EAI Endorsed Transactions on Energy Web

Improving Fault Classification Accuracy Using Wavelet Transform and Random Forest with STATCOM Integration

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ew 24(1):

Authors:

Shradha Umathe, Prema Daigavane, Manoj Daigavane

Abstract:

INTRODUCTION: Fault detection in transmission lines is critical for keeping the grid stable and reliable. This research offers a new methodology, the Wavelet Transform-Enhanced Random Forest Fault Classification System with STATCOM Integration (WERFCS-SI), to solve the shortcomings of existing faul…INTRODUCTION: Fault detection in transmission lines is critical for keeping the grid stable and reliable. This research offers a new methodology, the Wavelet Transform-Enhanced Random Forest Fault Classification System with STATCOM Integration (WERFCS-SI), to solve the shortcomings of existing fault detection approaches.
OBJECTIVES: The integration of STATCOM-compensated transmission lines improves fault detection capabilities. The Wavelet Transform finds faults by analysing approximation and detail coefficients, allowing for multiresolution analysis and exact fault localisation.
METHODS: Feature selection approaches, such as information gain, are used to discover and keep relevant features, increasing classification accuracy.
RESULTS: Due to its ability to process complex, high-dimensional data and identify minute feature connections, Random Forest (RF) is utilised for classification tasks. The proposed approach improves RF model performance while maintaining precision.
CONCLUSION: The integrated technique simplifies fault categorisation, increasing accuracy and efficiency by detecting problems in the transmission line system.
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Fuzzy Allocation Optimization Algorithm for High-Density Storage Locations with Low Energy Consumptions

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Authors:

Ziyi Gao, Linze Huang, Zhigang Wu, Zhenyan Wu, Chunhui Li

Abstract:

The global demand for stored and processed data has surged due to the development of IoTs and similar computational structures, which has led to further energy consumption by concentrated data storage facilities and thus the demands of global energy and environmental needs. The current paper introd…The global demand for stored and processed data has surged due to the development of IoTs and similar computational structures, which has led to further energy consumption by concentrated data storage facilities and thus the demands of global energy and environmental needs. The current paper introduces Fuzzy Allocation Optimization Algorithm to mitigate energy consumption in high storage density settings. It uses the principles of Fuzzy logic to determine the best way to assign the tasks in relation to storage density necessity, urgency and energy consumption. Thus, the proposed approach incorporates fuzzy inference systems with multi-objective optimization methods where location of storage is dynamically assessed and assigned according to energy efficiency parameters. The findings of the simulation and case study prove that the algorithm is successful in saving energy while at the same time lowering storage I/O response time, which provides a viable solution to energy issues in evolving data centres. This work satisfies the lack of energy efficient algorithms in high density storage areas and responds to the recent calls for green technology and smart utilization of resources in the energy field. The findings are used in the promotion of significant IT infrastructures towards developing the next generation of energy efficient data centers with respect to Future Internet and evolving energy web environments.
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Construction and Application Analysis of an Intelligent Distribution Network Identification System Based on Deep Neural Networks

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Author:

Yu Ma

Abstract:

INTRODUCTION: At present, the communication between measuring data and network topology in the distribution system cannot be accurately established. Therefore, deep neural networks were utilized to learn the mapping relationship between the measurement data and network topology, achieving topology …INTRODUCTION: At present, the communication between measuring data and network topology in the distribution system cannot be accurately established. Therefore, deep neural networks were utilized to learn the mapping relationship between the measurement data and network topology, achieving topology structure discrimination under different working conditions.
OBJECTIVES: This study aims to establish a machine learning-based Intelligent Distribution Network (IDN) online topology recognition model to address the limited measurement equipment in distribution networks and improve the accuracy and efficiency of network topology recognition.
METHODS: First, light GBM was used for feature selection to reduce computational complexity and improve learning efficiency. Then, a DNN model was constructed for topological identification and enhances the model scalability through incremental and transfer learning mechanisms. In addition, the Cross-Validation Grid Search Algorithm (GSA) was used to optimize the hyperparameters to ensure that the model can achieve the optimal performance on different data sets. Finally, a new intelligent distribution network identification model (Intelligent Distribution Electricity Network Identification System, IDENIS) was constructed.
RESULTS: The study was experimentally verified on the distribution system of IEEE 33 and PG&E 69. The experimental results showed that the accuracy of the DNN-based model reached 0.9817 on the test set, while the accuracy after feature selection only decreased by 1.3%, and the features decreased by 81.8%. In the PG&E 69 node system, the features were reduced by 85.5%, while the identification accuracy was decreased by only 0.51%. These results demonstrated that the proposed method maintained high identification accuracy while reducing the computational resource consumption.
CONCLUSION: Its efficient computing speed fully meets the real-time requirements in practical applications. This paper provides new ideas and methods for achieving intelligent distribution network topology recognition of high proportion distributed power sources.
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Research on Fault Diagnosis Method for Photovoltaic Array Based on XGBoost Algorithm

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Authors:

Zongyu Zhang, Bodi Liu, Chun Xie, Ermei Yan

Abstract:

INTRODUCTION: Photovoltaic (PV) energy sources frequently experience issues, including fragmentation, open-circuit, short-circuiting, and other common and hazardous problems. The current focus of PV research is on fault detection within solar arrays. Traditional models encounter challenges in ident…INTRODUCTION: Photovoltaic (PV) energy sources frequently experience issues, including fragmentation, open-circuit, short-circuiting, and other common and hazardous problems. The current focus of PV research is on fault detection within solar arrays. Traditional models encounter challenges in identifying errors due to uncertainties in panel settings and the complex nature of the actual PV structure.
OBJECTIVES: This study aims to introduce a novel Extreme Gradient Boosting (XGBoost) approach for fault diagnosis in PV arrays.
METHODS: The XGBoost algorithm is trained using collected PV array defect data samples. Data preprocessing is performed to manage missing values and remove noisy data. Feature extraction is conducted using Linear Discriminant Analysis (LDA) to improve detection accuracy. To further enhance XGBoost’s performance, the World Cup Optimization (WCO) approach is applied to select optimal features from the extracted data. Fault detection is then conducted using the XGBoost algorithm on the processed data. Various indicators are utilized for performance assessment within the Python environment.
RESULTS: The comparative analysis demonstrates that this research improves fault detection efficiency in PV arrays compared to existing methodologies.
CONCLUSION: The study presents an effective method for enhancing fault detection in PV systems, showcasing the advantages of the XGBoost and WCO-based approach over conventional methods.
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Research on a New Maximum Power Tracking Algorithm for Photovoltaic Power Generation Systems

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Authors:

Lei Shi, Zongyu Zhang, Yongrui Yu, Chun Xie, Tongbin Yang

Abstract:

INTRODUCTION: Significant advances have been made in photovoltaic (PV) systems, resulting in the development of new Maximum Power Point Tracking (MPPT) methods. The output of PV systems is heavily influenced by the varying performance of solar-facing PV panels under different weather conditions. Pa…INTRODUCTION: Significant advances have been made in photovoltaic (PV) systems, resulting in the development of new Maximum Power Point Tracking (MPPT) methods. The output of PV systems is heavily influenced by the varying performance of solar-facing PV panels under different weather conditions. Partial shading (PS) conditions pose additional challenges, leading to multiple peaks in the power-voltage (P-V) curve and reduced output power. Therefore, controlling MPPT under partial shading conditions is a complex task.
OBJECTIVES: This study aims to introduce a novel MMPT algorithm based on the ant colony incorporated bald eagle search optimization (AC-BESO) method to enhance the efficiency of PV systems.
METHODS: The effectiveness of the proposed MPPT algorithm was established through a series of experiments using MATLAB software, tested under various levels of solar irradiance.
RESULTS: Compared to existing methods, the proposed AC-BESO algorithm stands out for its simplicity in implementation and reduced computational complexity. Furthermore, its tracking performance surpasses that of conventional methods, as validated through comparative analyses.
CONCLUSION: This study confirms the efficacy of the AC-BESO method over traditional strategies. It serves as a framework for selecting an MPPT approach when designing PV systems.
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Harmonic Measurement Algorithm of Power System Integrating Wavelet Transform and Deep Learning

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ew 24(1):

Authors:

Hanshu Jiang, Yutian Li, Zhu Liu, Guanghao Wu, Zeyang Liu

Abstract:

INTRODUCTION: The problem of low accuracy in harmonic measurement is a significant challenge in power systems. Traditional methods often exhibit higher measurement errors, leading to unreliable detection of harmonics. To address this, the author proposes a new approach that integrates wavelet trans…INTRODUCTION: The problem of low accuracy in harmonic measurement is a significant challenge in power systems. Traditional methods often exhibit higher measurement errors, leading to unreliable detection of harmonics. To address this, the author proposes a new approach that integrates wavelet transform and deep learning techniques for enhanced harmonic measurement accuracy.
OBJECTIVES: The primary goal of this study is to develop a more accurate harmonic measurement algorithm by combining full phase fast Fourier transform (FFT) and adaptive neural networks. The research aims to automatically detect power system harmonics with minimal error and improve upon the limitations of traditional methods.
METHODS: The study implemented a harmonic measurement method using full phase FFT integrated with an adaptive neural network. This approach calculates harmonic amplitudes based on the fundamental component and its amplitude, while determining the precise start and end times of harmonics. The system also incorporates mean filtering for automatic detection of harmonics. The effectiveness of the proposed method was evaluated through experiments that compared it to traditional harmonic measurement techniques.
RESULTS: Experimental results demonstrated that the proposed method achieved an average measurement error of 0.02V, with a maximum error of 0.03V, both of which are below the acceptable error limit. In contrast, traditional methods exhibited significantly higher average errors of 3.31V and a maximum error of 5.17V. The new method consistently showed higher accuracy in harmonic detection compared to conventional approaches.
CONCLUSION: The study concludes that the proposed harmonic measurement algorithm significantly improves accuracy compared to traditional methods. With its lower measurement error and effective automatic detection capabilities, the method proves to be highly suitable for harmonic measurement in power systems.
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Research on Energy-Efficient Building Design Using Target Function Optimization and Genetic Neural Networks

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Author:

Youxiang Huan

Abstract:

OBJECTIVES: This research aims to provide data for decision-makers to achieve sustainability in building construction projects. METHODS: A multi-objective optimization method, using the non-sorting genetic algorithm (NSGA-II), assesses energy efficiency by determining optimal wall types, insulatio…OBJECTIVES: This research aims to provide data for decision-makers to achieve sustainability in building construction projects.
METHODS: A multi-objective optimization method, using the non-sorting genetic algorithm (NSGA-II), assesses energy efficiency by determining optimal wall types, insulation thickness, and insulation type. This paper utilizes the EnergyPlus API to directly call the simulation engine from within the optimization algorithm. The genetic neural network algorithm iteratively modifies design parameters (e.g., building orientation, insulation levels etc) and evaluates the resulting energy performance using EnergyPlus.
RESULTS: This reduces energy consumption and life cycle costs. The framework integrates Matlab-based approaches with traditional simulation tools like EnergyPlus. A data-driven technology compares the framework's effectiveness.
CONCLUSION: The study reveals that optimal design configurations can reduce energy consumption by 30% and life cycle costs by 20%, suggesting changes to window fenestration and envelope insulation are necessary. The framework's accuracy and simplicity make it valuable for optimizing building performance.
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Power System Operation Status Based on MRMR Algorithm and Multiple ELM

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ew 24(1):

Author:

Benkang Jia

Abstract:

To validate the operation status of the power system after encountering faults and restoring it to equilibrium, an efficient and accurate evaluation method is raised to promote the accuracy and efficiency of operation status evaluation model. The study first introduced the minimum redundancy maximu…To validate the operation status of the power system after encountering faults and restoring it to equilibrium, an efficient and accurate evaluation method is raised to promote the accuracy and efficiency of operation status evaluation model. The study first introduced the minimum redundancy maximum correlation algorithm and multiple extreme learning machine, and then constructed a multi-layer evaluation model grounded on multiple extreme learning machine. The experiment findings indicated that 1225 samples were sent to the second layer after the first evaluation layer, and 531 samples were sent to the third layer after the second evaluation layer. Only 10 samples could not be evaluated at the fifth level. Moreover, there were only 2 cases of missed judgments in the fifth layer. The experiment data indicated that the probability of missed judgments in the hierarchical evaluation model was very small, and it could evaluate almost all samples. This demonstrates that the power system operation state evaluation method based on the minimum redundancy maximum correlation algorithm and multiple extreme learning machine proposed by the research can timely and effectively evaluate feature samples, providing strong support for the stable operation of the power system.
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Distributed Photovoltaic Power Energy Generation Prediction Based on Improved Multi-objective Particle Algorithm

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ew 24(1):

Authors:

Yuanzheng Xiao, Huawei Hong, Feifei Chen, Xiaorui Qian, Ming Xu, Hanbin Ma

Abstract:

Accurate prediction of distributed photovoltaic (DPV) power generation is crucial for stable grid operation, yet existing methods struggle with the non-linear, intermittent nature of solar power, and traditional machine learning models face hyperparameter selection and overfitting challenges. This …Accurate prediction of distributed photovoltaic (DPV) power generation is crucial for stable grid operation, yet existing methods struggle with the non-linear, intermittent nature of solar power, and traditional machine learning models face hyperparameter selection and overfitting challenges. This study developed a highly accurate DPV power prediction method by optimizing a Long Short-Term Memory (LSTM) network's hyperparameters using an improved Multi-Objective Particle Swarm Optimization (MO-PSO) algorithm. A hybrid LSTM-PSO model was created, where the LSTM network served as the core prediction model, and the improved MO-PSO algorithm optimized its hyperparameters, enhancing generalization and avoiding overfitting. The LSTM-PSO model significantly improved prediction accuracy compared to traditional methods. Key results from two power stations included a maximum deviation of 6.2 MW at Power Station A, a peak time deviation of less than 0.1 MW at Power Station B, and a prediction interval error controlled below 30 MW at an 80% confidence level. The optimized LSTM-PSO model effectively captures DPV power generation dynamics, and the superior performance metrics demonstrate its potential for intelligent grid management. However, limitations include prediction accuracy under extreme weather and computational efficiency for large datasets. Future work will focus on broader applicability and more efficient algorithm variants.
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Overcoming Weak Grid Challenges: A Combined Approach to VSI Stability with Impedance Adjustment, Control Optimization, and Microgrid Integration

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Authors:

Harendra Pal Singh, Sourav Bose, Anurag K. Swami

Abstract:

This paper addresses the challenges in Voltage Source Inverter (VSI) systems connected to weak grids, where frequent impedance changes lead to instability and power quality issues. This research studies how changing grid impedance affects current distortion and the stability of a VSI. It proposes t…This paper addresses the challenges in Voltage Source Inverter (VSI) systems connected to weak grids, where frequent impedance changes lead to instability and power quality issues. This research studies how changing grid impedance affects current distortion and the stability of a VSI. It proposes the stability analysis of a single loop controller and optimize its settings using various techniques (ZN-method, PSO, GA) to ensure VSI stability and meet current distortion limits (THD compliance), when grid impedance varies. The primary focus revolves around addressing two key challenges: managing impedance variations at the PCC and enhancing the tracking performance of the PI controller. The VSI-based system connected to the weak grid and in standalone mode is simulated on Typhoon HIL, to validate the effectiveness of obtained optimized controller parameters by changing various conditions like, the output power regulation and sudden load change in a standalone distribution network. The MATLAB/SIMULINK with m-files is utilized for the parameters optimization and controller model simulation purposes. This research is important for developing more reliable and resilient power systems, specifically by investigating the transient behaviour of VSI frequency and voltage under sudden changes, to ensure an uninterruptible power supply to critical loads.
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Two-stage high-frequency switching power supply device design study

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ew 24(1):

Author:

Lijuan Zhang

Abstract:

The current volume and efficiency of high-frequency switching power supplies in power supply system cannot meet practical requirements. Therefore, a modular equipment was studied to optimize the design of PWM rectifiers and DC-DC converters, and corresponding control strategies were adopted. At the…The current volume and efficiency of high-frequency switching power supplies in power supply system cannot meet practical requirements. Therefore, a modular equipment was studied to optimize the design of PWM rectifiers and DC-DC converters, and corresponding control strategies were adopted. At the same time, experimental verification was performed. The experimental results show that before using the control, there are two large secondary voltage ripples in the PWM rectifier, with an amplitude of approximately 8 V; After using the control, the amplitude was approximately 1 V, a decrease of 87.5%. In addition, the DC-DC converter module may have fluctuations when it is lifted, but the amplitude of the voltage wave remains basically 62 A after steady state. In practical applications, the ripple is controlled at 1 V through the proposed control method, and the actual displayed current is a relatively standard sine wave with a low distortion rate. Meanwhile, compared with other methods, the efficiency of the studied method is as high as 89.110%. Overall, the control strategy proposed by the research institute can effectively control the front and rear modules in theory. In practical applications, it can effectively improve the power output and reduce the pollution in the power grid. It has high effectiveness and feasibility in practical industrial application.
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Construction of a Fast Monitoring System for Electric Energy Equipment Status Based on Data Mining

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ew 24(1):

Authors:

Fusheng Wei, Xue Li, Weiwen Chen, Zhaokai Liang, Zhaopeng Huang

Abstract:

In modern power system operation, it is crucial to achieve fast and accurate monitoring of the electrical equipment status. To achieve this fast and accurate detection, this study proposes a generative adversarial network that combines edge features to amplify and recognize infrared images of devic…In modern power system operation, it is crucial to achieve fast and accurate monitoring of the electrical equipment status. To achieve this fast and accurate detection, this study proposes a generative adversarial network that combines edge features to amplify and recognize infrared images of devices, aiming to improve the model’s training effect. This model extracted edge features from infrared images to eliminate background noise in infrared images to achieve the goal of improving the accurate monitoring of the status of electrical equipment. The results showed that on the balanced dataset, the recognition accuracy of the model could reach about 96%, and the recognition effect of the model was relatively stable. On imbalanced datasets, the highest model recognition accuracy was around 89%, and the model recognition accuracy fluctuated greatly. The constructed model effectively improves the accuracy of monitoring the operating status of electric energy equipment, achieving fast and accurate monitoring of this state. This study can achieve rapid monitoring of the operating status of electric energy equipment, effectively reducing the operation and maintenance costs of the power system.
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Risk prediction method for power Internet of Things operation based on ensemble learning

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ew 24(1):

Authors:

Chao Hong, Xiaoyun Kuang, Yiwei Yang, Yixin Jiang, Yunan Zhang

Abstract:

INTRODUCTION: The power Internet of Things is an important strategic support for the State Grid Corporation of China to build an international leading energy internet enterprise. However, the operating environment of the power Internet of Things is complex and varied, which has serious implications…INTRODUCTION: The power Internet of Things is an important strategic support for the State Grid Corporation of China to build an international leading energy internet enterprise. However, the operating environment of the power Internet of Things is complex and varied, which has serious implications for the safe operation of the power Internet of Things.
OBJECTIVES: To timely predict the various risk.
METHODS: A data set is fused based on time series. The training set is over-sampled using an adaptive synthetic oversampling method. Then, by jointly considering the contribution of features to classification and the correlation between features, a risk prediction method ground on ensemble learning is established.
RESULTS: From the results, the accuracy of predicting 5 risk categories increased by 7.00%, 1.10%, 2.20%, 2.30%, and 0.60%, respectively, reducing the features from the original 118 columns to 60 columns and reducing the data dimension by 49.00%. Compared with traditional models, the accuracy was 98.61%, and the overall accuracy was improved by 0.60%.
CONCLUSION: This risk prediction scheme can quickly and accurately predict the risk categories that affect its operation. It has high prediction accuracy and fast speed than other algorithms. This research can provide strong assistance for security decision-making in the power Internet of Things.
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CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup

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Authors:

K. Punitha, G. Sivapriya, T. Jayachitra

Abstract:

A Solar Photovoltaic (PV) System is an energy conversion system that uses the photovoltaic effect to convert sunlight into electricity. A fault in a Solar Photovoltaic (PV) system refers to any abnormal condition or defect that disrupts the normal operation and performance of the solar system. Thes…A Solar Photovoltaic (PV) System is an energy conversion system that uses the photovoltaic effect to convert sunlight into electricity. A fault in a Solar Photovoltaic (PV) system refers to any abnormal condition or defect that disrupts the normal operation and performance of the solar system. These faults can arise from a variety of factors, including environmental conditions, manufacturing defects, installation errors, and wear and tear of the components. Fault diagnosis in solar PV systems involves the detection, identification, and rectification of faults or abnormalities that can occur due to various reasons. By detecting and addressing faults early, systems can maintain optimal performance levels. Machine Learning (ML) in Solar Photovoltaic (PV) systems refers to the application of algorithms and statistical models that enable computers to perform specific tasks without using explicit instructions, relying instead on patterns and inference. In the context of solar PV systems, ML is used to analyse and interpret vast amounts of data generated by these systems to enhance their efficiency, predict energy production, detect and diagnose faults, and optimize maintenance and operation. By analysing data from sensors and system logs, ML algorithms can identify patterns indicative of faults or inefficiencies, such as shading, soiling, or equipment malfunctions, often before they become serious issues. Convolutional Neural Networks (CNNs) are a class of deep learning algorithms most commonly applied. They are particularly powerful for tasks involving data recognition, classification, and analysis due to their ability to automatically and adaptively learn spatial hierarchies of features. This research presents a unique machine learning model based fault diagnosis and detection method for a 33 KW solar PV system at P.S.R. Engineering College, Sivakasi. The real-time data from the PV system for five years, covering 23,000 instances of eight types of faults such as Cell Cracks or Hot Spots, Partial Shading, sensor fault, Module failure, Ground Faults, Communication Errors, Environmental Factors, Grid Connectivity Issues are collected. CNN is applied to the data and analysed their performance in terms of accuracy, precision, and standard deviation (SD)-score. It is found that CNN achieved the best results, with an accuracy of 98.7% a precision of 95%, a recall of 98%, and an F1 score of 96.5%. Therefore, CNN is used as the fault prediction also. The model is implemented using Python programming language and demonstrated its effectiveness on test cases. The smart data gathering system was achieved utilizing an ESP32 node with several sensors. The obtained data was stored in an authorized Google Sheet and compared to predetermined threshold ranges. When any parameter deviates from its threshold value, the ESP32 node starts a cooling and dust cleaning procedure with a water pump and drip pipe configuration. If the divergence persists, the ESP32 node activates a camera to capture an image of the panel and sends it to the Google Sheet via a connection for further analysis and fault correction.
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Enhancing Torque Smoothness in BLDC Motors with Built-in DC-DC Converter via Bitterling Fish Optimization Algorithm

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ew 24(1):

Authors:

K. Balamurugan, B Sri Revathi

Abstract:

Brushless DC (BLDC) motors are efficient and robust electric motors with fewer moving parts, but their application is often limited by torque ripple, which arises from current variations between the entering and exiting phases during commutation. This study aims to minimize torque ripple in BLDC mo…Brushless DC (BLDC) motors are efficient and robust electric motors with fewer moving parts, but their application is often limited by torque ripple, which arises from current variations between the entering and exiting phases during commutation. This study aims to minimize torque ripple in BLDC motors integrated with a DC-DC converter. The proposed optimization method utilizes the Bitterling Fish Optimization (BFO) Algorithm to effectively control torque error and speed, addressing the torque ripple caused by current variations during commutation. The proposed method is implemented using the MATLAB working environment and compared with various existing methods like Spider Web Algorithm (SWA), Improved Tunicate Swarm Optimization Algorithm (ITSA), and Harris Hawks Optimizer with Black Widow Optimization (HHO-BWO). The results indicate that the proposed method achieves a reduced torque ripple rate of 9.64, significantly lower than the rates of 17.32, 11.20 and 22.19 for ITSA, HHO-BWO and SWA respectively. Additionally, the proposed approach exhibits low error of 0.168, outperforming the existing methods errors of 0.287, 0.195 and 0.311. These findings demonstrate that the BFO algorithm effectively minimizes torque ripple more than existing optimization techniques, providing a promising solution for enhancing the performance of BLDC motors.
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Investigating Safe and Economic Adjustment of Power Balance in Smart Grids Based on Integration of Renewable Energy

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Author:

Hongyan Zhang

Abstract:

The present pace of integration of renewable sources into the electrical grid is insufficient, failing to fulfill the expectations of producers or coincide with sustainable national objectives. Furthermore, sustainable national policies are not being executed. Despite the growth of the solar and wi…The present pace of integration of renewable sources into the electrical grid is insufficient, failing to fulfill the expectations of producers or coincide with sustainable national objectives. Furthermore, sustainable national policies are not being executed. Despite the growth of the solar and wind energy industry and the installation of decentralized energy production systems, this scenario has emerged. Several factors contribute to this scenario, including advancements in administration, forecasting, and oversight, along with enhancements in infrastructure. These issues may arise notwithstanding the decentralized nature of renewable energy sources. The integration rate of renewable energy sources into networks, along with the efficiency of these networks, is clearly hindered as a result of this. Furthermore, we will examine the problems associated with the implementation of this network. We will focus on the low injection rate and the balance between supply and demand. Subsequently, we will examine the impact they have on the operation of the interconnected system. We will provide management solutions tailored to each detected issue, along with the suggested cures for any recognized concerns. The aim is to discover the structures, procedures, and tools that will enhance the network's reliability and energy efficiency while simultaneously reducing installation costs and fortifying the network. The findings indicate that the interruptions in voltage, frequency, and power have been mitigated due to the dynamic simulations using the proposed method. The calculations were predicated on an integration of solar and wind energy, with twenty percent of the energy derived from wind.
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Research on intelligent detection method of new energy vehicle power battery based on improved ViBe algorithm

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ew 24(1):

Author:

Lei Gu

Abstract:

Background: Traditional foreground detection methods for new energy vehicles using the ViBe algorithm often suffer from ghosting effects, which can obscure the accurate detection of moving targets. Aims: This study enhances foreground detection accuracy by addressing ghosting issues in the ViBe al…Background: Traditional foreground detection methods for new energy vehicles using the ViBe algorithm often suffer from ghosting effects, which can obscure the accurate detection of moving targets.
Aims: This study enhances foreground detection accuracy by addressing ghosting issues in the ViBe algorithm and improving the battery pack state detection system for new energy vehicles.
Method: The method includes analyzing global light changes before foreground detection and updating the background model using the three-frame difference method. The system integrates hardware and software to process data with the ViBe algorithm, measuring voltage from twelve 18650-type lithium batteries.
Results: The battery management system prototype exhibits an absolute measurement error within -1.2 mV compared to the high-precision multimeter. The system maintains measurement accuracy across varying temperatures, demonstrating effective environmental adaptability.
Conclusion: The enhanced system successfully reduces ghosting in foreground detection and provides reliable battery state monitoring. It is robust under extreme conditions, contributing to improved diagnostic capabilities and enhanced traffic safety.
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Reinforcement Learning Data-Driven Optimal Load-Frequency Control for Power Systems

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Author:

Yi Zhao

Abstract:

INTRODUCTION: Power systems are complex due to their time-varying and uncertain parameters, challenging conventional control methods. OBJECTIVES: This study proposes an adaptive dynamic programming (ADP) controller to address this limitation. The ADP controller eliminates the need for pre-existing…INTRODUCTION: Power systems are complex due to their time-varying and uncertain parameters, challenging conventional control methods.
OBJECTIVES: This study proposes an adaptive dynamic programming (ADP) controller to address this limitation. The ADP controller eliminates the need for pre-existing knowledge of the system dynamics, a significant advantage in real-world applications.
METHODS: By iteratively solving the Riccati equation using only system state and input data, the controller learns an approximate optimal control strategy. In this study, we use an iterative computational approach with an online adaptive optimal controller designed for unknown power system dynamics.
RESULTS: Utilizing real-time collected system states and input information, even in the absence of knowledge about the power system matrix, we achieve iterative solutions for the algebraic Riccati equation, enabling the computation of an optimal controller. Simulation results demonstrate the ease of implementation of this approach in power system load frequency control (LFC).
CONCLUSION: The proposed ADP controller exhibits good control performance of grid stability, making it a valuable reference for LFC, especially in scenarios with unknown system parameters.
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Research on anti-error operation warning of power grid dispatching based on deep bidirectional gated recurrent neural network

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Authors:

Jinglong He, Dunlin Zhu, Sheng Yang, Jinming Liu, Tianyun Luo, Yuan Fu

Abstract:

To improve the security and overall efficiency of grid scheduling work and accurately optimize scheduling decisions, a grid scheduling error-proof operation warning method based on a deep bidirectional gated recurrent neural network is proposed. This paper combines the principle of hierarchical dat…To improve the security and overall efficiency of grid scheduling work and accurately optimize scheduling decisions, a grid scheduling error-proof operation warning method based on a deep bidirectional gated recurrent neural network is proposed. This paper combines the principle of hierarchical data construction, summarizes the structured data of metadata operation tickets and maintenance plans of CIM model and OMS network frame model, and constructs the data warehouse of grid dispatching error prevention; based on the natural language processing (NLP) technology, key information and knowledge entities related to grid dispatching error prevention are automatically identified and extracted from the data warehouse. Based on the deep bidirectional gated recurrent neural network, the extracted information sequence is used as input to construct the grid scheduling operation state reconstruction model, and the error prevention warning is carried out according to the output prediction results. The experimental results show that: the data docking speed in different scheduling phases is fast with the fastest speed of 71.254MB/s, and the convergence speed of the analysis and calculation is within 0.01MB/s, indicating that the overall analysis efficiency is high, the application performance is good, and it can determine whether there is any misoperation in the process of grid scheduling and carry out highly efficient, accurate, and fast early warning.
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Distributed energy storage hierarchical partition dispatch control of virtual power plant based on SaDE-BBO algorithm

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Authors:

Tianyi Yu, Shijia Wei, Tao Lu, Zhipeng Zhang, Ning Sun

Abstract:

To improve the response ability of the virtual power plant during operation and the adjustment ability when the load fluctuates, and ensure its stable operation, a virtual power plant distributed energy storage hierarchical partition dispatch control method based on the SaDE-BBO algorithm is propos…To improve the response ability of the virtual power plant during operation and the adjustment ability when the load fluctuates, and ensure its stable operation, a virtual power plant distributed energy storage hierarchical partition dispatch control method based on the SaDE-BBO algorithm is proposed. This method is based on the operation structure of the virtual power plant, analyzes the operating characteristics of the distributed energy storage system and the output of uncertainty factors, considers the grid load, renewable energy and distributed energy storage on the time scale, and constructs hierarchical partitions of the virtual power plant. The dispatch model determines the day-ahead and day-in-day hierarchical partition dispatch control objective functions, and sets corresponding constraints; the dispatch control model based on the solution of the SaDE-BBO algorithm outputs the virtual power plant distributed energy storage hierarchical partition dispatch control optimization plan. The test results show that the maximum load peak value after dispatch control through this method is 40.9 MW; the active power loss results are all below 10 MW, real-time response to control instructions ensures the safety and stability of the voltage of the virtual power plant under the access of renewable energy, and the nodal voltage fluctuated within the permissible range of 0.95 to 1.05 p.u.
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Research on short-term power load forecasting based on deep reinforcement learning with multiple intelligences

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Authors:

Tianyun Luo, Dunlin Zhu, Jinming Liu, Sheng Yang, Jinglong He, Yuan Fu

Abstract:

A reliable supply of power systems is critical for industry, commerce, and residential life. Improving the accuracy and reliability of short-term electricity load forecasting plays a crucial role in ensuring the satisfaction of electricity demand and the stable operation of the power system. Theref…A reliable supply of power systems is critical for industry, commerce, and residential life. Improving the accuracy and reliability of short-term electricity load forecasting plays a crucial role in ensuring the satisfaction of electricity demand and the stable operation of the power system. Therefore, to realize accurate and efficient prediction of short-term power loads, a short-term power load prediction method based on multi-intelligence deep reinforcement learning is proposed to address the complex nonlinear characteristics of load data. In this paper, we analyze the multi-intelligence application architecture in power load forecasting, and analyze the function of each intelligent unit applied to short-term power load forecasting; based on clarifying the interaction relationship of each intelligent unit in short-term power load forecasting, we model short-term power load forecasting as a distributed and partially observable Markov decision-making process, which is suitable for multi-intelligence deep reinforcement learning; based on the MATD3 algorithm, a centralized training-distributed execution framework is used to train multiple intelligences within the model to achieve short-term power load forecasting. The experimental results show that in the August short-term electricity load forecasting using the design method, the obtained MAE value is 35.94 kW, MAPE value is 4.05%, and RMSE value is 32.71 kW. In the short-term power load forecasting evaluation conducted for December, the average absolute error (MAE) value obtained was 36.75 kilowatts, the average absolute percentage error (MAPE) value was 4.51%, and the root mean square error (RMSE) value was 34.82 kilowatts. These evaluation results fully demonstrate that the design method adopted has high prediction accuracy and forecast precision. This method has demonstrated good practical value and broad application prospects in practical applications due to its high-precision prediction performance and strong prediction stability.
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Power grid inspection based on multimodal foundation models

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ew 24(1):

Authors:

Jingbo Hao, Yang Tao

Abstract:

INTRODUCTION: With the development of large foundation models, power grid inspection is transmitting from traditional deep learning to multimodal foundation models. OBJECTIVES: This paper aims to boost the application of multimodal foundation models for power grid inspection. METHODS: Current res…INTRODUCTION: With the development of large foundation models, power grid inspection is transmitting from traditional deep learning to multimodal foundation models.
OBJECTIVES: This paper aims to boost the application of multimodal foundation models for power grid inspection.
METHODS: Current research on foundation models and multimodal large language models (LLMs) is introduced respectively. Three application forms of multimodal foundation models in power grid inspection are explored. The reliability of these models is discussed as well.
RESULTS: These techniques can significantly reduce the time and cost of inspection by automating the analysis of large amounts of sensor data. They can also improve the accuracy and reliability of inspection by leveraging the understanding and reasoning abilities of LLMs.
CONCLUSION: These advanced techniques have shown great application potential in power grid inspection. But it is important to note that they should not entirely replace human inspectors who can validate automatic findings and address possible issues not captured by these models alone.
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Research on multi-stage optimization planning of power internet of things based on seagull optimization algorithm

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ew 24(1):

Authors:

Peng Ye, Guanxian Liu, Shuo Yang, Shaotao Guo, Huan Wang, Yi Zhao, Mingli Zhang

Abstract:

The Power Internet of Things (PIoT) is a significant technology for realizing the transformation of future energy systems, with the Integrated Energy System (IES) playing a crucial role in realizing the value of PIoT. Traditional IES planning methods typically focus on a single-stage planning appro…The Power Internet of Things (PIoT) is a significant technology for realizing the transformation of future energy systems, with the Integrated Energy System (IES) playing a crucial role in realizing the value of PIoT. Traditional IES planning methods typically focus on a single-stage planning approach and involve complex solution models, often resulting in inefficient equipment configurations and resource wastage. This study proposes a multi-stage IES planning method aimed at enhancing both energy efficiency and the economic performance of IES. The method models the IES based on electric, gas, and thermal buses, considering the coupling, storage, and conversion of multiple energy sources. A range of constraints, such as energy coupling, equipment capacity, and energy purchases, are considered. The planning cycle is divided into multiple stages, and an economic model is developed that accounts for both system investment and operating costs. Given the complexity of the multi-stage planning model, the Seagull Optimization Algorithm (SOA) is introduced to solve the problem. The SOA leverages its strong global and local search capabilities to determine the optimal capacity configuration at each stage. The comparison of the single-stage planning method by a calculation example proves the economic advantage of the multi-stage planning scheme and effectiveness of SOA.
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Cloud model-based unconventional risk assessment method for flexible distribution system

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ew 24(1):

Authors:

Shaotao Guo, Peng Ye, Jinhang Shan, Yi Liang, Zhentao Han, Qixiang Wang, Na Zhang

Abstract:

The integration of high-penetration distributed resources has led to increased complexity and uncertainty in the unconventional risks of distribution networks, posing higher demands on the risk assessment of distribution networks. This paper proposes an unconventional risk assessment method for fle…The integration of high-penetration distributed resources has led to increased complexity and uncertainty in the unconventional risks of distribution networks, posing higher demands on the risk assessment of distribution networks. This paper proposes an unconventional risk assessment method for flexible distribution system based on cloud model. Firstly, an unconventional risk assessment system for distribution networks is constructed by considering the probability of unconventional risk occurrence and the severe consequences, and an improved AHP-entropy weight method for index weighting is proposed. Then, the cloud model for risk assessment is used to quantitatively evaluate the risk level of the distribution system. The variable weight cloud model is employed to replace the traditional cloud model to provide risk indicator evaluation information. The inverse cloud generator is used to infer and correct the risk cloud model parameters, and the assessment is completed by comparing with the digital characteristics of the standard cloud model. Finally, the effectiveness of the proposed assessment method is verified through an example analysis of a certain region in China.
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Modeling and simulation of control strategy for voltage source converter multi-terminal DC system

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ew 24(1):

Author:

Xingchao Zhang

Abstract:

An improved strategy for controlling DC voltage droop is proposed to solve the problems of conventional DC voltage droop control strategy, such as the received power of converter stations is distributed according to fixed proportion, the power variation amplitude is large, the power regulation abil…An improved strategy for controlling DC voltage droop is proposed to solve the problems of conventional DC voltage droop control strategy, such as the received power of converter stations is distributed according to fixed proportion, the power variation amplitude is large, the power regulation ability is easy to lose when reaching the upper limit, and there is obvious voltage deviation. A three-terminal VSC-MTDC system model including wind farm was constructed by Matlab/Simulink. According to different conditions of power fluctuation caused by wind power output variation, simulation and analysis of VSC active power and DC voltage variation were carried out. Simulation results showed that the improved strategy for controlling DC voltage droop proposed in this paper made the converter station retain more power regulation margin when the converter station absorbed more active power, and could prevent the converter station from losing its power regulation ability when the power reached the upper limit, and realize reasonable power distribution among converter stations. Meanwhile, the proposed control strategy could maintain DC voltage constant, which verified the feasibility of its application in wind power grid-connected flexible DC transmission system.
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Intelligent Equipment Scheduling Optimization Model for Transmission Lines Based on Improved BFO Algorithm

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ew 24(1):

Authors:

Wulue Zheng, Xin Zhang, Fuchun Zhang, Ning Wang, Yangliang Zheng, Zhi Wang

Abstract:

INTRODUCTION: In modern power systems, the optimization of intelligent equipment scheduling for transmission lines is a key task. OBJECTIVES: To improve the effectiveness of scheduling optimization, this study introduces an intelligent equipment scheduling optimization model for transmission lines…INTRODUCTION: In modern power systems, the optimization of intelligent equipment scheduling for transmission lines is a key task.
OBJECTIVES: To improve the effectiveness of scheduling optimization, this study introduces an intelligent equipment scheduling optimization model for transmission lines on the ground of the improved Bacterial Foraging Optimization algorithm.
METHODS: This model achieves global and local search capabilities through an improved Bacterial Foraging Optimization algorithm, maintaining the diversity of equipment states and effectively improving the optimization level of scheduling results.
RESULTS: At 3000 iterations, the model was able to reach its optimal state, and its optimization results showed excellent performance in terms of convergence and uniformity, which was very close to the optimal solution. In practical applications, the performance of the intelligent equipment scheduling optimization model for transmission lines on the ground of the improved Bacterial Foraging Optimization algorithm is also excellent. The average line usage rate of the scheduling scheme proposed by the model reached 70.69%, while the average line usage rate of the manual scheduling scheme was only 64.63%. In addition, the optimal relative error percentage of this model is less than 2.1%, while the BRE of other algorithms reaches around 10%.
CONCLUSION: The intelligent equipment scheduling optimization model for transmission lines on the ground of improved Bacterial Foraging Optimization algorithm has important practical significance for improving the operational efficiency of the power system, reducing operating costs, and making sure the stable and reliable operation of the power system.
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Investigation of the anchor chain tension distribution and six-degree-of-freedom motion characteristics of a floating fan platform in a wind-wave basin

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ew 24(1):

Authors:

Xi Zhang, Jie Dong, Jiankang Wang, Guirong Lu, Jingyi Wei, Yupeng Wang, Guoqiang Chen

Abstract:

INTRODUCTION: Because of its versatility and adaptability, floating wind turbine platforms have emerged as the go-to foundation type for deep sea wind power as offshore wind power production steadily expands into deeper waters. OBJECTIVES: But the floating platform's six-degree-of-freedom motion a…INTRODUCTION: Because of its versatility and adaptability, floating wind turbine platforms have emerged as the go-to foundation type for deep sea wind power as offshore wind power production steadily expands into deeper waters.
OBJECTIVES: But the floating platform's six-degree-of-freedom motion and the anchor chain system's mechanical reaction to the combined force of wind, wave, and current are incredibly intricate, and this directly affects the platform's stability and safety.
METHODS: Under various wind, wave, flow incidence angle, and chain length conditions, the platform's six-degree-of-freedom motion characteristics and anchor chain tension distribution are carefully studied using potential flow theory, the finite element method, and the fluid-structure coupling model. The numerical simulation combined JONSWAP wave spectrum and NPD wind spectrum to conduct multi-condition analysis.
RESULTS: the results show that the incidence Angle and anchor chain configuration have significant effects on the dynamic response of platform pitching and pitching.
CONCLUSION: This paper deeply discusses the platform motion response under the broken anchor chain, and puts forward the corresponding optimal design scheme.
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Power optimization and control strategy for new energy hybrid power generation system based on deep learning

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ew 24(1):

Author:

Fei Li

Abstract:

The continuous shortage of non-renewable energy and the increasingly serious environmental pollution have made clean renewable energy represented by wind and solar energy become the focus of attention. This paper mainly studies the power optimization and control strategy of a new energy hybrid powe…The continuous shortage of non-renewable energy and the increasingly serious environmental pollution have made clean renewable energy represented by wind and solar energy become the focus of attention. This paper mainly studies the power optimization and control strategy of a new energy hybrid power generation system based on deep learning. This paper introduces the basic principle and structure of a new energy hybrid power generation system and the application of deep learning technology in power optimization and control strategy. In this paper, a power optimization method based on deep learning is proposed, which realizes real-time optimization of power generation system powers by training neural network models. A control strategy based on deep learning is designed to improve the stability and efficiency of the power generation system. The effectiveness of the proposed method in practical application is verified by experiments.
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Energy management system design for high energy consuming enterprises integrating the Internet of Things and neural networks

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ew 24(1):

Authors:

Zhaolin Wang, Zhiping Zhang

Abstract:

INTRODUCTION: High energy consuming enterprises continue to pay increasing attention to energy consumption. Therefore, designing an energy management system is significant. OBJECTIVES: To improve the management level and economic benefits of enterprises, a high energy consuming enterprise energy m…INTRODUCTION: High energy consuming enterprises continue to pay increasing attention to energy consumption. Therefore, designing an energy management system is significant.
OBJECTIVES: To improve the management level and economic benefits of enterprises, a high energy consuming enterprise energy management system design based on Internet of Things technology and neural network algorithms is proposed.
METHODS: Internet of Things devices are used for data collection and transmission. The combination of neural network model prediction and optimization algorithms can achieve real-time monitoring, prediction, and optimization control of energy consumption.
RESULTS: The research results indicated that the response time of the high energy consuming enterprise energy management system proposed in the study was 80.2 ms when the number of people was 600. The fluctuation range of CPU usage within 24 hours was 14% to 45%.
CONCLUSION: A high energy consuming enterprise energy management system that integrates the Internet of Things and neural networks can manage energy more efficiently and intelligently, thereby improving the production efficiency and economic benefits of the enterprise. This helps companies gain greater advantages in fierce market competition.
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Power System Operation Stability Assessment Method Based on Deep Convolutional Neural Network

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ew 24(1):

Authors:

Jinman Luo, Yuqing Li, Qile Wang, Liyuan Liu

Abstract:

INTRODUCTION:  For the assessment of power system stability, a power system assessment method based on a deep convolutional neural network is studied. OBJECTIVES: Through the improvement of the integrated convolutional neural network (CNN) network model, the impact of insufficient transient stabil…INTRODUCTION:  For the assessment of power system stability, a power system assessment method based on a deep convolutional neural network is studied.
OBJECTIVES: Through the improvement of the integrated convolutional neural network (CNN) network model, the impact of insufficient transient stability assessment caused by sample misjudgment and sample omission is effectively reduced.
METHODS: We adopt the hierarchical real-time prediction model to evaluate the stability and instability of the determined stable samples and unstable samples, thereby improving the timeliness and accuracy of transient evaluation.
RESULTS: Through experimental comparison, the integrated CNN network model in this study has obvious advantages in accuracy compared with the single CNN network. Compared with other algorithm reference models, this model has a higher evaluation accuracy of 98.39%, far exceeding other comparison models.
CONCLUSION: By further evaluating the model’s accuracy, it is proved that the model can provide an effective reference for the follow-up power system stability prevention and has important application value.
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Optimized design and application of slot-type solar concentrator based on BIM technology

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ew 24(1):

Authors:

Miaoling Wang, Hao Wang

Abstract:

The application of BIM (Building Information Modeling) technology allows designers in the construction industry to complete their design work with lower energy consumption and higher efficiency and is therefore increasingly being used in other design fields. In this study, a finite element model of…The application of BIM (Building Information Modeling) technology allows designers in the construction industry to complete their design work with lower energy consumption and higher efficiency and is therefore increasingly being used in other design fields. In this study, a finite element model of the concentrator was constructed using BIM technology to reduce the amount of steel used in the manufacture of the trough solar concentrator and thus reduce its production cost, and static analysis and steel minimization planning analysis was conducted. The analysis results show that the multiple linear regression algorithm is more reasonable to calculate the structural optimization evaluation index. The steel consumption of the *02 solution is lower, and the lowest values are 805. 962 kg and 3974. 9￥ for the total steel consumption and steel cost, respectively, which is 10. 99% lower than the steel cost of 4465. 5￥ for the concentrator before optimization. Although its maximum displacement and maximum stress are not the lowest among the alternatives, they are still far below the corresponding allowable values of the structure, so this solution has the best comprehensive optimization effect. The results of this research show that the application of BIM technology to the design of trough solar concentrators can indeed save equipment production materials and reduce production costs, and has certain application prospects.
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Control Method of Microgrid Grid Connected Inverter Based on Quantum GA-PID

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ew 24(1):

Author:

Yunqing Qu

Abstract:

The current trend is towards ever-increasingly rigorous control performance requirements for grid-connected inverters. Therefore, a proportional integral derivative control method of the quantum genetic algorithm and particle swarm optimization has been proposed, which can achieve stable and effici…The current trend is towards ever-increasingly rigorous control performance requirements for grid-connected inverters. Therefore, a proportional integral derivative control method of the quantum genetic algorithm and particle swarm optimization has been proposed, which can achieve stable and efficient operation of microgrids.The total harmonic distortion of the improved controller was 1.03%, which was 1.98% lower than the traditional method and far below the national standard of 5%; The prediction accuracy wad on average 82%, 51%, and 54% higher than the other three classic algorithms; After the improvement of sag, the smoothness of microgrid switching had been increased, avoiding severe shaking. The convergence time was 68.3%, 54.5%, and 35.5% shorter, and the average convergence algebra was improved by 60.1%, 67.2%, and 87.3%, respectively; The control step response only approached 1.5 after 0.4 seconds, which was 75% longer than the improved time, and the overshoot was 0. Accordingly, the proposed method is able to utilizefor the regulation of actual microgrid grid-connected inverters, achieving effective voltage control in dynamic and complex environments.
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Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm

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ew 24(1):

Authors:

Youle Song, Yuting Duan, Tong Rao

Abstract:

Fault diagnosis of power equipment is a crucial task to credit the safe and stable operation of equipment. However, fault diagnosis of power equipment faces challenges such as high dimensionality, complexity, and nonlinearity. Therefore, this study proposes an improved support vector machine model,…Fault diagnosis of power equipment is a crucial task to credit the safe and stable operation of equipment. However, fault diagnosis of power equipment faces challenges such as high dimensionality, complexity, and nonlinearity. Therefore, this study proposes an improved support vector machine model, combined with grey wolf optimization algorithm, aimed at improving the accuracy and efficiency of power equipment fault diagnosis. To validate the model’s performance, this study divided a dataset of 3870 power equipment defects into training and testing sets using an 8:2 ratio, with evaluation metrics including accuracy, recall, and F1 score. The results showed that the fault recognition rate of the support vector machine model based on the improved grey wolf optimization algorithm reached 92.76%, with an accuracy close to 0.95 and a loss rate of 0.13. The model exhibited faster convergence speed, as well as better stability and convergence. At the same time, the optimized model had good feature extraction ability on different types of model faults, and the comprehensive recognition error of the model was basically stable in the interval of (-0.005, 0.005). The experiment validates that the research model improves the optimization algorithm through modal decomposition strategy. Meanwhile, the improvement of support vector machine parameter selection has strengthened the recognition and analysis of fault characteristics, providing an effective solution for power equipment fault diagnosis.
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Research on the Construction of a Short-Term Voltage Prediction Model Integrating Topological Data Analysis and Deep Neural Network under the Power System Resilience Assessment Framework

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ew 24(1):

Authors:

Hongjun Wang, Tao Li, Zhiliang Dong

Abstract:

INTRODUCTION: This paper examines the stability of small disturbances in wind farm grid-connected systems within the framework of power system resilience. With increasing renewable integration, minor disturbances can escalate into cascading failures, threatening grid reliability. OBJECTIVES: The g…INTRODUCTION: This paper examines the stability of small disturbances in wind farm grid-connected systems within the framework of power system resilience. With increasing renewable integration, minor disturbances can escalate into cascading failures, threatening grid reliability.
OBJECTIVES: The goal is to build a short-term voltage prediction model by integrating Topological Data Analysis (TDA) with Deep Belief Networks (DBN) and to propose a coordinated reactive power control strategy that enhances system dynamic performance under small disturbances.
METHODS: The study adopts a VSC-HVDC system based on Modular Multilevel Converters (MMC) to model wind farm connectivity. A cluster-based reactive power control approach is applied by grouping wind turbines with similar operational characteristics. Small disturbance signals are simulated, and both unified and decentralised Doubly Fed Induction Generator (DFIG) control schemes are compared using impedance modelling and time-domain analysis.
RESULTS: Simulations indicate that small AC-side disturbances have a significant impact on reactive power and system voltage, whereas DC-side faults affect frequency stability. The decentralised DFIG coordination strategy achieved a lower network loss (0.467 MW) compared to the unified approach (0.473 MW) while also improving reactive power allocation and system responsiveness.
CONCLUSION: By combining TDA and DBN with decentralised control, the proposed model enhances the stability of small disturbances in wind-integrated power systems. It enhances fault tolerance, mitigates power fluctuations, and facilitates the resilient operation of renewable-rich grids.
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Comparison of Machine Learning and Deep Learning Models Performance in predicting wind energy

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ew 24(1):

Authors:

Saswati Rakshit, Anal Ranjan Sengupta

Abstract:

The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and …The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. Each ML model underwent rigorous cross-validation to ensure optimal performance. The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. It was found that among the nine ML models, Random Forests, GBM and KNN consistently provided superior accuracy and robustness, making them the top choices for wind energy prediction whereas the performance of linear regression, SVM and SVR were very poor for the considered dataset. From the experiment, Random Forest, GBM, and KNN showed the best performance with low MSE values of 0.77, 1.95, and 1.51 respectively, while other models had MSEs above 7.5, with AdaBoost reaching 30. Their RMSEs (0.88, 1.40, 1.23) and MAEs (0.093, 0.73, 0.10) also indicate strong predictive accuracy compared to the rest.In this paper, time series forecasting, TCNs, LSTM and GRU networks showed strong capabilities in capturing temporal dependencies and trends within the wind energy data. Visualization techniques were employed to compare model performances comprehensively, providing clear insights into their predictive power. Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction.
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A Level-Shift Carrier PWM Modulation Technique and Predictive Current Control of a Multilevel DC-AC Converter for Interfacing Renewable Energy Sources

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ew 24(1):

Authors:

Vitor Pinto, Joao L. Afonso, Vitor Monteiro

Abstract:

The integration of renewable energy sources into the electrical power grid is a crucial aspect for achieving sustainable development and reducing environmental impacts. This paper explores the implementation of a three phase Neutral Point Clamped (NPC) DC AC converter with level shift carrier PWM m…The integration of renewable energy sources into the electrical power grid is a crucial aspect for achieving sustainable development and reducing environmental impacts. This paper explores the implementation of a three phase Neutral Point Clamped (NPC) DC AC converter with level shift carrier PWM modulation technique and predictive current control to facilitate the grid interconnection of renewable energy systems. Multilevel converters offer important advantages over traditional two level converters, such as reduced harmonic distortion and improved efficiency. Computational simulations using the PSIM software were conducted to validate the operation of the converter, both as active rectifier and as inverter. The results demonstrated the ability of the converter to adjust its current generation, even when considering sudden power variations in the load, as well as variations in the electrical power grid. Moreover, it effectively controls the DC link voltage, while maintaining a minimal ripple in the controlled AC current. The obtained results allow to verify the robustness and reliability of the converter for the integration of renewable energy (solar photovoltaic panels) into the power grid, contributing to the pursuit of sustainable energy solutions for a greener future.
 
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Coal Energy Production Efficiency Improvement Strategy with Integrated Genetic Algorithm and Its Economic Impact Assessment

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ew 24(1):

Authors:

Fangmin Chen, Zheng Ma

Abstract:

INTRODUCTION: Global energy systems heavily rely on coal energy generation, particularly in emerging nations. OBJECTIVES: Strategies that maximize the efficiency of coal energy generation while limiting environmental harm are essential to addressing these issues. With an emphasis on increasing pro…INTRODUCTION: Global energy systems heavily rely on coal energy generation, particularly in emerging nations.
OBJECTIVES: Strategies that maximize the efficiency of coal energy generation while limiting environmental harm are essential to addressing these issues. With an emphasis on increasing productivity and minimizing environmental effects, this study suggests an integrated strategy for optimizing coal energy production processes using Genetic Algorithms (GA).
METHODS: Key factors, including GDP growth rate, pollution abatement investment, coal intensity, and clean technology efficiency, are all optimized using GA in the suggested approach. Finding the best combination of these factors to maximize coal production efficiency while reducing CO2 emissions and other pollutants is made possible by GA-based optimization. A Social Cost-Benefit Analysis (SCBA) and environmental impact appraisal are also included to assess various scenarios' economic and environmental consequences. The findings show that, particularly in situations with slower GDP growth, more pollution abatement expenditures and cleaner technology adoption result in notable emissions reductions and increased overall efficiency.
RESULTS: The results show how crucial it is to balance environmental sustainability and economic prosperity. The study offers insightful information to industry executives and regulators, highlighting GA's potential to maximize the efficiency of coal energy generation.
CONCLUSION: Scenario A provided the best economic advantages, with a greater GDP growth rate and higher environmental costs.
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Smart Grid Implementation Based on Solar Generation for Load Demand Management in Najaf City, Iraq

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ew 24(1):

Author:

Dr. Hasan N. Muslim

Abstract:

Electrical Smart Grids (SG) contain numerous energy and operational measures, including renewable energy resources, energy efficiency resources, smart meters, and appliances. Investigating their applicability in a geographical area is important to provide a firm background to practical consideratio…Electrical Smart Grids (SG) contain numerous energy and operational measures, including renewable energy resources, energy efficiency resources, smart meters, and appliances. Investigating their applicability in a geographical area is important to provide a firm background to practical considerations. In this paper, the SG model is investigated, considering the available solar generation, hence enhancing residential load demand in Najaf city, Iraq. The analysis begins with an assessment of the electrical load profile characteristics, followed by choosing the optimum orientation of solar panels for the cities, which has been presented using MATLAB software. The optimum tilt angle for each month is estimated for three Photovoltaic (PV) systems of three sizes (1.5kWp, 2kWp, and 2.5 kWp). Furthermore, the study of their generation and impact on load demand is analyzed. The economic and financial analysis has been presented with payback period and benefits. The duck curve generated by the proposed PV systems is discussed. It is found that the annual demand saving of the proposed solar systems is 22%, 31%, and 36%, respectively. The hourly demand saving is presented, and it has been observed that there is overgeneration using 2kWp and 2.5 kWp PV systems, resulting in reshaping the load curve and producing the duck curve. The future work can be summarized by suggesting management of the battery system for the overgeneration.
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Deep Learning Model for Feature Extraction and Anomaly Recognition in High-Dimensional Energy Metering Data

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ew 24(1):

Authors:

Huakun Que, Zetao Jiang, Zhifeng Zhou, Yongsheng He, Xin Liu

Abstract:

Introduction:The rapid expansion of energy networks has significantly increased energy consumption, resulting in higher electricity costs. Abnormal energy usage in buildings and industries, often caused by system malfunctions, leads to substantial energy waste. Detecting such anomalies is essential…Introduction:The rapid expansion of energy networks has significantly increased energy consumption, resulting in higher electricity costs. Abnormal energy usage in buildings and industries, often caused by system malfunctions, leads to substantial energy waste. Detecting such anomalies is essential for cost control and efficient energy management.
Objectives:This study aims to develop a deep learning-based method to detect anomalies in high-dimensional energy metering data, overcoming the limitations of existing techniques that struggle with data complexity and lack effective contextual analysis.
Methods:High-dimensional metering data from a city energy provider is processed using a Convolutional Autoencoder (CAE) to extract deep features and reduce dimensionality. These features are then fed into a Cascaded Long Short-Term Memory (CLSTM) network, which identifies anomalous patterns in the data.
Results:The cascaded CLSTM model effectively detects anomalies in the energy consumption data by accurately predicting deviations from normal patterns.
Conclusion:The proposed CAE-CLSTM approach enhances anomaly detection in complex energy datasets, enabling more effective monitoring and reducing unnecessary energy waste and costs.
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Towards Real-Time Spatial Distance Monitoring of Power Transmission Lines Using LiDAR Point Clouds and Visual Imaging

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ew 24(1):

Authors:

Li Zhendong , Wang Feiran , Han Geng , Guo Xinyang , Shi Zhaoyang

Abstract:

INTRODUCTION: Efficient monitoring of power transmission lines is paramount to grid safety, clearance violation prevention, and uninterrupted supply of electricity. Classic inspection approaches like ground surveys by manual methods and visual inspections by drones are time-consuming, costly, and s…INTRODUCTION: Efficient monitoring of power transmission lines is paramount to grid safety, clearance violation prevention, and uninterrupted supply of electricity. Classic inspection approaches like ground surveys by manual methods and visual inspections by drones are time-consuming, costly, and susceptible to human error.
OBJECTIVES: Current LiDAR-based approaches are limited in automation, with extensive post-processing based on manual intervention. Additionally, most existing models are not scalable and fail under changing environmental conditions because of a lack of generalization. In this research, a spatial monitoring platform that combines LiDAR point clouds with high-resolution imagery through RandLA-Net is presented for semantic segmentation and hazard detection.
METHODS: Combining geometric information (LiDAR) and visual features (images) with an optimized RandLA-Net architecture allows for accurate, real-time infrastructure features and hazard detection in dense or cluttered scenarios.
RESULTS: The system presented here attained a semantic segmentation accuracy of 99.1% and a mean Intersection over Union (mIoU) of 93.2%. Spatial distance estimation had a low Mean Absolute Error (MAE) of 0.16 meters and Root Mean Square Error (RMSE) of 0.23 meters. The rate of safety violations detected never exceeded 4% among all object pairs. Compared to alternative techniques the proposed approach offers higher segmentation accuracy and more comprehensive hazard detection.
CONCLUSION: It uniquely combines LiDAR and image data with advanced algorithms for precise, real-time distance measurement and monitoring. This study provides a cost-effective, scalable, and real-time-enabled monitoring solution, lessening reliance on human inspections and hugely enhancing hazard detection accuracy for power transmission infrastructure.
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Research on the Mechanism of Power Quality in Renewable Energy Marketing Based on BMA Algorithm

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ew 24(1):

Author:

Qinghua Fan

Abstract:

INTRODUCTION: Based on the BMA (Bayesian Model Average) algorithm, this paper deeply studies the mechanism of power quality in renewable energy marketing. By collecting a large amount of renewable energy market data and utilising the BMA algorithm for model building and data analysis, this paper ai…INTRODUCTION: Based on the BMA (Bayesian Model Average) algorithm, this paper deeply studies the mechanism of power quality in renewable energy marketing. By collecting a large amount of renewable energy market data and utilising the BMA algorithm for model building and data analysis, this paper aims to reveal the internal relationship between power quality and renewable energy marketing and explore its impact on actual market operations.
OBJECTIVES: In the context of data analysis, this paper selects multidimensional information, including sales data, user feedback data, and power quality monitoring data, from the renewable energy market over recent years. By integrating and analysing these data with the BMA algorithm, we find that power quality has a significant positive impact on the marketing of renewable energy.
METHODS: Specifically, when power quality is improved by 10%, the sales of renewable energy products are expected to increase by approximately 6% on average, and customer satisfaction will rise by 7% accordingly. This data shows that improving power quality can effectively promote the expansion of the renewable energy market and enhance user loyalty. Our research, leveraging the BMA algorithm, reveals that improving power quality serves as a dual catalyst for promoting renewable energy.
RESULTS: Firstly, it directly bolsters consumer trust and purchase intention towards renewable energy products. Secondly, it indirectly enhances the market competitiveness of these products by optimising their performance and reducing failure rates. These insights provide renewable energy enterprises with a pivotal foundation for optimising their marketing strategies.
CONCLUSION: In conclusion, power quality emerges as a crucial factor in the marketing of renewable energy. By prioritising its improvement, enterprises can simultaneously enhance sales, customer satisfaction, and product competitiveness, thereby underscoring the importance of power quality as a core component of their marketing strategy.
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Intelligent Optimization of Combustion Process and NOx Emission Control of Power Plant Boiler Based on Deep Learning and Multi-objective Optimization

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ew 24(1):

Authors:

Xiaowei Chen, Lei Yan

Abstract:

This study proposes an intelligent optimization and nitrogen oxide (NOx) emission control method for power plant boiler combustion processes by integrating deep learning and multi-objective optimization. While traditional empirical tuning and single-objective algorithms struggle with dynamic, multi…This study proposes an intelligent optimization and nitrogen oxide (NOx) emission control method for power plant boiler combustion processes by integrating deep learning and multi-objective optimization. While traditional empirical tuning and single-objective algorithms struggle with dynamic, multi-variable combustion environments and lack real-time adaptability and synergistic optimization of efficiency and emissions, this research addresses these gaps by establishing a rolling optimization model that considers load and emissions. By analyzing the relationship between boiler combustion efficiency and nitrogen oxides generation, a rolling optimization model considering load and emission is established. The study analyzes and predicts the operation data and optimizes the combustion strategy in real time by a dynamic multi-objective optimization evolutionary algorithm. Performance evaluation shows that the model achieves high prediction accuracy, with an average absolute error of 2.36×10-5 kW for boiler load, and outperforms existing models in key metrics such as ignition success rate (98.7%) and load adjustment accuracy (3.4 MW). The approach significantly improves combustion efficiency and tightens NOx control, reducing energy waste and improving power plant energy efficiency. These advances demonstrate their effectiveness in improving combustion efficiency, enhancing nitrogen oxide control, and reducing energy waste, providing a powerful solution for operating smart power plants that integrates real-time adaptability and multi-objective synergy, outperforming traditional methods.
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Application of Offshore Wind Power Digital Twin Technology in Remote Operation and Maintenance

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Authors:

Huatao Si , Xiaotao Chen , Qingyu Men

Abstract:

INTRODUCTION: Offshore wind (OsW) energy has emerged as a key factor in the global transition towards high-energy-return renewable energy, with stable winds and minimal land take. However, the Maintenance and Operation (O&M) of OsW wind farms are very challenging due to their hostile marine conditi…INTRODUCTION: Offshore wind (OsW) energy has emerged as a key factor in the global transition towards high-energy-return renewable energy, with stable winds and minimal land take. However, the Maintenance and Operation (O&M) of OsW wind farms are very challenging due to their hostile marine conditions, huge running costs, and poor accessibility.
OBJECTIVES: To address these issues, the proposed method offers a Digital Twin (DT) strategy designed to enhance the remote operation and maintenance (O&M) of OsW wind power equipment. From the use of high-resolution global OSW wind turbine observations from Sentinel-1, combined with domain-specific feature engineering and data preprocessing, including outlier removal and normalization, the approach provides robust input for modelling and analysis.
METHODS: One of the most important aspects of the system is the integration of real-time sensor feeds through Arduino devices, secure data exchange through OPC UA, and middleware processing through Node-RED. The sensor-based data architecture feeds into a Unity 3D-based digital twin environment, which continuously synchronizes virtual models with the physical conditions of the turbines.
RESULTS: Besides, fault classification is handled with a Categorical Network (CatNet), where attention mechanisms and convolutional layers are used to detect abnormalities such as gearbox faults, generator faults and yaw misalignment. Interactive dashboards, 3D visualization, and predictive analytics are supported within the framework, enabling operators to monitor, diagnose, and control offshore turbines remotely.
CONCLUSION: Ultimately, this approach significantly reduces unplanned downtime, enhances safety, and maximizes power output through intelligent, data-driven decision-making.
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Data-Driven Decision-Making Method of Intelligent Supervision and Command Platform in Offshore Wind Power Operation and Maintenance

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ew 24(1):

Authors:

Jia Kun Wang, Yi Liu, Suowei Song

Abstract:

INTRODUCTION: Innovations in offshore wind farm operation and maintenance require intelligent monitoring platforms that can leverage high-resolution SCADA data to enhance predictive precision and operational effectiveness. With the use of deep learning algorithms, specifically LSTM, this work achie…INTRODUCTION: Innovations in offshore wind farm operation and maintenance require intelligent monitoring platforms that can leverage high-resolution SCADA data to enhance predictive precision and operational effectiveness. With the use of deep learning algorithms, specifically LSTM, this work achieves improved forecasting and anomaly detection accuracy on a real wind turbine dataset recorded in Turkey in 2018.
OBJECTIVES: The proposed approach involves extensive data preprocessing, including cleaning, synchronization, and normalization, followed by advanced feature extraction using signal processing transforms such as the Fast Fourier Transform and wavelet transforms.
METHODS: Different predictive models, including Linear Regression, Random Forest Regression, Support Vector Regression, Gradient Boosting Machines, and LSTM, were trained and tested within a Python setting. The LSTM model achieved a remarkable improvement, with a Mean Absolute Error of 78.6 kW, compared to traditional machine learning methods such as RF Regression, SV Regression, and Gradient Boosting Machines. The enhanced accuracy results from the LSTM's ability to derive intricate temporal patterns and nonlinear relationships inherent in sequential turbine operational data.
RESULTS: The results affirm the potential of deep learning approaches in reshaping offshore wind turbine management and highlight the importance of tailored temporal modeling for resolving the specific challenges of renewable energy systems.
CONCLUSION: The system not only accurately predicts power production but also performs anomaly detection and optimises maintenance scheduling, resulting in enhanced reliability and energy production for offshore wind farms. By integrating these data-oriented approaches with an intelligent command and supervision system, the strategy facilitates proactive decision-making and real-time operation control.
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Deep Learning and GIM Standard-Based Intelligent 3D Modeling Algorithm for Substations with Distributed Collaborative Design System

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Authors:

Zhongsheng Kan, Yu Xin, Delong Meng, Jie Chen, Xiaoming Ju

Abstract:

INTRODUCTION: Contemporary substation design methodologies encounter fundamental limitations in achieving optimal geometric precision and collaborative efficiency, particularly when addressing the integration of heterogeneous data sources while maintaining strict adherence to Geographic Information…INTRODUCTION: Contemporary substation design methodologies encounter fundamental limitations in achieving optimal geometric precision and collaborative efficiency, particularly when addressing the integration of heterogeneous data sources while maintaining strict adherence to Geographic Information Modeling (GIM) standards. Existing computational approaches demonstrate significant deficiencies characterized by prolonged processing durations and constrained accuracy levels, thereby necessitating the development of innovative solutions that leverage cutting-edge artificial intelligence techniques to overcome these systematic challenges.
OBJECTIVES: This investigation aims to develop and validate a comprehensive intelligent 3D modeling algorithm specifically designed for electrical substation applications that seamlessly integrates advanced deep learning methodologies with rigorous GIM standard compliance and sophisticated distributed collaborative design functionalities, while simultaneously achieving substantial improvements in geometric accuracy and computational efficiency compared to conventional design paradigms.
METHODS: The proposed algorithmic framework employs sophisticated hierarchical neural network architectures that incorporate multi-scale convolutional feature extraction mechanisms and adversarial generative training protocols. The comprehensive system architecture integrates four critical components: intelligent data acquisition and preprocessing modules, advanced deep learning computational engines, automated GIM standard compliance verification systems, and distributed collaborative design platforms. Experimental validation was conducted using an extensive dataset encompassing 12,847 technical engineering drawings, 1,156 high-resolution point cloud segments, and 3,428 photogrammetric image collections, with comprehensive field testing involving up to 32 concurrent collaborative users across diverse operational scenarios.
RESULTS: The developed intelligent modeling algorithm achieved exceptional geometric accuracy of 96.8% compared to 87.3% demonstrated by traditional methodologies, representing a substantial 9.5 percentage point improvement in modeling precision. Computational efficiency demonstrated remarkable optimization with processing time reduced by 94%, decreasing from the conventional range of 180-240 minutes to an unprecedented 12.4 minutes per complete substation model. Extensive field validation trials confirmed seamless collaborative scalability with negligible performance degradation under multi-user operational conditions, while maintaining GIM standard compliance exceeding 99.2% across all tested configurations and operational scenarios.
CONCLUSION: The developed intelligent 3D modeling system establishes a new technological paradigm for substation design applications, delivering exceptional improvements in both geometric accuracy and computational efficiency while maintaining stringent GIM compliance requirements. The framework's robust integration capabilities enable seamless deployment within existing power system management infrastructures without necessitating extensive modifications to established operational workflows, thereby providing a comprehensive foundation for next-generation collaborative engineering design platforms in critical infrastructure applications.
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Phase Robust Optimization of PV-Energy Storage Microgrid Based on Deep Reinforcement Learning and Mixed Integer Constraint Model

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Authors:

Wei Li, Zhihang Qin

Abstract:

The pronounced dependence of photovoltaic (PV) generation on meteorological conditions, coupled with substantial fluctuations in load demand, renders conventional deterministic optimization approaches inadequate. Addressing the need for robust multi-phase decision-making across temporal domains (e.…The pronounced dependence of photovoltaic (PV) generation on meteorological conditions, coupled with substantial fluctuations in load demand, renders conventional deterministic optimization approaches inadequate. Addressing the need for robust multi-phase decision-making across temporal domains (e.g., day-ahead scheduling and real-time adjustment) and the coordinated optimization of continuous variables (such as energy storage charge/discharge rates) and discrete variables (such as unit commitment states), this research proposes a phased robust optimization strategy for PV-storage microgrids. This strategy integrates Deep Reinforcement Learning (DRL) with a Mixed-Integer Constrained Model (M-ICM). The methodology explicitly accounts for the coupling effects between irradiance intensity, temporal sequence efficiency, and the state-of-charge of energy storage systems. This ensures that the microgrid control system provides sufficient resilience mechanisms for dynamic energy allocation in practical applications, facilitating global optimization of microgrid energy utilization. The simulation results show significant improvements over conventional methods, which includes reduction in time-to-peak under dynamic balancing conditions, maintenance of lower output current-to-power ratios, and enhanced convergence speed of the neural network model.
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Fine-Tuning the Qwen2.5-VL Model for Intelligent Applications in the Electrical Domain

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Authors:

Yao Song, Chunli Lv, Kun Zhu, Xiaobin Qiu

Abstract:

This study explores the fine-tuning application of the Qwen2.5-VL multi modal large model in the electrical domain. The electrical industry faces numerous challenges in maintaining and managing complex electrical systems. Traditional methods often rely on manual inspection and analysis. With the ra…This study explores the fine-tuning application of the Qwen2.5-VL multi modal large model in the electrical domain. The electrical industry faces numerous challenges in maintaining and managing complex electrical systems. Traditional methods often rely on manual inspection and analysis. With the rapid advancement of artificial intelligence (AI) technologies, there is a growing need to explore how these tools can be applied to improve efficiency and accuracy in the electrical domain. Qwen2.5-VL is a state-of-the-art visual language model. We adopted the LoRA (Low Rank Adaptive) method to fine tune the model, which enables efficient parameter updates in low resource environments while maintaining high performance. This study analyzes the data characteristics and task requirements in the electrical domain, designs fine-tuning strategies with a focus on image-based applications, including data preprocessing, model fine-tuning, and training parameter optimization. The experimental re-sults show that the fine tuned model has achieved significant performance im-provements in tasks such as electrical equipment fault detection, image recogni-tion, and text classification. This study provides new ideas and methods for the application of artificial intelligence in the electrical domain, which is of great significance for promoting the development of electrical intelligence.
 
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Demand Response Control Strategy of Photovoltaic Grid-connected Inverter Based on Improved Linear Active Disturbance Rejection Dynamic Decoupling

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Authors:

Chao Zhang, Yangrui Zhang, Yongliang Jia, Peng Tao, Guinan Han, Junpeng Zhao

Abstract:

In power systems, because the Auto Disturbances Rejection Controller (ADRC) performs relatively reliable stability and safety, it finds extensive application in the design of power inverters. However, its performance is constrained by inherent response lag and limited harmonic suppression capabilit…In power systems, because the Auto Disturbances Rejection Controller (ADRC) performs relatively reliable stability and safety, it finds extensive application in the design of power inverters. However, its performance is constrained by inherent response lag and limited harmonic suppression capabilities. To address the evolving demands of modern power systems, Linear Active Disturbance Rejection Control (LADRC) has emerged as an upgraded alternative to conventional control strategies through its enhanced adaptability in practical applications. We propose a frequency-domain-equivalent-based LADRC control strategy, where systematic parameter calibration is implemented through transfer function methodology. Subsequent fine-tuning of critical parameters, which includes proportional gain, derivative gain, observer's natural frequency, control gain and so on, are made fine adjustments to optimize circuit performance. Experimental validation confirms the efficacy of first-order LADRC in improving voltage-current conversion characteristics of LLC-operated transformers. Furthermore, an adaptive control framework for third-order LADRC is established to achieve coupled parameter optimization. This research innovatively incorporates Particle Swarm Optimization (PSO) into the LADRC parameter configuration process, enhancing adjustment efficiency and facilitating optimal solutions. Simulation results verify that the enhanced LADRC effectively mitigates phase delay and improves dynamic response characteristics. The modified PSO algorithm exhibits technical superiority with 11.7% precision enhancement. The response speed has obvious technical advantages, and the coefficient of variation is significantly lower than that of the comparison algorithm, which verifies the advantages of the improved PSO algorithm in robustness. The proposed control strategy successfully compensates for ADRC limitations, achieving an optimal balance between control precision and steady-state performance through gain regulation in repetitive control systems. This advancement will provide critical technical support for addressing control challenges in renewable energy integration.
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A fault diagnosis and location method for power grid simulators based on voltage threshold and MCNN

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ew 24(1):

Authors:

Jinyue Su, Yingwen Yang, YiLin Yu, Yueshuo Li, Shiwei Zhao, Zhidong Wang, Ling Yang, Fengqiang Deng

Abstract:

To accommodate the testing requirements of high-power wind turbines, this paper designs a power grid simulator topology and investigates fault diagnosis and localization methods by integrating mathematical models and neural networks. To address the drawback of lengthy computation times associated w…To accommodate the testing requirements of high-power wind turbines, this paper designs a power grid simulator topology and investigates fault diagnosis and localization methods by integrating mathematical models and neural networks. To address the drawback of lengthy computation times associated with intelligent diagnostic methods, this paper employs a threshold-based approach using voltage mathematical models to achieve rapid preliminary diagnostics. To address the positioning challenges brought about by symmetrical structures, a multi-layer convolutional neural network (MCNN) model is utilized to achieve accurate positioning. To tackle the issue of insufficient fault samples, a sliding window technique and frequency domain transformation methods are applied to expand the sample set, enabling the diagnosis and localization of 36 types of faults. This paper builds an inverter-side model of the power grid simulator using Simulink to verify the proposed method. And the diagnostic accuracy rate reaches 100%, and the overall localization accuracy exceeds 96%.
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On-Site Protection Scheme for Microgrid with High Penetration Rate of Grid-Forming Inverter Interfaced Distributed Energy Resources

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ew 24(1):

Authors:

Yubo Yuan, Juan Li, Yunlong Jiang, Sudi Xu, Haiou Cao

Abstract:

INTRODUCTION: Renewable energy represented by grid forming inverter interfaced distributed energy resources is increasingly connected to microgrids that can operate independently and have various modes, resulting in traditional relay protection schemes that cannot meet the requirements of protectio…INTRODUCTION: Renewable energy represented by grid forming inverter interfaced distributed energy resources is increasingly connected to microgrids that can operate independently and have various modes, resulting in traditional relay protection schemes that cannot meet the requirements of protection selectivity and speed.
OBJECTIVES: To this end, the fault characteristics of the grid forming distributed energy resources are analyzed. On this basis, the current, voltage and phase angle characteristics between them under the grid-connected/islanded operation mode of the microgrids are analyzed.
METHODS: Aiming at the problem that the traditional protection loses selectivity, the phase angle difference between the pre-fault voltage and the post-fault current is used to judge the fault direction, and the internal fault acceleration factor of the integrated phase angle difference and the measured impedance is used to improve the action equation of the inverse time overcurrent protection. Aiming at the problem that the fault current is too small and the inverse-time overcurrent protection cannot be started when the two-phase short-circuit fault occurs in the islanded state, a low-voltage protection criterion based on the comprehensive phase angle difference characteristics is set up, and a microgrid protection scheme combining improved inverse-time over-current protection and low-voltage protection is obtained.
RESULTS: The model is built by PSCAD, the theoretical analysis and simulation results show that the scheme can operate quickly and reliably in the grid-connected/islanded operation state of the microgrid.
CONCLUSION: The results verify the effectiveness of the proposed protection scheme.
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Short-term photovoltaic power prediction based on dual decomposition with TCN-Informer-xLSTM

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ew 24(1):

Authors:

Guancheng Jin, He Jiang, Mofan Wei, Rui Guo

Abstract:

As renewable energy generation is increasingly integrated into power grids worldwide, the random nature of renewable energy output poses significant challenges to the stability of power systems. Therefore, it is essential to accurately predict the output of renewable energy sources. In this paper, …As renewable energy generation is increasingly integrated into power grids worldwide, the random nature of renewable energy output poses significant challenges to the stability of power systems. Therefore, it is essential to accurately predict the output of renewable energy sources. In this paper, a dual decomposition algorithm based on variational mode decomposition (VMD) and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed to decompose the original photovoltaic power sequence and combine the entropy values of the subsequences to obtain the predicted sequences for the high frequency and low frequency components. Then, different prediction models are used for the high-frequency and low-frequency sequences to predict the photovoltaic outputs, where the Temporal Convolutional Networks (TCN)-Informer model is used for the high-frequency component and the xLSTM model is used for the low-frequency component, and finally, the RIME algorithm is applied to find the optimization of the hyperparameters. The results of simulation analysis show that the quadratic decomposition method proposed in this paper significantly improves the prediction accuracy of photovoltaic sequences and reduces the computational complexity
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A reliability enhancement strategy of critical nodes in power system communication network based on quantitative calculation method and criticality index

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ew 24(1):

Authors:

Xiaoyu Deng, Yabin Chen, Yu Sui, Hao Yu

Abstract:

INTRODUCTION: Power system communication networks are essential for smart grid operations, enabling real-time monitoring and control. Disruptions at critical communication nodes can jeopardize grid stability and lead to cascading failures, highlighting the need for accurate reliability assessment o…INTRODUCTION: Power system communication networks are essential for smart grid operations, enabling real-time monitoring and control. Disruptions at critical communication nodes can jeopardize grid stability and lead to cascading failures, highlighting the need for accurate reliability assessment of these vital components. However, traditional methods often overlook the complex, dynamic, and interdependent nature of modern communication infrastructures.
OBJECTIVES: This paper aims to develop a precise and scalable methodology for assessing and enhancing the reliability of critical nodes in smart grid communication networks.
METHODS: The proposed approach integrates probabilistic failure modeling, graph-theoretic analysis, and heuristic optimization. Key techniques include a newly designed Criticality Index (CI) accounting for failure probabilities, repair dynamics, and topological relevance; a Monte Carlo simulation framework to assess network behavior under stochastic disturbances; and a genetic algorithm (GA) for optimizing node reinforcement strategies.
RESULTS: Experiments conducted on the IEEE-118 bus system demonstrate that the GA-CI methodology improves the Network Robustness Index by 12.45%, consistently outperforming baseline methods with acceptable computational efficiency.
CONCLUSION: The proposed framework provides a robust and interpretable solution for reinforcing critical communication infrastructure in smart grids. It holds potential for broader application in the reliability assessment of other complex networked systems.
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Distributed photovoltaic power prediction considering spatiotemporal correlation and dual Attention-LSTM

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ew 24(1):

Authors:

Yueyuan Zhang, Yifan Zhang, Dazhi Pan, Mingyu Sun, Huawei Mei, Wangbin Cao

Abstract:

Predicting the power output of photovoltaic clusters is crucial for optimizing regional solar power scheduling. To enhance the accuracy of distributed photovoltaic station power forecasts, a method incorporating spatiotemporal correlation and dual Attention-LSTM is introduced. The K-means algorithm…Predicting the power output of photovoltaic clusters is crucial for optimizing regional solar power scheduling. To enhance the accuracy of distributed photovoltaic station power forecasts, a method incorporating spatiotemporal correlation and dual Attention-LSTM is introduced. The K-means algorithm is first employed to classify the distributed photovoltaic power station clusters in the area. The reference station for the target photovoltaic station is determined by calculating the Euclidean distance between the target station and the typical daily power profiles of other stations in the cluster. Simultaneously, pivotal weather features that influence photovoltaic output are ascertained through computation of the Pearson correlation coefficient. Subsequently, an Attention-LSTM-based power prediction and error correction model is constructed, utilizing both meteorological and power traits as input variables to finalize the photovoltaic power generation forecast. To validate the approach, a simulation analysis is conducted using empirical data from Arizona, USA. The experimental results indicate that the suggested method greatly improves the accuracy of predictions for distributed photovoltaic power.
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Intelligent substation communication network fault location method based on dynamic spatiotemporal graph association perception

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ew 24(1):

Authors:

Ligang Ye, Wenzhang Li, Jing Zhao, Yuanyuan Liu

Abstract:

INTRODUCTION: Accurately locating faults in intelligent substation communication networks is crucial for power grid safety. Existing methods fail to fully capture dynamic fault characteristic evolution and complex dependencies within network topologies OBJECTIVES: This paper aims to (1) model spat…INTRODUCTION: Accurately locating faults in intelligent substation communication networks is crucial for power grid safety. Existing methods fail to fully capture dynamic fault characteristic evolution and complex dependencies within network topologies
OBJECTIVES: This paper aims to (1) model spatiotemporal fault features in communication networks, (2) enhance fault pattern capture through multi-view learning, and (3) improve fault location accuracy.
METHODS: We propose a multi-view spatiotemporal dynamic graph network. First, a multi-view graph neural network models spatial dependencies via cross-view comparative learning using topological and attribute data. Second, a gated recurrent unit with dynamic time windows extracts temporal evolution trends, focusing on local fault patterns and short-term dependencies.
RESULTS: Evaluations on a 220kV substation communication network show our method achieves higher fault location accuracy versus baselines, effectively capturing spatiotemporal fault characteristics.
CONCLUSION: The proposed framework addresses dynamic fault evolution and topological dependencies, providing a robust solution for intelligent substation fault diagnosis.
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Consistency Monitoring of Wind Turbine Blade Loads Based on MEMS Optical Fiber Sensing

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ew 24(1):

Authors:

Yong Xue , Yang Li , Xiangye Fan , Binshan Xie , Zhiyuan Ma , Lin Lin , Mengnan Cao

Abstract:

INTRODUCTION: This paper presents a comprehensive structural monitoring framework for wind turbine blades based on MEMS-FBG (micro-electro-mechanical systems—fiber Bragg grating) sensor fusion technology. OBJECTIVES: The system integrates high-resolution strain and vibration sensing across multipl…INTRODUCTION: This paper presents a comprehensive structural monitoring framework for wind turbine blades based on MEMS-FBG (micro-electro-mechanical systems—fiber Bragg grating) sensor fusion technology.
OBJECTIVES: The system integrates high-resolution strain and vibration sensing across multiple blade segments, combined with real-time data processing, fault detection, and SCADA-level visualization.
METHODS: A multilayered load consistency model is introduced, incorporating thermal compensation, strain-to-load calibration, and a novel consistency index (η) to quantify inter-blade aerodynamic symmetry.
RESULTS: Experimental validation was conducted on a 2.0 MW wind turbine over a 60-day continuous monitoring campaign. Static calibration demonstrated a load reconstruction accuracy within ±3%, while dynamic data revealed a strong correlation between load and blade vibration (R ≥ 0.86).
CONCLUSION: Fault simulation through pitch angle manipulation confirmed the system’s rapid alarm response within 3 seconds for major asymmetry events. Additionally, signal drift testing showed MEMS-FBG sensors exhibited 80–95% lower drift than conventional resistance strain gauges under rotating and EMI-intensive conditions.
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Distribution Network Target Framework Planning Algorithm Based on Fuzzy Optimization and Grey System Theory

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Authors:

Jinsen Liu, Molin He, Jie Wang, Jie Lu

Abstract:

INTRODUCTION: The global energy transition, driven by the rapid growth of distributed renewable energy, stochastic load profiles (e.g., EV charging spikes), and conflicting stakeholder objectives, has brought unprecedented complexities to distribution network planning. Traditional deterministic met…INTRODUCTION: The global energy transition, driven by the rapid growth of distributed renewable energy, stochastic load profiles (e.g., EV charging spikes), and conflicting stakeholder objectives, has brought unprecedented complexities to distribution network planning. Traditional deterministic methods fail to handle qualitative fuzziness (e.g., subjective reliability thresholds) and quantitative uncertainty (e.g., sparse historical data), leading to inflexible and inefficient solutions. This study addresses these challenges by developing a hybrid planning framework.
OBJECTIVES: This paper aims to solve the dual challenges of qualitative fuzziness and quantitative uncertainty in distribution network planning, providing a systematic solution to accommodate distributed renewable energy, handle load uncertainty, and balance conflicting stakeholder preferences through integrating fuzzy optimization theory and grey system theory.
METHODS: The hybrid algorithm combines fuzzy optimization and grey system theory. Fuzzy optimization uses triangular fuzzy numbers for load growth rates ([3%, 5%, 8%]) and trapezoidal fuzzy intervals for voltage constraints ([−10%, −5%, 5%, 10%]) with membership functions (threshold λ≥0.8) to convert qualitative requirements into solvable constraints. Grey system theory applies the GM(1,1) model for load forecasting (achieving 4.2% MAPE with 15-month data) and grey relational analysis (GRA) for data-driven objective weighting to eliminate expert bias. An improved particle swarm optimization (IPSO) algorithm is used for optimization, validated in a 33-node network with 8.5 MW PV and 6 MW wind capacity.
RESULTS:  In the 33-node case study, compared to the deterministic genetic algorithm (D-GA), the hybrid algorithm reduces lifecycle costs by 19% (from $8.91M to $7.23M), increases renewable energy accommodation by 24% (from 9.8 MW to 12.3 MW), and improves the system average supply availability index (ASAI) from 99.92% to 99.95%. Under extreme uncertainties (±40% renewable output, ±30% load shifts), cost deviations remain within 6% and reliability metrics within 5%, demonstrating strong robustness.
CONCLUSION: This research presents a robust hybrid framework that bridges fuzzy qualitative reasoning and grey data efficiency, effectively addressing both qualitative fuzziness and quantitative uncertainty in distribution network planning. It provides a science-based tool for resilient grid design, with potential for extension to multi-energy system integration and real-time optimization in future work.
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Smart Grid and Economic Growth: Driving Industrial Upgrading through Efficient Energy Management

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Authors:

Xilin Zhang , Kaiyuan Dong , Yujiao Mao , Yanhong Wang , Yaqian Liu

Abstract:

INTRODUCTION: In an era marked by rising energy demand and environmental concerns, integrating smart grid technologies is a crucial solution for promoting industrial expansion through efficient electricity control. OBJECTIVES: Here, we examine the revolutionary capabilities of smart grids to enhan…INTRODUCTION: In an era marked by rising energy demand and environmental concerns, integrating smart grid technologies is a crucial solution for promoting industrial expansion through efficient electricity control.
OBJECTIVES: Here, we examine the revolutionary capabilities of smart grids to enhance economic growth by leveraging an innovative hybrid optimisation method, Trevally Optimisation (TrevOpt), at its core. The study's main focus is to show how Turnbridge's new approach can efficiently manage energy distribution and use within industrial ecosystems by leveraging TrevOpt's computational power, which combines the benefits of evolutionary algorithms and heuristics.
METHODS: The heart of this paper is a discussion of how the use of smart grid technologies can catalyse industrial development. Operation eff was on another level in this experiment, enabling the optimisation of production parameters using the TrevOpt model. Integrating real-time analytics into the TrevOpt framework allows proactive management of energy resources through dynamic tuning, thereby reducing waste and enhancing system reliability.
RESULTS: This highlights the potential of these technologies to inspire industrial competitiveness, drive investment in sustainability, and open new areas in energy-intensive industries to spur economic growth. The simulation presented at the end of the numerical section outlines the concrete benefits of using the TrevOpt method, including a 20% reduction in energy consumption, a 15% reduction in operational costs, and a 25% increase in overall system reliability.
CONCLUSION: This study therefore provides a solid foundation that enables industries to leverage smart grid developments as a cornerstone for transforming their businesses while also protecting the environments in which people live.
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State Estimation of Power System Using PMU Devices

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Authors:

S Shanmugapriya, Puspesh Pallav, Mukul Patel, Virendra Mulgir

Abstract:

This paper explains the role of Phasor Measurement Units (PMUs) in estimating the state of energy systems and suggests a linear state estimator involving PMU current and voltage measurements for tracking the system state. The state estimator carries out the estimation process in two phases. The fir…This paper explains the role of Phasor Measurement Units (PMUs) in estimating the state of energy systems and suggests a linear state estimator involving PMU current and voltage measurements for tracking the system state. The state estimator carries out the estimation process in two phases. The first phase uses the conventional SCADA measurements and applies the classical Weighted Least Square (WLS) approach for estimating the current system state, and the second phase corrects and tracks the system state using the PMU measurements in the subsequent intervals. It provides simulation results of the proposed method on IEEE 30 and 57 bus energy systems for exhibiting its superiority.
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A Hybrid WOA-DeepONet Framework for Data-Driven and Physics-Guided SOH/RUL Estimation in Lithium-Ion Batteries

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Authors:

Qiang Yang , Zhiquan Qin , Haifeng Zhang , Jian Shi , Chao Lu

Abstract:

INTRODUCTION: For energy storage systems to be safe, effective, and reliable, it is essential to accurately forecast the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries under complicated operating situations. OBJECTIVES: This research suggests a hybrid modeling appro…INTRODUCTION: For energy storage systems to be safe, effective, and reliable, it is essential to accurately forecast the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries under complicated operating situations.
OBJECTIVES: This research suggests a hybrid modeling approach that combines a physics-informed Deep Operator Network (DeepONet) supplemented by encoders with a neural network optimized by the Whale Optimization Algorithm (WOA).
METHODS: The framework first utilizes WOA to improve the initialization of Backpropagation (BP) neural networks, thus enhancing convergence speed and avoiding local minima in early-stage training. Then, a multi-physics informed DeepONet model is constructed to learn the spatiotemporal evolution of battery thermal and electrochemical variables from virtual heating profiles.
RESULTS: By integrating boundary conditions, starting restrictions, and partial differential equation (PDE) residuals into the loss function, the model integrates physical supervision and guarantees forecast consistency with battery dynamics.
CONCLUSION: Both COMSOL-generated synthetic data and publicly available battery aging datasets are used in extensive research. The suggested approach outperforms conventional MLP, LSTM, and even standard DeepONet models, according to the results, with an R2 of 0.99 and an MSE of 0.0009.
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Multi-Source Collaborative Optimization Scheduling Technology for New Energy Microgrid Based on Improved Particle Swarm Algorithm

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Author:

Xin Jin

Abstract:

INTRODUCTION: New energy microgrids face significant challenges in multi-source coordinated dispatching, primarily due to the high uncertainty in renewable energy output. Traditional optimization methods often suffer from local optimality and extended computation times, limiting their effectiveness…INTRODUCTION: New energy microgrids face significant challenges in multi-source coordinated dispatching, primarily due to the high uncertainty in renewable energy output. Traditional optimization methods often suffer from local optimality and extended computation times, limiting their effectiveness in real-time or complex environments. There is a critical need for enhanced strategies to improve both the economic efficiency and operational robustness of microgrids.
OBJECTIVES: This paper aims to propose and validate an optimization dispatching strategy based on an Improved Particle Swarm Optimization (IPSO) algorithm. The core objectives are to reduce the system's comprehensive operating cost, increase computational efficiency, and enhance the absorption rate of new energy within microgrid systems.
METHODS: The IPSO algorithm enhances the conventional PSO by incorporating dynamically adjusted inertia weights and adaptive learning factors, improving its global search ability and convergence speed. A multi-source collaborative optimization model is formulated with a primary objective of minimizing the total operating cost. The model accounts for fluctuations in load demand and constraints related to the charging and discharging efficiency of the energy storage system (ESS). The strategy is implemented and tested with consideration of Distributed Energy Resources (DERs)..
RESULTS: The improved IPSO algorithm demonstrated a 22.7% improvement in computational efficiency. It also achieved an average 16.2% reduction in the system’s comprehensive operating cost and increased the average absorption rate of new energy to 92.4%.
CONCLUSION: The proposed IPSO-based optimization strategy significantly enhances the economic and operational efficiency of new energy microgrids. By effectively integrating DER characteristics and operational constraints, this method provides a viable technical pathway for advancing the utilization of renewable energy and improving the overall performance of microgrid systems.
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Optimization of Deep Peak Shaving Methods for Fossil Fuel-Based Power Units Using the Improved Energy Consumption Framework

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Authors:

Xing Zhang, Yunlong Zhou

Abstract:

The design optimisation of Fossil Fuel-Based Power Plants is critical for improving energy efficiency and minimising environmental impact, particularly amid the increasing global demand for electricity. Fossil fuel plants are vital for supplying energy needs, but are hindered by fuel inefficiency a…The design optimisation of Fossil Fuel-Based Power Plants is critical for improving energy efficiency and minimising environmental impact, particularly amid the increasing global demand for electricity. Fossil fuel plants are vital for supplying energy needs, but are hindered by fuel inefficiency and emissions. The main aim of this research is to improve the performance of such power plants during peak demand hours and to reduce fuel consumption and emissions. The emphasis is placed on maximizing energy generation, enhancing operational effectiveness, and sustainability. The suggested work combines two advanced optimization methods.
 
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