Research Article
Stand-alone Micro Grid based on Artificially Intelligent Neural Network (AI-NN)
@ARTICLE{10.4108/ew.v9i6.147, author={Jenitha R. and K. Rajesh}, title={Stand-alone Micro Grid based on Artificially Intelligent Neural Network (AI-NN)}, journal={EAI Endorsed Transactions on Energy Web}, volume={10}, number={1}, publisher={EAI}, journal_a={EW}, year={2023}, month={6}, keywords={Neural Networks (NN), Maximum power point tracking (MPPT), Small Wind Turbine (SWT), Solar Photovoltaic (SPV), P\&O (Perturb and Observe), (Fuzzy logic controller)}, doi={10.4108/ew.v9i6.147} }- Jenitha R.
K. Rajesh
Year: 2023
Stand-alone Micro Grid based on Artificially Intelligent Neural Network (AI-NN)
EW
EAI
DOI: 10.4108/ew.v9i6.147
Abstract
INTRODUCTION: Hybrid stand-alone Small Wind Solar Energy System offers a feasible solution in remote areas where grid connectivity is either financially or physically unavailable. A small wind turbine (SWT) and a solar photovoltaic system are part of the hybrid energy system, which is effectively employed to meet the energy needs of rural household loads. OBJECTIVE: This research suggests an effective analysis of wind solar hybrid system controllers taking energy demands into account. The controller should be designed in such a way as to intelligently monitor the availability of wind energy and solar energy and store the energy without spilling it out. METHODS: In order to cope with the challenging factors involved in designing the controller, intelligent power tracking with an artificially intelligent neural network (AI-NN) is designed. Added to that, the whole process has been designed and analysed with the MATLAB SIMULINK tool. RESUSTS: The results of the simulation, infer that AI-NN achieved the regression value of 0.99 when compared with the Perturb & Observe algorithm (P&O), and the Fuzzy Logic Control (FLC) algorithm, and has a higher tracking speed. Also, the AI-NN attained 2.62kW whereas the P&O has attained 2.52kW and Fuzzy logic has attained 2.43W of power which is 3.89% higher than P&O algorithm and 7.52% higher than fuzzy MPPT algorithm. CONCLUSION: The designed controller module enhances the system by artificially intelligent algorithm. The AI-NN attains the better power performance with lesser tracking time and higher efficiency. Thus, it is evident that AI-NN MPPT suits well for the hybrid system.
Copyright © 2023 Jenitha R. et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
