Adaptive Weighted Kernel Principal Component Analysis and Multi-attribute Decision Making-based Judgment Model for Power Grid Forced Transmission

  INTRODUCTION:  response to the theoretical bottlenecks of the hybrid energy storage system (HESS) when participating in grid regulation. OBJECTIVES: Such as strong coupling of multi-dimensional state variables, difficulties in coordinating energy storage scheduling with grid stability, and prominent subjectivity in traditional power transfer decisions, a model for AI optimization collaborative control and grid state assessment with HESS as the core control object is proposed. METHODS: Firstly, based on traditional strong transfer features such as fault characteristics, grid topology, equipment status, and environmental disturbances, the operation state variables of HESS (including state of charge, charging and discharging power, health level, etc.) are introduced to construct a multi-dimensional feature matrix integrating energy storage and the grid, and the objective weighting of each dimension is achieved based on the information entropy theory. Secondly, an adaptive weighted kernel principal component analysis (AW-KPCA) algorithm is proposed, which uses the particle swarm optimization (PSO) algorithm to dynamically adjust the kernel function parameters and feature weights, and incorporates the HESS charging and discharging strategy into the optimization space to achieve joint optimization of energy storage scheduling and high-dimensional feature dimension reduction. The core novelty is the synergistic integration of HESS dynamics into grid feature modeling, PSO-based joint optimization of kernel parameters, feature weights, and storage dispatch, and TOPSIS-driven risk quantification for forced transmission decisions. RESULTS: Compared with the traditional kernel principal component analysis algorithm, the proposed method improved data processing efficiency by 41.3% and feature extraction accuracy by 15.7%. Finally, the TOPSIS multi-attribute decision-making theory is integrated to build a comprehensive judgment model on the adaptability of forced transportation, quantify the feasibility and risk level of forced transportation, and achieve optimal decision-making under multiple constraints. CONCLUSION: The simulation test was completed on the IEEE 300-node system and the Guizhou Power Grid Actual Fault Data Set. The accuracy of the proposed model reached 95.7, and its robustness was significantly stronger. The research provides new theoretical and algorithmic support for HESS to participate in the restoration of power grid failures.