Construction of an Intelligent Fault Identification System for Power Grid under Adaptive Genetic Algorithm

The increasingly complex structure of the power grid, as well as the integration of new energy and distributed generation resources, have led to a more diverse variety of types and modes of power grid faults. This study proposes an intelligent identification method for power grid faults based on the principle of Adaptive Genetic Algorithm (AGA). Firstly, it collects fault data through power grid monitoring equipment. Then, through an encoding scheme, these parameters are transformed into chromosomes that can be processed by genetic algorithms. In the design of the fitness function, the accuracy and real-time performance of fault detection are considered to ensure that the algorithm can effectively evaluate the quality of chromosomes. The control parameters of the genetic algorithm based on the performance of the process to enhance the convergence and robustness of the algorithm can be dynamically adjusted. Through comparative testing on multiple power grid fault data, the results show that the AGA based intelligent identification system for power grid faults is superior to traditional genetic algorithms (GA) and artificial neural networks (ANN) in terms of fitness function, convergence accuracy, and interpretability. Its fitness value can reach 96%, with a minimum relative error of 0.8% and an interpretability of 88%. This system can effectively process power grid fault data and provide a new intelligent tool for power grid operation and maintenance. Future work can focus on further optimizing the performance of AGA and achieving its widespread application.