Research On Distribution Network Fault Location Based On Intelligent Optimization Algorithm

As the size of power grids continuously increases,the application of automation equipment in power systems is also becoming more prevalent.When a power system fails,it sends a large number of alarm messages to the dispatch center,which can significantly challenge dispatchers’ decision-making.Therefore,developing a highly accurate fault localization method for power systems,to assist dispatch decisions,is of great significance for enhancing the reliability of power systems and ensuring their safe and stable operation.In light of the limitations of conventional fault location technologies—including low detection accuracy,poor adaptability,and insufficient fault tolerance—this study presents a Discrete Binary Particle Swarm Optimization algorithm.This algorithm improves the accuracy of fault location in power grids by optimizing the transfer function and inertia weight coefficient.This study analyzes and summarizes the mathematical model of fault location in distribution networks,viewing it as a "0-1" integer programming problem,a typical NP-HARD discrete problem.Swarm intelligence algorithms have great advantages in solving these kinds of problems,so this study introduces an improved discrete binary particle swarm optimization algorithm for solving this problem.In the improved method,the basic particle swarm algorithm,generally applicable to continuous search space problems,is discretized.Initially,considering that the rapid pace of speed updating in particle swarm algorithms would shorten the iteration time and improve operational efficiency,but might also miss the optimal solution,leading to incorrect convergence before the correct solution is found,this study introduces a linear constraint factor,p,to control it.Initially,p is large,allowing for quick speed updating,and then the updating speed slows down with the constraint factor,ensuring high accuracy while accelerating the algorithm and improving the search speed of particles.Secondly,considering the improvement of the adaptive inertia weight in the particle swarm algorithm,the size of the inertia weight is adaptively adjusted to control exploration and utilization during the search process.Through simulation testing,it was found that improving the adaptive inertia weight did not significantly improve the outcomes in three typical faults compared to only improving the adaptive transfer function.Finally,simulation analysis involving dual-power ring networks,traditional radiation-type distribution networks,and distribution networks with distributed power sources,showed that the BPSO(Binary Particle Swarm Optimization)algorithm with improved adaptive transfer functions can effectively locate fault sections,verifying the effectiveness and correctness of the improved BPSO algorithm.Simultaneously,the improved BPSO algorithm has a faster search speed than conventional algorithms,playing a vital role in assisting dispatchers in quickly identifying fault locations.