Abstract:With the development of new energy generation technology, more and more new energy and distributed energy access, the distribution network is increasingly complex and randomized, resulting in fault location difficulties and imprecise positioning problems occur frequently. In this paper, the characteristics of the distribution network when a fault occurs, a binary genetic algorithm fusion chaotic particle swarm algorithm (Genetic Algorithm-Chaotic Particle Swarm Optimization, GA-CPSO) fault location method is proposed. First, adaptive inertia weights and compression factors are introduced into the particle swarm algorithm. Secondly, a two-dimensional Logistic chaotic mapping is proposed on the basis of one-dimensional Logistic mapping, which initializes the particles with stronger traversal and greatly avoids the possibility of falling into local optimal solutions. Then, the excellent particles are selected to combine with the genetic algorithm for secondary optimization to improve the global optimization ability. Finally, the technical feasibility of the binary GA-CPSO fusion algorithm applied to radial distribution network fault localization and the effectiveness of the design scheme are verified through MATLAB simulation experiments. The results show that the algorithm converges faster, can realize the precise location of complex distribution network faults, and the fault localization results obtained by this method are still accurate under the situation of sudden change of some fault information.