Active Distribution Network Fault Localization based on Improved Chaotic Binary Particle Swarm Algorithm

Aiming at the existing intelligent optimization algorithms in Active Distribution Network (ADN) fault localization, such as long-time-consuming fault localization, slow convergence speed, and easy convergence of faults to local optimal solutions, this paper proposes an Improved Chaotic Binary Particle Swarm Algorithm (ICBPSO). Firstly, a switching function reconstruction method is proposed based on the switching function construction characteristics of the ADN section state and node state. Secondly, based on the fault section characteristics of ADN, algorithms such as initial particle swarm sparsification, optimal particle differentiation, and optimal particle mutation are proposed, further improving the Chaotic Binary Particle Swarm Algorithm(CBPSO). Lastly, simulation experiments are used to validate the efficacy of ICBPSO in ADN fault localization. The tests evaluate several ADN fault localization techniques and simulation results show that ICBPSO performs better in fault localization elapsed time and convergence speed.