A fault segment location method for distribution networks based on spiking neural P systems and Bayesian estimation

Abstract With the increasing scale of distribution networks and mass access distributed generation, traditional centralized fault location methods can no longer meet performance requirements speed high accuracy. Therefore, this paper proposes a segment method based on spiking neural P systems Bayesian estimation for with generation. First, network system topology is decoupled into single-branch networks. A excitatory inhibitory synapses then proposed to model suspected faulty segment, its matrix reasoning algorithm executed obtain preliminary set results. Finally, contradiction principle are applied verify correct initial results final Simulation IEEE 33-node validate feasibility effectiveness method.