Fault probability identification method for distribution networks based on mov-MF distribution

Abstract

To address the fault identification challenge in distribution networks, a method leveraging a mixture of the von Mises–Fisher (mov-MF) distribution model for fault probability identification is proposed. Initially, the synchronous phasor measuring unit is employed to gather the post-fault steady-state voltage phase quantities, and then, the voltage phase angle values are combined to form a three-dimensional feature quantity. Subsequently, the mov-MF distribution model is initialized through the spherical K-means algorithm and the minimum message length algorithm. This model is further refined via the expectation–maximization algorithm to iteratively optimize distribution parameters. The test set data are input into the mov-MF distribution model, which has been constructed using typical fault data, to discern fault types. Finally, the efficacy of the proposed method is validated through simulation verification conducted on the IEEE 33-node distribution system. The analysis of the examples demonstrates the accuracy of the mov-MF distribution model-based fault identification method in identifying single-phase ground, two-phase ground, two-phase interphase, and three-phase short-circuit faults.