Typical Fault Cause Recognition of Single-Phase-to-Ground Fault for Overhead Lines in Nonsolidly Earthed Distribution Networks

Abstract

The single-phase-to-ground fault (SPGF) affects the reliability and security of the distribution system greatly. Accurate online recognition of fault causes can help improve the efficiency of weak components finding and maintenance. In this article, various symptom features of the SPGF by typical causes are analyzed and a fuzzy inference system (FIS) for fault cause recognition is established for overhead lines in nonsolidly earthed distribution networks. Based on the survey of fault causes in a certain city in China, artificial grounding experiments are designed for six typical fault causes, including arrester breakdown, insulator flashover, line-to-crossbar discharge, line fallen on wet mud, line fallen on wet sand, and line fallen into the pond for waveform data collection. Through multiple time-frequency analysis on waveform data of various causes, five features are extracted and the statistical results are obtained, including self-recoverability, zero current time, transition time, degree of distortion, and randomness. Based on the above, a FIS for cause recognition for SPGFs is established. The experimental results and the comparison with the BPNN model show that the proposed method has good performance and feasibility.