Journal Article

Single Line-to-Ground Fault Type Multilevel Classification in Distribution Network Using Realistic Recorded Waveform

Li, Chenjing, Sun, Ji, Lin, Haokun, Liu, Yue, Liu, Jiajun

2023

Overview

The further identification of fault types for single line-to-ground faults (SLGFs) in distribution networks is conducive to determining the cause of grounding faults and formulating targeted measures for hidden danger treatment and fault prevention. For the six types of SLGFs generated in the actual power grid, this paper deeply studies their fault characteristics. Firstly, the classification criterion of fault transition resistance is derived by the generation mechanism of fault zero sequence voltage (ZSV). At the same time, by comparing and analyzing the same and different characteristics between faults, three criteria for fault classification are obtained. Based on the above four criteria, a multilevel and multicriteria fault classification method is proposed to judge six types of SLGFs. Then, the proposed method is verified by various fault state simulations of the distribution network model with a balanced topology and unbalanced topology. The engineering application of the method is demonstrated by the verification of actual power grid data. Finally, noise and data loss interference test results show the robustness of the method.

• Publisher: MDPI AG
• Subject: Algorithms / Artificial intelligence / Classification / fault recorder data / Methods / multi-type faults / multilevel classification / Neural networks / Simulation methods / single line-to-ground fault / Wavelet transforms