Distortion-Based Detection of High Impedance Fault in Distribution Systems

TL;DR

This paper introduces a distortion-based detection algorithm for high impedance faults in distribution systems, improving reliability by classifying fault types, extracting waveform distortion features, and validating with field data.

Contribution

The paper presents a novel distortion-based method utilizing interval slopes and robust filtering to enhance high impedance fault detection accuracy.

Findings

Improved detection reliability over existing algorithms.

Effective classification of HIF types based on waveform distortion.

Validation with real field data demonstrates robustness.

Abstract

Detecting the High impedance fault (HIF) in distribution systems plays an important role in power utilization safety. However, many HIFs are challenging to be identified due to their low currents and diverse characteristics. In particular, the slight nonlinearity during weak arcing processes, the distortion offset caused by the lag of heat dissipations, and the interference of background noises could lead to invalid of traditional detection algorithms. This paper proposes a distortion-based algorithm to improve the reliability of HIF detection. Firstly, the challenges brought by the diversity of HIF characteristics are illustrated with the experiments in a 10kV distribution system. Then, HIFs are classified into five types according to their characteristics. Secondly, an interval slope is defined to describe the waveform distortions of HIFs, and is extracted with the methods of the linear least square filtering (LLSF) and the Grubbs-criterion-based robust local regression smoothing (Grubbs-RLRS), so that feature descriptions under different fault conditions can be unified. Thirdly, an algorithm, as well as the criteria, is proposed to identify the fault features described by the interval slopes. Finally, the detection reliability of the algorithm is thoroughly verified with field HIF data, and the results show the improvements by comparing it to other ad-vanced algorithms.