A Comprehensive Review of Signal Processing and Machine Learning Technologies for UHF PD Detection and Diagnosis (I): Preprocessing and Localization Approaches

Jiachuan Long,Guowei Zhu,Xianpei Wang,Wei Zhou,Jun Zhang,Dangdang Dai

doi:10.1109/access.2021.3077483

Abstract

Partial discharge (PD) detection and diagnosis based on the ultra-high frequency (UHF) signals is one of the most widely adopted methods to evaluate the internal insulation status of high voltage equipment. Benefit from the rapid development of computing hardware and data processing algorithms, the intelligent PD fault diagnosis method based on the UHF data has made considerable progress in the past two decades. This two-part paper aims to give a comprehensive review about the application of signal processing and machine learning technologies in UHF PD detection and diagnosis. These technologies are divided into three categories according to their respective purpose, which are the preprocessing technology, source localization technology and pattern recognition technology. As the first one of the two-part review, we focus on the preprocessing and localization approaches in this paper. Specifically, for the preprocessing topic, the methods for signal denoising, multi-source separation, and pulse segmentation are included. While for the localization topic, the time difference of arrival (TDOA) method, direction of arrival (DOA) method, received signal strength indicator (RSSI) method, and other latest methods are reviewed. For each topic, the basic ideas, recent research progresses, advantages and limitations are discussed in detail. Before the conclusion, we also make a discussion about the application effects of the above technologies and prospect some future directions accordingly. In the second paper, the pattern recognition problems based on the UHF PD data will be concentrated, especially the application of deep learning algorithms.

Highlights

With the continuous expansion of the power grid, the risk of large-scale power outages caused by the failure or damage of key power equipment is increasing [1, 2]
In [95], Ha et al made a comprehensive investigation on the influence of noise to four commonly used time difference of arrival (TDOA) estimation methods, and the results showed that the energy-based method has the best noise robustness
Partial discharge (PD) monitoring and intelligent diagnosis based on the ultra-high frequency (UHF) data are essential to detect the insulation defects of the power equipment in time and formulate corresponding maintenance strategies

Summary

Introduction

With the continuous expansion of the power grid, the risk of large-scale power outages caused by the failure or damage of key power equipment is increasing [1, 2]. As a phenomenon of partial breakdown of insulation caused by electric field distortion, PD is an important symptom of insulation defects in power equipment, and the main inducement of insulation deterioration. Both of the industry and academia are committed to develop the PD detection and diagnosis techniques to obtain the location, type, and severity information of the insulation defects inside the high-voltage equipment, and strive to prevent insulation failure during its latent stage [6,7,8,9,10,11].

Objectives

Methods

Findings

Discussion

Conclusion