Partial Discharge Pattern Recognition Based on 3D Graphs of Phase Resolved Pulse Sequence

Simeng Song,Xiuchen Jiang,Gehao Sheng,Hui Wang,Yong Qian,Yiming Zang

doi:10.3390/en13164103

Simeng Song, Xiuchen Jiang + Show 4 more

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https://doi.org/10.3390/en13164103

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Journal: Energies	Publication Date: Aug 7, 2020
Citations: 13	License type: CC BY 4.0

Affiliation: Shanghai Jiao Tong University

Abstract

Partial discharge (PD) is an important phenomenon that reflects the insulation condition of electrical equipment. In order to protect the safety of power grids, it is of significance to diagnose the type of insulation defects inside the equipment accurately and early through PD pattern recognition. In this article, phase resolved pulse sequence (PRPS) graphs in 3D were constructed by the PD pulse data of the gas-insulated switchgear (GIS) acquired, then the histogram of oriented gradient (HOG) features were extracted directly from the 3D PRPS graphs, and finally the attribute selective Naïve Bayes classifier was used to recognize the discharge pattern. In addition, this method was compared with two traditional methods, i.e., the statistical method and the grayscale gradient co-occurrence matrix method, from three aspects. The result shows that 3D PRPS graphs have different morphology characteristics in vision under different defects, and the similarity among different voltages applied is higher than among different defects, so it is reasonable to use them as the basis for PD pattern recognition. The contrast indicates that the HOG method not only has the highest accuracy with the least requirement for pretreatment and training, but it also has robustness when the voltage applied changes. Consequently, this method has the universality for PD pattern recognition that is based on 3D PRPS graphs.

Highlights

With the gradual expansion of the power grid scale and the gradual enhancement of voltage level, the safety and reliability of electrical equipment becomes increasingly important
The Partial discharge (PD) signals caused by different insulation defects usually show different characteristics, so the type of insulation defects inside the equipment can be accurately diagnosed according to the characteristics
The PD patterns are mainly divided into three types: time resolved partial discharge (TRPD) [5,6], phase resolved partial discharge (PRPD) [7,8,9], and phase resolved pulse sequence (PRPS) [10]

Summary

Introduction

With the gradual expansion of the power grid scale and the gradual enhancement of voltage level, the safety and reliability of electrical equipment becomes increasingly important. Partial discharge (PD), as an important phenomenon of insulation deterioration of electrical equipment, has the risk of damaging the safety of a power grid if without any prevention from continually deteriorating the insulation condition of the equipment [1]. The maintainer can diagnose the existing defect and take appropriate measures in time in order to prevent accidents [2,3,4]. It is an important premise to construct an appropriate PD pattern, extract representative feature parameters, and design a classifier of discharge type with good performance. The PD patterns are mainly divided into three types: time resolved partial discharge (TRPD) [5,6], phase resolved partial discharge (PRPD) [7,8,9], and phase resolved pulse sequence (PRPS) [10]

Objectives

Methods

Conclusion