Partial Discharge Patterns Recognition of GIS with Denoising-stacked Autoencoder Networks

Partial discharge (PD) is the main characterization of gas insulated switchgear (GIS) insulation defects, which will further aggravate equipment aging. Therefore, monitoring the PD of GIS equipment is of great significance to detect insulation defects and avoid GIS equipment failure to ensure safe a...

Full description

Saved in:
Bibliographic Details
Published in:2020 5th Asia Conference on Power and Electrical Engineering (ACPEE) pp. 1815 - 1818
Main Authors: Zhao, Yiming, Yan, Jing, Wang, Yanxin, Liu, Tingliang, Jiang, Junjie
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2020
Subjects:
Classification algorithms
denoising-stacked autoencoder
Feature extraction
gas insulated switchgear
partial discharge
Partial discharges
Pattern recognition
Stacking
Support vector machines
Training
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Partial discharge (PD) is the main characterization of gas insulated switchgear (GIS) insulation defects, which will further aggravate equipment aging. Therefore, monitoring the PD of GIS equipment is of great significance to detect insulation defects and avoid GIS equipment failure to ensure safe and reliable operation of the grid. However, the traditional partial discharge pattern recognition mostly relies on artificial feature engineering, and the appropriateness of feature selection directly affects the recognition result. This paper proposes a pattern recognition classifier that directly and automatically selects and classifies fault features by denoising-stacked autoencoder. Automatic feature extraction effectively reduces the dependence of traditional pattern recognition classification algorithms based on expert systems and excessive human intervention. The results show that it not only inherits the advantages of the generalization ability of the denoising autoencoder model, but also has the advantages of easy stacking, faster convergence and higher accuracy.
DOI:10.1109/ACPEE48638.2020.9136370