Multi-Source Partial Discharge Pattern Recognition Algorithm Based on DCGAN-YOLOv5

This study aims to overcome the complication that deep learning-based pattern recognition in partial discharge (PD) diagnosis of gas-insulated switchgear (GIS) can only identify single-source partial discharge but not multi-source partial discharge. Specifically, a GIS multi-source partial discharge...

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Bibliographic Details
Published in:IEEE transactions on power delivery Vol. 40; no. 6; pp. 2983 - 2992
Main Authors: Wu, Min, Jiang, Wei, Shen, Daoyi, Luo, Yingting, Yang, Junjie
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Convolutional neural networks
DCGAN
Discharge
Feature extraction
Gas insulation
Generative adversarial networks
Generators
Machine learning
multi-source partial discharge
Object recognition
Partial discharges
Pattern recognition
PRPD
Substations
Switchgear
target detection
Training
YOLOv5
ISSN:0885-8977, 1937-4208
Online Access:Get full text
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Summary:This study aims to overcome the complication that deep learning-based pattern recognition in partial discharge (PD) diagnosis of gas-insulated switchgear (GIS) can only identify single-source partial discharge but not multi-source partial discharge. Specifically, a GIS multi-source partial discharge detection algorithm based on Deep Convolution Generative Adversarial Networks and You Only Look Once (DCGAN-YOLOv5) is proposed. First, the Phase Resolved Partial Discharge (PRPD) features of multi-source PD are analyzed, a GIS experiment platform is established, and four typical PD defects are simulated. Besides, sample data are collected, and the DCGAN network is used for sample expansion. Then, a YOLOv5 network model is designed, and a spatial and channel attention mechanism is added to the feature extraction network with a positive sample equilibrium strategy. Finally, the effectiveness of the proposed algorithm is verified using laboratory data and field data collected from a 220 kV substation. The experimental results demonstrate that the proposed algorithm can effectively detect the multi-source PD features in PRPD patterns under complex noise and thus successfully identify the types of multi-source PD. The mean Average Precision (mAP) can reach 98.4%. The precision of single-source PD and multi-source PD can reach 95.2% and 89.3%, when testing with field data.
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ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2022.3222317