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Partial Discharge Diagnosis in Gas-Insulated Switchgear Using a Fault-Mechanism-Enhanced Conditional Generative Adversarial Network
ID：50 View Protection：ATTENDEE Updated Time：2025-11-04 13:34:10 Hits：457 Oral Presentation

Start Time：2025-11-09 10:35（Asia/Shanghai）

Duration：15min

Session：S3 3. Power system and automation High voltage and insulation technology » S33.Power system and automation High voltage and insulation technology

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Abstract
 Data-driven insulation defect diagnosis models aim to extract health information of gas-insulated switchgear (GIS) from large-scale monitoring data, and have achieved promising progress in recent years. However, in practical operating environments, the available fault monitoring data are often limited in scale and diversity. Moreover, most existing models lack the integration of physical fault mechanisms, making it challenging to establish reliable models for real-world applications. To address these issues, this paper proposes a fault-mechanism-enhanced conditional generative adversarial network (FME-CGAN) for partial discharge (PD) diagnosis in GIS. First, based on GIS insulation failure mechanisms, the operating fault problem is modeled as a search space, where potential equipment states and fault causes are defined as states, and operational or decision actions are represented as state transitions. A Monte Carlo Tree Search (MCTS) is then employed to simulate fault logic graphs under specific states, which are further encoded into a multilayer perceptron to construct a logical verification model. Subsequently, a conditional generative adversarial network is used to generate synthetic fault samples, while the logical verification model is embedded behind the discriminator to perform anomaly detection on the generated data. This mechanism not only validates the generated samples against fault logic but also provides a new backpropagation pathway for hyperparameter optimization. Finally, the discriminator is leveraged to achieve accurate GIS PD classification. Experimental results demonstrate that the proposed FME-CGAN achieves a diagnostic accuracy of 98.93%, outperforming baseline models by over 5%. These results verify that the proposed method significantly enhances the reliability of GIS PD modeling and analysis while incorporating fault-mechanism constraints into the diagnostic framework.
 
Keywords
gas-insulated switchgear, partial discharge, fault-mechanism-enhanced, conditional generative adversarial network, Monte Carlo Tree Search.
Speaker
Zhengrun Zhang
Xi'an Jiaotong University

Submission Author
Yanxin Wang State Key Laboratory of Electrical Insulation and Power Equipment； Department of Electrical Engineering； Xi’an Jiaotong University
Zhengrun Zhang State Key Laboratory of Electrical Insulation and Power Equipment;Xi'an Jiaotong University
Jing YAN Xi'an Jiaotong University
zhiyuan liu Xi’an Jiaotong University；State Key Laboratory of Electric Power Equipment
Yingsan Geng Xi’an Jiaotong University；State Key Laboratory of Electric Power Equipment
Jianhua Wang Xi'an Jiaotong University
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Important Date

  • Conference Date

    Nov 07

    2025

    to

    Nov 09

    2025

  • Oct 30 2025

    Draft paper submission deadline

  • Nov 10 2025

    Registration deadline

Sponsored By
IEEE西南交通大学IAS学生分会
Organized By
西南交通大学电气工程学院
SPACI车网关系研究室
四川大学电力系统稳定与高压直流输电研究团队
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