Recognition of partial discharge in GIS based on image feature fusion

Ziqiang Xu; Honghua Xu; Chao Yuan; Shoulong Chen; Yini Chen; Ziqiang Xu; Honghua Xu; Chao Yuan; Shoulong Chen; Yini Chen

doi:10.3934/energy.2024052

AIMS Energy

2024, Volume 12, Issue 6: 1096-1112. doi: 10.3934/energy.2024052

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Research article

Recognition of partial discharge in GIS based on image feature fusion

Nanjing Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd, Nanjing 210000, China

Received: 29 July 2024 Revised: 11 October 2024 Accepted: 18 October 2024 Published: 28 October 2024

Partial discharge (PD) is a significant electrical fault in gas-insulated switchgear (GIS), with various types posing different risks to insulation. Accurate identification of PD types is essential for enhancing GIS management and ensuring the reliability of electrical grids. This study proposes a novel approach for PD identification in GIS integrating completed local binary pattern (CLBP) feature extraction, feature engineering, and an optimized support vector machine (SVM). PD faults were simulated in GIS and phase-resolved pulse sequence (PRPS) data for four different forms of PD were gathered. CLBP was used to extract image features, and then the support vector machine recursive feature elimination (SVM-RFE) algorithm was used to evaluate feature importance. Then, linear discriminant analysis (LDA) was used to fuse the selected features and reduce redundancy. The fused features were classified using a bald eagle search algorithm combined with differential evolution (IBES)-optimized SVM, achieving a recognition accuracy of 99.38%. The results indicate that the proposed method effectively distinguishes between different PD PRPS patterns in GIS.
- phase-resolved pulse sequence,
- gas-insulated switchgear,
- support vector machine,
- intelligent optimization algorithm,
- local binary patterns
Citation: Ziqiang Xu, Honghua Xu, Chao Yuan, Shoulong Chen, Yini Chen. Recognition of partial discharge in GIS based on image feature fusion[J]. AIMS Energy, 2024, 12(6): 1096-1112. doi: 10.3934/energy.2024052

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Abstract

Partial discharge (PD) is a significant electrical fault in gas-insulated switchgear (GIS), with various types posing different risks to insulation. Accurate identification of PD types is essential for enhancing GIS management and ensuring the reliability of electrical grids. This study proposes a novel approach for PD identification in GIS integrating completed local binary pattern (CLBP) feature extraction, feature engineering, and an optimized support vector machine (SVM). PD faults were simulated in GIS and phase-resolved pulse sequence (PRPS) data for four different forms of PD were gathered. CLBP was used to extract image features, and then the support vector machine recursive feature elimination (SVM-RFE) algorithm was used to evaluate feature importance. Then, linear discriminant analysis (LDA) was used to fuse the selected features and reduce redundancy. The fused features were classified using a bald eagle search algorithm combined with differential evolution (IBES)-optimized SVM, achieving a recognition accuracy of 99.38%. The results indicate that the proposed method effectively distinguishes between different PD PRPS patterns in GIS.

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