Article: Fault diagnosis method for substation relay protection equipment based on CNN-SVM model Journal: International Journal of Systems, Control and Communications (IJSCC) 2026 Vol.17 No.1 pp.71 - 87 Abstract: With the increasing complexity of power systems, diagnosing equipment faults has become increasingly challenging. Traditional methods often struggle to handle noise and nonlinear issues in power data effectively. To address these limitations, a fault diagnosis model for substation relay protection equipment was developed using a support vector machine (SVM), enhanced with a convolutional neural network (CNN) and a channel attention mechanism for further performance optimisation. Experimental results demonstrated that with a dataset size of 2,000, the proposed model achieved an accuracy of 97.2% and a false positive rate of 2.8%. Additionally, the model effectively diagnosed various fault types, attaining an average accuracy of 85% with a diagnosis time of approximately 1.3 seconds. These findings highlight the model's superior fault diagnosis capabilities, including reduced false alarm rates and stable performance with large-scale data, providing robust technical support for the reliable and efficient operation of power systems. Inderscience Publishers - linking academia, business and industry through research

Title: Fault diagnosis method for substation relay protection equipment based on CNN-SVM model

Authors: Bing Tang; Zhenguo Ma; Tianlei Xia; Yuming Huang

Addresses: Changzhou Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd., Changzhou, 213003, China ' Changzhou Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd., Changzhou, 213003, China ' Changzhou Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd., Changzhou, 213003, China ' Changzhou Power Supply Branch, State Grid Jiangsu Electric Power Co., Ltd., Changzhou, 213003, China

Abstract: With the increasing complexity of power systems, diagnosing equipment faults has become increasingly challenging. Traditional methods often struggle to handle noise and nonlinear issues in power data effectively. To address these limitations, a fault diagnosis model for substation relay protection equipment was developed using a support vector machine (SVM), enhanced with a convolutional neural network (CNN) and a channel attention mechanism for further performance optimisation. Experimental results demonstrated that with a dataset size of 2,000, the proposed model achieved an accuracy of 97.2% and a false positive rate of 2.8%. Additionally, the model effectively diagnosed various fault types, attaining an average accuracy of 85% with a diagnosis time of approximately 1.3 seconds. These findings highlight the model's superior fault diagnosis capabilities, including reduced false alarm rates and stable performance with large-scale data, providing robust technical support for the reliable and efficient operation of power systems.

Keywords: relay protection equipment; RPE; fault diagnosis; convolutional neural network; CNN; support vector machine; SVM.

DOI: 10.1504/IJSCC.2026.150316

International Journal of Systems, Control and Communications, 2026 Vol.17 No.1, pp.71 - 87

Received: 03 Jan 2025
Accepted: 12 May 2025
Published online: 09 Dec 2025 *

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Title: Fault diagnosis method for substation relay protection equipment based on CNN-SVM model

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