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Energies
Volume 18
Issue 14
10.3390/en18143837
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Research on Fault Diagnosis of High-Voltage Circuit Breakers Using Gramian-Angular-Field-Based Dual-Channel Convolutional Neural Network

by
Mingkun Yang
1,
Liangliang Wei
2,
Pengfeng Qiu
1,
Guangfu Hu
1,
Xingfu Liu
1,
Xiaohui He
1,
Zhaoyu Peng
1,
Fangrong Zhou
1,
Yun Zhang
3,
Xiangyu Tan
2 and
Xuetong Zhao
2,*
1
Yunnan Electric Power Research Institute, Kunming 650217, China
2
State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China
3
Yuxi Power Supply Bureau, Yunnan Power Grid, Yuxi 653100, China
*
Author to whom correspondence should be addressed.
Energies 2025, 18(14), 3837; https://doi.org/10.3390/en18143837
Submission received: 21 May 2025 / Revised: 1 July 2025 / Accepted: 17 July 2025 / Published: 18 July 2025
(This article belongs to the Special Issue Advances in Diagnostic Analysis, Strategic Management, and Proactive Maintenance of Electrical Equipment)
Versions Notes

Abstract

The challenge of accurately diagnosing mechanical failures in high-voltage circuit breakers is exacerbated by the non-stationary characteristics of vibration signals. This study proposes a Dual-Channel Convolutional Neural Network (DC-CNN) framework based on the Gramian Angular Field (GAF) transformation, which effectively captures both global and local information about faults. Specifically, vibration signals from circuit breaker sensors are firstly transformed into Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF) images. These images are then combined into multi-channel inputs for parallel CNN modules to extract and fuse complementary features. Experimental validation under six operational conditions of a 220 kV high-voltage circuit breaker demonstrates that the GAF-DC-CNN method achieves a fault diagnosis accuracy of 99.02%, confirming the model’s effectiveness. This work provides substantial support for high-precision and reliable fault diagnosis in high-voltage circuit breakers within power systems.
Keywords: high-voltage circuit breaker; vibration faults; Gramian Angular Field; convolutional neural network high-voltage circuit breaker; vibration faults; Gramian Angular Field; convolutional neural network

Share and Cite

MDPI and ACS Style

Yang, M.; Wei, L.; Qiu, P.; Hu, G.; Liu, X.; He, X.; Peng, Z.; Zhou, F.; Zhang, Y.; Tan, X.; et al. Research on Fault Diagnosis of High-Voltage Circuit Breakers Using Gramian-Angular-Field-Based Dual-Channel Convolutional Neural Network. Energies 2025, 18, 3837. https://doi.org/10.3390/en18143837

AMA Style

Yang M, Wei L, Qiu P, Hu G, Liu X, He X, Peng Z, Zhou F, Zhang Y, Tan X, et al. Research on Fault Diagnosis of High-Voltage Circuit Breakers Using Gramian-Angular-Field-Based Dual-Channel Convolutional Neural Network. Energies. 2025; 18(14):3837. https://doi.org/10.3390/en18143837

Chicago/Turabian Style

Yang, Mingkun, Liangliang Wei, Pengfeng Qiu, Guangfu Hu, Xingfu Liu, Xiaohui He, Zhaoyu Peng, Fangrong Zhou, Yun Zhang, Xiangyu Tan, and et al. 2025. "Research on Fault Diagnosis of High-Voltage Circuit Breakers Using Gramian-Angular-Field-Based Dual-Channel Convolutional Neural Network" Energies 18, no. 14: 3837. https://doi.org/10.3390/en18143837

APA Style

Yang, M., Wei, L., Qiu, P., Hu, G., Liu, X., He, X., Peng, Z., Zhou, F., Zhang, Y., Tan, X., & Zhao, X. (2025). Research on Fault Diagnosis of High-Voltage Circuit Breakers Using Gramian-Angular-Field-Based Dual-Channel Convolutional Neural Network. Energies, 18(14), 3837. https://doi.org/10.3390/en18143837

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