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

Mingkun Yang, Liangliang Wei, Pengfeng Qiu, Guangfu Hu, Xingfu Liu, Xiaohui He, Zhaoyu Peng, Fangrong Zhou, Yun Zhang, Xiangyu Tan and Xuetong Zhao ()
Additional contact information
Mingkun Yang: Yunnan Electric Power Research Institute, Kunming 650217, China
Liangliang Wei: State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China
Pengfeng Qiu: Yunnan Electric Power Research Institute, Kunming 650217, China
Guangfu Hu: Yunnan Electric Power Research Institute, Kunming 650217, China
Xingfu Liu: Yunnan Electric Power Research Institute, Kunming 650217, China
Xiaohui He: Yunnan Electric Power Research Institute, Kunming 650217, China
Zhaoyu Peng: Yunnan Electric Power Research Institute, Kunming 650217, China
Fangrong Zhou: Yunnan Electric Power Research Institute, Kunming 650217, China
Yun Zhang: Yuxi Power Supply Bureau, Yunnan Power Grid, Yuxi 653100, China
Xiangyu Tan: State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China
Xuetong Zhao: State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China

Energies, 2025, vol. 18, issue 14, 1-13

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/18/14/3837/pdf (application/pdf)
https://www.mdpi.com/1996-1073/18/14/3837/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:18:y:2025:i:14:p:3837-:d:1705027

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().