Journal of Power Electronics

  • 제24권1호
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  • Pages.78-91
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  • 2024
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  • 1598-2092(pISSN)
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  • 2093-4718(eISSN)
전력전자학회 (The Korean Institute of Power Electronics)
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DOI QR코드

DOI QR Code

Remaining useful life prediction of circuit breaker operating mechanisms based on wavelet-enhanced dual-tree residual networks

  • Tailong Wu (College of Computer Science and Artificial Intelligence, Wenzhou University) ;
  • Yuan Yao (College of Mechanical and Electrical Engineering, Wenzhou University) ;
  • Zhihao Li (College of Mechanical and Electrical Engineering, Wenzhou University) ;
  • Binqiang Chen (School of Aerospace Engineering, Xiamen University) ;
  • Yue Wu (College of Urban Transportation and Logistics, Shenzhen Technology University) ;
  • Weifang Sun (College of Mechanical and Electrical Engineering, Wenzhou University)
  • 투고 : 2023.02.19
  • 심사 : 2023.09.23
  • 발행 : 2024.01.20
⟨ 이전 논문 다음 논문 ⟩

초록

The remaining useful life prediction of circuit breaker operating mechanisms is crucial for the condition-based maintenance of national power grids. To realize accurate remaining useful life prediction, a novel wavelet-enhanced dual-tree residual network is proposed in this paper. Through this wavelet transform, the time series is decomposed into two components (high frequency and low frequency). Then the two decomposed components are fed into two lightweight residual neural network structures. By concatenating the dual-tree features, the remaining useful life of a circuit breaker operating mechanism can be predicted. The proposed network is validated using a full-life cycle experiment of the circuit breaker operating mechanism. Results show that the proposed method has good capability when it comes to predicting the remaining useful life of the circuit breaker operating mechanism. Along with application in the construction of smart grids and green energy, it is expected that the proposed method has potential in running state prognostics of circuit breakers.

키워드

과제정보

This work was supported in part by the National Natural Science Foundation of China under Grant 52205122 and Grant U1909217, in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LQ21E050003, and in part by the Wenzhou Municipal Key Science and Research Program under Grant ZG2021027 and Grant ZG2021019.

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