Research on intelligent detection method of high-voltage circuit breaker based on PSO–CNN

Xufen Hua and Zhigang Liu

International Journal of Low-Carbon Technologies, 2025, vol. 20, 421-427

Abstract: Through the intelligent detection of high-voltage circuit breakers, equipment faults can be detected and diagnosed in time, which can effectively prevent power accidents and ensure the safe operation of the power grid. In this paper, a detection method based on particle swarm optimization (PSO) and convolutional neural network (CNN) is proposed, and the parameter setting of CNN is optimized by PSO. The feature of vibration signal is extracted and the feature sample set is constructed. On this basis, the sample set is constructed and the experiment is carried out. The experimental results show that the proposed method has significant advantages in precision, recall rate, and F1 score compared with the traditional method; the precision can reach 0.926, the recall can reach 0.913, and the F1 score can reach 0.919. Future research could optimize the training process to reduce running time, as well as introduce more advanced data preprocessing techniques to improve data quality.

Keywords: PSO; CNN; intelligent detection; HVCB (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1093/ijlct/ctae257 (application/pdf)
Access to full text is restricted to subscribers.

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:oup:ijlctc:v:20:y:2025:i::p:421-427.

Access Statistics for this article

International Journal of Low-Carbon Technologies is currently edited by Saffa B. Riffat

More articles in International Journal of Low-Carbon Technologies from Oxford University Press
Bibliographic data for series maintained by Oxford University Press ().