Arc contacts ablation state assessment method based on machine learning multiple linear regression

Yijun Liu; Daopin Chen; Muxin Diao; Guangyu Xiao; Jing Yan; Zhenxing Wang

doi:10.1117/12.2611061

14 October 2021 Arc contacts ablation state assessment method based on machine learning multiple linear regression

Yijun Liu, Daopin Chen, Muxin Diao, Guangyu Xiao, Jing Yan, Zhenxing Wang

Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 119303L (2021) https://doi.org/10.1117/12.2611061
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China

Abstract

SF₆ circuit breakers are widely used in power systems to ensure the safe and stable operation of power systems. This paper considers circuit breaker condition-based maintenance and proposes a method for evaluating the ablation state of arc contacts based on machine learning multiple linear regression algorithm. The dynamic resistance measurement(DRM) is used to obtain the dynamic contact resistance-travel curves characterizing the full life state of the arc contacts, and the relevant diagnostic parameters are extracted from the curve as model inputs. The radial ablation coefficient, the axial ablation coefficient and the number of remaining closing times are used to comprehensively characterize the ablation state of arc contacts. The model solves multiple linear regression equation by gradient descent method and adopts the RMSE value to evaluate the model prediction results. The results show that model predictions are relatively consistent with the experimental results, which can provide a reference for condition maintenance of circuit breakers.

Citation Download Citation

Yijun Liu, Daopin Chen, Muxin Diao, Guangyu Xiao, Jing Yan, and Zhenxing Wang "Arc contacts ablation state assessment method based on machine learning multiple linear regression", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 119303L (14 October 2021); https://doi.org/10.1117/12.2611061

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