Forecast method of overhaul period of high-voltage circuit breaker based on FGSO-BP

Chaoyin An; Wenfeng Wang; Cunkai Liu; Min Yang; Min Tang; Xiangqian Zhu

doi:10.1117/12.2685637

15 August 2023 Forecast method of overhaul period of high-voltage circuit breaker based on FGSO-BP

Chaoyin An, Wenfeng Wang, Cunkai Liu, Min Yang, Min Tang, Xiangqian Zhu

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 1271938 (2023) https://doi.org/10.1117/12.2685637
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

High-voltage circuit breaker is an important component of the power system. To reduce the negative impact of the maintenance of high-voltage circuit breakers on the power system, a forecasting method of overhaul cycle of high-voltage circuit breakers based on FGSO-BP is proposed. Through the analysis of historical overhaul cycle data, the change law of the overhaul cycle of the high-voltage circuit breaker is determined. It obtains the operation data of high-voltage circuit breakers, uses the iteration of FGSO-BP algorithm to extract the operation characteristics of high-voltage circuit breaker, and detects the fault type and degree of the circuit breaker through feature matching. Combined with the change law of the overhaul cycle of circuit breakers with different fault types, the final forecast results of the overhaul cycle are obtained. Compared with the traditional prediction method, it is found that the average prediction error of the optimization design method is reduced by 0.49h, which means that the optimization design method has obvious advantages in prediction accuracy.

Citation Download Citation

Chaoyin An, Wenfeng Wang, Cunkai Liu, Min Yang, Min Tang, and Xiangqian Zhu "Forecast method of overhaul period of high-voltage circuit breaker based on FGSO-BP", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 1271938 (15 August 2023); https://doi.org/10.1117/12.2685637

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KEYWORDS

Evolutionary algorithms

Artificial neural networks

Data acquisition

Design and modelling

Fluorescence

Feature extraction

Mathematical optimization

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