Partial discharge diagnosis method for electrical equipment based on PSO-GRNN

Xingfu Yang; Jiajun Song; Xiang Zhai; Mao Liao

doi:10.1117/12.3025012

13 May 2024 Partial discharge diagnosis method for electrical equipment based on PSO-GRNN

Xingfu Yang, Jiajun Song, Xiang Zhai, Mao Liao

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 1315961 (2024) https://doi.org/10.1117/12.3025012
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

Localized discharge refers to the phenomenon of insulation material discharge in electrical equipment under high electric field strength due to uneven distribution of electric field. The occurrence of localized discharge in equipment poses significant risks to the insulation layer, and a rapid and accurate identification of discharge types is crucial for the normal operation of industrial systems. In regard to the problem of identifying localized discharge types in electrical equipment, considering the timeliness and accuracy requirements of the monitoring system, a method is proposed that involves constructing a phase distribution spectrogram of localized discharge to extract statistical features. The particle swarm optimization (PSO) algorithm is utilized to optimize a generalized regression neural network (GRNN) model. Finally, the statistical features are used as input to the neural network for discharge type identification. The results demonstrate that the proposed diagnostic method achieves high accuracy and efficiency in discharge type identification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xingfu Yang, Jiajun Song, Xiang Zhai, and Mao Liao "Partial discharge diagnosis method for electrical equipment based on PSO-GRNN", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 1315961 (13 May 2024); https://doi.org/10.1117/12.3025012

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