UHF partial discharge location method based on time fingerprint

Yong Zhang; Jun Chang; Jia Zheng; Yan Zhou; Runping He; Zhefei Wang

doi:10.1117/12.3014558

8 December 2023 UHF partial discharge location method based on time fingerprint

Yong Zhang, Jun Chang, Jia Zheng, Yan Zhou, Runping He, Zhefei Wang

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 129430M (2023) https://doi.org/10.1117/12.3014558
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

Partial discharge is not only a manifestation of electrical equipment insulation defects, but also aggravated the degree of equipment insulation deterioration. Therefore, effective partial discharge positioning is an important means of monitoring the status of power equipment. The existing partial discharge positioning methods are affected by the environment, and the positioning accuracy is unstable. This paper proposes a location method for UHF partial discharge based on time fingerprints. The method first uses UHF sensors to collect the arrival time of the partial discharge signal, and uses the arrival time difference between the sensors to form the time fingerprint of the monitoring point to obtain the measured Time fingerprint library of the area. When a partial discharge occurs, input the collected time fingerprints into the time fingerprint library, and use the neural network algorithm for matching to obtain the local discharge source location result. The experimental results show that the average positioning error of the positioning method proposed in this paper is 1.78m within the measured area of 900 square meters, which can lock about 60% of the positioning error within 2m.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yong Zhang, Jun Chang, Jia Zheng, Yan Zhou, Runping He, and Zhefei Wang "UHF partial discharge location method based on time fingerprint", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 129430M (8 December 2023); https://doi.org/10.1117/12.3014558

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