Research on identification and fault diagnosis of electrical equipment based on machine learning

Tao Sun

doi:10.1117/12.3026987

15 March 2024 Research on identification and fault diagnosis of electrical equipment based on machine learning

Tao Sun

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130751H (2024) https://doi.org/10.1117/12.3026987
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

This paper firstly summarizes the traditional methods of power fault diagnosis in recent years. It is found that the traditional manual method is greatly affected by the work experience and professional ability of the inspectors, and the diagnosis time is long, and the accuracy rate is low. Then, an electrical equipment identification method based on East algorithm is proposed, and based on machine learning technology, the type of identification of electrical equipment in substations is deeply studied. According to the different characteristics of the network model and the characteristics of experimental data, a neural network based on GRU is constructed. The fault diagnosis model is based on the GRU model, and the experimental diagnosis analysis is carried out based on the GRU model.

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

Citation Download Citation

Tao Sun "Research on identification and fault diagnosis of electrical equipment based on machine learning", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130751H (15 March 2024); https://doi.org/10.1117/12.3026987

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