Title: Research on fault diagnosis for power transformer based on random forests and wavelet transform

Authors: Ming Zhang; Chongfeng Fang; Shuang Ji

Addresses: State Grid Jilin Electric Power Co., Ltd., Siping Power Supply Company, Siping, Jilin, China ' State Grid Jilin Electric Power Co., Ltd., Siping Power Supply Company, Siping, Jilin, China ' State Grid Jilin Electric Power Co., Ltd., Siping Power Supply Company, Siping, Jilin, China

Abstract: Transformers are electrical equipment widely used in power systems and electronic circuits. In order to improve the accuracy of power transformer fault diagnosis and condition monitoring, this paper proposes a fault diagnosis method for power transformers based on Random Forests (RFs) and wavelet transform. Firstly, the wavelet transform method is adopted to decompose the noisy vibration signal into multi-scales, and then the detailed signals at different scales are processed to achieve fault feature extraction of the power transformer vibration signals. Secondly, the mapping relationship between fault features and fault types of vibration signals is established by RFs algorithm, and the fault diagnosis model is trained by RFs algorithm. Finally, by identifying the experimental data of the normal and fault states of the power transformers, the accuracy reached 96.52%, which is suitable for monitoring and diagnosing the different working states of the power transformers.

Keywords: fault diagnosis; power transformer; random forests; wavelet transform.

DOI: 10.1504/IJWMC.2024.138857

International Journal of Wireless and Mobile Computing, 2024 Vol.26 No.4, pp.354 - 360

Received: 22 Sep 2023
Accepted: 05 Dec 2023
Published online: 31 May 2024 *

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