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

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. © 2024 Inderscience Enterprises Ltd.