ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Smart Grids
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1645135
Early Fault Diagnosis of Transformer Windings Based on The Improved MVMD-ELM
Provisionally accepted
- Sanmen Uuclear Power Co Ltd., Taizhou, China
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Aiming at the problems of weak early fault characteristics of transformer windings, large noise interference and insufficient accuracy of traditional diagnostic methods, this paper proposes an early fault diagnosis method for transformer windings based on improved multivariable mode decomposition and optimized Extreme Learning Machine(ELM). Firstly, taking the leakage magnetic field as the fault characteristic state quantity, the decomposition parameters are adaptively adjusted through Multivariate Variational Mode Decomposition (MVMD) combined with the Dream Optimization Algorithm(DOA), and the wavelet threshold method is combined to efficiently denoise the noisy signal and improve the signal quality. Secondly, multi-dimensional fault features such as correlation coefficient, asymmetry degree, distribution difference degree and Hausdorff distance are extracted to construct the DOA-ELM diagnostic model. The relevant parameters of ELM are optimized by using DOA to improve the classification performance of the model. The simulation and dynamic model experiment results show that the proposed method can effectively identify early faults such as axial compression deformation of windings and inter-turn short circuits. The diagnostic accuracy rates reach 98.33% and 96.67% respectively. Compared with the traditional Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods, it has effectively improved in classification accuracy and computational efficiency. This method provides an effective solution for the precise diagnosis of early faults in transformer windings and has high engineering application value.
Keywords: Transformer early fault diagnosis, Leakage magnetic field detection, multivariate variational mode decomposition (MVMD), Dream optimization algorithm, Extreme learning machine (ELM)
Received: 11 Jun 2025; Accepted: 07 Aug 2025.
Copyright: © 2025 Lin, Wang, Zhang, Zhang, Zhao, Wei, Liu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Qiuyang Lin, Sanmen Uuclear Power Co Ltd., Taizhou, China
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