A Variational Mode Decomposition Approach for Degradation Assessment of Power Transformer Windings

Windings are the most critical components in power transformers, and the related mechanical degradation assessment has attracted an increasing attention in recent years. In this paper, a novel feature extraction method based on an amalgamation of variational mode decomposition (VMD) and wavelet tran...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2019-04, Vol.68 (4), p.1221-1229
Main Authors: Hong, Kaixing, Wang, Ling, Xu, Suan
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Bayesian analysis
Classifiers
Coils (windings)
Computer simulation
Critical components
Damping
Decomposition
Degradation
Degradation assessment
Feature extraction
Insulation
Neural networks
Parameters
Support vector machines
transformer windings
Transformers
Transient analysis
transient vibrations
variational mode decomposition (VMD)
Vibrations
Wavelet transforms
Winding
Windings
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Summary:Windings are the most critical components in power transformers, and the related mechanical degradation assessment has attracted an increasing attention in recent years. In this paper, a novel feature extraction method based on an amalgamation of variational mode decomposition (VMD) and wavelet transform (WT) is encouraged, which correlates the nonlinear structural parameters of windings with the mechanical degradation. First, the winding vibration mechanism is explored, and the relationship between the transient vibration of windings and the structural parameters is studied. Next, the vibrations are decomposed into multiple modes using VMD, and the feature vector is extracted from the modes by means of WT. After that, three classification algorithms are employed, including support vector machine, naive Bayes classifier, and artificial neural network. In the experiment, the winding structure degradation was simulated via adjusting the clamping force. The vibrations from three winding units are used to train and test the classifiers, and their performance is evaluated and compared. The results demonstrate that the proposed feature extraction method with an appropriate classifier is effective for winding degradation assessment.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2018.2865048