It has been over two decades since the publication of pioneer works about the power transformer diagnostics based on monitoring of their acoustic fingerprints. Since then, there has been great progress in this field and the methods used are as complex as ever. Any unnecessary intervention on a power transformer implies its temporary disconnection from the power grid. The inability to supply electricity to the customer means not only financial loss for the utility but also generates a non-material loss, e.g., the loss of reputation to the customer. Faster, more accurate, more reliable, and less invasive diagnosis is the main reason behind development and improvement in this field. The main goal of this paper is to categorize and review state-of-the art of vibro-acoustic diagnostic methods for power transformers. This paper opens with a brief note about continuous condition monitoring, after which we overview the causes of transformer vibrations as well as the collection and preprocessing of diagnostic data. Then, we review and categorize works related to the acoustic condition assessment of power transformers considering both: feature extraction in the time, frequency, time-frequency domain, and mathematical modeling and system identification of dynamic systems.

/pdf/vibro-acoustic-methods-in-the-condition-assessment-of-power-596hhz2h8b.pdf

Vibro-Acoustic Methods in the Condition Assessment of Power Transformers: A Survey

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

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

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