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Abstract
Frequency response analysis (FRA) is a powerful and widely used tool for condition assessment in power transformers. However, interpretation schemes are still challenging. Studies show that FRA data can be influenced by parameters other than winding deformation, including temperature. In this study, a machine-learning approach with temperature as an input attribute was used to objectively identify faults in FRA traces. To the best knowledge of the authors, this has not been reported in the literature. A single-phase transformer model was specifically designed and fabricated for use as a test object for the study. The model is unique in that it allows the non-destructive interchange of healthy and distorted winding sections and, hence, reproducible and repeatable FRA measurements. FRA measurements taken at temperatures ranging from −40 °C to 40 °C were used first to describe the impact of temperature on FRA traces and then to test the ability of the machine learning algorithms to discriminate between fault conditions and temperature variation. The results show that when temperature is not considered in the training dataset, the algorithm may misclassify healthy measurements, taken at different temperatures, as mechanical or electrical faults. However, once the influence of temperature was considered in the training set, the performance of the classifier as studied was restored. The results indicate the feasibility of using the proposed approach to prevent misclassification based on temperature changes.
Highlights
Power transformer monitoring is crucial to prevent unplanned service interruptions and maintain electric power system stability
comparative standard deviation (CSD) values were calculated for the different fault modes for further comparisons and classification algorithm implementation
The algorithm analyzed each of the 343 instances classified them into classes: no-fault, axial displacement (AD), radial deformation (RD), and classified them into 5 classes: no-fault, axial displacement (AD), radial deformation disc disc spacespace variation (DSV), or shorted turns turns (ST)
Summary
Power transformer monitoring is crucial to prevent unplanned service interruptions and maintain electric power system stability. Frequency response analysis (FRA) is a wellknown method for condition monitoring in power transformers that can identify changes in a transformer’s active parts. From early studies of the technique in the late 1970s [1] to the present, FRA has demonstrated an ability to detect mechanical and electrical faults in power transformers. As recommended by the principal FRA standards [2,3], a small sinusoidal voltage waveform is applied over a large frequency band (from a few Hz up to a couple of MHz) to one of the terminals of the transformer (input point), and the response is measured in terms of its amplitude (dB) and phase (degrees) at another available terminal (output point). The current and reference FRA traces are compared to interpret the FRA measurements, identify changes in the transformer’s active parts and relate these changes to faults
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