Frequency Response Analysis Interpretation Using Numerical Indices and Machine Learning: A Case Study Based on a Laboratory Model

Abstract

Frequency response analysis is a powerful tool for mechanical fault diagnostics in power transformers. However, interpretation schemes still today depend on expert analyses, mainly because of the complex structure of power transformers. One of the fundamental shortcomings of experimental investigations is that mechanical deformations cannot be managed on real transformers to obtain data for different scenarios because they are too destructive. To address this issue in a systematic way, the current research used a specially designed laboratory transformer model that allows mechanical defects to be introduced so its frequency response can be evaluated under different conditions. The key feature of this model is the non-destructive interchangeability of its winding sections, allowing reproducibility and repeatability of frequency response measurements. Numerical indices were compared over key performance indicators (linearity, sensitivity and monotonicity). The analysis indicated that comparative standard deviation offered promising results for evaluation of mechanical deformations on the laboratory winding model given its monotonic behaviour, sensitivity and linear increase with fault severity. Additionally, support vector machine learning, radial basis function neural network and the statistical k-nearest neighbour method were used for fault classification with different strategies and configurations. While limited data from different transformers are used in the available literature, the approach discussed here considers 371 measurements from the same transformer model. The test results are supportive and demonstrate great accuracy when machine learning is used for winding fault classification.INDEX TERMS Condition monitoring, frequency response analysis interpretation, machine learning, numerical indices, power transformers, radial basis function, support vector machines.

A Machine-Learning Approach to Identify the Influence of Temperature on FRA Measurements

“…A total of 343 FRA traces were used. Details of the FRA traces in the database are given in reference [13].…”

“…At least four measurements were taken at each temperature to ensure All measurements in the database were taken at 20 • C. A total of 343 FRA traces were used. Details of the FRA traces in the database are given in reference [13].…”

“…The index works well for frequency deviations, but its sensitivity is not as good for amplitude deviations [8,14]. In a comparison with other numerical indices, the CSD was deemed to offer good performance in evaluating deviations in FRA traces, given its monotonicity, linearity, and sensitivity [8,13].…”

“…The model allows fault modes to be introduced and FRA measurements to be replicated to generate a large database. A support vector machine (SVM) method with demonstrably good performance in fault identification [13] was tested. The SVM algorithm's ability to distinguish fault measurements from healthy measurements at different temperatures was evaluated.…”

“…The research was not intended to evaluate the numerical index used to quantify deviations between traces or the performance of the classification algorithms. The CSD index and the SVM were selected because they have been widely used in previous studies concerning FRA interpretation [6,8,13,14]. Other numerical indices and/or classification algorithms should offer similar conclusions.…”