A Comparative Study of Power Transformer Winding Fault Diagnosis Using Machine Learning Algorithms

In today’s distribution and transmission infrastructure, power transformers have proven to be one of the maximum crucial components. Assessing the integrity of power transformer winding is imperative as winding failure is the most dominant failure location. Based on the reported literature a common trend with the research work is that they only consider one machine learning method and do not use other existing methods as benchmarks to corroborate proposed approaches. In this work, a comparative analysis of two trained well known, and broadly applied Machine Learning Algorithms (MLAs): Artificial Neural Network (ANN) and Support Vector Machine (SVM) to assess the power transformer winding fault is carried out. This work also introduces a machine learning algorithm that has the highest performance in terms of accuracy (validation and testing) with measured data from 300 power transformers that are independently inspected by industry connoisseurs. The obtained results show that the ANN method yields the best results when compared to the other algorithm.