Partial Discharge Source Classification Using Machine Learning Algorithms

This article proposes a machine-learning algorithm for the automatic classification of single-source partial discharge (PD) in power transformers. PD testing provides valuable information of the state and deterioration of the insulation systems of transformer windings and core. A PD testing setup based on the IEC 60270 International standard is used to test PD from transformers of varying size and operational age. PD is recorded at different voltage levels applied to the transformer under test. Where the majority of PD classification literature has focused on laboratory developed artificial PD models, this work uses practical power transformers as a basis for testing and collecting the PD database. The PD database collected from this testing is utilized for the training, validation and testing of the machine learning algorithm. In this article, a comparative analysis of various trained machine learning algorithms for classifying PD is performed. The results of the classification show very pleasing performance from the tested classifier algorithms, with Bilayered Neural Network achieving a 96.97% validation accuracy of and a test accuracy of 97%.