Assessing the Degradation of Transformer Oil From Partial Discharge Measurement Data Using Histogram Similarity Measures

In this article, a method is proposed to classify the in-service transformer oil by assessing the level of degradation in oil. Initially, 135 oil samples are classified into Classes 1–3 as per IEEE C57.106 (2015) standard by the conventional techniques which require five tests. The proposed method attempts to classify the oil samples by a single nondestructive partial discharge (PD) test measurement. The PD data of the 135 transformer oil samples are represented as 1-D histograms and classified by statistical analysis using histogram similarity measures (HSM) including cross correlation test, Kolmogorov–Smirnov (KS) distances, and chi-square test. This classification technique achieves an accuracy of 94.9%. The results are further subjected to class likelihood measures in the postprocessing stage, and this improves the accuracy of classification to 97.5% establishing the efficiency of the proposed method.