Accurate Classification of Partial Discharge Phenomena in Power Transformers in the Presence of Noise

Abstract

The objective of this research is to accurately classify different types of Partial Discharge (PD) phenomenon that occurs in transformers in the presence of noise. A PD is an electrical discharge or spark that bridges a small portion of the insulation in electrical equipment, which causes progressive deterioration of high voltage equipment and could potentially lead to flashover. The data for the study is generated from a laboratory setup and it is 300 time series signals each with 2016 attributes corresponding to 3 types of PDs; namely: Porcelain, Cable and Corona. The data is collected from two sensors with different bandwidths, in which Channel A signals refer to the data collected from the higher frequency sensor and signals from Channel B refer to data of the lower frequency sensor. Different feature engineering approaches are investigated in order to find the set of the most discriminant features which help to achieve high levels of classification accuracy for Channel A and Channel B signals. First, features that describe the shape and pulse of signals in the time domain are extracted. Then frequency domain based statistical features are generated. In comparison with classification accuracies using frequency domain features, time domain based features gave higher accuracy of more than 90% on average for both channels in the absence of noise while frequency domain features allowed classification accuracy up to 80% on average. However, in the presence of noise, both methods degraded. To overcome this, Regularization techniques were applied on the features from the frequency domain which helped to maintain classification accuracy even in the presence of high levels of noise.