Recognition of partial discharge signals in impaired datasets using cumulative energy signatures

The problem of impaired data sets refers to data sets containing a vast majority of unwanted signals than signals of interest. With increased interest in partial discharge (PD) testing with arbitrary waveforms and transients, these kind of data sets are becoming more and more common. Traditional clustering techniques cannot be applied due to big differences in spatial densities of the existing clusters in the data set. This paper contributes a simple yet efficient technique to recognize PD signals from noise and other disturbances. The signal recognition features are based on two specific areas extracted from the cumulative energy signal (CE) of each recorded waveform. These areas weigh up the extent to which the recorded signals have a pulse-like shape. A third feature, defined as a shape factor, extracts additional metrics from the CE signal that serves the purpose of accounting for the factors affecting the computation of the proposed recognition features and threshold for data size reduction. These three CE-based features are used to create a graph from which a real PD can be spotted in large impaired data sets. The performance of this technique is tested using PD measurements from superimposed impulse tests on a 150 kV cable system.