Non linear support vector machine based partial discharge patterns recognition using fractal features

Abstract

Partial discharge (PD) is an important tool for assessing the quality of the insulation system in High Voltage (HV) power apparatus. In this work, four different PD sources namely corona and surface discharges in both air and oil are measured in the HV laboratory. Initially 3-D (Φ-q-n) PD patterns are extracted from the PD data. Then it is subjected to two different fractal image compression techniques namely box counting method and semi variance method. For box counting method, the fractal dimensions like fractal dimension average, standard deviation and lacunarity are evaluated. For semi variance method, horizontal and vertical fractal dimension averages are evaluated. The extracted fractal features from 3-D PD patterns are used as input parameters for non linear Support Vector Machine (SVM) for PD recognition. The performance of non linear SVM is compared with Artificial Neural Network (ANN) and linear SVM classifiers. The non linear SVM with semi variance method provides outer performance as compared with other methods due to its gain flexibility and good out-of-sample generalization.