Performance of the Support Vector Machine Partial Discharge classification method to noise contamination using phase synchronous measurements

The Support Vector Machine (SVM) method has been used with success in classifying Partial Discharge (PD) data of different sources. In this work it was investigated whether the previous success of the Support Vector Machine (SVM) could be extended to the case where a PD measurement was corrupted by Additive White Gaussian Noise (AWGN). Data was collected from experiments using PDs of different sources under controlled laboratory conditions at the Tony Davies High Voltage Laboratory, University of Southampton. Artificial PD signals were injected into the HV electrode of a bushing and a high frequency current transformer (HFCT) was used to monitor the current between the tap-point and earth. The signals produced by four different artificial PD sources (corona discharge in air, floating discharge in oil, internal discharge in oil and surface discharge in air) were acquired using the peak detection mode of the oscilloscope and were processed to extract the feature that was used by each algorithm. The feature extraction algorithm involved the use of the Wavelet Packet Transform (WPT) on phase synchronous measurements corrupted by artificial AWGN. Once the SVM was trained using part of the data acquired in the laboratory then the remaining data was corrupted by noise of two different amplitudes, giving SNRs of 7 dB and 3dB. These noisy data were classified using the SVM and the classification results were recorded. This procedure validated the SVM as an effective classification method that can be trained using laboratory noise free PD signals which can subsequently be used to classify field on-line measurements that have been corrupted with noise.