Partial discharge source discrimination using a support vector machine

Abstract

Partial discharge (PD) measurements are an important tool for assessing the health of power equipment. Different sources of PD have different effects on the insulation performance of power apparatus. Therefore, discrimination between PD sources is of great interest to both system utilities and equipment manufacturers. This paper investigates the use of a wide bandwidth PD on-line measurement system consisting of a radio frequency current transducer (RFCT) sensor, a digital storage oscilloscope and a high performance personal computer to facilitate automatic PD source identification. Three artificial PD models were used to simulate typical PD sources which may exist within power system apparatus. Wavelet analysis was applied to pre-process measurement data obtained from the wide bandwidth PD sensor. This data was then processed using correlation analysis to cluster the discharges into different groups. A machine learning technique, namely the support vector machine (SVM) was then used to identify between the different PD sources. The SVM is trained to differentiate between the inherent features of each discharge source signal. Laboratory experiments where the trained SVM was tested using measurement data from the RFCT as opposed to conventional measurement data indicate that this approach has a robust performance and has great potential for use with field measurement data.

Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

“…Furthermore, given that the constructed classifier depends only on the support vectors in the samples, abandonment of non-support vectors does not affect the classifier. Considering adaptability to small sample, nonlinear, high-dimensional samples, there have been some instances in which SVM has been applied in PD pattern recognition [11,12]. Nevertheless, SVM's advantage of fast adaptation has not been reflected in PD on-line monitoring.…”

Bayesian neural network and discrete wavelet transform for partial discharge pattern classification in high voltage equipment

“…Over the past two decades, a number of intelligent techniques have been developed for automatic PD pattern recognition. Some examples include statistical methods, various artificial neural networks (ANNs), genetic algorithms, expert systems, discrete wavelet transforms, and support vector machines (SVMs) [3]- [10].…”

Improved Bagging Algorithm for Pattern Recognition in UHF Signals of Partial Discharges

“…The existing literature covers a wide range of PD pattern recognition classifiers, including artificial neural network [1][2][3][4], support vector machine [5][6][7] and other methods [8][9][10], which all have achieved good performance. Nevertheless, variations will appear in the accuracy of a pattern recognition algorithm when the training sample data are changed [11,12].…”