An approach based on rough set theory for identification of single and multiple partial discharge source

This paper describes a methodology to detect the location of single as well as multiple partial discharge sources by sensing the optical radiation from the source. To establish the methodology, an experimental setup has been arranged in the laboratory for generation of partial discharge inside a steel tank provided with five optical sensors placed at the centre of all its five inside walls excepting the top. Analyzing the data by comparing the results from the five sensors give estimation about the position(s) of the partial discharge occurring inside the tank. For successful analysis in the present work, auto-correlation, an extension of correlation based feature extraction technique, is used to extract the features from the recorded signal of the sensors. To classify the extracted features, a rough set theory (RST) based decision support system is used in this work. The novelty of this present work is in locating single as well as multiple sources of partial discharges that emit optical radiation simultaneously. Results show that the auto-correlation based feature extraction technique in conjunction with RST based classifier can localize the sources of partial discharge inside the tank with reasonable degree of accuracy.

► Identification of several partial discharge sources (i.e. defects) within insulator. ► Partial discharge (PD) sources are recorded from insulator through optical sensor. ► Auto-correlation technique is used to extract features from recorded PD sources. ► To classify the recorded PD sources, rough set theory based classifier is used. ► Proposed technique can successfully localize several PD sources within insulator.