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

Abstract 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.

[1]  Ubiratan Holanda Bezerra,et al.  PREDICT – Decision support system for load forecasting and inference: A new undertaking for Brazilian power suppliers , 2012 .

[2]  Yiyu Yao,et al.  Rough sets, neighborhood systems and granular computing , 1999, Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411).

[3]  E. M. Lalitha,et al.  Wavelet analysis for classification of multi-source PD patterns , 2000 .

[4]  Matti Lehtonen,et al.  Wavelet-based de-noising of on-line PD signals captured by Pearson coil in covered-conductor overhead distribution networks , 2012 .

[5]  Z. Pawlak.  Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[6]  Mohammad Lutfi Othman,et al.  Rough-Set-and-Genetic-Algorithm based data mining and Rule Quality Measure to hypothesize distance protective relay operation characteristics from relay event report , 2011 .

[7]  M. Muhr,et al.  Experience with optical partial discharge detection , 2007 .

[8]  B. Chatterjee,et al.  Rough-granular approach for impulse fault classification of transformers using cross-wavelet transform , 2008, IEEE Transactions on Dielectrics and Electrical Insulation.

[9]  Rodrigo Quian Quiroga,et al.  Nonlinear multivariate analysis of neurophysiological signals , 2005, Progress in Neurobiology.

[10]  Magdy M. A. Salama,et al.  Partial discharge pattern classification using the fuzzy decision tree approach , 2005, IEEE Transactions on Instrumentation and Measurement.

[11]  K. P. S. Rana,et al.  Auto-correlation based intelligent technique for complex waveform presentation and measurement , 2009 .

[12]  Suwarno,et al.  Partial discharge diagnosis of Gas Insulated Station (GIS) using acoustic method , 2009, 2009 International Conference on Electrical Engineering and Informatics.

[13]  R. Bartnikas,et al.  Partial discharge pulse pattern recognition using an inductive inference algorithm , 2005, IEEE Transactions on Dielectrics and Electrical Insulation.

[14]  L. Hao,et al.  Extraction of PD Signals from an Electro-optic Modulator Based PD Measurement System , 2006, 2006 IEEE Conference on Electrical Insulation and Dielectric Phenomena.

[15]  P. Lewin,et al.  Comparison of on-line partial discharge detection methods for HV cable joints , 2002 .

[16]  Chen-Fu Chien,et al.  Rough set theory for data mining for fault diagnosis on distribution feeder , 2004 .

[17]  R. Bartnikas,et al.  Trends in partial discharge pattern classification: a survey , 2005, IEEE Transactions on Dielectrics and Electrical Insulation.

[18]  José Edison Cabral,et al.  Fraud detection in electrical energy consumers using rough sets , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[19]  Taher Niknam,et al.  Using correlation coefficients for locating partial discharge in power transformer , 2011 .

[20]  Andrew Kusiak,et al.  Rough set theory: a data mining tool for semiconductor manufacturing , 2001 .