NDLI: Towards automated statistical partial discharge source classification using pattern recognition techniques

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Towards automated statistical partial discharge source classification using pattern recognition techniques

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Towards automated statistical partial discharge source classification using pattern recognition techniques

Content Provider	IET Digital Library
Author	Janani, Hamed Kordi, Behzad
Abstract	This study presents a comprehensive review of the automated classification in partial discharge (PD) source identification and probabilistic interpretation of the classification results based on the relationship between the variation of the phase-resolved PD (PRPD) patterns and the source of the PD. The proposed automated classification system consists of modern, high-performance statistical feature extraction methods and classifier algorithms. Their application in online monitoring and recognition of the PD patterns is investigated based on their low-processing time and high-performance evaluation. The application of modern statistical algorithms and pre-processing methods configured in this automated classification system improves the pattern recognition accuracy of the different PD sources that are suitable to be employed in different high-voltage (HV) insulation media. To evaluate the performance of the different combinations of the feature extraction/classier pairs, laboratory setups are designed and built that simulate various types of PDs. The test cells include three sources of PD in S F 6 , two sources of PD in transformer oil, and corona in the air. Data samples for different classes of PD sources are captured under two levels of voltage and two different levels of noise. The results of this study evaluate the suitability of the proposed classification systems for probabilistic source identification in various insulation media. Furthermore, of importance to the problem of the PD source identification is to assign a ‘degree of membership’ to each PRPD pattern, besides assigning a class label to it. Some of the classifier algorithms studied in this study, such as fuzzy classifiers, are not only able to show high classification accuracy rate, but they also calculate the ‘degree of membership’ of a sample to a class of data. This enables probabilistic interpretation of a new PRPD pattern that is being classified. The determination of the degree of membership for future PRPD samples allows safer decision making based on the risk associated with the different sources of PD in HV apparatus.
Starting Page	162
Ending Page	169
Page Count	8
Volume Number	3
e-ISSN	23977264
Issue Number	Issue 3, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/hve/3/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/hve.2018.5048
Journal	High Voltage
Publisher	The Institution of Engineering and Technology CEPRI
Publisher Date	2018-07-26
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Automated Classification System Classification Result Classification System Classifier Algorithm Data Handling Technique Dielectric Breakdown And Discharges Electrical Engineering Computing Feature Extraction Feature Extraction/classier Pairs High-voltage Insulation Media Knowledge Engineering Technique Partial Discharge Source Identification Partial Discharges Pattern Classification Pattern Recognition Accuracy Pattern Recognition Technique PD Source PD Source Identification Phase-resolved PD Pattern Probabilistic Interpretation Probabilistic Source Identification PRPD Pattern Statistical Algorithm Statistical Analysis Statistical Feature Extraction Method Statistical Partial Discharge Source Statistics Transformer Oil
Content Type	Text
Resource Type	Article

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