Rough set theory applied to pattern recognition of partial discharge in noise affected cable data

Peng, Xiaosheng, Wen, Jinyu, Li, Zhaohui, Yang, Guangyao, Zhou, Chengke, Reid, Alistair ORCID: https://orcid.org/0000-0003-3058-9007, Hepburn, Donald M., Judd, Martin D. and Siew, W. H 2017. Rough set theory applied to pattern recognition of partial discharge in noise affected cable data. IEEE Transactions on Dielectrics and Electrical Insulation 24 (1) , pp. 147-156. 10.1109/TDEI.2016.006060

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Abstract

This paper presents an effective, Rough Set (RS) based, pattern recognition method for rejecting interference signals and recognising Partial Discharge (PD) signals from different sources. Firstly, RS theory is presented in terms of Information System, Lower and Upper Approximation, Signal Discretisation, Attribute Reduction and a flowchart of the RS based pattern recognition method. Secondly, PD testing of five types of artificial defect in ethylene-propylene rubber (EPR) cable is carried out and data pre-processing and feature extraction are employed to separate PD and interference signals. Thirdly, the RS based PD signal recognition method is applied to 4000 samples and is proven to have 99% accuracy. Fourthly, the RS based PD recognition method is applied to signals from five different sources and an accuracy of more than 93% is attained when a combination of signal discretisation and attribute reduction methods are applied. Finally, Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM) methods are studied and compared with the developed method. The proposed RS method is proven to have higher accuracy than SVM and BPNN and can be applied for on-line PD monitoring of cable systems after training with valid sample data.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
ISSN:	1070-9878
Related URLs:	http://ieeexplore.ieee.org/xpl/RecentIss...
Date of First Compliant Deposit:	22 September 2016
Date of Acceptance:	12 September 2016
Last Modified:	26 Nov 2024 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/94738

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