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

Peng, Xiaosheng and Wen, Jinyu and Li, Zhaohui and Yang, Guangyao and Zhou, Chengke and Reid, Alistair and Hepburn, Donald M. and 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. ISSN 1070-9878 (https://doi.org/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.

ORCID iDs

Peng, Xiaosheng, Wen, Jinyu, Li, Zhaohui, Yang, Guangyao, Zhou, Chengke, Reid, Alistair, Hepburn, Donald M., Judd, Martin D. and Siew, W. H. ORCID logoORCID: https://orcid.org/0000-0003-4000-6856;
  • Item type:Article
    ID code:57853
    Dates:
    Date
    Event
    7 March 2017
    Published
    7 March 2017
    Published Online
    11 September 2016
    Accepted
    Notes:© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:19 Sep 2016 14:30
    Last modified:11 Nov 2024 11:27
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/57853
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