Random forest based optimal feature selection for partial discharge pattern recognition in HV cables

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Peng, Xiaosheng and Li, Jinshu and Wang, Ganjun and Wu, Yiijiang and Li, Lee and Li, Zhaohui and Ahmed Bhatti, Ashfaque and Zhou, Chengke and Hepburn, Donald M. and Reid, Alistair J and Judd, Martin D. and Siew, W. H. (2019) Random forest based optimal feature selection for partial discharge pattern recognition in HV cables. IEEE Transactions on Power Delivery, 34 (4). pp. 1715-1724. ISSN 0885-8977 (https://doi.org/10.1109/TPWRD.2019.2918316)

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Abstract

Optimal selection of features of Partial Discharge (PD) signals recorded from defects in High Voltage (HV) cables will contribute not only to the improvement of PD pattern recognition accuracy and efficiency but also to PD parameter visualization in HV cable condition monitoring and diagnostics. This paper presents a novel Random Forest (RF)-based feature selection algorithm for PD pattern recognition of HV cables. The algorithm is applied to feature selection of both PD signals and interference signals with the aim of obtaining the optimal features for data processing. Firstly, the experimental data acquisition and feature extraction processes are introduced. PD signals were captured from faults created in a cable to obtain the raw PD data, then a set of 3500 transient PD pulses and a set of 3500 typical interference pulses were extracted, based on which 34 PD features were extracted for further processing. Furthermore, 119 two-dimensional features and 1082 three-dimensional features were generated. The paper then discusses the basic principle of the RF algorithm. Finally, RF-based feature selection was implemented to determine the optimal features for PD pattern recognition. The results were obtained and evaluated with the Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM). Results show that the proposed RF-based method is effective for PD feature selection of HV cables with the potential for application to additional HV power apparatus.

ORCID iDs

Peng, Xiaosheng, Li, Jinshu, Wang, Ganjun, Wu, Yiijiang, Li, Lee, Li, Zhaohui, Ahmed Bhatti, Ashfaque, Zhou, Chengke, Hepburn, Donald M., Reid, Alistair J, Judd, Martin D. and Siew, W. H. ORCID logoORCID: https://orcid.org/0000-0003-4000-6856;
  • Item type:Article
    ID code:67974
    Dates:
    Date
    Event
    22 May 2019
    Published
    18 May 2019
    Accepted
    Notes:© 2019 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. X. Peng et al., "Random Forest Based Optimal Feature Selection for Partial Discharge Pattern Recognition in HV Cables," in IEEE Transactions on Power Delivery, vol. 34, no. 4, pp. 1715-1724, Aug. 2019, doi: 10.1109/TPWRD.2019.2918316
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:22 May 2019 15:57
    Last modified:19 Dec 2024 04:24
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/67974
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