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

Peng, Xiaosheng, Li, Jinshu, Wang, Ganjun, Wu, Yijiang, Li, Lee, Li, Zhaohui, Bhatti, Ashfaque Ahmed, Zhou, Chengke, Hepburn, Donald M., Reid, Alistair J. ORCID: https://orcid.org/0000-0003-3058-9007, 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. 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. First, 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, and 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 and Support Vector Machine. 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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISSN:	0885-8977
Date of First Compliant Deposit:	24 May 2019
Date of Acceptance:	18 May 2019
Last Modified:	26 Nov 2024 06:45
URI:	https://orca.cardiff.ac.uk/id/eprint/122859

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