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Random Forest Based Optimal Feature Selection for Partial Discharge Pattern Recognition in HV Cables

Authors :
Jinshu Li
Alistair Reid
Martin D. Judd
Donald M. Hepburn
Lee Li
Ashfaque Ahmed Bhatti
Yijiang Wu
Wan Hoon Siew
Xiaosheng Peng
Zhaohui Li
Chengke Zhou
Ganjun Wang
Source :
IEEE Transactions on Power Delivery. 34:1715-1724
Publication Year :
2019
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2019.

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.

Details

ISSN :
19374208 and 08858977
Volume :
34
Database :
OpenAIRE
Journal :
IEEE Transactions on Power Delivery
Accession number :
edsair.doi.dedup.....b18df5d7c4aa7770bf9474eb36d76141