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Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance

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  • Ju Tang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Miao Jin

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Fuping Zeng

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Siyuan Zhou

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Xiaoxing Zhang

    (School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

  • Yi Yang

    (Shandong Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250002, China)

  • Yan Ma

    (Shandong Electric Power Research Institute, State Grid Shandong Electric Power Company, Jinan 250002, China)

Abstract

Scientific evaluation of partial discharge (PD) severity in gas-insulation switchgear (GIS) can assist in mastering the insulation condition of in-service GIS. Limited theoretical research on the laws of PD deterioration leads to a finite number of evaluation features extracted and subjective features selected for PD severity assessment. Therefore, this study proposes a minimum-redundancy maximum-relevance (mRMR) algorithm-based feature optimization selection method to realize the scientific and reasonable choice of PD severity features. PD ultra-high frequency data of varying severities are produced by simulating four typical insulation defects in GIS, which are then collected in the lab. A 16-dimension feature set describing PD original characteristics is abstracted in phase-resolved partial discharge (PRPD) mode, and the more informative evaluation feature set characterizing PD severity is further excavated by the mRMR method. Finally, a support vector machine (SVM) algorithm is employed as the classifier for intelligent evaluation to compare the evaluation effects of PD severity between the feature set selected by mRMR and the feature set is composed of discharge times, amplitude value, and time intervals obtained traditionally based on discharge change theory. The proposed comparison test showed the effectiveness of the mRMR method in informative feature selection and the accuracy of PD severity assessment for all defined defects.

Suggested Citation

  • Ju Tang & Miao Jin & Fuping Zeng & Siyuan Zhou & Xiaoxing Zhang & Yi Yang & Yan Ma, 2017. "Feature Selection for Partial Discharge Severity Assessment in Gas-Insulated Switchgear Based on Minimum Redundancy and Maximum Relevance," Energies, MDPI, vol. 10(10), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1516-:d:113875
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    References listed on IDEAS

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    1. Ming Ren & Ming Dong & Jialin Liu, 2016. "Statistical Analysis of Partial Discharges in SF 6 Gas via Optical Detection in Various Spectral Ranges," Energies, MDPI, vol. 9(3), pages 1-15, March.
    2. Stefan Tenbohlen & Sebastian Coenen & Mohammad Djamali & Andreas Müller & Mohammad Hamed Samimi & Martin Siegel, 2016. "Diagnostic Measurements for Power Transformers," Energies, MDPI, vol. 9(5), pages 1-25, May.
    3. Abdullahi Abubakar Mas’ud & Ricardo Albarracín & Jorge Alfredo Ardila-Rey & Firdaus Muhammad-Sukki & Hazlee Azil Illias & Nurul Aini Bani & Abu Bakar Munir, 2016. "Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions," Energies, MDPI, vol. 9(8), pages 1-18, July.
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    Cited by:

    1. Gaoyang Li & Xiaohua Wang & Aijun Yang & Mingzhe Rong & Kang Yang, 2017. "Failure Prognosis of High Voltage Circuit Breakers with Temporal Latent Dirichlet Allocation," Energies, MDPI, vol. 10(11), pages 1-20, November.
    2. Yang Qi & Yang Fan & Bing Gao & Yang Mengzhuo & Ammad Jadoon & Yu Peng & Tian Jie, 2018. "Study on the Propagation Characteristics of Partial Discharge in Switchgear Based on Near-Field to Far-Field Transformation," Energies, MDPI, vol. 11(7), pages 1-12, June.
    3. Jiaying Deng & Wenhai Zhang & Xiaomei Yang, 2019. "Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network," Energies, MDPI, vol. 12(10), pages 1-16, May.

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