IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i10p1516-d113875.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/10/1516/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/10/1516/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mehran Tahir & Stefan Tenbohlen, 2019. "A Comprehensive Analysis of Windings Electrical and Mechanical Faults Using a High-Frequency Model," Energies, MDPI, vol. 13(1), pages 1-25, December.
    2. Xiaoxin Chen & Chen Li & Liangjin Chen & Hui Wang & Yiming Zang & Weijia Yao, 2020. "Influence of Different Structure and Specification Parameters on the Propagation Characteristics of Optical Signals Generated by GIL Partial Discharge," Energies, MDPI, vol. 13(12), pages 1-18, June.
    3. Lefeng Cheng & Tao Yu & Guoping Wang & Bo Yang & Lv Zhou, 2018. "Hot Spot Temperature and Grey Target Theory-Based Dynamic Modelling for Reliability Assessment of Transformer Oil-Paper Insulation Systems: A Practical Case Study," Energies, MDPI, vol. 11(1), pages 1-26, January.
    4. Jiefeng Liu & Hanbo Zheng & Yiyi Zhang & Hua Wei & Ruijin Liao, 2017. "Grey Relational Analysis for Insulation Condition Assessment of Power Transformers Based Upon Conventional Dielectric Response Measurement," Energies, MDPI, vol. 10(10), pages 1-16, October.
    5. Mohammed A. Shams & Hussein I. Anis & Mohammed El-Shahat, 2021. "Denoising of Heavily Contaminated Partial Discharge Signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform," Energies, MDPI, vol. 14(20), pages 1-22, October.
    6. Yulong Wang & Xiaohong Zhang & Lili Li & Jinyang Du & Junguo Gao, 2019. "Design of Partial Discharge Test Environment for Oil-Filled Submarine Cable Terminals and Ultrasonic Monitoring," Energies, MDPI, vol. 12(24), pages 1-14, December.
    7. Marek Florkowski & Dariusz Krześniak & Maciej Kuniewski & Paweł Zydroń, 2020. "Partial Discharge Imaging Correlated with Phase-Resolved Patterns in Non-Uniform Electric Fields with Various Dielectric Barrier Materials," Energies, MDPI, vol. 13(11), pages 1-15, May.
    8. Szymon Banaszak & Konstanty Marek Gawrylczyk & Katarzyna Trela, 2020. "Frequency Response Modelling of Transformer Windings Connected in Parallel," Energies, MDPI, vol. 13(6), pages 1-13, March.
    9. Minh-Tuan Nguyen & Viet-Hung Nguyen & Suk-Jun Yun & Yong-Hwa Kim, 2018. "Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear," Energies, MDPI, vol. 11(5), pages 1-13, May.
    10. Szymon Banaszak & Wojciech Szoka, 2018. "Cross Test Comparison in Transformer Windings Frequency Response Analysis," Energies, MDPI, vol. 11(6), pages 1-12, May.
    11. 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.
    12. Haresh Kumar & Muhammad Shafiq & Kimmo Kauhaniemi & Mohammed Elmusrati, 2024. "A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques," Energies, MDPI, vol. 17(5), pages 1-31, February.
    13. Fatih Atalar & Aysel Ersoy & Pawel Rozga, 2022. "Investigation of Effects of Different High Voltage Types on Dielectric Strength of Insulating Liquids," Energies, MDPI, vol. 15(21), pages 1-25, October.
    14. Ana C. N. Pardauil & Thiago P. Nascimento & Marcelo R. S. Siqueira & Ubiratan H. Bezerra & Werbeston D. Oliveira, 2020. "Combined Approach Using Clustering-Random Forest to Evaluate Partial Discharge Patterns in Hydro Generators," Energies, MDPI, vol. 13(22), pages 1-18, November.
    15. Emilio Parrado-Hernández & Guillermo Robles & Jorge Alfredo Ardila-Rey & Juan Manuel Martínez-Tarifa, 2018. "Robust Condition Assessment of Electrical Equipment with One Class Support Vector Machines Based on the Measurement of Partial Discharges," Energies, MDPI, vol. 11(3), pages 1-18, February.
    16. Patryk Bohatyrewicz & Szymon Banaszak, 2022. "Assessment Criteria of Changes in Health Index Values over Time—A Transformer Population Study," Energies, MDPI, vol. 15(16), pages 1-15, August.
    17. Alexandra I. Khalyasmaa & Pavel V. Matrenin & Stanislav A. Eroshenko & Vadim Z. Manusov & Andrey M. Bramm & Alexey M. Romanov, 2022. "Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics," Mathematics, MDPI, vol. 10(14), pages 1-25, July.
    18. Mehran Tahir & Stefan Tenbohlen, 2021. "Transformer Winding Condition Assessment Using Feedforward Artificial Neural Network and Frequency Response Measurements," Energies, MDPI, vol. 14(11), pages 1-25, May.
    19. Satoru Miyazaki, 2021. "Detection of Winding Axial Displacement of a Real Transformer by Frequency Response Analysis without Fingerprint Data," Energies, MDPI, vol. 15(1), pages 1-14, December.
    20. Chunguang Suo & Yanan Ren & Wenbin Zhang & Yincheng Li & Yanyun Wang & Yi Ke, 2021. "Evaluation Method for Winding Performance of Distribution Transformer," Energies, MDPI, vol. 14(18), pages 1-25, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1516-:d:113875. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.