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Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer

Author

Listed:
  • Wen Si

    (Department of Electrical Engineering, Shandong University, Jinan 250061, China
    Shandong Provincial Key laboratory of UHV Transmission Technology and Equipments, #17923 Jingshi Road, Jinan 250061, China)

  • Simeng Li

    (Department of Electrical Engineering, Shandong University, Jinan 250061, China
    Shandong Provincial Key laboratory of UHV Transmission Technology and Equipments, #17923 Jingshi Road, Jinan 250061, China)

  • Huaishuo Xiao

    (Department of Electrical Engineering, Shandong University, Jinan 250061, China
    Shandong Provincial Key laboratory of UHV Transmission Technology and Equipments, #17923 Jingshi Road, Jinan 250061, China)

  • Qingquan Li

    (Department of Electrical Engineering, Shandong University, Jinan 250061, China
    Shandong Provincial Key laboratory of UHV Transmission Technology and Equipments, #17923 Jingshi Road, Jinan 250061, China)

  • Yalin Shi

    (Jinan Power Supply Company of State Grid Shandong Electric Power Company, #238 Luoyuan Road, Jinan 250012, China)

  • Tongqiao Zhang

    (Jinan Power Supply Company of State Grid Shandong Electric Power Company, #238 Luoyuan Road, Jinan 250012, China)

Abstract

The ultra high voltage direct current (UHVDC) transmission system has advantages in delivering electrical energy over long distance at high capacity. UHVDC converter transformer is a key apparatus and its insulation state greatly affects the safe operation of the transmission system. Partial discharge (PD) characteristics of oil-pressboard insulation under combined AC-DC voltage are the foundation for analyzing the insulation state of UHVDC converter transformers. The defect pattern recognition based on PD characteristics is an important part of the state monitoring of converter transformers. In this paper, PD characteristics are investigated with the established experimental platform of three defect models (needle-plate, surface discharge and air gap) under 1:1 combined AC-DC voltage. The different PD behaviors of three defect models are discussed and explained through simulation of electric field strength distribution and discharge mechanism. For the recognition of defect types when multiple types of sources coexist, the Random Forests algorithm is used for recognition. In order to reduce the computational layer and the loss of information caused by the extraction of traditional features, the preprocessed single PD pulses and phase information are chosen to be the features for learning and test. Zero-padding method is discussed for normalizing the features. Based on the experimental data, Random Forests and Least Squares Support Vector Machine are compared in the performance of computing time, recognition accuracy and adaptability. It is proved that Random Forests is more suitable for big data analysis.

Suggested Citation

  • Wen Si & Simeng Li & Huaishuo Xiao & Qingquan Li & Yalin Shi & Tongqiao Zhang, 2018. "Defect Pattern Recognition Based on Partial Discharge Characteristics of Oil-Pressboard Insulation for UHVDC Converter Transformer," Energies, MDPI, vol. 11(3), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:592-:d:135331
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    References listed on IDEAS

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    1. Wong Jee Keen Raymond & Hazlee Azil Illias & Ab Halim Abu Bakar, 2017. "Classification of Partial Discharge Measured under Different Levels of Noise Contamination," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-20, January.
    2. Abdullahi Abubakar Mas’ud & Jorge Alfredo Ardila-Rey & Ricardo Albarracín & Firdaus Muhammad-Sukki & Nurul Aini Bani, 2017. "Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources," Energies, MDPI, vol. 10(7), pages 1-20, July.
    3. Ang Ren & Qingquan Li & Huaishuo Xiao, 2017. "Influence Analysis and Prediction of ESDD and NSDD Based on Random Forests," Energies, MDPI, vol. 10(7), pages 1-19, June.
    4. Xinnian Li & Fengqi Li & Shuyong Chen & Yanan Li & Qiang Zou & Ziping Wu & Shaobo Lin, 2017. "An Improved Commutation Prediction Algorithm to Mitigate Commutation Failure in High Voltage Direct Current," Energies, MDPI, vol. 10(10), pages 1-16, September.
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