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A Review of Online Partial Discharge Measurement of Large Generators

Author

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  • Yuanlin Luo

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zhaohui Li

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Hong Wang

    (Three Gorges Hydropower Plant, China Yangtze Power Corporation, Yichang 443002, China)

Abstract

Online partial discharge (PD) measurements have long been used as an effective means to assess the condition of the stator windings of large generators. An increase in the use of PD online measurement systems during the last decade is evident. Improvements in the detection capabilities are partly the reason for the increased popularity. Another reason has been the development of digital signal processing techniques. In addition, rapid progress is being made in automated single PD source classification. However, there are still some factors hindering wider application of the system, such as the complex PD mechanism and PD pulse propagation in stator windings, the presence of detrimental noise and disturbances on-site, and multiple PD sources occurring simultaneously. To avoid repetition of past work and to provide an overview for fresh researchers in this area, this paper presents a comprehensive survey of the state-of-the-art knowledge on PD mechanism, PD pulse propagation in stator windings, PD signal detection methods and signal processing techniques. Areas for further research are also presented.

Suggested Citation

  • Yuanlin Luo & Zhaohui Li & Hong Wang, 2017. "A Review of Online Partial Discharge Measurement of Large Generators," Energies, MDPI, vol. 10(11), pages 1-32, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1694-:d:116397
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    References listed on IDEAS

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    1. 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.
    2. 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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    8. Christian Gianoglio & Edoardo Ragusa & Andrea Bruzzone & Paolo Gastaldo & Rodolfo Zunino & Francesco Guastavino, 2020. "Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus," Energies, MDPI, vol. 13(5), pages 1-16, March.
    9. Jonathan dos Santos Cruz & Fabiano Fruett & Renato da Rocha Lopes & Fabio Luiz Takaki & Claudia de Andrade Tambascia & Eduardo Rodrigues de Lima & Mateus Giesbrecht, 2022. "Partial Discharges Monitoring for Electric Machines Diagnosis: A Review," Energies, MDPI, vol. 15(21), pages 1-31, October.
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    11. Anderson J. C. Sena & Rodrigo M. S. de Oliveira & Júlio A. S. do Nascimento, 2021. "Frequency Resolved Partial Discharges Based on Spectral Pulse Counting," Energies, MDPI, vol. 14(21), pages 1-36, October.
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