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Research on a power quality monitoring technique for individual wind turbines

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  • Yang, Wenxian
  • Tian, Sunny W.

Abstract

The extensive deployment of megawatt-scale wind turbines is bringing more challenges to the safety and stability of electric grid than ever before. This is not only because of the unstable wind over time but the increased risk of power quality pollution by defective wind turbines particularly when the turbines today are still experiencing various reliability issues. To prevent the power quality pollution by defective turbines, a new power quality monitoring technique dedicated for individual wind turbines is developed in this paper, so that the quality of the power generated by an individual turbine can be monitored by the wind turbine condition monitoring system. Through simulated and physical experiments on a specially designed test rig, some encouraging results have been achieved. It has been shown that the proposed technique is not only valid for monitoring the power quality of an individual wind turbine, but helpful in detecting the mechanical and electrical faults occurring in the wind turbines.

Suggested Citation

  • Yang, Wenxian & Tian, Sunny W., 2015. "Research on a power quality monitoring technique for individual wind turbines," Renewable Energy, Elsevier, vol. 75(C), pages 187-198.
    • Handle: RePEc:eee:renene:v:75:y:2015:i:c:p:187-198
    DOI: 10.1016/j.renene.2014.09.037
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    References listed on IDEAS

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    1. Amirat, Y. & Benbouzid, M.E.H. & Al-Ahmar, E. & Bensaker, B. & Turri, S., 2009. "A brief status on condition monitoring and fault diagnosis in wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2629-2636, December.
    2. Hameed, Z. & Hong, Y.S. & Cho, Y.M. & Ahn, S.H. & Song, C.K., 2009. "Condition monitoring and fault detection of wind turbines and related algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(1), pages 1-39, January.
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    Cited by:

    1. Agalar, Sener & Kaplan, Yusuf Alper, 2018. "Power quality improvement using STS and DVR in wind energy system," Renewable Energy, Elsevier, vol. 118(C), pages 1031-1040.
    2. Chen, Jinglong & Pan, Jun & Zhang, Chunlin & Luo, Xiaoyu & Zhou, Zitong & Wang, Biao, 2017. "Specialization improved nonlocal means to detect periodic impulse feature for generator bearing fault identification," Renewable Energy, Elsevier, vol. 103(C), pages 448-467.

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