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Classification of operating conditions of wind turbines for a class-wise condition monitoring strategy

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  • Ha, Jong M.
  • Oh, Hyunseok
  • Park, Jungho
  • Youn, Byeng D.

Abstract

Relevant classification of the stationary operating conditions of wind turbines (WTs) aids in the selection of an optimal condition monitoring technique. This paper presents a general method that can be used to classify the operating conditions of WTs in terms of rotor speed and power. In this study, the ideal probability density functions (PDFs) of rotor speed and power are calculated using an analytic WT model and a wind speed profile. To estimate the PDFs of rotor speed and power with field data, two methods are employed: (1) empirical PDF-based and (2) Gaussian mixture model (GMM)-based. The individual PDFs estimated by the two methods are used to quantitatively define the range of the stationary WT operating conditions. The proposed methods and the range of stationary operating conditions established by the methods were evaluated using data from an analytical WT model and an actual 2.5 megawatt WT in the field. In addition, the paper presents the evaluation of the performance of the proposed class-wise condition monitoring strategy when used with vibration signals acquired from a two kilowatt WT testbed. In summary, the proposed strategy and methods are promising for effective condition monitoring of WTs.

Suggested Citation

  • Ha, Jong M. & Oh, Hyunseok & Park, Jungho & Youn, Byeng D., 2017. "Classification of operating conditions of wind turbines for a class-wise condition monitoring strategy," Renewable Energy, Elsevier, vol. 103(C), pages 594-605.
    • Handle: RePEc:eee:renene:v:103:y:2017:i:c:p:594-605
    DOI: 10.1016/j.renene.2016.10.071
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    References listed on IDEAS

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    1. Vaart,A. W. van der, 2000. "Asymptotic Statistics," Cambridge Books, Cambridge University Press, number 9780521784504.
    2. Hameed, Z. & Ahn, S.H. & Cho, Y.M., 2010. "Practical aspects of a condition monitoring system for a wind turbine with emphasis on its design, system architecture, testing and installation," Renewable Energy, Elsevier, vol. 35(5), pages 879-894.
    3. Tian, Zhigang & Jin, Tongdan & Wu, Bairong & Ding, Fangfang, 2011. "Condition based maintenance optimization for wind power generation systems under continuous monitoring," Renewable Energy, Elsevier, vol. 36(5), pages 1502-1509.
    4. Rodriguez-Hernandez, O. & Jaramillo, O.A. & Andaverde, J.A. & del Río, J.A., 2013. "Analysis about sampling, uncertainties and selection of a reliable probabilistic model of wind speed data used on resource assessment," Renewable Energy, Elsevier, vol. 50(C), pages 244-252.
    5. García Márquez, Fausto Pedro & Tobias, Andrew Mark & Pinar Pérez, Jesús María & Papaelias, Mayorkinos, 2012. "Condition monitoring of wind turbines: Techniques and methods," Renewable Energy, Elsevier, vol. 46(C), pages 169-178.
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    Cited by:

    1. Kim, Hyeonmin & Kim, Jung Taek & Heo, Gyunyoung, 2018. "Failure rate updates using condition-based prognostics in probabilistic safety assessments," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 225-233.
    2. Yang, Wenguang & Liu, Chao & Jiang, Dongxiang, 2018. "An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring," Renewable Energy, Elsevier, vol. 127(C), pages 230-241.

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