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Condition based maintenance optimization for wind power generation systems under continuous monitoring

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  • Tian, Zhigang
  • Jin, Tongdan
  • Wu, Bairong
  • Ding, Fangfang

Abstract

By utilizing condition monitoring information collected from wind turbine components, condition based maintenance (CBM) strategy can be used to reduce the operation and maintenance costs of wind power generation systems. The existing CBM methods for wind power generation systems deal with wind turbine components separately, that is, maintenance decisions are made on individual components, rather than the whole system. However, a wind farm generally consists of multiple wind turbines, and each wind turbine has multiple components including main bearing, gearbox, generator, etc. There are economic dependencies among wind turbines and their components. That is, once a maintenance team is sent to the wind farm, it may be more economical to take the opportunity to maintain multiple turbines, and when a turbine is stopped for maintenance, it may be more cost-effective to simultaneously replace multiple components which show relatively high risks. In this paper, we develop an optimal CBM solution to the above-mentioned issues. The proposed maintenance policy is defined by two failure probability threshold values at the wind turbine level. Based on the condition monitoring and prognostics information, the failure probability values at the component and the turbine levels can be calculated, and the optimal CBM decisions can be made accordingly. A simulation method is developed to evaluate the cost of the CBM policy. A numerical example is provided to illustrate the proposed CBM approach. A comparative study based on commonly used constant-interval maintenance policy demonstrates the advantage of the proposed CBM approach in reducing the maintenance cost.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:renene:v:36:y:2011:i:5:p:1502-1509
    DOI: 10.1016/j.renene.2010.10.028
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    References listed on IDEAS

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    1. Liu, Wenyi & Tang, Baoping & Jiang, Yonghua, 2010. "Status and problems of wind turbine structural health monitoring techniques in China," Renewable Energy, Elsevier, vol. 35(7), pages 1414-1418.
    2. Guo, Haitao & Watson, Simon & Tavner, Peter & Xiang, Jiangping, 2009. "Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1057-1063.
    3. Krokoszinski, H.-J., 2003. "Efficiency and effectiveness of wind farms—keys to cost optimized operation and maintenance," Renewable Energy, Elsevier, vol. 28(14), pages 2165-2178.
    4. 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.
    5. Martínez, E. & Sanz, F. & Pellegrini, S. & Jiménez, E. & Blanco, J., 2009. "Life cycle assessment of a multi-megawatt wind turbine," Renewable Energy, Elsevier, vol. 34(3), pages 667-673.
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