Optimal external opportunistic maintenance for wind turbines considering wind speed

ABSTRACT

A novel optimal opportunistic maintenance strategy for wind turbines considering wind speed as a stochastic process is proposed in this paper. Under this strategy, periodic preventive maintenance is conducted to reduce the probability of downtime due to failure, whereas external opportunities are leveraged to reduce the cost of preventive maintenance. This combined maintenance approach can also reduce costs associated with the wind turbine lifecycle. The formulated expected costs per unit time considered two scenarios, i.e. regular operation and energy generation interruption, to obtain the optimal solution via a genetic algorithm. Furthermore, we illustrated the economic advantages of the proposed strategy by comparing various maintenance strategies, as well as a sensitivity analysis to determine the effects of the different parameters in the proposed model. Experimental results demonstrate that the proposed opportunistic maintenance strategy is more cost-effective for wind turbines than comparable methods, with an optimal solution that varies according to variations in wind speed.

KEYWORDS:

• Condition-based maintenance
• external opportunistic maintenance
• genetic algorithm
• wind speed variability
• wind turbine

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 72071183), the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (STIP, Grant No. 2021L333), the Fundamental Research Program of Shanxi Province (Grant No. 202103021223291), Natural Science Foundation of Shanxi Province (202203021211194), Research Project Supported by Shanxi Scholarship Council of China (2022-161), the PhD Research Start-up Foundation of Taiyuan University of Science & Technology (Grant No. 20202027; 20212055), Taiyuan University of Science and Technology Graduate Joint Training Demonstration Base Project (JD2022008). Additionally, we would like to thank anonymous reviewers for their valuable comments, which have greatly improved our article. And we are also grateful to Editage [www.editage.cn] for English language editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China [72071183]; the PhD Research Start-up Foundation of Taiyuan University of Science & Technology [20202027; 20212055]; the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi [2021L333]; the Fundamental Research Program of Shanxi Province [202103021223291].