• Corpus ID: 233872222

Optimal maintenance schedule for a wind power turbine with aging components

@inproceedings{Yu2020OptimalMS,
  title={Optimal maintenance schedule for a wind power turbine with aging components},
  author={Quanjiang Yu and Ola Carlson and Serik Sagitov},
  year={2020}
}

• Quanjiang Yu, O. Carlson, S. Sagitov
• Published 14 December 2020
• Business

Wind power technology is one of the most important sources of the renewable energy available today. A large part of the wind energy cost is due to the cost of maintaining the wind power equipment, especially for offshore wind farms. To further reduce the maintenance cost, one can improve the design of the wind turbine components, making them more reliable. One can also reduce the maintenance costs by optimal scheduling of the component replacements. The latter task is the main motivation for this paper. When a wind turbine component fails to function, it might need to be replaced under the less than ideal circumstances. This is known as corrective maintenance. To minimise the unnecessary costs, a more active maintenance policy based on life expectancy of the key components is preferred. Optimal scheduling of preventive maintenance activities requires advanced mathematical modelling. In this paper an optimisation model is developed using the renewal-reward theorem. In the multi-component setting, our approach involves a new idea of virtual maintenance which allows us to treat each replacement event as a renewal event even if some components are not replaced by new ones. The proposed optimisation algorithm is applied to a four-component model of a wind turbine and the optimal maintenance plans are computed for various initial conditions. ∗Corresponding author Email address: [e-mail] (Quanjiang Yu) Preprint submitted to Journal of European Journal of Operational Research December 29, 2020 ar X iv :2 01 2. 07 30 7v 2 [ m at h. O C ] 2 7 D ec 2 02 0 The modelling results showed clearly the benefit of PM planning compared to pure CM strategy (about 30% lower maintenance cost). When we compare with another state-of-art optimization model, it showed a similar scheduling with a much faster CPU time. The comparison demonstrated that our model is both fast and accurate.