Opportunistic maintenance optimisation for offshore wind farm with considering random wind speed

ABSTRACT

A joint maintenance decision-making framework is proposed to optimise the long-term maintenance plan and lower the maintenance cost for offshore wind farms. The historical wind speed data are screened by using the method of k-means clustering, and Markov chains are established for the wind speed in different seasons. On this basis, the approach of Markov chain Monte Carlo is applied to simulate the distribution of repair vessel's waiting time for maintenance, where the impact of wind speed on maintenance availability is considered. Moreover, the components in wind turbines are divided into four states according to their effective ages, i.e. young, mature, old and failed, respectively. A maintenance decision model is established, with the objective to minimise maintenance cost. Besides, three types of opportunistic maintenance are considered, i.e. failure-based opportunistic maintenance (FBOM), event-based opportunistic maintenance (EBOM) and age-based opportunistic maintenance (ABOM), respectively. The enhanced elitist genetic algorithm (SEGA) is adopted to solve the optimisation problem. The results indicate that among the three types of opportunistic maintenance, ABOM can reduce maintenance cost more effectively, and it is more suitable for long-term maintenance plans of offshore wind farm.

KEYWORDS:

• Offshore wind farm
• wind speed
• repair vessel
• effective age
• opportunistic maintenance

Disclosure statement

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

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Additional information

Funding

This work was supported by from the National Natural Science Foundation of China (NSFC) under grant 71671035; the Open Fund of Hunan Provincial Key Laboratory of Health Maintenance for Mechanical Equipment, China under grant 201901; and the Open Fund of Jiangsu Wind Power Engineering Technology Center of China under grant ZK19-03-03.

Notes on contributors

Chun Su

Chun Su is a professor in the School of Mechanical Engineering at Southeast University, China. He obtained his PhD degree in Mechanical Manufacturing from Southeast University, China in 2001. He used to be a visiting scholar of European Organization for Nuclear Research (CERN); University of Geneva, Switzerland; and Rutgers University, USA. His research interests include reliability engineering, maintenance optimisation, warranty decision-making and production system engineering. He has published more than 170 journal articles and conference papers.

Lin Wu

Lin Wu received his Bachelor degree in Industrial Engineering from Hunan University, China in 2020. He is currently working toward his master degree in the Department of Industrial Engineering, School of Mechanical Engineering at Southeast University, China. His research interests include reliability assessment and maintenance optimisation.