Optimal group maintenance decision for a wind farm based on condition‐based maintenance

Abstract

AbstractThis study investigated the group maintenance (GM) strategy based on condition‐based maintenance (CBM) of a parallel wind farm with multiple identical wind turbines. In terms of this strategy, the state of deterioration of the system is determined by periodic inspection, with the GM activities at each inspection point performed according to the number of failed turbines, which are determined by the corresponding state of deterioration of the system. Under this strategy, the probabilities that influence GM are derived by the stationary probability density function, which is based on the stationary law of the state of the wind farm system. Furthermore, employing numerical experiments and a case study, we verified the correctness and effectiveness of the stationary probability density function under different deterioration‐related parameters and obtained the optimal number of failed turbines when GM is performed, as well as the optimal interval between two consecutive inspection points. Finally, through comparison of strategies and sensitivity analysis, we demonstrated the economic advantages and the effectiveness of the proposed strategy. For a parallel system with high maintenance set‐up cost owing to severe environmental conditions, our proposed GM policy based on CBM outperformed other existing strategies in terms of cost benefit and ease of implementation.Highlights Proposing group maintenance (GM) schedules for cost‐effectiveness of the whole wind farm. Determining optimal inspection interval and number of the failed wind turbines for GM. A stationary probability density function is developed to address maintenance relevance. Investigating effects of various parameters on the optimal policy performance. Saving huge maintenance cost over the other two maintenance strategies.