Abstract
Emerging urban wind farms (UMWF) are becoming increasingly prevalent, and optimizing maintenance strategy for UMWF has become crucial for reducing costs and improving wind power efficiency. This research formulates an optimization problem of UMWF by considering the maintenance route and resources. To solve this problem, firstly, the Efficient Maintenance Value (EMF) is proposed as an objective function to measure the maintenance value, based on which a novel Random Variable Neighborhood Descent and Cuckoo Search-based Hybrid Discretized Artificial Fish Swarm Algorithm (RVNDCS-HDAFSA) is proposed to search the optimal maintenance strategy. Experiments are conducted to verify the local search capabilities of the hybrid algorithm, and extensive comparison experiments with the other algorithms are conducted at different scales to validate the effectiveness of the RVNDCS-HDAFSA algorithm; the result of the comparison experiments and the ANOVA shows that the HDAFSA demonstrates a superior capability in solving the medium-scale and large-scale problems compared to the existing algorithm. In conclusion, the practical application experiments validate the effectiveness of our proposed approach.
Published Version
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