Power Optimization Strategy for Large Offshore Wind Farms Based on Adaptive Graph Clustering and BAS Algorithm
To address the challenges posed by large-scale offshore wind farms(OWFs)and their complex wake effects on centralized control,a distributed non-convex optimization control strategy,enhanced by an improved beetle antennae search algorithm is proposed to optimize power conversion in OWFs.Initially,an adaptive threshold algorithm is developed to establish a wake adaptive pruning directed graph,effectively maintaining essential wake propagation relationships between wind turbines.Subsequently,by utilizing the constraints of the wake adaptive pruning graph,a sub-directed graph is generated,dividing the wind farm into decoupled clustering communication subsets.On this foundation,targeting the maximization of OWF's output power,with the yaw angle and axial coefficient of wind turbines as the optimization variables,a power optimization strategy for the wind farm based on the Monte Carlo-beetle antennae search(MC-BAS)algorithm is introduced.The simulation results demonstrate that compared to traditional centralized control methods,the pro-posed algorithm significantly reduces computational costs while enhancing power conversion efficiency.
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维普
