This paper investigates the determination of the optimal number of clusters for wind farm layout optimization using wind speed data. The objective is to compare the clustering results obtained from two methods: Silhouette coefficient and Hopkins clustering index. The study focuses on the application of K-means clustering to identify the cluster configurations that best represent the wind farm layout. The wind speed data used in this research corresponds to an offshore wind farm, comprising 25 turbines over a 30-day period. The silhouette coefficient and Hopkins clustering index are utilized to determine the optimal number of clusters for the wind farm layout. The results of the study prove the effectiveness of both methodologies in estimating the optimal cluster number. The silhouette coefficient analysis reveals an optimal cluster number of 8, with a coefficient value of 0.1368. The wind farm layouts obtained from each method are compared and plotted to visualize the clustering configurations. In contrast, the Hopkins-based layout results in a smaller number of clusters, implying a more cohesive turbine distribution. The findings of this study provide insights into the determination of the optimal cluster number for wind farm layout optimization. The comparison of the silhouette coefficient and Hopkins clustering index serves as a valuable reference for wind farm developers and operators in making informed decisions regarding the number of clusters to consider in the layout design. Further research is needed to evaluate the impact of these clustering methods on power output and overall wind farm performance.

Cluster analysis,Hopkins statistics index,Offshore wind farms,Silhouette coefficient,Wind farm clustering