A Multilevel Modeling Approach Towards Wind Farm Aggregated Power Curve

Wind farm multiple aggregated power curve modeling plays an important role in reducing the complexity of analyses wind management and annual prediction. There is a trade-off between accuracy curves. In this paper, $K$ -Means clustering utilized to classify turbines into homogeneous groups according new set features based on overall performance turbines. We apply multilevel methods, including random intercept slope models turbine clusters, take account hidden correlation among different clusters. Results show that our proposed methods are higher than single method alongside equal complexity. The model can be analyze behavior simulations forecast power.