A multi-view and multi-scale transfer learning based wind farm equivalent method

Abstract With the increasing capacity of wind farm (WF), detailed WF model is not appropriate for power system studies, and the equivalence of WF with the required accuracy level poses a complicated technical challenge. In this paper, a multi-view and multi-scale transfer learning based WF equivalent method is proposed. Three steps are taken in this method. (a) Extract feature from active and reactive power of wind turbine (WT) using refined composite multi-scale entropy (RCMSE), on this basis, construct clustering indicator considering view and scale two aspects. (b) Aiming at the feature of the clustering indicator, a multi-view and multi-scale fuzzy C-means (VS-FCM) clustering algorithm is proposed, and transfer learning is used in it for better WTs cluster performance. (c) transfer Q-learning is adopted to optimize the parameters of collector network for each equivalent WT, so as to improve parameter optimization efficiency. To verify the effectiveness of the proposed method, an actual system in East Inner Mongolia of China is utilized for case study. Simulation results shows that the dynamic characteristics and the robustness of the proposed model perform a good behavior in different wind scenarios and voltage sag levels, besides, the method has an advantage in the efficiency of simulation time and parameter optimization.