Data-driven dynamic assessment method of wind farm frequency characteristics based on state space mapping

With the integration of large-scale wind turbines (WTs) into grids via electronic interfaces, power systems are suffering from increasingly serious frequency stability risks. Due to the large number of WTs and their complex dynamic characteristics, operators encounter challenges coordinate single WTs to provide frequency support directly, and it is necessary to assess primacy frequency regulation (PFR) capability of wind farms. In order to cope with the problems of solving complexity and incomplete parameters, this paper develops a data-driven state space mapping-based linear model for wind farms to assess the maximum PFR capability. With Koopman operator theory (KOT), the proposed method transforms wind farm PFR nonlinear dynamics into a linear lift-dimension algebraic model, which can assess the maximum PFR capability of wind farms based on historical data in real-time. The simulation results demonstrate that the proposed method has the advantages of fast solving, independence on model parameters and lower requirements of training data.