A dynamic clustering model of wind farm based on the operation data

Due to the increasingly serious environmental problems in China, more and more renewable energy sources, especially wind power, have accessed to the power grid. The unpredictability and the stochastic volatility of wind power has given the power grid a huge challenge. The existing wind farm models may cause the scheduling usually adopt random wind turbine removal, whose effect is often not ideal, and may have the secondary stress on the power grid. In order to solve the problems in the existing models, this paper discusses the dynamic clustering model for the wind farm, where the modeling process is based on the actual wind speed operation data and contains a rolling update process. Through adopting a relatively mature clustering method and the hierarchical clustering method, the precision of the model is obviously improved and the complexity of the model is greatly reduced. The simulation results show the effectiveness of the proposed method.