Probabilistic clustering of wind generators

Increasing number of wind farms (WFs) connected to power systems calls for efficient aggregate models so that large farms can be represented by only few equivalent wind turbines for steady state and dynamic system studies. A wind power plant consists of many small generators inside a wind farm. Future wind projects predict even greater number of wind turbines inside a wind farm for increased capacity. If each generator is represented individually this adds considerably towards calculation time for dynamic simulations. For this reason, wind farms are required to be modelled by few equivalent wind turbines which will reduce computation time. In this paper wind turbines (WTs) are clustered based on wind speed they receive using the Support Vector Clustering (SVC) technique. It was found that if set of clusters that reoccur several times during the year, as the best representation of the entire WF, can be obtained probabilistically, the highly frequent set then can be used to represent the wind farm for the entire year. This method can prevent time consuming way of choosing new set of clusters every time the wind speed (WS) and wind direction (WD) varies. The most probable equivalent set is also better than a single turbine equivalent model as the latter is good only at very high wind speeds (if same farm layout and site are considered) and would inaccurately represent the wind farm at other speeds