1. School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, Henan Province, China 2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China 3. School of New Energy, North China Electric Power University, Changping District, Beijing 102206, China
By identifying the similarity of different wind turbines in the wind farm unit of each unit is divided into different groups, each group of fleet power prediction model is set up, respectively, can not only improve the computing efficiency, improved at the same time a large wind farm short-term power prediction precision, better solved the volatility and intermittent wind effects on safe operation of power system. A wind farm grouping power prediction method based on Davidson-Boding index and clustering algorithm was proposed. The actual measured wind speed, measured power and the combination of the two were taken as the input of the grouping model, and the influence degree on the clustering accuracy was analyzed. After the wind power sequence is decomposed by the empirical mode decomposition method, the intrinsic mode function (IMF), which is highly correlated with the original signal, is reconstructed and used as the input of the k-means clustering algorithm to regroup the units in the field. Empirical Mode Decomposition and Relevance Vector Machine (EMD-RVM) models were constructed for each cluster, and the predicted power components were superimposed to obtain the predicted value of total power. The simulation results show that the wind speed is the main factor affecting the clustering results, and the power can be used as an important supplement to the input variables of different clustering models. Packet power prediction based on EMD-RVM improves the prediction accuracy and efficiency.
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