AN IMPROVED SPECTRAL CLUSTERING ALGORITHM FOR LARGE SCALE WIND FARM POWER PREDICTION

Keywords

Spectral Clustering, Power Prediction, Cuckoo Search Algorithm, Lévy Flight

Abstract

Aiming at reaching the balance between calculation efficiency and power prediction accuracy of wind farms, two improved spectral clustering algorithm s and their application framework are proposed. For classical k way NJW spectral clustering , the clustering sample space is composed of k eigenvectors, which may lose part of structural information and may not reach accurate clustering results. T o improve the accuracy and stability, we propose d to cluster with feature expansion and the Cuckoo Search algorithm . We extended the clustering eigenspace from k eigenvectors to 2k to improve the clustering accuracy. To avoid following into local optimum while extending the eigenspace, the cuckoo search algorithm was introduced to search for better initial points instead of the random choice method. To apply the proposed algorithm for wind power prediction, wind turbines with similar wind regime were designated to the same group using the proposed spectral clustering algorithm . The power prediction model was established for each wind turbine group , and the output power of the entire wind farm was obtained by superposition. Experimental results indicated that the clustering accuracy is improved and the results of multiple clustering hold steady, which meets the requirement of accurate and timely prediction of wind farm power.

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