Power Prediction of Wind Turbine Units Based on Spectral Clustering and Multivariate Variational Mode Decomposition
Traditional wind power output forecasting methods need to fully consider the similarities and differences between units,neglect the influence of environmental conditions on wind power output,and have single-feature extraction methods.Therefore,this paper proposes an artificial neural network wind power output forecasting method based on spectral clustering and multivariate variational mode decomposition.Firstly,to capture the similarities and differences between different units,the wind speed and wind direction are clustered using spectral clustering,and a two-dimensional wind speed-wind direction label cluster is constructed.The center unit of each cluster is selected to represent the output characteristics of the cluster.Then,to comprehensively describe the relationship between output and environmental conditions,the variational mode decomposition algorithm is used to decompose the output of the clustering center units,and the output is decomposed into different frequency modal components together with wind speed and wind direction data.Finally,in the prediction stage,a deep learning network based on the attention mechanism is introduced,and the feature intrinsic mode functions are input into the convolutional long short-term memory neural network model after adding the attention mechanism for training and prediction.The prediction results of other target units in the same cluster are obtained through an error correction module.Compared with traditional methods,this method significantly improves prediction accuracy and has practicality and effectiveness.
wind power forecastspectral clusteringMVMDCNN-LSTMattention
国家科技期刊平台
NSTL
万方数据
维普
