Extraction of typical combined output scenarios of wind-solar-hydropower generation based on deep embedding clustering

Abstract

It is a prerequisite for resource characteristics analysis and scheduling optimization research to screen out the representative typical scenarios of wind-solar-hydropower integrated generation systems. Due to the influence of the “dimension effect”， traditional clustering algorithms cannot be directly applied to high-dimensional data clustering， and the existing technical route based on “dimension reduction before clustering” cannot guarantee that low-dimensional features after dimensionality reduction are suitable for clustering tasks， resulting in unstable clustering results. In view of the existing problems， this paper proposes a method for extracting typical output scenarios of wind-solar-hydropower based on DEC （deep embedding clustering）. The method can realize high-dimensional output data clustering and avoid that low-dimensional features after dimensionality reduction are not suitable for clustering tasks. First， with the help of the nonlinear representation ability of the stacked autoencoder， the high-dimensional wind-solar-hydropower combined output data is deeply represented to achieve data dimensionality reduction. Then， the K-means clustering method is used to cluster the deep low-dimensional features， and the stacked encoder is optimized and adjusted at the same time in the clustering process to obtain the low-dimensional wind-solar-hydropower combined output feature suitable for the clustering space. Moreover， the precise division of wind-solar-hydropower combined output scenarios is realized. Finally， the DEC is performed on the wind-solar-hydropower output data of a region in south China. The PCA-K-means algorithm is used to set up a comparison example to verify the effectiveness of the DEC in selecting typical combined output scenarios of wind-solar-hydropower generation.

Keywords

• wind-solar-hydropower integration
• typical scenarios
• deep embedding clustering
• dimensionality reduction
• feature extraction