An improved density peaks clustering algorithm for power load profiles clustering analysis
DOI：10.7667/PSPC171386
Key Words:power big data  load profiles clustering  density peaks clustering algorithm  PCA  kd tree  KNN algorithm
Author NameAffiliation
CHEN Junyi School of Electrical Engineering, Wuhan University, Wuhan 430072, China 
DING Jianyong School of Electrical Engineering, Wuhan University, Wuhan 430072, China 
TIAN Shiming China Electric Power Research Institute, Beijing 100192, China 
BU Fanpeng China Electric Power Research Institute, Beijing 100192, China 
ZHU Bingxiang School of Electrical Engineering, Wuhan University, Wuhan 430072, China 
HUANG Shicheng School of Electrical Engineering, Wuhan University, Wuhan 430072, China 
ZHOU Kai School of Electrical Engineering, Wuhan University, Wuhan 430072, China 
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Abstract:Aiming at the problems of poor stability of clustering results, poor effectiveness in clustering, slow speed and high memory consumption when making traditional clustering analysis for a large dimensionality huge number of load profiles with huge difference between the clusters under the background of the big data, an improved density peaks clustering algorithm is proposed. Firstly, principle components analysis method is used to reduce dimensions of load curves after normalization in order to reduce the calculation of the Euclidean distance between the sample vectors and to speed up the subsequent operations. Then, the kd tree algorithm is used to carry out the fast k-nearest neighbor search to generate KNN matrix. Finally, the KNN matrix is used to replace the original distance matrix as the input data. Based on the KNN improved local density and distance calculation criterion, the density peaks clustering algorithm is used to cluster the load profiles. Experiments and case analysis show that the proposed method is practicable and effective. This work is supported by National High-tech R & D Program of China (863 Program) (No. 2015AA050203).
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