Load Decomposition Method Based on Improved EEMD-Person-PCA

With the rapid development of economy and society, the power demand of the whole society is becoming more and more huge and the types are becoming more and more complicated. The traditional power load modeling method is single, which leads to the problems of low classification of load modeling area decomposition and difficult detailed separation of features in the current situation. To solve these problems, a load decomposition method based on Ensemble Empirical Mode Decomposition (EEMD) -Person correlation coefficient - Principal Component Analysis (PCA) was proposed. It has good engineering significance for separating load characteristics in complex cases. First, a series of intrinsic mode functions (IMF) with mode aliasing were obtained by decomposing the original load data through EEMD. Then, the Person correlation coefficient and PCA method were used to decompose and reduce the dimension of IMF, eliminate the IMF component with weak correlation and obtain decomposition parameters with high similarity, thus alleviating the degree of modal aliasing and reducing the difficulty of parameter determination. The proposed method is applied to the typical load parameter decomposition problem, and the simulation results show that the proposed method has obvious advantages in the precision scale in the centralized modeling of multiple decomposition problems, indicating that the designed method can effectively deal with the typical load decomposition modeling problem.