Research on feature extraction and classification of load's reactive power based on data-driven

Abstract

Driven by the advancement of science and technology, the extensive access of high proportion of renewable distributed energy and power electronic equipment has changed the reactive power characteristics of the user side of the urban distribution network. In order to deeply explore the current status of reactive power characteristics on the urban user side, this paper takes the typical users of a regional power grid in Shanghai as the research object, whose power factor data are collected, and the power factor change curves of users are analyzed by combining deep learning and multidimensional clustering. Firstly, the collected data are preliminarily processed to remove the abnormal data in the total sample, and the incomplete data is filled by the interpolation method. Then, aiming at avoiding the influence of the data with an overhigh dimension on the clustering effect, this paper uses a multi-layer autoencoder deep neural network in deep learning to reduce the dimension of the typical daily power factor change curve set of input and extracts its deep features through unsupervised learning. Finally, the K-means clustering algorithm is used to conduct clustering analysis on the daily power factor change curve of typical users with different power consumption types, including large industry, commerce, resident, and charging service industry, etc. So as to summarize various users' reactive power consumption characteristics and reactive power distribution rules. The analysis results show that the shape of the power factor curves of some users vary significantly in different months, and even the factor fluctuates greatly in the same day.