A Method of Complementing Missing Power Data in Low-Voltage Stations Based on Improved Deep Convolutional Self-Encoding Network

Abstract

The irregularities in the collection and transmission of user power data in the low-voltage power distribution station area have led to errors in the subsequent application analysis of the station area. In order to ensure the integrity of power data in low-voltage stations, a multi-user power missing data complement method based on improved deep convolutional self-encoding is proposed. First, according to the characteristics of the lack of multi-user power data in the low-voltage station area, the power data is formed into a spatio-temporal tensor data format that can be used for one-dimensional convolution operations. Then use the encoding and decoding capabilities of the improved deep convolutional self-encoding network to realize the reconstruction of missing data, and optimize the network structure by introducing residual learning and batch normalization (BN). Finally, based on the proposed method, two cases of random and continuous loss of user power data in a certain area are complemented. The results show that the method can accurately complete 40% of randomly missing data and 2 consecutive days of missing data. The proposed method has improved completion accuracy compared with traditional methods to varying degrees.

Keywords

• Intelligent distribution network
• low-voltage power distribution station area
• deep convolutional autoencoder
• residual learning
• missing data completion