A power system missing data filling method based on correlation analysis and generative adversarial network

Abstract

In the novel power system of urban grid, the multiple resources increase and the data collection becomes more difficult, which lead to a higher random missing data rate. It is difficult to meet the demand for refined analysis and decision making. For the frequent missing data problem in the distribution network, a new missing data filling method for power systems based on fluctuation cross-correlation analysis (FCCA) and generative adversarial network (GAN) is proposed in this paper. Firstly, a multi-dimensional feature extraction method for strongly correlated grid data is proposed by fusing FCCA. Secondly, based on kernel principal component analysis (KPCA), the multi-dimensional feature dataset is dimensionally reduced. Finally, an improved GAN structure is designed, which integrates multi-dimensional features of power grid equipment data to reconstruct low dimensional vectors. The missing data is accurately filled in, and the integrity and availability of the new power system measurement data is improved. The algorithm is validated using real grid data, and the proposed method is also tested in a city grid. The results show that the proposed method has higher filling accuracy than the traditional data filling methods. Therefore, it is conformed that in the case of continuous and significant data environment, integrating strong correlation features for data filling has significant advantages in improving the integrity and availability of measurement data.

Keywords

• novel power systems
• fluctuating cross-correlation analysis (fcca)
• multi-dimensional features
• generative adversarial networks (gan)
• missing data
• kernel principal component analysis (kpca)
• intelligent filling