Multidimensional data processing and tensor analysis for large-scale power grids in a parallel computing framework

Abstract

Grid data is compressed and stored without processing. There are problems of large compression errors and long running time, which affect the compression and storage effect. Therefore, this paper proposes a method for storing grid data using parallel computing frameworks. While drawing on the task scheduling strategy of MapReduce distributed computing framework, combined with the current situation of centralized deployment of data in the information system of the State Grid Corporation, an efficient tensor-based parallel computing method is proposed. The massive multivariate and heterogeneous smart grid data is compressed using the Tucker decomposition method for big data compression of smart grids. This method’s line resistance and reactance dynamic parameters have average values of 0.033 and 0.520, respectively, which are very close to the actual values. The method used in this paper has a high degree of accuracy in estimating resistance dynamic parameters. It possesses certain practical application performance. By predicting the daily average load of the North China Power Grid, the accuracy of the prediction data of the gridded load analysis is close to the actual value, indicating the superiority of the grid multidimensional data processing method designed in this paper.