A Dimensional Reduction Visualization Method for Large Data of Power Grid Based on t-SNE Algorithms

Abstract

The large data of power grid includes the data generated or needed in the process of planning, operation, management, and operation of distribution power system, including power generation data, transmission data, distribution data, power consumption data, and external data (such as economic, social and meteorological data). Data dimension is high and visualization is difficult. In this paper, a dimensionality reduction visualization method based on the t-SNE algorithm for large data of power grid is proposed. The dimensionality reduction of large data of high-dimensional power grid is processed on the premise of maintaining local geometric characteristics. Because the t-SNE algorithm has the ability to process non-linear and high-dimensional data, it can meet the needs of multi-type data fusion analysis and processing in a smart grid. In this paper, the 96-dimensional load data in the Shanghai area is used for visualization simulation, and good results are obtained, which provides a novel slotion for the visualization of large data in the high-dimensional power grid.