Anomaly detection based on random matrix theory for industrial power systems

Abstract

Abstract The identification of abnormal power consumption state is an important but difficult issue in power consumption. The State Grid Corporation of China’s electric energy data acquisition system is only capable of acquiring power consumption big data collected by smart energy meter terminals. In view of this fact, this study presents a method for identifying abnormal power consumption state. First, the spectral distribution of eigenvalues of the covariance matrix of the high-dimensional random matrix of massive volumes of power consumption data is analyzed based on high-dimensional random matrix theory. Then, a power consumption big data-based abnormal power consumption state identification method is proposed based on the statistical properties of random matrices. Finally, simulations are performed based on actual power consumption data from Guizhou Province, China. The simulation results show that the proposed method can not only satisfy urgent requirements of power grids for visualization, timeliness, reliability and security but also provide a new approach for data-driven smart visual monitoring of power consumption.