Power Grid Situation Awareness Based on Random Matrix Theory

As the penetration ratio of renewable energy continues to increase, the structure of power systems is becoming increasingly complex. Traditional analysis methods are facing challenges in accurately modeling this complexity, highlighting the urgent need for a new research approach to break the current impasse. With the ongoing digitalization process, acquiring electricity data has become significantly more convenient. The vast amount of electricity data provides powerful support for power system analysis and also opens up a new avenue of research. This paper introduces a set of big data analysis methods, namely real-time big data analysis and augmented matrix method, based on the foundation of random matrix theory. These methods can fully utilize the characteristics of electricity big data, known as the 4Vs (Volume, Variety, Velocity, Veracity), and address the limitations of traditional methods in harnessing the driving force of heterogeneous spatio-temporal data. By visualizing case studies based on the single-ring theorem in random matrix theory and utilizing the statistical indicator - Mean Spectral Radius (MSR), this paper quantitatively analyzes the power system status from a high-dimensional perspective, achieving the situation awareness in the power grid through spatio-temporal integration. This approach effectively explores the inherent connections among electricity data, thereby providing a robust assessment of the power grid's operational status.