.png){kind=icon}
.png){kind=icon}
Return
A Data-Driven Vulnerability Evaluation Method in Grid Edge Based on Random Matrix Theory Indicators
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.2971030.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In order to solve the problem of vulnerability assessment of complex power systems facing complex structures and large sizes, a novel data driven method based on random matrix theory is proposed in this paper. Firstly, with the use of phasor measurement units (PMUs) big data, evaluation matrices are constructed to extract statistical characteristics of power systems operation. Then, with the combination of random matrix theory and entropy theory, vulnerability evaluation index are constructed considering the degree of influence of some faults in power systems. With full use of big data, the model-free method is more accurate and comprehensive. Simulation results in IEEE 39-bus test system and a real-world power grid in China verify the effectiveness of the method.
Keywords:
PMU measurement
voltage stability
power system vulnerability
random matrix theory
big data
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.