A Sample Covariance Model-Based Method for Topology Change Detection and Location of Power Grids With High-Level Renewables

Nan Zhou, Yan Xu, Lingen Luo, Guoming Ma

Abstract: The increasing penetration of renewable energy resources (RESs) has introduced diverse uncertainties in power distribution grids, necessitating the development of advanced grid topology estimation methods applicable in such situation. This article proposes a new method for topology change detection and location tailored for high renewable energy penetrated grids. First, we establish the power grid topology change model using the voltage magnitude measurements, addressing scenarios encompassing line tripping, reconfiguration, and active islanding. We then incorporate the sample covariance model into this framework, elucidating topology changes as low-rank perturbations in the eigenvalue distributions of the voltage matrix. Eventually, topology change detection and location are achieved by monitoring the behavior of the largest eigenvalue within the distribution. Simulation tests are conducted on several distribution power grids, demonstrating the method's efficacy with different levels of RESs penetration. Even in the utmost scenario of 65% RESs integration, our method consistently achieves an impressive topology detection rate of up to 95%. The method also shows high noise tolerance capability and low computational complexity, satisfying the needs for practical application.