A Data-Driven Approach for Quantifying and Evaluating Overloading Dependencies Among Power System Branches Under Load Redistribution Attacks

False data injection (FDI) for inducing load redistribution attacks can pose significant overloading risks to power systems. In this paper, we propose a data-driven approach to investigate the underlying dependencies and correlation of injected false data and the resulting overloading events, so that the detailed overloading mechanisms and propagation patterns of LR attacks can be revealed. Initially, we define two types of branches to capture the load distribution features of a network following an LR attack. Then, we develop a data-driven overloading dependency model to capture the pairwise dependencies among branches so that the overloading risks imposed by LR attacks can be captured. Finally, we propose three types of performance evaluation metrics to systematically quantify how the identified overloading dependencies can be used to evaluate the damaging effects of LR attack. Simulation results, conducted based on the IEEE 39-bus system, show that the proposed approach is more effective than the existing methods in mitigating the average overloading risks following an LR attack.