A Bi-Level Model for Detecting and Correcting Parameter Cyber-Attacks in Power System State Estimation

Abstract

Power system state estimation is an important component of the status and healthiness of the underlying electric power grid real-time monitoring. However, such a component is prone to cyber-physical attacks. The majority of research in cyber-physical power systems security focuses on detecting measurements False-Data Injection attacks. While this is important, measurement model parameters are also a most important part of the state estimation process. Measurement model parameters though, also known as static-data, are not monitored in real-life applications. Measurement model solutions ultimately provide estimated states. A state-of-the-art model presents a two-step process towards simultaneous false-data injection security: detection and correction. Detection steps are χ2 statistical hypothesis test based, while correction steps consider the augmented state vector approach. In addition, the correction step uses an iterative solution of a relaxed non-linear model with no guarantee of optimal solution. This paper presents a linear programming method to detect and correct cyber-attacks in the measurement model parameters. The presented bi-level model integrates the detection and correction steps. Temporal and spatio characteristics of the power grid are used to provide an online detection and correction tool for attacks pertaining the parameters of the measurement model. The presented model is implemented on the IEEE 118 bus system. Comparative test results with the state-of-the-art model highlight improved accuracy. An easy-to-implement model, built on the classical weighted least squares solution, without hard-to-derive parameters, highlights potential aspects towards real-life applications.