A Replay-Attack-Resilient Power System State Estimation Scheme

Replaying outdated or uncorrelated data can lead to a distorted view of the actual system state, resulting in suboptimal control decisions that potentially compromise grid stability, reliability, and efficiency. Replay Attacks (RA) are a kind of stealthy attacks which can mar one of the very key application of the Energy Management System (EMS), viz., Power System State Estimation (PSSE). This paper, therefore, proposes a novel scheme to make the PSSE resilient against RAs. To this end, first, a Power Transfer Distribution Factor (PTDF)-assisted vulnerability analysis is carried out to identify the critical SCADA measurements which may become a preferred choice of the attacker to launch RAs. Next, an optimal number of secured phasor measurements are exploited to detect and correct any RA in the SCADA measurement set, followed by a simple hybrid estimation scheme to reconstruct the falsified set of measurements. The proposed strategy’s effectiveness is validated through testing on three standard IEEE test systems, namely IEEE 14, New England (NE) 39, and IEEE 118 using simulated data generated from Real-time Digital Simulators (RTDS) and MATPOWER. Finally, the efficacy, feasibility and robustness of the proposed method under different variant of RA scenarios are compared with two existing Kalman Filter-based distributed state estimation methods associated with the three widely used statistical cyber attack detectors. Note to Practitioners—Power System State Estimation (PSSE) is a key application in the EMS, which further caters to many important real-time grid applications. If the SCADA measurements, which are fed to the PSSE program get corrupted with the Replay Attacks (RAs), it can jeopardize the smooth functioning of the power grids. Being motivated by this, this article presents a simple approach to detect and mitigate RAs in PSSE. The proposed scheme exploits a set of limited secured phasor measurements, along with the vulnerable SCADA measurements, in the form of Hybrid State Estimation (HYB-SE) to detect and correct any presence of RAs. The proposal is validated on the IEEE 14, NE 39, and IEEE 18 bus systems, modelled in Real Time Digital Simulator with a maximum detection accuracy of 94.6%, 94.2%, and 84.47% respectively. The superior performance of the proposed detection approach is also validated against two existing distributed state estimation methods accompanied with three statistical cyber attack detectors.