A Data-Driven Model Predictive Control for Alleviating Thermal Overloads in the Presence of Possible False Data

Abstract

Effective mitigation of thermal overloads is crucial in preventing power grid outages. With large-scale adoption of phasor measurement units, new and more effective mitigation opportunities have emerged. However, the possibility of false data attacks on the measured data, or the relay status information, threatens the promise of utilizing measurements for timely mitigation of thermal overloads. False data can lead to wrong estimation of the system states and the power flow model, which may eventually lead to wrong mitigations. To address these newly introduced challenges, in this article, a data-driven model-predictive control method is introduced for mitigation of the thermal overloads, which is resilient to false data injection attacks. This is achieved by constructing a data-driven power flow model from the trustworthy system measurements, which is independent from system topology and model parameters. Hence, the power flow model becomes immune to wrong system model information. To eliminate the adverse impacts of false data on the measurements, the actual system states are recovered from the historical trustworthy data if an attack is detected. Case studies demonstrate that the developed model predictive control methodology effectively identifies the optimal mitigations in a finite prediction horizon.