An Ensemble Classifier Based Scheme for Detection of False Data Attacks Aiming at Disruption of Electricity Market Operation

Wide area monitoring and control of modern power network demand real-time estimation of state variables from sensor measurements. Maintaining a high degree of reliability and accuracy in the state estimation process is important in avoiding any disruption in the electricity market operation. The market operation in power networks aims at providing a win-win situation for both the utility and consumer. The exposure and vulnerability of cyber components in smart grids allow for manipulating the electricity market by falsifying the state variables. The attacker can cause intentional profit/loss to the utility/consumer by misdirecting the estimated states through the injection of false data into the sensor information. Hence, maintaining integrity in the market operation demands a mechanism for detecting false data injection attack (FDIA). This paper proposes a classification-based approach for detecting FDIAs aiming at electricity market disruption. For any variation in the predicted and real-time nodal electricity price, the proposed decision tree (DT) based ensemble classifier is executed using state information to identify the prevailing scenario as a contingency or FDIA. The effectiveness of the proposed scheme has been extensively validated for various contingency and FDIA scenarios in IEEE 14 bus, 39 bus, and 57 bus test power systems.