Diagnosis of Outliers and Cyber Attacks in Dynamic PMU-Based Power State Estimation

Abstract

The paper introduces a robust method to detect random errors and cyber-attacks targeting alternating current (AC) dynamic state estimation through false data injection. The dynamic state estimator uses measurements that are delivered at a high reporting rate from synchrophasors or phasor measurement units (PMUs). A statistical outlier detection algorithm using the S-estimator is implemented on successive batch-mode regression representations of the extended Kalman filter (EKF). The S-estimator is a robust regression estimator that resists the effect of outliers and bad measurements. Theoretically, an S-estimator offers an improved detection of outliers compared with other methods implemented in the smart grid literature such as the Huber M-estimator and the Generalized Huber M-estimator (GM-estimator). The S-estimator has the advantage of offering a high breakdown point and can resist both observation outliers and leverage points. The introduced S-based EKF accurately tracks the fast variations of states when the PMU data is either clean or contaminated by bad measurements and cyber attacks. The proposed filter also accurately tracks the states when errors and cyber attacks target the topology of the grid. The simulations are conducted on the IEEE 14-bus system.