A hybrid robust forecasting-aided state estimator considering bimodal Gaussian mixture measurement errors

Abstract In this paper, a hybrid robust forecasting-aided state estimator (FASE) is proposed that can handle bimodal Gaussian mixture (BGM) distribution of PMU noise, bad data and sudden load changes. It is shown in this paper that the traditional methods will be biased in the presence of BGM noise of PMU measurements. To this end, the generalized-maximum likelihood cubature Kalman filter (GM-CKF) is developed and compared with existing GM-EKF, GM-UKF, CKF, EKF, UKF, and static state estimator (SSE) in different system operating scenarios. It is demonstrated that GM-CKF has better estimation accuracy than all other methods in the presence of BGM errors, and is more stable than GM-UKF and UKF. However, its estimation accuracy is lower than the other state estimators except for CKF in the initial estimation stage and when an unexpected sudden change occurs. This result also means that the GM-CKF is highly sensitive to the anomalies and is condusive for anomaly detection. Finally, a hybrid robust FASE method is proposed that balances well the trade-off between GM-CKF and SSE. Simulation results carried out on several IEEE benchmark systems demonstrate the effectiveness as well as the robustness of the proposed hybrid method.