An improved algorithm for cubature Kalman filter based forecasting-aided state estimation and anomaly detection

作者：Zhaoyang Jin; Saikat Chakrabarti; James Yu; Lei Ding; Vladimir Terzija

摘要：This article proposes a new algorithm for forecasting-aided state estimation based on the cubature Kalman filter (CKF) and new methods for detecting and identifying data anomalies. In this article, through extensive simulations, the CKF was compared to four different types of forecasting-aided state estimators (FASEs) including extended Kalman filter (EKF), iterated EKF, second-order Kalman filter and unscented Kalman filter under normal operation and bad data conditions. Identifying the challenge that the estimation accuracy of the existing CKF-based estimator is significantly lower than that of the other FASEs in the cases of sudden load change, and sudden topology change caused by faults an attempt to improve the CKF accuracy has been undertaken. The existing detection methods cannot accurately detect and distinguish those anomalies, and they cannot identify the anomaly location. This article proposes an improved algorithm for CKF-based FASE that overcomes the drawbacks of the existing CKF-based FASEs using a novel anomaly detection algorithm. The simulation results show that the new anomaly detection method is superior to the two existing anomaly detection algorithms. The simulations are performed in the above-mentioned four cases in IEEE 14 and 118 bus test systems in MATLAB.

發(fā)表于：International Transactions on Electrical Energy Systems ( Volume: 31, Issue: 5, May. 2021)