Comparing Kalman Filters and Observers for Power System Dynamic State Estimation With Model Uncertainty and Malicious Cyber Attacks

• Published in: IEEE access Vol. 6; pp. 77155 - 77168
• Main Authors: Qi, Junjian, Taha, Ahmad F., Wang, Jianhui
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cyber attack; Cyberattack; Cybersecurity; dynamic state estimation; Generators; Kalman filter; Kalman filters; model uncertainty; non-Gaussian noise; observer; Observers; phasor measurement unit (PMU); Power system dynamics; Routines; State estimation; State observers; Uncertainty
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2876883

Kalman filters (KFs) and dynamic observers are two main classes of the dynamic state estimation (DSE) routines. The Power system DSE has been implemented by various KFs, such as the extended KF (EKF) and the unscented KF (UKF). In this paper, we discuss two challenges for an effective power system DSE: 1) model uncertainty and 2) potential cyber attacks and measurement faults. To address this, the cubature KF (CKF) and a nonlinear observer are introduced and implemented. Various KFs and the dynamic observer are then tested on the 16-machine 68-bus system given realistic scenarios under model uncertainty and different types of cyber attacks against synchrophasor measurements. It is shown that the CKF and the observer are more robust to model uncertainty and cyber attacks than their counterparts. Based on the tests, a thorough qualitative comparison is also performed for KF routines and observers.