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

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 D...

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Bibliographic Details
Published in:IEEE access 2018, Vol.6, p.77155-77168
Main Authors: Qi, Junjian, Taha, Ahmad F., Wang, Jianhui
Format: Article
Language:English
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
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Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2876883