Abstract
Bad data may lead to performance degradation or even instability of a power system, which can be caused by various factors: unintentional PMU abnormalities, topology error, malicious cyber-attacks, electromagnetic interference, temporary loss of communication links, external disturbances, extraneous noise biases, etc. In order to develop a more resilient and reliable state estimation technique, this manuscript presents a novel two-step fault tolerant extended Kalman filter framework for discrete-time stochastic power systems, under bad data, PMU failures, external disturbances, extraneous noise, and bounded observer-gain perturbation conditions. The failure mechanisms of multiple phasor measurement units are assumed to be independent of each other with various bad data or malfunction rates. The benchmark IEEE standard test systems are utilized as a demonstrative example to carry out computer simulation studies and to examine different estimation algorithms. Experimental results demonstrates that the proposed second-order fault tolerant extended Kalman filter provides more accurate estimation results, in comparison with traditional first- and second-order extended Kalman filter, and the unscented Kalman filter. The proposed two-step fault-tolerant extended Kalman filter can serve as a powerful alternative to the existing dynamic power system state estimation techniques.
Highlights
M ODERN smart grid has been envisioned to improve the robustness, efficiency of the traditional power grid with the advancement of power electronics, computing, control and communication technologies
THE STRUCTURE OF THE TWO-STEP SECOND-ORDER FAULT TOLERANT EXTENDED KALMAN FILTER we propose a novel two-step fault tolerant extended Kalman filter, which is robust against external disturbances, extraneous noise, bad data, sensor failures, and bounded observer-gain perturbations
The performance indices of the traditional first-order EKF (EKF), the second order EKF (SOEKF), the unscented Kalman filter (UKF) and the proposed second-order fault tolerant extended Kalman filter (SOFTEKF) for the IEEE 14bus power system dynamic state estimation are summarized in Tab.1., under phasor measurement units (PMU) bad data rate 5%
Summary
M ODERN smart grid has been envisioned to improve the robustness, efficiency of the traditional power grid with the advancement of power electronics, computing, control and communication technologies. Different from the other power system DSE approaches, by modeling bad data as binary Bernoulli distributed random variables, Wang and Yaz proposed a first-order fault tolerant extended Kalman filter in [25], [26], which has been successfully implemented in CompactRIO and dSPACE hardware platform. In comparison with the traditional nonlinear estimation such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF), the proposed second-order fault-tolerant extended Kalman filter (SOFETKF) can provide significant improvements in estimation accuracy, without an increased level of computational effort The plan of this manuscript is organized as follows: In Section II, multi-machine power system dynamical model is formulated.
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