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To perform power system monitoring and control using synchrophasor measurements, various dynamic state estimators have been proposed in the literature, including the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). However, they are unable to handle system model parameter errors and any type of outliers, precluding them from being adopted for power system real-time applications. In this paper, we develop a robust iterated extended Kalman filter based on the generalized maximum likelihood approach (termed GM-IEKF) for dynamic state estimation. The proposed GM-IEKF can effectively suppress observation and innovation outliers, which may be induced by model parameter gross errors and cyber attacks. We assess its robustness by carrying out extensive simulations on the IEEE 39-bus test system. From the results, we find that the GM-IEKF is able to cope with at least 25% outliers, including in position of leverage.
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Adaptive compensation for multi-axial real-time hybrid simulation via nonlinear parameter estimation
For Real-time hybrid simulation (RTHS) to be stable and accurate, it is essential to address the time desynchronization issue between the numerical and physical substructures. Desynchronization is primarily caused by time delays, inherent dynamics of the control plant, system uncertainties, and noises. While existing adaptive compensators have shown effective tracking performance in single-input single-output (SISO) RTHS, their effectiveness in multi-input multi-output (MIMO) RTHS has not been fully demonstrated. MIMO-RTHS presents additional challenges due to its larger solution space, and significant dynamic coupling between actuators. To address these challenges, this study introduces an adaptive compensation framework for MIMO-RTHS. The proposed framework utilizes a control law based on the inverse dynamics of the control plant, incorporating real-time adaptive parameter updates through Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) methods. Both the transfer function (TF) and discrete-time state-space (SS) models of the plant are employed in distinct parameter estimation cases. The performance of the proposed compensation is validated through a multi-axial RTHS (maRTHS) benchmark problem. Extensive simulations on the maRTHS incorporating various earthquake inputs, sensor noise, and model uncertainties, demonstrated an excellent tracking performance and strong robustness across four parameter estimation cases (EKF-TF, UKF-TF, EKF-SS, and UKF-SS). The use of UKF with SS model (UKF-SS) achieved superior performance, effectively managing nonlinearities and noise without requiring low-pass filtering.
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- Award ID(s):
- 2011423
- PAR ID:
- 10649861
- Publisher / Repository:
- Frontiers in Built Environment
- Date Published:
- Journal Name:
- Frontiers in Built Environment
- Volume:
- 10
- ISSN:
- 2297-3362
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3389/fbuil.2024.1477804
