Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability
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MATLAB code for the paper:”Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter with Enhanced Numerical Stability”.
The Dynamic State Estimation Toolbox (DSET) is for performing power system dynamic state estimation by using the extended Kalman filter (EKF) and several variants of the unscented Kalman filter (UKF).
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| Authors | Junjian Qi, Kai Sun, Jianhui Wang, and Hui Liu |
| Journal/Conference Name | IEEE Transactions on Smart Grid |
| Paper Category | ECE |
| Paper Abstract | In this paper, in order to enhance the numerical stability of the unscented Kalman filter (UKF) used for power system dynamic state estimation, a new UKF with guaranteed positive semidifinite estimation error covariance (UKF-GPS) is proposed and compared with five existing approaches, including UKF-schol, UKF-κ, UKF-modified, UKF-AQ, and the squareroot UKF (SR-UKF). These methods and the extended Kalman filter (EKF) are tested by performing dynamic state estimation on WSCC 3-machine 9-bus system and NPCC 48-machine 140-bus system. For WSCC system, all methods obtain good estimates. However, for NPCC system, both EKF and the classic UKF fail. It is found that UKF-schol, UKF-κ, and UKF-AQ do not work well in some estimations while UKF-GPS works well in most cases. UKF-modified and SR-UKF can always work well, indicating their better scalability mainly due to the enhanced numerical stability. |
| Date of publication | 2016 |
| Code Programming Language | MATLAB |
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