Robust state estimation for power systems via moving horizon strategy
In this paper, I propose a re-weighted moving horizon estimation (RMHE) to improve the robustness for power systems. The RMHE reduces its sensitivity to the outliers by updating their error variances real-time and re-weighting their contributions adaptively for robust power system state estimation (...
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sg-ntu-dr.10356-840912020-03-07T14:00:31Z Robust state estimation for power systems via moving horizon strategy Chen, Tengpeng School of Electrical and Electronic Engineering Robust State Estimation In this paper, I propose a re-weighted moving horizon estimation (RMHE) to improve the robustness for power systems. The RMHE reduces its sensitivity to the outliers by updating their error variances real-time and re-weighting their contributions adaptively for robust power system state estimation (PSSE). Compared with the common robust state estimators such as the Quadratic-Constant (QC), Quadratic-Linear (QL), Square-Root (SR), Multiple-Segment (MS) and Least Absolute Value (LAV) estimator, one advance of RMHE is that the RMHE incorporates the uncertainty of process model and the arrival cost term during the optimization process. Constraints on states are also taken into account. The influence of the outliers can be further mitigated. Simulations on the IEEE 14-bus system show that the RMHE can obtain estimated results with smaller errors even when the outliers are present. NRF (Natl Research Foundation, S’pore) Accepted version 2017-07-21T03:45:39Z 2019-12-06T15:38:09Z 2017-07-21T03:45:39Z 2019-12-06T15:38:09Z 2017 Journal Article Chen, T. (2017). Robust state estimation for power systems via moving horizon strategy. Sustainable Energy, Grids and Networks, 10, 46-54. 2352-4677 https://hdl.handle.net/10356/84091 http://hdl.handle.net/10220/42960 10.1016/j.segan.2017.02.005 en Sustainable Energy, Grids and Networks © 2017 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Sustainable Energy, Grids and Networks, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.segan.2017.02.005]. 22 p. application/pdf |
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English |
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Robust State Estimation |
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Robust State Estimation Chen, Tengpeng Robust state estimation for power systems via moving horizon strategy |
| description |
In this paper, I propose a re-weighted moving horizon estimation (RMHE) to improve the robustness for power systems. The RMHE reduces its sensitivity to the outliers by updating their error variances real-time and re-weighting their contributions adaptively for robust power system state estimation (PSSE). Compared with the common robust state estimators such as the Quadratic-Constant (QC), Quadratic-Linear (QL), Square-Root (SR), Multiple-Segment (MS) and Least Absolute Value (LAV) estimator, one advance of RMHE is that the RMHE incorporates the uncertainty of process model and the arrival cost term during the optimization process. Constraints on states are also taken into account. The influence of the outliers can be further mitigated. Simulations on the IEEE 14-bus system show that the RMHE can obtain estimated results with smaller errors even when the outliers are present. |
| author2 |
School of Electrical and Electronic Engineering |
| author_facet |
School of Electrical and Electronic Engineering Chen, Tengpeng |
| format |
Article |
| author |
Chen, Tengpeng |
| author_sort |
Chen, Tengpeng |
| title |
Robust state estimation for power systems via moving horizon strategy |
| title_short |
Robust state estimation for power systems via moving horizon strategy |
| title_full |
Robust state estimation for power systems via moving horizon strategy |
| title_fullStr |
Robust state estimation for power systems via moving horizon strategy |
| title_full_unstemmed |
Robust state estimation for power systems via moving horizon strategy |
| title_sort |
robust state estimation for power systems via moving horizon strategy |
| publishDate |
2017 |
| url |
https://hdl.handle.net/10356/84091 http://hdl.handle.net/10220/42960 |
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