Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems
In this paper, a distributed state estimation method based on moving horizon estimation (MHE) is proposed for the large-scale power system state estimation. The proposed method partitions the power systems into several local areas with non-overlapping states. Unlike the centralized approach where al...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/87054 http://hdl.handle.net/10220/44301 |
| _version_ | 1811687097335021568 |
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| author | Chen, Tengpeng Chen, Xuebing Foo, Eddy Yi Shyh Ling, Keck Voon |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Chen, Tengpeng Chen, Xuebing Foo, Eddy Yi Shyh Ling, Keck Voon |
| author_sort | Chen, Tengpeng |
| collection | NTU |
| description | In this paper, a distributed state estimation method based on moving horizon estimation (MHE) is proposed for the large-scale power system state estimation. The proposed method partitions the power systems into several local areas with non-overlapping states. Unlike the centralized approach where all measurements are sent to a processing center, the proposed method distributes the state estimation task to the local processing centers where local measurements are collected. Inspired by the partitioned moving horizon estimation (PMHE) algorithm, each local area solves a smaller optimization problem to estimate its own local states by using local measurements and estimated results from its neighboring areas. In contrast with PMHE, the error from the process model is ignored in our method. The proposed modified PMHE (mPMHE) approach can also take constraints on states into account during the optimization process such that the influence of the outliers can be further mitigated. Simulation results on the IEEE 14-bus and 118-bus systems verify that our method achieves comparable state estimation accuracy but with a significant reduction in the overall computation load. |
| first_indexed | 2024-10-01T05:10:54Z |
| format | Journal Article |
| id | ntu-10356/87054 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:10:54Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/870542020-03-07T13:56:08Z Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems Chen, Tengpeng Chen, Xuebing Foo, Eddy Yi Shyh Ling, Keck Voon School of Electrical and Electronic Engineering Moving Horizon Estimation Distributed State Estimation In this paper, a distributed state estimation method based on moving horizon estimation (MHE) is proposed for the large-scale power system state estimation. The proposed method partitions the power systems into several local areas with non-overlapping states. Unlike the centralized approach where all measurements are sent to a processing center, the proposed method distributes the state estimation task to the local processing centers where local measurements are collected. Inspired by the partitioned moving horizon estimation (PMHE) algorithm, each local area solves a smaller optimization problem to estimate its own local states by using local measurements and estimated results from its neighboring areas. In contrast with PMHE, the error from the process model is ignored in our method. The proposed modified PMHE (mPMHE) approach can also take constraints on states into account during the optimization process such that the influence of the outliers can be further mitigated. Simulation results on the IEEE 14-bus and 118-bus systems verify that our method achieves comparable state estimation accuracy but with a significant reduction in the overall computation load. NRF (Natl Research Foundation, S’pore) Published version 2018-01-10T05:51:56Z 2019-12-06T16:34:07Z 2018-01-10T05:51:56Z 2019-12-06T16:34:07Z 2017 Journal Article Chen, T., Foo, E. Y. S., Ling, K. V., & Chen, X. (2017). Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems. Sensors, 17(10), 2310-. 1424-8220 https://hdl.handle.net/10356/87054 http://hdl.handle.net/10220/44301 10.3390/s17102310 en Sensors © 2017 by The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 21 p. application/pdf |
| spellingShingle | Moving Horizon Estimation Distributed State Estimation Chen, Tengpeng Chen, Xuebing Foo, Eddy Yi Shyh Ling, Keck Voon Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems |
| title | Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems |
| title_full | Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems |
| title_fullStr | Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems |
| title_full_unstemmed | Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems |
| title_short | Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems |
| title_sort | distributed state estimation using a modified partitioned moving horizon strategy for power systems |
| topic | Moving Horizon Estimation Distributed State Estimation |
| url | https://hdl.handle.net/10356/87054 http://hdl.handle.net/10220/44301 |
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