Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87054
Title: Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems
Authors: Chen, Tengpeng
Chen, Xuebing
Foo, Eddy Yi Shyh
Ling, Keck Voon
Keywords: Moving Horizon Estimation
Distributed State Estimation
Issue Date: 2017
Source: 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-.
Series/Report no.: Sensors
Abstract: 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.
URI: https://hdl.handle.net/10356/87054
http://hdl.handle.net/10220/44301
ISSN: 1424-8220
DOI: http://dx.doi.org/10.3390/s17102310
Rights: © 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/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
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