Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
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Sensors 2017, 17(10), 2310; https://doi.org/10.3390/s17102310
Received: 24 August 2017
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Revised: 8 October 2017
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Accepted: 9 October 2017
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Published: 11 October 2017
(This article belongs to the Section Physical Sensors)
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.
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Keywords:
distributed state estimation; moving horizon estimation; wide-area monitoring; sensor measurement; power systems; outliers
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MDPI and ACS Style
Chen, T.; Foo, Y.S.E.; Ling, K.V.; Chen, X. Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems. Sensors 2017, 17, 2310. https://doi.org/10.3390/s17102310
AMA Style
Chen T, Foo YSE, Ling KV, Chen X. Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems. Sensors. 2017; 17(10):2310. https://doi.org/10.3390/s17102310
Chicago/Turabian StyleChen, Tengpeng, Yi S.E. Foo, K.V. Ling, and Xuebing Chen. 2017. "Distributed State Estimation Using a Modified Partitioned Moving Horizon Strategy for Power Systems" Sensors 17, no. 10: 2310. https://doi.org/10.3390/s17102310
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