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Coordinated algorithms for distributed state estimation with synchronized phasor measurements

Author

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  • Yang, Xuan
  • Zhang, Xiao-Ping
  • Zhou, Suyang

Abstract

This paper presents a novel coordinated algorithm for distributed state estimation where Phasor Measurement Units (PMUs) measurements are considered at both subsystem level and coordination level. At the coordination level, the linear state estimation with PMUs is carried out to coordinate the voltage states of boundary buses where the computational time can be significantly reduced. Tests on the IEEE 30-bus and 118-bus system are used to show the performance of the novel distributed state estimation algorithms and compare results with the previous distributed state estimation algorithm in terms of both the estimation quality and computational performance. The distributed state estimation is compatible with the distributed control architecture for the operation of the future Smart Grid, where a large power system can be decomposed into subsystems and the subsystems can be estimated, operated and controlled in the distributed environments.

Suggested Citation

  • Yang, Xuan & Zhang, Xiao-Ping & Zhou, Suyang, 2012. "Coordinated algorithms for distributed state estimation with synchronized phasor measurements," Applied Energy, Elsevier, vol. 96(C), pages 253-260.
    • Handle: RePEc:eee:appene:v:96:y:2012:i:c:p:253-260
    DOI: 10.1016/j.apenergy.2011.11.010
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    Citations

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    Cited by:

    1. Zhang, Suhan & Gu, Wei & Qiu, Haifeng & Yao, Shuai & Pan, Guangsheng & Chen, Xiaogang, 2021. "State estimation models of district heating networks for integrated energy system considering incomplete measurements," Applied Energy, Elsevier, vol. 282(PA).
    2. Zhao, Zhida & Yu, Hao & Li, Peng & Li, Peng & Kong, Xiangyu & Wu, Jianzhong & Wang, Chengshan, 2019. "Optimal placement of PMUs and communication links for distributed state estimation in distribution networks," Applied Energy, Elsevier, vol. 256(C).
    3. Zou, Cong & Li, Bing & Liu, Feiyang & Xu, Bingrui, 2022. "Event-Triggered μ-state estimation for Markovian jumping neural networks with mixed time-delays," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    4. Luis Hernández-Callejo, 2019. "A Comprehensive Review of Operation and Control, Maintenance and Lifespan Management, Grid Planning and Design, and Metering in Smart Grids," Energies, MDPI, vol. 12(9), pages 1-50, April.
    5. Su, Hongzhi & Wang, Chengshan & Li, Peng & Liu, Zhelin & Yu, Li & Wu, Jianzhong, 2019. "Optimal placement of phasor measurement unit in distribution networks considering the changes in topology," Applied Energy, Elsevier, vol. 250(C), pages 313-322.
    6. Das, Laya & Garg, Dinesh & Srinivasan, Babji, 2020. "NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid," Applied Energy, Elsevier, vol. 257(C).
    7. Su, Hongzhi & Wang, Chengshan & Li, Peng & Li, Peng & Liu, Zhelin & Wu, Jianzhong, 2019. "Novel voltage-to-power sensitivity estimation for phasor measurement unit-unobservable distribution networks based on network equivalent," Applied Energy, Elsevier, vol. 250(C), pages 302-312.
    8. Luis Vargas & Henrry Moyano, 2023. "A Novel Multi-Area Distribution State Estimation Approach with Nodal Redundancy," Energies, MDPI, vol. 16(10), pages 1-19, May.
    9. Ruizi Ma, 2021. "Adaptive Tolerant State Estimation under Model Uncertainty in Power Systems," Energies, MDPI, vol. 14(8), pages 1-15, April.

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