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Distributed State Estimation of Multi-region Power System based on Consensus Theory

Author

Listed:
  • Shiwei Xia

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Qian Zhang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Jiangping Jing

    (State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China)

  • Zhaohao Ding

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Changping District, Beijing 102206, China)

  • Jing Yu

    (State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China)

  • Bing Chen

    (China State Grid Zhenjiang Power Supply Company, Zhenjiang 212000, China)

  • Haiwei Wu

    (State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China)

Abstract

Effective state estimation is critical to the security operation of power systems. With the rapid expansion of interconnected power grids, there are limitations of conventional centralized state estimation methods in terms of heavy and unbalanced communication and computation burdens for the control center. To address these limitations, this paper presents a multi-area state estimation model and afterwards proposes a consensus theory based distributed state estimation solution method. Firstly, considering the nonlinearity of state estimation, the original power system is divided into several non-overlapped subsystems. Correspondingly, the Lagrange multiplier method is adopted to decouple the state estimation equations into a multi-area state estimation model. Secondly, a fully distributed state estimation method based on the consensus algorithm is designed to solve the proposed model. The solution method does not need a centralized coordination system operator, but only requires a simple communication network for exchanging the limited data of boundary state variables and consensus variables among adjacent regions, thus it is quite flexible in terms of communication and computation for state estimation. In the end, the proposed method is tested by the IEEE 14-bus system and the IEEE 118-bus system, and the simulation results verify that the proposed multi-area state estimation model and the distributed solution method are effective for the state estimation of multi-area interconnected power systems.

Suggested Citation

  • Shiwei Xia & Qian Zhang & Jiangping Jing & Zhaohao Ding & Jing Yu & Bing Chen & Haiwei Wu, 2019. "Distributed State Estimation of Multi-region Power System based on Consensus Theory," Energies, MDPI, vol. 12(5), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:900-:d:212152
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    Citations

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    Cited by:

    1. 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).
    2. Israa T. Aziz & Hai Jin & Ihsan H. Abdulqadder & Sabah M. Alturfi & Wisam H. Alobaidi & Firas M.F. Flaih, 2019. "T 2 S 2 G: A Novel Two-Tier Secure Smart Grid Architecture to Protect Network Measurements," Energies, MDPI, vol. 12(13), pages 1-24, July.
    3. Meng Xia & Dajun Du & Minrui Fei & Xue Li & Taicheng Yang, 2020. "A Novel Sparse Attack Vector Construction Method for False Data Injection in Smart Grids," Energies, MDPI, vol. 13(11), pages 1-19, June.

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