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Fast and Robust State Estimation for Active Distribution Networks Considering Measurement Data Fusion and Network Topology Changes

Author

Listed:
  • Dai Wan

    (State Grid Hunan Electric Power Company Limited Research Institute, Changsha 410000, China
    State Grid Joint Laboratory for Intelligent Application and Key Equipment in Distribution Network, Changsha 410000, China)

  • Miao Zhao

    (State Grid Hunan Electric Power Company Limited Research Institute, Changsha 410000, China
    State Grid Joint Laboratory for Intelligent Application and Key Equipment in Distribution Network, Changsha 410000, China)

  • Guidong He

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Liang Che

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Qi Guo

    (College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)

  • Qianfan Zhou

    (State Grid Hunan Electric Power Company Limited Research Institute, Changsha 410000, China)

Abstract

With the integration of distributed generations (DGs), distribution networks are being transformed into active distribution networks (ADNs). Due to ADNs‘ complex operational scenarios, massive data, and fast-changing network topologies, traditional state-estimation (SE) methods are inadequate to meet the requirements of computational accuracy, computational speed, and robustness. Aiming at the SE of ADNs, this paper proposes a data-driven and classic-model-integrated SE method, which uses an SE neural network (NN) to perform an initial estimation, and then uses linear SE to refine the estimation. It applies PMU and SCADA data fusion and is robust to noise and ADN topology changes. The simulations on the IEEE standard system verify that the proposed method is superior to traditional SE methods in terms of estimation accuracy, calculation speed, and robustness. This study provides ADNS with a new effective estimation scheme, which is of great significance in the context of promoting the development of renewable energy.

Suggested Citation

  • Dai Wan & Miao Zhao & Guidong He & Liang Che & Qi Guo & Qianfan Zhou, 2023. "Fast and Robust State Estimation for Active Distribution Networks Considering Measurement Data Fusion and Network Topology Changes," Sustainability, MDPI, vol. 15(18), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13800-:d:1240938
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    References listed on IDEAS

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    1. Guo, Qi & Xiao, Fan & Tu, Chunming & Jiang, Fei & Zhu, Rongwu & Ye, Jian & Gao, Jiayuan, 2022. "An overview of series-connected power electronic converter with function extension strategies in the context of high-penetration of power electronics and renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Muhammad Huzaifa & Arif Hussain & Waseem Haider & Syed Ali Abbas Kazmi & Usman Ahmad & Habib Ur Rehman, 2023. "Optimal Planning Approaches under Various Seasonal Variations across an Active Distribution Grid Encapsulating Large-Scale Electrical Vehicle Fleets and Renewable Generation," Sustainability, MDPI, vol. 15(9), pages 1-32, May.
    3. Issarachai Ngamroo & Wikorn Kotesakha & Suntiti Yoomak & Atthapol Ngaopitakkul, 2023. "Characteristic Evaluation of Wind Power Distributed Generation Sizing in Distribution System," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
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