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A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges

Author

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  • Sepideh Radhoush

    (Electrical and Computer Engineering Department, Montana State University, Bozeman, MT 59718, USA)

  • Maryam Bahramipanah

    (Electrical and Computer Engineering Department, Montana State University, Bozeman, MT 59718, USA)

  • Hashem Nehrir

    (Electrical and Computer Engineering Department, Montana State University, Bozeman, MT 59718, USA)

  • Zagros Shahooei

    (Electrical and Computer Engineering Department, Montana State University, Bozeman, MT 59718, USA)

Abstract

This paper provides a comprehensive review of distribution system state estimation in terms of basic definition, different methods, and their application. In the last few years, the operation of distribution networks has been influenced by the installation of distributed generations. In order to control and manage an active distribution network’s performance, distribution system state estimation methods are introduced. A transmission system state estimation cannot be used directly in distribution networks since transmission and distribution networks are different due to topology configuration, the number of buses, line parameters, and the number of measurement instruments. So, the proper state estimation algorithms should be proposed according to the main distribution network features. Accuracy, computational efficiency, and practical implications should be considered in the designing of distribution state estimation techniques since technical issues and wrong decisions could emerge in the control center by inaccurate distribution state estimation results. In this study, conventional techniques are reviewed and compared with data-driven methods in order to highlight the pros and cons of different techniques. Furthermore, the integrated distribution state estimation methods are compared with the distributed approaches, and the different criteria, including the level of area overlapping execution time and computing architecture, are elaborated. Moreover, mathematical problem formulation and different measuring methods are discussed.

Suggested Citation

  • Sepideh Radhoush & Maryam Bahramipanah & Hashem Nehrir & Zagros Shahooei, 2022. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2520-:d:755712
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    References listed on IDEAS

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    Cited by:

    1. Paolo Tenti & Tommaso Caldognetto, 2023. "Integration of Local and Central Control Empowers Cooperation among Prosumers and Distributors towards Safe, Efficient, and Cost-Effective Operation of Microgrids," Energies, MDPI, vol. 16(5), pages 1-23, February.
    2. Eva Buchta & Mathias Duckheim & Michael Metzger & Paul Stursberg & Stefan Niessen, 2023. "Leveraging Behavioral Correlation in Distribution System State Estimation for the Recognition of Critical System States," Energies, MDPI, vol. 16(20), pages 1-21, October.
    3. Xianglong Zhang & Ying Liu & Songlin Gu & Yuzhou Tian & Yifan Gao, 2025. "Multi-Level Asynchronous Robust State Estimation for Distribution Networks Considering Communication Delays," Energies, MDPI, vol. 18(14), pages 1-20, July.
    4. Yu Shi & Yueting Hou & Yue Yu & Zhaoyang Jin & Mohamed A. Mohamed, 2023. "Robust Power System State Estimation Method Based on Generalized M-Estimation of Optimized Parameters Based on Sampling," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    5. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.

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