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Multi-Source Information Fusion Technology and Its Application in Smart Distribution Power System

Author

Listed:
  • Xi He

    (Department of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang 421002, China)

  • Heng Dong

    (Department of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang 421002, China)

  • Wanli Yang

    (Department of Electrical and Information Engineering, Hunan University, Changsha 410006, China)

  • Wei Li

    (Department of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang 421002, China)

Abstract

Compared to traditional measurement devices, the micro-synchrophasor measurement unit (D-PMU or μPMU) in the distribution power system has great differences in data acquisition frequency, data format, data dimension, time-stamped information, etc. Hence, it is imperative to research the integration mechanism of heterogeneous data from multiple sources. Based on the analysis of the current technology of multi-source information fusion, this paper proposes a novel approach, which considers two aspects: the interoperability of multi-source data and the real-time processing of large-scale streaming data. To solve the problem of data interoperability, we have modified the model of D-PMU data and established a unified information model. Meanwhile, an advanced distributed processing technology has been deployed to solve the problem of real-time processing of streaming data. Based on this approach, a smart distribution power system wide-area measurement and control station can be established, and the correctness and practicality of the proposed method are verified by an on-field project.

Suggested Citation

  • Xi He & Heng Dong & Wanli Yang & Wei Li, 2023. "Multi-Source Information Fusion Technology and Its Application in Smart Distribution Power System," Sustainability, MDPI, vol. 15(7), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6170-:d:1115171
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    References listed on IDEAS

    as
    1. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    2. Leijiao Ge & Yuanliang Li & Yuanliang Li & Jun Yan & Yonghui Sun, 2022. "Smart Distribution Network Situation Awareness for High-Quality Operation and Maintenance: A Brief Review," Energies, MDPI, vol. 15(3), pages 1-24, January.
    3. Shuo Chen & Falko Ebe & Jeromie Morris & Heiko Lorenz & Christoph Kondzialka & Gerd Heilscher, 2022. "Implementation and Test of an IEC 61850-Based Automation Framework for the Automated Data Model Integration of DES (ADMID) into DSO SCADA," Energies, MDPI, vol. 15(4), pages 1-30, February.
    4. Lilia Tightiz & Hyosik Yang, 2020. "A Comprehensive Review on IoT Protocols’ Features in Smart Grid Communication," Energies, MDPI, vol. 13(11), pages 1-24, June.
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