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Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters

Author

Listed:
  • Md Jakir Hossain

    (Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA)

  • Mia Naeini

    (Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA)

Abstract

Low-latency data processing is essential for wide-area monitoring of smart grids. Distributed and local data processing is a promising approach for enabling low-latency requirements and avoiding the large overhead of transferring large volumes of time-sensitive data to central processing units. State estimation in power systems is one of the key functions in wide-area monitoring, which can greatly benefit from distributed data processing and improve real-time system monitoring. In this paper, data-driven Kalman filters have been used for multi-area distributed state estimation. The presented state estimation approaches are data-driven and model-independent. The design phase is offline and involves modeling multivariate time-series measurements from PMUs using linear and non-linear system identification techniques. The measurements of the phase angle, voltage, reactive and real power are used for next-step prediction of the state of the buses. The performance of the presented data-driven, distributed state estimation techniques are evaluated for various numbers of regions and modes of information sharing on the IEEE 118 test case system.

Suggested Citation

  • Md Jakir Hossain & Mia Naeini, 2022. "Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters," Energies, MDPI, vol. 15(19), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7105-:d:926902
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    References listed on IDEAS

    as
    1. Minh-Quan Tran & Ahmed S. Zamzam & Phuong H. Nguyen & Guus Pemen, 2021. "Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks," Energies, MDPI, vol. 14(11), pages 1-13, May.
    2. D. A. Vaccari & H. -K. Wang, 2007. "Multivariate polynomial regression for identification of chaotic time series," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 13(4), pages 395-412, August.
    3. Zhou, Wei & Wu, Yue & Huang, Xiang & Lu, Renzhi & Zhang, Hai-Tao, 2022. "A group sparse Bayesian learning algorithm for harmonic state estimation in power systems," Applied Energy, Elsevier, vol. 306(PB).
    4. Upama Nakarmi & Mahshid Rahnamay Naeini & Md Jakir Hossain & Md Abul Hasnat, 2020. "Interaction Graphs for Cascading Failure Analysis in Power Grids: A Survey," Energies, MDPI, vol. 13(9), pages 1-25, May.
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    Cited by:

    1. Orestis Darmis & George Korres, 2023. "A Survey on Hybrid SCADA/WAMS State Estimation Methodologies in Electric Power Transmission Systems," Energies, MDPI, vol. 16(2), pages 1-20, January.
    2. 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.

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