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Multi-Area Distribution System State Estimation Using Decentralized Physics-Aware Neural Networks

Author

Listed:
  • Minh-Quan Tran

    (Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands)

  • Ahmed S. Zamzam

    (National Renewable Energy Laboratory, Golden, CO 80401, USA)

  • Phuong H. Nguyen

    (Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands)

  • Guus Pemen

    (Department of Electrical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands)

Abstract

The development of active distribution grids requires more accurate and lower computational cost state estimation. In this paper, the authors investigate a decentralized learning-based distribution system state estimation (DSSE) approach for large distribution grids. The proposed approach decomposes the feeder-level DSSE into subarea-level estimation problems that can be solved independently. The proposed method is decentralized pruned physics-aware neural network (D-P2N2). The physical grid topology is used to parsimoniously design the connections between different hidden layers of the D-P2N2. Monte Carlo simulations based on one-year of load consumption data collected from smart meters for a three-phase distribution system power flow are developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares and state-of-the-art learning-based DSSE approaches. Numerical results show that the D-P2N2 outperforms the state-of-the-art methods in terms of estimation accuracy and computational efficiency.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3025-:d:560712
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    References listed on IDEAS

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    1. Vasileios Boglou & Christos-Spyridon Karavas & Konstantinos Arvanitis & Athanasios Karlis, 2020. "A Fuzzy Energy Management Strategy for the Coordination of Electric Vehicle Charging in Low Voltage Distribution Grids," Energies, MDPI, vol. 13(14), pages 1-34, July.
    2. Marzband, Mousa & Sumper, Andreas & Ruiz-Álvarez, Albert & Domínguez-García, José Luis & Tomoiagă, Bogdan, 2013. "Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets," Applied Energy, Elsevier, vol. 106(C), pages 365-376.
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    Cited by:

    1. 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.
    2. Marco Pau & Paolo Attilio Pegoraro, 2022. "Monitoring and Automation of Complex Power Systems," Energies, MDPI, vol. 15(8), pages 1-3, April.

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