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A Distribution System State Estimator Based on an Extended Kalman Filter Enhanced with a Prior Evaluation of Power Injections at Unmonitored Buses

Author

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  • David Macii

    (Department of Industrial Engineering, University of Trento, via Sommarive, 9, 38123 Trento, Italy)

  • Daniele Fontanelli

    (Department of Industrial Engineering, University of Trento, via Sommarive, 9, 38123 Trento, Italy)

  • Grazia Barchi

    (EURAC Research, Institute for Renewable Energy, via Alessandro Volta, 13/A, 39100 Bozen-Bolzano, Italy)

Abstract

In the context of smart grids, Distribution Systems State Estimation (DSSE) is notoriously problematic because of the scarcity of available measurement points and the lack of real-time information on loads. The scarcity of measurement data influences on the effectiveness and applicability of dynamic estimators like the Kalman filters. However, if an Extended Kalman Filter (EKF) resulting from the linearization of the power flow equations is complemented by an ancillary prior least-squares estimation of the weekly active and reactive power injection variations at all buses, significant performance improvements can be achieved. Extensive simulation results obtained assuming to deploy an increasing number of next-generation smart meters and Phasor Measurement Units (PMUs) show that not only the proposed approach is generally more accurate and precise than the classic Weighted Least Squares (WLS) estimator (chosen as a benchmark algorithm), but it is also less sensitive to both the number and the metrological features of the PMUs. Thus, low-uncertainty state estimates can be obtained even though fewer and cheaper measurement devices are used.

Suggested Citation

  • David Macii & Daniele Fontanelli & Grazia Barchi, 2020. "A Distribution System State Estimator Based on an Extended Kalman Filter Enhanced with a Prior Evaluation of Power Injections at Unmonitored Buses," Energies, MDPI, vol. 13(22), pages 1-25, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6054-:d:447619
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    References listed on IDEAS

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    Cited by:

    1. Enrico Dalla Maria & Mattia Secchi & David Macii, 2021. "A Flexible Top-Down Data-Driven Stochastic Model for Synthetic Load Profiles Generation," Energies, MDPI, vol. 15(1), pages 1-20, December.
    2. Marco Pau & Paolo Attilio Pegoraro, 2022. "Monitoring and Automation of Complex Power Systems," Energies, MDPI, vol. 15(8), pages 1-3, April.
    3. Junjun Xu & Yulong Jin & Tao Zheng & Gaojun Meng, 2023. "On State Estimation Modeling of Smart Distribution Networks: A Technical Review," Energies, MDPI, vol. 16(4), pages 1-19, February.

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