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Robust and Fast State Estimation for Poorly-Observable Low Voltage Distribution Networks Based on the Kalman Filter Algorithm

Author

Listed:
  • Mitja Antončič

    (Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia)

  • Igor Papič

    (Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia)

  • Boštjan Blažič

    (Faculty of Electrical Engineering, University of Ljubljana, Tržaška 25, 1000 Ljubljana, Slovenia)

Abstract

This paper presents a novel approach for the state estimation of poorly-observable low voltage distribution networks, characterized by intermittent and erroneous measurements. The developed state estimation algorithm is based on the Extended Kalman filter, where we have modified the execution of the filtering process. Namely, we have fixed the Kalman gain and Jacobian matrices to constant matrices; their values change only after a larger disturbance in the network. This allows for a fast and robust estimation of the network state. The performance of the proposed state-estimation algorithm is validated by means of simulations of an actual low-voltage network with actual field measurement data. Two different cases are presented. The results of the developed state estimator are compared to a classical estimator based on the weighted least squares method. The comparison shows that the developed state estimator outperforms the classical one in terms of calculation speed and, in case of spurious measurements errors, also in terms of accuracy.

Suggested Citation

  • Mitja Antončič & Igor Papič & Boštjan Blažič, 2019. "Robust and Fast State Estimation for Poorly-Observable Low Voltage Distribution Networks Based on the Kalman Filter Algorithm," Energies, MDPI, vol. 12(23), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4457-:d:289990
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    Citations

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    Cited by:

    1. István Táczi & Bálint Sinkovics & István Vokony & Bálint Hartmann, 2021. "The Challenges of Low Voltage Distribution System State Estimation—An Application Oriented Review," Energies, MDPI, vol. 14(17), pages 1-17, August.
    2. Sylwester Kaczmarzewski & Piotr Olczak & Maciej Sołtysik, 2021. "The Impact of Electricity Consumption Profile in Underground Mines to Cooperate with RES," Energies, MDPI, vol. 14(18), pages 1-20, September.
    3. Feng Hua & Wengen Gao & Yunfei Li & Pengfei Hu & Lina Qiao, 2023. "Joint Detection and State Estimate with GSAs in PMU-Based Smart Grids," Energies, MDPI, vol. 16(15), pages 1-18, July.
    4. Jakub Matoušek & Jindřich Duník & Ondřej Straka, 2020. "Density Difference Grid Design in a Point-Mass Filter," Energies, MDPI, vol. 13(16), pages 1-17, August.

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