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Experimental Verification of Self-Adapting Data-Driven Controllers in Active Distribution Grids

Author

Listed:
  • Stavros Karagiannopoulos

    (EEH—Power Systems Laboratory, ETH Zurich, Physikstrasse 3, 8092 Zurich, Switzerland
    School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Athanasios Vasilakis

    (School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Panos Kotsampopoulos

    (School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Nikos Hatziargyriou

    (School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece)

  • Petros Aristidou

    (Department of Electrical Engineering, Cyprus University of Technology, Limassol 3036, Cyprus)

  • Gabriela Hug

    (EEH—Power Systems Laboratory, ETH Zurich, Physikstrasse 3, 8092 Zurich, Switzerland)

Abstract

Lately, data-driven algorithms have been proposed to design local controls for Distributed Generators (DGs) that can emulate the optimal behaviour without any need for communication or centralised control. The design is based on historical data, advanced off-line optimization techniques and machine learning methods, and has shown great potential when the operating conditions are similar to the training data. However, safety issues arise when the real-time conditions start to drift away from the training set, leading to the need for online self-adapting algorithms and experimental verification of data-driven controllers. In this paper, we propose an online self-adapting algorithm that adjusts the DG controls to tackle local power quality issues. Furthermore, we provide experimental verification of the data-driven controllers through power Hardware-in-the-Loop experiments using an industrial inverter. The results presented for a low-voltage distribution network show that data-driven schemes can emulate the optimal behaviour and the online modification scheme can mitigate local power quality issues.

Suggested Citation

  • Stavros Karagiannopoulos & Athanasios Vasilakis & Panos Kotsampopoulos & Nikos Hatziargyriou & Petros Aristidou & Gabriela Hug, 2021. "Experimental Verification of Self-Adapting Data-Driven Controllers in Active Distribution Grids," Energies, MDPI, vol. 14(10), pages 1-15, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2837-:d:554924
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    Citations

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    Cited by:

    1. Paweł Kelm & Irena Wasiak & Rozmysław Mieński & Andrzej Wędzik & Michał Szypowski & Ryszard Pawełek & Krzysztof Szaniawski, 2022. "Hardware-in-the-Loop Validation of an Energy Management System for LV Distribution Networks with Renewable Energy Sources," Energies, MDPI, vol. 15(7), pages 1-18, April.
    2. Yu Fujimoto & Akihisa Kaneko & Yutaka Iino & Hideo Ishii & Yasuhiro Hayashi, 2023. "Challenges in Smartizing Operational Management of Functionally-Smart Inverters for Distributed Energy Resources: A Review on Machine Learning Aspects," Energies, MDPI, vol. 16(3), pages 1-26, January.

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