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

Stavros Karagiannopoulos, Athanasios Vasilakis, Panos Kotsampopoulos, Nikos Hatziargyriou, Petros Aristidou and Gabriela Hug
Additional contact information
Stavros Karagiannopoulos: EEH—Power Systems Laboratory, ETH Zurich, Physikstrasse 3, 8092 Zurich, Switzerland
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

Energies, 2021, vol. 14, issue 10, 1-15

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.

Keywords: data-driven control design; active distribution networks; OPF; machine learning; Hardware-in-the-loop (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/10/2837/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/10/2837/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:10:p:2837-:d:554924

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().