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Τίτλος: Experimental verification of self-adapting data-driven controllers in active distribution grids
Συγγραφείς: Karagiannopoulos, Stavros 
Vasilakis, Athanasios 
Kotsampopoulos, Panos 
Hatziargyriou, Nikos 
Aristidou, Petros 
Hug, Gabriela 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Λέξεις-κλειδιά: Data-driven control design;Active distribution networks;OPF;Machine learning;Hardware-in-the-loop
Ημερομηνία Έκδοσης: 2-Μαΐ-2021
Πηγή: Energies, 2021, vol. 14, no. 10, articl. no. 2837
Volume: 14
Issue: 10
Περιοδικό: Energies 
Περίληψη: 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.
URI: https://hdl.handle.net/20.500.14279/22974
ISSN: 19961073
DOI: 10.3390/en14102837
Rights: © by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Type: Article
Affiliation: ETH Zurich 
National Technical University Of Athens 
Cyprus University of Technology 
Publication Type: Peer Reviewed
Εμφανίζεται στις συλλογές:Άρθρα/Articles

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