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

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...

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Bibliographic Details
Published in:Energies (Basel) 2021-05, Vol.14 (10), p.2837
Main Authors: Karagiannopoulos, Stavros, Vasilakis, Athanasios, Kotsampopoulos, Panos, Hatziargyriou, Nikos, Aristidou, Petros, Hug, Gabriela
Format: Article
Language:English
Subjects:
active distribution networks
Algorithms
Artificial intelligence
Communication
Control algorithms
Controllers
data-driven control design
Datasets
Distributed generation
Hardware-in-the-loop
Hardware-in-the-loop simulation
Historical account
Learning algorithms
Machine learning
Mathematical models
OPF
Optimization
Optimization techniques
Simulation
Verification
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14102837