Distributed Q-Learning-Based Voltage Restoration Algorithm in Isolated AC Microgrids Subject to Input Saturation

A model-free data-driven Q-learning-based distributed control is proposed for achieving autonomous voltage restoration in isolated AC microgrids (MGs) subject to input saturation. First, by defining the control objective in terms of local neighborhood tracking errors, the voltage restoration problem...

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Vydané v:	IEEE transactions on industry applications Ročník 60; číslo 4; s. 5447 - 5459
Hlavní autori:	Lin, Shih-Wen, Chu, Chia-Chi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Bellman theory consensus algorithm Decentralized control distributed control Distributed generation Electric potential Exact solutions Feedback Heuristic algorithms Internet of Things Machine learning Mathematical models Microgrid modified algebraic Riccati equation multi-agent reinforcement learning plug-and-play Q-learning Recursive functions Restoration Riccati equation Riccati equations saturation limit Tracking control Tracking errors Voltage Voltage control voltage restoration
ISSN:	0093-9994, 1939-9367
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