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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on industry applications Ročník 60; číslo 4; s. 5447 - 5459
Hlavní autoři: Lin, Shih-Wen, Chu, Chia-Chi
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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 can be solved by the distributed pinning-based consensus problem. To address the effect of input signal saturation in each distributed generator (DG), the low gain feedback method is considered to obtain these feedback gains. Since these feedback gain matrices are obtained by solving the modified algebraic Riccati equation (MARE) which needs the complete knowledge of DG dynamics, an iterative model-free data-driven Q-learning algorithm is presented. A Q-learning function and a Q-learning Bellman equation are defined for finding these feedback gain matrices. Finally, based on recursive least square techniques, an iterative Q-learning algorithm is proposed for achieving autonomous voltage restoration. To validate the performance of the proposed method, simulations of two isolated AC MG are performed. Simulation results demonstrate that the acquired feedback gains closely align with these analytical solutions of the MARE. Furthermore, the proposed model-free distributed Q-learning method remains its effectiveness even under model uncertainty and plug-and-play operations of DGs.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0093-9994
1939-9367
DOI:10.1109/TIA.2024.3379965