Adaptive, Optimal, Virtual Synchronous Generator Control of Three-Phase Grid-Connected Inverters Under Different Grid Conditions-An Adaptive Dynamic Programming Approach

This article proposes an adaptive, optimal, data-driven control approach based on reinforcement learning and adaptive dynamic programming to the three-phase grid-connected inverter employed in virtual synchronous generators (VSGs). This article takes into account unknown system dynamics and differen...

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Vydané v:IEEE transactions on industrial informatics Ročník 18; číslo 11; s. 7388 - 7399
Hlavní autori: Wang, Zhongyang, Yu, Yunjun, Gao, Weinan, Davari, Masoud, Deng, Chao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
adaptive
Adaptive control
Adaptive dynamic programming (ADP)
Damping
Dynamic programming
Inverters
Learning
Optimal control
Optimization
Power system stability
Reinforcement learning
Synchronous generators
System dynamics
value iteration
virtual synchronous generator (VSG)
Voltage control
Voltage drop
ISSN:1551-3203, 1941-0050
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Shrnutí:This article proposes an adaptive, optimal, data-driven control approach based on reinforcement learning and adaptive dynamic programming to the three-phase grid-connected inverter employed in virtual synchronous generators (VSGs). This article takes into account unknown system dynamics and different grid conditions, including balanced/unbalanced grids, voltage drop/sag, and weak grids. The proposed method is based on value iteration, which does not rely on an initial admissible control policy for learning. Considering the premise that the VSG control should stabilize the closed-loop dynamics, the VSG outputs are optimally regulated through the adaptive, optimal control strategy proposed in this article. Comparative simulations and experimental results validate the proposed method's effectiveness and reveal its practicality and implementation.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2021.3138893