A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Distributed Secondary Control Strategy Based on $Q$-learning and Pinning Control for Droop-controlled Microgrids
A distributed secondary control (DSC) strategy that combines $Q$-learning and pinning control is originally proposed to achieve a fully optimal DSC for droop-controlled microgrids (MGs). It takes advantages of cross-fusion of the two algorithms to realize the high efficiency and self-adaptive control in MGs. It has the following advantages. Firstly, it adopts the advantages of reinforcement learning in autonomous learning control and intelligent decision-making, driving the action value of pinning control for feedback adaptive correction. Secondly, only a small part of points selected as pinned points needs to be controlled and pre-learned, hence the actual control problem is transformed into a synchronous tracking problem and the installation number of controllers is further reduced. Thirdly, the pinning matrix can be modified to adapt to plug-and-play operation under the distributed control architecture. Finally, the effectiveness and versatility of the proposed strategy are demonstrated with a typical droop-controlled MG model.
Distributed Secondary Control Strategy Based on $Q$-learning and Pinning Control for Droop-controlled Microgrids
A distributed secondary control (DSC) strategy that combines $Q$-learning and pinning control is originally proposed to achieve a fully optimal DSC for droop-controlled microgrids (MGs). It takes advantages of cross-fusion of the two algorithms to realize the high efficiency and self-adaptive control in MGs. It has the following advantages. Firstly, it adopts the advantages of reinforcement learning in autonomous learning control and intelligent decision-making, driving the action value of pinning control for feedback adaptive correction. Secondly, only a small part of points selected as pinned points needs to be controlled and pre-learned, hence the actual control problem is transformed into a synchronous tracking problem and the installation number of controllers is further reduced. Thirdly, the pinning matrix can be modified to adapt to plug-and-play operation under the distributed control architecture. Finally, the effectiveness and versatility of the proposed strategy are demonstrated with a typical droop-controlled MG model.
Distributed Secondary Control Strategy Based on $Q$-learning and Pinning Control for Droop-controlled Microgrids
Wei Liu (author) / Jun Shen (author) / Sicong Zhang (author) / Na Li (author) / Ze Zhu (author) / Liang Liang (author) / Zhen Wen (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
A novel seamless droop control method for load-sharing in photovoltaic-based AC microgrids
| American Institute of Physics | 2019
Droop control method for load share and voltage regulation in high-voltage microgrids
| DOAJ | 2016
| DOAJ | 2023
| American Institute of Physics | 2014