Distributed Secondary Control Strategy Based on <tex>$Q$</tex>-learning and Pinning Control for Droop-controlled Microgrids
A distributed secondary control (DSC) strategy that combines <tex>$Q$</tex>-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 a...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | Journal of Modern Power Systems and Clean Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9853025/ |
| _version_ | 1828110465050345472 |
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| author | Wei Liu Jun Shen Sicong Zhang Na Li Ze Zhu Liang Liang Zhen Wen |
| author_facet | Wei Liu Jun Shen Sicong Zhang Na Li Ze Zhu Liang Liang Zhen Wen |
| author_sort | Wei Liu |
| collection | DOAJ |
| description | A distributed secondary control (DSC) strategy that combines <tex>$Q$</tex>-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. |
| first_indexed | 2024-04-11T11:19:29Z |
| format | Article |
| id | doaj.art-77a4ea3fbe1b4930a0f8ddf45bbd7e3f |
| institution | Directory Open Access Journal |
| issn | 2196-5420 |
| language | English |
| last_indexed | 2024-04-11T11:19:29Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | Journal of Modern Power Systems and Clean Energy |
| spelling | doaj.art-77a4ea3fbe1b4930a0f8ddf45bbd7e3f2022-12-22T04:27:07ZengIEEEJournal of Modern Power Systems and Clean Energy2196-54202022-01-011051314132510.35833/MPCE.2020.0007059853025Distributed Secondary Control Strategy Based on <tex>$Q$</tex>-learning and Pinning Control for Droop-controlled MicrogridsWei Liu0Jun Shen1Sicong Zhang2Na Li3Ze Zhu4Liang Liang5Zhen Wen6School of Automation, Nanjing University of Science and Technology,Department of Electrical Engineering,Nanjing,China,210094School of Automation, Nanjing University of Science and Technology,Department of Electrical Engineering,Nanjing,China,210094School of Automation, Nanjing University of Science and Technology,Department of Electrical Engineering,Nanjing,China,210094School of Automation, Nanjing University of Science and Technology,Department of Electrical Engineering,Nanjing,China,210094School of Automation, Nanjing University of Science and Technology,Department of Electrical Engineering,Nanjing,China,210094State Grid Zhejiang Electric Power Co., Ltd., Jiaxing Power Supply Company,Jiaxing,China,230022State Grid Zhejiang Electric Power Co., Ltd., Jiaxing Power Supply Company,Jiaxing,China,230022A distributed secondary control (DSC) strategy that combines <tex>$Q$</tex>-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.https://ieeexplore.ieee.org/document/9853025/Microgriddistributed secondary controlpinning controlQ-learning |
| spellingShingle | Wei Liu Jun Shen Sicong Zhang Na Li Ze Zhu Liang Liang Zhen Wen Distributed Secondary Control Strategy Based on <tex>$Q$</tex>-learning and Pinning Control for Droop-controlled Microgrids Journal of Modern Power Systems and Clean Energy Microgrid distributed secondary control pinning control Q-learning |
| title | Distributed Secondary Control Strategy Based on <tex>$Q$</tex>-learning and Pinning Control for Droop-controlled Microgrids |
| title_full | Distributed Secondary Control Strategy Based on <tex>$Q$</tex>-learning and Pinning Control for Droop-controlled Microgrids |
| title_fullStr | Distributed Secondary Control Strategy Based on <tex>$Q$</tex>-learning and Pinning Control for Droop-controlled Microgrids |
| title_full_unstemmed | Distributed Secondary Control Strategy Based on <tex>$Q$</tex>-learning and Pinning Control for Droop-controlled Microgrids |
| title_short | Distributed Secondary Control Strategy Based on <tex>$Q$</tex>-learning and Pinning Control for Droop-controlled Microgrids |
| title_sort | distributed secondary control strategy based on tex q tex learning and pinning control for droop controlled microgrids |
| topic | Microgrid distributed secondary control pinning control Q-learning |
| url | https://ieeexplore.ieee.org/document/9853025/ |
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