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Composite experience replay based deep reinforcement learning with application in wind farm control
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Dong, Hongyang and Zhao, Xiaowei (2022) Composite experience replay based deep reinforcement learning with application in wind farm control. IEEE Transactions on Control Systems Technology, 30 (3). pp. 1281-1295. doi:10.1109/TCST.2021.3102476 ISSN 1063-6536.
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WRAP-Composite-experience-replay-based-deep-wind-farm-control-2021.pdf - Accepted Version - Requires a PDF viewer. Download (9Mb) | Preview |
Official URL: https://doi.org/10.1109/TCST.2021.3102476
Abstract
In this article, a deep reinforcement learning (RL)-based control approach with enhanced learning efficiency and effectiveness is proposed to address the wind farm control problem. Specifically, a novel composite experience replay (CER) strategy is designed and embedded in the deep deterministic policy gradient (DDPG) algorithm. CER provides a new sampling scheme that can mine the information of stored transitions in-depth by making a tradeoff between rewards and temporal difference (TD) errors. Modified importance-sampling weights are introduced to the training process of neural networks (NNs) to deal with the distribution mismatching problem induced by CER. Then, our CER-DDPG approach is applied to optimizing the total power production of wind farms. The main challenge of this control problem comes from the strong wake effects among wind turbines and the stochastic features of environments, rendering it intractable for conventional control approaches. A reward regularization process is designed along with the CER-DDPG, which employs an additional NN to handle the bias of rewards caused by the stochastic wind speeds. Tests with a dynamic wind farm simulator (WFSim) show that our method achieves higher rewards with less training costs than conventional deep RL-based control approaches, and it has the ability to increase the total power generation of wind farms with different specifications.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TJ Mechanical engineering and machinery |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| Library of Congress Subject Headings (LCSH): | Intelligent control systems , Wind power plants , Reinforcement learning , Neural networks (Computer science) | ||||||||
| Journal or Publication Title: | IEEE Transactions on Control Systems Technology | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 1063-6536 | ||||||||
| Official Date: | May 2022 | ||||||||
| Dates: |
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| Volume: | 30 | ||||||||
| Number: | 3 | ||||||||
| Page Range: | pp. 1281-1295 | ||||||||
| DOI: | 10.1109/TCST.2021.3102476 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Date of first compliant deposit: | 14 August 2021 | ||||||||
| Date of first compliant Open Access: | 17 August 2021 | ||||||||
| RIOXX Funder/Project Grant: |
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