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Data-driven structural control of monopile wind turbine towers based on machine learning
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Zhang, Jincheng, Zhao, Xiaowei and Wei, Xing (2020) Data-driven structural control of monopile wind turbine towers based on machine learning. In: The 21st IFAC World Congress, Berlin, Germany, 12-17 Jul 2020. Published in: IFAC-PapersOnLine, 53 (2). pp. 7466-7471. doi:10.1016/j.ifacol.2020.12.1299 ISSN 2405-8963.
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WRAP-data-driven-structural-control-monopile-wind-turbine-towers-based-machine-learning-Zhao-2020.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (2111Kb) | Preview |
Official URL: https://doi.org/10.1016/j.ifacol.2020.12.1299
Abstract
This paper studies the data-driven structural control of monopile wind turbine towers based on machine learning approach, by using an active tuned mass damper (TMD) located in the nacelle. The adaptive dynamic programming (ADP) approach is employed to obtain the optimal controller which is derived on the modern large-scale machine learning platform Tensorflow. The proposed network structure includes three simple three-layer neural networks (NNs), i.e. a plant network, a critic network, and an action network. The plant network is used to capture the fully nonlinear dynamics of the structural system while the action network is used to approximate the optimal controller. Their training requires the gradient information flowing through the whole network. The automatic differentiation is used in this paper for all the gradient derivations, which greatly improves the employed ADP algorithm’s ability in solving complex practical problems. The simulation results of structural control of monopile turbine towers show that on average the active TMD achieves 15% performance improvement on tower fatigue load reduction over a passive TMD, with small active power consumption (less than 0.24% of the turbine’s nominal power production). Besides, the controller design considers the trade-off between control performance and power consumption.
| Item Type: | Conference Item (Paper) | ||||||||
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| Subjects: | T Technology > T Technology (General) T Technology > TH Building construction 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): | Wind turbines -- Mathematical models, Tuned mass dampers , Dynamic programming | ||||||||
| Journal or Publication Title: | IFAC-PapersOnLine | ||||||||
| Publisher: | Elsevier | ||||||||
| ISSN: | 2405-8963 | ||||||||
| Official Date: | 2020 | ||||||||
| Dates: |
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| Volume: | 53 | ||||||||
| Number: | 2 | ||||||||
| Page Range: | pp. 7466-7471 | ||||||||
| DOI: | 10.1016/j.ifacol.2020.12.1299 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2020 the authors. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-ND. | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Date of first compliant deposit: | 26 May 2020 | ||||||||
| Date of first compliant Open Access: | 26 May 2020 | ||||||||
| Grant number: | 765579 | ||||||||
| RIOXX Funder/Project Grant: |
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| Conference Paper Type: | Paper | ||||||||
| Title of Event: | The 21st IFAC World Congress | ||||||||
| Type of Event: | Conference | ||||||||
| Location of Event: | Berlin, Germany | ||||||||
| Date(s) of Event: | 12-17 Jul 2020 | ||||||||
| Related URLs: |
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