The Library
Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data
Tools
Tools
Tools
Yin, Xiuxing and Zhao, Xiaowei (2021) Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data. IEEE Transactions on Industrial Electronics, 68 (4). pp. 3251-3261. doi:10.1109/TIE.2020.2979560 ISSN 0278-0046.
|
PDF
WRAP-Deep-neural-predictive-control-offshore-wind-farm-Zhao-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1085Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TIE.2020.2979560
Abstract
The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations.
| Item Type: | Journal Article | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
||||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| Library of Congress Subject Headings (LCSH): | Neural networks (Computer science) , Offshore wind power plants, Predictive control, Eddies -- Simulation methods | ||||||||
| Journal or Publication Title: | IEEE Transactions on Industrial Electronics | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 0278-0046 | ||||||||
| Official Date: | April 2021 | ||||||||
| Dates: |
|
||||||||
| Volume: | 68 | ||||||||
| Number: | 4 | ||||||||
| Page Range: | pp. 3251-3261 | ||||||||
| DOI: | 10.1109/TIE.2020.2979560 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2020 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: | 4 May 2020 | ||||||||
| Date of first compliant Open Access: | 4 May 2020 | ||||||||
| RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
