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Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data

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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.

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Official URL: http://dx.doi.org/10.1109/TIE.2020.2979560

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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:
DateEvent
April 2021Published
16 March 2020Available
23 February 2020Accepted
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:
Project/Grant IDRIOXX Funder NameFunder ID
EP/R007470/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266

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