The Library
A novel dynamic wind farm wake model based on deep learning
Tools
Zhang, Jincheng and Zhao, Xiaowei (2020) A novel dynamic wind farm wake model based on deep learning. Applied Energy, 277 . 115552. doi:10.1016/j.apenergy.2020.115552 ISSN 0306-2619.
|
PDF
WRAP-novel-dynamic-wind-farm-wake-model-based-deep-learning-Zhang-2020.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (8Mb) | Preview |
Official URL: https://doi.org/10.1016/j.apenergy.2020.115552
Abstract
A deep learning based reduced order modelling method for general unsteady fluid systems is proposed, which is then applied to develop a novel dynamic wind farm wake model. The proposed method employs the proper orthogonal decomposition technique for reducing the flow field dimension and the long short-term memory network for predicting the reduced representation of the flow field at a future time step. The method is specifically designed to tackle distributed fluid systems (such as wind farm wakes) and to be control-oriented. For wind farm wake modelling, a set of large eddy simulations are first carried out to generate a series of flow field data for wind turbines operating in different conditions. Then the proposed method is employed to develop the data-based wake model. The results show that this novel dynamic wind farm wake model can predict the main features of unsteady wind turbine wakes similarly as high-fidelity wake models while running as fast as the low-fidelity static wake models and that the model’s overall prediction error is just 48% with respect to the freestream wind speed. As an illustrative example, the developed model can predict the unsteady turbine wakes of a 9-turbine test wind farm within several seconds based on a standard desktop while it requires tens of thousands of CPU hours on a high-performance computing cluster if a high-fidelity model is used. Thus the developed model can be used for fast yet accurate simulation of wind farms as well as for their predictions and control designs.
Item Type: | Journal Article | ||||||||
---|---|---|---|---|---|---|---|---|---|
Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TL Motor vehicles. Aeronautics. Astronautics |
||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Artificial intelligence -- Engineering applications, Wind power plants, Wakes (Aerodynamics) | ||||||||
Journal or Publication Title: | Applied Energy | ||||||||
Publisher: | Elsevier BV | ||||||||
ISSN: | 0306-2619 | ||||||||
Official Date: | 1 November 2020 | ||||||||
Dates: |
|
||||||||
Volume: | 277 | ||||||||
Article Number: | 115552 | ||||||||
DOI: | 10.1016/j.apenergy.2020.115552 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 25 September 2020 | ||||||||
Date of first compliant Open Access: | 30 July 2021 | ||||||||
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