File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.1016/j.apenergy.2019.114025
- Scopus: eid_2-s2.0-85073926191
- WOS: WOS:000506574700052
- Find via
Supplementary
- Citations:
- Appears in Collections:
Article: Wake modeling of wind turbines using machine learning
Title | Wake modeling of wind turbines using machine learning |
---|---|
Authors | |
Keywords | Wind turbine wake Wake model Artificial neural network (ANN) Machine learning ADM-R (actuator-disk model with rotation) |
Issue Date | 2020 |
Publisher | Pergamon. The Journal's web site is located at http://www.elsevier.com/locate/apenergy |
Citation | Applied Energy, 2020, v. 257, p. article no. 114025 How to Cite? |
Abstract | In the paper, a novel framework that employs the machine learning and CFD (computational fluid dynamics) simulation to develop new wake velocity and turbulence models with high accuracy and good efficiency is proposed to improve the turbine wake predictions. An ANN (artificial neural network) model based on the backpropagation (BP) algorithm is designed to build the underlying spatial relationship between the inflow conditions and the three-dimensional wake flows. To save the computational cost, a reduced-order turbine model ADM-R (actuator disk model with rotation), is incorporated into RANS (Reynolds-averaged Navier-Stokes equations) simulations coupled with a modified k - e turbulence model to provide big datasets of wake flow for training, testing, and validation of the ANN model. The numerical framework of RANS/ADM-R simulations is validated by a standalone Vestas V80 2MW wind turbine and NTNU wind tunnel test of double aligned turbines. In the ANN-based wake model, the inflow wind speed and turbulence intensity at hub height are selected as input variables, while the spatial velocity deficit and added turbulence kinetic energy (TKE) in wake field are taken as output variables. The ANN-based wake model is first deployed to a standalone turbine, and then the spatial wake characteristics and power generation of an aligned 8-turbine row as representation of Horns Rev wind farm are also validated against Large Eddy Simulations (LES) and field measurement. The results of ANNbased wake model show good agreement with the numerical simulations and measurement data, indicating that the ANN is capable of establishing the complex spatial relationship between inflow conditions and the wake flows. The machine learning techniques can remarkably improve the accuracy and efficiency of wake predictions. |
Persistent Identifier | http://hdl.handle.net/10722/294617 |
ISSN | 2023 Impact Factor: 10.1 2023 SCImago Journal Rankings: 2.820 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ti, Z | - |
dc.contributor.author | Deng, XW | - |
dc.contributor.author | Yang, H | - |
dc.date.accessioned | 2020-12-08T07:39:30Z | - |
dc.date.available | 2020-12-08T07:39:30Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Applied Energy, 2020, v. 257, p. article no. 114025 | - |
dc.identifier.issn | 0306-2619 | - |
dc.identifier.uri | http://hdl.handle.net/10722/294617 | - |
dc.description.abstract | In the paper, a novel framework that employs the machine learning and CFD (computational fluid dynamics) simulation to develop new wake velocity and turbulence models with high accuracy and good efficiency is proposed to improve the turbine wake predictions. An ANN (artificial neural network) model based on the backpropagation (BP) algorithm is designed to build the underlying spatial relationship between the inflow conditions and the three-dimensional wake flows. To save the computational cost, a reduced-order turbine model ADM-R (actuator disk model with rotation), is incorporated into RANS (Reynolds-averaged Navier-Stokes equations) simulations coupled with a modified k - e turbulence model to provide big datasets of wake flow for training, testing, and validation of the ANN model. The numerical framework of RANS/ADM-R simulations is validated by a standalone Vestas V80 2MW wind turbine and NTNU wind tunnel test of double aligned turbines. In the ANN-based wake model, the inflow wind speed and turbulence intensity at hub height are selected as input variables, while the spatial velocity deficit and added turbulence kinetic energy (TKE) in wake field are taken as output variables. The ANN-based wake model is first deployed to a standalone turbine, and then the spatial wake characteristics and power generation of an aligned 8-turbine row as representation of Horns Rev wind farm are also validated against Large Eddy Simulations (LES) and field measurement. The results of ANNbased wake model show good agreement with the numerical simulations and measurement data, indicating that the ANN is capable of establishing the complex spatial relationship between inflow conditions and the wake flows. The machine learning techniques can remarkably improve the accuracy and efficiency of wake predictions. | - |
dc.language | eng | - |
dc.publisher | Pergamon. The Journal's web site is located at http://www.elsevier.com/locate/apenergy | - |
dc.relation.ispartof | Applied Energy | - |
dc.subject | Wind turbine wake | - |
dc.subject | Wake model | - |
dc.subject | Artificial neural network (ANN) | - |
dc.subject | Machine learning | - |
dc.subject | ADM-R (actuator-disk model with rotation) | - |
dc.title | Wake modeling of wind turbines using machine learning | - |
dc.type | Article | - |
dc.identifier.email | Deng, XW: xwdeng@hku.hk | - |
dc.identifier.authority | Deng, XW=rp02223 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.apenergy.2019.114025 | - |
dc.identifier.scopus | eid_2-s2.0-85073926191 | - |
dc.identifier.hkuros | 320441 | - |
dc.identifier.volume | 257 | - |
dc.identifier.spage | article no. 114025 | - |
dc.identifier.epage | article no. 114025 | - |
dc.identifier.isi | WOS:000506574700052 | - |
dc.publisher.place | United Kingdom | - |