Machine learning-based approach to wind turbine wake prediction under yawed conditions
As wind energy continues to be a crucial part of sustainable power generation, the need for precise and efficient modeling of wind turbines, especially under yawed conditions, becomes increasingly significant. Addressing this, the current study introduces a machine learning-based symbolic regression...
Saved in:
Main Authors: | , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/173107 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-173107 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1731072024-01-13T16:48:30Z Machine learning-based approach to wind turbine wake prediction under yawed conditions Gajendran, Mohan Kumar Ijaz Fazil Syed Ahmed Kabir Vadivelu, Sudhakar Ng, Eddie Yin Kwee School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Yaw Wake Prediction Wind Energy Optimization As wind energy continues to be a crucial part of sustainable power generation, the need for precise and efficient modeling of wind turbines, especially under yawed conditions, becomes increasingly significant. Addressing this, the current study introduces a machine learning-based symbolic regression approach for elucidating wake dynamics. Utilizing WindSE’s actuator line method (ALM) and Large Eddy Simulation (LES), we model an NREL 5-MW wind turbine under yaw conditions ranging from no yaw to 40 degrees. Leveraging a hold-out validation strategy, the model achieves robust hyper-parameter optimization, resulting in high predictive accuracy. While the model demonstrates remarkable precision in predicting wake deflection and velocity deficit at both the wake center and hub height, it shows a slight deviation at low downstream distances, which is less critical to our focus on large wind farm design. Nonetheless, our approach sets the stage for advancements in academic research and practical applications in the wind energy sector by providing an accurate and computationally efficient tool for wind farm optimization. This study establishes a new standard, filling a significant gap in the literature on the application of machine learning-based wake models for wind turbine yaw wake prediction. Published version 2024-01-12T05:17:02Z 2024-01-12T05:17:02Z 2023 Journal Article Gajendran, M. K., Ijaz Fazil Syed Ahmed Kabir, Vadivelu, S. & Ng, E. Y. K. (2023). Machine learning-based approach to wind turbine wake prediction under yawed conditions. Journal of Marine Science and Engineering, 11(11), 2111-. https://dx.doi.org/10.3390/jmse11112111 2077-1312 https://hdl.handle.net/10356/173107 10.3390/jmse11112111 2-s2.0-85178362293 11 11 2111 en Journal of Marine Science and Engineering © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Mechanical engineering Yaw Wake Prediction Wind Energy Optimization |
spellingShingle |
Engineering::Mechanical engineering Yaw Wake Prediction Wind Energy Optimization Gajendran, Mohan Kumar Ijaz Fazil Syed Ahmed Kabir Vadivelu, Sudhakar Ng, Eddie Yin Kwee Machine learning-based approach to wind turbine wake prediction under yawed conditions |
description |
As wind energy continues to be a crucial part of sustainable power generation, the need for precise and efficient modeling of wind turbines, especially under yawed conditions, becomes increasingly significant. Addressing this, the current study introduces a machine learning-based symbolic regression approach for elucidating wake dynamics. Utilizing WindSE’s actuator line method (ALM) and Large Eddy Simulation (LES), we model an NREL 5-MW wind turbine under yaw conditions ranging from no yaw to 40 degrees. Leveraging a hold-out validation strategy, the model achieves robust hyper-parameter optimization, resulting in high predictive accuracy. While the model demonstrates remarkable precision in predicting wake deflection and velocity deficit at both the wake center and hub height, it shows a slight deviation at low downstream distances, which is less critical to our focus on large wind farm design. Nonetheless, our approach sets the stage for advancements in academic research and practical applications in the wind energy sector by providing an accurate and computationally efficient tool for wind farm optimization. This study establishes a new standard, filling a significant gap in the literature on the application of machine learning-based wake models for wind turbine yaw wake prediction. |
author2 |
School of Mechanical and Aerospace Engineering |
author_facet |
School of Mechanical and Aerospace Engineering Gajendran, Mohan Kumar Ijaz Fazil Syed Ahmed Kabir Vadivelu, Sudhakar Ng, Eddie Yin Kwee |
format |
Article |
author |
Gajendran, Mohan Kumar Ijaz Fazil Syed Ahmed Kabir Vadivelu, Sudhakar Ng, Eddie Yin Kwee |
author_sort |
Gajendran, Mohan Kumar |
title |
Machine learning-based approach to wind turbine wake prediction under yawed conditions |
title_short |
Machine learning-based approach to wind turbine wake prediction under yawed conditions |
title_full |
Machine learning-based approach to wind turbine wake prediction under yawed conditions |
title_fullStr |
Machine learning-based approach to wind turbine wake prediction under yawed conditions |
title_full_unstemmed |
Machine learning-based approach to wind turbine wake prediction under yawed conditions |
title_sort |
machine learning-based approach to wind turbine wake prediction under yawed conditions |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/173107 |
_version_ |
1789483183859826688 |