Wake and power prediction of horizontal-axis wind farm under yaw-controlled conditions with machine learning

Nakhchi, M. E.; Win Naung, S.; Rahmati, M.

Abstract

The main objective of this study is to employ the Extreme Gradient Boosting (XGBoost) machine learning algorithm to predict the power, wake, and turbulent characteristics of horizontal-axis wind farms under yaw-controlled conditions. For this purpose, a series of high-fidelity numerical simulations using LES method are performed over tandem NREL-5 MW wind turbines to generate the input data for training and testing in machine learning analysis. It is observed that XGBoost is more accurate for wake prediction of the yaw-controlled wind farms compared to ANN, which was used in previous studies. The results illustrate that XGBoost can predict the power with a mean deviation of 0.94 % for different yaw angles, while ANN can estimate the power generation with a mean deviation of 2.15 % for various tested yaw angles. At far wake regions (X > 2000 m) of the second wind turbine, the deviations reach below 1 %. Moreover, XGBoost requires a much shorter training time, 87.5 % faster than ANN. The power production of both wind turbines can be predicted more accurately with XGBoost compared to ANN. The wake prediction time of XGBoost is just 0.105 sec, while this time is 4.480 for the ANN model. In conclusion, XGBoost provides a significant reduction in error and training time compared to ANN and deep learning algorithms over yaw-misaligned wind farms.

• Journal Article Type: Article
• Acceptance Date: Sep 24, 2023
• Online Publication Date: Sep 30, 2023
• Publication Date: Nov 15, 2023
• Deposit Date: Oct 19, 2023
• Publicly Available Date: Oct 20, 2023
• Journal: Energy Conversion and Management
• Print ISSN: 0196-8904
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 296
• DOI: https://doi.org/10.1016/j.enconman.2023.117708
• Public URL: https://uwe-repository.worktribe.com/output/11354284