Redirigiendo al acceso original de articulo en 19 segundos...
ARTÍCULO
TITULO

Machine Learning-Based Approach to Wind Turbine Wake Prediction under Yawed Conditions

Mohan Kumar Gajendran    
Ijaz Fazil Syed Ahmed Kabir    
Sudhakar Vadivelu and E. Y. K. Ng    

Resumen

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.

 Artículos similares

       
 
Subin Kim, Heejin Hwang, Keunyeong Oh and Jiuk Shin    
The seismically deficient column details in existing reinforced concrete buildings affect the overall behavior of the building depending on the failure type of the column. The purpose of this study is to develop and validate a machine-learning-based pred... ver más
Revista: Applied Sciences

 
Myoung-Su Choi, Dong-Hun Han, Jun-Woo Choi and Min-Soo Kang    
Sleep apnea has emerged as a significant health issue in modern society, with self-diagnosis and effective management becoming increasingly important. Among the most renowned methods for self-diagnosis, the STOP-BANG questionnaire is widely recognized as... ver más
Revista: Applied Sciences

 
Xiaohui Yan, Tianqi Zhang, Wenying Du, Qingjia Meng, Xinghan Xu and Xiang Zhao    
Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the a... ver más

 
Saikat Das, Mohammad Ashrafuzzaman, Frederick T. Sheldon and Sajjan Shiva    
The distributed denial of service (DDoS) attack is one of the most pernicious threats in cyberspace. Catastrophic failures over the past two decades have resulted in catastrophic and costly disruption of services across all sectors and critical infrastru... ver más
Revista: Algorithms

 
Eike Blomeier, Sebastian Schmidt and Bernd Resch    
In the early stages of a disaster caused by a natural hazard (e.g., flood), the amount of available and useful information is low. To fill this informational gap, emergency responders are increasingly using data from geo-social media to gain insights fro... ver más
Revista: Information