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

Author:

Gajendran Mohan Kumar1ORCID,Kabir Ijaz Fazil Syed Ahmed2ORCID,Vadivelu Sudhakar3ORCID,Ng E. Y. K.2ORCID

Affiliation:

1. Division of Energy, Matter and Systems, University of Missouri-Kansas City, Kansas City, MO 64110, USA

2. School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Block N3, Singapore 639798, Singapore

3. Department of Aerospace Engineering, Toronto Metropolitan University (TMU), Toronto, ON M5B 2K3, Canada

Abstract

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.

Publisher

MDPI AG

Subject

Ocean Engineering,Water Science and Technology,Civil and Structural Engineering

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