AI-Machine Learning Method for Design and Predictive Analysis of Wind Turbine

Abstract

Prameet Guha, Sudipta Chakraborty and Pradip Majumdar

Wind turbines are electro-mechanical systems designed to harness kinetic energy from wind and convert it into electrical energy using rotor blades and a generator. They are considered one of the leading sources of sustainable power, offering a cleaner alternative to fossil fuels. However, the energy conversion efficiency of most commercial wind turbines remains limited, often between 25% to 40%, far below the theoretical maximum known as Betz’s limit (59.3%). This study aims to investigate design-based performance improvements to achieve a higher efficiency, employing both traditional approaches, such as Blade Element Momentum (BEM) Theory and Computational Fluid Dynamics, as well as advanced Artificial Intelligence techniques, including supervised regression-based Machine Learning (ML) algorithms. The research utilized meteorological data from 19 cities across Illinois, obtained from the Prairie Research Institute’s Illinois State Weather Survey (ISWS). The proposed ML models, trained on historical meteorological data, capture complex nonlinear relationships in turbine performance without requiring explicit physical modeling. By integrating site-specific wind data with aerodynamic modeling, this approach provides a scalable pathway to improving efficiency toward the Betz limit. Furthermore, the methodology incorporates computational strategies to optimize the selection and quantity of representative functions, alongside the weighting factors for critical features, by minimizing both training and validation losses. The proposed methodology offers valuable insight for future turbine designs aiming for high- performance renewable energy systems.

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