The increasing demand for renewable energy sources has highlighted the critical role of wind turbines in the global energy landscape. As wind energy continues to grow in importance, optimizing the performance and efficiency of wind turbines has become paramount. One of the key challenges in wind turbine operation is maintaining effective power regulation, especially under varying and unpredictable wind conditions. Traditional methods for power regulation often rely on linearized models that may not fully capture the complex, nonlinear dynamics of wind turbines. To address this challenge, this study proposes a novel approach that leverages machine learning techniques for model linearization of wind turbines, specifically aimed at improving power regulation.

Wind turbines operate in highly dynamic environments, where the relationship between wind speed, turbine control parameters, and power output is inherently nonlinear. Traditional linearization techniques, typically based on small perturbations around an operating point, can lead to suboptimal performance, particularly under rapidly changing wind conditions. This study aims to enhance the accuracy and reliability of wind turbine models by employing machine learning-based linearization, which can better capture the complexities of turbine dynamics and improve overall power regulation.

The proposed approach involves the use of machine learning algorithms to develop a more accurate linearized model of wind turbine dynamics. The model is trained on historical operational data from wind turbines, capturing a wide range of operating conditions and responses. Key machine learning techniques, including regression models, neural networks, and ensemble methods, are employed to identify and learn the underlying patterns and relationships in the data. The resulting model provides a linear approximation that is more representative of the turbine's behavior across different operating points, thereby enabling more precise control for power regulation.

The linearized model derived from machine learning is integrated into the wind turbine's control system, where it is used to adjust control parameters in real-time to maintain the desired power output. This approach is tested and validated using both simulated and real-world data from wind turbines, with performance metrics including power output stability, response time to wind speed changes, and overall energy efficiency.

Machine learning, model linearization, wind turbines, power regulation, control systems, predictive modeling, renewable energy, optimization, turbine dynamics, data-driven models

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