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Observational analysis demonstrates improved energy efficiency via dynamic blade angle control in wind turbines, indicating the role of AI-based systems.
In this study, This study examines the use of Dynamic Blade Angle Control (DBAC) to improve energy efficiency in wind turbines. DBAC adapts blade pitch based on wind speed fluctuations to optimize energy production while maintaining turbine safety. Using an artificial intelligence-based model, this study analyzes the effects of DBAC on energy efficiency. The model predicts energy production based on wind speed and blade pitch, showing that DBAC increases energy efficiency at low speeds and ensures turbine safety at high speeds. The model's performance is enhanced with feedforward control strategies and LiDAR-assisted systems. These results emphasize the importance of DBAC and AI-based control systems in enhancing energy efficiency in wind turbines
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Yönetken et al. (2025) studied this question.
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