Artificial-Intelligence Aerodynamics for Efficient Energy Systems: The Focus on Wind Turbines
Keywords:
Artificial intelligence, wind energy, turbine control, machine learning, fluid dynamics, prognostic and health monitoring, real-time control, wind farm efficiency, energy prognosis, sustainable resourcesAbstract
The incorporation of AI in wind energy systems has transformed the design, operation and management of wind turbines, wind farms increasing their effectiveness, resilience and viability. This paper explores the transformative impact of AI-driven technologies across various aspects of wind energy, focusing on five key areas: Lear two main areas: in turbine engineering, advanced concepts such as fluid dynamics and blade design, while in computer sciences, major components consist of machine learning for performance assessment of turbines, monitoring of turbines on real-time basis as well as for the purpose of maintenance, and optimization of wind farms. In the specific application of improving the efficiency of turbine blade design and function, AI continues to be useful as machine learning is used in creating new and more efficient and long lasting blades while dynamic real time monitoring systems are used in making adjustments based on external conditions. AI-based predictive maintenance enables for mechanical problems identification before they evolve, thus decreasing the time a machine spends out of service and operational expenses. Also, AI enhances the design of wind farm, control of wake and load balance to enhance efficiency of wind electricity generation. It allows for a more effective intro of energy into the larger grid and hydrates therefore increasing the availability of renewable energy with stability. Based on this paper, the future of AI remains evident in future enhancement of wind energy systems, hence guaranteeing sustainable energy, efficiency, and cost-effectiveness in energy solutions for the overall energy transformation.
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