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Terra Joule Journal

Abstract

Wind energy, a cornerstone of renewable energy solutions, provides a sustainable means of meeting global energy demands while minimizing environmental impact. This study investigates the influence of meteorological factors—wind speed, temperature, air pressure, and turbulence—on wind turbine performance and energy yield. Numerical techniques employed in the work include Euler's Method, Runge-Kutta 4th Order (RK4), and Physics-Informed Neural Networks, which were applied to simulate dynamics for turbine performance optimization. In addition, the Jensen's Wake Model has been applied for wake effect analysis and optimization of turbine spacing in wind farms. Moreover, yaw and pitch control strategies have been investigated to optimize energy capture efficiency. The cubic relationship of wind speed with power output, showing exponential gains in energy with the increment of wind velocity, has been derived from simulation-based analyses. Variations in temperature indicate air density; thus, higher energies would be produced in cold climates since the air would be denser. Yaw and pitch control strategies, incorporated within the model, demonstrated significant performance improvements, with yaw optimization alone increasing energy yield by X% and combined yaw-pitch control strategies enhancing production efficiency by Y% under variable wind conditions. Economic and environmental assessments underlined the advantages of reduced energy losses, optimized land use, and lower lifecycle carbon emissions. This study has identified the crucial role of atmospheric data and advanced optimization techniques in wind farm performance improvement. The findings provide valuable recommendations for improving turbine efficiency, reducing operational costs, and developing sustainable energy solutions.

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