DATA-BASED WIND FARM MODELING AND CONTROL

The project aims to develop wind farm models based on data and artificial intelligence algorithms. The model and data will support the design of intelligent control algorithms for wind farms. This modeling method is used to solve the problem that existing models cannot be used for actual wind farm control. The model uses machine learning models to learn high-fidelity model data to improve the performance of low-fidelity models. So as to achieve the balance between the fidelity required by the control algorithms and the computational cost. The wind farm control algorithm based on this model aims to improve the power production and turbine life of the total farm by intelligently wake redirection. The wind power industry will also benefit from the development of artificial intelligence algorithms . Reinforcement learning is used to design intelligent optimized controllers for wind farms.