Wind turbine blade rapid design optimization method based on reinforcement learning

The invention discloses a wind turbine blade rapid design optimization method based on reinforcement learning. The method is based on a reinforcement learning method, directional guidance is providedin the blade TAD optimization process, a blade model is promoted to evolve in a larger energy acquisition direction, and the optimization efficiency is greatly improved. Meanwhile, due to the reusability of the reinforcement learning method, the trained optimization model can be continuously reused at different wind speeds, and the search process of the optimal TAD of the blade at different wind speeds gets rid of the awkward situation starting from 0. The optimization model trained at the original wind speed is used as the initial model, and then the optimization model is adjusted to adapt toa new wind speed environment, so that the training time of the optimization model is greatly shortened, and the TAD optimization speed of the blade is increased. 本发明公开了一种基于强化学习的风力机叶片快速设计优化方法。该方法基于强化学习方法，在叶片TAD优化的过程中提供方向性指导，促进