Predictive control of wind power ramping based on wind turbine load optimization

A predictive control strategy considering ramping rate and power deviation is proposed, which is based on wind turbine load optimization. The wind turbine load model is established according to blade element momentum (BEM) theory, and the influence factors of wind turbine load are analysed based on this model. The control strategy is divided into 3 modules. The prediction module is based on ultra-short-term neural network wind speed prediction. The online optimization model is established with the objectives of minimizing the ramping rate, wind turbine load and power deviation. The feedback module compensates the deviation of optimal control under practical operation conditions. Simulation results show that the proposed strategy can improve the security and economy of wind turbines when dealing with ramping events.