Wind Farm Energy Storage System Based on Cat Swarm Optimization-Backpropagation Neural Network Wind Power Prediction

To solve the instability problem of wind turbine power output, the wind power was predicted, and a wind power prediction algorithm optimized by the backpropagation neural network based on the CSO (cat swarm optimization) algorithm was studied, and a wind farm energy storage system model was built on this basis. By collecting the wind power plant's historical wind speed, power, and other parameters, the short-term wind farm output power was predicted, and the operation of the wind farm energy storage system was controlled to suppress the output power of the wind farm when the wind farm was connected to the grid so as to improve the stability of the output power of the wind farm. At the same time, typical wind farm data were taken as an example to verify the feasibility of the proposed method.