Modeling method for wind-light-fire coupling system frequency response model based on GAN and GRU neural networks

The invention aims to provide a modeling method of a system frequency response model based on a generative adversarial network (GAN) and a gate cycle unit (GRU) neural network, aiming at complex dynamic characteristics such as time varying, nonlinearity, uncertainty, intermittency and the like of a wind-light-fire coupling system, and discloses a modeling method of a system frequency response model based on the GAN and the GRU neural network. The generative adversarial network is utilized to solve the problem that training samples are deficient in the building process of the system frequency response model based on data driving. In addition, the problem that an existing modeling method cannot accurately describe nonlinearity, uncertainty and the like of system frequency response is solved by utilizing a gate circulation unit neural network. 本发明的目的是针对风光火耦合系统时变、非线性、不确定性、间歇性等复杂的动态特性，提出了一种基于生成对抗网络(GenerativeAdversarial Networks，GAN)与门循环单元(Gate Recurrent Unit，GRU)神经网络的系统频率响应模型建模方法。利用生成对抗网络解决了基于数据驱动的系统频率响应模型建立过程中训练