结合生成对抗网络与支持向量回归提升发电厂励磁系统参数辨识的准确性OA

The existing methods for parameter identification of the excitation system in power plants usually have problems such as low identification accuracy and poor training stability.These issues lead to insufficient accuracy of parameter identification in the system,which affects the dynamic regulation and stability of the excitation system in power plants.Therefore,this paper combines the generative adversarial network(GAN)and the support vector regression(SVR)model to improve the accuracy of parameter identification in the excitation system of power plants.Firstly,the Wasserstein GAN structure is adopted,and through the adversarial training of the generator and the discriminator,feature data similar to the actual excitation system is generated.Then,the principal component analysis is used to extract and reduce the features of the generated data,mapping the high-dimensional data to a low-dimensional space,providing input for the subsequent SVR model.The SVR model uses the radial basis function kernel function to handle complex nonlinear relationships,thereby accurately predicting the key parameters of the excitation system.Finally,through the dual optimization of combining GAN and SVR,the simulation results show that the mean square error of the GAN+SVR optimization method is generally low,with the highest being 0.000 9 and the lowest being 0.000 1,indicating that the model can effectively capture the dynamic characteristics of the excitation current and maintain high accuracy.