Abstract:

When the neural network is used to identify and train the load,because the number of original samples of uncommon load is small and the number of common load samples is large,the distribution of training samples is unbalanced.Using the traditional generative adversarial networks (GAN) and the auxiliary classifier GAN (ACGAN),the original model is prone to the problems such as gradient disappearance when the sample is expanded and trained.Based on the original ACGAN model,the gradient penalty function is added to the loss function for distinguishing true and false,so that the gradient of the discriminator and generator will fall faster during the mutual optimization process,and it is easier to achieve Nash equilibrium.The imbalance of the original sample has been effectively improved.Through the test and analysis of the public data set PLAID,the effectiveness of the proposed method is verified.

Key words: generative adversarial networks (GAN), auxiliary classifier, load identification, sample imbalance, sample expansion