Research on Data Generation Based on the Combination of Growing-Pruning GAN and Intelligent Parameter Optimization

The amount of voltage fault data collection is limited to signal acquisition instruments and simulation software. Generative adversarial networks (GAN) have been successfully applied to the data generation tasks. However, there is no theoretical basis for the selection of the network structure and parameters of generators and discriminators in these GANs. It is difficult to achieve the optimal selection basically by experience or repeated attempts, resulting in high cost and time-consuming deployment of GAN computing in practical applications. The existing methods of neural network optimization are mainly used to compress and accelerate the deep neural network in classification tasks. Due to different goals and training processes, they cannot be directly applied to the data generation task of GAN. In the three-generation scenario, the hidden layer filter nodes of the initial GAN generator and discriminator are growing firstly, then the GAN parameters after the structure adjustment are optimized by particle swarm optimization (PSO), and then the node sensitivity is analyzed. The nodes with small contribution to the output are pruned, and then the GAN parameters after the structure adjustment are optimized using PSO algorithm to obtain the GAN with optimal structure and parameters (GP-PSO-GAN). The results show that GP-PSO-GAN has good performance. For example, the simulation results of generating unidirectional fault data show that the generated error of GP-PSO-GAN is reduced by 70.4% and 15.2% compared with parameters optimization only based on PSO (PSO-GAN) and pruning- PSO-GAN (P-PSO-GAN), respectively. The convergence curve shows that GP-PSO-GAN has good convergence.