Parameter identification of closed-wave characteristics of distributed power supply based on convolutional neural network optimization algorithm and measured data

In order to accurately model the low-voltage ripple characteristics of distributed power supply, the convolutional neural network algorithm is improved, and the convolutional neural network optimization algorithm is used to identify the low-voltage ripple characteristics of class B distributed power supply. Firstly, the sealing characteristics of class B distributed power supply are introduced, and the parameters to be identified are pointed out; Then, multiple groups of measured data to be identified are collected, and the convolution neural network algorithm is optimized from the structure and convolution mode of the convolution neural network algorithm. Then, the sealing wave characteristic parameters of distributed power supply are identified, and the optimal identification results are extracted from multiple groups of data; Finally, the optimal identification results are substituted into the model, and the modeling simulation data are compared with the measured data to judge the accuracy of the parameter identification results. In this method, the convolution neural network algorithm is applied to parameter identification, and the convolution operation type of convolution neural network algorithm convolution layer is optimized, which realizes the accurate identification of distributed power supply wave sealing characteristic parameters.