Design of electric drive cooperative control system based on graph convolution neural network

As an efficient and energy-saving system, the electric drive system has been widely promoted and popularized in various fields of society, and has a far-reaching impact on related fields, which has attracted more and more attention. In order to optimize the electric drive system, reduce the operating cost and promote its optimal performance, it is imperative to strengthen the research on the automatic control system of electric drive. Based on this, this paper analyzes the design requirements of the paper machine for its electric drive cooperative control system with the help of the graph convolution neural network, and according to the requirements of the control system, analyzes and studies the control principles of each division, and obtains the corresponding control scheme. The intelligent control system is integrated with the graph-volume neural network to control the electric drive cooperative control system. After systematically studying the structure and training algorithm of graph convolution neural network, the training platform of convolutional neural network is built by using cntk deep learning framework. The designed system has been put into operation in actual production, and the speed of the paper machine has been further improved, which meets the requirements of the paper machine for the control system.