As a large number of new energy is connected to the grid

the participation of supercritical thermal power units in peak regulation tends to cause the superheat of intermediate points to fluctuate greatly

resulting in superheated steam over temperature and other problems. In order to better control the intermediate point superheat to achieve stability

a prediction method of intermediate point superheat based on double-depth input convex neural network multi-model(muti-DDICNN model) was proposed. Sub-models with different prediction step sizes were trained respectively

and the intermediate point superheat state prediction network(SPNN) and error prediction network(EPNN) were constructed. Based on the convex property of prediction network

a multi-model predictive controller(DDICNN-MPC) based on convex neural network with double-depth input is designed. The control problem is transformed into a convex optimization problem

the Jacobian matrix of control matrix to objective function is obtained

and the optimal solution of control matrix is calculated by gradient descent method. The simulation results show that

the DDICNN-MPC can track the intermediate point superheat setting quickly and stably

and the steady-state error is small

so it has good adjustment ability.