Superheater Steam Temperature Control Based on the Expanded-Structure Neural Network Inverse Models

Abstract

In order to improve the control effect of the Superheater Steam Temperature (SST) for a 300MW boiler unit, this paper presents an inverse compensation control scheme based on the expanded-structure neural network inverse models. The input and output variables of the expanded–structure neural network Inverse Dynamic Process Models （IDPMs） for the superheater system are determined from understanding of the boiler operating characteristics. Then, two neural network (NN) inverse controllers are designed with the IDPMs as on-line output compensators for the original cascade PID controllers in order to improve the control effect. Detailed simulation tests are carried out on the full-scope simulator of the given 300MW power unit. It is shown by tests that the control effects of the NN-compensated control on the SST are significantly improved compared with the case of the original cascade PID control scheme.