Neural Network Inverse Control for the Coordinated System of a 600MW Supercritical Boiler Unit

Abstract

A supercritical (SC) once-through boiler unit is a typical multivariable system with large inertia and nonlinear, slow time-variant, and time-delay characteristics, which often makes the coordinated control quality deteriorate under wide-range loading conditions, and thus influences the unit load response speed and leads to heavy fluctuation of the main steam pressure. To improve the SC unit's coordinated control quality with advanced intelligent control strategy, the neural-network based inverse system models of a 600MW supercritical boiler unit were investigated. A feedforward neural network with time-delayed inputs and time-delayed output feedbacks was adopted to establish the inverse models for the load and the main steam pressure characteristics. Based on the model, neural network inverse coordinated control scheme was designed and tested in a full-scope power plant simulator of the given SC power unit, which showed that the proposed coordinated control scheme can achieve better control results compared to the original PID coordinated control.