Intelligent coordinated controller design for a 600 MW supercritical boiler unit based on expanded-structure neural network inverse models

Abstract

Under present widespread automatic generation control (AGC) centered on regional power grid, a large-capacity coal-fired supercritical (SC) power unit often operates under wide-range variable load conditions. Since a SC once-through boiler unit is represented by a typical multivariable system with large inertia and non-linear, slow time-variant and time-delay characteristics, it 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 (NN) based expanded-structure inverse system models of a 600MW SC 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, a 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.