单元机组负荷非线性预测控制及其仿真研究

Nonlinear Predictive Control on the Load System of a Thermal Power Unit and Its Simulation Study

大型单元机组负荷控制系统存在着强耦合、非线性等特性,常规线性控制策略难取得满意的控制效果。为此,该文提出了一种新的基于模糊模型和免疫优化的非线性预测控制方法,将离线辨识到的全局模糊模型作为预测模型,然后利用实数编码的免疫优化算法在线实现非线性预测控制的滚动优化,给出每个采样时刻的最优控制量。该方法还可通过修正的遗传算子方便地解决控制量受限问题。通过对一个500MW单元机组负荷控制系统的仿真试验,验证了该非线性预测控制方法的有效性。

Due to the strong coupling and nonlinear characteristics of large-scale boiler-turbine-generating unit load control systems, those conventional linear control systems cannot obtain satisfactory control performance. To avoid this difficulty, one novel nonlinear predictive control approach based on fuzzy model and immune optimization algorithm is proposed. Here, a global fuzzy model, which is obtained by off-line identification, is used for the prediction of future plant behavior, then the receding horizon optimization problem of nonlinear predictive controller is solved on-line by a particularly designed real-coded immune algorithm, thus to obtain the corresponding optimal control actions at each sampling instant. Moreover, with the modified genetic operators this approach can easily solve the nonlinear optimization problem under manipulated input constraints. Finally, an application to a 500MW boiler-turbine-generating unit load control system is given, and simulation results indicate the effectiveness of this new approach.