SCR脱硝系统的RBF条件积分滑模参数优化控制

Parameter Optimization Control of RBF Conditional Integral Sliding Mode for SCR Denitration System

针对选择性催化还原(SCR)脱硝系统在火电机组深度调峰下因系统特性随着机组负荷改变而难以控制的问题,提出基于径向基函数(RBF)的条件积分滑模优化控制方案,用条件积分滑模减少抖振与调节时间,用RBF实时逼近SCR脱硝系统特性改变下的未知扰动以提高系统鲁棒性。并提出一种纳什均衡量子粒子群寻优(NEQPSO)算法,以获得RBF条件积分滑模控制方案的最优参数,并在20%~100%的机组负荷下进行仿真实验。结果表明:优化后的RBF条件积分滑模控制与传统控制方案相比超调量降低50.26%、调节时间缩短13.55%,在干扰信号下超调量仅有1.50%,其响应速度更快、抗干扰能力更强、鲁棒性更好。

To address the issue that the selective catalytic reduction(SCR)denitration system is difficult to control due to the change of system characteristics along with power load under deep peak regulation of thermal power units,an optimization control scheme of conditional integral sliding mode based on radial basis function(RBF)was proposed.The conditional integration sliding mode was used to reduce the jitter and adjustment time,and the RBF was used to approximate the unknown disturbance under the change of the characteristics of the SCR denitration system in real time to improve the robustness of the system.A Nash equilibrium quantum particle swarm optimization(NEQPSO)algorithm was proposed to obtain the optimal parameters of the RBF conditional integral sliding mode control scheme,and the simulation experiments were carried out under the load of 20%-100%.Results show that compared with the traditional control scheme,the optimized RBF conditional integral sliding mode control reduces the overshoot by 50.26%,the adjustment time is shortened by 13.55%,and the overshoot is only 1.50%under the interference signal,which has faster response speed,stronger anti-interference ability and better robustness.