深度调峰超超临界机组过热汽温迭代学习预测控制  

Reheat Steam Temperature Prediction and Control Based on Iterative Learning Algorithm for Ultra-supercritical Units Under Deep Peak Shaving

作　　者：屈小凡 吕剑虹[1] QU Xiaofan;Lü Jianhong(School of Energy and Environment,Southeast University,Nanjing 210000,China)

机构地区：[1]东南大学能源与环境学院,南京210000

出　　处：《动力工程学报》2020年第8期614-620,634,共8页Journal of Chinese Society of Power Engineering

摘　　要：为了提高超超临界火电机组协调控制系统的控制性能,提出一种基于迭代学习预测控制的控制方法。将迭代学习算法用于改进预测控制器,基于状态空间预测模型,利用历史误差进行迭代校正,在性能指标中同时引入迭代序列和时间序列二次型项,计算最优控制律,从而保证在模型失配情况下对设定值的无偏跟踪。以某1000 MW超超临界机组为例,基于现场实验数据采用粒子群优化算法辨识得到过热汽温对象模型,并进行仿真实验。结果表明:该方法能够有效改善过热汽温的控制品质,对于输入、输出扰动以及负荷大范围变化所造成的模型失配具有较好抗性,鲁棒性强。To improve the control performance of the coordinated control system in an ultra-supercritical thermal power unit,a control method was proposed based on iterative learning algorithm,which was used to improve the performance of the predictive controller.Based on the state space predictive model,iterative correction was made using historical errors.Meanwhile,the iterative sequence and time series quadratic terms were introduced into the performance indicators to calculate the optimal control law,thereby ensuring unbiased tracking of set values in case of model mismatch.For a 1000 MW ultra-supercritical unit,an object model of superheated steam temperature was identified by particle swarm optimization algorithm based on field experimental data,while a simulation experiment was performed.Results show that the method proposed can effectively improve the control quality over superheated steam temperature,which has good resistance and robustness to I/O disturbance and model mismatch caused by wide load variations.

关 键 词：过热汽温 迭代学习算法 预测控制 粒子群优化算法 卡尔曼滤波 

分 类 号：TK323[动力工程及工程热物理—热能工程]