基于数字孪生模型的主汽温预测控制策略  

Main Steam Temperature Predictive Control Strategy Based on Digital Twin Model

作　　者：郭嘉曦 刘长良[1] 刘帅[1] 刘卫亮 GUO Jiaxi;LIU Changliang;LIU Shuai;LIU Weiliang(School of Control and Computer Engineering,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学控制与计算机工程学院,河北保定071003

出　　处：《华北电力大学学报（自然科学版）》2024年第5期118-125,共8页Journal of North China Electric Power University：Natural Science Edition

基　　金：中央高校基本科研业务费专项资金资助项目(2020JG006,2020MS117).

摘　　要：针对超临界机组主汽温对象大惯性、大时滞、时变及工况复杂等特点,提出一种基于数字孪生模型的主汽温预测控制策略。该控制策略通过主汽温数字孪生模型预测主汽温值,并采用反馈校正修正预测值,使用黄金分割法滚动优化目标函数,输出最优控制量。所建立的主汽温数字孪生模型,包含主汽温系统传递函数模型与Xgboost-LSTM偏差模型,采用迁移学习理论实现在线更新,具有了高预测精度、高效更新能力与强鲁棒性。以某1000 MW超临界机组末级过热系统为例,进行仿真实验。结果表明,基于数字孪生模型的主汽温预测控制策略有效改善了主汽温系统的控制品质与鲁棒性,具有良好的负荷适应能力。Aiming at the characteristics of large inertia,large time delay,time change and complex working conditions of the main steam temperature object of the supercritical unit,we proposed a main steam temperature predictive control strategy based on the digital twin model.We used the control strategy to predict the main steam temperature value through the digital twin model of main steam temperature,used feedback correction to correct the predicted value,and used the golden section method to roll the optimization objective function to output the optimal control amount.The established main steam temperature digital twin model,including the main steam temperature system transfer function model and the Xgboost-LSTM deviation model,used the transfer learning theory to achieve online update,with high prediction accuracy,efficient update ability and strong robustness.Taking the final stage superheating system of a 1000MW supercritical unit as an example,we conducted a simulation experiment.The results show that the main steam temperature predictive control strategy based on the digital twin model effectively improves the control quality and robustness of the main steam temperature system,and has good load adaptability.

关 键 词：主汽温 数字孪生 预测控制 迁移学习 

分 类 号：TM621[电气工程—电力系统及自动化]