基于扩张状态观测器的过热汽温系统建模与参数智能辨识  

Modeling and Intelligent Parameter Identification for Superheated Steam Temperature System Based on Extended State Observer

作　　者：孙明[1] 王胤开 白阳振 范延增 董泽[3] SUN Ming;WANG Yinkai;BAI Yangzhen;FAN Yanzeng;DONG Ze(Department of Automation,North China Electric Power University,Baoding 071003,Hebei Province,China;China United Engineering Corporation.Co.,Ltd.,Hangzhou 310052,Zhejiang Province,China;Hebei Technology Innovation Center of Simulation and Optimized Control for Power Generation(North China Electric Power University),Baoding 071003,Hebei Province,China)

机构地区：[1]华北电力大学自动化系,河北省保定市071003 [2]中国联合工程有限公司,浙江省杭州市310052 [3]河北省发电过程仿真与优化控制技术创新中心(华北电力大学),河北省保定市071003

出　　处：《中国电机工程学报》2024年第22期8957-8967,I0022,共12页PROCEEDINGS OF THE CHINESE SOCIETY FOR ELECTRICAL ENGINEERING

基　　金：河北省自然科学基金项目(E2018502111);河北省省级科技计划项目(22567643H)。

摘　　要：可再生能源的高渗透率给电网供需匹配带来严峻挑战的同时,燃煤机组需要承担着大量的调峰调频任务,这对过热汽温系统的安全稳定运行造成了一定威胁,因此有必要建立面向热工控制的汽温数学模型。考虑到迟延型扩张状态观测器(time-delayed extended state observer,TD-ESO)的总扰动信号中含有大量模型信息,提出一种基于ESO补偿模型的参数智能优化和信息提取方法,即以总扰动中未知信息量最小为目标,采用改进沙丘猫算法对模型参数优化并提取总扰动中已知模型信息补偿至ESO的输入端。在仿真算例方面,线性和非线性系统的测试结果表明,所提辨识方法对有无输入迟延的两种系统均有良好的适用性和较高的精度;在实际应用方面,基于超超临界二次再热机组的过热汽温系统数据进行模型辨识与验证,同样表明该建模方法是合理、准确的。因此,该文所建立的模型能够为汽温系统的控制策略设计和性能优化等方面提供有价值的参考。The high penetration of renewable energy presents a serious challenge for the matching between electricity supply and demand in the power grid.Simultaneously,coal-fired units need to undertake extensive tasks related to peak and frequency regulation,which poses a certain threat to the safe and stable operation of the superheated steam temperature system.Therefore,it is necessary to establish a thermal process control-oriented mathematical model for superheated steam temperature.Considering that the total disturbance signal of time-delayed extended state observer(TD-ESO)contains sufficient model information,a method for intelligent parameter optimization and information extraction based on ESO compensation model is proposed,which aims to minimize the amount of unknown information in total disturbance,and uses an improved sand cat swarm optimization algorithm to optimize the model parameters and extract known model information from the total disturbance for compensation to the input of ESO.Simulation results from tests on linear and nonlinear systems indicate that the proposed identification method demonstrates good applicability and high accuracy for both systems with and without input time-delay.In terms of practical application,model identification and validation are carried out by employing operational data from superheated steam temperature system of ultra-supercritical double reheat unit,which also indicates the reasonableness and accuracy of the proposed method. Furthermore, the identified model canprovide valuable reference for control strategy design andperformance optimization of the superheated steamtemperature system.

关 键 词：迟延型扩张状态观测器 数据驱动模型辨识 沙丘猫群优化算法 过热汽温系统 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]