基于多目标遗传算法的过热汽温建模与仿真

doi:10.16182/j.issn1004731x.joss.201709027

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (9): 2081-2086.doi: 10.16182/j.issn1004731x.joss.201709027

基于多目标遗传算法的过热汽温建模与仿真

吴振龙¹, 何婷¹, 王灵梅², 贾峰生³, 杨云凯⁴, 吴海曙⁴, 李东海^1*, 韩磊²

1.清华大学热能系电力系统国家重点实验室,北京 100084;
2.山西大学工程学院,山西太原 030013;
3.国网山西省电力公司电力科学研究院,山西太原 030001;
4.大同煤矿集团同达热电有限公司,山西大同 037001

收稿日期:2017-05-20 发布日期:2020-06-02
作者简介:吴振龙(1992-),男,河南商丘,博士生,研究方向为火电机组建模与自抗扰控制;何婷(1992-),女,四川巴中,博士生,研究方向为分布式能源系统建模与自抗扰控制。
基金资助:
山西省煤基重点科技攻关项目(MD2014-07)

Modeling and Simulation of Superheated Steam Temperature Based on Multi-objective Genetic Algorithm

Wu Zhenlong¹, He Ting¹, Wang Lingmei², Jia Fengsheng³, Yang Yunkai⁴, Wu Haishu⁴, Li Donghai^1*, Han Lei²

1. State Key Laboratory of Thermal Power System, Tsinghua University, Beijing 100084, China;
2. School of Engineering, Shanxi University, Taiyuan 030013, China;
3. State Grid Shanxi Electric Power Research Institute, Taiyuan 030001, China;
4. Datong Coal Mine Group Tongda Thermal Power Co., Ltd., Datong 037001, China

Received:2017-05-20 Published:2020-06-02

摘要/Abstract

摘要： 基于过热汽温的分布参数模型,采用高阶惯性传递函数进行逼近。为分析串级回路中导前区、惰性区模型阶次对辨识精度的影响和得到高阶惯性传递函数的动态参数,根据电厂实际运行的数据,采用多目标遗传算法对模型参数进行优化和仿真。通过分析仿真可知,阶次在合理的范围时阶次越高时帕累托前锋面越靠前,辨识具有更高的精确度。考虑实际需求与精度的要求,建立该机组过热汽温系统的串级模型。

关键词: 过热汽温模型, 串级系统, 多目标遗传算法, Pareto最优解集

Abstract: The high order inertial transfer functions were used to approximate the distribution parameter model. In order to compare the influence of the order of the leading region and the inertia object model on the accuracy and get the dynamic parameters, the multi-objective genetic algorithm was used to optimize the model parameters according to the actual operation data of the power plant. The higher the order, the Pareto front moves forward and recognizes the higher accuracy by the simulation when the orders are in a reasonable range. The reasonable superheated steam temperature system model was established considering the engineering and accuracy requirements.

Key words: superheated steam temperature model, cascade system, multi-objective genetic algorithm, Pareto optimal solution set

中图分类号:

TP27

吴振龙, 何婷, 王灵梅, 贾峰生, 杨云凯, 吴海曙, 李东海, 韩磊. 基于多目标遗传算法的过热汽温建模与仿真[J]. 系统仿真学报, 2017, 29(9): 2081-2086.

Wu Zhenlong, He Ting, Wang Lingmei, Jia Fengsheng, Yang Yunkai, Wu Haishu, Li Donghai, Han Lei. Modeling and Simulation of Superheated Steam Temperature Based on Multi-objective Genetic Algorithm[J]. Journal of System Simulation, 2017, 29(9): 2081-2086.

参考文献

[1] 刘吉臻, 曾德良, 田亮, 等. 新能源电力消纳与燃煤电厂弹性运行控制策略[J]. 中国电机工程学报, 2015, 35(21): 5385-5394.
(Liu Jizhen, Zeng Deliang, Tian Liang, et al.Control strategy for operating flexibility of coal-fired power plants in alternate electrical power systems[J]. Proceedings of the CSEE, 2015, 35(21): 5385-5394.)
[2] 王传峰, 李东海, 姜学智, 等. 基于概率鲁棒性的锅炉过热汽温串级PID控制器[J]. 清华大学学报(自然科学版), 2009, 49(2): 249-252.
(Wang Chuanfeng, Li Donghai, Jiang Xuezhi, et al.Cascade PID controllers for superheated steam boiler temperatures based on probabilistic robustness[J]. Journal of Tsinghua University (Science and Technology), 2009, 49(2): 249-252.
[3] Wu Z, Li D, Wang L.Control of the Superheated Steam Temperature: A Comparison Study Between PID and Fractional Order PID Controller[C]// Control Conference (CCC), 2016 35th Chinese. USA: IEEE, 2016: 10521-10526.
[4] 范永胜, 徐治皋. 基于动态特性机理分析的锅炉过热汽温自适应模糊控制系统研究[J]. 中国电机工程学报, 1997, 17(1): 23-28.
(Fan Yongsheng, Xu Zhigao.Study of Adaptive Fuzzy Control of Boiler Superheated Steam Temperature Based on Dynamic Mechanism Analysis[J]. Proceedings of the CSEE, 1997, 17(1): 23-28.)
[5] Wen X, Jian S.Modeling of Main Steam Parameters in Large Thermal Power Plant [C]// MATEC Web of Conferences. EDP Sciences. Paris, France: Atlantis Press, 2016, 77: 02011.
[6] Wang D, Yuan S.Identification of LPV Model for Superheated Steam Temperature System Using A-QPSO[J]. Simulation Modelling Practice and Theory (S1569-190X), 2016, 69(1): 1-13.
[7] Jiao S, He L, Wang Z, et al.Main Steam Temperature System Modeling Based on PCA and Neural Networks[C]// Computational Intelligence and Design, 2008. ISCID'08, International Symposium on. USA: IEEE, 2008, 1: 303-308.
[8] Deb K, Pratap A, Agarwal S, et al.A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation (S1089-778X), 2002, 6(2): 182-197.
[9] Liu X, Li D, Jiang X, et al.Simulation Study of Auto-disturbance-rejection Controller for High-order Systems[J]. Journal-Tsinghua University (S1007-0214), 2001, 41(6): 95-99.
[10] Blasco X, Herrero J, Sanchis J, et al.A New Graphical Visualization of N-dimensional Pareto Front for Decision-making in Multi-objective Optimization[J]. Information Science (S0020-0255), 2008, 178(20): 3908-3924.

基于多目标遗传算法的过热汽温建模与仿真

Modeling and Simulation of Superheated Steam Temperature Based on Multi-objective Genetic Algorithm

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 4

编辑推荐

Metrics

本文评价

[1]	付建林, 丁国富, 张剑, 江海凡, 郭沛佩. 基于响应面和NSGA-II的AGV系统多目标优化配置[J]. 系统仿真学报, 2022, 34(5): 994-1002.
[2]	刘延斌, 王亚威, 韩建海. 正负刚度并联的磁流变隔振器性能分析及优化[J]. 系统仿真学报, 2018, 30(10): 3965-3975.
[3]	李随科, 向建华, 王海军, 张明智, 李义. 基于模糊遗传的联合作战目标组合选择方法[J]. 系统仿真学报, 2017, 29(10): 2254-2261.
[4]	杨云飞, 顾岩, 范秀敏, 何其昌, 邱世广. 虚拟人装配操作智能仿真技术研究[J]. 系统仿真学报, 2015, 27(10): 2320-2327.