超超临界单元机组协调控制系统研究
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摘要
为实现社会经济可持续发展的目标,能源发电领域必须适应新形势的要求。因此,提高发电技术的安全性、稳定性、经济性以及节能降耗水平是十分必要的。超超临界发电技术是目前发电技术领域的先进技术,具有可靠、高效、环保的特点。超超临界机组相关的基本理论、技术策略的研究与应用,对电力工业的未来发展具有重要意义。
本文首先分析了超超临界单元机组的新特点,介绍了超超临界单元机组协调控制系统中包括模型建立、控制系统设计方面的研究现状。然后根据模糊建模理论,利用现场采集的超超临界单元机组运行数据,建立了单元机组的T-S模糊模型,辨识结果表明T-S模糊模型能有效地表达超超临界单元机组的输入输出特性。为了能够实现对所建立的协调控制系统的模型进行控制,提出了改进的广义预测控制策略,充分利用了预测信息并简化了多变量矩阵的求解过程,并在某1000MW机组的协调控制系统中进行了仿真研究,结果表明该控制算法比传统的广义预测算法具有更好的控制品质。最后,将控制算法与之前建立的超超临界单元机组的T-S模糊模型进行结合,研究了基于T-S模型的广义预测控制算法,仿真结果表明所设计的控制系统可以实现对各个输出量设定值的有效跟踪,且在出现干扰的情况下具有良好的抗扰性。
To achieve sustainable development goals of social and economic in China, the energy generation industry must adapt to new situations. Therefore, it's necessary for power generation units to improve the technology, security, stability and economy. Currently, ultra-supercritical power generation technology is advanced in area of power generation for its reliability, efficiency and environment-friendly. The relevant theory and technology strategy of ultra-supercritical unit is important to the future development of the electric power industry
In this paper, new features of ultra-supercritical power units are analyzed, and some research progresses about modeling and control system design of unit coordinated control system is summarized. Then according to fuzzy modeling theory, a T-S fuzzy model of unit plant is established by using the operation data from the field. The identification results show that the T-S fuzzy model can effectively express the input and output characteristics of ultra-supercritical power unit. In order to control the unit plant model established before, an improved generalized predictive control strategy is proposed. This control strategy takes advantage of the forecast information and simplifies the process of solving the multivariable matrix. Meanwhile, this method is applied to1000MW unit coordinated control system, simulation results show that the improved control method has better performance than the traditional generalized predictive control algorithm. Finally, the improved generalized predictive control is combined with the T-S fuzzy model of unit plant. Simulation results show that the proposed control system can follow the set value of output rapidly and has good immunity in case of interference.
本文首先分析了超超临界单元机组的新特点,介绍了超超临界单元机组协调控制系统中包括模型建立、控制系统设计方面的研究现状。然后根据模糊建模理论,利用现场采集的超超临界单元机组运行数据,建立了单元机组的T-S模糊模型,辨识结果表明T-S模糊模型能有效地表达超超临界单元机组的输入输出特性。为了能够实现对所建立的协调控制系统的模型进行控制,提出了改进的广义预测控制策略,充分利用了预测信息并简化了多变量矩阵的求解过程,并在某1000MW机组的协调控制系统中进行了仿真研究,结果表明该控制算法比传统的广义预测算法具有更好的控制品质。最后,将控制算法与之前建立的超超临界单元机组的T-S模糊模型进行结合,研究了基于T-S模型的广义预测控制算法,仿真结果表明所设计的控制系统可以实现对各个输出量设定值的有效跟踪,且在出现干扰的情况下具有良好的抗扰性。
To achieve sustainable development goals of social and economic in China, the energy generation industry must adapt to new situations. Therefore, it's necessary for power generation units to improve the technology, security, stability and economy. Currently, ultra-supercritical power generation technology is advanced in area of power generation for its reliability, efficiency and environment-friendly. The relevant theory and technology strategy of ultra-supercritical unit is important to the future development of the electric power industry
In this paper, new features of ultra-supercritical power units are analyzed, and some research progresses about modeling and control system design of unit coordinated control system is summarized. Then according to fuzzy modeling theory, a T-S fuzzy model of unit plant is established by using the operation data from the field. The identification results show that the T-S fuzzy model can effectively express the input and output characteristics of ultra-supercritical power unit. In order to control the unit plant model established before, an improved generalized predictive control strategy is proposed. This control strategy takes advantage of the forecast information and simplifies the process of solving the multivariable matrix. Meanwhile, this method is applied to1000MW unit coordinated control system, simulation results show that the improved control method has better performance than the traditional generalized predictive control algorithm. Finally, the improved generalized predictive control is combined with the T-S fuzzy model of unit plant. Simulation results show that the proposed control system can follow the set value of output rapidly and has good immunity in case of interference.
引文
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[41]Su Baili, Chen Zengqiang, Yuan Zhuzhi. Constrained predictive control based on T-S fuzzy model for nonlinear systems [J]. Systems Engineering and Electronics,2007,18(1):95-100.
[42]Fan Shaosheng, Wang Yaonan. Fuzzy Model Predictive Control for Alternating Current Excitation Generators[C]. Power Electronics and Motion Control Conference, Aug 2004,2:676-680
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[2]陈世和.1000MW超超临界火电机组热工自动化[M].北京：中国电力出版社,2010
[3]张静媛,刘明福.关于超临界超超临界发电机组的发展[J].山西科技,2006,4：7-8
[4]孙奎明,时海刚.热工自动化[M].北京：中国电力出版社,2009
[5]李冉.超临界机组协调控制策略的优化研究[D].安徽理工大学硕士学位论文,2009,6
[6]王远平,傅望安,时标等.华能玉环电厂4×1000MW超超临界机组燃水比控制策略[J].电力设备,2008,9(1)：8-12
[7]王中胜,夏明,赵松烈等.北仑1000MW超超临界机组协调控制策略分析及优化[J].电力建设,2010,31(1)：87-90
[8]刘潇,曹冬林,丁劲松.外高桥1000MW超超临界机组闭环控制系统设计[J].中国电力,2006,39(3)：70-73
[9]王家兴.模糊预测控制在单元机组协调控制中的研究与应用[D].华北电力大学硕士学位论文,2008,1
[10]房方.单元机组协调控制系统的先进控制策略研究[D].华北电力大学博士学位论文,2005,5
[11]刘红波,李少远,柴天佑.协调控制系统多变量PID控制器的自整定方法[J].自动化仪表,2003,24(6)：10-15
[12]Da Fei-peng, Song Wen-zhong. Adaptive Control for a Class of Nonli-ne ar System Based on FNNSMC[C]. Proceedings of the CSEE,2002,22(5): 78-83
[13]Roubos J A F, Mollov S, Babusks B. Fuzzy Model-Based Predictive Control Using Takogi-Sngeno Models[C]. Int J Approx Reas,1999,22:3-30
[14]谢谢,曾德良.基于遗传算法的协调控制系统鲁棒PID参数寻优[J].动力工程学报,2010,30(12)：937-940
[15]Chen-Kuo Weng, Asok Ray. Robust wide-range control of steam-electric power plants[J]. IEEE Trans.Control System Technology,1997,5(1):74-88
[16]席原,刘景宾.单元机组协调控制系统研究进展[J].工业仪表与自动化装置,2011,5：10-16
[17]Peng Wang, Ning Li, Shaoyuan Li. A method of T-S modeling based on hierarchical clustering[C].Chinese Control and Decision Conference, July 2008,4576-4579
[18]Takagi T, Sugeno M. Fuzzy identification of systems and its application to control[J]. IEEE Trans on SMC,1986,15(1):116-132
[19]Liu Jizhen, Chen Yanqiao, Zeng Deliang, Yang Guangjun. Identification of a boiler-turbine system using T-S fuzzy model[C]. Proceedings of IEEE TENCON,2003, (3):1278-1281
[20]曾凡春.基于T-S模糊模型的单元机组协调控制系统建模研究[D].华北电力大学硕士学位论文,2010,3
[21]陈彦桥.单元机组模糊多模型协调控制系统研究[D].华北电力大学工学博士学位论文,2003,4
[22]Zhenkai Guo, Jianqin Mao, Ping Wang. An Approach of Adaptive Fuzzy Modeling and Application to Inverted Pendulum Control[C]. Proceedings of the 6th World Congress on Intelligent Control, June 2006,1:1594-1598
[23]韩璞,施建中,王东风,焦嵩鸣.基于模糊聚类神经网络的T-S模糊模型辨识[J].计算机科学,2009,36(4A)：264-265
[24]Peng Wang, Ning Li, Shaoyuan Li. Interval Type-2 Fuzzy T-S Modeling for a Heat Exchange Process on CE117[C] Process Trainer. Proceedings of 2011 International Conference on, June 2011,457-462
[25]陈红,王广军,王志杰,沈曙光.基于最小二乘支持向量机的模糊辨识及其在热工对象逆系统建模中的应用[J].中国电机工程学报,2010,30(29)：103-106
[26]刘翠.基于T-S模糊模型的非线性系统辨识[D].哈尔滨理工大学硕士学位论文,2010,3
[27]牛丽霞.多变量智能预测控制方法及其应用研究[D].华北电力大学硕士学位论文,2008,1
[28]Culter C R, Ramaker BL. Dynamic Matrix Control Computer Control Algorithm[J]. Proe.JACC San Francisco,American Automatica Control Council.1980
[29]RouhaniR, MehraR.K. Model Algorithm Control(MAC)[J]. Basie Theoretical Pro Perties Automatica,1982,4(18):401-414
[30]Clarke.D.W. Application of Generalized Predictive Control to Industrial proeesses[J]. IEEE Control Systems,1988,8:49-55
[31]王景学.基于T-S模型的模糊预测控制在火电单元机组协调控制系统中的 应用研究[D].内蒙古工业大学硕士学位论文,2009,6
[32]凌呼君,王文兰,冯永祥.广义预测控制在火电厂单元机组协调控制中的研究[J].浙江电力,2003,(5)：1-4
[33]王文兰,马然.解耦广义预测控制(DGPC)在单元机组协调控制系统中的应用[J].控制理论与应用,2003,31(3)：17-19
[34]王文兰,马然.鲁棒广义预测控制在单元机组的协调控制[J].控制工程,2005,12(4)：327-330
[35]童一飞,金晓明.基于广义预测控制的循环流化床锅炉燃烧过程多目标优化控制策略[J].中国电机工程学报,2010,30(11)：38-43
[36]薛美盛,樊弟,魏衡华.多变量系统的广义预测控制解耦设计[J].控制工程,2011,18(1)：39-42
[37]高琳,孙海蓉,杨怀申.一种基于神经网络和参数优化的隐式广义预测控制[J].华北电力大学学报,2009,36(5)：66-72
[38]王爽心,董旸,刘海瑞.基于T-S模型和小世界优化算法的广义非线性预测控制[J].控制工程,2011,26(5)：673-678
[39]Igor Skrjanc, Drago Matko. Predictive Functional Control Based on Fuzzy Model for Heat-Exchanger Pilot Plant[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, Dec 2000,8(6):705-712
[40]张化光,吕剑虹,陈来九.模糊广义预测控制及其应用[J].自动化学报,1993,19(1)：9-17
[41]Su Baili, Chen Zengqiang, Yuan Zhuzhi. Constrained predictive control based on T-S fuzzy model for nonlinear systems [J]. Systems Engineering and Electronics,2007,18(1):95-100.
[42]Fan Shaosheng, Wang Yaonan. Fuzzy Model Predictive Control for Alternating Current Excitation Generators[C]. Power Electronics and Motion Control Conference, Aug 2004,2:676-680
[43]Doris Saez, Aldo Cipriano. Design of Fuzzy Model based Predictive Controllers and its Application to an Inverted Pendulum[C]. Proceedings of the Sixth IEEE International Conference on, July 1997,2:915-919
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