单元机组智能协调控制系统策略研究
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摘要
以某电厂300MW单元机组协调控制系统的改造项目作为研究背景，在自适应模糊控制理论的基础上，提出了采用智能复合控制实现火电单元机组的协调控制系统，提高协调控制系统(CCS)的控制品质以满足自动发电(AGC)技术对机组负荷控制的性能要求。
本文给出了智能复合控制系统的定义，介绍了其主要研究内容并着重阐述模糊控制在智能复合控制中的应用，提出模糊控制在智能控制领域应朝着自组织、自适应、自学习的方向发展。指出了单元机组的协调控制系统是实现AGC的重要技术基础之一，以及本项目研究内容对于AGC技术的意义。在参阅大量相关文献的基础上，对单元机组的动态特性作了详尽的分析，得出了单元机组作为协调控制系统的控制对象，其中锅炉对象存在的非线性特性是造成机组参数变化的主要原因，同时介绍了单元机组的运行方式及各自的特点。
针对单元机组这一具有非线性、时变性、大滞后的多变量耦合系统，本文从解耦控制的角度，首次提出了采用基于概率统计算法的耦合度分析来设计单元机组负荷控制系统。首次采用了一种利用模糊规则动态调整解耦参数的动态模糊解耦补偿器，试验表明解耦效果良好而且具有一定的鲁棒性。通过对模糊控制的本质分析，作者指出模糊控制的核心是规则库的建立，并提出了一种规则自适应的模糊控制算法，本算法结构简单，可在线建立或调整模糊控制规则。
本文将所设计的自适应模糊控制器用于协调控制系统，并与智能前馈控制等控制策略相结合，构成了一种新的智能复合协调控制系统。仿真结果及实际运行结果表明，控制效果良好。
The background of this paper is the renewal project of coordinated control system (CCS) of the 300MW-generating unit of a power plant. Based on the theory of self-adaptive fuzzy control, intelligent compound control strategy is employed in the CCS to improve the control effect and meet the request of Automatic Generation Control (AGC).
In the paper, firstly, the concept of intelligent compound control system is defined, and its main research fields are summarized. With that the paper points out three heading: self-organization, self -adaptive and self-learning in the fuzzy logic control as an important domain.
Secondly, the paper analyses dynamic characteristic of the boiler-turbine unit in detail, and draw a conclusion that the nonlinear feature of the boiler is the main reason to lead the variable parameter of the unit.
Then two ways are presented to be used in CCS. A dynamic fuzzy decoupling compensator is constructed in the multivariable fuzzy control system; the compensator is tuned by fuzzy rule through the related coefficients. And a rule self-adaptive fuzzy controller is also presented, owning the simple structure.
Finally CCS is based on the intelligent compound controller, which combined the rule self-adaptive fuzzy controller with the intelligent feed forward controller. The results of simulation and operation show the control effect is good.
本文给出了智能复合控制系统的定义，介绍了其主要研究内容并着重阐述模糊控制在智能复合控制中的应用，提出模糊控制在智能控制领域应朝着自组织、自适应、自学习的方向发展。指出了单元机组的协调控制系统是实现AGC的重要技术基础之一，以及本项目研究内容对于AGC技术的意义。在参阅大量相关文献的基础上，对单元机组的动态特性作了详尽的分析，得出了单元机组作为协调控制系统的控制对象，其中锅炉对象存在的非线性特性是造成机组参数变化的主要原因，同时介绍了单元机组的运行方式及各自的特点。
针对单元机组这一具有非线性、时变性、大滞后的多变量耦合系统，本文从解耦控制的角度，首次提出了采用基于概率统计算法的耦合度分析来设计单元机组负荷控制系统。首次采用了一种利用模糊规则动态调整解耦参数的动态模糊解耦补偿器，试验表明解耦效果良好而且具有一定的鲁棒性。通过对模糊控制的本质分析，作者指出模糊控制的核心是规则库的建立，并提出了一种规则自适应的模糊控制算法，本算法结构简单，可在线建立或调整模糊控制规则。
本文将所设计的自适应模糊控制器用于协调控制系统，并与智能前馈控制等控制策略相结合，构成了一种新的智能复合协调控制系统。仿真结果及实际运行结果表明，控制效果良好。
The background of this paper is the renewal project of coordinated control system (CCS) of the 300MW-generating unit of a power plant. Based on the theory of self-adaptive fuzzy control, intelligent compound control strategy is employed in the CCS to improve the control effect and meet the request of Automatic Generation Control (AGC).
In the paper, firstly, the concept of intelligent compound control system is defined, and its main research fields are summarized. With that the paper points out three heading: self-organization, self -adaptive and self-learning in the fuzzy logic control as an important domain.
Secondly, the paper analyses dynamic characteristic of the boiler-turbine unit in detail, and draw a conclusion that the nonlinear feature of the boiler is the main reason to lead the variable parameter of the unit.
Then two ways are presented to be used in CCS. A dynamic fuzzy decoupling compensator is constructed in the multivariable fuzzy control system; the compensator is tuned by fuzzy rule through the related coefficients. And a rule self-adaptive fuzzy controller is also presented, owning the simple structure.
Finally CCS is based on the intelligent compound controller, which combined the rule self-adaptive fuzzy controller with the intelligent feed forward controller. The results of simulation and operation show the control effect is good.
引文
[1] 李士勇．模糊控制 神经控制和智能控制理论．哈尔滨工业大学出版社，1996
[2] 易继锴．智能控制技术．北京工业大学出版社，1999
[3] 谢刚，阎高伟，马红卫，谢克明．采用智能复合控制器的单元机组协调控制系统．Proc.Of WCICA'2002，上海，中国，2002．6，(已录用待发表)
[4] Fu K S. Learning Control System and Intelligent Control System: An Intersection of Artificial Intelligence and Automatic Control. IEEE Trans, AC, 1971, 16(1): 70～72
[5] Saridis, G.N. Self-Organizing Control of Stochastic System. Marcel Dekker Inc. 1977
[6] 刘向杰，周孝信，柴天佑．模糊控制研究的现状与新发展．信息与控制，1999，28(4)：283～292
[7] 张曾科．模糊数学在自动化技术中的应用．[M]．清华大学出版社
[8] L. A .Zadeh. Fuzzy Sets. Information and Control, 1965, 8:338～353
[9] E H Mamdani. Applications of Fuzzy Algorithms for Control of Simple Dynamic Plant, Proc. IEE, 1974,121:1585～1588
[10] K J Astrom, Thomas J. Mc Avoy Intelligent Control. Proc. Cont. 1992, (3): 115～127
[11] Procyk T J, Mamdani E H. A Linguistic Self-organizing Process Controller, Automatica, 1979, 15(1):15～30
[12] Scharf E M, Mandic N J. The Application of a Fuzzy Controller to the Control of a Multi-degree Freedom Robot Arm. Industrial Application for Fuzzy Control M Sugeno(ed), North-Holland, 1985
[13] Shao S. Fuzzy Self-organizing Controller and its Application for Dynamic Processes. Fuzzy Sets and Systems, 1988, 26:151～164
[14] Dalay S, K F Gill. A Design Study of a Self-organizing Fuzzy Logic Controller. Proc Inst Mech Engrs? 1986, 200(cl): 59～69.
[15] Z Q Wuetal. A Rule Self-regulating Fuzzy Controller. Fuzzy Sets and Systems, 1992, 47:13～21
[16] Keming Xie, Changhua Mou, Gang Xie. A MEBML-based adaptive fuzzy logic controller. Proceedings. of 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation, October 22-28, 2000, Nagoya Congress Center, Nagoya, Japan, 1492-1496
[17] Keming Xie, Changhua Mou, Gang Xie. The multi-parameter combination mind-evolutionary-based machine learning and its application. Proceedings of 2000
IEEE International Conference on Systems, Man, and Cybernetics (SMC2000), 2000.10, Music City Sheraton, Nashville, Tennessee, USA, 183-187
[18] Keming Xie, Jing Yang, T. Y. Lin. Modeling Analysis for the Temperature System Using Rough Sets. Proceedings of 2000 5th International Conference on Signal Processing (ICSP 2000, WCC2000), Aug.21-25, 2000, Beijing, China, 1718-1721
[19] Chih-Hsun Chou, Hung-Ching Lu. A Heuristic Self-tuning Fuzzy Controller. Fuzzy Sets and Systems, 1994, 61:249～264
[20] Rhee F V D, Vander Rhee F, Van Na Alemke H R etal. Knowledge Based Fuzzy Control of Systems. IEEE Trans Automatic Control, 1990, 35(2): 148～155
[21] Chen, T C Tsao. A Description of the Dynamical Behavior of Fuzzy Systems, IEEE, Trans. Systems Man and Cybernetics, 1989, 19(4): 745～755
[22] 鲍新福，都志杰，王芳君．自调整比例因子控制器．自动化学报，1987，13(2)：129～133
[23] 毛宗源，狄净．自调整比例因子控制器控制锅炉燃烧过程．自动化学报，1991，17(5)：611～614
[24] Xiangjie Liu, Xiaoxin Zhou. Identification of Boiler Models and Its Fuzzy Logic Strategy. The 14th IFAC World Congress. B e ij i ng China. 1999
[25] Chul-Hwan Jung etal. A Real-time Self-tuning Fuzzy Controller Through Scaling Factor Adjustment for the Steam Generator of NPP. Fuzzy Sets and Systems, 1995, 74:53～60
[26] 刘向杰，柴天佑，刘红波．动力锅炉燃烧系统的模糊控制策略．自动化学报，1998，26(4)：534～538
[27] 谢克明，侯宏仑．复杂系统的智能控制方法．太原理工大学学报，1998，29(6)：569～576
[28] R Layneetal. Fuzzy Model Reference Learning Control for Cargo Ship Steering. IEEE Contr. Syst., 1993,13(5): 23～24
[29] Chen, P C Chen. A Fuzzy Model-following Control System Design Procedure, Proc. of the Int. Fuzzy Engineering Symposium'91, 1991: 718～729
[30] H Kang, G J Vachtsevanos. Model Reference Fuzzy Control, Proc. of the 28th Conf. On Decision and Control, Tampa, Florida, 1989:751～756
[31] VOurnas C D et al. A Flexible AGC Algorithm for the Hellennic Inter-Connected Svstem. IEEE Trans On Power System, PWRS-4, 1989. 2 (1)
[32] Wang Dingyi et al. Application of Operation Research in Automatic Generation Control of Hydropower Plants. IEEE Trans on Energy Conversion, 1989, 4(3): 375～380
[2] 易继锴．智能控制技术．北京工业大学出版社，1999
[3] 谢刚，阎高伟，马红卫，谢克明．采用智能复合控制器的单元机组协调控制系统．Proc.Of WCICA'2002，上海，中国，2002．6，(已录用待发表)
[4] Fu K S. Learning Control System and Intelligent Control System: An Intersection of Artificial Intelligence and Automatic Control. IEEE Trans, AC, 1971, 16(1): 70～72
[5] Saridis, G.N. Self-Organizing Control of Stochastic System. Marcel Dekker Inc. 1977
[6] 刘向杰，周孝信，柴天佑．模糊控制研究的现状与新发展．信息与控制，1999，28(4)：283～292
[7] 张曾科．模糊数学在自动化技术中的应用．[M]．清华大学出版社
[8] L. A .Zadeh. Fuzzy Sets. Information and Control, 1965, 8:338～353
[9] E H Mamdani. Applications of Fuzzy Algorithms for Control of Simple Dynamic Plant, Proc. IEE, 1974,121:1585～1588
[10] K J Astrom, Thomas J. Mc Avoy Intelligent Control. Proc. Cont. 1992, (3): 115～127
[11] Procyk T J, Mamdani E H. A Linguistic Self-organizing Process Controller, Automatica, 1979, 15(1):15～30
[12] Scharf E M, Mandic N J. The Application of a Fuzzy Controller to the Control of a Multi-degree Freedom Robot Arm. Industrial Application for Fuzzy Control M Sugeno(ed), North-Holland, 1985
[13] Shao S. Fuzzy Self-organizing Controller and its Application for Dynamic Processes. Fuzzy Sets and Systems, 1988, 26:151～164
[14] Dalay S, K F Gill. A Design Study of a Self-organizing Fuzzy Logic Controller. Proc Inst Mech Engrs? 1986, 200(cl): 59～69.
[15] Z Q Wuetal. A Rule Self-regulating Fuzzy Controller. Fuzzy Sets and Systems, 1992, 47:13～21
[16] Keming Xie, Changhua Mou, Gang Xie. A MEBML-based adaptive fuzzy logic controller. Proceedings. of 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation, October 22-28, 2000, Nagoya Congress Center, Nagoya, Japan, 1492-1496
[17] Keming Xie, Changhua Mou, Gang Xie. The multi-parameter combination mind-evolutionary-based machine learning and its application. Proceedings of 2000
IEEE International Conference on Systems, Man, and Cybernetics (SMC2000), 2000.10, Music City Sheraton, Nashville, Tennessee, USA, 183-187
[18] Keming Xie, Jing Yang, T. Y. Lin. Modeling Analysis for the Temperature System Using Rough Sets. Proceedings of 2000 5th International Conference on Signal Processing (ICSP 2000, WCC2000), Aug.21-25, 2000, Beijing, China, 1718-1721
[19] Chih-Hsun Chou, Hung-Ching Lu. A Heuristic Self-tuning Fuzzy Controller. Fuzzy Sets and Systems, 1994, 61:249～264
[20] Rhee F V D, Vander Rhee F, Van Na Alemke H R etal. Knowledge Based Fuzzy Control of Systems. IEEE Trans Automatic Control, 1990, 35(2): 148～155
[21] Chen, T C Tsao. A Description of the Dynamical Behavior of Fuzzy Systems, IEEE, Trans. Systems Man and Cybernetics, 1989, 19(4): 745～755
[22] 鲍新福，都志杰，王芳君．自调整比例因子控制器．自动化学报，1987，13(2)：129～133
[23] 毛宗源，狄净．自调整比例因子控制器控制锅炉燃烧过程．自动化学报，1991，17(5)：611～614
[24] Xiangjie Liu, Xiaoxin Zhou. Identification of Boiler Models and Its Fuzzy Logic Strategy. The 14th IFAC World Congress. B e ij i ng China. 1999
[25] Chul-Hwan Jung etal. A Real-time Self-tuning Fuzzy Controller Through Scaling Factor Adjustment for the Steam Generator of NPP. Fuzzy Sets and Systems, 1995, 74:53～60
[26] 刘向杰，柴天佑，刘红波．动力锅炉燃烧系统的模糊控制策略．自动化学报，1998，26(4)：534～538
[27] 谢克明，侯宏仑．复杂系统的智能控制方法．太原理工大学学报，1998，29(6)：569～576
[28] R Layneetal. Fuzzy Model Reference Learning Control for Cargo Ship Steering. IEEE Contr. Syst., 1993,13(5): 23～24
[29] Chen, P C Chen. A Fuzzy Model-following Control System Design Procedure, Proc. of the Int. Fuzzy Engineering Symposium'91, 1991: 718～729
[30] H Kang, G J Vachtsevanos. Model Reference Fuzzy Control, Proc. of the 28th Conf. On Decision and Control, Tampa, Florida, 1989:751～756
[31] VOurnas C D et al. A Flexible AGC Algorithm for the Hellennic Inter-Connected Svstem. IEEE Trans On Power System, PWRS-4, 1989. 2 (1)
[32] Wang Dingyi et al. Application of Operation Research in Automatic Generation Control of Hydropower Plants. IEEE Trans on Energy Conversion, 1989, 4(3): 375～380