基于遗传算法辨识的超超临界机组协调控制系统
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摘要
随着电力工业的发展,大容量、高参数发电机组迅猛普及,超超临界机组已经开始占据火力发电机组主导位置。而采用直流锅炉是超超临界机组的一个主要特点。由于超超临界机组的直流运行特性、变参数的运行方式、多变量的控制特点异于亚临界汽包炉,使得其在控制上具有很大的特殊性。因此研究超超临界机组协调控制系统,对机组的安全运行和节能降耗有着深远的意义。
协调控制系统是大型火力发电机组的重要组成部分,是把锅炉和汽轮机发电机组作为一个整体进行控制,具有多种控制功能以能够满足不同运行方式和不同工况下的控制要求。在运行过程中汽机调门开度、燃料量、给水量的扰动均会影响直流炉的功率、主汽压、主汽温、中间点焓值。因此,在本文中,将超超临界机组看成一个三输入三输出的多变量调节对象。
本文首先分析了直流锅炉的动静态特点,再对协调控制系统在机组的功能、分类和运行方式做了阐述,介绍了直流锅炉协调控制系统的主要任务,分析了现有协调控制策略。最后以北疆电厂的一号机组为研究对象,由机理分析得出协调控制对象的模型结构,再根据对象的实际运行的输入输出数据,采用实数编码自适应遗传算法辨识出模型的相关参数。在典型工况下,采用遗传算法辨识出协调控制对象的静态解耦网络,再分别设计压力回路、功率回路和焓回路的控制器进行仿真研究。仿真结果表明采用遗传算法辨识解耦的协调控制方法效果良好,调节控制品质令人满意。
With the development of power industry, large capacity, high parameter generating unit has a rapid popularization, ultra-supercritical unit have begun to occupy the leading position in generating thermal power generating unit. Once-through boiler is a key feature of the ultra-supercritical unit. As a result of the supercritical unit's direct-current-operating characteristic, the variable-element-movement way, the multivariable-control characteristic, and the big differences comparing with the subcritical steam drum stove in the control aspect, therefore it has the profound significance in the safety-operation and the energy-conservation-consume of studying the USC unit’s CCS.
The unit power coordinate control system is the important part of electric power plant, control the boiler and steam turbine as a whole, has a variety of control functions, can satisfy the different operation modes and control requirements under different working conditions. While running, the turbine valve position, feed water and feed fuel’s change will cause changes of the once through boiler’s power, main steam pressure and temperature, and intermediate point’s enthalpy. Therefore, the USC unit can be taken as a three input-output construed objects in this paper.
Firstly this article analyzes the static and dynamic characteristics of boiler, expounds the coordinate control system in unit's function, classification and the movement way, introduces the main tasks of coordinated control system and the coordinated control strategy. Finally with the Beijiang power plant’s first unit as the study object, gain the model framework by theoretical analysis and then use real-coded adaptive genetic algorithm to recognize the model parameter. In the typical working conditions, the static decoupling controllers of the coordinate control system can be schemed out by using the genetic algorithm. Then to design pressure circuit, power loop and enthalpy loop controller respectively for simulation. The results of simulation show that the coordinate control control system based on GA and decoupling control is effective to regulate and have a satisfactory control quality.
协调控制系统是大型火力发电机组的重要组成部分,是把锅炉和汽轮机发电机组作为一个整体进行控制,具有多种控制功能以能够满足不同运行方式和不同工况下的控制要求。在运行过程中汽机调门开度、燃料量、给水量的扰动均会影响直流炉的功率、主汽压、主汽温、中间点焓值。因此,在本文中,将超超临界机组看成一个三输入三输出的多变量调节对象。
本文首先分析了直流锅炉的动静态特点,再对协调控制系统在机组的功能、分类和运行方式做了阐述,介绍了直流锅炉协调控制系统的主要任务,分析了现有协调控制策略。最后以北疆电厂的一号机组为研究对象,由机理分析得出协调控制对象的模型结构,再根据对象的实际运行的输入输出数据,采用实数编码自适应遗传算法辨识出模型的相关参数。在典型工况下,采用遗传算法辨识出协调控制对象的静态解耦网络,再分别设计压力回路、功率回路和焓回路的控制器进行仿真研究。仿真结果表明采用遗传算法辨识解耦的协调控制方法效果良好,调节控制品质令人满意。
With the development of power industry, large capacity, high parameter generating unit has a rapid popularization, ultra-supercritical unit have begun to occupy the leading position in generating thermal power generating unit. Once-through boiler is a key feature of the ultra-supercritical unit. As a result of the supercritical unit's direct-current-operating characteristic, the variable-element-movement way, the multivariable-control characteristic, and the big differences comparing with the subcritical steam drum stove in the control aspect, therefore it has the profound significance in the safety-operation and the energy-conservation-consume of studying the USC unit’s CCS.
The unit power coordinate control system is the important part of electric power plant, control the boiler and steam turbine as a whole, has a variety of control functions, can satisfy the different operation modes and control requirements under different working conditions. While running, the turbine valve position, feed water and feed fuel’s change will cause changes of the once through boiler’s power, main steam pressure and temperature, and intermediate point’s enthalpy. Therefore, the USC unit can be taken as a three input-output construed objects in this paper.
Firstly this article analyzes the static and dynamic characteristics of boiler, expounds the coordinate control system in unit's function, classification and the movement way, introduces the main tasks of coordinated control system and the coordinated control strategy. Finally with the Beijiang power plant’s first unit as the study object, gain the model framework by theoretical analysis and then use real-coded adaptive genetic algorithm to recognize the model parameter. In the typical working conditions, the static decoupling controllers of the coordinate control system can be schemed out by using the genetic algorithm. Then to design pressure circuit, power loop and enthalpy loop controller respectively for simulation. The results of simulation show that the coordinate control control system based on GA and decoupling control is effective to regulate and have a satisfactory control quality.
引文
[1]樊泉桂.新一代超临界锅炉的技术分析[J].锅炉技术, 2005, 36(4): 13-15
[2]李遵基.热工自动控制系统[M].北京:中国电力出版社, 1997
[3]李冉.超临界机组协调控制策略的优化研究[D].安徽:安徽理工大学, 2009
[4]赵亮.遗传算法在热工过程建模与优化控制中的应用研究[D].江苏:东南大学, 2006
[5] Astrom K J, T Hagglund. Automatic tuning of Simple Regulators with Specification on Phase and Amplitude Margins[J]. Automatic, 1984, 20(5): 645-651
[6] Kraus T W, Myron T J. Self-Tuning PID Controller Users Pattern Recognition Approach[J]. Control Engineering, 1984, 6: 109-111
[7] Schraudolph N N, Belew R K. Dynamic Parameter Encoding for Genetic Algorithms[J]. Machine Learning, 1992, 9(1): 9-21
[8] Back T, Hoffmeister F. Extended Election Mechanisms in Genetic Algorithms. In[49], 1991: 92-99
[9] Maza M D L, Tidor B. An Analysis of Selection Procedures with Particular Attention Paid to Proportion and Boltzmann Selection. In[50], 1993: 124-131
[10] C.C.Hang, Astrom, W.K.Ho. Refinements of the Ziegler-Nichols tuning Formula[C]. IEEE Proceedings-D, 1992, 138(2): 111-118
[11] Michalewicz Z. Genetic Algorithms+Data structure=Evolution Programs[M]. Springer-Verlag, Berlin, 1992
[12] S W Mahfoud. Genetic Drift in Sharing Methods[C]. 0-7803-1899-4/94, 1994, IEEE, 62~72
[13] V Petridis, SKazarlis. Varying Quality Function in Genetic Algorithms and the Cutting Problem[C]. 0-7803-1899-4/94, 1994, IEEE, 166~169
[14] Back T. Selection pressure in evolutionary algorithms: a Characterization of selection mechanisms[C]. Proceeding of the First IEEE conference on Evolutionary computation, IEEE press Orlando, FL. 1994: 57-62
[15] Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithms, IEEE Trans on Systems, Man and Cybernetics, 1994, 24(4): 656-667
[16] Feng Q X, Francesco P. Theoretical Analysis of Evolutionary Algorithms with an Infinite Population Size in Continuous Space Part I and PartⅡ: and Basic Properties of Selection and Mutation[C]. IEEE Trans Neural Networks, 1994, 5(1): 102-129
[17] D T Pham, G Jin. Genetic Algorithms Using Gradient-like reproduction operator[J]. Electronics Letters, 1995, 31(18): 1558-1559
[18] Pan Z J, Kang L S, Nie S X. Evolving Both the Topology and Weights of Neural Network[J]. Parallel Algorithms and Applications. 1996, 9: 299-307
[19] Wang Lei, Jiao Licheng. Novel genetic algorithm based on immunity[C]. Proceedings-IEEE International Symposium on Circuits and Systems 5 May 28-May31 2000 Sponsored by: IEEE Circuits and Systems Society IEEE V385-V388
[20]张满良. 300MW火电机组系统辨识模型及仿真分析[D].北京:华北电力大学, 2010
[21]张思才,张方晓.一种遗传算法适应度函数的改进方法[J].计算机应用与软件, 2006, 23(2): 108-110
[22]菅倩.基于矩阵编码遗传算法的研究与应用[D].太原:太原理工大学, 2008
[23]李良敏,温广瑞,王生昌.遗传算法中遗传操作的改进策略[J].计算机应用与软件, 2009, 26 (6): 27-30
[24] Stephenson M, O'Reilly U M, Martin M C, et al. Genetic programming applied to compiler heuristic optimization[C]. In: Anon, eds. Genetic Programming 6th European Conference Proceedings. Heidelberg: Springer Verlag, 2003, 238-253.
[25]白焰,蒋毅恒,朱耀春等.基于遗传编程的火电厂主汽温系统建模研究[J].系仿真学报, 2008, 20(4): 1076-1079
[26] Ljung, L. Consistency analysis of parametric Identification methods. IEEE Trans. Autom. Control, AC-23, No. 5, pp770-783, 1978
[27]姚峻. 900MW超临界机组控制策略的研究和应用[D].江苏:东南大学, 2006
[28] Isidori A, Krener A J, Gori-Giorgi C, Monaco S. Nonlinear decoupling via feedback: a differential geometric approach[J]. IEEE Transactions on automatic control, 1981, April, 26(2): 331-345
[29]姚远,刘岩. 600MW超超临界机组协调控制系统设计[J].东北电力技术, 2009(01): 42-45
[30]刘廉隅.基于电厂实时运行数据的模型辨识方法研究[D].河北:华北电力大学, 2007
[31] M.E.Flynn, M.J.O Malley. A drum boiler model for long term power system dynamic simulation[C]. IEEE Transaction Power System, 1999, 14(1): 209-217
[32] A.Gebremedhin, B.Moshfegh. Modeling and optimization of district heating and industrial energy system[J], International Journal of Energy Research , 2004(5): 411-422
[33] Kishore J K, Patnaik L M, Mani V, et al. Application of genetic programming for multicategory pattern classification[J]. IEEE Transactions on Evolutionary Computation: (S1089-778X), 2000, 4(3): 242-258
[34]焦健,赵志强. 660MW超超临界机组协调控制策略[J].东北电力技术. 2010, 1:27-31
[35]刘吉臻.协调控制与给水全程控制[M].北京:水利电力出版社, 1995
[36] Akanyeti O, Kyriacou T, Nehmzow U, Iglesias R, Billings S A. Visual task identification and characterization using polynomial models[J]. Robotics and Autonomous Systems. 2007, 55(9): 711-719
[37]张法文.热工过程控制系统分析、设计和调试[M].中国电力出版社, 1997
[38]李敏强,寇纪淞,林丹等.遗传算法的基本理论与应用[M].北京:科学出版社, 2002
[39]焦力兴.基于遗传算法进行PID参数寻优的热工系统的应用研究[D].华北电力大学(河北), 2008, 12
[40]梁杨,谷俊杰.基于遗传算法的超临界机组模型的参数辨识[J].电力科学与工程, 2010, 26(7): 38-41
[41] Eshelman L, Schaffer J. Real-coded genetic algorithms and interval-schema[C]. In: Whitley Ded, Foundations of genetic algorithms 2, San Mateo: Morgan Kaufmann Publishers, 1992: 187~202
[42] Jazayeri P, Rosehart W, Westwick D T. A multistage algorithm for identification of nonlinear aggregate power system loads [J]. IEEE transactions on power systems, 2007, August, 22(3): 1072-1079
[43] Preciado V M, Preciado M A, Jaramillo M A. Genetic programming for automatic generation of image processing algorithms on the CNN neuroprocessing architecture [C] . In: Anon, eds. Current topics in artificial intelligence, 10th conference of the Spanish association for artificial intelligence. Heidelberg: Springer Verlag, 2004, 374-383
[44]朱小娟,高庆忠,宋福圣.基于自适应混合遗传算法的协调控制系统[J].自动化与仪器仪表, 2009, 3: 3-6
[45]蒋毅恒.基于遗传编程的协调控制系统建模和设计[D].河北:华北电力大学工学, 2008. 4
[46]张雷.遗传算法在控制系统闭环辨识中的应用研究[D].河北:华北电力大学, 2007
[47]王恭良,沈炯,李益国等.适用于控制的600MW超临界机组的简化模型[J].江苏电机工程, 2008, 27(4): 1-7
[48] Han Zhongxu,Qi Xiaohong,Zhou Guang.Feed-water control scheme design of two different structure double-hearth benson once-through boilers and its application[C] . 2006 1st IEEE Conference on Industrial Electronics and Applications,Singapore,2006
[2]李遵基.热工自动控制系统[M].北京:中国电力出版社, 1997
[3]李冉.超临界机组协调控制策略的优化研究[D].安徽:安徽理工大学, 2009
[4]赵亮.遗传算法在热工过程建模与优化控制中的应用研究[D].江苏:东南大学, 2006
[5] Astrom K J, T Hagglund. Automatic tuning of Simple Regulators with Specification on Phase and Amplitude Margins[J]. Automatic, 1984, 20(5): 645-651
[6] Kraus T W, Myron T J. Self-Tuning PID Controller Users Pattern Recognition Approach[J]. Control Engineering, 1984, 6: 109-111
[7] Schraudolph N N, Belew R K. Dynamic Parameter Encoding for Genetic Algorithms[J]. Machine Learning, 1992, 9(1): 9-21
[8] Back T, Hoffmeister F. Extended Election Mechanisms in Genetic Algorithms. In[49], 1991: 92-99
[9] Maza M D L, Tidor B. An Analysis of Selection Procedures with Particular Attention Paid to Proportion and Boltzmann Selection. In[50], 1993: 124-131
[10] C.C.Hang, Astrom, W.K.Ho. Refinements of the Ziegler-Nichols tuning Formula[C]. IEEE Proceedings-D, 1992, 138(2): 111-118
[11] Michalewicz Z. Genetic Algorithms+Data structure=Evolution Programs[M]. Springer-Verlag, Berlin, 1992
[12] S W Mahfoud. Genetic Drift in Sharing Methods[C]. 0-7803-1899-4/94, 1994, IEEE, 62~72
[13] V Petridis, SKazarlis. Varying Quality Function in Genetic Algorithms and the Cutting Problem[C]. 0-7803-1899-4/94, 1994, IEEE, 166~169
[14] Back T. Selection pressure in evolutionary algorithms: a Characterization of selection mechanisms[C]. Proceeding of the First IEEE conference on Evolutionary computation, IEEE press Orlando, FL. 1994: 57-62
[15] Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithms, IEEE Trans on Systems, Man and Cybernetics, 1994, 24(4): 656-667
[16] Feng Q X, Francesco P. Theoretical Analysis of Evolutionary Algorithms with an Infinite Population Size in Continuous Space Part I and PartⅡ: and Basic Properties of Selection and Mutation[C]. IEEE Trans Neural Networks, 1994, 5(1): 102-129
[17] D T Pham, G Jin. Genetic Algorithms Using Gradient-like reproduction operator[J]. Electronics Letters, 1995, 31(18): 1558-1559
[18] Pan Z J, Kang L S, Nie S X. Evolving Both the Topology and Weights of Neural Network[J]. Parallel Algorithms and Applications. 1996, 9: 299-307
[19] Wang Lei, Jiao Licheng. Novel genetic algorithm based on immunity[C]. Proceedings-IEEE International Symposium on Circuits and Systems 5 May 28-May31 2000 Sponsored by: IEEE Circuits and Systems Society IEEE V385-V388
[20]张满良. 300MW火电机组系统辨识模型及仿真分析[D].北京:华北电力大学, 2010
[21]张思才,张方晓.一种遗传算法适应度函数的改进方法[J].计算机应用与软件, 2006, 23(2): 108-110
[22]菅倩.基于矩阵编码遗传算法的研究与应用[D].太原:太原理工大学, 2008
[23]李良敏,温广瑞,王生昌.遗传算法中遗传操作的改进策略[J].计算机应用与软件, 2009, 26 (6): 27-30
[24] Stephenson M, O'Reilly U M, Martin M C, et al. Genetic programming applied to compiler heuristic optimization[C]. In: Anon, eds. Genetic Programming 6th European Conference Proceedings. Heidelberg: Springer Verlag, 2003, 238-253.
[25]白焰,蒋毅恒,朱耀春等.基于遗传编程的火电厂主汽温系统建模研究[J].系仿真学报, 2008, 20(4): 1076-1079
[26] Ljung, L. Consistency analysis of parametric Identification methods. IEEE Trans. Autom. Control, AC-23, No. 5, pp770-783, 1978
[27]姚峻. 900MW超临界机组控制策略的研究和应用[D].江苏:东南大学, 2006
[28] Isidori A, Krener A J, Gori-Giorgi C, Monaco S. Nonlinear decoupling via feedback: a differential geometric approach[J]. IEEE Transactions on automatic control, 1981, April, 26(2): 331-345
[29]姚远,刘岩. 600MW超超临界机组协调控制系统设计[J].东北电力技术, 2009(01): 42-45
[30]刘廉隅.基于电厂实时运行数据的模型辨识方法研究[D].河北:华北电力大学, 2007
[31] M.E.Flynn, M.J.O Malley. A drum boiler model for long term power system dynamic simulation[C]. IEEE Transaction Power System, 1999, 14(1): 209-217
[32] A.Gebremedhin, B.Moshfegh. Modeling and optimization of district heating and industrial energy system[J], International Journal of Energy Research , 2004(5): 411-422
[33] Kishore J K, Patnaik L M, Mani V, et al. Application of genetic programming for multicategory pattern classification[J]. IEEE Transactions on Evolutionary Computation: (S1089-778X), 2000, 4(3): 242-258
[34]焦健,赵志强. 660MW超超临界机组协调控制策略[J].东北电力技术. 2010, 1:27-31
[35]刘吉臻.协调控制与给水全程控制[M].北京:水利电力出版社, 1995
[36] Akanyeti O, Kyriacou T, Nehmzow U, Iglesias R, Billings S A. Visual task identification and characterization using polynomial models[J]. Robotics and Autonomous Systems. 2007, 55(9): 711-719
[37]张法文.热工过程控制系统分析、设计和调试[M].中国电力出版社, 1997
[38]李敏强,寇纪淞,林丹等.遗传算法的基本理论与应用[M].北京:科学出版社, 2002
[39]焦力兴.基于遗传算法进行PID参数寻优的热工系统的应用研究[D].华北电力大学(河北), 2008, 12
[40]梁杨,谷俊杰.基于遗传算法的超临界机组模型的参数辨识[J].电力科学与工程, 2010, 26(7): 38-41
[41] Eshelman L, Schaffer J. Real-coded genetic algorithms and interval-schema[C]. In: Whitley Ded, Foundations of genetic algorithms 2, San Mateo: Morgan Kaufmann Publishers, 1992: 187~202
[42] Jazayeri P, Rosehart W, Westwick D T. A multistage algorithm for identification of nonlinear aggregate power system loads [J]. IEEE transactions on power systems, 2007, August, 22(3): 1072-1079
[43] Preciado V M, Preciado M A, Jaramillo M A. Genetic programming for automatic generation of image processing algorithms on the CNN neuroprocessing architecture [C] . In: Anon, eds. Current topics in artificial intelligence, 10th conference of the Spanish association for artificial intelligence. Heidelberg: Springer Verlag, 2004, 374-383
[44]朱小娟,高庆忠,宋福圣.基于自适应混合遗传算法的协调控制系统[J].自动化与仪器仪表, 2009, 3: 3-6
[45]蒋毅恒.基于遗传编程的协调控制系统建模和设计[D].河北:华北电力大学工学, 2008. 4
[46]张雷.遗传算法在控制系统闭环辨识中的应用研究[D].河北:华北电力大学, 2007
[47]王恭良,沈炯,李益国等.适用于控制的600MW超临界机组的简化模型[J].江苏电机工程, 2008, 27(4): 1-7
[48] Han Zhongxu,Qi Xiaohong,Zhou Guang.Feed-water control scheme design of two different structure double-hearth benson once-through boilers and its application[C] . 2006 1st IEEE Conference on Industrial Electronics and Applications,Singapore,2006