基于遗传算法的异步电动机机械效率优化控制
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摘要
实现电能与机械能相互转换的电工设备总称为电机。电机是利用电磁感应原理实现电能与机械能的相互转换。把机械能转换成电能的设备称为发电机,而把电能转换成机械能的设备叫做电动机。在生产上主要用的是交流电动机,特别是三相异步电动机。异步电动机是量大、面广的耗能设备,我国异步电动机的年耗电量约占工业耗电量的80%。由于异步电动机具有结构简单、坚固耐用、运行可靠、价格低廉、维护方便等优点,被广泛地用来驱动各种金属切削机床、起重机、锻压机、传送带、铸造机械、功率不大的通风机及水泵等。但在实际工作中大部分异步电动机并非满载运行,或不是始终满载运行,电动机经常工作于满电压及负载较轻或负载波动频繁的状态,导致异步电动机功率因数和机械效率都很低,功率因数一般都不会超过0.8,这种电机的非经济运行浪费了大量能源,节能问题日益突出。
遗传算法是一种简单、高效的优化算法,它为我们指明了一种求解复杂系统优化问题的通用途径,也为三相异步电动机在轻载或空载时机械效率的优化问题提供了有力依据。本文通过理论分析三相异步电动机在轻载或空载时的机械效率,提出运用遗传算法原理提高异步电动机在轻载或空载时的机械效率,并从理论上对机械效率的优化进行了可靠的证明。通过实验和仿真结果可以看出,这种基于遗传算法的异步电动机机械效率的优化控制方法明显地提高了异步电动机在轻载或空载时的机械效率,具有极高理论和实际意义。
The electrical equipment which achieves electrical energy and mechanical energy conversion collectively known as the motor. And the motor converses electrical energy into mechanical energy by the principle of electromagnetic induction. The device which converses mechanical energy into electrical energy called the generator, and the device which converses mechanical energy into electrical energy called the electric motor. The AC motor is widely used in production, especially the three-phase asynchronous motor. Asynchronous motor is large, a wide range of energy-consuming equipment, the annual electricity consumption of the asynchronous motor accounted for about 80% of industrial power consumption. As the asynchronous motor has a simple structure, rugged, reliable, low cost, easy maintenance, etc., they are widely used to drive a variety of metal cutting machine tools, cranes, forging machines, conveyor belts, foundry machinery, low power fan, water pump, and so on. However, most of the asynchronous motors not run at fully loaded situation, or not always run at full capacity in practice work, the electric motor often works at full load voltage and light load or the state of frequent load fluctuations, which results in low power factor and efficiency of induction motor, generally the power factor is less than 0.8, and such non-economic operation of motor waste a lot of energy, energy conservation is becoming increasingly prominent.
Genetic algorithm is a simple and efficient optimization algorithm, which pointed out a general way for us to solve complex optimization problems, and also provides a strong basis for the optimization problem of three-phase asynchronous motor's mechanical efficiency at light load or no load. This paper theoretical analysises the mechanical efficiency of three-phase asynchronous motor at light load or no load, and put forward a way to improve the mechanical efficiency of three-phase asynchronous motor at light load or no load by the principle of genetic algorithm, then proves the reliability of the optimization in theory. From the experiment and simulation results, we can see that the optimal method based on genetic algorithms has significantly improved the mechanical efficiency of three-phase asynchronous motor at light load or no load, and it has a high theoretical and practical significance.
遗传算法是一种简单、高效的优化算法,它为我们指明了一种求解复杂系统优化问题的通用途径,也为三相异步电动机在轻载或空载时机械效率的优化问题提供了有力依据。本文通过理论分析三相异步电动机在轻载或空载时的机械效率,提出运用遗传算法原理提高异步电动机在轻载或空载时的机械效率,并从理论上对机械效率的优化进行了可靠的证明。通过实验和仿真结果可以看出,这种基于遗传算法的异步电动机机械效率的优化控制方法明显地提高了异步电动机在轻载或空载时的机械效率,具有极高理论和实际意义。
The electrical equipment which achieves electrical energy and mechanical energy conversion collectively known as the motor. And the motor converses electrical energy into mechanical energy by the principle of electromagnetic induction. The device which converses mechanical energy into electrical energy called the generator, and the device which converses mechanical energy into electrical energy called the electric motor. The AC motor is widely used in production, especially the three-phase asynchronous motor. Asynchronous motor is large, a wide range of energy-consuming equipment, the annual electricity consumption of the asynchronous motor accounted for about 80% of industrial power consumption. As the asynchronous motor has a simple structure, rugged, reliable, low cost, easy maintenance, etc., they are widely used to drive a variety of metal cutting machine tools, cranes, forging machines, conveyor belts, foundry machinery, low power fan, water pump, and so on. However, most of the asynchronous motors not run at fully loaded situation, or not always run at full capacity in practice work, the electric motor often works at full load voltage and light load or the state of frequent load fluctuations, which results in low power factor and efficiency of induction motor, generally the power factor is less than 0.8, and such non-economic operation of motor waste a lot of energy, energy conservation is becoming increasingly prominent.
Genetic algorithm is a simple and efficient optimization algorithm, which pointed out a general way for us to solve complex optimization problems, and also provides a strong basis for the optimization problem of three-phase asynchronous motor's mechanical efficiency at light load or no load. This paper theoretical analysises the mechanical efficiency of three-phase asynchronous motor at light load or no load, and put forward a way to improve the mechanical efficiency of three-phase asynchronous motor at light load or no load by the principle of genetic algorithm, then proves the reliability of the optimization in theory. From the experiment and simulation results, we can see that the optimal method based on genetic algorithms has significantly improved the mechanical efficiency of three-phase asynchronous motor at light load or no load, and it has a high theoretical and practical significance.
引文
[1]李发海,朱东起.电机学(第四版)[M].科学出版社, 2007
[2]李发海,王岩.电机与拖动基础(第二版)[M].北京:清华大学出版社, 1994
[3]周明,孙树栋.遗传算法原理及应用[M].国防工业出版社, 1999
[4]陈伯时主编.电机拖动自动控制系统[M].北京:机械工业出版社, 2003
[5]李敏强.遗传算法的基本理论与应用[M].北京:科学出版社,2002
[6]王小平,曹立明.遗传算法——理论、应用与软件实现[M].西安:西安交通大学出版社, 2002
[7]陈文清.遗传算法综述[J].洛阳工业高等专科学校学报, 2003(1)
[8]张文修,梁怡.遗传算法的数学基础(第2版)[M].西安:西安交通大学出版社,2003
[9]徐清振,肖成林.遗传算法的研究与应用[J].现代计算机,2006(5)
[10]徐阳,王友仁.演化硬件理论与应用技术研究[J].航空电子技术,2003(3)
[11]马玉明,贺爱玲,李爱民.遗传算法的理论研究综述[J].山东轻工业学院学报,2004,18(3):77-80
[12]王春水,肖学柱,陈汉明.遗传算法的应用举例[J].计算机仿真2005,22(6):155-157
[13]姚文俊.遗传算法及其研究进展[J].计算机与数字工程, 2004,32(4):41-43
[14]吉根林.遗传算法研究综述[J].计算机应用与软件, 2004,21(2):69-73
[15]潘正君等.演化计算[M].清华大学出版社,广西科学技术出版社,1998
[16]徐宗本,张讲社,郑亚林.计算智能中的仿生学——理论与算法[M].科学出版社,2003
[17]陈国良,王煦法,庄镇泉.遗传算法及其应用[M].国防出版社,2001
[18]云庆夏.遗传算法和遗传规划[M].冶金工业出版社,1997
[19]毕义明,李景文.遗传算法及其应用[M].解放军出版社,1998
[20]许力.智能控制与智能系统[M].北京:机械工业出版社,2006.12
[21]师黎,陈铁军,李晓媛,姚利娜.智能控制理论及应用[M].北京:清华大学出版社,2009.4
[22]郑力新.遗传算法在控制系统工程应用的研究[D].天津大学博士论文,2002
[23]杨斌.基于遗传算法的三相异步电动机轻载和空载时的节能控制研究[D].武汉科技大学硕士学位论文, 2006
[24]肖专文,徐日庆,龚晓南.求解复杂工程优化问题的一种实用方法[J].水利学报,1999.2
[25]玄光男,程润伟.遗传算法与工程优化[M].于歆杰,周根贵译[M].北京:清华大学出版社,2005
[26]孙增圻.智能控制理论与技术[M].北京:清华大学出版社,1997
[27]胡汉平,吴晓刚.基于遗传算法的模糊控制系统[J].电子学报,2000,28(7)
[28]张琨,毕靖,丛滨. MATLAB 7.6从入门到精通[M].电子工业出版社,2009
[29]雷英杰. MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005
[30]龚纯,王正林. MATLAB语言常用算法程序集[M].电子工业出版社,2008
[31] Davis L D. Handbook of genetic algorithms. New York: Van Nostrand Reinhold, 1991
[32] Mayr E. Animal species and evolution [M]. Cambridge, MA: Belknap Press, 1963
[33] Andreas Bortfeldt, Hermann Gehring. A Hybrid Genetic Algorithm for The Container Loading Problem[J]. European Journal of Operational Research, 2001(131):143-161
[34] D. Y. He, J. Z. Cha. Research on Solution to Complex Container Loading Problem Based on Genetic Algorithm[J]. The First International Conference on Machine Learning and Cybernetics. Beijing-China, 2002:78-82
[35] Goldberg D.Lingle R.Alleles,Loci and the Traveling Salesman Problem.In:Grefenstette J . Editor , Proceedings of First International Conference on Genetic Algorithms[J].Lawrence Erlbaum Associates,New Jersey,1985,154-159
[36] Davis L D . Genetic Algorithms and Simulated Annealing[M] . Morgan Kaufmann.LosAltos,1987
[37] Michalewicz Z . A Modified Genetic Algorithm for Optimal Control Problems[J].Computers Math.Application,1992,23(12): 83-94
[38] Alen Varsek, Tanja Urbancic, Bodgan Filipic, "Genetic Algorithms in Controller Design and Tuning", IEEE Trans. S. M. C, 1993, 5(23): 1330-1339
[39] Goldberg D E. Genetic Algorithm in Search, Optimization, and machine Learning. Addison-Wesley, Reading, MA, 1989
[40] Gunter Rudolph, Convergence Analysis of Canonical Genetic Algorithms, IEEE, Trans. on Neural Networks, 1994, 1(5): 96-101
[41] Holland J H. Adaptation in natural and artificial systems[M]. The University of Michigan press, 1975
[42] De Jong K A. An analysis of the behavior of a class of genetic adaptive systems[M]. University of Michigan, 1975
[2]李发海,王岩.电机与拖动基础(第二版)[M].北京:清华大学出版社, 1994
[3]周明,孙树栋.遗传算法原理及应用[M].国防工业出版社, 1999
[4]陈伯时主编.电机拖动自动控制系统[M].北京:机械工业出版社, 2003
[5]李敏强.遗传算法的基本理论与应用[M].北京:科学出版社,2002
[6]王小平,曹立明.遗传算法——理论、应用与软件实现[M].西安:西安交通大学出版社, 2002
[7]陈文清.遗传算法综述[J].洛阳工业高等专科学校学报, 2003(1)
[8]张文修,梁怡.遗传算法的数学基础(第2版)[M].西安:西安交通大学出版社,2003
[9]徐清振,肖成林.遗传算法的研究与应用[J].现代计算机,2006(5)
[10]徐阳,王友仁.演化硬件理论与应用技术研究[J].航空电子技术,2003(3)
[11]马玉明,贺爱玲,李爱民.遗传算法的理论研究综述[J].山东轻工业学院学报,2004,18(3):77-80
[12]王春水,肖学柱,陈汉明.遗传算法的应用举例[J].计算机仿真2005,22(6):155-157
[13]姚文俊.遗传算法及其研究进展[J].计算机与数字工程, 2004,32(4):41-43
[14]吉根林.遗传算法研究综述[J].计算机应用与软件, 2004,21(2):69-73
[15]潘正君等.演化计算[M].清华大学出版社,广西科学技术出版社,1998
[16]徐宗本,张讲社,郑亚林.计算智能中的仿生学——理论与算法[M].科学出版社,2003
[17]陈国良,王煦法,庄镇泉.遗传算法及其应用[M].国防出版社,2001
[18]云庆夏.遗传算法和遗传规划[M].冶金工业出版社,1997
[19]毕义明,李景文.遗传算法及其应用[M].解放军出版社,1998
[20]许力.智能控制与智能系统[M].北京:机械工业出版社,2006.12
[21]师黎,陈铁军,李晓媛,姚利娜.智能控制理论及应用[M].北京:清华大学出版社,2009.4
[22]郑力新.遗传算法在控制系统工程应用的研究[D].天津大学博士论文,2002
[23]杨斌.基于遗传算法的三相异步电动机轻载和空载时的节能控制研究[D].武汉科技大学硕士学位论文, 2006
[24]肖专文,徐日庆,龚晓南.求解复杂工程优化问题的一种实用方法[J].水利学报,1999.2
[25]玄光男,程润伟.遗传算法与工程优化[M].于歆杰,周根贵译[M].北京:清华大学出版社,2005
[26]孙增圻.智能控制理论与技术[M].北京:清华大学出版社,1997
[27]胡汉平,吴晓刚.基于遗传算法的模糊控制系统[J].电子学报,2000,28(7)
[28]张琨,毕靖,丛滨. MATLAB 7.6从入门到精通[M].电子工业出版社,2009
[29]雷英杰. MATLAB遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005
[30]龚纯,王正林. MATLAB语言常用算法程序集[M].电子工业出版社,2008
[31] Davis L D. Handbook of genetic algorithms. New York: Van Nostrand Reinhold, 1991
[32] Mayr E. Animal species and evolution [M]. Cambridge, MA: Belknap Press, 1963
[33] Andreas Bortfeldt, Hermann Gehring. A Hybrid Genetic Algorithm for The Container Loading Problem[J]. European Journal of Operational Research, 2001(131):143-161
[34] D. Y. He, J. Z. Cha. Research on Solution to Complex Container Loading Problem Based on Genetic Algorithm[J]. The First International Conference on Machine Learning and Cybernetics. Beijing-China, 2002:78-82
[35] Goldberg D.Lingle R.Alleles,Loci and the Traveling Salesman Problem.In:Grefenstette J . Editor , Proceedings of First International Conference on Genetic Algorithms[J].Lawrence Erlbaum Associates,New Jersey,1985,154-159
[36] Davis L D . Genetic Algorithms and Simulated Annealing[M] . Morgan Kaufmann.LosAltos,1987
[37] Michalewicz Z . A Modified Genetic Algorithm for Optimal Control Problems[J].Computers Math.Application,1992,23(12): 83-94
[38] Alen Varsek, Tanja Urbancic, Bodgan Filipic, "Genetic Algorithms in Controller Design and Tuning", IEEE Trans. S. M. C, 1993, 5(23): 1330-1339
[39] Goldberg D E. Genetic Algorithm in Search, Optimization, and machine Learning. Addison-Wesley, Reading, MA, 1989
[40] Gunter Rudolph, Convergence Analysis of Canonical Genetic Algorithms, IEEE, Trans. on Neural Networks, 1994, 1(5): 96-101
[41] Holland J H. Adaptation in natural and artificial systems[M]. The University of Michigan press, 1975
[42] De Jong K A. An analysis of the behavior of a class of genetic adaptive systems[M]. University of Michigan, 1975