改进粒子群优化算法在整流变压器设计中的应用研究
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摘要
整流变压器在电化学工业、电镀、直流电源以及直流输电等领域有着十分广泛的应用。随着工业水平的不断提高,对整流变压器的容量、结构形式也有了许多新的要求,这就要求设计人员根据不同的情况设计出不同特点的整流变压器。通过对ZS-2000/10kV整流变压器电磁部分采用手工计算,能够得知计算时间长。为了提高计算的效率,论文采用Visual C++软件对其计算过程进行程序设计。设计过程中对电磁部分进行分块,采用分块的方法编写程序,降低编程难度,提高工作效率,节省工作时间。
目前在整流变压器设计方面,许多的参数没有具体的国家规定。每个设计者的经验不同,选取的参数往往不同,计算结果也会有差异,故对其进行优化设计是十分必要的。本文通过查阅资料,分析研究各种优化算法的优劣,针对整流变压器计算参数的特点,选择了粒子群优化算法。粒子群优化算法是一种可以应用于多种优化模型的全局搜索算法,计算过程简单、快速、准确。但是该算法在初始参数不多的情况下容易陷入局部最优,故对其进行了改进。在基本的粒子群算法之中引进了控制因子α,以此来调整惯性权重w取值的优劣。控制因子将最大迭代次数分为若干段,每段迭代开始计算时每个粒子的惯性权重由其自身的适应度值和群体平均适应度值的比较结果来决定。适应值好的粒子趋向于局部搜索,惯性权重不作调整;适应值差的粒子将进行全局探测,调整惯性权重,以便发现最优解,使整个粒子群具有多样性和好的收敛性。
最后,本文将改进的粒子群优化算法应用到ZS-2000/10kV整流变压器优化设计中,并且在优化计算中采用计算机编程,以人机对话界面显示最终的结果,将优化结果与手工计算的结果进行比较可以看出,改进后的算法具有较好的可行性和有效性。
Rectifier transformer has a very wide range of applications in industrial field, including electrochemical industrial, electroplating, DC power and DC transmission. With the continuous improvement of industry standards, the new capacity and structure of the rectifier transformer have many new requirements, which require the designer to design different characteristics of the rectifier transformer according to different situations. It is too time-consuming to calculate the electromagnetic part of the ZS-2000/10 rectifier transformer manually. Therefore, in order to improve the calculation efficiency, the paper uses Visual C + + software for the calculating process, in which segmented partition method program is adopted for the design process of electromagnetic parts, as a result it reduces programming difficulty, improves efficiency and saves work time.
The current design of the rectifier transformer, many parameters have no specific national regulations. Each designer's experience and the selected parameters are often different, the results will be different, and so it is very necessary to optimize the design. This paper studies the advantages and disadvantages of various optimization algorithms through data access, aiming at the characteristics of the calculated parameters rectifier transformer, the particle swarm optimization algorithm is choused. PSO is an optimization model which can be applied to many of the global search algorithm; the calculation is simple, fast and accurate. However the algorithm is limited in the case of few initial parameters and it is easy to fall into local optimum, so it needs improvement. In the basic PSO, the control factorαis introduced, so as to adjust the value of inertia weight w . The control factors divide the maximum iterating times into several segments, each particle's inertia weigh is decided by the comparison results of self-adapting and group average fitness value when each iteration begins. Good fitness particle tend to local search, inertia weight is not made adjust; poor fitness particles will carry on global surveyor, adjust inertia weight to find the optimal solution, so that the whole particle swarm diversity and good convergence.
Finally, the improved particle swarm optimization algorithm is applied to ZS-2000/10 kV rectifier transformer design optimization, and the calculation of the electromagnetic part is calculated by Visual C + + programming, interactive interface to display the final results. By comparing the results with manual calculation shows that the improved algorithm is more feasible and effective.
目前在整流变压器设计方面,许多的参数没有具体的国家规定。每个设计者的经验不同,选取的参数往往不同,计算结果也会有差异,故对其进行优化设计是十分必要的。本文通过查阅资料,分析研究各种优化算法的优劣,针对整流变压器计算参数的特点,选择了粒子群优化算法。粒子群优化算法是一种可以应用于多种优化模型的全局搜索算法,计算过程简单、快速、准确。但是该算法在初始参数不多的情况下容易陷入局部最优,故对其进行了改进。在基本的粒子群算法之中引进了控制因子α,以此来调整惯性权重w取值的优劣。控制因子将最大迭代次数分为若干段,每段迭代开始计算时每个粒子的惯性权重由其自身的适应度值和群体平均适应度值的比较结果来决定。适应值好的粒子趋向于局部搜索,惯性权重不作调整;适应值差的粒子将进行全局探测,调整惯性权重,以便发现最优解,使整个粒子群具有多样性和好的收敛性。
最后,本文将改进的粒子群优化算法应用到ZS-2000/10kV整流变压器优化设计中,并且在优化计算中采用计算机编程,以人机对话界面显示最终的结果,将优化结果与手工计算的结果进行比较可以看出,改进后的算法具有较好的可行性和有效性。
Rectifier transformer has a very wide range of applications in industrial field, including electrochemical industrial, electroplating, DC power and DC transmission. With the continuous improvement of industry standards, the new capacity and structure of the rectifier transformer have many new requirements, which require the designer to design different characteristics of the rectifier transformer according to different situations. It is too time-consuming to calculate the electromagnetic part of the ZS-2000/10 rectifier transformer manually. Therefore, in order to improve the calculation efficiency, the paper uses Visual C + + software for the calculating process, in which segmented partition method program is adopted for the design process of electromagnetic parts, as a result it reduces programming difficulty, improves efficiency and saves work time.
The current design of the rectifier transformer, many parameters have no specific national regulations. Each designer's experience and the selected parameters are often different, the results will be different, and so it is very necessary to optimize the design. This paper studies the advantages and disadvantages of various optimization algorithms through data access, aiming at the characteristics of the calculated parameters rectifier transformer, the particle swarm optimization algorithm is choused. PSO is an optimization model which can be applied to many of the global search algorithm; the calculation is simple, fast and accurate. However the algorithm is limited in the case of few initial parameters and it is easy to fall into local optimum, so it needs improvement. In the basic PSO, the control factorαis introduced, so as to adjust the value of inertia weight w . The control factors divide the maximum iterating times into several segments, each particle's inertia weigh is decided by the comparison results of self-adapting and group average fitness value when each iteration begins. Good fitness particle tend to local search, inertia weight is not made adjust; poor fitness particles will carry on global surveyor, adjust inertia weight to find the optimal solution, so that the whole particle swarm diversity and good convergence.
Finally, the improved particle swarm optimization algorithm is applied to ZS-2000/10 kV rectifier transformer design optimization, and the calculation of the electromagnetic part is calculated by Visual C + + programming, interactive interface to display the final results. By comparing the results with manual calculation shows that the improved algorithm is more feasible and effective.
引文
[1]崔立君.特种变压器理论与设计[M].北京:科学技术文献出版社,1996
[2]姚志松,姚磊等.新型配电变压器机构、原理和应用[M].机械工业出版社,2006.12
[3]孙林,王梦云等.我国电力变压器发展现状及趋势[J].电力设备,2003,4(4):78-81
[4] G.L.Slibinski, N.Guskov and D.Zhou.Cost effective multi-pulse transformer solutions for harmonic mitigation in AC drive [J].IAS Annual Meeting Conf.Rec., 2003, 21(2):1488-1497
[5] Chen Jianzhong, Shi Yaowu, Zhao Haiyan.Optimum Design of Welding Transformer. 1999, Vol14, No.2, p11-15
[6]陈文彪.调容防窃电变压器只能控制一体化设计与数值计算:[硕士学位论文].哈尔滨:哈尔滨理工大学,2005
[7]崔杜武,王竹荣,张毅坤.整流变压器优化设计的研究[M].电工技术杂志,2002,12(5):40-42
[8]王祝华.高压整流变压器的优化设计方法.煤矿机电,2004,24(2):9-11
[9]尹克宁.变压器设计原理[M].北京:中国电力出版社,2003,1~3
[10]《电力变压器手册》编写组.电力变压器手册[M].辽宁科学技术出版社,1990, 25-85
[11]马丁,J·希思科特.变压器实用大全[M].北京:机械工业出版社,2004
[12] Alabiso B., Transformation of Data Flow Analysis Models to Object Oriented Design, Proceedings 9, 1998.pp.335–353
[13]张安红.电力变压器的损耗研究和优化设计:[硕士学位论文].长沙:湖南大学,2005
[14]路长柏,朱英浩.电力变压器计算[M].哈尔滨:黑龙江科学技术出版社,1986:1-9
[15]李磊源,施文康,周薇.变压器移相角的数字测量方法及其比较[J].计算机测量与控制, 2002,148-151
[16]姚志松,姚磊著.中小型变压器实用全书[M].北京:机械工业出版社,2003: 23-43
[17]方大千.变压器速查速算手册.北京:中国水利水电出版社,2004,66-87
[18]沈阳变压器研究所.变压器[M].辽宁:辽宁省科学技术情报研究所,2005,23-46.
[19]陈天华编著.面向对象程序设计与Visual C++6.0教程.北京:清华大学出版社,2006.1:7-8
[20]郑平安,曾大亮,杨有安,崔坷梅编著.程序设计基础(C语言),第二版.北京:清华大学出版社,2006.1
[21]刘路放. Visual C++面向对象程序设计教程[M].北京:高等教育出版社,2000
[22]任哲等编著. MFC windows应用程序设计[M].北京:清华大学出版社,2004.6:4-12
[23]付灵丽,柴欣. Visual C++程序设计基础[M].北京:中国铁道出版社,2004.8
[24]樊叔维,汪国梁,谢卫.遗传算法在电力变压器优化设计中的应用研究[J].中国电机I:程学报. 1996. Vol.16, No.5:346-348
[25]纪震,廖惠连,吴青华.粒子群算法及应用[M].北京:科学出版社,2009
[26] Kennedy, Eberhart. Particle swarm optimization[C].Proc IEEE international conference on Neural Net2works , USA : IEEE Press , 1995 ,4 :1942～1948
[27] Angelino, J Evolutionary. Optimization versus Particle Swarm Optimization: Philosophy and Performance Differences [J]. Evolutionary Programming, 1998, 256-260
[28] Eberhart and Shi Y. Particle swarm optimization: developments, applications and resources. Proc. Congress on Evolutionary Computation 2001.Seoul, Korea. Piscataway, NJ: IEEE Service Center
[29] Coello C A, Lechuga, MS.MOPSO. A proposal for multiple objective particle swarm optimization[C].IEEE: Int Congress on Evolutionary Computation. Piscataway, NJ. IEEE Service Center, 2002:1051-1056
[30] Shi Y C. Particle swarm optimization: developments, applications and resources[C].IEEE Int Congress on Evolutionary Computation. Piscataway, NJ: IEEE Service Center, 2001:81-86
[31]张文爱,刘丽芳,李孝荣.基于粒子进化的多粒子群优化算法[J].计算机工程与应用,2008,Vol.44,No.7,51一53
[32]雷秀娟,史忠科,周亦鹏. PSO优化算法演变及其融合策略[J].计算机工程与应用,2007,43
[33] Shi Y, Eberhart, R.Parameter. Selection in Particle Swarm Optimization[C].Evolutionary Programming VII: Proceedings of the Seventh Annual Conferenceon Evolutionary Programming, New York, 1998:591-600
[34]王凌,智能优化算法及其应用[M].北京:清华大学出版社,2001
[35]李爱国.多粒子群协同优化算法[J].复旦大学学报,2004,43(5):923-925
[36] Kennedy J. The particle swarm social Adaptation of Knowledge. Proc IEEE Int Confon Evolutionary Computation. Indianapolis, 1997.303-308.Piscataway, NJ: IEEE Service Center, 1999.1927-1930
[37] Parsopoulos, K.E Plagianakos, V P., Magoulas, G D, Vrahatis, M.N. Improving particle swarm optimizer by function "stretching". Advances in Convex Analysis and Global Optimization:445-457, 2001
[38]雷开友.粒子群算法及其应用研究.[学位论文].西南大学,2006
[39]柯晶,钱积新,乔谊正.一种改进粒子群优化算法[J].电路与系统学报,2003,8(5):87~91
[40] Vesterstroem, Jakob Sand Riget, Jacques. Particle improved local, mufti-modal, and dynamic swarms: extensions for search in numerical optimization. Master’s thesis Department of Computer Science, University of Aarhus, 2002
[41] Robinson J, Sinton S, and Rahmat, Samii Y. Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna.2002 IEEE Antennas and Propagation Society International Symposium and URSI National Radio Science Meeting, an Antonio, TX.2002
[42]付国江,王少梅,李宁.一种新型的粒子群算法[J].计算机工程与应用,2005(23):80-83
[2]姚志松,姚磊等.新型配电变压器机构、原理和应用[M].机械工业出版社,2006.12
[3]孙林,王梦云等.我国电力变压器发展现状及趋势[J].电力设备,2003,4(4):78-81
[4] G.L.Slibinski, N.Guskov and D.Zhou.Cost effective multi-pulse transformer solutions for harmonic mitigation in AC drive [J].IAS Annual Meeting Conf.Rec., 2003, 21(2):1488-1497
[5] Chen Jianzhong, Shi Yaowu, Zhao Haiyan.Optimum Design of Welding Transformer. 1999, Vol14, No.2, p11-15
[6]陈文彪.调容防窃电变压器只能控制一体化设计与数值计算:[硕士学位论文].哈尔滨:哈尔滨理工大学,2005
[7]崔杜武,王竹荣,张毅坤.整流变压器优化设计的研究[M].电工技术杂志,2002,12(5):40-42
[8]王祝华.高压整流变压器的优化设计方法.煤矿机电,2004,24(2):9-11
[9]尹克宁.变压器设计原理[M].北京:中国电力出版社,2003,1~3
[10]《电力变压器手册》编写组.电力变压器手册[M].辽宁科学技术出版社,1990, 25-85
[11]马丁,J·希思科特.变压器实用大全[M].北京:机械工业出版社,2004
[12] Alabiso B., Transformation of Data Flow Analysis Models to Object Oriented Design, Proceedings 9, 1998.pp.335–353
[13]张安红.电力变压器的损耗研究和优化设计:[硕士学位论文].长沙:湖南大学,2005
[14]路长柏,朱英浩.电力变压器计算[M].哈尔滨:黑龙江科学技术出版社,1986:1-9
[15]李磊源,施文康,周薇.变压器移相角的数字测量方法及其比较[J].计算机测量与控制, 2002,148-151
[16]姚志松,姚磊著.中小型变压器实用全书[M].北京:机械工业出版社,2003: 23-43
[17]方大千.变压器速查速算手册.北京:中国水利水电出版社,2004,66-87
[18]沈阳变压器研究所.变压器[M].辽宁:辽宁省科学技术情报研究所,2005,23-46.
[19]陈天华编著.面向对象程序设计与Visual C++6.0教程.北京:清华大学出版社,2006.1:7-8
[20]郑平安,曾大亮,杨有安,崔坷梅编著.程序设计基础(C语言),第二版.北京:清华大学出版社,2006.1
[21]刘路放. Visual C++面向对象程序设计教程[M].北京:高等教育出版社,2000
[22]任哲等编著. MFC windows应用程序设计[M].北京:清华大学出版社,2004.6:4-12
[23]付灵丽,柴欣. Visual C++程序设计基础[M].北京:中国铁道出版社,2004.8
[24]樊叔维,汪国梁,谢卫.遗传算法在电力变压器优化设计中的应用研究[J].中国电机I:程学报. 1996. Vol.16, No.5:346-348
[25]纪震,廖惠连,吴青华.粒子群算法及应用[M].北京:科学出版社,2009
[26] Kennedy, Eberhart. Particle swarm optimization[C].Proc IEEE international conference on Neural Net2works , USA : IEEE Press , 1995 ,4 :1942～1948
[27] Angelino, J Evolutionary. Optimization versus Particle Swarm Optimization: Philosophy and Performance Differences [J]. Evolutionary Programming, 1998, 256-260
[28] Eberhart and Shi Y. Particle swarm optimization: developments, applications and resources. Proc. Congress on Evolutionary Computation 2001.Seoul, Korea. Piscataway, NJ: IEEE Service Center
[29] Coello C A, Lechuga, MS.MOPSO. A proposal for multiple objective particle swarm optimization[C].IEEE: Int Congress on Evolutionary Computation. Piscataway, NJ. IEEE Service Center, 2002:1051-1056
[30] Shi Y C. Particle swarm optimization: developments, applications and resources[C].IEEE Int Congress on Evolutionary Computation. Piscataway, NJ: IEEE Service Center, 2001:81-86
[31]张文爱,刘丽芳,李孝荣.基于粒子进化的多粒子群优化算法[J].计算机工程与应用,2008,Vol.44,No.7,51一53
[32]雷秀娟,史忠科,周亦鹏. PSO优化算法演变及其融合策略[J].计算机工程与应用,2007,43
[33] Shi Y, Eberhart, R.Parameter. Selection in Particle Swarm Optimization[C].Evolutionary Programming VII: Proceedings of the Seventh Annual Conferenceon Evolutionary Programming, New York, 1998:591-600
[34]王凌,智能优化算法及其应用[M].北京:清华大学出版社,2001
[35]李爱国.多粒子群协同优化算法[J].复旦大学学报,2004,43(5):923-925
[36] Kennedy J. The particle swarm social Adaptation of Knowledge. Proc IEEE Int Confon Evolutionary Computation. Indianapolis, 1997.303-308.Piscataway, NJ: IEEE Service Center, 1999.1927-1930
[37] Parsopoulos, K.E Plagianakos, V P., Magoulas, G D, Vrahatis, M.N. Improving particle swarm optimizer by function "stretching". Advances in Convex Analysis and Global Optimization:445-457, 2001
[38]雷开友.粒子群算法及其应用研究.[学位论文].西南大学,2006
[39]柯晶,钱积新,乔谊正.一种改进粒子群优化算法[J].电路与系统学报,2003,8(5):87~91
[40] Vesterstroem, Jakob Sand Riget, Jacques. Particle improved local, mufti-modal, and dynamic swarms: extensions for search in numerical optimization. Master’s thesis Department of Computer Science, University of Aarhus, 2002
[41] Robinson J, Sinton S, and Rahmat, Samii Y. Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna.2002 IEEE Antennas and Propagation Society International Symposium and URSI National Radio Science Meeting, an Antonio, TX.2002
[42]付国江,王少梅,李宁.一种新型的粒子群算法[J].计算机工程与应用,2005(23):80-83