基于MOPSO的电力变换器多目标优化

doi:10.11985/2018.12.002

摘要/Abstract

摘要：

为克服电力变换器优化设计中各种目标的相互冲突,以效率、物理尺寸（体积、质量和面积）和成本为优化目标建立电力变换器多目标优化模型,采用多目标粒子群优化（MOPSO）算法对建立的模型进行求解,决策者可在最终提供的最优解集中找到不同折衷的最优解。本文以Vienna整流器和Buck变换器作为研究对象,分别建立各自的多目标优化函数,导入器件数据库,在Matlab上实现了MOPSO算法。仿真实验结果表明了MOPSO算法的可行性、覆盖性和收敛性,并能在比较短的时间里收敛到最优解集。

关键词: 电力变换器, MOPSO算法, 多目标优化

Abstract:

In order to overcome the conflicts among various objectives in the optimization design of power converters, a multi-objective optimization model of power converters with efficiency, physical size (volume, weight and area) and cost is established as optimization objectives in the paper. The model is solved by using multi-objective particle swarm optimization (MOPSO) algorithm and decision makers can find different compromise optimal solutions in the final set of optimal solutions provided. Vienna rectifier and Buck converter are taken as research objects. Their respective multi-objective optimization functions are established, and introduced into the device database, and the MOPSO algorithm is implemented on Matlab. Simulation results show that the MOPSO algorithm is feasible, coverage and convergence, and it can converge to the optimal solution set in a relatively short time.

Key words: Power converter, MOPSO algorithm, multi-objective optimization

中图分类号:

TM46

郑沛琪,王久和,陈启丽,马亮. 基于MOPSO的电力变换器多目标优化[J]. 电气工程学报, 2018, 13(12): 7-13.

Zheng Peiqi,Wang Jiuhe,Chen Qili,Ma Liang. Multi-Objective Optimization of Power Converter Based on MOPSO[J]. Journal of Electrical Engineering, 2018, 13(12): 7-13.

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参考文献 18

[1]	Mirjafari M, Balog R S. Multi-objective design optimization of renewable energy system inverters using a descriptive language for the components [C]. In Proc. IEEE 26th Annu. Appl. Power Electron. Conf. Expo. , 2011: 1838-1845.
[2]	Kolar J W, Biela J, Waffler S, et al. Performance trends and limitations of power electronic systems [C]. IEEE International Conference on Power Electronics Systems, Chicago, USA, 2010: 182-205.
[3]	Kolar J W, Friedli T, Krismer F , et al. Conceptualization and multiobjective optimization of the electric system of an airborne wind turbine[J]. IEEE J. Emerg. Sel. Topics Power Electron. , 2013,1(2) : 73-103. doi: 10.1109/JESTPE.2013.2269672
[4]	Kolar J W, Friedli T . The essence of three-phase PFC rectifer systems[J]. IEEE Transactions on Power Electronics, 2014,29(2) : 543-561. doi: 10.1109/TPEL.2013.2258472
[5]	Boussaïd I, Lepagnot J, Siarry P . A survey on optimization metaheuristics[J]. Inf. Sci. , 2013,237:82-117. doi: 10.1016/j.ins.2013.02.041
[6]	Marglin S. Public investment criteria[M]. MIT Press, Cambridge, Massachusetts: 1967.
[7]	Chew K C ,Kondapalli S R R. Modelling, analysis, simulation and design optimization (genetic algorithm) of DC-DC converter for uninterruptible power supply applications[J]. IEEE Transactions on Power Electronics and Drive Systems, 2006,24(15) : 1530-1535.
[8]	Visairo H, Medina M A, Ramirez J M. Use of evolutionary algorithms for design optimization of power converters [C]. IEEE Applied Electrical Communication and Computers Conference, 2012: 268-272.
[9]	Yoshida H, Fukuyama Y, Takayama S, et al. A particle swarm optimization for reactive power and voltage control in electric power systems considering voltage security assessment [C]. In Proc. IEEE Int. Conf. Syst. , Man, Cybern. , 1999,6:497-502.
[10]	Debnath S, Ray R N. Harmonic elimination in multilevel inverter using GA and PSO: A comparison [C]. In Proc. IEEE Students Conf. Electr., Electron. Comput. Sci. , 2012: 1-5.
[11]	AlRashidi M, El-Hawary M . A survey of particle swarm optimization applications in electric power systems[J]. IEEE Transactions on Evolutionary Computation, 2009,13(4) : 913-918. doi: 10.1109/TEVC.2006.880326
[12]	Jasour A M Z, Khazraei M, Rahmati A . Design of a MIMO controller for a multimodul DC-DC converter based on particle swarm optimized neural network[J]. IEEE Transaction on Power and Energy, 2008,46(12) : 143-156.
[13]	Li X . A nondominated sorting particle swarm optimizer for multiobjective optimization [C]. In Proceedings of the Genetic and Evolutionary Computation Conference GECCO 2003,2003:37-48.
[14]	Coello C A C, Lechuga M S . MOPSO: a proposal for multiple objective particle swarm optimization [C]. IEEE Congress on Evolutionary Computation, Piscataway, New Jersey, 2002: 1051-1056.
[15]	Coello C A C, Pulido G T, Lechuga M S . Handling multiple objectives with particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation, 2004,8(3) : 256-279. doi: 10.1109/TEVC.2004.826067
[16]	常伟, 王久和, 陈启丽 . 基于NSGA-II算法Vienna整流器多目标优化[J]. 中国电机工程学报, 2016,36(增刊) : 179-185.
	Chang Wei, Wang Jiuhe, Chen Qili . Multi-objective optimization of Vienna rectifier based on NSGA-II algorithm[J]. Proceedings of the CSEE, 2016,36(S) : 179-185.
[17]	Chang Wei, Wang Jiuhe, Chen Qili. Multi-objective optimization of synchronous Buck converter based on NSGA-II algorithm [C]. International Conference on Mechatronics and Automation, Harbin, China, 2016: 1391-1395.
[18]	Chen W N, Zhang J, Chung H S , et al. A novel set-based particle swarm optimization method for discrete optimization problems[J]. IEEE Transactions on Evolutionary Computation, 2010,14(2) : 278-300. doi: 10.1109/TEVC.2009.2030331

参数	设定值
种群数	200
迭代次数	200

参数	设定值
输入电压V_in/V	400
输出电压V_o/V	800
开关频率f_sw/Hz	50
系统功率P_o/kW	5

参数	设定值
输入电压V_in/V	12
输出电压V_o/V	1.5
最大输出电流I_o/A	15
开关频率f_sw/kHz	300

方案	功率损耗/W	面积/mm²	成本/元
A	1.82	257.58	14.01
B	4.50	119.62	37.61
C	2.83	197.54	8.00

方案	功率损耗/W	面积/mm²	成本/元
A	1.92	310.64	29.65
B	3.76	119.81	13.95
C	2.65	150.32	10.62