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浙江大学学报(工学版)
浙江大学学报(工学版)  2023, Vol. 57 Issue (3): 606-615    DOI: 10.3785/j.issn.1008-973X.2023.03.019
电气工程     
基于超平面NSGA-II的双输入双降压逆变器系统参数优化设计
李煌1(),葛红娟1,2,*(),马莹3,王永帅2
1. 南京航空航天大学 民航学院，江苏 南京 211106
2. 南京航空航天大学 自动化学院，江苏 南京 211106
3. 中国商飞上海飞机设计研究院，上海 201210
Parameters optimization design of dual-input dual-buck inverter system based on hyperplane NSGA-II
Huang LI1(),Hong-juan GE1,2,*(),Ying MA3,Yong-shuai WANG2
1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211016, China
3. COMAC Shanghai Aircraft Design and Research Institute, Shanghai 201210, China
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摘要：

针对第二代非支配排序遗传算法（NSGA-II）计算过程中存在种群分布不均匀、收敛性速度较慢的问题，提出超平面NSGA-II（HP-NSGA-II）. 该算法通过连接反映种群边缘分布的极值点构造超平面，以其法向量为进化趋势，对临界层个体在超平面进行投影，促使种群朝着分布均匀且收敛良好的最优解进化. 以双输入双降压型逆变器（DIDBI）为多目标优化对象，开关损耗、输出电压总谐波失真和滤波元件体积为优化目标，依据谐振频率、电感电流纹波和功率因数的要求，推导出滤波电容、滤波电感和开关频率的约束条件，比较分析HP-NSGA-II与NSGA-II、考虑各目标重要度的 ${\boldsymbol{\gamma}} $-NSGA-Ⅱ的应用场合和价值. 以某型逆变器样机为例，开展参数优化设计实验研究，结果表明了设计的有效性与正确性.
关键词： 双输入双降压型逆变器(DIDBI);  超平面第二代非支配排序遗传算法(HP-NSGA-II);  多目标;  系统参数    
Abstract:

Aiming at the problems of uneven population distribution and slow convergence in the calculation process of non-dominated sorting genetic algorithm II (NSGA-II), a hyperplane NSGA-II (HP-NSGA-II) was proposed. The algorithm constructed a hyperplane by connecting extreme points reflecting the edge distribution of the population, with its normal vector as the evolutionary trend, the critical layer individuals were projected on the hyperplane, which promoted the population to evolve towards the optimal solution with uniform distribution and good convergence. Dual-input dual-buck inverter (DIDBI) was a multi-objective optimization object, the switching loss, total harmonic distortion of output voltage, and volume of filter elements were regarded as optimization objectives. According to the requirements of resonant frequency, inductor current ripple, and the power factor, the constraint conditions of filter capacitance, filter inductance, and switching frequency were derived. The application situation and value of the HP-NSGA-II were compared with NSGA-II and γ-NSGA-II considering the importance of each target. An Inverter prototype was regarded as an example, the experimental research on parameters optimization design was carried out, and the results show the effectiveness and correctness of the design.
Key words: dual-input dual-buck inverter (DIDBI)    hyperplane-non-dominated sorting genetic algorithm II (HP-NSGA-II)    multi-objective    system parameter
收稿日期: 2022-03-23 出版日期: 2023-03-31
CLC:  TM 46  
基金资助: 国家自然科学基金委员会-中国民用航空局民航联合研究基金项目资助（U2233205，U1933115，U2133203）
通讯作者: 葛红娟     E-mail: 15834188137@163.com;allenge@nuaa.edu.cn
作者简介: 李煌（1992—），女，博士生，从事机载系统安全性技术研究. orcid.org/0000-0002-8454-9839. E-mail： 15834188137@163.com
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引用本文:

李煌,葛红娟,马莹,王永帅. 基于超平面NSGA-II的双输入双降压逆变器系统参数优化设计[J]. 浙江大学学报(工学版), 2023, 57(3): 606-615.

Huang LI,Hong-juan GE,Ying MA,Yong-shuai WANG. Parameters optimization design of dual-input dual-buck inverter system based on hyperplane NSGA-II. Journal of ZheJiang University (Engineering Science), 2023, 57(3): 606-615.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.03.019    或    https://www.zjujournals.com/eng/CN/Y2023/V57/I3/606

图 1  双输入双降压型逆变器拓扑
图 2  开关管和二极管的简化开关过程
图 3  超平面第二代非支配排序遗传算法的流程图
图 4  基于超平面第二代非支配排序遗传算法的临界层个体超平面构建
图 5  超平面第二代非支配排序遗传算法的性能测试实验
图 6  第二代非支配排序遗传算法改进前后各目标函数值进化过程
编号 NSGA-Ⅱ HP-NSGA-II γ-NSGA-Ⅱ,γ1 = [THD, VL,Ploss] γ-NSGA-Ⅱ,γ2 = [Ploss, THD,VL]
THD V/dm3 Ploss/W THD V/dm3 Ploss/W THD V/dm3 Ploss/W THD V/dm3 Ploss/W
1 0.041 0.060 12.27 0.036 0.055 12.01 0.028 0.061 12.41 0.040 0.064 11.78
2 0.043 0.064 12.30 0.034 0.057 12.16 0.030 0.063 12.33 0.039 0.065 11.71
3 0.040 0.058 12.28 0.035 0.054 12.05 0.029 0.062 12.30 0.042 0.064 11.77
4 0.039 0.059 12.23 0.035 0.052 12.05 0.029 0.059 12.41 0.041 0.066 11.82
5 0.042 0.057 12.25 0.037 0.055 12.02 0.030 0.059 12.41 0.043 0.067 11.69
6 0.040 0.060 12.20 0.036 0.053 12.01 0.028 0.061 12.39 0.042 0.068 11.75
7 0.041 0.062 12.25 0.033 0.051 12.06 0.030 0.058 12.35 0.044 0.064 11.76
8 0.042 0.060 12.26 0.036 0.054 12.02 0.030 0.059 12.37 0.041 0.066 11.83
9 0.042 0.058 12.31 0.037 0.053 12.08 0.031 0.063 12.32 0.042 0.065 11.80
10 0.041 0.059 12.24 0.035 0.050 12.09 0.029 0.062 12.33 0.041 0.063 11.77
平均 0.041 0.060 12.26 0.035 0.053 12.05 0.029 0.061 12.36 0.042 0.065 11.77
表 1  算法改进前后第50代Pareto解集对比
图 7  双输入双降压型逆变器的样机
图 8  双输入双降压型逆变器的输出电压波形
1 卢其威, 邓欢, 陈婷, 等 LLCC谐振滤波器在高频正弦波逆变器中的优化设计[J]. 电工技术学报, 2017, 32 (20): 142- 152
LU Qi-wei, DENG Huan, CHEN Ting, et al Parameters optimization and design of LLCC resonant converter in high frequency sinusoidal inverter[J]. Transactions of China Electrotechnical Society, 2017, 32 (20): 142- 152
2 张俊红, 郭迁, 王健, 等 塑料机油冷却器盖加强筋参数的多目标优化[J]. 浙江大学学报: 工学版, 2016, 50 (7): 1360- 1366
ZHANG Jun-hong, GUO Qian, WANG Jian, et al Multi-objective optimization of ribs design parameters for plastic oil cooler cover[J]. Journal of Zhejiang University: Engineering Science, 2016, 50 (7): 1360- 1366
3 CHEW C K, KONDAPALLI S R R. Modelling, analysis, simulation and design optimization (genetic algorithm) of DC-DC converter for uninterruptible power supply applications [C]// 2005 International Conference on Power Electronics and Drive Systems. Kuala Lumpur: IEEE, 2006: 1530-1535.
4 WU Biao, ZHANG Lei, MENG Jin An optimization design method of low frequency resonant filter based on NSGA-II[J]. Electrical Measurement and Instrumentation, 2018, 55 (10): 62- 67
5 ELKHOLY M M, EL-HAMEED M A, EL-FERGANY A A Harmonic analysis of hybrid renewable microgrids comprising optimal design of passive filters and uncertainties[J]. Electric Power Systems Research, 2018, 163: 491- 501
doi: 10.1016/j.jpgr.2018.07.023
6 SRINIVAS N, DEB K Multiobjective function optimization using nondominated sorting genetic algorithms[J]. Evolutionary Computation, 1994, 2 (3): 221- 248
doi: 10.1162/evco.1994.2.3.221
7 DEB K, PRATAP A, AGARWAL S, et al A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6 (2): 182- 197
doi: 10.1109/4235.996017
8 程虹, 高元海, 王淳, 等 基于无重访NSGA-Ⅱ算法的配电网多目标重构[J]. 电力系统保护与控制, 2016, 44 (23): 10- 16
CHENG Hong, GAO Yuan-hai, WANG Chun, et al Multi-objective distribution network reconfiguration based on non-revisiting NSGA-II algorithm[J]. Power System Protection and Control, 2016, 44 (23): 10- 16
9 苏玉刚, 陈苓芷, 唐春森, 等 基于NSGA-Ⅱ算法的ECPT系统PID参数寻优及输出稳压控制[J]. 电工技术学报, 2016, 31 (19): 106- 114
SU Yu-gang, CHEN Ling-zhi, TANG Chun-sen, et al Evolutionary multi-objective optimization of PID parameters for output voltage regulation in ECPT system based on NSGA-Ⅱ[J]. Transactions of China Electrotechnical Society, 2016, 31 (19): 106- 114
10 何忠华, 袁一星 基于剩余能量熵的供水管网可靠性优化设计[J]. 浙江大学学报: 工学版, 2014, 48 (7): 1188- 1194
HE Zhong-hua, YUAN Yi-xing Reliability optimization design of water distribution system based on surplus energy entropy[J]. Journal of Zhejiang University: Engineering Science, 2014, 48 (7): 1188- 1194
11 刘思. 基于NSGA-Ⅱ算法的SWISS整流器多目标优化研究 [D]. 哈尔滨: 哈尔滨理工大学, 2020.
LIU Si. Multi-objective optimization of SWISS rectifier based on NSGA-Ⅱ algorithm [D]. Harbin: Harbin University of Science and Technology, 2020.
12 王康, 王久和, 王路 低压开关槽式变换器多目标优化[J]. 西安科技大学学报, 2021, 41 (5): 938- 947
WANG Kang, WANG Jiu-he, WANG Lu Multi-objective optimization of switched-tank converter for low-voltage applications[J]. Journal of Xi’an University of Science and Technology, 2021, 41 (5): 938- 947
13 MOHAMMADI M Bacterial foraging optimization and adaptive version for economically optimum sitting, sizing and harmonic tuning orders setting of LC harmonic passive power filters in radial distribution systems with linear and nonlinear loads[J]. Applied Soft Computing, 2015, 29: 345- 356
doi: 10.1016/j.asoc.2015.01.021
14 杨帆, 葛红娟, 党润芸, 等 一种双直流输入多电平双Buck逆变器[J]. 电工技术学报, 2018, 33 (6): 1320- 1327
YANG Fan, GE Hong-juan, DANG Run-yun, et al A dual-DC-input multi-level dual-buck inverter[J]. Transactions of China Electrotechnical Society, 2018, 33 (6): 1320- 1327
15 中国民用航空局. 中国民用航空技术标准规定 航空静止变流器: CTSO-C73 [S]. 北京: [s.n], 2019.
16 ZHANG L, GE H J, MA Y, et al Multi-objective optimization design of a notch filter based on improved NSGA-II for conducted emissions[J]. IEEE Access, 2020, 8: 83213- 83223
doi: 10.1109/ACCESS.2020.2991576
17 YANG F, GE H J, YANG J F, et al A Family of dual-buck inverters with an extended low-voltage DC-input port for efficiency improvement based on dual-input pulsating voltage-source cells[J]. IEEE Transactions on Power Electronics, 2017, 33 (4): 3115- 3128
18 胡存刚, 姚培, 张云雷, 等 高效非隔离单相并网MOSFET逆变器拓扑及控制策略[J]. 电工技术学报, 2016, 31 (13): 82- 91
HU Cun-gang, YAO Pei, ZHANG Yun-lei, et al Topology and control strategy for high-efficient non-isolated single-phase grid-connected MOSFET inverter[J]. Transactions of China Electrotechnical Society, 2016, 31 (13): 82- 91
doi: 10.3969/j.issn.1000-6753.2016.13.010
19 全国电磁兼容标准化管理委员会. 电磁兼容 试验和测量技术供电系统及所连设备谐波、谐间波的测量和测量仪器导则: GB/T 17626.7—2008 [S]. 北京: [s.n.], 2009.
20 叶海云, 马海啸 三相四桥臂逆变器输出滤波器的优化设计[J]. 电力电子技术, 2015, 49 (5): 93- 95
YE Hai-yun, MA Hai-xiao The optimal design of the output filter for three-phase four-leg inverter[J]. Power Electronics, 2015, 49 (5): 93- 95
21 郑征, 高照阳, 张展 基于虚拟电阻的PWM逆变器LC输出滤波器的研究[J]. 测控技术, 2017, 36 (3): 127- 131
ZHENG Zheng, GAO Zhao-yang, ZHANG Zhan Research on PWM inverter LC output filter based on virtual resistance[J]. Measurement and Control Technology, 2017, 36 (3): 127- 131
22 全国能源基础与管理标准化技术委员会. 并网光伏发电专用逆变器技术条件: CGC/GF 004: 2011(CNCA/CTS 0004-2009A) [S]. 北京: [s.n.], 2011.
23 LI X, LI X, WANG K, et al Achievement scalarizing function sorting for strength Pareto evolutionary algorithm in many-objective optimization[J]. Neural Computing and Applications, 2021, 33: 6369- 6388
doi: 10.1007/s00521-020-05398-1
24 毕晓君, 王朝 基于超平面投影的高维多目标进化算法[J]. 浙江大学学报: 工学版, 2018, 52 (7): 1284- 1293
BI Xiao-jun, WANG Chao Many-objective evolutionary algorithm based on hyperplane projection[J]. Journal of Zhejiang University: Engineering Science, 2018, 52 (7): 1284- 1293
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