高维多目标极值优化算法的中点钳位型三电平逆变器设计方法*

doi:10.16628/j.cnki.2095-8188.2020.07.005

电器与能效管理技术 ›› 2020, Vol. 592 ›› Issue (7): 29-35.doi: 10.16628/j.cnki.2095-8188.2020.07.005

高维多目标极值优化算法的中点钳位型三电平逆变器设计方法^*

李民英¹, 陈宇¹, 戴瑜兴¹^,²

1.广东志成冠军集团有限公司, 广东东莞 523718;
2.湖南大学, 湖南长沙 410082

收稿日期:2020-05-27 出版日期:2020-07-30 发布日期:2020-08-10
作者简介:李民英(1961—),男,高级工程师,主要从事大容量电力电子装备与智能控制研究。|陈宇(1982—),男,高级工程师,主要从事大容量电力电子装备设计与研究。|戴瑜兴(1956—),男,教授,研究方向为大容量变流技术与微电网系统。
基金资助:
* 广东省“珠江人才计划”项目(2017ZT07G23)

SHEPWM Design Method for Mid-Point Clamped Three Level Inverter Based on Many Objective Population Extremal Optimization Algorithm

LI Minying¹, CHEN Yu¹, DAI Yuxing¹^,²

1. Guangdong Zhicheng Champion Group Co.,Ltd., Dongguan 523718, China;
2. Hunan University, Changsha 410082, China

Received:2020-05-27 Online:2020-07-30 Published:2020-08-10

摘要/Abstract

摘要：

特定谐波消除技术(SHEPWM)在降低直流侧电流纹波、提高电力质量等方面发挥着重要的作用。SHEPWM难题的本质是计及多性能指标的高维多目标约束优化问题,提出了基于高维多目标极值优化算法(MaOPEO)的三电平SHEPWM技术,克服了单目标智能优化算法性能评价函数过于简单、权重系数难以精准确定等问题。所提方法首先以特定阶次谐波最小化和输出电压波形的总谐波畸变率最小化建立高维多目标优化函数,再设计高效的MaOPEO进行求解,最后选取1/4周期3角度SHEPWM方程组为测试案例,与基于捕食者策略的遗传算法和多目标遗传算法的SHEPWM对比,验证了所提方法的有效性。

关键词: SHEPWM, 三电平, 高维多目标约束, 极值优化

Abstract:

The specific harmonic elimination technology (SHEPWM) plays an important role in reducing DC-side current ripple and improving power quality.The SHEPWM problem is essentially a high-dimensional multi-objective constraint optimization problem that takes into account multiple performance indicators.Therefore,the three-level SHEPWM technology based on the high-dimensional multi-objective extremal optimization algorithm (MaOPEO) is proposed,which overcomes the problems that the single-objective intelligent optimization algorithm performance evaluation function is too simple and the weight coefficient is difficult to determine accurately.The proposed method first establishes a high-dimensional multi-objective optimization function by minimizing specific order harmonics and THD minimization of the output voltage waveform,and then designs an efficient MaOPEO to solve it.Select the 1/4 period 3 angle SHEPWM equation system as the test.The simulation is compared with predator strategy genetic algorithm multi-objective genetic algorithm-based SHEPWM to verify the effectiveness of the proposed method.

Key words: SHEPWM, three-level, many-objective optimization, extremal optimization

中图分类号:

TM46

李民英, 陈宇, 戴瑜兴. 高维多目标极值优化算法的中点钳位型三电平逆变器设计方法^*[J]. 电器与能效管理技术, 2020, 592(7): 29-35.

LI Minying, CHEN Yu, DAI Yuxing. SHEPWM Design Method for Mid-Point Clamped Three Level Inverter Based on Many Objective Population Extremal Optimization Algorithm[J]. LOW VOLTAGE APPARATUS, 2020, 592(7): 29-35.

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算法	N	MG	其他
MaOPEO算法	56	200	b=random(2,5)
NSGA-II算法	56	1 500	P_c=2/Nv;P_m=2/Nv
PSGA算法	20	10 000	P_c=0.3~0.9;P_m=0.1~0.9

算法	α₁/(°)	α₂/(°)	α₃/(°)	THD/%	HV (平均)	t_average /s
MaOPEO	18.15	33.90	39.08	36.77	23.97	37.38
MaOPEO	18.24	33.90	39.15	36.83	23.97	37.38
NSGA-II	7.611	11.29	35.13	40.91	23.90	56.14
NSGA-II	6.87	18.72	31.93	44.29	23.90	56.14
SPGA	29.63	39.48	52.74	55.71	—	—
SPGA	12.09	68.94	84.19	81.15	—	—

算法	α₁/(°)	α₂/(°)	α₃/(°)	THD/%	HV (平均)	t_average /s
MaOPEO	2.297	12.97	24.79	44.08	23.96	40.63
MaOPEO	16.71	33.52	37.67	35.87	23.96	40.63
NSGA-II	11.88	83.70	85.04	38.94	23.69	55.21
NSGA-II	11.30	72.29	74.24	41.10	23.69	55.21
SPGA	26.78	38.89	50.74	50.85	—	—
SPGA	12.64	70.44	83.51	73.80	—	—

算法	α₁/(°)	α₂/(°)	α₃/(°)	THD/%	HV (平均)	t_average /s
MaOPEO	13.58	24.17	29.00	34.99	21.15	42.84
MaOPEO	13.61	24.03	30.17	35.81	21.15	42.84
NSGA-II	8.39	15.58	24.46	36.81	21.15	55.36
NSGA-II	1.13	10.36	19.79	43.28	21.15	55.36
SPGA	19.35	35.63	41.59	38.78	—	—
SPGA	14.29	77.49	82.18	48.24	—	—

算法	α₁/(°)	α₂/(°)	α₃/(°)	THD/%	HV (平均)	t_average /s
MaOPEO	15.08	25.45	28.60	33.20	19.23	41.59
MaOPEO	13.81	23.91	29.61	35.26	19.23	41.59
NSGA-II	14.37	85.55	89.70	46.67	19.15	57.97
NSGA-II	16.83	30.57	35.51	35.66	19.15	57.97
SPGA	17.13	31.60	36.38	36.06	—	—
SPGA	15.23	81.90	85.20	43.46	—	—

高维多目标极值优化算法的中点钳位型三电平逆变器设计方法^*

SHEPWM Design Method for Mid-Point Clamped Three Level Inverter Based on Many Objective Population Extremal Optimization Algorithm

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