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

doi:10.16628/j.cnki.2095-8188.2020.07.005

Abstract

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

CLC Number:

TM46

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.

Figures/Tables 11

References 23

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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	—	—