Multi-objective Collaborative Optimization Control Method of Composite Function Grid Connected Inverters Considering Variable Weight Hybrid Decision Evaluation

doi:10.11930/j.issn.1004-9649.202310052

Abstract

Abstract:

Multi-functional grid connected inverter (MFGCI) has the ability to solve various power quality problems in the distribution network while fulfilling the power output task simultaneously, but this ability is often limited by its compensation capacity that can be used for power quality management. Based on the control structure of MFGCI, this paper provides the current compensation order and grid connection tracking current order without phase-locked loop(PLL). A multi-objective collaborative optimization method based on variable weight mixed decision evaluation is proposed to better adapt to the fluctuations in power quality indicators caused by nonlinear load integration and uncertainty of new energy. A multi-objective function is constructed to achieve the best power quality compensation effect and the minimum required compensation capacity. Based on update mechanism from the multi-objective artificial hummingbird algorithm (MOAHA), an optimization algorithm is employed to solve the optimal capacity allocation coefficient for compensating various power quality problems. The correctness and effectiveness of the proposed method are verified through simulations in various scenarios.

Key words: power quality, multi-function grid connected inverter, collaborative optimization, variable weight hybrid decision, multi-objective artificial hummingbird algorithm

Fan YANG, Shuiping WEI, Yi REN, Zilong CHEN, Jian LE. Multi-objective Collaborative Optimization Control Method of Composite Function Grid Connected Inverters Considering Variable Weight Hybrid Decision Evaluation[J]. Electric Power, 2024, 57(3): 113-125.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202310052

https://www.electricpower.com.cn/EN/Y2024/V57/I3/113

Figures/Tables 17

References 25

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仿真模块	变量	参数值
PVMFGCI	开关频率/Hz	10 000
	直流母线电压U_dc/V	700
	滤波电感L/mH	2
	滤波电容C/μF	10
	阻尼电阻R/Ω	4
	并网功率跟踪指令值P_g, Q_g(kW, kV·A)	(18, 0)/(20, 0)/(16, 0)
线路	线路的单位电阻/ (Ω·km^–1)	0.642
线路	线路的单位电抗/ (Ω·km^–1)	0.083
配网电源	电压U_s/V	380
PCC1~3 非线性负荷	R/Ω, L/H	15, 0.005
		20, 0.010
		25, 0.015
PCC1~3 三相不平衡负荷	R/Ω, L/H	2.9, 0.17/4, 0.079/1.6, 0.04
		3.3, 0.14/4, 0.072/2.6, 0.035
		3.1, 0.12/4, 0.065/2.4, 0.046
PCC1~3 三相平衡负荷	R/Ω	(35, 35, 35)/(30, 30, 30)/ (25, 25, 25)
DG1/DG2/DG3	指令值P/kW, Q/(kV·A)	(36, 0)/(32, 0)/(36, 0)

仿真模块	变量	参数值
PVMFGCI	开关频率/Hz	10 000
	直流母线电压U_dc/V	700
	滤波电感L/mH	2
	滤波电容C/μF	10
	阻尼电阻R/Ω	4
	并网功率跟踪指令值P_g, Q_g(kW, kV·A)	(18, 0)/(20, 0)/(16, 0)
线路	线路的单位电阻/ (Ω·km^–1)	0.642
线路	线路的单位电抗/ (Ω·km^–1)	0.083
配网电源	电压U_s/V	380
PCC1~3 非线性负荷	R/Ω, L/H	15, 0.005
		20, 0.010
		25, 0.015
PCC1~3 三相不平衡负荷	R/Ω, L/H	2.9, 0.17/4, 0.079/1.6, 0.04
		3.3, 0.14/4, 0.072/2.6, 0.035
		3.1, 0.12/4, 0.065/2.4, 0.046
PCC1~3 三相平衡负荷	R/Ω	(35, 35, 35)/(30, 30, 30)/ (25, 25, 25)
DG1/DG2/DG3	指令值P/kW, Q/(kV·A)	(36, 0)/(32, 0)/(36, 0)

场景		设置
1		0~0.1 s， PVMFGCI 1~3均不投入
2		0.1~0.2 s，PVMFGCI 1~3分别补偿100%、80%和70%的谐波分量
3		0.2~0.3 s，PVMFGCI 1~3均同时补偿全部谐波和不平衡分量
4		0.3~0.4 s，PVMFGCI 1同时补偿全部谐波、不平衡分量以及60%的无功分量，PVMFGCI 2同时补偿全部谐波、不平衡分量以及70%的无功分量，PVMFGCI 3同时补偿全部谐波、不平衡分量以及80%的无功分量。
5		0.4~0.5 s，PVMFGCI 1~3均完全补偿
6		0.5~0.6 s，采用基于MOAHA的多目标优化补偿，多目标函数及决策变量按照3.1节原则设定，分别为各补偿点的电能质量综合指标最小、补偿容量最小及谐波畸变率、负序不平衡度、零序不平衡度、无功系数的补偿系数，MOAHA优化算法中种群设置为100，迭代次数为1 000，整体优化过程按照3.2中流程来实施

场景		设置
1		0~0.1 s， PVMFGCI 1~3均不投入
2		0.1~0.2 s，PVMFGCI 1~3分别补偿100%、80%和70%的谐波分量
3		0.2~0.3 s，PVMFGCI 1~3均同时补偿全部谐波和不平衡分量
4		0.3~0.4 s，PVMFGCI 1同时补偿全部谐波、不平衡分量以及60%的无功分量，PVMFGCI 2同时补偿全部谐波、不平衡分量以及70%的无功分量，PVMFGCI 3同时补偿全部谐波、不平衡分量以及80%的无功分量。
5		0.4~0.5 s，PVMFGCI 1~3均完全补偿
6		0.5~0.6 s，采用基于MOAHA的多目标优化补偿，多目标函数及决策变量按照3.1节原则设定，分别为各补偿点的电能质量综合指标最小、补偿容量最小及谐波畸变率、负序不平衡度、零序不平衡度、无功系数的补偿系数，MOAHA优化算法中种群设置为100，迭代次数为1 000，整体优化过程按照3.2中流程来实施

PCC	C₁	C₂	C₃	C₄
1	0.1 815	0.1 267	0.2 443	0.3 458
2	0.1 664	0.1 083	0.1 859	0.2 939
3	0.1 298	0.0 713	0.1 308	0.2 738