变权-混合决策评估的复合功能并网逆变器多目标协同优化控制方法

doi:10.11930/j.issn.1004-9649.202310052

摘要/Abstract

摘要：

复合功能并网逆变器（multi-functional grid-connected inverter，MFGCI）在完成功率输出的同时，具备解决配电网中多种电能质量问题的能力，但该能力往往受其可用于电能质量治理的补偿容量的限制。以MFGCI的控制结构为基础，给出了无锁相环补偿指令电流及并网跟踪电流指令，提出了基于变权-混合决策评估的多目标协同优化方法，以更好适用于因新能源不确定性及非线性负荷接入导致的电能质量指标波动问题。构建了以电能质量补偿效果最佳和所需补偿容量最小的多目标函数，采用基于多目标人工蜂鸟算法（multi-objective artificial hummingbird algorithm，MOAHA）更新机制的优化算法，求解补偿各电能质量问题的最优容量分配系数，并通过多种场景下仿真，验证了所提方法的正确性和有效性。

关键词: 电能质量, 复合功能并网逆变器, 协同优化, 变权-混合决策, 多目标人工蜂鸟算法

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

杨帆, 卫水平, 任意, 陈秭龙, 乐健. 变权-混合决策评估的复合功能并网逆变器多目标协同优化控制方法[J]. 中国电力, 2024, 57(3): 113-125.

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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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202310052

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

图/表 17

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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