面向分布式电源的改进GWO-GS电网承载力提升方法*

doi:10.11985/2021.02.018

摘要/Abstract

摘要：

分布式发电(Distributed generation, DG)单元高渗透性会造成各种电能质量问题,从而导致电网承载能力降低。为此,提出一种面向分布式电源的改进灰狼优化与引力搜索(Grey wolf optimization and gravity search,GWO-GS)电网承载力提升方法。提出一种谐波滤波器,大范围地抑制谐波并降低功率损耗,从而最大限度地提高谐波约束的随机性承载能力。利用控制系数的自适应调节和适应度值对权重的作用,同时采用改进灰狼算法进行优化,避免陷入局部最优。结合DG间歇输出功率、负载变化等不确定因素,构建基于改进GWO-GS的滤波器优化。利用Matlab仿真平台对所提优化算法进行验证,结果表明所提算法的收敛时间最短,性能优于其他算法。

关键词: 分布式发电, 改进灰狼优化, 引力搜索, 承载能力, 谐波滤波器

Abstract:

The high permeability of distributed generation (DG) units will cause various power quality problems, which will lead to the reduction of power grid carrying capacity. Therefore, a method to improve the load-carrying capacity of DG connected to power grid based on improved grey wolf optimization and gravity search (GWO-GS) is proposed. Firstly, a harmonic filter is proposed to suppress harmonics and reduce power loss, so as to maximize the random carrying capacity of harmonic constraints. Then, the adaptive adjustment of control coefficient and the effect of fitness value on weight are used, and the improved gray wolf algorithm is used to optimize to avoid falling into local optimum. Finally, combined with uncertain factors such as DG intermittent output power and load change, the filter optimization based on improved GWO-GS is constructed. In addition, Matlab simulation platform is used to verify the proposed optimization algorithm. The results show that the proposed algorithm has the shortest convergence time and better performance than other algorithms.

Key words: Distributed generation, improved gray wolf optimization, gravitational search, hosting capacity, harmonic filter

中图分类号:

TM73

许翰宸, 王立辉, 廖宇航. 面向分布式电源的改进GWO-GS电网承载力提升方法^*[J]. 电气工程学报, 2021, 16(2): 141-148.

XU Hanchen, WANG Lihui, LIAO Yuhang. Method of Improving Power Grid Hosting Capacity for Distributed Generation Based on Improved GWO-GS[J]. Journal of Electrical Engineering, 2021, 16(2): 141-148.

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参数	基准	补偿系统
参数	基准	改进GWO-GS	文献[7]	文献[12]
V_min/p.u.	0.958 3	0.935 3	0.978 5	0.987 2
TDD(%)	6.657 5	7.868 6	8.357 2	8.000 1
THDV(%)	5.454 3	4.042 2	4.123 3	4.145 1
滤波器损耗/p.u.	0.003 0	0.003 1	0.003 4	0.003 3
总功率损耗/p.u.	0.037 5	0.006 2	0.007 3	0.007 5
位移功率因素	0.921 3	0.998 2	0.953 6	0.975 2
功率因素	91.685 6	0.986 7	0.965 2	0.975 4
确定性承载能力(%)	50.00	56.51	50.72	49.18

参数	补偿系统
参数	改进GWO-GS	文献[7]	文献[12]
V_min/p.u.	0.994 3	0.988 5	0.987 2
TDD(%)	7.866 6	7.352 2	7.203 8
THDV(%)	4.432 1	4.191 3	4.143 1
滤波器损耗/p.u.	0.001 8	0.002 9	0.002 3
总功率损耗/p.u.	0.003 5	0.004 1	0.004 5
位移功率因素	0.999 2	0.972 6	0.961 4
功率因素	0.967 7	0.945 2	0.913 4
随机性承载能力(%)	74.05	70.38	69.98