基于改进飞蛾扑火优化算法的并网复合装置滑模变控制器参数优化

doi:10.11985/2023.04.031

摘要/Abstract

摘要：

针对背靠背MMC-HVDC并网复合装置滑模变控制参数整定困难的问题，提出一种基于改进飞蛾扑火优化算法的控制器优化方法。引入佳点集初始化，在初始化种群时增加变量取值的多样性，加快算法收敛速度，减少计算量；结合Lévy飞行更新机制，避免算法陷入局部最优。通过8个标准测试函数测试所提改进飞蛾扑火算法的性能，并与多个常见群智能优化算法进行对比，验证所提算法的优越性。研究实现Python-PSCAD联合仿真方法，以综合ITAE指标为目标函数，对滑模变控制器进行参数优化。仿真结果表明，经改进飞蛾扑火算法优化的控制器参数可以使并网复合装置具有更好的动态性能。

关键词: 背靠背MMC-HVDC, 并网复合装置, 飞蛾扑火优化算法, Python-PSCAD联合仿真

Abstract:

Aiming at the problem of difficult parameter tuning of sliding mode variable control for back-to-back MMC-HVDC grid connected composite device, a controller optimization method based on improved moth-flame optimization algorithm is proposed. Good point set initialization is introduced to increase the diversity of variable values when initializing the population, speed up the convergence speed of the algorithm and reduce the amount of calculation. Combined with Lévy flight update mechanism, the algorithm can avoid falling into local optimization. The performance of the proposed improved moth-flame optimization algorithm is tested by 8 standard test functions, and compared with several common swarm intelligence optimization algorithms to verify the superiority of the proposed algorithm. The Python-PSCAD joint simulation method is studied and realized. Taking the comprehensive ITAE index as the objective function, the parameters of the sliding mode variable controller are optimized. The simulation results show that the controller parameters optimized by the improved moth-flame optimization algorithm can make the grid connected composite device have better dynamic performance.

Key words: Back-to-back MMC-HVDC, grid connected composite device, moth-flame optimization algorithm, Python-PSCAD joint simulation

中图分类号:

TM712

刘家军, 林皓琨, 苗淼, 刘立鹏. 基于改进飞蛾扑火优化算法的并网复合装置滑模变控制器参数优化[J]. 电气工程学报, 2023, 18(4): 288-299.

LIU Jiajun, LIN Haokun, MIAO Miao, LIU Lipeng. Parameter Optimization of Sliding Mode Variable Controller for Grid Connected Compound Device Based on Improved Moth-flame Optimization Algorithm[J]. Journal of Electrical Engineering, 2023, 18(4): 288-299.

图/表 23

图1

图2

图3

图4

图5

表1

测试函数"

测试函数	$f min (x)$
$f 1 (x) = ∑ i = 1 n x i 2$	0
$f 2 (x) = ∑ i = 1 n x i + ∏ i = 1 n x i$	0
$f 3 (x) = ∑ i = 1 n (∑ j = 1 i x j) 2$	0
$f 4 (x) = max i x i, 1 ≤ i ≤ D$	0
$f 5 (x) = ∑ i = 1 n x i sin (x i) + 0.1 x i$	0
$f 6 (x) = ∑ i = 1 n x i 2 − 10 cos (2 π x i) + 10$	0
$f 7 (x) = − 20 e x p − 0.2 1 n ∑ i = 1 n x i 2 − exp 1 n ∑ i = 1 n cos 2 π x i + 20 + e$	0
$f 8 (x) = 1 4 000 ∑ i = 1 n (x i 2) − ∏ i = 1 n cos (x i i) + 1$	0

表1

表2

测试结果"

测试函数	算法	平均值	方差
$f 1$	PSO	2.642 245	0.144 362
	MFO	7.92×10^-30	1.49×10^-59
	改进MFO	1.10×10^-189	0
$f 2$	PSO	1.098 57	0.028 251
	MFO	1.333 333	3.80×10^-38
	改进MFO	4.50×10^-103	1.00×10^-208
$f 3$	PSO	19.212 38	88.857 96
	MFO	1.34×10^-6	2.13×10^-14
	改进MFO	2.80×10^-151	0
$f 4$	PSO	0.907 91	0.150 335
	MFO	2.852 998	5.537 056
	改进MFO	2.87×10^-88	2.80×10^-178
$f 5$	PSO	70.704 18	0.876 751
	MFO	12.259 06	4.81×10^-24
	改进MFO	3.60×10^-101	9.50×10^-212
$f 6$	PSO	26.165 55	110.614 7
	MFO	21.626 07	24.748 14
	改进MFO	0	0
$f 7$	PSO	1.565 486	0.038 066
	MFO	4.91×10^-15	0
	改进MFO	8.88×10^-16	0
$f 8$	PSO	0.829 78	0.009 495
	MFO	0.159 074	0.000 667
	改进MFO	0	0

表2

图6

表3

表4

图7

图8

图9

表5

图10

图11

表6

图12

图13

表7

图14

图15

表8

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参数	数值
系统电压/kV	220
直流侧电压额定值/kV	40
MMC容量/(MV·A)	40
桥臂模块数	40
子模块电容值/mF	10
桥臂电抗器电感值/mH	10

算法	综合ITAE值
优化前	0.442 789
MFO	0.366 769
改进MFO	0.358 213

算法	响应速度/s	超调情况/MW
优化前	0.14	0.094
MFO	0.09	0.037
改进MFO	0.08	0.021

算法	响应速度/s	超调情况/MVar
优化前	0.08	0.120
MFO	0.13	0.075
改进MFO	0.07	0.024