Parameter Optimization of Sliding Mode Variable Controller for Grid Connected Compound Device Based on Improved Moth-flame Optimization Algorithm

doi:10.11985/2023.04.031

Abstract

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

CLC Number:

TM712

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.

Figures/Tables 23

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

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

References 25

[1]	张兴, 李明, 郭梓暄, 等. 新能源并网逆变器控制策略研究综述与展望[J]. 全球能源互联网. 2021, 4(5):506-515.
	ZHANG Xing, LI Ming, GUO Zixuan, et al. Review and perspectives on control strategies for renewable energy grid-connected inverters[J]. Journal of Global Energy Interconnection, 2021, 4(5):506-515.
[2]	刘家军. 基于功率传递的电网间同期并列原理与技术[M]. 北京: 科学出版社, 2018.
	LIU Jiajun. Principle and technology of synchronization and paralleling between power grids based on power transfer[M]. Beijing: Science Press, 2018.
[3]	刘家军, 汤涌, 姚李孝, 等. 电压型换流器实现电网间同期并列的原理及仿真研究[J]. 中国电机工程学报, 2010, 30(S1):12-17.
	LIU Jiajun, TANG Yong, YAO Lixiao, et al. Research on the principle and simulation of synchronization parallel between grids by VSC[J]. Proceedings of the CSEE, 2010, 30(S1):12-17.
[4]	LIU Jiajun, TANG Yong, SUN Huadong, et al. Research on implementation of compound function based on back-to-back VSC in power grid parallel[C]// 2010 International Conference on Power System Technology, 2010:1-6.
[5]	支建刚. 基于VSC-HVDC的电网解列与并列的综合控制研究[D]. 西安: 西安理工大学, 2017.
	ZHI Jiangang. Research on integrated control of grid splitting and paralleling operation based on VSC-HVDC[D]. Xi’an: Xi’an University of Technology, 2017.
[6]	刘家军, 任娟, 王锟, 等. 基于背靠背VSC-HVDC同期并列装置转换为UPFC的策略研究[J]. 电网与清洁能源, 2018, 34(7):7-12,18.
	LIU Jiajun, REN Juan, WANG Kun, et al. Research on control strategy of synchronization paralleling device converted into unified power flow controller based on back-to-back VSC-HVDC[J]. Power System and Clean Energy, 2018, 34(7):7-12,18.
[7]	刘家军, 王锟, 杨松. 基于背靠背VSC-HVDC电网间同期并列装置实现UPFC的仿真研究[J]. 电力电容器与无功补偿, 2020, 41(1):151-157.
	LIU Jiajun, WANG Kun, YANG Song. Simulation study on achieving UPFC of simultaneous paralleling device based on back to back VSC-HVDC[J]. Power Capacitor & Reactive Power Compensation, 2020, 41(1):151-157.
[8]	刘家军, 杨松, 谭雅岚. 基于功率传递的同期并列装置实现SSSC功能的仿真[J]. 电力电容器与无功补偿, 2020, 41(2):103-109.
	LIU Jiajun, YANG Song, TAN Yalan. Simulation of implementing SSSC function converting from grid paralleling device based on power through transmission[J]. Power Capacitor & Reactive Power Compensation, 2020, 41(2):103-109.
[9]	孙骥. 基于虚拟同步发电机的并网复合装置在低惯量系统中的应用研究[D]. 西安: 西安理工大学, 2021.
	SUN Ji. Application of gird connected composite device based on virtual synchronous generator in low inertia system[D]. Xi’an: Xi’an University of Technology, 2021.
[10]	LIU Jiajun, YANG Jie. Research on power oscillation suppression strategy of grid-connected composite system based on impedance optimization[C]// 2021 4th International Conference on Energy,Electrical and Power Engineering(CEEPE), 2021:535-541.
[11]	王福忠, 陶新坤, 田广强, 等. 基于改进果蝇算法优化的微电网逆变器恒功率控制算法[J]. 电力系统保护与控制, 2021, 49(21):71-79.
	WANG Fuzhong, TAO Xinkun, TIAN Guangqiang, et al. Constant power control algorithm for a microgrid inverter based on an improved fruit fly algorithm[J]. Power System Protection and Control, 2021, 49(21):71-79.
[12]	林雪华, 洪国巍, 郭琦, 等. 基于改进MOPSO的MMC控制参数多机联合优化[J]. 电力系统保护与控制, 2017, 45(9):48-55.
	LIN Xuehua, HONG Guowei, GUO Qi, et al. Multi-machine joint parameters optimization of MMC controller based on improved MOPSO[J]. Power System Protection and Control, 2017, 45(9):48-55.
[13]	GUO Jinghua, LI Keqiang, FAN Jingjing, et al. Neural-fuzzy-based adaptive sliding mode automatic steering control of vision-based unmanned electric vehicles[J]. Chinese Journal of Mechanical Engineering, 2021, 34(1):1-13. doi: 10.1186/s10033-020-00524-5
[14]	MIRJALILI S. Moth-flame optimization algorithm:A novel nature-inspired heuristic paradigm[J]. Knowledge-based Systems, 2015, 89:228-249. doi: 10.1016/j.knosys.2015.07.006
[15]	黄琳妮. 基于群飞蛾扑火算法的风力发电系统PI控制参数优化整定[D]. 广州: 华南理工大学, 2018.
	HUANG Linni. Optimization of PI control parameters of wind energy system based on swarm moths flame algorithm[D]. Guangzhou: South China University of Technology, 2018.
[16]	潘晓杰, 张立伟, 张文朝, 等. 基于飞蛾扑火优化算法的多运行方式电力系统稳定器参数协调优化方法[J]. 电网技术, 2020, 44(8):3038-3046.
	PAN Xiaojie, ZHANG Liwei, ZHANG Wenchao, et al. Multi-operation mode PSS parameter coordination optimization method based on moth-flame optimization algorithm[J]. Power System Technology, 2020, 44(8):3038-3046.
[17]	王子琪, 陈金富, 张国芳, 等. 基于飞蛾扑火优化算法的电力系统最优潮流计算[J]. 电网技术, 2017, 44(11):3642-3647.
	WANG Ziqi, CHEN Jinfu, ZHANG Guofang, et al. Optimal power flow calculation with moth-flame optimization algorithm[J]. Power System Technology, 2017, 44(11):3642-3647.
[18]	刘金琨. 滑模变结构控制MATLAB仿真[M]. 北京: 清华大学出版社, 2015.
	LIU Jinkun. MATLAB simulation of sliding mode variable structure control[M]. Beijing: Tsinghua University Press, 2015.
[19]	曹亚丽, 曹竣奥, 宋昕, 等. 一种改进滑模观测器的PMSM矢量控制研究[J]. 电力系统保护与控制, 2021, 49(16):104-111.
	CAO Yali, CAO Junao, SONG Xin, et al. Research on vector control of PMSM based on an improved sliding mode observer[J]. Power System Protection and Control, 2021, 49(16):104-111.
[20]	赵盈盈. 基于滑模变结构的PMSM直接转矩控制[D]. 长沙: 湖南大学, 2015.
	ZHAO Yingying. Permanent magnet synchronous motor direct torque control based on sliding mode[D]. Changsha: Hunan University, 2015.
[21]	王雨薇. 基于混沌理论和改进人工神经网络的短期风速预测研究[D]. 北京: 华北电力大学, 2019.
	WANG Yuwei. Research on short-term wind speed forecasting based on chaotic theory and improved artificial neural network[D]. Beijing: North China Electric Power University, 2019.
[22]	何思名, 袁智勇, 雷金勇, 等. 基于改进灰狼算法的DG接入配电网反时限过电流保护定值优化[J]. 电力系统保护与控制, 2021, 49(18):173-181.
	HE Siming, YUAN Zhiyong, LEI Jinyong, et al. Optimal setting method of inverse time over-current protection for a distribution network based on the improved grey wolf optimization[J]. Power System Protection and Control, 2021, 49(18):173-181.
[23]	唐伦, 田立峰, 张曦, 等. 计及N-1安全约束的输电网阻塞管控数据驱动模型[J]. 电测与仪表, 2019, 56(23):11-17.
	TANG Lun, TIAN Lifeng, ZHANG Xi, et al. A data-driven model for transmission congestion management considering N-1 static security constraints[J]. Electrical Measurement & Instrumentation, 2019, 56(23):11-17.
[24]	袁性忠, 张鹭, 罗迪, 等. 基于边缘计算的能量自治区域调度策略[J]. 智慧电力, 2021, 49(8):46-54.
	YUAN Xingzhong, ZHANG Lu, LUO Di, et al. Energy autonomous region scheduling strategy based on edge computing[J]. Smart Power, 2021, 49(8):46-54.
[25]	张莹, 苏建徽, 汪海宁, 等. 基于改进磷虾群算法优化Elman神经网络的PEMFC电堆建模[J]. 电测与仪表, 2021, 58(3):23-27.
	ZHANG Ying, SU Jianhui, WANG Haining, et al. PEMFC stack modeling based on Elman neural network optimized by improved krill herd algorithm[J]. Electrical Measurement & Instrumentation, 2021, 58(3):23-27.

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