Citation: | Zhongqiang WU, Bilian CAO, Lincheng HOU, Xiaoyu HU, Boyan MA. Maximum Power Point Tracking for Photovoltaic System Based on Improved Multi-Verse Optimization[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3735-3742. doi: 10.11999/JEIT200599 |
[1] |
周孝信, 陈树勇, 鲁宗相, 等. 能源转型中我国新一代电力系统的技术特征[J]. 中国电机工程学报, 2018, 38(7): 1893–1904. doi: 10.13334/j.0258-8013.pcsee.180067
ZHOU Xiaoxin, CHEN Shuyong, LU Zongxiang, et al. Technology features of the new generation power system in China[J]. Proceedings of the CSEE, 2018, 38(7): 1893–1904. doi: 10.13334/j.0258-8013.pcsee.180067
|
[2] |
SANGWONGWANICH A, YANG Yongheng, and BLAABJERG F. High-performance constant power generation in grid-connected PV systems[J]. IEEE Transactions on Power Electronics, 2016, 31(3): 1822–1825. doi: 10.1109/TPEL.2015.2465151
|
[3] |
王立舒, 蒋赛加, 王君, 等. 基于混合策略的光伏MPPT算法优化控制[J]. 太阳能学报, 2016, 37(6): 1396–1402. doi: 10.3969/j.issn.0254-0096.2016.06.006
WANG Lishu, JIANG Saijia, WANG Jun, et al. Optimization control of PV MPPT algorithm based on mixed strategy[J]. Acta Energiae Solaris Sinica, 2016, 37(6): 1396–1402. doi: 10.3969/j.issn.0254-0096.2016.06.006
|
[4] |
ZHANG Longlong, HURLEY W G, and WÖLFLE W H. A new approach to achieve maximum power point tracking for PV system with a variable inductor[J]. IEEE Transactions on Power Electronics, 2011, 26(4): 1031–1037. doi: 10.1109/TPEL.2010.2089644
|
[5] |
张商州, 楚冰清, 袁训锋, 等. 光伏阵列模型分析及最大功率点跟踪研究[J]. 自动化与仪器仪表, 2019(10): 114–116. doi: 10.14016/j.cnki.1001-9227.2019.10.114
ZHANG Shangzhou, CHU Bingqing, YUAN Xunfeng, et al. Photovoltaic array model analysis and maximum power point tracking study[J]. Automation &Instrumentation, 2019(10): 114–116. doi: 10.14016/j.cnki.1001-9227.2019.10.114
|
[6] |
蔡小庆, 陈晓芳. 改进型扰动观察法在光伏发电MPPT中的应用[J]. 电子测试, 2019(1): 59–60, 90. doi: 10.3969/j.issn.1000-8519.2019.01.024
CAI Xiaoqing and CHEN Xiaofang. Application of improved perturbation observation method in MPPT of photovoltaic[J]. Electronic Test, 2019(1): 59–60, 90. doi: 10.3969/j.issn.1000-8519.2019.01.024
|
[7] |
苏有功, 王大成, 王毅, 等. 基于改进型变步长电导增量法的MPPT控制策略仿真[J]. 自动化技术与应用, 2019, 38(10): 11–15. doi: 10.3969/j.issn.1003-7241.2019.10.003
SU Yougong, WANG Dacheng, WANG Yi, et al. Simulation of MPPT control strategy based on improved variable step conductance increment method[J]. Techniques of Automation and Applications, 2019, 38(10): 11–15. doi: 10.3969/j.issn.1003-7241.2019.10.003
|
[8] |
贾林壮, 陈侃, 李国杰, 等. 局部阴影条件下光伏阵列MPPT算法研究[J]. 太阳能学报, 2014, 35(9): 1614–1621. doi: 10.19768/j.cnki.dgjs.2020.04.020
JIA Linzhuang, CHEN Kan, LI Guojie, et al. The MPPT method research for PV array under partially shaded conditions[J]. Acta Energiae Solaris Sinica, 2014, 35(9): 1614–1621. doi: 10.19768/j.cnki.dgjs.2020.04.020
|
[9] |
TESHOME D F, LEE C H, LIN Y W, et al. A modified firefly algorithm for photovoltaic maximum power point tracking control under partial shading[J]. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2017, 5(2): 661–671. doi: 10.1109/JESTPE.2016.2581858
|
[10] |
聂晓华, 王薇. 混沌改进猫群算法及其在光伏MPPT中的应用[J]. 中国电机工程学报, 2016, 36(22): 6103–6110. doi: 10.13334/j.0258-8013.pcsee.161022
NIE Xiaohua and WANG Wei. Chaos improved cat swarm optimization and its application in the PV MPPT[J]. Proceedings of the CSEE, 2016, 36(22): 6103–6110. doi: 10.13334/j.0258-8013.pcsee.161022
|
[11] |
王雨, 胡仁杰. 基于粒子群优化和爬山法的MPPT算法[J]. 太阳能学报, 2014, 35(1): 149–153. doi: 10.3969/j.issn.0254-0096.2014.01.025
WANG Yu and HU Renjie. MPPT algorithm based on particle swarm optimization with hill climbing method[J]. Acta Energiae Solaris Sinica, 2014, 35(1): 149–153. doi: 10.3969/j.issn.0254-0096.2014.01.025
|
[12] |
胡克用, 胥芳, 艾青林, 等. 自适应遗传算法在光伏发电系统中的应用[J]. 光子学报, 2016, 45(1): 158–166. doi: 10.3788/gzxb20164501.0135001
HU Keyong, XU Fang, AI Qinglin, et al. Adaptive genetic algorithm in the application of photovoltaic power generation system[J]. Acta Photonica Sinica, 2016, 45(1): 158–166. doi: 10.3788/gzxb20164501.0135001
|
[13] |
MIRJALILI S, MIRJALILI S M, and HATAMLOU A. Multi-verse optimizer: A nature-inspired algorithm for global optimization[J]. Neural Computing and Applications, 2016, 27(2): 495–513. doi: 10.1007/s00521-015-1870-7
|
[14] |
KUMAR P, GARG S, SINGH A, et al. MVO-based 2-D path planning scheme for providing quality of service in UAV environment[J]. IEEE Internet of Things Journal, 2018, 5(3): 1698–1707. doi: 10.1109/JIOT.2018.2796243
|
[15] |
刘小龙. 改进多元宇宙算法求解大规模实值优化问题[J]. 电子与信息学报, 2019, 41(7): 1666–1673. doi: 10.11999/JEIT180751
LIU Xiaolong. Application of improved multiverse algorithm to large scale optimization problems[J]. Journal of Electronics &Information Technology, 2019, 41(7): 1666–1673. doi: 10.11999/JEIT180751
|
[16] |
LAI Wenhao, ZHOU Mengran, HU Feng, et al. A new DBSCAN parameters determination method based on improved MVO[J]. IEEE Access, 2019, 7: 104085–104095. doi: 10.1109/ACCESS.2019.2931334
|
[17] |
MIRJALILI S and LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51–67. doi: 10.1016/j.advengsoft.2016.01.008
|
[18] |
KIM K A, XU C Y, JIN L, et al. A dynamic photovoltaic model incorporating capacitive and reverse-bias characteristics[J]. IEEE Journal of Photovoltaics, 2013, 3(4): 1334–1341. doi: 10.1109/JPHOTOV.2013.2276483
|
[19] |
邹德旋, 高立群, 段纳. 用修正的差分进化算法确定光电模型参数[J]. 电子与信息学报, 2014, 36(10): 2521–2525.
ZOU Dexuan, GAO Liqun, and DUAN Na. Determining the parameters of photovoltaic modules by a modified differential evolution algorithm[J]. Journal of Electronics &Information Technology, 2014, 36(10): 2521–2525.
|
[20] |
刘宜罡, 邹应全, 张晓强, 等. 基于差分进化的光伏MPPT算法改进[J]. 太阳能学报, 2020, 41(6): 264–271.
LIU Yigang, ZOU Yingquan, ZHANG Xiaoqiang, et al. An improved photovoltaic MPPT algorithm based on differential evolution algorithm[J]. Acta Energiae Solaris Sinica, 2020, 41(6): 264–271.
|
[21] |
石季英, 张登雨, 薛飞, 等. 基于改进灰狼优化-黄金分割混合算法的光伏阵列MPPT方法[J]. 电力系统及其自动化学报, 2019, 31(5): 21–26. doi: 10.19635/j.cnki.csu-epsa.000020
SHI Jiying, ZHANG Dengyu, XUE Fei, et al. Maximum power point tracking method for photovoltaic array based on modified hybrid method of grey wolf optimization and golden-section optimization[J]. Proceedings of the CSU-EPSA, 2019, 31(5): 21–26. doi: 10.19635/j.cnki.csu-epsa.000020
|