RESEARCH ON PHOTOVOLTAIC MULTI-PEAK MPPT BASED ON SIMPLIFIED SAND CAT SWARM OPTIMIZATION ALGORITHM

Wang Hailin, Zhang Wenfeng, Fan Xiang, Tang Jin, Zhang Yu, Zhang Lei

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 298-306.

PDF(3279 KB)
Welcome to visit Acta Energiae Solaris Sinica, Today is
PDF(3279 KB)
Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 298-306. DOI: 10.19912/j.0254-0096.tynxb.2024-1330

RESEARCH ON PHOTOVOLTAIC MULTI-PEAK MPPT BASED ON SIMPLIFIED SAND CAT SWARM OPTIMIZATION ALGORITHM

  • Wang Hailin1, Zhang Wenfeng1, Fan Xiang2, Tang Jin3, Zhang Yu3, Zhang Lei1
Author information +
History +

Abstract

The power-voltage output characteristic curves of PV series modules have multiple peaks when they are blocked by different shadows. The traditional maximum power point tracking algorithm has slow convergence speed and complex control parameters,so it can not achieve the global maximum power point tracking well. In this paper,a simplified sand cat swarm optimization algorithm is proposed,which is compared with particle swarm optimization algorithm and Gray Wolf algorithm respectively from static to dynamic under the complex lighting environment of shadow. Simulation and experiment verify that the simplified sand cat swarm optimization algorithm can realize the global photovoltaic maximum power point tracking. Under the same external conditions,compared with the other two algorithms,the simplified sand cat swarm optimization algorithm has fewer control variables affecting the experimental results. The tracking speed is increased by more than 60% and the tracking efficiency is increased by more than 0.5%. In addition,the power and voltage fluctuation in the convergence process is small,which improves the reliability of intelligent group algorithm applied in photovoltaic maximum power point tracking and improves the utilization rate of photovoltaic power generation.

Key words

photovoltaic power generation / maximum power point tracking(MPPT) / partial shading / particle swarm optimization(PSO) / sand cat swarm optimization algorithm(SCSO)

Cite this article

Download Citations
Wang Hailin, Zhang Wenfeng, Fan Xiang, Tang Jin, Zhang Yu, Zhang Lei. RESEARCH ON PHOTOVOLTAIC MULTI-PEAK MPPT BASED ON SIMPLIFIED SAND CAT SWARM OPTIMIZATION ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 298-306 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1330

References

[1] SAFARI A, MEKHILEF S.Simulation and hardware implementation of incremental conductance MPPT with direct control method using cuk converter[J]. IEEE transactions on industrial electronics, 2011, 58(4): 1154-1161.
[2] ALI A, ALMUTAIRI K, PADMANABAN S, et al.Investigation of MPPT techniques under uniform and non-uniform solar irradiation condition-a retrospection[J]. IEEE access, 2020, 8: 127368-127392.
[3] ABDELSALAM A K, MASSOUD A M, AHMED S, et al.High-performance adaptive perturb and observe MPPT technique for photovoltaic-based microgrids[J]. IEEE transactions on power electronics, 2011, 26(4): 1010-1021.
[4] GUPTA A K, PACHAURI R K, MAITY T, et al.Effect of various incremental conductance MPPT methods on the charging of battery load feed by solar panel[J]. IEEE access, 2021, 9: 90977-90988.
[5] SWAMINATHAN N, LAKSHMINARASAMMA N, CAO Y.A fixed zone perturb and observe MPPT technique for a standalone distributed PV system[J]. IEEE journal of emerging and selected topics in power electronics, 2021, 10(1): 361-374.
[6] MIRHASSANI S M, GOLROODBARI S Z M, GOLROODBARI S M M, et al. An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time[J]. International journal of electrical power & energy systems, 2015, 64: 761-770.
[7] MAO M X, ZHANG L, DUAN Q C, et al.A two-stage particle swarm optimization algorithm for MPPT of partially shaded PV arrays[J]. International journal of green energy, 2017, 14(8): 694-702.
[8] IBRAHIM M H, ANG S P, DANI M N, et al.Optimizing step-size of perturb & observe and incremental conductance MPPT techniques using PSO for grid-tied PV system[J]. IEEE access, 2023, 11: 13079-13090.
[9] OBUKHOV S, IBRAHIM A, ZAKI DIAB A A, et al. Optimal performance of dynamic particle swarm optimization based maximum power trackers for stand-alone PV system under partial shading conditions[J]. IEEE access, 2020, 8: 20770-20785.
[10] MOHANTY S, SUBUDHI B, RAY P K.A new MPPT design using grey wolf optimization technique for photovoltaic system under partial shading conditions[J]. IEEE transactions on sustainable energy, 2016, 7(1): 181-188.
[11] 毛明轩, 许钊, 崔立闯, 等. 基于改进灰狼优化算法的光伏阵列多峰MPPT研究[J]. 太阳能学报, 2023, 44(3): 450-456.
MAO M X, XU Z, CUI L C, et al.Research on multi-peak mppt of photovoltaic array based on modified gray wolf optimization algorithm[J]. Acta energiae solaris sinica, 2023, 44(3): 450-456.
[12] MOTAHHIR S, CHOUDER A, EL HAMMOUMI A, et al.Optimal energy harvesting from a multistrings PV generator based on artificial bee colony algorithm[J]. IEEE systems journal, 2021, 15(3): 4137-4144.
[13] 解宝, 李萍宇, 苏绎仁, 等. 局部阴影下光伏阵列的最大功率点跟踪算法研究[J]. 太阳能学报, 2023, 44(12): 47-52.
XIE B, LI P Y, SU Y R, et al.Research on maximum power point tracking algorithm of PV array under local shadow[J]. Acta energiae solaris sinica, 2023, 44(12): 47-52.
[14] 葛传九, 武鹏, 董祥祥, 等. 基于布谷鸟算法的光伏MPPT改进[J]. 太阳能学报, 2022, 43(10): 59-64.
GE C J, WU P, DONG X X, et al.Improved photovoltaic maximum power point tracking based on cuckoo search algorithm[J]. Acta energiae solaris sinica, 2022, 43(10): 59-64.
[15] SEYYEDABBASI A, KIANI F.Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems[J]. Engineering with computers, 2023, 39(4): 2627-2651.
[16] ZHANG L B, LUO Y, SHEN Z H, et al.Optimization design of the elbow inlet channel of a pipeline pump based on the SCSO-BP neural network[J]. Water, 2024, 16(1): 74.
[17] TAVAKOL AGHAEI V, SEYYEDABBASI A, RASHEED J, et al.Sand cat swarm optimization-based feedback controller design for nonlinear systems[J]. Heliyon, 2023, 9(3): e13885.
[18] 张永革, 石季英, 张文, 等. 复杂遮阴条件下光伏系统MPPT控制改进PSO算法仿真研究[J]. 中国电机工程学报, 2014, 34(S1): 39-46.
ZHANG Y G, SHI J Y, ZHANG W, et al.Simulation study on improved PSO algorithm for MPPT control of photovoltaic system under complex shading conditions[J]. Proceedings of the CSEE, 2014, 34(S1): 39-46.
PDF(3279 KB)

Accesses

Citation

Detail

Sections
Recommended

/