RESEARCH ON MPPT OF PHOTOVOLTAIC SYSTEM BASED ON IMPROVED SALP SWARM AlGORITHM

Wang Jiajian; Pazlai&#x000b7;Mahemuti; Kong Bolong

doi:10.19912/j.0254-0096.tynxb.2020-0800

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (4) : 191-197. DOI: 10.19912/j.0254-0096.tynxb.2020-0800

Topics on Key Technologies for Safety of Electrochemical Energy Storage Systems and Echelon Utilization of Decommissioned Power Batteries

RESEARCH ON MPPT OF PHOTOVOLTAIC SYSTEM BASED ON IMPROVED SALP SWARM AlGORITHM

Wang Jiajian, Pazlai·Mahemuti, Kong Bolong

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Abstract

Partial shading condition causes multiple peaks in power-voltage （P-V） curve of photovoltaic systems. And conventional maximum power point tracking （MPPT） algorithms are prone to be trapped at a local maximum power point. An improved salp swarm algorithm is proposed by expanding the zoom factor and introducing a difference strategy. The Pareto distribution and chaotic maps are added to improve the global search performance in the leader position update process; the difference strategy is introduced to improve the local search ability in the local search process. The improved method is applied to the MPPT of multi-peak photovoltaic system. The experimental results demonstrate that the improved salp swarm algorithm （ISSA） can achieve the fastest and most stable global MPPT under Partial shading in comparison to the conventional incremental conductance method （INC）, the perturbation observation method （P&O）, and the salp swarm algorithm （SSA）. It can be seen that this method has strong adaptability and stability in photovoltaic MPPT.

Key words

photovoltaic systems maximum power point tracking / difference strategy / salp swarm algorithm / Pareto distribution function / chaotic maps

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Wang Jiajian, Pazlai·Mahemuti, Kong Bolong. RESEARCH ON MPPT OF PHOTOVOLTAIC SYSTEM BASED ON IMPROVED SALP SWARM AlGORITHM[J]. Acta Energiae Solaris Sinica. 2022, 43(4): 191-197 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0800

References

[1] KAMARZAMAN N A, TAN C W.A comprehensive review of maximum power point tracking algorithms for photovoltaic systems[J]. Renewable and sustainable energy reviews, 2014, 37: 585-598.
[2] MAO M X, ZHANG L, HUANG H, et al. Maximum power exploitation for grid-connected PV system under fast-varying solar irradiation levels with modified salp swarm algorithm[J]. Journal of cleaner production, 2020(20) ,122-158.
[3] YANG B, ZHONG L N, ZHANG X S.Novel bio-inspired memetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition[J]. Journal of cleaner production, 2019, 215: 1203-1222.
[4] 潘健, 梁佳成, 黎家成, 等. 阴影光照条件下光伏阵列的最大功率点跟踪方法[J]. 华侨大学学报(自然科学版), 2020, 41(4): 541-548.
PAN J, LIANG J C, LI J C, et al. Maximum power point tracking method of photovoltaic array under shadow illumination[J]. Journal of Huaqiao University(natural science edition), 2020, 41(4): 541-548.
[5] HASSAN S Z, LI H, KAMAL T, et al. Neuro-fuzzy wavelet based adaptive MPPT algorithm for photovoltaic systems[J]. Energies, 2017, 10(3): 1-16.
[6] 李彦志.基于电流扰动的自适应最大功率跟踪算法研究[J]. 通信电源技术, 2020, 37(5): 34-36, 39.
LI Y Z.Research on adaptive maximum power tracking algorithm based on current disturbance[J]. Communication power technology, 2020, 37(5): 34-36, 39.
[7] 陈亚爱, 周京华, 李津, 等. 梯度式变步长MPPT算法在光伏系统中的应用[J]. 中国电机工程学报, 2014, 34(19): 3156-3161.
CHEN Y A, ZHOU J H, LI J, et al. Application of gradient variable step size MPPT algorithm in photovoltaic system[J]. Proceedings of the CSEE, 2014, 34(19): 3156-3161.
[8] SEYEDALI M, AMIR H G, A H, SEYEDEH Z M, et al. Salp swarm algorithm: A bio-inspired optimizer for engineering design problems[J]. Advancesin engineering software, 2017, 114(6): 163-191.
[9] MAJHI S K, MISHRA A, PRADHAN R, et al. A chaotic salp swarm algorithm based on quadratic integrate and fire neural model for function optimization[J]. Progress in artificial intelligence, 2019, 8(3): 343-358.
[10] 张严, 秦亮曦.基于Levy飞行策略的改进樽海鞘群算法[J]. 计算机科学, 2020, 47(7): 154-160.
ZHANG Y, QIN L X.Improved salvia squirt swarm algorithm based on Levy flight strategy[J]. Computer science, 2020, 47(7): 154-160.
[11] 陈雷, 蔺悦, 康志龙.基于衰减因子和动态学习的改进樽海鞘群算法[J]. 控制理论与应用, 2020, 37(8): 1766-1780.
CHEN L, LIN Y, KANG Z L.Improved salvia swarm algorithm based on attenuation factor and dynamic learning[J]. Control theory and application, 2020, 37(8): 1766-1780.
[12] 沈凯, 陈克, 杭丽君, 等. 一种可提升光伏系统在局部阴影时发电效率的阵列连接策略[J]. 太阳能学报, 2020, 41(4): 51-58.
SHEN K, CHEN K, HANG L J, et al. An array connection strategy that can improve the power generation efficiency of photovoltaic systems in partial shadows[J]. Acta energiae solaris sinica, 2020, 41(4): 51-58.
[13] 王彦军, 王秋萍, 王晓峰.改进的樽海鞘群算法及在焊接梁问题中的应用[J]. 西安理工大学学报, 2019, 35(4): 484-493.
WANG Y J, WANG Q P, WANG X F.Improved salvia algae group algorithm and its application in welded beam problem[J]. Journal of Xi’an University of Technology, 2019, 35(4): 484-493.
[14] 陈静, 毛林.一种基于整数小波变换和混沌映射的图像加密算法[J]. 许昌学院学报, 2020, 39(5): 123-127.
CHEN J, MAO L.An image encryption algorithm based on integer wavelet transform and chaotic mapping[J]. Journal of Xuchang University, 2020, 39(5): 123-127.
[15] FATHY A, ABDELKAREEM M A, OLABI A G, et al. A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell[J]. International journal of hydrogen energy, 2020(2):165-198.
[16] 龙文, 蔡绍洪, 焦建军, 等. 一种改进的灰狼优化算法[J]. 电子学报, 2019, 47(1): 169-175.
LONG W, CAI S H, JIAO J J, et al. An improved gray wolf optimization algorithm[J]. Chinese journal of electronics, 2019, 47(1): 169-175.