A Novel Nature-Inspired Maximum Power Point Tracking (MPPT) Controller Based on SSA-GWO Algorithm for Partially Shaded Photovoltaic Systems

被引：46

作者：

Wan, Yihao ^{[1
]}

Mao, Mingxuan ^{[1
,2
]}

Zhou, Lin ^{[1
]}

Zhang, Qianjin ^{[1
]}

Xi, Xinze ^{[1
,3
]}

Zheng, Chen ^{[4
]}

机构：

[1] Chongqing Univ, State Key Lab Power Transmiss Equipment & Syst Se, Chongqing 400044, Peoples R China

[2] Chongqing Univ, Postdoctoral Stn Elect Engn, Chongqing 400044, Peoples R China

[3] Yunnan Power Grid Co Ltd, Elect Power Res Inst, Kunming 650217, Yunnan, Peoples R China

[4] State Grid Henan Elect Power Res Inst, Zhengzhou 450052, Henan, Peoples R China

来源：

ELECTRONICS | 2019年 / 8卷 / 06期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Maximum Power Point Tracking (MPPT); Salp Swarm Algorithm (SSA); Grey Wolf optimizer (GWO); Partially Shaded Photovoltaic (PV) System; PARTICLE SWARM OPTIMIZATION; ARTIFICIAL BEE COLONY; PV SYSTEMS; PERTURB;

D O I：

10.3390/electronics8060680

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

To overcome the real-time problem of maximum power point tracking (MPPT) for partially shaded photovoltaic (PV) systems, a novel nature-inspired MPPT controller with fast convergence and high accuracy is proposed in this paper. The proposed MPPT controller is achieved by combining salp swarm algorithm (SSA) with grey wolf optimizer (GWO) (namely, SSA-GWO). The leader structure of the GWO algorithm is introduced into the basic SSA algorithm to enhance the global search capability. Numerical simulation on 13 benchmark functions was done to evaluate the proposed SSA-GWO algorithm. Finally, the MPPT performance on PV system with the proposed SSA-GWO algorithm under static and dynamic partial shading conditions was investigated and compared with conventional MPPT algorithms. The quantitative and simulation results validated the effectiveness and superiority of the proposed method.

引用

页数：17

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