An Overall Distribution Particle Swarm Optimization MPPT Algorithm for Photovoltaic System Under Partial Shading

被引：354

作者：

Li, Hong ^{[1
]}

Yang, Duo ^{[2
]}

Su, Wenzhe ^{[1
]}

Lu, Jinhu ^{[3
,4
,5
,6
]}

Yu, Xinghuo ^{[7
]}

机构：

[1] Beijing Jiaotong Univ, Sch Elect Engn, Beijing 100044, Peoples R China

[2] Railway Sci Corp Ltd, China Acad, Beijing 100081, Peoples R China

[3] Beihang Univ, Sch Automat Sci & Elect Engn, State Key Lab Software Dev Environm, Beijing 100083, Peoples R China

[4] Beihang Univ, Beijing Adv Innovat Ctr Big Data & Brain Machine, Beijing 100083, Peoples R China

[5] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China

[6] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[7] RMIT Univ, Melbourne, Vic 3000, Australia

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2019年 / 66卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Maximum power point tracking (MPPT); particle swarm optimization (PSO); partial shading conditions (PSCs); solar photovoltaic (PV) systems; POWER POINT TRACKING; P-AND-O; STEADY-STATE; SCHEME; ARRAY; PSO;

D O I：

10.1109/TIE.2018.2829668

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Solar photovoltaic (PV) systems under partial shading conditions (PSCs) have a nonmonotonic P-V characteristic with multiple local maximum power points, which makes the existing maximum power point tracking (MPPT) algorithms unsatisfactory performance for global MPPT, if not invalid. This paper proposes a novel overall distribution (OD) MPPT algorithm to rapidly search the area near the global maximum power points, which is further integrated with the particle swarm optimization (PSO) MPPT algorithm to improve the accuracy of MPPT. Through simulations and experimentations, the higher effectiveness and accuracy of the proposed OD-PSO MPPT algorithm in solar PV systems is demonstrated in comparison to two existing artificial intelligence MPPT algorithms.

引用

页码：265 / 275

页数：11

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