Collective intelligence-based maximum power point tracking of PV systems under partial shading condition

被引：0
|
作者：
Hu Y. [1 ]
Cheng K. [2 ]
Yang B. [3 ]
机构：
[1] Sino German Institute of Engineering, Yibin University, Yibin
[2] Sanjiang Institute of Artificial Intelligence and Robotics, Yibin University, Yibin
[3] Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2021年 / 49卷 / 24期
基金：
中国国家自然科学基金;
关键词：
Dynamic leader based collective intelligence; Hardware-in-loop; Maximum power point tracking; Partial shading condition; PV systems;
D O I：
10.19783/j.cnki.pspc.201086
中图分类号：
学科分类号：
摘要：
PV arrays are usually affected by a partial shading condition, which leads to a relatively low power production. This is because the power-voltage curve of a PV system contains multiple peaks while the traditional Maximum Power Point Tracking (MPPT) algorithm is easily trapped at the Local Maximum Power Point (LMPP). Hence, a novel MPPT approach is provided, i.e., Dynamic Leader-based Collective Intelligence (DLCI). Unlike traditional meta-heuristic algorithms, this algorithm has a multiple sub-optimizer which seeks the optimum independently. Then, the current best optimum will be chosen as the dynamic leader to guide the other sub-optimizers thereafter. Three case studies are carried out, i.e., constant climate conditions, varying climate conditions, and a large-scale photovoltaic station. Simulation outcomes of Matlab/Simulink prove that DLCI outperforms the traditional Incremental Conductance (INC) and five other typical meta-heuristic algorithms. It can achieve the fastest and most stable global MPPT. Lastly, a dSpace based Hardware-In-the-Loop (HIL) test is carried out to validate the implementation feasibility of the DLCI algorithm. © 2021 Power System Protection and Control Press.
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收藏
页码：78 / 87
页数：9
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