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基于粒子群优化算法的太阳能充电系统PI控制器设计

PSO based PI controller design for a solar charger system.

作者信息

Yau Her-Terng, Lin Chih-Jer, Liang Qin-Cheng

机构信息

Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan.

出版信息

ScientificWorldJournal. 2013 May 13;2013:815280. doi: 10.1155/2013/815280. Print 2013.

DOI:10.1155/2013/815280
PMID:23766713
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3666245/
Abstract

Due to global energy crisis and severe environmental pollution, the photovoltaic (PV) system has become one of the most important renewable energy sources. Many previous studies on solar charger integrated system only focus on load charge control or switching Maximum Power Point Tracking (MPPT) and charge control modes. This study used two-stage system, which allows the overall portable solar energy charging system to implement MPPT and optimal charge control of Li-ion battery simultaneously. First, this study designs a DC/DC boost converter of solar power generation, which uses variable step size incremental conductance method (VSINC) to enable the solar cell to track the maximum power point at any time. The voltage was exported from the DC/DC boost converter to the DC/DC buck converter, so that the voltage dropped to proper voltage for charging the battery. The charging system uses constant current/constant voltage (CC/CV) method to charge the lithium battery. In order to obtain the optimum PI charge controller parameters, this study used intelligent algorithm to determine the optimum parameters. According to the simulation and experimental results, the control parameters resulted from PSO have better performance than genetic algorithms (GAs).

摘要

由于全球能源危机和严重的环境污染，光伏（PV）系统已成为最重要的可再生能源之一。先前许多关于太阳能充电器集成系统的研究仅关注负载充电控制或切换最大功率点跟踪（MPPT）和充电控制模式。本研究采用两阶段系统，使整个便携式太阳能充电系统能够同时实现MPPT和锂离子电池的最优充电控制。首先，本研究设计了一种太阳能发电的DC/DC升压转换器，它采用可变步长增量电导法（VSINC）使太阳能电池随时跟踪最大功率点。电压从DC/DC升压转换器输出到DC/DC降压转换器，使电压降至适合给电池充电的电压。充电系统采用恒流/恒压（CC/CV）方法对锂电池充电。为了获得最优的PI充电控制器参数，本研究采用智能算法确定最优参数。根据仿真和实验结果，粒子群优化算法（PSO）得到的控制参数比遗传算法（GAs）具有更好的性能。

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