基于群智能算法的智能最大功率点跟踪(MPPT)技术设计

Tapas Chakrabarti, Udit Sharma, Suvrajit Manna, Tyajodeep Chakrabarti, S. Sarkar
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引用次数: 8

摘要

本文的主要目的是开发一种智能、高效的最大功率点跟踪(MPPT)技术。本研究采用了两种最新引入和流行的基于群体智能的算法:萤火虫算法(FA)和人工蜂群算法(ABC)来开发一种跟踪太阳能电池组件最大功率点(MPP)的新技术。在这种情况下,这两种算法的性能已经与其他流行的进化计算技术(如PSO, DE和GA)进行了比较。在MATLAB/SIMULINK环境下进行了仿真,仿真结果表明,该方法在不同太阳辐照度和环境温度下均能获得较好的MPP精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Design of intelligent Maximum Power Point Tracking (MPPT) technique based on swarm intelligence based algorithms
Main objective of this paper is to develop an intelligent and efficient Maximum Power Point Tracking (MPPT) technique. Two most recently introduced and popular swarm intelligent based algorithms: Firefly algorithm (FA) and Artificial Bee Colony (ABC) has been used in this study to develop a novel technique to track the Maximum Power Point (MPP) of a solar cell module. The performances of two algorithms in this context have been compared with other popular evolutionary computing techniques like PSO, DE and GA. Simulations were done in MATLAB/SIMULINK environment and simulation results show that proposed approach can obtain MPP to a good precision under different solar irradiance and environmental temperatures.
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