Design of intelligent Maximum Power Point Tracking (MPPT) technique based on swarm intelligence based algorithms

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

Abstract

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.
基于群智能算法的智能最大功率点跟踪(MPPT)技术设计
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。
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