Comparison of MPPT Algorithms Under Uniformly Varying Atmospheric Conditions

Chirag Kaushik, Rachana Garg, Prof. Priya Mahajan
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引用次数: 1

Abstract

Power output from a photovoltaic (PV) array changes based on the change in ambient temperature and solar irradiation. Conventional Maximum Power Point (MPP) techniques were adopted to extract maximum power from PV array which were easy to implement but multiple drawbacks were observed related to oscillations around MPP and slower response under varying atmospheric conditions. To overcome such drawbacks of conventional algorithms, PV system requires some intelligent techniques to track the maximum power point which may help to design more efficient PV system. Authors in the present study have highlighted the comparison of multiple conventional and intelligent control techniques such as Perturb and Observe(P&O), Incremental Conductance (INC), Type 1 Fuzzy logic controller (T1-FLC) and Artificial Neural Network (ANN) based techniques. Uniform variation of solar irradiation and ambient temperature has been considered to compare the oscillations in output power waveform, power loss and fill factor of PV array. Superiority of ANN based technique has been established for all the considered factors. The work has been carried out using MATLAB/SIMULINK environment.
均匀变化大气条件下MPPT算法的比较
光伏(PV)阵列的输出功率会随着环境温度和太阳辐照度的变化而变化。采用传统的最大功率点(MPP)技术从光伏阵列中提取最大功率,该技术易于实现,但存在MPP周围振荡和在不同大气条件下响应较慢的缺点。为了克服传统算法的这些缺点,光伏系统需要一些智能技术来跟踪最大功率点,这有助于设计更高效的光伏系统。本研究的作者强调了多种传统和智能控制技术的比较,如摄动和观察(P&O)、增量电导(INC)、1型模糊逻辑控制器(T1-FLC)和基于人工神经网络(ANN)的技术。考虑太阳辐照度和环境温度的均匀变化,比较了光伏阵列输出功率波形、功率损耗和填充系数的振荡。基于人工神经网络技术的优越性已经在所有考虑的因素中得到证实。本工作在MATLAB/SIMULINK环境下进行。
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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