A Novel Comparative Analysis of Solar P&O, ANN-based MPPT Controller under Different Irradiance Condition

Pavithra C, Dhayalan R, Anandha Kumar S, Dharshan Y, Haridharan R, Vijayadharshini M
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Abstract

The depletion of fossil fuels and rising energy demand have increased the use of renewable energy. Among all Solar PVs, system-based electricity production is increased due to multiple advantages. In this paper a Solar PV system with an Artificial Neural Network (ANN)-based Maximum Power Point Tracking (MPPT) controller is developed. ANN has multiple advantages like stability, improved dynamic response, and fast and precise output. The System is modelled with a DC-DC boost converter with Perturb and Observe (P&O)-based MPPT controller which is operated in MATLAB-based Simulink model. Both the controller output is analyzed and compared, among these two controllers ANN has very fast and more precise output under dynamic conditions.
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不同辐照度条件下太阳能 P&O 和基于 ANN 的 MPPT 控制器的新型对比分析
化石燃料的枯竭和能源需求的增长,增加了对可再生能源的使用。在所有太阳能光伏发电系统中,基于系统的发电量因其多重优势而得到提高。本文开发了一种基于人工神经网络(ANN)的最大功率点跟踪(MPPT)控制器的太阳能光伏系统。人工神经网络具有稳定性、改进的动态响应、快速和精确的输出等多重优势。该系统以直流-直流升压转换器为模型,采用基于 Perturb and Observe (P&O) 的 MPPT 控制器,在基于 MATLAB 的 Simulink 模型中运行。对两种控制器的输出进行了分析和比较,在这两种控制器中,ANN 在动态条件下具有非常快速和更精确的输出。
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