Artificial neural network-based modelling and fault detection of partial shaded photovoltaic modules

被引：156

作者：

Mekki, H. ^{[1
]}

Mellit, A. ^{[2
,3
]}

Salhi, H. ^{[1
]}

机构：

[1] Blida Univ 1, Fac Technol, Dept Elect, SET Lab, Blida 09000, Algeria

[2] Jijel Univ, Renewable Energy Lab, Jijel 18000, Algeria

[3] Abdus Salaam Int Ctr Theoret Phys, Str Costiera 11, I-34151 Trieste, Italy

来源：

SIMULATION MODELLING PRACTICE AND THEORY | 2016年 / 67卷

关键词：

Photovoltaic module; Partial shading; Fault detection; Modelling; Artificial neural networks; PARTIAL SHADING CONDITIONS; EXPERIMENTAL-VERIFICATION; MPPT METHOD; PV SYSTEMS; POWER; SIMULATION; DESIGN; PLANTS;

D O I：

10.1016/j.simpat.2016.05.005

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, a fault detection method for photovoltaic module under partially shaded conditions is introduced. It consists to use an artificial neural network in order to estimate the output photovoltaic current and voltage under variable working conditions. The measured data (solar irradiance, cell temperature, photovoltaic current and voltage) at Renewable Energy Laboratory REL, Jijel University (Algeria), have been used. The comparison between the estimated current and voltage with the ones measured gives useful information on the operating state of the considered photovoltaic module. To show the effectiveness of the proposed method, several shading patterns have been investigated. The results showed that the designed method accurately detects the shading effect on the photovoltaic module. (C) 2016Elsevier B.V. All rights reserved.

引用

页码：1 / 13

页数：13

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