Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling

In the photovoltaic (PV) field, the outdoor evaluation of a PV system is quite complex, due to the variations of temperature and irradiance. In fact, the diagnosis of the PV modules is extremely required in order to maintain the optimum performance. In this paper, an artificial neural network (ANN)...

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Main Authors:	Xiaobo Xu, Xiaocheng Zhang, Zhaowu Huang, Shaoyou Xie, Wenping Gu, Xiaoyan Wang, Lin Zhang, Zan Zhang
Format:	Article
Language:	English
Published:	MDPI AG 2019-09-01
Series:	Materials
Subjects:	artificial neural network PV module current characteristics prediction
Online Access:	https://www.mdpi.com/1996-1944/12/18/3037

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record_format	Article
spelling	doaj-59ae4f49c0f74989930aa63b8406243a2020-11-25T02:07:49ZengMDPI AGMaterials1996-19442019-09-011218303710.3390/ma12183037ma12183037Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network ModelingXiaobo Xu0Xiaocheng Zhang1Zhaowu Huang2Shaoyou Xie3Wenping Gu4Xiaoyan Wang5Lin Zhang6Zan Zhang7School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Electronic and Control Engineering, Chang’an University, Xi’an 710064, ChinaIn the photovoltaic (PV) field, the outdoor evaluation of a PV system is quite complex, due to the variations of temperature and irradiance. In fact, the diagnosis of the PV modules is extremely required in order to maintain the optimum performance. In this paper, an artificial neural network (ANN) is proposed to build and train the model, and evaluate the PV module performance by mean bias error, mean square error and the regression analysis. We take temperature, irradiance and a specific voltage for input, and a specific current value for output, repeat several times in order to obtain an I-V curve. The main feature lies to the data-driven black-box method, with the ignorance of any analytical equations and hence the conventional five parameters (serial resistance, shunt resistance, non-ideal factor, reverse saturation current, and photon current). The ANN is able to predict the I-V curves of the Si PV module at arbitrary irradiance and temperature. Finally, the proposed algorithm has proved to be valid in terms of comparison with the testing dataset.https://www.mdpi.com/1996-1944/12/18/3037artificial neural networkPV modulecurrent characteristics prediction
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Xiaobo Xu Xiaocheng Zhang Zhaowu Huang Shaoyou Xie Wenping Gu Xiaoyan Wang Lin Zhang Zan Zhang
spellingShingle	Xiaobo Xu Xiaocheng Zhang Zhaowu Huang Shaoyou Xie Wenping Gu Xiaoyan Wang Lin Zhang Zan Zhang Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling Materials artificial neural network PV module current characteristics prediction
author_facet	Xiaobo Xu Xiaocheng Zhang Zhaowu Huang Shaoyou Xie Wenping Gu Xiaoyan Wang Lin Zhang Zan Zhang
author_sort	Xiaobo Xu
title	Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling
title_short	Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling
title_full	Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling
title_fullStr	Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling
title_full_unstemmed	Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling
title_sort	current characteristics estimation of si pv modules based on artificial neural network modeling
publisher	MDPI AG
series	Materials
issn	1996-1944
publishDate	2019-09-01
description	In the photovoltaic (PV) field, the outdoor evaluation of a PV system is quite complex, due to the variations of temperature and irradiance. In fact, the diagnosis of the PV modules is extremely required in order to maintain the optimum performance. In this paper, an artificial neural network (ANN) is proposed to build and train the model, and evaluate the PV module performance by mean bias error, mean square error and the regression analysis. We take temperature, irradiance and a specific voltage for input, and a specific current value for output, repeat several times in order to obtain an I-V curve. The main feature lies to the data-driven black-box method, with the ignorance of any analytical equations and hence the conventional five parameters (serial resistance, shunt resistance, non-ideal factor, reverse saturation current, and photon current). The ANN is able to predict the I-V curves of the Si PV module at arbitrary irradiance and temperature. Finally, the proposed algorithm has proved to be valid in terms of comparison with the testing dataset.
topic	artificial neural network PV module current characteristics prediction
url	https://www.mdpi.com/1996-1944/12/18/3037
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Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling

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