Factorial design and response surface optimization for modeling photovoltaic module parameters

In this paper, based on factorial design of experiments method (DoE), predictive model and surface response analysis methodology was used for studying, modeling, characterizing and optimizing the parameters of a mono-crystalline photovoltaic (PV) panel behavior considering the interactive effects of...

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Main Authors: Fatma Zohra Kessaissia, Abdallah Zegaoui, Michel Aillerie, Mustapha Arab, Mohamed Boutoubat, Chahinez Fares
Format: Article
Language:English
Published: Elsevier 2020-02-01
Series:Energy Reports
Online Access:http://www.sciencedirect.com/science/article/pii/S2352484719312235
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spelling doaj-5bf2cc14d79143e3bd2079a3730b96d02020-11-25T02:58:10ZengElsevierEnergy Reports2352-48472020-02-016299309Factorial design and response surface optimization for modeling photovoltaic module parametersFatma Zohra Kessaissia0Abdallah Zegaoui1Michel Aillerie2Mustapha Arab3Mohamed Boutoubat4Chahinez Fares5Department of Electrical Engineering. Hassiba Benbouali University, Chlef, Algeria; Laboratoire Matériaux Optiques, Photonique et Systèmes, LMOPS, EA 4423, Université de Lorraine, 57070 Metz, FranceDepartment of Electrical Engineering. Hassiba Benbouali University, Chlef, Algeria; Laboratoire Matériaux Optiques, Photonique et Systèmes, LMOPS, EA 4423, Université de Lorraine, 57070 Metz, FranceLaboratoire Matériaux Optiques, Photonique et Systèmes, LMOPS, EA 4423, Université de Lorraine, 57070 Metz, France; Laboratoire Matériaux Optiques, Photonique et Systèmes, LMOPS, CentraleSupelec, Université Paris-Saclay, 57070 Metz, France; Corresponding author at: Laboratoire Matériaux Optiques, Photonique et Systèmes, LMOPS, EA 4423, Université de Lorraine, 57070 Metz, France.Department of Electrical Engineering. Hassiba Benbouali University, Chlef, AlgeriaUniversity Amar Thlidji, Laghouat, AlgeriaDepartment of Electrical Engineering. Hassiba Benbouali University, Chlef, AlgeriaIn this paper, based on factorial design of experiments method (DoE), predictive model and surface response analysis methodology was used for studying, modeling, characterizing and optimizing the parameters of a mono-crystalline photovoltaic (PV) panel behavior considering the interactive effects of two variables surface PV cell temperature and solar irradiation levels. The DoE concept allows finding the predictive model of each parameter behavior that uses the experimental data. It enables accurate predictions of the responses according to input factors variations. This contribution evaluates the output parameters by predicting these mathematical models of the three responses of a mono-crystalline PV panel: the maximum power Pm, the short-circuit current Iscand the open circuit voltage Vocas function of the influences of both input parameter factors: illumination and temperature. In addition, to validate the results of the DoE predictive models, the surface response and the contour curves analysis were used to bring out the optimum of each response in each operating point covering the domain of the study by the use of a script developed under Minitab is deduced. The obtain results are compared with experimental data. Keywords: Factorial design of experiments method, Experimental design method, Predictive model, Surface response analysis, Photovoltaic modules, ANOVAhttp://www.sciencedirect.com/science/article/pii/S2352484719312235
collection DOAJ
language English
format Article
sources DOAJ
author Fatma Zohra Kessaissia
Abdallah Zegaoui
Michel Aillerie
Mustapha Arab
Mohamed Boutoubat
Chahinez Fares
spellingShingle Fatma Zohra Kessaissia
Abdallah Zegaoui
Michel Aillerie
Mustapha Arab
Mohamed Boutoubat
Chahinez Fares
Factorial design and response surface optimization for modeling photovoltaic module parameters
Energy Reports
author_facet Fatma Zohra Kessaissia
Abdallah Zegaoui
Michel Aillerie
Mustapha Arab
Mohamed Boutoubat
Chahinez Fares
author_sort Fatma Zohra Kessaissia
title Factorial design and response surface optimization for modeling photovoltaic module parameters
title_short Factorial design and response surface optimization for modeling photovoltaic module parameters
title_full Factorial design and response surface optimization for modeling photovoltaic module parameters
title_fullStr Factorial design and response surface optimization for modeling photovoltaic module parameters
title_full_unstemmed Factorial design and response surface optimization for modeling photovoltaic module parameters
title_sort factorial design and response surface optimization for modeling photovoltaic module parameters
publisher Elsevier
series Energy Reports
issn 2352-4847
publishDate 2020-02-01
description In this paper, based on factorial design of experiments method (DoE), predictive model and surface response analysis methodology was used for studying, modeling, characterizing and optimizing the parameters of a mono-crystalline photovoltaic (PV) panel behavior considering the interactive effects of two variables surface PV cell temperature and solar irradiation levels. The DoE concept allows finding the predictive model of each parameter behavior that uses the experimental data. It enables accurate predictions of the responses according to input factors variations. This contribution evaluates the output parameters by predicting these mathematical models of the three responses of a mono-crystalline PV panel: the maximum power Pm, the short-circuit current Iscand the open circuit voltage Vocas function of the influences of both input parameter factors: illumination and temperature. In addition, to validate the results of the DoE predictive models, the surface response and the contour curves analysis were used to bring out the optimum of each response in each operating point covering the domain of the study by the use of a script developed under Minitab is deduced. The obtain results are compared with experimental data. Keywords: Factorial design of experiments method, Experimental design method, Predictive model, Surface response analysis, Photovoltaic modules, ANOVA
url http://www.sciencedirect.com/science/article/pii/S2352484719312235
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