A new approach to select an optimal PV module model under the outdoor conditions

Despite the great development in modeling the I-V characteristics of the PV module, the outdoor conditions variations still the main difficulty to predict its performances. In this work, anew approach proposed to reconstruct the I-V characteristic of a PV module under various real conditions of irradiance and temperature. Based on the characterization tests data, carried out on four different PV modules technologies (monocrystalline silicon, polycrystalline silicon, thin film CIS and amorphous silicon), under semi-arid environment conditions of Ghardaïa site, a developed methodology has been presented. It consists of exploiting a set of the five parameters data versus irradiance and temperature obtained via the five parameters model. The assembled data have been reconstructed via analytical and adaptive neuro-fuzzy inference system (ANFIS) models, for each PV module technology. The reconstructed of five parameters model obtained by ANFIS model give a good precision for all tested PV module types, in comparison to the analytical model.

[1]  Giacomo Capizzi,et al.  A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module , 2012, ArXiv.

[2]  A. Hadj Arab,et al.  Loss-of-load probability of photovoltaic water pumping systems , 2004 .

[3]  Mohammad Ali Abido,et al.  Parameter estimation for five- and seven-parameter photovoltaic electrical models using evolutionary algorithms , 2013, Appl. Soft Comput..

[4]  Antonino Laudani,et al.  High performing extraction procedure for the one-diode model of a photovoltaic panel from experimental I–V curves by using reduced forms , 2014 .

[5]  K. Ebihara,et al.  Estimation of equivalent circuit parameters of PV module and its application to optimal operation of PV system , 2001 .

[6]  Valerio Lo Brano,et al.  On the experimental validation of an improved five-parameter model for silicon photovoltaic modules , 2012 .

[7]  A. Sellami,et al.  Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction , 2010 .

[8]  Wei Zhou,et al.  A novel model for photovoltaic array performance prediction , 2007 .

[9]  Efim G. Evseev,et al.  The assessment of different models to predict the global solar radiation on a surface tilted to the south , 2009 .

[10]  Y. Bakelli,et al.  Selection of a proper model based on PV modules under outdoor characteristics , 2012, 2012 11th International Conference on Environment and Electrical Engineering.

[11]  Soteris A. Kalogirou,et al.  ANFIS-based modelling for photovoltaic power supply system: A case study , 2011 .

[12]  Li Zhang,et al.  Genetic algorithm-trained radial basis function neural networks for modelling photovoltaic panels , 2005, Eng. Appl. Artif. Intell..

[13]  Weerakorn Ongsakul,et al.  A simulation model for predicting the performance of a solar photovoltaic system with alternating current loads , 2002 .

[14]  Giuseppe Marco Tina,et al.  Comparison of different metaheuristic algorithms for parameter identification of photovoltaic cell/module , 2013 .

[15]  Kashif Ishaque,et al.  A critical evaluation of EA computational methods for Photovoltaic cell parameter extraction based on two diode model , 2011 .

[16]  Arif Hepbasli,et al.  Diffuse solar radiation estimation models for Turkey’s big cities , 2009 .

[17]  S. L. Shimi,et al.  Modeling of solar PV module and maximum power point tracking using ANFIS , 2014 .

[18]  Haralambos Sarimveis,et al.  A new neural network model for evaluating the performance of various hourly slope irradiation models: Implementation for the region of Athens , 2010 .

[19]  Yu Zhang,et al.  Development of a new compound method to extract the five parameters of PV modules , 2014 .

[20]  Marcelo Gradella Villalva,et al.  Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays , 2009, IEEE Transactions on Power Electronics.

[21]  Mutlu Boztepe,et al.  Neural network based solar cell model , 2006 .

[22]  J. A. Gow,et al.  Development of a photovoltaic array model for use in power-electronics simulation studies , 1999 .

[23]  William A. Beckman,et al.  Improvement and validation of a model for photovoltaic array performance , 2006 .

[24]  W. Beckman,et al.  A method for estimating the long-term performance of direct-coupled PV pumping systems , 1998 .

[25]  Ali Naci Celik,et al.  Artificial neural network modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules , 2011 .

[26]  E. Muljadi,et al.  A cell-to-module-to-array detailed model for photovoltaic panels , 2012 .

[27]  Ali Naci Celik,et al.  Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four- and five-parameter models , 2007 .

[28]  Kashif Ishaque,et al.  Simple, fast and accurate two-diode model for photovoltaic modules , 2011 .

[29]  Alain K. Tossa,et al.  A new approach to estimate the performance and energy productivity of photovoltaic modules in real operating conditions , 2014 .

[30]  Salhi Hanen,et al.  New relationships of dark diffusion and recombination currents as a function of temperature for a crystalline cell photovoltaic , 2014 .

[31]  Marcello Artioli,et al.  Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis , 2010 .