Exact analytical solutions of the parameters of real solar cells using Lambert W-function

Jain, Amit; Kapoor, Avinashi

doi:10.1016/j.solmat.2003.11.018

Cited by 486 publications

(200 citation statements)

References 10 publications

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“…(1), it is not possible to solve in terms of I(V) explicitly, except by means of the Lambert-W function. 10 Typically, these data may be used simply to estimate I sc ; V oc , maximum power point (P mp ), fill factor (FF).…”

Section: A Physics-based Diode Models For Photovoltaic Cellsmentioning

confidence: 99%

“…One common and straightforward manner for estimating the series and shunt resistance of the PV module is by simply calculating the pointwise resistance curve for an I-V curve, given by R(V) ¼ À1/(dI/dV), where R s ¼ R(V ¼ V oc ) and R sh ¼ RðV ¼ 0Þ. 12 Analytical solutions to the transcendental equation exist based upon the Lambert-W function, 10,21 that Ghani et al…”

Section: A Physics-based Diode Models For Photovoltaic Cellsmentioning

confidence: 99%

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Insights into metastability of photovoltaic materials at the mesoscale through massiveI–Vanalytics

Peshek

Fada

et al. 2016

Journal of Vacuum Science &Amp; Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phen

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The authors demonstrate the feasibility of quantifying cell-level performance heterogeneity from module-level I–V curves by determining conditions of bypass diode turn-on. Analysis of these curves falls outside of typical diode-based models of photovoltaic (PV) performance. The authors show that this approach can leverage statistical and machine learning techniques for broad application to massive datasets, and combine those insights with simulations and laboratory-based experiments to provide useful information into the metastability of the interfaces of a PV cell. The authors find good agreement between the experimentally determined curves and the simulated curves, which guide the variable selection in the massive dataset collected from sites in Cleveland, OH, USA, the Negev Desert, Israel, Isla Gran Canaria, Spain, and Mount Zugspitze, Germany.

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Section: A Physics-based Diode Models For Photovoltaic Cellsmentioning

confidence: 99%

Section: A Physics-based Diode Models For Photovoltaic Cellsmentioning

confidence: 99%

Insights into metastability of photovoltaic materials at the mesoscale through massiveI–Vanalytics

Peshek

Fada

et al. 2016

Journal of Vacuum Science &Amp; Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phen

View full text Add to dashboard Cite

show abstract

“…Analytical methods [5][6][7][8][9][10] required less computational time for parameter extraction due to their simple structure and perform efficiently at standard testing conditions (STC) for some models but these methods are found to be unsuitable for solar model for wide range of changing weather conditions. Because, the mathematical model of PV panel represented is implicit in nature.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Solar Cell Modelling Parameters Using Differential Evolution Algorithm

Tamrakar¹,

Gupta²

2015

International Journal of Innovative Research in Electrical, Ele

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Abstract:Energy from the sun is the best option for electricity generation as it is abundantly available everywhere and sustainable source. Advance of Photovoltaic (PV) technology in recent years has made solar energy one of the practical alternative energy sources available in the energy market. In order to make the PV energy more affordable and cost effective, major focus of the research community and industry is improvement on power efficiency of PV systems. Parameter extraction of solar cell plays a crucial role in the simulation and design calculation of PV system. In this paper, in order to fast and accurately extract the solar cell parameters, differential evolution (DE) is proposed. The single diode and double diode model of solar cell is used as the basis for the extraction problem. The analysis carried out using current-voltage (I-V) characteristics showed that the proposed DE outperforms other methods, namely the genetic algorithm (GA) and partical swarm optimization (PSO).

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“…The latter can be identified analytically [1] or numerically [2] for a specific temperature and irradiance. In other hand, the study of the behavior of solar cell requires the identification of its parameters for various values of irradiance and temperature.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Training By Gradient Descent Algorithms: Application on the Solar Cell

Dkhichi¹,

Oukarfi²

2014

IJIRSET

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This present paper deals with the parameter determination of solar cell by using an artificial neural network trained at every time, separately, by one algorithm among the optimization algorithms of gradient descent (Levenberg-Marquardt, Gauss-Newton, Quasi-Newton, steepest descent and conjugate gradient). This determination issue is made for different values of temperature and irradiance. The training process is insured by the minimization of the error generated at the network output. Therefore, from the outcomes obtained by each gradient descent algorithm, we conducted a comparative study between the overall of training algorithms in order to know which one had the best performances. As a result the Levenberg-Marquardt algorithm presents the best potential compared to the other investigated optimization algorithms of gradient descent.

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Exact analytical solutions of the parameters of real solar cells using Lambert W-function

Cited by 486 publications

References 10 publications

Insights into metastability of photovoltaic materials at the mesoscale through massiveI–Vanalytics

Insights into metastability of photovoltaic materials at the mesoscale through massiveI–Vanalytics

Extraction of Solar Cell Modelling Parameters Using Differential Evolution Algorithm

Neural Network Training By Gradient Descent Algorithms: Application on the Solar Cell

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