In this paper, a method based on genetic algorithms is proposed for improving the accuracy of solar cell parameters extracted using novel technique. We propose a computational based binary-coded genetic algorithm (GA) to extract the parameters (, , , and ) for a single diode model of solar cell from its current-voltage (I–V) characteristic. The algorithm was implemented using Matlab as a programming tool and validated by applying it to the I–V curve synthesized from the literature using reported values. The characterization, current-voltage data used was generated by simulating a one-diode solar cell model of specified parameters. The new approach is based on formulating I–V equation of solar cell, with Lambert function, the parameter extraction as a search and optimization problem. Compared with other optimization techniques in literatures, the approach proposed for the determination of parameters are in good agreement.
ChanDSHPhillipsJRPhangJCH, 1986. A comparative-study of extraction methods for solar-cell model parameters. Solid-State Electron. 29(3), 329–337.
2.
ZhouWYangHFangZ, 2007. A novel model for photovoltaic array performance prediction. Appl. Energy. 84(12), 1187–1198.
3.
SandroliniLArtioliMReggianiU, 2010. Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis. Appl. Energy. 87(2), 442–451.
4.
AmroucheBGuessoumABelhamelM, 2012. A simple behavioural model for solar module electric characteristics based on the first order system step response for MPPT study and comparison. Appl. Energy. 91(1), 395–404.
5.
TianHMMancilla-DavidFEllisKMuljadiEJenkinsP, 2012. A cell-to-module-to-array detailed model for photovoltaic panels. Sol. Energy Mater. Solar Cells. 86(9), 2695–2706.
6.
KhanFBaekSHParkYKimJH, 2013. Extraction of diode parameters of silicon solar cells under high illumination conditions. Energy Convers. Manage. 76, 421–429.
7.
OrioliAGangiAD, 2013. A procedure to calculate the five-parameter model of crystalline silicon photovoltaic modules on the basis of the tabular performance data. Appl. Energy. 102(SI), 1160–1177.
8.
CubasJPindadoSVictoriaM, 2014. On the analytical approach for modeling photovoltaic systems behavior. J. Power Sources. 247, 467–474.
9.
GottschalgRRommelMInfieldDGKearneyMJ, 1999. The infl uence of the measurement environment on the accuracy of the extraction of physical parameters of solar cells. Meas. Sci. Technol. 10796–804.
10.
Ortiz-CondeAMaYThomsonJSantosELiouJJGarciaFJLeiMFinolJLaymanP, 2000. Parameter extraction using lateral and vertical optimisation, in: Proc. 22nd Int. Conf. on Microelectronics, MIEL 2000 (Nis, 2000), Vol. 1, pp. 165–8.
11.
MartilIDiazGG, 1992. Determination of the dark and illuminated characteristic parameters of a solar cell for IV characteristics. Eur. J. Phys. 13193–7.
12.
AlRashidiMAlHajriMEl-NaggarKAl-OthmanA, 2011. A new estimation approach for determining the I–V characteristics of solar cells. Solar Energy. 85(7), 1543–1550.
13.
AlHajriMEl-NaggarKAlRashidiMAl-OthmanA, 2012. Optimal extraction of solar cell parameters using pattern search. Renewable Energy. 44(0), 238–245.
14.
CharlesJPIsmailMABordureG, 1985. A critical study of the effectiveness of the single and double exponential models for I–V characterization of solar cells. Solid-State Electron. 28(8), 807–820.
15.
SubudhiBPradhanR, December 2011. Characteristics Evaluation and Parameter Extraction of a Solar Array Based on Experimental Analysis, in: IEEE International Conference on Power Electronics and Drive System (PEDS), pp. 340–344.
16.
JainAKapoorA, 2004. Exact analytical solutions of the parameters of real solar cells using Lambert W-function. Solar Energy Materials and Solar Cells. 81(2), 269–277.
17.
ChanDSHPhangJCH, 1987. Analytical methods for the extraction of solar-cell single- and double-diode model parameters from I–V characteristics. IEEE Transactions on Electron Devices. 34(2), 286–293.
18.
SaleemHKarmalkarS, 2009. An analytical method to extract the physical parameters of a solar cell from four points on the illuminated J–V curve. IEEE Electron Device Letters. 30(4), 349–352.
19.
Ortiz-CondeAGarcia SanchezFJMuciJ, 2006. New method to extract the model parameters of solar cells from the explicit analytic solutions of their illuminated I–V characteristics. Solar Energy Materials and Solar Cells. 90(30), 352–361.
20.
FjeldlyTAMoonBJShurMS, 1991. Approximate analytical solution of generalized diode equation. IEEE Transactions on Electron Devices. 38(8), 1976–1977.
AppelbaumJPeledA, 2014. Parameters extraction of solar cells e a comparative examination of three methods. Sol Energy Mater Sol Cells. 122, 164–73.
23.
EaswarakhanthanTBottinJBouhouchIBoutritC, 1986. Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. International Journal of Solar Energy. 4, 1–12.
24.
AlRashidiMRAlHajriMFEl-NaggarKMAl-OthmanAK, 2011. A new estimation approach for determining the IeV characteristics of solar cells. Sol Energy. 85(7), 1543–50.
25.
JervaseJABourdoucenHAl-LawatiA, 2001. Solar cell parameter extraction using genetic algorithms. Meas Sci Technol. 12(11), 1922–5.
26.
YeMWangXXuY, 2009. Parameter extraction of solar cells using particle swarm optimization. J Appl Phys. 105(9), 094502–8.
27.
WeiHCongJLingyunXDeyunS, 2011. Extracting solar cell model parameters based on chaos particle swarm algorithm, in: International Conference on Electric Information and Control Engineering (ICEICE), pp. 398–402.
28.
El-NaggarKMAlRashidiMRAlHajriMFAl-OthmanAK, 2012. Simulated annealing algorithm for photovoltaic parameters identification. Sol Energy. 86(1), 266–74.
29.
AskarzadehARezazadehA, 2012. Parameter identification for solar cell models using harmony search-based algorithms. Sol Energy. 86(11), 3241–9.
30.
RajasekarNKrishnaKNVenugopalanR, 2013. Bacterial foraging algorithm based solar PV parameter estimation. Sol Energy. 97, 255–65.
31.
NiuQZhangHLiK, 2014. An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models. Int J Hydrogen Energy. 39(8), 3837e54.
32.
AskarzadehARezazadehA, 2013. Extraction of maximum power point in solar cells using bird mating optimizer-based parameters identification approach. Sol Energy. 90, 123–33.
33.
JervaseJABourdoucenHAl-LawatiA, 2001. Solar cell parameter extraction using genetic algorithms. Meas Sci Technol. 12(11), 1922–5.
34.
HrstkaOKučerováA, 2004. Improvements of real coded genetic algorithms based on differential operators preventing premature convergence. Adv Eng Softw. 35, 237–46.
35.
Ostadmohammadi AraniBMirzabeygiPShariatPM, 2013. An improved PSO algorithm with a territorial diversity-preserving scheme and enhanced exploration exploitation balance. Swarm Evol Comput. 11, 1–15.
36.
LingQWuGYangZWangQ, 2008. Crowding clustering genetic algorithm for multimodal function optimization. Appl Soft Comput. 8, 88–95.
37.
LiMLinDKouJ, 2012. A hybrid niching PSO enhanced with recombination-replacement crowding strategy for multimodal function optimization. Appl Soft Comput. 12, 975–87.
38.
AlRashidiMREl-NaggarKMAlHajriMF, 2012. Heuristic Approach for Estimating the Solar Cell Parameters. Recent Researches in Applied Information Science. 80–83. Published by WSEAS Press.
39.
DasSSuganthanPN, 2011. Differential evolution: A survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15, 4–31.
40.
BalzaniMReattiA, 2005. Neural network based model of a PV array for the optimum performance of PV system. Research in Microelectronics and Electronics, 2005 PhD.
41.
KaratepeEBoztepeM, et al., 2006. Neural network based solar cell model. Energy Conversion and Management. 47(9–10), 1159–1178.
42.
OlivaDCuevasEPajaresG, 2014. Parameter identification of solar cells using artificial bee colony optimization. Energy. 72, 93e102.
43.
GhaniFDukeMCarsonJ, 2013. Numerical calculation of series and shunt resistance of a photovoltaic cell using the Lambert W-function: Experimental evaluation. Solar Energy. 87, 246–253.
44.
JungWGuziewiczM, 2009. Schottky diode parameters extraction using Lambert W function, Materials Science and Engineering B. 165, 57–59.
45.
ChenYWangXLiDHongRShenH, 2011. Parameters extraction from commercial solar cells I–V characteristics and shunt analysis. Applied Energy. 88, 2239–2244.
46.
PengLSunYMengZWangYXuY, 2013. A new method for determining the characteristics of solar cells. Journal of Power Sources. 227, 131–136.
47.
JainAKapoorA, 2004. Exact analytical solutions of the parameters of real solar cells using Lambert W-function. Solar Energy Materials & Solar Cells. 81, 269–277.
48.
TowsendTUA, 1989. Method for estimating the long-term performance of direct-coupled photovoltaic systems, M.S. Thesis, Solar Energy Laboratory, University of Wisconsin.
49.
CelikANAcikgozN, 2007. Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four-and five-parameter models. Applied Energy. 84, 1–15.
50.
ChanDSPhangJCH, 1987. Analytical methods for the extraction of solar-cell singleand double-diode model parameters from I–V characteristics. IEEE Transactions on Electron Devices. 34, 286–293.
51.
ChanDPhangJ, 1987. Analytical methods for the extraction of solar-cell single- and double-diode model parameters from I–V characteristics. IEEE Transactions on Electron Devices. 34(2), 286– 293.
52.
Ortiz-CondeASnchezFJGMuciJ, 2006. New method to extract the model parameters of solar cells from the explicit analytic solutions of their illuminated I–V characteristics. Solar Energy Materials and Solar Cells. 90(3), 352–361.
53.
SaleemHKarmalkarS, 2009. An analytical method to extract the physical parameters of a solar cell from four points on the illuminated J–V curve. IEEE Electron Device Letters. 30(4), 349–352.
54.
PetroneGSpagnuoloGVitelliM, 2007. Analytical model of mismatched photovoltaic fields by means of Lambert W-function. Solar Energy Materials and Solar Cells. 91, 1652–1657.
55.
JainASharmaSKapoorA, 2006. Solar cell array parameters using Lambert W function. Solar Energy & Materials. 90, 25–31.
56.
JainAKapoorA, 2004. Exact analytical solutions of the parameters of real solar cells using Lambert W-function. Solar Energy & Materials. 81, 269–277.
57.
JainAKapoorA, 2005. A new method to determine the diode ideality factor of real solar cell using Lambert W-function. Solar Energy Materials & Solar Cells. 85, 391–396.
58.
JervaseJABourdoucenHAl-LawatiA, 2001. Solar cell parameter extraction using genetic algorithms. Meas Sci Technol. 12, 1922–5.
59.
MohamedFAKoivoHN, 2012. Online management genetic algorithms of microgrid for residential application. Energy Convers Manage. 64, 562–8.
60.
ShahatAE, 2010. Maximum power point genetic identification function for photovoltaic system. IJRRAS. 3, 264–73.
61.
CaoYJWuQH, 1999. Teaching genetic algorithm using matlab. Int. J. Elect. Enging. Educ. 36, 139–153. Manchester U.P. Printed in Great Britain.
62.
ZagroubaMSellamiABouaïchaMKsouriM, 2010. Identification of PV solar cells and modules parameters using the genetic algorithms: Application to maximum power extraction. Solar Energy. 84, 860–866.
63.
GongWCaiZ, 2013. Parameter extraction of solar cell models using repaired adaptive differential evolution. Solar Energy. 94, 209–220.
64.
OlivaDCuevasEPajaresG, 2014. Parameter identification of solar cells using artificial bee colony optimization. Energy. 72, 93–102.
65.
BonkoungouDKoalagaZNjomoD, March 2013. Modelling and simulation of photovoltaic module considering signale-diode equivalent circuit model in MATLAB. International Journal of Emerging Technology and Advanced Engineering. 3(Issue 3).
66.
LunSDuCYangGWangSGuoTSangJLiJ, 2013. An explicit approximate I–V characteristic model of a solar cell based on pade’ approximants. Solar Energy. 92, 147–159.
67.
TayyanAAEL. A simple method to extract the parameters of the single-diode model of a PV system. Turkish Journal of Physics. http://journals.tubitak.gov.tr/physics/.
