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Solar Energy 91 (2013) 337–349

Monitoring, modelling and simulation of PV systems using LabVIEW

Aissa Chouder a, Santiago Silvestre b,⇑, Bilal Taghezouit a, Engin Karatepe c

a Photovoltaic Laboratory, Development Centre of Renewable Energies, BP 62 Route de l’Observatoire, 16340 Bouzareh Algiers, Algeriab Electronic Engineering Department, Universitat Politecnica de Catalunya, C/Jordi Girona 1-3, Campus Nord UPC, 08034 Barcelona, Spain

c Department of Electrical and Electronics Engineering, Ege University, 35100 Bornova, Izmir, Turkey

Received 24 July 2012; received in revised form 25 September 2012; accepted 29 September 2012Available online 29 October 2012

Communicated by: Associate Editor Nicola Romeo

Abstract

This paper presents a detailed characterization of the performance and dynamic behaviour of photovoltaic systems by using Lab-VIEW real-time interface system. The developed software tool integrates several types of instruments into a single system which is ableto offer online measurements all data sources and comparison simulation results with monitored data in real-time. Comprehensive mon-itoring and analyzing of PV systems play a very important role. The proposed method is a low-cost solution to provide fast, secure andreliable system by making the system database-ready for performance analysis of PV systems. The proposed method is also applied to agrid connected PV system in the Centre de Developpement des Energies Renouvelables (CDER) in Algeria. The results show that there isa good agreement between the measured and simulation results values. The integration methodology of robust simulation and monitoreddata in real-time can be extended to study the fault diagnosis of a PV system.� 2012 Elsevier Ltd. All rights reserved.

Keywords: PV systems; Monitoring; Modelling; Simulation

1. Introduction

LabVIEWe (Laboratory Virtual Instrument Engineer-ing Workbench) is a graphical programming language byNational Instruments that uses icons instead of lines of textto create applications. Nowadays this programming envi-ronment has found its application in many scientific fieldsand technical engineering, so in this work we propose anintegral LabVIEW platform of monitoring, modellingand simulation tools for photovoltaic (PV) systems.

Many applications of LabVIEW for monitoring PV sys-tems have been reported before in the literature (Koutroulisand Kalaitzakis, 2003; Forero et al., 2006; MartınezBohorquez etal., 2009; Vergura and Natangelo, 2009; Ulieruet al., 2010).

0038-092X/$ - see front matter � 2012 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.solener.2012.09.016

⇑ Corresponding author. Tel.: +34 934017491; fax: +34 934016756.E-mail address: santi@eel.upc.edu (S. Silvestre).

On the other hand different commercial software solu-tions are available for PV systems simulation (Silvestre,2012) and standard simulation software, as Matlab (Yusofet al., 2004; Pater and Agarwal, 2008; Karatepe et al., 2008;Chouder and Silvestre, 2012) or Pspice (Castaner andSilvestre, 2002; Silvestre et al., 2009) have been also exten-sively used for this purpose. Furthermore Vergura andNatangelo (2010) have integrated Matlab and Simulinkfor monitoring energy performances of PV plants.

In this work we report the integration of monitoring,modelling and simulation of PV systems in the same envi-ronment able to give information of the system behaviourin real time. This solution allows the acquisition and con-trol of all necessary data from the PV system, evaluatemain model parameters of PV modules and array, calculatethe performance ratio (PR) and Yields of the system, createHTML and XLS report files and visualize all these dataand the dynamic system behaviour in real time. Moreoverthe integration of robust modelling and simulation gives

338 A. Chouder et al. / Solar Energy 91 (2013) 337–349

the opportunity to compare simulation results with moni-tored data in real time, allowing the development of newtools for fault detection as well as new prediction modelswith the objective of improve the performance and reliabil-ity of PV systems, optimizing the system output to achievehigher yields.

2. PV system description

The proposed method of monitoring, modelling andsimulation of PV systems has been applied to a grid con-nected PV system located in the Centre de Developpementdes Energies Renouvelables (CDER), Algerie. The PV sys-tem is formed by 90 PV modules (Isofoton 106Wp-12 atSTC) divided in three subgenerators of 3 kWp each one.The subgenerators are formed by two parallel strings of15 PV modules in series. Each subgenerator is connectedto a single phase inverter of 2.5 kW (IG30 Fronius) thatinjects the generated energy into a phase of the publiclow voltage distribution network of the National Company(Sonelgaz) 220 V–50 Hz (Hadj Arab etal., 2005). The blockdiagram of this PV system is shown in Fig. 1.

3. Monitoring PV systems using LabVIEW

Fig. 2 shows a schematic diagram of the sensors andacquisition data of the monitoring system implemented inthe PV system. Different sensors are included to measureirradiance, temperature, as well as current and voltages atthe DC and AC sides of the system.

The data acquisition is carried out using an Agilent34970A and dedicated multiplexer module Agilent 34902Awith sixteen channels. Data communication between PCand LabVIEW, where the incoming data is processed, is per-formed by GPIB bus.

Fig. 1. Schematic diagram of the CD

Two pyranometers (Kipp & Zonen CM 11 type) and areference solar cell are used to measure the irradiance.One of the pyranometers and the reference cell are installedat two different places of the PV plant to measure irradi-ance in the tilted plain. A second pyranometer measuresthe irradiance in the horizontal plane. Thermocouples havebeen used to measure the ambient temperature near the PVplant in order to predict the effective PV modulestemperature.

Eq. (1) is used to calculate this effective solar cell tem-perature, Tc.

T c ¼ T a þ ðNOCT � 20 �CÞ G800

ð1Þ

where Ta is the ambient temperature, G the irradiance andNOCT the Normal Operating Cell Temperature given bythe PV modules manufacturer.

For the measurements of currents, DC (Idc) and AC(Iac), we have used two CLSM-50 closed loop Hall effectcurrent sensors and a dual operational amplifier LM1458N.

The DC output voltage of the PV system is measured bymeans of a voltage divider, while the AC output voltage ismeasured at the secondary of the transformer used for thevoltage supply of the hall sensors. All data coming from theacquisition system are processed in LabVIEW using the VIshown by Fig. 3, where the appearing coefficients are usedto calibrate the different monitored parameter. The VIallows the following tasks: Communication with the dataacquisition Agilent 34970A in order to setup the differentchannels via GPIB bus (DC/AC and temperature measure-ments), processing the output string coming from the dataacquisition, splitting the output string to the correspondingmeasured variable and the calibration of each channel with

ER grid connected PV system.

Fig. 2. Sensors and acquisition data system to monitor the PV system.

Fig. 3. VI developed to obtain the monitored data.

Table 1Calibration factors and channels associated to each measured variable and sensitivity of the irradiance sensors used.

Measured variables Channel number Sensors Calibration factor

GI,c 101 Reference cell: Rsh ¼ 0:037 X 7901.4GI,p, GH,p 102,103 CM 11: sensitivity = 5 lV/(W m�2) 19,9203.2Tamb 104 k type Thermocouple Direct measure in Agilent 34,970VDC,meas 105 Resistive divider 29VAC,meas 106 AC adapter 220/18IDC, IAC,meas 107,108 Hall effect 1.6

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the corresponding scaling factor. Table 1 shows the calibra-tion factors and channels associated to each measuredvariable as well as the sensitivity of the irradiance sensorsused.

4. Description of PV system modelling and simulation

4.1. Modelling the PV module

The model of the PV module is based on the one diodemodel of the solar cell shown in Fig. 4, where G and T areirradiance and temperature respectively, Iph is the photogenerated current depending on irradiance and tempera-ture conditions, D is the diode modelling the P/N junctionof the solar cell and Rsh and Rs are the shunt and seriesresistances respectively, modelling the power losses in thedevice (Overstraeten and Mertens, 1986; Castaner and Sil-vestre, 2002). The output current of the solar cell, I, can bewritten as:

I ¼ Iph � Id � I sh ð2Þ

where Iph is the photo generated current, Id is the diodecurrent and Ish is the current across Rsh.

These currents are given as:

Iph ¼G

Gref

ðIph;ref þ lIccðT� T refÞÞ ð3Þ

where G and T are respectively the irradiance and tempera-ture conditions of work, Gref and Tref are irradiance and tem-perature at standard test conditions (STCs): 1000 W/m2 and25 �C, Iph,ref is the photo generated current at STC and lIcc

the temperature coefficient of current.

Id ¼ I sat expVþ RsI

nV t

� �� 1

� �ð4Þ

where Isat is the is the reverse saturation current of diode, n

is the diode ideality factor and Vt the thermal voltage.

I sh ¼Vþ RsI

Rsh

ð5Þ

Eq. (2) can be rewritten, considering Eqs. (3)–(5), as

I ¼ Iph � I sat expVþ RsI

nV t

� �� 1

� �� Vþ RsI

Rsh

� �ð6Þ

Eq. (6) is an implicit and not linear equation than givesthe I(V) characteristic of the solar cell. Commercial photo-voltaic modules are composed by association of solar cellsin series forming a branch. Some higher power PV modules

Fig. 4. Equivalent circuit of the solar cell.

include various branches in parallel. If we consider Ns solarcells in series in each branch and a total number of Np

branches for a PV module, the total number of solar cellsforming the PV module is Ns � Np. So, Eq. (6) can be con-veniently scaled to obtain a similar equation for the I(V)characteristic of a PV module, taking into account the fol-lowing equations (Castaner and Silvestre, 2002; Chenniet al., 2007; Karatepe et al., 2007):

Im ¼ N pI ð7ÞV m ¼ N sI ð8Þ

Rsm ¼N s

N p

Rs ð9Þ

Rshm ¼N s

N p

Rsh ð10Þ

where parameters with subscript m stands for the PVmodule.

The model that allows to obtain the I(V) characteristicof a PV module, considering Eqs. (6)–(10), has been imple-mented in LabVIEW environment. The I(V) and P(V)characteristics as well as the coordinates of the maximumpower point (MPP) of both, solar cell and PV moduleare available results of the developed model. The inputparameters for the calculations are: Open circuit voltageof the solar cell (Voc), short circuit current of the solar cell(Isc), Rs, Rsh, Ns, Np, G, T, lIcc and the solar cell idealityfactor (n). The flowchart for the obtention of the I(V)and P(V) characteristics is shown in Fig. 5.

4.2. Inverter model

The inverter model developed in LabVIEW environ-ment is based in the performance model inverter presentedby King et al. (2007). The AC output power of the inverter,PAC,sim is defined by the following equation:

P AC;sim ¼P aco

A� B

� �� C A� Bð Þ

� �P dc � Bð Þ þ C P dc � Bð Þ2 ð11Þ

where Paco is the maximum AC output power for inverterat reference or nominal rating conditions, Pdc is the DCpower at the inverter input and parameters A, B and C

are given by the following equations:

A ¼ P dco 1þ C1ðV DCsim � V dcoÞ½ � ð12ÞB ¼ P so 1þ C2ðV DCsim � V dcoÞ½ � ð13ÞC ¼ Co½1þ C3ðV DCsim � V dcoÞ� ð14Þ

where VDCsim is the DC voltage at the inverter input, Vdco

and Pdco are respectively the DC voltage and power inputsat which the AC-power rating is achieved at the referencerating condition, Pso is the DC power required at the inver-ter input to start working properly and C1, C2 and C3 areempirical coefficients to adjust the PAC(PDC) characteristicof the inverter.

The values of main parameters involved in Eq. (11) usedin this work for modelling the inverters are shown in Table 2.

Fig. 5. Flowchart for the obtention of the I(V) and P(V) characteristics.

Table 2Values used in the performance model inverter.

Paco (W) Pdco (W) Vdco (V) Pso (W) Co (W)�1 C1 (V)�1 C2 (V)�1 C3 (V)�1

2692 2900 275.6 25 �6.67 10�5 �0.00296 �0.00458 0.0255

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4.3. Grid connected PV system simulation

The main objective of the simulation of grid connectedPV system is to obtain expected evolution of voltagesand currents at the DC side of the system as well as atthe AC side, at the inverter output. So, simulation resultswill give the expected behaviour, in a dynamic way, ofthe whole system taking into account real conditions ofclimate parameters. From simulation results the values ofpower (P) and energy, instantaneous (Einst) and cumulative(Ecum) energies, can be evaluated as follows:

Einst ¼ PDt ð15Þ

Ecum ¼Xt

0

Einst ð16Þ

The simulation of the whole grid connected PV system isbased on the models presented above for PV modules andfor the inverter and is carried out also in LabVIEW envi-ronment. The flowchart of the simulation process isdepicted in Fig. 6.

The calculation of the power and cumulative and instan-taneous energies, AC and DC, generated by the photovol-taic system in dynamic regime is evaluated using anotherVI.

Fig. 6. Flowchart of the simulation process.

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Finally, the expected values of the system yields: Refer-ence yield (Yr), array yield (Ya) and final yield (Yf), as wellas the performance ratio (PR), can be evaluated from thesimulation results using the following expressions (Haber-lin and Beutler, 1995; Commission of the European Com-munities, 1997):

Y r ¼R Dt

0G

Gref

ð17Þ

Y a ¼R Dt

0Edc

Poð18Þ

Y f ¼R Dt

0Eac

P o

ð19Þ

PR ¼ Y r

Y f

ð20Þ

where Gref is the irradiance at STC, G is the measured irra-diance and Po is the nominal PV system power.

Fig. 7 shows the evolution of Yr along a day and itsvalue for this day obtained from LabVIEW simulations.

5. Results and discussion

5.1. PV module model validation

In order to validate the PV module model used to pre-dict the whole PV system performance, a procedure basedon outdoor measurement and analytic derivation of the

expected five main parameters (Iph, Isat, n, Rs, Rsh) (Chou-der and Silvestre, 2012) has been integrated in LabVIEWenvironment as a separated module. The developed proce-dure finds out the five parameters at STC (1000 W/m2 and25 �C) and then calculates them for any other real operat-ing condition. The effect of variation of the parameter val-ues of a single PV module affects the entire PV systemperformance (D’Alessandro et al., 2011).Our methodincludes parameter extraction techniques for the whole sys-tem that allow a good estimation of the system output. Ifsome parameter of a PV module changes and importantdifferences between simulation results and monitored dataare observed, is necessary to run again the parameterextraction algorithm and check again if simulation resultsshow a good agreement with monitored data.

Main results obtained, applying the parameters extrac-tion procedure, are summarized in Table 3. The visualiza-tion panel performing this task is shown in Fig. 8. Thevalidation of the procedure is carried out by comparingreal measurement of I–V characteristics measured at out-door conditions and the simulated one generated by intro-ducing the expected five model parameters. The result ofthis comparison is consolidated by plotting the error curvebetween simulated and measured I–V characteristics andquantifying the main error indicator as seen in Fig. 9 andreported in Table 4. These values are below previousreported values for errors obtained in simulations of PVmodules (Mahmoud et al., 2012; Villalva et al., 2009).

The resolution of Eq. (6) shows an increasing errorbetween simulation and measurement near the open circuitvoltage, as can be seen in Fig. 9. It is essentially due tonumerical stability, because the number of points generatedby the simulation is less than the number of points given bythe IV tracer PVPM2516, so the calculation of the error isalso affected at high voltage.

5.2. Analysis of PV system simulation Results

The variable climates used in the simulations carried outto analyze and validate the simulation procedure of the PVsystem, presented before in Section 4.2, are temperaturesand irradiances obtained from the monitoring systemshown in Fig. 10. The samples have been measured witha time step of 1 min.

The simulation of the PV system behaviour, followingthe flow chart shown in Fig. 6, was done in real time. Mainresults obtained for DC voltage and current at the outputof the PV array are shown in Fig. 11. As can be seen, a goodagreement is obtained between simulation results and mon-itored data. Main differences observed between 15.00 h and18.00 h for the current are due to inverter disconnectionforced by grid disturbances at the end of the day.

The results obtained for the generated power at the DCand AC sides of the system are shown in Fig. 12, where theeffect of the inverter disconnection is present again.

Table 5 summarizes the errors observed between moni-tored values and simulation results for power and energy

Fig. 7. Reference yield.

Table 3Extracted parameters for Isofoton 106-12 PV module at 762 W/m2 of irradiance and 26.2 �C of temperature.

Parameter Iph (A) Io (A) n Rs (X) Rsh (X) Isc (A) Voc (V) Im (A) Vm (V)

Value 5.1 1.9 10�8 1.14 0.33 144 5.07 20.4 4.53 16.31

Fig. 8. Visualization panel of parameters extraction of PV module.

A. Chouder et al. / Solar Energy 91 (2013) 337–349 343

generated by the PV system, giving a good approach to theaccuracy of the simulation procedure.

Root mean square errors (RMSEs) below 4% areobtained for power and energy delivered by the PV array,these values are below previous RMSE values reported inthe literature in simulations of PV systems (Chouder and

Silvestre, 2009) and in the same order of magnitude thanMSE errors obtained using neural networks algorithms(Yu and Chang, 2011).

The whole system has been monitored along the monthsof June, August and September 2012 using the presentedprocedure. Simulations of the dynamic system behaviour

Fig. 9. Plot of the error between measured and simulated I–V characteristic (G = 762 W/m2, T = 26.2 �C).

Table 4Main error values between measured and simulated parameters.

Relative error EIsc ð%Þ EV oc ð%Þ EIm ð%Þ EV m ð%Þ RMSEðAÞ RMSEð%ÞValue 0.97 �1.07 1.63 0 0.13 2.66

Fig. 10. Monitored irradiance and temperature profiles.

344 A. Chouder et al. / Solar Energy 91 (2013) 337–349

have been carried out in the same period of time. Fig. 13shows the values obtained for the reference yield, Yr, forthe second and third weeks of June and Fig. 14 showsthe same information for different days corresponding tothese 3 months.

The comparison between monitoring (meas) and simula-tion results (sim) for the array and final yields are given inFigs. 15 and 16 for the same weeks of June. This informa-tion is enlarged in Figs. 17 and 18 for several days for these3 months.

Fig. 11. Evolution of voltage and current at the output of the PV array, measured (meas) and simulation results (sim).

Fig. 12. Evolution of the power generated by the PV system, measured (meas) and simulation results (sim).

Table 5Indicators of the accuracy of the simulation procedure.

PDC PAC EDC EAC

Mean error 61.13 W 80.97 W 0.17 Wh 0.22 WhRMSE 6826 W 95.17 W 0.19 Wh 0.26 WhRMSE% 2.73 3.81 2.73 3.81

A. Chouder et al. / Solar Energy 91 (2013) 337–349 345

On June 19 we removed intentionally one string fromthe whole PV array to show the effectiveness of the modelto detect malfunctions. So in this day the simulation resultsgave the result of a healthy system, while the monitoreddata gives lower output power. As can be seen in Figs.15 and 16, the comparison of monitored data and simula-tion results gives the opportunity to detect a malfunction inthe system.

Finally the values obtained for the performance ratioare given in Figs. 19 and 20. As can be seen a good agree-ment is found between simulation results and monitoreddata.

6. Conclusion

This works presents an integral LabVIEW platform ofmonitoring, modelling and simulation of grid connectedPV systems. In the same platform, we propose the model-ling of the PV module identified with outdoor measure-ments of I–V curves in order to extract the main PVmodule parameters. The PV module modelling and extrac-tion parameters procedure has been successfully validatedexperimentally.

For the dynamic behaviour modelling of the PV system,an accurate model of the Inverter is included. The invertermodel allows to predict AC output power as a function ofDC input voltage and DC input power. The simulationmethodology of the PV system in real dynamic conditionsof work has also been been validated successfully in a gridconnected PV system located in Argelia.

The developed platform allows the acquisition and con-trol of all necessary data from the PV system, the simula-tion in real time of the whole PV system working in

Fig. 13. Reference yield June 2012.

Fig. 14. Reference yield along summer 2012.

Fig. 15. Array yield June 2012.

346 A. Chouder et al. / Solar Energy 91 (2013) 337–349

dynamic behaviour, calculates the performance ratio, PRand Yields of the system, create HTML and XLS reportfiles and visualize all these data and the dynamic system

behaviour in real time. This toolbox results in robust mod-elling, advanced simulation incorporating predictions ofsystem output with respect to solar resource, local weather

Fig. 16. Final yield June 2012.

Fig. 17. Array yield along summer 2012.

Fig. 18. Final yield along summer 2012.

A. Chouder et al. / Solar Energy 91 (2013) 337–349 347

Fig. 19. Performance ratio June 2012.

Fig. 20. Performance ratio along summer 2012.

348 A. Chouder et al. / Solar Energy 91 (2013) 337–349

and system behaviour. The output results obtained fromthis toolbox could allow the inclusion in the same platforman algorithm for automatic supervision and fault detectionof the PV system.

References

Castaner, L., Silvestre, S., 2002. Modelling Photovoltaic Systems usingPspice. John Wiley & Sons Ltd., Chichester.

Chenni, R., Makhlouf, M., Kerbache, T., Bouzid, A., 2007. A detailedmodeling method for photovoltaic cells. Energy 32, 1724–1730.

Chouder, A., Silvestre, S., 2009. Analysis model of mismatch power lossesin PV systems. Journal of Solar Energy Engineering 131 (2), 024504–024509.

Chouder, A., Silvestre, S., 2012. Modeling and simulation of a gridconnected PV system based on the evaluation of main PV moduleparameters. Simulation Modelling Practice and Theory 20 (1), 46–58.

Commission of the European Communities, 1997. Guidelines for theAssessment of Photovoltaic Plants, Document B, Analysis andPresentation of Monitoring Data. Issue 4.3. Joint Research Centre.

DAlessandro, V., Guerriero, P., Daliento, S., Gargiulo, M., 2011. Astraightforward method to extract the shunt resistance of photovoltaiccells from current–voltage characteristics of mounted arrays. Solid-State Electronics 63, 130–136.

Forero, N., Hernandez, J., Gordillo, G., 2006. Development of amonitoring system for a PV solar plant. Energy Conversion andManagement 47, 2329–2336.

Haberlin, H., Beutler, Ch., 1995. Normalized representation of energy andpower for analysis of performance and on-line error detection in PV-systems. In: Proceedings of the 13th EU PV Conference on PhotovoltaicSolar Energy Conversion, October 1995, Nice, France, pp. 934–937.

Hadj Arab, A., Cherfa, F., Chouder, A., Chenlo, F., 2005. Grid connectedphotovoltaic system at CDER-Algeria. In: Proceedings of the 20thEuropean Solar Energy Conf. and Exhibition, June 2005, Barcelona,Spain.

Karatepe, E., Boztepe, M., Colak, M., 2007. Development of a suitablemodel for characterizing photovoltaic arrays with shaded solar cells.Solar Energy 81, 977–992.

Karatepe, E., Hiyama, T., Boztepe, M., Colak, M., 2008. Voltage basedpower compensation system for photovoltaic generation system underpartially shaded insolation conditions. Energy Conversion and Man-agement 49 (8), 2307–2316.

A. Chouder et al. / Solar Energy 91 (2013) 337–349 349

King, D.L., Gonzalez, S., Galbraith, G.M., Boyson, W.E., 2007.Performance Model for Grid-Connected Photovoltaic Inverters. San-dia Report: SAND2007-5036.

Koutroulis, E., Kalaitzakis, K., 2003. Development of an integrated data-acquisition system for renewable energy sources systems monitoring.Renewable Energy 28 (1), 139–152.

Mahmoud, Y., Xiao, W., Zeineldin, H.H., 2012. A simple approach tomodeling and simulation of photovoltaic modules. IEEE Transactionson Sustainable Energy 3 (1), 185–186.

Martınez Bohorquez, M.A., Enrique Gomez, J.M., Andujar Marquez,J.M., 2009. A new and inexpensive temperature-measuring system:Application to photovoltaic solar facilities. Solar Energy 83, 883–890.

Overstraeten, R.J., Mertens, R.P., 1986. Characterisation and Testing ofSolar Cells and Modules. Physics, Technology and Use of Photovol-taic. Hilger. Bristol.

Pater, H., Agarwal, V., 2008. MATLAB-based modeling to study theeffects of partial shading on PV array characteristics. IEEE Transac-tions on Energy Conversion 23 (1), 302–310.

Silvestre, S., 2012. Review of system design and sizing tools. In: Mcevoy,A., Markvart, T., Castaner, L. (Eds.), Practical Hadook of Photovol-taics. Fundamentals and Applications. Elsevier, Oxford, pp. 684–694.

Silvestre, S., Boronat, A., Chouder, A., 2009. Study of bypass diodesconfiguration on PV modules. Applied Energy 86 (9), 1632–1640.

Ulieru, V.D., Cepisca, C., Ivanovici, T.D., Pohoata, A., Husu, A., Pascale,L., 2010. Measurement and analysis in PV systems. In: Proceedings ofthe International Conference on Circuits, ICC 2010, WSEAS, July2010, Korfu Island, Greece, pp. 137–142.

Vergura, S., Natangelo, E., 2009. Labview interface for data analysis ofPV. In: Proceedings of the International Conference on CleanElectrical Power, June 2009, Capri, Italy, pp. 236–241.

Vergura, S., Natangelo, E., 2010. Labview–Matlab integration foranalyzing energy data of PV plants 2010. In: Proceedings of theInternational Conference on Renewable Energies and Power Quality(ICREPQ’10), March 2010, Granada, Spain, pp. 635–640.

Villalva, M.G., Rafael, Gazoli.J., Ruppert, Filho.E., 2009. Comprehensiveapproach to modeling and simulation of photovoltaic arrays. IEEETransactions on Power Electronics 24 (5), 1198–1208.

Yu, T.S., Chang, H.T., 2011. The forecast of the electrical energygenerated by photovoltaic systems using neural network method. In:Proceedings of Conference on Electric Information and ControlEngineering (ICEICE), April 2011, Wuhan, China, pp. 2758–2761.

Yusof Y., Sayuti, S.H., Abdul Latif, M., Mohd. Zamri Che Wanik, 2004.Modeling and simulation of maximum power point tracker forphotovoltaic system. In: Proceedings of the Power and EnergyConference, November 2004, Kuala Lumpur, Malaysia, pp. 88–93.