Maximum power point tracking using fuzzy logic control

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    Abd EL-halim a, Mohamed Ezzat El Kotb Salem c,

    gy, E

    Photovoltaic (PV)

    DCDC Ck converter

    tellir vaa Dh itser pPT. T

    the efciency of energy production from PV.

    as is vourcesalmosing innd we

    and current feedback based MPPT to more improved power feed-back based MPPT such as the perturbation and observation (P&O)technique or the incremental conductance technique [1,2]. Re-cently intelligent based controls MPPT have been introduced.

    In this paper, an intelligent control technique using fuzzy logiccontrol is associated to an MPPT controller in order to improve en-ergy conversion efciency.

    age transfer function is explained in Fig. 2:The voltage transfer function of Ck converter is written as [15]:


    D1 D 1

    Its relationship to the duty cycle (D) is:

    If 0 < D < 0.5 the output is smaller than the input. If D = 0.5 the output is the same as the input. If 0.5 < D < 1 the output is larger than the input.

    Corresponding author. Tel.: +20 01001832433.

    Electrical Power and Energy Systems 39 (2012) 2128

    Contents lists available at


    .e lE-mail address: (M.E.E.K. Salem).the year, so we can use stand alone PV-powered water pumpingsystem to get water in non peopled areas. Unfortunately theactual energy conversion efciency of PV module is rather low.So to overcome this problem and to get the maximum possibleefciency, the design of all the elements of the PV system hasto be optimized.

    In order to increase this efciency, MPPT controllers are used.Such controllers are becoming an essential element in PV systems.A signicant number of MPPT control have been elaborated sincethe seventies, starting with simple techniques such as voltage

    2.1. PV module

    BP Solar BP SX 150S PV module is chosen for a MATLAB simula-tion model. The module is made of 72 multi-crystalline silicon so-lar cells in series and provides 150W of nominal maximum power[14]. Table 1 shows its electrical specication.

    2.2. DCDC Ck converter

    The basic operation of Ck converter and derivation of the volt-DC water pumpMATLAB/SIMULINK

    1. Introduction

    In our Arabian nation peopled aretotal area because fresh water resareas. Now, the existing fresh waterbut in the near future with increasbe huge problem. On the other ha0142-0615/$ - see front matter 2011 Elsevier Ltd. Adoi:10.1016/j.ijepes.2011.12.006ery small comparing toconcentrate in these

    t enough for our needs,people numbers it willhave shining sun all

    2. The proposed system

    The proposed system in this paper is stand-alone DC waterpumping without backup batteries. As shown in Fig. 1, the systemis very simple and consists of subsystems:Maximum power point tracking (MPPT)Fuzzy logic control (FLC)

    2011 Elsevier Ltd. All rights reserved.Maximum power point tracking using fu

    Mohamed M. Algazar a, Hamdy AL-monier b, Hamdya Electrical Power & Machines Dept., Faculty of Engineering, Al-Azhar University, Egyptb Electronics & Communication Dept., Naser High Institute for Engineering and TechnolocB. Sc.Electrical Power & Machines Engineering, Al Azhar University, Cairo 2004, Egypt

    a r t i c l e i n f o

    Article history:Received 12 July 2011Received in revised form 10 November 2011Accepted 5 December 2011Available online 29 February 2012


    a b s t r a c t

    This paper proposes an inphotovoltaic system undelogic controller applied toare presented together witon it is stand-alone PV watby the system without MP

    Electrical Power a

    journal homepage: wwwll rights control method for the maximum power point tracking (MPPT) of ariable temperature and insolation conditions. This method uses a fuzzyCDC converter device. The different steps of the design of this controllersimulation. The PV system that I chose to simulate to apply my techniquesumping system. Results of this simulation are compared to those obtainedhey show that the system with MPPT using fuzzy logic controller increasegypty logic control

    SciVerse ScienceDirect

    d Energy Systems

    sevier .com/locate / i jepes

  • Fig. 1. Block diagram of the proposed PV water pumping system.

    Fig. 2. Ck converter waveforms: (a

    Table 1Module electrical characteristics data of PV module taken from the datasheet [14].

    Electrical characteristics

    Maximum Power (Pmax) 150 WVoltage at Pmax (Vmp) 34.5 VCurrent at Pmax 4.35 AWarranted minimum Pmax 140 WShort-circuit current (Isc) 4.75 AOpen-circuit voltage (Voc) 43.5 VMaximum system voltage 600 VTemperature coefcient of Isc (0.065 0.015)%/CTemperature coefcient of Voc (160 20)mV/CTemperature coefcient of power (0.5 0.05)%/CNOCT5 47 2 C

    Table 2Design specication of the Ck converter [5].


    Input voltage (Vs) 2048 VInput current (Is) 05 A (

  • r andM.M. Algazar et al. / Electrical PoweHere, a Ck converter is designed based on the specicationshown in Table 2.

    2.3. FL controller

    This is the main subject of this paper; I will discuss it in the nextsections.

    Fig. 3. Kyocera SD 1230 water pump performance chart [16].

    Fig. 4. PV module is directly connected to a (variable) resistive load [5].

    Fig. 5. IV curves of BP SX 150S PV module [14] and various resistive loadssimulated with the MATLAB model (1 KW/m2, 25 C) [5].Recently fuzzy logic controllers have been introduced in thetracking of the MPP in PV systems [9,10]. They have the advantageto be robust and relatively simple to design as they do not requirethe knowledge of the exact model. They do require in the otherhand the complete knowledge of the operation of the PV systemby the designer.2.4. DC water pump

    Fig. 3 shows the relationship between ow rate of water and to-tal dynamic head for the Kyocera SD 1230 solar pump to be mod-eled. It has the normal operating voltage of 1230 V and themaximum power of 150 W.

    3. Maximum power point tracking algorithms

    When a PV module is directly coupled to a load, the PVmodulesoperating point will be at the intersection of its IV curve and theload line which is the IV relationship of load. For example in Fig. 4,a resistive load has a straight line with a slope of 1/Rload as shownin Fig. 5. In other words, the impedance of load dictates the oper-ating condition of the PV module. In general, this operating point isseldom at the PV modules MPP, the optimal adaptation occursonly at one particular operating point, called Maximum PowerPoint (MPP) and noted in our case Pmax.

    To overcome this problem, it is necessary to add an adaptationdevice, MPPT controller with a DCDC Ck converter, between thesource and the load [3].

    Furthermore the location of the MPP in the IV plane is notknown beforehand and always changes dynamically dependingon irradiance and temperature [6,7]. For example, Fig. 6 shows aset of PV IV curves under increasing irradiance at the constanttemperature (25 C), and Fig. 7 shows the IV curves at the sameirradiance values but with a higher temperature (50 C). Thereare observable voltage shifts where the MPP occurs. So, the MPPTcontroller is also required to track the new modied maximumpower point in its corresponding curve whenever temperatureand/or insolation variation occurs.

    Many MPPT control techniques have been conceived for thispurpose these last decades [1,4].

    Maximum power point tracking algorithms:

    Perturb and observe algorithm. Incremental conductance algorithm. Parasitic capacitances. Constant voltage control. Constant current control. Pilot cell. Articial intelligent method. (Fuzzy logic control, neuralnetwork. . .)

    They can be classied as: Voltage feedback based methods which compare the PV operat-ing voltage with a reference voltage in order to generate thePWM control signal of the DCDC converter [8].

    Current feedback based methods which use the PV moduleshort circuit current as a feedback in order to estimate the opti-mal current corresponding to the maximum power.

    Power based methods which are based on iterative algorithmsto track continuously the MPP through the current and voltagemeasurement of the PV module [5].

    4. Fuzzy logic MPPT controller

    Energy Systems 39 (2012) 2128 23The proposed system in this thesis consist of two input vari-ables: error (E) and change of error (CE), and one out variable: dutyratio or duty cycle (D), as shown in Fig. 8.

  • Fig. 6. IV curves for varying irradianc

    Fig. 7. IV curves for varying irradianc

    Fig. 8. General diagram of a fuzzy controller [3].

    24 M.M. Algazar et al. / Electrical Power and Energy Systems 39 (2012) 21284.1. Fuzzication

    Membership function values are assigned to the linguistic vari-ables, using ve fuzzy subsets: NB (negative big), NS (negativesmall), ZE (zero), PS (positive small), and PB (positive big). The par-tition of fuzzy subsets and the shape of membership function,which can adapt shape up to appropriate system, are shown inFig. 9. The value of input error (E) and change of error (CE) are nor-malized by an input scaling factor. In this system the input scalingfactor has been designed such that input values are between 1and 1.

    e and a trace of MPPs (25 C) [5].

    e and a trace of MPPs (50 C) [5].

  • r andM.M. Algazar et al. / Electrical PoweThe triangular shape of themembership function of this arrange-ment presumes that for any particular input there is only one dom-inant fuzzy subset. I chose the number of fuzzy subset depending onthe required accuracy, some paper [11] using seven subset for thesame problem but the increasing in accuracy is very small.

    The input variables error (E) and change of error (CE) for the fuz-zy logic controller can be calculated as follows:





    CEK EK EK 1 3where I is the output current from PV array; DI is I(k) I(k1); V isoutput voltage from PV array; DV is the V(k) V(k1).

    4.2. Inference method

    The composition operation by which a control output can begenerated. Several composition methods such as MAXMIN andMAXDOT have been proposed in fuzzy tool box in MATLAB. Thecommonly used method is MAXMIN is used in this thesis. Theoutput membership function of each rule is given by the MIN (min-imum) operator and MAX (maximum) operator. Table 3 shows therule table for fuzzy logic controller.

    4.3. Defuzzication

    Defuzzicaion for this system is the centre of gravity to com-pute the output of this FLC which is the duty ratio (cycle). The

    Fig. 9. Fuzzy logic membership functioEnergy Systems 39 (2012) 2128 25centre of gravity method is both very simple and very fast method.The centre of gravity defuzzication method in a system of rules byformally given by:

    D Pn

    j1lDj DjPn


    Duty ratio, the output of fuzzy logic control uses to controlthrough PWM which generated pulse to control MOSFET switchin DCDC converter.

    4.4. Fuzzy logic control simulation in MATLAB/SIMULINK

    Fig. 10 shows details of FLC block in MATLAB/SIMULINK. As Imentioned in beginning of this section, FLC for MPPT have two in-put variables and one out variables. The rst step in shown block isto calculate the input variables using Eqs. (1) and (2), then usingFLC block which programmed as mentioned in previous sectionsto calculate duty ration which is the Pulse Width Modulator input.

    ns for inputs and output variables.

    Table 3Fuzzy rule table.

    E CE



  • 5. Simulation of PV water pump system with MPPT

    In this Section 1 will simulate the system in MATLAB/SIMULINK.Fig. 11 shows block diagram stand-alone PV water pumping sys-tem with MPPT using FLC.

    I will rst test the whole system with two certain atmosphericconditions and comparing the result with theoretical data and di-rect-coupled system.

    Fig. 12 shows the test result for the rst test scenario with irra-diance 1000W/m2 and temperature 25 C. The PV system withoutMPPT has poor efciency because of mismatching between the PVmodule and the DC pump motor load. The power generated by PVsystem with MPPT (assume loss-less converter) is approximatelyequal the theoretical maximum generated power at this atmo-spheric condition. Assuming a DCDC converter has efciencymore than 95%, the system can increase the overall efciency com-pared to the system without MPPT.

    Fig. 13 shows the test result for the second test scenario withirradiance 500W/m2 and temperature 15 C. The result is showthat, the PV system with MPPT can increase the overall efciencycompared to the system without MPPT, like that shown in the rsttest scenario Fig. 12.

    Now for nal test scenario, I will simulate the system usingatmospheric data (irradiation and temperature) from NationalRenewable Energy Laboratory [12] for one day as shown in Figs.14 and 15 to provide a comparison with the system without MPPTin terms of energy produce and ow rates of water pumped.

    The day that I chose to using its atmospheric data for test thesystem is a very sunny day as shown in Fig. 14 (the irradiance

    increase gradually from sun rise 5:00 AM to noon then decreasegradually to sun set 6:42 PM). The reason for this choice is to viewclearly the effect of using MPPT in the system.

    The effect of changing temperature over day time for choosingday is small because the temperature value varies in the mostday hour around four degrees (1821 C), as show in Fig. 15.

    gic controller block.

    Fig. 12. Result of system with irradiance 1000 W/m2 and temperature 25 C.

    26 M.M. Algazar et al. / Electrical Power and Energy Systems 39 (2012) 2128Fig. 10. Fuzzy loFig. 11. PV water pump system with MPPT simulated in MATLAB/SIMULINK.

  • Fig. 14. Irradiance vs. time data from 5:00 AM to 6:42 PM for sunny day simulationpurpose.

    Fig. 15. Temperature vs. time data from 5:00 AM to 6:42 PM for sunny daysimulation purpose.

    Fig. 13. Result of system with irradiance 500 W/m2 and temperature 15 C.

    M.M. Algazar et al. / Electrical Power and Energy Systems 39 (2012) 2128 27Fig. 16 shows that, the generated power of PV system withMPPT is greater than the direct-coupled system. The difference willbe maximum at noon time. It shows also that the system withMPPT has smooth curve over the day, but direct-coupled systemhas oscillation over the day time. This oscillation will affect onthe performance of DC pump the long life run and its life time.

    As shown in Fig. 17, the ow rate of Kyocera SD 1230 waterpump is proportional to the power delivered. When the total dy-namic head is 30 m, the ow rate per watt is approximately86.7 cm3/Wmin. The minimum power requirement of pump mo-tor is 35 W [13]; therefore as long as the output power is higherthan 35W, it pumps water with the ow rate above.

    Fig. 17 shows also that the direct-coupled PV water pumpingsystem has a severe disadvantage because in the morning thepump will delay starting while the same system with MPPT is al-ready pumping water. Similarly, in the afternoon it goes idle nearlyearlier than the system with MPPT. The ow rate of water is alsolower throughout the operating period. The gures show thatMPPT offers signicant performance improvement.

    Fig. 16. Generating power of PV system for day time period (from 5:00 AM to6:42 PM).

    Fig. 17. Flow rates of PV system for day time period (from 5:00 AM to 6:42 PM).

  • 6. Conclusion

    The main purpose of this paper was to propose a new t...


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