2011_Control of a DCDC Converter by Fuzzy Controller for a Solar Pumping System_Paper N. Mazouz, A. Midoun

7/29/2019 2011_Control of a DCDC Converter by Fuzzy Controller for a Solar Pumping System_Paper N. Mazouz, A. Midoun

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Control of a DC/DC converter by fuzzy controller for a solar pumping system

N. Mazouz , A. Midoun

University of Sciences and Technology of Oran, Electrical Engineering Faculty, LEPES Laboratory, P.O. Box 1505, El Mnaouer USTO, Oran, Algeria

a r t i c l e i n f o

Article history:

Received 11 January 2010

Received in revised form 2 February 2011

Accepted 3 June 2011Available online 7 October 2011

Keywords:

Fuzzy logic

Maximum power point

Photovoltaic array

a b s t r a c t

The exploitation of the solar energy is very significant for the very sunny countries. Moreover the dryness

phenomenon in these country is imposes more and more the use of pumping plants. The storage of the

water in insulated basins from the wells has a double advantage. On the one hand, it is a technical storagesolution of the solar energy collected by the photovoltaic panels. On the other hand, it is a hygienic way

out to supply water for the rural population.

In our work, we propose a technique for the identification of the maximum power point (MPP) based

on fuzzy logic. This method is used to generate the cyclic ratio to operate the switcher within the max-

imum power of a photovoltaic array (PVA).

For simulation purpose we made a complete modeling of the entire system. The system carried out

consists of a photovoltaic array supplying, through a DC converter, a direct current (DC) engine coupled

to a centrifugal pump. Our experimental bench consists of two principal units. A DC converter module

composed of IGBT power transistors. And a processing module connected to a PC serial port, handling

the input signals delivered by photovoltaic generator and controlling the power unit.

The obtained experimental results confirm the simulation result which is very satisfactory and show

the utility of the fuzzy controller for the optimization of the system.

2011 Elsevier Ltd. All rights reserved.

1. Introduction

The PVA is a system providing a non-linear power. It is required

a real time identification and the tracking of the maximum opera-

tion point. This maximum power point varies largely in time

according to the climatic conditions such as the sunning and the

temperature.

When we connect a load at the outputs of a PVA, this load im-

poses a point of operation which is not necessarily the point of

maximum power. To ensure an optimal adaptation of energy be-

tween the PVA and the load it should be introduced an adapter

so that the PVA operates at its maximum power point. In our case

the adapter is a DC/DC converter whose we control the cyclic ratio

by regulation.The first used method of regulation is a traditional technique. It

has disadvantages, such as the oscillations around the point of

operation, and the choice of the step of the tracking of the maxi-

mum point. To go beyond these problems we chose a technique

more powerful, the fuzzy logic. In this technique, we used two

method of regulation, the first one that of the variation of the input

voltage by fixing the optimal voltage, and the second one that of

the variation of power per the current (dP/dI), the two methods

led to good results (i.e. good adaptation). The simulation of the sys-

tem (Fig. 1) was carried out in [1].

2. Followed process

2.1. Structure of the developed set up

The system consists of a PVA supplying a DC engine coupled to a

centrifugal pump, through a DC converter, allowing the tracking of

the optimum operation point. The developed controller is a micro-

controller based board connected to a PC through the serial port for

monitoring purpose (Fig. 3).

2.2. Presentation of the simulated system

The block diagram of the maximum power point tracking

(MPPT) system is composed of a PVA, a DC converter and a load

represented by an engine coupled to a pump. The point of opti-

mum power is controlled by the cyclic ratio generated by the fuzzy

controller whose the inputs are the voltage and the current of the

PVA (Fig. 1).

Before any synthesis of a control law, it is necessary to analyze

the process to be controlled and establish an appropriate model.

Regarding the PVA, we considered as an inputs, the current and

the voltage measured experimentally (I1, V1) with specific climatic

conditions of operation, sunning and temperature, respectively.

0142-0615/$ - see front matter 2011 Elsevier Ltd. All rights reserved.doi:10.1016/j.ijepes.2011.06.016

Corresponding author.

E-mail address: [email protected] (N. Mazouz).

Electrical Power and Energy Systems 33 (2011) 16231630

Contents lists available at SciVerse ScienceDirect

Electrical Power and Energy Systems

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / i j e p e s
http://dx.doi.org/10.1016/j.ijepes.2011.06.016mailto:[email protected]://dx.doi.org/10.1016/j.ijepes.2011.06.016http://www.sciencedirect.com/science/journal/01420615http://www.elsevier.com/locate/ijepeshttp://www.elsevier.com/locate/ijepeshttp://www.sciencedirect.com/science/journal/01420615http://dx.doi.org/10.1016/j.ijepes.2011.06.016mailto:[email protected]://dx.doi.org/10.1016/j.ijepes.2011.06.016


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The PVA includes 20 modules (LA361K51, SI-polycrystalline,Vopen circuit = 21.2 V, Ishort circuit = 3.25A, PMaximum = 51 Wp), each 10

are connected in series, and each module contains 36 cells.

The characteristic (I1, V1) obtained experimentally does not en-

sure a nominal operation at the optimum power point for our load,

from where the need for the dimensioning of the PVA.

The dimensioning of the characteristic by simulation consisted

in multiplying the currents and voltages of the characteristic raisedby suitably selected coefficients Ki and Kv.

With Ki = 2 and Kv = 10.

Starting from a characteristic of reference experimentally ob-

tained at the temperature T1 and the E1 sunning. The translation

of formulas (1), (2) would provide the characteristics at the T2 tem-

perature and the E2 sunning.

I2 I1ICCE2=E1 1 a

T2 T1 1

V2 V1 bT2 T1 RS

I2 I1 KI2T2 T1 2

with a = 1.6e3 A/C (coefficient which takes into account the influ-ence of the temperature on the current), b = 7.8e2 V/C (coeffi-

cient which takes account of the effect of the temperature on the

voltage), Rs = 0.4 5X (serial resistor), K= 5.5e3

X/C (factor ofcorrection of the curve), and ICC (A) is the current of short-circuit

measured at the outputs of the solar panel, (I1, V1) is a characteristic

of reference at E1 = 60% and T1 = 30 C.

The used engine is a kind of DC motor with permanent magnet.

His model is defined by the following two equations:

The equation of the electrical circuit of armature.

Va RaIa KUX LadIadt

3

The mechanical equation

KUI A BX JdX

dt Cc 4

These parameters are determined from information recorded on

the motor nameplate or from experimental tests made on the en-

gine. The values of the plate are given for nominal operating

conditions:

Va = 180 V.

Ia = 4.9 A. X = 1750 r/mn.

The counter electromotive force:

e P

a

NnU 5

Nis the number of conductors, n is the rotational speed of the motor

shaft (r/s), U the flow from a pole, a is the number of pair of way of

rolling up (way of rolling up: the circuits that are parallel to the

armature brushes), Pthe number of pole pairs, the voltage constant

is expressed by K.

K NP

2pa

6

We have:

e KUX 7

The useful power:Pu CX eI 8

The magnetic torque:C KUI 9

The used pump is centrifugal type. Its torque is a function of

speed X, and is expressed by the following equation:

Cc a bXn 10

The moment of inertia is approximated to JP = 5 Jm, whereJm is

the moment of inertia of the engine.

The parameters a, b and n must be chosen so that at the speed is

equal to the nominal speed Xn, and the torque of the pump is equal

to the nominal torque CmN [7,8].

CcX Xn CmN 11

where

CmN KmN XN 6 N m 12

We have used a step down converter, the model is defined in

Fig. 2.It symbolizes our converter by the switch S.

Fig. 1. Block diagram of the MPPT.

Fig. 2. Diagram of the buck converter.

Fig. 3. Test bench set up.

1624 N. Mazouz, A. Midoun/ Electrical Power and Energy Systems 33 (2011) 16231630


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First case: 0 < t< Ton: The switch S is closed, The voltage is given

by the following equation:

E VPV RLiL LdiL

dt VLt 13

Second case: Ton < t< T: The switch S is open, The voltage is given by

the following equation:

RLiL L diLdt

VLt 0 14

VLmoy 1

T

ZTon0

V1tdt 15

VLmoy Ton

T

Ea 16

E is the voltage of the PVA, a is the cyclic ratio or hash rate rangingfrom 0 to 1. The frequency of the converter was set to 20 kHz.

The variation in cyclic ratio will be made such that VLmoy equal

the optimum voltage Vopt of the PVA.

2.2.1. Digital simulation of a direct coupling (without regulation)

During the launching phase (Fig. 4) the engine/load assembly

need a large amount of current, which pushes the point of opera-

tion towards the area of the short-circuit of the PVA. This situation

disappears in the established mode, in which the load is some were

in the area of the slope of the PVA (not necessarily at the point of

maximum operation). Exceptional cases can appear, for example

an operation with weak sunning or high load. In this case the load

operates in the area of short-circuit and thus practically at almost

zero voltage, which does not make it possible to have a significant

couple to drive the load. A regulation trying to bring back the point

of operation to the optimal point is strongly recommended to opti-

mize the effectiveness of the system (operation in point of maxi-

mum power) and thus to ensure a sufficient couple to drive the

load. The change of sunning causes the displacement of the point

of operation on another characteristic I= f (V), which involves anincrease of the load voltage and thus an increase of the speed of

the engine, the current does not vary considerably.

For the high loads, the drawn current is large, which brings back

the current of the PVA towards the area of short-circuit. When the

load is decreased, the call of current decreases also, which causes

an increase in the output voltage of the PVA, and consequently

an increase of the engine speed. It is also noticed that for signifi-

cant sunning the point of optimum capacity approaches the

boundary point of operation to a = 1. To bring a good margin be-tween these two points of the characteristic, we slightly increased

the engine load and we will show it later in the results that the in-

crease in load extends the range values of the operation points of

the system.We note that the optimum capacity varies with the sunning, but

this power must always be close to the nominal power of the

engine.

By considering that the sunning is most of the day ranging be-

tween 60% and 80%, we can evaluated the range of the electric

parameters of operation (power, current, voltage) see (Table 2).

The simulations show the need for a tracking to exploit to the

maximum the PVA. In the following, the detailed study of the reg-

ulation techniques used will be presented.

2.2.2. Experimental and digital simulation system with regulation

Several techniques are used for the MPPT. The Hill Climbing

technique is based on the derivative of the power to the voltage

which is equal to zero within the MPP [911]. The double feedback

control technique which uses the fact that the voltage of the MPP

tends to being sufficiently close with a certain fixed percentage

to the voltage of the open circuit of the PVA. There are also, sophis-

ticated methods like the use of fuzzy logic, the use of DSP and neu-

rons arrays.

In our work, we propose some of methods of identification of

the MPP. Who will be used to generate the cyclic ratio to operate

the chopper for a maximum power of the PVA.

2.2.2.1. Traditional technique. This method allows the optimal point

tracking of a PVA by analyzing the difference in power between

two points of characteristic IV of a PVA [3].

We take two measurements of the power, P1 and P2.

We calculate the power difference DP.If DP is positive amount we decrease the step of displacement.

If DP is negative amount, we increase this step.

We carried out simulation tests, the obtained results, are pre-

sented in the figures below. Figs. 5a and 5b show the variation of

Fig. 4. Transient answers of the direct coupling system at E= 60%.

N. Mazouz, A. Midoun/ Electrical Power and Energy Systems 33 (2011) 16231630 1625


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the operation power during the tracking regarding small and large

steps.

From these figures, we can deduce that for a large tracking step,

the response time of the system decreases and the oscillations in

static mode increase. In the case of a small tracking step, the re-sponse time increases and the oscillations of the power around

the optimal point decrease. To improve this tracking a variable step

using a fuzzy logic control technique is adopted.

2.2.2.2. Fuzzy control technique. Since the temperature has only a

small effect on operation of the system, the only external distur-

bance that we consider in the following study is the variation of

sunning [6].

Information on the place of the point of operation compared to

the point of optimum capacity is necessary and can be known

through the parameter dP/dI (slope of the curve (IP)) at the point

of operation. This parameter is very useful since it informs us by

his sign about the place of the point of operation compared to

the point of optimum capacity. Zone 1 : operation in constantvoltage generator or zone 2: operation in constant current gener-

ator, And also by its amplitude about the degree of proximity to

this point(Figs. 6).

From these observations a procedure rises for the tracking of

the optimum capacity which consists in varying the cyclic ratio

in the opposite direction of the sign of dP/dI. This variation will

be weaker as the point of operation will be more close to the point

of optimum capacity.

The position of a point of operation on characteristic (IP) com-pared to the point of maximum power can be given according to

the slope dP/dI in this point. The sign of this slope informs us of

from which side of the optimal point is located the point of opera-

tion on the left or on the right of the optimal point, its amplitude

indicates the degree of proximity of this point to the optimal point.

In addition the variation of the cyclic ratio moves the point of oper-

ation on the curve (IP). The direction of the displacement of the

operation point depends on the sign of the cyclic ratio variation,

and the importance of these displacements is proportional to the

amplitude of the cyclic ratio variations.

The regulation is thus done by changing the cyclic ratio accord-

ing to the slope dP/dI in order to bring back the point of operation

on the optimal point where the slope is zero.Fig. 5a. Small step tracking.

Fig. 5b. Large step tracking.

Fig. 6a. Characteristic (IV) relocated at T= 30, Es = 60%.

Fig. 6b. Characteristic (PV) relocated at T= 30, Es = 60%.



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This method is based on a ratio calculation between a power

variation and a current variation. The block diagram of the regula-

tor is as follows (Fig. 7):

Where:

e dP=dI pk pk 1=Ik Ik 1: 17

de ek ek 1 18

P is the measured power on the PVA.

For the inputs variables, the error and its derivative, we chose

five subsets of triangular forms being spread out over the [1,1]

interval (Fig. 8).

However the output variable, which is the result of a deduction

between the two input values, representing in our controller the

cyclic ratio, the room values are spread out between 0.25 and

+ 0.5, with seven subsets for more precision [4] (Fig. 9).

In our regulation, we have used the SEGENO logic [4] whose

rule is: IF the error is PM AND derivative is NG THEN the cyclic

ratio is NG .

Table 1 gather the whole fuzzy rules.

To measure the slope dP/dI we must take two measurements of

I and V brought closer in time since we seek the slope in a point.Thereafter, the regulator determines the output corresponding to

the measured slope, that is to say the variation of cyclic ratio. This

variation is then added to the preceding value. The variation of cyc-

lic ratio is accompanied by a change of state which results in a dis-

placement of the point of operation.

After a time during which the point of operation will have suf-

ficiently progressed we take again the measurement of dP/dI. The

cycle measurement and regulation is continuously started again

in order to bring back the point of operation to the optimal point.

We define for the inputs and outputs variables 5 and 7 functions

of membership respectively. These functions of standard member-

ship have the same symmetrical form, of width equal and each

function has an overlapping of 50% with the closest functions. Also,

we distribute these functions on the fields of speech so that the

condition has a zero slope corresponds to a zero increase in cyclic

ratio is verified. For that, the functions of membership Z of the

inputs and outputs variables must be centered.

The results of the simulation of this fuzzy method are repre-

sented in Figs. 10.

To highlight this technique we carried out simulations tests

whose obtained results are indicated in Figs. 10 for an initial cyclic

ratio set to 0.7. Both curves show the results of simulation in the

case of a starting at a sunning of 600 W/m 2 of sunning. It is noted

that the optimum capacity is well reached.For an increase in the cyclic ratio the point of operation moves

on characteristic (IP) in the direction of the decreasing voltages by

exceeding the point, then returns slowly towards this point. Thus

more the variation of cyclic ratio is large more the going beyond

is important, and thus more the response time is large. This behav-

ior justifies why the going beyond, and thus the response time, are

more important when the initial cyclic ratio is more important.

Table 1

Table of fuzzy rules.

e/de NL NA AZ PA PL

NL PL PS AZ NL NL

NA PL PS AZ NL NL

AZ PH PA AZ NL NL

PA PH PA PS NA NA

PL PH PA PS NA NA

NL (Negative large), NA (Negative Average), AZ (Approximately Zero), PS (Positive

Small), PA (Positive Average), PL (Positive Large) is the total of the subsets [2].

Table 2

The range of the electric parameters.

Nominal electric

quantities

E= 80% E= 60%

Power, Pm (W) 1000 650 450

Voltage of armature, Vm (V) 180 150 140

Armature current, Im (A) 4.9 4 3

Fig. 7. Structure of the fuzzy logic control.

Fig. 8. Membership functions.

Fig. 9. Fuzzy singletons of the output. Fig. 10a. Simulation results of optimal operation voltage.



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We noted that in addition to the tracking of the point of opti-mum capacity the regulator also makes it possible to optimize

the response time and to decrease the oscillations of the power

around the point optimal.

The fuzzy control method of (dP/dI) converges quickly because

of the computation precision of the error and its derivative. Com-

paratively to the traditional method witch need the choosing of

the tracking step (small and precise) Despite of the compatibility

of the performances, the tracking obtained results from these

methods are satisfactory.

The experimental implementation of the algorithm of the fuzzy

controller on a Micro chip family microcontroller based board is

represented in Fig. 11. To highlight this technique we carried out

an experimental test with an initial cyclic ratio of 0.7.

To interface the PV array output to DC motor driven centrifugalpump, a microcontroller-based DC/DC buck converter was de-

signed and built (Fig. 3). By measuring PV array voltage and cur-

rent, voltage generated by an analog tachometer, and voltage

produced by a digital manometer, we can easily determine PV ar-

ray output power, rotational speed of the DC motor-pump, and

pump outlet pressure respectively. After that the controller on chip

10-bit PWM generator output drives the DC/DC buck converter

according to each algorithm. The buck converter comprises:

MOSFET switch IRF740, diode BYT08, coil (L = 100 lH) and PV array

voltage filtering capacity (C= 1000 lF) [12]. The switching fre-

quency (20 kHz) is designed to obtain low output ripple. PIC micro-controller can send data to PC with a line driver/receiver chip

(MAX232) and a null modem cable.

The test of MPPT algorithms was conducted on sunny day

(Average insolation: 600 W/m2, average temperature: 30 C).

Fig. 10b. Simulation results of optimal operation power.

Fig. 11. Experimental test bench.

Fig. 12. PIC control card and DC/DC converter.

Fig. 13. DC motor-pump.

Fig. 14. Optimal operation voltage.



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According to the PV array output power and voltage information,

the microcontroller computes the output and generates a com-

mand representing the duty cycle given by the microcontroller

PWM pin which is isolated by an optocoupler (6N135), amplified

by a hex buffer-inverter converter (HEF4049) and applied to MOS-

FET switch. The converter duty cycle is adjusted such that maxi-

mum PV array output power is extracted under all operating

conditions and transferred to DC motor-pump which in turn drawswater from a storage tank in a closed hydraulic system ( Figs. 3 and

1113).

Fig. 14 illustrates the operation of our regulator. Initially, the

assembly is in open circuit, this corresponds to a voltage VOC of

about 195 V. Once the program launched with a cyclic ratio of

one, the voltage falls to 50 V which corresponds to the point of

operation without regulator, then converges towards the optimal

tension Vopt around 145 V. What shows the good performance of

the tracking.

Fig. 15a, 15b, 15c show the shape of the current of the PVA and

the load following an increase in the cyclic ratio. It is noticed that

to provide current to the load, we should increase the cyclic ratio,

which mean that we should not boost the current beyond the opti-

mal current.

More the cyclic ratio increases more the current of charge and

discharge of the engine decreases. It is noticed that the consumed

current by the load is almost equal to the current of short-circuit.What justifies the call of current at the starting. As soon as flow

is established, the current decreases and the point of operation

positions on an area.

In the case of a weak load (see our case), we observe the current,

the voltage and the power consumption, this point is in the oper-

ation region of constant voltage generator. In the case of a strong

load, we work in the operation area of constant current generator.

In both cases the power consumption of the load does not match

with the maximum power which can provide the photovoltaic

Fig. 15a. Engine current, PVA current for cyclic ratio = 10%.

Fig. 15b. Engine current, PVA current for cyclic ratio = 50%.

Fig. 15c. Engine current, PVA current for cyclic ratio = 80%.



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generator, implying that the efficiency of the system is not opti-

mum. The introduction of the regulation loop resolves this problem

and contributes to the optimization of the efficiency.

Before launching the operation of tracking, knowledge of the

system is essential in order to control its behavior during the var-

iation of these parameters to predict the operation of the regulator.

For that we took samples of the current and voltage of the PVA,

the current of the engine thus the delivery and the speed of theassembly (engine-pump) by applying various values of the cyclic

ratio to the whole system carried out to a sunning of 60%. We

noted that more we increase the cyclic ratio, the voltage of the

PVA decreases, and the current of the PVA increases.

Figs. 15a, 15b and 15c show us that after a certain value of the

cyclic ratio which is about 0.8 the voltage and the current of the

PVA are stabilized what carries out us to conclude that the point

of operation of our load (engine and pump) is not far from the opti-

mum point (what was proven and shown in simulation), this con-

clusion is much more justified during sampling of the curves of the

currents of the PVA and the load.

3. Conclusion

This work is a contribution to the integration of Soft-Computing

and the artificial intelligence in the field of exploitation of energies

with the aim to improve the performances and the optimizing of

the efficiency of photovoltaic array, by making them working with

their maximum power, our contribution efforts are fixed on the

development of a linguistic tracking system based on fuzzy logic,

ensuring a good adaptation of the load. One of the specificity of

the fuzzy regulator proposed is that it does not require a prelimin-

ary knowledge of the sunning or the optimum power since the

slope dP/dI at the point of operation is only function of this point

position compared to the optimal operation point.

We noted that in addition to the optimum power tracking the

regulator also allows the optimization of the response time and

the reduction of the power oscillations around the optimal point.

The obtained experimental results show on the one hand the use-fulness of the fuzzy controller for the system optimization, and on

the other hand the match with the simulation results what is very

satisfactory.

Technological advances always renovated in the field of power

electronics. New technologies more efficient in quality and re-

sponse time are developed.

We are currently designing a new structure Superbuck step

down choppers whose literature is still limited, with operating

mode CCM (continuous conduction mode) [5], commanded bythe PCM (Peak Current Mode).

References

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[2] Walker G. Evaluating MPPT converter topologies using a Matlab PV model. JElectr Electron Eng 2001;21:4955.

[3] Singer S. Maximum poxer transfer from a non linear energy source to anarbitrar load. IEEE Proc 1987;134:2817.

[4] Mazouz N. Tracking of the optimal point of PV array through a DC/DC buckconverter in a pumping solar system by fuzzy logic. Master Thesis, July 2005,Electrical Engineering Faculty, Oran, Algeria; 2005.

[5] Erickson RW, Maksimovic D. Fundamentals of power electronics. 2nded. University of Colorado; 2006.

[6] Abd El-Shafy Nafeh A, Fahmy FH, Abou El-Zahab EM. Maximum-poweroperation of a stand-alone PV system using fuzzy logic control. Int J NumerModel Electron Networks Dev Fields 2002;15:38598.

[7] Jafar M. A model for small-scale photovoltaic solar water pumping. RenewEnergy 2000;19:8590.

[8] Koutroulis E, Klaitzakis K, Voulgaris N. Development of a microcontroller-based photovoltaic maximum power point tracking control system. IEEEPower Electron 2001;16:4654.

[9] Esposito F, Isastia V, Meo S, Piegari L. An improved perturbe and observealgorithm for tracking maximum power points of photovoltaic power systems.Int Rev Model Simul 2008;0(0):106.

[10] Esposito F, Isastia V, Meo S, Piegari L. A maximum power point trackingalgorithm for photovoltaic power systems with the control of the outputvoltage. In: Proceedings of the XVII international conference on electricalmachines, 2006, Chania, Crete Island, Greece; 2006.

[11] Dasgupta N, Pandey A, Mukerjee AK. Voltage-sensing-based photovoltaicMPPT with improved tracking and drift avoidance capabilities. Sol EnergyMater Sol Cells 2008;92:15528.

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Documents

2011_Control of a DCDC Converter by Fuzzy Controller for a Solar Pumping System_Paper N. Mazouz, A. Midoun