Comparison between fuzzy and p&o control for mppt for photovoltaic system using boost converter

Journal of Energy Technologies and Policy www.iiste.org
ISSN 2224-3232 (Paper) ISSN 2225-0573 (Online)
Vol.2, No.6, 2012

Comparison Between Fuzzy and P&O Control for MPPT for
Photovoltaic System Using Boost Converter

H.E.A. Ibrahim
Dept. of Electrical and Computer Control Engineering
Arab Academy for Science and Technology and marine transport, Cairo, Egypt
E-mail: hibrahim_eg@yahoo.com
Mahmoud Ibrahim
Master Degree Student
Arab Academy for Science and Technology and marine transport, Cairo, Egypt
E-mail: Mahmud_ibrahim@hotmail.com
Abstract
This paper presents fuzzy control method for maximum power point tracking (MPPT) of photovoltaic (PV)
system under varying irradiation and temperature conditions. The fuzzy control method has been compared with
perturb and observe (P&O) method as one of the most widely conventional method used in this area. Both
techniques have been analyzed and simulated. Fuzzy technique gives better and more reliable control for this
application.
Keywords: Photovoltaic, MPPT, P&O, FUZZY.

1. Introduction
Renewable energy sources play an important role in electricity generation. Various renewable energy sources like
wind, solar, geothermal and biomass can be used for generation of electricity and for meeting our daily energy
needs. Photovoltaic generation is becoming increasingly important as a renewable source since it offers many
advantages such as incurring no fuel costs, not being polluting, required little maintenance, and emitting no noise,
among others.
The photovoltaic voltage-current (V-I) characteristic is nonlinear and changes with irradiation and
temperature. In general, there is a point on the V-I or voltage-power (V-P) curves, called the Maximum power
point (MPP), at which PV operates with maximum efficiency and produces its maximum output power. The state
of the art techniques to track the maximum available output power of PV systems are called the maximum-power
point tracking (MPPT). Controlling MPPT for the solar array is essential in a PV system. There are many
techniques have been developed to implement MPPT, these techniques are different in there efficiency, speed,
hardware implementation, cost, popularity [1, 2].
One of the most widely used techniques in MPPT is P&O due to its simple and easily implementation. In
this paper, intelligent control technique using fuzzy logic control is associated to an MPPT controller in order to
improve energy conversion efficiency and compared with P&O method. Simulation and analysis of P&O and
fuzzy logic control are presented.

2. Modeling and Characteristic of solar panel
2.1 Modeling of solar panel
The model of solar cell can be categorized as p-n semiconductor junction, when exposed to light, the DC current
is generated. The PV cell equivalent circuit can be represented as an ideal current source, diode, parallel
resistance and series resistance as shown in Fig.1, where the current source is the light generated current which is
directly proportional to the solar irradiation. The series and the shunt resistances represent a voltage loss on the
way to the external contacts and the leakage current in the shunt path respectively [3, 4].

Fig.1. PV modeling

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Vol.2, No.6, 2012

The mathematical model which relates the output current to the output voltage is given by the equation (1) [5, 6].

q ሺv ൅ IR ୱ ሻ
I ൌ I୮୦ െ I୭ ቞exp ቀ ሺv ൅ IR ୗ ሻ െ 1ቁ െ ቟ ሺ1ሻ
kTA R ୱ୦

where Iph is the current generated by the incident light (it is directly proportional to the Sun irradiation),I& v are
the PV array output current and voltage respectively, Io is the reverse saturation or leakage current of the diode, q
is the electron charge (1.60217646 e-19 C), k is the Boltzmann constant (1.3806503 e−23 J/K), A is the diode
ideality constant, Rs & Rsh are series and shunt resistance of PV respectively, and T (in Kelvin) is the cell’s
working temperature.
The photocurrent (Iph) mainly depends on the solar insolation level and cell’s working temperature, which is
described by equation (2)

I୮୦ ൌ ൫Iୱୡ ൅ Kଵ ሺT െ T୰ୣ୤ ሻ൯λ ሺ2ሻ

Where Isc is the cells short-circuit current at a 25°C and1kW/m2, K1 is the cells short-circuit current temperature
coefficient, ), Tref is the cell’s reference temperature which equals to 25°C, and λ is the solar insolation level in
kW/m2.

2.2 Solar panel Characteristic
There are two main parameters that are used to draw I-V curve which are short circuit current (Isc) and open
circuit voltage (Voc).
• Short circuit current (Isc) is the maximum current that the cell can provide and it occurs when the cell is short
circuited.
• Open circuit voltage (Voc) is the maximum voltage that exists between the cell terminals.
The name plate data of the PV array used in our simulation has been taken from [7] as a simulation for
KC200GT array. The module I-V, P-V characteristics at different insolation and temperature levels are illustrated
in Fig .2, also the locus of MPP at each level is shown. It can be observed that the irradiation changes mainly
affect the PV output current and the temperature changes affect PV output voltage. For direct coupling between
the load and PV, the operating point is determined by the intersection between the load I-V curves and the PV
I-V curves whatever the insolation level. For example in Fig.3, for any resistive load R, it has straight line I-V
characteristic with slope 1/R, it gives one operating point for a certain insolation level, by varying the load value
R, it’s I-V curve slope varies also and so the operating point varies depending on these variation. Also for a
specified value of the resistive load Ropt, it gives the MPP of the PV as an operating point, but that could be
happened only for one insolation level.

250
10
0
T=25 C
1000 w/m2 1000 w/m2
200
8
MPP T=250C
800 w/m2 MPP
800 w/m2
6
150
Pow er
C u rre n t

600 w/m2
600 w/m2

4 400 w/m2 100
400 w/m2

200 w/m2
2 50 200 w/m2

0 0
0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 35 40
Voltage Voltage
(a)

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Vol.2, No.6, 2012

10
250
MPP
MPP

8
200
2
G=1000 w/m o
T=10 c
o
2 T=10 c
6 G=1000 w/m
C u rren t

150

C u r re n t
o
T=30 c
o
T=30 c

4 100 o
o T=50 c
T=50 c

o o
2 T=70 c 50 T=70 c

0 0
0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30 35 40
Voltage Voltage

(b)
Fig.2. (a) V-I ,P-V Characteristics of PV module at constant temperature and varyinginsolations,(b) V-I ,P-V
Characteristics of PV module at constant insolationsand varying temperature.
10

8
Slope=1/R Ropt MPP

6
Current

4

2

increase R
0
0 5 10 15 20 25 30 35
Voltage

Fig.3. I-V curves of PV array and various resistive loads
3. Mppt Model
The circuit diagram of the energy conversion system is shown in Fig.4. The system consists of photovoltaic
panel, a DC-DC boost converter, a fuzzy-based MPP tracker and a resistive load.
The PV array consists of 50 series PV cells. The I-V characteristic of array depends on the temperature and
solar insolation level. The photovoltaic array operation depends on the load characteristics at which it is
connected to. So when connected to load directly, the output of the PV array rarely works at MPP. However, to
adapt the load and extract maximum power from a PV module, a DC-DC boost converter is used by adjusting its
duty cycle under control of selected controller (in our case fuzzy and P&O algorithms) based MPPT controller
such that the maximum solar panel output power is extracted under all operating conditions [8].

Fig.4. MPPT system

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Vol.2, No.6, 2012

3.1 DC-DC converter
A boost converter is a step-up DC-DC power converter. Fig.5 shows the boost converter circuit using MOSFET
switch. The converter operation can be divided into two modes. Mode 1 begins when the transistor is switched
ON, the current in the boost inductor increases linearly, and the diode is OFF state, mode 2 begins when the
transistor is switched OFF, the energy stored in the inductor is released through the diode to the load. The power
flow is controlled by varying the on/off time of the MOSFET. The relationship between input and output
voltages is given by equation (3) [9].
V୭ 1
ൌ ሺ3ሻ
V୧ ሺ1 െ Dሻ
Where Vi is the PV output voltage, Vo voltage of boost converter, D is duty cycle, that can be expressed by
equation (4).

T୭୬
Dൌ ሺ4ሻ
T

Where Ton is time when MOSFET is switched on, T is cycle period time. The transistor operates as a switch; it is
turned on and off depending on pulse width modulated (PWM) control signal. PWM operates at constant
frequency i.e T is constant and Ton is varying, so D can be varied from 0 to 1.

Fig.5. Boost converter Circuit diagram

4. MPPT controller
MPPT is essentially a real time process to search for the operating point which gives the maximum available
power that can be extracted from the PV array at any insolation level. Two MPPT techniques will be presented
and simulated.

4.1 perturb & observe
The principle of P&O is to perturbation by acting decrease or increase on the PWM duty cycle of boost converter
and then observing the direction of change of PV output power, If at any instant j the output PV power P (j) &
voltage V (j) is greater than the previous computed power P (j−1) & V (j-1), then the direction of perturbation is
maintained otherwise it is reversed [10, 11]. The flow chart of algorithm has 4 cases as shown in Fig.6 and can
be detailed as following
• When ∆P <0 & V(j)>V(j-1), this yields to D (j+1) = D (j) - ∆D
• When ∆P <0 & V(j)<V(j-1), this yields to D (j+1) = D (j) + ∆D
• When ∆P >0 & V(j)<V(j-1), this yields to D (j+1) = D (j) - ∆D
• When ∆P >0 & V(j)<V(j-1), this yields to D (j+1) = D (j) + ∆D

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Vol.2, No.6, 2012

Fig.6. P&O flowchart
Where ∆D is chosen value by trial and error in simulation. A simulation of the P&O algorithm has been
implemented by using MATLAB; Fig.7 shows the simulation results for different values of ∆D.
Despite the P&O algorithm is easy to implement it has mainly the following drawbacks:
• Cannot always operate at the maximum power point due to the slow trial and error process, and thus the
maximum available solar energy from the PV arrays cannot be extracted all the time..
• the PV system always operates in an oscillating mode which leads to the need of complicated input and
output filters to absorb the harmonics generated.
DeltaD=0.01 DeltaD=0.005

200 200

150 150
Power (w)

P o w er (w )

100 100

50 50

0 0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Time (sec) Time (sec)

Fig.7. P&O MPPT for different ∆D

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Vol.2, No.6, 2012

4.2. MPPT using Fuzzy Logic Control
Fuzzy logic is one of the most powerful control methods. It is known by multi-rules-based resolution and
multivariable consideration. Fuzzy MPPT is popular for over last decade. Fuzzy logic controllers (FLC) have the
advantages of working with imprecise inputs, no need to have accurate mathematical model, and it can handle
the nonlinearity [12]. The proposed FLC is shown in Fig.8; it consists of two inputs and one output. The two
FLC input variables are the error (E) and change of error (CE) that expressed by equation (5), (6).
P୮୴ ሺjሻ െ P୮୴ ሺj െ 1ሻ
Eሺjሻ ൌ ሺ5ሻ
V୮୴ ሺjሻ െ V୮୴ ሺj െ 1ሻ

CEሺjሻ ൌ Eሺjሻ െ Eሺj െ 1ሻ ሺ6ሻ

Where Ppv, Vpv are the PV power and voltage respectively at instant j. E (j) shows if the load operating point at
the instant j is located on the left or on the right of the maximum power point on the P-V characteristic where it
is equals to zero at MPP as shown in Fig.9 while the change of error CE(j) expresses the moving direction of this
point. Where the control action duty cycle D used for the tracking of the maximum power point by comparing
with the saw tooth waveform to generate a PWM signal for the boost converter.

dP/dV=0
200

150
P o w er

100
dP/dV>0 dP/dV<0

50

0
0 5 10 15 20 25 30 35 40
Voltage

Fig.8. Fuzzy controller diagram Fig.9.p-v curve at 1000 w/m2

The fuzzy controller design contains the three following steps:
• Fuzzification
The fuzzification is the process of converting the system actual inputs values E and CE into linguistic fuzzy sets
using fuzzy membership function. These variables are expressed in terms of five linguistic variables (such as ZE
(zero), PB (positive big), PS (positive small), NB (negative big), NS (negative small)) using basic fuzzy subsets
as shown in Fig.10.
• Rule base & inference engine
Fuzzy rule base is a collection of if-then rules that contain all the information for the controlled parameters. It is
set according to professional experience and the operation of the system control. The fuzzy rule algorithm
includes 25 fuzzy control rules listed in table I.
Fuzzy inference engine is an operating method that formulates a logical decision based on the fuzzy rule setting
and transforms the fuzzy rule base into fuzzy linguistic output. In this paper Mamdani’s fuzzy inference method,
with Max-Min operation fuzzy combination has been used.

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Vol.2, No.6, 2012

1.2 1.2
1.2
NS PB NB NS ZE PS PB
NB NS ZE PS PB NB ZE PS
1 1
1

0.8 0.8
0.8

0.6 0.6 0.6

0.4 0.4 0.4

0.2 0.2 0.2

0 0 0
-10 -8 -6 -4 -2 0 2 4 6 8 10 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1
Error Change of Error Duty cycle

(a) (b) (c)
Fig.10. Membership function of E, CE and D

Table I. FLC Rules base

CE NB NS ZE PS PB

E

NB ZE ZE PB PB PB

NS ZE ZE PS PS PS

ZE PS ZE ZE ZE NS

PS NS NS NS ZE ZE

PB NB NB NB ZE ZE
• Defuzzification
Defuzzification of the inference engine, which evaluates the rules based on a set of control actions for a given
fuzzy inputs set. This operation converts the inferred fuzzy control action into a numerical value at the output by
forming the union of the outputs resulting from each rule. The center of area (COA) algorithm is used for
defuzzification of output duty control parameter. i.e If E is NB and CE is ZO then crisp D is PB, it means that if
the operating point is far away from the MPP by the right side, and the variation of the slope of the curve is
almost Zero; then increase the duty cycle.

5. Simulation results
An extensive simulation for both techniques has been done using MATLAB. Some selected results are presented
with a comparison between fuzzy and P&O MPPT controllers. The following simulation were presented for
different insolation levels from 200 to 1000 W/m2 at fixed temperature of 25°C as shown in Fig.11, and at
different temperature levels from 10oc to 70oc at fixed insolation of 1000 W/m2 shown in Fig.12.
Fig.11 (a), (b), (c) and (d) show the simulation results of PV operating point and load curves for simulation time
10 sec (varying insolation level from 20% to 100%). The figure shows the result when using fuzzy and P&O as
MPPT controller.
Fig 12(a), (b),(c )and (d)show the simulation results of PV operating power at maximum power point and
load curves at varying temperature and constant irradiation at 1000 W/m2which is being tracked by fuzzy and
P&O controllers.
Fig.13 shows the output power of PV at fixed insolation level and temperature for both controllers. As shown
fuzzy controller shows smother power signal line, less oscillating and better stable operating point than P&O.
From the simulation results, it can be deduced that the fuzzy controller has better performance than P&O, and it
has more accuracy for operating at MPP.

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Vol.2, No.6, 2012

1000

800
Insolation (w/m )
2

600

400

200

0
0 1 2 3 4 5 6 7 8 9 10
Time (sec)

(a)

200 P&O
FUZZY
180

160
PV power (w)

140

120

100

80

60

40

20
0 1 2 3 4 5 6 7 8 9 10
Time (sec)

(b)

80
P&O
FUZZY
70
Load Voltage (v)

60

50

40

30

20
0 1 2 3 4 5 6 7 8 9 10
Time (sec)

8

Vol.2, No.6, 2012

(c)

3
P&O
FUZZY
2.5
Load Current (A)

2

1.5

1

0.5

0
0 1 2 3 4 5 6 7 8 9 10
Time (sec)

(d)

Fig11.Simulation result at varying insolation

80

70

60
Temperature (c )
o

50

40

30

20

10

0
0 1 2 3 4 5 6 7 8 9 10
Time (sec)

(a)

220

210

200
PV Power (w)

190

180

170

160
P&O
150 FUZZY
140
0 1 2 3 4 5 6 7 8 9 10
Time (sec)

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Vol.2, No.6, 2012

(b)

80

75

70
Load voltage (v)

65

60

55

50
P&O
45 FUZZY
40
0 1 2 3 4 5 6 7 8 9 10
Time (sec)

(c)

3
Load current (A)

2.5

2

P&O
FUZZY

1.5
0 1 2 3 4 5 6 7 8 9 10
Time (sec)

(d)
Fig12.Simulation result at varying temperature

205
P&O
FUZZY
er )
PV pow (w

200

195
5 5.005 5.01 5.015 5.02 5.025 5.03 5.035 5.04 5.045 5.05
Time (sec)

Fig13.Simulation result at constant temperature and insolation

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Vol.2, No.6, 2012

6. Conclusion
This paper presented a mathematical model for PV. It also included MPPT at varying irradiation and temperature
conditions. P&O and fuzzy logic controller have been designed and simulated for the proposed PV system,
comparison for simulation results have been presented for different irradiation condition. Fuzzy controller
showed better performance with lower oscillation.

References
[1] A. Dolara, R. Faranda, S. Leva., ”Energy Comparison of Seven MPPT Techniques for PV Systems”,
Journal of Electromagnetic Analysis and Applications, vol.1, no.3, pp.152-162, Sep 2009.
[2] Trishan Esram, Patrick L. Chapman, ”Comparison of Photovoltaic Array Maximum Power Point Tracking
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pp.81-107, 2010.
[4] Luis Castafier and Santiago Silvestre, "Modeling Photovoltaic Systems Using Pspice", Chichester: John
Wiley & Sons Ltd, ISBN 0-470-845279, pp.41-75, 2002.
[5] Chokri Ben Salah, Mohamed Ouali, "Comparison of Fuzzy Logic and Neural Network in Maximum Power
Point Tracker for PV Systems", Elsevier, Electric Power Systems Research, vol.81, pp.43-50, July 2011.
[6] Mohamed Salhi1 ,Rachid El-Bachtri2, "Maximum Power Point Tracker Using Fuzzy Control for
Photovoltaic System", International Journal of Research and Reviews in Electrical and Computer Engineering,
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[7] Marcelo Gradella Villalva, Jonas Rafael Gazoli, and Ernesto Ruppert Filho, ”Comprehensive Approach to
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[8] C. Larbes, S.M.Ait Cheikh, T. Obeidi, A. Zerguerras, "Genetic Algorithms Optimized Fuzzy Logic Control
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11

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