Download Fuzzy Incremental Conductance for Maximum Power Point

ISSN: 2319-5967
ISO 9001:2008 Certified
International Journal of Engineering Science and Innovative Technology (IJESIT)
Volume 3, Issue 6, November 2014
Fuzzy Incremental Conductance for Maximum
Power Point Tracking in Photovoltaic System
Ratna Ika Putri1, Sapto Wibowo2, M. Rifa’i3, Taufik4
Abstract— Continuing world’s population growth has led to an increase in electricity demand. This further promotes
the need for diversification of renewable energy to meet the demand. Indonesia has a huge potential of solar energy
because the sun shines all year round and hence the prevalent use of photovoltaics (PV). To maximize the electrical
power produced by PV, maximum power point tracking (MPPT) is often used. This paper presents an MPPT technique
utilizing Fuzzy Incremental Conductance to drive a buck boost converter. Computer simulation using Matlab/Simulink
has been performed to demonstrate the performance of the controller. Results show that the Fuzzy IC algorithm can
follow the change in solar irradiation to produce a varying duty cycle to achieve the maximum power point. .
Index Terms— Photovoltaic, MPPT, Fuzzy IC.
I. INTRODUCTION
World’s electricity demand continues to rise as world’s population keeps going on the upward swing. At present,
meeting the demand still depends on the availability of dwindling fossil fuels. Realizing that a sustainable
solution to meet electricity demand becomes an important issue, there has been global efforts in utilizing an
alternative energy such as renewable energy sources. As a country geographically located on the equator,
Indonesia receives a significant amount of sun light annually. This makes solar energy through PVs the most
potential and widely used renewable energy for electricity in the country. For example, the western region of
Indonesia receives solar energy on average around 4,5KWh/m2/day with monthly variation of about 10%. The
eastern Indonesian region all the while receives around 5,1KWh/m2/day with monthly variation of about 9%.
Overall, the potential of solar energy in Indonesia averages approximately 4.8 kWh/m2/day with monthly
variation of about 9% [1]. To exploit this potential, photovoltaics have been used with their known advantages of
being environmentally friendly, non-polluting, low maintenance costs and widely available. However,
photovoltaics suffer from their low efficiency and being strongly influenced by external factors such as
temperature and solar irradiation with very non-linear change.
PV efficiency may be increased it is operated at the maximum power point (MPP). A device that forces PV to
operate at the MPP is called maximum power point tracking (MPPT) which typically employs a dc-dc converter
that connects the PV system to the load [4]. As temperature or solar irradiation changes, the MPP will shift
accordingly. Similarly, any change in the load will also cause the MPP to move the power generated by
photovoltaic. Therefore, the MPP does not remain at a single point, but rather moves along the PV V-I curve
depending on solar irradiation and temperature [3]. One commonly used method in MPPT is based on the voltage
of the PV array by using the dc-dc converter. This method has the advantage that the array voltage is measured
directly which implies lower cost compared to the method that makes use of solar irradiation measurement and
other environmental factors. Additionally, MPPT using a dc-dc converter does not require measurement of the
flow so that the system becomes simpler [2]. There are several MPPT techniques that can be used to maintain PV
working at the maximum power point (MPP). Examples are the Perturb & Observe (P & O), incremental
conductance (IC), and constant voltage methods. Compared with the incremental conductance method, the P & O
method takes a faster convergence to achieve the maximum power point [11]. However, the method will
potentially generate steady state oscillations with a large enough voltage variations which will result in failure to
determine the MPP especially in areas with rapid variation in solar irradiation [5][6]. The use of incremental
conductance method reduces oscillation in the time it reaches the point of maximum power, but requires a longer
convergence time [7][9]. The constant voltage method offers fast tracking ability, but its performance
significantly degrades as weather conditions change. Comparing the two methods, the IC shows a more stable
performance in different weather conditions and reduces the oscillations in the MPP [10]. Conventional MPPT
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methods of convergence time depends on how a step change in duty cycle is fixed. Duty cycle step in turn
determines the speed and precision of the method to quickly find the point of maximum power.
Fuzzy logic has been applied also to determine the maximum power point of a PV system. Fuzzy logic is based on
the knowledge so as to reduce the use of complex mathematical problem solving. Fuzzy logic controller has been
found to produce very good performance with a very fast response, no overshoot and lower oscillations in steady
state condition [4]. Fuzzy logic controller has also been used as an MPPT on a hybrid system that combines PV
systems and wind turbines [8]. MPPT with fuzzy logic controller produces better performance with lower
oscillation compared with the P & O method [5]. In addition, the fuzzy logic method has been found to generate
smaller voltage noise and to have better performance than the P & O method; thus improving the overall
efficiency of PV [4].
II. PHOTOVOLTAIC SYSTEM
Photovoltaic systems (PV) is a device that converts solar energy into electrical energy. The PV system consists of
several solar cells, the respective cells linked to each other either in series or parallel to form a PV which generally
makes a series called “PV Module”. A PV module typically consists of 36 cells or 72 solar cells. The efficiency of
photovoltaic energy conversion related to the maximum operating point (MPP) of PV systems [12]. PV panels
work at the maximum point that produces maximum output power. MPP is strongly influenced by the non-linear
changes of solar irradiation and the cell junction temperature. In sunny weather, scattering energy of sunlight
may only be 15-20% of the global irradiance while the percentage reaches 100% in otherwise a cloudy day.
Photovoltaic can be modeled by a circuit consisting of a current source in parallel with a diode. The current source
represents the current generated from solar irradiation [13]. In practice, PV is modelled with a current source
(Isol) parallel to a diode with diode current (Id) and a shunt resistance (Rsh) that is connected in series with
resistance Rs, as shown in Figure 1.
Fig 1. Equivalent Circuit of Solar Cell
Based on KCL, the current through resistance Rs can be expressed by the equation
I = Isol – ID
(1)
The magnitude of the diode current can be determined based on the diode reverse saturation current (Ios), cell
temperature (T), Boltzmann’s constant (1,381e-23 J/K), electric charge (q), the cell output current (I) and the cell
output voltage (V). The diode current can be determined by

 q(V  RS I  
I D  I OS  exp
  1
kT

 

(2)
In the above equation, the diode reverse saturation current (Ios) is determined based on the short circuit current at
25C, the temperature coefficient of Isc (Ki), solar irradiation (λ), the band gap for silicon (EGO) at 1:12 eV,
ideality factor of 1.74 (γ), the reference temperature for 298,18K (Tr), and saturation current at Tr cells (Ior).
 qE  1 1 
T 
 I or   exp GO   
 Tr 
 k  Tr T 
3
I os
(3)

I sol  I SC  K i (T  298.18)
1000
(4)
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Photovoltaic characteristics are depicted by their V-I and P-V curves with variations in temperature, solar
irradiation and the load, as shown in Figure 2.
4
Irradiation 1000w/m2
3.5
3
Irradiation 800w/m2
Current (A)
2.5
Irradiation 600w/m2
2
Irradiation 500w/m2
1.5
1
0.5
0
0
2
4
6
8
10
12
14
16
18
20
22
Voltage (V)
60
Irradiation 1000w/m2
50
irradiation 800w/m2
40
Power (W)
irradiation 600w/m2
30
irradiation 500w/m2
20
10
0
0
2
4
6
8
10
12
14
16
18
20
22
Voltage (V)
Fig 2. Characteristic curves P-V dan I-V
PV’s output power is highly dependent on solar irradiation. As the solar irradiation increases, the PV output
power and the maximum power will increase accordingly. However, as indicated by Figure 2 PV’s, the increase
of output power continues until it reaches a certain voltage (Vmpp) beyond which the power then drops
dramatically
III. DESIGN FUZZY-INCREMENTAL CONDUCTANCE
Fuzzy controller incremental conductance (Fuzzy IC) is a fuzzy logic controller for MPPT which is based on the
incremental conductance method. The block diagram of a PV system with Fuzzy IC controller is shown in Figure
3. It consists of PV panels, PV power calculation, incremental conductance fuzzy controller, buck boost converter
and the load.
Buck boost
converter
Photovoltaic
I
Load
Duty
cycle
V
dV/dI
Power
calculation
Z-1
Fuzzy
Incremental
Conductance
I/V
Fig 3. Diagram Block of PV System with Fuzzy IC based MPPT
Using the IC method, the MPP can be determined based on the rate of change or slope of dP/dV on the PV curve.
As illustrated in Figure 2, at dP/dV = 0 at maximum power point (MPP), larger than 0 (increasing slope) to left
of the maximum point, and less than 0 (decreasing) to the right of the maximum point. Based on these
observations, the algorithm for MPPT IC may take the following form:
(5)
=
(6)
By the time it reaches MPP dP/dV = 0 so that
(7)
To determine the output power of the PV, it is necessary to perform power calculation based on the output voltage
and current of PV before it is further processed in the fuzzy controller. The IC fuzzy controller has dV/dI and I/V
as the two inputs, and duty cycle as the output for controlling the MOSFET in the buck boost converter. Input
354
ISSN: 2319-5967
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Volume 3, Issue 6, November 2014
dV/dI is the ratio of output voltage change and output current change of PV while input I/V is the ratio between
the current and the output voltage that can be expressed by the equation
(8)
For implementing the fuzzy controller, the input and output membership functions use triangular fuzzy with five
membership functions, as shown in Figure 4.
(a) Membership Function of dI/dV
(b) Membership Function of I/V
(c) Membership Function of Duty Cycle
Fig 4. Membershipfunction For Input and Output Fuzzy
Determination of the rule-base of IF-THEN in the controller contains all the information to control the parameters
and is based on incremental conductance, as shown in Table 1.
Table 1. Fuzzy Rule
dI/dV
I/V
NK
K
Z
P
PB
NK
N
Z
P
PK
PB
B
Z
NK
K
B
B
Z
NK
NK
NK
K
Z
B
PB
B
B
Z
NK
NK
PB
B
Z
K
NK
IV. DESIGN OF BUCK BOOST CONVERTER
Buck-boost converter is a dc-dc converter circuit that connects the photovoltaic with its load. With buck-boost
converter, the output voltage may be greater or smaller than the input voltage depending on the operating duty
cycle of the circuit. If the duty cycle is less than 0.5, the output voltage will be less than the input voltage and vice
versa. When the duty cycle is equal to 0.5 the output voltage equals the input voltage. The power stage of a buck
boost converter is shown in Figure 5/ The converter stage consists of a DC source, MOSFET as the main switch,
inductor, capacitor, diode and resistor load. The DC source at the input of the circuit comes from the photovoltaic
output voltage with a maximum power of 50 W.
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Scope 1
+ i
g
Duty Cycle , f=20 KHz
D
+ -i
I
S
Scope
I1
+v
-
L 0.02mH
C 500uF
DC Voltage Source
V
Fig 5 Buck Boost Converter Circuit
MOSFET functions as the switching component that will receive a PWM signal from the controller. The
switching frequency of the PWM signal is chosen to be 20 kHz. Input power to the converter is the maximum
output power produced by the photovoltaic. If the converter is designed with an efficiency of 90%, the output
power of the converter can be determined from
Efficiency ( η ) =
90% =
The buck-boost converter will be designed to output 12 V since it will be used to charge a 12V battery. Its output
current be calculated by
Current output (Io) =
=
The minimum value for inductor and capacitor can be determined by using a duty cycle of 0.5 applied to the
following equations:
(9)
(10)
V. SIMULATION RESULTS
The input to MPPT is the voltage and current output from the photovoltaic, while the output of the MPPT is a
varying duty cycle to drive the buck boost converter circuit. Simulation for the MPPT with fuzzy incremental
conductance has been performed using Matlab/Simulink whose block diagram is shown in Figure 6.
Discrete,
Ts = 1e-006 s.
MPPT -IC
Scope 7
powergui
dI
dV
Delta D
Duty
I
Ipv
V
dI/dV
Vpv
I/V
Ipv
dV
dP/dV
DeltaD
Scope 5
DdP/dV
dI
Vpv
Fuzzy Kontroller
Subsystem
Scope 3
Scope 4
D
Vout
+
Pout
Vin
Scope 1
Voubattery
-
Scope 6
Iin
Scope 8
Iout1
Buck boost konverter
PV panel 1
Scope 9
Scope 2
Ipv
Scope 12
Vpv
Product 2
Scope 11
+ i
-
+
Current Measurement
Series RLC Branch
+
- v
Voltage
Scope 10
-
PV panel 2
Fig 6. PV System Connected to MPPT with Fuzzy IC
The simulation is done by providing solar irradiation of 1000W/m 2 and the junction temperature of 30C. The
resulting duty cycle for the MPPT is shown in Figure 7.
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Duty Cycle Dengan Algoritma Fuzzy IC
0.56
0.54
Duty Cycle
0.52
0.5
0.48
0.46
0
0.01
0.02
0.03
0.04
0.05
Time
0.06
0.07
0.08
0.09
0.1
Fig 7. Duty Cycle
Converter Output Voltage With MPPT
25
X: 0.05761
Y: 24.41
20
15
10
5
0
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.07
0.08
0.09
0.1
Converter Output Voltage Without MPPT
Voltage (V)
15
10
5
0
0
0.01
0.02
0.03
0.04
0.05
Time
0.06
(a) Output Voltage Controller
Output Power of Converter
25
With MPPT
20
Without MPPT
Power (W)
15
10
5
0
0
0.01
0.02
0.03
0.04
0.05
Time
0.06
0.07
0.08
0.09
0.1
(b) Output Power Controller
Fig 8. Output Voltage and Power Controller
Fuzzy controller can determine the change in duty cycle that it will be the input for incremental conductance
algorithm. So that the photovoltaic system work at the maximum power point. Based on simulation results, the
MPPT can determine the appropriate duty cycle to achieve the maximum power point. Converter output voltage
is generated in the PV system with MPPT of 25V while the system without MPPT produces output voltage of 13V.
The output power of the PV system with MPPT of 22W while the photovoltaic system without MPPT generate
output power of only 15W. Based on the transient response, with MPPT photovoltaic systems have settling time
of 0.05 seconds, rise time of 0:02 seconds and has no overshoot
With the changes in solar irradiation, the IC fuzzy algorithm is able to recognize these changes and provide the
duty cycle that yields the maximum power point. By providing solar irradiation changes from 600W/m 2 to
900W/m2 and falling back to 800W/m2, the fuzzy IC algorithm generates duty cycle as shown in Figure 9. When
compared to the PV system without the Fuzzy IC, the output power generated by the PV system is higher as shown
in Figure 10.
Duty Cycle
0.62
0.6
Duty Cycle
0.58
0.56
0.54
0.52
0.5
0
0.01
0.02
0.03
0.04
0.05
Time
0.06
0.07
0.08
0.09
Fig 9. Output MPPT with Change of Sun Irradiation
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Output Voltage Converer With MPPT
50
Voltage (V)
40
30
20
10
0
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.14
0.16
0.18
0.2
0.14
0.16
0.18
0.2
Output Voltage Converter Without MPPT
Voltage (V)
15
10
5
0
0
0.02
0.04
0.06
0.08
a.
0.1
Time
0.12
Output Voltage
Output Power Converter
30
25
With MPPT
20
Power (W)
Without MPPT
15
10
5
0
0
0.02
0.04
0.06
0.08
0.1
Time
0.12
b. Output Power
Fig. 10 Output Converter With Change of Sun Irradiation
Compared with the photovoltaic system without MPPT, MPPT produce greater output voltage. With irradiation
600W / m2, the use of MPPT Fuzzy IC generates the output power of 20.5W. This indicates an increase in the
output power of 9W when compared to the system without MPPT PV. Changes of sun irradiation, causing
changes in the point of maximum power, has to be recognized by the MPPT. This is indicated by a change in solar
irradiation into 900W /m2, MPPT has been able to increase the duty cycle so that the resulting output power is
also increased to 28W. Compared with the system without MPPT photovoltaic, the PV system with MPPT has
been able to increase the output power of 14W system.
VI. CONCLUSION
Maximum power point tracking with the fuzzy incremental conductance method has been presented. Fuzzy
incremental conductance method is an extension of the IC method to improve PV’s output power. Fuzzy logic
results in a faster determination of the duty cycle for the buck-boost converter used in MPPT. Simulation results
of the Fuzzy IC algorithm demonstrate that the Fuzzy IC algorithm is able to follow the change in solar irradiation
to produce a varying duty cycle to achieve the maximum power point.
ACKNOWLEDGMENT
The authors would like to thank you to the Indonesian Ministry of Education and Culture which sponsored this
research by using National Research Competition 2014 funding scheme.
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ISSN: 2319-5967
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International Journal of Engineering Science and Innovative Technology (IJESIT)
Volume 3, Issue 6, November 2014
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AUTHOR BIOGRAPHY
Ratna Ika Putri, received the BSc and M.E Degree in Electrical Engineering from Brawijaya
University, East Java Indonesia in 1994 and 2006. She has got teaching experience nearly 12
years. Currently a lecturer of electronics Department at Malang State Polytechnic, East Java,
Indonesia. Her current research interest in control, power electronics and artificial intelligent
technique. She is a member of IAENG.
Sapto Wibowo completed his MSc degree in Embedded System and Control from University of
Leicester, United Kingdom in 2010. He awarded Dunlop Polymer Engineering Prize 2010. He
earned his BSc from Brawijaya University, Malang, Indonesia in 2000 in Electrical
Engineering. He has been working as R&D Engineer at Windstrich Engineering from 2000.
Since 2003, he is also working as a Lecturer in the Electrical Engineering Department at State
Polytechnic of Malang, Indonesia. He got some training such as Training on Information
Technology in University of Canberra, Australia in 2007. His areas of interest are embedded
system, advanced control, instrumentation, industrial automation, and SCADA system. He is
also keen to conducts industrial projects and has a strong relation with some industries.
M. Rifa’i received the B.E degree from Brawijaya Univ. and the M.E degrees, from Brawijaya
Univ. in 2000 and 2009. Joint with Electronic Department at Malang State Polytechnic from
2005. Her research interest in power electronics.
Taufik received his BS in Electrical Engineering with minor in Computer Science from
Northern Arizona University, MS in Electrical Engineering from University of Illinois Chicago,
and Doctor of Engineering from Cleveland State University. He joined the Electrical
Engineering department at Cal Poly State University in 1999 where he is currently a Full
Professor. He is a Senior Member of IEEE and has been employed by several companies
including Capstone Micro turbine, Rockwell Automation (Allen-Bradley), Picker International,
San Diego Gas & Electric, APD Semiconductor, Diodes Inc., Partoe Inc., and Enerpro.
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