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Indonesian Journal of Electrical Engineering and Computer Science
Vol. 18, No. 2, May 2020, pp. 666~677
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v18.i2.pp666-677 666
Journal homepage: http://ijeecs.iaescore.com
Modeling and simulation of solar PV module for comparison of
two MPPT algorithms (P&O & INC) in MATLAB/Simulink
Hayder Moayad Abd Alhussain, Naseer Yasin
Electrical Power Engineering Techniques, Department of Electrical Engineering Technical College
Middle Technical University Baghdad, Iraq
Article Info
ABSTRACT
Article history:
Received Aug 2, 2019
Revised Nov 4, 2019
Accepted Nov 18, 2019
This paper introduces a procedure for the modeling of a Photovoltaic (PV)
cell and the application of“maximum power point tracking (MPPT)”in step-
by-step with MATLAB/Simulink. The model of one diode is used to explore
the characteristics of I
ــ
V and P
ــ
V curves of 60W PV module. Due to the
non-linear and time-varying of PV characteristics, the generated power of the
PV is continually varying with atmospheric conditions like temperature and
irradiation, the MPPT technology is very important to chase“maximum
power point (MPP)”on the P
ــ
V curve to obtain maximum output power from
PV array. This study focuses on two common types of algorithms of
MPPT,“namely perturb and observe (P&O) and incremental conductance
(INC)”. A DC
ــ
DC boost converter is implemented to regulate the voltage
output from the PV array's and for the application of the MPPT algorithm.
Keywords:
DCــDC boost converter
Incremental conductance(INC)
Maximum power point tracking
(MPPT)
Perturb and observe (P&O)
Photovoltaic (PV)
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Hayder Moayad Abd Alhussain,
Electrical Power Engineering Techniques, Department of Electrical Engineering Technical College,
Middle Technical University Baghdad, Iraq.
Email: hayder_moayad@yahoo.com
1. INTRODUCTION
The Photovoltaic power could be considered among the renewable energy resources as the most
essential resource with the greatest development potential, so it attracts human attention because of it is
ubiquitous, cost reduction, clean energy, continuity and reliability, and there is plenty of solar radiant free
energy. Researchers have the best understanding of PV working principles because of the continuous
updating of the mathematical modeling of solar PV cells [1]. However, the variation of the PV power
generation with different atmosphere circumstances is the main challenge for the PV system applications and
it is the main case that must be taken into account. Therefore, it is important to increase the efficiency of the
PV system, which must operate at its maximum power point, so the maximum power point tracking MPPT is
a process that responsible for obtaining the information about the highest PV power usage in the design of
the console [2].
The solar cell's efficiency depends on many factors such as irradiance, temperature, shadow, dirt,
spectral characteristics of sunlight, etc. The changing in insolation on PV panels due to rapid climatic
changes such as an increase in ambient temperature and cloudy weather can reduce the PV panels output
power. In another word, each photovoltaic cell produces energy related to its operational and environmental
conditions [3]. The maximum power of the PV module generates at a single operating point. On the other
hand, the operating point of the PV system can be controlled by adjusting the output power or voltage of the
PV system. The output voltage and power of the PV system can be controlled by a power electronic
converter which is the most common method, which in turn is controlled by a specific control algorithm to
drive this procedure [4].
Therefore, most environmental factors such as ambient temperature and solar radiation greatly
determine the amount of energy that can be produced. So, an MPPT is required with a control unit to reach
Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752
Modeling and simulation of solar PV module for comparison of two… (Hayder Moayad Abd Alhussain)
667
the maximum power generated from the resulting PV array [5], and also as the characteristics of PــV and IــV
curves are non-linear and time-varying as in Figure 1, it is necessary to implement a“maximum power point
tracking MPPT” system to chase the on the PــV curve, so that maximum power output can be gained
from the PV array system [6].
Two MPPT algorithm methods are proposed in this literature; Perturb and Observe (P&O) and
Incremental Conductance (INC). For the representation of the MPPT, the PV system needs to have a DCــDC
converter, the DCــDC converter can be either boost converter or buckــboost converter, they are usually used
because of their efficiency high [7]. The boost converter is used in this paper to track the MPP.
The purpose of this paper is to study and compare the most suitable MPPT methods for PV
applications and evaluate their performance under irradiation changes using perturb and observe method and
incremental conduction method. The simulation study is designed to create an implementation of two MPPT
algorithms for the PV modules connected to the load, giving a satisfying response to the problem of
irradiation changes using MATLAB/Simulink program.
Figure 1. IV and PV characteristic curves of a solar panel
2. NOMENCLATURE
[] PV output current in (A), [] PV output voltage in (V), [] reference temperature = 298K,
[] operating temperature in Kelvins, [] PV saturation current in (A), [] PV ideality--factor,
which = 1.6,
[] PV light generated current in (A), [] Boltzman-constant, which= (1.3805×10-23 J/K), []
Charge of electron, which = (1.6×10-19 C), [] series resistance of a PV, which =0.0111Ω, [] parallel
resistance of a PV which =1000 Ω, [] the band gap of the silicon, which =1.1 eV, [] short circuit
current temperature cــefficient, which =0.0032A/oC, [] the illumination of PV in (W/m2) which
=1000W/m2, [] short ـcircuit current with irradiation (1000W/m2) and temperature 25oC= 2.55A, []
Cells number in series, []Cells number in parallel.
3. PHOTOVOLTAIC MODELING
3.1. Model Reference
PV module of MXS 60W is taken as a reference for the simulation as given in Table 1 [8].
Table 1. 60W PV Module Specifications
Rated Power
60 W
Voltage at Maximum power (Vmp)
17.1 V
Current at Maximum power (Imp)
3.5 A
Open circuit voltage (VOC)
21.1 V
Short circuit current (ISCr)
3.8 A
Total number of cells in series (Ns)
36
Total number of cells in parallel (Np)
1
3.2. Solar Cell Module
A solar panel can be built by connecting series cells and parallel cells to form the PV. In this paper
60W PV model is used with NS = 36 and NP = 1. The solar cells are a p-n semiconductor junction, which
can be represented as a diode circuit, as shown in Figure 2. This circuit includes photocurrent Iph , Rs and
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Rsh are the series and parallel resistance of the PV cell respectively. The PV panel can be formed
mathematically as shown [9-11].
3.2.1. Shunt Current Ish Module
Matlab Simulation of shunt current Ish as shown in Figure 3.
(1)
Figure 2. Solar cell circuit
Figure 3. Matlab Simulation of shunt current Ish
3.2.2. Temperature module from degrees to Kelvin
Matlab simulation of temperature conversion from degrees Celsius to Kelvin as shown in Figure 4.
(2)
3.2.3. Photo Current Iph Module
Matlab simulation of photo current as shown in Figure 5.
(3)
Figure 4. Matlab simulation of
temperature conversion from degrees
Celsius to Kelvin
Figure 5. Matlab simulation of photo current
3.2.4. Reverse Saturation Current Module Irs
Simulation of the reverse saturation current as shown in Figure 6.
(4)
3.2.5. Saturation Current Io Module
Matlab simulation of saturation current as shown in Figure 7.
(5)
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Figure 6. Simulation of the reverse saturation current
Figure 7. Matlab simulation of saturation current
3.2.6. The current output of PV module IPV
Simulation of output current of PV module as shown in Figure 7.
(6)
Figure 8. Simulation of output current of PV module
All six models above are connected as shown in Figure 9, and a ramp function is used to simulate
the whole axis of voltage for the characteristics of IــV and PــV curves, and the slope of the ramp function is
set to 10.
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Figure 9. Simulation of all six models
4. ALGORITHM OF MPPT CONTROL
PV system curves are influenced by temperature and solar irradiation. Furthermore, daily
irradiation behavior has abrupt variations during day time. The MPP of the PV arrays under these conditions
keeps changing continuously, and the operating point of the PV cell should also change consequently to
maximize the extracted energy. The MPPT technique is useful to maintain the operation point of a PV array
at its maximum [12].
4.1. DC-DC Boost Converter
The DC-DC boost converter circuit shown in Figure 10, the circuit consists of DC input source
voltage Vin to represent the PV output voltage, L is boost inductor, boost controlled switch IGBT, C2 is
boost filter capacitor, boost diode D and R is load resistance [13-14].
Figure 10. DC-DC converter
The boost controller switch IGBT is controlled by a duty-cycle (d) and the gain DC voltage of the
DC-DC boost converter is:
(7)
Vout is the voltage output from the boost converter, Vin is the voltage input to the boost converter
from the PV array and (d) is the duty-cycle generated from MPPT controller either by P&O or INC to boost
the input voltage using pulse width modulation (PWM) technique as shown in Figure 11 [15].
Details of the boost converter are given in Table 2. These details can be determined mathematically
from the designed circuit of a dcــdc boost converter [16].
Table 2. DC-DC Boost Converter Component Values
Component
Rating
L
150[μH]
C1
100[μF]
C2
470[μF]
R
30[Ω]
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Two methods of MPPT are taken in this paper.
Figure 11. Configuration of PV system to control the boost converter with MPPT
4.2. Perturb and Observe Method (P&O)
The algorithm based on the process of perturbation (increase or decrease) of power based on
increases or decrease the voltage of the PV array's, based on the observation of the power output of the PV.
The algorithm of P&O is continuously increasing or decreasing the voltage reference by depending on the
previous value of the power sample. The P&O method is simpler as it requires sensors for the voltage and
current only, and it is easier to be implemented [17]. In this algorithm, the time taken to reach the MPP is
longer than that in INC. When the MPP point is reached, P&O keeps alternating around the point and would
never stop on it.
According to the algorithm of P&O, when a small increase in the operating voltage of the PV array
is perturbed, and if the power change ΔP is (positive), it is going in a direction towards MPP and it should
keep on the perturbing along the same direction. If the changes in power ΔP is (negative), then the operating
point is moving away from the MPP point and the sign has to be changed of the delivered perturbation.
The algorithm of P&O is shown in Figure 12; it is based on perturbing the operating voltage
periodically and comparing the previous one with it. If the difference in voltage ΔV and the difference in
power ΔP are positive then the PV array voltage is increased. If both ΔP and ΔV are negative then there is
also an increase in the PV array voltage, otherwise, the voltage is decreased. Similarly, the next period is
reiterated until the point of maximum power is reached [18-19].
(a). Flowchart of P&O
(b). P-V graph
Figure 12. P&O control technique
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The simulink model of the MPPT P&O algorithm shown in Figure 11 includes the 60W PV array
and contains the equations required for its modeling. The DC voltage source of the boost converter is
replaced by a MATLAB subsystem integrated with the PV array. Perturbing the duty ratio of the dc ــ dc boost
converter will perturb the current of the PV array and consequently perturbs the voltage of the PV array. To
calculate the power at different duty cycles and to compare it with the current operating point power, the
MPPT subsystem is used. The duty cycle either decreases or increases or remains the same.
Figure 13 shows the configuration of the MPPT algorithm in MATLAB/Simulink according to the
flowchart of the P&O method expounded in Figure 12 [20].
Figure 14 shows the configuration of PWM to increase or decrease the duty-cycle used to control
the boost switching IGBT, with pulse generator period 1/5000 and pulse width 50 [20].
Figure 13. Configurations of (P&O) MPPT algorithm in Simulink
Figure 14. Configuration of PWM
4.3. Incremental Conductance (INC)
INC is another method for tracking the MPP. This method is used to counter the weakness of the
P&O method [21]. The P&O method is not capable to compare the actual operating voltage at a maximum
power point with the terminal voltage of the (PV) array. The INC method is easier to implement, it has a
higher tracking speed, and better efficiency, this makes INC algorithm better than P&O [22]. The algorithm
of INC is derived by differentiating the power of the PV with respect to its voltage, then the result is set equal
to zero as in (8) and (9) [23-24].
At point of MPP
(8)
Rearranging as shown in (8),
(9)
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From (9), the (left-hand side) is the incremental conductance, while the (right-hand side) is the
opposite instantaneous conductance. When the operating point reaches the MPP, both sides of (9) are
opposite in the sign but equal in magnitude.
When an operating point is away from the MPP, two possibilities exist, either the operating point is
at the (left-hand side) of MPP when incremental conductance is greater than instantaneous conductance, or at
the (right-hand side) of MPP when incremental conductance is less than instantaneous conductance as shown
in (10), (11), (12). The Simulink model of the InC algorithm is the same as the P&O condition, but only
different in MPPT algorithms.
(10)
(11)
(12)
The operation of the incremental conductance idea is shown in Figure 15.
(a). Flow chart of INC
(b). P-V graph
Figure 15. Incremental conductance control technique
5. SIMULATION RESULTS
5.1. Simulation Results of The PV Array Without DC-DC Converter
In Figure 16 (a), the input irradiation until time 1 sec, is set to 1000W/m2, 1 & 2sec is set to
800W/m2, 2 & 3sec it is set to 600W/m2, 3 & 4 sec the setting is 400W/m2, and between 4 & 5 sec, it is set
to is 200W/m2. With input temperature set at 250C during the whole simulation.
In Figure 16 (b), the input temperature from 0 to 1sec is 250C, between 1 & 2sec it is 500C, 2 & 3s
it is set to 750C. With input irradiation set at 1000W/m2 during the whole simulation.
Figure 17 shows that the current output from the PV is decreasing, and the voltage output is also
decreasing when the irradiation decreases. This would result in a net decrease in output power with a
decrease in irradiation when the temperatures are constant.
In Figure 18 when the operating temperature increases, the voltage output from the PV decreases
drastically, but the current output increases marginally. This results in a net reduction in the power output
with a rise in temperatures.
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(a). Input-Time varying irradiation
(b). Input-time varying temperature
Figure 16. Input-Time varying irradiation and temperature
(a). Output IــV
(b). Output PــV
Figure 17. Output characteristic of the PV while varying irradiation
(a). Output IــV
(b). Output PــV
Figure 18. Output characteristic of the PV with temperature varying
5.2. Simulation Results of The PV Array with Resistive Load
The irradiation is set to1000W/m2 and the temperature is set to 250C, it is seen in Figure 19 that the
output DC power is about 14.41W which is equal to:
(13)
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(a). Power (Ppv)
(b). Voltage (Vpv)
Figure 19. Output of the PV array with resistive load
5.3. Simulation Results with DC-DC Boost Converter Using MPPT (P&O, INC)
As seen in Figure 20, the output power from the P&O method is 56.81W and the output voltage is
41.29V which represents the maximum power (Pmax) and (Vmax) that can be drawn from the PV module
and the maximum power was achieved in about 0.09 sec, the final value of output power has a ripple of about
1.56 as seen in Figure 20 (a).
While in the INC method the maximum power output (Pmax) of value 58W is achieved with a rise
time of about 0.65 sec, with a ripple of about 1.31 as seen in Figure 21 (a).
(a). Output power
(b). Output voltage
Figure 20. The output power and voltage of the DCــDC boost converter with P&O method
(a). Output power
(b). Output voltage
Figure 21. The output voltage of the dcــdc boost converter with INC method
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It can be noted that after applying the MPPT algorithm the operating point at maximum power is
reached at lesser rise time with INC. The result of the simulation show that the energy production from solar
panels being independent and maximized of weather conditions.
5.4. The Comparison Between P&O and InC MPPT Algorithms
MPPT P&O and InC algorithms are simulated and compared under the same conditions. When
weather conditions are steady or change slowly, P&O MPPT oscillates near the MPP but InC MPPT
accurately finds the MPP at changing weather conditions as well. There is a comparison between these two
algorithms for the different parameters are given in Table 3. The output current can be determined by (14).
The results obtained in this paper from 60-watt solar panels can be compared to the results obtained from 60-
watt and 70-watt solar panels in [25-26].
(14)
Table 3. Comparison between the MPPT P&O and InC Algorithms
PV
Output Power
Output Voltage
Output Current
Time Response
Accuracy
Without MPPT
14.41 W
20.77 V
0.693 A
0.0 sec
Less
With MPPT P&O
56.81 W
41.29 V
1.375 A
0.09 sec
Less
With MPPT InC
58 W
41.72 V
1.4 A
0.065 sec
Accurate
6. CONCLUSIONS
In this paper, a procedure for the mathematical modeling of the PV module is demonstrated to earn
more understanding of the characteristics of PــV and IــV curves of PV module. The MPPT algorithms of
P&O and INC are discussed and simulated by MATLAB/Simulink. The simulated MPPT controller achieved
and maintained the MPP efficiently at different temperatures and irradiation levels. The simulation proved
that the INC method has a higher speed, better performance with more accuracy in tracking the MPP than
)P&O( method. These two methods improve the steady-state and dynamic performances of a photovoltaic
system and improve the efficiency of the dcــdc boost converter system.
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BIOGRAPHIES OF AUTHORS
Hayder Moayad Abd AlHussain was born in Baghdad, Iraq, on March 15, 1994. He received his
Bachelor's degree in electrical engineering in 2016 from Electrical Power Engineering
Techniques Department, Al-Mamoun University College, Baghdad, Iraq. His current research
works include the development & Improve the efficiency of renewable energy systems, control
techniques, and artificial intelligence.
Dr. Naseer M. Yasin was born in Messan city, iraq, on May 20, 1965. He was an Assistant
Professor of Electrical Engineering, at the Electrical Engineering Technical College, Middle
Technical University - Iraq, in 2019. He received the PhD. degree from College of Engineering,
University of Basrah, in 2014. He received the MASTER degree in Electrical Engineering from
Electrical Engineering Technical College, Middle Technical University, in 2007. His research
interests include electrical motor drives, renewable energy and artificial intelligence.
