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Comparison of MPPT Algorithms for PV Systems Based DC-DC Converter
Article in Advances in Natural and Applied Sciences · March 2015
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ADVANCES
in
NATURAL and APPLIED SCIENCES
ISSN: 1995-0772
EISSN: 1998-1090
2015 Special; 9(17): pages 212-221
Published BY AENSI Publication
http://www.aensiweb.com/ANAS
Open Access Journal
Comparison of MPPT Algorithms for PV
Systems Based DC – DC Converter
1M.Premkumar, 2N.Dhanasekar, 3R.Dhivakar, 4P.Arunkumar
1Assistant
2,3,4UG
Professor, Department of EEE, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.
scholar, Department of EEE, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.
Received 12 February 2015; accepted 20 March 2016; published 25 March 2016
Address For Correspondence:
M.Premkumar, Assistant Professor, Department of EEE, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu,
India.
E-mail: mprem.me@gmail.com
Copyright © 2016 by authors and American-Eurasian Network for Scientific Information (AENSI Publication).
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
ABSTRACT
In the recent years, developing new topologies for power conditioning units and applying new control techniques were the focus
of many studies, almost saturating this field of research. This paper presents the simulation of a photovoltaic system using various
Maximum Power Point Tracking (MPPT) algorithms with DC-DC converter. The conventional MPPT algorithms are not capable of
solving the problems of multiple peaks that established in the P-V characteristic curves of the Photovoltaic (PV) systems due to
partial shaded conditions. Different combinations of voltage, current and power output have been compared to obtain MPP.
MATLAB Simulink tools have been used to compare the performance of the MPPT algorithms. This manuscript should serve as a
convenient reference for future work in photovoltaic power generation.
KEYWORDS: Power conditioning, partial shading, Maximum Power Point Tracking, Photovoltaic Systems
INTRODUCTION
Tracking the maximum power point (MPP) of a photovoltaic (PV) array is usually an essential part of a PV
system. As such, many MPP tracking (MPPT) methods have been developed and implemented. The methods
vary in complexity, sensors required, convergence speed, range of effectiveness, popularity, etc. In fact, so
many methods have been developed, as it has become tedious to find which method, newly proposed or
existing, is most appropriate for a given PV system [1].
A MPPT is used for extracting the maximum power from the PV module and transferring that power to the
load [2]. A DC-DC converter as shown in the figure 1 serves the purpose of transferring maximum power from
the PV module to the load. By changing the duty cycle of the PWM control signal, the load voltage varies and
matches at the point of the peak power with the source so as to transfer the maximum power .A study in
Germany, of pv system revealed that inverters contribute for 63%of failures, PV module 15% and other
components 23% on an average, every 4.5 years .when PV module maximum power point comes into case of a
real time is a well-known Tracking problem. Using a solar panel or an array of panels without controller that can
perform Maximum Power Point Tracking will often result in wasted power. An efficient MPPT algorithm is
important criteria to increase the efficiency of PV system to maintain the PV array’s operating point at this MPP
at all environment conditions. Therefore MPPT techniques are needed to maintain the PV array’s operating at
its MPPT. Many MPPT techniques have been proposed in the literature example are the Perturb and Observe
(P&O) methods[3], Model reference Adaptive control method [4], Incremental Conductance (IC) methods [5]
etc.
To Cite This Article: M.Premkumar, N.Dhanasekar, R.Dhivakar, P.Arunkumar., Comparison of MPPT Algorithms for PV Systems
Based DC – DC Converter, 2015. Advances in Natural and Applied Sciences. 9(17); Pages: 212-221
213
M.Premkumar et al., 2015/ Advances in Natural and Applied Sciences. 9(17) Special 2015, Pages: 212-221
Fig. 1: DC-DC Converter for Operation at the MPP
2. Problem overview:
The problem of the MPPT techniques is to automatically find the voltage VMPP or current IMPP at which a
PV module should operate to obtain the maximum power output PMPP under a given temperature and irradiance.
It is noted that under partial shading conditions, it is possible to have multiple local maxima, but overall there is
only one true maximum power point. Most techniques respond to changes in both irradiance and temperature,
but some are specifically more useful if temperature is approximately constant. Most techniques would
automatically respond to changes in the array due to aging, though some are open-loop and would require
periodic fine tuning. In our project, the array will be connected to a power converter that can vary the current
coming from the PV module.
3. MPPT Control algorithm:
3.1. Incremental Conductance:
The incremental conductance method is based on the fact that the slope of the PV array power curve is zero
( dP/dV = 0)at the MPP, positive on the left(dP/dV > 0) of the MPP, and negative on the right(dP/dV < 0) of the
MPP as shown in figure 2.The basic equations of this method are as follows,
dI/dV=-I/V ....... At MPP
dI/dV>-I/V .......Left of MPP
dI/dV<-I/V .......Right of MPP
Fig. 2: Maximum Power Point in P-V curve of Photovoltaic System
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M.Premkumar et al., 2015/ Advances in Natural and Applied Sciences. 9(17) Special 2015, Pages: 212-221
As shown in figure 3, the MPP can be tracked by comparing the instantaneous conductance (I/V) to the
incremental conductance (I/V). Vref is the reference voltage at which the PV array is forced to operate. At the
MPP, Vref equals to VMPP. Once the MPP is reached, the operation of the PV module is maintained at this point
unless indicating a change in atmospheric conditions and the MPP. The algorithm decrements or increments Vref
to track the new MPP [6].
The increment size determines how fast the MPP is tracked. With bigger increments, fast tracking can be
achieved but the system might not operate exactly at the MPP and oscillate about it instead, so there is a trade
off. By proper control of the power converter, the initial operating point is set to match a load resistance
proportional to the ratio of the open-circuit voltage (VOC) to the short-circuit current (ISC) of the PV module. The
real MPP is tracked in case of multiple local maxima. The operating point is brought into this area and then
Incremental Conductance is used to reach the MPP. A less obvious, but effective way of performing the
Incremental Conductance technique is to use the instantaneous conductance and the incremental conductance to
generate an error signal
Fig. 3: Flow Chart of Incremental Conductance
Measurements of the instantaneous PV array voltage and current require two sensors. Incremental
Conductance method lends itself well to DSP and microcontroller control, which can easily keep track of
previous values of voltage and current and make all the decisions.
3.2. Perturb and Observe (P&O):
In algorithm shown in figure 4 a slight perturbation is introduced in system which causes the power of the
solar module to change. If the power increases (dP/dt>0) due to the perturbation then the perturbation is
continued in that direction. After the peak power is reached the power at the next instant decreases (dP/dt<0)
and hence after that the perturbation reverses. When the steady state is reached the algorithm oscillates around
the peak point. The perturbation size is kept small in order to keep the power variation small. PI controller then
acts moving the operating point of the module to that particular voltage level [7]. Due to perturbation, it is
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M.Premkumar et al., 2015/ Advances in Natural and Applied Sciences. 9(17) Special 2015, Pages: 212-221
observed that it fails to track the power under fast varying atmospheric conditions. But still this algorithm is
very popular and simple.
Fig. 4: Flow Chart of P&O algorithm.
3.3. Adaptive P&O Algorithm:
The MPPT algorithm proposed is a variation of the classic P&O method [8]. The main limitation of the
P&O algorithm is the compromise between a fast dynamic response and a reduced oscillation at steady-state.
Large perturbation steps imply a fast dynamic performance, whereas small perturbation steps ensure little
oscillations at steady-state. In certain conditions, during cloudy days, the solar irradiation can vary many times.
These particular conditions can cause reduction of efficiency in MPP trackers. In order to reduce this problem
some studies has been proposed to increase the efficiency. Now, it is necessary to achieve a good dynamic
response to avoid losing a great quantity of energy. The efficiency of the P&O technique depends on the
perturbation step. It is certainly difficult to find out the optimal perturbation step, because it depends on the
panel characteristics, the converter’s topology and working conditions. In particular, when the operating point is
far from the MPP, large perturbation steps are chosen, whereas small ones are used in proximity to the
maximum. This algorithm is formulated in such a manner that when the power changes is less than or equal to
the set lowest limit , the algorithm assumes that the system enters the steady state and the value of perturbation
becomes small otherwise the perturbation becomes large[9]. To obtain faster dynamic with small oscillations
without knowing accurately the technical details of the PV system, the proposed adaptive P&O method as
shown in figure 5 operates with variable perturbation step adapted to the actual operating conditions. However,
a precise knowledge is not required because the P&O technique always works around the real maximum.
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Fig. 5: Flow Chart of Adaptive P&O Algorithm.
4. Buck Boost Converter:
The Peak Power Tracker is a microprocessor controlled DC/DC converter used by a photovoltaic power
system. The microprocessor tries to maximize the output power from the solar panel by controlling the
conversion ratio of the DC/DC converter to keep the solar panel operating at its MPP. A DC-DC converter acts
as an interface between the load and the module. The Buck-Boost mode DC/DC converter is the last and most
important type of switching regulator. In this converter, the buck and boost topologies are combined into one.
The output voltage produced may be less than or greater than the input voltage hence it is named as “buckboost”. Also this is known as an inverting regulator. The buck-boost converter steps the voltage down when the
duty cycle is less than 50% and steps it up when the duty cycle is greater than 50%. The Peak Power Tracker
uses an iterative approach to finding this constantly changing MPP [10].The Simulink model of Buck Boost
converter is shown in the figure 6.
Fig. 6: Buck boost converter.
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5. Simulation results:
Here all the simulation results have been done to make sure the comparison of the circuit can be determined
accurately.
5.1. Simulink Model for Incremental Conductance Method:
Fig. 7: Simulink Model for Incremental Conductance Method.
Fig. 8: Power Output of MPPT and Buck Boost Converter
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Fig. 9: Voltage Output of Buck Boost Converter
5.2. Simulink Model for P&O Method:
Fig. 10: Simulink model for P&O method
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Fig. 11: Output Waveform of PV Array System with MPPT.
5.3. Simulink Model for Adaptive P&O Method:
Fig. 12: Simulink Model for Adaptive P&O Method
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Fig. 13: PV Panel Current Waveform (Current vs Time)
Fig. 14: PV Panel Power Waveform (Power vs Time)
Conclusion:
A Adaptive P&O MPPT algorithm was proposed for changes in PV current limit violation and irradiance.
The proposed method focus on features -- speeds up the dynamic performance under sudden operating limits
violation, provides variable perturbation to reduce the oscillations around MPP and steadily tracks the MPP
under normal condition. The proposed algorithm has faster response with reduced oscillations around MPP than
the conventional P&O. The computational efforts caused due to the derivatives as explained in INC method are
absent. Unlike the fractional open circuit voltage and FSCC (Fractional Short Circuit Current) methods, it tracks
true maximum power. Also, it does not require a large amount of data for training and extensive computation to
deal various stages, as required by FLC (Fuzzy Logic Control) and NN (Neutral Network). The simulation
studies confirm that the proposed algorithm gives faster response than the conventional algorithms.
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