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A Comprehensive Review on Various
Optimization Techniques Assisted Perturb
and Observe MPPT Algorithm for a PV Panel 1T.R. Premila and
2R. Krishna Kumar
1Department of EEE,
Vels Institute of Science,
Technology and Advanced Studies,
Chennai, India.
[email protected] 2Department of EEE,
Vels Institute of Science,
Technology and Advanced Studies,
Chennai, India.
Abstract This paper explains the partial shading conditions (PSC) of the PV array
for the improvement of a brand new set of rules for hybrid Maximum
Power Point Tracking. The new algorithm combines the procedure of Gray
Wolf Optimization (GWO) for MPPT all through the initial ranges of
monitoring and then employs the existing perturb and observe algorithm at
the final stages. Therefore the Grey wolf optimization algorithm replaces
the conventional MPPT algorithm drawback. But, in partly shaded
situation wherein there are multiple MPPs inside the p–v curve, the
conservative algorithms are unsuccessful in figuring out the global MPP
(GMPP) the various nearby MPPs (IMPPs), therefore reducing the general
PV system efficiency. The main variations among those algorithms are
analog or digital implementation, sensor necessities, the design simplicity,
range of effectiveness, convergence speed, in addition to the hardware
costs. Therefore, deciding on the right set of rules is very vital to the
customers, as it influences the electric performance of PV array and also
reduces the solar panel cost. The GWO technique is compared to the
conventional 21 techniques to track the Global peak of the MPP.
International Journal of Pure and Applied MathematicsVolume 119 No. 10 2018, 323-336ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu
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1. Introduction
A mastermind call for-facet control, alternative of electricity conservation, and
renewable power, the use of efficient, modern photovoltaic solar cells (PVSCs)
has been featured. From the software grid, PVSCs have not yet been a precisely
a captivating, offbeat for electrical utilization who can purchase much less high-
priced electrical energy because of their underlying high cost. But, the software
strength isn't handy or is quite luxurious to move they have been used
extensively for air habituation in water pumping, far off and remote or isolated
areas. Due to new improvements within the movie innovation and assembling
procedure, PVSC costs have faded substantially amid the maximum latest years.
The hassle of performance and the products are solved by way of using the
exceptional techniques of MPPT. The MPPT trajectory the MP in the
photovoltaic module had been endorsed and had been made and now
commercially available for customers [Ahmed M. Atallah et al, 2014].
Because of the developing insist on power, the restricted rising and stock costs
of existing resources (collectively with petroleum and coal, and plenty of
others.), photovoltaic (pv) power turns into a promising possibility as it's miles
omnipresent, environment great, freely available, and has lots less operational
and renovation prices [Bidyadhar Subudhi,2013]. Consequently, the decision
for of PV system appears to be extended for every grid-connected and
standalone modes of PV structures. MPPT is a major role in the PV array,
because it is used to track the MPP from the photovoltaic panel under all
environmental conditions.
The GWO based MPPT optimization algorithm is used to track the global peak
power[Mohammad Amin Ghasemi, 2015] from the photovoltaic panel under
partial shading conditions. Partial shading condition is famous, because it has
multiple peak points and it compared with the conventional MPPT algorithms,
Such as P&O-MPPT, INC-MPPT [SatyajitMohanty, 2015]. [Hadeed Ahmed
Sher, 2015] explain the (P&O) perturb and observe oscillations. By using
perturb and observe algorithm oscillations are reduced, but in INC algorithm the
oscillation is reduced but not entirely. Both IC and perturb and observe
techniques miscarry at some point of the ones time periods characterized by
way of converting atmospheric situations.
Thesolar radiation level, (Ahmad Al-Diab, 2010) working temperature and load
current which rely on the non-linear variety in the yield voltage and current, can
bring about low electrical efficiency. The MPP of the PV framework is
followed, utilizing disconnected or online calculations are utilized to tackle
issues with the usage of sunlight based exhibits for electrical power, [Emad M.
Ahmed, 2010].Compared to other methods such us genetic algorithms and
neural network, using only the expert knowledge it provides fast results
[NoppadolKhaehintung, 2004]. The fundamental structures the with the
objective to be reusable and in a similar time, it follows the calculation
International Journal of Pure and Applied Mathematics Special Issue
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capacities of present-day inserted processors [Ali. M. Eltamaly, 2010]. In the
simulations, the considered MPPT systems have been executed entirely taking
after the description demonstrated in the references: no MPPT calculation is
favored and no MPPT strategies have been acknowledged with more
consideration regard to the others [M. Berrera, 2009; J. Prasanth Ram, 2016].
This paper presents a quick assessment among one of a kind strategies to assist
the customers for a selected application to choose an MPPT method. The
contrast between the MPPT strategies consists of cost, convergence speed,
sensor dependence, hardware complexity, analog or digital implementation, and
effectiveness. The PSC consists of multiple global peaks are highlighted, inside
the first step the global peak power is detected and the second step which is
compared to the conventional MPPT, then only the exact GMPP is obtained[A.
R.Mehrabian, 2006].
The following ways the paper was discussed. In section I explains the PV
system modeling. Problem formulation is discussed in section II. In section III
explain the Comparison between MPPT techniques.
2. PV System Modeling
The solar cell is the fundamental unit of a photovoltaic framework. The
photovoltaic cell modeled is shown in figure 1. (Saravana Selvan. D, 2013).
For simplicity, Figure 1 is used for the single-diode model. With the basic
structure, this model has the good relationship between accuracy and simplicity.
It has a parallel diode and a current source.
The output current I of a single- diode model is given by
(1)
Where, Ipv and I speak to the photovoltaic and output current of cell.
is the intrinsic series resistance of the cell .
The current generated by the light is .It is proportional to irradiance and can
be written as G,
(2)
Where ,Gis the irradiance and G0is the nominal irradiance, G0 current is Ig0,
temperature coefficient of Ipv is J0, T is the actual temperature of the cell and the
cell nominal temperature is Tref.
According to Eq.(2) the photocurrent module IPV of the photovoltaic array be
determined by directly on the solar irradiation and is likewise preferential by
the temperature.
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The saturation current of the diode is expressed as following, which is depends
of the temperature.
(3)
Where, saturation and nominal saturation current is I0, I0,n, respectively, charge
of electron is q, band gap energy is Eg, Boltzmann’s constant is k, diode
ideality constant is a.
Fig.1: Single-Diode Model of a Photovoltaic Cell
At the nominal condition the photovoltaic current is Iph (A) (25◦C and
1000W/m2), the short-circuit current/temperature coefficient is Ki (0.0017A/K),
the actual and reference temperatures are Tkand Tref in Kelvin, respectively, the
irradiation on the gadget surface (W/m2) is λ, and 1000W/m
2 is nominal
irradiation. Fig.2. shows the module configuration. Here the Fig.2 (a) shows the
PV panel connected in series, three panels are used. In Fig.2 (b) shows PV
panels are connected in series and parallel
Fig.2: (a) PV Panels Connected in Series (3s)(b)PV Panels Connected in
Series and Parallel(3s2p)
Rp
I
I
0
Rs
Ideal photovoltaic cell
Practical photovoltaic cell
Iph
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3. Problem Formulation
In any other application the Maximum Power Point Tracking is used to track the
maximum power [Tse et al. 2002]. MPPT is famous one in PV array. By using
MPPT can achieve the maximum power from the photovoltaic panel. This
MPPT will give the maximum efficiency of the whole system. Various
controller techniques are used inside the MPPT algorithm. The quantity of
power won by using PV system is predicated upon on numerous elements
collectively with temperature, irradiance, and partial shading. These algorithms
need to remember the modifications in those factors. An optimization problem
of the proposed MPP extraction can be formulated as below:
Maximize P(d) (4)
Subject to (5)
The output power of the of the photovoltaic is P(d), converter duty ratio is d
and the min and the max value of the duty ratio is dmin and dmax., which is taken
as 10% and 90% respectively.
Table 1: MPPT Algorithm Parameter Comparison
Parameters Statement
PV array dependent/
independent
Some PV panels are not suitable for any other converter, so the PV array is
dependent or independent that’s need to check, it is necessary one.
Circuitry types Digital or analog
Periodic tuning Periodic tuning is required; because the oscillation is there in the output of
the MPP the tuning option is needed.
Sensors
It depends on the range of variables beneath attention
Implementation
complexity
This preferred describes the approach in fashionable
True MPPT The used MPPT is true or not, ie if we use the Maximum Power Point
Tracking is to track the maximum power from the panel.
4. Relationship between MPPT Techniques
In table 2, the relationship between 21 techniques are proven. In step with the
table, the maximum not unusual algorithms are P&O, INC, FLC and GWO. We
have to compare our work to the existing MPPT techniques.
Table 2: Various MPPT Algorithm Comparison
Techniques of MPPT
digital /
Analog
Convergence
speed
Tuning is needed
periodically
How much difficulty to
implement
True
MPPT
Sensors PV array
Dependence/Independence
P&O (Sera, 2006; Kamarzaman; Jusoh
et al. 2014;
2014Busa , 2012)
Both vary Not needed Low Yes V and I Independence
Incremental conductance
(Esram, 2007;
Yadav, 2012;
Rashid, 2011;
Zainudin,2010;
Jusoh, 2014;
Digital vary Not needed Medium Yes V and I Independence
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Kamarzaman, 2014)
Fractional Voc
(Kumari, 2011; Lee, 2011; Jusoh, 2014;
Kamarzaman, 2014)
Both Medium Needed Low No V Dependedence
Fractional Isc
(Kumari, 2011; Lee,
2011; Jusoh, 2014;
Kamarzaman, 2014)
Both Medium Needed Medium No I Dependedence
Fuzzy logic control (Ali, 2012; Rezaei,
2013; Takun, 2011;
Rahmani, 2013; Jusoh, 2014;
Kamarzaman,
2014).
Digital Fast Needed High Yes Varies Dependedence
System oscillation method (Ali, 2012)
Analog N/A Not needed Low Yes V Dependedence
Online MPP search
algorithm (Ali,
2012)
Digital Fast Not needed High Yes V and I Independence
VMPP and IMPP calculation
(Morales, 2010)
Digital N/A Needed Medium Yes T and Ir Dependedence
Temperature method (Ali, 2012;
Faranda, 2008;
Brito, 2013)
Digital Medium Needed Low Yes V and I Dependedence
Constant voltage tracker (Ali, 2012;
Coelho, 2010)
Digital Medium Needed Low No V Dependedence
State based MPPT (Ali, 2012)
Both Fast Needed High Yes V and I Dependedence
IC Based On PI
(Brito, 2013; Lyden, 2015)
Digital Fast Not needed Medium Yes V and I Independence
Parasitic
capacitances
(Zainudin, 2010; Rekioua, 2012;
Hohm, 2003).
Analog High Not needed Low Yes V and I Independence
Modified Perturb and Observe (Liu,
2004)
Digital High Not needed Medium Yes V and I Independence
Particle swarm
optimization PSO algorithm
(Mandour, 2013;
Lyden, 2015)
Digital High Not needed Low Yes V and I Independence
PSO-INC structure (Mandour, 2013)
Digital High Not needed Low Yes V and I Independence
Ant colony algorithm (Qiang,
2013)
Independence
GA-optimized ANN (Kulaksiz, 2012)
Digital Fast Needed High Yes V, T and Ir
Independence
DE-Differential Evaluation
(Kamarzaman,
2014)
- Fast Not needed Low Yes V and I Independence
Simulated annealing (Lyden, 2015)
Digital Varies Not needed Low/Moderate Yes - Independence
Grey wolf
optimization (SatyajitMohanty,
2015)
Both Fast Not needed Low Yes V, T and
Ir
Dependedence
In this exertion, we carried out a literature examine to what's to be had in
International Journal of Pure and Applied Mathematics Special Issue
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phrases of MPPT algorithms. The proposed GWO optimization is compared
with the conventional MPPT algorithm is mentioned in table 2. Here we
compared 21 techniques.
GWO-Grey Wolf Optimization
In nature the grey wolf performs hunting mechanism and it is in the leadership
hierarchy, proposed [S. Mirjalili, 2014].In top of the food chain grey wolf is
considered and they prefer to live in a prey. There are four types of grey wolves,
such as α-alpha, β-beta, δ-delta, andω-omega. They live in a cycle and give the
instruction to administrator. From the cycle the first place isα-alpha, second
place isβ-beta, andδ-delta is as a third place. Inω-omega watching all wolves are
doing their duty as regular and inform to the administrator. The rules are
hunting, chasing and tracking for prey. This is shown in Fig.3.
Fig.3: Grey Wolves Hunting Behavior: (a)–(c) Tracking and Chasing
Food;(d) Surrounding foOd; and (e) Violent Food
Grey wolves encircle a prey at some stage to hunt to take the food surrounding
behavior can be modeled by way of the subsequent equations:
(6)
(7)
In Eq.(7), t denotes the current iteration, the coefficient vectors are D, A, and
C, the prey position vector is denoted as Xp and the grey wolf position vector is
X. The calculated vectors are given as:
= 2 . - (8)
(9)
The components are decrease linearly from 2 to 0 in the iteration of the
direction and the random vectors are denoted as r1,and r2in [0, 1]. The alpha is a
leader of the all grey wolf. It takes the decision. The delta and beta are chasing
to prefer the prey.
International Journal of Pure and Applied Mathematics Special Issue
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Perturb and Observe MPPT
Perturb and Observe based MPPT, the maximum power point is tracking by the
way of perturbing the strolling element after which watching the trade in energy
before and after perturbations. The famous techniques of P&O have easy to
implement, cost is low, structure is simple and the parameters are measured
easily. The V-Voltage and I-Current is measured from the photovoltaic array.
The P&O split the power output of the photovoltaic array into smaller step size.
The step size is smaller one, which is constant one to all. Therefore the constant
step size, which reduces the oscillation of the PV panel output. By using this
method the photovoltaic panel output voltage and current can be managed and
which is referred to as “perturbation”. But this technique is fail because of the
rapidly change weather condition. It is not suitable for all weather conditions
(busaet al. 2012;sera et al. 2006).
The perturbation cycle is as follows:
(10)
From Eq. (10) perturbation responsibility is denoted as φ and if φ is large, that
means faster convergence and φ is low, that means lower convergence.
Extra variety of wolf’s consequences in better MPP precision however also will
increase the computational burden. The set of rules are to be adopted in the
proposed MPPT is as follows,
1. In the same area the grey wolves position is initialize and which is lie in the
duty ratio is in 10 to 90% respectively.
2. The wolf function is used to noted the photovoltaic array output power at every
cycle and then the output power of the PV array is:
(11)
3. In step 3 grey wolf location is modify
4. Repeat the 3 and 4 the procedure.
5. The MPP is reached the GWO to track the maximum power from the multiple
GP and also the small step size is selected to reduce the oscillations and for
better tracking performance.
5. Conclusion
This paper is proposed Grey wolf optimization based Maximum Power Point
Tracking using photovoltaic array under partial shading condition. The
proposed optimization technique was compared with the 21 conventions MPPT
techniques. The proposed techniques give best result as compared to all. PSC
consist of multiple global peaks, which were found by using the GWO
algorithm is better than the existing techniques. The proposed GWO technique
has tracking response is higher and convergence is faster towards to the GP
(Global Peak).
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