A Comprehensive Review on Various Optimization Techniques … · 2018. 3. 24. · A Comprehensive Review on Various Optimization Techniques Assisted Perturb and Observe MPPT Algorithm

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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International Journal of Pure and Applied Mathematics Special Issue

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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


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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


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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.


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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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A Comprehensive Review on Various Optimization Techniques … · 2018. 3. 24. · A Comprehensive Review on Various Optimization Techniques Assisted Perturb and Observe MPPT Algorithm