[IEEE 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT) -...

2013 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2013]

A New Adaptive P&O MPPT Algorithm Based on FSCC Method for Photovoltaic System

Sathish Kumar Kollimalla Student Member, IEEE

Department of Electrical Engineering, Indian Institute of Technology

Madras, Chennai, India. [email protected]

Abstract-Maximum power point tracking (MPPT) techniques are used in photovoltaic (PV) systems to extract maximum power from the PV module. There are number of techniques available in literature. In this paper, an adaptive P&O MPPT algorithm is proposed using conventional perturb and observe (P&O) and fractional short circuit current (FSCC) methods. P&O method is widely used because of its low-cost and ease of implementation. The P&O method oscillates close to maximum power point (MPP) , when atmospheric conditions are constant or slowly varying. However, when irradiance and temperature are changing rapidly, this method fails to track MPP with rapid speed. In order to consider rapidly varying atmospheric conditions an adaptive control algorithm is proposed, by measuring the short circuit current at different irradiance levels and temperatures. In this proposed method current perturbation is considered instead of voltage perturbation. The proposed method has faster dynamics and improved stability compared to the conventional P&O method. The effectiveness of proposed MPPT algorithm is verified using digital simulations.

Index Terms-PV module; maximum power point tracking

(MPPT); perturb and observe (P&O) method; fractional short

circuit current (FSCC) method; adaptive control.

I. INTRODUCT ION

Non-conventional energy sources are expected to play an

important role in meeting the world's power demand, due to

their independence from availability of limited power sources

and their less impact on the environment. Solar power gener

ation is currently considered as one of the most useful renew

able energy sources as it is pollution free, maintenance free,

fast technological progress and continuous cost reduction. The

fundamental element in solar power generation system is the

solar cell or photovoltaic (PV) cell that converts sunlight into

direct current (DC) electricity. A photovoltaic (PV) module is

an interconnected collection of cells combined as one item.

Multiple modules can be wired together either in parallel or

in series or in both to form an array. In general, the larger

the area of a module or array, the more electricity that it can

produce.

The main hindrance to solar energy going widespread is

the cost of installing solar modules. The biggest disadvantage

of solar energy production revolve around the fact that power

generation is not constant throughout the day, it is always

changing with weather conditions, i.e., irradiation and temper

ature. Furthermore, the efficiency of solar energy conversion

to electrical energy is very low which is only in the range of

9-17% [1], especially under low irradiation states, this means

Mahesh Kumar Mishra Senior Member, IEEE

Department of Electrical Engineering, Indian Institute of Technology

Madras, Chennai, India.

that a fairly vast amount of surface area is required to produce

a lot of electricity. Therefore, maximum power point tracking

(MPPT) is an essential part of the photovoltaic system to

ensure that power converter circuits operate at the maximum

power point of the solar array. Various MPPT algorithms have

been developed [2] - [3]. These algorithms are differ from

each other in terms of number of sensors used, complexity

in algorithm and cost to implement the algorithm. The main

objective of all these MPPT algorithms is to achieve fast

and accurate tracking performance and minimize oscillations

due to varying weather conditions. Each algorithm can be

categorized based on the type of the control variable it uses: a)

voltage, b) current, or c) duty cycle. Among different MPPT

methods, much focus has been on perturb and observe (P&O)

[4] - [5] and hill climbing (HC) [6] - [7] methods. The P&O

method involves a perturbation in the operating voltage of solar

array and hill climbing involves a perturbation in the duty ratio

of power converter.

In P&O method the voltage is being increased or decreased

with fixed step size in the direction of reaching the maximum

power point (MPP). The process is repeated periodically until

the MPP is reached. At steady state, the operating point

oscillates around the MPP giving rise to the wastage of some

amount of available energy. These oscillations can be mini

mized by reducing the fixed step size, but it takes relatively

more time to reach MPP. The solution to this conflicting

situation is to have a variable step size as suggested in [8] - [9].

Although, the implementation of these methods are simple,

but it is not very accurate and rapid, since the effects of

temperature and irradiation are not taken into consideration.

Several methods are proposed to address these issues by

considering adaptive perturbation [10] - [11].

Another method is fractional open circuit voltage (or frac

tional short circuit current) based algorithm, the MPP voltage

V MP P (or MPP current IMP p) with respect to the open

circuit voltage Vac (or short circuit current Iso) [12] -

[14] is monitored. Since this method approximates a constant

ratio, its accuracy cannot be guaranteed under varying weather

conditions.

To overcome the above mentioned drawbacks, several meth

ods have been proposed using artificial intelligence (AI) based

algorithms such as neural network (NN) [15] and fuzzy logic

controller (FLC) [16]. But these methods also have drawbacks

like, they require large data storage and extensive compu

tation. For instance, NN requires large amount of data for

978-1-4673-4922-2113/$31.00 ©20 13 IEEE 406


training which is the major constraint. Similarly, FLC requires

extensive computation to deal with a) fuzzification b) rule

base storage c) inference mechanism and d) defuzzification

operations. Furthermore, low cost hardware processors cannot

be used for these applications because the MPP continuously

changes with atmospheric conditions in real time.

In this paper, an adaptive P&O algorithm is designed in

order to overcome the drawbacks in the conventional P&O

method. In this proposed method, current perturbation is

considered to speed up the tracking performance. The current

perturbation can be realized by using sliding mode controller

(SMC) as discussed in [17]. In order to handle the rapidly

varying atmospheric conditions an adaptive control algorithm

is used.

This paper is organized as follows. In Section IT, modeling

of PV module and array is discussed. The proposed adaptive

P&O MPPT algorithm is designed and analyzed in Section Ill.

Simulation study is reported in Section IV. Finally, conclusions

are summarized in Section V.

11. MODELLING OF PV MODULE

The photovoltaic (PV) cell is basically a p-n junction

fabricated in a thin wafer of semiconductor. The solar energy

is directly converted to electricity through photovoltaic effect.

PV cell exhibits a nonlinear P-V and I-V characteristics

which vary with cell temperature (T) and solar irradiance

(S). Different equivalent circuit models of PV cell has been

discussed in literature [18]-[20].

A. PV Cell Model

The equivalent circuit of the general model of PV cell,

which consists of a photo current source, a parallel resistor

representing a leakage current, a diode, and a series resistor

describing an internal resistance to the current flow. This is

shown in Fig. 1.

Rs Ipv �

s 1 � ! Irs Rsh Vpv Iph I

Fig. 1. Equivalent circuit of PV cell

The nonlinear voltage-current characteristic equation of a

PV cell is given as [21], ( q(Vpv+Jc�vRs) ) Vpv + IpvRs I h - I e Akl - 1 - (1) p rs

Rsh where, Ipv and Vpv are terminal current and voltage of

PV cell respectively, Irs is diode reverse saturation cur

rent, q ( = 1.609 x 1O-19C) is an electron charge, k ( = 1.38 X 10-23 J / K) is a Boltzmann's constant and other pa

rameters can be obtained from specifications given in [22].

B. PV Module Model

s

� Ns

Fig. 2. Equivalent circuit of PV module

NSR N S p

1 r

PV cells are connected in series-parallel configuration in

order to produce enough power, because the power production

from a typical PV cell is less than 2 W. The equivalent circuit

of PV module is shown in Fig. 2. The nonlinear voltage-current

characteristic equation of PV module is given as [21],

(2)

Rsh where, N p is number of cells connected in parallel and N s is number of cells connected in series as shown in Fig. 2.

C. Solution for Nonlinear Equation

The model is processed in a MATLAB script file and the

output voltage or current is evaluated in terms of the irradiance

and cell temperature. Newton Raphson (NR) method is used

to solve the nonlinear equations as given [23],

f(xn) Xn+l = Xn -

f'(xn) (3)

where, Xn is the value of x at nth instant, l' is the derivative

of f w.r.t. x and f can be derived from (2) as given,

f(x) = Ipv - Nplph + NpVpv/�s + IpvRs

sh ( q(Vpv/NS+fpvRs/Np) ) +Nplrs e AkT - 1

(4)

where, x can be either Ipv or Vpv' In this paper, Vpv is

considered as x.

D. Characteristics of PV Array

The PV module considered for simulation is Solarex

MSX60 [23]. A PV array is formed by connecting 6 modules

in series and 6 modules in parallel. The specifications of PV

array at Standard Temperature Condition (STC) is given in

Table I.

The impact of temperature and irradiance on the I-V and

P-V characteristics are shown in Fig. 3. As the temperature

is increased, the output voltage decreases drastically whereas

the output current increases marginally; henceforth there is a

net reduction in output power. As the irradiance is increased,

the output current increases significantly, as a result of which

407


TABLE 1 SPECIFICATIONS OF PV ARRAY AT STC (S = 1000 W/m2, T = 25°C)

Parameters Symbol Value Maximum Power PMPP 2160 W Voltage at Maximum Power VMPP 102.6 V Current at Maximum Power IMPP 21 A Open Circuit Voltage Vac 126.6 V Short Circuit Current Isc 22.8 A

-- Power -- Current S=500W/m2

10 1000 � $ ... ;: Q) Q) � � 5 0 500 �

80 Voltage (V) 120 (a)

-- Power -- Current T= lODe

1500 � 1000 i> � 500

o 50 100 Voltage (V) 150 (b)

Fig. 3. Simulated I-V and P-V characteristics of PV array, (a) at constant solar irradiance 500 W / m 2 (b) at constant temperature 10° C

the output power also increases. Therefore, from Fig. 3 it can

be concluded that the PV array output current is significantly

affected by irradiation level, whereas the PV array output

voltage is affected by temperature.

Ill. MPPT ALGORITHM

The objective of MPPT algorithm is to automatically track

the current (IMP p ) and voltage (V MP p ) of PV array at which

maximum output power (PMPP) is obtained under a specific

irradiance and temperature. In this paper, an adaptive P&O

MPPT algorithm is proposed using conventional P&O and

fractional short circuit current (FSCC) methods. These two

methods are explained briefly.

A. Perturb and Observe Method

Conventional P&O method involves a perturbation in the

operating voltage b. V of the PV array as shown in Table 11. The performance of P&O method is heavily dependent on the

trade off between the tracking speed and the oscillations that

occurs around the MPP.

B. Fractional Short-Circuit Current (FSCC) Method

The output current of PV array is almost constant in the

voltage region of 0 to V MP P under varying atmospheric

Perturbation Positive Positive Negative Negative

TABLE II SUMMARY OF P & 0 METHOD

Change in Power Next Perturbation Positive Positive Negative Negative Positive Negative Negative Positive

conditions as shown in Fig. 3, and IMPP is approximately

linearly related to the short circuit current Ise as given in [2]

(5)

where, ksc is a proportionality constant, and it lies in between

0.78 and 0.92.

The short circuit current Ise is measured by shorting the PV

array periodically. It requires an additional switch. Measuring

Ise during operation is complicated. During this period the

output power is reduced. Since (5) is an approximation, the

PV array will never perfectly match the MPP.

C. Adaptive P&O MPPT Algorithm

i-Adaptive control-I L ___ al.1l�!!'� ___ :

Fig. 4. Flowchart of the proposed MPPT algorithm

This algorithm incorporates two major modifications to

conventional P&O method:

1) Considering current perturbation instead of voltage per

turbation to speed up the tracking performance as ex

plained using flowchart Fig. 4.

2) Moving the operating point to left hand side of MPP to

handle the sudden changes in weather conditions.

In Fig. 4, Ipv( k), Vpv( k) and Ppv( k) are output current,

voltage and power of PV array at kth iteration respectively

and b.I is current perturbation size. The generalized equation

is derived for the proposed MPPT algorithm as given,

Ipv( k + 2) = Ipv( k + 1) + sign(Ipv( k + 1) - Ipv( k)) (6)

*sign(Ppv( k + 1) - Ppv( k)) * b.I.

408


where, the function sign(.) gives either + 1 or -1 depending

on positive or negative value inside the function respectively.

The idea behind considering current perturbation is ex

plained as follows. At a given temperature and irradiance, the

output current of PV array in the voltage region of 0 to V MP P i.e. left hand side (LHS) of MPP is almost constant as shown

in Fig. 3. On the other hand the current is drastically changing

in the right hand side (RHS). Therefore, when the operating

point lies in the left hand side of MPP, then even for relatively

smaller perturbation in current compared to voltage makes the

PV system to reach MPP faster with reduced oscillations. If

the operating point lies in the right hand side of MPP and

operating current Ipv( k) is less than [IMPP - (Ise -IMPP )], then current perturbation gives slower response. To avoid this

situation an adaptive control algorithm is proposed.

Adaptive control algorithm always tries to keep the operat

ing point in the left hand side of MPP. Once MPP is reached

then VMPP & IMPP oscillates around MPP depending on

perturbation size. Let assume that, initially PV array is op

erating at point A as shown in Fig. 3(a) and Fig. 3(b). The

operating point A corresponds to MPP indicated by E. If there

is a sudden increase in temperature or irradiance, then the

operating point changes from A to B i.e., left hand side (LHS)

of new MPP denoted by F as shown in Fig. 3(a) and Fig.

3(b) respectively. The MPPT algorithm directs the PV array to

change the operating point from B to C. The operating point C

corresponds to new MPP as indicated by F. If there is a sudden

decrease in temperature or irradiance, then the operating point

changes from C to D i.e., right hand side (RHS) of new MPP

denoted by E as shown in Fig. 3(a) and Fig. 3(b) respectively.

Therefore, whenever there is a change in temperature or

irradiance then the operating point (Vpv, Ipv) is to be shifted

to (O,Ise) i.e., left hand side of MPP. This can be done

by obtaining the short circuit current. Different methods are

available for obtaining the short circuit current as mentioned

in fractional short-circuit current method. The short circuit

current Ise is measured by shorting the PV array periodically.

Once the short circuit current Ise is measured, then the new

operating point on LHS of MPP is calculated as explained in

fractional short circuit method [2]. In (5), ksc varies between

0.78 and 0.92 depending on weather conditions. In order

to avoid the operating point on RHS of MPP, consider the

maximum value of ksc. Therefore, the new operating point is

given as

Ipv( k) = 0.92 Ise . (7)

IV. SIMULAT ION ST UDIES

The proposed MPPT algorithm is verified for sudden

changes in weather conditions through digital simulations

using MATLAB. In the following section, MPPT algorithm

has been studied for different conditions of irradiation and

temperatures. The specifications of PV array are given in Table

I. The perturbation considered for current and voltage are

6.1 = 0.06A and 6.V = 1.25 V.

A. Sudden increase in irradiance

In this simulation the PV array is simulated for sudden

increase in irradiance from 400 Wjm2 to 800 Wjm2 at 50th

- Proposed MPPT - - PV Curve - Conventional MPPT

2000 .------�-----r-:=_.......__--_, � � 1000 g:.

0 0 50 Ca) 100 Voltage CV) 150

;�F:;.�.6�.... .• . •.. HHS.H:::�.= ... .

.. . . . ......

.

. . o · · · · · · · · · 8.4� · · · · · · · · · · · · · · · · : · · · · · · · · · · · · · · · · · . · · · · · . . . . . . . . . . . . . _IOL----'-----'------'---..I...----' o 20 40 (b) 60 Iterations 100

Cc) 60 Iterations 100 � :::: � . . . . . . . . . . . . . . �.. •. 197j� .. . .. . .

� 10009r�

.··

�i19J ..

.•. .

..

...

.. . . . . . . . .

� _T 93� . _ o o 20 40 Cd) 60 Iterations 100

Fig. 5. Simulation results of sudden increase in irradiance from 400 W / m 2 to 800 W/m2

iteration, assuming constant temperature of 100G. Fig. 5(a)

shows tracking of MPP for conventional and proposed MPPT.

Figs. 5(b )-( d) shows the simulation results of current, voltage

and powers of PV array against number of iterations to reach

the MPP. For conventional MPPT the operating point starts

from point A and reaches the maximum power point B as

shown in Fig. 5(a), at 50th iteration the operating point follows

the path B-C-D-C-E to reach the new maximum power point

E as irradiance is increased. For proposed MPPT the operating

point starts from F and reaches the maximum power point B, at

50th iteration the operating point follows the path B-E to reach

the new maximum power point E as irradiance is increased.

Figs. 5(b )-( d) shows that, the number of iterations to reach

MPP is less for proposed MPPT. The graphs are zoomed in

the intervals of (20 - 40) and (70 - 90) iterations to observe

the oscillations and perturbations. It is also observed that, for

proposed MPPT the oscillations around MPP are minimized

when compared to conventional MPPT.

B. Sudden decrease in irradiance


decrease in irradiance from 800 Wjm2 to 400 Wjm2 at 50th

iteration, assuming constant temperature of 100G. Fig. 6(a)

shows tracking of MPP for conventional and proposed MPPT.

Figs. 6(b)-(d) shows the simulation results of current, voltage

and powers of PV array against number of iterations to reach

the MPP. Here B and D are maximum power points. Fig.

6 shows that proposed MPPT tracks the MPP faster and

minimizes the oscillations.

409



2000 ,---------�-----r-:;;;_,,3I....._--___, � � 1000 &

$

! u

0 0 50 (a) 100 Voltage (V) 150

10 �16.& · ..

. ..

. ..

. = .

. : ...... � .. .. ��J7'�� . . . . . . . . . ".".".

·.' 8 .65� . . . . •• . . . . . . . . ' . . .

-1� L..._ ... _ .. _ ... _ ... _ ........... _ ... _ ... _ ... _ .. _ ... _ . . .

.J... .•.. _ .. _ ... _ ... _ ... _ ... _:'-8_04_5 __ ·--....1..·. _. _._ ... _ .. ---, . .

o 20 40 (b) 60 Iterations 100

r:r L--_ .. ..

. .

_:::...I...-.�_ . . . • . _' --L.-. �--• • _-_----'-:--._::�_=----'-. · ._ ...

.

_ ... .

----' ... . .

o 20 40 (c) 60 Iterations 100

i ::!k LL-_·····_:::---'-lmYb_:_ · _···· ·····...l._··_ ·····_·� ·----'i'--: _:iid_ ·· ·--,-· .•• _ .• ..

... _ .

. .

__' ..... o 20 40 (d) 60 Iterations 100

Fig. 6. Simulation results of sudden decrease in irradiance from 800 W/m2 to 400 W/m2

C. Sudden increase in temperature

� 1000

i> 500 � "'"


40 (a) 80 Voltage (V) 120

I :: �tt�%@------,------· · · �

. . . . . . . ----,----:::iM!f1----'---------.. . . . . . . .

o 20 40 (b) 60 Iterations 100

r�L..-t- ·· ···- :--L-�-· · -··· · ·····----'.· .• _ ..... _< _ ••••• -'----!_:�_. ---,--.. ... _ .. _ ..... ----' .... o 20 40 (c) 60 Iterations 100

1500 �

i 1� LL-�L_ •••• · _��:---'-.ymr_·.····· _···.·····_·· •• ·...l._ · ••••• _ ••••• ·_ ••• ----'i'--:4_�_· .. --'-. _ .... _ ..••• ----' ••••• o 20 40 (d) 60 Iterations 100

increase in temperature from 40°C to 80°C at 50th iteration,

assuming constant irradiance of 500 W/m2. Fig. 7(a) shows

tracking of MPP for conventional and proposed MPPT. Figs.

7(b)-(d) shows the simulation results of current, voltage and

powers of PV array against number of iterations to reach

the MPP. For conventional MPPT the operating point starts

from point A and reaches the maximum power point B as

shown in Fig. 7(a), at 50th iteration the operating point

follows the path B-C-D to reach the new MPP as temperature

is increased. For proposed MPPT the operating point starts

from E and reaches the maximum power point B, at 50th

iteration the operating point follows the path B-D to reach the

new maximum power point D as temperature is increased.

It is observed that proposed MPPT tracks MPP faster and

minimizes the oscillations.

D. Sudden decrease in temperature


decrease in temperature from 80°C to 40°C at 50th iteration,

assuming constant irradiance of 500 W/m2. Fig. 8(a) shows

tracking of MPP for conventional and proposed MPPT. Figs.

8(b)-(d) shows the simulation results of current, voltage and

powers of PV array against number of iterations to reach

the MPP. Here B and C are maximum power points. Fig. 8

shows that proposed MPPT algorithm tracks the maximum

power point faster than conventional P&O MPPT algorithm,

and minimizes the oscillations.

� 1000

1 500


40 (a) 80 Voltage (V) 120

:: tt:� ···· l:� o . o 20 40 60 iteratIOns 100 (b)

r�L-E_-:�....L.. _·_'-----·....L..: _�_�_'___----' o 20 40 ( C) 60 Iterations 100

f� LL-ff_·····_ ::---'-rvvE_ ·· ._ .• . _ .. ...l._i _····· ·_·· .. ----' ...• _:�_�_ ..... --'-•.•.• _ ... .. _ . . . . . __' •..•. o 20 40 (d) 60 Iterations 100

Fig. 7. Simulation results of sudden increase in temperature from 400e to Fig. 8. Simulation results of sudden decrease in temperature from 800e to

�e �e

In this simulation the PV array is simulated for sudden From the above simulations, it is clear that the proposed

410


MPPT algorithm effectively reduces the sustained oscillations

and tracks the MPP faster, irrespective of increase or decrease

of irradiance and temperatures.

V. ACKNOWLEDGEMENT

This work is supported by the Ministry of Science and Tech

nology, DST, India (Project No.: DSTffM/SERII2klO/47). The

authors would like to thank the Ministry of Science and

Technology, DST, India.

VI. CONCLUSION

In this paper, an adaptive P&O MPPT algorithm is proposed

using conventional P&O and fractional short circuit current

(FSCC) methods. In order to speed up the tracking perfor

mance, current perturbation is used in the conventional P&O

instead of voltage perturbation. In order to consider the sudden

changes in irradiance and temperature, the operating point is

shifted to left hand side of the MPP in PV curve by measuring

the short circuit current. Results presented in the paper clearly

demonstrate that, the proposed algorithm has faster dynamics

and improved stability compared to the conventional P&O

algorithm. The steady state and transient states of the proposed

MPPT algorithm is successfully demonstrated using digital

simulations.

REFERENCES

[1] S. L. Roberto Faranda, "Energy Comparison of MPPT Techniques for PV Systems;' WSEAS Trans. On Power Systems, vol. 3, no. 6, Jun. 2008.

[2] T. Esram and P. L. Chapman, "Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques," IEEE Trans. on Energy

Convers., vol. 22, no. 2, pp. 439-449, Jun. 2007. [3] G. M. S. Azevedo, M. C. Cavalcanti, K. C. Oliveira, F. A. S. Neves,

and Z. D. Lins, "Comparative Evaluation of Maximum Power Point Tracking Methods for Photovoltaic Systems," Journal of Solar Energy Engineering, vol. 131, no. 3, p. 31006, Aug. 2009.

[4] J. J. Nedumgatt, K. B. Jayakrishnan, S. Umashankar, D. Vijayakumar, and D. P. Kothari, "Perturb and Observe MPPT Algorithm for Solar PV Systems-modeling and Simulation," in Annual IEEE India Conference

(INDICaN), Dec. 2011, pp. 1-6. [5] L. Chun-xia and L. Li-qun, "An Improved Perturbation and Observation

MPPT Method of Photovoltaic Generate System," in 4th IEEE Con·

ference on Industrial Electronics and Applications, ICIEA 2009., May. 2009, pp. 2966-2970.

[6] W. Xiao and W. Dunford, "A Modified Adaptive Hill Climbing MPPT Method for Photovoltaic Power Systems," in IEEE 35th Annual Power Electronics Specialists Conference, PESC 04., vol. 3. IEEE, Jun. 2004, pp. 1957-1963.

[7] F. Liu, Y Kang, Y Zhang, and S. Duan, "Comparison of P&O and Hill Climbing MPPT Methods for Grid-connected PV Converter," in 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA

2008. IEEE, Jun. 2008, pp. 804-807. [8] c. W. Tan, T. C. Green, and C. A. Hernandez-Aramburo, "An Improved

Maximum Power Point Tracking Algorithm with Current-Mode Control for Photovoltaic Applications," in International Conference on Power Electronics and Drives Systems, PEDS 2005., vol. I, 2005, pp. 489-494.

[9] c. W. Tan, T. C. Green, and Hernandez-Aramburo, "Analysis of Perturb and Observe Maximum Power Point Tracking Algorithm for Photovoltaic Applications," in IEEE 2nd International Power and Energy Conference, PECon 2008., Dec. 2008, pp. 237-242.

[l0] L. Piegari and R. Rizzo, "Adaptive Perturb and Observe Algorithm for Photovoltaic Maximum Power Point Tracking," lET Renewable Power Generation, vol. 4, no. 4, pp. 317-328, Jul. 2010.

[11] N. Fermia, D. Granozio, G. Petrone, and M. Vitelli, "Predictive & adaptive mppt perturb and observe method," IEEE Trans. on Aerosp. Electron. Syst., vol. 43, no. 3, pp. 934-950, Jul. 2007.

411

[12] M. A. S. Masoum, H. Dehbonei, and E. F. Fuchs, "Theoretical and Experimental Analyses of Photovoltaic Systems with Voltage and Current-based Maximum Power-Point Tracking," IEEE Trans. on Energy

Convers., vol. 17, no. 4, pp. 514-522, Dec. 2002. [l3] S. Yuvarajan and S. Xu, "Photo-voltaic Power Converter with a Simple

Maximum-Power-Point-Tracker," in P roceedings of the 2003 Interna· tional Symposium on Circuits and Systems, ISCAS'03., vol. 3. IEEE, May. 2003, pp. III-399 - III-402.

[14] D. P. Quoc, Q. N. Nhat, N. T. D. Vu, A. N. Bao, H. H. Lee et al., "The New Combined Maximum Power Point Tracking Algorithm using Fractional Estimation in Photovoltaic Systems," in IEEE Ninth Inter· national Conference on Power Electronics and Drive Systems (PEDS), 20ll. IEEE, Dec. 2011, pp. 919-923.

[15] A. K. Rai, N. D. Kaushika, B. Singh, and N. Agarwal, "Simulation Model of ANN based Maximum Power Point Tracking Controller for Solar PV System," Solar Energy Materials and Solar Cells, vol. 95, no. 2, pp. 773-778, Feb. 2011.

[16] B. Alajmi, K. Ahmed, S. Finney, and B. Williams, "Fuzzy-Logic-ContTOI Approach of a Modified Hill-Climbing Method for Maximum Power Point in Microgrid Standalone Photovoltaic System," IEEE Trans. on Power Electron., vol. 26, no. 4, pp. 1022-1030,2011.

[17] K. Sathish Kumar and Mahesh K. Mishra, "Analysis and Control of DCDC Boost Converter using Average Power Balance Control (APBC)," in 4th International Conference on Intelligent and Advanced Systems

(ICIAS), 2012, vol. 2, Jun. 2012, pp. 513-518. [l8] J. Gow and C. Manning, "Development of a Photovoltaic Array Model

for use in Power-electronics Simulation Studies," lEE Proceedings . Electric Power Applications, vol. 146, no. 2, pp. 193 -200, Mar. 1999.

[l9] G. Farivar and B. Asaei, "A New Approach for Solar Module Temperature Estimation Using the Simple Diode Model," IEEE Trans. on Energy Convers., vol. 26, no. 4, pp. 1118 -1126, Dec. 2011.

[20] S. A. Mahmoud, M. M. Alsari, E. I. Reda, and R. M. Alhammadi, "MATLAB Modeling and Simulation of Photovoltaic Modules," in IEEE 55th International Midwest Symposium on Circuits and Systems

(MWSCAS), Aug. 2012, pp. 786-789. [21] H.-L. Tsai, c.-S. Tu, and Y-J. Su, "Development of Generalized

Photovoltaic Model using MATLAB/SIMULINK," in P roceedings of the

World Congress on Engineering and Computer Science 2008, WCECS

'08, October 22 - 24, 2008, San Francisco, USA. [22] F. M. Gonzalez-Longmatt, "Model of Photovoltaic Module in MAT

LAB," in 2Do Congreso Iberoamericano De Estudiantes De Ingenieria Electrica, Electronica Y Computacion (11 CIBELEC 2005).

[23] G. Walker, "Evaluating MPPT Converter Topologies using a MATLAB PV Model," Journal of Electrical & Electronics Engineering, vol. 21, no. I, pp. 49-56, 2001.

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