International Journal of Advances in Power Systems (IJAPS) Vol. 1, No. 3, December 2013

ISSN: 2335-1772

1

Abstract—In this paper the grid disturbances effects on a grid

connected PV array were studied while considering different

maximum power point tracking algorithms. The maximum

power point tracking techniques included in this study are; the

perturb & observe technique (P&O), incremental conductance

technique (ICT), fuzzy logic based technique. The grid

disturbances involved in this paper are the different types of

faults, voltage sag, and voltage swell. A comparative study of the

grid disturbances effect on the three maximum power point

tracking algorithms is obtained. A 100 kW photovoltaic array

connected to the grid via a voltage source inverter through a

boost converter is modeled and simulated under the

MATLAB/SIMULINK in order to accomplish this study. The

simulation results show that the fuzzy logic based technique gives

the best response under steady state and transient conditions.

Index Terms — Maximum Power Point Tracking; Fuzzy Logic

controller; Photovoltaic System; Transient Analysis

I. INTRODUCTION

ver the past few years, the demand for renewable energy

resources has increased significantly due to the fact that

the fossil fuels will run out in the near future and the

harmful environmental effects of the fossil fuels. Among

various types of renewable energy resources, solar energy has

become one of the most promising and attractive resource.

Nodaway solar energy based photovoltaic is widely used in

many applications as it owns the advantage of being

maintenance and pollution free. Recently, the use of

photovoltaic panel has grown consistently due to the following

factors; the PV efficiency is enhanced, the manufacturing

technology is improved and the PV panel's cost is decreased.

Recently, a large number of PV modules are connected to

utility grid in many countries.

The output power of PV arrays is mainly influenced by the

irradiance (amount of solar radiation) and temperature.

Moreover for a certain irradiance and temperature, the output

power of the PV array is function of its terminal voltage and

there is only one value for the PV's terminal voltage at which

the PV panel is utilized efficiently. The procedure of searching

for this voltage called maximum power point tracking MPPT.

Recently, several algorithms have been developed to achieve

MPPT technique, such as; Perturb and Observe (P&O) [1],

incremental conductance [2], open circuit voltage, short circuit

current, fuzzy or neural network based algorithms, etc [3, 4].

The PV array generates DC power so power electronics

converters are essential. Actually power electronics converters

are required to converter the generated DC power to AC and to

achieve the MPPT. The MPPT can be accomplished either in

single stage or in a double stage. In single stage, the PV array is

connected to the grid through an AC/DC converter and the

converter is utilized to obtain both the MPPT and the

conversion of the generated voltage to DC. In the double stage,

the PV array is connected to the grid though an AC/DC

converter via a DC/DC converter. In this case, the MPPT is

obtained via the DC/DC converter by controlling its input

voltage. The function of the inverter is to convert the output DC

voltage of the PV into AC and to maintain the output voltage of

the DC/DC converter constant.

In this paper, maximum power point tracking for a grid

connected PV array is executed and evaluated for three

different MPPT algorithms. The evaluated methods are; (i)

Perturb & Observe (P&O) (ii) Incremental conductance

Technique (ICT) and (iii) Fuzzy logic based (FLC) [3-5].

Furthermore the effects of different grid disturbances on the PV

array and the MPPT algorithms are studied. The considered

disturbances in this study are; the different type of faults,

Transient Analysis of Grid-Connected

Photovoltaic System Based on Comparative Study

of Maximum Power Point Tracking Techniques

Almoataz Y. Abdelaziz1, Hadi M. El-Helw

2, Basem Abdelhamed

3

1Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt

e-mail: almoatazbdelaziz@hotmail.com

2Electrical and Control Department, Arab Academy for Science, Technology and Maritime Transport, Cairo,

Egypt

e-mail: elhelw@cairo.aast.edu

3Department of Electrical Power, El-Shorouk Academy, Cairo, Egypt

e-mail: eng_basemabdelhamed@yahoo.com

O

International Journal of Advances in Power Systems (IJAPS) Vol. 1, No. 3, December 2013

ISSN: 2335-1772

2

voltage sag, and voltage swell. In order to accomplish this study

the system shown in Fig. 1 is modeled and simulated using the

MATLAB/SIMULINK.

Fig.1 Block diagram of the grid connected photovoltaic system discussed in

this paper.

II. THE PV MODEL

A. Mathematical model

A PV model based current source is illustreated in this

section [6]. Fig. 2 shows the PV circuit diagram.

V

I

Fig. 2 Equivalent Circuit of photovoltaic cells.

Where Np is the number of parallel modules , Ns is the

number of series modules, Rp is the array parallel resistance,

and Rs is the array series resistance. The module current Im can

be calculated from:

1expst

p

ss

popPVmaNV

IN

NRV

NINII (1)

Ipv can be expressed by:

n

ipvnpvG

GTKII (2)

Where Ipvn is the generated output current at 1000 W/m2

and

25oC as nominal condition, ∆T is the difference between the

real and the nominal temperatures in Kelvins, Ki is the current

temperature coefficient, G is the irradiance and Gn is the

irradiance at nominal conditions. While Io can be given by:

1exp

t

vocn

iscno

aV

TKV

TKII

(3)

Kv is the volatge temperature coefficient, Iscn , Vocn are the

short circuit current and open circuit voltage at nominal

condition respectively,a is the diode ideality constant and Vt is

the thermal voltage of the array and can be calculated from:

q

kTNV cs

t (4)

Where Ncs is the number of cells connected in series,q is

electron charge, K is Boltzmann constant and, T is the

temperature of the P-N junction in Kelvin`s

B. Model Verification

In order to accomplish this paper, the mathematical model

discribed in the previous sectionis is modeled under the

MATLAB/Simulink.The developed Model is vrefied utilizing

the parameters of a real PV module (Kyocera- KD 200GT)

manufactured by Kyocera (see table I)[7]. Fig. 3 shows the I-

V and P-V characteristics which developed by the MATLAB

model at different irradiance and constant temperature (25oC).

Fig. 4 shows I-V and P-V characteristics which developed by

the MATLAB model at constant irradiance (1000 W/m2) and

variable temperature. The results shown in Fig. 3 and Fig. 4

are similar to that shown in the PV module datasheet [6].

TABLE I

PRAMETERS OF PV MODULE

Parameter Value

Open circuit voltage (VOC) of a PV module 32.9.0 V

Short circuit current (ISC) of a PV module 8.21 A

Module voltage at maximum power point (Vm) 26.3 V

Module current at maximum power point (Im) 7.61 A

Maximum Power (Pm) of a PV module 200 W

Reference temperature 25º C

Reference solar radiation (1 sun) 1000W/m2

Fig. 3 I-V and P-V characteristics of the PV module at constant temperature

25°C and various irradiances

0 5 10 15 20 25 30 350

2

4

6

8

curr

ent

(A)

Voltage (V)

PV module : Kyocera KC200GT at constant temperature (25°C)

0 5 10 15 20 25 30 350

50

100

150

200

Pow

er

(W)

Voltage (V)

800W/m2

600W/m2

400W/m2

200W/m2

1000W/m2

1000 W/m2

800 W/m2

600 W/m2

400 W/m2

200 W/m2

International Journal of Advances in Power Systems (IJAPS) Vol. 1, No. 3, December 2013

ISSN: 2335-1772

3

Fig. 4 I-V and P-V characteristics of the PV module under constant

irradiance and different temperature.

III. MPPT ALGORITHMS

As mentioned above the MPPT can be achieved eithier in

single stage or in double stages. In this paper the double stages

scheme is utilized. With the help of the DC/DC converter the

maximum power can be excuted by controlling the duty cycle

of the DC/DC converter in order to control the PV array

terminal voltage. The tracking of the optimam termanl voltage

can be performed by various algorithms.In this section the

MPPTalgorithms[1-5], which are used for the comparative

study in this paper, will be illustrated.

A. Perturb and Observe (P&O)Algorithm.

In this algorithm, a small perturbation is introduced in the

duty cycle of the power electronics converter and then the

output power of the PV module is observed. If the power

increases due to this perturbation, then the perturbation is

carried on in the same direction. On the other hand, if the PV

output power is decreased then the direction of the

perturbation has to be reversed. The P&O technique is the

simplest MPPT algorithm; however it owns the disadvantage

of oscillation around the final maximum power point (MPP)

[8]. The MATLAB/SIMULINK model of Perturb and

Observe (P&O) Algorithm is shown in Fig. 5.

Fig. 5 MATLAB / SIMULINK model of P&O algorithm

B. Incremental Conductance (ICT) Algorithm

The ICTalgotithm is built on the principle that the

derivative of the PV array power curve is zero at the

maximum power point(i.e. the slope of the power curve is

zero)[4] . The slope of the power curve is positive on the left of

the MPP and negative on the right.In this algorithm the duty

cycle of the power electronics converter is changed and then

the derivative of the array output power (slope) is caculated.

Accordinding to the slop of the power curve the duty cycle of

the converter is adjusted [2]. The MATLAB / SIMULINK

model of the Incremental Conductance Algorithm (ICT) is

shown in Fig. 6.

Fig.6 MATLAB / SIMULINK model of ICT algorithm

It is very rare for the ICT to reach exactly to the maximum

power point MPP. Therefore, in this technique the MPP is

considered reached when the operating voltage is within a

certain error limit [2].

C. Proposed Fuzzy Logic Control (FLC) Algorithm

Maximum power point tracking based Fuzzy logic has the

advantage of being robust and fast in response. In this paper,

the input variables of the proposed fuzzy controller are ΔP(k)

and ΔV(k), where P(k) is PV array output power and V(k) is PV

array output voltage [5]. These variables are expressed in

terms of seven linguistic fuzzy sets; Negative Big (NB),

Negative Medium (NM), Negative Small (NS), Zero (ZO),

Positive Big (PB), Positive Medium (PM) and Positive Small

(PS) using basic fuzzy subset. The MATLAB / SIMULINK

model of the proposed fuzzy logic controller (FLC) is shown

in Fig.7.

Fig. 7 MATLAB / SIMULINK model of the proposed fuzzy based algorithm

The proposed fuzzy logic controller comprises three

function blocks; fuzzification, Fuzzy rule base, and

defuzzification. An error function (E) and a change of error

(CH_E) are created during fuzzification. In the fuzzy rule

base stage, these variables are then compared to a set of pre-

0 5 10 15 20 25 30 350

5

10curr

ent

(A)

Voltage (V)

PV module : Kyocera KC200GT at 1 kW/m2

0 5 10 15 20 25 30 350

100

200

Pow

er

(W)

Voltage (V)

50°C25°C

75°C

75°C

25°C

50°C

International Journal of Advances in Power Systems (IJAPS) Vol. 1, No. 3, December 2013

ISSN: 2335-1772

4

designed value in order to determine the appropriate

response. In the defuzzification block, the aggregated fuzzy

set is employed to create the simple crisp value of output duty

cycle D. The seven rules which used for tracking the MPP in

the proposed technique are shown in Table II.

TABLE II.

FUZZY RULES

E↓ /CE→ NB NM NS ZE PS PM PB

NB ZE ZE ZE NB NB NB NB

NM ZE ZE ZE NM NM NM NM

NS NS ZE ZE NS NS NS NS

ZE NM NS ZE ZE ZE PS PM

PS PM PS PS PS ZE ZE PS

PM PM PM PM PM ZE ZE ZE

PB PB PB PB PB ZE ZE ZE

The output of FLC is utilized to control the DC-to-DC

converter. The membership functions of the input and output

variables are shown in Fig. 8, Fig. 9 and Fig. 10 respectively.

Fig. 8 Membership function for input variable (E)

Fig. 9 Membership function for input variable (CH_E)

Fig. 10 Membership function for output variable (D)

The proposed fuzzy logic controller utilizes duty cycle with

variable steps for controlling the boost converter and

therefore provides quicker convergence to the maximum

power point [9].

IV. SYSTEM DISCRIBTION

In this paper a 100 kW PV array is utilized. The 100 kW

PV array is modeled using 63 parallel connected strings with

each string having 8 series connected PV modules (Kyocera-

KD 200GT). The output of the array is connected to the grid

via a voltage source inverter through a boost converter. The

function of the boost converter is to control the terminal

voltage of the PV array in order to accomplish the MPPT.

ADC link capacitor is placed after the boost converter and

acts as a temporary power storage device to provide the

voltage source inverter with a steady flow of power. The

capacitor's voltage is regulated using a DC link controller

that balances input and output powers of the capacitor. The

parameter of the system under study is shown in Table III.

TABLE III

PRAMETERS OF THE SYSTEM UNDER STUDY

Quantity Value

Gridvoltage 260V

Frequency 60 Hz

Switching frequency 5kHz

DC link capacitor C 100µF

DC link voltage 500V

Boost converter inductance 5mH

Boost converter capacitor 1.2mF

Grid voltage 20kV

Inverter voltage 260V

Transformer 260V /20kV

load at bus1 300 + j200 k VA

load at bus 2 200kW

The voltage source converter (VSC) is controlled utilizing

vector control in order to provide a controllable three phase

AC current to the grid. To attain unity power factor

operation, current is injected in phase with the grid voltage.

A phase locked loop (PLL) is utilized in order t o lock on the

grid frequency and provide a reference synchronization signal

for the inverter control system [10]. The

MATLAB/SIMULINK model of the VSC is shown in Fig.

11.

Fig. 11 MATLAB/SIMULINK model of the VSC

V. SIMULATION RESULTS AND DISCUSSION

In this section, the system shown in Fig. 1 is simulated

using the MATLAB/SIMULINK under condition of applying

three maximum power point tracking techniques. The

simulation is run several times in order to study the effect of

different disturbances on the three MPPT algorithms

mentioned above. The MATLAB/SIMULINK model is

shown in Fig. 12.

International Journal of Advances in Power Systems (IJAPS) Vol. 1, No. 3, December 2013

ISSN: 2335-1772

5

Fig. 12 The MATLAB/SIMULINK model of the grid connected PV system

A. STEADY STATE ANALYSIS

First the simulation is accomplished while the PV array

operates at nominal conditions (1000W/m2&25°C). The

simulation is run three times; first, the Perturb and Observe

algorithm is applied, second the incremental conductance

algorithm is applied, and finally the fuzzy logic algorithm is

applied. The output voltage, current, and power of the PV

array while applying the three different algorithms P&O, ICT

and FLC are shown in Fig.13, Fig. 14 and Fig. 15

respectively. It can be observed that PV array feeds100kW to

the grid while utilizing the three algorithms but the

proposed FLC gives a faster response when compared with

the others.

Fig.13 The output power, voltage and current of the PV array

With MPPT Based P&O

Fig. 14 The output power, voltage and current of the PV array

With MPPT Based ICT

Fig. 15 The output power, voltage and current of the PV array with

MPPT Based FLC.

B. FAULT ANALYSIS

In this section the MATLAB/SIMULINK model shown in

Fig.12 is simulated under different fault conditions. The

simulation is accomplished under nominal condition (G =

1000 W/m2 and T=25

0C). As shown in Fig. 12 the fault is

applied on the grid side. The fault duration is 0.1s from 0.2

to 0.3 s. In this section all types of faults will be discussed

under the same condition.

Line-to-ground fault

The model shown in Fig. 12 is simulated while applying

single line to ground fault (1L-G) on phase A. the fault

location is illustrated in Fig. 12 and the fault duration is 100

ms. The output voltage and current at the point of common

coupling PCC are shown in Fig. 16.

Fig. 16 The output voltage and current at the PCC with single line to ground

fault

The simulation was run three times under condition of

applying a single line to ground fault while utilizing the three

MPPT algorithms discussed in section III. Fig. 17 shows the

output power at the array terminal for the three cases.

0 0.5 1 1.5 20

200

400

Voltage (

V)

Time (S)

PV Array : Kyocera KC200GT of 8 series modules; 63 parallel strings

0 0.5 1 1.5 20

500

1000

Curr

ent

(A)

Time (S)

0 0.5 1 1.5 20

100

200

Pow

er

(kW

)

Time (S)

0 0.5 1 1.5 20

200

400

Voltage (

V)

Time (S)

PV Array : Kyocera KC200GT of 8 series modules; 63 parallel strings

0 0.5 1 1.5 20

500

1000

Curr

ent

(A)

Time (S)

0 0.5 1 1.5 20

100

200

Pow

er

(kW

)

Time (S)

0 0.5 1 1.5 20

200

400

Voltage (

V)

Time (S)

PV Array : Kyocera KC200GT of 8 series modules; 63 parallel strings

0 0.5 1 1.5 20

500

1000

Curr

ent

(A)

Time (S)

0 0.5 1 1.5 20

100

200

Pow

er

(kW

)

Time (S)

0 0.1 0.2 0.3 0.4 0.5-5

0

5x 10

4

Voltage (

V)

Time (S)

Grid Voltage at PCC With 1L-G

0 0.1 0.2 0.3 0.4 0.5-20

0

20

40

Curr

ent

(A)

Time (S)

Grid Current at PCC With 1L-G

International Journal of Advances in Power Systems (IJAPS) Vol. 1, No. 3, December 2013

ISSN: 2335-1772

6

Fig.17 The output power of the PV array using the three different

algorithms with 1LG fault

Line-to-line Fault

In this case the model shown in Fig. 12 is simulated with

applying a line to line fault (L-LF) between phases A and B.

The voltage and the current at the PCC are shown in Fig.18.

Fig.18 The output voltage and current at the PCC with L-L fault

In order to compare the performance of the three MPPT

algorithms mention above to this type of fault, the model is

run three times; each time one algorithm is implemented.

The output power of the PV array under the three cases is

shown in Fig. 19.

Fig.19 The output power of the PV array using the three different algorithms with L-L fault

Line-to-line-to-ground fault

A line-to-line-to ground (L-L-G) fault is applied to the

model shown in Fig. 12. The voltage and current waveforms

for this case at the point of common coupling are shown in

Fig. 20.

The output power at the array terminal of the three

different maximum power point tracking algorithms while

applying this type of fault is shown in Fig. 21.

Fig. 20 The output voltage and current at the PCC with L-L-G fault

Fig. 21 Output power of The PV array using the three different algorithms with

L-L-G fault

Three line to ground fault

A three line to ground (L-L-L-G) fault is applied to the

model shown in Figure 12. Fig. 22 shows the output voltage

and current at the PCC. Fig. 23 shows the output power of

the PV array while applying the three MPPT techniques for

this type of fault.It noteworthy that the proposed FLC

succeeds to sustain the stability of the MPPT during the fault

while the other two conventional techniques fail.

Fig.22 The output voltage and current at point of common coupling (PCC)

with L-L-L-G fault.

0 0.5 1 1.5 20

50

100

Pow

er

(kW

)

Time (S)

Output Power of the PV array using the three different algorithms at 1L-G

P&O

ICT

FLC

0 0.1 0.2 0.3 0.4 0.5-5

0

5x 10

4

Voltage (

V)

Time (S)

Grid Voltage at PCC With L-L

0 0.1 0.2 0.3 0.4 0.5-20

0

20

Curr

ent

(A)

Time (S)

Grid Current at PCC With L-L

0 0.5 1 1.5 20

50

100

Pow

er

(kW

)

Time (S)

Output Power of the PV array using the three different algorithms at L-L

P&O

ICT

FLC

0 0.1 0.2 0.3 0.4 0.5-5

0

5x 10

4

Voltage (

V)

Time (S)

Grid Voltage at PCC With L-L-G

0 0.1 0.2 0.3 0.4 0.5-40

-20

0

20

40

Curr

ent

(A)

Time (S)

Grid Current at PCC With L-L-G

0 0.5 1 1.5 20

50

100

Pow

er

(kW

)

Time (S)

Output Power of the PV array using the three different algorithms at L-L-G

P&O

ICT

FLC

0 0.1 0.2 0.3 0.4 0.5-5

0

5x 10

4

Voltage (

V)

Time (S)

Grid Voltage at PCC With L-L-L-G

0 0.1 0.2 0.3 0.4 0.5-40

-20

0

20

Curr

ent

(A)

Time (S)

Grid Current at PCC With L-L-L-G

International Journal of Advances in Power Systems (IJAPS) Vol. 1, No. 3, December 2013

ISSN: 2335-1772

7

Fig.23 The output power of the PV array using the three different algorithms

with L-L-L-G fault

C. VOLTAGE SAGS ANALYSIS

The decrease in the RMS value of the voltage or current

between 0.9 to 0.1 p.u. for duration of 0.5 cycle to 1 minute is

defined as voltage sag. Voltage sags are generally caused by

over loading or grid faults. The MATLAB/SIMULINK

model shown in Fig. 24 is utilized to conduct the analysis in

this section. The model shown in Fig. 24 is simulated under

condition of voltage sag at the point of common coupling for

a duration of 0.15 s.

Fig. 24 Grid Connected PV system under sag Analysis

In order to study the effect of voltage sag on the

performance of the three MPPT algorithms under study in this

paper, the voltage at the PCC is reduced from 20kV to 10kV.

The output voltage and current at the PCC is shown in Fig.

25.

Fig.25 The output voltage and current at PCC in case of voltage decrease by

50%

The simulation is run three times and each time one of the

MPPT algorithms is employed while operating the PV array

at nominal condition. Fig. 26 shows the output power of the

PV array in the three cases. It can be observed that FLC has

a faster response and is not affected with the disturbances

occurred on the grid side.

Fig.26 Output power of The PV array using the three different algorithms under

voltage sag

D. VOLTAGE SWELLS

The increase in the RMS voltage or current between 1.1 to

1.8 p.u. for a duration of 0.5 cycle to 1 minute is defined as

voltage swell. Voltage swells are normally initiated by the

disconnection of a very large load, the energization of a large

capacitor bank and voltage swells are usually associated with

the system fault conditions. Fig. 24 shows the grid connected

PV array MATLAB/SIMULINK model which utilizes in this

section. The system is studied under voltage swells of 0.15 s

duration.

In order to studying the effect of voltage swells, the voltage

at the PCC is increased from 20 kV to 26 kV as shown in Fig.

27.

Fig.27 The output voltage at the PCC in case of voltage increase by 30%

The PV array output power of the three MPPT algorithms

in case of voltage swell is shown in Fig. 28. It can be

observed that, the FLC has a good response and is not

affected with the disturbances occurred on the grid side.

Fig.28 Output power of the PV Array using the three different algorithms

under voltage swell condition

0 0.2 0.4 0.6 0.8 10

50

100

Pow

er (

kW)

Time (S)

Output Power of the PV array using the three different algorithms at L-L-L-G

P&O

ICT

FLC

0 0.1 0.2 0.3 0.4 0.5-5

0

5x 10

4

Vol

tage

(V

)

Time (S)

Grid Voltage at PCC With Voltage Dips

0 0.1 0.2 0.3 0.4 0.5-20

0

20

Cur

rent

(A

)

Time (S)

Grid Current at PCC With Voltage Dips

0 0.5 1 1.5 20

50

100

Pow

er (

kW)

Time (S)

Output Power of the PV array using the three different algorithms at Voltage Dips

P&O

ICT

FLC

0 0.1 0.2 0.3 0.4 0.5-5

0

5x 10

4

Vol

tage

(V

)

Time (S)

Grid Voltage at PCC With Voltage Swell

0 0.1 0.2 0.3 0.4 0.5-20

0

20

Cur

rent

(A

)

Time (S)

Grid Current at PCC With Voltage Swell

0 0.5 1 1.5 20

50

100

Pow

er

(kW

)

Time (S)

Output Power of the PV array using the three different algorithms at Voltage Swell

P&O

ICT

FLC

International Journal of Advances in Power Systems (IJAPS) Vol. 1, No. 3, December 2013

ISSN: 2335-1772

8

VI. CONCLUSION

In this paper, a 100 kW grid connected photovoltaic array

is studied under steady state and transient conditions while

utilizing three different maximum power point tracking

algorithms. The three algorithms employed in this paper are:

the perturb and observe (P&O) algorithm; the incremental

conductance (ICT) algorithm and the fuzzy logic control

(FLC) algorithm. The simulated results under steady state

condition show the effectiveness of the MPPT on increasing

the output power of the PV array for the three techniques.

However the FLC algorithm offers accurate and faster

compared to the conventional techniques. The simulation

results under transient conditions show that the output power

injected to grid from PV array is approximately constant

while utilizing the proposed FLC and the PV system can still

connect to grid and deliver power to grid without any damage

to the inverter switches.

REFERENCES

[1] A.Durgadevi, S. Arulselvi, S. P. Natarajan, “Study and implementation

of Maximum Power Point Tracking (MPPT) algorithm for Photovoltaic

systems,” 1st International Conference on Electrical Energy Systems

(ICEES), 2011, pp. 240 – 245.

[2] A. Safri and S. Mekhilef, “Incremental conductance MPPT method for

PV systems”, Canadian Conference on Electrical and Computer

Engineering (CCECE), 2011, pp. 345 -347.

[3] Sung-Jun Kang, Jae-Sub Ko, Jung-Sik Choi, Mi-Geum Jang, Ju-HuiMun

,Jin-Gook Lee, Dong-Hwa Chung, “A Novel MPPT Control of

photovoltaic system using FLC algorithm,” 11th International

Conference on Control, Automation and Systems (ICCAS), 2011, pp.

434 - 439.

[4] K.Rouzbehi, A.Miranian, A.Luna, P.Rodriguez, “Identification and

maximum power point tracking of photovoltaic generation by a local

neuro-fuzzy model,” 38th Annual Conference on IEEE Industrial

Electronics Society, 2012 , pp. 1019 – 1024.

[5] Shun Tao, Lin Liu, Xiang-ning Xiao, Xiao-chen Zhen, Yang Yang,

“Photovoltaic MPPT algorithm based on hysteresis comparison and

optimal gradient,” IEEE 15th International Conference on Harmonics

and Quality of Power (ICHQP), 2012, pp. 733 – 738.

[6] M.G.Villalva, J.R.Gazoli, E.R.Filho, “Comprehensive Approach to

Modeling and Simulation of Photovoltaic Arrays,” IEEE Transactions

on Power Electronics, vol. 24 , no. 5, pp. 1198 – 1208, 2009.

[7] KYOCERA KC200GT high efficiency photovoltaic module data

sheet (2013, Oct.), [Online] Available:

http://www.kyocerasolar.com/assets/001/5195.pdf.

[8] M.G.Villalva, F, E.Ruppert, “Analysis and simulation of the P&O

MPPT algorithm using a linearized PV array model,” 35th Annual

Conference of IEEE Industrial Electronics, 2009, pp. 231 - 236.

[9] T. Esram and P. Chapman, “Comparison of photovoltaic array

maximum power point tracking techniques”, IEEE Transactions on

Energy Conversion, Vol. 22, no. 2, pp.439 - 449, June 2007.

[10] Guan-Chyun Hsieh , J. C. Hung, , “Phase Lock Loop Techniques - A

Survey”, IEEE Transactions on Industrial Electronics, vol. 43, no. 6, pp.

609 - 615, Dec. 1996.