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Leonardo Electronic Journal of Practices and Technologies

ISSN 1583-1078

Issue 31, July-December 2017 p. 111-128

111

Engineering, Environment

Control and performance analysis of grid connected photovoltaic systems of

two different technologies in a desert environment

Layachi ZAGHBA, Messaouda KHENNANE, Abdelhalim BORNI, Amor FEZZANI

Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de

Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Algeria

E-mail(s): [email protected]

* Corresponding author, phone: 00(213)029 87 01 26, fax: 00(213)029 87 01 46

Received: January 13, 2017 / Accepted: November 25, 2017 / Published: December 30, 2017

Abstract

In this study, is to investigate the effect of real climatic conditions on the performance

parameters of a 9 kWp grid connected photovoltaic plant during one-year using typical

days installed in the desert environment in south of Algeria (Ghardaia site). The PV

plant contain the following components: solar PV array, with a DC/DC boost converter,

neural MPPT, that allow maximal power conversion into the grid, have been included.

These methods can extract maximum power from each of the independent PV arrays

connected to DC link voltage level, a DC/AC inverter and a PI current control system.

The PV array is divides in two parallel PV technology types; the first includes 100 PV

modules mono-crystalline silicon (mc-Si) arranged in 20 parallel groups of 5 modules

in series, and the second of composed of 24 amorphous modules (Inventux X series),

arranged in 6 parallel groups of 4 modules in series. The proposed system tested using

MATLAB/SIMULINK platform in which a maximum power tracked under constant

and real varying solar irradiance. The study concluded that output power and energy

from two PV technology types (mc-Si and Amorphous-Si) increases linearly with

increase of solar irradiance.

Keywords

Grid-connected PV system; Performance evaluation; PV technology types; Neural

MPPT control strategy; Boost converter; Desert environment

mailto:[email protected]

Control and performance analysis of grid connected photovoltaic systems of two different

technologies in a desert environment


112

Introduction

As the conventional fuel, resources throughout the world continue to reduce, the

promising renewable energy resources as photovoltaic energy technologies have been widely

used to compensate the depletion of the conventional fuel resources [1-2]. Renewable energy

uses inexhaustible flow of natural energy sources (sun, wind, water, plant growth). These

energies of the future not yet cover only 22% of global electricity consumption with the

importance of hydropower, which accounts for three quarters of electricity from renewable

energy. The main advantages of renewable energy are not their inexhaustible and their very

limited emissions of greenhouse gases as evidenced by the analysis of the life cycle of

exploitation [3-5]. PV system installation has played an important role in worldwide and

becoming one of the most popular sources since it is clean, inexhaustible, environment friendly,

secure energy sources and requires little maintenance. The geographical location of Algeria

promotes the development an ambitious program launching in 2012 for development of

renewable energy and energy efficiency. This program includes of installing a renewable power

of nearly 22 000MW between 2012 and 2030, will be dedicated to cover the national demand

for electricity [6–8]. In 2030, about 40% of the production of electricity for domestic

consumption is from renewable sources [8]. The average annual irradiation levels in Algeria

are around 2000kWh/m2. In the Local of study (Ghardaia site), the global horizontal solar

radiation is varied between 3 kWh/m2/day and 7.4 kWh/m2/day, with annual average value of

5.4 kWh/m2/day [9]. Variations in solar radiation level and ambient temperature from each

season and year-to-year influence the PV system performance. Therefore, it need to be

considered and is important to identify the effects of climate during different seasons and which

performance are suitable for which system evaluations based on their weather-dependence.

The focus of this present work is to study the effect of the real climatic conditions on

the performance of a 9 kWp grid connected photovoltaic plant during one-year of 2013 using

typical days with variation in different seasons using two different PV technology under an

environmental condition of Algeria (Ghardaia site).

The main contributions of this paper are:

• Input real experimental weather data of the location site (irradiation and temperature)

applied to analysis and evaluation the impact of desert environment on the performance

of grid connected photovoltaic plant during twelve typical days using two PV array


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Issue 31, July-December 2017

p. 111-128

113

technology types (Amorphous and Monocrystalline Solar PV panels);

• Neural network mode based MPPT control is proposed;

• The method exhibits good transient performance and fast convergence.

Material and method

Principle of the method

In this paper, maximum power point tracking (MPPT) of a photovoltaic system using

artificial neural network control is developed and simulated in MATLAB SIMULINK at

varying atmospheric conditions to increase the output power of a photovoltaic system.

The inputs variables for ANN are the output PV current and output PV voltage

corresponding to a given insolation and operating cell temperature conditions, which they have

significant influence on the ANN response; the output variable of ANN is the corresponding

normalized increasing or decreasing duty cycle. The proposed neural network MPPT is tested

and validated using Matlab/Simulink model for different atmospheric conditions.

Location of study area

The study region id located in the southern of Algeria (Latitude 32.23°N, Longitude

3.45°E, Altitude 600 km).

The measured data are recorded averaged every 5 min and stored on a disk for analyzing

and evaluating of the various climatic parameters (Figure 1).

https://www.google.dz/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiW5KD6kITOAhVGnBoKHWNeAgMQFggaMAA&url=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FAmorphous_silicon&usg=AFQjCNFKtR9J-50Ot4Kwm6Y80LMwcKF04g

https://www.google.dz/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiW5KD6kITOAhVGnBoKHWNeAgMQFggaMAA&url=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FAmorphous_silicon&usg=AFQjCNFKtR9J-50Ot4Kwm6Y80LMwcKF04g




114

Figure 1. Solar photovoltaic station in Algeria map

For irradiation measurement, the measured data on solar radiation used in the present

study were recorded collected by a radiometric station with high precision installed on the solar

radiation laboratory roof of applied research unit for renewable energies (URAER).

Real sun irradiance and temperature profile of studied location are presented above in

Figure 2 and 3.

Figure 2. Real sun irradiance profile during full one year (2015)

Ghardaia


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115

Figure 3. Real temperature profile for three months of studied location (2015)

The daily meteorological output parameter data (temperature, irradiance) were acquired

and recorded with a time step of 5 minutes using Agilent 34970A data logger via RS485

interface.

The meteorological parameters that were monitored include: solar irradiance (measured

at plane of sub-array) and ambient air temperature.

PV plant description

The conception of three-phase grid-connected PV system is shown in Figure 4.

05

1015202530354045

0:0

6

0:5

6

1:4

6

2:3

6

3:2

6

4:1

6

5:0

6

5:5

6

6:4

6

7:3

6

8:2

6

9:1

6

10

:06

10

:56

11

:45

12

:35

13

:25

14

:16

15

:06

15

:56

16

:46

17

:36

18

:26

19

:16

20

:06

20

:56

21

:46

22

:36

23

:26

Tem

per

atu

re

time (hours)

January March May




116

Figure 4. Schematic block circuit diagram of the grid connected PV system

The solar energy converted to DC electricity by PV arrays, and converted to AC

electricity by inverter after dc-dc boost converter, the neural network MPPT control to extract

the maximum power point at each sub system array and a voltage DC link capacitor, then

supplied to the grid through the harmonic filter circuit RL.

PV array modelling

The electrical properties of the modules summarized in Table 1.

Table 1. Specification of the two different technology modules used

PV Module technologies Mono-crystalline

modules

Amorphous silicon

modules

Maximum Power (PPV) 60 W 125 W

Voltage at Pmax (VMPP) 17.1 V 1 V

Current at Pmax (MPP) 3.5A 125A

Open Circuit Voltage (Voc) 21.1V 165V

Short Circuit Current (Isc) 3.8 A 1.14A

Vdq

Inverter

MPPT

MPPT

Control

abc

dq

abc

dq

abc dq

Current

control

Idq

Iabc Vabc

PWM

DC

DC

DC

DC

Grid

Filter

DC/DC

Converte

Ipv

Vpv

Vpv

Ipv

D

D

Power

control

P

Q=0

Idref

Iqref

DC Link

Monocristallines PV

modules

Amorphous silicon

modules


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117

The PV array is divides in two PV technology types; the first includes 100 PV modules

mono-crystalline silicon (mc-Si) of 60 W arranged in 20 parallel groups of 5 modules in series,

and the second of composed of 24 amorphous modules (Inventux X series) of 125 W, arranged

in 6 parallel groups of 4 modules in series. Both PV arrays are set in a fixed tilt of 320 with

azimuth of 00 (south).

DC–DC Boost converter model

The input source is DC Type (Inductor in series with a voltage source) and output load

voltage type continuous (capacitor in parallel with the resistive load) as presented in Figure 5.

Figure 5. DC-to-DC Boost Converter

The DC-DC converter is capable to elevate the voltage without changing the polarity

respecting to a common ground. It therefore behaves as an autotransformer that operates in

continued current DC. By adding to its control loop, we obtained a stabilized output voltage.

The mathematical model using Kirchhoff’s laws around the two loops (transistor

switching on off), in Eq. (1) [10-11]:

Ldi

dt=VPv-V0(1-D)

CdV0

dt=iL(1-D)-

V0

R

(1)

Where: L- DC-DC converter inductance; C - DC-DC converter capacity; iL- inductance current;

D - Duty-cycler; Vpv - photovoltaic panel output voltage; V0 - DC-DC converter output

Voltage.

MPPT by Using Neural Network

Artificial neural network has memory, which include to the weights in the neurons.




118

The weights and biases of the network tuning by the learning rate in order to move the

network output closer to the targets. To find the output optimal voltage, the Feed-forward neural

network

Architecture MPPT control proposed shown in Figure 6.

Figure 6. Feed-forward neural network architecture

Inputs are the current and voltage of PV generator [12–15]. In this work, Artificial

Neural Network (ANN) have been developed to track the maximum power point [16–17].

The relationship between the inputs and output formulated as, Eq. (2):

y =∑ Wiφ(xki=1 )+w0 (2)

Where: x - neural inputs, Wi - neuronal network weights, y - the output of the system.

Back propagation algorithm used to update the weights and biases and to minimize a

mean squared-error performance index given as, Eq. (3):

J =1

2∑ (yi

des-yi)2N

i=1 (3)

Where: J - the criterion to be minimized; yides - the i- th desired output of the system; yi - the i-

th output of the system.

The update of weight Wi is done according to the following rule, Eq. (4):

wijk(t+1) = wij

k(t)-∆w (4)

Where: wijk is the weights in in the layer K; t - is the time; Δw - is the variations of the weights.

Grid side control

The following equations describe the voltage of balanced three-phase power grid, Eq.

(5) [18-19]:

Hidden Layer

Input Layer Output Layer

Current

Vopt

Voltage

(V)


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119

ex = Em cos wt

ey = Em cos (wt -2п

3)

ez = Em cos (wt +2п

3)

(5)

Where: Em - the maximum value of power grid voltage; w - the angular frequency of power

grid; suppose L2=L3=L4=L, R1=R2=R3=R.

To study the system connected to the grid in Figure 3, standard Kirchhoff’s voltage law

is given, thus the voltage at the grid side of the converter described as Eq. (6) [18-19]:

ex= L

d

dtix+Rix+vx

ey= Ld

dtiy+Riy+vy

ez= Ld

dtiz +Riz + vz

(6)

Where: exyz - grid three phase’s voltages; vxyz - inverter three phase’s voltages; L - filter

inductance; R - filter resistance.

Using park transformation, from three phase stationary abc reference frame to two

phases synchronous rotating dq reference frame, Eq. (7).

(

did

dtdiq

dt

) = 1

L(

-R wL

wL -R) (

idiq) -

1

L(ed

eq) +

1

L(vd

vq) (7)

Where: L - filter inductance; R - filter resistance; edq - grid voltage (park transformation); vdq -

inverter voltage (park transformation).

From the above d-q mathematical model, the decoupling of the d-axis and q-axis

variables makes it complex to design the controller. The voltage drops due to the line impedance

compensated using a PI regulator in each loop.

The control equations are as below Eq. (8) [18-19]:

vd = (Kp

+Ki

S)(idref-Id)-wLiq+ed

vq = (Kp+

Ki

S)(iqref-Iq)+wLid+eq

(8)

Where: L - filter inductance; edq - grid voltage (park transformation); vdq - inverter voltage (park

transformation); Kp and Ki - gain of the PI regulator; Idred and Iqref - reference current (park

transformation); Id and Iq - measured current (park transformation).

The active power P and reactive power Q under dq synchronous rotating reference frame

expressed as below Eq. (9) [9]:




120

P =

2

3(edi

d+eqi

q)

Q =2

3(eqi

d-edi

q) (9)

Where: P - Active power; Q - Reactive power; edq - grid voltage (park transformation); vdq -

inverter voltage (park transformation).

If d-axis is positioned as grid voltage synthesized vector, namely eq=0, equation (9) can

be expressed as follows Eq. (10) [18-19]:

P =

2

3edi

d

Q = -2

3e

diq

(10)

Where: P - Active power; Q - Reactive power; edq - grid voltage (park transformation); Id and

Iq - grid current (park transformation).

The decoupled current control scheme of grid-connected inverter is shown in Figure 7.

Figure 7. The control scheme of grid-connected inverter

Results and discussion

In order to present, the effects of variation in solar irradiation and temperature on grid

connected photovoltaic performance, under real outdoor daily operating condition for one

year 2013 (using typical days), are shown in Table 2.


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121

Table 2. Typical days used

Months Type of days Months Type of days

January 17-01-2013 July 17-07-2013

February 16-02-2013 August 16-08-2013

March 15-03-2013 September 16-09-2013

Avril 13-04-2013 October 15-10-2013

May 22-05-2013 November 15-11-2013

June 11-06-2013 December 10-11-2013

Figure 8. Simulink model of grid connected PV system

Complete Matlab/Simulink simulation of the grid connected PV System with the Neural

MPPT algorithm and the power active and reactive control of the grid-side inverter controlled

by the PI current regulator with the following parameters (see Figure. 8).

• The PV array is divides in two PV technology types; the first includes 100 PV modules

mono-crystalline silicon (mc-Si) of 60 W arranged in 20 parallel groups of 5 modules

in series, and the second of composed of 24 amorphous modules (Inventux X series) of

125 W, arranged in 6 parallel groups of 4 modules in series.

• The DC-link capacitance: C= 22000e-6 F.

• The grid filter: R= 0.1 Ω, L= 3e-3 H

• The grid voltage: 400/50 Hz.

Radial Basis Network

MPPT

MPPT

lrradiation

v + -

VM2

v + -

VM1 g

A

B

C

+

-

Universal Bridge

1/z Unit Delay

Vabc Iabc

A

B

C

a b c

Three-Phase V-I Measurement1

T

S

Uout

I

Subsystem2

T

S

Uout

I

Subsystem1

Saturation2

Saturation1

NNET Input Output

NNET Input Output

+ R2

+ R1

+ La3

+ La2

+ La1

+ L1

+ L

Irradiation

g

C

E

IGBT1

g

C

E

IGBT

N A B C

Grid

Vabc(pu) Freq wt Sin_Cos

Discrete 3-phase PLL

Diode1

Diode I_load

sin_cos

pulse

Current control

s

- +

s

- +

25 Constant2

25 Constant

i + - CM3

i +

- CM1

+

C5

+

C3

+

C1




122

Figure 9. Real sun irradiance of studied location using typical days

Sun irradiance during one year of studied location using typical days (year 2013)

presented in Figure 9. To simulate the system, the PV model described above implemented in

Matlab Simulink and placed in real outdoor conditions of irradiation and temperature, and the

neural network controller has been defined and designed using the box neural network tools.

All Simulation results above show that the system controlled by the neural network

adapts to changing external disturbances and show their effectiveness not only for continued

maximum power point but also for response time and stability after a transient regime of time

equal to 10 ms. The different electrical parameters (power, voltage and current) stabilize to

optimal values. The power provided by the first PV generator stabilizes around 6 kW and the

second PV generator stabilizes around 3 kW at G=1000 W/m² (Figure 10).

Figure 10. Annual PV array Power (PV1 and PV2) using typical days (2013)

0

200

400

600

800

1000

1200

12:10:00AM

1:00:00AM

1:50:00AM

2:40:00AM

3:30:00AM

4:20:00AM

5:10:00AM

6:00:00AM

6:50:00AM

7:40:00AM

8:30:00AM

9:20:00AM

Irra

dia

tio

n (W

/m²)

time (hours)

0 1 2 3 4 5 6 7 8 9 10 11 12 -1000

0

1000

2000

3000

4000

5000

6000

7000

Months

PV

Po

we

r (W

)

The first PV array Power (PV1) The second PV array Power (PV2)

2013-04-13

2013-05-22 2013-06-11

2013-07-17 2013-08-16

2013-09-16

2013-10-15

2012-12-10

2013-11-15

2013-03-15

2013-02-16

2013-01-17


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123

As shown in Figure 10, at G=1000W/m² ant T=250C, the maximum output power of

first PV system is 6000 W and the second PV system is 3000 W.

Figure 11, present the annual PV array current (PV1 and PV2) during one-year using

typical days (2013).

Figure 11. Annual PV array current (PV1 and PV2) using typical days (2013)

The current provided by the first PV generator stabilizes around 25 A, and the second

PV generator stabilizes around 5 kW at G=1000 W/m² (Figure 11).

In Figure 12 the current loop controller is validate and can be seen that the Id and Iq

measured signals are tracking very well their references after a very short time.

Figure 12. Id current reference and measured (Id and Iq) using typical days (2013)

0 1 2 3 4 5 6 7 8 9 10 11 12 -5

0

5

10

15

20

25

30

35

40

Months

PV

arr

ay c

urr

en

t(A

)

First PV array current (PV1) Second PV array current (PV2)

2013-02-16

2013-03-15 2013-04-13

2013-01-17

2013-10-15 2013-11-15

2013-07-17 2013-06-11 2013-05-22

2013-08-16 2013-09-16

2012-12-10

0 1 2 3 4 5 6 7 8 9 10 11 12 -30

-20

-10

0

10

20

30

40

50

Months

Cu

rre

nt

Id a

nd

Iq

Idmes Idref iqref

2013-01-17

2013-02-16

2013-04-13 2013-03-15

2013-05-22

2013-06-11

2013-07-17 2013-08-16

2013-09-16 2013-10-15

2013-11-15

2012-12-10




124

We note that the effect of the rapid increase in power caused by increased irradiation.

The overall results show that the DC-DC converter and MPPT control properly perform their

roles. The converter provides optimal conditions in a voltage at its output than that provided by

the PV array. The MPPT control adjusts the PV array to the charge transfer maximum power

provided by the PV generator.

Figure 13, show the sinusoidal three current waveforms on the grid side (Iabc) during

one-year using typical days (2013).

Figure 13. Current waveform on the grid side (Iabc) using typical days (2013)

The figure 13 shows the characteristics of the three-phase currents injected into the

network (Iabc). They have almost sinusoidal waveform. The increase and decrease of the

current "Iabc" reflects the variation of the irradiation. Increasing the intensity of sunlight causes

an increase in power.

Figure 14, shows the voltage waveform on the inverter side during one-year using

typical days (2013)

0 1 2 3 4 5 6 7 8 9 10 11 12 -30

-20

-10

0

10

20

30

40

50

Months

Cu

rre

nt

Id a

nd

Iq

3.4 3.6 3.8 4 -20

0

20 Ia Ib Ic

2013-09-16 2013-10-15

2012-12-10

2013-11-15

2013-08-16 2013-07-17

2013-05-22

2013-02-16 2013-01-17

2013-03-15 2013-04-13

2013-06-11


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125

Figure 14. Voltage waveform on the inverter side using typical days (2013)

It can be seen from Figure 14, the three-phase voltages of the network have a sinusoidal

shape of the same frequency 50 Hz, phase shifted by 2 π / 3, and having the same effective

value 220 V, form a balanced three-phase system.

Figure 15 present the profile active and reactive power which is null during one-year

using typical days during one-year (2013).

Figure 15. Active and reactive power using typical days (2013)

Figure 15 shows daily production for each month for one year for tow PV array (PV1

and PV2). It can be noted that the maximum value of energy is in the summer period (June-

August).

Figure 16 shows the daily PV array energy for one year (2013), where serie 1 represent

the first PV array (PV1) and Serie 2 represent second PV array (PV 2).

0 1 2 3 4 5 6 7 8 9 10 11 12

-300

-200

-100

0

100

200

300

400

500

600

Months

Gri

d v

olt

ag

e V

ab

c (

V)

2 2.2 2.4 -200

0 200 400

0 1 2 3 4 5 6 7 8 9 10 11 12 0

2000

4000

6000

8000

10000

12000

Months

Act

ive

an

d R

eact

ive

po

wer

Active Power (W) Reactive power (Var)

2013-10-15

2013-09-16

2013-08-16 2013-07-17

2013-06-11 2013-05-22

2012-12-10

2013-11-15

2013-04-13

2013-01-17

2013-02-16

2013-03-15




126

Figure 16. Daily PV array energy (2013)

Figure 16 describe the daily PV array energy for one year (2013) for tow PV array (PV1

and PV2). It is noted that the energy produced is much higher during the in the summer period

(June-August) compared to the winter season (December-February), because the solar

illumination received at the level of the photovoltaic field is very high in this season.

Conclusions

In this paper, an experiment to assess the performance of a grid connected system PV

under real conditions of exploitation in a desert environment of two different technologies

(Amorphous and Monocrystalline Solar PV panels).

The neural network based MPPT control has clearly demonstrated its utility and the

effectiveness in tracking the maximum power point of the photovoltaic power plant. The

simulation results obtained of the whole system done in Matlab-Simulink have shown an

excellent performance, high efficiency, low error, very short response time, high dynamics for

both inverter and MPPT, the DC bus voltage and the power output value from two PV

technology types.

Appendix

The following Matlab code creates a feed-forward neural network:

P= [Current Data; Voltage Data];

T= [Optimal voltage data];

net=newff(minmax(P),[40,3,1], 'tansig','tansig','purelin','traingd');

0

20000

40000

60000

80000

1 2 3 4 5 6 7 8 9 10 11 12

PV

arr

ay e

ner

gy (

Wh

)

Months

Series1 Series2


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net.trainParam.epochs=1000;

net.trainParam.goal=1e-3;

net.trainParam.show=50;

net.trainParam.lr=0.05;

[net,tr]=train(net,P,T);

a=sim(net,P)

gensim(net)

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