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where,Vpv: PV panel voltage (V)
Rsh: Shunt resistance (Ÿ)
Iph: PV Cell current (A)
Rse: Series resistance (Ÿ)
K: Boltzman constant (1.38 *10-23
J/K)
IPV: PV panel current (A)
IPVRSC: Reverse saturation current (A)
η
: Ideality factor
q: Electron charge (1.60217*10-19
C)
T: Ambient temperature (K)
The BP Solar SX3190 PV module is considered for
analysis of integrated Boost and Cuk converter and for
obtaining the rating of 568W PV panel, three strings are
connected parallel (IMP=3*7.8294=23.4A) and one series
connected module per sting (VMP=1*24.3003=24.3003V) then
the maximum power, PMP=IMP*VMP=23.4*24.3003=568W.
The design specifications are listed in Table I.
TABLE I. PV PANEL PARAMETER SPECIFICATIONS
Description Rating
Maximum power (PMP) 190W
Maximum current (IMP) 7.82945 A
Maximum voltage (VMP) 24.3003 V
Short circuit current (ISC) 8.51029 A
Temperature (T) 250
C
Open circuit voltage (Voc) 30.6021V
Number of parallel strings 3
Number of series-connected modules per string 1
Irradiation (R) 1000 W/m2
Characteristics of BP Solar SX3190 PV module are shown
in Fig. 2 under different irradiation conditions (1000 W/m2
,
750 W/m2
, 500 W/m2
, 250 W/m2
) [9-11].
0 5 10 15 20 25 30
0
10
20
30
1000 W/m2
Current
(A)
Voltage (V)
Array type: BP Solar SX3190; 1 series modules; 3 parallel strings
750 W/m2
500 W/m2
250 W/m2
0 5 10 15 20 25 30
0
200
400
600
1000 W/m2
Power
(W)
Voltage (V)
750 W/m2
500 W/m2
250 W/m2
Fig. 2 Under different irradiation conditions, PV panel I-V and P-V
characteristics
The MPPT algorithm is necessary for PV system in order
to improve the system efficiency and to yield the maximum
possible power from the dynamically varying PV source, due
to the solar irradiations and temperature variations.
Several MPPT algorithms are available in the literature
[12,13], such as perturb and observation method, incremental
conduction method and various soft computing control
techniques [9].
In order to validate the proposed converter, Radial basis
function network (RBFN) model MPPT controller is
considered. The basic structure of the RBFN is shown in the
Fig. 3. It has the faster learning capability and more compact
topology compared to the other MPPT control techniques. It
consists of three layers input, hidden layer, and output layer.
The inputs to the input layer are PV panel voltage, Vpv and
current Ipv and it computed the duty cycle, D for obtaining
maximum possible power from the PV source in the output
layer. The activation function in the hidden layer is
determined by the distance between the input and the
prototype vectors.
Fig. 3 RBFN basic structure
III. DESIGN OF INTEGRATED BOOST-CUKCONVERTER
The PV source low voltage characteristics are step-up by
using the traditional DC-DC Boost converter, but it has the
limitations of low voltage transfer gain and high stress on the
power semiconductor switches. As a result, a high step-up
DC-DC converter is designed by integrating the traditional
Boost and Cuk converter.
A. Boost converter
A Boost converter is a type of step-up DC-DC converter, it
consists of the single semiconductor switch (S), single diode
(D), two energy storage elements inductor (L) and capacitor
(C) as shown in Fig. 4. The key principle for the operation of
Boost converter depends on the inductor, L. Its output voltage,
Vo is always much higher than the input voltage, Vpv. The
output voltage, current, and voltage transfer gain are given in
Eq. (3), (4) and (5) respectively.
The output voltage, Vo, current, Io and transfer gain, M of
the Boost converter [7] are
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TABLE II. COMPARISON OF CONVENTIONAL AND INTEGRATED CONVERTER VOLTAGE TRANSFER GAINS
Converter Gain, M D = 0.1 D = 0.2 D = 0.3 D = 0.4 D = 0.5 D = 0.6 D = 0.7 D = 0.8 D = 0.9
Boost D
V
V
pv
o
−
=
1
1
1.11 1.25 1.42 1.66 2 2.5 3.33 5 10
Cuk
D
D
V
V
pv
o
−
=
1
0.11 0.25 0.42 0.66 1 1.5 2.3 4 9
Integrated D
D
V
V
pv
o
−
+
=
1
1
1.22 1.5 1.85 2.33 3 4 5.56 9 19
IV. ANALYSIS OF DEVELOPED CONVERTER
The three operating regions of the proposed converter are
explained in the following section based on the availability of
the PV source.
(a)
(b)
(c)
Fig. 7(a-c) Operating regions of the proposed integrated converter
Region-I:
When a switch, S is ON then the converter operates in the
region-I as shown in Fig. 7 (a). In this region the inductors L1
and L2 are in charging mode and D1 and D2 are in reverse bias
condition due to the negative voltages of VC1 and VC2.
Whereas the capacitor, C3 is in discharging mode.
Region-II:
When a switch, S is OFF and VC3 is smaller than the VC1,
then the converter operates in Region-II as shown in Fig. 7(b).
In this region the capacitor C3 is in charging mode and the
inductors L1 and L2 are in discharging mode. During this
condition, D1 is in reverse bias and D2 is in the forward bias
condition.
Region-III:
When a switch, S is OFF and VC3 is greater than the VC1,
then the converter operates in Region-III as shown in Fig.7(c).
In this region both diodes D1 and D2 are in forward bias
condition. Then the inductors L1 and L2 are in discharging
mode and Capacitors C1 and C3 are in charging mode through
the inductor current IL1.
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V. SIMULATION AND RESULT DISCUSSION
To validate the performance of the proposed integrated
Boost-Cuk converter Matlab/Simulink model is implemented.
The converter parameters specifications are listed in the Table
III.
TABLE III. INTEGRATED BOOST AND CUK CONVERTER
SPECIFICATIONS
Description Ratings
PV rating 24 V, 23.4 A, 568 W
Common inductor L1=1e-3
H
Cuk converter inductor L2=1e-3
H
DC link capacitors C1=100e-6
F, C2=100e-6
F
Cuk converter capacitor C3=2e-6
F
Load resistance R=104 Ÿ
Switching frequency fs=10 kHz
A 568W PV panel is considered for the analysis of the
developed converter with RBFN based MPPT technique. Fig.
8 shows the PV output voltage, current and power waveforms.
The PV panel output power is given as input to the proposed
converter, which converts input 24V DC into a 230V DC by
varying the duty cycle of the converter with RBFN method.
The simulated converter output voltage, current, and power
are shown in Fig. 9 and a comparative analysis is carried out
on the proposed converter based on the MPPT techniques.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
0
10
20
30
Voltage(V)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
0
10
20
30
Current(A)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
-500
0
500
1000
Time(Sec)
Power(W)
Fig. 8 simulated output voltage, current and power from the PV source
The conventional P&O algorithm is applied to the
developed converter and the results are compared with the
implemented RBFN based MPPT technique. The comparative
results are listed in the Table IV.
TABLE IV. COMPARISON OF PROPOSED INTEGRATED CONVERTER
WITH MPPT
The average output with MPPT
P&O RBFN
Voltage (V) 221 230
Current (A) 2.2 2.3
Power (W) 486 529
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
0
100
200
300
Voltage(V)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
0
1
2
3
Current(A)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
0
200
400
600
Time(Sec)
Power(W)
Fig. 9 Simulated output voltage, current and power from load side.
VI. CONCLUSION
In this paper, analysis of integrated Boost and Cuk
converter with RBFN based MPPT has been presented for
obtaining the high voltage gain and to reduce the voltage
stress on the power converter components in the solar PV
system. By integrating the two conventional converters high
voltage gain is obtained and the neural network based MPPT
algorithm is applied to the integrated converter to validate the
effectiveness of the converter. A comparative study is carried
out based on the MPPT related to the RBFN and P&O
methods. The proposed converter with the RBFN algorithm
based MPPT shows better performance by achieving
maximum power output and provides constant 230 V DC to
the load.
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