IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 4, AUGUST 2003
749
Neural-Network-Based Maximum-Power-Point
Tracking of Coupled-Inductor
Interleaved-Boost-Converter-Supplied
PV System Using Fuzzy Controller
Mummadi Veerachary, Member, IEEE, Tomonobu Senjyu, Member, IEEE, and Katsumi Uezato
Abstract—The photovoltaic (PV) generator exhibits a nonlinear
– characteristic and its maximum power (MP) point varies
with solar insolation. In this paper, a feedforward MP-point
tracking scheme is developed for the coupled-inductor interleaved-boost-converter-fed PV system using a fuzzy controller.
The proposed converter has lower switch current stress and
improved efficiency over the noncoupled converter system. For
a given solar insolation, the tracking algorithm changes the duty
ratio of the converter such that the solar cell array voltage equals
the voltage corresponding to the MP point. This is done by the
feedforward loop, which generates an error signal by comparing
the instantaneous array voltage and reference voltage corresponding to the MP point. Depending on the error and change of
error signals, the fuzzy controller generates a control signal for
the pulsewidth-modulation generator which in turn adjusts the
duty ratio of the converter. The reference voltage corresponding
to the MP point for the feedforward loop is obtained by an offline
trained neural network. Experimental data are used for offline
training of the neural network, which employs a backpropagation
algorithm. The proposed peak power tracking effectiveness is
demonstrated through simulation and experimental results.
Tracking performance of the proposed controller is also compared
with the conventional proportional-plus-integral-controller-based
system. These studies reveal that the fuzzy controller results in
better tracking performance.
Index Terms—Coupled-inductor interleaved boost converter,
feedforward loop, fuzzy controller, maximum power (MP) operation, neural network, proportional plus integral controller, solar
cell array (SCA).
I. INTRODUCTION
T
HE rapid trend of industrialization of nations and increased
interest in environmental issues led recently to the exploration of the use of renewable forms such as solar energy [1]–[3].
Photovoltaic (PV) generation is gaining increased importance as
a renewable source due to its advantages like absence of fuel cost,
little maintenance, no noise and wear due to absence of moving
parts etc. In particular, energy conversion from a solar cell array
Manuscript received December 12, 2001; revised August 21, 2002. Abstract
published on the Internet May 26, 2003.
M. Veerachary was with the Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Nakagami, Okinawa 903-0213, Japan. He is now with the Department of Electrical Engineering, Indian Institute of Technology, New Delhi 110 016, India.
T. Senjyu and K. Uezato are with the Department of Electrical and Electronics
Engineering, Faculty of Engineering, University of the Ryukyus, Nakagami,
Okinawa 903-0213, Japan.
Digital Object Identifier 10.1109/TIE.2003.814762
(SCA) received considerable attention in the last two decades.
Since the PV generator exhibits a nonlinear – characteristic,
its maximum power (MP) point varies with the solar insolation
and temperature. At a particular solar insolation, there is a unique
operating point of the PV generator at which its power output is
maximum. Therefore, for maximum utilization efficiency, it is
necessary to match the PV generator to the load such that the
equilibrium operating point coincides with the MP point of the
PV source. However, since the MP point varies with insolation
and seasons, it is difficult to maintain MP operation at all solar insolations without changes in the system parameters. To overcome
this problem, use of an intermediate dc–dc converter is proposed
[1]–[4], which continuously adjusts the voltage, current levels,
and matches the PV source to the load.
Different peak power tracking schemes have been proposed by
using different control strategies [5]. Array-voltage-based maximum-power-point tracking (MPPT) using dc–dc converters has
been reported [6]–[8]. This method of MP tracking has the advantages of straightforward array voltage measurement, no need
of current sensors (which introduces losses and complexity in the
system), etc. An interleaved dual boost converter scheme is proposed by the authors for the array-voltage-based MP extraction
[9] by controlling the converter in an interleaved fashion. Advantages of the present converter system [10] are: 1) ripple cancellation both in the input and output waveforms to the maximum
extent,; 2) lower value of ripple amplitude, high ripple frequency
in the resulting input and output waveforms; and 3) reduced electromagnetic interference because of low ripple amplitude of the
SCA current. Although the interleaving technique increases the
number of components, the actual increase of cost may not be
significant. This is because when a large number of converter
cells are used they can share the current flow in the inductors
and switching devices, so that lower current rating devices can
be employed. Further, parallel connection of converters has many
desirable properties, such as reduced device stresses, fault tolerance for the system, flexibility in the system design, etc.
A simple two-cell interleaved boost converter is capable
of extracting MP from the SCA and exhibits improved
performance as compared to conventional boost converter.
However, there is still a need to improve the performance
(both steady state and dynamic) of the two-cell interleaved
boost converter. Steady-state performance can be improved
by using large inductances, which reduces the current ripple
0278-0046/03$17.00 © 2003 IEEE
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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 4, AUGUST 2003
and conduction losses both in the switching device and filter
capacitance. However, use of larger inductances slows down
the transient response. To alleviate the problem of the contradictory inductance requirement for steady-state and dynamic
performance, a coupled inductance approach has been reported
for buck converters [11]. By introducing coupling among the
parallel branches it is possible to obtain different equivalent
inductances, which makes for the possibility of improving
both steady-state and dynamic performance simultaneously,
whereas if we use a simple two-cell interleaved converter
without any coupling, one has to sacrifice the steady-state
performance to achieve a faster dynamic performance or
vice versa. Therefore, a coupled-inductor interleaved boost
converter (CIBC) is proposed for PV applications in this paper.
Use of coupled inductors [12] reduces the number of magnetic
cores, exhibits lower switch stress, lower conduction losses
both in the switching device and filter capacitor on account of
smaller current ripple, and improves the converter efficiency as
compared to the noncoupled case.
In the feedforward-array-voltage-based tracking scheme,
the MP point tracking depends on the adjustment of reference
voltage for the feedforward loop that corresponds to the optimal
array voltage at that solar insolation. If the solar insolation
changes, then the optimal array voltage also changes. Therefore,
an online estimation of the optimal array voltage is required
for the MP point tracking control. To cope with this situation
an offline artificial neural network (ANN) is proposed in this
paper to estimate the optimal array voltage variation with solar
insolation. For controlling the dc–dc converters several control
strategies are reported in the literature. These controllers are
simple to implement and easy to design. However, there are
several drawbacks [13] that hinder the conventional controllers,
such as performance being dependent on the working point, the
necessity for tuning of control parameters against changes in
supply voltage and load parameters, complex design of control
parameters and stabilization problems, etc.
To overcome some of the disadvantages mentioned above,
fuzzy logic controllers (FLCs) are appearing in industrial
processes [14]–[16] owing to their heuristic nature associated
with simplicity and effectiveness for both linear and nonlinear
systems. The main difference from conventional methods is that
the accurate description of the system to be controlled is not
required. Fuzzy logic allows the determination of the rule base
by linguistic terms and, therefore, the tuning of the controller in
a very simple way which is qualitatively different from conventional design techniques. Furthermore, fuzzy control is nonlinear
and adaptive in nature, which gives it robust performance under
parameter variation, load and supply voltage disturbances. In
this paper, fuzzy logic has been applied to track the MP from the
interleaved-boost-converter-supplied PV system. The control
inputs to the fuzzy logic controller are voltage error and change
of errors, while the output is the change of control signal for the
pulsewidth-modulation (PWM) generator.
Fig. 1. Experimental setup of the PV system.
controller and the data acquisition system are set up by using a
PC, A/D and D/A conversion interfaces, and the other peripherals such as voltage sensing and scaling devices, etc. The analysis of the system is carried out under the following assumptions.
1) Switching elements (MOSFET and diode) of the converter are assumed to be ideal.
2) The equivalent series resistance of the capacitance and
stray capacitances are neglected.
3) The two parallel converter cells operate in the continuous inductor current mode.
4) The switches ( , ) operate in an interleaved fashion.
Mathematical models for the individual components are developed in the following sections.
A. PV Generator Model
The PV generator is formed by the combination of many PV
cells connected in series and parallel fashion to provide desired
value of output voltage and current. This PV generator exhibits a
nonlinear insolation-dependent – characteristic, mathematicells in series
cally expressed for the SCA [4] consisting of
cells in parallel as
and
(1)
II. MATHEMATICAL MODEL OF THE SYSTEM
The basic configuration of the proposed fuzzy-neural-network-based MP tracking controller is shown in Fig. 1. Both the
where
pletion factor;
temperature;
, is the electric charge; is the comis the Boltzmann’s constant; is the absolute
is the cell series resistance;
is the photo
VEERACHARY et al.: MPPT OF CIBC-SUPPLIED PV SYSTEM USING FUZZY CONTROLLER
current; is the cell reverse saturation current; and
and
are the SCA current and voltage, respectively. For given values
characteristic depends on the
of SCA parameters, the
solar insolation and the MP point varies with the solar insolation. Rewrite (1) as
(2)
. Expanding the term
into Taylor series and neglecting higher order
terms results in the following equation [9].
in
matrices
751
,
and
. With these assumptions the simplified
are
where
The steady-state behavior can be obtained from the following
expression:
(3)
(6)
. Equation (3) defines the simpliwhere
fied – characteristic, which is used in the simulation studies.
(7)
B. CIBC Model
The intermediate CIBC produces a chopped output dc voltage
and controls the average dc voltage applied to the load. Further, the converter continuously matches the output characteristic of the PV generator to the input characteristic of the load
so that MP is extracted from the SCA. The mathematical model
of this converter operating in continuous current mode is derived using state-space averaging technique [17]. In the general
case for this converter system more topologies are possible depending on the control signals, switching frequency, and load
value. The analysis presented here is only for an interleaved
. However, the state-space model for
operation with
can easily be obtained on similar lines. The conducting
are Mode-1: ( ,
devices in one cycle time period for
); Mode-2: ( ,
); Mode-3: ( , ); Mode-4: (
, ).
The state-space equations for these four different modes of operation, respectively, are (see Appendix for detailed equations)
From the above equations the steady-state voltage gain of the
converter is
(8)
If
then the relationship between load and input voltage
is
(9)
III. PROPORTIONAL PLUS INTEGRAL (PI) CONTROLLER
The duty ratio control law is chosen as
(10)
For MP tracking the above control law becomes
(4)
Taking the average of the above four state models results in the
following average state-space model:
(5)
and
, and
matrices are
Integrating the above equation, the resulting duty ratio in the
form of PI control law is
(12)
,
where
(11)
. The corresponding
In real time the duty ratio for the converter is generated by
with control (or) refcomparing a triangular carrier signal
, i.e., the duty ratio of the converter is
erence signal
(13)
For a given triangular carrier signal the duty ratio is propor. Therefore, the control law (12)
tional to the control signal
becomes
(14)
The above control law is implemented in real time using the
following discrete form:
where
identical branches (
,
,
. Assuming
) results
(15)
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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 4, AUGUST 2003
TABLE I
RULE BASE TABLE FOR THE FUZZY LOGIC CONTROLLER
Fig. 2.
Membership functions for E ,
1E , and 1U .
IV. FUZZY CONTROLLER
In recent years, FLCs have been widely used for industrial
processes owing to their heuristic nature associated with simplicity and effectiveness for both linear and nonlinear systems.
Advantages of fuzzy logic controllers over the conventional
controllers are: 1) they do not need accurate mathematical
model; 2) they can work with imprecise inputs; 3) they can
handle nonlinearity; 4) they are more robust than conventional
nonlinear controllers. This section will briefly describe the
techniques used in fuzzy logic controller, viz., fuzzification,
fuzzy knowledge base, and defuzzification.
In the fuzzification process the numerical variable is converted into a linguistic variable. The following five fuzzy levels
are chosen for the controlling inputs of the fuzzy controller
) in the fuzzification:
(error: , change of error:
NB negative big;
NS
negative small;
ZE
zero;
PS
positive small;
PB
positive big.
and it must be large so as to bring the terminal voltage
.
to
, then the
3) When the array voltage is close to the
incremental duty ratio is small.
4) When the array voltage is near to MP point voltage
and is approaching it rapidly, then the change of duty
ratio should be zero so as to prevent operating point
deviation away from the MP point.
5) When the array voltage is equal to the MP point
voltage, then the change of duty ratio should be maintained at zero.
Taking the above points into consideration the fuzzy rules
are derived and the corresponding rule base is given in Table I.
These rules can be employed in any PV system for MP point
tracking irrespective of size and type of converter used. There
are several possible combinations of the degree of supports with
varying strengths to the corresponding rules, to satisfy different
conditions. However, for the present problem one such combination for the degree of support, resulting in better tracking performance, is 1.00, 0.5, 0.3, 0.0.
and
Membership functions for controller inputs, i.e., ,
are defined
incremental change in the controller output
on the common normalized range of [ 1,1]. In this paper, asymmetric triangular membership functions are considered and their
representation is shown in Fig. 2. These membership functions
are more dense at the center and, thus, provide more sensitivity
against variation in the SCA terminal voltage. This occurs particularly in cases where the change in the terminal voltage tends
to zero.
B. Defuzzification
A. Fuzzy Knowledge Base
is the
where is the maximum number of effective rules,
is the value corresponding to the
weighting factor, and
.
membership function of
Using the steps mentioned above, the fuzzy controller is implemented in real time for MP point tracking. The data acquisition system samples the terminal and reference voltages of the
, change
feedforward loop and computes error
of error signals at each sampling time. Employing these error
and change of error signals, the fuzzy controller determines the
control action required from the fuzzy knowledge base. Then
it computes the required change in the control voltage for the
PWM generator, which changes the duty ratio of the converter
so as to bring the terminal voltage to the desired value. In the
The rule base that associates the fuzzy output to the fuzzy
inputs is derived by understanding the system behavior. In
this paper, the fuzzy rules are designed to incorporate the
following considerations keeping in view the overall tracking
performance.
1) When the SCA terminal voltage is much greater than
, then change the duty ratio
the MP point voltage
of the converter so as to bring the terminal voltage to
.
2) When the SCA terminal voltage is less than the MP
point voltage, then the change of duty ratio is negative
In the defuzzification process the crisp value of the change of
duty cycle is obtained. This paper uses the well-known center
of gravity [17] method for defuzzification. The crisp value of
is computed by the following equation:
control output
(16)
VEERACHARY et al.: MPPT OF CIBC-SUPPLIED PV SYSTEM USING FUZZY CONTROLLER
753
The learning stage of the network is performed by updating
the weights and biases using the backpropagation algorithm
with the gradient-descent method in order to minimize a
mean-squared-error performance index
given as
(21)
The synaptic weights updating expressions are
(22)
(23)
Fig. 3.
where and are learning and momentum factors, respectively.
With the above equations in the forward direction, the ANN
training is performed.
Feedforward neural network.
following section the method of estimating the reference voltage
for the feedforward loop using neural networks is discussed.
V. ANN
ANNs are widely accepted as a technology offering an alternative way to solve complex problems. Particularly, in recent
years the application of ANN models in various fields is increasing because these ANNs operate like a black box model, requiring no detailed information about the system. They learn the
relationship between the input and output variables by studying
the previously recorded data. These trained ANNs can be used to
approximate an arbitrary input–output mapping of the system.
Among the available training algorithms, the backpropagation algorithm [18] is one of the most widely used, because it
is stable, robust, and easy to implement. We now begin by considering the feedforward neural network consisting of a single
hidden layer with sigmoid activation function. An input vector
is applied to the input layer of
hidden
the network as shown in Fig. 3. The net input of the
unit is
(17)
is the weight on the connection from the th input
where
for
represents the bias for hidden layer
unit,
neurons. Now, the output of the neurons in the hidden layer is
written as
(18)
VI. EXPERIMENTAL SYSTEM DESCRIPTION
The basic configuration of the proposed PV system is shown
in Fig. 1. The data acquisition system is set up by using PC, an
Interface AZI-3503 card, which mainly consists of 8-channel
12-bit A/D, D/A converters. For power measurements a digital
power meter (YOKOGAWA-WT130) is used, through which a
general purpose interface bus (GPIB) interface is connected to
the PC to record the SCA power data. The PWM modulator is
a voltage comparator made of an LF311 operational amplifier.
The reference signal to this comparator is the signal obtained
from the D/A converter, generated by means of the MPPT algorithm. A synthesized YOKOGAWA function generator (FG120)
was used to obtain phase displaced triangular carrier signals
to the PWM generator. The experimental prototype circuit was
built with an International Rectifier IRF530 N MOSFET with
suitable driver circuit, and the diode FML-32S. The artificial sun
is realized in the laboratory by means of an incandescent lamp
set. Further, the solar insolation level illuminated on the solar
panel is adjusted by controlling the power to this incandescent
lamp set. The coupled inductor is made of a toroidal core (TDK:
HF70 T) whose dimensions are 25 13 15 mm. To avoid saturation an air gap of about 1 mm is introduced in the core. The
mH,
measured values of this coupled inductor are:
mH, and
mH. To make fair comparison and to improve the performance of the converter, the
inductance values both for coupled and noncoupled cases are
, where
designed [11] satisfying the relation
is the coupling coefficient, and
and
are the self inductances of noncoupled and coupled cases, respectively.
VII. EXPERIMENTAL RESULTS AND DISCUSSIONS
and the net input to the neurons in the output layer becomes
(19)
Finally, the output of the neurons (reference voltage for the feed) in the output layer is
forward loop—
(20)
Comprehensive simulation studies were made to investigate
the feedforward-voltage-based MP point tracking capability of
CIBC-supplied PV system. To verify the theoretical analysis
and modeling equations developed in the previous sections, a
design example was considered. The SCA and converter parameters are given in Tables II and III, respectively. The PV array is
simulated using (3), whereas the converter and duty ratio control are defined by (7), (15), and (16). These sets of equations
are programmed in the MATLAB environment. The simulated
peak power tracking characteristics for one solar insolation
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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 4, AUGUST 2003
TABLE II
PV ARRAY PARAMETERS
TABLE III
CONVERTER PARAMETERS
Fig. 4. Experimental V
different solar insolations.
are shown in Figs. 6–8. To validate the simulation results, experimental investigations are presented in the following paragraphs.
In order to confirm the analysis and to evaluate the performance of the proposed fuzzy feedforward-voltage-based MP
point tracking, a prototype experimental PV-supplied converter
,
system was constructed. The experimental
characteristics of the PV generator for different solar insolations
are shown in Fig. 4. The comprehensive experimental studies
were made to investigate the influence of coupled inductor interleaved boost converter as an intermediate MP point tracker
for the PV-supplied system. Based on the theory given in the
preceding sections, a program was developed in the C environment for real-time MP tracking of the SCA. The solar insolation equivalent voltage signal was measured with the help of an
insolation meter, and the SCA input voltage was measured by
a sensing and scaling device. These two voltage signals were
sensed by the A/D converter of the data acquisition system. The
offline ANN sets the reference voltage to the feedforward loop
from the known solar insolation equivalent voltage signal.
At a given solar insolation for MP operation of the SCA, the
array operating voltage must be made equal to the MP point
by changing the converter duty ratio. The duty ratio
voltage
,
is controlled by means of a PWM generator control signal
obtained from the fuzzy controller. This fuzzy controller works
0I
=V
0P
characteristics of SCA module for
based on the controlling inputs, error
, and change of error
signals, generated with the help of the feedforward loop.
The error signal generated by the feedforward loop depends on
and the reference voltage
the instantaneous array voltage
. The reference voltage for the feedforward loop is to be
set such that it is equal to the MP point voltage at that solar insolation. Since the solar insolation is varying, the corresponding
for the feedforward loop should
reference voltage
also change according to the insolation variation. Therefore, for
MP point tracking control, an online determination of insolation-dependent reference voltage for the feedforward loop is essential. Since, the optimum array voltage is nonlinearly related
to the solar insolation, the linear function approximation techniques were not suitable. Under these circumstances, the ANNs
provide a viable solution for the online estimation of the insolation-dependent reference voltage. In these studies a three-layer
feedforward neural network with sigmoid activation function is
considered for the online estimation of reference voltage.
Experiments were conducted on the chosen PV generator to
determine the MP point voltages. At different solar insolations
the SCA terminal voltage, corresponding to MP point operation, was experimentally determined. For illustration its variawith solar insolation is shown in Fig. 5. Taking these
tion
values as reference patterns, the feedforward ANN is trained.
The gradient-descent algorithm is used in training, as it improves the performance of the ANN, reducing the total error
by changing the weights along its gradient. The learning rate
parameter is 0.5 and a momentum factor of 0.9 is used for satisfactory performance. This trained ANN is tested for different
solar insolations other than reference patterns. The test results
are also plotted in Fig. 5. The results obtained from the offline-trained ANN are in close agreement with the experimental
results. Now, this trained ANN is combined with the peak power
tracking scheme for online estimation of the reference voltage
to the feedforward loop at different solar insolations. The reference voltage variation with solar insolation, estimated from the
offline-trained ANN, is also shown in Fig. 5.
VEERACHARY et al.: MPPT OF CIBC-SUPPLIED PV SYSTEM USING FUZZY CONTROLLER
Fig. 7.
Fig. 5.
755
Error in the terminal voltage tracking at solar insolation
9.
Feedforward loop reference voltage variation with solar insolation.
Fig. 6. Experimental SCA terminal voltage tracking characteristics at solar
.
insolation
9
The developed scheme tracks the MP point continuously by
adjusting the SCA terminal voltage to MP point voltage. At a
the experimental tracking characteristics,
solar insolation
viz., terminal voltage, error in the terminal voltage, and SCA
power are shown in Figs. 6–8, respectively. In Fig. 8, simulation
results are slightly higher than the experimental values. This is
mainly due to the factors that the simulated characteristic was
obtained assuming ideal conditions, whereas the experimental
observations include nonidealities of the converter, changes in
the parameter values of the PV generator, errors involved in
measuring systems, etc. Studies were also made to observe the
effectiveness of the developed tracking scheme for changing
solar insolations. Experimental observations show that the
developed algorithm is capable of tracking MP point even
for variable solar insolations. The experimental array power
tracking characteristics for different solar insolations ( ,
,
, and
) are also obtained as shown in Fig. 9. For
verification, MP points were determined experimentally by
Fig. 8. Experimental SCA power tracking characteristics at solar insolation
.
9
connecting a variable load resistance. Comparing the tracking
characteristics (Fig. 9) with the MP points, it can be noticed
that the feedforward control strategy is capable of extracting
MP from the SCA. The tracking capability of the converter
system is also verified under partial shading conditions. For
illustration, array power tracking characteristic, when four cells
shaded by 50% is shown in Fig. 10. Under this condition, the
SCA power output decreases and settles to a new MP point as
shown in Fig. 10.
The proposed fuzzy-controller-based scheme is evaluated
by comparing its tracking performance with that of a conventional PI-controller-based scheme. Among the possible
set of controller parameters, we have chosen the optimum
parameters for the PI controller (based on the operator experience, trial and error experiments) that results in better tracking
performance. For illustration, the experimental MP tracking
with the conventional PI
characteristics at solar insolation
controller are plotted in Figs. 6 –8. It can be noticed from these
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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, VOL. 50, NO. 4, AUGUST 2003
Fig. 9. Experimental SCA power tracking characteristics for different solar
insolations.
Fig. 10. Experimental SCA power tracking characteristics for partial shading
condition.
characteristics that the fuzzy controller improves the tracking
performance over the conventional PI-controller-based PV
system. In particular, for variable solar insolations the fuzzy
controller provides better performance compared to the PI
controller. Under variable solar insolation conditions, if the
conventional PI controller is used for achieving the optimum
tracking performance, it requires tuning of the controller
gains appropriately depending on the solar insolation. From
the experimental results, it is observed that the feedforward
control strategy with fuzzy controller is a promising one with
reference to peak power tracking. Furthermore, it does not
require any tuning of the parameters, which is the case with
the conventional PI controller, wherein the controller gain
parameters need to be changed when solar insolation changes.
The validity of the proposed tracking scheme is also verified for
a battery-charging application. For illustration, experimental
results were presented for one particular solar insolation
Fig. 11. Experimental SCA power tracking characteristics for battery load.
Fig. 12. Experimental SCA power tracking characteristics with different
membership functions.
in Fig. 11. From these observations it is understood that, in
this case the fuzzy control also improves the power tracking
performance as compared to the PI-controller-based power
tracking.
The tracking performance of the converter with fuzzy
controller also depends on the type of membership functions
considered. In these studies, triangular membership functions
,
are used. Experimental
as shown in Fig. 2 for ,
tracking performance is obtained with: 1) triangular membership functions concentrated at the center (Membership-1:
,
) and 2) equal base and 50% overlap with
other neighboring membership functions (Membership-2:
,
). These power tracking characteristics are
plotted in Fig. 12. From these results, it is observed that the
,
triangular membership functions (Membership-1:
), result in faster tracking performance as compared
to equal base and 50% overlap with other neighboring membership functions. Faster tracking response may be due to the
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757
TABLE IV
COMPARISON OF EXPERIMENTAL RIPPLE AMPLITUDES AND EFFICIENCY FOR NONCOUPLED AND COUPLED CONVERTER SYSTEM
shape of membership functions. The more dense at the center
of membership functions the more will be the sensitivity.
However, experimental results show slight overshoot with
dense membership functions. This may be due to the degree of
support given for the fuzzy rules and errors involved in the data
acquisition system.
A comparative study of noncoupled and coupled interleavedboost-converte-supplied PV systems is made and their steadystate performance parameters were measured. For illustration,
experimental ripple amplitudes of array current, load voltage,
switch current, and efficiencies at different loads are tabulated
in Table IV for comparison purposes. Tabulated measurements
show that the inverse coupling among the inductors will have
a substantial effect on reducing the ripple content, especially
load voltage and switch current stresses. The improvement in
the efficiency is about 2%–5%. The observed reduction in load
voltage ripple is about 50%–70%, while in the switch current
stress is 70%–80%. This point clearly indicates that lower current rating switching devices are sufficient for the coupled converter system.
The above experimental results agree well with those obtained from computer simulations. However, slight differences
in these results are attributed to the following factors: 1) the
analysis was made on the assumption that all the switching
devices are ideal, i.e., nonidealities of the converter parameters
such as forward voltage drops, on-state resistances of the
switching devices, and other parasitics are not taken into
account; 2) use of simplified PV array model; and 3) errors in
the measuring system, drops in the parasitics, etc.
VIII. CONCLUSIONS
A fuzzy feedforward-voltage-based MP point tracking
scheme was developed for the CIBC-supplied PV system.
Analytical expressions for the PV source and converters were
derived. An offline ANN, trained using the backpropagation
algorithm, was utilized for online estimation of reference
voltage in the feedforward loop. Simulation and experimental
results demonstrated the peak power tracking capability of
the proposed scheme. It was also demonstrated that the fuzzy
control improves the tracking performance compared to the
conventional PI controller and, thus, avoids the tuning of
controller parameters. Furthermore, use of coupling among
the inductor branches improves the steady-state performance
without deteriorating the dynamic performance.
APPENDIX
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Mummadi Veerachary (M’99) was born in Survail,
India, in 1968. He received the Bachelor degree from
the College of Engineering, Anantapur, JNT University, Hyderabad, India, in 1992, the Master of Technology degree from the Regional Engineering College, Warangal, India, in 1994, and the Dr.Eng. degree from the University of the Ryukyus, Nakagami,
Okinawa, Japan, in 2002.
From 1994 to 1999, he was an Assistant Professor
in the Department of Electrical Engineering, JNTU
College of Engineering, Anatapur, India. From October 1999 to March 2002, he was a Research Scholar in the Department of
Electrical and Electronics Engineering, University of the Ryukyus. Since July
2002, he has been with the Department of Electrical Engineering, Indian Institute of Technology, New Delhi, India, where he is currently an Assistant Professor. His fields of interest are power electronics and applications, modeling
and simulation of large power electronic systems, design of power supplies for
spacecraft systems, control theory application to power electronic systems, and
fuzzy neuro-controller applications to photovoltaic systems.
Dr. Veerachary was the recipient of the IEEE Industrial Electronics Society
Student Travel Grant Award for the year 2001, Best Paper Award at the International Conference on Electrical Engineering (ICEE-2000) held in Kitakyushu,
Japan, and Best Researcher Award for the year 2002 from the President of the
University of the Ryukyus. He is a Member of the IEEE Industrial Electronics
Society, Institute of Electrical Engineers of Japan, and Institution of Engineers
India.
Tomonobu Senjyu (A’89–M’02) was born in Saga
Prefecture, Japan, in 1963. He received the B.S. and
M.S. degrees from the University of the Ryukyus,
Nakagami, Okinawa, Japan, in 1986 and 1988, respectively, and the Ph.D. degree from Nagoya University, Nagoya, Japan, in 1994, all in electrical engineering.
Since 1988, he has been with the Department of
Electrical and Electronics Engineering, Faculty of
Engineering, University of the Ryukyus, where he
is currently a Professor. His research interests are in
the areas of stability of ac machines, advanced control of electrical machines,
and power electronics.
Dr. Senjyu is a Member of the Institute of Electrical Engineers of Japan.
Katsumi Uezato was born in Okinawa Prefecture,
Japan, in 1940. He received the B.S. degree from
the University of the Ryukyus, Nakagami, Okinawa,
Japan, in 1963, the M.S. degree from Kagoshima
University, Kagoshima, Japan, in 1972, and the
Ph.D. degree from Nagoya University, Nagoya,
Japan, in 1983, all in electrical engineering.
Since 1972, he has been with the Department of
Electrical and Electronics Engineering, Faculty of
Engineering, University of the Ryukyus, where he
is currently a Professor. He is engaged in research on stability and control of
synchronous machines.
Dr. Uezato is a Member of the Institute of Electrical Engineers of Japan.