Fuzzy based Modular Cascaded H-Bridge Multilevel PV Inverter with Distributed MPPT for Grid - Connected Applications

52 International Journal for Modern Trends in Science and Technology
International Journal for Modern Trends in Science and Technology
Volume: 02, Issue No: 11, November 2016
http://www.ijmtst.com
ISSN: 2455-3778
Fuzzy based Modular Cascaded H-Bridge Multilevel
PV Inverter with Distributed MPPT for Grid -
Connected Applications
G. Mani1
| B. Gopal2
1PG Scholar, Department of EEE, KITS Engineering College, Khammam (Dt), Telangana, India.
2Associate Professor, Department of EEE, KITS Engineering College, Khammam (Dt), Telangana, India.
To Cite this Article
G. Mani, B. Gopal Fuzzy based Modular Cascaded H-Bridge Multilevel PV Inverter with Distributed MPPT for Grid -
Connected Applications, International Journal for Modern Trends in Science and Technology, Vol. 02, Issue 11, 2016, pp.
52-59.
The inverters are categorized according to the configuration of the PV system, the configuration of the
conversion stages within the inverter and whether they use transformers. After the introduction of the state of
the art of inverters for PV systems with and without transformers, the paper focuses on some known
problems and challenges for transformer less inverters. Topologies without transformers have big
advantages like low weight, volume and cost. In addition they often reach higher efficiencies than topologies
with transformers. Eliminating the leakage current is one of the most important issues for transformer less
inverters in grid-connected photovoltaic system applications, where the technical challenge is how to keep the
system common-mode voltage constant to reduce the leakage current.
To realize better utilization of PV modules and maximize the solar energy extraction, a distributed
maximum power point tracking control scheme is applied to both single- and three-phase multilevel inverters,
which allows independent control of each dc-link voltage. For three-phase grid-connected applications, PV
mismatches may introduce unbalanced supplied power, leading to unbalanced grid current.
KEYWORDS: cascaded multilevel inverter, distributed maximum power point (MPP) tracking (MPPT),
modular, modulation Compensation.
Copyright © 2016 International Journal for Modern Trends in Science and Technology
All rights reserved.
I. INTRODUCTION
In recent years, the efforts to spread the use of
renewable energy resources instead of pollutant
fossil fuels and other forms have increased.
Photovoltaic systems offer the possibility of
converting sunlight into electricity. The
transformation of electricity through photovoltaic
provides case of installation, maintenance and
become more affordable. One of the most common
control strategies structures applied to
decentralized power generator is based on power
direct control employing a controller for the dc link
voltage and a controller to regulate the injected
current to the utility network. The system
components and power control scheme are
modeled in terms of dynamic behaviors. An
improved MPPT converter with current
compensation method for small-scaled
PV-applications is presented in [1-3]. He proposed
method implements maximum power point
tracking (MPPT) by variable reference current
which is continuously changed during one
sampling period. Lot of research has been done on
maximum power point tracking of Photovoltaic cell.
He introduced a new maximum power point
ABSTRACT

G. Mani, B. Gopal : Fuzzy based Modular Cascaded H-Bridge Multilevel PV Inverter with Distributed MPPT for Grid -
tracking algorithm for photovoltaic arrays is
proposed in [4]. The algorithm detects the
maximum power point of the PV. The computed
maximum power is used as a reference value of the
control system. The proposed MPPT has several
advantages: simplicity, high convergence speed,
and independent on PV array characteristics. The
many different techniques for maximum power
point tracking of photovoltaic (PV) arrays are
discussed in [5]. Paper should serve as a
convenient reference for future work in PV power
generation in [6].
The modular cascaded H-bridge multilevel
inverter, which requires an isolated dc source for
each H-bridge, is one dc/ac cascaded inverter
topology. The separate dc links in the multilevel
inverter make independent voltage control
possible. As a result, individual MPPT control in
each PV module can be achieved, and the energy
harvested from PV panels can be maximized.
Meanwhile, the modularity and low cost of
multilevel converters would position them as a
prime candidate for the next generation of efficient,
robust, and reliable grid connected solar power
electronics. A modular cascaded H-bridge
multilevel inverter topology for single- or
three-phase grid-connected PV systems is
presented in this paper. The panel mismatch
issues are addressed to show the necessity of
individual MPPT control, and a control scheme
with distributed MPPT control is then proposed.
The distributed MPPT control scheme can be
applied to both single and three-phase systems
[7-9].
In addition, for the presented three-phase
grid-connected PV system, if each PV module is
operated at its own MPP, PV mismatches may
introduce unbalanced power supplied to the
three-phase multilevel inverter, leading to
unbalanced injected grid current. To balance the
three-phase grid current, modulation
compensation is also added to the control system.
An improved perturbation and observation
maximum power point tracking algorithm for PV
arrays. Improved perturbation and observation
method of Maximum Power Point Tracking control
for photovoltaic power systems in [10]. He
explained about the perturbation observation
method. Maximum photovoltaic power tracking an
algorithm for rapidly changing atmospheric
conditions explained in [11]. Evaluation of
maximum power point tracking methods for grid
connected photovoltaic systems discussed in [12].
In the maximum power point tracking method so
many methods are available but he used the
suitable tracker. The fuzzy inference is carried out
by using Sugeno‘s method in [13]. So this is
Sugeno, or TakagiSugeno-Kang, method of fuzzy
inference. Introduced in 1985[9], it is similar to the
Mamdani method in many respects. Hardware
Implementation of Fuzzy Logic based Maximum
Power Point Tracking Controller for PV System
explained in [14]. The electric power supplied by a
photovoltaic power generation systems depends on
the solar irradiation and temperature. A
Rule-Based Fuzzy Logic Controller for a PWM
Inverter in Photo-voltaic Energy Conversion
Scheme discussed in [15]. The modeling and
simulation of the electric part of a grid connected
photovoltaic generation system explained. This
work proposed a fuzzy logic based controller to
track MPPT in photovoltaic cell.
II. SYSTEM DESCRIPTION
Modular cascaded H-bridge multilevel inverters
for single and three-phase grid-connected PV
systems are shown in Fig.1. Each phase consists of
n H-bridge converters connected in series, and the
dc link of each H-bridge can be fed by a PV panel or
a short string of PV panels. The cascaded multilevel
inverter is connected to the grid through L filters,
which are used to reduce the switching harmonics
in the current.
By different combinations of the four switches in
each H-bridge module, three output voltage levels
can be generated: −vdc, 0, or +vdc. A cascaded
multilevel inverter with n input sources will provide
2n + 1 levels to synthesize the ac output waveform.
This (2n + 1)-level voltage waveform enables the
reduction of harmonics in the synthesized current,
reducing the size of the needed output filters.
Multilevel inverters also have other advantages
such as reduced voltage stresses on the
semiconductor switches and having higher
efficiency when compared to other converter
topologies.
III. PANEL MISMATCHES
PV mismatch is an important issue in the PV
system. Due to the unequal received irradiance,
different temperatures, and aging of the PV panels,
the MPP of each PV module may be different. If
each PV module is not controlled independently,
the efficiency of the overall PV system will be
decreased.
To show the necessity of individual MPPT
control, a five-level two-H-bridge single-phase

inverter is simulated in MATLAB/SIMULINK. Each
H-bridge has its own 185-W PV panel connected as
an isolated dc source. The PV panel is modeled
according to the specification of the commercial PV
panel from Astronergy CHSM-5612M.
Consider an operating condition that each panel
has a different irradiation from the sun; panel 1
has irradiance S =1000 W/m2, and panel 2 has S =
600 W/m2. If only panel 1 is tracked and its MPPT
controller determines the average voltage of the two
panels, the power extracted from panel 1 would be
133 W, and the power from panel 2 would be 70 W,
as can be seen. Without individual MPPT control,
the total power harvested from the PV system is
203 W.
However, the MPPs of the PV panels under the
different irradiance. The maximum output power
values will be 185 and 108.5 W when the S values
are 1000 and 600 W/m2, respectively, which
means that the total power harvested from the PV
system would be 293.5 W if individual MPPT can be
achieved. This higher value is about 1.45 times of
the one before. Thus, individual MPPT control in
each PV module is required to increase the
efficiency of the PV system.
In a three-phase grid-connected PV system, a PV
mismatch may cause more problems. Aside from
decreasing the overall efficiency, this could even
introduce unbalanced power supplied to the
three-phase grid-connected system. If there are PV
mismatches between phases, the input power of
each phase would be different. Since the grid
voltage is balanced, this difference in input power
will cause unbalanced current to the grid, which is
not allowed by grid standards. For example, to
unbalance the current per phase more than 10% is
not allowed for some utilities, where the percentage
imbalance is calculated by taking the maximum
deviation from the average current and dividing it
by the average current.
Fig. 1. Topology of the modular cascaded H-bridge multilevel
inverter for grid-connected PV systems.
To solve the PV mismatch issue, a control
scheme with individual MPPT control and
modulation compensation is proposed. The details
of the control scheme will be discussed in the next
section.
IV. CONTROL SCHEME
A.Distributed MPPT Control
In order to eliminate the adverse effect of the
mismatches and increase the efficiency of the PV
system, the PV modules need to operate at different
voltages to improve the utilization per PV module.
The separate dc links in the cascaded H-bridge
multilevel inverter make independent voltage
control possible. To realize individual MPPT control
in each PV module, the control scheme proposed is
updated for this application.
The distributed MPPT control of the
three-phase cascaded H-bridge inverter is shown
in Fig.2. In each H-bridge module, an MPPT
controller is added to generate the dc-link voltage
reference. Each dc-link voltage is compared to the
corresponding voltage reference, and the sum of all
errors is controlled through a total voltage
controller that determines the current reference
Idref. The reactive current reference Iqref can be set to
zero, or if reactive power compensation is required,
Iqref can also be given by a reactive current
calculator. The synchronous reference frame
phase-locked loop (PLL) has been used to find the
phase angle of the grid voltage. As the classic
control scheme in three-phase systems, the grid
currents in abc coordinates are converted to
dqcoordinates and regulated through
proportional–integral (PI) controllers to generate

the modulation index in the dqcoordinates, which
is then converted back to three phases.
The distributed MPPT control scheme for the
single-phase system is nearly the same. The total
voltage controller gives the magnitude of the active
current reference, and a PLL provides the
frequency and phase angle of the active current
reference. The current loop then gives the
modulation index.
To make each PV module operate at its own
MPP, take phase a as an example; the voltages vdca2
to vdcanare controlled individually through n − 1
loops. Each voltage controller gives the modulation
index proportion of one H-bridge module in phase
a. After multiplied by the modulation index of
phase a, n − 1 modulation indices can be obtained.
Also, the modulation index for the first H-bridge
can be obtained by subtraction. The control
schemes in phases b and c are almost the same.
The only difference is that all dc-link voltages are
regulated through PI controllers, and n modulation
index proportions are obtained for each phase.
Fig.2. Control scheme for three-phase modular cascaded
H-bridge multilevel PV inverter.
A phase-shifted sinusoidal pulse width
modulation switching scheme is then applied to
control the switching devices of each H-bridge.
It can be seen that there is one H-bridge module
out of N modules whose modulation index is
obtained by subtraction. For single-phase systems,
N = n, and for three-phase systems, N = 3n, where n
is the number of H-bridge modules per phase. The
reason is that N voltage loops are necessary to
manage different voltage levels on N H-bridges, and
one is the total voltage loop, which gives the
current reference. So, only N − 1 modulation
indices can be determined by the last N – 1 voltage
loops, and one modulation index has to be
obtained by subtraction.
Many MPPT methods have been developed and
implemented. The incremental conductance
method has been used in this paper. It lends itself
well to digital control, which can easily keep track
of previous values of voltage and current and make
all decisions.
B. Modulation Compensation
As mentioned earlier, a PV mismatch may cause
more problems to a three-phase modular cascaded
H-bridge multilevel PV inverter. With the individual
MPPT control in each H-bridge module, the input
solar power of each phase would be different, which
introduces unbalanced current to the grid. To solve
the issue, a zero sequence voltage can be imposed
upon the phase legs in order to affect the current
flowing into each phase. If the updated inverter
output phase voltage is proportional to the
unbalanced power, the current will be balanced.
Thus, the modulation compensation block, as
shown in Fig. 3, is added to the control system of
three-phase modular cascaded multilevel PV
inverters. The key is how to update the modulation
index of each phase without increasing the
complexity of the control system. First, the
unbalanced power is weighted by ratio rj, which is
calculated as
Fig. 3. Modulation compensation scheme.
(1)
Where Pinjis the input power of phase j (j = a, b, c),
and Pinav is the average input power.
Then, the injected zero sequence modulation index
can be generated as
(2)
Where djis the modulation index of phase j (j = a, b,
c) and is determined by the current loop controller.
The modulation index of each phase is updated by

(3)
Only simple calculations are needed in the scheme,
which will not increase the complexity of the
control system. An example is presented to show
the modulationcompensation scheme more clearly.
Assume that the input power of each phase is
unequal
(4)
V. FUZZY CONTROLLER
The control scheme consists of Fuzzy controller,
limiter, and three phase sine wave generator for
reference current generation and generation of
switching signals. The peak value of reference
currents is estimated by regulating the DC link
voltage. The actual capacitor voltage is compared
with a set reference value. The error signal is then
processed through a Fuzzy controller, which
contributes to zero steady error in tracking the
reference current signal.
A fuzzy controller converts a linguistic control
strategy into an automatic control strategy, and
fuzzy rules are constructed by expert experience or
knowledge database. Firstly, input voltage Vdc and
the input reference voltage Vdc-ref have been
placed of the angular velocity to be the input
variables of the fuzzy logic controller. Then the
output variable of the fuzzy logic controller is
presented by the control Current Imax. To convert
these numerical variables into linguistic variables,
the following seven fuzzy levels or sets are chosen
as: NB (negative big), NM (negative medium), NS
(negative small), ZE (zero), PS (positive small), PM
(positive medium), and PB (positive big) as shown
in Figure 10.
The fuzzy controller is characterized as follows:
1) Seven fuzzy sets for each input and output;
2) Fuzzification using continuous universe of
dis-course;
3) Implication using Mamdani's ‗min‘ operator;
4) De-fuzzification using the ‗centroid‘ method.
Fig 4 Conventional fuzzy controller
Fig 5(a) Input Vdc normalized membership function; (b) Input
Vdc-ref Normalized Membership Function; (c) Output Imax
Normalized Membership Function
Fuzzification: the process of converting a
numerical variable (real number) convert to a
linguistic variable (fuzzy number) is called
fuzzification.
De-fuzzification: the rules of FLC generate
required output in a linguistic variable (Fuzzy
Number), according to real world requirements,
linguistic variables have to be transformed to crisp
output (Real number).
Database: the Database stores the definition of the
membership Function required by fuzzifier and
defuzzifier.
Rule Base: the elements of this rule base table are
determined based on the theory that in the
transient state, large errors need coarse control,
which requires coarse in-put/output variables; in
the steady state, small errors need fine control,
which requires fine input/output variables. Based
on this the elements of the rule table are obtained
as shown in Table 1, with ‗Vdc‘ and ‗Vdc-ref‘ as
inputs.

VI. MATLAB/SIMULATION RESULTS
Fig 6 Simulation model of modular cascaded distributed MPPT
control based grid connected system
Fig 7 Simulation waveform of after and before compensation
Fig 8 Simulation waveform of grid current
Fig 9 Simulation waveform of output MLI voltage
Fig 10 Simulation waveform of Power extracted from PV panel
Fig 11 Simulation waveform of output grid power
Fig 12 Simulation waveform of harmonic analysis
Fig 13 Simulation waveform of fuzzy based distributed MPPT MLI
grid connected system
Fig 14 Simulation waveform of Fuzzy control circuit diagram

Fig 15 Simulation waveform of grid current
Fig 16 Simulation waveform of Inverter voltage
Fig 17 Simulation waveform of Three phase Output power
Fig 18 Simulation waveform of Harmonic analysis
VII. CONCLUSION
The purpose of this paper is to study the
harmonic profile of Grid connected PV system for
different MPPT methods including perturbation &
observation incremental conductance and Fuzzy
logic controller. The PV simulation system used in
this paper is set up under Matlab/Simulink
environment. After accomplishing the model of PV
modules, the models of Multi level inverter and
MPPT systems are combined with it to complete the
PV simulation system with the MPPT functions.
The proposed fuzzy MPPT sets the optimum DC
bus voltage reference for the inverter without the
need for a dc–dc converter. The control of the active
and reactive power is done using a PI controller.
Fuzzy based three phase single stage grid
connected PV system has been proposed. The
harmonics are eliminated by the double tuned
resonant filter. The power extracted from the PV
array increased by the fuzzy based MPPT which
develop the system performance in varying weather
condition.
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Fuzzy based Modular Cascaded H-Bridge Multilevel PV Inverter with Distributed MPPT for Grid - Connected Applications

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