The Fig 4.9 shows output waveform of
multilevel inverter with Fuzzy logic controller. It produces four level output.
The output of each phase is shown in Fig 4.9. The Fuzzy logic controller can
generate pulse from error calculation. From this, the controller can provide a
smart control strategy for the multilevel inverter. The output level of
multilevel inverter is four. The Total harmonic Distortion is about 6.87% is
shown in Fig 4.9.
total simulation period is 0.5 second. Using the facilities available in MATLAB
the fuzzy logic controller is simulated to be in operation. The error between
the output of pi controller & reference voltage can be send as input to the
fuzzy logic controller. By using this error calculation the fuzzy logic
controller has able to produce suitable control for this multilevel inverter.
In this model, fuzzy logic control rules are used
for an effective operation and also it offers an outstanding control for
multilevel inverter and also it eliminates the voltage and current magnitude of
harmonics with good dynamic response. The different waveforms are analyzed and
conferred with the proposed scheme.
Fig 4.6 shows the software implementation model of the proposed scheme. The
development software here used is MATLAB version 13.0, the tool which confers
with this implementation is SIMULINK. The implementation block consists of
source, multilevel inverter, load and fuzzy logic controller. In this
multilevel inverter the bidirectional switches are used. It is a four-level
structure with two bidirectional switches (Q10 and Q11)
used for sharing among the three phases. This multilevel inverter is used to
convert DC voltage of renewable energy source into AC voltage and also reduce
Total Harmonic Distortion (THD).
a non adaptive fuzzy logic controller, the methodology used and the results of
the nine steps mentioned above are fixed, whereas in an adaptive fuzzy logic
controller, they are adaptively modified based on some adaption law in order to
optimize the controller.
defuzzification to form a crisp output.
the fuzzy outputs recommended by each rule.
fuzzy approximate reasoning to infer the output contributed to each rule.
the inputs to the controller.
appropriate scaling factors for the input and the output variables in order to
normalize the variables to the 0,1 or the -1,1 interval(optional).
the fuzzy relationships between the inputs or states fuzzy subsets on the one
hand and the outputs fuzzy subsets on the other hand, thus forming the rule
or determine a membership function for each fuzzy subset.
the universe of discourse or the interval spanned by each variable into a
number of fuzzy subsets, assigning each a linguistic label (subset include all
the elements in the universe).
the variables (inputs, states and outputs) of the plant.
Fuzzy logic controller is
explained by TimothyJ.Ross (1997). The steps in designing a fuzzy logic control
system are as follows.
B. Fuzzy Logic Controller – Design Steps
It is more robust than conventional
It can handle non-linearity.
It does not need accurate mathematical
Recently, fuzzy logic controller (FLC) is used in
power electronic systems for adjustable motor drives and active power filter
applications. FLCs are widely used because of the following reasons.
When designing a fuzzy logic controller, the
information obtained from the operator is more important than the dynamic
mathematical model of the system. The real problem in a control system is the
output signal or error signal in the physical environment. This information
plays an important role in the processes of closed loop systems because the
closed loop systems are commanded according to this information. The aim of
fuzzy logic control is to reduce the error of the system to a minimum. The size
of the controller input is associated with the size of error. The rate of
change of error affects the determination of the controller input. Therefore,
as the linguistic changes, the error and the change of error are used to give
the decision according to the controller rules. The most important state for
solving a problem by using fuzzy logic theories is the determination of the
membership functions. In many studies, it has been shown that the degree of
membership of a fuzzy logic set is directly related to the senses obtained from
samples in some applications. In other applications, it is related to
statistical or mathematical estimations under specific assumptions.
of Fuzzy Logic Controller
III CONTROL SYSTEM:
improved control strategy for the switch-sharing-based multilevel inverter
suitable for PV applications has been presented in this report. The strategy is
based on the Fuzzy logic controller. This multilevel inverter act as interface
between renewable energy sources and load. By using this multilevel inverter,
the total harmonics distortion will be reduced and the power quality of the
system will be increased. The THD reduction is done by the fuzzy logic
controller which provides an effective control strategy for this three phase
multilevel inverter. The
proposed control strategy enhances the controller’s ability to produce good
quality of the load current even during disturbances. The performance of the
proposed control strategy has been experimentally assessed and the results show
that the desired outputs have been fully accomplished. It reduces the Total
Harmonic Distortion about 6.87%.