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.

The

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.

The

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).

IV SIMULATION

RESULTS:

In

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.

9.

Apply

defuzzification to form a crisp output.

8.

Aggregate

the fuzzy outputs recommended by each rule.

7.

Use

fuzzy approximate reasoning to infer the output contributed to each rule.

6.

Fuzzify

the inputs to the controller.

5.

Choose

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).

4.

Assign

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

base.

3.

Assign

or determine a membership function for each fuzzy subset.

2.

Partitioning

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).

1.

Identify

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

controllers.

·

It can handle non-linearity.

·

It does not need accurate mathematical

model.

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.

A. Design

of Fuzzy Logic Controller

III CONTROL SYSTEM:

V CONCLUSION

An

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%.