Dynamic Voltage Restorer With Artificial Intelligent Controller For Voltage Sag Mitigation cover
perpustakaan.uns.ac.id
digilib.uns.ac.id
CHAPTER IV
DATA AND ANALYSIS
There were four simulations done in this study, i.e., simulation of
distribution network without installation of DVR, simulation of DVR based on the
Park’s traansformation, simulation of DVR based on Mamdani-type fuzzy logic,
and simulation of dynamic voltage restorer based on Sugeno-type fuzzy logic in
distribuion network. DVR were tested using MATLAB SIMULINK, result were
analyzed. The votage sag occured at the time duration of o.4 second.
4.1 Control Surfaces using Mamdani-type and Sugenu-type Fuzzy Logic
The plot was of simulation of Mamdani-type fuzzy logic and Sugeno-type
fuzzy logic. The surface viewer of Mamdani-type fuzzy logic and Sugeno-type
Output of controller
fuzzy logic is presented in figure 4.1 and 4.2
200
100
0
-100
-200
200
1
0
Delta Error
-200
-0.5
1
0
-0.5
Input of controller
Figure 4.1 Control Surfaces of Mandani-Type Fuzzy Logic
commit to user
23
perpustakaan.uns.ac.id
digilib.uns.ac.id
Output of controller
200
100
0
-100
-200
-1
200
Delta Error
0.5
0
0
- 0.5
-200
1
Input of controller
Figure 4.2 Control Surfaces of Sugeno-Type Fuzzy Logic
Figure 4.1 and 4.2 shows the control surface of the logic based system, in
Mamdani-type and Sugeno-type fuzzy systems the antecedent and consequent
fuzzy sets were often chosen to be triangular or Gaussian. It was also prevalent
that the input membership functions depost in such a way that the membership
rate of the rule promoted always sum up to one. In this case, and if the rule base
was on connective form, Rule can beconstrued each rule as defining the output
rate for one point in the input value. The rate in the input space was acquired by
taking the centers of the input fuzzy logicsets. Then the output value was the
center of the output fuzzy logic set (center-of-area method). The fuzzy reasoning
resulted in a smooth interpolation between the values in the input space as it can
be seen in figure above with this interjection. Mamdani-type fuzzy logic can be
observed as defining apiecewise constant function with interjection. All the rules
that apply were proceeding, using the membership functions and truth values
gained from the inputs, to determine the result of the rule, and result in turn was
mapped into a membership function and truth amount controlling the output
commit to user
variable.
24
perpustakaan.uns.ac.id
digilib.uns.ac.id
4.2 Simulation of Dynamic Voltage Restorer System
The first simulation was done without dynamic voltage restorer and a three
phase fault is applied to the system for a time duration of 400 ms The second
simulation is carried out at the same scenario as above but using dynamic voltage
restorer with Park’s transformation controller. The simulations were carried out at
the same scenario as above but have different scenatio usage of dynamic voltage
restorer with Sugeno-type and Mamdani-type fuzzy logic. Below is the result of
the reduction of sag on a network system with the application of the three
methods, the Park’s tranformation controller, Sugeno-type fuzzy logic and
Mamdani-type fuzzy logicon the same distribution network. Here Sugeno-type
fuzzy logic and Mamdani-type fuzzy logic have the functionin order to mitigate
the value of voltage sag during single, two, and three phase distribution with a
level of handling most perfect way.
Voltage (p.u)
Park’s Tranformation
88%
Without Injection 14%
Time (sec)
Figure 4.3 Compensation of Sag by Single-Phase Park’s Transformation and
)))))))))))))))Without Injection
commit to user
25
perpustakaan.uns.ac.id
digilib.uns.ac.id
Figure 4.3 shows the single-phase fault scenario results with comparison
without injection, and the park’s transformation controller when system gave
single-phase fault with connected to dynamic voltage restorer with Park’s
transformation controller. The disadvantage of Park’s transformation mainly lies
in the unable nature of the control, which makes it was not good enough for this
application of dynamic voltage restorer. An error was not detected perfectly, the
Park's transformation controller was not operated to alter the setting of the final
control element in perfect way such a way as to minimize the error in the least
possible time with the minimum disturbance to the distribution networks. To
achieve this objective, different actions could be taken by the controller and hence
different signals were sent to the final control element. This can be illustrated by
an experiment used as a scenario of dynamic voltage restorer.
Voltage (p.u)
Sugeno-type fuzzy
logic 98%
Mamdani-type fuzzy
logic 98%
Without Injection 14%
Time (sec)
Figure 4.4 Compensation of Voltage Sag by Single-Phase Sugeno-Type and
>>>>>>>>>Mamdani-Type fuzzy Logic and Without Injection
From Figure 4.4 it wasclear that the result of Sugeno-type, Mamdani-type
to user
fuzzy logic,and withoutinjection commit
when gave
single-phasein distribution network.
26
perpustakaan.uns.ac.id
digilib.uns.ac.id
The voltage restore based on Sugeno-type fuzzy logic and Mamdani-type fuzzy
logic can be overcome by 0.98 p.u respectively. From Sugeno-type fuzzy logic,
and Mamdani-type fuzzy logic showed above the result the best solving control to
restore the voltage in distribution network. From the single, two, andthree-phase
distribution cases of voltage sag. In the end, it can be clear that the Sugenno-type
fuzzy logic and Mamdani-type fuzzy logic was able to overcome the problem of
voltage sagin order to obtaind the best reference voltage during controllingin
distribution network.
Voltage (p.u)
Sugeno-type fuzzy
logic 96%
Mamdani-type fuzzy
logic 96%
Park’s Tranformation
84%
Without Injection 14%
Time (sec)
Figure 4.5 Compensation of Sag by two-phase Park’s Transformation, Sugeno>>>>>>>>>Type, Mamdani-Type Fuzzy Logic and without Injection
Figure 4.5 it can be seen the result of Sugeno-type, Mamdani-type fuzzy
Logic, Park’s transformation, and without injection when given two-phase in
distribution network. The voltage restore based on Sugeno-type fuzzy logic and
mamdani-type fuzzy logic was able to overcome by 0.96 p.u respectively. Park’s
transformation was able to overcome by 0.84 p.u.
commit to user
27
perpustakaan.uns.ac.id
digilib.uns.ac.id
Sugeno-type fuzzy logic and Mamdani-type fuzzy logic showed that the
best solving control to restore the voltage sag during distribution. From the single,
two, and three-phase distribution cases of voltage sag. In the end, it was clear that
the Sugenno-type fuzzy logic and Mamdani-type fuzzy logic was able to
overcome the problem of voltage sag in order to obtain the best reference voltage
during controlling system.
Voltage (p.u)
Sugeno-type fuzzy
logic 94%
Sugeno-type fuzzy
logic 94%
Park’s
Transformation
81%
Without injection 14%
Time (sec)
Figure 4.6 Compensation of Sag by Three-Phase Park’s Transformation,
___________Sugeno-Type and Mamdani-type Fuzzy Logic and Without Injection
Figure 4.6 it is clear that the result of Sugeno-type, Mamdani-type fuzzy
logic, park’s transformation, and without injection when gave three-phase in
distribution network. The voltage restore based on Sugeno-type fuzzy logic and
mamdani-type fuzzy logic can able to overcome by 0.94 p.u respectively. Park’s
transformation was able to overcome by 0.81 p.u.
commit to user
28
perpustakaan.uns.ac.id
digilib.uns.ac.id
Sugeno-type fuzzy logic andMamdani-type fuzzy Logic showed that the
best solving control to restore the voltage. This was because Sugenno-type fuzzy
logic and Mamdani-type fuzzy logic were successfully used in dynamic voltage
restorer process control systems. Sugenno-type fuzzy logic and Mamdani-type
fuzzy logic addressed dynamic voltage restorer perfectly as it resembled human
decision making with an ability to generate meticulous and good deal offers
innovative solutions to mitigate voltage sag from uncertain or approximate data
that impliment in SIMULINK. From the single, two, and three-phase distribution
cases of voltage sag. In the end, it can be clear that the Sugenno-type fuzzy logic
and Mamdani-type fuzzy logic was able to overcome the problem of voltage sag
in order to obtain the best reference voltage during controlling system in
distribution network.
commit to user
29
digilib.uns.ac.id
CHAPTER IV
DATA AND ANALYSIS
There were four simulations done in this study, i.e., simulation of
distribution network without installation of DVR, simulation of DVR based on the
Park’s traansformation, simulation of DVR based on Mamdani-type fuzzy logic,
and simulation of dynamic voltage restorer based on Sugeno-type fuzzy logic in
distribuion network. DVR were tested using MATLAB SIMULINK, result were
analyzed. The votage sag occured at the time duration of o.4 second.
4.1 Control Surfaces using Mamdani-type and Sugenu-type Fuzzy Logic
The plot was of simulation of Mamdani-type fuzzy logic and Sugeno-type
fuzzy logic. The surface viewer of Mamdani-type fuzzy logic and Sugeno-type
Output of controller
fuzzy logic is presented in figure 4.1 and 4.2
200
100
0
-100
-200
200
1
0
Delta Error
-200
-0.5
1
0
-0.5
Input of controller
Figure 4.1 Control Surfaces of Mandani-Type Fuzzy Logic
commit to user
23
perpustakaan.uns.ac.id
digilib.uns.ac.id
Output of controller
200
100
0
-100
-200
-1
200
Delta Error
0.5
0
0
- 0.5
-200
1
Input of controller
Figure 4.2 Control Surfaces of Sugeno-Type Fuzzy Logic
Figure 4.1 and 4.2 shows the control surface of the logic based system, in
Mamdani-type and Sugeno-type fuzzy systems the antecedent and consequent
fuzzy sets were often chosen to be triangular or Gaussian. It was also prevalent
that the input membership functions depost in such a way that the membership
rate of the rule promoted always sum up to one. In this case, and if the rule base
was on connective form, Rule can beconstrued each rule as defining the output
rate for one point in the input value. The rate in the input space was acquired by
taking the centers of the input fuzzy logicsets. Then the output value was the
center of the output fuzzy logic set (center-of-area method). The fuzzy reasoning
resulted in a smooth interpolation between the values in the input space as it can
be seen in figure above with this interjection. Mamdani-type fuzzy logic can be
observed as defining apiecewise constant function with interjection. All the rules
that apply were proceeding, using the membership functions and truth values
gained from the inputs, to determine the result of the rule, and result in turn was
mapped into a membership function and truth amount controlling the output
commit to user
variable.
24
perpustakaan.uns.ac.id
digilib.uns.ac.id
4.2 Simulation of Dynamic Voltage Restorer System
The first simulation was done without dynamic voltage restorer and a three
phase fault is applied to the system for a time duration of 400 ms The second
simulation is carried out at the same scenario as above but using dynamic voltage
restorer with Park’s transformation controller. The simulations were carried out at
the same scenario as above but have different scenatio usage of dynamic voltage
restorer with Sugeno-type and Mamdani-type fuzzy logic. Below is the result of
the reduction of sag on a network system with the application of the three
methods, the Park’s tranformation controller, Sugeno-type fuzzy logic and
Mamdani-type fuzzy logicon the same distribution network. Here Sugeno-type
fuzzy logic and Mamdani-type fuzzy logic have the functionin order to mitigate
the value of voltage sag during single, two, and three phase distribution with a
level of handling most perfect way.
Voltage (p.u)
Park’s Tranformation
88%
Without Injection 14%
Time (sec)
Figure 4.3 Compensation of Sag by Single-Phase Park’s Transformation and
)))))))))))))))Without Injection
commit to user
25
perpustakaan.uns.ac.id
digilib.uns.ac.id
Figure 4.3 shows the single-phase fault scenario results with comparison
without injection, and the park’s transformation controller when system gave
single-phase fault with connected to dynamic voltage restorer with Park’s
transformation controller. The disadvantage of Park’s transformation mainly lies
in the unable nature of the control, which makes it was not good enough for this
application of dynamic voltage restorer. An error was not detected perfectly, the
Park's transformation controller was not operated to alter the setting of the final
control element in perfect way such a way as to minimize the error in the least
possible time with the minimum disturbance to the distribution networks. To
achieve this objective, different actions could be taken by the controller and hence
different signals were sent to the final control element. This can be illustrated by
an experiment used as a scenario of dynamic voltage restorer.
Voltage (p.u)
Sugeno-type fuzzy
logic 98%
Mamdani-type fuzzy
logic 98%
Without Injection 14%
Time (sec)
Figure 4.4 Compensation of Voltage Sag by Single-Phase Sugeno-Type and
>>>>>>>>>Mamdani-Type fuzzy Logic and Without Injection
From Figure 4.4 it wasclear that the result of Sugeno-type, Mamdani-type
to user
fuzzy logic,and withoutinjection commit
when gave
single-phasein distribution network.
26
perpustakaan.uns.ac.id
digilib.uns.ac.id
The voltage restore based on Sugeno-type fuzzy logic and Mamdani-type fuzzy
logic can be overcome by 0.98 p.u respectively. From Sugeno-type fuzzy logic,
and Mamdani-type fuzzy logic showed above the result the best solving control to
restore the voltage in distribution network. From the single, two, andthree-phase
distribution cases of voltage sag. In the end, it can be clear that the Sugenno-type
fuzzy logic and Mamdani-type fuzzy logic was able to overcome the problem of
voltage sagin order to obtaind the best reference voltage during controllingin
distribution network.
Voltage (p.u)
Sugeno-type fuzzy
logic 96%
Mamdani-type fuzzy
logic 96%
Park’s Tranformation
84%
Without Injection 14%
Time (sec)
Figure 4.5 Compensation of Sag by two-phase Park’s Transformation, Sugeno>>>>>>>>>Type, Mamdani-Type Fuzzy Logic and without Injection
Figure 4.5 it can be seen the result of Sugeno-type, Mamdani-type fuzzy
Logic, Park’s transformation, and without injection when given two-phase in
distribution network. The voltage restore based on Sugeno-type fuzzy logic and
mamdani-type fuzzy logic was able to overcome by 0.96 p.u respectively. Park’s
transformation was able to overcome by 0.84 p.u.
commit to user
27
perpustakaan.uns.ac.id
digilib.uns.ac.id
Sugeno-type fuzzy logic and Mamdani-type fuzzy logic showed that the
best solving control to restore the voltage sag during distribution. From the single,
two, and three-phase distribution cases of voltage sag. In the end, it was clear that
the Sugenno-type fuzzy logic and Mamdani-type fuzzy logic was able to
overcome the problem of voltage sag in order to obtain the best reference voltage
during controlling system.
Voltage (p.u)
Sugeno-type fuzzy
logic 94%
Sugeno-type fuzzy
logic 94%
Park’s
Transformation
81%
Without injection 14%
Time (sec)
Figure 4.6 Compensation of Sag by Three-Phase Park’s Transformation,
___________Sugeno-Type and Mamdani-type Fuzzy Logic and Without Injection
Figure 4.6 it is clear that the result of Sugeno-type, Mamdani-type fuzzy
logic, park’s transformation, and without injection when gave three-phase in
distribution network. The voltage restore based on Sugeno-type fuzzy logic and
mamdani-type fuzzy logic can able to overcome by 0.94 p.u respectively. Park’s
transformation was able to overcome by 0.81 p.u.
commit to user
28
perpustakaan.uns.ac.id
digilib.uns.ac.id
Sugeno-type fuzzy logic andMamdani-type fuzzy Logic showed that the
best solving control to restore the voltage. This was because Sugenno-type fuzzy
logic and Mamdani-type fuzzy logic were successfully used in dynamic voltage
restorer process control systems. Sugenno-type fuzzy logic and Mamdani-type
fuzzy logic addressed dynamic voltage restorer perfectly as it resembled human
decision making with an ability to generate meticulous and good deal offers
innovative solutions to mitigate voltage sag from uncertain or approximate data
that impliment in SIMULINK. From the single, two, and three-phase distribution
cases of voltage sag. In the end, it can be clear that the Sugenno-type fuzzy logic
and Mamdani-type fuzzy logic was able to overcome the problem of voltage sag
in order to obtain the best reference voltage during controlling system in
distribution network.
commit to user
29