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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 fuzzy logic is presented in figure 4.1 and 4.2
Figure 4.1 Control Surfaces of Mandani-Type Fuzzy Logic
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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 variable.
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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.
Figure 4.3 Compensation of Sag by Single-Phase Park’s Transformation and )))))))))))))))Without Injection
Park’s Tranformation 88%
Without Injection 14%
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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.
From Figure 4.4 it wasclear that the result of Sugeno-type, Mamdani-type fuzzy logic,and withoutinjection when gave single-phasein distribution network.
Figure 4.4 Compensation of Voltage Sag by Single-Phase Sugeno-Type and >>>>>>>>>Mamdani-Type fuzzy Logic and Without Injection
Without Injection 14% Sugeno-type fuzzy
logic 98%
Mamdani-type fuzzy logic 98%
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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.
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.
Sugeno-type fuzzy logic 96%
Mamdani-type fuzzy logic 96%
Park’s Tranformation 84%
Without Injection 14%
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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.
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.
Sugeno-type fuzzy logic 94%
Sugeno-type fuzzy logic 94%
Park’s Transformation
81%
Without injection 14%
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