Transmission line is one the important compnent in protection of electric power system because the transmission line connects the power station with load centers. The fault includes storms, lightning, snow, damage to insulation, short circuit fault [1]. Fault needs to be predicted earlier in order to be prevented before it occur

1. UTMUNIVERSITI TEKNOLOGI MALAYSIA
PROPOSED
FAULT DETECTION
ON OVERHEAD TRANSMISSION LINE
USING PARTICLE SWARM OPTIMIZATION
By
MAKMUR SAINI
SUPERVISOR BY
PROF.IR.DR.HJ.ABDULLAH ASUHAIMI BIN MOHD ZIN
CO SUPERVISOR BY
ASSOC.PROF.DR.MOHD WAZIR BIN MUSTAFA
20112011

2. TABLE OF CONTENT
I. INTRODUCTION
Background
Problem Statement
Objective of the Researach
Scope of the Research
Significance of the Research
II. LITERATURE REVIEW
III. RESEARCH
METHODOLOGY
The Proposed Design
Expected Result
Research Planning and Schedule
IV. PRELIMINARY RESULT
V. CONCLUSION
VI. REFERENCE

3. BACKGROUND
 Transmission line is one the important compnent in
protection of electric power system because
the transmission line connects the power station with
load centers.
 The fault includes storms, lightning, snow, damage to
insulation, short circuit fault [1].
 Fault needs to be predicted earlier in order to be
prevented before it occur.

4. BACKGROUND
The fault must be detected early; hence the
possibility of disturbance with the transmission
can be reduced. It can be improved by
predicting early signs of the fault [2].
The signs of the fault can be made in the form
of algorithms in which algorithms are able to
specify the a parameters before fault occurs.

5. BACKGROUND
Alternative algorithms that can be used include
ANN, ACO, FUZZY LOGIC and PSO
This study will use Particle Swarm
Optimization (PSO).
This aims of this study are simulate the
occurrence of fault on the transmission line.

6. BACKGROUND
The types of fault that will be simulated are:
The single line to ground fault
The line to line fault
The double line to ground fault
Three phases of to ground fault
 The lighting Strike fault

7. PROBLEM STATEMENT
 The overhead transmission line , which often has varieties of
small or large disturbances is highly susceptible to interference
and it is necessary for fault detection.
 Fault detection must be able to quickly determine the location
of interference as well as to classify type of fault quickly to
stabilize the electric power system .
 Particle Swarm Optimization ( PSO) is able to detect
interference very quickly and with good accuracy. Hence it is
used in this study

8. OBJECTIVES
1. To identify and simulate conventional type of
disturbance on the overhead transmission line by
using PSCAD / EMTDC software package
2. To develop mathematical model for various type of
disturbance on overhead transmission line.
3. To develop a smart algorithm for fault detection
using Particle Swarm Optimization (PSO).

9. SCOPE OF THE RESEARCH
1. Identification and simulation of various of
disturbance on overhead transmission line by
using PSCAD/EMTDC software. Version 4.2.0
2. Preparing suitable mathematical model for voltage
and current signals of the above disturbances.
3. Development of the proposed smart algorithm by
using Particle Swarm Optimization (PSO) method
in fault detection of overhead transmission line.

10. SIGNIFICANCE OF THE RESEARCH
1.The developed system aims to inform or warn the
operator that there is a possibility of fault occurs on
the transmission line. Then the operator can react to
the warning before the fault happens.
2.The numerical simulation program was developed
for fault detection will be based on PSO
optimization. Code optimization will be developed in
MATLAB, then the results of PSCAD-EMTDC will
be used in the MATLAB program

11. LITERATURE REVIEW
The last ten years, many literatures and researches on
particle swarm optimization applications to power
system have seen found [3]
 Fault classification on transmission by combining the discrete
wavelet transform [4]
 Fault location on transmission with a combination of least
squares method [5]
 Load forecasting by combination of Neural Networks [6]
 The induction motor stator fault Estimation [7],
 Planning of electrical distribution network distribution [8],
 Power transformer protection using neural network [9].

12. LITERATURE REVIEW
There are few methods have been previously
performed to detect fault on the transmission us
such :
 Wavelet Singular Entropy [10]
 Transform Wavelet and ANN [11]
 Coordinating fuzzy ART Neural Networks [12].
 High impedance to high impedance transform wavelet
approach [13]
 High impedance approach morels a wavelet transform [14]
 Transmission line fault detection using the Intelligent power
system [15].

13. LITERATURE REVIEW
Moreover, some are using
 Time and frequency analysis [16],
 Fiber grating sensor [17]
Online fault detection among others,
 Online fault detection for power system using wavelet and
ANN [18].
 Online fault detection of transmission line using ANN [19]
 Online fault detection using adaptive distance relaying
algorithm [20]

14. LITERATURE REVIEW
PSO has been widely applied in recent
transmission researches, such as , in the
reactive power control, the economic
dispatch, power system reliability, load
flow and electric machinery [3.33].
However there is a opportunity to study
fault detection in electric power
transmission systems using PSO.

15. LITERATURE REVIEW
 New PSO method [21.34] does not use crossover and
mutation operators as in the GA [22] and this is the
advantage of this using method.
 Other advantage of PSO method is a derivative-free
algorithm which is flexible and could be integrated
with other algorithms (GA, ANN, Fuzzy). Moreover, it
is easy to apply in mathematical model and does not
have the Initial Solution (23).

16. LITERATURE REVIEW
Fault detection on AC Induction Motor (35)
found that the PSO gives better result which is
above 90% compared with GA and also better
when compared to the PCA (Principle
Component Analysis).
The study of induction motor stator fault (7), it
is found that the application of the PSO based
method is more optimal and also improves the
detection speed.

17. LITERATURE REVIEW
Reactive power dispatch problem was
solved using the Particle Swarm
Optimization model for continuous
variables with discrete control variables
better than the main classical approach,
Gradient Based Optimal Power Flow with
P-Q decomposition [36]

18. LITERATURE REVIEW
The state estimation problem was solved
using the Particle Swarm Optimization
based on the well known Weighted Least
Squares Estimation method approach
achieved a better estimation than an
iterative Newton method using a Mean
Square Error (MSE) analysis [36]

19. LITERATURE REVIEW
The unit commitment problem was solved using
the Particle Swarm Optimization model for
binary variables. The results were compared
with the results obtained from a Dynamic
Programming approach. The same global
solution was found showing the robustness of
the Particle Swarm Optimization model for
binary variables [36].

20. LITERATURE REVIEW
Among The advantages of using PSO are : [3]
 PSO has a derivative-free algorithms
 PSO has the flexibility that is integrated with optimization techniques
to form hybrid device.
 PSO less sensitive to the objective function, continuity and
convexity.
 PSO has little parameter adjustments than other evolutionary
techniques.
 PSO has a very easy application in mathematics and logic circuits
operating.
 PSO can handle objective functions with a stochastic nature in the
case of one of the optimization as a random variable.
 PSO does not require any initial solution to start the iteration
process.

21. Mathematical Model of PSO
Vi
K+1
= Vi
K
+ C1 r1 ( pbesti
K
– xi
K
) + C2r2 (gbestK
– xi
K
)
xi
K+1
= xi
K
+ Vi
K+1
X = Position
V = Velocity
where
c1 and c2 are two positive constants;
r1 and r2 are two randomly generated numbers with a
range of [0,1];
Pbest is the best position particle achieved
based on its own experience;
gbest is the best particle position based
on overall swarm’s experience;
k is the iteration index

22. Particle Swarm Optimization Algorithms

23. Research Methodology
 Fault detection is proposed by creating
a simulation current and voltage signals
at several fault conditions that obtained
through simulation using PSCAD/
EMTDC.
The waveforms obtained in simulation
PSCAD will be trained using the PSO
method with the Matlab program

24. Research Methodology
The results form the signal currents and
voltages are similar when compared to
results obtained from the pattern of
training PSO
Expected result to generate a simulation
model of fault detection and faults on
overhead transmission line path by using
PSO.

25. Research Methodology
The results of this study will be validated by
Comparing with another methods
Compared with the real data which is carried
out in this field in the case of electric
transmission systems in South Sulawesi
Indonesia

26. ALOGARITHMS FAULT DETECTION

27. ALOGARITHMS FAULT DETECTION

28. Expected Result
The expected output by using the PSO
method in the detection process can produce
a fault detection system effectively and
accurately so that electric power system
stability is maintained .
To identify fault detection of the transmission
system before the disturbance in the system
so that operators can take corrective action.

29. Planning and Schedule

30. PRELIMINARY
RESULT
The study was conducted using of
PSCAD/EMTDC that generate current and
voltage wave signal. Below are the 5 types of
fault
 The line to ground fault
 The line to line fault
 The line-line to ground fault
 The three phase to ground fault
 The lightning strike fault

31. PRELIMINARY RESULT

32. PRA FAULT

33. Fault Line to Ground

34. Fault Line to Ground

35. Fault Line-Line to
Ground (LLG )

36. Fault 3 Phase to Ground
(LLLG)

37. Fault Line to
Line (L-L)

38. Fault Lightning Strike

39. PRELIMINARY
RESULT
The result of the current and voltage wave
signal will be made in the mathematical model,
mathematical model will be processed using
PSO method with the program MATLAB.
The results mentioned above will be compared
with the results of current and voltage
waveform signal obtained from the
PSCAD/EMTDC.

40. CONCLUSION
In this study the new method is proposed
to detect the disturbance which includes :
Simulation and Identification of disturbance on
the transmission line using PSCAD/EMTDC
software.
Voltage and current waveform of disturbance
signals are also simulated using a mathematical
model

41. CONCLUSION
 Smart algorithm will be developed using
Particle Swarm Optimization (PSO) in
MATLAB program using the result obtained
from the mathematical modulation.
The results of new PSO based method would be
compared with the another method.

42. REFERENCE

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