UTMUNIVERSITI TEKNOLOGI MALAYSIA 

PROPOSED FAULT DETECTION

ON OVERHEAD TRANSMISSION LINE USING PARTICLE SWARM OPTIMIZATION

ByMAKMUR SAINI

SUPERVISOR BYPROF.IR.DR.HJ.ABDULLAH ASUHAIMI BIN MOHD ZIN

CO SUPERVISOR BYASSOC.PROF.DR.MOHD WAZIR BIN MUSTAFA

2011 2011

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

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.

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.

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.

BACKGROUND

The types of fault that will be simulated are: The single line to ground faultThe line to line fault The double line to ground fault Three phases of to ground fault The lighting Strike fault

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

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).

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.

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

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].

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].

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]

 

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.

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).

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.

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]

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]

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].

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.

Mathematical Model of PSO

Vi

K+1 = ViK + C1 r1 ( pbesti

K – xiK ) + C2r2 (gbestK – xi

K )   xi

K+1 = xiK + 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

Particle Swarm Optimization Algorithms

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

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.

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

ALOGARITHMS FAULT DETECTION

ALOGARITHMS FAULT DETECTION

Expected ResultThe 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.

 

Planning and Schedule

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

PRELIMINARY RESULT

PRA FAULT

Fault Line to Ground

Fault Line to Ground

Fault Line-Line to Ground (LLG )

Fault 3 Phase to Ground (LLLG)

Fault Line to Line (L-L)

Fault Lightning Strike

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.

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

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.

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