Transmission Line Fault Detection & Phase Selection using ANN
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15-04-2010, 01:19 AM


Abstract “
A novel application of neural network approach to protection of transmission line is demonstrated in this paper. Different system faults on a protected transmission line should be detected and classified rapidly and correctly. This paper presents the use of neural networks as a protective relaying pattern classifier algorithm. The proposed method uses current signals to learn the hidden relationship in the input patterns. Using the proposed approach, fault detection, classification and faulted phase selection could be achieved within a quarter of cycle. An improved performance is experienced once the neural network is trained sufficiently and suitably, thus performing correctly when faced with different system parameters and conditions. Results of performance studies show that the proposed neural network- based module can improve the performance of conventional fault selection algorithms.

Presented By:
M. Sanaye-Pasand, H. Khorashadi-Zadeh

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04-05-2011, 10:00 AM

Abstract
A novel application of neural network approach to
protection of transmission line is demonstrated in this paper.
Different system faults on a protected transmission line
should be detected and classified rapidly and correctly. This
paper presents the use of neural networks as a protective
relaying pattern classifier algorithm. The proposed method
uses current signals to learn the hidden relationship in the
input patterns. Using the proposed approach, fault detection,
classification and faulted phase selection could be achieved
within a quarter of cycle. An improved performance is
experienced once the neural network is trained sufficiently
and suitably, thus performing correctly when faced with
different system parameters and conditions. Results of
performance studies show that the proposed neural networkbased
module can improve the performance of conventional
fault selection algorithms.
Keywords – Fault Detection, Phase Selection, Transmission Line
Protection, Neural Networks
I. INTRODUCTION
In an electric power system comprising of different
complex interacting elements, there always exists a
possibility of disturbance and fault. The advent of large
generating stations and highly interconnected power
systems makes early fault detection and rapid equipment
isolation imperative to maintain system stability. Faults on
power system transmission lines need to be detected and
located rapidly, classified correctly and cleared as fast as
possible. Fault detector module of a transmission line
protective scheme can be used to start other relaying
modules. Fault detectors provide an additional level of
security in a relaying application as well.
Application of a pattern recognition technique could be
useful in discriminating between power system healthy
and/or faulty states. It could also be used to distinguish
which of the phases of a three phase power system is
faulty. Artificial Neural Networks (ANNs) are powerful in
pattern recognition and classification. Consequently,
various ANN-based algorithms have been investigated and
implemented in power systems in recent years [1].
A well developed protective scheme should perform
well for different system conditions and parameters. ANNs
possess excellent features such as generalization capability,
noise immunity, robustness and fault tolerance. Therefore,
the decision made by an ANN-based relay would not be
seriously affected by variations in system parameters.
ANN-based techniques have been used in power system
protection and encouraging results are obtained [1-6].
In this paper, a new scheme is proposed for fast and
reliable fault detection and phase selection. The proposed
method uses an artificial neural network-based scheme.
Various transient system faults are modeled and an ANNbased
algorithm is used for recognition of these patterns.
Performance of the proposed scheme is evaluated using
various fault types and encouraging results are obtained. It
is shown that the algorithm is able to perform fast and
correctly for different combinations of fault conditions,
e.g. fault type, fault resistance, fault inception angle, fault
location, prefault power flow direction and system short
circuit level.
II. FAULT TYPE CLASSIFIERS
Conventional fault detection algorithms are designed
based on current or voltage magnitude measurements [7,8].
Increase of current magnitude or decrease of
voltage/impedance magnitude could be considered as a
measure to detect a system fault. These algorithms are
dependent on various factors such as fault resistance and
power system short circuit capacity.
Current based starters get confused when load current is
significant compared to fault current. Conventional
overcurrent based starters may not be able to detect faults
with high amount of fault resistance. For remote low
current faults, no clear undervoltage condition arises at the
relay location. In the case of a close-in fault on a weak
system, all voltages deviate from the nominal value.
Therefore, the voltage based starters might not be able to
perform correctly for different fault conditions.
For the conventional based fault detectors, current and
voltage magnitudes should be estimated correctly using
appropriate filtering algorithms. When a fault happens on a
transmission line, the power system goes through a
transient period. It might not be easy to determine
current/voltage signal magnitude fast and precisely during
the transient period after the occurrence of the fault.
As power systems grow both in size and complexity, it
becomes necessary to identify different system faults faster
and more accurately using more powerful algorithms. It
would be desirable to design a reliable and fast algorithm
to classify different power system faults for various system
parameters and fault states. An ANN-based algorithm in
proposed in this paper as a transmission line fault detector
and fault type classifier module.

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29-11-2012, 11:59 AM

to get information about the topic "TRANSMISSION LINE FAULT DETECTION AND CLASSIFICATION USING WAVELET TRANSFORM AND NEURAL NETWORKS" full report ppt and related topic refer the link bellow

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