Review Report on Fault Detection an d Classification
Methodologies o n Overhead Transmission Lines
Sravan Kumar Kotha (DT18EEE086), Dr. Bhooshan Rajpathak
Department of Electrical Engineering
Visvesaraya National Institute of Technology, Nagpur.
ABSTRACT: Transmission line is the one which transmits power from generating stations
to load; hence it is the most crucial elements in the power system network . Faults on
transmission lines not only affect the equipment but also the power quality. Disturbances
(Faults) which affect the transmission line system should be detected accurate ly and
pro mptly. Fault investigation on transmission lines is imperative for economic operation of
power system components as it facilitates quicker repair, improve system availability, reduce
operating costs, and saves time and space. Various fault clas sification m ethodologies for
detection and classification of faults on transmission lines has been presented in this paper . In
this review almost all the techniques and philosophies are discussed in the literature.
Key Words: Three phase two terminal transmission line , wavelet transform, Artificial
Neural Networks (ANNs), Support Vector Machine (SVM), feature extraction, fau lt
classification accuracy .
Abbreviations: DWT: Discrete wavelet transform, ANN: Artificial Neural Networks ,
SVM: Support Vector Machine .
For proper functioning of power system network the protection of transmission lines
against uncovered deficit is the most basic task . Fault on transmission system is an
abnormal condition when the system quantities like voltage, current and phase angle etc
exceeds its threshold values. Faults on overhead lines can be identified by examining the
phase voltages and phase currents as open circuit (series) faults and the ot her one is short
circuit (shunt) faults . Short circuit faults again classi fied into LG, LL, LLG, and LLL faults .
Here L indicates each phase and G indicates ground. Among all the faults m ost severe fault
on transmission line is LLL fault or 3 -phase .
For maintaining the reliability of power system continuous operation of transmis sion line
system is having prime importance . Disturbance (Fault) which affects the transmission line
should be detected accurately and fast. Accurate fault classification facilitates quick repair,
improves avai lability of system , reduce maintenance cost during operation , and saves time.
Various approaches are used for fault type identification and classification . Every
methodology has its merits and flaws. So it is difficult to select a n accurate fault classificatio n
for users. Hence, an effort is made in this paper t o make a review of all the efficacious fault
classification method s. In this review, fault classification methods have been related based on
their techniques and simulation tools used. The power system model considered is simulated
by using di fferent tools like MATLAB, EMTP, EMTDC/PSCAD.
Survey on Fault Classification Techniques :
In this review paper popular and hybrid techniques have been discussed. In addition to
this newly proposed methodologies are also presented for the ease to approach.
Popular methodologies used for detection and classification of fault types. They are
1. Wavelet -Transform (WT) approach
2. Artificial Neural Network approach (ANN)
3. Support Vector Machine Technique (SVM) .
The above techniques are explained below.
1. Wavelet -Transform (WT) Approach
In earlier days Fourier transforms were used to extract features of the signals in frequency
domain . There may be a possibility of losing time information by using Fourier transform.
This may be overcome by using Mul ti resolution analysis (MRA ) in Wavelet transforms
which can decompose the signal into different frequency bands . It allows the decomposition
of signal into various resolution levels. This is done by implementing a discrete wavelet
tran sform (DWT) using a bank of HP and LP filters .
To implement wavelet MRA, the selection of Mother Wavelet  is essential. An attempt
was made by using Db4 mother wavelet and the use of discrete wavelet transform (DWT) for
classification of faults on transmission lines . In th is method fourth level detailed coefficients
of the three phase fault current signals have been used.
An approach has been made by utilising discrete wavelet transform (DWT) for classifying
faults on ove rhead lines [2 ]. In this method the detailed an d approximation coefficients of
three phase currents and voltages are utilises to identify the occurrence of faul t on lines and
the type can also be identified .
2. Artificial Neural Network Approach
Earlier fault classification was done by phase selector, but was unable to classify the correct
fault type under different system operating conditio ns like fault resistances. Artificial Neural
networks (ANNs) can map abnormal patterns which intern provides potential solution to the
long -standing problems of accurat e fault classification .
Back propag ation (BP) neural system is utilised in ANN for fault classification [ 3]. A feed
forward network with back propagation algorithm by the use of radial basis function  has
been developed for cla ssification of faults on d ouble c ircuit transmission lines.
3. Support Vector Machine (SVM ) Approach
Support Vector Machine optimally se parates data into two groups by constructing an N –
dimensional hyper plane. SVM model using a sigmoid kernel functions which are similar to
a two layer perceptron neural -networks. From detailed coefficients of current signals obtained
from wavelet transform some number of pre -faulted samples and some number of post –
faulted samples are taken from each signal sample for training of SVM. Pre -faul ted samples
are given with zero label and post -faulted samples are given with some integer valued label
for ex. 1 for AG fault, 2 for BG fault etc. After extracting the useful features from detailed
coefficients of three current signals are used as input to the SVM .
Hybrid techniques utilises the comb ination of above three methodologie s. Concoction has
been done to mitigate the deficiency in one approach during its application.
In , the author classifies the faults on transmission lines in three stages. From sending end
of transmission line t he current and voltage signals were obtained in first stage and these are
processed to get detailed coefficients by discrete wavelet tran sform (DWT). In second and
third stages multi class support vector machine (MCSVM) is employed for classification and
The feature extraction of faulty phase current and voltage signals is done by a hybrid
technique  by utilising discrete wavelet transform (DWT). These are used to train the SVM
classifier to make a decision of fault or no fault. Ground detection is done by using proposed
Global positioning system (GPS) has been in troduced  for fault identification and
categorisation on overhead lines. Wavelet transform (WT) is used to obtain detailed
coefficients which are utilized to get fault indices. Threshold value is set based on
enormous test signals. If fault indices are compared with this threshold values for fa ult
classification. ANN is used to locate the faults.
A new approach has been discussed in  ; The features of faulty phase currents were
decomposed to first level by using discrete wavelet transform and these are given as inputs
to train and test support vector machine and extreme learning machine ELM.
The presence of non -linear loads has been considered while detecting and classifying faults
on transmission line  by using SVM and ANN techniques. T o estimate the harmonic
components Kalman filter is use d to process the post fault voltage signals, which can be
served as input vectors for SVM and ANN.
To find the distance of fault from generating end is proposed in . The wavelet
technique and neural network techniques were used. This method produces ve ry
imperative fault distance results with a relative error around 3.2%.
Availability of fault detection and classification methodologies have been presented in
thi s review report which are helpful for selecting the basic technologies and
amalgamation of different technologies used for protectio n of overhead lines. This report
explains the different techniques based on the technique and simulation tools used i.e.
popular and combination of b asic techniques. One can extend the work by utilisi ng the
methods discussed in this article.
Acknowledgement: I convey my deep sense of gratitude to VNIT Nagpur for providing me
the opportunity to do research.
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