This document presents a novel technique for fault detection and classification on double circuit transmission lines using artificial neural networks (ANNs). The technique uses high frequency transients caused by faults to identify internal and external faults. ANNs with suitable numbers of neurons are used to analyze voltage and current signals and decompose them to identify faults. Extensive simulation studies show the proposed approach can accurately discriminate between internal and external faults, providing fast, effective, and efficient protection. The document describes modeling a double circuit transmission line system in MATLAB, selecting ANN input/output parameters, developing a 3-layer 45-neuron ANN structure, and presenting simulation results demonstrating the ANN can detect and classify different fault types within a few milliseconds.

1. A Novel Fault detection and classification technique
for double circuit transmission line using Artificial
Neural Network
Neeraj Kumar Singh
Department of Electrical Engineering
P.E.S College of Engineering Aurangabad
neerajksssingh@gmail.com
Shilpa S Badge
Department of Electronic Engineering
HI-Tech Institute of Technology Aurangabad
Shilpasbadge15@gmail.com
Abstract-Protection and security of double circuit transmission
lines is a challenging task. This paper presents an auspice
technique predicated on the high-frequency transients. These
transients are engendered by the fault to cover virtually the
entire length of double circuit transmission lines.
For this purpose, felicitously designed high frequency line traps
are installed at terminals of the protected transmission line, and
the Artificial Neural Network with suitable number of Neurons is
used to identify internal and external faults predicated on the
frequency spectrum of the current and voltage signals
decomposed by Artificial Neural Network logic. Extensive
simulation studies designate that the proposed approach is well
capable of discriminating between the internal and external
faults and provides a secure, very fast, effective and efficient
protection technique.
The simulation model is done in the MATLAB simulink for
system analysis.
Keyword-Artificial neural network (ANN), Support vector
machine (SVM), Phasor measurement unit (PMU)
I. INTRODUCTION
Development of any nation depends on availability of
continuous power supply. An electric power system is one of
the tools for converting and transporting electrical energy. The
only means of transport electrical energy is through
transmission lines. The majority of the transmission line
network is an overhead line system which is open to the
surrounding. Because of which it more exposed to the faults.
For the sake of continuity of electric power it is necessary to
detect and repair the faulty parts as early as possible. Still,
most of the transmission line I protected using distance
relaying. Distance relay performs two main functions that are
primary and secondary back-up protection. With no
intentional delay primary protection scheme is first to respond
to the transmission fault. If primary protection fails, then
secondary back-up protection comes into play to protect the
transmission system. Multiple zone protection schemes are
provided by the distance relaying system. Zone 1 provides the
most expeditious aegis with no deliberate time delay.
Zone 1 cover 85% of the line length because owing to the
problem in distinctive between faults which are proximate to
remote bus. Due to co-ordination time interval zone 2
protection is delayed. The main function of second zone is to
protect primary circuit and to act as secondary back-up
protection which protects the 50% part of adjacent circuit
0.25–0.4 s time delay. Zone 3 settings make it able to cover
finish essential circuit and nearby line and up to 25% of
remote area likewise with extra deferral. The performance of
distance relay is degraded by fault-path resistance, shunt
capacitance and remote in-feed currents [1]. To protect the
entire line current differential protection scheme is applied
successfully. However, it is a difficult task to choose the relay
setting due to presence of line-charging currents and current
variation amid a high resistance fault. To improve relay
sensitivity composite current and voltage parameters are
measured [2]. Travelling wave relaying scheme proposed to
improve circuit stability and fast response for clearing the fault
[3, 4]. However, using this scheme, it is difficult to detect zero
voltage and close-in fault. Till now different sort of protection
technique for transmission lines have been proposed in the
past for fault detection, classification and distance location [5–
10]. But none of the technique identifies the type and direction
of fault.
A adaptable PMU (phasor measurement unit)-based fault
location, detection, classification and direction identification
techniques are developed using 2-terminal integrate phasors
[6], for which required the reach setting 85% of line length
and the communication link for other end data.
Communication link is the important parameters for the
directional relay based on propagation of surge wave
developed in [7]. For series compensated line an algorithm for
positive sequence relay is reported in [8] but it doesn’t
relegate the type of fault. Few ANN based directional relay
technique is disused in [8, 10], however these technique are
useless as it does not indicate type and faulty phase.
A portion of the transmission line protection scheme
systems created by specialists over the most recent 2 years are
additionally examined here with their drawback. ANN based
transmission line fault detection and protection with 1st order
1-phase model is developed in [11] with 90% reach setting.
For single line to ground fault (SLG) neural back propagation
technique for parallel transmission line is reported in [12] with
reach setting 80%. Both techniques stated above do not
classify the fault nor does it estimate the zone and direction of
fault. Wavelet transform-based fault, Support vector machine
(SVM) and arrangement and separation area on transmission
line is accounted for with achieves setting 85% as it was [6].
Cumulated wavelet and neural back proliferation technique -
predicated fault analysis for single circuit line has been
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2. developed [13]. Papers [14,15] discussed single circuit
transmission line without identifying zone and fault direction
which is a major concern of fault analysis and the reach setting
is kept 80–90% only.
Accordingly subsequent to checking on different
procedures detailed till date, it is felt that there is an incredible
need of advancement of effective security strategy for
transmission lines for blame discovery, arrangement, bearing
and zone evaluation with enhanced 1st zone achieve setting.
The fundamental targets of venture are as take after enhance
security of twofold circuit transmission line assurance because
of issue of reach of transfer, arrange correct sort of blame
utilizing ANN and using high transient surge vitality for
insurance of transmission line.
II. POWER SYSTEM MODELING USING MATLAB
The proposed grid system model consists of two circuit of
400kv.The proposed transmission line is divided in to 3
sections of length 150 Km each and connected to source at
each end. Balanced 3- phase source G1and G2 of 400kv and
50Hz are connected to proposed power system model. Short
circuit capacity of G1 is 1200 MVA and X/R ratio is 10. The
proposed power system model is simulated in MATLAB 13 as
shown in the Fig.1
Fig. 1 Proposed Transmission Line in MATLAB 13
Double circuit proposed transmission line consists of different
parameters which are stated in Table I.
TABLE I. PARAMETER OF PROPOSED CIRCUIT
Parameters Values
Positive sequence resistance 0.0191Ω/
Zero sequence resistance 0.229 Ω/
Positive sequence inductance 0.000829 H/
Zero sequence inductance 0.00318 H/
Positive sequence capacitance 1.267 e-008 F/
Zero sequence capacitance 7.835 e-009 F/
Here we are neglecting the mutual inductance and capacitance
values because of their small values.
Different fault conditions are taken in to studies which
include the followings:
1) Type of Faults
2) Fault location
3) Fault inception angle
4) Fault resistance
III. PROPOSED AAN STRUCTURE
A. ANN input and output parameters selection
Artificial intelligence introduces ANN as a new tool to
solve complex problems consist of different parameters at a
time. ANN is more efficient, accurate and faster than the
conventional automation techniques. The proposed
methodology is shown through flow chart given in Fig. 2.
Fig. 2 Flow chart of Proposed Method
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3. The proposed ANN structure in Fig. 3 is divided in to three
layer of intelligence, the first layer is for fault detection,
second layer is used to estimate the faulty section and the third
layer is to classify the fault.
Fig.3 ANN Structure for the proposed system
The proposed ANN structure consists of 9 inputs and 1
output. The 9 inputs consist of Phase voltage of the circuits
and current flowing through it. The input parameter is denoted
by I and output parameter by O.
= [ , , , 1, 1, 1, 2, 2, 2] (1)
= = [ 1, 2, 3, 1, 2, 3, 1, 2, 3, ] (2)
Where,
, , = Phase voltages,
, , = Phase current of circuit 1,
, , = Phase current of circuit 2,
The ANN structure consists of 45 neurons in different
layers (Fig. 4) such that the estimation of the type and location
of the fault can be easily determined.
Fig.4 ANN Neuron Structure
B. ANN fault detection sequence
The output of ANN structure consists of different
parameters which combine to form a single unique output
which determine the type of faults. The ANN parameters for
output combinations indicating different faults are given in the
Table II.
TABLE.II ANN FAULT SEQUENCE
Faults Set Combinations
LG X1N, X2N, Y1N, Y2N, Z1N,
Z2N
LL X1Y1, X2Y2, Y1Z1, Y2Z2,
X1Z1, X2Z2
LLG X1Y1N, X2Y2N, Y1Z1N,
Y2Z2N, X1Z1N, X2Z2N
LLL X1Y1Z1, X2Y2ZD2
The logic behind the fault detection and determination is given
by the equation (3)
Fig.5 Logic of ANN neurons
Where,
Θ = External noise,
= Control Logic,
= Inputs,
= Output,
Then the output equation can be expressed as follow,
= Ѱ( =1
+ Θ ) (3)
Every time the system sense the disturbance and the ANN
computation start in progress. All the input parameters are
given to the ANN system which specifies how each unit is
connected to the other units in the network. Each unit send its
output to several units and receives inputs from different units
to identify and determine the faults which are predefine in the
rules of ANN using logics. The neuron are define with the
logic given in equation (4),
= ( ) − ( − 1) = [ ] (4)
If the neuron logic match with the predefine login then the
fault is easily determined but if the neuron logics and
calculation do not match the predefine logic then it start
SMART ANN
BASED FAULT
DETECTION
USING HIGH-
FREQUENCY
TRANSIENT
(45 NEURON)
LAYER
[ , , ]
[ , , ]
[ , , ]
OUTPUT
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4. random calculation to match with the predefine logic so that
no time should waste for long and step to step logic
calculation.
IV. SIMULATION RESULTS
The purposed ANN architecture is simulated in Matlab
during which purposed ANN neuron fault detector tested. In
the test the distance was chosen between 0-100 Km having
fault resistance between 0-100Ω, and fault inception angle
between 0º-360º. Table III show the simulation results.
TABLE III SIMULATION RESULT
Fault
Location
(Km)
Fault
inception time
(ms)
Fault
detection time
(ms)
Relay
operating
Time (ms)
80 58 61 3
85 64 68 4
90 68 72 4
95 74 79 5
97 80 85 5
99 84 88 4
The ANN Logic sense the change in voltage and current
level of the circuits just after the inception of fault and give
the better result by reducing the operating time of relay. Only
in few millisecond the proposed ANN structure logic detect
the fault and operate the relay. Fig. 6 and Fig. 7 show the three
phase instantaneous voltage and current waveform of LG
fault.
Fig.6 Three phase instantaneous voltage of LG fault
Fig.7 Three phase instantaneous current of LG fault
Simulation result for current waveform of SLG (Single
line to ground), DLG (Double line to ground) and LL (Line to
line) fault is shown in the Fig. 8, Fig.9 and Fig. 10. X- Axis
denotes current level and Y- axis denotes time. Table IV show
the detail of which type of fault occurs at different location as
mention in table III.
Fig.8 Current waveform of SLG fault
Fig.9 Current waveform of DLG fault
Fig.10 Current waveform of LL fault
Current
Time
CurrentCurrent
Time
Time
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5. TABLE IV TYE OF FAULT
Fault
Location
(Km)
Fault
inception
time (ms)
Fault
detection
time (ms)
Relay
operating
Time (ms)
Fault
Type
80 58 61 3 X1Y1N
85 64 68 4 X1Z1
90 68 72 4 Y1N
95 74 79 5 Y2Z2N
97 80 85 5 X2Z2N
99 84 88 4 X2Z2
V. CONCLUSION
This paper proposed a new ANN method to locate the
transmission line fault and also classify which type of fault
occurs. Proposed ANN neuron circuit technique is tested in
Matlab model with different parameters of double circuit
transmission line. The simulation results show that the
proposed scheme is more effective in terms of primary and
secondary protection both. Proposed scheme take very few
steps and calculation to determine the fault types and location,
because of which operating time of relay decreases. So this
technique is highly effective and efficient in terms of fault
detection and classification can be implemented for the
protection of transmission line.
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