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International Journal of Electrical andElectronics Engineering Research (IJEEER)ISSN(P): 2250-155X; ISSN(E): 2278-943XVol. 4, Issue 5, Oct 2014, 63-74
TJPRC Pvt. Ltd.
A NOVEL METHOD FOR DETECTION OF ELECTRIC TRANSMISSION LINE
FAULTS USING DISCRETE WAVELET TRANSFORM
NAVEEN GAUR1, RAM NIWASH MAHIA
2& OM PRAKAS H MAHELA
3
1Principal, Aryan Polytechnic College, Ajmer, Rajasthan, India
2Research Scholar, Indian Ins titute of Technology, Jodhpur, Rajasthan, India
3Research Scholar, IIT Jodhpur, Rajasthan, India
3Assistant Engineer, RRVPNL, Jodhpur, Rajasthan , India
ABSTRACT
The power utility companies have been trying to identify and locate three-phase transmission line faults in the
minimum possible time in order to prevent economic losses. In the last few decades technology used for power system
protection has evolved and shifted from electromechanical devices to solid state and micro-processor based intelligent
devices, which require fast and accurate detection of faults in the transmission lines. This paper presents a novel discrete
wavelet transform (DWT) based multi-resolution analysis technique for detection of transmiss ion line faults including and
without including ground. A comparative study of all types of faults is presented. A test system having generation, load
and transmission line in two parts is modeled in MATLAB/ Simulink environment. The MATLAB programming is used
for DWT analysis of the faults.
KEYWORDS:
Fault Detection, Discrete Wavelet Transform, Power System, Transmission Line, Transmission LineFault
1. INTRODUCTION
Transmission and distribution lines are important parts of the electrical power system and provide the path to
transfer electrical power from central generating stations to load centers. These lines are susceptible to faults due to
continuous environment e xpos ition and switching operations [1]. The performance of power system is affected by faults on
transmission lines, which results in interruption by power flow [2]. The fast and accurate detection, classification and
location of the transmission line fault must be done to de-energize the faulted line, protecting the power system network
from the harmful effects of the fault [3].
The continuous expansion of power networks in both scale and complexity has been imposing a requirement for
fast fault clearance to improve system stability and reliability [4]. For the same, efficient and reliable protection techniques
have been developed. In [5], authors proposed a digital distance protection scheme for transmission lines based on
analyzing the measured voltage and current signals at the relay location using wavelet transform (WT) with
multi-resolution analysis (MRA). In [6], authors presented a scheme for the protection of parallel transmission lines in
which WT with its magnificent characteristics is employed to detect the disturbances in the current signals during faulty
conditions and to estimate the phasors of all the signals as well as to achieve high-speed relaying. The wavelet-based ultra
high speed directional transmiss ion line protection has been proposed in [7].
A prototype Kerr Cell has been constructed by the authors in [8] and tested for detecting and identifying faults by
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monitoring high voltages in power system. An approach to automated transmission line fault analysis using synchronized
sampling at two ends of the line is presented in [9]. In [10], authors presented a new high-impedance fault detection
method based on the WT for feature extraction, and principal component analysis for the selection of features, and a fuzzy
inference system for decision making. A novel hybrid framework that is able to detect rapidly and locate a fault on power
transmiss ion lines is presented in [11].
This paper presents, a discrete wavelet transform based approach for detection of transmission line faults which
involves the capturing current signals generated in a transmission line under faulty conditions. The detection process is
performed through s ignal decomposition using db4 as mother wavelet. In this paper, we have used the moving window of
size 32 samples and average is calculated. The window is moved by one sample in each step and iterated successively to
cover all the samples available in wavelet coefficients. In each step, average of samples is calculated and plotted.
The results of this proposed technique are compared with the absolute values of the wavelet coefficients .
This paper is divided into six sections . Starting with an introduction in section 1, the section 2 covers the discretewavelet transform analysis for detection of transmission line faults and section 3 describes the test power system model
used for the detection of transmiss ion line faults. Section 4 includes proposed algorithm and the s imulation results and their
discussion are presented in section 5. Finally the concluding remark is included in the section 6.
2. WAVELET TRANSFORM ANALYSIS FOR FAULT DETECTION
The digital signal processing techniques are widely us ed for processing the s ignals associated with power system.
These techniques have been classified into two categories, the frequency-based and time-frequency techniques.
The frequency based techniques, such as Fourier transform, are used for stationary signal analysis. The time-frequency
based techniques, such as short- time Fourier t ransform (STFT), wavelet t ransform (WT), ambiguity function (AF),
and wigner-ville distribution (WVD) are usually used for extracting transient features from the non-stationary signals [12].
The wavelet transform is a mathematical tool, much like a Fourier transform in analyzing a stationary signal that
decomposes a signal into different scales with different levels of resolution by dilating a single prototype function.
The decomposition into scales is made possible by the fact that the WT is based on a square-integrable funct ion and group
theory representation. The wavelet transform provides a local representation, in both time and frequency, of a given signal.
Therefore, it is suitable for analyzing a signal where time-frequency resolution is needed such as disturbance transition
events during faulty conditions in the transmission lines [13]-[14].
The discrete wavelet transform (DWT) is the bas ic tool for the feature extraction. DWT is the discrete counter part
of the continuous wavelet transform (CWT). The CWT of a continuous time signal is defined as [15]
(1)
(2)
where (t) is the mother wavelet, the asterisks denote complex conjugates, a and bare scaling and translating
parameters respectively.
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For detection of transmission line faults, the DWT is used instead of the CWT. This is implemented by using
discrete values and for the scaling parameter and translation parameter, respectively. Then, the mother
wavelet is given as
(3)
Where m and n indicate the frequency localization and the time localization, respectively. When a0=2 and b0=1
are used, then the WT is known as dyadic-orthonormal wavelet transform and bas is for multi-resolution analysis (MRA).
In MRA, signal is passed through a series of high pass filters (HPF) to analyze the high frequencies, and it is also
passed through a s eries of low pass filters (LPF) to analyze the low frequencies. The signal (S) is decomposed into two
types of components: approximation (A) and detail (D). The approximat ion is h igh scale, low frequency component of the
signal. The detail is low scale, high frequency component. The decomposition process can be iterated, with successiveapproximations being decomposed in turn, so that one signal into many lower resolution components which is called the
wavelet decomposition tree as shown in Figure 1 [16]. The LPF and HPF filters form a family of scaling (t) and wavelet
(t)functions as given below.
(4)
(5)
Where , his low pass filter andgis high pas s filter.
Figure 1: W avelet Decomposition Tree
The choice of filters h and g with four coefficients is known as daubechies wavelet with four filter coefficients
(or Daub4). Daub4 wavelet and Daub4 scaling functions are shown in Figure 2.
0 1 2 3 4 5 6 7
-0.2
0
0.2
0.4
0.6
0.8
1
Scaling function phi
(a)0 1 2 3 4 5 6 7
-0.5
0
0.5
1
Wavelet function psi
(b)
Figure 2: (a) Daub4 Scaling Function and (b) Daub4 Wavelet Function
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3. PROPOSED POWER SYSTEM MODEL
For detections of transmission line faults, s ingle line d iagram of the experimental s et up used is cons isting of three
buses, one source, and one load as shown in Figure 3. The tes t system trans mission line parameters are given in Tab le 1.
The transmission lines with two sections of 100 km length each are used. The type of transmission line is -section.
In transmission lines, the positive and negative sequence parameters are same; therefore, only positive sequence parameter
values are given in the table.
Table 1: Test System Transmiss ion Line Parameters
S. No. Attributes Value
1 Positive s equence resistance R1 (/km) 0.01273
2 Zero sequence resistance R0 (/km) 0.3864
3 Positive sequence inductance L1 (H/km) 0.9337e-3
4 Zero sequence inductance L0 (H/km) 4.1264e-3
5 Positive sequence capacitance C1 (F/km) 12.74e-9
6 Zero sequence capacitance C0 (F/km) 7.751e-9
The details of transformers used at the generating end (XG-1) for step-up of the voltage and load end (XL-2) for
step-down of the voltage are given in Table 2. The X/R ratio of the source generator is 7 and 12 MVA load is used.
The supply frequency used is 60 Hz.
Table 2: Transformer Parameters
Transformer MVA Kv-High Kv-LowHV Winding LV Windi ng
R ( ) X ( ) R ( ) X ( )
XG-1 25 220 33 29.095 211.60 2.1100 4.8312
XL-2 25 220 11 29.095 211.60 0.1142 0.8306
The current signals for feature extraction of faults are captured at bus 1 near the generator. The fault detection
point in the system may be taken depending on the type and locat ion of protection scheme provided in the s ystem. In the
proposed study the fault is located at bus no. 3. Three types of faults v iz. line to ground (LG), double line to ground (LLG),
double line (LL) and three-phase faults are created at bus no. 3 one by one and analyzed WT with db4 as mother wavelet.
Figure 3: Propose d Model of Power System for Detection of Trans mission Line Faults
4. PROPOSED TRANSMISSION LINE FAULT DETECTION ALGORITHM
In the power system, faults are abnormal events which are not part of normal operation and unwanted by the
network operator. A fault detector must detect the fault inception and to issue an output signal indicating this condition.
During normal operating conditions the currents and voltage of the power system are sinusoidal signals. Load variation
with t ime may produce low amplitude changes in current signals and, in lesser extent, in voltage signals. The inception of
fault introduces abrupt changes of amplitude and phase in current and voltage signals [17]. After fault occurs in the power
system, a non-linear signal of transient travelling wave is generated and runs along faulted transmiss ion line to both ends of
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the line. Those travelling waves contain information about fault nature. The fault initial travelling wave has a wide
frequency spectrum from DC co mponent to high frequencies. When such fault travelling wave arrives at the substat ion bus
bar, it will change incisively, i.e . travelling wave head will present the sudden change in the time-frequency diagram.
In that way, travelling wave arr ival to the measuring point (usually the busbar voltage transformers) is e xactly a moment of
sudden change recorded on measuring substation [18]. The system is simulated in MATLAB/simulink environment.
The fault is created at 10th
cycle from the start of the simulation and cleared at 20th
cycle from start of the simulation.
The detected three phase current signals at bus no. 3 are passed through DWT with db4 as mother wavelet and different
details up to level 4 and approximation at level 4 are obtained. The sampling frequency of 1920 Hz is used for DWT
decomposition. If the value of h igh frequency detail (HFD) coefficients is greater than the threshold value (Td) the fault is
detected, otherwise no fault is detected. The flow chart of proposed algorithm is shown in Figure 4. The absolute values of
wavelet coefficients are plotted in each case. In this paper, we have used the moving window of size 32 samples and
average is calculated. The window is moved by one sample in each step and iterated successively to cover all the samples
available in wavelet coefficients. In each step, average of samples is calculated and plotted. The results of this proposed
technique are compared with the abs olute values of the wavelet coefficients.
Figure 4: Flow Chart of DWT Based Fault Detection Algorithm
5. SIMULATION RESULTS AND DISCUSSIONS
The power system model shown in Figure 3 is simulated in MATLAB/Simulink environment with fault created
on bus no. 3 on phase-A at 10th
cycle from start of the simulation and cleared at 20th
cycle from the start of the simulation
in each case. The current signal of phase-A, is passed through DWT with db4 as mother wavelet. The absolute values of
the wavelet coefficients as well as average values of wavelet coefficients as calculated by the proposed method mentioned
in section 4 are p lotted in each case and compared.
5.1 LG Fault on Power System
The current signal of phase-A at bus 1 with LG fault on bus 3 and the absolute value of the detail coefficients up
to level 4 and approximation coefficient at level 4 are shown in Figure 5. The current signal and detail coefficient upto
level 4 and appro ximation coefficient at level 4, as calculated by the proposed method are shown in Figure 6.
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0 500 1000 1500 2000-5000
0
5000
Original Signal
20 40 60 80 100 1200
2000
4000
6000
Absolute value of approximation Coefficient cA4
0 20 40 60 80 100 120 1400
5000
10000
Absolute value of detail Coefficient cD4
50 100 150 200 2500
500
1000
Absolute coefficient of detail Coefficient cD3
50 100 150 200 250 300 350 400 450 5000
50
100
150
200
Absolute coefficient of detail Coefficient cD2
0 200 400 600 800 10000
50
100
Absolute coefficient of detail Coefficient cD1
Figure 5: Absol ute Wavelet Coefficients Using Db4 wi th LG Faul t at Bus-3
0 500 1000 1500 2000-5000
0
5000
Original Signal
0 20 40 60 80 100 1200
5
10x 10
4 Approximation coefficient Coefficient cD1
0 20 40 60 80 100 1200
1
2
3x 10
5 Detail Coefficient cD4
0 50 100 150 200 2500
1
2
3x 10
4 Detail Coefficient cD3
0 100 200 300 400 5000
500
1000
1500
Detail Coefficient cD2
0 200 400 600 800 10000
1000
2000
Detail Coefficient cD1
Figure 6: Absolute Wavelet Coefficients Using Db4 wi th LG Faul t at Bus-3 with Proposed Algorithm
5.2 LL Faul t on Power System
The current s ignal of phase-A at bus 1 with LL fault on bus 3 and the absolute value of the detail coefficients up
to level 4 and approximation coefficient at level 4 are shown in Figure 7. The current signal and detail coefficient upto
level 4 and appro ximation coefficient at level 4, as calculated by the proposed method are shown in Figure 8.
0 500 1000 1500 2000-5000
0
5000
10000
Original Signal
20 40 60 80 100 1200
5000
10000
15000
Absolute value of approximation Coefficient cA4
0 20 40 60 80 100 120 1400
5000
10000
Absolute value of detail Coefficient cD4
50 100 150 200 2500
1000
2000
3000
Absolute coefficient of detail Coefficient cD3
0 100 200 300 400 5000
500
1000
1500
Absolute coefficient of detail Coefficient cD2
0 200 400 600 800 10000
500
1000
1500
2000
Absolute coefficient of detail Coefficient cD1
Figure 7: Absolute Wavelet Coefficients Using Db4 wi th LL Fault at Bus-3
0 500 1000 1500 2000-5000
0
5000
10000
Original Signal
0 20 40 60 80 100 1200
5
10
15x 10
4 Approximation coefficient Coefficient cD1
0 20 40 60 80 100 1200
1
2
3x 10
5 Detail Coefficient cD4
0 50 100 150 200 2500
1
2
3x 10
4 Detail Coefficient cD3
0 100 200 300 400 5000
0.5
1
1.5
2x 10
4 Detail Coefficient cD2
0 200 400 600 800 10000
1
2
3
4x 10
4 Detail Coefficient cD1
Figure 8: Absol ute Wavelet Coefficients Using Db4 wi th LL Fault at Bus-3 wi th Propose d Algorithm
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5. 3 LLG Fault on Power System
The current signal of phase-A at bus 1 with LLG fault on bus 3 and the absolute value of the detail coefficients up
to level 4 and approximation coefficient at level 4 are shown in Figure 9. The current signal and detail coefficient up to
level 4 and appro ximation coefficient at level 4, as calculated by the proposed method are shown in Figure 10.
0 500 1000 1500 2000-5000
0
5000
10000
Original Signal
20 40 60 80 100 1200
5000
10000
15000
Absolute value of approximation Coefficient cA4
0 20 40 60 80 100 120 1400
5000
10000
15000
Absolute value of detail Coefficient cD4
50 100 150 200 2500
1000
2000
3000
Absolute coefficient of detail Coefficient cD3
0 100 200 300 400 5000
500
1000
1500
Absolute coefficient of detail Coefficient cD2
0 200 400 600 800 10000
1000
2000
3000
Absolute coefficient of detail Coefficient cD1
Figure 9: Absolute Wavelet Coefficients Using Db4 wi th LLG Faul t at Bus-3
0 500 1000 1500 2000-5000
0
5000
10000
Original Signal
0 20 40 60 80 100 1200
5
10
15x 10
4 Approximation coefficient Coefficient cD1
0 20 40 60 80 100 1200
1
2
3x 10
5 Detail Coefficient cD4
0 50 100 150 200 2500
1
2
3x 10
4 Detail Coefficient cD3
0 100 200 300 400 5000
0.5
1
1.5
2x 10
4 Detail Coefficient cD2
0 200 400 600 800 10000
1
2
3
4x 10
4 Detail Coefficient cD1
Figure 10: Absolute Wavelet Coefficients Using Db4 wi th LLG Fault at Bus-3 wi th Propose d Algorithm
5.4 Three-Phase Fault on Power System
The current signal of phase-A at bus 1 with three-phase fault on bus 3 and the absolute value of the detail
coefficients up to level 4 and approximation coefficient at level 4 are shown in Figure 11. The current signal and detail
coefficient upto level 4 and approximation coefficient at level 4, as calculated by the proposed method are shown in
Figure 12.
0 500 1000 1500 2000-5000
0
5000
10000
Original Signal
20 40 60 80 100 1200
5000
10000
15000
Absolute value of approximation Coefficient cA4
0 20 40 60 80 100 120 1400
5000
10000
15000
Absolute value of detail Coefficient cD4
50 100 150 200 2500
500
1000
1500
Absolute coefficient of detail Coefficient cD3
0 100 200 300 400 5000
500
1000
1500
Absolute coefficient of detail Coefficient cD2
0 200 400 600 800 10000
500
1000
1500
2000
Absolute coefficient of detail Coefficient cD1
Figure 11: Absolute Wavelet Coefficients Using Db4 wi th Three-Phase Fault at Bus-3
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0 500 1000 1500 2000-5000
0
5000
10000
Original Signal
0 20 40 60 80 100 1200
5
10
15x 10
4 Approximation coefficient Coefficient cD1
0 20 40 60 80 100 1200
2
4x 10
5 Detail Coefficient cD4
0 50 100 150 200 2500
1
2
3x 10
4 Detail Coefficient cD3
0 100 200 300 400 5000
5000
10000
15000
Detail Coefficient cD2
0 200 400 600 800 10000
1
2
3x 10
4 Detail Coefficient cD1
Figure 12: Absolute Wavelet Coefficients Using Db4 wi th Three-Phase Fault at Bus-3 with Propose d Algorithm
5.5 Discussions of Simulation Results
The maximum values of absolute wavelet coefficients and average wavelet coefficients in proposed method for
each type of transmission line faults are provided in Table 3. The current of phase-A captured at bus-1 of the proposed
power system model is used for analysis. Comparing the results of Figure 5 and 6 for LG fault, Figure 7 and 8 for LL fault,
Figure 9 and 10 for LLG fault and Figure 11 and 12 for three-phase fault, it is concluded that proposed algorithm is more
effective for detection of all types of transmission line faults. The magnitude of wavelet coeff icients, as provided in table 3,
is high in the proposed algorithm as compared to the original coefficients which clearly detects the faults. In LG fault, the
magnitude of absolute coefficients is very low, which is not efficient to detect the faults as such magnitude of coefficients
may also be obtained due to other type of power system transients. The presence of fault is detected by the high values of
wavelet coefficients as compared to normal conditions. Double line to ground fault is most severe leading to maximum
unbalancing in the system, which is indicated by the highest values of wavelet coefficients. The three-phase fault, actually
results in same changes in all the phases.
Table 3: Maximum Values of Wavelet Coefficients
Type of Fault
Absolute Maximum Value of Wavelet
Coefficient
Maximum Value of Wavelet Coefficient with
Proposed Algorithm
Cd1 Cd2 Cd3 Cd4 Ca4 Cd1 Cd2 Cd3 Cd4 Ca4
LG 95 140 800 8000 5500 1200 1300 2*104 2.6*10
4 8*10
4
LL 2000 1100 2000 10000 10000 3.8*104 1.6*10
4 2.8*10
4 2.8*10
5 10*10
4
LLG 2000 1400 2000 10500 10000 4*104 1.7*10
4 2.8*10
4 2.9*10
5 11*10
4
Three-phase 1600 1200 1400 12500 10000 2.8*10 12000 2.9*10 3*10 15*10
6. CONCLUSIONS
In this paper, a new DWT based technique using moving window of size 32 samples is proposed for detection of
transmission line faults. The proposed model of the power system is simulated in the MATLAB/Simulink environment.
The results show the relative severity of transmission line faults on the power system. The LL and LLG faults are more
severe and produce maximum unbalancing in the system. The proposed method effectively detects the transmission line
faults, which further can be used for the on-line protection system in the power system. The accuracy of the proposed
method has been found to be high and the consistency of the results demonstrates the effectiveness of the method.
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AUTHORS DETAILS
Naveen Gaur received Engineering Diploma, from Govt. Polytechnic College, Ajmer, India in 2000. He received
B. E. (Electrical) fro m Rajasthan Institute of Engg. and Tech. Jaipur, India in 2004 and M. Tech. (Power System) from
Bhagwant University, Ajmer, India, in 2014.
Presently he is working as a Principal at Aryan Polytechnic College, Ajmer, India. He also worked as a Principal
at Santosh Adarsh Pvt. ITI, Riya Badi, Nagaur, since July-2013 to Aug-14 and also worked as a Lecturer at Aryan
Polytechnic College, Ajmer from Oct-2011 to July-2013. His research interest includes the power system and power
electronics.
Ram Niwash Mahiareceived his B.E. degree in Electronics instrumentation and Control Engineering from Govt.
Engineering College Bikaner, Bikaner, India and his M.E. degree in Control and Instrumentation under Electrical
Department from Delhi College of Engineering, Delhi, India in 2007 and 2009, respectively. He is pursuing Ph. D. degree
in Information Communication and Technology from Indian Institute of Technology Jodhpur, Rajasthan, India, since
August-2011. From March 2010 to july-2011, he was an Assistant Professor with the Department of Electronics
Instrumentation and Control Engineering, Global Institute of Technology, Jaipur, Rajasthan, India. His research interests
include control of multi-agent systems, nonlinear control, robust control and its applications for uncertain s ystems.
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A Novel Method for Detection of Electric Transmission Line Faults Using Discrete Wavelet Transform 73
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Om Prakash Mahelawas born in Sabalpura (Kuchaman City) in the Rajasthan state of India, on April 11, 1977.
He studied at Govt. College of Engineering and Technology (CTAE), Udaipur, and received the electrical engineering
degree from Maharana Pratap University of Agriculture and Technology (MPUAT), Udaipur, India in 2002. He received
M. Tech. in 2013. He is currently pursuing PhD from Indian Institute of Technology, Jodhpur, India.
From 2002 to 2004, he was Assistant Professor with the RIET, Jaipur. From 2004 to 2013, he has been Junior
Engineer-I with the Rajasthan Rajya Vidhyut Prasaran Nigam Ltd. (RRVPNL), India.. Presently he has been Assistant
Engineer with RRVPNL. His special fields of interest are Transmission and Distribution (T&D) grid operations, Power
Electronics, Power Quality, Renewable energy s ources and Load Forecasting. He is an author of 34 International Journals
and Conference papers. He is a Member of IEEE. He is Member of IEEE Power & Energy Society. Mr. Mahela is recipient
of University Rank certificate from MPUAT, Udaipur, India, in 2002 and Gold Medal in 2013.
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