A Novel Composite High Impedance Faults Detection System
MING-TA YANG1 JHY-CHERNG GU2
Department of Electrical Engineering
St. John’s University1 National Taiwan university of Science and Technology2
499, Sec. 4, Tamking Rd., Tamsui, Taipei Hsien 251, Taiwan1
43, Sec. 3, Keelung Rd., Taipei 106, Taiwan2
TAIWAN
mtyang@mail.sju.edu.tw1 http://www.sju.edu.tw1
jcgu@mouse.ee.ntust.edu.tw2 http://www.ntust.edu.tw2
Abstract: - Protection of aerial lines from high impedance faults (HIFs) is a signification topic for worldwide
electric utilities. Arcing phenomena between a fallen conductor and the ground are usually linked with HIFs,
which may lead to a fire hazard or endanger beings. This work successfully develops a new composite HIF
detection system that adopts 3-phase currents and 3I0 zero sequence current to solve HIF problems in aerial
lines. Two staged fault tests were performed to study the feasibility of the proposed algorithm and evaluate its
performance. Experimental results reveal that the proposed composite HIF relay is feasible performance well.
Key-Words: - Fault diagnosis, high impedance faults, neural networks, protection, statistical confidence,
wavelet transform
1 Introduction
HIFs constitute the most common problem in
electric utilities. HIFs can be described as
distribution system faults that are not detected by
traditional protection devices with adequate fault
current. Many systems, mainly involving
mechanical or electrical detection, have been
presented in the past two decades to enhance HIFs
detection in power distribution system.
Mechanical detection methods [1] comprise
devices that provide a low impedance ground path
by catching the falling conductor. The main flaws of
these methods are the high installation and
maintenance costs.
The electrical detection methods may be
categorized into three classes, time domain,
frequency domain and wavelet transform. The time
domain detection algorithms utilize the random
behaviour of the fault pattern. The relay schemes are
based on pattern identification of the fault voltage,
fault current and arc flicker [2-4]. The detection
algorithm of the frequency domain applies Fourier
transform to extract the features of the harmonic
components. The identification approach involved
low-frequency, high-frequency and inter-harmonic
parts of the arcing fault signals [5-6]. Wavelet
transform can be employed to examine the transient
phenomena of HIFs signals in both the time and
frequency domains. The algorithm is ideally for
nonstationary signals such as those encountered
under HIF arcing faults [7-8].
2 Problem Formulation
The composite HIFs detection system includes two
individual algorithms for individually detecting
HIFs are shown in Fig. 1. These relaying algorithms
are based on third-order harmonic current and
intelligent HIFs detector.
Fig. 1. Composite HIFs detection system
2.1 Third-order Harmonic Current Relaying
Algorithm [9]
The variation in amplitude of the third-order
harmonic current and the variations of ratio of third–
order harmonic to fundamental are monitored. An
HIF is issued if both the variation of amplitude and
the ratio both exceed the threshold level.
2.2 Proposed Intelligent
Relaying Algorithm
HIFs
Detector
A self-turning scheme based on the chi-square
distribution and 95 ％ confidence interval is first
applied to set the threshold level automatically for
the 3I0 zero sequence current variances examined.
The feature extraction scheme based on wavelet
transform and the pattern recognition technique
found on neural networks are then applied to
identify HIFs.
2.2.1 Statistical Confidence [10]
The magnitude of the 3I0 zero sequence current is a
normal distribution random variable. Let X 1 , X 2 , . . .
, X n be a random sample from a 3I0 zero sequence
current with mean  and variance  2 , and let S 2
denote the sample variance.
Then, the random variable X
chi-square
 
2
 , 
2
 X - 
Z=
n-1 S 2

=
2
has a
distribution with n-1 degrees of
freedom.
If X ~N
2
 X - 
and Z =
2
2
, then
2

2
~ 12
(1)
 
The random variable Z has a chi-square  2
distribution with 1 degree of freedom. The 95%
confidence internal on  2 is constructed as
2
 =1-0.05=0.95
P  Z  1,0.05
(2)
2
X threshold = + 1,0.05
 2
(3)
From Equ. (2) and the confidence internal for  ,
the threshold can be derived as
To enhance the reliability of HIFs detection and
prevent power supply interruption, the confidence
level was adopted to identify the abnormal variation.
The analysis of the staged test data reveals that
intermittent arcing phenomena arose in most HIFs,
and that the waveform of the arcing current had an
unsymmetrical shape in each cycle. The same was
true of arcing in switching transient condition of
load or capacitor bank, except for the sustained time
of arcing, which was longer in the HIFs than in the
switching transient. Hence, the statistical confidence
was applied for 3I0 zero sequence current
estimation. A 3I0 zero sequence current above
threshold, or an excessive difference between
successive samples, indicate an HIF-suspected.
2.2.2 Wavelet Transform
A wavelet representation will give the location in
both time and frequency simultaneously. The
literatures demonstrate that the sharp signal
variations can be regarded as features of the faults.
The HIFs can be identified from the sharp variations.
The proposed approach is to detect the HIFs using
the multiresolution analysis (MRA) output. The
MRA can easily extract important features implicit
in the distribution feeder voltage and current signals,
even when these features are very weak [11]. This
investigation utilized the wavelet transform as a
feature extraction tool.
Filter
bank
theory
provides
efficient
computational schemes for wavelet analysis.
Selecting an adequate wavelet filter is crucial to
identifying the HIF features of 3I0 zero sequence
current signal. The “optimal” wavelet for extracting
a given signal is that which can generate the most
coefficients with maximal values within the timescale domain, to represent the feature of a signal.
This wavelet should also have sharp cut-off
frequencies. This study employed the Daubechies
D-20 (denote Db10) wavelet, since it has been
demonstrated to perform well and satisfy the
aforementioned conditions.
In this study, the sampling frequency fs was set to
6 kHz, and the resolution level of the MRA filter
banks was set to 4.
2.2.3 Neural Networks
Back propagation artificial neural networks (BPANN) are highly effective for pattern recognition.
Using BP-ANN is an attempt to diagnose the HIF
and distinguish it from normal system conditions. In
this study, the neural networks used the back
propagation Scaled Conjugate Gradient training
algorithm, and adjusted the weight update step at
each iterations. The network stops learning when the
mean square error (MSE) or number of iterations
reached a predetermined target value. In this
investigation, the MSE was set to 0.0001 and the
number of times of training was set to 4000 [12].
The wavelet coefficient norm is adopted as the
input to neural networks. This approach not only
reduces the size of the neural networks, but also
retains the key features of the original signals [1314]. This study calculated the norm using 0.1 ms of
the data window.
Owing to the load nonlinear and system
imbalance, the 3I0 zero sequence current includes
harmonics. Accordingly, the corresponding signals
of the details d1[n] are not appropriate for HIF
identification for the 3I0 zero sequence current.
Therefore, four norm coefficients (d2[n], d3[n], d4[n]
and c4[n]) were fed into the neural networks in every
data window period for the 3I0 zero sequence
current. The neural networks topology consists of
two independent multilayers of a feedforward
network, which includes four neurons in hidden
layer and one neuron in the output layer. The neural
networks output includes one target. The target is
for fault detection, and has only two possible values:
“0” means no fault, and “1” indicates that an HIF
exists in the feeder.
Fig. 2 depicts the proposed protection flowchart
for distinguishing normal system operating events
from an HIF by combining the statistical confidence
and wavelet transform with neural networks.
The 95% confidence interval of 3I0 zero
sequence current is applied first, to calculate
thresholds for determining HIF-suspected.
If the results exceed the thresholds, then the
wavelet transform is employed to extract the
distinctive features of the 3I0 zero sequence current,
and transform them into a series of detail and
approximation wavelet components. The norm of
the data window of the wavelet components is then
computed and used to train and test neural networks
to identify an HIF from the normal system
operations as a result of features of wavelet
extraction [15].
If the characteristics of HIF are recognized, then
an HIF tripping signal is issued; otherwise, an HIFsuspected alarm signal is released, and the sample
duration is moved to next sliding window for HIF
detection.
Additionally, a broken conductor was tested in air
for HIFs. An XLPE covered conductor, a copper
bare conductor and ACSR were employed in this
test.
1) August, 2004 testing: This staged fault testing
was conducted to collect HIF data and explore the
feasibility of the proposed composite HIFs detection
system. The 25 faults were staged in conducted on
phase C. The three-phase voltages and currents of
the Tai-16 feeder were measured at the Taishi
substations. Additionally, the arcing voltage and
current were measured at the staged fault testing
site. The staged fault testing records were later
employed to train neural networks. The types of
conductor and ground of staged fault testing is
shown in Table 1
2) March, 2005 testing: These staged fault tests
were undertaken to evaluate the performance of the
proposed algorithm. The 59 faults were staged on
phase A, B, and C, and three capacitor banks
switching tests executed.
3 Staged Fault Testing For HIFs
An 11.4 kV Tai-16 feeder served from Taishi
substation Yunlin Administration Division of
Taipower near Mailiao, was chosen as the testing
site. The testing site was located about 10 km away
from the substation, as displayed in Fig. 3. This
feeder mostly supplies aquaculture load, and has an
average load of about 100－110 A. The A/D event
recorder ADX-3000 was installed to record threephase voltages and currents of fault feeder in
substation. By switching the fuse cutout and oil
circuit breaker the broken conductor can be
grounded or ungrounded in testing site.
The HIF faults were staged at electric pole #61,
and tested on either the ground or ground objects,
which were dry and wet sand, gravel, asphalt,
concrete, grass, tussock, shrub and electric pole.
Fig. 2. Algorithm of the intelligent HIFs detector
scheme
Fig. 3. One-line diagram of field staged faults
distribution networks
Fig. 4. XLPE conductor phase C HIF on asphalt:
four-level wavelet analysis of 3I0 zero sequence
current
4 Test Results
Fig. 4－11 respectively demonstrate that an XLPE
conductor HIF occurred at roughly 0.4 s on asphalt,
7.5 s on dry sand, 3.5 s － 6.5 s on grass and
approximately 9 s in air. The response of the neural
networks also exhibit relative to the HIF event.
Table 2 shows the analytical results of the staged
fault test. An HIF was incorrectly identified 6 times.
However, the intelligent HIF detector issued an
HIF-suspected alarm signal, reminding operators to
be aware of possible HIF events. Therefore, about
89.8% (53/59) of the HIF events were identified
correctly, and roughly 93.8% (45/48) arcing faults
were acknowledged appropriately. Since the
sustained time of the capacitor bank switching was
not longer than the HIF arcing, three switching
events were detected properly.
Fig. 5. Response of an XLPE conductor phase C
HIF on asphalt
5 Conclusions
HIFs are generally accompanied by weak arc
phenomena between the fallen conductor and the
interface, which may be a potential fire hazard or
other public danger. Numerous detection methods
have been recommended for detecting HIFs. The
proposed composite HIF detection system enables
HIFs detection and identification requires with
three-phase currents. The arcing faults detection rate
of the proposed detector is about 93.8%, which is
much better than commercially available relay in the
market. The proposed composite HIF detection
system identifies HIF more accurately than other
available algorithms. If this function HIF-suspected
is viewed as HIF detection, then the detection rate is
almost 100%.
There is a room for further high impedance faults
investigation, including faulted phase recognition
and fault location.
Fig. 6. XLPE conductor phase C HIF on dry sand:
four-level wavelet analysis of 3I0 zero sequence
current
Fig. 7. Response of an XLPE conductor phase C
HIF on dry sand
from Yunlin Administration Division of Taipower is
also acknowledged.
Fig. 8. XLPE conductor phase A HIF on grass: fourlevel wavelet analysis of 3I0 zero sequence current
Fig. 9. Response of an XLPE conductor phase A
HIF on grass
Fig. 10. XLPE conductor phase A HIF in air: fourlevel wavelet analysis of 3I0 zero sequence current
Fig. 11. Response of an XLPE conductor phase A
HIF in air
Acknowledgment
The authors would like to thank the Taipower
Research Institute for financial supporting on this
project. The technical support on field staged tests
References:
[1] C. G. Wester, High impedance fault detection
on distribution system, in Proc. 42nd Annu.
Rural Electric Power Conf., 1998, C5, pp. 1-5.
[2] C.-L. Huang, H.-Y. Chu, and M.-T. Chen,
Algorithm comparison for high impedance
fault detection based on staged fault test, IEEE
Trans. Power Delivery, Vol. 3, No. 4, Oct.
1988, pp. 1427-1435.
[3] H. Calhoun, M. T. Bishop, C. H. Eichler, and
R. E. Lee, Development and testing of an
electro-mechanical relay to detect fallen
distribution conductors, IEEE Trans. Power
Apparatus and Systems, Vol. PAS-101, No. 6,
June 1982, pp. 1643-1650.
[4] S. F. Sultan, G. W. Swift, D. J. Fedirchuk,
Detecting arcing downed-wires using fault
current flicker and cycle asymmetry, IEEE
Trans. Power Delivery, Vol. 9, No. 1, 1994, pp.
461-470.
[5] H.-Y. Chu, M.-T. Chen and C.-L. Huang, High
impedance fault tests on the Taipower primary
distribution system, Electric Power Systems
Research, Vol.19, 1990, pp. 105-114.
[6] B. D. Russell and R. P. Chinchali, A digital
signal processing algorithm for detecting arcing
faults on power distribution feeders, IEEE
Trans. Power Delivery, Vol. 4, No. 1, Jan.
1989, pp. 132-140.
[7] A. Lazkano, J. Ruiz, E. Aramendi, and L. A.
Leturiondo, A new approach to high impedance
fault detection using wavelet packet analysis, in
Proc. 2000 IEEE Int. Conf. on Harmonics and
Quality of Power, pp. 1005-1010.
[8] D. C. T. Wai and X. Yibin, A novel technique
for high impedance fault identification, IEEE
Trans. Power Delivery, Vol. 13, No. 3, July
1998, pp. 738-744.
[9] M.-T. Chen, H.-Y. Chu, C.-L. Huang, and F.-R.
Wu, Performance evaluation of high impedance
fault detection algorithms based on staged fault
tests, Electric Power Systems Research,
Vol.18, 1990, pp. 75-82.
[10] D. C. Montgomery and G. C. Runger, Applied
Statistics and Probability for Engineers, New
York: John Wiley & Sons, 2003, ch. 8.
[11] O. A. S. Youssef, A wavelet-based technique
for discrimination between faults and
magnetizing inrush currents in transformers,
IEEE Trans. Power Delivery, Vol. 18, No. 1,
Nov. 2003, pp. 170-176.
[12] K. Swingler, Applying neural networks-A
practical guide, Building a network, Academic
Press, 1996, pp. 51-76.
[13] F. Martin and J. A. Aguado, Wavelet-based
ANN approach for transmission line protection,
IEEE Trans. Power Delivery, Vol. 18, No. 4,
Oct. 2003, pp. 1572-1574.
[14] P. L. Mao and R. K. Aggarwal, A novel
approach to the classification of the transient
phenomena in power transformers using
combined wavelet transform and neural
network, IEEE Trans. power Delivery, Vol. 11,
No. 4, Oct. 2001, pp. 654-660.
[15] M.-T. Yang and J.-C. Gu, Detection high
impedance faults utilizing combined phase
voltages with neutral line current, International
Journal of Emerging Electric Power Systems,
Vol. 2, Issue 2, June 2005, Article 1051.
Table 1 The types of conductor and ground of August, 2004 staged fault testing
Conductor
type
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
Item
1
2
3
4
5
6
7
8
9
E. pole： Electrical pole
Surface
type
Grass
Shrub
Wet Gravel
Wet Gravel
Wet Gravel
Grass
Wet Grave
Wet Grave
In Air
Item
10
11
12
13
14
15
16
17
18
Conductor
type
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
Surface
type
Wet Grave
Tussock
Tussock
Tussock
Asphalt
Grass
Concrete
E. pole
Concrete
Item
19
20
21
22
23
24
25
Conductor
type
Bare
Bare
Bare
Bare
Bare
Bare
Bare
Surface
type
Shrub
Wet Gravel
Asphalt
Dry Sand
Tussock
Wet Gravel
Concrete
Table 2 The results of March, 2005 staged fault testing
Item
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Fault
phase
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
B
B
B
B
Conductor Surface
HIF
Arcing ？
type
type
detectable
XLPE
Concrete
Yes
Yes
XLPE
Shrub
Yes
Yes
XLPE
Asphalt
No
Yes
XLPE
Gravel
Yes
Yes
XLPE
Dry Sand
Yes
Yes
XLPE
Grass
Yes
Yes
XLPE
Grass
Yes
Yes
XLPE
Tussock
Yes
Yes
XLPE
In air
Yes
Yes
XLPE
Concrete
Yes
Yes
XLPE
Concrete
Yes
Yes
Bare
Shrub
Yes
Yes
Yes
Bare
Tussock
Yes
Yes
Bare
Asphalt
No
Bare
W.Asphalt
Yes
Yes
Bare
Dry Gravel
No
Suspected
Bare
W. Gravel
Yes
Yes
Bare
Dry Sand
Yes
Yes
Bare
Grass
Yes
Yes
Bare
Concrete
Yes
Yes
Bare
Concrete
Yes
Yes
Bare
Concrete
Yes
Yes
Bare
Concrete
Yes
Yes
ACSR
Concrete
Yes
Yes
ACSR
Shrub
Yes
Suspected
ACSR
Tussock
Yes
Yes
XLPE
Concrete
Yes
Yes
Yes
XLPE
Concrete
Yes
Yes
XLPE
Shrub
Yes
XLPE
Tussock
Yes
Yes
Item
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
Suspected：HIF-suspected； W. Asphalt：Wet Asphalt； W. Gravel：Wet Gravel
Fault
phase
B
B
B
B
B
B
B
B
B
B
B
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
C
Conductor
type
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
Bare
Bare
Bare
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
XLPE
Bare
Bare
Bare
Bare
Bare
Bare
Bare
Bare
Bare
Bare
Bare
Surface
Arcing ？
type
Asphalt
No
Asphalt
No
Gravel
Yes
Gravel
No
Dry sand
Yes
Concrete
Yes
Grass
Yes
Grass
Yes
Concrete
Yes
Grass
Yes
Sand
Yes
Shrub
Yes
Asphalt
No
Gravel
No
Dry sand
Yes
Dry sand
Yes
Concrete
Yes
Tussock
Yes
Concrete
Yes
Sand
Yes
Tussock
Yes
Asphalt
No
W.Asphalt
Yes
Gravel
No
W. Gravel
Yes
Sand
Yes
Grass
Yes
Concrete
Yes
Concrete
No
HIF
detectable
Suspected
Yes
Yes
Yes
Suspected
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Suspected
Yes
Suspected
Yes
Yes
Yes
Yes
Yes
Yes
Yes