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Approaches in High Impedance Fault Detection - A Chronological Review
Article in Advances in Electrical and Computer Engineering · August 2010
DOI: 10.4316/aece.2010.03019 · Source: DOAJ
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Advances in Electrical and Computer Engineering Volume 10, Number 3, 2010
Approaches in High Impedance Fault Detection
A Chronological Review
M. SEDIGHIZADEH1,2
, A. REZAZADEH2
, Nagy I. ELKALASHY3
1
Faculty of Engineering and Technology, Imam Khomeini International University, Qazvin, Iran
2
Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G. C., Evin
1983963113, Tehran, Iran
3
Department of Electrical Engineering, Helsinki University of Technology (TKK), PO Box 3000, FI-
02015 TKK, Finland
E-mail: m_sedighi@sbu.ac.ir
Abstract— This paper reviews the major contributions to the
high impedance fault (HIF) detection field throughout a 48-
year period, from 1960 up to 2008, from classic approaches to
heuristic algorithms. After surveying around 225 papers in the
field, the amount of existing works for each method is
identified and classified. The paper concludes with
comparative tables and graphs demonstrating the frequency of
each high impedance fault detection methods, and so it can be
used as a guideline for researchers in this field.
Index Terms— Chronological, Classic, Detection, High
Impedance Fault, Heuristic
I. INTRODUCTION
A high impedance fault results either from high
impedance fault object or when a primary circuit conductor
makes an unwanted electrical contact, which restricts the
flow of current below the detection level of the protective
devices. High impedance faults often occur when a
conductor breaks and falls to the ground. Such a situation
leaves an energized conductor at ground level, creating a
public hazard, and any unsafe condition is of concern to
utilities. For this reason, the detection of high impedance
faults (HIF) in electric distribution systems has been the
subject of intense interest over the history of utility systems.
When a HIF occurs, the sparks are usually created between
downed conductor and contact surface and the conductor is
bouncing until the protection system remove the faulted
system from the energy source. It causes to increase the
public hazard. Fig 1 shows a typical high impedance fault on
the wet grass ground.
Figure 1. A typical high impedance fault on the wet grass ground
[212]
The limitations of conventional protection are clear. As an
example, a utility may set phase relays at 960 A and ground
relays at 480 A. The smallest fuse size may be 40 A, but in
many locations, 100 A fuses are used. When a high
impedance fault with a typical current of 10-50 A occurs, it
is likely that conventional protection will not operate [53].
Reference [3] explains that over current devices fail to
operate in 32% of such faults. Table 1 provides typical fault
currents on different surfaces [110].
TABLE I. TYPICAL FAULT CURRENTS ON VARIOUS SURFACES
Surface Current (A)
Dry asphalt or sand 0
Wet sand 15
Dry soil 20
Dry grass 25
Wet soil 40
Wet grass 50
Reinforced concrete 75
II. NATURE OF HIF AND MODELING AND GENERAL
CONSIDERATION
A. Nature
The nature and extent of the high impedance fault
problem has been well-documented [8, 52, 57, 67, 84, 118,
and 145]. The HIFs are generally difficult for conventional
over current protection devices to detect in view of the fact
that they have high impedance at the fault point and do not
cause an excessive change of current in the feeder affected.
This fault has special nature such as low currents and the
associated arcs. These arcs are created as a result of air gaps
due to the poor contact made with the earth or with the
earthed object. Such arcs provide asymmetry in the
waveforms and generate low and high frequencies. While it
is likely that only a few percentages of all faults are high
impedance faults, it is estimated that one-third to one-half of
downed conductor faults are high impedance faults. These
estimates are based on discussions with line crews and
reflect those faults which do not involve a grounded
conductor or grounded object. Regarding that HIF has a low
current, so the conventional protection system could no
detect it and thus, the researchers realized that the methods
based on the magnitude of fault current did not success to
114
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Figure 2. Taxonomy of HIF Detection Algorithms
detect HIF and the approaches based on the characteristics
of fault current had to be proposed. Therefore, the
researchers at Texas A&M University as well as other
researchers studied the nature and characteristics of high
impedance and arcing faults in electrical systems [4, 6, 20,
55, and 138].
A. Modeling
A number of authors introduced models to simulate the HIF
that could analyze the behavior of these faults [2,113]. In
reference [1], a model describing the phenomenon using a
spark gap is developed. In [3] a model is proposed for HIF
based on the spark gap model. Reference [75] describes a
computer model developed to compute the effects of
harmonics generated by high-impedance faults. Reference
[141] explains the historical evolution of arc modeling for
HIF. In [157] a model was introduced for HIF utilizing two
series time varying resistances. Another model is presented
in [192] which include two DC voltage sources representing
the arcing voltage of air in soil and/or between trees and the
distribution line. In [60], the authors suggest two DC
sources connected anti-parallel to two diodes to simulate
zero periods of arcing and asymmetry. In [219], a HIF
model based on a time-varied earth resistance in series with
a dynamic arc resistance is proposed. In [203], a high
impedance arcing fault due to a leaning tree in medium
voltage (MV) networks is modeled. The fault is represented
in two parts: an arc model; and a high resistance. The arc is
generated by a leaning tree towards the network conductor
and the tree resistance limits the fault current.
B. General considerations
By analyzing of HIF characteristics, it can be seen that
the HIF frequency spectrum is very similar to capacitor
switching and other events in distribution systems. Thus,
this problem sophisticates the detection of HIF from other
events [11, 72]. Many researchers have reviewed and
documented the methods to detect of HIF [154,166] and
others [81,216] have denoted the challenges to improve the
safety of detecting these faults. In [19], several complex
technical, legal, economical, and operational problems
involved in high impedance fault detection are discussed.
Reference [110] discusses technical as well as non-technical
issues associated with applying high impedance fault
detectors. Besides, there is not a single method to
completely solve the problem of HIF identification [121].
In the following sections, various approaches for HIF
detection are reviewed and in the conclusions, these
algorithms are compared. The taxonomy of these algorithms
is shown in Fig. 2. This figure declares that the HIF
detection methods are either depending on classically
extracting the features or based on intelligently decisive
tools.
III. HIF FEATURES USED IN DETECTION APPROACHES
A. Magnitude of Phase or Natural Fault Current and
Voltage
There were approaches presented based on the magnitude
of phase or natural fault current measurement [80,132] and
as the HIF has a little current, these algorithms frequently
failed to detect the fault. Founded on these approaches, the
authors in [9] proposed a proportional relaying algorithm
and introduced a new electromechanical relay. The
unbalancing in phases currents in fault duration was a
115

criterion applied to detect the HIF. According to such
criterion, in [12,15,21,33], the ratio ground relaying (RGR)
algorithm and in [16,128], other schemes appropriate for
electromechanical relays were presented. In [135], a
comparison was performed between a ratio ground relay and
a proportional relay. The proposed algorithm in [144] was
based on the change of the neutral voltage and zero-
sequence currents. In [153], an algorithm consisting of
monitoring the shunt resistance of each feeder derived from
substation was considered as a tool to detect the high
impedance fault. In [164], a modified directional earth fault
relay was suggested to identify HIF. The differences
between HIF detection for ungrounded systems and multi-
grounded systems are expressed in [204] and next, a method
based on the system fundamental frequency component of
residual current and voltage was proposed to determine HIF.
In [172], another protection scheme based on active power
variation was developed, and a dissipation factor (DF)-based
criterion was proposed to identify load switching operation.
This method was implemented by cross-correlation analysis
between phase voltage and residual current in single-phase
network. Reference [173] proposed the algorithm which
calculated the influence of feeder admittance unbalancing in
neutral voltage in order to detect the HIF.
As aforementioned, current waveforms of the HIF is
contaminated by low and high order harmonics and sub-
harmonics which are used as indexes to detect these faults.
To extract the noted low and high components from fault
current waveforms, several authors have used the
approaches based on signal processing schemes as follows:
B. Low Order Harmonics, Sub-harmonics and Low
Frequency Spectrum
The vast frequency spectrum can be derived via analyzing
the HIF current [73, 95,171]. In this subsection, the HIF
features supporting its detection are explained. They are
based on the variation of one or more characterizing or non-
characterizing harmonics fault current signal. Also using 3rd
harmonic voltage and current changes, relay performance
similar to a conventional 60Hz directional impedance relay
was attained for fault currents detection [61]. In [10], the
third harmonic current relaying algorithm and in [14], a
more sophisticated technique that involved finding a Chi-
squared test statistic using 60 Hz sequence components and
harmonics were suggested to detect HIF. In [160], the
authors proposed a method based on negative sequence
current. An algorithm based on the symmetric components
was proposed to detect and identify the balance condition of
the system during the fault [165]. Next, other ideas such as
using the information contained in the low frequency
spectral behavior in terms of both magnitude and phase
[23,149], increasing frequency components near to 60 Hz
[28], focusing on the low order harmonic current in order to
obtain the features to distinguish arcing faults from
switching events [29, 36, 45, 46, 60, 66], and detection
method using even order harmonics [27, 30] were proposed.
The technique presented in [25, 26] monitors the
unbalancing in the fundamental, third, and fifth harmonic
feeder currents at the substation and performs a statistical
evaluation of the present unbalancing relative to past levels
of it. References [49, 62, 71] introduced three criteria of an
even order power, an even order ratio, and an even order
incremental variance criterion for the fault detection. Energy
variance criteria determination and threshold tuning scheme
for high impedance fault detection were explained in [143]
and self-tuning of fault detection threshold was described in
[133]. In [69], a relay was explained based on second order
harmonic current algorithm. An adaptive detection method
founded on low order harmonics and harmonic ratio
characteristics was described [100]. In [34, 35], an algorithm
was proposed regarding to the increase in the energy level
(off harmonics) and the degree of randomness associated
with arcing HIF. In [44], a combined algorithm was
presented, which consists of five sub-algorithms including:
Energy, Randomness, Arcing phase signature, Spectral
analysis, and Load analysis algorithm. In [32], a comparison
among four high impedance fault detection algorithms was
made, namely the proportional relaying algorithm, the ratio
ground relaying algorithm, the second order harmonic
current relaying algorithm, and the third order harmonic
current relaying algorithm. In [5], a communication link
relaying algorithm is explained as the sub-harmonic relaying
algorithm. In [98], the authors have proposed a new adaptive
algorithm with using harmonic characteristics of HIF. A
multiple algorithm based mainly on energy, randomness,
and inter-harmonics was presented in [104]. In [114], a
scheme was developed based on a concept of quasi-static
ripple harmonics and sub/super harmonic frequencies. In
[151], energy content of even, odd, and in-between
harmonics is utilized as a criterion to detect the HIF. In
[222], the authors have used fault features ranging from
transient high frequency harmonic distortion to fundamental
intermittent in order to determine the HIF. The use of sub
harmonic oscillation and harmonic distortion was
investigated in [161] as a means of anticipating of HIFs. The
high impedance fault detection system reported in
[178,179,188,212] was based on algorithms that use all
harmonic and non-harmonic current components in all of the
3-phases and ground.
C. High Frequency Spectrum
The HIF causes the creation of high frequency
components in current signals. So, one of the criteria to
detect the HIF was localizing the high frequency energy
level in current signals. Naturally, normal events such as
capacitor switching cause the high frequency energy level to
increase similar to HIF. Regarding that energy level
increasing in HIF has random nature, thus to solve such a
problem, other criteria named randomness criteria were
defined. Some researchers [11, 13, 18, 50, and 99] have
employed one or two of above mentioned criteria to detect
the HIF. The monitored system impedance was affected by
high frequency components in current signal. Therefore in
[7], the HIF was detected by high frequency impedance
monitoring. Reference [39] presented a novel algorithm
based on random behavior of HIF. The reference [119]
described a conventional detection technique that compared
energy values of load currents to preset thresholds in order
to detect an arcing fault on a power line. In [68], the authors
describe an identification approach involving low frequency,
high frequency, and inter harmonic parts for detecting HIF.
116

In [86], design and implementation of an inexpensive
prototype fault detector on the Seattle City light street
network was described based on the algorithm mentioned in
[11]. In [207], an operational algorithm solved the problem
of determining the HIF from other events was proposed. The
discrimination was based on the magnitude, randomness,
and steadiness of harmonic energy content of the current
waveform.
IV. HIF FEATURE EXTRACTORS
As it can be concluded in previous section, HIFs are
accompanied by variations in the 60 Hz and harmonic
components. However, these variations are not stationary
and time varying in nature due to the dynamics of arc faults.
Therefore, approaches that accounts for the time varying
nature of the fundamental and harmonic components using
feature extractors such as Kalman filtering, wavelet
transform, chaos and fractal theorem, and other techniques
to recognize the pattern of fault current signals are
convenience to identify the HIF. These algorithms are
classified as follows:
A. Algorithms Based on Kalman Filtering and Pattern
Recognition
In Reference [42] the authors briefly discussed high
impedance faults, current and voltage patterns, and arcing
faults analysis and introduced a protection scheme based on
microprocessor to correctly discriminate between normal
and high impedance fault conditions. In [56, 87], techniques
were proposed for detecting high impedance faults using
signal processing and pattern recognition methods. In [148],
the scheme was based on graphical image feature extraction
of diagnostic feature vector carrying information about the
fault types. In [82,103], the authors presented a technique
for high impedance fault detection. The method comprised
of injecting an Impulsive signal in the feeder and monitoring
its response. HIFs were identified by comparing the current
pattern in normal condition and under fault condition. In
methodology developed in [209], instead of injecting current
through the neutral grounding, a voltage of certain
frequency was superposed in the bus.
An approach based on Kalman filtering theory for
obtaining the best estimation for time variation of the
fundamentals and harmonic components was suggested in
[58]. Time variation of fundamental and low order harmonic
components contributed to detection of high impedance
faults.
B. Algorithms Based on Fractal Theorem
Analysis of HIFs using fractal techniques was suggested
in [112]. The detection using the combination of neural
network and chaotic degree was presented by [131].
C. Algorithms Based on Wavelet Transform
In [127], application of continues wavelet transform to
detect HIF was introduced. Reference [129] presented a
detection using wavelet analysis filter banks method where a
sophisticated TACS switch which controls the open/closed
loop of a HIF in order to introduce Nonlinearity and
asymmetry and then, HIF is detected using wavelet analysis
filter banks method. The authors in [130] utilized discrete
wavelet transform for HIF detection. In [134], a wavelet
analysis filter bank was presented to identify the HIF.
Reference [142] uses multi-resolution analysis based on
Morlet wavelet transform approach to detect the HIF. In
[162], a technique was proposed using an absolute sum
value of coefficients in multi-resolution signal
decomposition (MSD) founded on the discrete wavelet
transform (DWT). In [163], the wavelet transform was
applied to filter out some frequency bands of harmonics
from residual current and line current. Then, the root mean
square (RMS) value of these harmonics were directly
calculated using their wavelet coefficients and the fault was
identified from disturbance by the RMS difference between
the residual current and the line current. An algorithm in
[176] was based on the phase comparison between wavelet
coefficients of zero sequence voltage and current signals.
Reference [181] presented an algorithm based on an analysis
of three-phase unbalanced current, feeder 3I0, and using
decomposition of the signal by means of wavelet transform
techniques. In [183], wavelet transform was used for the
decomposition of signals and feature extraction; then,
feature selection was done by principal component analysis.
In addition, Bayes classifier was used for the classification
of HIF from other events. Reference [187] proposed to use
the Discrete Wavelet Transform (DWT) as well as
frequency range and RMS conversion to apply a pattern
recognition based detection algorithm for HIF detection.
The protection algorithm developed in [197] observed the
phase displacement between wavelet coefficients calculated
for zero-sequence voltage and current signals at a chosen
high-level frequency. A new model was presented in
[200,203,206] to simulate the HIF. In these references,
Discrete Wavelet Transform (DWT) is used to extract the
fault features and to verify the HIF model. Reference [201]
used discrete wavelet transform (DWT) in order to yields
three phase voltage and current in the low frequency range
which was fed to a classifier for pattern recognition. The
classifier was based on the algorithm that used nearest
neighbor rule approach. A new method to extract HIF
features by means of discrete wavelet transform was
introduced in [208,210]. Reference [214] introduced
Discrete Wavelet Transform (DWT) to extract residual
currents throughout unearthed MV networks for detecting
high-impedance faults due to leaning trees. References
[206,225] proposed a novel method to determine the HIF
based on the computation of feeder power. This power was
mathematically processed by multiplying the DWT detail
coefficients of the phase voltage and current for each feeder.
Its polarity identified the faulty feeder. In [220], the
mentioned technique in [206,225] was evaluated using 17
staged earth fault cases at a sampling frequency of 10 kHz.
In [221], the algorithm performance defined by [225] was
tested with resistance earth faults over a wide range of the
fault resistance values as well as concerning practical fault
cases such as arcing faults.
117

D. Algorithms Based on Crest Factor
In [106], an algorithm was presented to analyze the transient
behavior of various events and HIFs on distribution feeders
by quantifying wave distortion with the crest factor.
E. Algorithms Based on Fault Current Flicker
The authors in [101] denoted a fault current flicker
criterion and half cycle asymmetry method to identify HIF.
F. Algorithms Based on Cross-Winger-Ville distribution
Preliminary investigation on the use of Cross-Winger-
Ville distribution to detect high impedance earth faults in
power system was performed in [170].
V. HEURISTIC APPROACHES
A. The Expert System
One of the effective tools utilized to detect HIFs is the
expert system that many researchers have used it during the
previous decade. Hence, a knowledge-based (Expert) system
has been developed in [37, 51] to detect faults by monitoring
frequency changes. The second and third laws of induction
along with a minimum entropy method (Expert System)
were utilized in [43, 48] to provide a decision of the HIF
detection. The use of knowledge based decision making
under incomplete knowledge was a method introduced in
[63, 64]. Reference [117] described the use of multiple
algorithms to detect various types of faults and the use of an
expert decision maker to decipher incoming data in order to
determine the status and health of a distribution feeder. In
addition, requirements for a practical, secure, and high-
impedance fault relay were also discussed. In [54], an
intelligent detector founded on decision making system was
proposed. A learning detection system was proposed in [70]
which synthesize the status output of the decision rule and
an event detector to identify the system status. In [78], the
combination of two schemes were presented; the first one
was based on measuring varied phase angles between phase
current and bus voltages and the second scheme, including
several existing detection algorithms, incorporated an expert
system. The combination of several algorithms with the
expert system was presented in [83].
Expert system techniques were used in [97] to detect the
HIF. Detection was accomplished through the combination
of these algorithms: Energy Algorithm, Randomness
Algorithm, Expert Arc Detector Algorithm, Load Event
Detector Algorithm, Load Analysis Algorithm, Load
Extraction Algorithm, Arc Burst Pattern Analysis
Algorithm, Spectral Analysis Algorithm, and Arcing
Suspected Identifier Algorithm. A comprehensive expert
system combined with conventional algorithms was
suggested in [105]. The researchers in Texas A&M
University have developed a detector based on the
combination of energy and randomness algorithms with
expert system [124]. Decision tree based methodology for
high impedance fault detection was suggested in [174]. This
method was founded on Kalman filtering and expert system.
In [196], high impedance fault detection using harmonics
energy decision tree algorithm was proposed.
B. The Neural Network
Due to the fact that HIF has an extremely nonlinear and
time varying behavior, many authors have proposed neural
networks as a power full tool to detect and discriminate the
HIF from other normal events in a system. References [38,
59, 47] presented an approach to detect HIF. This approach
consisted of collecting samples of substation current during
normal and abnormal feeder operation and using these
samples to learn a neural network the rules for fault
detection. So in [77], a feed-forward three-layer perceptron
was trained by high impedance fault, fault-like load, and
normal load current patterns using the back-propagation
training algorithm to detect HIF. The authors in [79,85]
introduced schemes based on neural network to identify the
HIF. Reference [218] provided an evaluation of two new
structures of Artificial Neural Networks (ANNs) that may
be used for reliable HIF detection. A small number of
necessary neurons in developed ANNs, short measuring
sliding data window, and easy interpretation of obtained
output signals were the main advantages of the proposed
approach. In [90, 92, 93, and 94], using Residual third
harmonic and MLP neural network, a new algorithm was
suggested. The method consists of a feature extractor based
on a grid description of the feeder by impulse responses. A
neural network for ground fault localization was proposed in
[139]. An intelligent approach for high impedance fault
(HIF) detection using advanced signal-processing
techniques such as time-time and time-frequency transforms
combined with neural network was presented in [217].
Reference [140] investigated a technique for accurate high
impedance fault detection employing artificial neural
networks (ANN). The ANN uses back-propagation learning
algorithm for adjusting the weights in a multilayer neural
network. In [150], a two-stage supervised clustering-based
neural network was developed to perform the classification
and identification functions of the HIF. In [152], an
intelligence method based on Radial BASIS Function (RBF)
neural network was proposed. The authors in [175]
developed a detection scheme which only utilizes low
harmonics of residual quantities and applied them to a
neural network in order to detect the HIF. Reference [193]
suggested multi-parametric approach based on artificial
neural networks for identification and classification
purposed of high-impedance faults in distribution systems.
The authors in [194] have presented the application of
Neural Network technique as pattern recognition for high
impedance faults. The relay was founded on a low-
frequency (3rd and 5th
) harmonic feature diagnostic vector.
The currents and voltages were used as featured extracted
signals for fault discrimination. References [91,137,168]
also expressed an algorithm based on neural network to
classify arcing faults. The reference [120] described a
conventional high impedance fault detection technique using
a neural network to detect high impedance faults and relied
on derivatives of the maxima and minima of the load
current. In [123], a practical artificial neural network-based
relay algorithm was presented and in [109,125], an
implementation of hybrid intelligence was investigated. The
authors in [155] proposed a high impedance fault detector
using a neural network and sub-band decomposition. In
[167], a scheme was presented that recognized the distortion
118

of the voltage and current waveforms caused by HIF. The
analysis using Finite Impulse Response (FIR) filter bank
yields three-phase voltage and current in the low frequency
range which were fed to a classifier for pattern recognition.
The classifier was based on the algorithm that used neural
network approach. The authors in [213] proposed a
methodology which made use of a Learning Vector
Quantization (LVQ) neural network as a classifier for
identifying high impedance arc-type faults.
C. The Neuro-Fuzzy Network
In [89], the authors proposed a neuro-fuzzy based relay
algorithm. An algorithm composed of a case database and a
rule database that operated with fuzzy numbers was
introduced in [115]. In [215], a feature vector comprising
the 3rd and 5th harmonics of the voltage, current, and power
signals was developed. Next, this vector was applied to
fuzzy ARTMAP to investigate HIF. In [146], a novel
technique was proposed. The technique consisted of making
comparative analysis of feeder responses to pulses injected
at the feeder inlet for different normal and abnormal
configurations. Furthermore, an artificial neuron set
composed of 'neo-fuzzy' neurons, was trained to recognize
normal events from HIF.
D. The Fuzzy Logic
In [136], a detection method by means of fuzzy logic
reasoning was proposed. This method was based on the
analysis of feeder responses to impulse waves. In [158],
fuzzy decision making techniques were developed as an
option to improve the new DSTRP high impedance fault
detection algorithm.
E. Genetic Algorithm
In [182], two methods—one based on genetic algorithm
(GA), and the other based on neural networks—were
proposed for high impedance fault detection in distribution
systems. The reference [202] presented a new method for
detecting HIF using Real Coded Genetic Algorithm (RCGA)
to analyze the harmonics and phase angles of the fault
current signals.
F. Combination of Neural Network and Wavelet
Transform
In references [147,159], a novel algorithm, with
combination of wavelet transform and neural network, was
suggested to detect HIF. In these papers, the Discrete
Wavelet Transform (DWT) was used as a preprocessing
stage for feature extraction, which prepared the data for the
artificial neural network (ANN) stage. In
[177,180,185,186,191,205], discrete wavelet transforms
(DWT) were initially utilized to extract distinctive features
of the voltage and current signals; and were transformed into
a series of detailed and approximated wavelet components.
The coefficients of wavelet components variation were then
calculated. This information was introduced into the training
artificial neural networks (ANN) to verify an HIF from the
operations of the switches. The authors in [184] employed
the Finite impulse response artificial neural network
(FIRANN) type. The inputs to the FIRANN were selected to
be the details coefficients of the voltage waveform as
measured at the relaying point. In [189], using an accurate
model for high impedance faults, a new wavelet-based
method was presented. The proposed method, which
employed a 3-level neural network system, successfully
differentiated high impedance faults from other transients.
The paper also thoroughly analyzed the effect of choosing
mother wavelet on the detection performance.
In [190], phase displacement between discrete wavelet
coefficients calculated for zero sequence voltage, and
current signals at natural network frequency was tracked for
HIF detection. In [195], combining Chi square distribution,
wavelet transform, and neural network, a novel algorithm
was proposed.
G. Combination of Fuzzy Logic, Genetic Algorithm and
Wavelet Transform
In [199], wavelet transform and principal component
analysis were used for feature extraction/selection of fault
current signal and a fuzzy inference system was
implemented for fault classification and a genetic algorithm
was applied for input membership functions adjustment.
H. The Neuro-Fuzzy Network and Wavelet Transform
In [224], a wavelet multi-resolution signal decomposition
method was employed for feature extraction. Extracted
features were fed to an adaptive neural fuzzy inference
system (ANFIS) for identification and classification.
VI. FIELD EXPERIENCE AND HIF DETECTOR
IMPLEMENTATION
The first effort to implement the HIF detector was the
implementation of High impedance fault detection algorithm
that was presented in [14]. However, after implementing this
detector, it failed in 60% cases [22]. In [24], a micro
computer based data acquisition system is employed for
high impedance fault analysis. Reference [31] described the
experience of Houston Lighting & Power Company with the
field operation of the Feeder Protection and Monitoring
System developed by Texas A&M University. Reference
[211] presented a practical adaptive strategy for HIF
protection with new implementations using digital signal
processing (DSP) technology and modern computer
networking technology. Reference [40] explained
techniques for detecting the HIF based on the combination
of events from laboratory tests and digital simulation
methods under arcing faults condition. In [41], the
researchers in Texas A&M were conferred with utility
companies to determine practical constraints for
implementation of a system to effectively deal with high-
impedance faults. In [65], the authors explained a procedure
for safely making fault tests of 13kV line to confirm the
performance of the relay mentioned in [61] without
jeopardizing customer service. In [96,223], some of
119

TABLE 2. ABBREVIATIONS DEFINED FOR VARIOUS ALGORITHMS
METHOD ALGORITHM ABB.
Magnitude of Phase or Natural Fault Current and Voltage Measurement PNCV
Low Order Harmonics and Sub-harmonics and Low Frequency Spectrum LH
High Frequency Spectrum HH
Kalman Filtering and pattern recognition KFPR
Fractal Theorem FT
Wavelet Transform WT
Crest Factor CF
Fault Current Flicker FF
Classic
Approaches
Cross-Winger-Ville distribution CWVD
Expert System ES
Neural Network NN
Neuro-Fuzzy Network NF
Fuzzy Logic FL
Genetic Algorithm GA
Neural Network and wavelet Transform NW
Fuzzy Logic, Genetic Algorithm and wavelet Transform FGW
Heuristic
Approaches
The Neuro-Fuzzy Network and Wavelet Transform NFW
industry survey results about HIF were denoted and the
authors in [102] define a design and implementation method
to detect 11.4 kV downed conductor. In [107], the
application of high impedance fault detectors has been
explained. The author described how advances in digital
technology have enabled a practical solution for the
detection [108].the authors in [116,198] have reported some
problems of relays for detecting the HIF. The reference [74]
reports Manhole explosions due to arcing faults on
underground secondary distribution cables in Ducts and
[156] described the faults due to leaning trees. In [111,126],
a test apparatus and procedures was introduced for testing
high impedance fault detector. The authors in [122]
introduced the development of a methodology to detect high
impedance fault in distribution feeders and have explained
some of field tests.
VII. CHRONOLOGICAL ANALYSIS
A total of 225 papers are surveyed in this paper, covering
a sufficient depth of works in the HIF detection field for the
time span of 1960 to 2008. Nearly 15 percent of the
reviewed papers deal with determination of nature and
behavior of HIF as well as the presentation of models for
simulation of these types of faults and general
considerations about HIF. These papers were explained in
section II. Authors present their experiences about HIF
events in field and introduce their efforts to design,
implement, and test of the implemented HIF detectors in
about 8 percent of papers. These papers were described in
section V. Other papers, i.e., near to 77 percent of the
surveyed papers belong to several methods of HIF detection
which are presented in Fig.2. To simplify the analyzing
process, abbreviations are used for the name of each
approach. These abbreviations are summarized in Table. 2.
A comparison of major HIF detection algorithms are
performed for a five-year period in Table 3. In this table, a
number and percentage of papers for each algorithm are
denoted. According to this survey, from the start of HIF
detection in 1960, about 60% of papers are related to classic
algorithms and 40% to heuristic algorithms. The survey
reveals that from 1960 to 1985, all papers are within the
realms of Classic approaches. During 1985 to 1990, 27% of
papers deal with heuristic algorithms and 73% cope with
Classic algorithms. This meaningful raising in using the
heuristic method is originated from the emergence of HIF
detection based on Expert System Methods and neural
networks. From 1991 to 1995, using Neural Network to
identify the HIF cause to increase the proportion of
Heuristic Methods to 53.5% against Classic Methods
(46.5%). During 1996 to 2000, the emersion of Wavelet
Transform as a classic method to help HIF detection
changed the proportion of heuristic/classic methods and it
reduced to 0.55 (35.5% for heuristic method against 64.5%
for classic methods). However, using the combination of
Wavelet transform and neural network as a heuristic
method, this proportion grew to 1 during 2001 to 2005.
Finally, it decreased to 0.81 during 2006 to 2008.
Obviously, the latest period is not a five-year period and the
proportion may change up to 2010. The summary of above
mentioned analysis is shown in Fig. 3.
The portion of contribution for both Classic and heuristic
algorithms are shown in Fig. 4 and Fig. 5, respectively.
According to Table 3 and Fig. 4, among 103 papers about
Classic method, 43 papers belong to algorithms based on
Low Order Harmonics and Sub-harmonics and Low
Frequency Spectrum (LH algorithm) and this algorithm has
the maximum contribution among classic methods (41%).
As stated in Table 3 and Fig. 5, among 67 papers concerning
heuristic methods, 29 papers are classified in neural network
algorithm category and this algorithm has the highest
proportion in heuristic methods with 43.2%. Fig. 6 shows
120

TABLE 3. A COMPARISON OF MAJOR HIF DETECTION ALGORITHMS
Method Abb. Before
1980
81-85
(%)
86-90
(%)
91-95
(%)
96-00
(%)
01-05
(%)
06 -08
(%)
Total (#/%)
PNCV 0 3 0.6 0.6 2.3 2.3 0.6 16/9.4
LH 0.6 1.8 11.8 3.5 3 3.5 1.1 43/25.3
HH 0.6 1.8 1.8 0.6 0.6 0 0.6 10/6
KFPR 0 0 1.8 1.8 0.6 0 0.6 8/4.8
FT 0 0 0 0 1.2 0 0 2/1.2
WT 0 0 0 0 3 3.5 6 21/12.5
CF 0 0 0 0.6 0 0 0 1/0.6
FF 0 0 0 0.6 0 0 0 1/0.6
CWVD 0 0 0 0 0 0.6 0 1/0.6
Classic
Approaches
Total 1.2 6.6 16 7.7 10.7 9.9 8.9 103/61
ES 0 0 4.1 3 1.2 0.6 0.6 16/9.5
NN 0 0 1.8 5.3 4.1 3 3 29/17.2
NF 0 0 0 0.6 1.2 0 0.6 4/2.4
FL 0 0 0 0 0.6 0.6 0 2/1.2
GA 0 0 0 0 0 0.6 0.6 2/1.2
NW 0 0 0 0 0.6 5.3 1.2 12/7.1
FGW 0 0 0 0 0 0 0.6 1/0.6
NFW 0 0 0 0 0 0 0.6 1/0.6
Heuristic
Approaches
Total 0 0 5.9 8.8 7.6 10 7.2 67/39
TOTAL 1.2 6.6 22 16.5 18.4 20.1 16 100
the percentage of published papers about HIF detection
versus a five-year period from 1960 up to 2008. It can be
surveyed that during 1986 to 1990, the maximum number of
papers are published about this field (22%) and after that,
the duration of 2001 to 2005 is placed in second place with
20.1%.
Figure 3. Application of Classic and heuristic algorithms
Figure 4. The portion of contribution of each Classic algorithm
Figure 5. The portion of contribution of each heuristic algorithm
Figure 6. Percentage of published papers about HIF detection
VIII. CONCLUSIONS
In this paper, near to 225 papers are surveyed about HIF
detection. Among these papers, 56 papers are about the
identification of nature, behavior, and modeling of HIF.
121

Remainder of papers, i.e. 169 papers, concern about HIF
detection algorithms. This paper classifies the mentioned
algorithms to two major groups: classic methods and
heuristic methods. Next, the algorithms belonging to each
method are introduced. It terminates with tables and graphs
determining the frequency of each algorithm. This paper can
be utilized as a guideline for researchers in this field where
the HIF detection is still a bending research area.
ACKNOWLEDGMENT
The authors were supported in part by a Research grant
from Shahid Beheshti University with No.s/650/266.
REFERENCES
1960
[1] R. H. Kaufman, J. C. Page, "Arcing fault protection for low voltage
power distribution systems- nature of the problem," AIEE Trans, pp. 160-
167, June 1960.
1972
[2] L. J. R. Dunki-Jacobs, "The effects of arcing ground faults on low-
voltage system design," IEEE Transactions on Industry Applications, Vol.
IA-8, No.3, pp. 223-30, 1972.
1976
[3] “The interruption of downed conductors on low voltage distribution
systems" IEEE Power System Relaying Committee Report, October, 1976
1979
[4] R. E. Lee, L. A. Kilar," Summary and status report on research to detect
and de-energize high impedance faults on three phase four wire distribution
circuit," IEEE PES Summer Meeting, Vancouver, B. C. ,Canada, July 1979,
Paper No. A79-516-6
[5] L. A. Kilar, M. Rosado, H. F. Farnsler, R.E. Lee," Innovative relay
methods for detecting high impedance faults on distribution circuits," Proc.
Of the American Power Conference , Vol. 41, 1979, pp.1180-1183
[6] J. Carr, G. L. Hood," High impedance fault detection on primary
distribution systems", Report for the Canadian Electrical Association,
November 1979, pp. 43-44
1980
[7] H. L. Graham, A. J. Carlson, T. A. Granberg," Broken conductor and
high impedance fault detection by high frequency impedance monitoring,"
IEEE PES Winter Meeting, Vancouver, 1980, Paper No. A80-064-6
[8] P.S. Wessels,’ Ground fault interruption, personal protection on the
overhead electric power distribution line," Hazard Prevention, 1980, pp..9-
15, 32
1981
[9] J. Carr, “ Detection of high impedance fault on multi-grounded primary
distribution systems," IEEE Transaction on Power Apparatus and System,
Vol. PAS-100, No. 4, April 1981,pp. 2008-2016
1982
[10] I. Lee, " High impedance fault Detection using third harmonics
current," EPRI Report EL-2430, Prepared by Hughes Aircraft Company ,
June 1982
[11] B. M, Aucoin, B. D. Russell, "Distribution high impedance fault
detection utilizing high impedance frequency current components," IEEE
Transaction on Power Apparatus and System, Vol. PAS-101, No. 6, June
1982, pp. 1595-1606
[12] H. Calhoun, M. T. Bishop, C. H. Eichler, R. E. Lee, Development and
testing of an electro-mechanical relay to detect fallen distribution
conductors", IEEE Transaction on Power Apparatus and System, Vol. PAS-
101, No. 6, June 1982, pp. 1643-1650
[13] B. D. Russell, B. M, Aucoin, T. J. Talley, “Detection of arcing faults
on distribution feeders", EPRI Final Report EL-2757, Prepared by Texas
A&M University December 1982
[14] S.J.Balser, K.A.Clements, E. Kallauer, “Detection of high impedance
faults", EPRI Report EL-2413, Power Technologies, Inc., June 1982.
1983
[15] R.E.Lee, M.T.Bishop ,"Performance Testing of the Ratio Ground
Relay on a Four-Wire Distribution Feeder", IEEE Transaction on Power
Apparatus and System, Vol. PAS-102, No. 9, Sept 1983, pp.2943-2949
[16] R. M. Reedy, W. A. Elsmore," Electromechanical relay to detect fallen
distribution conductors", American public power association conference,
San Antonio, Feb 1983
1984
[17] H. L. Jou," Analysis and detection simulation of high impedance fault
in distribution system, “ M.S. Thesis of national Cheng Kung University ,
Taiwan , June 1984
[18] B. Don Russell, Jr., 1984, “High Impedance Fault Detection Apparatus
and Method", U.S. Patent No. 4,466,071
1985
[19] B. M, Aucoin, “ Status of high impedance fault detection ," IEEE
Transaction on Power Apparatus and System, Vol. PAS-104, No. 3, March
1985, pp. 638-644
[20] C.L. Huang, H.Y. Chu, M,T. Chen," Analysis and detection algorithm
of high impedance fault improving distribution system", EPRI Research
Project NSC74-0404-E006-06, National Science Council Taiwan ,
December 1985
[21] R. E. Lee, M. T. Bishop," A comparison of measured high impedance
fault data to digital computer modeling results", IEEE Transaction on
Power Apparatus and System, Vol. PAS-104, No. 10, October 1985, pp.
2754-2758
[22] “Implementation of a high impedance fault detection algorithm", EPRI
Report EL-4022, Prepared by Power Technologies, Inc., May 1985.
[23] A. G. Phadke, H. E. Hankun," Detection of broken distribution
conductors", Proc. IEEE Southern Conf., Raleigh, N. C., CH2161-
8/85/0000-0074
[24] R.E. Lee, H. R. Osborn," A micro computer based data acquisition
system for high impedance fault analysis," IEEE Transaction on power
apparatus and systems, Vol-PAS-104, No.10, pp. 27348-53
1986
[25] S. J. Basler, K. A. Clements, D. J. Lawrence, “ A microprocessor-
based technique for detection of high impedance faults", IEEE Transaction
on Power Delivery, Vol. PWRD-1, No. 3, July 1986, pp. 252-258
[26] S. J. Basler, K. A. Clements, D. J. Lawrence, “ A microprocessor-
based technique for detection of high impedance faults", IEEE PES Winter
Meeting, 1986, Paper No. 86WM152-3
[27] G. W. Lee," High impedance fault detection algorithm based on
frequency domain analysis" , Seoul National University, M. Sc.
Dissertation, 1986
1987
[28] B. M, Aucoin, B. D. Russell," Detection of distribution high
impedance faults using burst noise signal near 60 Hz ," IEEE Transaction
on Power Delivery, Vol. PWRD-2, No. 2, April 1987, pp. 342-348
[29] B. D. Russell, R.P. Chinchali, C.J. Kim," Behavior of low frequency
spectra during arcing fault and switching event," IEEE PES Summer
Meeting, San Francisco, CA, 1987, Paper No. 87SM633-1
122

[30] W. H. Kwon, G. W. Lee, Y. M. Park, “A study for the improvement of
the protection relay scheme on multi-grounded distribution system,"
KEPCO Project Report, KRC-85A-JO5, 1987
[31] B. D. Russell, M. Narendorf, “Microcomputer based feeder protection
and monitoring system-Utility experience", IEEE Transaction on Power
Delivery, Vol. PWRD-2, No. 2, Oct. 1987, pp. 1046-1052
1988
[32] C.L. Huang, H.Y. Chu, M. T. Chen," Algorithm comparison for high
impedance fault detection based on staged fault test," IEEE Transaction on
Power Delivery, Vol. PWRD-3, No. 4, October 1988, pp. 1427-1435
[33] C.J. Kim, B. D. Russell," Harmonic behavior during arcing faults on
power distribution feeders," Electric Power System Research, Vol. 14,
No.3, June 1988, pp. 219-225
[34] C. L. Benner, P. W. Carswell, B. D. Russell," Improved algorithm for
detecting arcing faults using random fault behavior," Southern Electric
Industry Application Symposium, New Orleans, November 1988
[35] B. D. Russell, K. Mehta, R.P. Chinchali, “ An arcing fault detection
technique using low frequency current components- performance
evaluation using recorded field data," IEEE Transaction on Power Delivery,
Vol. PWRD-3, No. 4, October 1988, pp. 1493-1500
[36] B. D. Russell, R.P. Chinchali, C.J. Kim," Behavior of low frequency
spectra during arcing fault and switching event," IEEE Transaction on
Power Delivery, Vol. PWRD-3, No. 4, October 1988, pp. 1485-1492
[37] B. D. Russell, R.P. Chinchali, “ A digital signal processing algorithm
for detecting arcing faults on power distribution feeders", IEEE PES Winter
Meeting, New York, NY, 1988, Paper No. 88, WM 123-2
[38] S. Ebron," A neural network processing strategy for the detection of
high impedance faults", Master’s Thesis, Electrical and Computer
Engineering Department, NCSU, 1988
[39] P. W. Carswell,"The detectin of high impedance faults using random
fault behavior", Master’s Thesis, Texas A&M University, August 1988.
[40] M. Al-Dabbagh, R. Daoud, R. Coulter," Technique for modeling and
detection of high impedance faults in high voltage distribution networks",
IEEE industrial electronics conference, Singapore , 1988
[41] B. Don Russell and Carl L. Benner, High Impedance Fault Detection
Workshop, Industry interaction seminar, conducted jointly by Texas A&M
University and the Electric Power Research Institute, New Orleans,
November 1988
1989
[42] M. Al-Dabbagh, R. Daoud, R. Coulter," Improved microprocessor
based distribution feeder earth fault protection using pattern recognition",
Fourth International Conference on Developments in Power Protection, 11-
13 Apr 1989, pp. 172-176
[43] C.J. Kim, B. D. Russell," Classification of faults and switching events
by inductive reasoning and expert system methodology", IEEE PES Winter
Meeting, New York, January 1989, Paper No. 89WM058-9-PWRD
[44] M. Aucoin, B. D. Russell, and C. L. Benner, “High impedance fault
detection for industrial power systems," in Proc. Conf. Rec., IEEE Industry
Applications Soc. Annu. Meeting, vol. 2, 1989, pp. 1788-1792.
[45] D. I. Jeerings, I. R. Linders," Unique aspects of distribution system
harmonics due to high impedance ground faults", IEEE Paper No.89 TD
379-9 PWRD, presented at the IEEE/PES 1989 Transmission and
Distribution Conference, New Orleans, LA.
[46] A. E. Emanual, E. M. Gulachenski," High impedance fault arcing on
sandy soil in 15 kV distribution feeders: contributions to the evaluation of
the low frequency spectrum", IEEE Paper No. 89 SM 784-0 PWRD,
presented in IEEE Summer Meeting 1989
[47] S. Ebron, D. L. Lubkeman, M. White," A neural network approach to
the detection of incipient fault on power distribution feeders," IEEE Paper
No.89 TD 377-3 PWRD, presented at the IEEE/PES 1989 Transmission
and Distribution Conference, New Orleans, LA.
[48] C.J. Kim, B. D. Russell," Classification of faults and switching events
by inductive reasoning and expert system methodology", IEEE Transaction
[49] W. H. Kwon, G. W. Lee, Y. M. Park, M. C. Yoon, M. H. Yoo,"
Detection of high impedance faults using the randomness of even harmonic
currents", IFAC 1989 International Symposium on Power Systems and
Power Plant Control, August 22-25, Seoul, Korea, 1989
[50] C. L. Benner, P. W. Carswell, B. D. Russell," Improved algorithm for
detecting arcing faults using random fault behavior," Electric Power System
Research, Vol. 17, No.1 ,June 1989, pp. 49-56
[51] B. D. Russell, R.P. Chinchali, “ A digital signal processing algorithm
for detecting arcing faults on power distribution feeders", IEEE Transaction
[52] B. D. Russell, et. al. ," IEEE tutorial course: detection of downed
conductors on utility distribution systems" , Publication No: 90EH0310-3
PWR, The IEEE, Inc. 1989(blue book)
[53] "Downed Power Lines: Why They Can't Always Be Detected" IEEE
Power Engineering Society Public Affairs Document February 1989 (green
book)
[54] C.J. Kim," An intelligent decision making system for detecting high
impedance faults", PhD dissertation, Texas A&M university, 1989
[55] D. I. Jeerings, I. R. Linders," Ground resistance-revisited", IEEE
Transaction on Power Delivery, Vol. PWRD-4, No. 1, 1989, pp. 949-956
[56] B. D. Russell, B. M, Aucoin, , C. L. Benner," Computer relaying
techniques for the detection of high impedance faults using signal
processing and pattern recognition methods", Proc. Int. Conf. power system
protection, Singapore, Sept. 1989
[57] R. M. Reedy," Minimize the public risk of downed conductors",
Electrical World, Sept. 1989, S-36, S-38, S-40
1990
[58] A. A. Girgis, W. Chang, E. B. Makram," Analysis of high impedance
fault generated signals using a kalman filtering approach," IEEE
Transaction on Power Delivery, Vol. PWRD-5, No. 4, November 1990, pp.
1714-1724
[59] S. Ebron, D. L. Lubkeman, M. White," A neural network approach to
the detection of incipient fault on power distribution feeders," IEEE
Transaction on Power Delivery, Vol. PWRD-5, No. 2, April 1990, pp. 905-
912
[60] A. E. Emanual, E. M. Gulachenski, D. Cyganski, J. A. Orr, S. Shiller, "
High impedance fault arcing on sandy soil in 15 kV distribution feeders:
contributions to the evaluation of the low frequency spectrum", IEEE
Transaction on Power Delivery, Vol. PWRD-5, No. 2, April 1990, pp. 676-
683
[61] D. I. Jeerings, I. R. Linders," A practical protective relay for down-
conductor faults", Paper No.90 SM 333-5 PWRD , IEEE/PES 1990,
Summer Meeting , July 1990
[62] W. H. Kwon, G. W. Lee, Y. M. Park, M. C. Yoon, M. H. Yoo," High
impedance fault detection utilizing incremental variance of normalized
even order harmonic power", Paper No.90 SM 349-1 PWRD , IEEE/PES
1990, Summer Meeting , July 1990
[63] C. J. Kim, B. D. Russell, K. Watson, “A parameter based process for
selecting high impedance fault detection techniques using decision making
under incomplete knowledge" Paper No.90 WM 043-0 PWRD , IEEE/PES
1990, Winter Meeting , 1990
[64] C.J. Kim, B. D. Russell, K. Watson," A Parameter-Based Process For
Selecting High Impedance Fault Detection Techniques Using Decision
Making Under Incomplete Knowledge", IEEE Transaction on Power
Delivery, vol. 5, No. 3, Jul. 1990
[65] E. A. Atwell, A. W. Shaffer, D.I. Jeerings, J.R. Linders,"Performance
Testing of the Nordon High Impedance Ground Fault Detector on a
123

Distribution Feeder" 34th Rural Electric Power Conference IEEE/IAS April
29 - May 1, 1990 Orlando FL
[66] D. I. Jeerings, I. R. Linders," Unique aspects of distribution system
harmonics due to high impedance ground faults", IEEE Transaction on
Power Delivery, Vol. PWRD-5, No. 2, April 1990, pp. 1082-92
[67] IEEE, Tutorial Course, Detection of downed conductors on utility
distribution systems, Publication 90 EHO310-3-PWR, IEEE, Piscataway,
NJ 1990
[68] H. Y. Chu, M. T. Chen, C. L. Huang, S. L. Chen, S.S. Yen,"High
impedance fault tests on the Taipower primary distribution system",
Electric Power System Research, Vol. 19, 1990, pp. 105-114
[69] M. T. Chen, H. Y. Chu, C.L. Huang, F.R. Wu," Performance
evaluation of high impedance fault detection algorithms based on staged
fault tests", Electric Power System Research, Vol. 18, pp.75-82, 1990
1991
[70] C.J. Kim, B. D. Russell, “ A learning method for use in intelligent
computer relays for high impedance faults", IEEE Transaction on Power
Delivery, Vol. PWRD-6, No. 1, January 1991, pp. 109-111
[71] W. H. Kwon, G. W. Lee, Y. M. Park, M. C. Yoon, M. H. Yoo," High
impedance fault detection utilizing incremental variance of normalized
even order harmonic power", IEEE Transaction on Power Delivery, Vol.
PWRD-6, No. 2, April 1991, pp. 557-563
[72] A.F. Sultan, G.W. Swift," Security testing of high impedance fault
detectors", IEEE/WESCANEX, May 1991, Regina, Saskatchewan, Canada
[73] D. I. Jeerings, I. R. Linders ,"A Practical Protective Relay For Down
Conductor Faults", IEEE Transaction on Power Delivery, Vol. PWRD-6,
No. 2, pp. 565-574, April 1991
[74] B. Kock, Y. Carpentier," Manhole explosions due to arcing faults on
underground secondary distribution cables in Ducts", Paper 91 SM 302-0
PWRD, IEEE PES Summer meeting , San Diego, 1991
[75] Gajjar, J.T., "Efficient model for computing high-impedance fault
generated harmonic propagation effects on radial power distribution
feeders", IEEE International Symposium on Circuits and Systems, 11-14
June 1991. Vol. 5, pp.3031 - 3034
1992
[76] C.J. Kim, B. D. Russell, K. Watson ," A parameter based process for
selecting high impedance fault detection techniques using decision making
under incomplete knowledge", Submitted to IEEE Transaction on Power
Delivery
[77] A. F. Sultan, G. W. Swift, D. J. Fedirchuk," Detection of high
impedance arcing faults using a multi-layer perceptron", IEEE Transaction
on Power Delivery, Vol. PWRD-7, No. 4, October 1992, pp. 1871-1877
[78] J. A. Momoh, A. U. Chuku, L.G. Dias, Z. Z. Zhang," Integrated
detection and protection schemes for high impedance faults on distribution
systems", IEEE International Conference on Systems, Man and
Cybernetics, 18-21 Oct 1992, Vol.2, pp. 1102-1109
[79] A.P.Apostolov, J. Bronfeld, C. H. M. Saylor, P.B.Snow ,“An Artificial
Neural Network Approach to the Detection of High Impedance Faults",
EPRI Conference on Artificial Intelligence Applications in Power Systems,
Dallas TX December 1992
[80] AL-Dabbagh, M. Technisearch Ltd. -RMT University, “ High
impedance fault detector", Australian Patent PL3451, July 1992
[81] B. M. Aucoin, B. D. Russell," Fallen conductor accidents: The
challenge to improve safety", Public Utilities Fortnightly, Vol.129, No. 3,
Feb. 1992, pp.38-40
[82] SILVA, P.R.: ‘Alternative technique for detection of high impedance
faults’. Mater’s Thesis, Federal University of Minas Gerais, 1992 (in
Portuguese)
1993
[83] C.J. Kim, B. D. Russell," High impedance fault detection system using
an adaptive element model", IEE Proceedings-C, Vol. 140, No. 2, March
1993, pp. 153-159
[84] D. I. Jeerings, I. R. Linders,"Down Conductor Detection: Theory and
Practice", PSRC/IEEE Vancouver BC Section Conference on Downed
Conductors May 1993
[85] A. M. Sharaf, L. A. Snider, and K. Debnath, “A neural network based
relaying scheme for distribution system high impedance fault detection," in
Proc. 1st New Zealand Int. Two-Stream Conf. Artificial Neural Networks
Expert Systems, 1993, pp. 321-324.
[86] R. D. Christie, H. Zadehgol, M. M. Habib," High impedance fault
detection in low voltage networks", IEEE Transaction on Power Delivery,
Vol. PWRD-8, No. 4, October 1993, pp. 1829-1834
[87] AL-Dabbagh, R. J. Zhu, P. Rai, R. Coulter," High impedance Fault
Diagnosis for accurate fault detection on power lines using Microprocessor
and pattern recognition:, Proc of the Tooldiag’93, International Conference
on Fault Diagnosis, Toulouse, France, April 1993
[88] K.Y. Lein, M. F. Su, S.L. Chen, C. S. ChangS. S. Liu, H. Y. Shen,"
Evaluation of numerical algorithm for high impedance fault detection",
Proc. Of 3rd
International Symposium on Electricity and Energy
Management , Oct. 1993, Singapore, pp.386-392
[89] A. M. Sharaf, L. A. Snider, K. Debnath," A neuro-Fuzzy based relay
for global ground fault detection in radial electrical distribution networks",
Internatinal Conference of Electrical Engineering, Tehran, Iran, May 1993
[90] A. M. Sharaf, L. A. Snider, K. Debnath," Residual third harmonic
detection of high impedance faults in distribution systems using perceptron
neural networks", Proc. Of the ISEDEM93,Singapore, Oct. 1993
[91] K. Butler, J.A. Momoh,"Neural network based classification of arcing
faults in a power distribution system", Proc. Of the north American power
symposium, Washington ,DC, pp.322-328, 1993
[92] A. M. Sharaf, L. A. Snider, K. Debnath," harmonic based detection of
high impedance faults in distribution networks using neural networks",
Proc. Of the IASTED Conference, 1993, Pittsburg, PA
[93] A. M. Sharaf, L. A. Snider, K. Debnath," A third harmonic sequence
and based detection scheme for high impedance faults “, IEEE Canadian
Conference on Electrical and Computer Engineering, 14-17 Sep 1993,
vol.2, pp. 802-806
[94] A. M. Sharaf, L. A. Snider, K. Debnath," A neural network based back
error propagation relay algorithm for distribution system high impedance
fault detection", APSCOM, Hong Kong, Dec. 1993
1994
[95] J. Reason ,"Relay Detects Down Wires By Fault Current Harmonics",
Electrical World, Vol. 208, No. 12 December 1994 pp 58-59
[96] “Distribution Line Protection Practices - Industry Survey Results",
PSRC Committee Report IEEE T&D Conference April 1994 94CH3428-0
pp 291-301
[97] W. Tyska, B. D. Russell, B. M. Aucoin ,“A Microprocessor-Based
Digital Feeder Monitor with High Impedance Fault Detection" , 47th
Annual Texas A&M Relay Conference March 21-23,1994
[98] D. C. Yu, S. H. Khan," An Adaptive High and Low Impedance Fault
Detection Method", IEEE Transactions on Power Delivery, vol. 9, No. 4,
Oct. 1994; pp.1812-21
[99] M. El-Hami, "A distribution system fault location technique utilizing
high frequency spectrum of fault current," Galway, Ireland, 1994
[100] D. C. Yu and S. H. Khan, "An adaptive high and low impedance fault
detection method," IEEE Transactions on Power Delivery, vol. 9, No. 4, pp.
1812-1821, Oct. 1994
[101] A. F. Sultan, G. W. Swift, and D. J. Fedirchuk, “Detecting arcing
downed wires using fault current flicker and half-cycle asymmetry," IEEE
Trans. Power Delivery, vol. 9, No. 1, pp. 461-470, Jan. 1994
124

[102] K.Y. Lein, C. K. Teng, S.L. Chen,C. S. Chang, H. Y. Shen, T. M.
Lin," Method design and implementation to detect 11.4 kV downed
conductor", by Tsing Hua University, Dec , 1994
[103] P. R. Silva, A. Santos Jr., W. C. Boaventura, G. C. Miranda, J. A.
Scott", Impulsive response analysis of a real feeder for high impedance
fault detection", IEEE Transmission and Distribution Conference, Chicago,
April 1994
[104] R. Patterson, W. Tyska, B. Don Russell and B. Michael Aucoin,
1994, “A Microprocessor-Based Digital Feeder Monitor With High-
Impedance Fault Detection", 47th Annual Conference for Protective Relay
Engineers,Texas A&M University, College Station, TX, March 21-23
1995
[105] B.D.Russell, C.L.Benner , “Arcing Fault Detection for Distribution
Feeders: Security Assessment in Long Term Field Trials", IEEE
Transaction on Power Delivery, Vol. PWRD-10, No. 2, April 1995, pp.
676-683
[106] C. J. Kim, B.D. Russell, Analysis of distribution disturbances and
arcing faults using the Crest factor, Electric Power Systems Research, Vol.
35, 1995, pp. 141-148
[107] J.T. Tengdin et al," Application of High Impedance Fault Detectors",
A Summary of the Panel Session Held at the 1995 IEEE PES Summer
Meeting
[108] Ron Patterson," Signatures and Software Find High-Impedance
Faults", IEEE Computer Applications in Power, Vol.8, No. 3, Jul. 1995,
pp.12-15
[109] SILVA, P.R., SANTOS, A., and JOTA, F.G.: ‘An intelligent system
for automatic detection of high impedance faults in electrical distribution
systems’. 38th MIDWEST, Rio de Janeiro, Brazil, August 1995. VD. 453-
456
1996
[110] B. M. Aucoin, R. H. Jones , “High Impedance Fault Implementation
Issues", IEEE Transactions on Power Delivery, January 1996, Volume 11,
Number 1, pp. 139-148
[111] Buchholz et al," High Impedance Fault Detection Device Tester",
IEEE Transactions on Power Delivery, vol. 11, No. 1, Jan. 1996
[112] A. V. Mamishev, B. D. Russell, and Carl L. Benner, “Analysis of
high impedance faults using fractal techniques," IEEE Trans. Power
Systems,vol. 11, No. 1, pp. 435-440, Feb. 1996.
[113] X. Yibin, D. Chan, T. Wai,"An improved model of high impedance
arcing fault in distribution systems", Proc. Of AUPEC96, Australia, Vol.2,
Oct.1996, pp.411-415
[114] A. M. Sharaf, R. M. El-Sharkawy, R. Al-Fatih, M. Al-Ketbi,"High
impedance fault detection on radial distribution and utilization
systems",Proc. Of CCEC96, pp.1012-1013
[115] P. R. Silva, A. Santos Jr., F.G. Jota,"An intelligent system for
automatic detection of high impedance faults in electrical distribution
systems", Proceedings of the 38th Midwest Symposium on Circuits and
Systems, 1996, Vol. 1, 13-16 Aug. 1996, pp.453 - 456
[116] J. T. Tengdim and et. al," Application of high impedance fault
detectors: a summery of the panel session held at the 1995 IEEE PES
summer meeting", Transmission and Distribution Conference, IEEE, 15-20,
Sept. 1996, pp.116-122
[117] C. L. Benner and B. D. Russell, "Practical high impedance fault
detection for distribution feeders," in Proceedings 1996 Annual Conference
of Rural Electric Power, pp. B2- IVB2-6.
[118] Report of Power System Relaying Committee (PSRC) Working
Group D15, “High Impedance Fault Detection Technology", 1996
[119] B. Don Russell and B. Michael Aucoin, 1996, “Energy Analysis Fault
Detection System", U.S. Patent No. 5,512,832
[120] Peter B. Snow, Alexander P. Apostolov and Jefferson D. Bronfeld,
1996, “Method and Apparatus For Detecting High-Impedance Faults In
Electrical Power Systems", U.S. Patent No.5,537,327.
[121] J. Tengdin, R. Westfall, and K. Stephan, “High Impedance Fault
Detection Technology," PSRC Working Group Members, Rep. PSRC
Working Group D15.
[122] JOTA, P.R.S.: ‘Development of a methodology to detect high
impedance fault in distribution feeders.’ PhD Thesis, State University of
Campinas, Brazil, 1996 (in Portuguese)
1997
[123] L.A. Snider, Y.Y. Shan," The Artificial Neural Networks Based
Relay Algorithm For Distribution System High Impedance Fault
Detection", Proceedings of the 4th International Conference on Advances in
Power System Control, Operation and Management, APSCOM-97, Hong
Kong, Nov. 1997, pp.100-103
[124] C. L. Benner, B. Russell," Practical High-Impedance Fault Detection
on Distribution Feeders," IEEE Transactions on Industry Applications. vol.
33, No. 3, May/Jun. 1997, pp.635-640
[125] J. A. Momoh, L. G. Dias, D. N. Larid," An implementation of hybrid
intelligent tool for distribution system fault diagnosis", IEEE Transaction
on power delivery, Vol. 12, No. 2, April 1997, pp. 1035-1040
[126] K. Y. Lein, W.Y. Wang, H. L. Lai, S.L. Chen, T.Y. Guo, J. S. Yang,
J. L. Liao, C. J. Liao," Performance testing and improvement of high
impedance fault detector", Final Report of Taipower Research Project
Conducted by Tsing Hua University, May 1997
[127] L. L. Lai, E. Styvaktakis, A. G. Sichanie," Wavelet transform for high
impedance fault detection", Proceeding of the 4th
International conference
on advance in power system control, operation and management,
APSCOM-97, IEE, Hong Kong, November 1997
1998
[128] C. G. Wester, "High impedance fault detection on distribution
systems," presented at 1998 Rural Electric Power Conference Presented at
42nd
Annual Conference, 26-28 April 1998, St. Louis, MO, USA, 1998.
[129] D.C. Chan, T. Wai and X. Yibin, "A novel technique for high
impedance fault identification," IEEE Transactions on Power Delivery, vol.
13, No.3,pp. 738-744, 1998
[130] L. L. Lai, E. Styvaktakis, A. G. Sichanie," Application of discrete
wavelet transform to high impedance fault identification", IEEE
Transactions on Power Delivery, Vol.2, pp. 689-693, 1998
[131] J. H. Ko, J. C. Shim, C. W. Ryu, C.G. Park, W. Y. Yim," Detection
of high impedance fault using neural Nets and chaotic Degree",
Proceedings of energy management and power delivery, Vol.2, pp.399-404,
1998
[132] A. Nikander, P. Jarventausta," Methods for earth fault identification
and distance estimation in a compensated medium voltage distribution
network", Proceedings of the energy management and power delivery,
pp.595-600, 1998
[133] K.Y. Lien, S.L. Chen, “Self tuning of fault detection threshold for
high impedance fault detection," journal of the Chinese Institute of
Electrical Engineering, Vol.5, No.4, pp.277-285, 1998
[134] X. Yibin, Q. Li, D. C. T. Wai, "DSP Implementation of a wavelet
Analysis Filter Bank for High Impedance Fault Detection", Proceedings of
International Conference on Energy Management and Power Delivery,
EMPD '98, Vol. 2, 3-5 March 1998, pp.417 - 421
[135] Huang, C.L.,Chu,H.Y., and Chen, M.T.: ‘Algorithm comparison for
high-impedance fault detection based on staged fault test’, IEEE Trans.
Power Deliv., 1998, 3, (4), pp. 1427-1435
[136]Jota, F.G., and Jota, P.R.S.: ‘High-impedance fault identification
using a fuzzy reasoning system’, IEE Proc. Gener. Transm. Distrib., 1998,
45, (6), pp. 656-661
125

[137]L. A. Snider and Y. S. Yuen, “The artificial neural networks based
relay for the detection of stochastic high impedance faults," Neurocomput.,
vol. 23, pp. 243-254, 1998
[138] S. Hanninen, M. Lehtonen," Characteristics of earth faults in
electrical distribution networks with high impedance earthing", Electric
Power System Research, 44(3), 1998, pp.151-161
[139] K.J. Jensen, S. M. Munk, J.A. Sorensen," Feature extraction method
for high impedance ground fault localization in radial power distribution
networks", IEEE Proceedings of International Conference on Acoustics,
Speech and Signal Processing, Vol. 2, 12-15 May 1998, pp.1177 - 1180
vol.2
1999
[140] M. Al-Dabbagh, L. Al-Dabbagh," Neural Networks Based Algorithm
For Detecting High Impedance Faults on Power Distribution Lines," IEEE
International Joint Conference on Neural Networks, IJCNN '99.,Vol. 5, 10-
16 July 1999, pp.3386 - 3390
[141] T. Gammon and J. Matthews, "The historical evolution of arcing-
fault models for low-voltage systems", Industrial and commercial Power
Systems Technical Conference, 1999.
[142] S. J. Huang, C.T. Hsieh, “High impedance fault detection utilizing a
Morlet wavelet transform approach," IEEE Trans. Power Delivery, vol. 14,
pp. 1401-1410, Oct. 1999
[143] K. Y. Lien, S. L. Chen, C. J. Liao, T. Y. Guo, T. M. Lin, J. S. Shen,"
Energy variance criterion and threshold tuning scheme for high impedance
fault detection", IEEE Transaction on power delivery, Vol. 14, No. 3, July
1999, pp. 810-817
[144] S. Hanninen and M. Lehtonen," Method for Detection and Location
of Very High Resistive Earth Faults", Europ. Trans. Electr. Power, ETEP,
Vol. 9, pp. 285-291, 1999.
[145] C. L. Benner and B. D. Russell, "Characteristic Behavior of Downed
Electrical Lines Including Evaluation of Various Electrocution Scenarios,"
51st Annual Meeting, American Academy of Forensic Sciences, Orlando,
FL, February 15-20, 1999.
[146] Patricia R.S. Jota, Fa´bio G. Jota," Fuzzy detection of high impedance
faults in radial distribution Feeders," Electric Power Systems Research 49
(1999) 169-174
2000
[147] Lazkano, J. Ruiz, E. Aramendi, A. Leturiondo, " A New Approach
To High Impedance Fault Detection Using Wavelet Packet Analysis",
Proceedings of Ninth International Conference on Harmonics & Quality of
Power, vol. 3, pp. 1005-1010, 2000
[148] A. M. Sharaf and S. I. Abu-Azab, "A smart relaying scheme for high
impedance faults in Distribution and utilization networks," Proceedings of
the canadian conf. on electrical and computer engineering, Vol.2, March
2000, Halifax, Canada, pp. 740-744.
[149] A. Lazkano, J. Ruiz, L. A. Leturiondo, and E. Aramendi, "High
impedance arcing fault detector for three-wire power distribution
networks," in Proc. 10th. Mediterranean Electrotechnical Conf., vol. 3, May
29-31, 2000, Lemesol, Cyprus, pp. 899-902.
[150] K. L. Butler, J.A. Momoh," A neural net based approach for fault
diagnosis in distribution networks", IEEE power engineering society,
Winter meeting, Vol.2, pp.1275-1278, 2000
[151] A. Lazkano, J. Ruiz, E. Aramendi, J. A. Gonzalez," Study of High
Impedance Fault Detection in Levante Area in Spain", IEEE Proceedings.
Ninth International Conference on Harmonics and Quality of Power, 1-4
Oct. 2000, Vol. 3, pp.101 1-1016
[152] M. Farrokhi, M. Sedighizadeh, S. M. Shahrtash, "High impedance
fault detection using radial basis function neural network", 15th
International Power System Conference, PSC2000, Nov. 2000, Tehran, Iran
2001
[153] A. Nikander, P. Jarventausta, J. Myllymaki," Novel algorithm for
earth fault indication based on monitoring of shunt resistance of MV feeder
as a part of relay protection," Seventh international conference on
development in power system protection, pp.430-433, 2001
[154] L. Li and M. A. Redfern, “A review of techniques to detect downed
conductors in overhead distribution systems," in Proc. Inst. Elect. Eng. 7th.
Int. Conf. Developments in Power System Protection, 2001, pp. 169-172
[155] R. Keyhani, M. Deriche, and E. Palmer, “A high impedance fault
detector using a neural network and subband decomposition," in Proc. 6th
International Symposium on Signal Processing and its Applications
(ISSPA), Kuala Lumpur, Malaysia, 13 - 16 August, 2001.vol. 2, pp.458-
461.
[156] S. Hanninen, M. Lehtonen and T. Hakola "Earth Faults and Related
Disturbances in Distribution Networks", Proc. IEEE/PES SM2001,
Vancouver, Canada, Vol. 2, 15-19 July 2001, pp.1181 - 1186
[157] Nam, S.R., et al. at KEPCO, "Modeling of a High Impedance Fault in
a Distribution System Using Two Series Time-Varying Resistances in
EMTP", IEEE, Vol. 2, pp. 1175-1180, 2001.
[158] Ruz, F., and Fuentes, J.A. ‘Fuzzy decision making applied to high
impedance fault detection in compensated neutral grounded MV
distribution systems’. Proc. of 7th IEE Int. Conf. on Developments in
Power System Protection, 2001, pp. 307-310
[159] Lazkano, J. Ruiz, E. Aramendi, A. Leturiondo, " Evaluation of a new
Approach for arcing Fault Detection method based on Wavelet Packet
Analysis", the 2001 Power Engineering Society Summer Meeting, 2001,
Vol.3, pp.1328-1333
[160] Zeng Xiangjun; Li, K.K.; Chan, W.L.; Yin Xianggen ,"Novel
techniques for earth fault feeder detection based on negative sequence
current in industry power systems",Thirty-Sixth IAS Annual Meeting. The
2001 IEEE Conference Record of Industry Applications Conference, 2001.
Vol. 3, 30 Sept.-4 Oct. 2001, pp.1831 - 1837
2002
[161] Hanninen, S.; Lehtonen, M.; Hakola, T ,"Earth faults and related
disturbances in distribution networks, IEE Proceedings Generation,
Transmission and Distribution,Vol. 149, 3, May 2002, pp.283 - 288
[162] K. Chul-Hwan, K. Hyun, K. Young-Hun, B. Sung-Hyun, R. K.
Aggarwal, and A. T. Johns, "A novel fault-detection technique of high
impedance arcing faults in transmission lines using the wavelet transform,"
IEEE Transactions on Power Delivery, vol. 17, pp. 921-9, 2002.
[163] Z. Xiangjum, K.K. Li, W.L.Chan," Wavelet analysis based
protection for high impedance ground fault in power systems", IEEE
Proceedings International Conference on Power System Technology,
PowerCon 2002.Vol. 1, 13-17 Oct. 2002, pp.275 - 279
[164] Z. Gan, X. Z. Dong, Z. Q. Bo, B. R. J. Caunce, D. Montjean," A new
protection scheme for high impedance fault using adaptive trip and recloser
technique", Proceedings International Conference on Power System
Technology, PowerCon 2002, Vol. 1, 13-17 Oct. 2002 , pp.295 - 299 vol.1
[165] Z Q Bo, X Z Dong, S X Shi, Z Gan and B R J Caunce, "Detection of
High Impedance Fault Using Adaptive Non-communication Protection
Technique" IEEE Power Engineering Society Summer Meeting, Vol. 1, 25-
25 July 2002, pp.376 - 381
[166] M. Sedighizadeh, S. M. Shahrtash, M. Farmahini, " Analysis and
comparison of high impedance fault detection methods", 17th
International
Power System Conference, PSC2002, Nov. 2002, Tehran, Iran
2003
[167]T. M. Lai, L. A. Snider, E. Lo, C. H. Cheung, and K. W. Chan, "High
impedance faults detection using artificial neural network," Sixth
International Conference on Advances in Power System Control, Operation
and Management, APSCOM 2003.Vol. 2, 11-14 Nov. 2003, Hong Kong,
China, pp.821 - 826
[168] E. Lokman, "Artificial neural networks high impedance arcing fault
detection," Ph.D. dissertation Dept. of Electrical, Computer and System
Eng., Faculty of Rensselaer Polytechnic Institute, 2003.
126

[169] A. M. Sharaf, G. Wang," High impedance fault detection using
feature pattern based relaying", IEEE PES Transmission and Distribution
Conference and Exposition, Vol. 1, 7-12 Sept. 2003, pp.222 - 226
[170]E. Palmer, G. Ledwich, M Deriche," Preliminary investigation into the
use of the Cross-Winger-Ville distribution to detect high impedance earth
faults in power system", IEEE Proceedings Seventh International
Symposium on Signal Processing and Its Applications, Vol. 1, 1-4 July
2003, pp.597 - 600
[171] Fabian Marcel Uriarte, "Modeling, Detection, And Localization of
High-Impedance Faults In Low-Voltage Distribution Feeders," Msc Thesis,
Virginia Tech Polytechnic Institute, December 15, 2003
[172] K. K. Li, W. L. Chan, "Novel Methods for High-Impedance Ground-
Fault Protection in Low-Voltage Supply Systems", Electric Power
Components and Systems, 2003, 31:1133-1150
[173] F. Ruz, A. Quijano, E. Gdmez," DSTRP: A New Algorithm for High
Impedance Fault Detection in Compensated Neutral Grounded M.V. Power
Systems," ETEP Vol. 13, No. 1, January/February, pp.23-28, 2003
2004
[174] Y. Sheng and S. M. Rovnyak, "Decision tree based methodology for
high impedance fault detection," IEEE Transactions on Power Delivery,
vol. 19,No. 2, pp. 533-536, 2004
[175] H. Khorashadi-Zadeh, "A novel approach to detection high
impedance faults using artificial Neural network," Universities Power
Systems Conference, UPEC2004, Bristol, UK, 2004. pp.373-376
[176] M. Michalik and H. Belka, “Application of the continuous wavelet
transform to intermittent high impedance ground fault detection in MV
networks," in Proc. 8th Inst. Elect. Eng. DPSP Conf., Amsterdam, The
Netherlands, Apr. 2004, vol. 2, pp. 473-476
[177] M.T. Yang, J.C. Gu, C.Y. Jeng, W.-S. Kao ," Detection of high
impedance Fault in Distribution Feeder using Wavelet transform and
Artificial Neural Networks," 2004 international Conference on Power
System Technology - POWERCON 2004,Singapore, 21-24 November
2004
[178] R. Das and S. A. Kunsman, 2004, “A Novel Approach for Ground
Fault Detection", presented to the Fifty-Seventh Annual Conference for
Protective Relay Engineers, Texas A&M University, College Station, TX,
March 30-April 1
[179] R. Das, 2004, “A New Approach to High Impedance Fault
Detection", Electrical System Protection and Control Handbook, Vol. 2,
The Electricity Forum, Ajax, ON, Canada, pp. 83-88
[180] M. T. Yang, J. C. Gu,W. S. Hsu, Y.C. Chang, C. Cheng," A novel
intelligent protection scheme for high impedance fault detection in
distribution feeder," Conference TENCON 2004. IEEE Region 10, Vol. C,
21-24 Nov. 2004, pp.401 - 404
[181] Andoni Lazkano, Jesus Ruiz, Elisabete Aramendi, Luis A.
Leturiondo," Evaluation of a new proposal for an arcing fault detection
method based on wavelet packet analysis," European Transactions on
Electrical Power Vol. 14, Issue 3, May/June 2004, pp. 161-174
2005
[182]R. Sedighi, M.-R. Haghifam, O.P. Malik," Soft computing
applications in high impedance fault detection in distribution systems,"
Electric Power Systems Research ,Volume 76, Issues 1-3, September 2005,
Pages 136-144
[183] A. R. Sedighi, M. R. Haghifam, O. P. Malik, M. H. Ghassemian,
"High impedance fault detection based on wavelet transform and statistical
pattern recognition," IEEE Transactions on Power Delivery, vol. 20,
pp.2414-21, 2005
[184] H. M. Jabr, A. I. Megahed, “A Wavelet-FIRANN Technique for
High-Impedance Arcing Faults Detection in Distribution Systems Presented
at the International Conference on Power Systems Transients (IPST’05) in
Montreal, Canada on June 19-23, 2005 Paper No. IPST05 - 035
[185] M. T. Yang, J. C. Gu," Detecting of high impedance faults utilizing
combined phase voltages with neutral line current," International journal of
emerging electric power systems, Vol.2, Issue 2, Article 1051, June 2005
[186] M. T. Yang, J. C. Gu, J.L. Guan, C. Y. Cheng," Detection of High
Impedance Faults in Distribution System," 2005 IEEE/PES Transmission
and Distribution Conference & Exhibition, Asia and Pacific, Dalian, China,
2005, pp.1 - 5
[187] T. M. Lai, L. A. Snider and E. Lo,"High Impedance Fault detection
using discrete wavelet transform and frequency range and RMS conversion
", IEEE Transactions on Power Delivery, Vol. 20, No. 1, pp. 397-407, Jan
2005.
[188] M. Carpenter, R.F. Hoad ,T.D. Bruton, R. Das ,S.A. Kunsman, J.M.
Peterson," Staged-Fault Testing For High Impedance Fault Data
Collection," 58th Annual Conference for Protective Relay Engineers, 5-7
April 2005, pp.9 - 17
[189] H. Mokhtari, R. Aghatehrani," A new wavelet based method for
detection of high impedance faults"; International Conference on Future
Power Systems, 16-18 Nov. 2005
[ 190] M. Michalik, W. Rebizant, M. Lukowicz S. J. Lee, S. H. Kang,"
Wavelet Transform Approach to High Impedance Fault Detection in MV
Networks", IEEE Russia Power Tech, 27-30 June 2005, pp.1 - 7
[191] M. T. Yang, J. C. Gu, and J. L. Guan," Detection of Downed
Conductor in Distribution System", IEEE Power Engineering Society
General Meeting, 12-16 June 2005, Vol. 2, pp.1107 - 1114
2006
[192] N. Zamanan and J. K. Sykulski, "Modelling arcing high impedances
faults in relation to the physical processes in the electric arc," Proceedings
of the 6th WSEAS International Conference on Power Systems, Lisbon,
Portugal, September 22-24, 2006
[193] R. A. Flauzino, V.Ziolkowski, I. N. da Silva," Using neural network
techniques for identification of high impedance faults in distribution
systems", IEEE/PES Transmission & Distribution Conference and
Exposition: Latin America, 2006. TDC '06., 15-18 Aug. 2006, pp.1 - 5
[194] M.M. Eissa, G.M. A. Sowilam, A. M. Sharaf," A new protection
detection technique for high impedance fault using neural network", IEEE
Power Engineering Conference on Large Engineering Systems, July 2006,
pp.146 - 151
[195] M. T. Yang, J. C. Gu, J. L. Guan, C.Y. Cheng," Evaluation of
algorithms for high impedance faults identification based on staged fault
tests", IEEE Power Engineering Society General Meeting,18-22 June 2006,
pp.8
[196] S. M. Shahrtash, M. Sarlak," High Impedance Fault Detection Using
Harmonics Energy Decision Tree Algorithm," International Conference on
Power System Technology, PowerCon2006, 22-26 Oct. 2006, pp.1 - 5
[197] M. Michalik, W. Rebizant, M. Lukowicz, S. J. Lee, S. H. Kang,"
High-Impedance Fault Detection in Distribution Networks With Use of
Wavelet-Based Algorithm," IEEE Transactions on Power Delivery, vol. 21,
No. 4, Oct 2006, pp.1793-1802
[198] A. C. Depew, J. M. Parsick, R. W. Dempsey, C. L. Benner, B. Don
Russell, M. G. Adamiak," Field Experience with High-Impedance Fault
Detection Relays," IEEE PES Transmission and Distribution Conference
and Exhibition 2005/2006, 21-24 May 2006 pp.868 - 873
[199] M.-R. Haghifam, A.-R. Sedighi, O.P. Malik," Development of a
fuzzy inference system based on genetic algorithm for high-impedance fault
detection", IEE Proc.-Gener. Transm. Distrib., Vol. 153, No. 3, May 2006,
pp. 359-367
[200] N. I. Elkalashy, M. Lehtonen, H. A. Darwish, M. A. Izzularab, and A.
I. Taalab, "Modeling and Experimental Verification of a High Impedance
Arcing Fault in MV Networks", IEEE PES Power Systems Conference and
Exposition, PSCE'06, Oct. 29 2006-Nov. 1 2006, pp.1950 - 1956
127

[213] A. Bansal and G. N. Pillai," High Impedance Fault Detection using
LVQ Neural Networks," International Journal of Computer, Information,
and Systems Science, and Engineering (WASET) 1(3), 2007
[201] T.M. Lai, L.A. Snider, E. Lo," Wavelet transform based relay
algorithm for the detection of stochastic high impedance faults," Electric
Power Systems Research 76 (2006) 626-633
[214] N. I. Elkalashy, M. Lehtonen1, H. A. Darwish, A. M. I. Taalab, M. A.
Izzularab," DWT-based extraction of residual currents throughout
unearthed MV networks for detecting high-impedance faults due to leaning
trees," EUROPEAN TRANSACTIONS ON ELECTRICAL POWER,
Euro. Trans. Electr. Power 2007; 17:597-614
2007
[202] N. Zamanan,J. Sykulski, A. K. Al-Othman," Arcing High Impedance
Fault Detection Using Real Coded Genetic Algorithm“, Proceeding of the
Third IASTED Asian Conference, Power and Energy Systems, 2-4 April,
Phuket, Thailand 2008
[215] S. M. A. Saleema, A. M. Sharaf ," A Fuzzy ARTMAP Based High
Impedance Arc Fault Detection Scheme", Canadian Conference on
Electrical and Computer Engineering, CCECE 2008, 4-7 May 2008,
pp.000871 - 000876
[203] N. I. Elkalashy, M. Lehtonen, H. A. Darwish, M. A. Izzularab , A. M.
I. Taalab," Modeling and Experimental Verification of High Impedance
Arcing Fault in Medium Voltage Networks," IEEE Transactions on
Dielectrics and Electrical Insulation Vol. 14, No. 2; April 2007, pp.375 -
383 [216] M. Adamiak," Safety First: Detection of Downed Conductors and
Arcing on Overhead Distribution Lines", 55th
IEEE Petroleum and
Chemical Industry Technical Conference, PCIC 2008, 22-24 Sept. 2008,
pp.1 - 8
[204] Hou. Daqing , N. Fischer," Deterministic High-Impedance Fault
Detection and Phase Selection on Ungrounded Distribution Systems",
IEEE/IAS Industrial & Commercial Power Systems Technical Conference,
ICPS 2007. 6-11 May 2007, pp.1 - 10
[217] S.R. Samantaray, B.K. Panigrahi and P.K. Dash," High impedance
fault detection in power distribution networks using time-frequency
transform and probabilistic neural network", IET Generation, Transmission
& Distribution,Vol. 2, Issue 2, March 2008 ,pp.261 - 270
[205] M.T. Yang and J.C. Gu," Using Wavelet Transform and Neural
Networks Detection High-Impedance Fault ,"International Journal of Power
and Energy Systems,2007 [218] M. Michalik, M. Łukowicz, W. Rebizant, S. J. Lee, and S. H. Kang,"
New ANN-Based Algorithms for Detecting HIFs in Multi grounded MV
Networks", 43rd
International Universities Power Engineering Conference,
UPEC2008, 1-4 Sept. 2008, pp.1 - 5
[206] N. I. Elkalashy , M. Lehtonen , H. A. Darwish, A. M. I. Taalab, M. A.
Izzularab," DWT-based extraction of residual currents throughout
unearthed MV networks for detecting high-impedance faults due to leaning
trees," IEEE Power Engineering Society General Meeting, 24-28 June
2007, pp.1 - 7
[219] T. Cui, X. Dong,Z. Bo, A. Klimek, A. Edwards," Modeling Study for
High Impedance Fault Detection in MV Distribution System" 43rd
International Universities Power Engineering Conference, UPEC 2008, 1-4
Sept. 2008, pp. 1-5
[207] N. Ramezani, M. Sarlak, S.M. Shahrtash, D.A. Khabori, "Design and
implementation of an adaptive high impedance relay", International Power
Engineering Conference, IPEC2007, 3-6 Dec. 2007, pp.1131 - 1136 [220] N. 1. Elkalashy, M. Lehtonen, H. A. Darwish, A-M. 1. Taalab, M. A.
Izzularab," Verification of DWT-Based Detection of High Impedance
Faults in MV Networks', IET 9th
International Conference on Developments
in Power System Protection, DPSP 2008.
17-20 March 2008, pp.344 - 348
[208] N. I. Elkalashy, M. Lehtonen, H. A. Darwish, A-M. I. Taalab and M.
A. Izzularab," Feature Extraction of High Impedance Arcing Faults in
Compensated MV Networks.Part I: DWT-Based Analysis of Phase
Quantities", IEEE Power Engineering Society Conference and Exposition
in Africa, PowerAfrica '07, 16-20 July 2007, pp.1 - 6 [221] N. 1. Elkalashy, M. Lehtonen, H. A. Darwish, A-M. 1. Taalab, M. A.
Izzularab," Operation Evaluation of DWT-Based Earth Fault Detection in
Unearthed MV Networks", 12th International Power System Conference,
MEPCON2008., Middle-East, 12-15 March 2008, pp.208 - 212
[209] I. Zamora, A. J. Mazón, K. J. Sagastabeitia, and J. J. Zamora," New
Method for Detecting Low Current Faults in Electrical Distribution
Systems", IEEE Transactions on Power Delivery, Vol. 22, Issue 4, Oct.
2007, pp.2072 - 2079 [222] T. Cui, X. Dong, Z. Bo and S. Richards," Integrated Scheme for High
Impedance Fault Detection in MV Distribution System" IEEE/PES
Transmission and Distribution Conference and Exposition, Latin America,
13-15 Aug. 2008, pp.1 - 6
[210] N. I. Elkalashy, M. Lehtonen, H. A. Darwish, A-M. I. Taalab and M.
A. Izzularab," Feature Extraction of High Impedance Arcing Faults in
Compensated MV Networks. Part II: DWT-Based Analysis of Residual
Components," IEEE Power Engineering Society Conference and Exposition
in Africa, PowerAfrica '07, 16-20 July 2007, pp.1 - 5
[223] V. Ziolkowski ,"High-Impedance Fault Experiments in Power
Distribution Lines for Automation Purposes" IASTED, Power and Energy
Systems - 2008
[211] L. Wang, F. Chen, G. Allen, H. Cheung, T. Mander, R. Cheung
,"Network-Integrated DSP-based Adaptive High Impedance Ground Fault
Feeder Protection", IEEE Power Engineering Society General Meeting, 24-
28 June 2007, pp.1 - 7
[224] Etemadi, AH; Sanaye-Pasand, M, " High-impedance fault detection
using multi-resolution signal decomposition and adaptive neural fuzzy
inference system," IET GENERATION TRANSMISSION &
DISTRIBUTION Volume: 2 Issue: 1, 2008, pp. 110-118
[212] R. Das, D. Bayoumi, System for Detection of High Impedance Fault,"
19th International Conference on Electricity Distribution Vienna, 21-24
May 2007, Paper - 0876, CIRED2007 Session 3, Paper No 0876, pp.1 - 4
[225] N. 1. Elkalashy, M. Lehtonen, H. A. Darwish, A-M. I. Taalab, M. A.
Izzularab,"A novel selectivity technique for high impedance arcing fault
detection in compensated MV networks, "EUROPEAN TRANSACTIONS
ON ELECTRICAL POWER, Euro. Trans. Electr. Power 2008; 18:344-3
128
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