ACKNOWLEDGMENTS

The authors thank the following PRC employees: Kevin C. Schuster (now with LTV Steel) and George F. Persetic
Michael R. Yenchek for his role as principal investigator of the _for their assistance during field visits.
project, Arthur J. Hudson for his instrumentation work, and

REFERENCES

Hall PM, Myers K, Vilchek WS [1978]. Arcing faults on direct current Paice DA, Shimp AB, Putkovich RP [1974]. Circuit breaker development
trolley systems. In: Proceedings of the 4th WVU Conference on Coal Mine and application; phase I. Pittsburgh, PA: Westinghouse Electric Corp. U.S.
Electrotechnology. Morgantown, WV: West Virginia University, pp. 21-1 to Bureau of Mines contract No. H0122058.

21-19.

Paice DA [1978]. Portable calibrator for de circuit breakers. Pittsburgh, PA:

Westinghouse Electric Corp. U.S. Bureau of Mines contract No. H0122058.


DETECTION OF DOWNED TROLLEY LINES USING ARC
SIGNATURE ANALYSIS

Jeffrey Shawn Peterson’ and Gregory P. Cole”

ABSTRACT

‘The Pittsburgh Research Center’ conducted research to study and improve electrical fault detection on coal
mine direct-current (dc) trolley systems. A suspended trolley line delivers power at voltages of 300 or 600
'V dc to the haulage equipment and other loads. Roof falls or other accidents may force the trolley line down
so that an electrical fault current may flow between the trolley line and rail. This fault current could result in
localized heating, arcing, and perhaps fire. Such faults are not always preventable by the existing circuit
protection because the current magnitude is well below typical operating levels of the trolley system. The
research discussed in this report studies the applicability of computerized signal analysis techniques to solve
this problem. Tests were conducted at cooperating coal mines. Measurements were taken during normal
loading of the trolley system (light, intermediate, and heavy traffic), during induced fault conditions (arcing
and bolted faults), and during transitions between the two. These data were then used to train and test a
microprocessor-based fault detection algorithm. Later, field tests of the detection algorithm found that it
correctly classified the state of the trolley system greater than 95% of the time.

‘Blectrical engineer.

Computer engineer.

Pittsburgh Research Center, National Institute for Occupational Safety and Health, Pittsburgh, PA.

“This research originated under the former U.S. Bureau of Mines prior to transferring to the National Institute for Occupational Safety and Health in
1996.


minimizing leakage. The recording sample rate accomplished
this without applying another transformation to the data.

Regions of interest were then extracted from the tests and
divided into successive windows. These regions included
trolley loads, arcing loads, light loading (little trolley activity),
and transition points to and from arcing. Using DADiSP, FFT's
were generated for each window. Key areas would be zeroed
in upon, with an average FFT generated from the windows
spanning each area. Using the average of several adjacent
spectra reduced noise, providing better harmonic resolution.

At this point, some observations can be made about the
trolley signals:

1. Spectra based on the voltage output of the rectifier show
a strong fundamental frequency of 360 Hz with many
harmonics. This represents the voltage ripple generated from
the fully rectified three-phase ac inputs to the rectifier. This
effect appears in the de current and power wave forms as well.

2. The most promise lies in the de current signal output of
the rectifier. There are several reasons for this. First, the
system is monitoring the very phenomenon to be interrupted in

case of a fault. Secondly, the current FFT's show a wider
variation in components when comparing normal and faulted
spectra (figure 4) than does the voltage. Figure 4 shows that
there are significant differences in the two current spectra up to
10 kHz, whereas the two voltage spectra are comparatively the
same above 5 kHz.

With regard to the frequency content of the signals, there
were also some conclusions:

1, Most of the frequency content for all of the modes
observed is contained in the 0 to 20-kHz range. Further, neural
network tests conducted on the data suggest that there will be
sufficient information in the 0 to 10-kHz range. Detection
algorithms built keying upon the 10- to 20-kHz band had
significantly worse root-mean-square errors and classification
rates than algorithms using 0 to 10-kHz data.

2. The high-frequency analysis yielded no telltale frequency
components unique to an arcing fault, i.e. no magnitude
components in the 20- to 80-kHz range proved to be con-
sistently unique to an arcing fault.

60 T T T T
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20

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Comparison of Voltage FFTs, Under Normal and Arcing Conditions

T T T T T

Key
— Arcing FFT 4
--- Normal load FFT

0
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000.:10,000
FREQUENCY, Hz

Figure 4.—Comparisons of the variations between the current and voltage fast Fourler transform spectra under normal and arcing

fault conditions,


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Figure 2.—Trolley arcing fault test circuit used at Lake Lynn
Laboratory.

held the electrodes in place, and 18 AWG copper wire served to
complete the initial current path (figure 3). After the fuse wire
melted away, arcing continued between the metallic electrodes.
Finally, a contactor switched the fault into the trolley circuit

either manually or via a fiber optically isolated control switch.
This fault circuit was simply clamped to the trolley wire and rail
system. Except for the air core inductors, this equipment was
later used during recording and monitoring sessions at
cooperating mines. Shunts installed on the rectifier negative
conductor provided the current signal, and the voltage was
measured with a voltage divider installed directly across the
rectifier output.

‘A Thorn EMI magnetic tape recorder collected early test data
at Lake Lynn. This eight-channel device had a bandwidth of 40
kHz at a tape-recording speed of 150 cm/s (60 in/s). Typically,
only a voltage and a current channel were recorded, but on
occasion, redundant voltage and current data were collected.
For safety purposes, the data channels were electrically isolated
with signal conditioning modules that had a 10-kHz response.
This equipment was used during the Lake Lynn phase of the


INTRODUCTION

Direct-current (dc) trolley haulage systems move personnel,
supplies, and coal in approximately 50 U.S. mines. A
suspended trolley line energized at 300 or 600 V de provides
electrical power, and a system of steel track serves as the return
path. Inductance of the trolley system depends on the physical
separation of the trolley line and the rail, but generally for
300-V systems, it is 0.347 mH per 300 m (1,000 ft); for 600-V
systems, it is 0.368 mH per 300 m (1,000 ft) [Paice et al.
1974]. Haulage distances may extend for kilometers;
therefore, significant inductances may be inherent in the trolley
system. When roof falls and other events force the trolley line
down near the ground return rail, this inductance facilitates
continued current flow along an ionized path between the line
and rail. This releases a significant amount of energy in the arc
and may damage and/or ignite surrounding material.
Conventional circuit breaker systems usually cannot prevent
this continued arcing because the magnitudes of the currents
involved may be significantly less than typical breaker trip
settings. Because of the sheer size of some trolley systems, an
arcing fault may exist undetected for long periods in low traffic
areas.

In 1980, the former U.S. Bureau of Mines demonstrated
research to detect arcing and other types of trolley faults [Paice
1978]. The system required an oscillator to superimpose a
3-KHz signal in the trolley line, a signal wire suspended
parallel to the trolley line for circuit breaker coordination, and
a filtering system on locomotives larger than 25 tons, Proper
data had to be received via the signal wire for the circuit
breakers to close. Thus, signal transmission was halted and de
power cut if the signal wire was broken. If the system detected
a fault condition, both inby and outby circuit breakers would

open. A third measure of safety employed conventional over-
current tripping. Although the system functioned satisfactorily,
the coal industry did not adopt it because of the complexity and
cost of the hardware.

Today, the dangers of trolley-fault-induced fires and
accidents still exist. As recently as 1989, a trolley system fire
struck the Mathies Mine near Pittsburgh, PA, and burned for 1
month before it was extinguished. A similar fire at Mathies in
1990 prevented access to 27 million t (30 million st) of
reserves and resulted in the temporary closing of the facility.
It has since reopened under new ownership. The Pittsburgh
Research Center (PRC) has sought new solutions that would
require minimal hardware maintenance and would be cost-
effective. It was unlikely that the coal industry would attempt
to improve trolley circuit protection since any single company
may own only a few mines with trolley systems. Additionally,
coal mines that employ de trolley systems tend to be larger,
older mines in which a coal company may not be willing to
invest significant resources to solve this problem.

Advances in computer software have made it possible to
employ rapid classification algorithms to recognize obscure
patterns or characteristics. Adept use of signal analysis
techniques make it possible to develop a microprocessor-based
system employing a detection algorithm. The system would
monitor the trolley rectifier electrical signature, and data would
be collected and analyzed to classify the present state of the
trolley system, fault or no-fault. An indicator signal could be
provided to break the trolley circuit or provide an audio or
visual alarm if a fault was detected. This approach eliminates
modification of the trolley line, feeder, or its vehicles,
lessening maintenance concerns and costs.

DEVELOPMENT OF TEST PROCEDURE

Critical to developing a detection scheme was the resolution
of several key elements. Finding a location to conduct early
shakedown tests was of primary importance. There, safe
methods to collect baseline information to assist in defining a
course of action had to be found. These included defining a
safe procedure for inducing faults on an existing trolley system,
determining relevant electrical signature characteristics, and
developing the required platform to record and analyze data.

Our Lake Lynn Laboratory facility near Fairchance, Fayette
County, PA, was chosen as the initial test location (figure 1).
The facility had an on-site trolley system that was unused for
several years, but was suitable for use after refurbishing and
making minor safety modifications. A 12.47/2.3-kV, three-
phase transformer supplied electrical power for the Lake Lynn

trolley. This fed an oil break switch and a 2,300/222-V delta-
delta transformer in a rectifier building. A 300-kW, 300-V de
rectifier employed a wye-wye bridge arrangement. This was
connected to 4/0 American wire gauge (AWG), figure-eight
trolley wire and 1.1-m (42-in) gauge track at approximately the
midpoint of a 96.3-m (316-ft) straightaway with a 4% grade. A
10-ton locomotive and several 10-ton rotary dump cars stored
‘on a 39.6-m (130-ft) spur were available for use.

Techniques were developed at Lake Lynn to safely induce
arcing faults on trolley systems. Multiple 0.3-ohm power
resistors and air core inductors limited the fault current and
simulated increased haulage distance, respectively (figure 2).
Copper, steel, and aluminum rods served as electrodes through
which the arcing current flowed. A wooden and steel bracket


and manual logging detailed information about the starting and
ending of faults and any other relevant information, such as
types of traffic on the system during a test.

Test data were collected with the PC-based data acquisition
system using a C programming language custom program,
XTRIG3. This program allowed recording on two, four, or six

channels at rapid sampling rates. Data collection could be
initiated and terminated simply by a key press of the PC. Data
recordings were collected and stored in individual data files
with descriptive names. These were also logged manually
along with other pertinent details of the test.

DATA ANALYSIS

The bulk of the analysis relied on examining the data in
digital form. An important part of the project was the
application of a new software technique requiring digital data
accessible from a computer. Tests recorded early in the project
on FM tape (analog form) were digitized upon returning to the
laboratory. This was not necessary after deployment of the
portable PC-based recording system.

A brief discussion is necessary to cover the basic principles
of DDA to clarify the parameters of the system. The basic goal
of recording data digitally is to make it accessible to computer-
based analysis and manipulation programs. It is also important
that noise and distortion be kept to a minimum when recording
digitally. One source of noise generation in DDA is aliasing.
This occurs in sampled systems when input frequencies interact
with the DDA sampling rate. Without the proper filtering, input
signals above the Nyquist rate (one-half the sample rate) would
generate false frequency components in the sampled data.
Coordinating the corner frequency setting of the low-pass filters
with the sample rate eliminates this effect. Reducing data
distortion required sampling the data in "window"-grouped
samples of a power of two in length. These windows are the
basis of the fast Fourier transform (FFT) calculations. The
windows also need to span one cycle of the fundamental
frequency of the alternating current (ac) power distribution,
60 Hz. Both conditions are necessary to optimize the FFT
calculation, Having one cycle of the fundamental frequency
span this window reduces the effects of leakage. This occurs
when energy at a given frequency smears into adjacent
frequencies of the spectrum. Leakage has the effect of
broadening the lobes (spectral lines or peaks) of the FFT and
making them less distinct. Considering these factors, along
with a minimum desired bandwidth of 20 kHz, a sample rate of
61,440 Hz was chosen. This met antialiasing criteria by having
a Nyquist rate (0.5 x 61,440 = 30,720) greater than the desired
bandwidth, The rate also reflects acquiring 1,024 (2") points in
the span of one 60-Hz cycle. It creates a window that is a
power of two in length (2") for the FFT and spans the
fundamental frequency, reducing leakage in the FFT
calculation.

Several additional sample rates were briefly tested during
high-frequency analysis of the data. These tests were conducted
with the use of both the PC and FM tape-based systems.
Exploiting the high bandwidth properties of the FM tape
systems allowed frequency analysis up to the 40- and 80-kHz

ranges. These ranges exceeded the parameters of the PC-based
system alone. However, used with the FM tape system, the PC-
based system successfully digitized the data to resolve the
higher frequencies. By slowing the playback of the FM tape
recorder by one-half or one-fourth of the record speed, it was
possible to double or quadruple the effective sample rate of the
DDA system. This resulted in effective sample rates of 122,880
and 245,760 Hz, easily encompassing the 40- and 80-kHz
ranges, respectively. These higher ranges were the maximum
frequency responses of the two FM tape recorders used.

After digitally recording and storing the data files to disk,
they were analyzed using a commercially available software
package, DADiSP (by DSP Development Corp.). The FFT
analysis concentrated on the magnitude components of the
frequency spectrum. Analysis was completed using a window
length of 1,024 points; additional limited tests were conducted
using a 2,048-point window. The initial digital analysis
employed a rectangular windowing scheme. This means
applying a weighting function of 1 to each of the sample points
in the window according to the equation

Ol 6, cherie * oO
where n= the sample point,
w[n] = the weighting factor at sample point n,
and M = the window length.
From this, the resultant signal is simply
h{n]) = k[n}w[n], (2)

where h[n] = the resultant signal,
and k[n] = the sampled input signal.

Because the sample rate accounted for encompassing the
fundamental frequency within the window, nothing was gained
when special windowing functions such as the Hanning or
Hamming algorithms were used. These algorithms force the
data to be periodic within the window, improving resolution by


IMIOSH

RI 9639 REPORT OF INVESTIGATIONS/1997

Detection of Downed Trolley Lines Using Arc
Signature Analysis

Centers for Disease Control and Prevention

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CENTERS FOR DISEASE CONTROL
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hidden nodes, and 1 output node. Each node, or processing
element, represents a single neuron that, when grouped
together, form a network. The 23 inputs to the network are the
FFT spectrum components of the current. They cover the 60-
Hz harmonics starting at 1,440 Hz and ending at 2,760 Hz.
Connections from the input layer to the hidden layer and from
the hidden layer to the output node form the "memory" of the

network. It is through these connection weights, adjusted
during the training phase, where the network learns the solution.
After training and testing the network in the laboratory, it
was converted into a C language module. This is done last
because learning is disabled after conversion. This module was
then incorporated into a program written to run on the DDA
platform, where it was taken into the field for further testing.

TROLLEY SYSTEM MONITORING SESSIONS

The monitoring program used to test the neural network
detection algorithm is DOS-based and written in the
C programming language. Installed on the DDA platform, it
runs in an online mode examining the system status ap-
proximately once per second.

During each pass, the program simultaneously acquires a
buffer of data from two input channels. Both channels monitor
the rectifier current, with the secondary channel having one-half
the gain of the primary. This allows for maximum resolution
with a higher gain on the primary channel while providing a
backup channel in case of clipping. Data are clipped when the
input signal range exceeds the DDA input range. If clipping
occurs on the secondary channel, the buffer is ignored as the
current levels are now approaching the normal circuit breaker
settings. Conversely, the buffer is also ignored if the average
current is below the minimum level (200 A), where arcs are less
likely to be self-sustaining [Hall et al. 1978].

Buffers of valid data are logged on the DDA hard drive.
This provides valuable data for analysis back at the laboratory.
The logs provide information on rectifier usage, are the source
for additional training patterns for new networks, and allow for
manual checking of network accuracy. After logging the
buffer, it is processed via an FFT. Each buffer holds sufficient
data to compute four FFT's. The four spectra are averaged, with
the resulting pattern used as input to the detection network.
Using the average of several spectra helps reduce the noise level
in the FFT's.

‘The network classification is output to the user via the DDA
display after several postprocessing steps. First, the raw
network output is time-stamped and logged to the DDA hard
drive along with several other parameters. Next, the raw output
is "normalized" to +1. For deployment purposes, the varying
degree of confidence in the raw output has no use. The desired
network output, especially if interfaced directly to the circuit
breaker, is a normal or fault indication. There are two steps to
the normalizing process. The first is to set a maximum
threshold value to be considered a fault. With network output
ranging between -1 (fault) and +1 (normal), a maximum
threshold of -0.5 was set. This means that all values below

-0.5 are considered a fault, whereas all values above are
considered normal. Figure 6 illustrates the normalizing scheme,
with the results displayed above the corresponding rectifier
current log. Region A is where the fault occurs in this test amid
normal loading from the locomotive. Applying the threshold
increases the accuracy of the network through accepting only
classifications with a high degree of confidence, thus decreasing
the chances of a false alarm. After setting the threshold, the
second step is to round the output to either a +1 or -1 value and
display it on the DDA screen.

‘The monitoring program has some additional user functions
and logging abilities. The program operator can manually
toggle a flag indicating the presence of an arcing fault on the
system. This is a helpful option for field testing in the monitor
mode and provides validation data for off-line analysis. The
flag is also logged to disk along with the time-stamped network
output. Another function of the program deals with the output
of the network to the user. Output of the system status is via
audio and visual display. The addition of sound allows the user
to maintain awareness of the program while keeping watch on
the various hardware connected to the trolley system during the
monitoring sessions.

The performance of the final detection network proved to be
excellent. Accuracy of the network is calculated based on the
results from running in a "monitoring" mode. This was done to
keep data used for training purposes isolated from the data used
for testing and verification. In 62 monitoring passes, the
network correctly classified the normal/fault status of the
system 97.9% of the time. Of the 2.1% wrong classifications,
1,9% were missed fault classifications and 0.2% were false
alarms. It is important to eliminate the occurrence of false
alarms. In the production-oriented environment of coal mine
haulage, false alarms could result in costly and unnecessary
interruptions. As an additional measure to further decrease the
chance of false alarms, the system could be set to wait for
several fault indications in sequence before activating an alarm
or circuit breaker. This additional step would slow the response
time to approximately once every 3 s.


DETECTION ALGORITHM

Artificial neural networks (ANN's) formed the basis of the
new fault detection algorithm. It is a technology that mimics
the learning ability of neurons in the human brain. The power
of the ANN lies in its ability to learn by example and model
nonlinear phenomenon. Problems that are very difficult to
model by a direct equation can be successfully modeled with an
ANN. The paradigm used in the solution explained here is the
Back Propagation network, an architecture that performs well
on pattern classification problems,

Part of the process of developing an ANN solution is the
training phase. During this phase, the data are presented to the
network for training. Key areas representing all of the normal
and fault conditions are extracted from the field test recordings.
The corresponding FFT spectrum components are generated to
create teaching patterns and are split into a training and test set.
Along with each generated pattern is the correct classification
value for that pattern, either a +1 for normal or a -1 for a fault.
The ANN learns by randomly selecting patterns from the
training set. It calculates an output based on the input pattern

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and compares it with the desired output. Error values calculated
from that comparison are used to correct the network
architecture to be closer to the desired output. This process runs
repeatedly until the accuracy becomes acceptable. The input is
then switched to the test set where the network does a one-time
pass to generate independent error values. The purpose of this
is to test the network on controlled data not used in training and
check that the training set was not memorized. A network that
memorizes the training data becomes too specific and will not
perform well on new patterns. Further, examining the network
output and its variances between the set values of +1 (normal or
fault) help identify weak areas in the training set. As the neural
network predictions gravitate toward each extreme, it signifies
a high level of confidence in the classification. When the
output shifts toward the zero range, network confidence
decreases and suggests possible weak areas. Additional training
patterns could then be added to help improve network accuracy.

Figure 5 shows the topography of the neural network
deployed for field testing. It consists of 23 input nodes, 11

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Figure 5.—Topography of the neural network showing the layers and interconnections.


Figure 3.—Metallic electrode configuration during an arcing fault.

project and during early field tests at mines in Pennsylvania and
West Virginia. To increase the bandwidth, a Honeywell eight-
channel tape recorder was used during later tests. At a tape
speed of 300 cm/s (120 in/s), its frequency response was 80
kHz. An ISO-4 voltage isolator provided electrical isolation for
the data channels and had a bandwidth from de to 100 kHz.
Although FM tape w
collection, several problems were inherent to the technology
that made it a poor choice for later stages of the project. FM
tape recorders are not designed to function reliably in hostile
field environments. Proper alignment of the recording heads is
critical in avoiding interchannel phase skewing. Shock and
vibration hazards commonly present at test sites could possibly
create such problems. Also, FM tape did not provide for on-
the-spot analysis of test data; therefore, early data were
analyzed later in the laboratory. To move toward the goal of

an adequate choice for early data

developing and testing a detection algorithm, clearly the data
must be collected and analyzed digitally and essentially in real
time. No off-the-shelf data collection and analysis equipment
was found that met exacting standards for ruggedness,
versatility, and weight considerations. Therefore, it was
necessary to develop a custom system component by
component, each of which met specific requirements.

An industrialized PC built for field use was necessary to
serve as the backbone of the data acquisition system. The PC
had to be lightweight, have sufficient throughput for high-speed
data collection, have sufficient space to accept full-sized cards,
and be able to function on battery power. A FieldWorks
75-MHz 486 industrialized PC met these requirements. A Data
Translation DT2833 was chosen as the analog-to-digital
converter (ADC) for this application. The DT2833 is a 12-bit,
8-channel ADC that can write data directly to the PC's hard
disk, increasing throughput. It also employs simultaneous
sample-and-hold technology that eliminates multiplexing
between data channels, minimizing phase shift and allowing
power calculations (voltage x current). Additionally, the
sampling rate of the ADC was software-selectable. The ISO-4
mentioned earlier is a four-channel device that provides
electrical isolation up to 1,000 V de channel-to-channel and
channel-to-ground. It has switchable gains of x10, x1, x0.1,
and x0.01 and an output of +10 V, scaled to the input. For a
digital data acquisition (DDA) system, filtering of the data
channels is imperative. This called for the installation of
Frequency Devices elliptic low-pass filters on each data
channel. Elliptics were chosen for their sharp attenuation at the
corner frequency (f.), allowance of a large pass band compared
with other types of filters, and essentially unity gain in the pass
band. The f, of these filters was switch-selectable from 200 Hz
to 51.2 kHz in 200-Hz steps. Several amplifiers boosted current
shunt signals to increase data resolution. These amplifiers all
had frequency responses beyond 100 kHz.

DATA-RECORDING SESSIONS

PRC researchers visited several coal mines in Pennsylvania
and West Virginia to conduct tests of 300- and 600-V de trolley
systems under various operating conditions. These conditions
comprised traffic present on the system at any given time:
large- and small-vehicle traffic; pump activity; and heavy,
intermediate, and minimal loading. Typically, voltage was
measured across the rectifier output, and current signatures were
derived from shunts. Test lengths varied from several seconds
to as much as 45 s, depending on the existing conditions of the
trolley system. These tests represent the no-fault portion of the
data collected. Duplication of these tests with the inclusion of
induced arcing or bolted faults comprised the faulted data tests.

Carefully selected sites both on the surface and underground
and at various distances from the trolley rectifier served as fault
locations, ensuring observation of a variety of possible test
conditions. During arcing fault tests, as the electrode material
melted away, the gap resistance increased until the are was
ly quenched, Most arcing faults continued until this
ed; only a limited portion were intentionally extinguished
prematurely.

‘Throughout the recording sessions, personnel at the fault site
and at the trolley rectifier maintained voice contact via walkie-
talkies or mine telephone, which allowed proper test co-
ordination. During tape recording sessions, voice annotation



UNIT OF MEASURE ABBREVIATIONS USED IN THIS REPORT
A ampere m meter
cm/s centimeter per second mH millihenry
dB decibel MHz megahertz
ft foot s second
Hz hertz st short ton
in inch t ton (metric)
in/s inch per second Vv volt
kHz kilohertz Vde volt, direct current
kv kilovolt % percent
kW kilowatt

Mention of any company name or product does not constitute endorsement by the National Institute for Occupational Safety and
Health.

To receive additional information about mining issues or other occupational safety and health problems, call 1-800-35-NIOSH
(1-800-356-4674), or visit the NIOSH Home Page on the World Wide Web at http://www.cdc.gov/niosh/homepage.html


CONTENTS

Abstract
Introduction
Development of test procedure .
Data-recording sessions
Data analysis
Detection algorithm
Trolley system monitoring sessions
Conclusions ....
Acknowledgments .
References

ILLUSTRATIONS.

Lake Lynn Laboratory trolley system
Trolley arcing fault test circuit used at Lake Lynn Laboratory . oe
Metallic electrode configuration during an arcing fault... 00.00. .6.c0eeeceeeeeeeeeeeeeeeeeeer seuss
Comparisons of the variations between the current and voltage fast Fourier transform spectra under normal

and arcing fault conditions ..... 6.6... ce eee e eect eee een n Ene tne eee ene n eee
Topography of the neural network
6. Comparison of raw and normalized neural network output with the corresponding rectifier current signal .

Peper

oa

Page

SSecoriHwannye


Report of Investigations 9639

Detection of Downed Trolley Lines Using Arc
Signature Analysis

Jeffrey Shawn Peterson and Gregory P. Cole

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES
Public Health Service
Centers for Disease Control and Prevention
National Institute for Occupational Safety and Health
Pittsburgh Research Center
Pittsburgh, PA

May 1997


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Region A
! i] ! |

RELATIVE AMPLITUDE

=n ! 1

i 200
. 0

200 400 600

800

1,000 1,200 1,400 1,600

DISCRETE SAMPLES, 102

Figure 6 Comparison of the raw and normalized neural network output with the corresponding rectifier current signal. Region A is
where the fault occurs In this test amid normal loading from the locomotive.

It was originally planned that a single neural network be
trained to generalize across different mine dc trolley systems.
This was not accomplished for several reasons. The data
gathered from the different mines may not have provided a
sufficient generalized profile, Further, the variations between
mines may be too complex to model with the existing
technique. These variations can include different rectifier types,

operating voltages, equipment running on the system, and
system size and complexity. The network discussed here is
rectifier-specific, i.e., it was trained solely on patterns obtained
from a single mine rectifier. This means that in deploying this
network to mines, it would have to be trained for each
individual mine system. Deploying the network manually or in
a self-learning configuration could achieve this.

CONCLUSIONS

The monitoring test results have displayed the network's
ability to accurately detect faults on de trolley systems. Arcing
faults are successfully detected using only the frequency content
of the rectifier current and the imposed fault signal. Used with
the existing overcurrent circuit breakers, this scheme can add
another layer of protection for both mine personnel and
material. Self-sustaining arcs caused by roof falls or similar

events could be extinguished before igniting adjacent material
and causing fire hazards. This system can be implemented
using a self-contained microprocessor mounted on each
rectifier, where it would monitor the de current and provide an
improvement over prior techniques in terms of both cost and
complexity,