000 4004008001900 42001400 ROO
DISCRETE SAMPLES, 107

Figure 7. Comparison of neural network
classification and current signal.

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 sec.

The performance of the final mine-specific network
Proved to be excellent. Accuracy of the network is
calculated based upon 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 tests, the
network correctly classified the normal/fault status of
the system with an accuracy of 97.9%. Of the 2.1%
wrong classifications, 1.9% were missed fault
classifications and 0.2% were false alarms. This means
that for approximately each second a fault exists on the
system, there is a 97.9% change that it will be detected.
The monitoring tests generated 4,338 patterns for the
network, covering approximately 30 minutes of on-line
detection time. Each test was conducted in the same
manner as a recording test with the exception that the
data feeds directly into the detection network where it
displays the fault status. Also important to note is that
all of the false alarms were single instance occurrences.
These are eliminated if there is no alarm until fault
detection occurs two or three times in sequence as

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 in this paper is rectifier and arcing fault

specific. That is, it was trained solely on patterns
obtained from a single mine rectifier and induced arcing
faults. A detection network for another mine was
successfully trained using bolted and arcing fault
pattems but lacked sufficient verification tests for
adequate discussion here. To deploy this detection
scheme to mines would require training the network for
each individual mine system. Deploying the network
manually or in a self-learning configuration could
achieve this.

Summary

The monitoring test results have displayed the
artificial neural network's ability to accurately detect
faults on dc 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 over-current 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 current,
representing an improvement over prior techniques.

Acknowledgments

The authors thank Michael R. Yenchek, for his role
as principle investigator of the project, Arthur J.
Hudson, for his instrumentation work, and Kevin
Schuster (retired) and George Persetic, for their
assistance during field visits. All are employees of
NIOSH.

References

Hall, P. M., Myers, K., and Vilchek, W. S., 1978,
*Arcing Faults on Direct Current Trolley Systems",
Proceedings of the 4th West Virginia University
Conference on Coal Mine Electrotechnology,
Morgantown, WV, pp. 21-1-21-19.

Paice, D. A., 1980, "Discriminating Circuit Breaker
System for Trolley Wire Protection", (Westinghouse
Electric Corp., contract H0122058); USBM OFR 73-
79, NTIS PB-297-832, 35 pp.

Paice, D. A., Shimp, A. B., and Putkovich, R. P.,
1973, "Circuit Breaker Development and
Applications", (Westinghouse Electric Corp., contract
0122058); USBM Contract Phase I Report,
pp. 2-15.


Figure 4. Induced fault and traffic current
averaged Fast Fourier Transforms.

r T T T

4B
883s 83 3a 8

Figure 5. Fast Fourier Transform of current data
collected during a locomotive returning empty trolley
cars to the mine.

the RMS errors due to less data content. Plots of
FFT's extending up to 40 kHz (Figure 6) also showed
no consistent markers unique to a fault. Therefore, all
analysis was limited to frequency components within
the 0- to 10-kHz range. This range shows the greatest
spectral variations between normal and faulted
conditions and the highest signal to background noise
Tatio.

Monitoring Sessi

After rigorous training and testing in the laboratory,
the detection algorithm was installed on the ruggedized
PC for field testing under conditions similar to those of
the recording sessions. Raw data in 1/15-sec bursts
were collected, analyzed, and then classified as either a
normal or faulted condition. The PC then provided a
visual and audio beep indication of the trolley systems
status. This process continued repeatedly while the
algorithm monitored the trolley system.

Results

‘The resulting artificial neural network monitoring
tests provided information on both the field testing and
training accuracy of the network. The raw network
output and its variances between the set values of +1
(normal or fault) provided important information. As
the neural network predictions gravitated toward +1, it
signified a high level of confidence in the classification.
When the output shifted toward zero, network
in the training set. Additional training patterns could
then be added to help improve network accuracy.

For deployment purposes, however, this amount of
detail was not necessary. The required output from the
detection network was a normal or fault indication.
Another tradeoff must also be considered. False alarms
in the production-oriented environment of coal mine
haulage could result in costly and unnecessary
interruptions. To account for these factors, thresholding
and rounding of the raw network output helped
eliminate false alarms at the cost of decreasing overall
accuracy. The network output is forced to +1 (normal)
or -1 (fault) only. The threshold represents the
minimum prediction value to be treated as a valid fault
classification. Figure 7 illustrates this scheme using a
threshold of (-0.5). For comparison purposes, the raw
artificial neural network output and the ‘normalized’
output are superimposed above the current wave of a
monitoring test. Region A is where the fault occurs in
this test amid normal loading from the locomotive.
When deployed, the detection network runs in an on-
line mode examining the system approximately once a
second. Therefore, a network with a classification rate
of 75% and no false alarms is much more beneficial
than a network with a rate of 90% with some false
alarms. This network will not false trip the breaker and
has a 75% chance each successive second of detecting
the fault, while still providing a strong measure of
protection. As an additional measure to further decrease
the chance of false alarms, the system could be set to

08000 10000 18000 20000 25;000 sap00 36000 <apoo
FREQUENCY, Hr

Figure 6, Comparison of normal and faulted
Fast Fourier Transforms, de to 40 kHz.


ELECTRICAL FAULT DETECTION ON DOWNED DC TROLLEY LINES

J. S. Peterson

G. P. Cole

NIOSH
Pittsburgh, PA


Abstract. The National Institute for Occupational
Safety and Health (NIOSH), Pittsburgh Research
Center (PRC), has conducted research to improve
electrical fault detection on coal mine direct current
(dc) trolley systems. Present circuit breaker protection
systems are current-magnitude-based and cannot discern
between normal traffic and high resistance electrical
faults. Tests were conducted at cooperating mines to
assist in developing a neural-network-based detection
algorithm for distinguishing between electrical faults
and normal operation. The Fast Fourier Transform of
the trolley rectifier current signature served as baseline
data for the neural network. During field tests, the
algorithm performed with greater than 90% accuracy.

Introduction

Direct current (dc) trolley systems move personnel,
supplies, and coal in roughly 50 U.S. mines. A
suspended trolley line energized at 300 or 600 Vdc
provides electrical power while a system of steel rails
serves as the return path. Inductance of the trolley
system is dependent upon the physical separation of the
trolley line and the rail. Generally, for 300-V systems,
it is 347 4H/1,000 ft and for 600-V systems, it is
368 H/1,000 ft (Paice, 1973). Because haulage
distances may extend for miles, significant inductances
may be inherent in the trolley system. When roof falls
and other accidents force the trolley line down near a
ground retum rail, this inductance facilitates arcing
between the line and rail. Such arcing may be self
sustaining at currents above 200 amps (Hall, 1978).
Electrical accidents like this are not always preventable
by conventional circuit breakers because the magnitudes
of the currents involved may be significantly less than
typical breaker trip settings. In 1989, a trolley system
fire struck the Mathies mine near Pittsburgh, PA, and
bumed for one month before it was extinguished, In
1990, a similar fire at the Mathies mine prevented
access to 30,000,000 tons of reserves and resulted in
the closing of the facility. It has since reopened under
new ownership.

NIOSH demonstrated a method to detect trolley
faults in 1980 (Paice, 1980). This system required that
a signal wire be suspended parallel to the trolley line,
an oscillator to superimpose a 3-kHz signal on the
trolley wire, and a filtering system on locomotives
larger than 25 tons. The coal industry did not adopt the
system because of the complexity and cost of the
hardware. Advances in computer software have made
it possible to employ rapid classification algorithms to
detect obscure solutions. A detection algorithm running
on a microprocessor-based system would be installed to
monitor the de current of the trolley rectifier. Its
output would be analyzed to determine if there is any
fault activity. If a fault is detected, an indicator signal
would be provided to break the trolley circuit, or
provide an audio or visual alarm. This approach

eliminates the modification of the trolley line or its
vehicles, lessening maintenance concerns and costs.

Data Collection and Signal Analysis

Development of a detection algorithm required several
steps which included:

1, Configuring an adequate data acquisition system;

2. Using the data acquisition system to record the
electrical signature (current and voltage) of
normal traffic and induced faults at a cooperating
mine;

3. Using the collected data to train and test a
detection algorithm;

4, Returning to the cooperating mines and testing
the detection algorithm.

It was necessary that the data acquisition system be
light weight, versatile, and rugged enough to withstand
less than optimal test conditions. Magnetic tape
recording was initially considered as a viable option,
and it was used for data collection early in the project.
However, magnetic tape allowed for no onboard data
analysis and this effectively eliminated it as an option.
No off-the-shelf recording devices were found that met
the particular requirements, so a unique system was
developed in-house. The system was composed of a
module to provide electrical isolation and signal gain,
elliptic low-pass filters, an analog-to-digital converter
(ADC), an industrial PC, and signal amplifiers
(Figure 1). The isolation module provided electrical
isolation between data channels and between data
channels and ground. It also provided signal gain and
attenuation for input signal scaling. Low-pass elliptic
filters with switch selectable cutoff frequencies filtered
cout data signals whose frequencies were above those of
interest. An 8-channel ADC recorded the data for use
later during the analysis portion of the research. A
ruggedized, 486-based 75-MHz PC housed the ADC
and allowed on-board high-speed data collection and
analysis. When needed, signal amplifiers boosted low
level current shunt signals.

Figure 1. Custom data acquisition system used for field
work


Figure 2 shows the hardware used for inducing
resistive faults on a trolley system. This included
multiple 0.3-ohm power resistors used to limit the fault
current. A wooden and steel bracket was used to
secure copper, steel, or aluminum electrodes in place.
Copper fuse wire served as the initial current path
between the electrodes. For bolted faults, the trolley
line and rails were simply shorted together and did not
induce arcing. A contactor switched the faults into the
trolley circuit either by push button switch or via a
fiber-optic isolated control switch.

Fast Fourier Transforms (FFT's) converted the test
data from the time domain to the frequency domain for
evaluation of the signal energy at the fundamental
frequency (60 Hz) and its harmonics. Once this was
done, a custom C-based software program converted
the FFT results into training data for the detection
algorithm. Software technology has evolved to the
point where artificial neural networks (ANN's) are now
viable tools for nonlinear applications such as this.
Neural networks learn by example and must be
"trained" on a particular problem. Data collected under
known conditions, e.g., fault/no-fault, trains the ANN.
The neural network then learns the relevant features of
the data that discern between the possible inputs.

Later, the ANN is tested with data not used for training
to find if the network will make the correct evaluation.

Field Work
Recording Sessions

Several Pennsylvania and West Virginia coal mines
were visited to conduct tests of 300 and 600 Vdc trolley
systems under assorted 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 current
draw. These tests represent the no-fault portion of the
data collected. Duplication of these tests with the
inclusion of an induced resistive or bolted fault
composed the faulted data tests. Carefully selected sites
both on the surface and underground and at various

Figure 2. Trolley system fault hardware.

distances from the trolley rectifier served as fault
locations. This ensured observation of a variety of
possible test conditions.

Figure 3 illustrates an actual data file of trolley
current plotted vs. time. This field test covered the time
before, during, and after an induced resistive fault.
Besides the fault, there was minimal background traffic
‘on the system as well. While this fault arced for
approximately 14 sec, the peak current of roughly
650 amps was never close to the circuit breaker setting
of several thousand amperes. Figure 4 shows the
average FFT's of data taken from the data file shown in
Figure 3. For each FFT, four successive, 1/60-sec time
windows of data (1,024 points) were selected for
analysis. The solid plot is from data collected during
the induced fault while the dashed curve is data
collected before the fault. The FFT of the faulted data
exhibits significantly more leakage than the no-fault
data. When leakage occurs, the energy component at
one frequency leaks into the vicinity of other energy
components resulting in spectral smearing. This has the
effect of broadening the lobes (spectral lines or peaks)
of the FFT and making them less distinct. For
comparison, similar evaluations produced Figure 5.
Here, an average FFT of a locomotive returning empty
trolley cars underground is shown. During this period,
trolley current draw was roughly 750 amps. The
significant leakage so evident in the faulted data of
Figure 4 is not present in this plot, i.e., the lobes of this
FFT are distinct.

The detection network developed keyed upon the
frequency content of the current spectrum, ranging from
0 to 10 kHz. More precisely, several networks were
developed that keyed upon different bands in this range.
They included 0 to 1,380 Hz, 1,440 to 2,820 Hz, and 0
to 9,960 Hz. The network with the best results, and
discussed later, was the network keying upon the 1,440-
to 2,820-Hz frequency band. Several tests were
undertaken to examine higher frequency components
with poor results. Detection networks were built
keying upon the 10- to 20-kHz band but had double

CURRENT, A
88888 38

2 4 6 8 10 12 4 16 18 20 2 m 26
TIME, sec

°

Figure 3. Induced fault and traffic current vs. time.