Ryan Habib
Wilkes University
Huy Tran
Richland College
1
Purpose

Construct a simple data acquisition
system to mimic the measuring of an arc
flash incident
2
Arc Flash

A rapid release of
energy in the form
of an electrical
explosion that
results from a low
impedance
connection
between lines of
different voltage or
phases
3
Arc Flash damage
Most burns from
electrical accidents
are a result of arc
flash
 Temperatures can
reach up to 20,000⁰C
 Most occurrences are in
industrial settings due
to required power levels

4
Arc Flash Experimentation
Fiber Optic Internet
Connection
Slug calorimeters
and
Pressure sensors
AD210 + MOV NI cRio:
16 Differential AI
Analog Devices
16 TTL Compatible DI/O
7B-47-K-04-1
(Build-in CJC)
Experimental DAQ System
5
Arc Flash Simulation
6
7
SCADA (Supervisory Control And
Data Acquisition) Systems
Versatile industrial control system
 Components

 Sensor
 Remote terminal unit
 Central computer
8
Sensors

Reads a signal from a physical property
and converts it into one usable by a
control system
Photoresistor
Hall effect sensor
9
Thermocouple

Type K thermocouple
 Produces output voltage
dependent on
temperature
 Made of two metals with
different conducting
properties
 Temperature range of
-200⁰C to 1350⁰C
10
Types of Thermocouples
Type
Materials
Traits
B/R/S
Platinum-Rhodium
Low Sensitivity, High
Cost
E
Chromel-Constantan
High Sensitivity, Nonmetallic
J
Iron-Constantan
Low Range, High
Sensitivity
K
Chromel-Alumel
Inexpensive, Versatile,
Reliable
N
Nicrosil-Nisil
More stable in highenergy environments
T
Copper-Constantan
Very Stable, especially
at lower temperatures
11
Analog to Digital Conversion
7B47 Signal Conditioning Module
 Successive Approximation ADC

12
Data Logger
Records digital data from the sensors
 Easily connected to other machines to
display information in real time

13
GL800

Simultaneously displays and records
data from up to 20 inputs
14
LabVIEW
Large quantity of functions for data
acquisition, signal conditioning, and data
analysis purposes
 Extensive support for accessing
instrumentation hardware

15
System Setup
Seven thermocouples were each
connected to their own 7B47 signal
conditioning module
 Each module was connected to an input
of the GL800
 USB/Ethernet cable connected GL800
to computer

16
Test Process
Place thermocouple in water to be
measured
 Send digital pulse to trigger the GL800
data recording
 Connect computer to GL800 to record
data on the computer
 Convert data from GL800 from voltage
to temperature

17
Setup
DAQ System Utilized in
Power System
Control & Monitor
System
Internet (Wireless or LAN)
Communication System
Industrial &
Commercial
Power Grid
Data Acquisition &
Logger System
CH1
CH2
CH3
CH4
CH5
CH6
CH7
CH8
7
B
4
7
7
B
4
7
7
B
4
7
7
B
4
7
7
B
4
7
7
B
4
7
7
B
4
7
7
B
4
7
+-
+-
+-
+-
+-
+-
+-
+-
A/D Converter Equipment
& Protection System
18
19
Results: Change from Air to Hot Water
Air to Hot Water
70
60
Temperature (oC)
50
Thermocouple 1
40
Thermocouple 2
Thermocouple 3
Thermocouple 4
30
Thermocouple 5
Thermocouple 6
20
Thermocouple 7
10
0
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
20
Results: Change from Air to Hot
Water (Average)
Air to Hot Water
70
60
Temperature (oC)
50
40
average
30
20
10
0
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
21
Results: Change from Air to Cold Water
Air to Cold Water
30
25
Temperature (oC)
20
Thermocouple 1
Thermocouple 2
Thermocouple 3
15
Thermocouple 4
Thermocouple 5
10
Thermocouple 6
Thermocouple 7
5
0
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
22
Results: Change from Air to Cold
Water (Average)
Air to Cold Water
30
Temperature (oC)
25
20
15
average
10
5
0
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
23
Results: Change from Hot Water to Air
Hot Water to Air
60
50
Temperature (oC)
40
Thermocouple 1
Thermocouple 2
Thermocouple 3
30
Thermocouple 4
Thermocouple 5
20
Thermocouple 6
Thermocouple 7
10
0
0
1
2
3
Time (s)
4
5
6
24
Results: Change from Hot Water
to Air (Average)
Hot Water to Air
60
Temperature (oC)
50
40
30
average
20
10
0
0
1
2
3
Time (s)
4
5
6
25
Results: Change from Cold Water to Air
Cold Water to Air
18
16
Temperature (oC)
14
12
Thermocouple 1
Thermocouple 2
10
Thermocouple 3
8
Thermocouple 4
Thermocouple 5
6
Thermocouple 6
Thermocouple 7
4
2
0
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
26
Results: Change from Cold Water
to Air (Average)
Cold Water to Air
15.5
Temperature (oC)
15
14.5
14
Average
13.5
13
12.5
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
27
Results: Change from Hot Water to
Cold Water
Hot Water to Cold Water
50
45
40
Temperature (oC)
35
Thermocouple 1
30
Thermocouple 2
Thermocouple 3
25
Thermocouple 4
20
Thermocouple 5
Thermocouple 6
15
Thermocouple 7
10
5
0
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
28
Results: Change from Hot Water
to Cold Water (Average)
Hot Water to Cold Water
45
40
Temperature (oC)
35
30
25
20
average
15
10
5
0
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
29
Results: Change from Cold Water to
Hot Water
Cold to Hot Water
50
45
40
Temperature (oC)
35
Thermocouple 1
30
Thermocouple 2
Thermocouple 3
25
Thermocouple 4
20
Thermocouple 5
Thermocouple 6
15
Thermocouple 7
10
5
0
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
30
Results: Change from Cold Water
to Hot Water (Average)
Cold to Hot Water
50
45
40
Temperature (oC)
35
30
25
average
20
15
10
5
0
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
31
Results: Change from Adding Hot
Water to Cold Water
Adding Hot Water to Cold Water
30
Temperature (oC)
25
20
Thermocouple 1
Thermocouple 2
Thermocouple 3
15
Thermocouple 4
Thermocouple 5
10
Thermocouple 6
Thermocouple 7
5
0
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
32
Results: Change from Adding Hot
Water to Cold Water (Average)
Adding Hot Water to Cold Water
30
Temperature (oC)
25
20
15
average
10
5
0
0
0.5
1
1.5
2
Time (s)
2.5
3
3.5
4
33
Data Analysis
System did a solid, yet unspectacular,
job of reading changes in water
temperature
 Variance in quality of measurements
throughout the different tests
 Could be attributed to variety of factors,
including low sample rate and lack of
memory

34
Comparisons with LabVIEW
Using LabVIEW would’ve solved the
issues with sample rate and memory
 Interface is much less intuitive
 Weeks/months to master skills
necessary for this type of task

35
Conclusion
The DAQ system was able to measure
changes in temperature in a relatively
effective manner
 The components in the system are
versatile enough to be used in a wide
array of situations
 For these specific tests, a data logger
with a higher sampling rate, along with a
sensor with a more narrow range, would
have been more effective

36
Acknowledgements
Dr. Wei-Jen Lee
 Zhenyuan Zhang
 Zhaohao Ding
 The University of Texas at Arlington
 National Science Foundation

37