curve itic cbema labview
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Meter Data to Metrics
First Semester Report
Fall Semester 2013
-Full Report-
By
Keaton Andersen
Chad Brotherton
Jeremy Eldridge
Prepared to partially fulfill the requirements for ECE 401
Department of Electrical and Computer Engineering
Colorado State University
Fort Collins, CO 80523
Project adviser: Dr. Siddharth Suryanarayanan
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Abstract
Quality of power signals is one of the most important and heavily regulated
standards in the power and electronics industry. Without proper monitoring and
regulation, harmonics generated by non-linear loads would cause major heating
and stability issues amongst the grid that could disrupt power systems locally and
globally. In order to maintain order and ensure a lasting and continuous power
source for everyone, power quality metrics were determined. These metrics are
relatively easy to understand but require repetitive and time consuming
calculations that could easily be done by a computer application to save a
potential power industry worker time and effort.
Due to potential issues associated with maintain the grid and power stability,
many standards have been established. These standards such as IEEE 519 or IEC
61000-3 help regulate how far generated and distributed power can deviate from
an ideal situation. For example, IEE 519 gives an acceptable range for power levels
in terms of magnitudes. If a distributed signal falls too far (10%) outside of this
level for too long the system must be flagged for instability and the issue
corrected. Ignoring potential issues at a given moment could lead to catastrophic
results for the entire system.
In order to rectify these problematic system errors, software has been developed
to measure the real time quality of supplied power signals. This software is
cutting edge and enables a system operator to make real time judgments about
problems that may arise within the grid. If handled properly, these problems pass
easily and without a large area of affected customers. However if handled
improperly the situation can “snowball” and cause large scale problems as one
did back in August of 2003 in the northeast of the United States. Future systems
may look to incorporating residential and individual state managers in order to
minimize potential non-linear loads upon the grid to keep power quality at a
maximum for increased durations.
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Table of Contents
Title i
Abstract ii
Table of Contents iii
List of Figures and Equations iv
I. Introduction 1
II. Power Quality Metrics Discussion 2
A. Asymmetry Factor 2
B.
Total Harmonic Distortion 4
C. CBEMA Compliance 6
III. Ethical Considerations 8
IV. Conclusion 8
References 9
Bibliography 10
Appendix A - Abbreviations A-1
Appendix B – Budget B-1
Appendix C – Timelines C-1
Appendix D – Letter of Proposal to National Instruments D-1
Acknowledgements E-1
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List of Figures
Figure 1 Front Panel 2
Figure 2 2nd
Harmonic Effects on the Fundamental Waveform 3
Figure 3 Asymmetry Factor Block Diagram 4
Figure 4 Block Diagram of THD calculation 5
Figure 5 CBEMA Compliance Curves 6
Figure 6 CBEMA Threshold Block Diagram 6
Figure 7 CBEMA Magnitude Logging Block Diagram 7
Figure 8 CBEMA Compliance Calculation Block Diagram 7
List of Equations
Equation 1 Asymmetry Factor Calculation 3
Equation 2 Total Harmonic Distortion Calculation 5
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I. Introduction
Quality of power is a very important issue in the world of electronics and computer systems, as well as
for the health of the grid. With this in mind, there are many different metrics that power systems mustadhere to so as to promote the performance and reliability of the grid and the equipment powered by it.
All pieces of equipment powered by the grid are going to have some sensitivity to the quality of power
received. It is for this reason that the quality of power must be monitored. Normally, power quality is
not continuously monitored on the distribution level as the equipment to do so is rather expensive.
With the advent of ‘smart meters’, there are more advanced technologies embedded allowing the meter
its self to monitor some power quality metrics, and collect raw data. Anticipating more and more
advanced meters being deployed more broadly, the “Meter Data to Metrics” project was born.
The “Meter Data to Metrics” project is a 2013-2014 Senior Design project created at CSU with
sponsorship from the customer Schneider Electric. The goal of this project is to develop a dashboard
application that allows the user to easily and intuitively interpret the quality of supplied power signals
via a simple and clean front end interface. While this project was previously done at CSU, all intellectual
developments made on this project are freestanding and independent of what was previously done. This
project is being worked on by senior ECE students: Keaton Andersen, Chad Brotherton and Jeremy
Eldridge with supervision and advising from Dr. Siddharth Suryanarayanan and Ms. Oilvera Notaros.
In order to develop the dashboard application for this project the team has researched various power
quality metrics to full understand how they affect the faithfulness of the supplied power signals as they
relate to an ideal sine wave at 60 hertz. The development for this application was all done using National
Instruments LabView development software. LabView was chosen due to the project member’s level of
knowledge in this coding environment and LabView’s ability to translate from software development
into hardware realization.
Using software, the project will utilize data that is already being collected to calculate power quality
metrics. This allows for cheaper surveying of power quality, as the current cost of a stand-alone power
quality meter can range from $2000 to more than $10,000.
The team has successfully completed the first iteration of the project which runs currently using
simulated data. Future iterations of the project will run off of actual data received from City of Fort
Collins that has been “scrubbed” so as to maintain ethical standards. Intermediate testing between
these stages is planned using data gathered from looking at the current drawn by a compact fluorescentlamp. Shown in Figure 1 below are the current pictures of the Front End Panel of the project
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Figure 1: Front Panel
As shown in the Front Panel Diagram, there are various graphs, tables and indicators that create an
interface which should show whatever a user might need to know about a supplied power signal.
Through the rest of this paper we will discuss varying power quality metrics and how they are analyzed
through the application.
Contained in this report are the major power quality metrics and standards that have been the focus of
the project. Chapter II discusses in detail Asymmetry Factor, Total Harmonic Distortion, and CBEMA
compliance. Throughout each of the metric discussions, the implications of noncompliance as well as
the Labview code responsible for each of the calculations is discussed. Also discussed are ethical and
budget considerations for this project.
II. Power Quality Metrics Discussion
a. Asymmetry Factor
When looking at quality of supplied power signals one of the most important factors is harmonics.
Harmonics present in a signal can be detrimental towards end delivered devices such as motor drives
and can cause overheating which can lead to permanent damage. As part of the project, an emphasis
was placed on checking various metrics associated with harmonics present in the signal, including
Asymmetry Factor and Total Harmonic Distortion.
Asymmetry Factor describes possible distortions in the negative and positive half cycles of a sinusoid
that are associated with even harmonics being present in the signal. To check for the presence of
asymmetry factor, the absolute values of the maximum and minimum points on the sinusoid are
compared to each other via division. With no even harmonic distortion, the maximum and minimum
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values should be identical in magnitude but opposite in sign. If this is not the case, when the Fourier
Transform is taken of the signal, it will show not only the presence of odd harmonics (which is expected)
but the presence of even harmonics as well. Figure 2 shown below helps illustrate this
In Figure 2, the green arrows represent the maximum and minimum values of the fundamental
waveform with no distortion. The orange arrows represent the maximum and minimum values when aneven harmonic is added to the signal. As seen in Figure 2, if an even harmonic is present it will affect the
fundamental waveform by complimentary or destructive interference. This will cause the maximum and
minimum values to no longer be equal in magnitude. In order to test this numerically, the equation
is used. When using this equation it is important to note that it is unlikely to ever get exactly one, so for
the purposes of this project a small tolerance (0.5% range) is added so that the calculations done by our
application do not always flag a relatively good signal as bad for it not having an asymmetry factor ofexactly one.
When designing the asymmetry factor portion of our application, many different functions from
LabView were utilized. The block diagram of this section, shown in Figure 3 below, is simplistic and clean
to make it easy for future edits to be made if necessary.
Figure 2: 2nd
Harmonic Effects on the Fundamental Waveform
%||
||=1
3
Equation 1: Asymmetry Factor Calculation
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Data is pulled in from the supplied power signal and fed into a minimum/maximum value calculator.
From here, the polarity of the minimum value of the waveform is reversed by multiplying by negative
one. It is important to note that a large assumption is made that the minimum value of the signal is
negative. If this is not the case the waveform received would be so distorted that it will fail other metrics
present in the system therefore it is safe to assume for the purposes of this application. After flipping
the polarity of the maximum negative value, the code then processes the data through the equation
discussed previously and checks it against a low tolerance. If the test passes, it will keep the associated
indicator light on the front panel lit green for “pass”. However, if the test fails it will change the color of
the indicator red to let the user know in a simple manner that there is an issue with the signal. In the
current data set up, this light does not need to be latched due to a single input of data but in the future
this indicator may need to latch if the application becomes real time.
b. Total Harmonic Distortion (THD)
Another important Power Quality metric that our program measures is Total Harmonic
Distortion (THD). The presence of THD distorts the current and voltage waveforms, which can be very
detrimental to a power system. Harmonics are introduced when non-linear loads or components are
present in the gird. The non-linearity of these devices causes extra current to be introduced back into
the grid. At a first glance this could seem like a beneficial effect, but the unpredictability of this
introduced current causes problems. This metric is highly regulated by The Institute of Electrical and
Electronics Engineers (IEEE) and the Public Utility Commission (PUC) to ensure that the Power Quality ismaintained. Figure 4 shows the Labview block diagram of how the THD is calculated.
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Figure 3: Asymmetry Factor Block Diagram
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Figure 4: Block Diagram of THD calculation
THD can be calculated using Equation 2. This is accomplished in the block diagram above. By taking the
Fast Fourier Transform of the raw voltage or current measurements you can sum all the harmonics and
divide by the fundamental frequency to obtain the THD percentage. In order to calculate THD you must
take into account all of the harmonics, both even and odd. The even part of the harmonics is associated
with asymmetric waveforms, while the odd part contributes to distortion. In industry, THD is monitored
into the 70th
harmonic in the United States and even higher harmonics in Europe. In order to double
check our program’s correctness, we will do hand calculations first, and then run our program with
known distortion limits. Once this step is complete, we will then use a power quality meter to have
another sanity check on our program’s effectiveness.
Equation 2: Total Harmonic Distortion Calculation
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c. CBEMA Compliance
The Computer & Business Equipment Manufacturers Association (CBEMA) curve gives a guideline for the
magnitude and duration of voltage sags and swells from the nominal voltage. The bottom of the curve is
approximated by the equation V3.142
*T=12455, with V as the voltage, and T as the duration. Any voltage
that falls below 10% of the nominal voltage is then used to calculate CBEMA compliance. If the duration
of the sag, along with the magnitude, is above the line, it is acceptable. For example, a sag down to 50%
of the nominal voltage would be acceptable for one half cycle at 60 Hz, where it would no longer be
acceptable for a full cycle. Figure 5 shows the acceptable window for this power quality metric.
This particular metric for power quality was agreed upon by manufacturers of equipment in
acknowledgement of the fact that incoming power will not be perfect, and that all equipment must have
some tolerance to voltage sags or swells. In a fault situation on the grid, the voltage may drop abruptly
for a duration, and it is important that equipment, particularly computers, be able to withstand such
inconsistencies. Any deviation outside of this metric can lead to noticeable effects in lighting andperformance of equipment, up to total malfunction and loss of data on a computer. Other places where
non-compliance with this metric can be troublesome are industrial applications, specifically production
line equipment and automated systems. Voltage sags can cause these pieces of equipment to reset, and
can even lead to unusable product.
The Labview code programmed to fulfill this task is shown below in Figure 6 and Figure 7. In the first
module, Figure 6, each value is run in to the module and checked whether or not it is within 10% of the
nominal value. If the value is determined to be outside this window, it is passed to the next module in
Figure 7.
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Figure 5: CBEMA Compliance Curves
Figure 6: CBEMA Threshold Block Diagram
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The next module, Figure 7, averages the value of the sag along with determining its duration. The
duration and magnitude of each set of values outside 10% are then sent to the next module to calculate
their compliance with the CBEMA curve.
In this module, Figure 8, the magnitude and duration of the sag are compared to what is acceptable via
the equation given for the lower limit of the CBEMA curve. It is important to note that currently the
project is only capable of evaluating the lower limit of the curve, as that is the only part that has a
readily available equation associated with it. Later, the hope is to have the top portion of the curve
modeled as well to check for voltage swells. This module serves the function of calculating CBEMA
compliance of the voltage sag as well as latching an alarm, and sending information about the sag to a
table that can be reviewed.
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Figure 7: CBEMA Magnitude Logging Block Diagram
Figure 8: CBEMA Compliance Calculation Block Diagram
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III. Ethical Considerations
During the search for real world data collected out in the field from meters capable of the resolution,
there were some information privacy issues. According to the Public Utility Commission (PUC), data
pertaining to power use by a customer is protected information. This concern was brought up by a
contact at City of Fort Collins Utilities Light & Power. To be able to continue with this project, City of
Fort Collins Utilities provided a scrubbed version of data suitable for the project, but will not contain any
identifiable information about the customer from whom it was collected. Power use, as well as any
information pertaining to power is considered to be sensitive information, so it was essential for the
utility to safeguard it as such.
IV. Conclusion
The first semester of our project has been a success, and we plan to continue this progress into
the next semester as well. We have successfully created a dashboard application program that
calculates Total Harmonic Distortion, asymmetric sinusoids, and CBEMA compliance. This has been
done using Labview development software, and a user friendly front panel has been generated to
display these Power Quality metrics. It is important to note that our software only takes into account
the lower limit of the CBEMA curve at the moment, but we are investigating the upper limit and plan to
incorporate it next semester. Additional plans for next semester include: running our program with
current measurements from a compact fluorescent light (CFL), running our program with meter data
from Schneider Electric, cross referencing our results with that of a power quality meter, and exploring
other power quality metrics.
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References
Fundamental and 2nd Harmonic/Fundamental and 3rd Harmonic. N.d. Photograph. Web. 13 Dec. 2013.
<http://www.vias.org/crowhurstba/img/crowhurst_basic_audio_vol2-134.gif>.
"Voltage Problems, Voltage Irregularities, Overvoltage, Undervoltage, Power Quality."Voltage Problems,
Voltage Irregularities, Overvoltage, Undervoltage, Power Quality . N.p., n.d. Web. 13 Dec. 2013.
<http://www.hersheyenergy.com/voltage_irregularities.html>.
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Biography
Alexander K, Marc T.Thompson. Power Quality in Electrical Systems. Beijing : Science
Press,2009.6-9.
Heydt, G.T., "Electric power quality: a tutorial introduction," Computer Applications in
Power, IEEE , vol.11, no.1, pp.15,19, Jan 1998
Heydt, G.T., "Electric power quality: a tutorial introduction," Computer Applications in
Power, IEEE , vol.11, no.1, pp.15,19, Jan 1998
IEEE Std 1100-1999", Powering and Grounding Electronic Equipment , 1999
Jewell, W.T., "Quality electric power," Potentials, IEEE , vol.13, no.2, pp.29,32, April 1994
K. Tsuji , K. Nara , J. Hasegawa and T. Oyama "Flexible, Reliable and Intelligent Electric
Energy Delivery System: Concepts and Perspective", Proceedings of the American PowerConference, vol. 1, pp.504 -511 1999
M. H.J. Bollen Understanding Power Quality Problems, :IEEE Press
Thallam, R.S.; Grady, W.M.; Samotyj, M.J., "Estimating Future Harmonic Distortion Levels In
Distribution Systems Due To Single-Phase Adjustable-Speed-Drive Air Conditioners: A Case
Study," Harmonics in Power Systems., ICHPS V International Conference on , vol., no.,
pp.65,69, 22-25 Sep 1992
Thallam, R.S.; Grady, W.M.; Samotyj, M.J., "Estimating Future Harmonic Distortion Levels In
Distribution Systems Due To Single-Phase Adjustable-Speed-Drive Air Conditioners: A Case
Study," Harmonics in Power Systems., ICHPS V International Conference on , vol., no.,
pp.65,69, 22-25 Sep 1992
Thallam, R.S.; Mogri, S.; Burton, R. S., "Harmonic impedance and harmonic interaction of an
AC system with multiple DC infeeds," Power Delivery, IEEE Transactions on , vol.3, no.4,
pp.2064,2071, Oct 1988
Zhang Lin-li; Wang Guang-zhu.New Artificial Neural Network Approach for Measuring
Harmonics. Proceedings of the EPSA, 2004,16(2):40-43.
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Appendix A - Abbreviations
AF – Asymmetry Factor
CBEMA – Computer & Business Equipment Manufacturer’s Association
CFL – Compact Fluorescent Lamp
CSU – Colorado State University
IEEE – Institute of Electrical and Electronics Engineers
IEC – Internation Electrotechnical Commision
PUC – Public Utilities Commission
THD – Total Harmonic Distortion
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Appendix B - Budget
Budget Considerations
For this project, there have been minimal concerns with budget as it is a software based project.
Currently, the project is being put together with the Labview Student Edition which was free for thegroup. The project was also recently sponsored by National Instruments and will be receiving a MyDAQ
device for later integration to the project if time permits. Other than this hardware, there are no other
looming topics in the budget that need attention. The only exception to this will be presentation
materials for the E-Days event next semester. Total, expenditures for the project are unlikely to exceed
$50, and as of the current date are at $0.
Future Budget Items Estimated Cost ($)
Presentation materials (poster board etc.) $30
Circuitry and materials to test CFL $20
B
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Appendix C - Timelines
Timeline as of September 11th
, 2013
All research complete, Begin deliverables October 1, 2013
Front-End Panel established and polished November 1, 2013
Data received and able to be viewed December 1, 2013
THD graphic and numeric displays added January 1, 2014
Voltage sag metrics completed February 1, 2014
Further metrics explored March 1, 2014
Quality testing and results April 1, 2014
Final polishes and project completed May 1, 2014
Timeline as of October 25th
, 2013
Task Start End Name
Begin preliminary research and
deliver project outline rough
draft
August 28, 2013 September 11, 2013 Keaton, Jeremy, Chad
Work on project website September 12, 2013 September 18, 2013 Jeremy
Update project outline September 12, 2013 September 23, 2013 Keaton, Jeremy
Testing and Measurement plan October 10, 2013 October 25, 2013 Chad
Review and update project
timeline; assess progress
October 25,2013 October 26, 2013 Keaton
Update project website with
progress
October 26, 2013 November 1, 2013 Jeremy
Oral Presentation and Written
Report
November 16, 2013 December 7, 2013 Keaton, Jeremy, Chad
Update timeline for second
semester
December 3, 2013 December 9, 2013 Keaton, Jeremy, Chad
Front-End complete with all
Metrics displayed using
synthetic data
December 3, 2013 December 6, 2013 Keaton, Jeremy, Chad
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Timeline as of November 7th
, 2013
Task Start End Name Completed
Begin preliminary
research and
deliver project
outline rough draft
August 28, 2013 September 11, 2013 Keaton, Jeremy,
Chad
X
Work on project
website
September 12, 2013 September 18, 2013 Jeremy X
Update project
outline
September 12, 2013 September 23, 2013 Keaton, Jeremy X
Testing and
Measurement plan
October 10, 2013 October 25, 2013 Chad X
Review and update
project timeline;
assess progress
October 25,2013 October 26, 2013 Keaton
Update project
website with
progress
October 26, 2013 November 1, 2013 Jeremy
Oral Presentation
and Written
Report
November 16, 2013 December 7, 2013 Keaton, Jeremy,
Chad
Update timeline
for second
semester
December 3, 2013 December 9, 2013 Keaton, Jeremy,
Chad
C
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Timeline as of December 13th
Task Start End Name Completed
Begin preliminary
research and
deliver project
outline rough draft
August 28, 2013 September 11, 2013 Keaton, Jeremy,
Chad
X
Work on project
website
September 12, 2013 September 18, 2013 Jeremy X
Update project
outline
September 12, 2013 September 23, 2013 Keaton, Jeremy X
Testing and
Measurement plan
October 10, 2013 October 25, 2013 Chad X
Review and update
project timeline;
assess progress
October 25,2013 October 26, 2013 Keaton X
Update project
website with
progress
October 26, 2013 November 1, 2013 Jeremy X
Oral Presentation
and Written
Report
November 16, 2013 December 7, 2013 Keaton, Jeremy,
Chad
X
Update timeline
for second
semester
December 3, 2013 December 9, 2013 Keaton, Jeremy,
Chad
C
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Appendix D – Letter of Proposal to National Instruments
METER DATA TO METRICS: A SMART GRID APPLICATION
Jeremy Eldridge, Keaton Andersen, and Chad Brotherton
Supervisor: Siddharth Suryanarayanan
OVERVIEW
Meter Data to Metrics is a senior project centered on using meter data collected by a smart meter which
is processed through Labview to obtain and display meaningful power quality metrics. The main task of
our project is to create an intuitive program which will interpret and analyze power quality data
received from smart meters. We chose LabView to tackle this project because of its strong set of data
analysis features and ease of use in creating intuitive and user friendly front-end interfaces. Using
LabView, we will create a smooth, easy to understand user interface that will display industry accepted
power quality metrics based on the data supplied from a smart meter. Specifically, the user interface
will display the total harmonic distortion as well as individual harmonics of the signal both numerically
and graphically. Additionally, the program will assess voltage sag and surge in both magnitude and
duration with the goal of supplying alarms if the quality of the power falls outside of industry accepted
standards.
The Objective
Develop a program in Labview to process data previously collected by a smart meter.
Display metrics along with warnings and alarms corresponding to each metric.
Compare our results against data collected and analyzed by a power quality meter to measure accuracy.
Collect our own data from a MyDAQ to analyze power quality, essentially creating our own power quality meter.
OUR PROPOSAL
With sponsorship from National Instruments, we would have the ability to take our project to the next
logical step. Originally the project specifies that we be able to process data that has already been
collected by a smart meter. The addition of a MyDAQ from NI would allow us to collect our own data
from a source of our choosing. This would also allow us to collect more interesting data about specific
devices of interest. Shown in Figure 1 is the current rough draft of our code to date.
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Execution Strategy
Figure 1: Current Rough Draft of Labview Code
Project Deliverables
Following is a complete list of all project deliverables:
Deliverable Description
Front End Display Intuitive interface that allows the user to view important metrics quickly
Total Harmonic Distortion Test Signal’s proportional amount of distortion as it compares to industry standards
Even Harmonics Test Comparing max and min levels of the signal to ensure no even harmonics
Signal CBEMA compliance Will verify the signal does not sag or swell outside of acceptable range
Alarms Will provide the user with a “quick glance” of how the signal is performing
Timeline for Execution
Key project dates are outlined below. Dates are tied to deliverables schedule for senior design.
Description Start Date End Date Name
Begin preliminary research and deliver project
outline rough draft
August 28, 2013 September 11, 2013 Keaton, Jeremy,
Chad
Work on project website September 12, 2013 September 18, 2013 Jeremy
Update project outline September 12, 2013 September 18, 2013 Jeremy
Testing and Measurement plan October 10, 2013 October 25, 2013 Chad
Review and update project timeline; assess
progress
October 25,2013 October 26, 2013 Keaton
Update project website with progress October 26, 2013 November 1, 2013 Jeremy
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Oral Presentation and Written Report November 16, 2013 December 7, 2013 Keaton, Jeremy,
Chad
Update timeline for second semester December 3, 2013 December 9, 2013 Keaton, Jeremy,
Chad
CONCLUSION
We look forward to working with National Instruments and continuing to demonstrate the value of the
LabView software and development suite as well as the MyDAQ hardware. We are confident that we
can meet the challenges ahead, and we expect to produce a better final product with the help of
National Instruments!
If you have questions about this proposal, feel free to contact Jeremy Eldridge at your convenience by
email at [email protected] or by phone at (720)-951-2316.
Thank you for your consideration,
Jeremy Eldridge, Keaton Andersen, and Chad Brotherton
The Meter Data to Metrics Senior Design Team
Meter Data to Metrics Website
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Appendix E – Acknowledgements
Special thanks to:
• Siddharth Suryanarayanan – Project Adviser and Supervisor
• Olivera Notaros – Head of Senior Design
• Scott Higgins – Connected the team with City of Fort Collins
• Kraig Bader – Provided scrubbed data for project
• John Seim – Lab testing and procedure
• Schneider Electric – Customer and Sponsor
• National Instruments – Provided LabView development software
• City of Fort Collins – Provided data for our team
Additional thanks to:
• Terry Comerford
• Dan Zimmerle
• Jennifer Curtis
• Bob Lachenmayer
• John Durkin
• Steve Catanach
E-