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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



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.

i



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

iii



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

iv



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

1



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

2



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



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.

4

Figure 3: Asymmetry Factor Block Diagram



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

5



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.

6

Figure 5: CBEMA Compliance Curves

Figure 6: CBEMA Threshold Block Diagram



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.

7

Figure 7: CBEMA Magnitude Logging Block Diagram

Figure 8: CBEMA Compliance Calculation Block Diagram



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.

8



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>.

9



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.

1



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

A



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



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

C



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


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



Timeline as of December 13th

Task Start End Name Completed

Begin preliminary

research and

deliver project

outline rough draft


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



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.

D



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


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


Update project website with progress October 26, 2013 November 1, 2013 Jeremy

D



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

D-3

http://www.engr.colostate.edu/ece-sr-design/AY13/storage/index.html





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-

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