Vac Work Utillabs

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UNIVERSITY OF WITWATERSRAND

Vacation Work: MECN310

Util Labs Pty. Ltd.

Joseph Thomas

0710343E

1/20/2012


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Executive Summary

The following document is details the work undertaken while at Util Labs. The problem statement

and the focus of the six weeks were to do with pattern recognition of electricity consumption in a

household. This is to combat incorrect billing. A method for non-intrusive application load

monitoring was discovered.

With programmed logic, applications can be identified depending on the magnitude of the power

draw. These techniques reduce computational intensity compared to other matching techniques

(convolution). This ensures that the data can be processed and sent from the MCU without further

modifications.

The deliverable was to find a method to characterize a house to prevent cable switching and

incorrect metering. Two types of data were analysed with different data intervals. Data with

intervals of one second and five minutes needed deferent techniques to analyse. Per second data

was found to be easy to establish an application class, providing a characteristic matrix of a

h h ld i h l i l li i l (T bl 1 5) S i i i l i d d d


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Declaration

Blank Page


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Table of Contents

Executive Summary .................................................................................................................................. i

Declaration .............................................................................................................................................. ii

List of Tables .......................................................................................................................................... iv

List of Figures .......................................................................................................................................... v

List of Abbreviations .............................................................................................................................. vi

1. Introduction .................................................................................................................................... 1

1.1 Company ....................................................................................................................................... 1

1.2 Project ........................................................................................................................................... 2

2. Objectives........................................................................................................................................ 3

3. Methodology ....................................................................................................................................... 3

3.1 Process .......................................................................................................................................... 4

4. Observations ................................................................................................................................... 5

4.1 Logic .......................................................................................................................................... 5

4.2 Sensitivity Analysis .................................................................................................................... 6

4.3 Habitual Data ............................................................................................................................ 7

5. Analysis ........................................................................................................................................... 8

6 C l i 9


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List of Tables

Table 1: Power Signature Matrix ............................................................................................................ 5

Table 2: Habitual Matrix ......................................................................................................................... 7


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List of Figures

Figure 1: Frequency Spectrum .............................................................................................................. 11

Figure 2: Sample data ........................................................................................................................... 11

Figure 3: Normalized frequency spectrum ........................................................................................... 11

Figure 4: Localised Differential vs Ordinary .......................................................................................... 12

Figure 5: Discrete pulse data and Localised rate of change (pulse/sec) vs time (sec) .......................... 12

Figure 6: Pulse and rate of change data: 2 second interval .................................................................. 13

Figure 7: Pulse and rate of change data: 10 second interval ................................................................ 13

Figure 8: Pulse and rate of change data: 60 second interval ................................................................ 13

Figure 9: Pulse and rate of change data: 5 minute interval .................................................................. 14

Figure 10: Per second data testing ....................................................................................................... 15

Figure 11: Habitual testing of 5 minute data ........................................................................................ 15


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List of Abbreviations

Abbreviation Meaning

NIALMS Non-Intrusive Application Load Monitoring

eddi Electricity Demand Display Unit

w.r.t. with respect to

ULMS Utility Load Monitoring System

MCU Master Control Unit

D&D Design and Development

OCP Operations Centre Personnel

VAR Volt Ampere Reactive

kW.h Kilo Watt . Hour


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1. Introduction1.1 Company

Founded in 2008, Util Labs has 60 employees working at the office in Midrand. The product is a

Utility Load Management System (ULMS). This device measures and displays the power consumption

of households in real time. This, in effect, empowers customers to change their consumption habits

and monitor the improvements. Additionally the ULMS allows municipalities to monitor and

communicate to individuals. This in turn leads to better customer management and support.

An organogram of the organisation is provided on the last page of this document. The structure of

the organisation is roughly separated into three groups:

Design and Development (D&D) Operations Finance/Administration

The ULMS has the technology capable of making the grid smart. The eddi is a plug and play device

that measured and displays the power consumption of the house. It works by fitting into any three

pin plug. The display unit alerts customers at times of high load to change their consumption to


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1.2 Project

Incorrect billing is a serious problem for all parties involved. Customers are annoyed at no end to pay

for something that they are not consuming. To rectify the problem manually can take time and

expend resources. The company involved can ultimately lose their customers if the problem is not

speedily resolved. Most of the time, however the customer and the provider are not aware that

wires have been switched. The purpose of the investigation is to develop software to recognise the

appliances of a house from the power usage data. This recognition can differentiate houses, allowing

for digital switching of meters that are tampered with in the field.

An appliance can be identified by its pattern of power usage, the time in the day it is used and the

length of time that is operates for. Assuming different houses have different applications, a

distinction between different houses as opposed to the same house with a new appliance must be

found. The input data was power consumption data (kW.h) from test units in the field.

The data was handles in two separate stages. The first stage was per second power data and the

second stage used per five minute data. The five minute data is the actual collection rate for all the

devices in the field. The problems that face the analysis include aliasing and filtering of the data.

N i t d b it it i t f ith th t t M lti l li


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2. Objectives

1) Could houses be differentiated and characterised by their power consumption characteristics?2) Identify an identification technique for a house using power usage data (kW.h).3) Separate appliance classes from power usage data into4) Find the usage characteristics for each appliance.5) Normalize the data and compile a matrix of power usage to characterize a house.6) Identify a matching criterion to differentiate houses from a group.

3. Methodology

The following is an example of a non-intrusive appliance load monitoring system (NIALMS). The

received input is discrete power data from the electronic demand display instrument (eddi

) for

particular houses. Each measurement is taken as a pulse. Each pulse represents 8.5742 W of power

used per seconds. The data acquisition rate is at 1 Hz for the initial analysis. The usability of the code

developed for 1 Hz is then tested and modified for an acquisition rate of 0.003 Hz (pulse per five

i t )


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3.1 Process

The Nyquist theorem states that the sampling rate must be at least twice the maximum frequency

of the system.[3].This avoids aliasing therefore smoothens spurious data patterns. The solution to

the problem is to run the data through a low-pass filter. The formula is presented below.[4]

( )

[] [ ] [] [ ]

Where:

RC: Time constant, or frequency below which the frequencies are cut out

T: Sampling frequency

: Smoothing factor

Y: Discrete power point

X: Discrete time point

Excel

has an add-in that can find the Fourier transform of given data. The given power

(Watt.second) was transformed and converted from the time domain to the frequency domain. The

f t f th l d t i h i A di A Th d ti f th i ft th


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4. Observations

The first differential represents the discrete pulses with respect to time. The second differential

represents the absolute change of power consumption. To reliably analyse the data with logic, the

peaks must be distinct. The differential data and the given data are shown in Appendix B, page 12.

Using logic in Excel

, with the data from Figure 5, page 12, appliance usage is extracted. There were

two apparent trends in the power consumption. There were repeated cycles with inductive kick of a

motor corresponding to a fridge. The second involved high peak of a resistive element of a heater.

The total usage time and average cycle time was compiled for each appliance in a matrix. This matrix

corresponds to a houses power usage signature. An example of such a matrix is presented in Table

1.

Table 1: Power Signature Matrix

Total on (sec) Average Period (sec) Peak change (pulse/sec)

Element 3605 901.25 175

fridge 27087 2083.615 30


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The fridge could be marked at each inductive spike. This marked the start of a new cycle and the end

of the previous one. A different counter found the time between each cycle and the cumulative

usage time. The running time of a fridge is continuous, so the critical point is the average cycle time

and not the total.

4.2 Sensitivity Analysis

The data collection rate used on the grid has five minute intervals. This is done due to the large

customer base and to relieve the processing done by the central server. The five minute interval

makes data handling easier, but data analysis trickier. As the data is gradually converted from per

second to per 5 minute there are a few things that stop working.

The low pass filter has negligible effect on the disturbance. This is shown in Appendix A, Figure 3 on

page 11. The frequency spectrum is normalized and there is only a noticeable difference when the

graph is magnified (notice the maximum is 0.02 instead of 1).

Using the current analysis for per second data, the data was extended to longer intervals to study

the limits of its usability. As the intervals get longer, the period of the fridge jumps in and out of sync

i h h ll i Thi d i f i i i h d diff i l Th l i f


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4.3 Habitual Data

This sort of analysis uses the fact that the power habits of an average working household stays

constant during the week. The day is segmented into three parts: morning, day and night. Using

similar logic as before, the applications are identified and compiled in a matrix depending on the

time of day it is used. An additional characteristic is sleep. This is the time where the minimum

amount of power is used. In terms of logic, it is the time between the last switch off, and the first

switch on.

A sample matrix of habitual data is presented below, in Table 2.This will represent a houses

characteristic power usage. Correlation techniques are then used and the normalised matrix pair

with the minimum difference will produce a match. Once the match passes a number of tests, which

are trade secrets, the digital switch of the two houses can take place.

Table 2: Habitual Matrix

Whole Morning Day Night

Length (5min/period) 5.53 5.52 4.38 6.92

N b f i d 3 47 2 74 0 38 0 35


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5.Analysis

Sample data has been used to check for the application of the code. The testing, as with the project

was done in two stages depending on the collection rate. The results of the per second and five

minute data are shown graphically in Appendix D, Figure 10 and Figure 11 respectively on page 15.

These figures show the base power data overlaid with the identified application via logic. The graphs

illustrate that appliances can be monitored and identifies using power data alone.

The per-second data can be segmented into application use of different classes without distortion or

significant uncertainty. This is not true for the five minute data. The identification of secondary

appliances, such as motors and pumps, proves to be more complex. Primary appliances, such as

heating elements, and sleeping patterns are used to form the basis for the analysis. The analysis

works for data intervals up to two minutes, as discussed in section 4.2 Sensitivity Analysis, page 6.

Habitual data provides more variables to the characteristic matrix. This analysis used a limited

number of appliances, with data intervals of five minutes. However there is still a variation of

potential power consumption as humans are not habitual by nature. This technique relies on the

users being highly regular and employed.


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6. Conclusions

1. Differentiation of the power data with logic provides a reasonable approach to patternrecognition.

2. The analysis using per second data intervals can be applied to data intervals of up to twominutes. This is as discussed in section4.2 Sensitivity Analysis, page 6.

3. Habitual data analysis provides a more accurate characteristic for houses using five minutedata intervals.

4. The buffer size needed to incorporate this analysis is small. The MCU need not be upgraded.5. Limited number of applications could be identified. Resistive elements and long cycle motors

could be discerned.

6. The detection of reactive power is the key to accurately find the type of application that isbeing used, using the phase angle.

7. Recommendations


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References

[1] Popular Mechanics, January 2012,Page 71.[2] http://www.utillabs.com/[3] http://www.wordiq.com/definition/Nyquist_theorem[4] http://www.dsplog.com/2007/12/02/digital-implementation-of-rc-low-pass-filter/[5] http://www.holoborodko.com/pavel/numerical-methods/numerical-derivative/central-

differences/

[6] Robert Schalkoff, Pattern Recognition Statistical, Structural and Neural Approaches, 1992, page329

All websites last visited 12th

March 2012


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Appendix A: Filtering

Figure 1: Frequency Spectrum


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Appendix B

Figure 4: Localised Differential vs Ordinary


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Appendix C: Sensitivity Analysis

Figure 6: Pulse and rate of change data: 2 second interval


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Figure 9: Pulse and rate of change data: 5 minute interval


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Appendix D: Testing

Fridge

Element

Normalised

Power


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1

UTIL LABS (PTY) LTD BOARD OF DIRECTORS

Chief

Executive

Officer

Joe Paul

Engineering DivisionOperations

Corporate

Paroshen Naidoo

Group Leader /

System Analyst

Andrew

Goedhart

Engineering

Projects

Management

Hartmut Bohmer

Testing &

Verification

Cedric

D'Abreton

Production

Engineering

Jan

Olwagen

Configurati

ons

Controller

Marketin

g

Senosha

Naidoo

Haneefa

Montani

Producti

on

(Compani

es

0; 1 & 2)

Network

Ops

Installatio

ns Projects

Managem

ent

Field

Installati

ons

ULM

Technic

al

Suppor

t

Finance

Quality

Assuran

ce

(QMR)

Personn

el

Norveshen

Pillay

Riaad

Perreira

Adheesh

Sewrajan

A N Other

Jon

Bonsignor

e

Stephe

n vd

Merwe

Britto

Philipose

Luigi

Slaviero

Senior

Developer

Carl

HeymannDeveloper

Nicholas

Prozesky

Developer

ULM

Syste

m

Securi

ty

EngineerProducti

on Ops

Network

Monitor

Field

Team 1

Procurem

ent

Officer

QA

Officer

(Assist

QMR)

Pierre vd

Riet

Edwar

d vd

Vyver

Japie

Greeff

Gregory

Moyce

Thamesh

an

Moodley

Deven

Pillay

Stanley

Mkhize

Farai

Nyereygon

a

Jodash

Singh

Develop

er

Develop

erDeveloper

Technici

an

Warehou

se

Controller

Public

Liaison

Officer

Field

Team 2

Test

Dept

Duane

McKibbi

n

Rajesh

Thiru

Sethu

Govindasa

my

Themba

Gama

Kenneth

Hlatshwa

yo

Dave

Hinchcliff

e

Andries

Kekana

Peter

Mataban

e

Siphelel

e

Msezan

e

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