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IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 27, NO. 4, OCTOBER 2012 1937

Assessing the Collective Harmonic Impact of ModernResidential Loads—Part I: Methodology

Diogo Salles , Student Member, IEEE , Chen Jiang , Student Member, IEEE , Wilsun Xu , Fellow, IEEE ,Walmir Freitas , Member, IEEE , and Hooman Erfanian Mazin , Student Member, IEEE

Abstract—The proliferation of power-electronic-based residen-tial loads has resulted in significant harmonic distortion in thevoltages and currents of residential distribution systems. There isan urgent need for techniques that can determine the collectiveharmonic impact of these modern residential loads. These tech-niques can be used, for example, to predict the harmonic effectsof mass adoption of compact fluorescent lights. In response to theneed, this paper proposes a bottom-up, probabilistic harmonicassessment technique for residential feeders. The method modelsthe random harmonic injections of residential loads by simulatingtheir random operating states. This is performed by determining

the switching-on probability of a residential load based on theload research results. The result is a randomly varying harmonicequivalent circuit representing a residential house. By combiningmultiple residential houses supplied with a service transformer,a probabilistic model for service transformers is also derived.Measurement results have confirmed the validity of the proposedtechnique. The proposed model is ideally suited for studying theconsequences of consumer behavior or regulatory policy changes.

Index Terms—Harmonic analysis, residential loads, statisticalanalysis, time-varying harmonics.

I. INTRODUCTION

THE proliferation of power-electronic-based modern resi-dential loads has resulted in significant harmonic distor-

tions in the voltages and currents of residential power distri-

bution systems. These new harmonic sources have comparable

sizes and are distributed all over a network. Although they pro-

duce insignificant amount harmonic currents individually, the

collective effect of a large number of such loads can be substan-

tial [1]–[3]. At present, there is an urgent need for techniques

that can determine the collective harmonic impact of modern

residential loads. Such techniques can be used, for example, to

predict the harmonic effects of mass adoption of compact fluo-

rescent lights (CFLs) and to quantify the effectiveness of certain

Manuscript received August 26, 2011; revised March 24, 2012; acceptedMay 19, 2012. Date of publication August 14, 2012; date of current ver-sion September 19, 2012. This work was supported in part by the NationalSciences and Engineering Research Council of Canada, in part by the Al-berta Power Industry Consortium, and in part by FAPESP, Brazil. Paper no.TPWRD-00718-2011.

D. Salles and W. Freitas are with the Department of Electrical EnergySystems, University of Campinas, Campinas 13083-852, Brazil (e-mail:[email protected]; [email protected]).

C. Jiang, W. Xu, and H. E. Mazin are with the Department of Electrical andComputer Engineering, University of Alberta, Edmonton, AB T6G 2V4 Canada(e-mail: [email protected]; [email protected]; [email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TPWRD.2012.2207132

harmonic control measures. One such measure is to adopt the

IEC device level limits in North America [4].

The main challenge to developing the above-mentioned tech-

niques is how to model the random nature of the harmonic cur-

rents produced by residential loads. Over the past many years,

some researchers have investigated the summation of random

harmonic phasors [5], [6], the algorithms of stochastic harmonic

power flows [7], [8], and methods to predict the mean values

of harmonic indices [9]. All of these works have greatly con-

tributed to our understanding on the modeling and analysis of systems with randomly varying harmonic loads. Unfortunately,

the available techniques are still not in a shape to fulfill the needs

of predicting harmonic distortions caused by consumer behavior

or regulatory policy changes.

The objective of this paper is to present a systematic, versa-

tile technique based on Monte Carlo simulation to study the har-

monic impact of residential loads. The main idea is originated

from the following observation: the random harmonic genera-

tion of residential loads is almost exclusively due to the random

on/off states of the loads. For example, a CFL can be in an ON

or OFF state randomly at any given time. But once it is turned

on, its harmonic currents essentially follow a known, determin-istic spectrum. Therefore, the key to develop the aforementioned

harmonic assessment technique is to model the random ON / OFF

state change events of residential loads properly. Once the states

of all residential loads are known, the problem becomes a de-

terministic harmonic power flow problem. Fortunately, a body

of knowledge on residential load behaviors has been developed

for load research purposes [10]–[13]. These techniques can be

adapted to solve the problem of predicting the operating states

of residential loads. The result is a bottom-up-based harmonic

analysis technique ideally suited for studying the consequences

of consumer behavior or regulatory policy changes.

The aforementioned idea has been applied to create a simula-

tion technique for predicting the harmonic impacts in secondarydistribution systems. Such a system typically consists of 5–20

single-detached houses in North America. An aggregate model

for service transformers is also derived with the technique. The

model can help to determine the harmonic conditions in primary

distribution systems. Field measurement results taken from a

dozen of service transformers in Canada have validated the pro-

posed modeling technique.

The paper is organized as follows. Section II describes

the electrical models of typical residential loads. Section III

presents a procedure to determine the ON / OFF state of a res-

idential load based on the behaviors of inhabitants and the

usage characteristics of the residential load. Build on the above

0885-8977/$31.00 © 2012 IEEE



1938 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 27, NO. 4, OCTOBER 2012

results, the harmonic model of a single house is developed in

Section IV. Section V derives a service transformer model.

Verification results are also presented. Section VI summarizes

the conclusions.

II. MODELING OF RESIDENTIAL LOADS

There are two aspects to model a residential load. One is itselectrical model and the other is its operating state model. This

section is focused on the electrical model.

Residential loads can be divided into linear and nonlinear

loads. In this paper, linear loads are modeled as constant power

loads at the fundamental frequency (60 Hz) and as impedance

at harmonic frequencies (h) [14]. The model parameters can be

obtained from the measurements or derived from the load’s elec-

tric characteristics. The nonlinear residential loads are modeled

as constant power loads at the fundamental frequency (60 Hz)

and as current sources at harmonic frequencies [14]. The mag-

nitude and phase of the source is calculated from the harmonic

current spectrum of the residential load using the well-knownprocedure recommended in [14] as follows.

1) The harmonic-producing load is treated as a constant

power load at the fundamental frequency, and the funda-

mental frequency power flow of the system is solved.

2) The current injected from the load to the system is then

calculated and is denoted as .

3) The magnitude and phase angle of the harmonic current

source representing the load are determined as follows:

-

-(1)

- - (2)

where the subscript “spectrum” stands for the typical harmonic

current spectrum of the residential load. In this study, harmonic

spectra of more than 20 types of residential loads (which adds

up to about 100 individual loads) have been measured.

The measurement activities have identified a need to deter-

mine which residential loads are linear or nonlinear. In a dis-

torted supply voltage scenario, even a resistive load could have

a distorted current waveform, appearing as a nonlinear source.

In this paper, the correlation of the instantaneous current and

voltage waveforms ( – plot) are used to separate linear loads

from nonlinear loads. The method is illustrated in Fig. 1 for two

residential loads. If the load is linear, the - plot is shaped ei-ther as a straight line (resistive load) or as a ring (reactive load).

The voltage versus current plots for nonlinear residential loads

are neither a straight line nor a ring, for example, the microwave

oven in Fig. 1.

When adopting the current source model for nonlinear loads,

the issue of whether to include the attenuation and diversity ef-

fects has been examined. Attenuation refers to the reduction of

the magnitude of harmonic currents produced by a residential

load when its supply voltage is distorted [15]. Based on the pub-

lished research works [8], [14], [15] and extensive lab tests con-

ducted by the authors [16], we concluded that the attenuation

effect becomes significant only when the voltage distortion is

quite high (for example, THD above 10%). We have not ob-served such a high-voltage distortion level in the field nor found

Fig. 1. Correlation between instantaneous voltage and current of a load. (a) – plot of the dryer (linear). (b) – plot of microwave (nonlinear).

Fig. 2. Current waveform measured from CFLs of different vendors.

fromsimulation results. So the attenuation effect is omitted from

the model proposed at present. This will lead to slightly higher

harmonic levels in the system. If the need to include the effect

arises, iterative harmonic power-flow algorithms can be used

[14], [17].

The diversity effect refers to the differences of harmonic cur-

rent phase angles associated with a residential load [14], [15]. It

is common knowledge that a residential load always producesharmonic currents at the same phase angle with respect to its

supply voltage (if given in the same operating condition). This

is why typical spectrum can be used to model a harmonic-pro-

ducing load. Fig. 2 shows the waveforms of multiple CFLs from

different vendors. It can be seen that the waveforms are similar,

meaning the harmonic angle (and magnitude) differences are

small. As a result, the random variation of phase angles from

their mean values is not included in the electrical model. The

variation due to different brands can be included in the Monte

Carlo sampling explained later. It is important to note that the

variation of phase angles causedby the variation of the phase an-

gles of supply voltages is included through (2). The harmonic

cancellation effect caused by voltage phase differences at dif-

ferent points of a feeder because of the branch impedances is

also modeled in the simulation through (2) and the multiphase

network model.

III. MODELING OF RESIDENTIAL HOUSES

This section develops a probabilistic bottom-up model that

provides the time distribution of the ON / OFF state of linear and

nonlinear residential loads during the course of the day. It is

useful to note that each load may switch ON / OFF randomly. But

the switching activities of all types of loads follow some statis-

tical distributions. These distributions are used to determine the

ON / OFF states of the loads. From this perspective, the loads donot operate in random completely.



SALLES et al.: ASSESSING COLLECTIVE HARMONIC IMPACT OF MODERN RESIDENTIAL LOADS—PART I 1939

Fig. 3. Time-of-use probability profile for cooking activity.

Fig. 4. Time- of-use probability profile for laundry activity.

A. Residential Load Profile Modeling

Considerable research efforts have been made in the past onthe subject of developing detailed residential electrical load pro-

files (i.e., reconstructing the expected daily electrical loads of a

household based on residential load sets, occupancy patterns,

and statistical data). Based on the published works [10]–[13],

the basic idea of home load profile modeling is to address three

factors:

1) When a residential load will be turned on. This can be

simulated using the Daily Time of Use Probability Profiles

of the residential loads.

2) How long a residential load will stay on (i.e., working cycle

duration). The duration can be determined from measured

data and from understanding the purpose of the residentialloads involved.

3) How to include the impact of the habits and population of

a household.

A load profile is created by combining these factors. Each

factor is presented in detail in the following subsections.

1) Daily Time of Use Probability Profiles: In [10], electric

load profiles are constructed from individual residential loads to

predict the tendency of the occupants to switch on a residential

load at any given time. Since then, many researchers [11]–[13]

have investigated how to quantify the probability of the speci-

fied activity being undertaken as a function of time of day, which

is called the Time of Use Probability Profiles. It represents the

probability of a household performing a specific activity duringa 24-h period. In this paper, each activity profile data is mainly

TABLE IUSAGE PATTERN FOR MAJOR RESIDENTIAL LOADS

TABLE IIAVERAGE USAGE PATTERN FOR OTHER RESIDENTIAL LOADS

TABLE IIIOCCUPANCY PATTERN FOR A TYPICAL HOUSEHOLD

collected from [12] and [18], which uses the survey data pro-

vided by [19]. For the sake of space, the probability profile of a

few activities is discussed and illustrated as follows.

Most of the kitchen loads are related to cooking activities.

They share the same probability of switching on at a given

time of the day. As shown in Fig. 3, the curves are applied to

predict the occupants’ cooking-related actions and to establish

the probability of a load switch-on event, such as using mi-

crowave, blender or griddle, etc. The probability profile is given

with 1-min resolution. The higher the value in the profile, the

higher the probability that the load switches on. For example, a

microwave switch-on event would be far more likely to occurat 17:00 than at 4:00. The profile on weekends is flatter than

the profile for weekdays because people tend to cook at more

random times on weekends.

In addition, people are more likely to use a toaster and waffle

iron in the morning, and stove at noon and in the evening. Spe-

cific profiles for these residential loads are also considered in

the proposed method.

Daily time of use of washer machines is determined by the

laundry activity profile shown in Fig. 4. The time-of-use pro-

file for dryers is generally the same shape as the washer profile,

and is offset from the washer profile in time [13]. People usu-

ally turn on dryers between 10 and 30 min following the end

of the washer cycle. The time-of-use probability profile associ-ated with other activities is also included in the methodology,




including TVs, computers, lighting, house cleaning, and occa-

sional switch-on events (garage door and furnace).

2) Residential Load Duration Characteristics: For major

residential loads, the cycle duration can be established ac-

cording to the measurement data from the Canadian Center for

Housing Technology (CCHT) [20]. At the CCHT, a simulated

occupancy system triggers daily residential load ON / OFF statechange events in a real single detached home. The average

cycles per year is derived from standard residential load test

methods of the Canadian Standards Association [21], [22].

Details of some major residential loads and working cycle

durations are presented in Table I.

Information regarding other residential loads was extracted

from buyers’ guides [23], [24] and from field measurements.

Several other residential loads were surveyed and a partial list

of the expected hours that each residential load remains on

per month is presented in Table II. The data in the table below

only include the total working hours per month. The number of

switch-on events per day is determined as follows: the average

working hours for an electric kettle is 15 per month per Table II.If the average working cycle duration of a kettle is 3 min, one can

obtain 15 60 30 3 10 switch-on events per day.

3) Size and Occupancy Pattern of the Household: The size of

household has a significant impact on daily electricity demand.

In order to include the impact of different household sizes, a

household size factor is introduced. When modeling a residen-

tial house with a specified number of occupants, is equal to

the ratio between and the average number of people per house-

hold (assumed to be 2.5 in this paper based on [25]). The value

of average hours per month for each residential load provided

in Table II cannot be used directly for different household sizes

since, for example, a house with more people will lead to an in-crease in load usage. In order to take this into account, the usage

times provided in Table II will be multiplied by the household

size factor .

An occupancy pattern (i.e., when occupants of a residence are

at home, and using residential loads) affects the ON / OFF state

of the loads. The common factors influencing the occupancy

pattern are as follows [12]: (a) the time of the first person getting

up in the morning and the last person to go to sleep and (b) the

period of inactive house occupancy during working hours. Due

to a lack of information about the house occupancy pattern, the

five most typical scenarios of the household occupancy pattern

in Canada are proposed. Table III lists these possible scenarios.

4) Probabilistic Model of Residential Load Switching-On:

Based on the usage pattern of residential loads discussed in the

previous sections and the method of [12], a procedure to deter-

mine loadswitch-on events at a given time of a day (which is also

called a simulation step) hasbeen established.The procedure is a

form of Monte Carlo simulation and implemented in a computer

program. At each simulation step, a list of residential loads that

are on is generated, which forms the load profile at that step. The

simulation procedure is shown Fig. 5 and explained as follows.

Step 1) The time-of-use probability profile is selected ac-

cording to the chosen load and whether it is a

weekend or not; the occupancy pattern is also se-

lected according to the working type and whether itis a weekend or not.

Step 2) When the simulation starts, the probability of load

activation (Pr) can be read from its activity profile.

Step 3) The number of residential load switch-on events (m)

are modified to consider household size

, where is the household size factor.

Step 4) The probability ( ) of a residential load to switch

on at the present instant in time ( ) is equal to theprevious probability Pr multiplied by the modified

number of load switch-ons in the simulation time

period and a calibration scalar ( ),

. A discussion of how the calibration scalar

is derived is presented below.

Step 5) The calculated probability is compared to a nor-

mally distributed random number ( ) between 0 and

1. If is larger than , go to Step 6); otherwise, go

to Step 7).

Step 6) The residential load is switched on and the present

simulation time step is updated to , where

is the load working cycle duration. After that, go

back to Step 2).Step 7) The load remains off and the present simulation time

step is updated to . is the simulation res-

olution (e.g., 1 min). After that, go back to Step 2).

The calibration scalar (c) is introduced to reflect the influence

of the household occupancy pattern [12]. This paper uses the oc-

cupancy function, as follows, to represent the occupancy pattern

when house is actively occupied

(e.g., morning, evening)

when house is inactively occupied

(e.g., daytime, midnight)

where “inactively occupied ” refers to the scenario where no-body is at home or awake.

If , the calibration scalar c is made 0 for most

residential loads, which makes the probability of certain load

switch-on to be zero when nobody is at home or awake.

If , the calibration scalar c is introduced in order

to make the mean probability of an activity taking place, when

multiplied by the calibration scalar, equal to the mean proba-

bility of a load switch-on event. As shown in Fig. 6, before cal-

ibration, we have

After introducing the occupancy function and calibration

scalar c

(n) = 0

(n)= 1.




Fig. 8. Simulated usage time for different residential loads.

and 2 at around 20:00. The usage duration for one time is 4–5

min, and total usage time is 21 min/day.

The same procedure is conducted for each installed residen-

tial load at each simulation step. An example use pattern of some

loads as simulated by the procedure is shown in Fig. 8. One can

observe that the PC is used between 17:00 and 24:00 and the

washer and dryer are not used throughout the day. Televisions

and PCs are used for a relatively long period throughout the day,

while cooking loads are used for much shorter periods but mul-

tiple times.

At the bottom of Fig. 8, the refrigerator and freezer are acti-

vated periodically throughout the entire day and not associated

with household occupancy pattern.

IV. SINGLE-HOUSE HARMONIC LOAD MODEL

Once the residential load usage time information is derived, aload with ON state will be represented with its electrical model

and be connected to the electric circuit of a house. Note that

there could be different electrical models of the same load if

one wants to consider different brands or consumer trends. A

particular model is obtained by drawing randomly from a data-

base of residential loads.

In North America, residential customers are usually supplied

through three-wire single-phase distribution transformers. The

secondary of these transformers has a neutral and two hot phases

carrying 120 V with respect to the neutral. In a residence, resi-

dential loads are connected in an essentially indeterminate way

with respect to the circuits, and this arrangement makes it diffi-cult to electrically model a residence. Since the objective of the

proposed technique is to predict the harmonic impact on power

systems, an equivalent circuit can be developed for a house.

Based on the theoretical analysis presented in [26], the model

of Fig. 9 has been established.

In each simulation step, the house impedance and

current source are established randomly based on the

procedure described earlier. Impedance represents

the linearloads ina house, andcurrent source represents

the nonlinear loads. An example simulation output is shown

in Fig. 10, for only the fundamental, third, and fifth harmonic

components.

The results of seven days simulation, which contains 5 week-days and 2 weekend days, are shown in Table IV. The table lists

Fig. 9. Equivalent circuit model to represent a residential house.

Fig. 10. Simulation output of a house during one day. (a) Funda-mental component. (b) Third harmonic component. (c) Fifth harmonic compo-nent.

the mean value and standard deviation of the total house funda-mental, 3rd, and 5th harmonic currents for each day.




TABLE IVSIMULATION RESULTS OF A RESIDENTIAL HOUSE

TABLE VMEASUREMENTS RESULTS OF A RESIDENTIAL HOUSE

In order to validate the simulation results, field measurements

were conducted for typical residential houses and the results are

shown in Table V. The measured sample house has some occu-

pants that do not need to go to work, so “no working”-type oc-

cupancy pattern is chosen for simulation. Comparing Table IVagainst Table V, the mean values of the fundamental and each

harmonic current matched quite well.

V. SERVICE TRANSFORMER HARMONIC LOAD MODEL

Residential houses are supplied through single-phase ser-

vice transformers connecting the primary to the secondary

system. The secondary is a 120/240-V three-wire service.

Each distribution transformer normally supplies 10–20 houses.

The loads are modeled collectively as one load connected to

the secondary side of the service transformer (the “service

transformer model”). This model is needed for studying the

harmonic impact on primary distribution systems.

The steps for constructing such a model are as follows.

1) Generate harmonic models of 10–20 houses according to

the proposed approach presented in Section IV.

2) Connect the house models as shown in Fig. 11(a).

3) Build the equivalent circuit as shown in Fig. 11(b).

The aforementioned service transformer model has been ver-

ified by comparing its results against those from field measure-

ments. Field measurements of harmonic currents in a sample

service transformer are shown in Fig. 12 (only the 1st and 3rd

harmonic components are illustrated). These data were collected

from ten different transformers serving residential loads in Ed-

monton, AB, Canada, in Winter 2008. There is a total of 55 daysmeasurement data. The current variation of each transformer is

Fig. 11. Equivalent service transformer circuit model. (a) Service transformercircuit model. (b) Equivalent model.

Fig. 12. Example of the transformer current output during one weekday ob-tained from real field measurements. (a) Fundamental component. (b) Thirdhar-monic component.

TABLE VIPERCENTAGE OF VARIANCE OF THE FIRST PRINCIPAL

COMPONENT OF THE TRANSFORMER CURRENT

unique, so it is not easy to compare the results with those ob-

tained from simulation which also exhibit random characteris-

tics. The approach implemented in this paper is to extract and

compare the principal components of the current profiles.

The principal component analysis (PCA) is a mathematical

procedure that transforms a number of possibly correlated

variables of the original data into a smaller number of uncor-

related variables called principal components. Mathematically,

PCA is a linear transformation that converts the data to a

new coordinate system so that the greatest variance by any

projection of the data comes to lie on the first coordinate, the

second greatest variance on the second coordinate, and so on.

This way, one can choose not to use all of the components and

still capture the most important part of the data. More details

can be found in [27].

Table VI lists the variance given by the first principal com-ponents for the magnitudes of measured transformers. Data are




Fig. 13. Comparison of the first principal components (weekdays).

grouped into weekday and weekend. As can be seen, for the

fundamental and third harmonic current, first principal compo-nents can represent almost 60% of the original data; however,

for higher harmonics, these percentages drop to 20%–30%. The

reason is that higher harmonics are more difficult to predict and

do not seem to show similar trends.

Based on the aforementioned analysis, two methods are pro-

posed for the transformer model verification as follows.

1) For fundamental and third harmonic current, the verifica-

tion method is to extract the first principal components

from the measurement data and the simulation results, re-

spectively. The components are then correlated to verify

their consistency.

2) For higher order harmonic currents, the verification is to

compare the normalized probability distribution of the

measured and calculated data.

Fig. 13 shows the daily variation of the first components of

the fundamental and third harmonic currents from both mea-

surement and simulation on weekdays. The first component of

the simulated fundamental current fits quite well with that of

the measured one. The correlation factor is 0.94. There is an

acceptable difference between the first components of the third

harmonic current from simulation and measurements. The cor-

relation factor is 0.7.

The remaining part of the fundamental and 3rd harmonic

components contains almost 40% of the original data. A

comparison is made in the form of statistical distributions in

Fig. 14. The distributions exhibit similar characteristics. For

higher order harmonics, their random variation is too strong

for the principal components to yield meaningful results. So

the components are not used for verification. Instead, normal-

ized harmonic distributions are used for comparison. Fig. 15

shows the results for a weekday. Table VII shows the standard

deviation of the harmonics for both measurements (Meas.)

and simulation (Sim.) results. The results show some form

of consistency for weekdays and weekends. Based on this

verification analysis, it can be concluded that the proposed

probabilistic residential house model is accurate and can be

included in the modeling of distribution systems for harmonicimpact assessment.

Fig. 14. Probability distribution of measured and simulated residue part for aweekday.

Fig. 15. Probability distribution function (PDF) curves of higher harmonics.

TABLE VIISTANDARD DEVIATION OF HIGHER ORDER HARMONIC CURRENTS

VI. CONCLUSION

A probabilistic method to determine the harmonic impact of

residential loads and houses has been presented in this paper.

The method models the random harmonic generations of resi-

dential loads by simulating the random operating states of the

loads. This is done through determining the switching-on prob-ability of a residential load based on the load research results.




The result is a randomly varying harmonic equivalent circuit

representing a residential house. By combining multiple resi-

dential houses served by a service transformer, a model for ser-

vice transformers is also derived. PCA and statistical analysis

on the simulated and measurement results have confirmed the

validity of the proposed modeling approach.

One of the attractive characteristics of the proposed method isits bottom-up approach. As a result, one can simulate the effect

of market trends and policy changes. For example, the harmonic

impact of CFLs can be studied by adjusting the composition of

lighting fixtures in the residential load database. An example

application of the proposed method, assessing the harmonic im-

pact of residential loads on secondary distribution systems, is

described in a companion paper.

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Diogo Salles (S’04) received the B.Sc. and M.Sc. degrees in electrical engi-neering from the University of Campinas, Campinas, Brazil, in 2006 and 2008,respectively, where he is currently pursuing the Ph.D. degree in electrical engi-neering.

From 2010 to 2011, he was a Visiting Doctoral Scholar at the University of Alberta, Edmonton, AB, Canada. His research interests focus on power qualityand analysis of distribution systems.

Chen Jiang (S’09) received the B.Eng. degree in electric engineering and au-tomation from the Huazhong University of Science and Technology (HUST),Wuhan, China, in 2008, and is currently pursuing the M.Sc degree in electricalengineering at the University of Alberta, Edmonton, AB, Canada.

His main research interests are power quality and smart-grid technology of distribution systems.

Wilsun Xu (M’90–SM’95–F’05) received the Ph.D. degree in electrical engi-neering from the University of British Columbia, Vancouver, BC, Canada, in1989.

Currently, he is a Professor and an NSERC/iCORE Industrial Research Chairin Power Quality at the University of Alberta, Edmonton, AB, Canada. His cur-

rent research interests are power quality, harmonics, and information extractionfrom power disturbances.

Walmir Freitas (M’02) receivedthe Ph.D. degree in electricalengineering fromthe University of Campinas, Campinas, Brazil, in 2001.

Currently he is an Associate Professor at the University of Campinas. Hisareas of research interest are the analysis of distribution systems and distributedgeneration.

Hooman Erfanian Mazin (S’08)was born in Tehran,Iran,in 1981. He receivedthe B.Sc. and M.Sc. degrees in electrical engineering from the Amirkabir Uni-versity of Technology, Tehran, Iran, in 2004 and 2006, respectively, and is cur-rently pursuing the Ph.D. degree in electrical engineering at the University of Alberta, Edmonton, AB, Canada.

2013-assessing the collective harmonic impact of modern residential loads-part i methodology.pdf

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