Comparative Analysis Between Measurements and Simulations of Voltage Sags

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1472 IEEE TRANSACTIONS ON POWER DELIVERY, VOL. 23, NO. 3, JULY 2008

Validation of Voltage Sag Simulation Tools: ATP andShort-Circuit Calculation Versus Field Measurements

Jose Maria Carvalho Filho, Roberto Chouhy Leborgne, Member, IEEE, Jos Policarpo G. de Abreu,Eder G. C. Novaes, and Math H. J. Bollen, Fellow, IEEE

AbstractTwo methods to calculate voltage sags are validatedagainst actual measurements. One of the methods is a short-circuitcalculation program resulting directly in sag magnitude during thefault. The other is an electromagnetic transient program resultingin voltage waveform as a function of time. Individual sagcharacter-istics and system performance obtained by deterministic simula-tion and measurement are compared. The influence of the randomvariables (prefault voltage, fault location and fault impedance) isinvestigated.

Index TermsMeasurement, power quality (PQ), simulation,voltage sag (dip).

NOMENCLATURE

PQ Power quality.

ATP Alternative Transients Program.

SARFI System average rms variation frequency index.

LG Line-to-ground fault.

LL Line-to-line fault.

LLG Double line-to-ground fault.

LLL Three-phase fault.

I. INTRODUCTION

VOLTAGE sags are a temporary reduction of the rms

voltage at a point of the electrical system below a certain

threshold. Voltage sags are mainly evaluated in terms of mag-

nitude, duration, and frequency of occurrence [1].

The sag characteristics can be obtained through measure-

ments or simulations. The system performance can be assessed

by long-term measurements or stochastic simulations [2].

The main limitation of a measurement program is the longtime needed to obtain accurate results [3]. Another limitation is

Manuscript received January 29, 2007; revised August 29, 2007. This workwas supported by the CAPES (Brazil). Paper no. TPWRD-00032-2007.

J. M. C. Filho and J. P. G. de Abreu are with Itajuba Federal University, Ita-juba 37500-903, Brazil (e-mail: [email protected]; [email protected]).

R. C. Leborgne is with theFederal University of RioGrande doSul (UFRGS),Porto Alegre 90035-190, Brazil (e-mail: [email protected]).

E. G. C. Novaes is with Petrobras, Rio de Janeiro 01311-936, Brazil (e-mail:[email protected]).

M. H. J. Bollen is with STRI AB, Ludvika 771 80, Sweden. He is also withthe Lule University of Technology, Skellefte 931 87, Sweden (e-mail [email protected]).

Digital Object Identifier 10.1109/TPWRD.2008.916752

the high cost of buying and installing power quality monitors

in the whole network. Furthermore, the changes in the network

topology and the installation of new generation plants change

the expected voltage sags statistics.

Considering that most of the severe voltage sags are caused

by faults in the networks, fault simulation has been used for

voltage sag estimation [2], [4]. Both electromagnetic transient

programs and short-circuit calculation programs are available

for fault analysis.

The power system performance obtained from the method of

fault positions was compared with measurements [5]. However,the accuracy of the computational tools used for the determin-

istic assessment of voltage sags has not been investigated in

depth. To provide this missing information, this paper investi-

gates the accuracy of the simulation of voltage sag magnitude

and frequency.

The random variables that affect voltage sag characteristics

are analyzed. An ideal 1.0 p.u. prefault voltage and the actual

prefault voltage are simulated. The estimated fault location

and a variation of % of the line length are admitted for

simulation. Several fault impedances (0, 5, 25, and 40 ) are

considered.

II. VOLTAGE SAG SIMULATION

Simulation methods are an inexpensive choice to obtain

voltage sags characteristics, thus avoiding long and expensive

periods of measurements. The two main tools used to calcu-

late voltage sag magnitude are electromagnetic transient and

short-circuit calculation programs [5], [6].

Electromagnetic transient programs calculate the voltage

waveform and therefore provide a complete characterization of

the disturbance [7], [8].

Short-circuit calculation programs are more popular for

voltage sag assessment due to their easy application and simple

network modeling. The voltage sag magnitude is obtained from

the bus impedance matrix. The sag duration can be estimatedusing a typical fault-clearing time [9], [10].

In this paper, sag magnitude and sag frequency are consid-

ered for the simulation validation. The sag frequency (SARFI)

represents the system performance. This index can be estimated

for one bus, a group of buses, or the whole network. The system

performance can be calculated through stochastic or determin-

istic simulation.

The stochastic assessment gives the expected performance

of the network. The method of fault positions and the Monte

Carlo simulation are used for stochastic assessment [11], [12].

The random variables, such as fault location, fault type, fault

impedance, etc., are modeled by a probability distribution. This

0885-8977/$25.00 2008 IEEE


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FILHO et al.: VALIDATION OF VOLTAGE SAG SIMULATION TOOLS 1473

Fig. 1. Reduced diagram of the monitored power system.

simulation approach can be used to analyze the likelihood of

a system performance obtained from short-term measurements

[13].

The deterministic assessment of the system performance

is carried out using sag measurements or simulating a set

of recorded faults. The fault-events are characterized by the

location, type, impedance, and system configuration. The result

of this assessment shows the system performance for a specific

period.

III. STUDY CASE

A. Power System

A reduced version of the system studied is shown in Fig. 1.

The whole network contains 67 transmission lines (138 and 230

kV) with a total length of 6619 km. There are 46 substations

with a transformer-installed capacity of 2076 MVA. The gen-

eration capacity is larger than the present demand. The excess

of generated power is exported to another regional grid through

the substation where the PQ-monitor P9 is installed.

A total of 12 buses located at five substations were selected

for voltage sag monitoring. The measured buses

are indicated in Fig. 1. The criteria for the bus choice werenetwork topology, load concentration, sensitive-loads location,

main generation plants, and transformer connections.

The monitoring system consists of 12 synchronized PQ mon-

itors. The synchronization was needed to do a correct cause/ef-

fect correlation.

The measurements included: sag magnitude, sag duration and

voltage phasors. The measurements are based on phase-to-neu-

tral voltages. The time resolution of the rms values and pha-

sors were 8.33 ms (half cycle). The selected sag threshold was

0.85 p.u. to avoid the overload of the PQ-monitor system with

shallow sags.

During the six-month monitoring period a total of 30 events

had the fault characterized by the location and the fault type,according to Table IX.

The fault location is performed by the distance relays using

current and voltage information at only one of the line termi-

nals. This terminal is called From in Table IX. The difference

between the actual fault location and the location estimated by

the relays does not exceed 5% of the line length. Table IX shows

the fault location in kilometers from the bus From.

There are no fault impedance estimators in the system.

Therefore, the actual fault impedance is unknown and 0 fault

impedance is adopted for the base case.

B. Calculation Programs

The electromagnetic transient program used here is the

well-known Alternative Transients Program (ATP) [14]. The

short-circuit calculation program employed in this research

is the ANAFAS [15]. As different programs use similar algo-

rithms, the choice of program is not expected to impact the

results.

The ATP calculates the instantaneous voltage waveform.

The model of the large generators includes the effect of the

voltage regulation. The long transmission lines are modeled by

distributed parameters. The model of the transformers takesaccount of the short-circuit impedance, the saturation, and the

phase shift between the primary and the secondary voltages.

Usually the loads are modeled as constant resistances and reac-

tances. However, in a grid with a high penetration of induction

machines a more detailed load modeling is needed.

The ANAFAS calculates the voltage magnitude using the

bus impedance matrix. The generators are modeled as an

ideal voltage source behind the sub-synchronous reactance of

the machines. The model adopted for the transmission lines

considers the resistance and the reactance, neglecting the shunt

capacitance. The transformer model includes the short-circuit

impedance and the phase shift due to the winding configuration.

Usually, the loads are neglected when performing this staticsimulation, with the exception of large motor loads.


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TABLE ISYSTEM MODELING

Table I summarizes the system modeling for the ATP and the

short-circuit program. The system models were given by the

utility.The prefault voltage adopted by the ATP was the result of the

steady-state solution of the network. The prefault voltage used

by the short-circuit program was 1.0 p.u. Therefore, in order

to be able to compare the results obtained from both programs,

the loads simulated by the ATP have been slightly adjusted to

obtain prefault voltages close to 1.0 p.u. This adjustment was

done considering historical values of P and Q.

Using the ATP and the short-circuit calculation program, each

fault is simulated to obtain the sag indices at the 12 monitored

buses. Therefore, the sag assessment is indeed deterministic.

Then, the simulated indices are compared with the measured

ones to evaluate the accuracy of the simulation approach.

IV. SAG MAGNITUDE VALIDATION

A. Sag Magnitude ErrorBase Case

The set of simulation described in this section is called base

case. The fault location is the one given by the distance relays,

the fault impedance is 0 and the prefault voltages have not

been adjusted.

The magnitude of the three-phase sags is determined by the

sag magnitude of the critical phase (i.e., the minimum rms value

of the three phases). The error of the simulated sag magnitude

is estimated using (1)

(1)

where and are the measured and simulated

voltage sag magnitudes in per unit.

The histograms of the sag magnitude error obtained using the

ATP and the short-circuit calculation program are shown in Fig.

2. The error distribution for the ATP is more symmetrical around

zero; there is a similar probability of over and underestimating

the sag magnitude when using the ATP. On the other hand, the

fault calculation program estimates sag magnitudes larger than

the measured ones in 73% of the cases.

The errors are summarized in Table II, where the averagevalues, the standard deviation, the minimum, and the maximum

Fig. 2. Histogram of errors of simulated voltage sag magnitude.

TABLE IIMAGNITUDE ERROR FOR ATP AND FAULT CALCULATION PROGRAM

Note: the average error and standard deviation is calculated considering the

absolute value of the errors.

values are given. In most of the cases (91% and 93% for the

ATP and the fault calculation program, respectively) the abso-lute error is less than 0.10 p.u. However, some of the results de-

viate considerably. The largest errors are 0.57 and 0.64 p.u. for

the ATP and the fault calculation program, respectively. These

errors on the magnitude estimation are found for the same event

registered at P6. This event is the No. 2 shown in Table IX,

caused by a LG fault in a 230-kV line close to P6.

The accuracy of the results is affected by some uncertainties

such as the prefault voltage, the precise location of the fault,

and the fault impedance. The results are also affected by some

errors associated to the measurement system, such as voltage

transformers and PQ-monitor.

In the next sections the results of a new set of simulations aredescribed, compared with the measurements and the previous

simulated results. These new results are obtained after making

some changes in the three variables considered responsible for

the actual divergences: prefault voltage, fault location, and fault

impedance.

B. Adjusting Prefault Voltage

In some cases, the measured prefault voltage reached values

below 0.92 p.u., whereas the ATP simulated prefault voltages

were about 0.98 p.u. and the ones adopted for the short-circuit

calculation program were 1.0 p.u. Hence, these variations were

recorded and the largest absolute variation at each monitor lo-cation is presented in Table III.


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TABLE IIILARGEST ABSOLUTE PREFAULT VOLTAGE VARIATIONS (IN p.u.)

TABLE IVMAGNITUDE ERROR FOR ATP AND FAULT CALCULATION

PROGRAM AFTER PREFAULT VOLTAGE NORMALIZATION

Note: the average error and standard deviation are calculated considering the


The deviations are lower than 0.10 p.u. as expected consid-

ering normal system operation and the voltage regulation ac-

cepted by the national standards.

The voltage sag magnitude at bus due to symmetrical fault

at bus is given by (2)

(2)

where and are the prefault voltages at the

buses and , respectively. is the transfer impedance be-

tween bus and , and is the driving point impedance at

bus [16].

Therefore, in order to avoid the error due to the use of dif-

ferent prefault voltage at the monitored bus, the error estimated

using (1) is recalculated normalized by the prefault voltages (3)

(3)

where and are the measured and simulated

voltage sag magnitudes and are the

prefault voltages obtained from the measurements and simula-

tions at the monitored bus.

The new average errors are shown in Table IV. The average

magnitude error for the ATP results decreased from 0.050 to

0.044 p.u. However, the average error for the short-circuit cal-culation program is still 0.050 p.u.

Fig. 3. Sag magnitude error obtained by using the ATP after considering pre-fault voltage adjustment.

Fig. 4. Sag magnitude error obtained by using the short-circuit program afterconsidering prefault voltage adjustment.

The error variation on the voltage magnitude obtained using

the ATP is shown in Fig. 3. With most of the monitored buses,

the error is lower when the results are normalized using the pre-

fault voltages. On the other hand, the results obtained using the

short-circuit calculation program do not show the same favor-

able trend. Just half of the monitored buses improved the results

when the sag magnitude was normalized considering the pre-

fault voltage, as shown in Fig. 4. However, the average errors

are still below 5% at most of the buses.

The sag magnitude adjustment is partial because the prefault

voltage at the fault location is not known. Therefore, there is still

a considerable error in the simulations according to Table IV.

However, the error could be a consequence of other variables,

such as the exact fault location and the fault impedance.

C. Adjusting Fault Location

The fault location is calculated by the distance relays used for

the line protection. The utility stated that the maximum error of

the fault location is lower than 5% of the line length. This result

was obtained comparing the actual fault location and the loca-

tion given by the relays. Therefore, it was decided to repeat the

simulation considering two new fault locations at the boundaries

of the uncertainty interval.

The sag magnitude errors obtained for the new fault locationsare presented in Figs. 5 and 6 for the ATP and the short-circuit


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Fig. 5. Sag magnitude error obtained by using the ATP after considering a 5%variation of the fault location.

Fig. 6. Sag magnitude error obtained by using the short-circuit program afterconsidering a 5% variation of the fault location.

program, respectively. The variations are related to the average

error found at each bus, estimated using (3).

In order to analyze the sag magnitude error variation at a spe-

cific bus for each simulated event, the results obtained at the bus

P6 are shown in Fig. 7. The event 2 is the one with the largest

error and also the one whose error varies most when the fault lo-

cation is adjusted. The initial error was 0.64 p.u. and for the new

fault location the error reduced to 0.32 p.u. This large variation

is a consequence of the close location of the event 2 from the

bus P6. After adjusting the fault location, the error obtained in

the calculated sag magnitude is still considerable large. There-fore, another variable such as the fault impedance is suspected

of influencing this result.

D. Adjusting Fault Impedance

The consideration of a 0- fault impedance for the base case

affects the sag magnitude, especially for systems where large

fault impedances are common. Therefore, the sag magnitude es-

timation must be redone considering other values of fault imped-

ances. According to the utility experience, another three values

are simulated (5, 25, and 40 ).

In order to quantify the influence of the fault impedance, the

sag assessment is repeated for the 17 faults to ground consid-ering the aforementioned fault impedances.

Fig. 7. Sag magnitude error obtained at P6 by using the short-circuit programafter considering a 5% variation of the fault location.

Fig. 8. Sag magnitude error obtained by using the ATP after considering var-ious fault impedances.

Fig. 9. Sag magnitude error obtained by using the short-circuit program afterconsidering various fault impedances.

The average magnitude errors acquired at each bus are

plotted in Figs. 8 and 9. The simulations are done considering

the former fault location provided by the utility.

The results indicate that at most of the buses, the magnitude

error increases in negative values when larger fault impedances

are chosen. This was expected, because the simulated sag mag-

nitude increases when the fault impedance increases according

to (3). The fault impedance, that minimizes the error, could bethe best choice to simulate fault for forecast purposes.


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Fig. 10. Sag magnitude error obtained at P6 by using the short-circuit programafter considering various fault impedances.

TABLE VMAGNITUDE ERROR FOR ATP

Note: the average error and std. deviation are calculated considering theabsolute value of the errors.

TABLE VIMAGNITUDE ERROR FOR FAULT CALCULATION PROGRAM

Note: the average error and standard deviation are calculated considering the


The magnitude error for each voltage sag obtained at P6 is

shown in Fig. 10 for several fault impedances. It is interesting

to see that the event 2, the one that presented the largest mag-

nitude error, is the one that presents the largest variation whenhigher values of fault impedance are considered. The error is

minimized when a fault impedance of 40 is considered. This

is a consequence of the close fault location to the bus P6; there-

fore, the sag magnitude at P6 for the event 2 is highly sensitive

to the fault impedance used in the simulation.

E. Summary for Sag Magnitude

The error of the sag magnitude is summarized in Tables V and

VI for each of the analyzed cases for the ATP simulation and the

short-circuit calculation. The sag magnitude error for the base

case was presented before in Table II and the sag magnitude

error after the adjustment of prefault voltage was presented inTable IV.

TABLE VIIVOLTAGE SAG FREQUENCY FOR DIVERSE THRESHOLDS

Both the average of absolute errors and standard deviation

are slightly sensitive to variations of the fault location and fault

impedance. Moreover, the ATP and the short-circuit program re-

sults show the same trend. In addition, both of them presented

better performance for magnitude estimation when the fault lo-

cation is shifted by 5% (i.e., when the fault location is shifted

towards the bus called From) in Table IX.

The fault impedance greatly influenced the estimated sagmagnitude when the fault was located close to the observation

bus. When the fault impedance is increased the maximum

magnitude error becomes lower. However, the average absolute

error increases. Therefore, it is not possible to improve all the

results by changing the fault impedance. The impedance value

that minimizes the error for the system could not be the same

as the one that minimizes the error at each monitored bus.

V. SAG FREQUENCY VALIDATION

A. SARFI-X Error-Base Case

The sag frequency index is deterministically calculated for

each of the monitored buses. Three different sag thresholds are

chosen (0.85, 0.70, and 0.50 p.u.). The system indices for the

three sag thresholds are shown in Table VII. The results indi-

cate that the ATP overestimates the number of sags for the three

analyzed thresholds, whereas the fault calculation program es-

timates a number of sags closer to the actual values.

The sag frequency as a function of the sag magnitude

obtained by the two simulation approaches and by the mea-

surements is shown in Fig. 11. The frequency error is rather

small for sags with magnitude below 0.45 p.u. After that

the frequency deviation is to some extent proportional to the

number of sags. It seems that the frequency deviation is mainlydue to line-to-ground faults that give more shallow sags. The

deviation may be due to an inaccurate representation of the

zero sequence impedance.

TheSARFI-85% obtained for each bus is presented in Fig. 12.

In some buses, the simulated sag frequency diverges consider-

ably from the measured one. One of the reasons of the high di-

vergence is that the selected threshold (0.85 p.u.) is in the region

where most of the sags are located, as shown in Fig. 11. Conse-

quently, for this threshold, the largest variations are expected.

B. Adjusting Prefault Voltage

After the sag magnitudes have been adjusted by the pre-fault voltage, the sag frequency is again estimated. The new


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Fig. 11. Cumulative frequency of sags.

Fig. 12. SARFI-85% for each monitored bus.

Fig. 13. SARFI-85% for each monitored bus after prefault voltage adjustment.

SARFI-85% obtained for each of the monitored buses is pre-

sented in Fig. 13.

The adjustment of the prefault voltage keeps the tendency

of the base case: The simulated sag frequency is greater than

the measured one, with the exception of bus P6. But this bus

presented a larger actual frequency than the simulated one for

the base case.

The sag frequency at the bus P7 is the same for simulations

and measurement after the adjustment. On the other hand, the

sag frequency at the bus P12 obtained after the adjustment is

not as good as the base case result.

C. Adjusting Fault Location

As shown in Figs. 14 and 15, the variation of the fault loca-

tion in 5% has a small influence on the calculated sag fre-quency. For example, at the buses P1, P2, P3, the sag frequency

Fig. 14. SARFI-85% for each monitored bus after the fault location has shifted0 5%.

Fig. 15. SARFI-85% for each monitored bus after the fault location has shifted+ 5%.

increased in one sag when the fault is shifted 5% for the ATP

and the short-circuit calculation. In this case, the new simulated

sag frequencies diverge more from the actual frequencies than

in the base case.

D. Adjusting Fault Impedance

The influence of the fault impedance on the calculated sag

frequency is shown in Figs. 16 and 17 for the impedance values

of 25 and 40 , respectively. The number of sags obtained by

simulation is reduced when the fault impedance increases, as

expected. A fault impedance of 40 improves the sag frequency

index in most of the buses. However, in some buses, such as P4

and P6, considering larger fault impedance increases the error

of the index. .

The fault impedance is a random variable. However, it would

be better to choose an impedance value to perform the voltage

sag calculations. The analysis performed permits the calibration

of the simulation programs to minimize the errors. Nevertheless,

the adjusting is not unique. The values of fault impedance that

minimize the error for a given bus can be different from the

values that minimize the error at another bus or at a system level.

The choice of the right fault impedance is mostly affected by the

fault distance from the monitored bus.

E. System Frequency Index

The frequency index estimated for the whole system is pre-sented in Table VIII for the several simulated cases: prefault


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Fig. 16. SARFI-85% considering a fault impedance of 25 .

Fig. 17. SARFI-85% considering a fault impedance of 40 .

TABLE VIIISARFI-85% FOR THE SEVERAL SIMULATED CASES

voltage adjustment, fault location shifted, and other values of

fault impedance.The system frequency index estimated by simulation over-

estimates the actual value for most of the simulated scenarios.

Moreover, the best result for the system index is found by the

short-circuit program for a fault impedance of 25 .

VI. CONCLUSION

In order to estimate the accuracy of voltage sag magnitude

and frequency obtained by an electromagnetic transient pro-

gram and a short-circuit calculation program, the result of a

six-month sag survey was compared with the simulation of the

faults recorded during this period.

Voltage sag magnitudes obtained from the simulations are ingeneral very close to the measured ones. In more than 90% of

TABLE IXLIST OF REGISTERED FAULTS

the simulated cases, the error of the sag magnitude is lower than

10%. The few cases that presented large errors are due to faults

located near the monitored bus where the exact fault location

and fault impedance significantly affect the calculated sag mag-

nitude.The simulated frequency is larger than the actual one at most

of the monitored buses. The prefault voltage and the exact fault

location do not have a great impact on the simulated frequency.

However, the fault impedance has large influence on the sim-

ulated sag frequency, particularly for the events that happened

close to the observed buses.

We conclude that for the study case the simulations agreed

well with the measurements. However, the choice of the right

values for the fault characteristics is still a great challenge when

running simulations.

The choice of an electromagnetic transient program or a

short-circuit calculation program depends on the kind of study.

The short-circuit program is recommended for stochastic calcu-lation of the system performance, because of simpler equipment


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modeling and faster computation algorithm. In contrast to that,

the ATP is advisable for the detailed study of individual events.

APPENDIX

The recorded faults are listed in Table IX. These faults have

been simulated to estimate the voltage sag characteristics.

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bility,in Proc.9th IEEE Power Eng.Soc. Int. Conf. HarmonicsQualityof Power, Orlando, FL, Oct. 2000, vol. 3, pp. 784789.

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[14] CAUE-Comite Argentino de Usuarios de EMTP-ATP, ATPRuleBook. Buenos Aires, Argentina, Dec. 2001.

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Jos Maria Carvalho Filho received the M.Sc. and D.Sc degrees in electricalengineering from the Itajub Federal University, Itajub, Brazil, in 1996 and2000, respectively.

Currently, he is AssociateProfessor at Itajub Federal University anda PowerQuality Study Group Member. His fields of interest include voltage sags and

other power-quality issues. He is also a Specialized Consultant in industrialplanning.

Roberto Chouhy Leborgne (M01) received the electrical engineering degreeand the M.Sc. degree in electric power engineering from the Itajub FederalUniversity, Itajub, Brazil, in 1998 and 2003, respectively, and the Ph.D. degreefrom Chalmers University of Technology, Gothenburg, Sweden, in 2007.

He was with ABB-Daimler Benz Transportation Brazil, So Paulo, andTeyma Abengoa, Montevideo, Uruguay. Currently, he is an Associate Re-searcher at the Federal University of Rio Grande do Sul, Porto Alegre, Brazil.His fields of interest include power quality and power system planning andoperation.

Jos Policarpo G. de Abreu was born on Madeira Island, Portugal, in 1952. Hereceivedthe B.S.E.E. and M.Sc. degrees fromthe Itajub Federal University, Ita-

jub, Brazil, in 1975 and 1980, respectively, and the D.Sc. degree in electricalengineering from the University of Campinas, Campinas, Brazil, in 1991. From1999 to 2000, he attended a postdoctoral program at Worcester Polytechnic In-stitute, Worcester, MA, on leave from the University of Campinas.

He is a Full Professor at the Itajub Federal University, where he also is thePower Quality Study Group Coordinator. His research interests include power-quality issues, such as power definitions, harmonics, imbalance, and voltagesags. induction motors, transformers, and converter transformers.

Eder G. C. Novaes was born in So Paulo, Brazil, in 1981. He received theB.S.E.E. and the M.Sc. degrees from the Itajub Federal University, Itajub,

Brazil, in 2003 and 2007, respectively.He is currently with Petrobras, Rio de Janeiro, Brazil. His fields of interestinclude power quality, electrical transients in power systems, and stability ofelectric power systems.

Math H. J. Bollen (F04) receivedthe M.Sc. and Ph.D.degreesfrom EindhovenUniversity of Technology, Eindhoven, The Netherlands, in 1985 and 1989, re-spectively.

Dr. Bollen is Manager of EMC and Power Quality at STRI AB, Ludvika,Sweden, and Guest Professor at EMC-On-Site, LuleUniversity of Technology,Skellefte, Sweden. Before joining STRI in 2003, he was a Research Asso-ciate at the Eindhoven University of Technology from 1989 to 1993; Lecturer atthe University of Manchester Institute of Science and Technology, Manchester,U.K., from 1993 to 1996; and Professor in Electric Power Systems at ChalmersUniversity of Technology, Gothenburg, Sweden, from 1997 to 2003. His re-search interests cover a wide range of power system issues, with a special em-phasis on power quality and reliability. He has published a number of funda-mental papers on voltage dip analysis and two textbooks on power quality. Heis active in several IEEE, CIGRE, and IEC working groups on power quality.

Comparative Analysis Between Measurements and Simulations of Voltage Sags

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