comparative analysis between measurements and simulations of voltage sags
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8/14/2019 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: jmaria@unifei.edu.br; polica@unifei.edu.br).
R. C. Leborgne is with theFederal University of RioGrande doSul (UFRGS),Porto Alegre 90035-190, Brazil (e-mail: rcl@ece.ufrgs.br).
E. G. C. Novaes is with Petrobras, Rio de Janeiro 01311-936, Brazil (e-mail:egcnovaes@yahoo.com.br).
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 math.bollen@stri.se).
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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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
absolute value of the errors.
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
absolute value of the errors.
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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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.
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