International Journal of Engineering, Science and Technology
Vol. 3, No. 11, 2011, pp. 35-45
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Comparison of metal oxide surge arrester models in overvoltage studies
André Meister1, Rafael Amaral Shayani2, Marco Aurélio Gonçalves de Oliveira2
2
1
Agência Nacional de Energia Elétrica (ANEEL)
Universidade de Brasília - Faculdade de Tecnologia - Departamento de Engenharia Elétrica – Campus, Universitário Darcy Ribeiro,
Caixa Postal 4386, 70.910-900 - Brasília – DF, BRAZIL
*
Corresponding Author: e-mail: mago@unb.br, Tel: +55 61 3107-5580; fax: +55 61 3107-5590,
andremeister@aneel.gov.br(André Meister), shayani@unb.br (Rafael Amaral Shayani)
Abstract
During switching or lightning overvoltages, surge arresters play an important role in limiting voltage levels and protecting
substation equipment, by conducting the excess of current in the system, which would otherwise damage the equipment. This
paper aimed to conduct a technical comparison of the models developed to represent the frequency dependent characteristic of
metal oxide surge arresters. The scope of this paper also included outlining a methodology to choose which one would be more
appropriate. The major contribution of this article is the validation of the models in a typical 500 kV substation insulation
coordination study.
Keywords: Overvoltages, Insulation Coordination, Surge Arrester, Surge Arrester Modeling, ATP program
DOI: http://dx.doi.org/10.4314/ijest.v3i11.4S
1. Introduction
The substitution of silicon carbide arresters for metal oxide surge arresters has brought benefits to overvoltage protection.
However, there is the demand for the development of a model to be used in transient overvoltage and switching surge studies. The
ATP – Alternative Transients Program – program allows the modeling of this non-linear resistance through the ZnO Fitter routine
and the Type 92 card (ATPRB, 1997). Laboratory test data of metal oxide arrester discharge voltage and current have indicated
that the arrester has dynamic characteristics that are significant for studies involving fast front surges, which are not well
represented by the ATP model previously mentioned. Technical data show that for fast front surges, with rise time less than 8µs,
the voltage waveform peak occurs before the current waveform peak and the residual voltage across the arrester increases as the
time to crest of the arrester discharge current decreases. The increase could reach approximately 6% when the front time of the
discharge is reduced from 8µs to 1.3µs. According to the IEEE Working Group 3.4.11 (1992), this peak can reach up to 12%. It
may be pointed out that the voltage across the arrester is not only a function of the magnitude of the discharge current, but is also
dependent on the rate of increase. This fact is particularly important in lightning studies. Several models, at different voltage
levels, have been proposed to represent the frequency dependant characteristic of metal oxide surge arresters. The model proposed
by the IEEE Working Group, although having the purpose of finding a mathematical model that adequately reproduces these
effects without requiring excessive computing time, uses a trial and error procedure. Besides, it is necessary to have physical
parameters (e.g. overall height, block diameter, column numbers), which makes modeling more difficult. Other models have been
developed, but adjustment of parameters, in some cases, requires iterative procedures. Furthermore, the technical data necessary
for other models are not always easily obtained through catalogues.
The purpose of this study was to conduct a comparison of surge arrester models. The results show that all models have similar
performance when subjected to fast front surges. Conclusions are drawn that will help the selection of which model would be more
appropriate for each kind of study. Lastly, the advantages, disadvantages, and errors associated with the use of these models are
mentioned. The main innovation of this paper lies in the technical comparison of the models with the results of an insulation
coordination study in a typical 500 kV substation subjected to fast-front surges. To be represented in the ATP, each piece of
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Meister et al. / International Journal of Engineering, Science and Technology, Vol. 3, No. 11, 2011, pp. 35-45
equipment was modeled for high frequency studies. The substation was modeled with distributed parameters through their
impedance surge, time travel and length. The transmission lines were modeled through their surge impedance. All other equipment
was modeled for the phenomenon studied. Lastly, the surge arresters models were introduced in the study for analysis.
2. Surge arrester models
2.1 The ATP conventional model
In the ATP Program, despite the existence of many types of surge arrester models, the exponential non-linear resistive device is
the most widely used. The voltage-current characteristic is represented by several exponential segments, each one defined by eq.
(1).
⎛ v
i = p⎜⎜
⎝ Vref
⎞
⎟⎟
⎠
q
(1)
In this equation q is the exponent, p is a multiplier, and Vref is an arbitrary reference voltage that normalizes the equation and
prevents numerical overflow during exponentiation. The first segment of the device is linear which speeds up the simulation. The
second segment is defined by parameters p, q and a minimum voltage level. When the voltage across the surge arrester reaches a
predefined minimum level, the algorithm tries to find a solution to the equation. The more exponential the model, the more precise
are the results. The simulation of this model shows that for fast front surges, the peak voltage and current occur at the same time.
Therefore it is not suitable to represent phenomena which are frequency dependent.
2.2 The Tominaga et al. model
The aim of having a frequency dependent model, based on the desired behavior between voltage and current, led to a model with
a varistor in series with an inductance (Figure 1) (Tominaga et al., 1979). Although the voltage across the device increases with a
higher current level, this model is a good approximation to specific situations. For example, a chosen inductance for the model
could produce accurate results for an 8µs surge front. But for a 2µs surge front, the results would not be satisfactory.
Figure 1 – The Tominaga et al. model.
2.3 The Kim et al. Model
As in the previous model, a non-linear inductance was placed in series with the varistor (Figure 2) (Kim et al., 1996). The major
problem with this model is that it is necessary to build a program to calculate the non-linear inductance. Furthermore, many
voltage-current points are necessary to represent the surge characteristic.
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Figure 2 – The Kim et al. model.
2.4 The IEEE Model
Due to the difficulties pointed out, it was necessary to study frequency dependent models. This was the task of the IEEE
Working Group [2], that developed a model in which the non-linear V-I characteristic is represented by two sections of non-linear
resistances designated by A0 and A1. These resistances are separated by a R-L filter (Figure 3).
Figure 3 – The IEEE Model.
For slow front surges, the R-L filter has very little impedance and the two non-linear sections of the model are essentially in
parallel. For fast front surges, the impedance of the R-L filter becomes more significant. This results in more current in the nonlinear section designated by A0, than in the section designated by A1. Since characteristic A0 has a higher voltage for a given
current than A1, the result is that the arrester model generates a higher voltage. This model yields good results for arrester
discharge voltages, when the discharge current has a time to crest within the range of 0.5 to 45µs. The major problem associated to
it is to calculate its parameters. The IEEE Working Group suggests an iterative method, where adjustments are necessary to
achieve better results. The initial parameters depend on electrical and physical data.
2.5 The Mardira and Saha model
The IEEE model was simplified, the resistive devices were eliminated, and another way to define the parameters was chosen
(Figure 4) (Mardira and Saha, 2011). The authors state that the model yields good results for a current discharge with a 8x20µs
waveform, and does not require an iterative process. However, it does not work properly for a wide variety of waveforms.
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Figure 4 –The Mandira et al. model.
2.6 The Pinceti and Giannettoni model
In this model, also derived from the IEEE Model, all necessary data are easily collected in datasheets, there is no need for an
iterative correction of the parameters, and the model’s performance is accurate (Figure 5) (Meister and Oliveira, 2005a). Besides,
the capacitance is eliminated, and only electrical parameters are used. The two parallel resistances are substituted for only one, in
order to avoid numerical overflow.
Figure 5 –The Pinceti and Giannettoni model.
2.7 The Fernandez and Diaz model
The IEEE Model is also simplified in this model (Fernandez and Diaz, 2001). This time, both electrical and physical parameters
are necessary. The two parallel resistances are substituted for only one, in parallel to a capacitance, and one of the inductive
devices is eliminated (Figure 6). The relation between the A0 and A1 currents and their voltages must necessarily be 0.02 (Pinceti
and Giannettoni, 1999).
Figure 6 – The Fernandez and Diaz model
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3. Model implementation
The Conventional, IEEE, Pinceti and Giannettoni, and Fernandez and Diaz models were compared using the ATP Program,
version ATPDRAW 3.9. The other models were not considered, because their results were not accurate. The waveforms used in
the simulations were developed with the ATP Source 15 – Heidler Type. The use of Source 13 does not demonstrate the frequency
dependent characteristic, where the current signal leads the voltage signal. The other sources available did not allow correct
modeling of the desired waveform. To compare models, the ABB EXLIM P-E - 444 kV varistor was used (Table 1). The model
parameters are shown in Table 2.
Rated
Voltage
[kV]
Table 1 – Technical data for ABB EXLIM P-E - 444 kV
Max. Residual Voltage (kVcrest)
Nominal
Discharge
Height
30x60µs
8x20µs
1x(2-20)µs
Current
[mm]
0.5
1.0
2.0 5.0
10
20
10
[kAcrest]
[kA]
[kA] [kA] [kA] [kA] [kA]
[kA]
444
20
4500
866
897
920
Table 2 – Model parameters
L1[µH]
L0[µH]
IEEE
21,75
0,29
Pinceti and Giannettoni
0,487
0,162
Fernandez and Diaz model
0,69
Model
960
1015 1111
R1[Ω]
94,25
1000
1000
R0[Ω]
145
-
1106
C[pF]
68,97
228,3
4. Simulation results
Results for both the 8x20µs waveform with 10 kA, and the 1x2µs waveform with 10 kA are shown in Table 3, together with the
error as compared to datasheet values (Table 1). Time to voltage crest for each model is presented in Table 4. The response of each
model to waveforms 8x20µs with 10 kA, and 1x2µs with 10 kA are shown in Figures 7 and 8 respectively. Figures 9, 10, 11, and
12 present their dynamic behavior.
Model
Conventional
IEEE
Pinceti and Giannettoni
Fernandez and Diaz model
Table 3 – Models Comparison - Voltage results
8x20µs - 10 kA
Voltage [kV]
1028.77
1023.62
1017.60
998.25
Error
1.30 %
0.84 %
0.25 %
1.60 %
1x2µs - 10 kA
Voltage [kV]
Error
1028.77
7.5 %
1158.03
4.7 %
1121.57
1.4 %
1137.79
2.8 %
Table 4 – Models Comparison – Time to voltage crest.
Time to crest (µs)
Model
8x20µs – 10 kA
1x2µs – 10 kA
Conventional
8.00
1.00
IEEE
6.00
0.57
Pinceti and Giannettoni
6.19
0.53
Fernandez and Diaz
5.58
0.46
model
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Meister et al. / International Journal of Engineering, Science and Technology, Vol. 3, No. 11, 2011, pp. 35-45
Figure 7 – Response to waveform 8x20µs – 10 kA
Figure 8 – Response to waveform 1x2µs – 10 kA
Figure 9 – Dynamic behavior: Conventional Model.
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Figure 10 – Dynamic behavior: IEEE Model
Figure 11 – Dynamic behavior: Pinceti and Giannettoni Model
Figure 12 – Dynamic behavior: Fernandez and Diaz Model
This study shows that the Conventional Model does not represent the dynamic behavior of the varistor, since the current and the
voltage peak occur at the same time (Figures 7 and 8). Besides, the Conventional Model reaches the same voltage peak value for
both waveforms (Table 3). The IEEE Model presents a relatively higher error for faster front surges (Table 3). It also requires
physical data and an iterative method to determine the parameters. The occurrence of the voltage maximum before the current
maximum was observed in all models, except for the Conventional (Figures 7 and 8). The results of the Pinceti and Giannettoni
model are very accurate. Physical data are not needed, nor does it require the use of iterative methods, thus not needing much
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computational effort. The Fernandez and Diaz model also presents good results. The major difficulty associated to using this
model is determining the inductance value, which is related to curves between the waveform time to crest and the residual voltage.
5. Insulation coordination study
In order to study energy absorption, models were compared through an insulation coordination study in a typical 500 kV
substation (Figure 13), subjected to fast-front surges. The substation was modeled with distributed parameters through their
impedance surge, time travel and length; and the transmission line through its surge impedance (the value of 300 Ω was adopted) .
The transformer 525/440/13,8 kV was modeled as in Figure 13. All other equipments were adequately modeled for the phenomena
studied as described in Table 7. (Meister and Oliveira, 2005b).
Figure 13 – Substation diagram and Transformer model
The line is protected by the ABB EXLIM P-E - 444 kV varistor (Table 1), while the transformer is protected by the ABB
EXLIM T - 420 kV varistor (Table 5). Surge arrester models were introduced to represent both varistors and their parameters are
shown in Table 6. The surge current was injected from the line using the ATP Source 15 and Table 8 presents the results, while
Figures 14 to 16 present energy waveforms.
Rated
Voltage
[kV]
420
Table 5 – Technical data for ABB EXLIM T- 420 kV
Max. Residual Voltage (kVcrest)
Nominal
Discharge
Height
30x60µs
8x20µs
1x(2-20)µs
Current
[mm]
0.5
1.0
2.0 5.0
10
20
10
[kAcrest]
[kA]
[kA] [kA] [kA] [kA] [kA]
[kA]
20
4500
807
830
846
888
Table 6 – Surge Arrester model parameters
L1[µH]
L0[µH] R1[Ω]
IEEE
6,90
0,1472
47,84
Pinceti and Giannettoni
1,30
0,433
1000
Fernandez and Diaz model
0,56
1000
Model
924
R0[Ω]
73,6
-
Table 7 – Other equipments parameters modeling
Equipment
C[nF]
Potencial transformers
5,00
Circuit breakers
0,10
Switches
0,10
Current transformers
0,50
Reactors
4,00
998
C[pF]
73,60
1358,6
998
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Table 8 – Insulation coordination study results.
Varistors
Model
Line
[kV]
Line
[J]
Transformer
[kV]
Transformer
[J]
IEEE
1344.25
3.62E+04
1003.84
1.15E+05
Pinceti and Giannettoni
1352.76
4.83E+04
984.99
1.06E+05
Fernandez and Diaz
1355.08
4.86E+04
989.52
1.06E+05
Figure 14 – IEEE Model
Figure 15 – Pinceti and Giannettoni Model
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Figure 16 – Fernandez and Diaz Model
The results show that in the voltage level studies, the highest difference was approximately 2% between the models. In the
energy absorption studies, the Fernandez and Diaz as well as Pinceti and Giannettoni models seemed to yield more conservative
results.
6. Conclusions
In this study, a simulation of the dynamic behavior and energy absorption of metal oxide surge arrester models was conducted.
Model implementation was performed with the Alternative Transients Program. All data necessary for the models was found in
manufacturers’ catalogues, although this was not always a simple task. Regarding dynamic behavior, the Conventional model was
the only one that did not work properly, with the current and voltage peaks occurring at the same time. The comparison with
manufacturers’ data showed that the IEEE, Pinceti and Giannettoni as well as Fernandez and Diaz models acceptably reproduced
the peak voltages. In the insulation coordination study the Pinceti and Giannettoni as well as Fernandez and Diaz models presented
more conservative results regarding the energy absorption, but the difference between these results was insignificant. For the
overvoltage coordination studies, all three models were appropriate.
References
Alternative Transient Program Rule Book (ATPRB), 1997. Can/Am EMTP User Group,USA.
Fernandez F., Diaz R., 2001. Metal-oxide surge arrester model for fast transient simulations , paper 144, International Conference
On Power System Transients, IPST’01, 20 -24 June.
IEEE Working Group 3.4.11, 1992. Modeling of metal oxide surge arresters, IEEE Transactions on Power Delivery, Vol. 7, No. 1,
pp. 302-309.
Kim, I., FunabashI, T., Sasaki, H., HagiwarA, T. Kobayashi, M. 1996. Study of ZnO arrester model for steep front wave, IEEE
Transactions on Power Delivery, Vol. 11, No. 2, pp. 834-841.
Mardira, K. P., Saha, T. K., 2011. A simplified lightning model for metal oxide surge arrester , The University of Queensland –
Austrália, Downloaded September 2011.
Meister, A., Oliveira, M.A.G. 2005a. Modeling ZnO arrestes in ATP for surge protection - Comparação da representação de
varistores de Óxido de Zinco no programa ATP para proteção contra sobretensões, XVIII SNPTEE, Curitiba, Paraná, Brasil,
Outubro.
Meister, A., Oliveira, M.A.G. 2005b. Modeling ZnO arrestes in ATP for inslulation coordination studies – Modelagem de
varistores de Óxido de Zinco para estudos de coordenação de isolamento . Dissertação de Mestrado em sistemas elétricos de
potência – Universidade de Brasília . PPGENE.DM-245A/05.
Pinceti, P., Giannettoni, 1999. M., A simplified model for zinc oxide surge arresters, IEEE Transactions on Power Delivery, Vol.
14, No. 2, pp.393-398.
Tominaga, S., Azumi, K., Shibuya, Y., Imataki, M., Fujiwara, Y., Nichida, S., 1979. Protective performance of metal oxide surge
arrester based on the dynamic v-i characteristics , IEEE Trans. Power App. Syst., Vol. PAS-98, pp. 1860-1871.
Biographical notes
Andre Meister was born in Brasilia, Brazil, on October 8, 1976. He received the Bachelor of Electrical Engineering degree and the M.Sc. degree from the
University of Brasília, Brasília, Brazil, in 2000 and 2005, respectively. His professional experience includes Marte Engenharia (Brasil), where he was involved with
load flow, stability, and electromagnetic transient studies. He joined the Agência Nacional de Energia Elétrica (National Regulatory Agency) in 2005, where he
works with electrical transmission systems. His areas of interest include transmission systems and electromagnetic transient studies.
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Meister et al. / International Journal of Engineering, Science and Technology, Vol. 3, No. 11, 2011, pp. 35-45
Rafael Anaral Shayani was born in São Paulo, Brazil, on March 25, 1976. He received the Bachelor of Electrical Engineering degree from the Polytechnical
School of the University of São Paulo, São Paulo, Brazil, in 1998 and the M.Sc. and Ph.D. degrees from the University of Brasília, Brasília, Brazil, in 2006 and
2010, respectively. His professional experience includes the Centro de Gestão e Estudos Estratégicos (Brazil), where he worked as a consultant in the Prospective
Study on Photovoltaic Energy, and Johnson Controls, where he supervised electric power and air conditioning systems. He joined the University of Brasilia in
2009, where he teaches a course on energy conversion. His areas of interest include electrical power systems, renewable energy sources, power quality, and energy
efficiency. Mr. Shayani received an award at the 8th Brazilian Energy Congress in 1999, presenting a comparison on overall costs between energy production from
hydroelectric power plants and from natural gas plants.
Marco Aurélio Gonçalves de Oliveira was born on December 20, 1958, in Rio de Janeiro, Brazil. He received the Bachelor of Electrical Engineering degree from
the University of Brasília, Brasília, Brazil, in 1982 and the M.Sc. and Ph.D. degrees from the University of Paris, Paris, France, in 1989 and 1994, respectively.
From 1982 to 1988, he was with the Operation Division of Eletronorte (Brazil) where he was involved with load flow, stability, and electromagnetic transient
studies. He joined the University of Brasilia in 1994, where he was the Head of the Department of Electrical Engineering from 2006 to 2010. His research interests
include power electronics, power quality, renewables, and energy efficiency. He has published over 50 papers in those fields. Dr. de Oliveira was Chair of the IEEE
Brasilia Section and IEEE Brazil Council.
Received September 2011
Accepted October 2011
Final acceptance in revised form October 2012