The effect of repetitive swells on metal-oxide varistors 

Eric S. Lagergren, François D. Martzloff, Mark E. Parker, and Susannah B. Schiller 

National Institute of Standards and Technology 

Gaithersburg MD 

Reprinted from Proceedings, PQA’92, Atlanta GA, 1992 

Significance - 2004 perspective

Part 7 – Protection techniques 

Neither the effects of repetitive swells on metal-oxide varistors, nor the natural occurrence of swells had

been documented in the literature. The paper briefly described a laboratory system capable of generatin

arbitrary swells and applying them to test varistors. A statistical experiment on five lots of varistors was 

performed and preliminary results reported. Effects of amplitude, duration, and number of swell 

occurrences were assessed, using as a criterion the change in varistor nominal voltage from before to 

after the swell sequence. 

The factors that affect the varistor response are the amplitude of the swell, the duration of the swell, and 

the number of swells experienced in the life of the varistor. It seems that failure by thermal runaway 

occurs quickly when amplitude or duration settings are large. Failure caused by gradual aging (the 

classic 10% limit quoted by industry) appears to require a larger number of swells than those applied in 

the experiments conducted in what was intended to be the first phase of a continuing investigation. 

While these results suggested an agenda for additional research, limited NIST resources prevented 

further investigation of the effect. We had encouraged all interested parties to contribute support and 

share new information on the subject, which might have provided significant information on the issue of 

varistor ageing/degradation, for which industry consensus has not yet been reached fourteen years later
THE EFFECT OF REPETITIVE SWELLS ON METAL-OXIDE VARISTORS 

Eric S. Lagergren, Franpis D. Martzloff, Mark E. Parker, and Susannah B. Schiller 

National Institute of Standards and Technology t 

Gaithersburg MD 

Absaact - Neither the effects of repetitive swells on metaloxide varistors, nor the natural occurrence of swells have 

been documented in the literature. The paper briefly 

describes a laboratory system capable of generating 

arbitrary swells and applying them to test varistors. A 

statistical experiment on jive lots of varistors has been 

performed and preliminary results are reported. Effects of 

amplitude, duration, and number of swell occurrences are 

assessed, using as a criterion the change in varistor nominal 

voltage from before to after the swell sequence. 

MOTIVATION FOR A SWELL EFFECTS STUDY 

Metal-oxide varistors, introduced in the early seventies, 

are used extensively as the basic nonlinear element in surge 

arresters built for electric utilities and in 'surge suppressors' 

for end-use protection at utilization voltage levels. These 

devices operate by diverting the surge currents away from 

sensitive loads and, in combination with the source 

impedance of the surge, limit the voltage across their 

terminals by a 'clamping' action. 'Ihus, the prime function 

of the varistor is to offer a low (but not zero) impedance to 

surge currents, resulting in power being dissipated in the 

bulk material. For surges of short duration, the peak power 

can be high but the total energy deposited in the material 

during one typical surge event remains low enough to keep 

the material from reaching excessive temperatures. 

Another power system disturbance involves a temporary 

overvoltage at the power frequency, with amplitudes ranging 

from a modest increase in the rms value of the line voltage 

to slightly less than twice the system voltage. The term 

'swell' was proposed to describe this type of disturbance, 

and has now received acceptance in the U.S. engineering 

community [ANSIAEEE C62.411. Occurrences of swells 

have not been documented as much as occurrences of 

surges, but some data are now being collected and published 

[Hairabedian, 19921. A question then arises on the possible 

effect that these small but long swells could have on varistor 

life, compared with the effect of short but large surges. 

It is known that conducting high-peak current surges can 

cause a progressive degradation of the varistor material. 

Manufacturers of electronic-grade varistors have recognized 

this process, and publish "Pulse Rating" charts describing the 

permissible stress [Harris, 19901. These pulse rating charts 

present a set of permissible values of the amplitude, 

duration, and number of occurrences of surges that will not 

produce a change of more than 10% in the varistor nominal 

voltage (the voltage corresponding to a 1 mA dc current). 

Figure 1 shows a typical Pulse Rating chart, with pulse 

duration, pulse amplitude and number of allowable pulses 

before the varistor reaches the criterion of expended rating, 

that is, a 10% change in characteristics. The surges are 

understood to be repeated events with cooling between 

repetitive surges. The surge duration covered by the charts 

provided by the manufacturers ranges from 20 to 10 000 ps, 

and amplitudes from a few amperes to a few kiloarnperes. 

Number of allowable pulses 

Figure 1 

Pulse Rating chart for 20-mm dia. metal-oxide varistors 

Armed with this information, designers can select a 

varistor size commensurate with the expected level of surges 

in the application of interest [Martzloff, 19851. Nevertheless, anecdotes of unexplained varistor failures occurring 

in the absence of accurately documented highenergy surges 

are sometimes heard. 

One of several possible explanations would be that 

varistors selected with a relatively low clamping voltage will 

be subjected to greater stress than varistors with a relatively 

higher clamping voltage when swells occur in the power 

system [Martzloff & Leedy, 19871. The effect of repetitive 

swells on the durability of varistors is not discussed in the 

information provided by manufacturers. 

Therefore, we speculate that an aging mechanism, 

perhaps similar to that characterized by the pulse rating 

charts, might be induced by the stress associated with 

repetitive swells applied during the lifetime of a varistor. 

Rather than identifying the aging mechanism itself - a task 

we leave to those versed in varistor physics or ceramics - we seek to establish an empirical but predictable relationship 

f Technology Administration, US. Depanment of Commerce 

Contributions of the National Institute of Staodards and Technology are not subject to U.S. Copyright 

C-13: 1 
between, on the one hand, the amplitude, the duration, and 

the number of occurrences of swells, and, on the other hand, 

the aging of the varistor. 

In keeping with the practice of the low-voltage varistor 

industry, we use as aging criterion the change in the nominal 

voltage. We have also considered measuring the power 

dissipation at the nominal ac system voltage, but have not 

implemented the more complex insertion of a wattmeter in 

the instrumentation sequence. 

Ultimately, the duration-amplitude-number relationship 

for repetitive swells might be described by extending the 

pulse rating lines of Figure 1 to longer durations and lower 

amplitudes, or by creating an entirely new chart. Our 

purpose is to provide an initial set of generic data, based on 

several brands of varistors, that might motivate varistor 

manufacturers to provide their customers with a complete set 

of data. Combii with results of ongoing powerquality 

surveys that will give new data on the occurrence of swells, 

these swell rating charts, just like the existing pulse rating 

charts, would result in greater reliability in the application of 

metal-oxide varistors. 

Another result of our work will be the in-house capability 

of producing wellcontrolled, arbitrary power-line 

disturbances of any waveform. These disturbances may be 

repetitive or single events. This capability will allow 

investigation of the effects of disturbances on end-user loads 

other than varistors. 

STATISTICAL EXPERIMENT DESIGN 

'Ihe objective of the experiment is to develop an 

empirical model relating the percentage change in nominal 

varistor voltage before and after application of the swell 

(called a response and labelled y) to the voltage amplitude 

(percentage above nominal line voltage), swell duration 

(number of cycles) and number of swells applied. The last 

three variables are called factors and are labelled xl, x2 and 

x3, respectively. The technique of statistically designed 

experiments [Box et al., 19781 was used to generate valid 

experimental data. This technique uses a systematic 

approach to generate data which yield unambiguous results 

at minimum cost and time. 

Changes in the varistor nominal voltage were measured 

in both the forward and reverse directions, giving nearly 

identical values. The analysis for the forward direction is 

presented here. The factor settings to be studied are given 

in Table 1. 

For each lot a 2x3~3 full factorial design was run. This 

design consists of all combinations of settings of the three 

factors and hence has 18 (= 2x3~3) runs. Since the test rack 

can hold 20 varistors, two control varistors (no applied 

swell) were also included from the same lot. 

Table 1 

Factors and settings for the first experiment 

The 20 varistors were randomly sampled from a given 

lot. This was done for each of the five lots and hence 5 lots 

containing 20 varistors each were run for a total of 100 

varistors in one experiment. The order of applied swell 

conditions was randomized to reduce the potential 

confounding of factor effects with any time drift effect 

Some care should be taken when making inference from 

these experiments to the population of varistors in the world. 

When we acquired approximately 200 varistors from each of 

5 manufacturers, neither the manufacturers nor the varistors 

themselves were chosen at random or to be representative of 

their respective populations. Rather, we were given what 

was readily available from local distributors. This approach 

was simply motivated by the idea of expanding the data base 

to more than one source and thus obtain more general 

information. An additional motivation was to avoid the 

possibility of working with specimens specially selected by 

the manufacturer. Had we wanted to make inference, we 

would have selected manufacturers at random from all 

varistor manufacturers, and we would have selected varistors 

at random from several lots from each manufacturer. 

Therefore, although the random sampling we do from our 

pool for each experiment allows us to make inference to the 

rest of the pool, we cannot safely make inference beyond 

that group in any formal way. 

i 

Amplitude 

(% above 

nornimal) 

Hits 

(Number of 

swells) 

Duration 

(Number of 

cvdes) 
IMPLEMENTATION OF A SWELL SYSTEM 

The swell system used in our experiment is a computercontrolled power source designed to deliver well-defined 

sinusoidal waveforms to large dynamic loads. A regulated 

ac source provides the nominal 120 V source to maintain the 

varistors energized before and after the swell. The swell 

itself is generated by momentary addition of a 60-Hz signal 

to a steady 60-Hz signal. The composite signal is then fed 

to a precision 3 kW amplifier capable of delivering 300 V 

swells at 10 A. Varistors to be tested are connected to the 

swell circuit by computer-controlled relays that apply the 

sequence of scheduled tests, including the initial and 

post-swell determination of the varistor nominal voltage. 

Th& software allows the operator to define, for a given 

group of specimens, the three characteristics of swells as 

they would be deftned by the results of power-quality 

sweys: voltage amplitude, duration, and number of 

occurrences. On the other hand, the stress in the varistors is 

likely to be associated with the current flowing in the 

varistor, which is determined by the relative voltage 

amplitude of the swell and the rated voltage of the varistor. 

Thus, the analysis of the experiment needs to focus on swell 

current rather than swell voltage. 

By characterizing the nominal voltage of each specimen 

varistor before applying the swell, the system can introduce 

a scaliig factor that will adjust the generated swell voltage 

to obtain a predictable swell current, which is the desired 

parameter for the statistical experiment. In this manner, the 

parameters of the experiment become the amplitude of the 

current (peak), the duration (number of cycles), and the 

number of applied swells (hits). 

Figure 2 shows a block diagram of the swell generator. 

Several distinct subsystems operate to produce the controlled 

swells: a master control computer, a steady-state voltage 

source, a swell source (signal generator and amplifier) and 

a relay-controlled test rack. 'Ihe parameters of the swell are 

recorded by instrumentation actuated by the controller. We 

give a brief description of the system, starting from the final 

stage - the varistors to be subjected to the swells and are 

installed in a test rack - and proceeding upstream to the 

other elements of the complete system 

Test rack and test sequence 

The test rack allows the connection of up to 20 varistors 

to the various power sources and instruments involved in the 

measurements and application of the swell(s). These 

connections are performed by a set of relays actuated by the 

master controller. The test sequence involves the several 

stages described below. Before starting the test sequence 

involving the varistors, a self-test sequence is performed by 

the controller. 

Figure 2 

Block diagram of the swell system 
A detailed description of the self-test sequence is beyond 

the scope of this paper. A brief description of the tests 

applied to the varistors will serve to provide information on 

the conditions of the experiment. The varistors, selected at 

random from one lot, are subjected to a steady 120-V stress 

that stabilizes their standby condition, an operation often 

called 'forming'. The varistors remain connected in parallel 

to the 120-V forming bus until subjected to a swell. The 

sequence, driven by the controller, unfolds as follows: 

Disconnect the varistors fiom the forming bus, one at a 

time, to insert the varistor in a circuit capable of 

injecting a controlled 1 mA dc current (both directions) 

through the varistor. Record the voltage across the 

varistor at a fied time after applying the dc voltage, to 

establish the nominal voltage of the varistor. Return the 

varistors to the forrning bus. 

Disconnect the varistor selected for swell application 

from the forming bus, and insert it in the swelling bus. 

Compare the measured nominal voltage of that varistor 

with the average nominal voltage of the five lots 

(obtained from an initial characterization of the 

varistors), and let the controller compute the adjusted 

value of the swell amplitude to be applied to that 

particular varistor. 

Apply the swell with the amplitude just computed, and 

a duration (number of cycles) preset by the experiment 

design Record and store (digitally) the instantaneous 

values of voltage across the varistor and current through 

the varistor, for later processing. 

As an option (selected before the test sequence is 

initiated), display on the computer monitor the voltage 

and current waveforms during the swell. 

If the experiment design calls for repeated swells, allow 

a faed cooling time for the varistor to return to room 

temperature (Figure 3), then apply the next swell called 

for by the experiment design. 

Reconnect the varistor just tested to the 120-V forming 

bus, and start the swell-application sequence for the 

next varistor. 

When the last swell of the sequence has been applied to 

the last varistor on the test rack, repeat step 2 to record 

the post-swell voltage at 1 mA dc. 

Using the data stored during the application of the 

swell, initiate the computation of several parameters of 

the swell event, such as total energy deposited in the 

varistor, total coulombs transferred, percent change in 

nominal voltage, etc. 

Initiate plotting of hard copies for the voltage and 

current during the swell, including the results of the 

computations, for each of the applied swells, followed 

by printing a summary sheet of the test data. 

Figure 3 

Temperature profile during repetitive swells 

Power sources 

Three power sources are involved in the swell system: the 

120-V fonning supply, the 1 mA dc source, and the swell 

generator. The 120-V ac fonning supply is provided by a 

commercial calibrator that supplies a regulated sinewave to 

the bus of the test rack. The fixed 1 mA dc used for the 

measurement of the nominal voltage is obtained from an 

operational amplifier with sufficient voltage compliance to 

drive the 1 mA current at the varistor nominal voltage. 

The swell generator consists of two parts: a pure 

sinewave generator and an arbitrary waveform generator that 

produces a sinewave of programmable value, synchronized 

with the fist generator. The two low-level (10 V) 

sinewaves are added, producing a swell waveform. A linear 

power amplifier delivers the voltage waveform at the desired 

level, that is, 120 V rms before and after the swell, and 

peaks up to 300 V during the swell. The power amplifier is 

capable of delivering up to 15 A of peak current as the 

varistor responds to the swell according to its power law 

characteristic 

I=AVa (1) 

where: 

V is the voltage applied to the varistor, 

I is the resulting current through the varistor, 

a is the characteristic exponent of the varistor material, and 

A is a factor reflecting the dimensions of the varistor disc. 

Instrumentation 

Figure 4 shows a typical recording of a swell as printed 

after the test (step 9 of the sequence). Note the 120-V rms 

sinevawe before and after the swell, and the occurrence of 

the swell at the peak of the 120-V waveform. The timing of 

the beginning of the swell has been selected at the 90" point 

of the wave to avoid an offset in the transient response of 

the output transformer of the amplifier. The current has the 

typical peaked waveform of the highly nonlinear response of 

a varistor to an applied sinewave voltage. The plot program 

includes identification of the test number and results of 

computation of the parameters of the varistor response. 
CODE: 6- 3 snaplng Factor: $000 

Full Scale Voltage: 5OOV Cnarge: 0.1290-0t Cnlilnmhs 

Full Scale Current' 10 Energy: 0.3240tOO Joules 

Figure 4 

Typical record of a swell 

The varistor current has a wide dynamic range, from 

milliamperes to amperes, while the desired plot has a linear 

scale. To obtain adequate resolution over this wide range, 

the instrumentation includes three current sensors in series, 

with a maximum range of respectively 50 mA, 500 mA, and 

20 A. Each sensor has a digital output channel which is fed 

to the fourchannel data acquisition card The controller 

detects any saturation in the output of a channel, and 

automatically selects the next higher level channel to 

perform the plot and the various computations. 

Controller functions 

The functions of the controller are performed by 'an AT 

type computer with an IEEE-488 interface to provide the 

waveform parameters to the swell generator, drive the 

sequencing relays of the test rack, receive inputs from the 

voltage and current sensors, and provide an output to drive 

a laser printer. 'Ihe controller is programmed in BASIC, 

providing a dialogue with the operator to select the test 

program according to the parameters def~ned in the statistical 

experiment design. This software allows a simple set of 

commands to initiate a test sequence that can involve tens of 

hours of test time for a set of 20 varistors to be subjected to 

many repetitive swells. The program also allows access to 

its 2000 lines of custom-developed code if modifications are 

desired in the options or operation of the system 

After a sequence has been completed, all the voltage and 

current data are stored in the hard-disk memory of the 

computer. These data can also be transferred onto diskettes 

so that the plotling can be performed through another 

computer with remote data processing capability. 

The controller wmputer is then free to run another 

experiment while the printing of the plots - which can take 

several horn for a sequence involving printing out 

individual records of repetitive swells -can proceed without 

interference. Thanks to this custom program, the result of 

a long evolution of hardware and software development, we 

have been able to perform overnight test sequences, 

unattended, and find the results ready the next day. Running 

an experiment on a group of varistors is almost anticlimactic 

after the long series of experiments that were conducted 

during the development of the system. Now that the system 

is fully operational, we intend to run several more 

experiments to explore the range of possible swells and their 

effects. So far we have been able to conduct one 

experiment on five sets of 20 varistors, and obtain 

preliminary data on a second set with parameters close to the 

failure point for some combinations, as described in the next 

section. 

ANALYSIS OF EXPERIMENTAL DATA 

The objective of this analysis is to develop an empirical 

model relating percentage change in varistor nominal voltage 

(y) to amplitude (x,), swell duration (x2) and number of 

swells (x3) and ultimately to identify swell conditions 

producing a 10% change in nominal voltage. Fit we 

perform a graphical analysis. Graphical analysis permits 

detection of outlying data points and allows the data to 

suggest an appropriate model form, rather than relying on 

prior assumptions about the form of the model. Once a 

candidate model is identified, the model is fit to the data by 

least squares regression meter et a]., 19901. The model is 

then validated This validation involves checking that the 

assumptions made in fitting the model are reasonable. If 

they are not, the model is modified in an appropriate fashion 

and the entire process is repeated. This analysis approach is 

used for each lot of varistors. We will describe in some 

detail the analysis for one lot and present a summary of the 

results for the remaining lots. 

Figure 5 gives a 3 x 3 matrix of scatter plots. The first 

row gives plots of nominal voltage change versus amplitude 

(xl), duration (x2) and number of swells (x3). In each plot 

the vertical lines cover the range of data at the given x 

setting and the lines intersecting these vertical lines connect 

the mean at each x setting. These plots are used for 

detecting outlying data and checking in advance the 

assumptions required for least squares regression. The four 

assumptions of least squares regression are: 

1. correct model 

2. constant variation in each set of measurements 

3. independent data 

4. normality of the residuals 
tl I 

32 64 

AMPLITUDE (% over nominal) 

2 4 8 16 32 

DURATION (Number of cycles) 

1 2 4 8 16 

HITS (Number of swells) 

Figure 5 

Plots of nominal voltage change versus factors (first row), log [voltage change] versus factors (second row) 

and log [voltage change] versus log [factors] (third row) 

The fourth assumption is required only for performing 

statistical inference (such as hypothesis tests, coddence 

intervals, ...). From the Fist row of plots in Figure 5 we can 

see that the second assumption is violated. Variation in 

nominal voltage change is not constant, in fact it increases 

as amplitude or duration or number of swells increases. 

There are several ways to correct this problem. One 

particularly effective way is to identify a transformation of 

the y's such that the transformed y's have constant variation. 

When the variation in y increases as x increases, a log 

transformation of the y's often yields transformed data 

having constant variation. The second row in Figure 5 gives 

plots of the log (y) versus the three factors. The log (y) 

clearly satisfies the assumption of constant variation better 

than the original y's. From this second row of plots we 

observe curvature in the plot of y versus amplitude and y 

versus duration (there were only two settings of number of 

swells, so curvature, even if present, cannot be detected). A 

second use of transformations is to simplify the relationship 

between the y's and x's. The bottom row gives plots of log 

(y) versus the log (x). We see that log (y) is now a linear 

function of log (xl) and log (xz). 'Ihese plots lead us to 

postulate the following empirical model 

where J = logb). (For lots C and E, y* = log(y+O.l), since 

a few small negative values were recorded.) The term & 

represents the combined effect of all x's that affect y, but 

have not been included in the model, and is called a 

"random error" tenn By taking the exponential of both 

sides of equation (2), we obtain equation (3), a power model 

in the original units, 

y = pg* x14 x2P2 X34 &* (3) 
where 

po* = epo 

and E* = eE. We fit the model (2) using least squares 

regression. 

Note that this postulated model is linear in the log (x). 

It is entirely possible that a better model is obtained by 

including cross-product terms. We considered such models 

and concluded that they were not an improvement over 

model (2). The details in performing such tests can be 

found in [Neter, 19901. 

The fitted model is given in equation (5) 

The terms bo, bl, b2 and are least squares estimates of 

the parameters Po, PI, P2 and P3, respectively. The term 9* 

is the y value predicted by the fitted model at xl, x2 and x3. 

The values f - f are called residuals. The model is then 

validated by a thorough graphical analysis of these residuals. 

Residuals without structure indicate a satisfactory model. 

This analysis was performed for each lot, see [Neter, 19901 

for details on this type of analysis. The least squares 

estimates for the fitted model (5) for each lot are given in 

Table 2. 

Table 2 

Least square estimates for model (5) for five lots 

Residual Response 

deviation 

Number of swells = 1 

Blw 

In Table 2, the residual standard deviation is given for 

each fitted model and indicates how closely the model 

predicts the original data. We 0bse~e that the coefficient of 

amplitude @,) is substantially larger than that for duration 

or number of swells. 

Once the empirical model is validated, we can interpret 

it. An effective way of interpreting such models is by 

contour plots. A contour plot displays "traces" (contours) 

which have equal values of the response, y, as a function of 

two x variables. Since we have three x variables, we 

generate contour plots for each setting of a third variable. 

Since x3 (number of swells) has only two settings, 1 and 10, 

we generate two contour plots, one for each setting of x3. 

Figure 6 gives two such plots for lot D. 

The contours of nominal voltage change are plotted over 

the experimental region (amplitude 35 to 52% and duration 

3 to 30 cycles). l3e maximum nominal voltage change 

contour over this region for 1 swell is 1.18% and for 10 

swells is 1.95%. These fall well short of the desired change 

of 10%. ?he shape of the contour lines indicate that 

nominal voltage change increases more rapidly when 

amplitude is increased rather than duration (or number of 

swells) and therefore, to reach 10% nominal voltage change 

most rapidly, we should increase the amplitude. 

The swell generator, however, has an upper limit of 15 A 

peak for the current it can deliver. For some specimens with 

low I-V characteristic, this limit is almost reached with a 

52% voltage level and, therefore, the amplitude cannot be 

increased 

Since we are interested in applications of one to 

thousands of swells, we need to increase swell duration if 

the criterion of 10% change is to be reached after only a few 

swells. Informal tests were run at longer swell duration to 

determine the duration required to identify the point at 

which a 10% change occurred or the varistor burned out. 

This point was identified and a second experiment was 

in this region. 

0 

Number of swells = 10 

DURATION (Number of cycles) 

Figure 6 

Contours plots of amplitude versus duration with nominal voltage change contours for 1 and 10 swells 
Since nominal voltage changed very little at 35% 

amplitude, this setting was dropped, and a thiird setting of 

number of swells (30) was added. Also, the duration now 

depends on the number of swells. For example, 30 swells 

of 600 cycles each could be too severe and 1 swell of 6 

cycles too mild A 2x3~3 full factorial design with two 

controls was planned for each lot, similar to the fmt series, 

but with the new settings, as shown in Table 3. 

Table 3 

Factors and settings for the second experiment 

Amplitude Hits Duration 

(% Above 

nornimal) 

Tests on new varistors from the same five lots were 

performed with the new settings shown in Table 3. A 

thorough statistical analysis is currently underway. 

However, inspection of the test results already provides 

interesting information. 

First, the point of failure, which was not reached in the 

first experiment, was reached for three lots. Several 

varistors failed in short-circuit mode, indicating that the new 

settings might be within range of significant change. 

Second, none of the five sets of lots had degradation 

close to 10%. Most varistors tested degraded less than 296, 

while a few completely failed. A graphical analysis of the 

data for one lot indicates that the failures that did occur were 

for settings of long duration and high amplitude (Figure 7). 

However, no quantification of this apparent phenomenon has 

been done at this time. In the discussion below, we offer 

some speculation on what may be the cause of this behavior. 

Figure 7 

Three-dimension plot of failure distribution for two swell 

amplitudes: 45% and 52% - shifts and failures 

DISCUSSION AND DIRECTION OF FUTURE WORK 

Test results 

The results obtained in the two experiments reported 

here are the beginning of a more comprehensive series of 

experiments from which more general conclusions will be 

drawn. Nevertheless, we can make several observations that 

will guide us toward the next series. 

From the first experiment, we obtained statistical 

confition that the most powerful factor for change is the 

amplitude of the swell (the high value of coefficient bl in 

Table 2). Given the nonlinear response of the varistors, we 

can expect this finding. The other two factors, duration of 

the swell and number of swells, were found quite smaller 

and of comparable magnitude. 

This statistical finding that duration and number of swells 

have comparable effect must be reconciled with intuitive 

expectations. We would expect a difference in the behavior 

of varistors exposed to a few longduration swells versus 

varistors exposed to many short-duration swells. We can 

readily accept that the longer the swell, the higher the 

temperature reached in the body of the varistor [Martzloff & 

Leedy, 19871. To validate this expectation, we applied a 

sequence of 10 swells of 60 cycles each (a total of 10 

seconds), allowing the varistor to cool between swells. That 

test is illustrated in the profile shown in Figure 3. The 

highest temperature rise of the surface was less than 10 "C, 

and the actual temperature returned to room temperature 

before application of the next swell. Then, we applied to the 

same varistor, with the same swell amplitude, an 

uninterrupted swell of 10 seconds. In that second test, the 

temperature rise at the surface reached 45 OC. 
Varistor aging considerations 

Not knowing the physics of varistor aging, we can 

speculate that the internal temperature of the varistor body 

(higher than the measured surface temperature) plays a dual 

role in the process. On the one hand, the accumulation of 

small temperature rise events - repetitive 'benign' swells - 

may be the aging mechanism that we are attempting to 

accelerate by our laboratory tests. On the other hand, a 

single, large temperature rise caused by a large swell may 

launch a thermal runaway of the varistor, without involving 

much aging. 

Examination of the plot of Figure 7 shows two distinct 

behaviors of the test varistors. Among those that failed 

catastrophically, two were exposed to a single long duration 

swell, one failed during a sequence planned for 10 swells. 

The data stored by the computer include all the swell history 

for each application; by searching through the records we 

will be able to pinpoint the time of failure within one 

exposure to the swell(s) and refine the statistical analysis. 

Looking at the pattern of voltage changes for varistors 

that did not fail, we see only a maximum of 4% for one, the 

rest being less Ulan 2%. That behavior may be genuine 

aging, as opposed to catastrophic failure caused by thermal 

runaway. These varistors might have aged further if a 

greater number of swells had been applied. 

Our software system includes the acquisition and storage 

for time, voltage, and current. From these data, the total 

energy deposition (joules) and total charge transfer 

(coulombs) are computed for each swell. A statistical 

analysis of these parameters might reveal a stronger 

conelation for one of those energy or charge parameters, 

pointing toward a hypothesis on the nature of the effect. 

In summary, increasing the amplitude of the swells could 

produce faster aging, as long as the temperature rise during 

a single swell will not reach the thennal runaway threshold. 

Inaeasing the duration of the swell does bring the varistor 

quickly to this threshold. That situation could point toward 

short swells of high amplitude, but our system does not 

produce these high swell currents. Furthermore, a 1-cycle 

(16 ms) swell current amplitude of more than 10 A begins 

to look very much like a 16-111s surge, a stress defined on 

the long time range (10 000 J.IS) of the pulse rating chart of 

Figure 1. 

Therefore, our next direction will be toward increasing 

the nvmber of swells. We will apply those amplitudes and 

durations in the ranges used during the second experiment 

that did not produce failure, but substantially increase the 

number of swells and observe whether the changes increase 

beyond the present 2-4% range. 

An alternative approach might be to accelerate the 

process by conducting the experiment at higher ambient 

temperatures, on the assumption that some Arrhenius-type 

relationship could be found. The manufacturers' data show 

such a relationship for aging under exposure to rated line 

voltage [Harris, 19901. However, to make this demonstration, several ambient temperatures must be applied to 

allow extrapolation, introducing the risk of modifying the 

failure mechanism Therefore, with the limited resources 

available at this point of the investigation, we prefer 

conducting the experiments at room temperature, rather than 

introduce one more variable in the process. 

ACTION ITEMS 

Just as we are seeking new knowledge on the effects of 

repetitive swells on varistors, we clearly need to fmd out 

more about the occurrence of swells in practical applications. 

When the two bodies of information will have matured, then 

it will be possible for application engineers to consider the 

aging effects of swells, as they do now for the pulse ratings. 

Therefore, we can define an agenda for future work, inviting 

the surge-protection engineering community to participate: 

1. Characterize swells as they occur in power systems 

How often do swells occur? 

How long are these swells? 

How large are these swells? 

Several organizations are currently conducting surveys of 

power quality on low-voltage power systems. Some of these 

have been published [Hairabedian, 19921, while others 

remain unheralded or proprietary. In the past, emphasis was 

often given to characterization of surges. More recently, 

characterization of harmonics has become a subject of 

intense interest. To these disturbances, we now add swells 

as a subject that needs more attention, in view of the 

anecdotes of varistor failures. Sharing information on the 

data collection is a must, if the situation is to be corrected. 

2. Conduct further experiments on aging caused by swells 

Increase the number of swells in a sequence 

Investigate acceleration by ambient temperature 

Extend the range of specimen collection 

Correlate aging with joules and coulombs 

We intend to conduct further research on these items, but 

also invite other researchers to join in the quest, and share 

the results. 

3. Investigate the physics of aging caused by swells 

As non-specialists in this field, we refrain from making 

any suggestion, but invite a dialogue with interested parties. 

4. Combine, share, and recommend 

These three areas of research, if pursued aggressively, 

reported candidly, and shared openly, will serve as the basis 

for a new consensus on varistor applications. 
CONCLUSIONS 

1. Applying swells produced by a computerdriven system 

is a practical method for subjecting varistors to repetitive 

swells under controlled conditions. 

2. The factors that affect the varistor response are the 

amplitude of the swell, the duration of the swell, and the 

number of swells experienced in the life of the varistor. 

3. It wems that failure by thermal runaway occurs quickly 

when amplitude or duration settings are large. Failure 

caused by gradual aging (the 10% limit quoted by industry) 

appears to require a larger number of swells than those 

applied in our current experiments. 

4. These results suggest an agenda for additional research. 

We encourage all interested parties to contribute and share 

new information on the subject. 

REFERENCES 

ANSUEEE C62.33-1987 - Test SpecSfications for Varistor 

Surge-Protective Devices. 

ANSIDEEE C62.41-1991- IEEE Recommended Practice on 

Surge Voltages in Low-Voltage AC Power Circuits. 

Box, G.E.P., Hunter, J.S. and Hunter, W.G. - Statistics for 

Experimenters, Wiley, New York NY 1978. 

Hairabedian, B. - A Survey of Power Line Disturbances at 

Typical JBM Computer Installations in the U.S. for the 

Period 1988-1992, Document TR 21.1507, IBM Corporation, 

Pougkkeepsie NY, 1992. 

Harris Corporation - Transient Voltage Suppression. 

Sommerville NJ, 1990. 

Martzloff, F.D. - Matching Surge Protective Devices to Their 

Environment, IEEE Transactions L4-21, No. 1, JanIFeb 1985, 

pp 99-106. 

Maft~loff. F.D. & My, T.F. - Selecting Varistor Clamping 

Voltage: Lower is not better! Proceedings, 1989 Zurich 

International EMC Symposium, pp 137-142. 

Neter, J., Wasserman, W..and Kutner, M.H. - Applied Linear 

Statistical Models, Irwin, Boston MA, 1990. 

Eric S. Lagergren is a mathematical statistician at the 

National Institute of Standards and Technology (NIST), 

Gaithersburg, MD. He received his M.S. and Ph.D. degrees 

in Applied Statistics from the University of California at 

Riverside and has prior work experience at Eastman Kodak 

Company and IBM. He has been a member of the NIST 

staff for four years promoting the use of experiment design 

in ceramic superconductor development, ceramic powder 

milling and size measurement and temperature measurement 

of hot-rolled aluminum His specialties include experiment 

design, quality engineering and linear models. 

Franpis D. Martzloff is an electronics engineer at NIST. 

After undergraduate studies in France, he obtained an M.S., 

E.E. at Georgia Tech in 1952 and, twenty years later, an 

M.S., LA. at Union College. After- 29 years with GE, he 

joined the National Institute of Standards and Technology. 

His early professional experience included the design of 

high-voltage fuses and high-voltage bushings. He changed 

to semiconductor technology, then to transients measurement 

and surge protection. A Fellow of the IEEE, he has 

contributed a number of papers and led the development of 

several standards on surge characterization and surge testing. 

Mark E. Parker is an electronics technician at NIST. He 

has been with the Electronic Instrumentation and Metrology 

Group for the past 8 years where he contributes to the 

development and characterization of ac voltage and current 

sources, and of measuring devices. He was responsible for 

the design and implementation of the hardware and the 

software of the swell system, as weU as performing the tests. 

Susannah B. Schiller is a mathematical statistician at NIST. 

She received her M.S. degree in Statistics from the 

University of Illinois, and has been a member of the NIST 

staff for five years. At NIST, her focus has been on 

designing and analyzing experiments for Standard Reference 

Material certification. In addition, she was a member of the 

NIST Power Quality Committee, providing the data 

encoding, analysis, and graphical display of data in a recent 

survey of NIST power quality. She has also organized a 

tutorial lecture series on statistical methods, which promote 

a high standard of statistical practice by NIST scientists and 

engineers.