A Practical Implementation of Statistical Principles to Select Insulators ...

A Practical Implementation of Statistical Principles to Select Insulators with Respect to Polluted Conditions on Overhead A.C. Lines Christiaan S. Engelbrecht, Igor Gutman and Ralf Hartings, Senior Member, IEEE

T

I. INTRODUCTION

he essence of dimensioning insulators with respect to polluted, or contaminated, conditions is to select the insulator dimensions to obtain an acceptable level of flashover performance in the network. This means that the insulator’s flashover probability must be sufficiently low not to flash over at the highest pollution severity that it may be subjected to during its service life. One way of achieving this is by evaluating the risk for flashover in terms of the statistical variables that describe the site contamination severity and the insulator’s flashover characteristic. In recent papers the mathematical basis for such a method[1][5] and its software implementation[8][6] have been presented. Up to now however, statistical principles are rarely used to dimension insulators with respect to contamination, because these methods require detailed input data, which are often not available. Instead, the insulators are simply dimensioned on the creepage distance requirements published in national or international guidelines (e.g. [10]), without taking account of the statistical nature of the input variables. In this paper, a statistical analysis is performed to obtain

C. Engelbrecht is with KEMA T&D Consulting, PO. Box 9035, Arnhem, 6800 ET; The Netherlands (e-mail: [email protected]). I. Gutman and R. Hartings are with STRI AB, Box 707, Ludvika, 77180; Sweden (e-mail: [email protected]).

A. Statistical method A statistical method for dimensioning insulators with respect to polluted conditions entails the selection of the dielectric strength of an insulator with respect to the voltage and environmental stresses to which it is subjected, in order to fulfill a specific performance requirement[1]. The distribution of the environmental stress is obtained from site severity measurements, and the electrical strength of the insulators can be obtained through laboratory testing, e.g. by performing standardized tests[11], or tests that simulate a specific operating environment[4]. The multiplication of the stress and strength probability functions gives the probability density for flashover of the insulator at the given site; the area below this curve expresses the risk for flashover as is shown in Fig. 1. Strength P(γ)

Stress f(γ)

f (γ ) • P (γ ) Risk for flashover

Strength: probability for flashover

Index Terms—Insulator contamination, Power overhead lines, risk analysis, insulator testing.

II. DIMENSIONING PROCEDURES FOR INSULATORS

Stress: density of occurrence

Abstract—A practical and simplified implementation is presented for the dimensioning of insulators with respect to contamination conditions based on a statistical analysis. This simplified statistical method entails the selection of a minimum insulator withstand severity relative to the site contamination severity by making use of a coordination factor. Evidence is presented to show that the proposed dimensioning procedure agrees well with present IEC creepage distance requirements for cap & pin overhead line insulators, based on a commonly used availability requirement and a realistic number of insulator strings on the line. Results from contamination laboratory test can be used to verify the withstand characteristic of the selected insulators. This simplified method may be used for dimensioning overhead a.c. line insulation when there is insufficient statistical information available to warrant a full risk analysis.

suitable “safety” factors that can be used in a deterministic method for the selection of cap-and-pin type insulators for overhead a.c. lines.

Pollution severity(γ)

Fig. 1. The stress-strength concept for the calculation of the risk for flashover with respect to polluted conditions.

To perform this method, it is necessary to obtain the parameters describing the stress and strength probability functions. The parameters describing the stress are derived from site severity measurements as is explained in Section III. The insulator strength characteristic is determined by performing laboratory pollution tests to determine the 50% flashover voltage and standard deviation of the candidate insulator as a function of the contamination severity[9]. Commercially available software can then be used to perform a complete risk for flashover analysis[8][6].

Co-ordination Factor, Kcs

Uncertain stress

Uncertain strength

Strength:Probability for flashover

Coordination withstand severity, γcw

Statistical severity, γs2

Stress: Density of occurence

B. Simplified method A simplified (deterministic) approach is followed when insufficient information is available to allow a complete risk for flashover analysis. With this method, the insulation withstand strength is selected relative to the site severity with a safety, or coordination, factor to cover the uncertainties in the designer's evaluation of the strength and stress parameters. This is illustrated in Fig. 2. The demand of this approach on the quality of the input data is much less than that of the statistical method.

• Effect of ageing on the pollution catch and wettability of the insulators during its expected lifetime. Step 3: Perform laboratory tests to verify the insulator withstand level. III.

One of the most common methods to quantify site severity is to perform Equivalent Salt Deposit Density (ESDD) measurements over a considerable period of time. Results from ESDD measurements, performed according to the same procedure at different sites in Sweden (2 sites), the U.K. (1 site) and South Africa (4 sites) are presented in Fig. 3. For each site a lognormal distribution function has been fitted through the data points. These fitted distribution functions are also presented in Fig. 3, and their parameters are contained in Table I. These curves support the finding[4][6] that the variation in site severity can adequately be described by a lognormal distribution function. It is interesting to note that the standard deviation of Ln(ESDD) varies in a fairly narrow range, i.e. between 0,4 and 0,8, although the statistical severities, or 2% values, range over more than two orders of a magnitude.

Pollution Severity( γ)

The simplified method consists of the following three steps (a similar approach is described in IEC 60071[12] for switching overvoltages): Step 1: Determine the site severity. The site severity is characterized by the “statistical severity”, γs2, which is the level of severity that has a 2% probability of being exceeded. Step 2: Determine the minimum insulator withstand severity also known as “coordination withstand severity”, γcw. At this severity level the insulator should have a 90% probability of withstand. The relationship between the statistical severity, γs2, and the coordination withstand severity, γcw, is given by the statistical co-ordination factor, Kcs: γ (1) K = cw cs

γ s2

The statistical coordination factor is determined by taking account of the required performance of the insulator (i.e. risk for flashover), and it includes a number of additional factors to convert the measured site severity into an equivalent laboratory test severity. These additional factors are not discussed in any detail in the rest of this paper, but it could include corrections for the following[7]: • If contamination measurements were performed on unenergized insulators, it could be necessary to adjust the measured values if, for example, electric field or leakage current enhances the contamination deposit. • If the salts in the natural contamination deposit differ from that used in the laboratory (e.g. less soluble salts). • Differences in the equipment assembly, such as string orientation. • Differences in shape and housing material of the insulators.

1

Probability of exceeding the ESDD value

Fig. 2. An illustration of the primary parameters used in a simplified design approach.

THE SITE SEVERITY (STEP 1)

Ludvika Barsebäck Sasolburg Brandsebaai Dungeness Kelso Koeberg

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.0001

0.001

0.01

0.1

1

Contamination severity (ESDD: mg/cm2)

Fig. 3: The cumulative distributions of monthly ESDD measurements from 7 measurement stations. (The measurements were performed according to the same procedure on a non-energized standard shape cap-and-pin insulator). TABLE I CONTAMINATION SEVERITY PARAMETERS FOR SEVEN SITES Site name

ESDD50 (mg/cm2)

Ludvika Barsebäck Sasolburg Brandsebaai Dungeness Kelso Koeberg

0,0005 0,002 0,007 0,018 0,043 0,072 0,16

IV.

Std. deviation of Ln(ESDD) 0,52 0,77 0,35 0,54 0,45 0,73 0,62

Statistical severity (γs2, mg/cm2) 0,0014 0,009 0,014 0,056 0,11 0,32 0,59

THE INSULATOR WITHSTAND SEVERITY (STEP 2)

A. Characterizing the insulator strength The contamination flashover characteristic of insulators can be expressed in terms of a power law relationship, which is derived from the physics of the pollution mechanism[2]:

Creepage Distance per kV of applied voltage (mm/kV)

50 45 40 35 30

Flashover Probability 0 0.1 0.5 0.9 0.999

(Line 1)

(Line 2)

25 20 15 10 5 0 0.001

0.01

0.1

1 2

Contamination severity (ESDD: mg/cm ) Fig. 4: Insulator flashover probability as a function of applied voltage and the contamination severity. A standard deviation of 8% is assumed.

For example, along line 1 in Fig. 4, which represents a constant contamination severity, the flashover probability increases with an increase in the applied voltage (i.e. a reduction in the creepage distance per kV of the applied voltage). This is the probability function that is usually determined through variable voltage laboratory flashover tests. It can adequately be described by, amongst others, a Weibull distribution function[1]. An important feature of this distribution function is that it is truncated, i.e. there is a voltage below which the probability for flashover is zero. It has been shown[1] that this truncation lies at 2,5 standard deviations below the 50% flashover voltage. The standard deviation of this probability function is normally expressed in per unit of the 50% flashover voltage. This normalised standard deviation, c, typically varies between 0,06 and 0,10 in artificial contamination tests [3]. The effect of a variation in the standard deviation is illustrated

1 0.9

Probability for flashover (p.u.)

V10

This equation forms the basis of the analysis presented in the rest of the paper. The contour graph in Fig. 4 shows that the insulator’s flashover probability is a function of both the applied voltage and the contamination severity. An increase in any of these parameters results in a higher flashover probability. Each curve in Fig. 4 represents a particular probability for flashover; the withstand characteristic (V10 from equation 3) is represented by the thick curve.

in Fig. 5 for a site severity of 0,056 mg/cm2 and an insulator with a creepage distance of 2484 mm.

0.8 0.7 0.6 0.5

per unit standard deviation "c" 0.06 0.08 0.1 0.15

0.4 0.3 0.2

Probability of withstand

0.1 0 60

80

100

120

140

160

180

Applied Voltage (kV) Fig. 5: The probability for flashover as a function of applied voltage for an insulator with a creepage distance of 2484 mm and at a contamination severity of 0,056 mg/cm2.

Line 2 in Fig. 4 represents actual service conditions where the insulator is energised to a constant voltage gradient (i.e. the maximum operating voltage), but the contamination severity varies over time. In this case the probability for flashover increases with an increase in the contamination severity level. Reference [1] describes a method whereby the probability as a function of the contamination severity is derived from the probability as a function of applied voltage. These derived functions are presented in Fig. 6 for an insulator with a creepage distance of 2484 mm, and an applied voltage of 145/√3 kV (i.e. 29,7 mm/kV). Both sets of curves presented in Fig. 5 and Fig. 6, are derived from the insulator withstand characteristic (3); as a result, the different probability curves cross each other at a 10% probability of flashover. 1

0.9

Probability for flashover (p.u.)

CD = B ⋅γ α V (2) CD is the creepage distance of the insulator in mm, V is the flashover voltage of the insulator in kV and γ is the contamination severity. The contamination performance of the insulator is characterized by the constants B and α, which are determined empirically. A recent compilation of results from artificial contamination tests indicates that the average withstand characteristic (i.e. 10% flashover voltage, V10) of standard shape cap-and-pin insulators [4] can be described as: CD (3) = 49.0 ⋅ γ 0.22

0.8 0.7 0.6 0.5

per unit standard deviation "c" 0.06 0.08 0.1 0.15

0.4 0.3 0.2 0.1

Probability of withstand

0 0.01

0.1

Contamination severity (ESDD: mg/cm2)

1

Fig. 6: The probability for flashover as a function of contamination severity for an insulator with a creepage distance of 2484 mm and at an applied voltage of 83,7 kV (i.e. 145 kV maximum operating voltage).

So far, the flashover probability of a single insulator has been discussed. Usually there are, however, multiple (or parallel) insulators exposed to the same environmental conditions (e.g. a section of overhead line), which results in an higher risk for flashover of the total insulation system, as is illustrated in Fig. 7. The flashover probability, Pn, of “n” parallel insulators may be calculated from flashover probability of one insulator, P1 by:

Pn (γ ) = 1 − [1 − P1 (γ )]n

(4)

1

Probability of flashover (p.u.)

0.9 0.8 0.7 0.6 0.5 Number of insulators

0.4

1 10 100 1000 100000

0.3 0.2

Truncation point (γ0)

Probability of withstand

0.1 0 0.01

0.1

2

Contamination severity (ESDD: mg/cm )

1

Fig. 7: The effect of the number of parallel insulators on the flashover probability. The insulator characteristics are the same as for Fig. 6 with a standard deviation of 8%.

It should be noted that as the number of insulators increases, the probability function approaches a step function with the step occurring at the truncation point, γ0, of the flashover probability function. B. Selecting the coordination factor As mentioned earlier, the coordination factor describes the separation between the probability functions describing the site severity and the insulator strength. A decrease in the coordination factor leads to an increase in the risk of flashover. This relationship between the coordination factor and the flashover risk is independent of the applied voltage and pollution severity, which makes it ideal as a dimensioning parameter. In this section two approaches to choose a coordination factor for line insulators are discussed. Approach 1: Worst case analysis The worst case is based on the following assumptions: • There are a very large number of parallel insulators exposed to the same conditions. • A risk of flashover of 0,02 (i.e. one flashover in 50 pollution events) is considered reasonable. In the previous section it is shown that the flashover probability curve of the insulator approaches a unit step function located at the truncation point, γ0, of the insulator flashover probability – see Fig. 7. In this case the risk for

flashover is approximated by: ∞

R≈

∫ f (γ )dγ γ

(5)

0

Where f(γ) is the probability density function describing the site severity – see Fig. 1. If the insulator characteristic is selected so that γ0 = γs2, the performance becomes: (6) R ≈ 0,02 Results from laboratory- and field-testing indicates that the truncation point lies at 2,5 standard deviations below the 50% flashover voltage. Therefore, the performance criterion is fulfilled if, V50, is selected relative to the maximum system voltage, Vm as follows[1]: Vm (7) = V50 (γ s 2 ) ⋅ (1 − 2,5 ⋅ c) 3 In this case the coordination factor is given by[1]:

  2,5 ⋅ c (8) K cs ≈ α 1 + 0,498 ( 1 − 2 , 5 ⋅ c )   It is seen from (8) that the coordination factor is a function of both “α” and “c”. For α = 0,22 as in (3) the coordination factor is: • Kcs = 1,5 for c = 0,06; • Kcs = 1,7 for c = 0,08; • Kcs = 2,0 for c = 0,10. Approach 2: Risk for flashover analysis The Insulator Selection Tool (IST) program[8][6] has been used to calculate the risk for flashover for different combinations of standard deviations of the site pollution severity and the insulator strength, as described in the previous sections. In this analysis the following assumptions have been used: • A risk of flashover of 0,02 (i.e. one flashover in 50 pollution events) is considered reasonable. • Typical line sections are considered where the number of parallel insulators exposed to the environment may vary between 100 and 1000 strings. • The normalized standard deviation of laboratory tests lies between 0,06 and 0,1. • The standard deviation of Ln(ESDD) describing the site severity may vary between 0,4 and 0,8 – see Table I. In Fig. 8, the calculated risk for flashover is presented as a function of the co-ordination factor for three levels of standard deviation, c, and the variation in standard deviation of Ln(ESDD). For these calculations, it was assumed that 1000 insulators are exposed to the same environmental conditions. This corresponds to a line length of approximately 100 km. These curves can be used to find a statistical co-ordination factor if the standard deviations of the site severity and the insulator flashover strength are known.

10000

c = 0.06 Nins = 1000 Number of parallel insulators exposed to the environment

Risk for flashover during one event

0.1

0.01

c=0,06

a

1

1.5

2

0.6

0.7

2.5

0.8

10

Risk for Flashover = 0,02 Std Deviation of Ln(γ) between 0,4 and 0,8

3.5

4

1 0.5

Risk for flashover during one event

1

1.5

2

Statistical coordination factor (Kcs)

Fig. 9: The influence of the number of parallel insulators, the standard deviation of the flashover voltage from laboratory tests and the site severity on the statistical coordination factor to obtain a risk for flashover of 0,02.

c = 0.08 Nins = 1000

C. A comparison with commonly used recommendations 0.01

Std. Deviation of Ln(ESDD) 0.4

0.001 1

1.5

2

0.5

0.6

2.5

0.7

0.8

3

0.9 3.5

4

Statistical coordination factor (Kcs) 0.1

To establish the validity of the simplified method presented here, the creepage distances that will result from the application of the proposed coordination factors are compared with the specific creepage distance requirements derived from the IEC 60815-1985[10]. Again, the “standard CIGRÉ”[4] curve for flashover performance of cap-and-pin insulators (equation 3) and a standard deviation c = 0,08 is used. One thousand parallel insulators are considered. 40

c = 0.10 Nins = 1000 Specific creepage diststance (mm/kVp-p)

Risk for flashover during one event

c

100

Statistical coordination factor (Kcs) 0.1

b

Typical lines

0.9

3

c=0,10

1000

Std. Deviation of Ln(ESDD) 0.4 0.5

0.001

c=0,08

0.01

0.9 Std. Deviation of Ln(ESDD)

0.001 1

1.5

2

0.4 2.5

0.5

0.6 3

0.7 3.5

0.8 4

Statistical coordination factor (Kcs)

35 30 25 20 15

Based on IEC Kcs=2.21 (1 in 200) Kcs=1.57 (1 in 50) Kcs=0.95 (1 in 10)

10 5 0 0.01

0.1

1

10 2

Pollution Severity (2% ESDD:mg/cm )

Fig. 8: Risk for flashover as a function of the statistical coordination factor for one thousand parallel insulators. The assumed normalized standard deviation for the insulator flashover strength, c, and the standard deviation of Ln(ESDD) are indicated in each figure.

Fig. 10: A comparison of the choice of the level of the statistical co-ordination factor and current IEC practice based on the performance of a typical standard cap-and-pin insulator disc.

The effect of the number of insulators exposed to the same environmental conditions has been further investigated for the selected risk for flashover of 0,02. Fig. 9 shows that as the number of parallel insulators increases, a larger coordination factor is needed to maintain the same performance. However this trend saturates for more than 10.000 insulators, when Kcs approach the level calculated in the worst-case analysis. The points labeled “a”, “b” and “c” in Fig. 9 corresponds approximately to the crossover points in Fig. 8, which lies at a flashover risk of 0,02. Typical line sections will have between 100 and 1000 insulators, which is indicated in the figure. This area shows that in practical cases for a.c. overhead line insulation, the statistical co-ordination factor could range from 1,3 to 1,8.

The results are presented in Fig. 10, which shows that the presently used creepage distance criteria correspond to a coordination factor of between 0,95 and 2,2. The value of Kcs = 1,57, which corresponds to a flashover risk of 0,02, agrees well with the average specific creepage distance requirement as is presently used. However, it should be noted that this analysis has been based on standard shape cap-and-pin insulators. Other line insulator types may require different coordination factors, for instance if they collect a different amount of pollution to that of the standard type insulator under the same environmental conditions. Furthermore this analysis has been based on a performance requirement of one flashover in 50 pollution events. For sites with an extreme number of wetting events, this performance

requirement may be too low[5]. In these cases it is better to perform a complete risk of flashover evaluation. Also for apparatus and other substation insulators, the coordination factor could be different. In substations the number of parallel insulator are generally less than on lines, but according to the present practice of power utilities the performance required of these insulators are higher. In these cases it is however advisable to perform a full statistical analysis, based on actual flashover characteristics and ESDD field data, using commercially available software[8][6]. V.

The last step in the simplified dimensioning process is to verify that a candidate insulator will fulfill the required performance. This can be done by performing laboratory tests to verify that the insulator does not have a higher than 10% flashover probability at the coordination withstand severity. In the present standard[11], withstand tests are described which consist of a maximum of four tests during which only one flashover is allowed. However, for line insulators, it may be more feasible, to perform variable voltage (or quick flashover tests), tests to determine the 50% flashover voltage, V50. The withstand voltage, U10, for the tested insulator can then be calculated from V50 and standard deviation, “c”. Creepage Distance per kV of applied voltage (mm/kV)

34

Statistical severity

30

Test point 1

28

Flashover Probability 26

24

Test point 2

0 0.1 0.5 0.9 0.999

22

20 0.01

(9) For the example illustrated in Fig. 11, the laboratory test severity is calculated as 0,056 x 1,7 = 0,095 mg/cm2, indicated as “test point 1”. Case 2: Test at a higher voltage: Another option is to perform the laboratory withstand test at the statistical severity, γs2, but with a higher test voltage. Since the withstand characteristic is described by a power law, the test voltage can be calculated as: α Vtest = K cw ⋅

VERIFICATION LABORATORY TESTS (STEP 3)

32

γ cw = K cw ⋅ γ s 2

0.1 2

Contamination severity (ESDD: mg/cm )

Fig. 11: Insulator flashover probability as a function of applied voltage and the contamination severity. A standard deviation of 8% is assumed. The statistical severity and laboratory test points are indicated.

Since the flashover probability at the statistical severity, or service stress, is usually chosen to be below 10% it means that the laboratory verification tests need to be performed at either higher pollution severity levels or higher voltage stresses. Fig. 11 shows an example for a “standard CIGRÉ”[4] insulator that is selected according to the worst-case analysis, i.e. equation (7). At the statistical severity (ESDD2% = 0,056 mg/cm2) and a voltage stress of 29,2 mm/kV (145 kV / √3 / 2447 mm) the probability for flashover is selected to be zero, indicated as “statistical severity” in Fig. 11. Case 1: Test at a higher contamination severity: In this case, laboratory withstand tests are performed at the maximum service voltage stress across the insulator and at the coordination withstand severity, γcw:

Vm

(10)

3

For this example, the test voltage becomes 94,1 kV (i.e. 1,70,22 x 145 kV / √3) or a test gradient of 26 mm/kV (i.e. 2447 mm/94,1) indicated as “Test point 2” in Fig. 11. VI. CONCLUSIONS This paper presents a practical and feasible alternative for selecting insulators for a.c. overhead lines with respect to pollution. With this simplified statistical method a minimum insulator withstand severity is selected relative to the site severity by making use of a coordination factor. An analysis has been performed to obtain suitable values for the coordination factor for overhead line standard shape cap-and-pin insulator strings: • The site severity is characterized with ESDD measurements. An analysis performed on measurements from various sites showed that, the variation of site severity is adequately represented by a lognormal distribution function and that the standard deviation of Ln(ESDD) varies in fairy narrow range 0,4-0,8 despite a large variation in the 2% values. • Good correlation is obtained between the results of dimensioning using this simplified statistical method and present IEC creepage distance recommendations, based on a commonly used availability requirement and a realistic number of insulator strings on the line. A corresponding range for the coordination factor is indicated. • Results from laboratory tests can be used to verify that candidate insulators conform to the required performance criterion. A full statistical evaluation is needed for those insulator types and conditions not covered in this analysis. There are software packages commercially available to do such analyses. VII. ACKNOWLEDGMENT The authors gratefully acknowledge W. L. Vosloo (ESKOM, South Africa) and D. Connor (NGT, UK) for making their site severity measurements available for this analysis.

VIII. REFERENCES Periodicals: [1] [2]

C.S. Engelbrecht, R. Hartings, J. Lundquist: “Statistical dimensioning of insulators with respect to polluted conditions”, IEE Proc.-Gener. Transm. Distrib, Vol. 151 No.3, May 2004, pp. 321-326. F.A.M. Rizk, “Mathematical models for pollution flashover”, Electra, 1981, n. 78, p.p. 71-103

Books: [3]

N.N. Tikhodeev, S.S. Shur: “Insulation of electrical network”, Leningrad, “Energy”, Russia, 1979 (in Russian).

Technical Reports: [4]

‘Polluted insulators: a review of current knowledge’. CIGRÉ´ technical publication 158, June 2000.

Papers Presented at Conferences (Unpublished): [5]

[6]

[7]

E.A. Solomonik, N.N. Tikhodeev, L.L. Vladimirsky, and I. Gutman, “Methods of Statistical Dimensioning of the Outdoor Insulation with Respect to Polluted Conditions”, to be presented at IEEE PowerTech’ 2005, 2005, paper 670. I. Gutman, and W.L. Vosloo, “Application of Statistical Principles of Insulator Dimensioning with Respect to Polluted Conditions to Select Line Insulators Based on Test Station Results”, to be presented at IEEE PowerTech’ 2005, 2005, paper 355. S. Zhiyi, L. Xidong, Y. Yu, “Important correction factors in HVDC line insulation selection”, Submitted to the 14th International Symposium on High Voltage Engineering, Beijing, China August 25-29, 2005.

Papers from Conference Proceedings (Published): [8]

[9]

I. Gutman, K. Halsan, D. Hübinette, E. Solomonik, L. Vladimirsky: “New developed Insulator Selection Tool (IST) software: Results of application using known Russian service experience”, Proceedings of the 12th Asian Conference on Electrical Discharge, 19-22 November 2004, Shenzhen, China, p.p. 10-15. C.S. Engelbrecht, S.M. Berlijn, K.Å. Halsan, B Engström, R. Hartings: “Dimensioning of insulators for salt pollution: A novel procedure and a laboratory test method,” Cigré Paris Session 2002, Paper 33-410.

Standards: [10] IEC: “Guide for the selection of insulators in respect of polluted conditions”, IEC/TR 60815 (1986-05). [11] IEC, ‘Artificial pollution tests on high-voltage insulators to be used on AC systems’. IEC Standard 60507, 1991, 2nd ed. [12] IEC: “Insulation co-ordination - Part 2: Application guide” IEC 60071-2 (1996-12).

IX. BIOGRAPHIES Christiaan Engelbrecht is a Senior Consultant with KEMA T&D consulting based in Arnhem, The Netherlands. In 1989 he received an M.Eng degree in electrical engineering from the University of Stellenbosch in South Africa. Professionally he has more than 15 years experience in the contamination design of insulators and insulation co-ordination studies, having also worked with ESKOM in South Africa (1989-1997) and STRI in Sweden (19982003). He is convener of the newly formed CIGRÉ Working Group C4AG03-03, “Pollution and Environmental Influence on the Electrical Performance of Power Systems,” and a member of IEC TC36 WG 11, which deals with the revision of IEC 60815, “Selection and Dimensioning of High-Voltage Insulators for Polluted Conditions.” He has also been involved in insulation coordination audits of transmission and distribution systems and the study of corona losses due to hoarfrost. Igor Gutman was born in Leningrad (Russia) in 1958. He graduated from the Technical University, Leningrad, where he received the M.Sc. and Ph.D. degrees in 1981 and 1990, both in high voltage engineering. His employment experience included the Leningrad HVDC Power Transmission Research Institute since 1981, where his work has been connected with outdoor line and station insulation, particularly with composite insulators. In 1994 he joined STRI, Sweden. He is now a Senior Specialist, Product Area Coordinator “Insulation Solutions” at the Technology & Consulting Services Department. His areas of activity are mainly dimensioning and maintenance of outdoor insulation intended to operate in clean and polluted environments; ageing characteristics and accelerated ageing tests of composite insulators. At present Dr. Gutman is a member of IEEE TF 15.09.09.03 (icing performance of insulators) and is active in a number of IEC working groups. Igor Gutman has published about 80 magazine and conference papers on various aspects of insulation performance (37 in Russian). Ralf Hartings (Senior Member ’98) received the M.Sc. degree in electrotechnical engineering from the University of Technology, Eindhoven, The Netherlands, in 1981. From 1982 to 1987, he has worked with circuit breaker research and development at ASEA, Ludvika, Sweden. In 1987, he entered the area of outdoor insulation, first at ABB and in 1989 at STRI in Ludvika, Sweden. Currently, he is the manager of the department for Technology & Consulting Services at STRI. He is a member of IEEE 15.09.03 and 15.09.04, including several TFs dealing with outdoor insulation and composite insulators and has also been a member of several CIGRÉ WG/TF. Ralf Hartings has contributed to numerous international papers and conferences and has received in 2002 the CIGRÉ Technical Committee Award.