A-60
2012 IEEE International Conference on Condition Monitoring and Diagnosis
23-27 September 2012, Bali, Indonesia
Insulation Ageing Diagnosis of XLPE Power Cables
under Service Conditions
Fei Liu, Xingyi Huang, Jing Wang, Pingkai Jiang*
Shanghai Key Lab of Electrical Insulation and Thermal Aging, Department of Polymer Science and Engineering
Shanghai Jiao Tong University
Shanghai 200240, People’s Republic of China
*E-mail: pkjiang@sjtu.edu.cn
parameters were usually electrical parameters commonly used
in preventive tests of cable, and the thresholds of membership
functions were just determined by consulting division rules of
insulation state from previous evaluation standards. While
there are sound bases for the determination of characteristic
parameters and thresholds of membership functions in this
paper, which is superior to previous researches.
Abstract—Forty percent of transmission and distribution power
grids are composed of underground cables in Shanghai, a city
with abundance surface water. The cables are subjected to a
variety of stress factors due to the complicated underground
environment and the ageing process is accelerated consequently.
Lots of cables have been in service for over 20 years and there
tends to be frequent accidents due to cable failures. So it’s
necessary to research the insulation ageing state of cables under
unique service conditions. Six physical and chemical analysis had
been performed on samples of about 300 cable lines in Shanghai,
including water tree investigation, tensile test, dielectric
spectroscopy test, TGA, FTIR and electrical ageing test. By
statistical analysis and fuzzy clustering, the parameters for
identifying and quantifying the XLPE cable insulation aging state
were presented and the influence of laying method and sampling
section on the degradation of cable insulation was also studied.
Based on fuzzy theory, a cable insulation ageing diagnosis model
was established, which was proved to be applicable by case study.
A database was also developed for cable maintenance according
to the multi-parameter test data and diagnosis model.
II.
Six insulation tests were performed on 466 samples
removed by utility from about 300 in-service power cable
lines due to insulation failure or replacement schedule.
A. Water Tree Investigation
The cable insulation was sliced into wafers, which were
dyed and placed under optical microscope to investigate water
treeing situation. In order to characterize water treeing degree
of a service aged cable, the maximum length of water trees C1
was measured and the amount of water trees per unit volume
was counted as water tree content C2.
Keywords- XLPE cable; insulation diagnosis; statistical
analysis; fuzzy clustering
I.
B. Dielectric Spectroscopy Test
A characteristic dielectric loss (tanį) peak of cable samples
was found to appear in the frequency range of 30M to 50MHz.
A thermal ageing test on 10kV new cables in an air oven was
designed and test results indicate that the peak value increases
with temperature and ageing time. So the dielectric loss peak
value C3 was used for characterization of the degree of
thermo-oxidative ageing in cable insulation.
INTRODUCTION
Deterioration in XLPE cable insulation often take place due
to electrical, thermal, mechanical and environmental stress.
Water treeing phenomenon is the main reason causing the
ageing of XLPE cables, especially the middle and low voltage
cables. In some countries such as Germany, Japan ,etc., it was
proposed that the insulation state of in-service cables can be
inferred by testing fault samples removed from the same cable
lines [1].
Lots of underground cables have been in service for over 20
years in Shanghai, a city with abundance surface water and
there tends to be frequent accidents due to cable failures. In
order to diagnose the insulation ageing state of cables under
unique service conditions, a large number of 10-110kV XLPE
cable samples were removed from in-service cable lines by
Shanghai Electric Power Company and some physical and
chemical properties of the samples were tested.
Fuzzy diagnosis has been used extensively in the fault
diagnosis and condition assessment of power transformer[2].
For the fuzzy diagnosis of cable insulation, few literatures are
involved[3-5].In those researches, the characteristic
978-1-4673-1018-5/12/$31.00 ©2012 IEEE
INSULATION TESTS OF CABLE
C. FTIR Spectroscopy
The carbonyl index was calculated from the FTIR curve,
which was defined as the ratio of the absorption at 1720cm-1
(carbonyl band) to the absorption at 2010cm-1[6]. The
carbonyl index C4 can be also used for characterization of the
degree of thermo-oxidative ageing in cable insulation.
D. Tensile Test
The wafers of cable insulation were further processed into
dumbbell test pieces for tensile test. The tensile strength C5
and elongation at break C6 were used to represent the degree
of thermal decomposition and thermal oxidation
decomposition in cable insulation.
647
E. Electrical Ageing Test
The AC step voltage breakdown test was performed on
wafers sliced from cable insulation at the room temperature. In
this paper, the accumulative breakdown strength C7 was
defined as the sum of products of each step applied electric
filed strength and the corresponding time of duration, which
was used to characterize the ability of electrical ageing
resistance of cable insulation.
to be accelerated (activation energy decreases) as the degree
of in-service ageing increases (tensile strength decreases).
There is strong positive correlation relationship between
carbonyl index C4 and accumulative breakdown strength C7.
It is known that the increase of microscopic imperfections will
enhance the dielectric breakdown strength for solid
material[7]. Obviously, the increase of Carbonyl index
indicates that the thermo-oxidative ageing causes enhanced
polarity of XLPE.
F. Thermogravimetric Analysis
Thermogravimetric analysis performed under N2 flow was
used to investigate the thermal degradation of cable insulation.
The activation energy C8 was calculated from the TG curve
by Coast-redfern method. The lower the activation energy, the
more easily the thermal decomposition reaction goes on.
III.
B. Two Sample t-Test
According to cable laying method, the tensile strength test
data were divided into two subsets, one for direct-buried
cables and the other for duct cables. The p-values obtained
with MinitabTM software for the Andersen-Darling tests on the
two subsets are 0.451 and 0.103 respectively. The AndersenDarling test is a widely used statistical test for normality. If pvalue exceeds 0.005, the data are assumed to be normally
distributed. Then a Two Sample t-Test was performed. If pvalue is below 0.01, it is believed there exists significant
difference between data subsets. The p-value obtained with
MinitabTM software for t-Test is 0.003 and the subset means of
direct-buried and duct cables are 26.36 and 27.37 respectively
according to the Andersen-Darling tests. It indicates that the
direct-buried cables are more susceptible to insulation
degradation than duct cables. The inference is reasonable
because the surrounding environment of direct-buried cables is
usually believed to be more hash than duct cables. And tensile
strength is also proved to be an effective test parameter for
identifying the cable insulation ageing state.
STATISTICAL ANALYSIS OF INSULATION TEST DATA
The regression analysis has been performed between every
two test parameters. In addition, for each test parameter, the
Two Sample t-Test and Analysis of Variance (ANOVA) have
been performed to analyze the influence of laying method and
sampling section on the degradation of cable insulation,
respectively. All the statistical analysis was performed in
MinitabTM software. The results of statistical analysis with
strong correlation in the regression analysis and significant
differences in the t-Test or ANOVA Analysis are displayed as
follows:
A. Regression Analysis
The results of regression analysis are shown in Tab.1.
The symbol “ĝ” in Tab.1 means positive correlation. In
statistical hypothesis testing, a probability (P-value) is usually
used to quantify the strength of the evidence against the null
hypothesis. For the distribution hypothesis test of a linear
regression, P<0.05 indicates a linear relationship is
established.
There is very strong positive correlation relationship
between water tree content C1 and maximum water tree length
C2. The water tree investigation method by microscope is
proved to be effective.
There is very strong positive correlation relationship
between tensile strength C5 and elongation at break C6. The
result is logical and the tensile test is also proved to be
effective.
There is very strong positive correlation relationship
between tensile strength C5 and activation energy C8. The
result indicates that the degradation of cable insulation tends
TABLE I.
C. Analysis of Variance (ANOVA)
1) Influence of sampling section on tensile strength
According to cable sampling section, the tensile strength
test data was divided into three subsets, one for samples
removed from non-fault section of cable line, one for samples
from fault section due to operating troubles of cable line and
one for samples from fault section due to external injury of
cable insulation. All the subsets have passed the AndersenDarling test for normality. The p-values obtained with
MinitabTM software for these tests are 0.514, 0.125 and 0.297
respectively. Analysis of Variance (ANOVA) was used to find
how significantly subsets are different from each other. If pvalue doesn’t exceed 0.01, the subset means are assumed to be
significantly different. The p-value obtained with MinitabTM
software for ANOVA is 0.01, indicating that the tensile
strength is significantly different with respect to different
sampling sections. And the 95% confidence intervals for mean
indicates that the tensile property of cables removed from fault
section due to external injury is inferior to cables from fault
section due to operating troubles on the whole, with cables
from non-fault section in between.
2) Influence of sampling section on the accumulative
breakdown strength
The accumulative breakdown strength test data was also
divided into three subsets according to cable sampling section.
All the subsets have passed the Ryan-Joiner test for normality,
another statistical test for normality based on correlation. The
RESULTS OF REGRESSION ANALYSIS
Correlated
Variables
Pvalue
Amount
of Data
Sets
Amount of
Abnormal
Observation
Points
Percentage of
The Total
C1ĝC2
C4ĝC7
C5ĝC6
C5ĝC8
0.000
0.015
0.000
0.001
278
266
265
265
17
14
15
22
6.1%
5.3%
5.7%
8.3%
648
correlation coefficient R obtained with MinitabTM software for
these tests are 0.98, 0.96 and 0. 97 respectively.
The p-value obtained with MinitabTM software for ANOVA
is 0.003, indicating that the accumulative breakdown strength
is significantly different with respect to different sampling
sections. The 95% confidence intervals for mean indicates that
the breakdown strength of cables removed from fault section
due to external injury is inferior to cables from fault section
due to operating troubles on the whole, with cables from nonfault section in between. The conclusion is consistent with the
Analysis of Variance for tensile strength. It is known that the
tensile strength and breakdown strength are just the two
commonly used functional parameters for cable insulation
evaluation. As is mentioned above, the Two Sample t-Test and
ANOVA test have been performed for all the 8 test
parameters. But there exists no significant difference in all the
other test parameters except tensile strength and breakdown
strength. So it can be sure that the tensile strength and
breakdown strength are effective diagnosis parameters for
identifying the ageing state of service aged cable insulation.
IV.
seemingly disordered test data of service aged cable samples.
Therefore the data of strongly correlated parameters water tree
content C1 and maximum water tree length C2, tensile
strength C5 and activation energy C8, carbonyl index C4 and
accumulative breakdown strength C7 were combined into
three two-dimensional data sets. The clustering centers
obtained by fuzzy C-means clustering performed in matlab
software on the above three two-dimensional data sets are
shown in Tab.3.
C. Translation of input and output variables into fuzzy
variables
1) Characteristic fuzzy variables and state fuzzy variables
The input and output variables were translated into three
fuzzy linguistic variables - mild, moderate, severe. The fuzzy
variables are shown in Tab.4 and Tab.5 .
2) Establishment of membership function
a) Type of membership function
The trapezoid type membership function μVS (x) and μVL (x)
was adopted to describe the fuzzy variable with the trend of
being small and the fuzzy variable with the trend of being
large respectively, the triangle type membership function
μVM (x) was adopted to describe the fuzzy variable with the
trend of being moderate in this paper. The three membership
functions adopted are shown in Fig.1.
b) Threshold of membership function
The clustering centers obtained by fuzzy clustering were
taken as the thresholds of membership functions describing
the characteristic fuzzy variables (Tab.6)
FUZZY DIAGNOSIS OF CABLE INSULATION
The fuzzy clustering diagnosis method was adopted in the
paper and fuzzy C-means clustering was applied.
A. Determination of input and output variables
A set of characteristic parameters showing strong
correlation in the regression analysis or significant differences
in the T-test or ANOVA Analysis were selected as input
variables of fuzzy diagnosis, which comprehensively reflects
various ageing phenomenon(Tab.2).
The state parameter A was defined as output variable of
fuzzy diagnosis. It is a comprehensive evaluation parameter of
cable insulation ageing state.
TABLE III.
B. Fuzzy clustering
It is not difficult to understand that the characteristic
parameters reflecting the same ageing phenomenon must be
strongly correlated with each other. So the data of test
parameters showing strong correlation in regression analysis
can be combined into a multi-dimensional data set for fuzzy
clustering. And the obtained clustering centers can be taken as
the thresholds between insulation ageing states of the
corresponding ageing phenomenon reflected by the correlated
test parameters. By this means, the valuable data for fuzzy
clustering are able to be mined from a huge amount of
TABLE II.
Symb
ol
Characteristic
Parameter
C1
Water tree content
C5
Tensile strength
C7
C8
Accumulative
breakdown strength
Activation energy
INPUT VARIABLES
CLUSTERING CENTERS
Clustering center
C1
C5
C7
C8
1
2
3
17.6
240.8
2752.8
27.3
26.3
25.8
1127.2
790.3
413.8
287.0
266.2
207.5
TABLE IV.
Characteristic
Fuzzy Variable
C11, C12 ,C13
C51 ,C52, C53
CHARACTERISTIC FUZZY VARIABLES
Comment
Water tree content is mildly, moderately, severely large
Tensile strength is mildly, moderately, severely small
Accumulative breakdown strength is mildly,
moderately ,severely small
Activation energy is mildly, moderately, severely small
C71 ,C72, C73
C81, C82, C83
TABLE V.
State Fuzzy Variable
A1, A2, A3
STATE FUZZY VARIABLES
Comment
Cable insulation is mildly, moderately, severely aged
Comment
Characterization of water treeing degree
Characterization of thermal decomposition
and thermal oxidation decomposition
Characterization of electrical ageing
Figure 1. Fuzzy distribution
Characterization of thermal decomposition
649
TABLE VI.
MEMBERSHIP FUNCTIONS OF CHARACTERISTIC FUZZY
V.
VARIABLES
Characteristic
Fuzzy Variable
Function Type
Thresholds of Membership
Function (a, b, c)
C11 , C12, C13
μVS (x ) , μ VM (x ) , μVL (x )
(17.6, 240.8, 2752.8)
C51, C52, C53
C71, C72, C73
C81, C82 , C83
μVL (x ) , μVM (x) , μVS (x )
μVL (x ) , μVM (x) , μVS (x )
μVL (x ) , μVM (x) , μVS (x )
The information of cable lines for case study is shown in
Tab.8. The results of fuzzy diagnosis are shown in Tab.9,
which can reflect the comprehensive ageing state of cable
insulation well. Thus a cable insulation ageing diagnosis
model based on the above fuzzy inference was established. A
database was also developed for cable maintenance according
to the cable information presented by utility, multi-parameter
test data and diagnosis model.
(25.8, 26.3, 27.3)
(413.8, 790.3, 1127.2)
(207.5, 266.2, 287.0)
D. Rules of fuzzy diagnosis
Consulting the results of fuzzy clustering and experience of
experts, the rules of fuzzy diagnosis were summed up as the
following 10 items using fuzzy if-then language(Tab.7).
The accuracy of fuzzy rules is influenced by expertise and
experience of diagnosis experts, sample capacity and source.
With the supplement of fault examples, the rules can be
updated to improve the accuracy of diagnosis.
VI.
No.
1
2
3
4
5
6
7
8
9
10
TABLE VIII.
1
2
3
4
5
6
7
8
9
[2]
[3]
Laying
Method
1
2001-10-20
direct-buried
2
1996-10-18
direct-buried
Water Tree
Content C1
(pcs /cm3)
5793.2
85.5
305.7
243.4
239.8
326.3
7.6
0.0
0.0
Tensile
Strength C5
(MPa )
25.2
25.7
23.5
28.30
26.6
31.9
30
26.7
32.2
3
2006-08-24
duct
4
5
6
7
8
9
2001-09-14
2006-04-27
2006-11-01
2004.03.01
2007-01-29
2005-10-24
direct-buried
direct-buried
duct
duct
duct
direct-buried
Sampling Section
fault section ( external
injury)
fault section (operating
troubles)
fault section (external
injury)
non fault section
non fault section
non fault section
non fault section
non fault section
non fault section
CASE STUDY
Accumulative Breakdown
Strength C7 (kV/mm•min
)
652
855
1023
703
842
685
988
1043
1233
Du B0-Xue, MA Zong-le, and HUO Zhen-xing, “Recent research status
of techniques for power cables,” High Voltage Apparatus, vol. 46, pp.
100–104, 2010.
Yann-Chang Huang, “A new data mining approach to dissolved gas
analysis of oil-insulated power apparatus,” IEEE TRANSACTIONS ON
POWER DELIVERY, vol. 18, pp. 1257-1261, 2010.
Jiantao Sun, Guangfan Li, and Keli Gao, “Fuzzy comprehensive
evaluation for insulation diagnosis of high voltage cable,” 2010
International Conference on Power System Technology. Hangzhou,
pp.1-3, 2010.
[5]
[6]
[7]
650
Membership Grades of
Result of Fuzzy
State Fuzzy Variables /
Diagnosis
˄ȝA1, ȝA2, ȝA3˅
278.8
severely aged
˄0, 0, 1˅
severely aged
216.9
˄0, 0.2, 0.8˅
severely aged
104.0
˄0, 0, 1˅
274.9
moderately aged
˄0ˈ0.6ˈ0.2˅
267.4
( 0, 0.8, 0 )
moderately aged
277.7
moderately aged
˄0, 0.6, 0.3˅
291.2
( 0.6, 0, 0 )
mildly aged
283.5
mildly aged
˄0.6, 0, 0˅
303.8
mildly aged
˄1, 0, 0˅
Jia-jia Huan, Gang Wang, and Hai-feng Li, “Risk assessment of XLPE
power cables based on fuzzy comprehensive evalution method,” 2010
Asia-Pacific Power and Energy Engineering Conference. Chengdu, pp.
1-4, 2010.
Jochen Bühler, Gerd Balzer, “Evaluation of the condition of medium
voltage urban cable networks using fuzzy logic,” 2009 IEEE Bucharest
Power Tech Conference. Bucharest, Romania, pp. 1-8, 2009.
Joy K. Mishra, Young-Wook Chang, and Byung Chul Lee, “Mechanical
properties and heat shrinkability of electron beam crosslinked
polyethylene-octene copolymer,” Radiation Physics and Chemistry, vol.
77, pp. 675-679, 2008.
CHEN ji-dan, LIU Zi-yu, “Dielectric physics,” Beijing: China Machine
Press, 1980.
Activation Energy
C8 (KJ/mol )
[4]
REFERENCES
[1]
Date of
Bringing Into
Service
THE RULES OF FUZZY DIAGNOSIS
TABLE IX.
INFORMATION OF CABLE LINES
No. of
Cable Line
Fuzzy If-Then Rules
If C13 then A3
If C73 then A3
If C53 and C83 then A3
If C12 and C52 and C83 then A3
If C12 and C72 and C81 then A2
If C12 and C72 and C82 then A2
If C12 and C51and C72 and C83 then A2
If C12 and C52 and C71and C82 then A2
If C11 and C51 and C71 and C81 then A1
If C11 and C52 and C71 and C81 then A1
No. of
Cable Line
CONCLUSION
Based on multi-parameter test data of a large number of
service aged cable samples, the parameters for identifying and
quantifying the XLPE cable insulation ageing state under
unique service conditions were presented by statistical
analysis and fuzzy clustering. And a cable insulation ageing
diagnosis model was established by fuzzy inference, which
was proved to be applicable by case study.
E. Fuzzy inference
The fuzzy inference was performed using intensity transfer
method. In this paper, the conclusion of fuzzy diagnosis is Aj,
where ȝAj(x)=max(ȝA1(x), ȝA2(x), ȝA3(x)).
TABLE VII.
CASE STUDY