materials
Article
Monitoring Porcelain Insulator Condition Based on Leakage
Current Characteristics
Ali Ahmed Salem 1 , Kwan Yiew Lau 1, * , Mohd Taufiq Ishak 2, * , Zulkurnain Abdul-Malek 1 ,
Samir A. Al-Gailani 3 , Salem Mgammal Al-Ameri 1 , Ammar Mohammed 4 , Abdulaziz Ali Saleh Alashbi 4
and Sherif S. M. Ghoneim 5
1
2
3
4
5
*
Citation: Salem, A.A.; Lau, K.Y.;
Ishak, M.T.; Abdul-Malek, Z.;
Al-Gailani, S.A.; Al-Ameri, S.M.;
Mohammed, A.; Alashbi, A.A.S.;
Ghoneim, S.S.M. Monitoring
Porcelain Insulator Condition Based
on Leakage Current Characteristics.
Institute of High Voltage and High Current, School of Electrical Engineering, Universiti Teknologi Malaysia,
Johor Bahru 81310, Malaysia
Faculty of Engineering, National Defence University of Malaysia (UPNM), Kuala Lumpur 57000, Malaysia
School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Malaysia
School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
Electrical Engineering Department, College of Engineering, Taif University, Taif 21944, Saudi Arabia
Correspondence: mtaufiq@upnm.edu.my (M.T.I.); kwanyiew@utm.my (K.Y.L.)
Abstract: Insulator monitoring using leakage current characteristics is essential for predicting an
insulator’s health. To evaluate the risk of flashover on the porcelain insulator using leakage current,
experimental investigation of leakage current indices was carried out. In the first stage of the experiment, the effect of contamination, insoluble deposit density, wetting rate, and uneven distribution
pollution were determined on the porcelain insulator under test. Then, based on the laboratory
test results, leakage current information in time and frequency characteristics was extracted and
employed as assessment indicators for the insulator’s health. Six indicators, namely, peak current
indicator, phase shift indicator, slope indicator, crest factor indicator, total harmonic distortion indicator, and odd harmonics indicator, are introduced in this work. The obtained results indicated
that the proposed indicators had a significant role in evaluating the insulator’s health. To evaluate
the insulator’s health levels based on the extracted indicator values, this work presents the naïve
Bayes technique for the classification and prediction of the insulator’s health. Finally, the confusion
matrix for the experimental and prediction results for each indicator was established to determine
the appropriateness of each indicator in determining the insulator’s health status.
Materials 2022, 15, 6370. https://
doi.org/10.3390/ma15186370
Keywords: porcelain insulator; contamination; leakage current characteristics; classification
Academic Editors: Hone-Zern Chen
and Albena Paskaleva
Received: 28 July 2022
Accepted: 28 August 2022
Published: 14 September 2022
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4.0/).
1. Introduction
Outdoor insulators are important in electrical power transmission systems. However,
the efficiency and health of the insulators are greatly impacted by numerous environmental
factors, including wetness and contamination. Outdoor environmental factors, such as
contamination and moisture, have a significant impact on insulator effectiveness. Moisture,
such as rain and fog, in the presence of pollution reduces the surface resistance of insulators.
Surface resistance lowering may result in higher leakage current flowing on the surface and
dry band arcing. A large magnitude of leakage currents flowing on the surface over an extended length of time may produce insulator surface deterioration, which might eventually
lead to flashover [1–6]. The unwanted discharge may result in a flashover phenomenon that
leads to electrical grid disruption or even failure [7–9]. It is, therefore, crucial to monitor the
status of the insulators to ensure that they are fit for purpose [10,11]. This would further
strengthen the efficiency of the power grid and decrease its failure probability.
The evaluation of the performance of outdoor insulators continues to be a significant
topic in the high-voltage community [12–15]. The use of the leakage current (LC) parameter
in observing the performance of outdoor insulators has been popular and offers many
advantages. This is because LC monitoring considers various environmental conditions,
Materials 2022, 15, 6370. https://doi.org/10.3390/ma15186370
https://www.mdpi.com/journal/materials
Materials 2022, 15, 6370
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such as temperature, humidity, pollution, and rain [16]. Moreover, LC monitoring can be
done online on a continuing basis. Examples of LC monitoring techniques include the
use of the microwave reflectometer system, which has been used to monitor LC for dry
insulator surfaces [17]. However, this type of monitoring system is less cost-effective. The
authors in [18] offer an alternative method that uses an antenna to monitor LC on polluted
insulators by capturing electromagnetic radiation resulting from partial discharge on the
insulator. The advantage of this system is that the components used are not damaged by
flashover voltage, as is the case of other systems. To the best of our knowledge, however,
this system has yet to be tested under large electromagnetic interference caused by coronas
and other effects of high-voltage conductors.
Another issue of interest in the monitoring technique of polluted insulator LC refers
to the competence to establish a strong link between the LC and the condition of the
insulator under service. Many researchers have offered various approaches to evaluate the
physical state of insulators [16,19–21]. Improvement in LC-based monitoring is achieved by
extracting information from the LC components. The LC statistical results, such as average,
maximum, and standard deviation, recommended by [22] were used for evaluating the
level of contamination. These researchers suggested that these parameters allow the
quantification of the dimensions and density of the contamination layer over the surface
of the insulator. Another study [19] assessed the contaminated insulator conditions by
evaluating the phase angle between current and voltage signals. According to the results
in [19], phase angle differences are a helpful indication for assessing contaminants and
humidity fluctuations in a clean environment.
The assessment of the LC signal in the frequency domain through fast Fourier transform (FFT) and wavelet transformations is also a relevant technique used to predict insulators’ pollution status [23–25]. Overall, the results have indicated that contaminants on the
insulator amplify the harmonic of leakage current components, especially the harmonics
in an odd order. The results imply that the contamination leads to the rise of the first and
third harmonics and the total harmonic distortion (THD) [26]. The concerned harmonics
are the first and third component harmonics under an AC voltage. Accordingly, the study
found that increasing these harmonics causes a rise in THD, which changes according to
the change in the level of contamination and harmonics of the supplied voltage [27].
Some publications in the literature have proposed several numerical indices to identify
the performance of overhead line insulators. As far as we are aware, none of them evaluated
the performance of insulators using one specific indicator. In direct connection, acoustic
and thermal-based diagnostic methods, such as ultrasonic wave [28] and acoustic fault
diagnosis [29], have been used to evaluate the health of overhead lines insulator. However,
the performance of these techniques is affected by the acoustic frequency noise generated
from the electromagnetic field of the overhead line. Infrared thermal imaging [30] and
temperature analysis of infrared insulator images [31] are utilized to diagnose polluted
insulators as well. However, these techniques rely only on the thermal behavior of the
insulator, which is limited in revealing an accurate insulator state. Although the wirelessbased system [32] and network sensors [18] show good performance in monitoring insulator
conditions, their high sensitivity to environmental weather, such as dust and rain, influences
the accuracy of the results.
It is essential to have an indicator reflecting the condition of the insulators [16,20].
The extraction of the LC components based on the frequency domain to calculate the
relevant indicators were performed. For example, the ratio of leakage current’s third to fifth
harmonic indicator (fifth/third) was extracted to estimate flashover incidences [27]. The
published findings for silicon rubber and glass insulators show good correlation between
the magnitude of the contamination and the reading of this indicator. In addition, the
literature review demonstrated that no attempt has been made to investigate the insulators’
conditions using indices considering the slope of the signal in the time domain and the odd
harmonic components between 0 and 500 Hz for LC. This technique is supposed to yield a
more reliable prediction when compared with the other leakage current indicators.
Materials 2022, 15, 6370
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Regarding the classification and prediction of insulator health, the previous work
in [33] introduces the support vector machine (SVM) for classifying the condition of the
insulator under the effect of the different coating damage modes. The assessment of
the contaminated insulators was performed using computer vision in [34] and k-nearest
neighbors (k-NN) [35]. The results of these studies indicated that these techniques provide
adequate efficiency and accuracy and that they are promising approaches since they are
quick and easy to perform. Some researchers have evaluated the conditions of insulators
using different artificial intelligence methods, as reported above. However, the application
of the naïve Bayes method to estimate the insulator conditions based on the leakage current
characteristics has not been investigated, and this will be discussed in our current work.
Our current work contributes to estimating the conditions of porcelain insulators,
as follows:
•
•
•
Six indicators based on the measured LC characteristics in the time domain and
frequency domain, namely, the peak current indicator, phase shift indicator, slope
indicator, crest factor indicator, total harmonic distortion indicator, and odd harmonic
indicator, were extracted by taking into consideration the significant effect of environmental factors on the performance of the overhead line insulators. Environmental
factors, including the soluble deposit density, wetting rate, insoluble deposit density,
and contamination ratio of the upper to the lower side of the insulator were taken into
consideration while simulating the nature of insulators in service.
The classification of the state of insulators based on the proposed indicator values
using the naïve Bayes approach was conducted.
The comparison of the performance of the proposed indicators using the confusion
matrix for the actual insulator conditions and naïve Bayes prediction results was
carried out.
The rest of this paper is structured as follows. Section 2 reviews the process of pollution
of the insulators and discusses how LC is measured. Section 3 illustrates the proposed
leakage current indicator expressions. Section 4 presents the experimental and modeling
results. Finally, the conclusion is presented in Section 5.
The advantages of this study are:
•
•
•
•
The proposed indicators are useful to monitor the condition of overhead line insulators
in real time.
Insulator condition estimation using LC indicators is simple, low cost, and accurate.
Applicable for any insulator type and any voltage level.
Monitoring insulator conditions on the transmission line (without removing the insulator and without interrupting the power line).
2. Materials and Methods
2.1. Test Sample
The porcelain insulators to be tested were collected from the transmission division
of the national network in Malaysia. The selected insulators’ main shape is portrayed in
Figure 1. The insulators’ actual specifications are tabulated as in Table 1. In this paper,
a single disk of porcelain insulators and a string of three units of porcelain insulators
were tested.
Materials 2022, 15, 6370
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Figure 1. Insulator sample.
Table 1. Test insulator structure characteristics.
Parameter
Symbol
Length (cm)
Creepage distance
L
32
Insulator height
H
14.6
Insulator diameter
D
25.5
Rib diameters
d1
d2
d3
dc
19.5
14.5
10.5
5
2.2. Test Facilities and Procedures
The IEC-507 standard, namely, “Artificial pollution tests on high-voltage ceramic and
glass insulators to be used on AC systems” [36] was referred to while performing the
experimental setup. All experiments were carried out in an artificial test chamber with
dimensions 50 cm × 50 cm × 75 cm in which the walls were polycarbonate sheets. The
chamber was installed with four inlet valves used for spraying insulators under test for the
purpose of wetting. Figure 2a shows a schematic diagram for the high-voltage polluted
insulator experimental setup. A photograph of the test setup and the equipment used
in the high-voltage laboratory is given in Figure 2b. The experimental circuit setup was
composed of the following components: A is a transformer (220 V/100 kV, 5 kVA, 50 Hz),
B is a capacitive voltage divider, C is the test sample inside the chamber, D is a monitoring
system to measure the leakage current, E is a resistive step-down divider (100:1) employed
for the measurement of the LC and protection of the DAQ device, and F is a fog generator
with a rate controller for wetting.
Materials 2022, 15, 6370
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(a)
(b)
Figure 2. (a) Diagram of insulators’ test arrangement; (b) photograph of the insulat
Figure 2. (a) Diagram of insulators’ test arrangement; (b) photograph of the insulators under test.
2.3. Wetting and Polluting
Prior to commencing the experiment, traces of grease and dirt were removed carefully
for all specimens using alcohol liquid. Next, the insulators were dried for 24 h naturally.
The contamination was then applied to the insulator surface based on the solid layer
method [37–40]. The pollution was made up of two types of deposits: soluble deposit
density (SDD), which is represented by sodium chloride salt (NaCl), and insoluble deposit
density (NSDD), which was represented by kaolin. To prepare the SDD, the required
amount of NaCl salt was mixed with 1000 mg of water to establish the pollution solution.
Then, a conductivity meter was used to measure the conductivity of the polluted solution
σσ at room temperature
for three amounts of salt (10 g, 20 g, and 30 g) to compute three
σσ
levels of SDD. Also, three amounts of kaolin (20 g, 40 g, and 60 g) were used to produce the
NSDD levels listed in Table 2. Next, the conductivity of the pollution solution at 20 ◦ C was
calculated using Equation (1):
σ20 =20σσ × [1[1− b((θ −20)]
20)]
(1)
Materials 2022, 15, 6370
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where σ20 and σσ , represent the solution conductivity at 20 ◦ C, and the solution conductivity
at the room temperature. θ is the temperature of the solution and b is a temperaturedependent factor that is determined to be 0.02 at = 27.5 ◦ C using Equation (2) [36].
b = −3.2 × 10−8 θ 3 + 1.032 × 10−5 θ 2 − 8.272 × 10−4 θ + 3.544 × 10−2
(2)
Table 2. Pollution layer characteristics.
σ20 (S/m)
SDD (mg/cm2 )
NSDD (mg/cm2 )
Wt (mL/h)
Pollution Level
0
0.39
0.72
1.38
0
0.05
0.1
0.2
0
0.15
0.25
0.35
0
3
6
9
Clean
Low
Medium
High
The SDD was calculated based on the IEC 60507 [36] and IEC 60815 [41] standards
using Equation (3):
(5.7 × σ20 )1.03 × V
(3)
SDD =
S
where V and S represent the volume of pollution solution, and the insulator surface area,
respectively. Meanwhile, Equation (4) was used to calculate NSDD according to the IEC-507
standard [36]:
[(ws − wi ) × 103 ]
(4)
NSDD =
S
where ws and wi are the mass of the filter paper under contamination and under dry
conditions, respectively.
As shown in Table 2, three levels were determined for both SSD and NSDD: light,
moderate, and high contamination. The sample was then polluted and hung in the test
room and left drying for around 1 day to ensure that the polluted insulator was dried. The
test room pressure remained constant throughout the experiment, matching the laboratory’s
ambient pressure. The temperature in the testing room was roughly 27.9 ◦ C, which was
about the same as the indoor temperature in the laboratory. The relative humidity inside
the test room was set to 75% and monitored during the testing with the help of a humidity
sensor. The spray technique was used to wet the insulator, with six nozzles regularly spaced
around the test room wall. A control panel outside the test room was used to automatically
calculate the fog flow rate. To achieve the moisture of insulators at different levels, three
degrees of wetting rates Wt were applied, i.e., 3 mL/h, 6 mL/h, and 9 mL/h.
Under both uniform and uneven contamination distributions, the porcelain was
investigated. Also, three contamination ratios of the upper to the lower side of the insulator
( PL /Pu ), i.e., 1/3, 1/5, and 1/8, were selected in the uneven contamination case. The upper
and lower surfaces of the insulator were polluted separately in the nonuniform pollution
case to yield SDDu and SDDL , whereas the overall SDD can be met by Equation (5) [5,40]:
SDD =
SDDu × Su + SDDL × S L
Su + S L
(5)
where Su and S L are the area of the upper and lower surface of insulator, respectively.
According to these selected pollution ratios, the SDD of the upper and lower sides (SDDu
and SDDu ) can be satisfied by Equation (6):
SDDPu =
2 × SDD
2 × SDD
, SDDPL =
1 + ( PL /Pu )
1 + ( Pu /PL )
(6)
Materials 2022, 15, 6370
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2.4. Data Monitoring
As illustrated in the experimental setup in Figure 2, the input voltage values were
obtained using a divider consisting of two capacitors with a ratio (C1 to C2 ) of 10,000:1. On
the other side of the test chamber, the monitoring system consisted of a data acquisition
(DAQ) card, oscilloscope, and a PC, which was used to measure LC. A resistive divider
(100:1) was employed, since the DAQ’s input voltage range is just 10 V. During the measurement, the LC data were captured from the divider and then transferred using a DAQ card
to the PC. The captured data were saved in a CSV file after being displayed on a graphical
user interface of the LabVIEW software. For measurement validation, the oscilloscope was
utilized to compare with DAQ data reading. Finally, with the help of MATLAB software,
the LC data saved were converted into the frequency domain using FFT.
3. Characteristic Parameter of Leakage Current
In this paper, six characteristics/indices were extracted in both the time and frequency
domains of LC to predict the health of polluted insulators.
3.1. Leakage Current Indicators in Time Domain
The time and frequency domain signal of LC was used to derive all the leakage current
indicator equations. The formulae presented in this section were used as the indicators of
change in LC signal that was affected by the insulator conditions. In the time domain of
LC, four indicators were selected. The first and second indicators, which were the peak of
leakage current Im (denoted as x1 ), found based on the absolute value of the current signal,
and the phase shift φ between the applied voltage and LC (denoted as x2 ) were extracted
from the LC general equation, expressed as in Equation (7) [42]:
I = Im sin(ωt + φ)
(7)
where ω is the angular frequency equivalent to 2πf, with the value of frequency f in this
study 50 Hz. As such, the first two characteristics can be defined as in Equations (8) and (9):
x1 = Im
(8)
∆t
360◦
(9)
T
Figure 3 illustrates how the phase difference φ between the applied voltage and LC
can be determined for a clean and a polluted insulator. For a clean insulator, the phase
difference φ between the applied voltage and LC appeared to be nearly 90◦ (see Figure 3a).
As the amount of pollution on the insulator increased, the phase difference φ between
the applied voltage and LC decreased. Under heavily polluted circumstances, the phase
difference φ between the applied voltage and LC became nearly zero (see Figure 3b).
The third indicator x3 was extracted by calculating the slope of the line between two
consecutive peaks of LC signal, expressed as in Equation (10):
x2 = φ =
m
m
∑ | y n − y n −1 |
x3 =
n =1
x n − x n −1
∑|∆yn |
=
0
∆xn
(10)
where ∆yn is the LC difference for adjacent peaks at the nth point for time and ∆xn is the
time between these peaks. Figure 4a illustrates how the LC signal slope was calculated. The
fourth indicator x4 was obtained from the crest factor, which was calculated by dividing
the peak value with the RMS value of the LC waveform, as shown in Figure 4b. As such, x4
is expressed as in Equation (11):
I peak
(11)
x4 =
IRMS
Materials 2022, 15, 6370
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IRMS =
s
1
ii2
n∑
i
(12)
where RMS is root mean square,−ii is each measured
value, and n is the number of measurements.
=
−
=
−
=
−
where ∆
nth point for time and ∆
=
=
(a)
(b)
Figure 3. Leakage current and applied voltage phase angle: (a) clean; (b) pollution.
3
1
1
0
1
∆
∆
(a)
(b)
4
Figure 4. (a) Slope curve extracted
from leakage current waveform; (b) crest factor indicator extracted
from leakage current waveform.
1
2
3.2. Leakage Current Indicators in Frequency
Domain
The frequency-domain signals of LC for polluted insulators have characteristic features at frequencies below 500 Hz. In this paper, the odd harmonic and total harmonic
distortion (THD) of LC under 500 Hz were used to propose indicators for insulator condition assessment. The frequency characteristics of the LC are defined by the x5 and x6
indicators, as in Equations (13) and (14), respectively:
x5 = THD =
s
∞
∑ In 2
n =2
I1
(13)
Materials 2022, 15, 6370
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∑ In
x6 =
n
I3
n = 5, 7, 9
(14)
where In represent the nth harmonic and n is the odd-order harmonic number.
4. Classification Model
The naïve Bayesian classifier is a classification algorithm based on Bayes’s theorem. Its
underlying idea and building approach are more straightforward and simpler than those of
support vector machines and neural networks. Furthermore, compared to other algorithms,
the naïve Bayesian classifier algorithm suits the approach of this work, and brought great
accuracy in classifying the insulator situations. The naïve Bayesian classifier theorem,
which is employed in the classification of the suggested LC indicators, is explained in
this section. Figure 5 depicts the flowchart of the procedures involved in extracting and
categorizing the recommended indicators.
Start
Select test insulators
Insulators Arrangement
and performed test
Leakage current
measurement and saving data
as CSV file to PC
Change new
pollution
level
Convert leakage
current to FFT
Extract the useful
characteristic of LC
Define the LC
indicators
Determine of indicators
values in the pollution
levels
No
Are all sample
s diagnosed?
Performance assessment
End
Comparison
Insulator condition
recognition using
NAIVE BAYESIAN
CLASSIFIER based on
LC indicators
Trained NAIVE
BAYESIAN
CLASSIFIER model
NAIVE BAYESIAN
CLASSIFIER model
establishment
Test
dataset
Classifying LC indicators
test data according the
condition insulator under
test to
(Normal, Abnormal,
Critical),
Training dataset
Yes
Figure 5. Data extraction and classification of the proposed indicators.
arg max{ ( | )}
To understand the naïve Bayesian, method,
consider y to be a collection of samples.
1,2,...,
Each sample contains n condition characteristics that represent
its special traits, as well as
( ) ( | )
one class label. All LC features are assumed
in
this work, while the class
arg maxto be interrelated
, 1,2,...,
( )
all training
label is assumed to be separated. Assume
that
examples
are classified into m
classifications and that the class labelarg
of max
each sample
changes
from
{z
1 , z2 , . . . , zm }. As a
( ) ( | )→
,
1,2,...,
result, any sample can be shown as an n-dimensional vector. y = (y1 , y2 , . . . , yn ) would be a
piece of testing data whose class label has to be determined. A naïve Bayesian classifier may
( )
compute the posterior probabilities and decide the class label for the new sample based
( )
/
( | )
( | )
Naïve Bayesian Classifier
Materials 2022, 15, 6370
10 of 22
on the previous and class-conditional possibilities of the new sample. A naïve Bayesian
classifier uses Equation (15) to characterize the new sample’s class label [43]:
n
o
→
z = argmax P(zk y )
zk ,k =1,2,...,m
→
( y|zk )
= argmax P(zk ) P→
(15)
P(y)
zk ,k =1,2,...,m
→
= argmax P(zk ) P( y|zk )
zk ,k =1,2,...,m
where P(zk ) represents the previous probability of the zk class that can be found from
→
P(zk ) = Nk /N, Nk is the number of samples within zk class, N is data set size, and P( y|zk )
represent the class-conditional probability. The main aim of the naïve Bayesian classifier is
→
to determine P( y|zk ) based on the training samples in the zk class.
All features are assumed to be independent by the naïve Bayesian classifier. As a
result, the class-conditional probability can be written as in Equation (16):
f
→
P ( y | z k ) = P ( y1 , y2 , . . . , y n | z k ) =
∏ P(y f
zk )
(16)
i =1
→
To find the class label of y , the Naïve Bayesian classifier can substitute the classconditional probability with Equation (16) and yield the decision function in Equation (17).
z = argmax
zk ,k =1,2,...,m
(
f
nk
P(y f zk )
N i∏
=1
)
(17)
where P(y f zk )(1 ≤ f ≤ n) is an essential factor in determining the class label of the
new sample.
5. Results and Discussion
5.1. Leakage Current Results
The LC findings of uniformly polluted insulators in both time and frequency domains
with various SDD levels but fixed NSDD of 0.15 mg/cm2 and Wt of 3 mL/h are shown in
Figures 6 and 7. Figure 6 indicates that increased pollution severity causes a considerable
rise in LC under specific values of NSDD, Wt, and Pu /PL . The LC increase can be explained
by the increase in conductivity of the pollution layer on the insulator’s surface once
subjected to wetness. Consequently, the LC flowed from the high voltage terminal to the
ground in the form of positively and negatively charged ions. Some spot arcs were observed
on occasion under high-contamination conditions, particularly in the existence of moisture.
During the flashover, the signal of the LC appeared to be severely distorted, as shown in
Figure 6d. Furthermore, when LC increased, the THD and harmonic values increased (see
Figure 7), while the phase angle between the current and voltage decreased. The decrease
in phase angle between the LC and voltage is due to the resistive current increasing with
constant capacitive current. Once the contamination level on the insulator surface was
raised, a clear change in the odd harmonics (3rd, 5th, 7th, and 9th) was observed (see
Figure 7). As seen in Figure 8, the 3rd harmonic will increase to surpass the 5th, 7th,
and 9th harmonics, with a clear increase in the 7th and 9th harmonics. Furthermore,
during discharge activity on the insulator’s surface, the 3rd harmonic is often substantially
high [44,45].
ϕ
Materials 2022, 15, 6370
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(a)
(b)
(c)
(d)
Leakage
current
(mA)(mA)
Leakage
current
Figure 6. Leakage current waveform under NSDD = 0.15 mg/cm2 , wt = 3 mL/h and different SDD:
(a) SDD = 0.00 mg/cm2 ; (b) SDD = 0.05 mg/cm2 , (c) SDD = 0.12 mg/cm2 , (d) SDD = 0.2 mg/cm2 .
40
4
40
30
30
20
2
0
20
10
10
0
0
5
4
2
0
0
0
3 th
11 kV
33 kV
th
11 kV
33thkV
7
5 th
3 th
0
100
0
100
100
7 th
200
200
200
300
300
300
Frequency (Hz)
100
200
300
(a) (Hz)
Frequency
400
400
400
400
40
40
30
30
20
th
20
10
3
100
3 th
0
40
11 kV
33 kV
11 kV
33 kV
0
0
100
5
th
5 th
200
100
200
30
20
7 th
300
300
Frequency
(b) (Hz)
3 th
20
10
7 th
Frequency (Hz)
11 kV
33 kV
11 kV
33 kV
40
30
100
400
0
400
(c)
0
0
100
7 th
5 th
3 th
7 th
5 th
200
300
Frequency (Hz)
100
200
300
Frequency
(d) (Hz)
400
400
Figure 7. FFT of leakage current waveform under NSDD = 0.15 mg/cm2 , Wt = 3 mL/h and
different SDD: (a) SDD = 0.00 mg/cm2 ; (b) SDD = 0.05 mg/cm2 , (c) SDD = 0.12 mg/cm2 ,
(d) SDD = 0.2 mg/cm2 .
Figure 8. Odd harmonics of leakage current under pollution grading [2].
Table 3 provides the test results of the LC harmonic components’ values (magnitude
Im , harmonics, THD, and phase shift angle φ) for different pollution levels under uniform
pollution distribution for all investigated
conditions. Under the clean condition, the ϕ
5th
ϕ
and 7th harmonics were greater than the 3rd harmonic. Furthermore, there were no signs
≈ϕ
≈ϕ
of flashover. The LC rose marginally as
the wetting rate increased when the clean insulators
≈≈ indicates that wetting the insulator surface caused
≈
were tested under a specific NSDD. This
≈
the flow of the charges from the high-voltage
terminal to the ground to rise noticeably.
Materials 2022, 15, 6370
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Table 3. LC characteristics for insulators under different contamination degree for uniform distribution.
Single Insulator under 11 kV
SDD
Wt
Im
3rd
5th
7th
9th
THD
φ
Im
3rd
5th
7th
9th
THD
φ
0
0
0
3
6
9
0
3
6
9
0
3
6
9
1.807
1.975
4.344
7.109
8.886
2.172
6.615
8.590
9.577
4.048
7.306
8.984
10.367
0.005
0.006
0.030
0.039
0.069
0.030
0.099
0.107
0.168
0.057
0.178
0.197
0.326
0.020
0.030
0.149
0.125
0.168
0.138
0.405
0.395
0.592
0.118
0.592
0.627
0.721
0.012
0.010
0.020
0.079
0.099
0.020
0.049
0.079
0.099
0.089
0.049
0.059
0.069
0.020
0.005
0.008
0.007
0.059
0.020
0.059
0.069
0.079
0.089
0.030
0.049
0.089
7.727
7.795
8.492
8.595
9.190
7.944
9.064
9.133
9.578
8.355
9.601
9.853
9.978
≈90
≈90
87.026
86.956
85.838
88.802
87.076
87.166
86.277
88.084
85.888
85.269
84.870
1.778
1.944
4.276
6.997
8.746
2.138
6.511
8.454
9.426
3.984
7.191
8.843
10.203
0.005
0.006
0.029
0.039
0.068
0.029
0.097
0.105
0.165
0.056
0.175
0.194
0.321
0.019
0.029
0.147
0.123
0.165
0.136
0.398
0.389
0.583
0.117
0.583
0.617
0.709
0.012
0.010
0.019
0.078
0.097
0.019
0.049
0.078
0.097
0.087
0.049
0.058
0.068
0.019
0.005
0.008
0.007
0.058
0.019
0.058
0.068
0.078
0.087
0.029
0.049
0.087
8.523
8.598
9.367
9.481
10.136
8.762
9.998
10.073
10.565
9.216
10.590
10.867
11.006
≈ 90
86.370
82.100
82.034
80.980
83.776
82.147
82.232
81.394
83.098
81.027
80.443
80.066
0
3
6
9
0
3
6
9
0
3
6
9
5.134
8.886
11.749
15.501
7.207
12.144
14.711
18.166
8.984
16.093
20.141
25.769
0.089
1.234
1.343
1.777
0.138
1.402
1.649
1.767
0.178
1.876
2.172
2.577
0.425
3.159
2.962
3.554
0.553
2.281
2.271
2.281
0.642
2.488
2.370
3.159
0.089
0.592
0.819
0.908
0.079
0.839
1.382
1.481
0.128
1.007
1.382
1.185
0.039
0.296
0.395
0.760
0.069
0.652
0.691
0.790
0.099
0.553
0.889
1.086
9.384
11.693
12.904
16.482
10.174
12.447
14.688
17.179
11.008
15.248
18.025
21.534
82.036
79.152
67.365
57.495
80.589
66.078
54.142
42.575
78.164
56.098
40.050
33.154
5.053
8.746
11.564
15.257
7.094
11.953
14.479
17.880
8.843
15.840
19.824
25.363
0.087
1.215
1.322
1.749
0.136
1.380
1.623
1.739
0.175
1.846
2.138
2.536
0.418
3.110
2.915
3.498
0.544
2.245
2.235
2.245
0.632
2.449
2.332
3.110
0.087
0.583
0.807
0.894
0.078
0.826
1.360
1.458
0.126
0.991
1.360
1.166
0.039
0.292
0.389
0.748
0.068
0.641
0.680
0.777
0.097
0.544
0.875
1.069
10.351
12.897
14.234
18.180
11.222
13.729
16.200
18.949
12.142
16.818
19.882
23.752
77.392
74.671
63.552
54.241
76.027
62.338
51.077
40.165
73.739
52.922
37.783
31.277
0
3
6
9
0
3
6
9
0
3
6
9
6.319
14.612
16.093
19.055
7.405
16.982
19.351
23.103
9.281
19.549
20.141
25.769
0.217
3.258
4.048
4.542
0.316
3.456
5.233
5.628
0.405
5.529
7.109
6.121
0.711
2.666
3.159
4.048
0.790
3.061
3.653
4.048
0.790
3.554
4.048
4.147
0.109
0.395
0.612
0.938
0.128
1.027
1.086
1.283
0.306
1.283
1.481
1.678
0.079
0.790
1.185
0.790
0.207
0.889
0.968
1.086
0.227
1.580
1.283
1.777
9.384
17.522
21.580
26.575
11.293
24.849
28.861
36.542
11.327
34.873
39.582
48.052
71.058
55.100
38.453
27.106
75.739
52.415
30.160
25.070
70.289
39.581
20.589
14.920
6.553
15.153
16.688
19.760
7.679
17.610
20.067
23.958
9.624
20.272
20.886
26.722
0.225
3.379
4.198
4.710
0.328
3.583
5.426
5.836
0.420
5.733
7.372
6.348
0.737
2.764
3.276
4.198
0.819
3.174
3.788
4.198
0.819
3.686
4.198
4.300
0.113
0.410
0.635
0.973
0.133
1.065
1.126
1.331
0.317
1.331
1.536
1.741
0.082
0.819
1.229
0.819
0.215
0.921
1.003
1.126
0.235
1.638
1.331
1.843
10.444
19.502
24.018
29.578
12.569
27.657
32.122
40.671
12.607
38.814
44.055
53.482
68.988
53.495
37.333
26.316
73.533
50.889
29.281
24.340
68.242
38.428
19.989
14.486
0
3
6
9
0
3
6
9
0
3
6
9
8.313
26.065
29.027
36.036
10.169
28.928
32.482
39.393
10.564
34.950
51.735
64.273
0.523
9.478
9.774
10.732
0.612
9.379
10.762
12.835
0.642
13.743
15.994
17.673
0.948
2.764
2.271
2.271
0.987
2.764
2.666
3.159
1.007
2.962
3.456
2.666
0.444
2.073
2.567
2.666
0.622
2.073
2.271
2.370
0.474
1.185
2.073
1.283
0.316
1.481
1.086
1.283
0.316
1.086
1.185
1.185
0.494
1.481
1.185
1.678
11.339
41.788
47.229
50.029
12.322
52.155
54.681
61.482
12.150
59.059
67.768
69.563
56.727
32.056
17.545
11.537
42.455
26.397
10.709
1.038
32.774
3.912
2.066
0.828
8.621
27.029
30.101
37.370
10.545
29.998
33.684
40.851
10.955
36.244
53.649
66.651
0.543
9.829
10.136
11.129
0.635
9.726
11.160
13.310
0.665
14.252
16.586
18.327
0.983
2.867
2.355
2.355
1.024
2.867
2.764
3.276
1.044
3.071
3.583
2.764
0.461
2.150
2.662
2.764
0.645
2.150
2.355
2.457
0.491
1.229
2.150
1.331
0.328
1.536
1.126
1.331
0.328
1.126
1.229
1.229
0.512
1.536
1.229
1.741
12.620
46.510
52.566
55.682
13.714
58.049
60.860
68.429
13.523
65.732
75.426
77.424
55.074
31.122
17.034
11.201
41.219
25.628
10.397
1.008
31.820
3.798
2.006
0.804
0.15
0.00
0.25
0.35
0.15
0.05
0.25
0.35
0.15
0.1
0.25
0.35
0.15
0.2
3 Unit Insulator String under 33kV
NSDD
0.25
0.35
Table 3 also shows the results of the uniformly polluted insulators with the change in
SDD, Wt, and NSDD. Table 3 shows that the LC on a clean insulator’s surface is very low,
approximately 1.81 mA for single disk and 1.78 for string insulators. This indicates that
under clean circumstances, the performance of the insulators is capacitive. Because of the
capacitive property of LC, the phase shift angle between current and voltage will be about
90◦ . Under the clean and dry scenario, the 5th harmonic is higher than the 3rd harmonic.
Generally, the LC component test results in Table 3 show that:
1.
2.
3.
4.
Under dry conditions, surface conductivity was minimal. Therefore, the influence of
increasing SDD and NSDD on LC and LC characteristics in this condition was minor.
The LC magnitude grew substantially as the contamination severity of SDD, NSDD,
and Wt increased.
As SDD, NSDD, and Wt increased and Pu /PL decreased, odd harmonic values and
THD increased. In contrast, the phase angle decreased.
When the Wt was changed for a clean insulator under a specific NSDD, the LC value
varies somewhat, as do the temporal and frequency characteristics of the LC.
5.2. Leakage Current Indices Finding
Figure 9 shows the LC indices of a clean (0.00 mg/cm2 of SDD) insulator under
different Wt and NSDD. Each indicator demonstrates a unique behavior when the wetting
rate and NSDD were changed. Of note, there is no significant difference between different
NSDD under the same Wt. The x1 , x3 , x4 , and x5 indices increased with the rise in both the
Materials 2022, 15, 6370
13 of 22
NSDD and Wt. For example, when the Wt was increased from 3 mL/h to 9 mL/h under
0.15 mg/cm2 of NSDD, the x1 increased from 4.2 to 8.88, x3 increased from 0.065 to 0.11, x4
increased from 1.56 to 1.585, and x5 increased from 7.6 to 10.49.
(a)
(b)
(c)
(d)
(e)
(f)
Figure 9. LC indices of a clean insulator under different Wt and NSDD; (a) x1 ; (b) x2 ; (c) x3 ; (d) x4 ;
(e) x5 ; (f) x6 .
In contrast, the x2 and x6 decreased with the increase in both the Wt and NSDD. For
example, when the Wt increased from 3 mL/h to 9 mL/h under NSDD = 0.15 mg/cm2 , the
x2 decreased from 87.2 to 86.01 and x6 decreased from 5.9 to 4.7.
5.2.1. Indicators Trends under Different SDD
The leakage current indices x1 , x3 , x4 , and x5 under different SDD, NSDD, Wt, and
Pu /PL for single and string insulators are presented in Figure 10 and Table 4. The LC
indicators of insulators under test increased with the increase in SDD under specific NSDD,
Wt, and Pu /PL . On the contrary, the indices x2 and x6 of insulators decreased with the
increase in SDD under the same conditions. For example, under NSDD of 0.25 mg/cm2 ,
Wt of 6 mL/h and Pu /PL of 1/3, when SDD was 0.05, 0.1 and 0.2 mg/cm2 , the x1 for the
single disk insulator corresponded to 12.2, 15.4, and 26.6 mA, respectively. The x1 also
increased by 26.2% and 118.03% when the SDD increased from 0.05 to 0.1 mg/cm2 and
from 0.1 to 0.2 mg/cm2 , respectively. For x6 , when the SDD was 0.05, 0.1 and 0.2 mg/cm2 ,
x6 corresponded to 2.69, 1.31, and 0.63 mA, respectively. The LC indicators showed a
similar trend and performance for the insulator string under 33 kV with minor variations,
as illustrated in Table 5.
Materials 2022, 15, 6370
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x
NSDD Wt
0.15
0.25
0.35
0
0.842
0.636
0.522
3
2.64
1.48
0.9
0.44
2.479
6
2.94
1.63
1.19
0.72
9
3.65
1.93
1.57
0.9
0
1.03
0.75
0.73
0.22
0.9493 0.6912 0.6728 0.2028
3
2.93
1.72
1.23
0.67
2.7
6
3.29
1.96
1.49
0.872
9
3.99
2.34
1.84
0.969
1.39
0.8451 0.4131
2.761
1.53
1.117 0.6761
3.427
1.812
1.474 0.8451
1.585
1.134 0.6175
3.032
1.806
1.373 0.8037
3.677
2.157
1.696 0.8931
0
1.07
0.94
0.91
0.413
1.024 0.8995 0.8708 0.3952
3
3.54
1.98
1.63
0.742
3.388
1.895
6
5.24
2.04
1.74
0.911
5.014
2.522
1.952 0.8718
9
6.51
2.61
2.31
1.046
6.23
2.498
2.098
1.001
0.2
0.1
0.05
0
0.2
0.1
0.05
0
11 kV
1
0.196 0.7906 0.5972 0.4901 0.184
1.56
6
5
4
3
2
0.71
1
33 kV
2
SDD (mg/cm )
(a)
NSDD Wt
0.15
0.25
0.35
0
0.842
1.27
0.636
0.78
0.522
0.17
3
2.64
7.29
1.48
2.8
0.9
0.45
0.44
0.06
2.479
7.64
1.39 0.8451
2.934
0.472 0.4131
0.063
6
2.94
9.46
1.63
5.82
1.19
0.96
0.72
0.09
2.761
9.914
1.53
6.099
0.196
0.064 0.7906
0.178 0.184
1.331 0.5972
0.817 0.4901
0.067
9
3.65
13.97
1.93
10.03
1.57
1.27
0.9
0.103
3.427
14.64
0
1.03
1.57
0.75
0.97
0.73
0.37
0.22
0.07
0.9493
1.026 0.6728
1.661 0.6912
0.391 0.2028
0.074
3
2.93
11.86
1.72
6.98
1.23
0.85
0.67
0.063
2.7
12.55
1.585
7.385
1.134
0.899 0.6175
0.067
6
3.29
13.85
1.96
8.37
1.49
1.33
0.872
0.104
3.032
14.65
1.806
8.855
1.373
1.407 0.8037
0.11
9
3.99
18.66
2.34
10.64
1.84
3.83
0.969
0.148
3.677
19.74
2.157
11.26
1.696
4.052 0.8931
0.157
1.812
10.51
0.15
20
1.474
1.331 0.8451
0.108
0
1.07
0.78
0.94
0.98
0.91
0.64
0.413
0.082
1.024
1.027 0.8708
0.817 0.8995
0.671 0.3952
0.086
3
3.54
12.85
1.98
6.39
1.63
1.08
0.742
0.113
3.388
13.47
1.895
6.697
1.56
1.132
6
5.24
15.84
2.04
9.11
1.74
3.76
0.911
0.162
5.014
16.6
2.522
9.547
1.952
0.17
3.94 0.8718
9
6.51
24.87
2.61
12.84
2.31
6.98
1.046
0.139
6.23
26.06
2.498
13.46
2.098
7.315
1.001
0.146
0.2
1
0.1
0.05
0
0.2
0.1
0.05
0
11 kV
25
1.117
1.006 0.6761
0.094
15
0.25
10
0.71
0.118
(b)
NSDD Wt
x3
5
0.35
x4
0.842
0 2.145
1.772
0.636
1.539
0.522
1.917 0.4901
1.665 0.184
1.65
1.525
0.196 0.7906
2.299 0.5972
2.64
2.59
1.48
2.391
1.592
0.9
1.563
0.44
2.776
2.479
1.723 0.4131
1.692
1.39 0.8451
2.587
2.94
6 2.739
1.63
2.521
1.19
2.234
1.575
0.72
2.936
2.761
1.53
2.728
1.704
1.117 0.6761
2.417
3.65
9 2.832
1.93
2.71
1.57
2.43
1.586
0.9
3.036
3.427
1.812
2.932
1.716
1.474 0.8451
2.629
1.03
0 2.216
1.824
0.75
1.609
0.73
1.654
1.937 0.6728
1.709 0.2028
1.557
0.22 0.9493
2.353 0.6912
3
3
2.63
2.93
1.72
2.467
1.23
2.181
1.567
0.67
2.793
2.7
1.585
2.62
1.664
1.134 0.6175
2.316
6 2.762
3.29
1.96
2.485
1.49
2.419
1.582
0.872
2.933
3.032
1.806
2.639
1.68
1.373 0.8037
2.569
9 2.846
3.99
2.34
2.64
1.84
2.596
1.599
0.969
3.022
3.677
2.804
2.157
1.698
1.696 0.8931
2.757
1.07
0 2.312
1.934
0.94
1.623
0.91
1.562
0.413
1.659
1.724 0.3952
1.024 0.8995
2.495
2.087 0.8708
3 2.815
3.54
1.98
2.501
1.63
2.219
1.575
0.742
3.038
3.388
1.895
2.699
1.56
2.357
6 2.862
5.24
2.04
2.558
1.74
2.492
1.591
0.911
3.089
5.014
2.761
2.522
1.69
2.647
1.952 0.8718
9 2.861
6.51
2.61
2.79
2.657
2.31
1.608
1.046
3.088
6.23
3.011
2.498
2.822
2.098
1.708
1.001
0.1
0.05
0
0.2
0.1
0.05
0
0.2
33 kV
2
SDD (mg/cm )
11 kV
2
SDD (mg/cm )
(c)
0.25
0.35
x
0.196
6.82 0.7906
10.45 0.5972
8.645 0.4901
8.645 0.184
7.181
2.6
2.4
2.2
2
1.673
0.71
1.8
1.6
33 kV
0.842
9.92
0.636
8.21
8.21
0.522
3
2.64
36.56
1.48
15.33
0.9
10.23
0.44
7.43
2.479
38.5
1.39 0.8451
16.14
10.77 0.4131
7.824
6
2.94
41.32
1.63
18.88
1.19
11.29
0.72
7.52
2.761
43.51
1.53
19.88
1.117
11.89 0.6761
7.919
9
3.65
43.77
1.93
23.25
1.57
14.42
0.9
8.04
3.427
46.09
1.812
24.48
1.474
8.466
15.18 0.8451
0
1.03
10.78
0.75
9.88
0.73
8.901
0.22
6.95
0.9493
11.14 0.6912
10.21 0.6728
9.203 0.2028
7.186
3
2.93
45.63
1.72
21.74
1.23
10.89
0.67
7.93
2.7
47.18
1.585
22.48
1.134
8.199
11.26 0.6175
6
3.29
47.84
1.96
25.25
1.49
12.85
0.872
7.99
3.032
49.46
1.806
26.11
1.373
13.29 0.8037
8.261
9
3.99
53.79
2.34
31.97
1.84
15.03
0.969
8.38
3.677
55.61
2.157
33.05
1.696
8.664
15.54 0.8931
30
0
1.07
10.63
0.94
9.91
0.91
9.631
0.413
7.31
1.024
11.31 0.8995
10.24 0.3952
10.54 0.8708
7.776
25
3
3.54
51.67
1.98
30.51
1.63
13.34
0.742
8.4
3.388
54.97
1.895
32.46
1.56
14.19
0.71
8.936
20
6
5.24
59.29
2.04
34.63
1.74
15.77
0.911
8.62
5.014
63.07
2.522
36.84
1.952
16.78 0.8718
9.17
15
9
6.51
60.86
2.61
42.04
2.31
18.84
1.046
8.73
6.23
64.74
2.498
44.72
2.098
20.04
1.001
9.287
10
0.2
0.1
0.05
0
0.2
0.1
0.05
0
2
SDD (mg/cm )
x6
NSDD Wt
5
0
11 kV
2.8
(d)
NSDD Wt
0.15
3
60
0.15
55
50
45
40
0.25
35
0.35
0.522
6.222
3.414 0.5972
7.845
4.327 0.4901
6.508 0.184
0.196
7.5 0.7906
0
0.842
3.264
0.636
4.136
3
2.64
0.667
1.48
1.182
0.9
3.28
0.44
5.967
2.479
0.697
1.39 0.8451
1.236
6.241
3.431 0.4131
6
2.94
0.606
1.63
1.224
1.19
3.11
0.72
5.35
2.761
0.634
1.53
1.281
1.117
5.596
3.253 0.6761
9
3.65
0.58
1.93
1.272
1.57
2.939
0.9
4.714
3.427
0.606
1.812
1.33
1.474
4.931
3.074 0.8451
0
1.03
3.145
0.75
3.688
0.73
5.071
0.22
6
0.9493
3.29 0.6912
6.276
5.305 0.2028
3.857 0.6728
3
2.93
0.632
1.72
1.44
1.23
2.69
0.67
5.2
2.7
0.654
1.585
1.492
1.134
5.387
2.787 0.6175
6
3.29
0.569
1.96
1.091
1.49
2.635
0.872
5.093
3.032
0.589
1.806
1.13
1.373
5.276
2.73 0.8037
2.157
1.181
1.696
4.753
2.668 0.8931
9
3.99
0.523
2.34
1.14
1.84
2.575
0.969
4.588
3.677
0.542
0
1.07
3.077
0.94
3.463
0.91
4.889
0.413
5.172
1.024
5.359
5.065 0.3952
3.188 0.8995
3.588 0.8708
3
3.54
0.409
1.98
1.161
1.63
2.158
0.742
3.778
3.388
0.432
1.895
1.226
1.56
2.279
6
5.24
0.42
2.04
0.958
1.74
2.136
0.911
3.725
5.014
0.443
2.522
1.012
1.952
2.256 0.8718
3.934
9
6.51
0.318
2.61
1.242
2.31
2.107
1.046
2.697
6.23
0.336
2.498
1.311
2.098
2.225 1.001
2.848
0.2
0.1
0.05
0
0.2
0.1
33 kV
11 kV
2
SDD (mg/cm )
(e)
7
6
5
4
3
2
0.71
3.989
0.05
1
0
33 kV
(f)
Figure 10. Leakage current indices of uniform polluted insulators under various wetting rate Wt and
NSDD: (a) x1 ; (b) x2 ; (c) x3 ; (d) x4 ; (e) x5 ; (f) x6 .
x1
70
60
x1 50
40
x2
x3
x4
x5
x6
x2
18
15
x3
20
2.90
16
2.85
12
2.80
12
9
6
x4
x5
60
55
50
x6
0.8
0.7
0.6
45
2.75
0.5
Materials 2022, 15, 6370
15 of 22
Table 4. LC indices of nonuniformly polluted insulators under different Wt and NSDD for
11 kV insulators.
SDD
mg/cm2
Pu/PL
NSDD
mg/cm2
0.15
0.05
0.25
0.35
0.15
0.1
0.25
0.35
0.15
0.2
0.25
0.35
1/3
Wt
mL/h
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
1/5
1/8
x1
x2
x3
x4
x5
x6
x1
x2
x3
x4
x5
x6
x1
x2
x3
x4
x5
x6
4.2
7.3
9.6
12.7
6
10.1
12.2
15.1
7.4
13.3
16.6
21.3
5.1
12
13.3
15.7
5.9
13.5
15.4
17.5
7.6
16
16.5
21.1
6.8
21.4
23.8
29.5
8.3
23.7
26.6
32.3
8.7
30.5
46.2
52.7
84.67
81.69
69.53
59.34
83.17
68.20
55.88
43.94
80.67
57.90
41.33
34.22
73.33
56.87
39.69
27.97
78.17
54.10
31.13
25.87
72.54
40.85
21.25
15.40
58.55
33.08
18.11
11.91
43.82
27.24
11.05
1.07
33.83
4.04
2.13
0.00
0.14
0.37
0.78
1.03
0.30
0.69
1.07
3.09
0.52
0.87
3.03
5.63
0.63
2.25
4.69
8.09
0.78
5.63
6.75
8.58
0.79
5.15
7.35
10.35
1.03
5.88
7.63
11.27
1.26
9.56
11.17
15.05
0.63
10.36
12.77
20.06
1.43
1.52
1.56
1.57
1.58
1.55
1.56
1.58
1.59
1.56
1.57
1.58
1.60
1.53
1.59
2.22
2.42
1.60
2.17
2.41
2.58
1.62
2.21
2.48
2.65
1.76
2.38
2.51
2.70
1.82
2.46
2.47
2.63
1.93
2.49
2.55
7.97
9.93
10.96
14.00
8.64
10.57
12.48
14.59
9.35
12.95
15.31
18.29
7.97
14.88
18.33
22.57
9.59
21.11
24.51
31.04
9.62
29.62
33.62
40.82
9.63
35.50
40.12
42.50
10.47
44.30
46.45
52.22
10.32
50.17
57.56
59.09
6.96
3.42
3.18
3.04
6.84
2.82
2.69
2.67
6.25
2.18
2.20
2.12
5.34
1.30
1.33
1.48
5.06
1.44
1.31
1.31
4.16
1.16
1.16
1.20
3.42
0.67
0.63
0.60
3.28
0.71
0.63
0.54
3.21
0.56
0.67
0.46
3.7
6.3
8.4
11.1
5.2
8.8
10.7
13.2
6.5
11.6
14.5
18.5
4.5
10.5
11.6
13.7
5.1
11.8
13.4
15.3
6.6
14
14.4
18.4
5.7
18.6
20.7
25.7
7.3
20.7
23.2
28.1
7.5
27.9
32.9
45.9
86.36
83.32
70.92
60.53
84.84
69.56
57.00
44.82
82.28
59.05
42.16
34.90
74.80
58.00
40.48
28.53
79.73
55.18
31.75
26.39
73.99
41.67
21.67
15.71
59.72
33.75
18.47
12.14
44.69
27.79
11.27
1.09
34.50
4.12
2.17
0.00
0.13
0.36
0.77
1.02
0.30
0.69
1.06
3.07
0.52
0.87
3.02
5.60
0.62
2.24
4.67
8.04
0.78
5.60
6.71
8.53
0.79
5.13
7.31
10.29
1.02
5.85
7.58
11.21
1.26
9.51
11.10
14.96
0.63
10.30
12.70
18.95
1.43
1.52
1.55
1.57
1.58
1.55
1.56
1.57
1.59
1.55
1.57
1.58
1.60
1.53
1.58
2.22
2.42
1.60
2.17
2.41
2.58
1.61
2.21
2.48
2.64
1.76
2.38
2.51
2.69
1.81
2.45
2.47
2.63
1.92
2.49
2.54
7.93
9.88
10.91
13.93
8.60
10.52
12.41
14.52
9.30
12.89
15.23
18.20
7.93
14.81
18.24
22.46
9.54
21.00
24.39
30.89
9.57
29.47
33.45
40.61
9.58
35.32
39.92
42.28
10.41
44.08
46.22
51.96
10.27
49.92
57.28
58.79
8.42
3.53
3.35
3.07
7.88
2.96
2.80
2.83
7.42
2.36
2.41
2.30
6.29
1.62
1.50
1.53
5.73
1.56
1.48
1.56
4.70
1.37
1.28
1.23
3.78
0.76
0.66
0.57
3.58
0.72
0.71
0.58
3.48
0.64
0.57
0.53
3.4
5.9
7.8
10.3
4.9
8.2
10
12.3
6
10.8
13.5
17.3
4.2
9.8
10.8
12.8
4.8
11
12.5
14.3
6.2
13
13.4
17.2
5.4
17.4
19.4
24.1
6.8
19.3
21.7
26.3
7.1
16.7
21.4
42.9
86.62
83.57
71.13
60.71
85.09
69.77
57.17
44.95
82.53
59.23
42.29
35.01
75.03
58.18
40.60
28.62
79.97
55.34
31.84
26.47
74.22
41.79
21.74
15.75
59.90
33.85
18.52
12.18
44.83
27.87
11.31
1.10
34.61
4.13
2.18
0.00
0.13
0.36
0.77
1.01
0.30
0.68
1.05
3.04
0.51
0.86
2.99
5.55
0.62
2.22
4.62
7.97
0.77
5.55
6.65
8.45
0.78
5.08
7.24
10.20
1.01
5.79
7.51
11.10
1.24
9.42
11.00
14.82
0.62
10.20
12.58
16.76
1.42
1.51
1.55
1.56
1.57
1.54
1.55
1.57
1.58
1.55
1.56
1.58
1.59
1.52
1.58
2.21
2.41
1.59
2.16
2.40
2.57
1.61
2.20
2.47
2.63
1.76
2.37
2.50
2.68
1.81
2.44
2.46
2.61
1.92
2.48
2.53
7.85
9.78
10.79
13.78
8.51
10.41
12.28
14.37
9.21
12.75
15.08
18.01
7.85
14.65
18.05
22.23
9.44
20.78
24.14
30.56
9.47
29.17
33.10
40.19
9.48
34.95
39.50
41.84
10.30
43.62
45.73
51.42
10.16
49.39
56.68
58.18
8.99
3.72
3.51
3.45
8.22
3.08
3.02
2.83
8.30
2.48
2.53
2.40
7.88
1.95
1.68
1.71
7.35
1.64
1.55
1.60
5.90
1.45
1.52
1.41
4.72
0.91
0.79
0.72
4.31
0.76
0.71
0.65
4.05
0.78
0.67
0.60
Table 5. LC indices of nonuniformly polluted insulators under different Wt and NSDD for insulator
string 33 kV.
SDD
mg/cm2
Pu/PL
NSDD
mg/cm2
0.15
0.05
0.25
0.35
0.15
0.12
0.25
0.35
0.15
0.2
0.25
0.35
1/3
Wt
mL/h
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
0
3
6
9
1/5
1/8
x1
x2
x3
x4
x5
x6
x1
x2
x3
x4
x5
x6
x1
x2
x3
x4
x5
x6
4
7
9.2
12.2
5.8
9.7
11.7
14.5
7.1
12.8
15.9
20.4
4.9
11.5
12.8
15.1
5.7
12.9
14.8
16.8
7.3
15.3
15.8
20.2
6.5
20.5
22.8
28.3
8
22.7
25.5
31
8.3
29.2
44.3
50.5
79.80
76.99
65.53
55.93
78.39
64.28
52.67
41.41
76.03
54.57
38.95
32.25
69.11
53.60
37.41
26.36
73.68
50.99
29.34
24.38
68.37
38.50
20.03
14.51
55.18
31.18
17.07
11.23
41.30
25.67
10.41
1.01
31.89
3.81
2.01
0.79
0.15
0.39
0.83
1.09
0.32
0.73
1.14
3.28
0.55
0.92
3.21
5.97
0.67
2.39
4.98
8.58
0.83
5.97
7.16
9.10
0.84
5.46
7.80
10.98
1.09
6.24
8.10
11.96
1.34
10.14
11.85
15.97
0.67
10.99
13.55
21.28
1.47
1.57
1.61
1.62
1.63
1.60
1.61
1.63
1.64
1.61
1.62
1.63
1.65
1.58
1.64
2.29
2.50
1.65
2.24
2.48
2.66
1.67
2.28
2.56
2.73
1.81
2.45
2.59
2.78
1.88
2.54
2.55
2.71
1.99
2.57
2.63
8.18
10.19
11.24
14.36
8.86
10.84
12.80
14.97
9.59
13.29
15.71
18.77
8.18
15.27
18.81
23.16
9.84
21.66
25.15
31.85
9.87
30.39
34.49
41.88
9.88
36.42
41.16
43.61
10.74
45.45
47.66
53.58
10.59
51.47
59.06
60.63
7.21
3.54
3.29
3.15
7.09
2.92
2.79
2.77
6.48
2.26
2.28
2.20
5.53
1.35
1.38
1.53
5.24
1.49
1.36
1.36
4.31
1.20
1.20
1.24
3.54
0.69
0.65
0.62
3.40
0.74
0.65
0.56
3.33
0.58
0.69
0.48
3.5
6
8.1
10.6
5
8.4
10.3
12.7
6.2
11.1
13.9
17.7
4.3
10.1
11.1
13.1
4.9
11.3
12.8
14.7
6.3
13.4
13.8
17.6
5.5
17.8
19.8
24.6
7
19.8
22.2
26.9
7.2
26.7
31.5
44
81.39
78.53
66.84
57.05
79.96
65.56
53.72
42.24
77.55
55.66
39.74
32.89
70.50
54.67
38.15
26.89
75.15
52.01
29.92
24.87
69.74
39.27
20.42
14.81
56.29
31.81
17.41
11.44
42.12
26.19
10.62
1.03
32.52
3.88
2.05
0.32
0.14
0.38
0.82
1.08
0.32
0.73
1.12
3.26
0.55
0.92
3.20
5.94
0.66
2.38
4.95
8.53
0.83
5.94
7.12
9.05
0.84
5.44
7.76
10.92
1.08
6.21
8.04
11.89
1.34
10.09
11.78
15.87
0.67
10.93
13.47
20.11
1.47
1.57
1.60
1.62
1.63
1.60
1.61
1.62
1.64
1.60
1.62
1.63
1.65
1.58
1.63
2.29
2.50
1.65
2.24
2.48
2.66
1.66
2.28
2.56
2.72
1.81
2.45
2.59
2.77
1.87
2.53
2.55
2.71
1.98
2.57
2.62
8.14
10.14
11.19
14.29
8.82
10.79
12.73
14.90
9.54
13.23
15.63
18.67
8.14
15.20
18.71
23.04
9.79
21.55
25.02
31.69
9.82
30.24
34.32
41.67
9.83
36.24
40.96
43.38
10.68
45.23
47.42
53.31
10.54
51.22
58.77
60.32
8.72
3.66
3.47
3.18
8.16
3.07
2.90
2.93
7.69
2.44
2.50
2.38
6.52
1.68
1.55
1.59
5.94
1.62
1.53
1.62
4.87
1.42
1.33
1.27
3.92
0.79
0.68
0.59
3.71
0.75
0.74
0.60
3.61
0.66
0.59
0.55
3.3
5.7
7.5
9.9
4.7
7.9
9.6
11.8
5.8
10.4
12.9
16.6
4
9.4
10.4
12.3
4.6
10.5
12
13.7
5.9
12.5
12.8
16.5
5.2
16.7
18.6
23.1
6.5
18.5
20.8
25.2
6.8
16
20.5
41.1
81.64
78.77
67.04
57.22
80.20
65.76
53.88
42.37
77.79
55.82
39.86
33.00
70.72
54.84
38.27
26.97
75.37
52.16
30.01
24.95
69.95
39.39
20.49
14.84
56.46
31.90
17.46
11.48
42.25
26.27
10.66
1.04
32.62
3.89
2.05
0.56
0.14
0.38
0.82
1.07
0.32
0.72
1.11
3.23
0.54
0.91
3.17
5.89
0.66
2.36
4.90
8.46
0.82
5.89
7.06
8.97
0.83
5.39
7.68
10.82
1.07
6.14
7.97
11.78
1.32
9.99
11.67
15.72
0.66
10.82
13.35
17.78
1.46
1.56
1.60
1.61
1.62
1.59
1.60
1.62
1.63
1.60
1.61
1.63
1.64
1.57
1.63
2.28
2.48
1.64
2.23
2.47
2.65
1.66
2.27
2.55
2.71
1.81
2.44
2.58
2.76
1.87
2.52
2.54
2.69
1.98
2.56
2.61
8.05
10.03
11.07
14.14
8.73
10.68
12.60
14.74
9.45
13.08
15.47
18.48
8.05
15.03
18.52
22.81
9.69
21.32
24.77
31.35
9.72
29.93
33.96
41.23
9.73
35.86
40.53
42.93
10.57
44.75
46.92
52.76
10.42
50.67
58.15
59.69
9.31
3.85
3.64
3.57
8.52
3.19
3.13
2.93
8.60
2.57
2.62
2.49
8.16
2.02
1.74
1.77
7.61
1.70
1.61
1.66
6.11
1.50
1.57
1.46
4.89
0.94
0.82
0.75
4.47
0.79
0.74
0.67
4.20
0.81
0.69
0.62
5.2.2. Indicator Trends under Different NSDD
The differences in the indicators are comparable to the previous case (pollution variation), and changes in the amount of increment/or decrement can be detected. The test
Materials 2022, 15, 6370
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findings in Table 5 demonstrate that under constant SDD, Wt, and Pu/PL, increasing the
NSDD increases the x1 , x3 , x4 , and x5 , but decreases the x2 and x6 . To further understand
the relationship between NSDD and the suggested indices, Figure 11 displays the x1 , x3 , x4 ,
x5 , and x6 vs. NSDD curves with SDD of 0.2 mg/cm2 , Wt of 6 mL/h, and Pu /PL of 1/1.
x1
x2
x3
x4
x5
x2
x6
70
18
15
60
x3
20
2.90
16
2.85
12
2.80
12
x1 50
9
40
6
30
3
0.15
0.20
0.25
0.30
x4
8
60
55
50
x6
0.8
0.7
0.6
45
2.75
2.70
0.35
x5
40
35
0.5
0.4
NSDD (mg/cm2)
Figure 11. The impact of NSDD on indicator shift.
5.2.3. Indicator Trends under Different Wt
The relationship between the proposed indices x1 –x6 and Wt for porcelain insulator
under SDD of 0.2 mg/cm2 , NSDD of 0.35 mg/cm2 , and Pu/PL of 1/1 and different Wt is
demonstrated in Figure 12. It is worth noting that when Wt increases, the x2 and x6 fall
while the x1 , x3 , x4 , and x5 increase. For example, under SDD of 0.2 mg/cm2 , NSDD of
0.35 mg/cm2 , and Pu /PL of 1/1, the x1 increased by 13.4% and 15.4% when Wt increased
from 3 to 6 mL/h and from 3 to 9 mL/h, respectively, whereas under the same conditions,
the x2 decreased by 72.1% and 57.2% when Wt increased from 3 to 6 mL/h and from 6 to
9 mL/h, respectively.
Figure 12. The impact of Wt on indicators shift.
5.2.4. Indicator Trends under Different Nonuniform Pollution Distribution (Pu /PL )
The relationship between proposed indices x1 –x6 and nonuniform pollution distribution Pu/PL for a polluted porcelain insulator under SDD of 0.2 mg/cm2 , NSDD of
0.35 mg/cm2 , and Wt of 9 mL/h and different Pu/PL as an example is shown in Figure 13.
It can be observed that an increase in Pu/PL causes an increase in the x2 and x6 and a
decrease in the x1 , x3 , x4 , and x5 . This means that the sample under uniform contamination conditions is more dangerous in terms of flashover incidence than the sample under
nonuniform pollution levels.
x1
x2
x3
x4
x5
x6
x2
2.5
70
2.0
x1
60
1.5
x3
28
26
x4
2.8
24
22
x5
x6
61
0.6
60
0.5
59
0.4
2.6
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x1
x2
x3
x4
x5
x6
x2
2.5
70
2.0
x1
60
1.5
50
40
1.0
1/1
1/3
1/5
1/8
0.5
x3
28
26
x4
2.8
24
22
x6
61
0.6
60
0.5
59
0.4
58
0.3
2.6
20
18
x5
2.4
16
Pu/PL
Figure 13. The impact of Pu /PL on indicator shift.
5.3. Insulator Condition Based on the Test Data of Indices
5.3.1. Insulator Condition Classification Based on Test Preparation
In this section, the ranges of the indicators corresponded to the level of SDD, NSDD,
Wt, and Pu /PL are classified. The experimental data indicated that the values of x1 , x3 , x4 ,
and x5 increased in proportion to an increase in SDD, NSDD, and Wt, but a decrease in
Pu /PL . Meanwhile, the indicators x2 and x6 decreased with an increase in SDD, NSDD, and
Wt, and decrease in Pu /PL . The proposed index values in the normal range were observed
under the clean and low-pollution cases with Wt less than 4 mL/h and NSDD less than
0.2 mg/cm2 . In this case, the possibility of discharge occurrence is almost nonexistent.
According to indicator results in Table 5, the insulator was in an abnormal state under
low contamination (0.05 mg/cm2 ) with heavy wetting Wt (9 mL/h) and medium and
high NSDD (0.25 and 0.35 mg/cm2 ) for all contamination distribution (Pu/PL), except
when Pu /PL = 1/8. In addition, the insulator under examination displayed an abnormal
condition in the presence of moderate pollution (0.12 mg/cm2 ) under moderate wetting
Wt of 6 mL/h, NSDD of 0.25 mg/cm2 , and Pu /PL of 1/1 and 1/5. The probability of a
discharge occurring in these conditions is low, except in cases of extreme wetting, where the
possibility of flashover increases. Meanwhile, the critical condition of the insulator under
test was found under two circumstances: first, under medium contamination conditions
with Wt of 9 mL/h, NSDD of 0.35 mg/cm2 , and all contamination distribution Pu /PL cases;
and second, when SDD is high under medium and heavy levels for Wt, NSDD, and all
Pu /PL cases. The flashover possibility occurring in these conditions is high, especially
under high wetting and high NSDD.
5.3.2. Insulator Condition Classification Based on Proposed Indicators
To develop a statistical technique for identifying diagnostic indicator borders based on
the x1 , x2 , x3 , x4 , x5 , and x6 inputs, the naïve Bayes classification algorithm [46] was trained
with the experimental data to predict the insulator’s state. In this study, MATLAB’s Deep
Learning Toolbox was employed to develop the classification model. In the classification
procedure, 952 data sets for each indicator were used in the classification process using
the naïve Bayes classifier algorithm, where 70% of the data (666 data) were chosen for
the model training, 15% of the data (143 data) were utilized for the model performance
verification, and the other 15% of the data (143 data) were selected for the model testing, as
shown in Figure 14. The boundary classification results of indicators x1 , x2 , x3 , x4 , x5 , and
x6 based on naïve Bayes classification are also shown in Figure 14.
Materials 2022, 15, 6370
60
Training
Testing
Validation
x3
x1
40
18 of 22
20
0
Normal
Abnormal
Critical
Class
(a)
3
60
Training
Testing
Validation
40
20
1.5
1
Training
Testing
Validation
5
2
(c)
x
x4
2.5
(b)
Normal
Abnormal
Class
Critical
(d)
0
Normal
Abnormal
Class
(e)
Critical
(f)
Figure 14. Visualization of the training samples of leakage current indicators: (a) x1 ; (b) x2 ; (c) x3 ;
(d) x4 ; (e) x5 ; (f) x6 .
Figure 15 depicts the limitations of the suggested indicators for diagnosing insulator
conditions (normal, abnormal, and critical). Taking indicator x1 as an example, the insulator
condition is normal when the x1 value is less than 7.59 mA, abnormal when it is about
20.88 mA, and critical when it is more than 46.6 mA. Of note, the restriction was set at 75%
of each level’s data in the box plot (Figure 14). Therefore, the insulator state is normal when
x1 is less than 9.33 and abnormal when x1 is between 15.9 mA and 25.3 mA; otherwise, the
insulator condition is critical.
5.4. Determination of Indices’ Performance
To determine the performance of the proposed indices precisely, the ability of the
indicators to accurately estimate the insulator state taken from the data set of 952 tests
observation was examined. The sensitivity, precision, and accuracy of these indices were
calculated using the confusion matrix shown in Figure 16 during the assessment based on
the naïve Bayes classification. The parameters of the confusion matrix were specified based
on the insulator condition preparation. The decision of test results and proposed indices
are defined as:
1.
2.
3.
4.
True positive (TP) represents the number of insulator tests that have been properly
classified using the indicators to have pollution, meaning they have the pollution.
True negative (TN) represents the number of correctly classified insulator tests using
the indicators as clean and it in fact is clean.
False positive (FP) represents the number of misclassified insulator tests using the
indicators as polluted, but in fact they are polluted.
False negative (FN) represents the number of insulator tests misclassified using the
indicators as polluted, but in fact are not polluted.
Materials 2022, 15, 6370
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(a)
(b)
(c)
(d)
(e)
(f)
Figure 15. Limitation of insulator condition indicators: (a) x1 ; (b) x2 ; (c) x3 ; (d) x4 ; (e) x5 ; (f) x6 .
Materials 2022, 15, 6370
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Figure 16. Confusion matrix for evaluating of the proposed indicators.
Experimentally, 905 test results out of 952 total tests reflected the insulator condition
correctly, whereas 47 test results provided negative results. The explanation for the erroneous test results is a lack of implementation of pollutants on the insulator surface in a
proper way or measuring-device error. In comparison to the total number of test outcomes,
the number of prediction results for each indicator varied.
The variation in the prediction results may assist in determining which indicators are
the best. It can be observed that the indices with a high number of correct prediction results
are the best and will achieve high accuracy.
According to the indicator-prediction output, the greatest number of correct predicted
results was observed for indicator x6 (906), followed by x3 (894), x4 (890), x1 (883), x5 (879),
and x2 (873). As a result, the highest indicator accuracy is x6 , followed by x3 , x4 , x1 , x5 , and
x2 accordingly, as shown in Table 6. Meanwhile, the greatest sensitivity was in indicator
x3 , followed by x6 , x4 , x1 , x5 , and x2 accordingly. Moreover, the “best” performance is
that where the variance between sensitivity and specificity is the least [47]. According
to Table 5, the smallest difference between sensitivity and specificity is shown with the
indicator x6 , followed by x3 , x4 , x1 , x5 and x2 accordingly. The sensitivity, precision, and
accuracy of these indices were calculated using the confusion matrix shown in Figure 16
during the assessment.
Table 6. The indices’ sensitivity, specificity, and accuracy for the 143 tests based on naïve
Bayes algorithm.
Indicator
Sensitivity
Specificity
Accuracy
x1
x2
x3
x4
x5
x6
0.932
0.947
0.951
0.948
0.950
0.954
0.329
0.295
0.452
0.413
0.338
0.540
0.868
0.849
0.901
0.892
0.868
0.918
6. Conclusions
This paper reports on an experimental investigation of leakage current indices to
determine the stability of polluted insulators. Porcelain insulators of 11 kV and 33 kV
distribution lines under different levels of contamination (SDD), wetting rate (Wt), insoluble
deposit density (NSDD), and nonuniform distribution pollution (Pu/PL) were investigated.
The temporal and frequency characteristics of the leakage current were then derived from
the laboratory test results and used as assessment indicators for the insulators’ physical
properties. The leakage current indication ranges were classified using the naïve Bayes
technique into three levels of insulator health status. The suggested indicator’s performance
was evaluated using the confusion matrix approach. The indicators used in this study are
all useful in detecting the pollution levels of the insulators. According to the confusion
matrix study, the odd harmonic indicator x6 , slope indicator x3 , and crest factor indicator x4
perform better than the others in terms of accuracy, with 0.918, 0.901, and 0.892, respectively.
Materials 2022, 15, 6370
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Author Contributions: Conceptualization, A.A.S., K.Y.L. and S.A.A.-G.; methodology, A.A.S.; K.Y.L.
and Z.A.-M.; software, A.A.S., S.M.A.-A., A.M., A.A.S.A. and S.S.M.G.; validation, A.A.S. and Z.A.-M.;
formal analysis, A.A.S., K.Y.L., S.A.A.-G. and M.T.I.; investigation, A.A.S., K.Y.L., S.M.A.-A. and
M.T.I.; resources, A.A.S. and K.Y.L.; data curation, A.A.S.; writing—original draft preparation, A.A.S.
and K.Y.L.; writing—review and editing, A.A.S., S.A.A.-G., K.Y.L., M.T.I., S.M.A.-A., S.S.M.G., A.M,
A.A.S.A. and Z.A.-M.; visualization, A.A.S. and K.Y.L.; supervision, A.A.S., K.Y.L., M.T.I., S.S.M.G.
and Z.A.-M.; project administration, A.A.S. and K.Y.L.; funding acquisition, K.Y.L., M.T.I. and Z.A.-M.
All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by collaboration between National Defence University of
Malaysia (UPNM) and the Post-Doctoral Fellowship Scheme under the Professional Development
Research University Grant (05E68) from Universiti Teknologi Malaysia (UTM).
Acknowledgments: This work was supported by collaboration between National Defence University
of Malaysia (UPNM) and the Post-Doctoral Fellowship Scheme under the Professional Development
Research University Grant (05E68) from Universiti Teknologi Malaysia (UTM).
Conflicts of Interest: The authors declare no conflict of interest.
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