2016China International Conference on Electricity Distribution (CICED 2016)
Xi’an,
Sep. 2016
附件 2
Based on Sugeno fuzzy integral combined multi-parameter partial
discharge diagnosis
WEIQIANG QI, BAICHAO CHEN , JIAXIN YUAN
School of Electrical Engineering Wuhan University；Beijing Electric Power Research Institute
Abstract:
This paper presents a comprehensive multi-parameter diagnostic methods, Based on the
high-frequency current method, ultrasonic method, UHF signals and other railway administration put
signal. First of all, Collection of various types of defects under high-frequency current, ultrasound, UHF PD
data, extraction nine basic characteristic parameters, eight phase characteristic parameters. Proposed
information fusion and semi-supervised learning method for high-frequency current PD data for diagnosis,
proposed mutation particle swarm optimization support vector machine diagnostic ultrasound signals,
proposed IIA-ART2A neural network for UHF signal diagnosis, Then diagnostic results matrix brightest
PD signal integration, based on Sugeno fuzzy integral multiple classifier fusion analysis, get the final
diagnosis. A large number of experimental data show, The proposed partial discharge diagnostic method to
solve the problem of different partial discharge detection methods can not detect data fusion analysis, the
method for different types of defects, diagnostic accuracy rate is higher than a single methods.
1.
High frequency current data PD diagnostic methods based on information fusion and
semi-supervised learning
Diagnostic information fusion and semi-supervised learning method for high-frequency current PD
data. Flow chart shown in Figure 1.
Enter the high frequency
current PD data
High frequency current PD signal
feature extraction (including the nine
basic characteristic parameters, eight
phase characteristic parameters, etc.)
The use of information gain feature
fusion method for imension reduction
dimension reduction
Diagnosis using graph-based
semi-supervised learning method
Output diagnosis results
Figure1 Discharge data based on information fusion and high-frequency current Board
semi-supervised learning method of diagnosis flowchart
Information gain feature selection methods can estimate the characteristics of class prediction
capability to characterize the ability to select a subset of features.
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Semi-supervised learning method based on graph, the sample set is mapped to the right to a free
connection diagram nodes in the graph as a sample set of all tagged and untagged data, the value of the
right side reflects the similarity between adjacent nodes degree. Graph model constructed in only a few
nodes are marked, most nodes are unlabeled. Marked edge nodes can be expanded by connecting to its
neighbor, which categories of information dissemination to the unlabeled nodes to achieve the purpose
of classification. Definition of undirected weighted graph
G  v, e 
, among them v V
representative samples of the training, e  E , On behalf of the similarity between two vertices
connected.
Four discharge model, semi-supervised learning recognition results are shown in Table 1, Spiked
accuracy rate of discharge 77%, Particulate discharge accuracy rate 83%,Air Discharge accuracy 83%.
Table 1 Recognition of the results of semi-supervised learning method
Defect Type
Spiked
particle
Suspension
Gap
Spiked
770
107
51
72
particle
23
830
36
111
Suspension
34
65
860
41
Gap
62
143
55
740
2.
Diagnosis based on ultrasonic signals mutation particle swarm optimization support vector
machine
Particle Swarm based on the parameters of support vector machine optimized , Ultrasound is used
to diagnose PD data, Flow chart shown in Figure 2.
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2016China International Conference on Electricity Distribution (CICED 2016)
Xi’an,
Sep. 2016
Start
Raw data
PD ultrasonic signal feature extraction
(including the nine basic characteristic
parameters, eight phase characteristic
parameters, etc.)
Normalized
Normalization
PCA Dimensionality reduction
EPPSO Parameter optimization
Support Vector Machine Model
Diagnosis
Calculation results
End
Figure 2 Diagnosis based on ultrasonic signals mutation particle swarm optimization support vector
machine
The proposed algorithm is adaptive mutation particle entropy based parameter (Referred to as
EPPSO，PSO based on Swarm Entropy) Process is as follows:
(1) In the random data field to determine the initial position and velocity of each particle, Setting
the threshold particle entropy stable
E0 ;
(2) The particles pbest Set current optimal position,
population;
(3) Update position and velocity of the particles;
(4) If the particle is superior fitness
particle is superior fitness
p gd
w
global optimal position to the initial
fitness, With the current location update
fitness, With the current location updates
(5) Calculate the particle entropy sets
predetermined threshold value
p gd
p gd
E
k
i
p gd
p gd
, If the
;
, Analyzing the entropy of each particle is less than a
E0 E, If that is true, executed(6), Otherwise skip;
p
gd
(6)Put w as 0 , Compute
Variation value, Continue to update iteration;
(7) Analyzing the convergence criteria are met(Fitness variance is less than the set value), If so,
execute(8); Otherwise, continue iteration;
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p
gd
(8) Export
, end run.
Use established classification 4 EPPSO-SVM model, Respectively, four types of test samples to
identify. In order to verify the test results of the model, in order to correct recognition rate of the test
sample as an evaluation criterion.
Use established classification model 4(EPPSO-SVM). In order to verify the test results of the
model, Correct recognition rate of the test sample as an evaluation criterion.
Recognition results, as shown in Table 2.
Defect model
Spiked
particle
Suspension
Gap
Table 2 EPPSO-SVM Recognition results
The total number of The total number of
Correct
test samples
training samples
identification
number
1000
500
810
1000
500
770
1000
500
730
1000
500
840
Correct
recognition
rate
81%
77%
76%
84%
Seen from the table, the correct recognition rate of all types of defects in more than 75%, of which
the correct recognition rate spikes defects 81% correct recognition rate of 77% particulate discharge,
floating electrode discharge correctly identify 76% correct recognition gap discharge was 84%.
3
UHF signal diagnosis based on IIA-ART2A Neural Networks
By immune algorithm based on improved natural cycle variation Correct ART2 Neural Networks
Optimizing parameters, Diagnosis for UHF PD data, Flow chart shown in Figure 3.
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2016China International Conference on Electricity Distribution (CICED 2016)
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Create ART2 Network Model
Enter training data set
input UHF PD data
Using wavelet transform
Denoising signal
processing
Obtain initial network
weights and thresholds
Avidity calculation
Antibody
selection
Antibodies
variation
UHF PD signal feature
value extraction
Termination
condition is
satisfied
using IIA-ART2A neural
network model for defect
diagnosis
Antibodies
clonal expansion
After optimizing the output
weights and thresholds
ART2A Network Model
Output Signal Intelligent
UHF PD diagnosis
Figure3 UHF signal diagnosis based on IIA-ART2A Neural Networks flow chart
IIA-ART2A net is unsupervised learning, So it requires the training sample number of samples is
not how much, but rather to be able to try to reflect the training samples mapped substance problem.
Just select the correct input vector, Requires minimal sample can be obtained high recognition accuracy
rate.because ART2A has runs while studying the characteristics, So for a new input sample, both
training and learning, you can also test its recognition accuracy, which can be trained side edge test.
According to the results of the training has been basically able to see recognition success rate of
the network is very high. In this type of defect for each selected 1000 new sample data, and the feature
quantity extraction,Use IIA-ART2A neural networks for pattern recognition. Fractal dimension values
are listed only four layers wavelet packet decomposition of the first four nodes on, Matching nodes 1-4
represent spikes, particles, suspended four types of air gap, Correct recognition rate spikes defects
91%,Correct identification rate of particulate discharge 85%,Correct recognition rate floating electrode
discharge 84%,Correct recognition rate gap discharge 77%.
Table 3 IIA-ART2A net defect Recognition Results
No
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Fractal
dimension
Session x
Damping
Similarity
Paper No xxx
……
Matching
nodes
2016China International Conference on Electricity Distribution (CICED 2016)
Xi’an,
Sep. 2016
4
0
0.072
0.01
0.9912
……
1
1
0.183
0.001
1
……
2
2
0.183
0.001
1
……
2
3
0.071
0.01
0.9912
……
1
4
0.073
0.01
0.9278
……
1
5
0.095
0.02
0.9966
……
3
6
0.098
0.02
0.9966
……
3
7
0.096
0.02
0.9380
……
3
8
0.071
0.01
0.9912
……
1
9
0.071
0.01
0.9912
……
1
10
0.132
0.1
0.8959
……
4
11
0.131
0.1
0.9411
……
4
12
0.132
0.1
0.8959
……
4
13
0.073
0.01
0.9278
……
1
14
0.072
0.01
0.9912
……
1
……
……
……
……
……
……
Multiple classifiers fusion analysis base on Sugeno fuzzy Integral
In this paper, use multiple classifiers fusion analysis base on Sugeno fuzzy Integral, put discharge
diagnosis data based on high frequency current station information fusion and semi-supervised learning
method and ultrasound Bureau mutation particle swarm optimization based on support vector machine
discharge diagnosis data and UHF PD diagnosis data based on IIA-ART2A neural Networks together,
Calculated the final diagnosis, Flow chart shown in Figure 4.
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2016China International Conference on Electricity Distribution (CICED 2016)
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Data discharge
Ultrasound Bureau
diagnosis based on
mutation particle swarm
information fusion and
optimization based on
semi-supervised
support vector machine
learning frequency
high
frequency
current
Board
discharge diagnosis
data
Ultrasound
current PD
diagnosis matrix
diagnosis of PD
matrix
Diagnosis based on data
put UHF Bureau
IIA-ART2A Neural
Network
UHF PD diagnosis
matrix
Fuzzy determine density
Construct fuzzy
measure
Count Sugeno fuzzy Integral
Fuzzy integral value greater as the final
diagnosis
Figure 4
Multiple classifiers fusion analysis base on Sugeno fuzzy Integral flow chart
Fault diagnosis is essentially a decision-making process of classification, single classification
structure by increasing complexity to improve the classification accuracy, the results are often
unsatisfactory, and a plurality of relatively simple structure classifier fusion to improve the overall
classification accuracy , after all, a wise choice, so a combination of several different classifiers to
obtain high precision is an important research topic. There is interaction between the classifier, rather
than independent. Fuzzy integral is based on fuzzy measure nonlinear integral, fuzzy measure is a
non-negative non-additive set function, fuzzy measures of non-additive precisely describe interactions
between classifiers. Therefore, to explore fusion technology based on fuzzy integral multiple classifiers
is to study the contents of this article.
C  {C1 , C2 ,, Cn }
Assume
classifiers.
Zk
as n set target-category,
is K-th identified objects.
Zk
X  {x1 , x2 ,, xm }
after identification of the classifications, it can be a
matrix, That is the decision-making model for decision-making in sectional,mark as
 h11k h12k  hlnk 


    
DP ( Z K )   hik1 hik2  hink 
    


k
hmk 1 hmk 2  hmn

h1  (hik1 , hik2 ,, hink ), (i  1,2,, m)
Each row vector
results
of
the
sample,is
xi
h j  (h , h ,, h ), ( j  1,2,, n)
k
1j
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k
2j
Session x
k
mj
is m set of
output
is Classifier
vector;
Each
represents a relative category
Paper No xxx
xi
DP (Z K )
Recognition
column
Cj
Zk
vector
, Each classifier
2016China International Conference on Electricity Distribution (CICED 2016)
Xi’an,
Sep. 2016
recognition results of the sample, Called
xi
function,
Cj
mapped as
together
intersecter
h 1
k
ij
extent ,when
Cj
k
ij
h
Cj
fusion vectors. When the sample is fixed, see
Fusion vectors corresponding component
represents classifier
, Classifier
xi
see
Zk
as
xi
Cj
put sample
Zk
hij
.
xi
as a
output vector and
assigned to
, in contrast,when
hj
Cj
determine
h  0 Zk
,
does not
k
ij
C
j
belong
.
Hypothesis
g
as
X
power
g  g ({xi }), (i  1,2,, m)
set
P(X)
sugeno
j
represents classifier
xi
fuzzy
measure,
Fuzzy
Density
the credibility of the decisions made.
g ( A) show X subset A the reliability of the local decision-making. Each fuzzy integral classifier see
Cj
Zk
belongs to class
objective estimation and the credibility of the classifier fusion , The integral
C
j
value of the system is derived sample belongs
the overall objective estimation.as so, system has
an integral value category, Take maximum points corresponding to the value category as a system of
Z
Z
k discrimination category . Corresponding category as a system of sample
k
sample
discrimination category.
Therefore, the basic steps with fuzzy integral can be summarized as:
(1)Fuzzy determine density
(2)Construct fuzzy measures is to determine the parameters of the fuzzy density, and to determine
fuzzy measures.
(3)Fuzzy integral calculation, that is, after the fusion object to be identified belongs to a
comprehensive degree of certainty for each category.
(4)Compare the size of the integral value is determined to be the recognition object category.
In order to facilitate the integration of fuzzy arithmetic, you must first set the output probability of
w (i  1,2,, m)
i
failure, Hypothesis
as m fault sample data corresponding feature magnitude, the
occurrence of the i-th fault probability is defined as:
i 
k i
wi
, wk  min ( wk )
k 1, 2,, m
wi  wk
0   i  1 , i reflecting sample and i category binding energy differences and second only to the
class fault binding energy, When the two values are equal
 i  0.5
 i  0.5 ,Classified as category I,  i
, critical state ,
larger , The stronger certainty Classification.
Data on the high-voltage conductor tip surface partial discharge failure, free metal particles,
suspended electrode, solid insulation gap four states, each state take 1000 samples, which changes
depending on the conditions of training samples, test samples 500, and build three classifier categories:
(1)Ttraining samples taken 1000, presented in accordance with article section before discharge
data diagnosis based on information fusion and semi-supervised learning frequency current Board,
Construction M1 classification;
(2)Training sample taken 1000, in accordance with article proposed section before discharge data
diagnostic method based on modified particle swarm optimization support vector machine ultrasonic
Bureau, Construction M2 classification;
(3)1000 training sample taken in accordance with proposed section before the article based on
neural network IIA-ART2A UHF PD diagnosis data to construct M3 classification;
Accuracy rate when identifying four types of samples obtained, as shown in Table 4.
Defect
model
Table 4 classifier accuracy
The total number of
The total number of
test samples
training samples
A Spiked
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500
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M1
M2
M3
0.77
0.81
0.91
2016China International Conference on Electricity Distribution (CICED 2016)
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B particle
1000
500
0.83
0.77
0.85
C
Suspension
1000
500
0.86
0.76
0.84
D Gap
1000
500
0.74
0.84
0.77
Fuzzy density is defined based on the accuracy of the method, the fuzzy density matrix is obtained
0.2592
0.2645
DP  
0.2419

0.2581
0.2577 0.2737
0.2338 0.2465
0.2628 0.2593

0.2255 0.2367
A sample to be tested, for example, through the sample M1, M2, M3 identification method,
according to the formula (6.9) calculating the probability of A, B, C, D which belongs to the class,
Table 3-10, Table 11 directly through classification, the M1, M3 to be divided into class D, M2 be
divided into A category.
Table 5 different classification probability sample characteristics in terms of quantity
Defect model
M1
M2
M3
ASpiked
0.2514
0.5423
0.3525
B particle
0.1375
0.2585
0.2352
C Suspension
0.4686
0.3242
0.1554
D Gap
0.7445
0.5186
0.6673
h(x) as Measurable function,
g () as according to the
Results are listed in descending order of the corresponding item in Table 5,
Table 6 shows the results of fuzzy measure and fuzzy integral,
fuzzy density and measurable function
h(x) resulting fuzzy measure , S v as h(x) and g ()
get on Sugeno valued Fuzzy Integral, according to
Sv
judge, test samples belong to the class D.
h(x) median . Since fuzzy measure from a variety of classification accuracy,
Fuzzy Integral been
this calculation more categorical values referenced "Value" and reduce the multi-classifier
"uncertainty".
Table 6 fuzzy measure and fuzzy integral Results
h(x)
g ()
Sv
[0.5423 0.3525 0.2514]
[0.3411 0.6878 1.0000]
0.3525
[0.2352 0.2024 0.1375]
[0.3263 0.6121 1.0000]
0.2352
[0.4686 0.4515 0.1554]
[0.3396 0.6794 1.0000]
0.4515
[0.7445 0.6673 0.5186]
[0.3419 0.6530 1.0000]
0.6673
Defect Type
ASpiked
B particle
C Suspension
D Gap
Use DP as blurred density for various types of partial discharge samples fuzzy integral decisions,
recognition results are shown in Table 3-12, Table 3-9 compares it can be seen, the use of fuzzy
integral fusion after the decision, and error-prone sample classification (such as C, class D) recognition
accuracy has significantly improved, the reason, the original feature vector error-prone sub-sample of
small differences, the recognition process randomness and uncertainty, it is difficult to correctly
classify fuzzy integration of multi-classifier fusion, to some extent, to avoid the kind of "uncertainty" to
improve the recognition accuracy.
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Table 7 Fuzzy classification results Integral Fusion
Defect model
The total number of
test samples
The total number of
training samples
Accuracy
A Spiked
1000
500
94%
B particle
1000
500
95%
C Suspension
1000
500
89%
D Gap
1000
500
91%
In many classification problems, they are not independent of each other but there is interaction
between the various classifiers. Fuzzy measures can describe non-additive interactions between
classifiers, fuzzy measure fuzzy integral has become multi-classifier fusion process of a new tool. The
interaction between classifiers into account, can improve the classification accuracy fusion system,
enhances fault tolerance of the system. Comparison Chart diagnostic accuracy of this method, as shown
in figure6.
1
0.95
诊断准确率
0.9
0.85
0.8
0.75
0.7
0.65
0.6
尖刺缺陷
高频电流诊断
超声诊断
特高频诊断
多参量诊断
微粒缺陷
悬浮缺陷
气隙缺陷
Figure 6 Diagnosis accuracy comparison chart
6.
Conciusion
The proposed multi-parameter partial discharge diagnosis integrated approach to solve the
different partial discharge detection method information can not be integrated, the problem of high
probability of detecting false test results show that the discharge spikes defects, particulate discharge
defects, defect floating potential discharge, gas gap discharge defect diagnostic accuracy rate is higher
than a single partial discharge diagnosis.
Keywords:
Partial Discharge; Joint Diagnosis; Fusion Analysis; High-frequency current; Ultrasonic; UHF.
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Author’s brief introduction and contact information:
Weiqiang Qi was born in Shanxi Province, China, on April 20, 1985. He received the master's degree
in the school of electrical engineering from Wuhan University, Wuhan, China in 2009
Currently, He is an full-time doctor in Wuhan University.
His main research interests are Network equipment assessments and high-voltage electrical
equipment condition monitoring, fault diagnosis and fault analysis.
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