CN110161389A - A kind of Electric Power Equipment Insulation defect identification method and AEVB self-encoding encoder - Google Patents
A kind of Electric Power Equipment Insulation defect identification method and AEVB self-encoding encoder Download PDFInfo
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- G—PHYSICS
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
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Abstract
The invention discloses a kind of Electric Power Equipment Insulation defect identification methods comprising step: (1) constructing AEVB self-encoding encoder, AEVB self-encoding encoder includes probability encoding device and probability decoder;(2) AEVB self-encoding encoder is trained using the history Partial Discharge Data of power equipment, so that: characteristic value of the history Partial Discharge Data of input in the output end output Partial Discharge Data of probability encoding device, the characteristic value input probability decoder of the Partial Discharge Data, to export corresponding insulation defect type in the output end of probability decoder;(3) by the local discharge signal input AEVB self-encoding encoder of power equipment to be identified, AEVB self-encoding encoder exports insulation defect type.In addition, the invention also discloses a kind of AEVB self-encoding encoder for Electric Power Equipment Insulation defect recognition, AEVB self-encoding encoder includes probability encoding device and probability decoder.
Description
Technical field
The present invention relates to a kind of recognition methods and its neural network more particularly to a kind of identifications of Electric Power Equipment Insulation defect
Method and its neural network.
Background technique
Currently used insulation defect diagnostic method is to be constructed of partial discharge map progress feature to local discharge information to mention
It takes and pattern-recognition.Rapidly and accurately carrying out identification to the partial discharge map of acquisition facilitates insulation defect diagnosis, and then grasps electricity
The state of insulation of power equipment.It can be repaired at the first time in the presence of having insulation defect.But current partial discharge spectrum recognition
Technology still remains the lower problem of accuracy rate, and failure is easy to cause to misjudge.
Based on this, it is expected that obtaining a kind of Electric Power Equipment Insulation defect identification method, partial discharge map can be significantly improved
Recognition accuracy, to preferably assess status of electric power, to be conducive to grasp the state of insulation of power equipment.
Summary of the invention
One of the objects of the present invention is to provide a kind of Electric Power Equipment Insulation defect identification method, which is lacked
Sunken recognition methods can effectively improve the recognition accuracy of partial discharge map, so that preferably status of electric power is assessed,
To be conducive to grasp the state of insulation of power equipment.
Based on above-mentioned purpose, the invention proposes a kind of Electric Power Equipment Insulation defect identification methods comprising step:
(1) AEVB self-encoding encoder is constructed, AEVB self-encoding encoder includes probability encoding device and probability decoder;
(2) AEVB self-encoding encoder is trained using the history Partial Discharge Data of power equipment, so that: input is gone through
Characteristic value of the history Partial Discharge Data in the output end output Partial Discharge Data of probability encoding device, the spy of the Partial Discharge Data
Value indicative input probability decoder, to export corresponding insulation defect type in the output end of probability decoder;
(3) by the local discharge signal input AEVB self-encoding encoder of power equipment to be identified, AEVB self-encoding encoder is defeated
Insulation defect type out.
It is accurate to the identification of partial discharge map in order to improve in Electric Power Equipment Insulation defect identification method of the present invention
Rate, inventor propose the Electric Power Equipment Insulation defect identification method of this case according to depth self-encoding encoder matching algorithm, first
Variation Bayes self-encoding encoder (hereinafter referred to as AEVB self-encoding encoder) first is constructed, which includes probability encoding device
And probability decoder, then AEVB self-encoding encoder is trained using the history Partial Discharge Data of power equipment, so that: it is defeated
Characteristic value of the history Partial Discharge Data entered in the output end output Partial Discharge Data of probability encoding device, the shelf depreciation number
According to characteristic value input probability decoder, to export corresponding insulation defect type, the history in the output end of probability decoder
Partial Discharge Data can be acquired from case information library, finally, by the local discharge signal of power equipment to be identified
AEVB self-encoding encoder is inputted, insulation defect type is exported by AEVB self-encoding encoder.
As a result, the Electric Power Equipment Insulation defect identification method by this case can be accurately and effectively to power equipment
Operating condition carries out assessment understanding, the recognition accuracy of partial discharge map is improved, to preferably comment status of electric power
Estimate, to be conducive to grasp the state of insulation of power equipment.
Further, in Electric Power Equipment Insulation defect identification method of the present invention, AEVB self-encoding encoder has one
A input layer, an output layer, a hidden variable layer and four middle layers;One of input layer, two middle layers and one
Hidden variable layer constitutes the probability encoding device, and hidden variable layer exports the characteristic value of Partial Discharge Data;Two middle layers and one
A output layer constitutes the probability decoder, and the output layer exports insulation defect type.
Further, in Electric Power Equipment Insulation defect identification method of the present invention, using stochastic gradient descent method
Optimize the parameter of probability encoding device and probability decoder.
Further, in Electric Power Equipment Insulation defect identification method of the present invention, probability encoding device is using following
Formula characterization:
Z indicates the output of hidden variable layer in formula, is the characteristic value of Partial Discharge Data;σencIndicate scale parameter;ε table
Show the random parameter for meeting N (0,1) distribution;N (0, I) is standardized normal distribution;F indicates activation primitive;Expression will solve
Scale parameter;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;Indicate the translation to be solved
Amount;Indicate the h to be solvedlScale parameter;Indicate the h to be solvedlTranslational movement;X is the Partial Discharge Data of input.
Further, in Electric Power Equipment Insulation defect identification method of the present invention, probability decoder is using following
Formula characterization:
In formula, x indicates the Partial Discharge Data of input;The characteristic value of z expression Partial Discharge Data;P (x | z) indicate condition
Probability-distribution function;N indicates normal distyribution function;μdecIndicate probability likelihood parameter, σdecIndicate scale parameter;I indicates offset
Amount;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;Indicate the h to be solved2Scale parameter;Indicate the h to be solved2Translational movement.
Correspondingly, another object of the present invention is to provide a kind of AEVB for Electric Power Equipment Insulation defect recognition is self-editing
Code device, can effectively improve the recognition accuracy of partial discharge map by the AEVB self-encoding encoder, thus preferably to power equipment
State is assessed, to be conducive to grasp the state of insulation of power equipment.
Based on above-mentioned purpose, the invention also provides a kind of AEV B for Electric Power Equipment Insulation defect recognition to encode certainly
Device, AEVB self-encoding encoder include probability encoding device and probability decoder;Wherein probability encoding device is configured to: to its input electric power
The local discharge signal of equipment, then its export Partial Discharge Data characteristic value;The probability decoder is configured to: it is inputted
The output of input probability encoder is held, insulation defect type is exported.
Further, in AEVB self-encoding encoder of the present invention, AEVB self-encoding encoder has an input layer, one
Output layer, a hidden variable layer and four middle layers;One of input layer, two middle layers and a hidden variable layer constitute
The probability encoding device, the characteristic value of the hidden variable layer output Partial Discharge Data;Two middle layers and an output layer group
At the probability decoder, the output layer exports insulation defect type.
Further, in AEVB self-encoding encoder of the present invention, the parameter of probability encoding device and probability decoder is adopted
It is optimized with stochastic gradient descent method.
Further, in AEVB self-encoding encoder of the present invention, probability encoding device is characterized using following formula:
Z indicates the output of hidden variable layer in formula, is the characteristic value of Partial Discharge Data;σencIndicate scale parameter;ε table
Show the random parameter for meeting N (0,1) distribution;N (0, I) is standardized normal distribution;F indicates activation primitive;Expression will solve
Scale parameter;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;Indicate the translation to be solved
Amount;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;X is the Partial Discharge Data of input.
Further, in AEVB self-encoding encoder of the present invention, probability decoder is characterized using following formula:
In formula, x indicates the Partial Discharge Data of input;The characteristic value of z expression Partial Discharge Data;P (x | z) indicate condition
Probability-distribution function;N indicates normal distyribution function;μdecIndicate probability likelihood parameter, σdecIndicate scale parameter;I indicates offset
Amount;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;
Indicate the translational movement to be solved;Indicate the h to be solved2Scale parameter;Indicate the h to be solved2Translational movement.
Electric Power Equipment Insulation defect identification method of the present invention and AEVB self-encoding encoder have the advantages described below
And the utility model has the advantages that
Electric Power Equipment Insulation defect identification method of the present invention can effectively improve the recognition accuracy of partial discharge map,
To preferably assess status of electric power, to be conducive to grasp the state of insulation of power equipment.
In addition, AEVB self-encoding encoder of the present invention similarly has above advantages and beneficial effect.
Detailed description of the invention
Fig. 1 is the process signal of Electric Power Equipment Insulation defect identification method of the present invention in one embodiment
Figure.
Fig. 2 schematically shows the structure of AEVB self-encoding encoder of the present invention in one embodiment.
Fig. 3 to Fig. 6 respectively illustrates Electric Power Equipment Insulation defect identification method of the present invention in different embodiment party
Detected detection data in formula.
Specific embodiment
It below will according to specific embodiment and Figure of description is to Electric Power Equipment Insulation defect recognition side of the present invention
Method and AEVB self-encoding encoder are described further, but the explanation does not constitute the improper restriction to technical solution of the present invention.
Fig. 1 is the process signal of Electric Power Equipment Insulation defect identification method of the present invention in one embodiment
Figure.
As shown in Figure 1, in the present embodiment, Electric Power Equipment Insulation defect identification method comprising steps of
Step 100: building AEVB self-encoding encoder, the AEVB self-encoding encoder includes probability encoding device and probability decoder;
Step 200: AEVB self-encoding encoder is trained using the history Partial Discharge Data of power equipment, so that: it is defeated
Characteristic value of the history Partial Discharge Data entered in the output end output Partial Discharge Data of probability encoding device, the shelf depreciation number
According to characteristic value input probability decoder, to export corresponding insulation defect type in the output end of probability decoder;
Step 300: by the local discharge signal input AEVB self-encoding encoder of power equipment to be identified, AEVB is encoded certainly
Device exports insulation defect type.
It should be noted that AEVB self-encoding encoder is with an input layer, an output layer, a hidden variable layer and four
Middle layer;One of input layer, two middle layers and a hidden variable layer constitute the probability encoding device, the hidden variable
The characteristic value of layer output Partial Discharge Data;Two middle layers and an output layer constitute the probability decoder, output layer
Export insulation defect type.
And Fig. 2 schematically shows the structure of AEVB self-encoding encoder of the present invention in one embodiment.
As shown in Fig. 2, using the parameter of stochastic gradient descent method optimization probability encoding device and probability decoder.Wherein, generally
Rate encoder is characterized using following formula:
Z indicates the output of hidden variable layer in formula, is the characteristic value of Partial Discharge Data;σencIndicate scale parameter;ε table
Show the random parameter for meeting N (0,1) distribution;N (0, I) is standardized normal distribution;F indicates activation primitive;Expression will solve
Scale parameter;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;Indicate the translation to be solved
Amount;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;X is the Partial Discharge Data of input.
And probability decoder is characterized using following formula:
In formula, x indicates the Partial Discharge Data of input;The characteristic value of z expression Partial Discharge Data;P (x | z) indicate condition
Probability-distribution function;N indicates normal distyribution function;μdecIndicate probability likelihood parameter, σdecIndicate scale parameter;I indicates offset
Amount;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;
Indicate the translational movement to be solved;Indicate the h to be solved2Scale parameter;Indicate the h to be solved2Translational movement.
In order to be better described this case Electric Power Equipment Insulation defect identification method recognition effect, Fig. 3 to Fig. 5 shows respectively
It has anticipated Electric Power Equipment Insulation defect identification method of the present invention detected detection data in various embodiments.
The relationship explanation that table 1 lists the electric discharge type that different local discharge signals detects between position of discharging.
Table 1.
Case No | Electric discharge type | Electric discharge position |
1 | Suspended discharge | Isolation switch insulated pull rod and transmission mechanism connecting portion |
2 | Suspended discharge | Isolation switch insulated pull rod and transmission mechanism connecting portion |
3 | Suspended discharge | Built-in sensor joint area |
4 | The class that insulate electric discharge | The tag of GIS cable termination storehouse is damaged |
In addition, it should be noted that, case information is as shown in table 1.Wherein case 1 is identical equipment factory with case 2
Family, same model equipment, the electric discharge case detected in same position.Case 3 is identical as case 1 and 2 electric discharge type of case, but
Equipment manufacturer and electric discharge position are different, and case 4 is the comparison case of different electric discharge types.It is detected in above four cases
The Partial Discharge Data arrived is as shown in Figures 3 to 6.By the various features for extracting Fig. 3 to Partial Discharge Data shown in fig. 6
Value, mutual matching degree is calculated in conjunction with cosine-algorithm, obtains the flux matched degree result of different characteristic as shown in following table table 2.
Table 2.
Note: statistical characteristics can obtain in the following way in table 2: statistical characteristics be by shelf depreciation amplitude and time
Number is in entire power frequency period and the degree of skewness Sk of power frequency positive-negative half-cycle, steepness Ku, degree of asymmetry Q and cross-correlation coefficient Cc etc. 16
A characteristic parameter composition;
DBN characteristic value obtains in the following way: deepness belief network is that Boltzmann machine superposition extension is limited by multilayer
Made of network structure model.It is herein 6 layers for the DBN network of comparison, number of nodes 3600,1000,500,100,10,
4.5th layer of output is as the characteristic value extracted;
CNN characteristic value obtains in the following way: depth convolutional network is operated using convolution, the pondization of multilayer to obtain
The network structure model of data further feature may be implemented the translation to data, scaling, distort invariance.The CNN applied herein
Network input layer is 50 × 72, and two convolutional layers are respectively 63 × 3 convolution kernels and 36 3 × 3 convolution kernels, corresponding pond
Changing layer is 1 × 2,1 × 11.Two full articulamentums are 500,10, output layer 4.The output of 2nd full articulamentum is as extraction
Characteristic value;
Principal component analysis (principal component analysis, abbreviation PCA)+linear discriminant analysis (linear
Discriminant analysis, abbreviation LDA) characteristic value obtains in the following way: sample dropped first with PCA
Dimension, eliminates the redundancy of sample, to be ensured of the nonsingularity of scatter matrix.LDA is recycled to solve optimal transformation feature
Information.
It should be noted that the number in table 2, which refers to, carries out characteristics extraction for two Case Nos in table 1, and tie
The matching degree that cosine-algorithm calculates the two is closed, furthermore case 1-2, which refers to, utilizes a variety of spies for extracting case 1 and case 2
Value indicative, in conjunction with the matching degree that cosine-algorithm both calculates, similarly, case 2-3 refers to using extracting the more of case 2 and case 3
Kind characteristic value, the matching degree of the two is calculated in conjunction with cosine-algorithm.
From Table 2, it can be seen that case 1-2 has compared with other cases in the obtained matching result of AEVB self-encoding encoder
Higher matching degree is combined, compared with case 1-3, matching degree is high by 23.09%.Compared with case 1-4, matching degree is high by 89.94%.
The counted matching degree of statistical characteristics meter as a comparison then relatively seldom goes out evident regularity, between the data of identical electric discharge type
It is closer to degree, the matching degree between the data of different electric discharge types is slightly lower, but the result effect obtained compared with AEVB self-encoding encoder
Fruit is poor.DBN model, CNN model and the resulting result of PCA+LDA model can be seen that between case 1-4, case 2-4
It is lower with spending, therefore it can obtain preferable recognition effect to the data of different electric discharge types, but for case 1-2, case
1-3, case 2-3, obtained matching degree are not much different, and cannot distinguish between similar cases, therefore poor as matching application effect.
In order to verify this case AEVB self-encoding encoder recognition effect, based on AEVB self-encoding encoder carry out characteristics extraction,
It is then utilized respectively cosine-algorithm, Euclidean distance and best entropy calculate the matching degree between case data, and the results are shown in Table 3.
Table 3.
Case No | Cosine-algorithm | Euclidean distance | Best entropy |
Case 1-2 | 96.87% | 93.03% | 98.62% |
Case 1-3 | 73.78% | 75.23% | 74.44% |
Case 1-4 | 6.93% | 8.31% | 5.00% |
Case 2-3 | 61.75% | 60.84% | 77.43% |
Case 2-4 | 1.92% | 6.80% | 3.34% |
Case 3-4 | 9.51% | 7.18% | 7.15% |
Note: cosine-algorithm can be obtained by mode as described below in table 3: the cosine-algorithm of following formula calculates shelf depreciation number
The distance between according to, it can be obtained the matching degree MR of Partial Discharge Data.
V in formulaa、VbRespectively two extracted feature vectors of Partial Discharge Data, | | | | indicate vector field homoemorphism;
The matching degree of Euclidean distance obtains as described below: the Euclidean distance based on two groups of vectors obtains its matching degree.It is based on
The matching degree of Euclidean distance is to be difficult to determine suitable measurement standard there are problem, and therefore, it is difficult to normalize.It chooses herein all
Maximum distance in sample data calculates its matching degree according to following formula as standard:
Matching degree based on best entropy obtains as described below: the main entropy for calculating signal proposes that calculating parameter is few, can be
Error caused by reducing to a certain extent because of time irreversibility.
As can be seen from Table 3, Euclidean distance is utilized respectively for above-mentioned case and best entropy calculates matching degree and cosine is calculated
The difference of method is little.
Matching degree calculating further is carried out to the data in 200,000,000 cases, is extracted using AEVB self-encoding encoder special
Value indicative is utilized respectively cosine-algorithm, the matching degree of Euclidean distance and best entropy calculating between any two.With similar type case lower
It is higher than 80% with degree, matching degree is correct as matching lower than 20% under dissimilar case, calculates matching accuracy and obtains such as table 4
Statistical information.
Table 4.
Electric discharge type | Suspended discharge | Discharge in insulation |
Cosine-algorithm | 82.6% | 85.7% |
Euclidean distance | 63.6% | 75.3% |
Best entropy | 47.5% | 72.4% |
As can be seen from Table 4, for a large amount of case, the matching degree accuracy based on Euclidean distance and best entropy is lower than
Cosine-algorithm, since in the matching degree calculating of Euclidean distance, all sample standard deviations and fixed maximum distance are compared, therefore are easy out
Existing singular value, causes overall effect poor, and the matching degree based on best entropy, which calculates, has faster calculating speed, but due to best
Entropy is related to the degree of scatter of sample, for the Partial Discharge Data similitude less effective under measurement complex data source.
In summary as can be seen that Electric Power Equipment Insulation defect identification method of the present invention can effectively improve partial discharge
The recognition accuracy of map, to preferably assess status of electric power, to be conducive to grasp the insulation of power equipment
State.
In addition, AEVB self-encoding encoder of the present invention similarly has above advantages and beneficial effect.
It should be noted that prior art part is not limited to given by present specification in protection scope of the present invention
Embodiment, all prior arts not contradicted with the solution of the present invention, including but not limited to first patent document, formerly
Public publication, formerly openly use etc., it can all be included in protection scope of the present invention.
In addition, it should also be noted that, institute in the combination of each technical characteristic and unlimited this case claim in this case
Combination documented by the combination or specific embodiment of record, all technical characteristics documented by this case can be to appoint
Where formula is freely combined or is combined, unless generating contradiction between each other.
The above embodiment is a preferred embodiment of the present invention, but embodiments of the present invention are not by above-described embodiment
Limitation, other any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the present invention,
It should be equivalent substitute mode, be included within the scope of the present invention.
Claims (10)
1. a kind of Electric Power Equipment Insulation defect identification method, which is characterized in that comprising steps of
(1) AEVB self-encoding encoder is constructed, the AEVB self-encoding encoder includes probability encoding device and probability decoder;
(2) AEVB self-encoding encoder is trained using the history Partial Discharge Data of power equipment, so that: the history office of input
Characteristic value of portion's discharge data in the output end output Partial Discharge Data of probability encoding device, the characteristic value of the Partial Discharge Data
Input probability decoder, to export corresponding insulation defect type in the output end of probability decoder;
(3) by the local discharge signal input AEVB self-encoding encoder of power equipment to be identified, the AEVB self-encoding encoder is defeated
Insulation defect type out.
2. Electric Power Equipment Insulation defect identification method as described in claim 1, which is characterized in that the AEVB self-encoding encoder tool
There are an input layer, an output layer, a hidden variable layer and four middle layers;One of input layer, two middle layers and
One hidden variable layer constitutes the probability encoding device, the characteristic value of the hidden variable layer output Partial Discharge Data;In two
Interbed and an output layer constitute the probability decoder, and the output layer exports insulation defect type.
3. Electric Power Equipment Insulation defect identification method as described in claim 1, which is characterized in that use stochastic gradient descent method
Optimize the parameter of probability encoding device and probability decoder.
4. Electric Power Equipment Insulation defect identification method as described in claim 1, which is characterized in that the probability encoding device uses
Following formula characterizations:
Z indicates the output of hidden variable layer in formula, is the characteristic value of Partial Discharge Data;σencIndicate scale parameter;ε indicates full
The random parameter of sufficient N (0,1) distribution;N (0, I) is standardized normal distribution;F indicates activation primitive;Indicate the ratio to be solved
Example parameter;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;X is the Partial Discharge Data of input.
5. Electric Power Equipment Insulation defect identification method as claimed in claim 4, which is characterized in that the probability decoder uses
Following formula characterizations:
In formula, x indicates the Partial Discharge Data of input;The characteristic value of z expression Partial Discharge Data;P (x | z) indicate conditional probability
Distribution function;N indicates normal distyribution function;μdecIndicate probability likelihood parameter, σdecIndicate scale parameter;I indicates offset;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;Table
Show the translational movement to be solved;Indicate the h to be solved2Scale parameter;Indicate the h to be solved2Translational movement.
6. a kind of AEVB self-encoding encoder for Electric Power Equipment Insulation defect recognition, which is characterized in that the AEVB self-encoding encoder
Including probability encoding device and probability decoder;Wherein probability encoding device is configured to: to the shelf depreciation of its input electric power equipment
Signal, then its export Partial Discharge Data characteristic value;The probability decoder is configured to: its input terminal input probability coding
The output of device exports insulation defect type.
7. AEVB self-encoding encoder as claimed in claim 6, which is characterized in that the AEVB self-encoding encoder has an input
Layer, an output layer, a hidden variable layer and four middle layers;One of input layer, two middle layers and a hidden variable
Layer constitutes the probability encoding device, the characteristic value of the hidden variable layer output Partial Discharge Data;Two middle layers and one
Output layer constitutes the probability decoder, and the output layer exports insulation defect type.
8. AEVB self-encoding encoder as claimed in claim 6, which is characterized in that the ginseng of the probability encoding device and probability decoder
Number is optimized using stochastic gradient descent method.
9. AEVB self-encoding encoder as claimed in claim 6, which is characterized in that the probability encoding device uses following formula tables
Sign:
Z indicates the output of hidden variable layer in formula, is the characteristic value of Partial Discharge Data;σencIndicate scale parameter;ε indicates full
The random parameter of sufficient N (0,1) distribution;N (0, I) is standardized normal distribution;F indicates activation primitive;Indicate the ratio to be solved
Example parameter;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;X is the Partial Discharge Data of input.
10. AEVB self-encoding encoder as claimed in claim 9, which is characterized in that the probability decoder uses following formula tables
Sign:
In formula, x indicates the Partial Discharge Data of input;The characteristic value of z expression Partial Discharge Data;P (x | z) indicate conditional probability
Distribution function;N indicates normal distyribution function;μdecIndicate probability likelihood parameter, σdecIndicate scale parameter;I indicates offset;Indicate the scale parameter to be solved;Indicate the translational movement to be solved;Indicate the scale parameter to be solved;Table
Show the translational movement to be solved;Indicate the h to be solved2Scale parameter;Indicate the h to be solved2Translational movement.
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CN113506247A (en) * | 2021-06-16 | 2021-10-15 | 国网湖北省电力有限公司孝感供电公司 | Transmission line inspection defect detection method based on variational Bayesian inference |
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