CN106934802A - A kind of cracking porcelain insulator based on decision tree judges diagnostic method - Google Patents
A kind of cracking porcelain insulator based on decision tree judges diagnostic method Download PDFInfo
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- 239000012212 insulator Substances 0.000 title claims abstract description 74
- 238000005336 cracking Methods 0.000 title claims abstract description 31
- 229910052573 porcelain Inorganic materials 0.000 title claims abstract description 31
- 238000003066 decision tree Methods 0.000 title claims abstract description 22
- 238000002405 diagnostic procedure Methods 0.000 title claims abstract description 10
- 238000004458 analytical method Methods 0.000 claims abstract description 4
- 239000011159 matrix material Substances 0.000 claims abstract description 4
- 238000013507 mapping Methods 0.000 claims description 9
- 229910000831 Steel Inorganic materials 0.000 claims description 8
- 239000010959 steel Substances 0.000 claims description 8
- 238000003709 image segmentation Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 13
- 238000000034 method Methods 0.000 abstract description 6
- 238000007689 inspection Methods 0.000 abstract description 4
- 230000007812 deficiency Effects 0.000 abstract description 2
- 230000008569 process Effects 0.000 abstract description 2
- 239000000463 material Substances 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000003331 infrared imaging Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/0008—Industrial image inspection checking presence/absence
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
- G01J2005/0077—Imaging
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10048—Infrared image
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
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Abstract
The present invention discloses a kind of cracking porcelain insulator based on decision tree and judges diagnostic method, comprises the following steps：A, insulator is taken pictures, image recognition is carried out, to distinguish different insulators；The temperature value that B, analysis different insulative show in the picture, arranges to temperature value, and forming eigenvalue matrix carries out decision tree judgement identification cracking insulator.The present invention can improve the deficiencies in the prior art, reduce and normal piece flase drop occurs in infrared inspection process, the phenomenon of cracking piece missing inspection improves the accuracy rate of cracking porcelain insulator detection, the substantial amounts of infrared picture for the treatment of of energy precise and high efficiency during patrolling and examining, and find out cracking piece.
Description
Technical field
The present invention relates to electric network fault identifying and diagnosing technical field, especially a kind of cracking porcelain insulation based on decision tree
Son judges diagnostic method.
Background technology
Insulator is substantial amounts of in transmission line of electricity at present uses, and its running environment is outdoor transmission line of electricity, traditional detection
Method must carry out stepping on bar detection, need to spend substantial amounts of manpower and material resources, and its accuracy rate by environment, manually adjust spark between
Stand-off distance from and operating personnel the influence of the factor such as experience it is very big.Even if carrying out one-time detection every year, a considerable amount of lacking also is had
Sunken insulator is still run on the line, and hidden danger is run as line security.
The method of conventional detection defect porcelain suspension insulator is not that workload is exactly the shadow for receiving environment or instrument greatly at present
Ring, it is impossible to reach simple, quick, effective testing goal, infrared imaging method has at a distance, untouchable, easy to operate etc.
The incomparable advantage of traditional common detection methods, is gradually widely used in power system context of detection in recent years, it is infrared into
As instrument is converted into electric signal by the thermal radiation signal of target, by being shown as after enhanced processing with the heat of temperature different distributions
Image, equipment heating performance can be intuitively detected by infrared imaging.
Influenceed by shape of a saddle voltage curve, the voltage that the sub-pieces at insulator chain two ends undertake is larger, in high humility
Under climatic environment, the leakage current increase of insulator is flowed through in pollution severity of insulators dissolving, causes the temperature rise of the normal piece in two ends tight
Weight, even more than normal piece is easily caused the generation of flase drop.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of cracking porcelain insulator based on decision tree and judges diagnosis side
Method, can solve the problem that the deficiencies in the prior art, reduce and normal piece flase drop occurs in infrared inspection process, and cracking piece missing inspection shows
As improve the accuracy rate of cracking porcelain insulator detection, the substantial amounts of infrared figure for the treatment of of energy precise and high efficiency during patrolling and examining
Piece, and find out cracking piece.
In order to solve the above technical problems, the technical solution used in the present invention is as follows.
A kind of cracking porcelain insulator based on decision tree judges diagnostic method, comprises the following steps：
A, insulator is taken pictures, image recognition is carried out, to distinguish different insulators；
The temperature value that B, analysis different insulative show in the picture, arranges to temperature value, forms eigenvalue matrix
Carry out decision tree and judge identification cracking insulator.
Preferably, in step A, all of surf characteristic points in infrared image are calculated first, then with template data
Feature point set carries out k nearest neighbor matching, screens out the characteristic point for being not belonging to insulator, and all characteristic points then are carried out into space clustering
To distinguish different insulators.
Preferably, during K- neighborhood matchings are carried out, calculate by feature point group into feature space center, calculate
Mapping relations between each characteristic point and feature space central point, the component of mapping relations each dimensions is carried out with characteristic point
Compare, if characteristic point is less than threshold value with the degree of correlation of the component of mapping relations, this feature point is deleted.
Preferably, in step B, judge that identification cracking insulator is comprised the following steps,
B1, the color legend for extracting infrared image, draw the corresponding Temperature numerical of each group of RGB, with insulator chain middle part
Insulator porcelain piece temperature is used as environment temperature；
B2, to insulator chain image segmentation treatment, obtain monolithic insulator image, and obtain the porcelain piece temperature of monolithic insulator
Degree, steel cap temperature, steel cap porcelain piece temperature difference, porcelain piece ambient temperature differences, monolithic insulator maximum temperature and monolithic insulator temperature
Variance this six characteristic quantities；
B3, using six characteristic quantities as the input quantity of decision tree, insulator state is estimated；
B4, find out cracking insulator.
Preferably, carrying out the selection of metric attribute with information gain, the maximum attribute of information gain is carried out after selection division
Division, possible decision space is traveled through using top-down greedy search；The value of characteristic quantity X is { x1, xn, each takes
The probability for arriving is { p1, pn, the entropy of x is defined as
Information gain is directed to for feature one by one, exactly sees a feature, when system has it and do not have it
Information content is respectively how many, and both differences are exactly the information content that this feature is brought to system, i.e. information gain；
S is wherein whole sample sets, and V (A) is the set of all values of attribute A, one of property value that v is, Sv
The value for being attribute A in S is the sample set of v；
Before each n omicronn-leaf child node of decision tree is divided, the information gain that each attribute is brought first is calculated,
The attribute of maximum information gain is selected to divide.
The beneficial effect brought using above-mentioned technical proposal is：Porcelain piece temperature by extracting monolithic insulator of the invention
Degree, steel cap temperature, porcelain piece steel cap temperature difference, porcelain piece ambient temperature differences, monolithic insulator temperature variance and monolithic insulator are maximum
Temperature this 6 characteristic quantities, and using decision tree differentiate which kind of characteristic quantity or which plant the combination of characteristic quantity can maximally effective judgement
Identification cracking insulator.Conventional method is general proposition threshold method, it is impossible to overcome ambient humidity and insulator position pair
The influence of temperature rise.The present invention is favorably improved the accuracy of detection cracking porcelain insulator, is improving detection efficiency, saves manpower
The aspects such as material resources are significant.
Brief description of the drawings
Fig. 1 is flow chart of the invention.
Specific embodiment
Reference picture 1 a, specific embodiment of the invention is comprised the following steps：
A, insulator is taken pictures, image recognition is carried out, to distinguish different insulators；
The temperature value that B, analysis different insulative show in the picture, arranges to temperature value, forms eigenvalue matrix
Carry out decision tree and judge identification cracking insulator.
In step A, all of surf characteristic points in infrared image are calculated first, then the feature point set with template data enters
Row k nearest neighbor is matched, and screens out the characteristic point for being not belonging to insulator, then carries out space clustering to distinguish difference by all characteristic points
Insulator.
During K- neighborhood matchings are carried out, calculate by feature point group into feature space center, calculate each feature
Mapping relations between point and feature space central point, the component of mapping relations each dimensions is compared with characteristic point, if
Characteristic point is less than threshold value with the degree of correlation of the component of mapping relations, then deleted this feature point.
In step B, judge that identification cracking insulator is comprised the following steps,
B1, the color legend for extracting infrared image, draw the corresponding Temperature numerical of each group of RGB, with insulator chain middle part
Insulator porcelain piece temperature is used as environment temperature；
B2, to insulator chain image segmentation treatment, obtain monolithic insulator image, and obtain the porcelain piece temperature of monolithic insulator
Degree, steel cap temperature, steel cap porcelain piece temperature difference, porcelain piece ambient temperature differences, monolithic insulator maximum temperature and monolithic insulator temperature
Variance this six characteristic quantities；
B3, using six characteristic quantities as the input quantity of decision tree, insulator state is estimated；
B4, find out cracking insulator.
Carry out the selection of metric attribute with information gain, the maximum attribute of information gain enters line splitting after selection division, uses
Top-down greedy search travels through possible decision space；The value of characteristic quantity X is { x1, xn, each probability got is
{p1, pn, the entropy of x is defined as
Information gain is directed to for feature one by one, exactly sees a feature, when system has it and do not have it
Information content is respectively how many, and both differences are exactly the information content that this feature is brought to system, i.e. information gain；
S is wherein whole sample sets, and V (A) is the set of all values of attribute A, one of property value that v is, Sv
The value for being attribute A in S is the sample set of v；
Before each n omicronn-leaf child node of decision tree is divided, the information gain that each attribute is brought first is calculated,
The attribute of maximum information gain is selected to divide.
The present invention extracts some temperature profile values of monolithic insulator, and and ring by splitting insulator chain infrared image
Border temperature compare to come diagnose differentiate cracking piece.The standard of detection cracking porcelain insulator is favorably improved with this detection method
True property, improving detection efficiency, the aspect such as use manpower and material resources sparingly is significant.
In the description of the invention, it is to be understood that term " longitudinal direction ", " transverse direction ", " on ", D score, "front", "rear",
The orientation or position relationship of the instruction such as "left", "right", " vertical ", " level ", " top ", " bottom ", " interior ", " outward " are based on accompanying drawing institute
The orientation or position relationship for showing, are for only for ease of the description present invention, must rather than the device or element for indicating or imply meaning
With specific orientation, with specific azimuth configuration and operation, therefore must be not considered as limiting the invention.
General principle of the invention and principal character and advantages of the present invention has been shown and described above.The technology of the industry
Personnel it should be appreciated that the present invention is not limited to the above embodiments, simply explanation described in above-described embodiment and specification this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and modifications of the present invention are possible, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appending claims and its
Equivalent thereof.
Claims (5)
1. a kind of cracking porcelain insulator based on decision tree judges diagnostic method, it is characterised in that comprise the following steps：
A, insulator is taken pictures, image recognition is carried out, to distinguish different insulators；
The temperature value that B, analysis different insulative show in the picture, is arranged to temperature value, and forming eigenvalue matrix is carried out
Decision tree judges identification cracking insulator.
2. the cracking porcelain insulator based on decision tree according to claim 1 judges diagnostic method, it is characterised in that：Step
In rapid A, all of surf characteristic points in infrared image are calculated first, then the feature point set with template data carries out k nearest neighbor
Match somebody with somebody, screen out the characteristic point for being not belonging to insulator, then carry out space clustering to distinguish different insulators by all characteristic points.
3. the cracking porcelain insulator based on decision tree according to claim 2 judges diagnostic method, it is characterised in that its
It is characterised by：During K- neighborhood matchings are carried out, calculate by feature point group into feature space center, calculate each feature
Mapping relations between point and feature space central point, the component of mapping relations each dimensions is compared with characteristic point, if
Characteristic point is less than threshold value with the degree of correlation of the component of mapping relations, then deleted this feature point.
4. the cracking porcelain insulator based on decision tree according to claim 1 judges diagnostic method, it is characterised in that：Step
In rapid B, judge that identification cracking insulator is comprised the following steps,
B1, the color legend for extracting infrared image, draw the corresponding Temperature numerical of each group of RGB, are insulated with insulator chain middle part
Sub- porcelain piece temperature is used as environment temperature；
B2, to the treatment of insulator chain image segmentation, obtain monolithic insulator image, and obtain monolithic insulator porcelain piece temperature,
Steel cap temperature, steel cap porcelain piece temperature difference, porcelain piece ambient temperature differences, monolithic insulator maximum temperature and monolithic insulator temperature variance
This six characteristic quantities；
B3, using six characteristic quantities as the input quantity of decision tree, insulator state is estimated；
B4, find out cracking insulator.
5. the cracking porcelain insulator based on decision tree according to claim 4 judges diagnostic method, it is characterised in that：With
Information gain carrys out the selection of metric attribute, and the maximum attribute of information gain enters line splitting after selection division, using top-down
Greedy search travels through possible decision space；The value of characteristic quantity X is { x1, xn, the probability that each is got is { p1, pn, x
Entropy be defined as
Information gain is directed to for feature one by one, exactly sees a feature, and system has it and without information when it
Amount is respectively how many, and both differences are exactly the information content that this feature is brought to system, i.e. information gain；
S is wherein whole sample sets, and V (A) is the set of all values of attribute A, one of property value that v is, SvIn being S
The value of attribute A is the sample set of v；
Before each n omicronn-leaf child node of decision tree is divided, the information gain that each attribute is brought first is calculated, selected
The attribute of maximum information gain is divided.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109142991A (en) * | 2018-07-05 | 2019-01-04 | 国网湖南省电力有限公司电力科学研究院 | A kind of infrared survey zero-temperature coefficient threshold determination method of porcelain insulator based on Burr distribution |
| CN112700424A (en) * | 2021-01-07 | 2021-04-23 | 国网山东省电力公司电力科学研究院 | Infrared detection quality evaluation method for live detection of power transformation equipment |
| CN114236327A (en) * | 2021-11-29 | 2022-03-25 | 国网福建省电力有限公司检修分公司 | Detection device and detection method for composite insulator core rod rotting defect |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7349889B1 (en) * | 2000-11-20 | 2008-03-25 | Rohm And Haas Electronic Materials Llc | System and method for remotely diagnosing faults |
| CN102519625A (en) * | 2011-12-20 | 2012-06-27 | 昆明理工大学 | Method for measuring temperature of porcelain insulators by aid of optical fiber Bragg grating temperature sensors |
| CN103792238A (en) * | 2014-02-07 | 2014-05-14 | 国家电网公司 | Porcelain suspension type insulator fault diagnosis method |
| CN105846780A (en) * | 2016-03-19 | 2016-08-10 | 上海大学 | Decision tree model-based photovoltaic assembly fault diagnosis method |
| CN106124949A (en) * | 2016-08-30 | 2016-11-16 | 国网山东省电力公司济南供电公司 | A kind of based on thermal infrared imaging technology to insulator breakdown on-line monitoring method |
-
2017
- 2017-03-09 CN CN201710137103.6A patent/CN106934802B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7349889B1 (en) * | 2000-11-20 | 2008-03-25 | Rohm And Haas Electronic Materials Llc | System and method for remotely diagnosing faults |
| CN102519625A (en) * | 2011-12-20 | 2012-06-27 | 昆明理工大学 | Method for measuring temperature of porcelain insulators by aid of optical fiber Bragg grating temperature sensors |
| CN103792238A (en) * | 2014-02-07 | 2014-05-14 | 国家电网公司 | Porcelain suspension type insulator fault diagnosis method |
| CN105846780A (en) * | 2016-03-19 | 2016-08-10 | 上海大学 | Decision tree model-based photovoltaic assembly fault diagnosis method |
| CN106124949A (en) * | 2016-08-30 | 2016-11-16 | 国网山东省电力公司济南供电公司 | A kind of based on thermal infrared imaging technology to insulator breakdown on-line monitoring method |
Non-Patent Citations (3)
| Title |
|---|
| 敬忠良: "《航天器自主操作的测量与控制》", 31 December 2011, 中国宇航出版社 * |
| 董明等: "基于组合决策树的油浸式电力变压器故障诊断", 《中国电机工程学报》 * |
| 袁安富等: "一种基于SURF特征的零件识别算法", 《计算机应用与软件》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109142991A (en) * | 2018-07-05 | 2019-01-04 | 国网湖南省电力有限公司电力科学研究院 | A kind of infrared survey zero-temperature coefficient threshold determination method of porcelain insulator based on Burr distribution |
| CN112700424A (en) * | 2021-01-07 | 2021-04-23 | 国网山东省电力公司电力科学研究院 | Infrared detection quality evaluation method for live detection of power transformation equipment |
| CN112700424B (en) * | 2021-01-07 | 2022-11-11 | 国网山东省电力公司电力科学研究院 | Infrared detection quality evaluation method for live detection of power transformation equipment |
| CN114236327A (en) * | 2021-11-29 | 2022-03-25 | 国网福建省电力有限公司检修分公司 | Detection device and detection method for composite insulator core rod rotting defect |
| CN114236327B (en) * | 2021-11-29 | 2024-05-31 | 国网福建省电力有限公司检修分公司 | Detection device and detection method for core rod decay defect of composite insulator |
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