CN102175953A - Discharge online monitoring and fault positioning method for power transmission line insulator - Google Patents
Discharge online monitoring and fault positioning method for power transmission line insulator Download PDFInfo
- Publication number
- CN102175953A CN102175953A CN2011100473143A CN201110047314A CN102175953A CN 102175953 A CN102175953 A CN 102175953A CN 2011100473143 A CN2011100473143 A CN 2011100473143A CN 201110047314 A CN201110047314 A CN 201110047314A CN 102175953 A CN102175953 A CN 102175953A
- Authority
- CN
- China
- Prior art keywords
- image
- gray
- scale
- fault
- handled
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 230000005540 biological transmission Effects 0.000 title claims abstract description 14
- 238000012544 monitoring process Methods 0.000 title abstract description 14
- 239000012212 insulator Substances 0.000 title abstract description 13
- 238000001914 filtration Methods 0.000 claims abstract description 7
- 238000003860 storage Methods 0.000 claims abstract description 7
- 238000003331 infrared imaging Methods 0.000 claims abstract description 5
- 239000004744 fabric Substances 0.000 claims description 7
- 238000009826 distribution Methods 0.000 claims description 6
- 230000004069 differentiation Effects 0.000 claims description 3
- 230000007613 environmental effect Effects 0.000 claims description 3
- 241001101720 Murgantia histrionica Species 0.000 claims description 2
- 230000008569 process Effects 0.000 abstract description 7
- 238000003384 imaging method Methods 0.000 abstract description 4
- 238000012545 processing Methods 0.000 abstract description 4
- 238000007781 pre-processing Methods 0.000 abstract 1
- 238000001514 detection method Methods 0.000 description 9
- 238000004458 analytical method Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 5
- 238000000926 separation method Methods 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 3
- 230000009977 dual effect Effects 0.000 description 3
- 238000003708 edge detection Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000004807 localization Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000009413 insulation Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 241001270131 Agaricus moelleri Species 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000005336 cracking Methods 0.000 description 1
- 238000000151 deposition Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Landscapes
- Image Processing (AREA)
Abstract
一种输电线路绝缘子放电在线监测故障定位方法，包括：(1)图像的获得：以现有红外成像、紫外成像或航拍技术获得输电线路绝缘子RGB真彩图像；(2)图像预处理：将所述RGB真彩图像转换为储存空间更小且不丢失有效信息的灰度图，然后利用滤波算法去噪；(3)确定故障位置：利用阈值法提取一次图像，检测图像边缘，对阈值法处理后的二值图像和边缘化处理后二值图像取反，将得到的两幅取反图像叠加，得到故障目标图像。它对在线监测所采集图像能快速进行故障定位，算法简便，为电力系统快速应用图像处理获得故障定位提供了一种图像识别技术。
A fault location method for on-line monitoring of transmission line insulator discharge, including: (1) image acquisition: obtain RGB true-color images of transmission line insulators with existing infrared imaging, ultraviolet imaging or aerial photography technology; (2) image preprocessing: convert all The above RGB true color image is converted into a grayscale image with smaller storage space and no loss of effective information, and then the filtering algorithm is used to denoise; (3) Determine the fault location: use the threshold method to extract an image, detect the edge of the image, and process the image with the threshold method The final binary image and the marginalized binary image are reversed, and the obtained two reversed images are superimposed to obtain the fault target image. It can quickly locate the fault on the image collected by online monitoring, and the algorithm is simple, which provides an image recognition technology for the power system to quickly apply image processing to obtain fault location.
Description
Technical field
The present invention relates to electric transmission line isolator On-line Discharge monitoring Fault Locating Method.
Background technology
Be installed in insulator on the transmission line of electricity be in operation because of stand for a long time dynamo-electricly to load, expose to the sun and rain, effect such as cold and hot variation, faults such as insulation resistance reduction, cracking even puncture may appear, power supply reliability is brought potential threat, and therefore, the online detection of insulator is significant.Line insulator is extensive because of the special and distributed areas of its installation site, always is a difficult point of Insulation Live Testing, the several years, is seeking effective solution both at home and abroad always.
In the existing insulator on-line monitoring technique, heating causes the fault of device external temperature variation to internal fault, can use the infrared image method and diagnose.Ultraviolet imagery technology is to utilize specific apparatus to receive the UV signal that corona discharge produces, imaging after treatment and with the visible images stack, reach position and the intensity of determining corona.Along with the raising of electric pressure, use the helicopter line walking and the technology of taking photo by plane, fast because of its reaction velocity, the image data amount is big, and can overcome obstacle such as topography and geomorphology, will come into one's own day by day in electric system.
Existing image that imaging technique obtains is the rgb image of RGB mostly, the picture quality height, and this proposes very high request to Computer Processing.Be better analysis image, it is littler and do not lose the image of effective information to need to change image into a kind of storage; And carrying out edge of image when detecting, abort situation is not obvious, for the image background complexity abort situation that is not easily distinguishable.
Summary of the invention
The technical problem to be solved in the present invention is, exist not enough at prior art, a kind of electric transmission line isolator On-line Discharge monitoring Fault Locating Method is proposed, it can carry out localization of fault fast to on-line monitoring institute images acquired, algorithm is easy, checking by experiment can present the insulator breakdown position in simple mode directly perceived on picture.
Technical scheme of the present invention is that described electric transmission line isolator On-line Discharge monitoring Fault Locating Method comprises:
(1) acquisition of image: obtain electric transmission line isolator RGB rgb image with existing infrared imaging, ultraviolet imagery or the technology of taking photo by plane;
(2) image pre-service: it is littler and do not lose the gray-scale map of effective information that described RGB rgb image is converted to the storage area; Utilize filtering algorithm to get rid of environmental interference information, i.e. denoising in the picture then;
(3) determine abort situation, step is:
A. utilize threshold method to extract an image: with the pixel definition of gray-scale map is gray-scale value, and gray-scale value size is 0-255, and wherein 0 is white, from 0 to 255 gradually from vain to ash, become black at last; Go out the number of times that each gray-scale value occurs among the 0-255 by statistics with histogram, obtain the histogram of the distribution of gray scale in the image, utilize the differentiation gray-scale value of the distribution failure judgement and the background of gray scale, should distinguish gray-scale value and be defined as threshold value; Find out this threshold value,
When the gray-scale value of image during greater than described threshold value pictorial element keep or be changed to 1; Pictorial element is lost and is changed to 0 when the gray-scale value of image is less than or equal to described threshold value, obtains the bianry image after threshold method is handled; Be expressed as follows with mathematical relation:
In the formula, (u is v) for handling back bianry image mid point (u, gray-scale value v) for H; (u v) is (u, gray-scale value v) before handling to D; D
0Be the threshold value of selecting, (u v) is the coordinate of image mid point;
B. detected image edge: gray-scale map is carried out marginalisation handles, the back bianry image is handled in marginalisation;
C. back bianry image negate (mirror image figure) is handled in bianry image and marginalisation after respectively threshold method being handled, gets the negate image of the bianry image after threshold method is handled and the negate image that the back bianry image is handled in marginalisation;
D. two width of cloth negate image overlay that will be obtained by step c obtain the fault target image.
Below the present invention made further specify.
In described step (2) the image pre-service, the wavelet analysis tool box of the filtering algorithm MATLAB of prior art provides the gang's algorithm that signal is carried out denoising, this paper adopts medfilt2 function (medium filtering) simple process, because emphasis of the present invention is in the trouble spot analytically, related Flame Image Process is mainly at fault and edge extracting, add suitable denoising function and can obtain more clearly handling the back image, can consult conventional images treatment technology data the detail knowledge of denoising.
Among described step (3) a, the principle of the selected foundation of threshold value is the sudden change of trouble spot pixel on the picture, for the fault picture, the trouble spot generally shows as the form of bright spot or hot spot, and tangible separation is arranged with respect to black background on every side, after we are gray-scale value with the pixel transitions of separation, the point of separation correspondence is left threshold value, utilize MATLAB to obtain the image size and make up once dual circulation, everyly be defined as white, all be defined as black, like this so that the human visual custom greater than threshold value less than setting threshold, algorithm has two kinds, one is that this is more accurate, is exactly in addition to select parameter by threshold function table automatic setting threshold value among the MATLAB by the artificial given threshold value of histogram, the effect that can not do well for some picture of the threshold value of Xuan Zeing by this method, the present invention preferentially adopts the former.
For passing through the artificial given threshold value of histogram, for example (referring to Fig. 2), because the trouble spot mostly is bright spot or hot spot greatly, there is black-ash-Bai continually varying gray-scale value to be quantified as 256 gray levels as handle, the scope of gray-scale value is 0-255, to shallow, the color in the correspondence image be from deceiving in vain from deeply in expression brightness.So the pixel near 255 gray scales shows as bright more on image more, as seen from Figure 2, reduce gradually after the gray-scale value 200 (hot spot accounts for the image scaled fraction on the correspondence image), and the gray-scale value still more saturated (background colour is in the great majority on the correspondence image) before 200, so can judge that greater than 200 later gray-scale values be the fault correspondence position, bring proof of algorithm by artificial observation into the 220-230 threshold value then, find that 230 can obtain spot size more clearly.
Among described step (3) b, for the fault picture, different with the handled emphasis of threshold method, threshold method is at the trouble spot, and rim detection is the profile at whole faulty equipment, and we need extract the edge of whole faulty equipment by rim detection, such as the joint number of insulator, the trouble spot that so comprehensive threshold method extracts just can know very clearly fault occurs on which joint of insulator actually.Because noise and fuzzy existence, the border at detected edge may broaden or be interrupted at certain some place, therefore rim detection comprises two substances, the marginal point of abstraction reaction grey scale change at first, reject some frontier point then or fill up the border and cut short a little, and these edges are connected into complete line.
The edge function that MATLAB Flame Image Process tool box provides can realize detecting the function at edge, and its syntax format is:
BW=edge (J, ' canny '), wherein J is a target image file, and canny is the canny operator of edge function)
Among the present invention,, the most important thing is in great amount of images information, to extract effective information, how to utilize these information interpretation images, see through the essence of phenomenon analysis fault for image detecting system.In order to analyze effectively and to understand image, often need given image and the image-region cut apart are removed garbage, and adopt more simple clear and definite numerical value, symbol or diagrammatic representation to come out useful information.The image information that electric system obtained because must there be influence in the complicacy of noise to image, for obtaining better monitoring effect, is carried out fault analysis more accurately, identification and location during actual monitoring, must carry out pre-service to gathering the image that comes.Usually adopt specific filtering method elimination picture noise, promptly image denoising is analyzed to judge the particular location of corona generation the pretreated image of process again.Existing image that imaging technique obtains is the rgb image of RGB mostly, the picture quality height, and this proposes very high request to Computer Processing.Be better analysis image, it is littler and do not lose image---the gray-scale map of effective information to need to change image into a kind of storage.With respect to the RGB image, the storage space that gray level image needs is little, and the position of corona discharge is determined not influence.
Among the present invention, threshold method has been adopted in the establishment of electric transmission line isolator abort situation.Threshold method is to select appropriate threshold that image is handled according to the intensity profile in the histogram.The RGB image becomes gray-scale map after treatment, the image size does not change, but originally position is become by a numerical value by pixel R (Red), G (Green), the B (Blue) of three numerical value decisions and determines that we are defined as gray-scale value with this numerical value, and size is 0-255.Wherein 0 be white, from 0 to 255 gradually from vain to ash, become black at last.Go out the number of times that each gray-scale value occurs among the 0-255 by statistics with histogram.Can see the distribution of gray scale in the image very intuitively by histogram, utilize the differentiation gray-scale value of the distribution failure judgement and the background of gray scale, we are called threshold value to it.Threshold method is found out this exactly and is distinguished gray-scale value, when the gray-scale value of image during greater than the threshold value selected pictorial element keep or be changed to 1; When the gray-scale value of image during less than the threshold value selected pictorial element lose and be changed to 0.
Among the present invention, the edge of image detection technique is extremely important for processing digital images, because the edge is the separatrix of target device to be extracted and background, extracting the edge could make a distinction target and background.In image, the border shows the termination of a characteristic area and the beginning of another characteristic area, the internal feature or the attribute of border institute separation region are consistent, and the feature of zones of different inside or attribute are different, the detection at edge confirms to utilize object and the difference of background on certain picture characteristics to realize that these differences comprise gray scale, color or unity and coherence in writing feature.In fact rim detection is exactly the position that the detected image characteristic changes.Its major advantage is to extract the exterior insulator feature more intuitively, detect under the little situation of images acquired noise the edge weak, may take place corona than the zonule, its shortcoming is that the fault position is not obvious, for the image background complexity abort situation that is not easily distinguishable.
The present invention with existing infrared imaging, ultraviolet imagery or the image that technology was obtained of taking photo by plane serve as research and process object, equipment under test is carried out multi-facetedly and long-time taking and adopting the zone technology of taking photo by plane to obtain the great amount of images data, according to existing threshold method and two kinds of mathematical image detection methods of Image Edge-Detection method, utilize that bit arithmetic is ingenious to overcome that both are not enough separately, proposed a kind of image-recognizing method after making improvements.
The present invention is directed to monitoring photo information, earlier true coloured picture sheet is converted into gray-scale map, can reduce deal with data like this and don't lose useful information, utilize filtering algorithm to get rid of environmental interference information in the picture (get rid of rapidly to disturb and show fault position in picture, the saving plenty of time) then as far as possible.Picture through denoising is the useful information picture, utilizing threshold method to extract an image earlier preserves, obtain an other image once more at the information picture then, utilize bit arithmetic to realize stack at last, the picture stack is only stayed the two-value picture of the clear position of failure message.By the relative merits of two kinds of methods of threshold method and Image Edge-Detection method are complementary comprehensive, it is not clear both to have overcome the threshold method abort situation, solve the unconspicuous problem of rim detection fault signature again, obtain a bianry image at last, image is simple, intuitive not only, and image storage space is little, and computing is also very fast, and is not high to hardware requirement.Can lay the foundation as setting up database, express-analysis discharge position, automatic digital assay failure cause etc. for follow-up work.
As known from the above, the present invention is a kind of electric transmission line isolator On-line Discharge monitoring Fault Locating Method, it can carry out localization of fault fast to on-line monitoring institute images acquired, and algorithm is easy, handles the acquisition localization of fault for the quick application image of electric system a kind of image recognition technology is provided.
Description of drawings
Fig. 1 is the gray-scale map of the electric transmission line isolator joint fault graph picture of infrared imaging;
Fig. 2 is the histogram of fault graph picture shown in Figure 1;
Fig. 3 is the bianry image of gray-scale map shown in Figure 1 after threshold method is handled;
Fig. 4 is the bianry image of gray-scale map shown in Figure 1 after handling through marginalisation;
Fig. 5 is the negate image (threshold method computing negate) of image shown in Figure 3;
Fig. 6 is the negate image (Canny operator computing negate) of image shown in Figure 4;
Fig. 7 is the targeted graphical that obtains after Fig. 5 and Fig. 6 two width of cloth doublings of the image;
Embodiment
Now detecting insulator joint fault with Fig. 1 infrared imagery technique is example:
Fig. 1 compute histograms is obtained image pattern 2, can find out from histogram, threshold value is generally greater than 200, and this paper is 230 for the threshold setting of Fig. 1, adopts the dual loop statement of MATLAB to remove threshold value and obtains after less than 230 pixel.
Find out that by Fig. 3 though algorithm has extracted fault-signal, region of discharge is obvious, can't obtain its particular location on electrical equipment, be not easy to maintenance and the maintenance of people electrical equipment.
Fig. 4 major advantage is to extract the exterior insulator feature more intuitively, detect under the little situation of images acquired noise the edge weak, may take place corona than the zonule, its shortcoming is that the fault position is not obvious, for the image background complexity abort situation that is not easily distinguishable.
Obtaining two width of cloth bianry images by above two kinds of methods all can not well react abort situation and obtain useful information, consider that its gained image slices vegetarian refreshments is 0 or 1, present technique adopts the MATLAB bit arithmetic, with threshold method gained image graph 3 and 4 negates of edge detection method gained image, obtain Fig. 5 and Fig. 6 earlier.
At last Fig. 5 and Fig. 6 two width of cloth bianry images are done and computing, be about to get targeted graphical shown in Figure 7 after this two width of cloth doubling of the image.
The realization of algorithm on MATLAB for the present invention relates to below:
% Flame Image Process original program
Clear; Close; % releasing memory capacity
I=imread (' E: STUDY MATLAB71 work tuxiangchuli TestD ', ' tif '); % opens image file, is example with the TestD fault
G=rgb2gray (I); % becomes gray level image with image by true coloured silk
G1＝medfilt2(G)；
Subplot (221); Imshow (G1); Title (' fault gray-scale map A '); Grid on; Gray level image after the % demonstration conversion
[x, y]=size (G1); % obtains the gray level image size
Subplot (222); Imhist (G); % shows histogram
G2=repmat (logical (uint8 (0)), x, y); % make up one with the big or small identical null matrix of source images, be used for depositing bianry image 0,1 data
For fanzhii=1:1:x% selects in the fault graph picture less than the gray-scale value of threshold value with a dual circulation, is set at 170 according to histogram thresholding in this example
for?fanzhij＝1:1:y
IfG (fanzhii, fanzhij)＞the 170%TestD threshold value is chosen as 170
G2(fanzhii，fanzhij)＝1；
end
end
end
Subplot (222), imshow (～G2); Title (' handles A-＞B ' through threshold method); Grid on; The %G2 image is the bianry image after selecting through threshold method
%[BW, thresh]=edge (G, ' canny '); % carries out marginalisation by MATLAB acquiescence mode selected threshold to be handled
G3=edge (G1, ' canny ', 0.18); The % marginalisation is handled, and takes the canny operator, and the TestD value is 0.181
Subplot (223); Imshow (G3); Title (' is A-＞C ' after marginalisation is handled); Grid on; BWl=(～G2) ﹠amp; (～G3); % is by merging on the piece image two width of cloth images and display result subplot (224) with computing; Imshow (BW1); Title (' merges the back image '); Grid on; Imwrite (BW1, ' result2 ', ' gif '); % is saved to memory device with the result with the GIF form.
Claims (1)
1. an electric transmission line isolator On-line Discharge is monitored Fault Locating Method, it is characterized in that this method is:
(1) acquisition of image: obtain electric transmission line isolator RGB rgb image with existing infrared imaging, ultraviolet imagery or the technology of taking photo by plane;
(2) image pre-service: it is littler and do not lose the gray-scale map of effective information that described RGB rgb image is converted to the storage area; Utilize filtering algorithm to get rid of environmental interference information, i.e. denoising in the picture then;
(3) determine abort situation, comprising:
A. utilize threshold method to extract an image: with the pixel definition of gray-scale map is gray-scale value, and gray-scale value size is 0-255, and wherein 0 is white, from 0 to 255 gradually from vain to ash, become black at last; Go out the number of times that each gray-scale value occurs among the 0-255 by statistics with histogram, obtain the histogram of the distribution of gray scale in the image, utilize the differentiation gray-scale value of the distribution failure judgement and the background of gray scale, should distinguish gray-scale value and be defined as threshold value; Find out this threshold value, when the gray-scale value of image during greater than described threshold value pictorial element keep or be changed to 1; Pictorial element is lost and is changed to 0 when the gray-scale value of image is less than or equal to described threshold value, obtains the bianry image after threshold method is handled;
B. detected image edge: gray-scale map is carried out marginalisation handles, the back bianry image is handled in marginalisation;
C. back bianry image negate (mirror image figure) is handled in bianry image and marginalisation after respectively threshold method being handled, gets the negate image of the bianry image after threshold method is handled and the negate image that the back bianry image is handled in marginalisation;
D. two width of cloth negate image overlay that will be obtained by step c obtain the fault target image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100473143A CN102175953A (en) | 2011-02-28 | 2011-02-28 | Discharge online monitoring and fault positioning method for power transmission line insulator |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100473143A CN102175953A (en) | 2011-02-28 | 2011-02-28 | Discharge online monitoring and fault positioning method for power transmission line insulator |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102175953A true CN102175953A (en) | 2011-09-07 |
Family
ID=44519158
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011100473143A Pending CN102175953A (en) | 2011-02-28 | 2011-02-28 | Discharge online monitoring and fault positioning method for power transmission line insulator |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102175953A (en) |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102565577A (en) * | 2011-12-15 | 2012-07-11 | 国网电力科学研究院 | Method for detecting high optical spectrum of composite insulator |
| CN103018640A (en) * | 2012-11-27 | 2013-04-03 | 华北电力大学（保定） | Method for testing electricity discharge intensity of corona on surface of high-voltage insulator |
| CN103033722A (en) * | 2011-09-29 | 2013-04-10 | 本德尔有限两合公司 | Insulated fault positioner and apparatus performing insulated fault positioning in ungrounded power-supply network |
| CN103353507A (en) * | 2013-06-20 | 2013-10-16 | 国家电网公司 | Automatic insulator flaw detection instrument and method |
| CN103412246A (en) * | 2013-06-28 | 2013-11-27 | 广东电网公司电力科学研究院 | Automatic detection method of abnormal discharge of power equipment based on ultraviolet video |
| CN103954897A (en) * | 2014-05-20 | 2014-07-30 | 电子科技大学 | Intelligent power grid high-voltage insulation damage monitoring system and method based on ultraviolet imaging |
| CN104459457A (en) * | 2013-09-25 | 2015-03-25 | 北京环境特性研究所 | Infrared and ultraviolet dual-path imaging power detector |
| CN105098651A (en) * | 2014-12-26 | 2015-11-25 | 天津航天中为数据系统科技有限公司 | Method and system for positioning insulator of power transmission line |
| CN105572541A (en) * | 2015-12-07 | 2016-05-11 | 浙江大学 | High-voltage line patrol fault detection method and system based on visual attention mechanism |
| CN106846304A (en) * | 2017-01-03 | 2017-06-13 | 中国特种设备检测研究院 | Electrical equipment detection method and device based on infrared detection |
| CN106934418A (en) * | 2017-03-09 | 2017-07-07 | 国家电网公司 | A kind of insulator infrared diagnostics method based on convolution Recursive Networks |
| CN107464044A (en) * | 2017-07-21 | 2017-12-12 | 国网浙江省电力公司绍兴供电公司 | Grid operation situation cognitive method and system based on focus edge extracting |
| CN107677944A (en) * | 2017-10-31 | 2018-02-09 | 成都意町工业产品设计有限公司 | It is a kind of to be used to detect the abnormal system of insulator in high-voltage transmission line |
| CN107729907A (en) * | 2016-08-12 | 2018-02-23 | 南京理工大学 | A kind of fault recognition method based on infra-red thermal imaging system |
| CN108177660A (en) * | 2016-08-30 | 2018-06-19 | 大连民族大学 | The laser image preprocess method of rail |
| CN109708877A (en) * | 2018-12-27 | 2019-05-03 | 昆明理工大学 | Mechanical fault analysis method based on wavelet fuzzy recognition and image analysis theory |
| CN110018389A (en) * | 2019-02-21 | 2019-07-16 | 国网山东省电力公司临沂供电公司 | A kind of transmission line of electricity on-line fault monitoring method and system |
| CN111882537A (en) * | 2020-07-28 | 2020-11-03 | 研祥智能科技股份有限公司 | Visual inspection method and system |
| CN112818998A (en) * | 2021-02-08 | 2021-05-18 | 上海电力大学 | Overhead line insulator fault monitoring system based on Lora communication |
| CN113376478A (en) * | 2021-06-22 | 2021-09-10 | 清华大学 | Power transmission line lightning stroke or short circuit fault positioning method based on edge detection |
| CN114240986A (en) * | 2021-11-12 | 2022-03-25 | 东莞拓斯达技术有限公司 | Image processing method, terminal device and computer-readable storage medium |
| CN115542100A (en) * | 2022-11-29 | 2022-12-30 | 广东电网有限责任公司东莞供电局 | Insulator fault detection method, device, equipment and medium |
| CN118777780A (en) * | 2024-07-05 | 2024-10-15 | 杭州和继能源有限公司 | A method and device for optimizing transmission line fault location data |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1557982A1 (en) * | 2004-01-26 | 2005-07-27 | STMicroelectronics S.r.l. | Method and system for admission control in communication networks |
| US20070194795A1 (en) * | 2006-01-31 | 2007-08-23 | Advantest Corporation | Test Apparatus and test method |
| CN101551435A (en) * | 2009-05-15 | 2009-10-07 | 湖北省电力试验研究院 | Method for detecting corona discharging and device thereof |
-
2011
- 2011-02-28 CN CN2011100473143A patent/CN102175953A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1557982A1 (en) * | 2004-01-26 | 2005-07-27 | STMicroelectronics S.r.l. | Method and system for admission control in communication networks |
| US20070194795A1 (en) * | 2006-01-31 | 2007-08-23 | Advantest Corporation | Test Apparatus and test method |
| CN101551435A (en) * | 2009-05-15 | 2009-10-07 | 湖北省电力试验研究院 | Method for detecting corona discharging and device thereof |
Non-Patent Citations (1)
| Title |
|---|
| 刘建友: "航拍绝缘子图像的边缘检测研究", 《中国高等学校电力系统及其自动化专业第二十五届学术年会》 * |
Cited By (29)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103033722A (en) * | 2011-09-29 | 2013-04-10 | 本德尔有限两合公司 | Insulated fault positioner and apparatus performing insulated fault positioning in ungrounded power-supply network |
| CN102565577A (en) * | 2011-12-15 | 2012-07-11 | 国网电力科学研究院 | Method for detecting high optical spectrum of composite insulator |
| CN103018640A (en) * | 2012-11-27 | 2013-04-03 | 华北电力大学（保定） | Method for testing electricity discharge intensity of corona on surface of high-voltage insulator |
| CN103353507A (en) * | 2013-06-20 | 2013-10-16 | 国家电网公司 | Automatic insulator flaw detection instrument and method |
| CN103412246A (en) * | 2013-06-28 | 2013-11-27 | 广东电网公司电力科学研究院 | Automatic detection method of abnormal discharge of power equipment based on ultraviolet video |
| CN103412246B (en) * | 2013-06-28 | 2016-03-02 | 广东电网公司电力科学研究院 | Based on the Automatic detection method of abnormal discharge of power equipment of ultraviolet video |
| CN104459457A (en) * | 2013-09-25 | 2015-03-25 | 北京环境特性研究所 | Infrared and ultraviolet dual-path imaging power detector |
| CN103954897A (en) * | 2014-05-20 | 2014-07-30 | 电子科技大学 | Intelligent power grid high-voltage insulation damage monitoring system and method based on ultraviolet imaging |
| CN105098651A (en) * | 2014-12-26 | 2015-11-25 | 天津航天中为数据系统科技有限公司 | Method and system for positioning insulator of power transmission line |
| CN105572541A (en) * | 2015-12-07 | 2016-05-11 | 浙江大学 | High-voltage line patrol fault detection method and system based on visual attention mechanism |
| CN107729907A (en) * | 2016-08-12 | 2018-02-23 | 南京理工大学 | A kind of fault recognition method based on infra-red thermal imaging system |
| CN108177660A (en) * | 2016-08-30 | 2018-06-19 | 大连民族大学 | The laser image preprocess method of rail |
| CN108177660B (en) * | 2016-08-30 | 2020-07-14 | 大连民族大学 | Steel rail abrasion detection method with laser image processing step |
| CN106846304B (en) * | 2017-01-03 | 2020-08-04 | 中国特种设备检测研究院 | Electrical equipment detection method and device based on infrared detection |
| CN106846304A (en) * | 2017-01-03 | 2017-06-13 | 中国特种设备检测研究院 | Electrical equipment detection method and device based on infrared detection |
| CN106934418A (en) * | 2017-03-09 | 2017-07-07 | 国家电网公司 | A kind of insulator infrared diagnostics method based on convolution Recursive Networks |
| CN107464044A (en) * | 2017-07-21 | 2017-12-12 | 国网浙江省电力公司绍兴供电公司 | Grid operation situation cognitive method and system based on focus edge extracting |
| CN107677944A (en) * | 2017-10-31 | 2018-02-09 | 成都意町工业产品设计有限公司 | It is a kind of to be used to detect the abnormal system of insulator in high-voltage transmission line |
| CN109708877A (en) * | 2018-12-27 | 2019-05-03 | 昆明理工大学 | Mechanical fault analysis method based on wavelet fuzzy recognition and image analysis theory |
| CN109708877B (en) * | 2018-12-27 | 2020-11-24 | 昆明理工大学 | Mechanical fault analysis method based on wavelet fuzzy recognition and image analysis theory |
| CN110018389B (en) * | 2019-02-21 | 2021-11-12 | 国网山东省电力公司临沂供电公司 | Online fault monitoring method and system for power transmission line |
| CN110018389A (en) * | 2019-02-21 | 2019-07-16 | 国网山东省电力公司临沂供电公司 | A kind of transmission line of electricity on-line fault monitoring method and system |
| CN111882537A (en) * | 2020-07-28 | 2020-11-03 | 研祥智能科技股份有限公司 | Visual inspection method and system |
| CN111882537B (en) * | 2020-07-28 | 2023-12-15 | 研祥智能科技股份有限公司 | Visual detection method and system |
| CN112818998A (en) * | 2021-02-08 | 2021-05-18 | 上海电力大学 | Overhead line insulator fault monitoring system based on Lora communication |
| CN113376478A (en) * | 2021-06-22 | 2021-09-10 | 清华大学 | Power transmission line lightning stroke or short circuit fault positioning method based on edge detection |
| CN114240986A (en) * | 2021-11-12 | 2022-03-25 | 东莞拓斯达技术有限公司 | Image processing method, terminal device and computer-readable storage medium |
| CN115542100A (en) * | 2022-11-29 | 2022-12-30 | 广东电网有限责任公司东莞供电局 | Insulator fault detection method, device, equipment and medium |
| CN118777780A (en) * | 2024-07-05 | 2024-10-15 | 杭州和继能源有限公司 | A method and device for optimizing transmission line fault location data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102175953A (en) | Discharge online monitoring and fault positioning method for power transmission line insulator | |
| US11221107B2 (en) | Method for leakage detection of underground pipeline corridor based on dynamic infrared thermal image processing | |
| CN107483014B (en) | A kind of photovoltaic panel failure automatic detection method | |
| Cha et al. | Vision-based detection of loosened bolts using the Hough transform and support vector machines | |
| Li et al. | Integrated processing of image and GPR data for automated pothole detection | |
| CN104331521B (en) | Transformer anomaly identification method based on image procossing | |
| Tian et al. | Power line recognition and tracking method for UAVs inspection | |
| CN104217443B (en) | Electric transmission and transformation equipment infrared fault image segmentation method based on HSV (Hue, Saturation, Value) space | |
| Chanda et al. | Automatic bridge crack detection–a texture analysis-based approach | |
| CN107729907A (en) | A kind of fault recognition method based on infra-red thermal imaging system | |
| CN106296670B (en) | A kind of Edge detection of infrared image based on the watershed Retinex--Canny operator | |
| CN101901477A (en) | Method and system for edge extraction of field images of plant leaves | |
| CN105572541A (en) | High-voltage line patrol fault detection method and system based on visual attention mechanism | |
| CN106228541A (en) | Screen positioning method and device in visual inspection | |
| CN109345586A (en) | Discharge feature extraction method of electrical equipment based on ultraviolet imaging technology | |
| CN106570515A (en) | Method and system for treating medical images | |
| CN108090459A (en) | A kind of road traffic sign detection recognition methods suitable for vehicle-mounted vision system | |
| CN103412246A (en) | Automatic detection method of abnormal discharge of power equipment based on ultraviolet video | |
| CN100500100C (en) | Method and apparatus for recognizing intelligently ultrasound womb contraceptive ring image | |
| CN102646191B (en) | Method applied to recognition of flame image generated by gas combustion associated in oil drilling | |
| CN112967221A (en) | Shield constructs section of jurisdiction production and assembles information management system | |
| CN102509299A (en) | Image salient area detection method based on visual attention mechanism | |
| CN105787955A (en) | Sparse segmentation method and device of strip steel defect | |
| CN111524146A (en) | Fault diagnosis method and system for HVAC hot water pipeline based on infrared image | |
| CN107231553A (en) | Corner location acquisition methods and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C12 | Rejection of a patent application after its publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20110907 |