CN117054824A - Power similarity-based power distribution network fault positioning method and device and related equipment - Google Patents
Power similarity-based power distribution network fault positioning method and device and related equipment Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
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Abstract
The application discloses a power distribution network fault positioning method and device based on power similarity and related equipment, wherein the method comprises the following steps: acquiring a first power value of each line head end and a second power value of each line tail end; calculating the similarity degree of the first power value and the second power value of each circuit to obtain the similarity value of each circuit; acquiring a plurality of photos on each line, and performing anomaly diagnosis on each photo on each line to obtain the category of each photo; based on the similarity value of each line and the class of the photo on the line, the fault location is determined. According to the application, the fault location is realized by combining the photo information and the electrical information, so that the effectiveness of fault location is improved; in addition, since the photograph information contains positioning information of the photographing point, the fault positioning accuracy is improved.
Description
Technical Field
The application relates to the technical field of power distribution networks, in particular to a power distribution network fault positioning method and device based on power similarity and related equipment.
Background
The distribution network is used as the tail end of the power network, closely contacts with users, plays an important role in a power system, and the normal operation of the distribution network is related to the reliability, safety and economy of power supply. However, the distribution network has complex circuit structure, numerous branches, various environments where the circuits are located, is easily affected by various factors (such as lightning, lightning strike, artificial damage and the like) to cause faults, and is easy to cause unexpected power failure. In addition, the distribution network can have the hidden danger such as circuit shaft tower slope collapse, circuit and surrounding trees safety distance are not enough in operation, and circuit is not enough to ground distance, leads to the emergence of trouble, even endangers personal safety. Therefore, the fault positioning is realized rapidly, and the method has important significance for recovering the stable operation of the power distribution network and guaranteeing the normal living order of the national people.
Disclosure of Invention
In view of the above, the application provides a power distribution network fault positioning method and device based on power similarity and related equipment, so as to realize automatic positioning of power distribution network fault points.
In order to achieve the above object, a first aspect of the present application provides a power distribution network fault location method based on power similarity, including:
acquiring a first power value of each line head end and a second power value of each line tail end;
calculating the similarity degree of the first power value and the second power value of each circuit to obtain the similarity value of each circuit;
acquiring a plurality of photos on each line, and performing anomaly diagnosis on each photo on each line to obtain the category of each photo;
based on the similarity value of each line and the class of the photo on the line, the fault location is determined.
Preferably, the process of calculating the similarity between the first power value and the second power value of each line to obtain the similarity value of each line includes:
the similarity value of each line is calculated by the following equation:
；
wherein,is the firstjSimilarity value of lines, < >>Is the firstjIn the first lineiFirst power values corresponding to the sampling points, +.>Is the firstjIn the first lineiSecond power values corresponding to the sampling points, +.>Is the firstjAverage value of the first power value of the lines in a preset time period, < >>Is the firstjThe average value of the second power values of the lines in the preset time period is njThe number of head or tail sampling points in the line.
Preferably, the process of performing abnormality diagnosis on each photo on each line to obtain a category of each photo includes:
preprocessing an image of each photo on each line to obtain a processed image corresponding to the photo;
and performing anomaly diagnosis on the processed image of each photo by using a cluster analysis method to obtain the category of the photo.
Preferably, the process of preprocessing the image of each photo on each line to obtain a processed image corresponding to the photo includes:
carrying out gray scale treatment on the image of each photo on each line to obtain a gray scale image of each photo on each line;
and carrying out noise reduction treatment on the gray-scale image of each photo on each line by utilizing Gaussian filtering and bilateral filtering to obtain a treated image corresponding to the photo.
Preferably, the process of graying the image of each photo on each line to obtain a gray-scale image of each photo on each line includes:
for each photo on each line, determining a gray-scale value of each pixel in an image of the photo by using the following equation to obtain a gray-scale map of the photo:
；
wherein,for the gray-scale value of the ith pixel in the gray-scale map of the image,/for the gray-scale value of the ith pixel>For the red gradation value of the i-th pixel in the image,/th pixel is selected from the group consisting of>For the green gradation value of the i-th pixel in the image,/th pixel>And the black level value of the ith pixel in the image.
Preferably, the process of performing anomaly diagnosis on the processed image of each photo by using a cluster analysis method to obtain the category of the photo includes:
converting, for a processed image of each photograph, the processed image into a binary image corresponding to the photograph;
determining an initial clustering center based on the data characteristics of each binary image;
and carrying out Kmeans clustering operation on each binary image based on a preset K value and the initial clustering center, determining the category of each binary image based on an operation result, and determining the category of each binary image as the category of the photo corresponding to the binary image, wherein the category comprises normal and abnormal.
Preferably, the process of determining the fault location based on the similarity value of each line and the class of the photo on the line includes:
judging whether the similarity value of each line is smaller than 0 or not according to each line;
if yes, screening out the photos with abnormal categories on the line as target photos, and determining fault positions based on the position information of the target photos.
Preferably, the process of obtaining a plurality of photographs on each line includes:
and utilizing the unmanned aerial vehicle to fly along a preset flying line, and shooting the photo of each line in a plurality of preset shooting points in the flying line to obtain a plurality of photos on each line.
The second aspect of the present application provides a power distribution network fault location device based on power similarity, including:
the power value acquisition unit is used for acquiring a first power value of the head end of each line and a second power value of the tail end of each line;
the similarity calculation unit is used for calculating the similarity of the first power value and the second power value of each circuit to obtain a similarity value of each circuit;
the abnormality diagnosis unit is used for acquiring a plurality of photos on each line, and performing abnormality diagnosis on each photo on each line to obtain the category of each photo;
and the fault locating unit is used for determining the fault position based on the similarity value of each line and the type of the photo on the line.
A third aspect of the present application provides a power distribution network fault location device based on power similarity, including: a memory and a processor;
the memory is used for storing programs;
the processor is used for executing the program to realize each step of the power distribution network fault positioning method based on the power similarity.
A fourth aspect of the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the power similarity based power distribution network fault location method described above.
According to the technical scheme, the first power value of the head end of each line and the second power value of the tail end of each line are obtained. And then, calculating the similarity degree of the first power value and the second power value of each circuit to obtain the similarity value of each circuit. It will be appreciated that the similarity value of a certain line characterizes the degree of consistency of the power values of the first and the last ends of the line. And then, acquiring a plurality of photos on each line, and carrying out abnormality diagnosis on each photo on each line to obtain the category of each photo. It will be appreciated that each photograph contains image information as well as location information for the photograph taking point. Wherein the categories include normal and abnormal. Finally, the fault location is determined based on the similarity value of each line and the class of the photo on the line. According to the application, the fault location is realized by combining the photo information and the electrical information, so that the effectiveness of fault location is improved; in addition, since the photograph information contains positioning information of the photographing point, the fault positioning accuracy is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a power distribution network fault location method based on power similarity according to an embodiment of the present application;
fig. 2 is a schematic diagram of a fault location device for a power distribution network based on power similarity according to an embodiment of the present application;
fig. 3 is a schematic diagram of a power distribution network fault location device based on power similarity according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The power distribution network fault positioning method based on the power similarity provided by the embodiment of the application is described below. Referring to fig. 1, the power similarity-based power distribution network fault positioning method provided by the embodiment of the application may include the following steps:
step S101, a first power value of each line head end and a second power value of each line tail end are obtained.
It should be noted that the first power value is the active power of the head end of each line, and the second power value is the active power of the tail end of each line. For example, the active power at the end of each line may be collected by using a measuring device disposed at the end of each line.
Step S102, the similarity degree of the first power value and the second power value of each circuit is calculated, and the similarity value of each circuit is obtained.
Wherein the similarity value of a certain line reflects the consistency of the active power on that line. It can be understood that if a certain line does not have any fault, the active power of each point on the line is relatively close, i.e. a certain consistency is maintained; conversely, the active power between certain points on the line may deviate to some extent, resulting in an affected consistency.
Step S103, obtaining a plurality of photos on each line, and performing anomaly diagnosis on each photo on each line to obtain the category of each photo.
It should be noted that each photo includes image information and positioning information of a shooting point, and typically, the positioning information is obtained by positioning a GPS module, so that a relatively high positioning accuracy can be achieved. After the abnormality diagnosis is carried out on the photo, whether the photo is abnormal or not can be judged.
Step S104, determining the fault position based on the similarity value of each line and the type of the photo on the line.
The fault position is a position on the line where a fault exists. For example, assuming that the similarity value of a certain line is smaller than a preset threshold value and at the same time, a certain photo on the line is abnormal, the position of the photo is considered to be the fault position.
The application firstly obtains the first power value of the head end of each line and the second power value of the tail end of each line. And then, calculating the similarity degree of the first power value and the second power value of each circuit to obtain the similarity value of each circuit. It will be appreciated that the similarity value of a certain line characterizes the degree of consistency of the power values of the first and the last ends of the line. And then, acquiring a plurality of photos on each line, and carrying out abnormality diagnosis on each photo on each line to obtain the category of each photo. It will be appreciated that each photograph contains image information as well as location information for the photograph taking point. Wherein the categories include normal and abnormal. Finally, the fault location is determined based on the similarity value of each line and the class of the photo on the line. According to the application, the fault location is realized by combining the photo information and the electrical information, so that the effectiveness of fault location is improved; in addition, since the photograph information contains positioning information of the photographing point, the fault positioning accuracy is improved.
In some embodiments of the present application, the step S102 of calculating the similarity between the first power value and the second power value of each line to obtain the similarity value of each line may include:
s1, calculating to obtain similarity values of all lines by using the following equation:
（1）
wherein,is the firstjSimilarity value of lines, < >>Is the firstjIn the first lineiFirst power values corresponding to the sampling points, +.>Is the firstjIn the first lineiSecond power values corresponding to the sampling points, +.>Is the firstjAverage value of the first power value of the lines in a preset time period, < >>And n is the number of sampling points at the head end or the tail end of the jth line, and is the average value of the second power value of the jth line in the preset time period.
In some embodiments of the present application, the process of acquiring multiple photos on each line in step S103 may include:
and utilizing the unmanned aerial vehicle to fly along a preset flying line, and shooting the photo of each line in a plurality of preset shooting points in the flying line to obtain a plurality of photos on each line.
It can be understood that the unmanned aerial vehicle is equipped with a relatively accurate positioning function, photographs on various lines are taken by the unmanned aerial vehicle, the obtained photographs are accompanied with position information, and the position of an abnormality can be more accurately positioned for the photographs with the abnormality. In addition, the unmanned aerial vehicle is used as a photographing means, is not influenced by factors such as topography, environment and the like, and has certain stability. Further, by combining the photos shot by the unmanned aerial vehicle with the traditional electric quantity to perform fault detection, when a high-resistance ground fault or other faults with unobvious electric quantity occur, the abnormal position can be rapidly positioned, and the positioning efficiency is improved.
In some embodiments of the present application, step S103 performs anomaly diagnosis on each photo on each line, and the process of obtaining a category of each photo may include:
s1, preprocessing an image of each photo on each line to obtain a processed image corresponding to the photo.
S2, performing anomaly diagnosis on the processed image of each photo by using a cluster analysis method to obtain the category of the photo.
In some embodiments of the present application, the step S1 of preprocessing the image of each photo on each line to obtain a processed image corresponding to the photo may include:
s11, carrying out gray scale treatment on the image of each photo on each line to obtain a gray scale image of each photo on each line;
s12, carrying out noise reduction processing on the gray-scale image of each photo on each line by utilizing Gaussian filtering and bilateral filtering to obtain a processed image corresponding to the photo.
In some embodiments of the present application, the step S11 of graying the image of each photo on each line to obtain a gray-scale image of each photo on each line may include:
for each photo on each line, determining the gray-scale value of each pixel in the image of the photo by using the following equation to obtain the gray-scale image of the photo:
（2）
wherein,is the gray level value of the ith pixel in the gray level diagram of the image, which is essentially the Y component in YUV color space,/for the pixel>For the red gradation value of the i-th pixel in the image,>for the green gradation value of the i-th pixel in the image,/th pixel is set to be the same as the first pixel>Is the black level value of the i-th pixel in the image.
In some embodiments of the present application, the step S2 of performing anomaly diagnosis on the processed image of each photo by using a cluster analysis method to obtain a category of the photo may include:
s21, for each processed image of the photo, converting the processed image into a binary image corresponding to the photo.
S22, determining an initial clustering center based on the data characteristics of each binary image.
S23, carrying out Kmeans clustering operation on each binary image based on a preset K value and the initial clustering center, and determining the category of each binary image based on an operation result.
The Kmeans clustering algorithm (K-means Clustering Algorithm) is an iteratively solved cluster analysis algorithm that pre-groups data into K groups, selects K objects as initial cluster centers, then calculates the distance between each object and each seed cluster center, and assigns each object to its nearest cluster center. It will be appreciated that the K value is predetermined according to the particular application scenario.
Wherein the class of each binary image is determined as the class of the photograph corresponding to the binary image, the class including normal and abnormal.
In some embodiments of the present application, the step S104 of determining the fault location based on the similarity value of each line and the class of the photo on the line may include:
s1, judging whether the similarity value of each line is smaller than 0 according to each line. If yes, S2 is executed.
S2, screening out photos with abnormal categories on the line as target photos, and determining fault positions based on position information of the target photos.
The foregoing embodiment describes how to determine the fault location, and in fact, the location where the fault hidden trouble exists may also be determined by combining the similarity value with the target photograph.
In some embodiments of the present application, the power similarity-based power distribution network fault location method may further include:
step S105, determining the position of the fault hidden trouble based on the similarity value of each line and the class of the photo on the line.
For example, when the similarity value of a line is a positive value, that is, there is no obvious difference in the power values of the measurement points on the line, but a certain photo or photos are identified as abnormal, then it is considered that there may be a fault hidden danger.
In some embodiments of the present application, the step S105 of determining the location of the fault hidden trouble based on the similarity value of each line and the class of the photo on the line may include:
s1, judging whether the similarity value of each line is larger than 0 according to each line. If yes, S2 is executed.
S2, screening out photos with abnormal categories on the line as target hidden danger photos, and determining fault hidden danger positions based on the position information of the target hidden danger photos.
The power similarity-based power distribution network fault positioning device provided by the embodiment of the application is described below, and the power similarity-based power distribution network fault positioning device described below and the power similarity-based power distribution network fault positioning method described above can be referred to correspondingly.
Referring to fig. 2, a power distribution network fault location device based on power similarity provided in an embodiment of the present application may include:
a power value obtaining unit 21, configured to obtain a first power value of a head end of each line and a second power value of a tail end of each line;
a similarity calculating unit 22, configured to calculate a similarity between the first power value and the second power value of each line, so as to obtain a similarity value of each line;
an anomaly diagnosis unit 23 for acquiring a plurality of photographs on each line, and performing anomaly diagnosis on each photograph on each line to obtain a category of each photograph;
the fault locating unit 24 is configured to determine a fault location based on the similarity value of each line and the class of the photo on the line.
In some embodiments of the present application, the process of calculating the degree of similarity between the first power value and the second power value of each line by the similarity calculating unit 22 to obtain the similarity value of each line may include:
the similarity value of each line is calculated by the following equation:
；
wherein,is the firstjSimilarity value of lines, < >>Is the firstjIn the first lineiFirst power values corresponding to the sampling points, +.>Is the firstjIn the first lineiSecond power values corresponding to the sampling points, +.>Is the firstjAverage value of the first power value of the lines in a preset time period, < >>Is the firstjThe average value of the second power values of the lines in the preset time period is njThe number of head or tail sampling points in the line.
In some embodiments of the present application, the process of acquiring a plurality of photographs on each line by the abnormality diagnosis unit 23 may include:
and utilizing the unmanned aerial vehicle to fly along a preset flying line, and shooting the photo of each line in a plurality of preset shooting points in the flying line to obtain a plurality of photos on each line.
In some embodiments of the present application, the abnormality diagnosis unit 23 performs abnormality diagnosis on each photograph on each line, and the process of obtaining the category of each photograph may include:
preprocessing an image of each photo on each line to obtain a processed image corresponding to the photo;
and performing anomaly diagnosis on the processed image of each photo by using a cluster analysis method to obtain the category of the photo.
In some embodiments of the present application, the process of preprocessing the image of each photo on each line by the abnormality diagnosis unit 23 to obtain a processed image corresponding to the photo may include:
carrying out gray scale treatment on the image of each photo on each line to obtain a gray scale image of each photo on each line;
and carrying out noise reduction treatment on the gray-scale image of each photo on each line by utilizing Gaussian filtering and bilateral filtering to obtain a treated image corresponding to the photo.
In some embodiments of the present application, the process of the abnormality diagnosis unit 23 subjecting the image of each photograph on each line to the graying process to obtain the gray-scale image of each photograph on each line may include:
for each photo on each line, determining a gray-scale value of each pixel in an image of the photo by using the following equation to obtain a gray-scale map of the photo:
；
wherein,for the gray-scale value of the ith pixel in the gray-scale map of the image,/for the gray-scale value of the ith pixel>For the red gradation value of the i-th pixel in the image,/th pixel is selected from the group consisting of>For the green gradation value of the i-th pixel in the image,/th pixel>And the black level value of the ith pixel in the image.
In some embodiments of the present application, the abnormality diagnosis unit 23 performs abnormality diagnosis on the processed image of each photograph using a cluster analysis method, and the process of obtaining the category of the photograph may include:
converting, for a processed image of each photograph, the processed image into a binary image corresponding to the photograph;
determining an initial clustering center based on the data characteristics of each binary image;
and carrying out Kmeans clustering operation on each binary image based on a preset K value and the clustering center, determining the category of each binary image based on an operation result, and determining the category of each binary image as the category of the photo corresponding to the binary image, wherein the category comprises normal and abnormal.
In some embodiments of the present application, the fault location unit 24 determines the fault location based on the similarity value of each line and the class of the photo on the line, and may include:
judging whether the similarity value of each line is smaller than 0 or not according to each line;
if yes, screening out the photos with abnormal categories on the line as target photos, and determining fault positions based on the position information of the target photos.
The power distribution network fault positioning device based on the power similarity provided by the embodiment of the application can be applied to power distribution network fault positioning equipment based on the power similarity, such as a computer and the like. Optionally, fig. 3 shows a hardware architecture block diagram of a power distribution network fault location device based on power similarity, and referring to fig. 3, the hardware architecture of the power distribution network fault location device based on power similarity may include: at least one processor 31, at least one communication interface 32, at least one memory 33 and at least one communication bus 34.
In the embodiment of the present application, the number of the processor 31, the communication interface 32, the memory 33 and the communication bus 34 is at least one, and the processor 31, the communication interface 32 and the memory 33 complete the communication with each other through the communication bus 34;
the processor 31 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present application, etc.;
the memory 33 may include a high-speed RAM memory, and may further include a non-volatile memory (non-volatile memory) or the like, such as at least one magnetic disk memory;
wherein the memory 33 stores a program, the processor 31 may call the program stored in the memory 33, the program being for:
acquiring a first power value of each line head end and a second power value of each line tail end;
calculating the similarity degree of the first power value and the second power value of each circuit to obtain the similarity value of each circuit;
acquiring a plurality of photos on each line, and performing anomaly diagnosis on each photo on each line to obtain the category of each photo;
based on the similarity value of each line and the class of the photo on the line, the fault location is determined.
Alternatively, the refinement function and the extension function of the program may be described with reference to the above.
The embodiment of the present application also provides a storage medium storing a program adapted to be executed by a processor, the program being configured to:
acquiring a first power value of each line head end and a second power value of each line tail end;
calculating the similarity degree of the first power value and the second power value of each circuit to obtain the similarity value of each circuit;
acquiring a plurality of photos on each line, and performing anomaly diagnosis on each photo on each line to obtain the category of each photo;
based on the similarity value of each line and the class of the photo on the line, the fault location is determined.
Alternatively, the refinement function and the extension function of the program may be described with reference to the above.
To sum up:
the application firstly obtains the first power value of the head end of each line and the second power value of the tail end of each line. And then, calculating the similarity degree of the first power value and the second power value of each circuit to obtain the similarity value of each circuit. It will be appreciated that the similarity value of a certain line characterizes the degree of consistency of the power values of the first and the last ends of the line. And then, acquiring a plurality of photos on each line, and carrying out abnormality diagnosis on each photo on each line to obtain the category of each photo. It will be appreciated that each photograph contains image information as well as location information for the photograph taking point. Wherein the categories include normal and abnormal. Finally, the fault location is determined based on the similarity value of each line and the class of the photo on the line. According to the application, the fault location is realized by combining the photo information and the electrical information, so that the effectiveness of fault location is improved; in addition, since the photograph information contains positioning information of the photographing point, the fault positioning accuracy is improved.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the present specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and may be combined according to needs, and the same similar parts may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The utility model provides a distribution network fault location method based on power similarity, which is characterized by comprising the following steps:
acquiring a first power value of each line head end and a second power value of each line tail end;
calculating the similarity degree of the first power value and the second power value of each circuit to obtain the similarity value of each circuit;
acquiring a plurality of photos on each line, and performing anomaly diagnosis on each photo on each line to obtain the category of each photo;
based on the similarity value of each line and the class of the photo on the line, the fault location is determined.
2. The method of claim 1, wherein the step of calculating the degree of similarity between the first power value and the second power value for each line to obtain the similarity value for each line comprises:
the similarity value of each line is calculated by the following equation:
；
wherein,is the firstjSimilarity value of lines, < >>Is the firstjIn the first lineiThe first power value corresponding to the sampling point,is the firstjIn the first lineiSecond power values corresponding to the sampling points, +.>Is the firstjAverage value of the first power value of the lines in a preset time period, < >>Is the firstjThe average value of the second power value of the lines in the preset time period,nis the firstjThe number of head or tail sampling points in the line.
3. The method of claim 2, wherein the step of performing anomaly diagnosis on each of the photographs on the lines to obtain a category of each of the photographs comprises:
preprocessing an image of each photo on each line to obtain a processed image corresponding to the photo;
and performing anomaly diagnosis on the processed image of each photo by using a cluster analysis method to obtain the category of the photo.
4. A method according to claim 3, wherein preprocessing the image of each photograph on each line to obtain a processed image corresponding to the photograph comprises:
carrying out gray scale treatment on the image of each photo on each line to obtain a gray scale image of each photo on each line;
and carrying out noise reduction treatment on the gray-scale image of each photo on each line by utilizing Gaussian filtering and bilateral filtering to obtain a treated image corresponding to the photo.
5. The method of claim 4, wherein the step of graying the image of each photograph on each line to obtain a gray-scale image of each photograph on each line comprises:
for each photo on each line, determining a gray-scale value of each pixel in an image of the photo by using the following equation to obtain a gray-scale map of the photo:
；
wherein,for the gray-scale value of the ith pixel in the gray-scale map of the image,/for the gray-scale value of the ith pixel>For the red gradation value of the i-th pixel in the image,/th pixel is selected from the group consisting of>For the green gradation value of the i-th pixel in the image,/th pixel>And the black level value of the ith pixel in the image.
6. A method according to claim 3, wherein the process of performing anomaly diagnosis on the processed image of each photograph using a cluster analysis method to obtain the category of the photograph comprises:
converting, for a processed image of each photograph, the processed image into a binary image corresponding to the photograph;
determining an initial clustering center based on the data characteristics of each binary image;
and carrying out Kmeans clustering operation on each binary image based on a preset K value and the initial clustering center, determining the category of each binary image based on an operation result, and determining the category of each binary image as the category of the photo corresponding to the binary image, wherein the category comprises normal and abnormal.
7. The method of claim 6, wherein determining the location of the fault based on the similarity value for each line and the class of photographs on the line comprises:
judging whether the similarity value of each line is smaller than 0 or not according to each line;
if yes, screening out the photos with abnormal categories on the line as target photos, and determining fault positions based on the position information of the target photos.
8. Power distribution network fault locating device based on power similarity, characterized by comprising:
the power value acquisition unit is used for acquiring a first power value of the head end of each line and a second power value of the tail end of each line;
the similarity calculation unit is used for calculating the similarity of the first power value and the second power value of each circuit to obtain a similarity value of each circuit;
the abnormality diagnosis unit is used for acquiring a plurality of photos on each line, and performing abnormality diagnosis on each photo on each line to obtain the category of each photo;
and the fault locating unit is used for determining the fault position based on the similarity value of each line and the type of the photo on the line.
9. A power distribution network fault location device based on power similarity, comprising: a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the power similarity-based power distribution network fault location method according to any one of claims 1 to 7.
10. A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the power similarity based power distribution network fault location method of any one of claims 1 to 7.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019232595A1 (en) * | 2018-06-07 | 2019-12-12 | Federation University Australia | A method of estimating the location of a fault on an electrical distribution network and an associated system |
| KR102058230B1 (en) * | 2018-08-27 | 2019-12-20 | 연세대학교 산학협력단 | Device, method and system to predict fault of cable |
| JP2021050954A (en) * | 2019-09-24 | 2021-04-01 | 株式会社日立製作所 | Power grid monitoring device and method |
| CN113469488A (en) * | 2021-05-08 | 2021-10-01 | 国网宁夏电力有限公司银川供电公司 | Online diagnosis and analysis system for topological structure of power distribution network equipment |
| CN113515655A (en) * | 2021-06-24 | 2021-10-19 | 国网山东省电力公司邹城市供电公司 | Fault identification method and device based on image classification |
| CN114140731A (en) * | 2021-12-08 | 2022-03-04 | 西南交通大学 | Traction substation abnormity detection method |
| CN114720812A (en) * | 2022-02-17 | 2022-07-08 | 郭海军 | Method and system for processing and positioning faults of power distribution network |
| CN115021356A (en) * | 2022-06-16 | 2022-09-06 | 南方电网电力科技股份有限公司 | Wireless charging method, system and equipment for power transmission line fault positioning device |
| CN115356593A (en) * | 2022-08-30 | 2022-11-18 | 中国矿业大学 | Direct-current power distribution network fault positioning method and system based on voltage traveling wave similarity |
| CN116523329A (en) * | 2023-03-20 | 2023-08-01 | 国网浙江省电力有限公司营销服务中心 | Method for diagnosing abnormal line loss of transformer area |
-
2023
- 2023-10-12 CN CN202311320948.0A patent/CN117054824A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019232595A1 (en) * | 2018-06-07 | 2019-12-12 | Federation University Australia | A method of estimating the location of a fault on an electrical distribution network and an associated system |
| KR102058230B1 (en) * | 2018-08-27 | 2019-12-20 | 연세대학교 산학협력단 | Device, method and system to predict fault of cable |
| JP2021050954A (en) * | 2019-09-24 | 2021-04-01 | 株式会社日立製作所 | Power grid monitoring device and method |
| CN113469488A (en) * | 2021-05-08 | 2021-10-01 | 国网宁夏电力有限公司银川供电公司 | Online diagnosis and analysis system for topological structure of power distribution network equipment |
| CN113515655A (en) * | 2021-06-24 | 2021-10-19 | 国网山东省电力公司邹城市供电公司 | Fault identification method and device based on image classification |
| CN114140731A (en) * | 2021-12-08 | 2022-03-04 | 西南交通大学 | Traction substation abnormity detection method |
| CN114720812A (en) * | 2022-02-17 | 2022-07-08 | 郭海军 | Method and system for processing and positioning faults of power distribution network |
| CN115021356A (en) * | 2022-06-16 | 2022-09-06 | 南方电网电力科技股份有限公司 | Wireless charging method, system and equipment for power transmission line fault positioning device |
| CN115356593A (en) * | 2022-08-30 | 2022-11-18 | 中国矿业大学 | Direct-current power distribution network fault positioning method and system based on voltage traveling wave similarity |
| CN116523329A (en) * | 2023-03-20 | 2023-08-01 | 国网浙江省电力有限公司营销服务中心 | Method for diagnosing abnormal line loss of transformer area |
Non-Patent Citations (2)
| Title |
|---|
| 王晓卫: "基于暂态功率相关分析的配电网故障定位研究", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, no. 06, pages 042 - 473 * |
| 程子夜: "红外图像处理在架空线路故障诊断中的应用", 中国优秀硕士学位论文全文数据库工程科技Ⅱ辑, no. 07, pages 042 - 736 * |
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