CN117074869B - Distribution line fault positioning method and system - Google Patents
Distribution line fault positioning method and system Download PDFInfo
- Publication number
- CN117074869B CN117074869B CN202311336205.2A CN202311336205A CN117074869B CN 117074869 B CN117074869 B CN 117074869B CN 202311336205 A CN202311336205 A CN 202311336205A CN 117074869 B CN117074869 B CN 117074869B
- Authority
- CN
- China
- Prior art keywords
- distribution line
- fault
- weather
- point
- information
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 38
- 238000001514 detection method Methods 0.000 claims abstract description 21
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims description 67
- 239000011159 matrix material Substances 0.000 claims description 52
- 238000005259 measurement Methods 0.000 claims description 9
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000004891 communication Methods 0.000 description 8
- 230000006870 function Effects 0.000 description 6
- 238000004590 computer program Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 4
- 238000010606 normalization Methods 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000007689 inspection Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- KLDZYURQCUYZBL-UHFFFAOYSA-N 2-[3-[(2-hydroxyphenyl)methylideneamino]propyliminomethyl]phenol Chemical compound OC1=CC=CC=C1C=NCCCN=CC1=CC=CC=C1O KLDZYURQCUYZBL-UHFFFAOYSA-N 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 201000001098 delayed sleep phase syndrome Diseases 0.000 description 1
- 208000033921 delayed sleep phase type circadian rhythm sleep disease Diseases 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Classifications
-
- 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
-
- 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
-
- 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
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
- G01N21/952—Inspecting the exterior surface of cylindrical bodies or wires
-
- 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
- G01R31/085—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution lines, e.g. overhead
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
- G01S17/08—Systems determining position data of a target for measuring distance only
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
- G01W1/10—Devices for predicting weather conditions
-
- 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
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Analytical Chemistry (AREA)
- Environmental & Geological Engineering (AREA)
- Electromagnetism (AREA)
- Pathology (AREA)
- Immunology (AREA)
- General Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Mathematical Physics (AREA)
- Environmental Sciences (AREA)
- Ecology (AREA)
- Biodiversity & Conservation Biology (AREA)
- Atmospheric Sciences (AREA)
- Signal Processing (AREA)
- Quality & Reliability (AREA)
- Computer Networks & Wireless Communication (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Locating Faults (AREA)
Abstract
The invention relates to the technical field of power grid line fault detection, in particular to a distribution line fault positioning method and a distribution line fault positioning system, wherein the method comprises the steps of obtaining weather data at the current moment; preprocessing weather data at the current moment to obtain preprocessed weather data; the preprocessed meteorological data are sent to a trained weather prediction model, so that a weather prediction result is obtained, wherein the weather prediction result comprises weather conditions after the current moment; determining a positioning mode of the distribution line fault according to weather prediction results; the invention predicts the weather by using the positioning mode of the fault of the distribution line to position the fault position of the distribution line, thereby realizing that different positioning modes are adopted according to the prediction result of the weather so as to improve the accuracy of the fault positioning of the distribution line.
Description
Technical Field
The invention relates to the technical field of power grid line fault detection, in particular to a distribution line fault positioning method and system.
Background
Along with the gradual progress of the distribution network to the intelligent distribution network, the existing distribution lines are more and more complex, so that fault location of the distribution lines is more difficult, in the prior art, unmanned aerial vehicles are generally adopted to carry out inspection on the distribution lines, but due to the fact that the distribution lines are complex in environment, the defects of low detection precision, large weather influence and the like exist in the prior art, and therefore, a distribution line fault location method is needed for locating fault positions of distribution lines in a corresponding location mode according to different weather conditions.
Disclosure of Invention
The invention aims to provide a distribution line fault positioning method and system so as to solve the problems.
In order to achieve the above purpose, the embodiment of the present application provides the following technical solutions:
in one aspect, embodiments of the present application provide a method comprising:
acquiring meteorological data at the current moment;
preprocessing the weather data at the current moment to obtain preprocessed weather data;
the preprocessed meteorological data are sent to a trained weather prediction model, and a weather prediction result is obtained, wherein the weather prediction result comprises weather conditions after the current moment;
determining a positioning mode of the distribution line fault according to the weather prediction result;
and positioning the fault position of the distribution line by using the positioning mode of the fault of the distribution line.
In a second aspect, embodiments of the present application provide a distribution line fault location system, the system including:
the acquisition module is used for acquiring weather data at the current moment;
the first processing module is used for preprocessing the weather data at the current moment to obtain preprocessed weather data;
the second processing module is used for sending the preprocessed meteorological data to a trained weather prediction model to obtain a weather prediction result, wherein the weather prediction result comprises weather conditions after the current moment;
the determining module is used for determining the positioning mode of the distribution line faults according to the weather prediction result;
and the third processing module is used for positioning the fault position of the distribution line by utilizing the positioning mode of the fault of the distribution line.
In a third aspect, embodiments of the present application provide a distribution line fault location device that includes a memory and a processor. The memory is used for storing a computer program; the processor is configured to implement the steps of the distribution line fault locating method described above when executing the computer program.
In a fourth aspect, embodiments of the present application provide a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the distribution line fault location method described above.
The beneficial effects of the invention are as follows:
according to the invention, the preprocessed meteorological data are sent to the trained weather prediction model, and the prediction is carried out on the weather after the current moment, so that the position of the broken strand of the distribution line is positioned according to different weather by adopting different positioning modes, when the weather is abnormal weather, the unmanned aerial vehicle is greatly subjected to weather interference, so that the detection precision is reduced, meanwhile, the unmanned aerial vehicle is possibly damaged due to the weather interference, and when the weather is normal weather, the broken strand position can be quickly and accurately positioned by adopting the unmanned aerial vehicle for inspection, therefore, the accuracy of the fault positioning of the distribution line can be effectively improved by adopting different positioning modes according to different weather, and the cost is reduced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a fault locating method for distribution lines according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a distribution line fault locating system according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a distribution line fault locating device according to an embodiment of the present invention.
The drawing is marked: 901. an acquisition module; 902. a first processing module; 903. a second processing module; 904. a determining module; 905. a third processing module; 9041. a judging unit; 90411. a first acquisition unit; 90412. a first processing unit; 90413. a second processing unit; 90414. a third processing unit; 90415. a fourth processing unit; 90416. a fifth processing unit; 90417. an eighth processing unit; 90418. a ninth processing unit; 90419. a tenth processing unit; 904110, eleventh processing unit; 904161, a second acquisition unit; 904162, sixth processing unit; 904163, seventh processing unit; 800. distribution line fault locating equipment; 801. a processor; 802. a memory; 803. a multimedia component; 804. an I/O interface; 805. a communication component.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Example 1:
the present embodiment provides a fault locating method for a distribution line, and it can be understood that a scenario may be laid in this embodiment, for example, a scenario in which, when a fault occurs in the distribution line, location detection is required at a broken strand of the distribution line.
Referring to fig. 1, the method includes a step S1, a step S2, a step S3, a step S4, and a step S5, where the method specifically includes:
step S1, acquiring meteorological data at the current moment;
it can be understood that the meteorological data is meteorological observation data collected at least one measuring point around the distribution line;
s2, preprocessing the weather data at the current moment to obtain preprocessed weather data;
it can be understood that, in order to improve the convergence speed and the prediction accuracy of the weather prediction model after training, firstly, the normalization processing is performed on the weather data collected at the current moment, the normalization processing of the weather data can eliminate the dimensional influence among the data indexes, the data is limited in a certain range by transforming the data, and the normalization processing is performed on the weather data at the current moment, which is a technical scheme well known to those skilled in the art, so that the description is omitted herein.
Step S3, the preprocessed meteorological data are sent to a trained weather prediction model, and a weather prediction result is obtained, wherein the weather prediction result comprises weather conditions after the current moment;
it can be appreciated that, the weather prediction model after the current moment can be predicted by sending the weather data after the normalization processing to the weather prediction model after training, where the weather prediction model can be an RBF model, and it is to be noted that training the weather prediction model based on the historical weather data set collected by only one measuring point is a technical scheme well known to those skilled in the art, so that details are not repeated herein.
S4, determining a positioning mode of the distribution line fault according to the weather prediction result;
it can be understood that the weather prediction results include, but are not limited to, sunny days, rainy days and thunder-rain days, and the accuracy of strand breakage positioning of the distribution line can be effectively improved by adopting different positioning modes according to different weather.
It may be understood that step S4 further includes step S41, where specific details are:
determining a positioning mode of the distribution line fault according to the weather prediction result, wherein if the weather prediction result is normal weather, acquiring first image information, wherein the first image information comprises image information of the distribution line shot by the unmanned aerial vehicle; positioning the fault position of the distribution line according to the first image information; if the weather prediction result is abnormal weather, acquiring the position information of each distribution line branch point and the position information of each distribution line terminal point, and positioning the fault position of the distribution line according to the position information of each distribution line branch point and the position information of each distribution line terminal point.
It can be understood that when the weather prediction result is a sunny day, the unmanned aerial vehicle can be adopted to collect the first image information to realize the inspection of the distribution line so as to position the broken strand position of the distribution line; when weather prediction results are rainy days or thunderstorm days, the position information of each distribution line branch point and the position information of each distribution line terminal point can be obtained, and the fault positions of the distribution lines are positioned according to the position information of each distribution line branch point and the position information of each distribution line terminal point, so that the problem that when an unmanned aerial vehicle is adopted to detect the broken strand of the distribution lines in rainy days, the interference of the weather on the detection precision is effectively solved, and meanwhile, when the weather is bad, unmanned aerial vehicle is used for detection, the unmanned aerial vehicle is damaged, and the fault positioning cost is greatly improved.
It may be understood that the step S41 further includes a step S41, a step S42, a step S43, a step S44, a step S45, and a step S46, where specifically:
step S41, acquiring a first matrix, wherein the first matrix is a time matrix for a fault traveling wave to reach the terminal point of each distribution line;
it can be understood that the first matrix is a time matrix that the fault traveling wave on the distribution line reaches the end point of each distribution line, and it is to be noted that the time that the fault traveling wave on the distribution line reaches the end point of each distribution line is collected according to the historical data set.
Step S42, obtaining distance information according to the position information of each distribution line branch point and the position information of each distribution line terminal point, wherein the distance information comprises the distance between any distribution line branch point and any distribution line terminal point;
it will be appreciated that a may be used, for example, when a distribution line has n branch points in total k Representing m line ends, b is used i It is shown that when the distribution line structure and the length of each section are known, the distance between any line branching point and end point can be obtained and is denoted as d akbi (k=1, 2 … … n; i=1, 2 … … m), whereby the distance from each branching point to any ending point can be obtained, a distance matrix D is constructed, wherein the distance matrix D is specifically:
in the above-mentioned method, the step of,representation a 1 Branch point reaching b 1 Distance between end points.
S43, constructing a second matrix according to the distance information, wherein the second matrix is a time matrix;
it can be understood that the calculation is performed according to the distance information in the distance matrix D and the velocity V of the traveling wave, that is, the distance matrix D is divided by the velocity V of the traveling wave to obtain a time matrix, where the time matrix specifically includes:
in the above-mentioned method, the step of,representation a 1 Branch point reaching b 1 The time it takes for the end point to travel.
Step S44, calculating according to the first matrix and the second matrix to obtain a third matrix;
it will be appreciated that the first matrix is subtracted from the second matrix to yield a third matrix.
Step S45, judging the searching range of the distribution line fault points according to the third matrix;
it can be appreciated that the second norm of the third matrix is calculated to obtain at least one norm value; judging the magnitude of the norm value, wherein if the minimum value of the norm value is U (k), judging the fault point and a k More closely, if the minimum value of the norm values is U (k) and U (k) =0, the fault point is determined to be at the branch point a k And (3) upper part.
And step S46, positioning the fault point in the searching range of the fault point of the distribution line.
It can be understood that the searching range of the fault point can be determined according to the norm value obtained by comparison and calculation, and the fault defect detection efficiency is effectively improved.
It can be understood that the step S46 further includes a step S461, a step S462, and a step S463, where specifically:
step S461, acquiring laser ranging information and actual measurement information;
it can be understood that the laser rangefinder can be used for accurately positioning the broken strand of the distribution line after the search range of the fault point is determined, and the laser rangefinder is used for acquiring the laser ranging information.
Step S462, fitting the laser ranging information and the actual measurement information by using a least square method to obtain a fitting function;
it can be understood that the laser range finder is affected by light intensity, climate and other interference factors to cause a certain error in the measurement result, so that a least square method is adopted to fit the distance between the laser range finding signal collected by the laser range finder and the real measurement, and the fitting function can effectively eliminate the interference factors and realize accurate positioning of the broken strand of the distribution line.
And step 463, calculating according to the fitting function to obtain the coordinate position of the fault point.
It can be understood that the accurate coordinate position of the broken strand of the distribution line can be calculated by substituting the data measured by the laser range finder into the fitting function.
It may be understood that the step S41 further includes a step S47, a step S48, a step S49, and a step S410, where specific details are:
step S47, detecting straight line segments according to the first image information to obtain a detection result;
it can be understood that the low contrast condition can be detected by detecting the straight line segment by using the phase grouping algorithm, and it should be noted that the detection of the straight line segment by using the phase grouping algorithm is a technical scheme well known to those skilled in the art, and thus will not be described herein.
Step S48, connecting straight lines in the detection result by using preset first constraint, second constraint and third constraint to obtain second image information, wherein the second image information comprises distribution lines after connection;
it will be appreciated that the first constraint is an approximation constraint, which is described by the distance between two line segments being smaller than the length of the shorter line segment, wherein specifically:
in the above, L 1 For the first distribution lineLength of line segment, length of line segment 2 is L 2 The length of the second distribution line segment is L, the distance between the first distribution line segment and the second distribution line segment is L, and the first distribution line segment and the second distribution line segment are two adjacent distribution line segments.
The second constraint is a continuity constraint, wherein the continuity is described as that the change of the angle between the endpoints of the two distribution line segments is smaller than a preset angle threshold, and the method specifically comprises the following steps:
in the above, θ 1 For the direction angle, θ, of the first distribution line segment 2 The angle of orientation of the second distribution line segment,for a preset angle threshold +.>The value is 3 degrees.
The third constraint is a colinear constraint, and the colinear is described as whether there is a parallel relationship between two power distribution line segments, where the specific is:
in the above, D is the vertical distance from the end point of the first distribution line segment to the second distribution line segment,and->The width of the first distribution line segment and the width of the second distribution line segment, respectively.
On the image of the extracted line segments, the connected distribution lines can be obtained by searching the line segments of the distribution lines which meet the preset first constraint, the preset second constraint and the preset third constraint, and the intermittent distribution line segments can be effectively combined into long distribution lines through the preset first constraint, the preset second constraint and the preset third constraint, so that the interference of the outdoor environment on the extraction of the distribution lines is avoided.
Step S49, detecting the intersection point in the second image information to obtain third image information;
it can be understood that, according to the flight trajectory of the unmanned aerial vehicle, the aerial photographing wires and the optical cables are distributed in parallel in a straight line in the photo, and generally no crossing phenomenon occurs, and if crossing occurs, the wires at the crossing point have possibility of strand breakage defects. Therefore, the position of the strand breakage defect of the power transmission line can be preliminarily judged by detecting the intersection in the second image information.
And step S410, judging whether the intersection point is a fault point according to the third image information, and positioning the fault point.
It is understood that whether the average gray level difference of the distribution lines forming the cross point in the third image information is smaller than the first threshold information is determined to obtain a determination result, wherein if the average gray level difference of the distribution lines forming the cross point is smaller than the first threshold information, the phase gradient variance of the edge points of the distribution lines forming the cross point in the third image information is calculated, the magnitude of the phase gradient variance of the edge points of the distribution lines forming the cross point and the magnitude of the preset second threshold information are further determined, and if the phase gradient variance of the edge points of the distribution lines forming the cross point is larger than the preset second threshold information, the cross point is determined to be the broken point, and the corresponding coordinate information is displayed in the third image information.
And S5, positioning the fault position of the distribution line by using a positioning mode of the fault of the distribution line.
It can be understood that the invention realizes the positioning of the broken strand of the distribution line by adopting different positioning modes according to different weather, when the weather is abnormal weather, the unmanned aerial vehicle is greatly affected by weather interference, so that the detection precision is reduced, and meanwhile, the unmanned aerial vehicle is possibly damaged due to the weather interference.
Example 2:
as shown in fig. 2, the present embodiment provides a distribution line fault location system, which includes an acquisition module 901, a first processing module 902, a second processing module 903, a determination module 904, and a third processing module 905, where the system specifically includes:
an acquisition module 901, configured to acquire weather data at a current moment;
the first processing module 902 is configured to perform preprocessing on the weather data at the current moment to obtain preprocessed weather data;
the second processing module 903 is configured to send the preprocessed meteorological data to a trained weather prediction model, so as to obtain a weather prediction result, where the weather prediction result includes a weather condition after the current moment;
a determining module 904, configured to determine a positioning manner of the distribution line fault according to the weather prediction result;
and a third processing module 905, configured to locate the fault location of the distribution line by using the location mode of the fault of the distribution line.
In a specific embodiment of the disclosure, the determining module 904 further includes a determining unit 9041, where the determining unit specifically includes:
a judging unit 9041, configured to determine a positioning manner of a distribution line fault according to the weather prediction result, where if the weather prediction result is normal weather, first image information is obtained, where the first image information includes image information of the distribution line shot by the unmanned aerial vehicle; positioning the fault position of the distribution line according to the first image information; if the weather prediction result is abnormal weather, acquiring the position information of each distribution line branch point and the position information of each distribution line terminal point, and positioning the fault position of the distribution line according to the position information of each distribution line branch point and the position information of each distribution line terminal point.
In a specific embodiment of the disclosure, the determining unit 9041 further includes a first acquiring unit 90411, a first processing unit 90412, a second processing unit 90413, a third processing unit 90414, a fourth processing unit 90415, and a fifth processing unit 90416, where specifically:
the first obtaining unit 90411 is configured to obtain a first matrix, where the first matrix is a time matrix when the fault traveling wave reaches an end point of each distribution line;
a first processing unit 90412, configured to obtain distance information according to the position information of each distribution line branching point and the position information of each distribution line end point, where the distance information includes a distance between any distribution line branching point and any distribution line end point;
a second processing unit 90413, configured to construct a second matrix according to the distance information, where the second matrix is a time matrix;
a third processing unit 90414, configured to calculate according to the first matrix and the second matrix, to obtain a third matrix;
a fourth processing unit 90415, configured to determine a search range of distribution line fault points according to the third matrix;
and a fifth processing unit 90416, configured to locate a fault point within the search range of the fault point of the distribution line.
In a specific embodiment of the disclosure, the fifth processing unit 90416 further includes a second obtaining unit 904161, a sixth processing unit 904162, and a seventh processing unit 904163, where specific details are:
a second acquisition unit 904161 for acquiring laser ranging information and actual measurement information;
a sixth processing unit 904162, configured to fit the laser ranging information and the actual measurement information by using a least square method, so as to obtain a fitting function;
and a seventh processing unit 904163, configured to calculate according to the fitting function, to obtain a coordinate position of the fault point.
In a specific embodiment of the disclosure, the judging unit 9041 further includes an eighth processing unit 90417, a ninth processing unit 90418, a tenth processing unit 90419, and an eleventh processing unit 904110, where:
an eighth processing unit 90417, configured to perform straight line segment detection according to the first image information, to obtain a detection result;
a ninth processing unit 90418, configured to connect the straight lines in the detection result by using a preset first constraint, a preset second constraint, and a preset third constraint, so as to obtain second image information, where the second image information includes a distribution line after connection;
a tenth processing unit 90419, configured to detect an intersection in the second image information to obtain third image information;
the eleventh processing unit 904110 is configured to determine whether the intersection is a fault point according to the third image information, and locate the fault point.
It should be noted that, regarding the system in the above embodiment, the specific manner in which the respective modules perform the operations has been described in detail in the embodiment regarding the method, and will not be described in detail herein.
Example 3:
corresponding to the above method embodiment, there is also provided a distribution line fault locating device in this embodiment, and a distribution line fault locating device described below and a distribution line fault locating method described above may be referred to correspondingly with each other.
Fig. 3 is a block diagram illustrating a distribution line fault location device 800 according to an exemplary embodiment. As shown in fig. 3, the distribution line fault location apparatus 800 may include: a processor 801, a memory 802. The distribution line fault location device 800 may also include one or more of a multimedia component 803, an I/O interface 804, and a communication component 805.
Wherein the processor 801 is configured to control the overall operation of the distribution line fault location device 800 to perform all or part of the steps of the distribution line fault location method described above. The memory 802 is used to store various types of data to support operation of the line fault location device 800, which may include, for example, instructions for any application or method operating on the line fault location device 800, as well as application related data, such as contact data, messages, pictures, audio, video, and the like. The Memory 802 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (ElectricallyErasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is configured to perform wired or wireless communication between the distribution line fault location device 800 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near FieldCommunication, NFC for short), 2G, 3G or 4G, or a combination of one or more thereof, the respective communication component 805 may thus comprise: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the distribution line fault location device 800 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated ASIC), digital signal processors (DigitalSignal Processor, abbreviated DSP), digital signal processing devices (Digital Signal Processing Device, abbreviated DSPD), programmable logic devices (ProgrammableLogic Device, abbreviated PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the distribution line fault location methods described above.
In another exemplary embodiment, a computer readable storage medium is also provided that includes program instructions that, when executed by a processor, implement the steps of the distribution line fault location method described above. For example, the computer readable storage medium may be the memory 802 described above including program instructions executable by the processor 801 of the distribution line fault location device 800 to perform the distribution line fault location method described above.
Example 4:
corresponding to the above method embodiment, there is also provided a readable storage medium in this embodiment, and a readable storage medium described below and a distribution line fault locating method described above may be referred to correspondingly with each other.
A readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the distribution line fault location method of the above method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RandomAccess Memory, RAM), a magnetic disk, or an optical disk, and the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (6)
1. A method of fault location for a distribution line, comprising:
acquiring meteorological data at the current moment;
preprocessing the weather data at the current moment to obtain preprocessed weather data;
the preprocessed meteorological data are sent to a trained weather prediction model, and a weather prediction result is obtained, wherein the weather prediction result comprises weather conditions after the current moment;
determining a positioning mode of the distribution line fault according to the weather prediction result;
positioning the fault position of the distribution line by using a positioning mode of the fault of the distribution line;
the method for determining the positioning mode of the distribution line fault according to the weather prediction result comprises the following steps:
determining a positioning mode of the distribution line fault according to the weather prediction result, wherein if the weather prediction result is normal weather, acquiring first image information, wherein the first image information comprises image information of the distribution line shot by the unmanned aerial vehicle; positioning the fault position of the distribution line according to the first image information; if the weather prediction result is abnormal weather, acquiring the position information of each distribution line branch point and the position information of each distribution line terminal point, and positioning the fault position of the distribution line according to the position information of each distribution line branch point and the position information of each distribution line terminal point;
the positioning of the fault position of the distribution line according to the position information of each distribution line branch point and the position information of each distribution line terminal point comprises the following steps:
acquiring a first matrix, wherein the first matrix is a time matrix for a fault traveling wave to reach the terminal point of each distribution line;
obtaining distance information according to the position information of each distribution line branch point and the position information of each distribution line terminal point, wherein the distance information comprises the distance between any distribution line branch point and any distribution line terminal point;
constructing a second matrix according to the distance information, wherein the second matrix is a time matrix;
calculating according to the first matrix and the second matrix to obtain a third matrix;
judging the searching range of the distribution line fault points according to the third matrix;
and positioning the fault point in the searching range of the fault point of the distribution line.
2. The distribution line fault locating method according to claim 1, wherein locating the fault point within the search range of the fault point of the distribution line comprises:
acquiring laser ranging information and actual measuring information;
fitting the laser ranging information and the actual measurement information by using a least square method to obtain a fitting function;
and calculating according to the fitting function to obtain the coordinate position of the fault point.
3. The distribution line fault location method according to claim 1, wherein locating a distribution line fault location based on the first image information comprises:
performing straight line segment detection according to the first image information to obtain a detection result;
connecting straight lines in the detection result by using preset first constraint, second constraint and third constraint to obtain second image information, wherein the second image information comprises a distribution line after connection;
detecting the intersection point in the second image information to obtain third image information;
judging whether the intersection point is a fault point according to the third image information, and positioning the fault point.
4. A distribution line fault location system, comprising:
the acquisition module is used for acquiring weather data at the current moment;
the first processing module is used for preprocessing the weather data at the current moment to obtain preprocessed weather data;
the second processing module is used for sending the preprocessed meteorological data to a trained weather prediction model to obtain a weather prediction result, wherein the weather prediction result comprises weather conditions after the current moment;
the determining module is used for determining the positioning mode of the distribution line faults according to the weather prediction result;
the third processing module is used for positioning the fault position of the distribution line by utilizing the positioning mode of the fault of the distribution line;
wherein, the determining module includes:
the judging unit is used for determining a positioning mode of the distribution line fault according to the weather prediction result, wherein if the weather prediction result is normal weather, first image information is obtained, and the first image information comprises image information of the distribution line shot by the unmanned aerial vehicle; positioning the fault position of the distribution line according to the first image information; if the weather prediction result is abnormal weather, acquiring the position information of each distribution line branch point and the position information of each distribution line terminal point, and positioning the fault position of the distribution line according to the position information of each distribution line branch point and the position information of each distribution line terminal point;
wherein the judging unit includes:
the first acquisition unit is used for acquiring a first matrix, wherein the first matrix is a time matrix for the fault traveling wave to reach the terminal point of each distribution line;
the first processing unit is used for obtaining distance information according to the position information of each distribution line branch point and the position information of each distribution line terminal point, wherein the distance information comprises the distance between any distribution line branch point and any distribution line terminal point;
the second processing unit is used for constructing a second matrix according to the distance information, wherein the second matrix is a time matrix;
the third processing unit is used for calculating according to the first matrix and the second matrix to obtain a third matrix;
the fourth processing unit is used for judging the searching range of the distribution line fault points according to the third matrix;
and the fifth processing unit is used for positioning the fault point in the searching range of the fault point of the distribution line.
5. The distribution line fault location system of claim 4, wherein the fifth processing unit comprises:
the second acquisition unit is used for acquiring laser ranging information and actual measurement information;
the sixth processing unit is used for fitting the laser ranging information and the actual measurement information by using a least square method to obtain a fitting function;
and the seventh processing unit is used for calculating according to the fitting function to obtain the coordinate position of the fault point.
6. The distribution line fault location system according to claim 4, wherein the judging unit includes:
an eighth processing unit, configured to perform straight line segment detection according to the first image information, to obtain a detection result;
a ninth processing unit, configured to connect the straight lines in the detection result by using a preset first constraint, a preset second constraint, and a preset third constraint, so as to obtain second image information, where the second image information includes a distribution line after connection;
a tenth processing unit, configured to detect an intersection point in the second image information, and obtain third image information;
and the eleventh processing unit is used for judging whether the intersection point is a fault point according to the third image information and positioning the fault point.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311336205.2A CN117074869B (en) | 2023-10-16 | 2023-10-16 | Distribution line fault positioning method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311336205.2A CN117074869B (en) | 2023-10-16 | 2023-10-16 | Distribution line fault positioning method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117074869A CN117074869A (en) | 2023-11-17 |
| CN117074869B true CN117074869B (en) | 2023-12-19 |
Family
ID=88715669
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311336205.2A Active CN117074869B (en) | 2023-10-16 | 2023-10-16 | Distribution line fault positioning method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117074869B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100957812B1 (en) * | 2009-10-06 | 2010-05-13 | 주식회사 호크마이엔지 | Method for locatingfault distance of trolley line and system thereof |
| CN108646146A (en) * | 2018-08-20 | 2018-10-12 | 国网河北省电力有限公司沧州供电分公司 | Distribution wire On-line Fault monitoring method and terminal device |
| CN111582697A (en) * | 2020-04-29 | 2020-08-25 | 西安交通大学 | Power distribution network fault assessment and scheduling method and system |
| CN112016739A (en) * | 2020-08-17 | 2020-12-01 | 国网山东省电力公司潍坊供电公司 | Fault detection method and device, electronic equipment and storage medium |
| WO2021053118A1 (en) * | 2019-09-17 | 2021-03-25 | University College Dublin | A decision support system and method for agriculture |
| CN114689994A (en) * | 2022-04-27 | 2022-07-01 | 云南电网有限责任公司普洱供电局 | System and method for online positioning and monitoring fault of transmission line |
| CN115270965A (en) * | 2022-07-29 | 2022-11-01 | 国网浙江省电力有限公司经济技术研究院 | Power distribution network line fault prediction method and device |
| CN115578651A (en) * | 2022-08-15 | 2023-01-06 | 金华八达集团有限公司 | Intelligent unmanned aerial vehicle inspection and dynamic inventory management system for distribution network line |
| CN115575768A (en) * | 2022-11-04 | 2023-01-06 | 广东电网有限责任公司 | Power transmission line fault positioning method and device based on inspection unmanned aerial vehicle |
| CN115656731A (en) * | 2022-12-09 | 2023-01-31 | 武汉格蓝若智能技术股份有限公司 | Overhead distribution line arc grounding fault point positioning method |
| CN115713038A (en) * | 2022-11-24 | 2023-02-24 | 盛隆电气集团有限公司 | Distribution and utilization fault detection method and system based on deep circulation neural network |
| CN116298670A (en) * | 2023-01-28 | 2023-06-23 | 北京交通大学 | Intelligent fault positioning method and system suitable for multi-branch distribution line |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110927520A (en) * | 2019-11-25 | 2020-03-27 | 山东理工大学 | Direct-current distribution line multi-end traveling wave fault positioning method and positioning device |
| US11674994B2 (en) * | 2021-02-24 | 2023-06-13 | Mitsubishi Electric Research Laboratories, Inc. | Distribution fault location using graph neural network with both node and link attributes |
-
2023
- 2023-10-16 CN CN202311336205.2A patent/CN117074869B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100957812B1 (en) * | 2009-10-06 | 2010-05-13 | 주식회사 호크마이엔지 | Method for locatingfault distance of trolley line and system thereof |
| CN108646146A (en) * | 2018-08-20 | 2018-10-12 | 国网河北省电力有限公司沧州供电分公司 | Distribution wire On-line Fault monitoring method and terminal device |
| WO2021053118A1 (en) * | 2019-09-17 | 2021-03-25 | University College Dublin | A decision support system and method for agriculture |
| CN111582697A (en) * | 2020-04-29 | 2020-08-25 | 西安交通大学 | Power distribution network fault assessment and scheduling method and system |
| CN112016739A (en) * | 2020-08-17 | 2020-12-01 | 国网山东省电力公司潍坊供电公司 | Fault detection method and device, electronic equipment and storage medium |
| CN114689994A (en) * | 2022-04-27 | 2022-07-01 | 云南电网有限责任公司普洱供电局 | System and method for online positioning and monitoring fault of transmission line |
| CN115270965A (en) * | 2022-07-29 | 2022-11-01 | 国网浙江省电力有限公司经济技术研究院 | Power distribution network line fault prediction method and device |
| CN115578651A (en) * | 2022-08-15 | 2023-01-06 | 金华八达集团有限公司 | Intelligent unmanned aerial vehicle inspection and dynamic inventory management system for distribution network line |
| CN115575768A (en) * | 2022-11-04 | 2023-01-06 | 广东电网有限责任公司 | Power transmission line fault positioning method and device based on inspection unmanned aerial vehicle |
| CN115713038A (en) * | 2022-11-24 | 2023-02-24 | 盛隆电气集团有限公司 | Distribution and utilization fault detection method and system based on deep circulation neural network |
| CN115656731A (en) * | 2022-12-09 | 2023-01-31 | 武汉格蓝若智能技术股份有限公司 | Overhead distribution line arc grounding fault point positioning method |
| CN116298670A (en) * | 2023-01-28 | 2023-06-23 | 北京交通大学 | Intelligent fault positioning method and system suitable for multi-branch distribution line |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117074869A (en) | 2023-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107944412A (en) | Transmission line of electricity automatic recognition system and method based on multilayer convolutional neural networks | |
| CN105628951A (en) | Method and device for measuring object speed | |
| US11823330B2 (en) | Detection device, detection method and detection program for linear structure | |
| JP2018090099A (en) | System to patrol facility and method to patrol facility | |
| EP3633551A1 (en) | Obstacle distribution simulation method, device and terminal based on a probability graph | |
| Manninen et al. | Toward automatic condition assessment of high-voltage transmission infrastructure using deep learning techniques | |
| CN112802004A (en) | Portable intelligent video detection device for health of transmission line and tower | |
| CN113959457A (en) | Positioning method and device for automatic driving vehicle, vehicle and medium | |
| CN117074869B (en) | Distribution line fault positioning method and system | |
| CN116989842A (en) | Equipment fault diagnosis method and device based on big data and storage medium | |
| CN116089406B (en) | Barrier visualization processing system for ocean mapping | |
| CN111738259A (en) | Tower state detection method and device | |
| CN115902839A (en) | Port laser radar calibration method and device, storage medium and electronic equipment | |
| CN116359909A (en) | Driving track determining method | |
| CN113810102B (en) | Optical cable detection equipment, system and method | |
| CN115494871A (en) | Unmanned aerial vehicle inspection method, device and system for power transmission line abnormity | |
| CN113496182B (en) | Road extraction method and device based on remote sensing image, storage medium and equipment | |
| CN112558091B (en) | Real-time detection method and device for tree space distance of transmission line and terminal equipment | |
| CN113010834A (en) | Ground inspection robot inspection coverage verification method and device based on GIS | |
| CN115575857B (en) | Emergency protection method and device for high-voltage wire breakage | |
| CN115880276B (en) | Tower pole operation state assessment method and system based on multi-period point cloud comparison | |
| CN117310772B (en) | Electromagnetic target positioning method based on map information visual distance or non-visual distance detection | |
| CN111366172B (en) | Quality detection method and device of digital elevation model and storage medium | |
| US20230419656A1 (en) | Method and system for analyzing geo-spatial data regarding spatial object representations | |
| CN115856971A (en) | Monitoring method, system and device for power transmission line tower pole |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |