System for identifying foreign objects on overhead power lines

Abstract

The application discloses a system for identifying and detecting foreign matters of power grid overhead lines, relates to the technical field of transmission line detection, and solves the technical problem that the prior art cannot efficiently determine whether foreign matters exist in overhead lines, leading to the problem that foreign matters of power grid overhead lines cannot be automatically identified and detected. The application separately or jointly analyzes line image data and line strain data in basic state data, determines foreign matter information, and then performs early warning processing according to the foreign matter information. The application can judge whether foreign matters exist in power grid overhead lines through automatic data processing, and provides a data basis for the identification and detection of foreign matters. When the line image data and the line strain data are jointly analyzed, the position of foreign matters is analyzed through the line strain data first, and then corresponding line image data is combined to analyze and determine foreign matter information. Whether foreign matters exist is determined through a low-cost scheme first, and then fine identification is performed, so that the identification and detection cost of foreign matters can be effectively reduced.

Description

Technical Field

[0001] This invention belongs to the field of power transmission line inspection and relates to a technology for identifying and detecting foreign objects in overhead power lines, specifically a system for identifying and detecting foreign objects in overhead power lines. Background Technology

[0002] Power transmission lines are exposed to the outdoors year-round, and foreign objects often end up hanging on them. These objects can damage the lines and even disrupt people's lives. Currently, the main method for detecting foreign objects on power transmission lines is manual inspection, which is costly and lacks sufficient accuracy.

[0003] Existing technology (patent application number 2019101287529) discloses a foreign object detection method for overhead transmission lines based on image processing. This method acquires three resolution images of the overhead transmission line containing foreign objects, preprocesses them, assigns foreign object tags, and analyzes the three resolution images using a multi-scale convolutional neural network model to determine the type of foreign object, thus improving detection accuracy and efficiency. However, existing technology only determines the type of foreign object based on the image of the foreign object on the overhead line when its presence is known, and cannot efficiently determine whether foreign objects exist on the overhead line, resulting in the inability to automatically identify and detect foreign objects on power grid overhead lines. Therefore, there is an urgent need for a foreign object identification and detection system for power grid overhead lines. Summary of the Invention

[0004] The present invention aims to at least solve one of the technical problems existing in the prior art; to this end, the present invention proposes a foreign object identification and detection system for power grid overhead lines, which solves the technical problem that the prior art can only determine the type of foreign object based on the image of the foreign object on the overhead line when the foreign object is known to exist, but cannot efficiently determine whether there is a foreign object on the overhead line, resulting in the inability to automatically identify and detect foreign objects on power grid overhead lines.

[0005] To achieve the above objectives, a first aspect of the present invention provides a foreign object identification and detection system for overhead power lines, comprising a central analysis module and an edge acquisition module connected thereto, wherein the edge acquisition module is connected to a data acquisition device.

[0006] Edge acquisition module: After receiving the data acquisition signal, it controls the data acquisition device to acquire the basic status data of the overhead power line; the basic status data includes line image data or line strain data, and the line strain data is acquired by uniformly set strain sensors.

[0007] Central Analysis Module: Analyzes the distribution environment of overhead power lines in the target area, generating data acquisition signals when the distribution environment is abnormal, or periodically generating data acquisition signals; and

[0008] After receiving the basic status data, the line image data and line strain data are analyzed individually or jointly to determine the foreign object information; the foreign object information includes the type and location of the foreign object.

[0009] Preferably, the central analysis module communicates and / or is electrically connected to several types of edge acquisition modules, and each type of edge acquisition module communicates and / or is electrically connected to a type of data acquisition device;

[0010] The data acquisition equipment includes smart terminals, drones, and strain sensors; the smart terminals include mobile phones and computers, and the strain sensors are evenly distributed on the overhead power lines.

[0011] Preferably, the central analysis module analyzes the distribution environment of overhead power lines in the target area based on historical foreign object data, including:

[0012] The historical foreign object data is acquired, and a GIS map of the target area is constructed and marked as the target map; wherein, the historical foreign object data is the data recorded when foreign objects appear on each power grid overhead line;

[0013] Mark the locations of foreign objects in the historical foreign object data on the target map; determine several target sub-regions based on the number of times foreign objects appear and the distance between the locations of each foreign object;

[0014] When the frequency of foreign objects appearing in the target sub-region is greater than the frequency threshold, the target sub-region is determined to be abnormal, and the data acquisition signal is generated accordingly; wherein, the frequency threshold is set based on experience.

[0015] Preferably, after receiving the data acquisition signal, the edge acquisition module acquires line image data via a drone or line pressure data via a strain sensor when the weather conditions meet the requirements; wherein, the weather conditions meet the requirements, meaning that the drone or strain sensor can acquire valid data;

[0016] When acquiring the line image data through a smart terminal, it is unrelated to the data acquisition signal.

[0017] Preferably, the central analysis module analyzes the line image data separately to determine the corresponding foreign object information, including:

[0018] Extract the line image data from the basic status data;

[0019] The foreign object type in the line image data is identified by image recognition technology, and the location of the foreign object is determined according to the acquisition location of the line image data, thereby obtaining the foreign object information.

[0020] Preferably, the central analysis module performs joint analysis on the line strain data and the line image data to determine the foreign object information, including:

[0021] Extract and analyze the strain data of the line to determine the location of strain anomalies in the strain data; wherein, the location of strain anomalies is a point or segment in the overhead power line where the strain does not conform to the pattern.

[0022] Collect or extract the line image data corresponding to the location of the strain anomaly, and determine the foreign object information based on the corresponding line image data.

[0023] Preferably, the central analysis module analyzes the line strain data using the strain difference method to determine the location of the strain anomaly, including:

[0024] The line strain data is divided into several strain sub-data according to the line poles; wherein, the strain sub-data is the line strain data between two adjacent line poles;

[0025] The overhead power line corresponding to the strain gauge data is divided into several segments and marked as segment strain data; the strain difference between two adjacent segment strain data is calculated, and the strain difference is compared with the difference threshold to determine the location of the strain anomaly; wherein, the difference threshold is set based on experience.

[0026] Preferably, the central analysis module analyzes the line strain data using a curve comparison method to determine the location of the strain anomaly, including:

[0027] The line strain data is divided into several strain sub-data according to the line poles; curve fitting is performed on the strain sub-data to obtain the strain fitting curve;

[0028] When the strain fitting curve has the same shape as the strain standard curve, the prominent point in the strain fitting curve is identified as the strain anomaly point, and the position corresponding to the strain anomaly point is the strain anomaly position.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] 1. In this invention, the edge acquisition module controls the data acquisition device to collect basic status data of the overhead power line, and performs individual or joint analysis on the line image data and line strain data to determine foreign object information, and then performs early warning processing based on the foreign object information; this invention application can determine whether there are foreign objects in the overhead power line through automated data processing, providing a data basis for the identification and detection of foreign objects.

[0031] 2. When jointly analyzing line image data and line strain data, this invention first analyzes the location of foreign objects through line strain data, and then combines the corresponding line image data to determine the information of foreign objects; it first determines whether foreign objects exist through a low-cost solution, and then performs fine identification, which can effectively reduce the cost of foreign object identification and detection. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of the working steps of the present invention;

[0034] Figure 2 This is a schematic diagram of the system principle of the present invention. Detailed Implementation

[0035] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Please see Figures 1-2 The first aspect of the present invention provides a foreign object identification and detection system for overhead power lines, including a central analysis module and an edge acquisition module connected thereto, wherein the edge acquisition module is connected to a data acquisition device.

[0037] Edge acquisition module: After receiving the data acquisition signal, it controls the data acquisition device to acquire the basic status data of the overhead power line; the basic status data includes line image data or line strain data, and the line strain data is acquired by uniformly set strain sensors.

[0038] Central Analysis Module: Analyzes the distribution environment of overhead power lines in the target area, generates data acquisition signals when the distribution environment is abnormal, or generates data acquisition signals periodically; and after receiving basic status data, performs separate or joint analysis on line image data and line strain data to determine foreign object information; among which, foreign object information includes foreign object type and foreign object location.

[0039] Current technologies for identifying and detecting foreign objects on overhead power lines typically rely on pre-existing knowledge of object locations. The identification process involves capturing images of the objects at those locations to determine their type. However, knowing the locations of foreign objects primarily depends on manual inspection, which reduces the efficiency of foreign object identification. Therefore, fully automated identification and detection of foreign objects on overhead power lines is essential.

[0040] In this invention application, the edge acquisition module controls the data acquisition device to collect basic status data of the overhead power line, and performs individual or joint analysis on the line image data and line strain data to determine foreign object information, and then performs early warning processing based on the foreign object information; this invention application can determine whether there are foreign objects in the overhead power line through automated data processing, providing a data foundation for the identification and detection of foreign objects.

[0041] The central analysis module of this invention communicates and / or is electrically connected to several types of edge acquisition modules, and each type of edge acquisition module communicates and / or is electrically connected to a type of data acquisition device; the data acquisition device includes a smart terminal, a drone, and a strain sensor.

[0042] Smart terminals include mobile phones and computers. Anyone can send data containing foreign object information (image data, video data, etc.) to the central analysis module through a smart terminal, and the data transmission from the smart terminal to the central analysis module is not controlled by the data acquisition signal. Strain sensors are evenly distributed on the overhead power lines; that is, strain sensors can be placed on top of the overhead power lines, or they can be evenly spaced on the overhead power lines.

[0043] Before data collection, the central analysis module analyzes the distribution environment of overhead power lines in the target area based on historical foreign object data, including:

[0044] Acquire historical foreign object data and construct a GIS map of the target area, marking it as the target map; mark the locations of foreign objects in the historical foreign object data on the target map; determine several target sub-regions based on the frequency of foreign object occurrences and the distance between the locations of each foreign object occurrence; when the frequency of foreign object occurrences in a target sub-region is greater than the frequency threshold, the target sub-region is determined to be abnormal, and a data acquisition signal is generated.

[0045] While foreign objects do appear on overhead power lines, the frequency is infrequent, making real-time monitoring unnecessary and a waste of resources. Periodic monitoring, which involves periodically generating data acquisition signals, can be performed according to a set schedule. Furthermore, the frequency of foreign object appearances on the overhead power lines can be used to determine whether monitoring is necessary.

[0046] Extract the data recorded when foreign objects appear on each overhead power line in the target area and mark them on the GIS map. This will reveal the presence of foreign objects at several locations on the GIS map corresponding to the target area. Several target sub-regions will be determined sequentially according to the following steps:

[0047] Select the point where the foreign object appears most frequently as the center. From other locations where foreign objects appear, select the point closest to the center as the edge point. When the distance difference between the center and the edge point is less than a set threshold (e.g., 10m), use this distance difference as the radius to determine a circular area. Then, abstract this circular area as a center, and then obtain suitable edge points. Expand the circular area based on the suitable edge points. When no suitable edge points exist, the current circular area is taken as a target sub-region. Analyze the remaining points in this way to obtain several target sub-regions.

[0048] It is worth noting that after determining the target sub-region, a reasonable data acquisition cycle should be set for each target sub-region. For example, the frequency of foreign object occurrence in a certain target sub-region can be marked as YCP. When YCP > PY, a data acquisition signal is generated, and the data acquisition cycle SCZ is obtained by the formula SCZ = α × N / YCP. After the edge acquisition module receives the data acquisition signal, it acquires the basic state data of the corresponding target sub-region according to the data acquisition cycle. Here, α is a proportionality coefficient greater than 0, and N is the duration of YCP.

[0049] The purpose of setting a target sub-region is to minimize the working time of the drone and strain sensor and save resources. When the target sub-region cannot be extracted, data can be collected periodically from the entire target area.

[0050] In this invention application, after the edge acquisition module receives the data acquisition signal, it acquires line image data via a drone or line pressure data via a strain sensor when the weather conditions meet the requirements; when acquiring line image data via a smart terminal, it is unrelated to the data acquisition signal.

[0051] After receiving the data acquisition signal, the edge acquisition module can flexibly collect basic status data through drones or strain sensors. During this process, it can still collect line image data through a smart terminal. Meeting the weather requirements means that the drone or strain sensor can collect valid data; that is, the drone can operate safely and collect line image data of the appropriate resolution, and the strain sensor can function normally and collect valid line strain data.

[0052] The central analysis module in this invention application can independently analyze line image data to determine the corresponding foreign object information, including:

[0053] Line image data is extracted from basic status data; the type of foreign object in the line image data is identified through image recognition technology, and the location of the foreign object is determined based on the acquisition location of the line image data, thereby obtaining foreign object information.

[0054] It is worth noting that simply using drones for data collection requires the drones to periodically patrol the target area or sub-area and acquire line image data. The line image data also needs to be identified and forwarded. The central analysis module also needs to identify the line image data through various algorithms, which consumes a lot of resources. Therefore, this invention also proposes the joint analysis of line strain data and line image data.

[0055] In this invention application, the central analysis module performs joint analysis of line strain data and line image data to determine foreign object information, including:

[0056] Extract and analyze the strain data of the line to determine the location of strain anomalies in the strain data; collect or extract the line image data corresponding to the strain anomaly location, and determine the foreign object information based on the corresponding line image data.

[0057] By directly analyzing the line strain data in the basic condition data, the location of strain anomalies can be identified. At this point, it can be determined that a foreign object exists on the overhead power line, specifically at the location of the strain anomaly. Once the location of the foreign object is determined, corresponding line image data can be obtained by using a drone or by personnel to take targeted photographs. At this stage, the amount of line image data is very limited, and information about the foreign object is easily extracted from it.

[0058] The location of strain anomalies is a point or segment in an overhead power line where the strain does not conform to a certain pattern. In this invention application, the central analysis module analyzes line strain data using the strain difference method to determine the location of strain anomalies, including:

[0059] The line strain data is divided into several strain sub-data according to the line poles; the overhead power line corresponding to the strain sub-data is divided into several segments and marked as segment strain data; the strain difference between two adjacent segment strain data is calculated, and the strain difference is compared with the difference threshold to determine the location of the strain anomaly.

[0060] When there are no abnormalities on the overhead power line, the line between two poles is only subjected to natural forces (gravity and wind). Since the line structure remains unchanged, the natural forces acting on it are basically the same. In other words, under normal circumstances, the strain of each section of the line is related. However, once a foreign object is present, such as a kite or balloon, the strain at the corresponding location will change abruptly under the influence of natural forces. Identifying the location of the abrupt change can pinpoint the location of the foreign object.

[0061] The strain gauge data is divided into several segments, thus obtaining several segments of strain data. Each segment of strain data corresponds to a line segment. When the strain difference between a certain segment of strain data and the strain differences between the segments before and after it is less than or equal to the strain difference, then there is no abrupt change in the strain data of that segment; otherwise, there is a foreign object in the line corresponding to that segment of strain data.

[0062] The central analysis module in this invention application analyzes line strain data using a curve comparison method to determine the location of strain anomalies, including:

[0063] The line strain data is divided into several strain sub-data according to the line poles; curve fitting is performed on the strain sub-data to obtain the strain fitting curve; when the strain fitting curve is consistent with the strain standard curve, the protruding point in the strain fitting curve is identified as the strain anomaly point, and the position corresponding to the strain anomaly point is the strain anomaly position.

[0064] The strain standard curve is the curve showing the change in strain data of an overhead power line under natural forces. Consistency between the strain fitting curve and the strain standard curve means that their approximate shapes are similar; for example, both are "U"-shaped, which is considered consistent, but a very high degree of similarity is not required. If the strain standard curve is "U"-shaped while the strain fitting curve is "I"-shaped, they are inconsistent, and the strain sensor and its control device may be malfunctioning. When they are consistent, the strain fitting curve is analyzed to identify any abrupt changes. The strain data of the line with these abrupt changes can be extracted, and their corresponding locations can be considered the locations of foreign objects.

[0065] The data in the above formula are all calculated by removing the dimensions and taking the numerical values. The formula is the closest to the real situation obtained by software simulation of a large amount of collected data. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0066] Working principle of the invention:

[0067] The central analysis module analyzes the distribution environment of overhead power lines in the target area. When the distribution environment is abnormal, it generates data acquisition signals or generates data acquisition signals periodically.

[0068] After receiving the data acquisition signal, the edge acquisition module controls the data acquisition equipment to collect basic status data of the overhead power line.

[0069] After receiving the basic status data, the central analysis module performs individual or joint analysis on the line image data and line strain data to determine the foreign object information.

[0070] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A system for identifying and detecting foreign objects on overhead power lines, comprising a central analysis module and an edge acquisition module connected thereto, the edge acquisition module being connected to a data acquisition device, characterized in that: Edge acquisition module: After receiving the data acquisition signal, it controls the data acquisition equipment to acquire the basic status data of the overhead power line; the basic status data includes line image data and line strain data, and the line strain data is acquired through uniformly set strain sensors; Central Analysis Module: Analyzes the distribution environment of overhead power lines in the target area, generating data acquisition signals when the distribution environment is abnormal, or periodically generating data acquisition signals; and After receiving the basic status data, the line image data and line strain data are jointly analyzed to determine the foreign object information; the foreign object information includes the type and location of the foreign object. The central analysis module analyzes the distribution environment of overhead power lines in the target area based on historical foreign object data, including: The historical foreign object data is acquired, and a GIS map of the target area is constructed and marked as the target map; wherein, the historical foreign object data is the data recorded when foreign objects appear on each power grid overhead line; Mark the locations of foreign objects in the historical foreign object data on the target map; determine several target sub-regions based on the number of times foreign objects appear and the distance between the locations of each foreign object; When the frequency of foreign objects appearing in the target sub-region is greater than the frequency threshold, the target sub-region is determined to be abnormal, and the data acquisition signal is generated accordingly; wherein, the frequency threshold is set based on experience. The central analysis module performs joint analysis on the line strain data and the line image data to determine the foreign object information, including: Extract and analyze the strain data of the line to determine the location of strain anomalies in the strain data; wherein, the location of strain anomalies is a point or segment in the overhead power line where the strain does not conform to the pattern. Collect or extract the line image data corresponding to the location of the strain anomaly, and determine the foreign object information based on the corresponding line image data; The central analysis module analyzes the line strain data using the strain difference method to determine the location of the strain anomaly, including: The line strain data is divided into several strain sub-data according to the line poles; wherein, the strain sub-data is the line strain data between two adjacent line poles; The overhead power line corresponding to the strain gauge data is divided into several segments and marked as segment strain data; the strain difference between two adjacent segment strain data is calculated, and the strain difference is compared with the difference threshold to determine the location of the strain anomaly; wherein, the difference threshold is set based on experience.

2. The foreign object identification and detection system for overhead power lines according to claim 1, characterized in that, The central analysis module communicates and / or is electrically connected to several types of edge acquisition modules, and each type of edge acquisition module communicates and / or is electrically connected to a type of data acquisition device; The data acquisition equipment includes smart terminals, drones, and strain sensors; the smart terminals include mobile phones and computers, and the strain sensors are evenly distributed on the overhead power lines.

3. The foreign object identification and detection system for overhead power lines according to claim 1, characterized in that, After receiving the data acquisition signal, the edge acquisition module acquires line image data via drone and line pressure data via strain sensor when the weather conditions meet the requirements. When acquiring the line image data through a smart terminal, it is unrelated to the data acquisition signal.

4. The foreign object identification and detection system for overhead power lines according to claim 1, characterized in that, The central analysis module analyzes the line strain data using a curve comparison method to determine the location of the strain anomaly, including: The line strain data is divided into several strain sub-data according to the line poles; curve fitting is performed on the strain sub-data to obtain the strain fitting curve; When the strain fitting curve has the same shape as the strain standard curve, the prominent point in the strain fitting curve is identified as the strain anomaly point, and the position corresponding to the strain anomaly point is the strain anomaly position.

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