A power transmission line fault prediction system and method

Abstract

The present application relates to the technical field of electric performance fault detection, and particularly relates to a power transmission line fault prediction system and method. The method comprises the following steps: acquiring meteorological monitoring data along a power transmission line and collecting vibration displacement data of fittings; taking images of the fittings surface in the running area of the power transmission line by a UAV, identifying the size of the fitting wear area, and determining the wear degree of the fittings; determining the meteorological action intensity on the fittings according to the meteorological monitoring data along the line, and setting the fitting wear coefficient according to the meteorological action intensity and the wear degree of the fittings. The present application identifies the wear area of the fittings of the power transmission line, and adapts the wear coefficient in combination with meteorological factor data to calculate the wear amount of the fittings of the power transmission line, thereby improving the accuracy of the power transmission line fault prediction.

Description

Technical Field

[0001] This invention relates to the field of electrical performance fault detection technology, and in particular to a transmission line fault prediction system and method. Background Technology

[0002] During the operation of power transmission lines, wear of the transmission line fittings is a key issue affecting their service life and transmission line faults. The occurrence and development of wear of transmission line fittings are influenced by the coupling effect of multiple factors. A comprehensive consideration and accurate analysis of these factors is the foundation for effective prediction of fitting wear. The core influencing factors of fitting wear include not only the traditionally recognized material surface characteristics (such as surface morphology and surface composition), contact characteristics (such as contact mode and force transmission), and relative motion, but also meteorological conditions and dynamic vibration state in the operating environment of the fittings. Meteorological factors such as temperature and humidity will change the surface friction coefficient and material properties of the fittings. The combined effect of icing and wind will aggravate the stress and movement amplitude of the fittings. The vibration displacement of the fittings during operation directly determines the relative friction intensity between them and the conductor. All these factors will have a significant impact on the wear rate of the fittings.

[0003] Existing research mainly focuses on the impact of specific operating conditions (such as secondary span oscillation) on the wear of transmission line fittings. Although targeted vibration prevention and fracture resistance schemes have been proposed, they have failed to incorporate meteorological factors such as temperature, humidity, and icing into the analysis system of transmission line faults. They have also failed to systematically combine actual vibration displacement data of fittings to carry out multi-dimensional transmission line fault prediction analysis. This results in limitations in the analysis of transmission line faults, making it difficult to fully reflect the true wear state of transmission line fittings under complex operating environments, and thus failing to provide accurate and comprehensive technical support for transmission line maintenance. Summary of the Invention

[0004] Therefore, it is necessary to provide a power transmission line fault prediction system and method to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a method for predicting transmission line faults is provided, applied to the detection of wear on hardware in transmission lines. The method includes the following steps: Step S1: Obtain meteorological monitoring data along the operating area of ​​the transmission line fittings and collect vibration displacement data of the fittings; Step S2: Take images of the hardware surface in the power transmission line operating area using a drone, identify the size of the hardware wear area, and determine the degree of hardware wear; Step S3: Determine the intensity of meteorological action on the fittings based on meteorological monitoring data along the route, and set the fittings wear coefficient according to the intensity of meteorological action and the degree of fittings wear; Step S4: Calculate the cumulative relative motion travel between the hardware and the conductor within the preset period by combining the hardware vibration displacement data, multiply the cumulative relative motion travel by the hardware wear coefficient, and correlate it with the hardware wear area size ratio to calculate the hardware wear amount within the preset period. Step S5: Determine the wear risk level based on the wear amount of the hardware, match the hardware maintenance information, and complete the transmission line fault prediction.

[0006] The present invention also provides a power transmission line fault prediction system for the above-described power transmission line fault prediction method, the power transmission line fault prediction system comprising: The meteorological and vibration data acquisition module is used to acquire meteorological monitoring data along the operating area of ​​transmission line fittings and to collect vibration displacement data of the fittings. The hardware wear image recognition module is used to capture images of the hardware surface in the power transmission line operating area using drones, identify the size of the hardware wear area, and determine the degree of hardware wear. The wear coefficient setting module is used to determine the intensity of meteorological action on the fittings based on meteorological monitoring data along the route, and to set the wear coefficient of the fittings according to the intensity of meteorological action and the degree of wear of the fittings; The hardware wear calculation module is used to calculate the cumulative relative motion travel between the hardware and the conductor within a preset period by combining the hardware vibration displacement data. The cumulative relative motion travel is multiplied by the hardware wear coefficient and associated with the hardware wear area size ratio to calculate the hardware wear amount within the preset period. The wear risk assessment module is used to determine the wear risk level based on the wear amount of the fittings and match the fittings maintenance information to complete the prediction of power transmission line faults.

[0007] The beneficial effects of this invention are: I. Improve the accuracy of surface image acquisition and wear area identification of hardware fittings, and provide a reliable basis for determining the degree of wear. By forming an equilateral triangle flight trajectory around the key stress points of the hardware fittings, and keeping the lens perpendicular to the stress surface and the focal length fixed at each vertex, and after setting the wear coefficient of the hardware fittings in step S3, the system also includes three consecutive image pixel superposition processing steps. This can fully cover the key areas of the hardware fittings that are prone to wear, avoid the loss of image information caused by traditional single-angle shooting, effectively solve the problem of difficulty in accurately quantifying the size of irregular wear areas of hardware fittings, and significantly reduce the measurement error of the actual size parameters of the wear area.

[0008] 2. Achieve precise quantification of meteorological action intensity and dynamic optimization of wear coefficient, improving the adaptability of wear coefficient; When determining the intensity of meteorological action, stable wind periods are screened through stratified statistics and combined with temperature and humidity weighted correction. In particular, for icing conditions, after setting the wear coefficient of hardware in step S3, step S3 also includes the wind synergy value. The weighted duration of stable wind periods is adjusted according to this value, so that the grading and quantification of meteorological action intensity is more in line with the actual working conditions, and the wear coefficient can match the environment and wear status of the hardware in real time.

[0009] Third, the system accurately calculates the cumulative relative motion travel between the fittings and the conductor, providing realistic basic data for wear calculation. By extracting the daily displacement peaks in three directions and calculating the vector sum to obtain the daily comprehensive displacement value, and then accumulating them to obtain the total cumulative relative motion travel within a preset period, the system fully considers the three-dimensional coordinated vibration characteristics of the fittings under the influence of wind loads, icing loads, etc. during actual operation. This system can accurately restore the true relative motion trajectory between the fittings and the conductor, avoiding the deviation in cumulative relative motion travel caused by simplified displacement calculation. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of a method for predicting power transmission line faults. Figure 2 A cruise diagram of the wear area of ​​power transmission line hardware captured by a drone; Figure 3 A schematic diagram of the area for photographing and collecting images of power transmission line fittings; Figure 4 A schematic diagram of the wear area of ​​transmission line hardware; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0011] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0012] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0013] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0014] To achieve the above objectives, please refer to Figures 1 to 4 A method for predicting transmission line faults, applied to the detection of wear on hardware in transmission lines, the method comprising the following steps: Preferably, step S1: Obtain meteorological monitoring data along the operating area of ​​the transmission line fittings and collect fitting vibration displacement data; In one embodiment, a rotatable meteorological acquisition component and a three-dimensional vibration displacement sensor are installed on the tower corresponding to the transmission line hardware, wherein the rotatable meteorological acquisition component integrates a wind speed sensor, a temperature sensor and a humidity sensor. It should be noted that the wind speed sensor has a measurement range of 0-60m / s and an accuracy of ±0.3m / s, the temperature sensor has a measurement range of -40℃-85℃ and an accuracy of ±0.5℃, and the humidity sensor has a measurement range of 0-100%RH and an accuracy of ±3%RH. The component is set to rotate around the tower axis once per hour, and collect a set of data every 30 degrees during the rotation. Each set of data is collected for 10 seconds, and the collected data is stored in real time to the component's built-in 2TB capacity SD memory card. The data sampling rate is set to 1Hz. In this embodiment, the three-dimensional vibration displacement sensor is installed at the top, middle and bottom of the hardware clamp. The sensor measurement range is set to ±50mm, resolution to 0.01mm and sampling frequency to 100Hz. It is connected to the 12V DC power supply data acquisition terminal at the bottom of the tower via RS485 communication protocol. Every 5 minutes, the collected axial, vertical line direction and vertical displacement data are packaged and transmitted to the data acquisition terminal. In another embodiment, the data acquisition terminal uses a 5th-order Butterworth low-pass filter to filter the displacement data, with a cutoff frequency set to 10Hz. Simultaneously, using the acquisition timestamp as a unique identifier, the wind speed, temperature, and humidity data acquired by the rotatable meteorological acquisition component at the same time are associated with the filtered displacement data to form a transmission line fitting operation monitoring dataset containing fields such as acquisition timestamp, wind speed value, temperature value, humidity value, axial displacement value, vertical line displacement value, and vertical displacement value. At 24:00 every day, the data acquisition terminal uploads the daily dataset to the remote monitoring platform via a 5G wireless network, completing the acquisition of meteorological monitoring data and fitting vibration displacement data along the line.

[0015] Please see Figure 2 This is a cruise diagram of the wear area of ​​power transmission line hardware captured by a drone.

[0016] Preferably, step S2: take images of the hardware surface in the power transmission line operating area by drone, identify the size of the hardware wear area, and determine the degree of hardware wear; Optionally, step S2, which involves taking images of the hardware surface in the power transmission line operating area using a drone, specifically involves: When the drone takes pictures of the surface of the hardware, the flight trajectory forms an equilateral triangle path around the power transmission line hardware. The three vertices of the equilateral triangle correspond to the three key stress points of the power transmission line hardware. When shooting at each vertex, the lens focal length is fixed and the shooting direction is perpendicular to the stress surface of the power transmission line fittings. After shooting, the three consecutive images of the same vertex are superimposed pixel by pixel to output the surface image of the fittings.

[0017] In one embodiment, a multi-rotor drone equipped with a three-axis stabilization gimbal and a 20-megapixel industrial-grade camera is selected to perform the task of taking images of the surface of power transmission line fittings. Before the drone takes off, the flight parameters need to be preset, the flight speed is set to 3m / s, the flight altitude is set to be flush with the plane where the fittings are located, and the sensitivity of the industrial-grade camera is fixed at ISO400, the shutter speed is fixed at 1 / 1000s, and the white balance mode is set to cloudy mode.

[0018] It should be noted that during the flight, the drone flies along a preset equilateral triangle path centered on the power transmission line fittings. The side length of the equilateral triangle path is set to 5m, and the three vertices are respectively aligned with the connection point between the fitting clamp and the conductor, the connection point between the fitting and the insulator string, and the stress concentration point in the middle of the fitting. The drone hovers at each vertex for 5 seconds. During hovering, the focal length of the industrial-grade camera lens is adjusted to 50mm and kept fixed. The shooting direction is calibrated by a three-axis stabilization gimbal to ensure that the optical axis of the lens is perpendicular to the stress surface of the fitting at the corresponding vertex. Then, three images of the fitting surface are taken continuously within 1 second. The image resolution is set to 5472×3648 pixels and the image format is saved as RAW.

[0019] In another embodiment, after the shooting is completed, three consecutive images of the same vertex are imported into the image preprocessing device. During processing, the first image is used as a reference, and the pixel positions of the latter two images are aligned with the reference image. The alignment accuracy is controlled within 1 pixel. Then, the gray values ​​of the corresponding pixels in the three images are averaged to generate a single high-definition image of the metal surface.

[0020] Of particular importance is that the drone, flying along an equilateral triangle path around the power transmission line fittings, includes the following: The locations of three key stress points were determined through mechanical analysis of the fittings, and virtual label points were set in the non-wear area of ​​the fittings next to each stress point. After importing the preset position parameters of the virtual tag point into the drone flight control system, the first flight moves from the left side of the hardware to the first key force point. When it reaches the first vertex of the equilateral triangle, it hovers and takes 3 images with a 10-second interval. During this period, the distance to the reflective tag is read in real time through the airborne laser rangefinder. After completing the first vertex shot, the drone moves along the side of the equilateral triangle to the second vertex, maintaining a constant flight altitude during the movement. Upon reaching the second vertex, it switches to shooting from the right side of the hardware, and also takes 3 images. Move to the third vertex, and upon arrival, use a sliding shooting method, that is, the drone slowly moves 5cm at the vertex position along a direction parallel to the axis of the hardware, and simultaneously takes 5 images. After shooting, the position of all images is calibrated by the positioning code of the reflective tag, and the image with the highest clarity under different shooting methods is selected for the same stress point.

[0021] In one embodiment, a finite element analysis tool is used to apply a rated working load to the transmission line fittings for mechanical analysis. Based on the stress peak value, the three-dimensional coordinates of three key stress points are determined: the connection point between the fitting clamp and the conductor, the connection point between the fitting and the insulator string, and the stress concentration point in the middle of the fitting. A 2mm diameter circular virtual label point is set in the non-wear zone 5mm next to each stress point, and the coordinates are recorded to form a table of key point coordinate parameters of the fitting.

[0022] It should be noted that a multi-rotor UAV equipped with a flight control system, a three-axis stabilization gimbal, a 20-megapixel industrial camera, and a 10-meter range airborne laser rangefinder was selected. After importing the coordinate parameter table, the flight speed was set to 2 m / s, the altitude was aligned with the axis of the fitting, the industrial camera was set to ISO 600, shutter speed 1 / 800s, focal length 50mm, resolution 5472×3648 pixels, RAW format, and the laser rangefinder was set to accuracy ±2mm and sampling rate 1Hz.

[0023] In another embodiment, the drone takes off 10m from the left side of the fitting, hovers at the first vertex of the equilateral triangle (the label point next to the wire clamp-conductor connection point), adjusts the lens optical axis to be perpendicular to the stress surface, and takes 3 pictures at 10-second intervals. The rangefinder reads the distance in real time and stores the image metadata. It then moves along a 5m side length to the second vertex (the label point next to the fitting-insulator string connection point), and takes 3 pictures at the right side of the fitting. It then moves to the third vertex (the label point next to the stress concentration point in the middle), moves 5cm along the fitting axis at 0.1m / s, and takes 5 pictures at 0.5-second intervals.

[0024] It should be noted that after shooting, the images and distance data are imported into the terminal, and the images are calibrated by the coordinate encoding of the tag points. The clearest image of the same stress point is selected to form a high-definition image set of key stress points of power transmission line fittings.

[0025] Most importantly, the output image of the hardware surface is specifically as follows: The angle between the lens and the force-bearing surface of the fitting is calibrated using an airborne tilt sensor, wherein the perpendicular deviation between the shooting direction and the force-bearing surface is less than 0.5°. At the same time, the focal length of the lens is fixed according to the area of ​​the force-bearing surface of the fitting. When the area of ​​the force-bearing surface is <100cm², the focal length is set to 85mm; when the area of ​​the force-bearing surface is between 100-200cm², it is set to 70mm; and when the area of ​​the force-bearing surface is >200cm², it is set to 50mm. Three images are captured at each vertex at 1-second intervals. During the capture process, the ambient light intensity is monitored in real time by an onboard light intensity sensor. If the light intensity fluctuates by more than 100 lux, the image is captured again. After the shooting is completed, the three images of the same vertex are imported into the image processing module. The average value of the RGB three color channels is calculated point by point according to the pixel position, and abnormal pixels in a single image whose gray value exceeds the average value by ±15% are removed. A single image of the hardware surface is generated by overlaying the corrected pixel data.

[0026] In this embodiment, a multi-rotor UAV equipped with a flight control system, a three-axis gimbal stabilization unit, a 20-megapixel industrial camera, an airborne tilt sensor, and an airborne light intensity sensor is selected. The airborne tilt sensor has a measurement range of ±90° and an accuracy of ±0.1°, and is used to monitor the angle between the lens and the force-bearing surface of the fitting in real time. The airborne light intensity sensor has a measurement range of 0-100,000 lux and an accuracy of ±5 lux, and is used to monitor the ambient light intensity during shooting. The industrial camera has a fixed resolution of 5472×3648 pixels, an image format of RAW, and a lens focal length that can be adjusted between 50mm and 85mm.

[0027] It should be noted that before shooting, the length and width of each key stress surface of the hardware should be measured, and the area of ​​the stress surface should be calculated: if the area is <100cm², the focal length of the industrial camera lens should be adjusted to 85mm; if the area is between 100-200cm², the focal length should be adjusted to 70mm; if the area is >200cm², the focal length should be adjusted to 50mm.

[0028] Specifically, after the drone flies to the target vertex along a preset equilateral triangle path, it activates the hovering mode, with the hovering error controlled within ±10cm. At the same time, the onboard tilt sensor is activated to acquire the angle data between the lens and the force-bearing surface of the fitting in real time. The data is transmitted to the three-axis stabilization gimbal control system. The gimbal then fine-tunes the lens angle based on the data until the vertical deviation between the shooting direction and the force-bearing surface of the fitting is less than 0.5°. The deviation value is confirmed in real time through feedback from the sensor.

[0029] It should be noted that after the angle requirement is met, the industrial camera is started to take pictures, and three images are taken continuously at a 1-second interval. During the shooting, the onboard light intensity sensor collects ambient light intensity data once per second. If the difference between the maximum and minimum light intensity values ​​exceeds 100 lux during the three shooting processes, the shooting is stopped immediately, the captured images are deleted, and the shooting is restarted after the light intensity fluctuation stabilizes, until three images with light intensity fluctuations that meet the requirements are obtained.

[0030] After capturing images of the first vertex, the drone flies along a 5m path along the equilateral triangle to the next vertex, repeating the focus adjustment, angle calibration, light intensity monitoring, and shooting operations to acquire qualified images for all vertices. After shooting, the three images of the same vertex are imported into the image processing module. The module first aligns the pixel positions of the three images using an image registration algorithm, controlling the registration accuracy within one pixel. Then, based on the aligned pixel positions, it calculates the grayscale values ​​of the RGB channels for each image point by point, taking the average grayscale value of the corresponding channels from the three images. The deviation of each pixel's grayscale value from the average is compared, and abnormal pixels with deviations exceeding ±15% are removed, retaining only valid pixel data. Finally, the valid pixel data from the three images are superimposed, and the average of the RGB channel grayscale values ​​of the corresponding pixels is taken as the pixel value of the superimposed image, generating a single image of the metal surface.

[0031] Optionally, in step S2, identifying the size of the wear area of ​​the hardware and determining the degree of wear includes: When identifying the size of the wear area of ​​the hardware, a polar coordinate system is established with the center of the bolt hole of the hardware clamp as the origin, and a measurement ray is drawn at equal intervals along the polar radius direction; Scan along each ray from the origin to the edge of the clamp, and record the locations where pixel grayscale values ​​change abruptly during the scanning process; Connect all location points to form a closed profile, measure the maximum polar diameter and polar angle coverage of the profile, and convert them into the actual size parameters of the wear area.

[0032] In this embodiment, a clear image of the hardware surface taken by a drone and processed by pixel overlay is selected, imported into the image processing terminal, and the image annotation tool is started. The bolt hole of the hardware clamp is located in the image, the edge of the bolt hole is identified, the center coordinates of the edge pixel points are calculated and set as the origin of the polar coordinate system, and the polar axis direction of the polar coordinate system coincides with the horizontal center line of the hardware clamp.

[0033] It should be noted that the polar angle interval parameter is set to 5°, and a measurement ray is drawn every 5° along the polar radius starting from the origin. The length of each ray covers the image area from the origin to the edge of the fitting clamp.

[0034] In this embodiment, an image grayscale scanning program is initiated, scanning pixel by pixel along each measurement ray from the origin to the edge of the line clamp. The scanning step size is set to 1 pixel, and the grayscale value of each pixel on the scanning path is recorded in real time. When the grayscale value change of 3 consecutive pixels exceeds 80 (grayscale value range 0-255), it is determined to be a pixel grayscale value mutation, and the polar coordinates (polar radius) of the mutation location point are recorded. Polar angle ).

[0035] It should be noted that after scanning all measurement rays, all recorded locations of abrupt changes in grayscale values ​​are imported into the contour generation module. The module automatically connects these locations in ascending order of polar angle to form a closed contour of the wear area. The polar radius values ​​of each point on the closed contour are then read using an image measurement tool to determine the maximum polar radius. Simultaneously, the polar angle difference between the first and last points of the closed contour is calculated to obtain the polar angle coverage range. .

[0036] In another implementation of this embodiment, the image calibration parameters are called (these parameters are pre-calculated based on the known distance between the drone and the hardware and the camera focal length before shooting; the calibration value is 1 pixel corresponding to an actual size of 0.02 mm), and the maximum polar radius is set to... Multiply by the calibration value to obtain the actual maximum radius R of the wear area, and then cover the polar angle range. Combined with the maximum radius Calculate the actual area S of the wear zone. ,in (In radians), a table of actual dimensions of the wear area of ​​the hardware is generated. The table includes the actual maximum radius R of the wear area and the polar angle coverage range. The three fields are: (converted to angle value), actual area S.

[0037] Preferably, step S3: determine the intensity of meteorological action on the fittings based on meteorological monitoring data along the route, and set the fittings wear coefficient according to the intensity of meteorological action and the degree of fittings wear; Optionally, step S3, when acquiring meteorological monitoring data along the route, includes: A rotatable meteorological data acquisition unit is installed on the poles of the line section where the hardware is located. The unit rotates once around the pole axis every hour. During the rotation, a set of wind speed, temperature, and humidity data was collected every 30 degrees. Meanwhile, the drone is equipped with a dual-frequency ultrasonic thickness gauge to simultaneously measure the ice thickness at six symmetrical points of the hardware clamps. Multiple sets of cross-validation were performed on wind speed, temperature, humidity, and ice thickness collected at the same time. Valid samples with data deviations less than the set range were retained as meteorological monitoring data along the route.

[0038] In one embodiment, a rotatable meteorological data acquisition component is installed on the top crossbar of the tower in the section of the transmission line where the hardware is located. The component integrates a wind speed sensor (measurement range 0-60m / s, accuracy ±0.3m / s), a temperature sensor (measurement range -40℃-85℃, accuracy ±0.5℃), a humidity sensor (measurement range 0-100%RH, accuracy ±3%RH), and a stepper motor rotation mechanism. The rotation mechanism is set to rotate around the tower axis once per hour at an angular velocity of 1° / s. During the rotation, data acquisition is triggered every 30 degrees of rotation. Each acquisition lasts for 5 seconds at a sampling rate of 1Hz. The wind speed, temperature, and humidity values ​​are recorded simultaneously and stored on a built-in 64GB SD memory card. The memory card names the data files in the format of "year-month-day-hour-minute-second".

[0039] In another embodiment, a multi-rotor UAV is equipped with a dual-frequency ultrasonic thickness gauge (operating frequencies of 1MHz and 5MHz, measurement range of 0-100mm, accuracy of ±0.1mm), flies to a position 5m directly in front of the fitting and hovers (error ±10cm), selects six symmetrical points on the top left and right, middle left and right, and bottom left and right of the fitting clamp, starts the thickness gauge to simultaneously measure the ice thickness at each point, takes three measurements at each point and takes the average value, and transmits the data to the ground data terminal via a 5G network.

[0040] In another embodiment, the ground data terminal matches the meteorological data and icing data of the same period according to the collection timestamp (accurate to the second) and performs multiple sets of cross-validation: the deviation of the wind speed value compared with the data of the adjacent towers of the same period must be <2m / s, the deviation of the temperature value and humidity value compared with the data collected by the component in two consecutive times must be <1℃ and <5%RH respectively, and the difference of the icing thickness relative to the point location must be <0.5mm. The data group that meets all the deviation requirements is retained to form the meteorological monitoring data along the line that includes the collection timestamp, wind speed value (m / s), temperature value (℃), humidity value (%RH), and average icing thickness of the hardware clamps (mm).

[0041] Optionally, in step S3, determining the intensity of the meteorological effects on the hardware specifically involves: The wind speed data acquired by the rotatable meteorological acquisition component is statistically analyzed in layers to filter out time periods when the wind speed exceeds a specific value and lasts for more than 1 hour. Calculate the standard deviation of wind speed within each time period, and define the time period with a standard deviation less than a specific value as a stable wind period; The duration of all stable wind periods is summed up, and the duration is weighted and corrected according to the average temperature and average humidity within each stable wind period to obtain the graded quantitative value of meteorological effect intensity.

[0042] In one embodiment, wind speed data from the meteorological monitoring data along the route is exported from the 2TB SD storage card built into the rotatable meteorological acquisition component, imported into the data statistics terminal, and the data stratification statistics program is started. The wind speed stratification intervals are set as 0-5m / s, 5-10m / s, 10-15m / s, and above 15m / s. Time periods with wind speeds exceeding 10m / s and lasting for more than 1 hour are selected. Each time period is continuously divided according to the acquisition timestamp to ensure that there are no intervals with wind speeds below 10m / s within the time period.

[0043] In another embodiment, for each selected time period, the standard deviation of all wind speed values ​​within that time period is calculated by a data statistics terminal. A specific value of 1.5 m / s is set for the standard deviation. Time periods with a standard deviation less than 1.5 m / s are marked as stable wind periods. The start time, end time, average temperature value, and average humidity value within each stable wind period are recorded simultaneously.

[0044] In another embodiment, a time-weighted correction procedure is initiated, and temperature correction coefficients are set as follows: when the average temperature is between -20℃ and 0℃, the correction coefficient is 1.2; when the average temperature is between 0℃ and 20℃, the correction coefficient is 1.0; when the average temperature is between 20℃ and 40℃, the correction coefficient is 0.8. Humidity correction coefficients are set as follows: when the average humidity is between 0% and 40%RH, the correction coefficient is 1.0; when the average humidity is between 40% and 70%RH, the correction coefficient is 1.1; when the average humidity is between 70% and 100%RH, the correction coefficient is 1.3. The weighted correction duration for each stable wind period is calculated as follows: actual duration of the period × temperature correction factor × humidity correction factor. The total weighted duration is obtained by summing the weighted correction durations for all stable wind periods.

[0045] In another embodiment, the intensity of meteorological effects is classified into levels based on the total weighted duration: Level 1 for total weighted duration < 10 hours, Level 2 for 10-20 hours, Level 3 for 20-30 hours, Level 4 for 30-40 hours, and Level 5 for > 40 hours. This classification and quantification value is the final quantification result of the intensity of meteorological effects on the fittings, forming a classification and quantification table of meteorological effects on fittings. The table includes fields such as stable wind period number, start time, end time, average temperature value (°C), average humidity value (%RH), weighted correction duration (hours), total weighted duration (hours), and classification and quantification value of meteorological effects intensity.

[0046] Optionally, weighting the duration based on the average temperature and average humidity during each stable wind period also includes: When the dual-frequency ultrasonic thickness gauge detects icing on the hardware clamp, the product of the icing thickness and the weighted correction duration of the corresponding stable wind period is calculated to obtain the icing-wind synergistic effect value. If the combined effect of icing and wind exceeds the set threshold, the weighted correction duration of the original stable wind period is multiplied by the correction coefficient. The correction coefficient increases linearly with the increase of icing thickness. The meteorological effect intensity level is then reclassified based on the graded quantitative value using the corrected duration.

[0047] In one embodiment, the average ice thickness of the fitting clamps recorded by the dual-frequency ultrasonic thickness gauge in the meteorological monitoring data along the route is retrieved from the ground data terminal. Stable wind periods with an average ice thickness > 0 mm (i.e. ice exists) are selected, and the weighted correction duration (the duration after correction by average temperature and average humidity) corresponding to these periods is extracted simultaneously.

[0048] It should be noted that the data calculation program is started, and the icing-wind synergy value is calculated for each stable wind period with icing according to the formula: "Icing-Wind Synergy Value = Average Icing Thickness of Fittings Clamps (mm) × Weighted Correction Duration for Stable Wind Periods (hours)". The threshold for this synergy value is set at 50 mm / hour. The calculated icing-wind synergy value is then evaluated. If the value is ≤ 50 mm / hour, the original weighted correction duration for stable wind periods remains unchanged. If the value is > 50 mm / hour, the correction coefficient calculation is started. The initial value of the correction coefficient is set to 1.0. For every 1 mm increase in the average icing thickness, the correction coefficient increases linearly by 0.1 (i.e., correction coefficient = 1.0 + 0.1 × average icing thickness of fittings clamps). Then, the new weighted duration is calculated according to "Corrected Weighted Duration = Original Weighted Correction Duration for Stable Wind Periods × Correction Coefficient".

[0049] In another embodiment, after correcting the duration of all stable wind periods with icing, the total weighted duration of all stable wind periods (including those without icing and those with icing and corrected duration) is re-accumulated, and the meteorological intensity levels are reclassified according to the new total weighted duration: Level 1 for total weighted duration < 12 hours, Level 2 for 12-24 hours, Level 3 for 24-36 hours, Level 4 for 36-48 hours, and Level 5 for > 48 hours, forming a corrected quantification table of meteorological intensity of fittings. The table adds fields such as average icing thickness (mm), icing-wind synergistic effect value (mm·hour), correction coefficient, and corrected weighted duration (hours), which correspond one-to-one with the original stable wind period number, start time, end time, and other fields.

[0050] Optionally, in step S3, the hardware wear coefficient is set according to the intensity of meteorological action and the degree of hardware wear as follows: Assign basic wear coefficients to different meteorological intensity levels; Calculate the equivalent wear area of ​​the wear region based on the maximum polar diameter and polar angle coverage of the wear region; Wear levels are classified according to the equivalent wear area. Each wear level corresponds to a wear coefficient adjustment factor. The final wear coefficient of the hardware is obtained by multiplying the basic wear coefficient by the adjustment factor.

[0051] In one embodiment, meteorological intensity grading values ​​are extracted from the revised hardware meteorological intensity grading quantification table, and basic wear coefficients are assigned to different grades: Grade 1 corresponds to a basic wear coefficient of 0.8, Grade 2 to 1.0, Grade 3 to 1.2, Grade 4 to 1.5, and Grade 5 to 1.8. The allocation results are recorded in the hardware wear coefficient calculation table. The maximum extreme diameter of the wear area is retrieved from the actual size parameter table of the hardware wear area. (Unit: mm) and polar angle coverage area (Unit: radians), start the area calculation program and calculate the equivalent wear area for each hardware wear zone using the following formula: ; In another embodiment, an equivalent wear area grading standard is set: <50mm² is considered light wear, 50-100mm² is moderate wear, and >100mm² is heavy wear. Wear coefficient adjustment factors are assigned to each level: 1.0 for light wear, 1.2 for moderate wear, and 1.5 for heavy wear. These adjustment factor data are also entered into the hardware wear coefficient calculation table. Using the data association function, the base wear coefficient of the same hardware is matched with the adjustment factor for the corresponding wear level, and the final hardware wear coefficient is calculated using the following formula: K = Basic wear coefficient × Wear coefficient adjustment factor; In another embodiment, the hardware wear coefficient calculation table includes the hardware number, the quantified value of meteorological action intensity classification, the basic wear coefficient, and the maximum extreme diameter of the wear area. (mm), polar angle coverage range (radian), equivalent wear area The data includes fields for wear level (mm²), wear coefficient adjustment factor, and final hardware wear coefficient K. All data are stored synchronously and correspond one-to-one.

[0052] Optionally, after setting the hardware wear coefficient in step S3, the following steps are also included: Each quarter, select transmission line fittings under different meteorological intensity levels within the line section; Collect data on the change in equivalent wear area of ​​the hardware wear zone, and compare this data with the change in equivalent wear area calculated based on the current wear coefficient; If the deviation exceeds the set range, adjust the basic wear coefficient for the corresponding meteorological action intensity level, and recalculate the wear coefficient after adjustment until the deviation meets the requirements.

[0053] In this embodiment, at the beginning of each quarter, hardware corresponding to each meteorological intensity level (level 1-5) within the transmission line section is selected from the hardware ledger. Ten sample hardware are selected for each level. The sample hardware must meet the conditions of consistent operating time (all of which have been in operation for more than 3 months since the last coefficient adjustment) and no maintenance records.

[0054] A drone equipped with a 20-megapixel industrial camera (resolution 5472×3648 pixels, focal length set according to the area of ​​the stress surface of the fitting) was used to capture images of the sample fitting surface. The wear area was identified by the polar coordinate system scanning method (with the center of the bolt hole as the origin, and a measurement ray drawn every 5°). The current equivalent wear area of ​​each sample fitting was calculated. Then, the equivalent wear area of ​​the fitting at the end of the previous quarter was retrieved (equivalent wear area at the end of the previous quarter). The change data of the equivalent wear area was calculated (actual equivalent wear area change value = current equivalent wear area - equivalent wear area at the end of the previous quarter).

[0055] Extract the final wear coefficient of each sample hardware from the hardware wear coefficient calculation table. Combine this with the total weighted duration of stable winds in the quarter in which the sample hardware is located (total weighted duration of stable winds, obtained from the revised hardware meteorological action intensity classification and quantification table). Calculate the equivalent wear area change value obtained based on the current wear coefficient according to the formula: (Theoretical equivalent wear area change value = final wear coefficient × total weighted duration of stable winds × 0.1) (unit: square millimeters, 0.1 is the preset duration-area conversion coefficient).

[0056] The deviation between the actual equivalent wear area change value and the theoretical equivalent wear area change value is calculated, and the allowable deviation range is set to ±15%. If the deviation of a single sample hardware exceeds this range, the corresponding meteorological action intensity level is marked. When the number of samples exceeding the deviation at a certain meteorological action intensity level is ≥3, the basic wear coefficient adjustment is initiated: if the actual equivalent wear area change value of most samples at this level is > the theoretical equivalent wear area change value, the original basic wear coefficient is increased by 0.1; if the actual equivalent wear area change value of most samples is < the theoretical equivalent wear area change value, the original basic wear coefficient is decreased by 0.1. After adjustment, the final wear coefficient of all hardware at this level is recalculated. The final hardware wear coefficient = adjusted basic wear coefficient × wear coefficient adjustment factor, and the deviation between the theoretical equivalent wear area change value and the actual equivalent wear area change value is calculated again. The adjustment is repeated until the number of samples exceeding the deviation at this level is <3. Finally, the basic wear coefficient of the corresponding level and the final wear coefficient of all related hardware are updated in the hardware wear coefficient calculation table.

[0057] Preferably, step S4: calculate the cumulative relative motion travel between the hardware and the conductor within a preset period by combining the hardware vibration displacement data, multiply the cumulative relative motion travel by the hardware wear coefficient, and correlate it with the hardware wear area size ratio to calculate the hardware wear amount within the preset period; Optionally, step S4, which calculates the cumulative relative motion travel between the hardware and the conductor within a preset period using the hardware vibration displacement data, includes: When calculating the cumulative relative motion travel between the hardware and the conductor within a preset period by combining the hardware vibration displacement data, a three-dimensional vibration displacement sensor is installed on the hardware clamp to collect displacement data along the line axis, perpendicular to the line direction, and vertical direction, respectively. The peak displacement values ​​in three directions are extracted daily within a preset period. The vector sum of the peak values ​​in the three directions is calculated to obtain the daily comprehensive displacement value. The total cumulative relative motion distance is obtained by accumulating the daily comprehensive displacement values.

[0058] In this embodiment, a three-dimensional vibration displacement sensor is attached and installed on the outer surface of the middle part of the fitting clamp. The sensor's measurement range is set to -50 mm to 50 mm, the measurement accuracy is ±0.01 mm, and the data sampling rate is set to 100 Hz. The sensor is connected to the data acquisition unit via a data cable. The data acquisition unit has a built-in 16GB storage module and automatically names and stores the collected data in the format of "year-month-day-hour-minute-second". The preset period is set to 90 days. Within the preset period, the data acquisition unit continuously collects displacement data along the axial direction, perpendicular to the line, and vertical direction. At 23:59 every day, the data extraction program is automatically triggered to extract the maximum value (displacement peak) of the displacement data in the three directions for that day, which is recorded as the peak value of the axial displacement, the peak value of the vertical displacement, and the peak value of the vertical displacement, respectively. The vector sum calculation program is started. First, the peak values ​​of the axial displacement, the peak value of the vertical displacement, and the peak value of the vertical displacement are squared respectively. Then, the three squared results are added together to obtain the sum. Finally, the square root of the sum is performed. The result is the daily comprehensive displacement value. Four significant figures are retained in the calculation process. The data acquisition unit has a built-in accumulation module. The daily comprehensive displacement value calculated each day is automatically added to the total relative motion cumulative stroke. During accumulation, the total value is updated in real time and stored in the storage module. After the preset period ends, the total relative motion cumulative stroke data is exported from the data acquisition unit. This data is the cumulative relative motion stroke between the fittings and the conductors within the preset period. The data unit is millimeters. The exported file includes the preset period start time, end time, daily comprehensive displacement value (90 sets in total), and total relative motion cumulative stroke fields. All data is associated with the corresponding fitting number.

[0059] Of particular importance is that the calculation of hardware wear within the preset period in step S4 is as follows: When calculating the wear amount of fittings based on the size ratio of the wear area, the ratio of the equivalent wear area of ​​the wear area to the total wear-sensitive area of ​​the fitting clamp is calculated to obtain the size ratio of the wear area. Multiply the total relative motion cumulative travel by the hardware wear coefficient, and then multiply the product by the proportion of the wear area size to obtain the hardware wear amount within the preset period.

[0060] In this embodiment, the equivalent wear area of ​​each hardware is retrieved from the actual size parameter table of the hardware wear area, and the total wear-prone area of ​​the corresponding hardware clamp is obtained from the power transmission line hardware design ledger (this area is the total area of ​​the area on the surface of the hardware clamp that is prone to wear, which is determined in advance by measurement through CAD drawings, and the unit is square millimeters). The equivalent wear area and the total wear-prone area are imported into the data calculation terminal, and the ratio calculation program is started. The wear area size ratio of each hardware is calculated according to "wear area size ratio = equivalent wear area ÷ total wear-prone area". The calculation result is retained to four decimal places and is simultaneously stored in the hardware wear amount calculation table. The total relative motion cumulative stroke (in millimeters) of the hardware within the preset period is extracted from the file exported by the data acquisition device. The final hardware wear coefficient of the corresponding hardware is retrieved from the hardware wear coefficient calculation table. The three data items of total relative motion cumulative stroke, final hardware wear coefficient, and wear area size ratio are correlated and matched to ensure that the hardware number and preset period corresponding to the data are completely consistent. The fitting wear calculation program is initiated by first multiplying the total cumulative relative motion travel by the final fitting wear coefficient to obtain a first intermediate calculated value. Then, this first intermediate calculated value is multiplied by the proportion of the wear area size to obtain the fitting wear amount within a preset period (in square millimeters). Three significant decimal places are retained during the calculation. The fitting wear calculation table includes fields for fitting number, equivalent wear area (square millimeters), total wear-sensitive area of ​​the fitting clamp (square millimeters), proportion of the wear area size, total cumulative relative motion travel (millimeters), final fitting wear coefficient, and fitting wear amount within a preset period (square millimeters). All data corresponds one-to-one and is synchronously uploaded to the transmission line fitting monitoring database.

[0061] Preferably, step S5: determine the wear risk level based on the wear amount of the hardware, and match the hardware maintenance information to complete the hardware wear prediction.

[0062] Of particular importance is that step S5 includes the following steps: Step S51: When determining the wear risk level based on the wear amount of the hardware, divide it into three risk ranges: wear amount less than 30% of the hardware's design wear limit is low risk, 30%-70% is medium risk, and greater than 70% is high risk; Step S52: After determining the risk level, call the preset hardware maintenance information database. The database stores maintenance plans according to hardware type. Low risk corresponds to quarterly inspection records, medium risk corresponds to monthly special inspection and grease replenishment, and high risk corresponds to immediate shutdown and hardware replacement. Step S53: Generate a visual report by analyzing the wear level, risk level, and matching maintenance information. The report will indicate the line segment number where the hardware is located, the installation time, and the deadline for the next maintenance, for use by maintenance personnel to perform maintenance operations.

[0063] In this embodiment, the wear amount of the fittings within a preset period is retrieved from the fitting wear calculation table. Combined with the design wear limits of the corresponding fittings in the fitting design ledger (50 square millimeters for tension clamps, 40 square millimeters for suspension clamps), the wear percentage is calculated (wear percentage = wear amount of fittings within the preset period ÷ design wear limit × 100%). Risk levels are then classified according to the percentage: <30% is low risk, 30%-70% is medium risk, and >70% is high risk. The results are entered into an evaluation form. The fitting maintenance information database is accessed, and a matching plan is applied based on the type: low risk requires quarterly inspections (including visual inspection and wear retesting); medium risk requires monthly special inspections and replenishment of lithium-based grease (50 grams for tension clamps, 30 grams for suspension clamps); and high risk requires shutdown and replacement of the same model of fittings within 72 hours. Generate a PDF visual report, including fitting number, line segment number, installation time, wear amount, risk level, maintenance plan, and next maintenance deadline (low risk +3 months, medium risk +1 month, high risk +72 hours). The report is stored in the database and pushed to the operation and maintenance terminal.

[0064] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0065] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for predicting transmission line faults, characterized in that, The method for detecting wear on hardware used in power transmission lines includes the following steps: Step S1: Obtain meteorological monitoring data along the operating area of ​​the transmission line fittings and collect vibration displacement data of the fittings; Step S2: Take images of the hardware surface in the power transmission line operating area using a drone, identify the size of the hardware wear area, and determine the degree of hardware wear; Step S3: Determine the intensity of meteorological action on the fittings based on meteorological monitoring data along the route, and set the fitting wear coefficient according to the intensity of meteorological action and the degree of fitting wear; Step S3, which involves acquiring meteorological monitoring data along the route, includes: A rotatable meteorological data acquisition unit is installed on the poles of the line section where the hardware is located. The unit rotates once around the pole axis every hour. During the rotation, a set of wind speed, temperature, and humidity data was collected every 30 degrees. Meanwhile, the drone is equipped with a dual-frequency ultrasonic thickness gauge to simultaneously measure the ice thickness at six symmetrical points of the hardware clamps. Multiple sets of cross-validation were performed on wind speed, temperature, humidity, and ice thickness collected at the same time. Valid samples with data deviations less than the set range were retained as meteorological monitoring data along the route. Specifically, determining the intensity of meteorological effects on the hardware in step S3 involves: The wind speed data acquired by the rotatable meteorological acquisition component is statistically analyzed in layers to filter out time periods when the wind speed exceeds a specific value and lasts for more than 1 hour. Calculate the standard deviation of wind speed within each time period, and define the time period with a standard deviation less than a specific value as a stable wind period; The duration of all stable wind periods is summed up, and the duration is weighted and corrected according to the average temperature and average humidity within each stable wind period to obtain the graded quantitative value of meteorological effect intensity. The weighted correction of duration based on the average temperature and average humidity during each stable wind period also includes: When the dual-frequency ultrasonic thickness gauge detects icing on the hardware clamp, the product of the icing thickness and the weighted correction duration of the corresponding stable wind period is calculated to obtain the icing-wind synergistic effect value. If the combined effect of icing and wind exceeds the set threshold, the weighted correction duration of the original stable wind period is multiplied by the correction coefficient. The correction coefficient increases linearly with the increase of icing thickness. The meteorological effect intensity level is then reclassified based on the graded quantitative value using the corrected duration. Specifically, in step S3, the hardware wear coefficient is set according to the intensity of meteorological action and the degree of hardware wear as follows: Assign basic wear coefficients to different meteorological intensity levels; Calculate the equivalent wear area of ​​the wear region based on the maximum polar diameter and polar angle coverage of the wear region; Wear levels are classified according to the equivalent wear area. Each wear level corresponds to a wear coefficient adjustment factor. The final wear coefficient of the hardware is obtained by multiplying the base wear coefficient by the adjustment factor. Step S4: Calculate the cumulative relative motion travel between the hardware and the conductor within the preset period by combining the hardware vibration displacement data, multiply the cumulative relative motion travel by the hardware wear coefficient, and correlate it with the hardware wear area size ratio to calculate the hardware wear amount within the preset period. Step S5: Determine the wear risk level based on the wear amount of the hardware, match the hardware maintenance information, and complete the transmission line fault prediction.

2. The transmission line fault prediction method according to claim 1, characterized in that, In step S2, the process of taking images of the hardware surface of the power transmission line operating area using a drone is as follows: When the drone takes images of the hardware surface, the flight trajectory forms an equilateral triangle path around the power transmission line hardware. The three vertices of the equilateral triangle correspond to the three key stress points of the power transmission line hardware. When taking images at each vertex, the lens focal length is fixed and the shooting direction is perpendicular to the stress surface of the power transmission line hardware. After taking the images, the three consecutive images of the same vertex are superimposed to output the hardware surface image.

3. The transmission line fault prediction method according to claim 1, characterized in that, Step S2, which involves identifying the size of the wear area of ​​the hardware and determining the degree of wear, includes: When identifying the size of the wear area of ​​the hardware, a polar coordinate system is established with the center of the bolt hole of the hardware clamp as the origin, and a measurement ray is drawn at equal intervals along the polar radius direction; Scan along each ray from the origin to the edge of the clamp, and record the locations where pixel grayscale values ​​change abruptly during the scanning process; Connect all location points to form a closed profile, measure the maximum polar diameter and polar angle coverage of the profile, and convert them into the actual size parameters of the wear area.

4. The transmission line fault prediction method according to claim 1, characterized in that, After setting the wear coefficient of the hardware in step S3, the following steps are also included: Each quarter, select transmission line fittings under different meteorological intensity levels within the line section; Collect data on the change in equivalent wear area of ​​the hardware wear zone, and compare this data with the change in equivalent wear area calculated based on the current wear coefficient; If the deviation exceeds the set range, adjust the basic wear coefficient for the corresponding meteorological action intensity level, and recalculate the wear coefficient after adjustment until the deviation meets the requirements.

5. The transmission line fault prediction method according to claim 1, characterized in that, Step S4, which calculates the cumulative relative motion between the hardware and the conductor within a preset period based on the hardware vibration displacement data, includes: When calculating the cumulative relative motion travel between the hardware and the conductor within a preset period by combining the hardware vibration displacement data, a three-dimensional vibration displacement sensor is installed on the hardware clamp to collect displacement data along the line axis, perpendicular to the line direction, and vertical direction, respectively. The peak displacement values ​​in three directions are extracted daily within a preset period. The vector sum of the peak values ​​in the three directions is calculated to obtain the daily comprehensive displacement value. The total cumulative relative motion distance is obtained by accumulating the daily comprehensive displacement values.

6. A power transmission line fault prediction system, characterized in that, For performing the transmission line fault prediction method as described in claim 1, the transmission line fault prediction system includes: The meteorological and vibration data acquisition module is used to acquire meteorological monitoring data along the operating area of ​​transmission line fittings and to collect vibration displacement data of the fittings. The hardware wear image recognition module is used to capture images of the hardware surface in the power transmission line operating area using drones, identify the size of the hardware wear area, and determine the degree of hardware wear. The wear coefficient setting module is used to determine the intensity of meteorological action on the fittings based on meteorological monitoring data along the route, and to set the wear coefficient of the fittings according to the intensity of meteorological action and the degree of wear of the fittings; The hardware wear calculation module is used to calculate the cumulative relative motion travel between the hardware and the conductor within a preset period by combining the hardware vibration displacement data. The cumulative relative motion travel is multiplied by the hardware wear coefficient and associated with the hardware wear area size ratio to calculate the hardware wear amount within the preset period. The wear risk assessment module is used to determine the wear risk level based on the wear amount of the fittings and match the fittings maintenance information to complete the prediction of power transmission line faults.

Images