Power transmission line icing thickness real-time monitoring system based on optical fiber sensor

Abstract

The invention relates to the technical field of intelligent sensors, in particular to a power transmission line icing thickness real-time monitoring system based on an optical fiber sensor, which comprises a temperature monitoring and judging module, an image acquisition and processing module and an icing thickness calculation module, generating a temperature distribution curve along the optical fiber, and judging whether the power transmission line is iced or not; when the image acquisition and processing module senses that the power transmission line is iced, a camera carried by the unmanned aerial vehicle is adopted to acquire an iced image of the power transmission line and a normal image of the power transmission line; respectively extracting edge pixel points of the icing image of the power transmission line and the normal image of the power transmission line by adopting an image processing method; and the icing thickness calculation module adopts a least square method to fit the circle centers and the radiuses of the icing edge pixel points and the normal edge pixel points, and then calculates the icing thickness of the power transmission line.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent sensors, and in particular to a real-time monitoring system for ice thickness on power transmission lines based on optical fiber sensors. Background Art

[0002] In power transmission networks, transmission lines are key carriers of electrical energy, and their safe and stable operation is crucial to the power system. However, in harsh weather conditions such as cold and humid weather, transmission lines are prone to icing. Icing increases line loads and can cause serious accidents such as conductor swaying, line breakage, tower tilting, and even collapse, threatening power grid security and causing significant economic losses and social impacts.

[0003] In the operation and maintenance of power transmission lines, to accurately determine whether the lines are threatened by icing, existing technical approaches typically rely on fiber optic sensors to build temperature monitoring networks. Leveraging the temperature sensitivity of optical fibers, laser pulses are injected into the fibers to collect anti-Stokes and Stokes light signals generated by Raman scattering during transmission. Based on the correspondence between the intensity ratio of the two and the temperature, and in conjunction with the principle of optical time domain reflectometry (OTDR), the time delay of the optical signal is converted into spatial position information, thereby obtaining the temperature distribution at different points along the transmission line. If the temperature at a certain location is determined to be consistently below a set threshold (based on the thermodynamic conditions for ice formation, the condensation of water into ice on the conductor surface is accompanied by heat exchange, resulting in a significant drop in local temperature), then ice is inferred in that area.

[0004] After determining the presence and location of ice on the transmission line surface through temperature, the thickness of the ice is essentially the quantification of the physical size of the ice layer on the line surface (such as radial increment), while temperature changes are the indirect result of heat exchange during the ice formation process. There is no direct physical quantity conversion relationship between the two. For example, different types of ice (sleet and rime) have different densities and porosities, and even if the thickness is the same, they will have different effects on the line temperature, making it impossible to determine the ice thickness of the transmission line in one step. In view of this, we propose a real-time monitoring system for transmission line ice thickness based on fiber optic sensors. Summary of the Invention

[0005] The purpose of the present invention is to solve the problem in the prior art that after determining the presence and location of ice on a transmission line only by temperature, the ice thickness cannot be determined in one step because there is no direct physical quantity conversion relationship between the ice thickness and temperature change.

[0006] To achieve the above objectives, the present invention provides a real-time monitoring system for ice thickness on power transmission lines based on optical fiber sensors, comprising a temperature monitoring and judgment module, an image acquisition and processing module, and an ice thickness calculation module, wherein:

[0007] The temperature monitoring and judgment module uses an optical fiber sensor to monitor the temperature of the transmission line in real time, generates a temperature distribution curve along the optical fiber, and determines whether the transmission line is covered with ice; when the image acquisition and processing module senses that the transmission line is covered with ice, it uses the camera carried by the drone to collect the ice-covered image and the normal image of the transmission line; the image processing method is used to extract the edge pixel points of the ice-covered image and the normal image of the transmission line respectively; the ice thickness calculation module uses the least squares method to fit the center and radius of the ice edge pixel points and the normal edge pixel points, and then calculates the ice thickness of the transmission line.

[0008] As a further improvement of this technical solution, the temperature monitoring and judgment module monitors the temperature of the transmission line in the following steps:

[0009] S1: Fiber optic sensors use pulsed lasers to emit laser pulses into the optical fiber. During the transmission of the laser pulses in the optical fiber, the laser pulses react with the optical fiber lattice to generate backscattered light. The backscattered light includes Rayleigh scattering and Raman scattering.

[0010] S2: After the backscattered light returns through the optical fiber, two sets of fiber Bragg gratings are used to separate the Stokes light and anti-Stokes light in the Raman scattering;

[0011] S3: Based on the photoelectric effect, the separated anti-Stokes light and Stokes light are converted into light intensity, the intensity ratio of anti-Stokes light to Stokes light is calculated, and the light intensity ratio is converted into the transmission line temperature value:

[0012] The anti-Stokes light and the Stokes light separated in the temperature monitoring and judgment module are incident on a photodetector, and the photosensitive material in the photodetector absorbs the anti-Stokes light and the Stokes light to generate a photocurrent; the photocurrents generated by the anti-Stokes light and the Stokes light are measured respectively, and the photocurrent ratio is the ratio of the anti-Stokes light intensity to the Stokes light intensity;

[0013] By utilizing the intensity-temperature characteristics of Raman scattering, a simultaneous correlation expression is established to establish the corresponding relationship between the intensity ratio and temperature, thereby converting the intensity ratio into the corresponding transmission line temperature value.

[0014] As a further improvement of the present technical solution, the temperature monitoring and judgment module senses the return time of the anti-Stokes light and the Stokes light, determines the spatial position of each temperature point on the optical fiber through S4, traverses the entire length of the optical fiber, associates the temperature values ​​at different spatial positions, and generates a temperature distribution curve of the transmission line along the optical fiber.

[0015] As a further improvement of the present technical solution, the temperature monitoring and judgment module sets a temperature threshold. If the temperature T at a certain position in the transmission line is less than the temperature threshold, it is determined that ice is present on the surface of the transmission line at this position.

[0016] The beneficial effects of the above further scheme are: first, by sensing the return time of anti-Stokes light and Stokes light, combining the principle of optical time domain reflection, the spatial position of each temperature point on the optical fiber is determined, and the temperature values ​​at different positions are correlated along the entire length of the optical fiber to generate a temperature distribution curve of the transmission line along the optical fiber. This can accurately and comprehensively present the temperature conditions of various parts of the transmission line, provide a continuous and complete temperature data basis for icing monitoring, and facilitate subsequent analysis of the relationship between temperature change trends and icing; second, a temperature threshold is set, and the temperature at the transmission line location is compared with the threshold to determine whether there is icing. This method realizes rapid identification of icing based on temperature differences. It is simple, direct, and highly operational. It can promptly discover icing hazards, provide a clear basis for icing monitoring and subsequent operation and maintenance decisions on transmission lines, and effectively improve the safety assurance capability of transmission line operation. The two improvements form a complete and efficient icing monitoring link from temperature data acquisition to icing determination, enhancing the practicality and functionality of the technical solution.

[0017] On the basis of the above technical solution, the present invention can also be improved as follows.

[0018] As a further improvement of this technical solution, the two sets of fiber Bragg gratings in the temperature monitoring and judgment module separate the Stokes light from the anti-Stokes light based on the wavelength difference between the Stokes light and the anti-Stokes light:

[0019] Setting the reflection wavelength of the first group of fiber Bragg gratings to be equal to the wavelength of the anti-Stokes light, so as to reflect only the anti-Stokes light and transmit other light. Specifically, when the backscattered light is incident on the first group of fiber Bragg gratings, the anti-Stokes light satisfies the Bragg condition, so that the first group of fiber Bragg gratings only reflects the anti-Stokes light and transmits the Stokes light and other light.

[0020] The reflection wavelength of the second group of fiber Bragg gratings is set to be equal to the wavelength of the Stokes light, and only the Stokes light is reflected, while other light is transmitted. Specifically, when the Stokes light transmitted by the first group of fiber Bragg gratings and other light are incident on the second group of fiber Bragg gratings, the Stokes light meets the Bragg condition, so that the second group of fiber Bragg gratings only reflects the Stokes light and transmits the incident light.

[0021] As a further improvement of the present technical solution, the image acquisition and processing module includes an image acquisition unit and an edge pixel extraction unit;

[0022] The image acquisition unit senses the location where ice is present on the surface of the transmission line, and outputs the ice location to the drone. The camera carried by the drone captures an image of the transmission line at the ice location, and defines the captured image as an ice-covered transmission line image. The image acquisition unit also senses the location closest to the ice location where the temperature T of the transmission line is greater than or equal to a temperature threshold, and defines the location as a normal location. The image captured at the normal location is the normal image of the transmission line.

[0023] The beneficial effect of the above further scheme is that the image acquisition logic of ice cover and normal position is clarified through the image acquisition unit and the edge pixel extraction unit, the ice cover position is first located by optical fiber sensing, and the image of the ice cover is collected by the camera mounted on the drone, and the normal position is defined and the image is collected by the temperature threshold; its beneficial effect is significant, the optical fiber sensing is used to accurately locate the ice cover position, combined with the flexible image collection of the drone, the line image data under ice cover and normal state can be obtained, and real and reliable visual materials are provided for the subsequent analysis of ice cover characteristics and the distinction between different states. Moreover, the normal position is determined by temperature, and a comparison sample of ice cover and normal image can be established, which helps to identify ice cover details more accurately, make up for the deficiency of ice cover thickness being difficult to judge based on temperature alone, enrich the monitoring dimension, and improve the accuracy of parameters such as ice cover thickness based on image analysis, thus laying a solid data foundation for ice cover monitoring and thickness assessment of transmission lines.

[0024] On the basis of the above technical solution, the present invention can also be improved as follows.

[0025] As a further improvement of the present technical solution, the edge pixel point extraction unit senses the ice-covered image of the transmission line and the normal image of the transmission line, and uses an image processing method to extract edge pixel points of the ice-covered image of the transmission line and the normal image of the transmission line respectively:

[0026] Grayscale conversion: Each pixel in the power line image is assigned a different channel coefficient based on the pixel values ​​of the red, green, and blue channels. Each channel pixel value is multiplied by the channel coefficient, and then the multiplication results are added together. The sum is the grayscale value of the pixel, thus grayscale conversion is performed;

[0027] Gaussian smoothing denoising: A Gaussian kernel is used as a sliding window, moving pixel by pixel across the transmission line image. For each area covered by the window, each pixel value within the window is multiplied by the corresponding Gaussian kernel weight. All products are then added together, and the result is used as the new value of the center pixel.

[0028] Calculate the gradient magnitude and direction: Use the horizontal gradient kernel and the vertical gradient kernel to convolve each pixel in the Gaussian smoothed and denoised power line image. Align the center of the convolution kernel with the pixel point in the Gaussian smoothed and denoised power line image, and multiply each element in the convolution kernel with the pixel value at the corresponding position. Finally, add all the products to obtain the horizontal and vertical gradient components of the pixel.

[0029] Based on the horizontal and vertical gradient components, the two directional components are regarded as orthogonal components of a two-dimensional vector through the Euclidean norm. The gradient amplitude that comprehensively reflects the intensity of grayscale change is obtained by first summing the squares and then performing square root operations.

[0030] Based on the ratio of the vertical gradient component to the horizontal gradient component, the gradient direction is determined by the inverse tangent function operation;

[0031] Non-maximum suppression: For each pixel, compare the two adjacent pixels along its gradient direction: if the gradient amplitude of the pixel is greater than that of the adjacent pixels on both sides, then retain the pixel; otherwise, set the gradient amplitude of the pixel to 0;

[0032] Dual-threshold detection and edge connection: set a high threshold Thigh and a low threshold Tlow; if the gradient amplitude M(x,y) of a pixel point is greater than the high threshold Thigh, the pixel point is judged to be the pixel point corresponding to the edge pixel point of the transmission line; if the low threshold Tlow is less than the gradient amplitude M(x,y) ≤ the high threshold Thigh, the pixel point is judged to be a weak edge point. If the weak edge point is adjacent to the determined edge pixel point, the pixel point is judged to be a transmission line edge pixel point, otherwise it is judged not to be a transmission line edge pixel point; if the gradient amplitude M(x,y) ≤ the low threshold Tlow, the pixel point is judged not to be an edge pixel point.

[0033] The beneficial effect of the above further scheme is that by using image processing methods to extract edge pixel points of ice-covered images and normal images, basic data is provided for subsequent image-based analysis of parameters such as ice thickness; its beneficial effect is that by accurately extracting edge pixel points of the two images, the contour difference between ice-covered and normal lines can be clearly defined, providing a key basis for subsequent comparative analysis (such as using edge differences to calculate ice thickness). With the objectivity and accuracy of image processing technology, manual judgment errors are reduced, and ice feature identification is made more accurate, thereby improving the accuracy of ice thickness monitoring, making up for the shortcoming that ice thickness cannot be quantified by relying solely on temperature monitoring, and making the entire ice thickness monitoring system based on optical fiber sensors more perfect. From temperature positioning of ice, to image acquisition and edge extraction, the realization of ice thickness monitoring is gradually promoted, and the system's comprehensive perception and precise analysis capabilities of the ice status of transmission lines are enhanced.

[0034] On the basis of the above technical solution, the present invention can also be improved as follows.

[0035] As a further improvement of the technical solution, the ice thickness calculation module includes a circle center and radius fitting unit and an ice thickness calculation unit;

[0036] The center and radius fitting unit defines the edge pixel points of the ice-covered image of the transmission line and the normal image of the transmission line as ice-covered edge pixel points and normal edge pixel points respectively; the least squares method is used to fit the center and radius of the ice-covered edge pixel points and normal edge pixel points; the ice thickness calculation unit is used to calculate the difference between the edge pixel points when the transmission line is iced and the edge pixel points when the transmission line is normal, and determine the ice thickness on the transmission line surface.

[0037] As a further improvement of the present technical solution, the least squares method in the center and radius fitting unit first converts the geometric equation of the standard circle into an algebraic equation form, and clarifies the relationship between the parameters in the algebraic equation and the coordinates of the center and radius; the edge pixel points in the edge pixel extraction unit are perceived, the coordinates of the edge pixel points are substituted into the algebraic equation, an error function is constructed, the partial derivatives of the error function with respect to the parameters are respectively calculated, the partial derivatives are set to 0, a linear equation group is established, and the linear equation group is solved to obtain the parameters; the correspondence between the algebraic equation parameters and the center coordinates and radius is used to calculate the center coordinates and radius of the fitted circle from the parameters.

[0038] As a further improvement of the present technical solution, the ice thickness calculation unit selects multiple pixel points on the edge pixel points of the ice fitting circle, calculates the distance from each pixel point to the center of the normal fitting circle, and then calculates the average value of the distances from the multiple pixel points to the center of the normal fitting circle, and then subtracts the radius of the normal transmission line edge pixel fitting circle to obtain the ice thickness.

[0039] The beneficial effect of the above further scheme is that, through the center and radius fitting unit and the ice thickness calculation unit, a complete process from image edge pixel fitting to ice thickness calculation is constructed, which corresponds to the problem in the background technology that the existing temperature monitoring cannot accurately determine the ice thickness; its beneficial effect is that the least squares method is first used to fit the center and radius of the edge pixel points of the ice and normal images, and a quantitative model of the line contour is established, and then the thickness is determined by calculating the difference between the edge pixel points in the ice and normal states, and the image features are associated with the physical size, breaking through the limitations of temperature monitoring, and making up for the inaccurate temperature judgment of thickness due to differences in density and other factors of different ice types. With the help of the accuracy of image processing and mathematical fitting, a direct and quantitative method is provided for ice thickness monitoring of transmission lines, and the monitoring system based on optical fiber sensors is improved. The accuracy and reliability of ice thickness judgment are improved, and the safe operation of transmission lines and the threat of ice are more effectively guaranteed.

[0040] On the basis of the above technical solution, the present invention can also be improved as follows.

[0041] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is the overall module principle diagram of the present invention;

[0043] Figure 2 This is a flow chart of the working principle of separating Raman scattering using two groups of fiber Bragg gratings in the present invention;

[0044] Figure 3 This is a flow chart of the working principle of the ice thickness calculation module of the present invention.

[0045] The meaning of each number in the figure is:

[0046] 100. Temperature monitoring and judgment module; 200. Image acquisition and processing module; 210. Image acquisition unit; 220. Edge pixel extraction unit; 300. Ice thickness calculation module; 310. Circle center and radius fitting unit; 320. Ice thickness calculation unit. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] References Figure 1-Figure 3 As shown, the real-time monitoring system for ice thickness of transmission lines based on optical fiber sensors includes a temperature monitoring and judgment module 100, an image acquisition and processing module 200, and an ice thickness calculation module 300, wherein:

[0049] The temperature monitoring and judgment module 100 uses an optical fiber sensor to monitor the temperature of the transmission line in real time and generates a temperature distribution curve along the optical fiber. The steps are as follows:

[0050] S1: Fiber optic sensors use pulsed lasers to emit laser pulses into the optical fiber. During the transmission of the laser pulses in the optical fiber, they generate backscattered light with the optical fiber lattice:

[0051] Backscattered light includes Rayleigh scattering (when laser photons collide elastically with atoms or molecules in the optical fiber lattice, the energy and frequency of the photons remain unchanged, but the propagation direction changes due to the microscopic inhomogeneity of the lattice) and Raman scattering (when laser photons collide inelastically with optical fiber molecules, the photons exchange energy with the molecules, causing the frequency of the scattered light to shift);

[0052] S2: After the backscattered light returns through the optical fiber, two sets of fiber Bragg gratings are used to separate the Stokes light (photons transfer part of their energy to molecules, the scattered light frequency is lower than the incident light frequency (the wavelength becomes longer), and its intensity is basically unaffected by temperature (only related to the molecular vibration energy level)) and the anti-Stokes light (molecules transfer part of their thermal motion energy to photons, the scattered light frequency is higher than the incident light frequency (the wavelength becomes shorter), and its intensity is positively correlated with the intensity of the molecular thermal motion (i.e., temperature) - the higher the temperature, the greater the molecular thermal motion energy, and the higher the intensity of the anti-Stokes light). Specifically, the Stokes light and the anti-Stokes light are separated based on the wavelength difference between the two lights:

[0053] Since the reflection wavelength of the two sets of fiber Bragg gratings is determined by the effective refractive index n of the fiber eff and the grating period ∧, satisfying the Bragg condition: λ B =2n eff ∧, where λ B is the reflection wavelength of the fiber Bragg grating, n eff is the effective refractive index of the optical fiber, ∧ is the grating period;

[0054] Therefore, the reflection wavelength of the first group of fiber Bragg gratings is set to be equal to the anti-Stokes light wavelength λ B1 =λ AS , only anti-Stokes light is reflected and other light is transmitted. Specifically, when the backscattered light is incident on the first group of fiber Bragg gratings, the anti-Stokes light λ AS The Bragg condition is satisfied, so that the first group of fiber Bragg gratings only reflects anti-Stokes light and transmits Stokes light λ S , incident light λ0;

[0055] Set the reflection wavelength of the second set of fiber Bragg gratings to be equal to the Stokes light wavelength λ B2 =λ S , only reflects the Stokes light and transmits the incident light, specifically the Stokes light λ transmitted by the first group of fiber Bragg gratings S When the incident light λ0 is incident on the second set of fiber Bragg gratings, the Stokes light λ S The Bragg condition is satisfied, so that the second set of fiber Bragg gratings only reflects Stokes light and transmits the incident light λ0;

[0056] S3: Based on the photoelectric effect, the separated anti-Stokes light and Stokes light are converted into light intensity, and the intensity ratio of the anti-Stokes light to the Stokes light is calculated. Since the intensity of the anti-Stokes light varies with temperature, while the intensity of the Stokes light is relatively stable, the light intensity ratio is converted into the temperature value of the transmission line;

[0057] The separated anti-Stokes light λ AS and Stokes light λ S The incident light enters the photodetector and is absorbed by the photosensitive material inside the photodetector, causing the electrons in the material to gain energy and jump to the conduction band, generating a photocurrent I. The photocurrent and light intensity satisfy the following equation: I = R·P, where R is the detector responsivity, which is related to the detector material and wavelength, and P is the light power.

[0058] The intensity of the perceived anti-Stokes light is P AS , the intensity of Stokes light is P S , then the output current of the photodetector is: I AS =R AS ·P AS , I S =R S ·P S , where I AS , I S are the photocurrents corresponding to anti-Stokes light and Stokes light, R AS 、R S The photodetectors are for anti-Stokes light and Stokes light;

[0059] In Raman scattering, anti-Stokes light and Stokes light are scattered by the same fiber molecule, and the ratio of the detector's response to the two types of light is relatively stable, so it can be approximately considered that Therefore, the intensity ratio of anti-Stokes light to Stokes light is:

[0060]

[0061] Temperature characteristics of Raman scattering light intensity: Stokes light intensity decreases slightly with increasing temperature The anti-Stokes light intensity increases significantly with increasing temperature Where N is the total number of molecules involved in Raman scattering, E 振 is the molecular vibration energy level difference, k is the Boltzmann constant;

[0062] Eliminate the total number of molecules N to derive the relationship between light intensity ratio and temperature: Combined R AS 、R S The expression of , after eliminating N, is: Further sorting out the temperature calculation formula: Where N is the number of optical fiber molecules: the total number of molecules involved in Raman scattering, E 振is the vibration energy level difference of optical fiber molecules, k is the Boltzmann constant, and T is the thermodynamic temperature of the transmission line / optical fiber environment;

[0063] S4: The time delay of backscattered light is converted into spatial position through the principle of optical time domain reflection. When the laser pulse is transmitted in the optical fiber, the time it takes for the backscattered light to return to the detector is proportional to the transmission distance, following the formula: Where L is the distance from the scattering point, c is the speed of light in vacuum, t is the time delay, and n is the refractive index of the optical fiber;

[0064] S5: By measuring the return time t of anti-Stokes light and Stokes light AS , t S , determine the spatial position L of each temperature point on the optical fiber through the calculation formula in S4, traverse the entire length of the optical fiber, and associate the temperature values ​​at different spatial positions L to generate the transmission line temperature distribution curve T(L) along the optical fiber;

[0065] S6: The physical mechanism of ice formation is strongly related to temperature. When the ambient temperature is below the freezing point (the freezing point of water, usually set as a temperature threshold, such as 0°C), water vapor and precipitation in the air will cause the temperature to drop further due to heat exchange after contacting the surface of the transmission line, satisfying the thermodynamic conditions for water to condense into ice, and ice will easily form on the surface of the line. The temperature distribution of the line is monitored in real time through optical fiber sensing, and the spatial position of the temperature point is accurately located in combination with the principle of optical time domain reflection. Specifically, a temperature threshold is set. If the temperature T at a certain position in the transmission line is less than the temperature threshold, it is determined that there is ice on the surface of the transmission line at this position, realizing "early detection and early warning" of ice, which helps operation and maintenance personnel to take de-icing, reinforcement and other measures in time to avoid accidents such as line dancing, breaking, and tower collapse caused by icing, ensuring the safe and stable operation of the transmission system, and improving the reliability and resilience of the power grid under complex climatic conditions.

[0066] In order to avoid the situation where transmission lines in mountainous areas are often built between steep mountains or canyons, which are difficult for manual inspections to reach; ground monitoring equipment cannot cover the entire range of lines with large spans (such as those across rivers), resulting in missed ice detection, the image acquisition and processing module 200 includes an image acquisition unit 210 and an edge pixel point extraction unit 220;

[0067] The image acquisition unit 210 senses the location where ice is present on the surface of the transmission line, records that ice location and outputs it to the drone. The camera carried by the drone captures an image of the transmission line at the ice location and defines the captured image as an ice image of the transmission line. At the same time, considering that the transmission line is subjected to alternating loads (such as its own weight, ice weight, and wind vibration) for a long time during long-term use, the metal lattice of the conductor will creep, which will cause changes in the transmission line. Therefore, the image acquisition unit 210 also senses the location of the transmission line closest to the ice location where the temperature T is greater than or equal to the temperature threshold, and defines the location as a normal location. The image captured at the normal location is the normal image of the transmission line, so that the ice thickness calculation unit 320 can compare and analyze it with the contour of the ice image to accurately determine the ice thickness of the transmission line.

[0068] The edge pixel point extraction unit 220 senses the ice-covered image of the transmission line and the normal image of the transmission line, and uses an image processing method to extract edge pixel points of the ice-covered image of the transmission line and the normal image of the transmission line respectively:

[0069] Grayscale conversion: The power transmission line images (ice-covered images, normal images) captured by the camera technology are converted into grayscale images: Gray(i,j) = 0.299 r(i,j) + 0.587 g(i,j) + 0.114 b(i,j), where (i,j) is the coordinates (row and column indices) of the pixel in the power transmission line image (ice-covered image, normal image), r(i,j), g(i,j), and b(i,j) are the red, green, and blue channel pixel values ​​(0-255) of the power transmission line image at the pixel coordinate (i,j), and 0.299, 0.587, and 0.114 are the corresponding channel coefficients.

[0070] Gaussian smoothing denoising: through Gaussian kernel As a sliding window, it moves pixel by pixel on the transmission line image (ice-covered image, normal image). For each area covered by the window, each pixel value in the window is multiplied by the corresponding Gaussian kernel weight, and then all the products are added together to obtain the new value of the center pixel.

[0071] Calculate gradient magnitude and direction: Use horizontal gradient kernel and vertical gradient kernel Convolve each pixel in the Gaussian smoothed denoised transmission line image (ice-covered image, normal image) respectively: align the center of the convolution kernel with the pixel point in the Gaussian smoothed denoised transmission line image (ice-covered image, normal image), and multiply each element in the convolution kernel with the pixel value at the corresponding position. Finally, add all the products to obtain the gradient component G of the pixel in the horizontal and vertical directions. x and G y ;

[0072] Based on the horizontal and vertical gradient components G x and G y , the gradient amplitude is calculated by the Euclidean norm

[0073] The gradient direction θ indicates the direction in which the grayscale changes most dramatically, and is perpendicular to the edge direction (the edge is the grayscale mutation area, and the gradient direction points to the direction of the fastest change, that is, perpendicular to the edge tangent). The calculation formula is:

[0074] Non-maximum suppression: For each pixel, compare the two adjacent pixels along its gradient direction: if the gradient amplitude of the pixel is greater than that of the adjacent pixels on both sides, then retain the pixel; otherwise, set the gradient amplitude of the pixel to 0;

[0075] Dual-threshold detection and edge connection: set a high threshold Thigh and a low threshold Tlow; if the gradient amplitude M(x,y) of a pixel point is greater than the high threshold Thigh, the pixel point is judged to be the corresponding pixel point of the transmission line edge pixel point (ice-covered image, normal image); if the low threshold Tlow is less than the gradient amplitude M(x,y) ≤ the high threshold Thigh, the pixel point is judged to be a weak edge point. If the weak edge point is adjacent to the determined edge pixel point, the pixel point is judged to be a transmission line edge pixel point, otherwise it is judged not to be a transmission line edge pixel point; if the gradient amplitude M(x,y) ≤ the low threshold Tlow, the pixel point is judged not to be an edge pixel point.

[0076] The ice thickness calculation module 300 includes a circle center and radius fitting unit 310 and an ice thickness calculation unit 320. The circle center and radius fitting unit 310 defines edge pixels of an iced transmission line image and a normal transmission line image as iced edge pixels and normal edge pixels, respectively. The least squares method is used to fit the circle center and radius of the iced edge pixels and normal edge pixels.

[0077] The standard geometric equation of a circle is expressed in terms of the coordinates of the center (a, b) and the radius r: (xa) 2 +(yb) 2 =r 2 , and then convert the circle geometry equation into algebraic equation form: 2 +y 2 +Dx+Ey+F=0, where the relationship between D, E, F and the center (a, b) and radius r is: a=-D / 2; b=-E / 2;

[0078] The edge pixel point set (ice-covered edge pixel point, normal edge pixel point) in the perception edge pixel point extraction unit 220 After each pixel is substituted into the algebraic equation, the sum of square errors of all pixels substituted into the algebraic equation is minimized. Ideally, it should satisfy: But there is actually an error, and the error function is defined as: The error function represents the sum of squared deviations between all pixels and the fitted circle. The goal of the least squares method is to find D, E, and F to minimize J.

[0079] In order to solve the algebraic equation parameters that minimize the sum of squared errors, the partial derivatives of the error function J with respect to D, E, and F are calculated and set to 0, thus establishing a linear equation system:

[0080]

[0081] Then, the linear equations are solved by matrix inversion or Gaussian elimination to obtain the parameters D, E, and F;

[0082] After obtaining the parameters D, E, F of the algebraic equation, we can calculate the value of the equation according to a=-D / 2、b=-E / 2、 Calculate the center coordinates and radius of the circle.

[0083] The ice thickness calculation unit 320 senses the center and radius of ice edge pixels and normal edge pixels, and calculates the difference between the edge pixels when the transmission line is iced and the edge pixels when the transmission line is normal, thereby determining the ice thickness on the transmission line surface:

[0084] The perceptually normal fitting circle is: Where (x n0 ,y n0 ) The center of the normal fitting circle, r n is the radius of the normal fitting circle; the ice fitting circle is: Where (x i0 ,y i0 ) The center of the ice-covered fitting circle, r i The radius of the circle fitted for ice cover;

[0085] Select multiple pixel points N on the edge of the ice fitting circle, and calculate the distance from each pixel point to the center of the normal fitting circle (x i0 ,y i0 ), and then calculate the average distance from multiple pixel points N to the center of the normal fitting circle, and then subtract the radius r of the fitting circle of the normal transmission line edge pixel point n , which is the ice thickness where d j is the distance from the jth pixel of the ice fitting circle to the center of the normal fitting circle;

[0086] Because the least squares method fits the center and radius of the circle of the ice edge pixel points and the normal edge pixel points to accurately describe the line contour, the radius difference is directly related to the physical change of ice thickening, and multi-pixel statistics can eliminate interference such as local edge noise and uneven ice coverage (such as local bulges or depressions in the ice cover). It not only relies on basic geometric relationships to achieve thickness quantification, but also reduces errors through statistical averaging, ensuring accurate and stable thickness calculation in complex icing scenarios, providing reliable data for transmission line icing monitoring, supporting operation and maintenance decisions, and improving the accuracy of line safety assessment and icing warning. It effectively avoids misjudgment caused by single pixel deviation or abnormal local ice morphology, and ensures the robustness and accuracy of ice thickness calculation.

[0087] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A real-time monitoring system for ice thickness on power transmission lines based on optical fiber sensors, characterized in that: It comprises a temperature monitoring and judging module (100), an image acquisition and processing module (200) and an ice thickness calculation module (300), wherein: The temperature monitoring and judgment module (100) uses an optical fiber sensor to monitor the temperature of the transmission line in real time, generates a temperature distribution curve along the optical fiber, and judges whether the transmission line is covered with ice; when the image acquisition and processing module (200) senses that the transmission line is covered with ice, it uses a camera carried by a drone to collect an iced image of the transmission line and a normal image of the transmission line; uses an image processing method to extract edge pixel points of the iced image of the transmission line and the normal image of the transmission line; and the ice thickness calculation module (300) uses a least squares method to fit the center and radius of the iced edge pixel points and the normal edge pixel points, and then calculates the ice thickness of the transmission line.

2. The optical fiber sensor-based real-time monitoring system for ice thickness on power transmission lines according to claim 1 is characterized in that: The temperature monitoring and judgment module (100) monitors the temperature of the power transmission line in the following steps: S1: Fiber optic sensors use pulsed lasers to emit laser pulses into the optical fiber. During the transmission of the laser pulses in the optical fiber, the laser pulses react with the optical fiber lattice to generate backscattered light. The backscattered light includes Rayleigh scattering and Raman scattering. S2: After the backscattered light returns through the optical fiber, two sets of fiber Bragg gratings are used to separate the Stokes light and anti-Stokes light in the Raman scattering; S3: Based on the photoelectric effect, the separated anti-Stokes light and Stokes light are converted into light intensity, the intensity ratio of anti-Stokes light to Stokes light is calculated, and the light intensity ratio is converted into the transmission line temperature value: The anti-Stokes light and the Stokes light separated in the temperature monitoring and judgment module (100) are incident on a photodetector, and the photosensitive material in the photodetector absorbs the anti-Stokes light and the Stokes light to generate a photocurrent; the photocurrents generated by the anti-Stokes light and the Stokes light are measured respectively, and the photocurrent ratio is the ratio of the anti-Stokes light intensity to the Stokes light intensity; By utilizing the intensity-temperature characteristics of Raman scattering, a simultaneous correlation expression is established to establish the corresponding relationship between the intensity ratio and temperature, thereby converting the intensity ratio into the corresponding transmission line temperature value.

3. The optical fiber sensor-based real-time monitoring system for ice thickness on power transmission lines according to claim 2 is characterized in that: The temperature monitoring and judgment module (100) senses the return time of anti-Stokes light and Stokes light, determines the spatial position of each temperature point on the optical fiber through S4, traverses the entire length of the optical fiber, associates the temperature values ​​at different spatial positions, and generates a temperature distribution curve of the power transmission line along the optical fiber.

4. The optical fiber sensor-based real-time monitoring system for ice thickness on power transmission lines according to claim 2 is characterized in that: The temperature monitoring and judging module (100) sets a temperature threshold, and if the temperature T at a certain position in the power transmission line is less than the temperature threshold, it is determined that ice is present on the surface of the power transmission line at this position.

5. The optical fiber sensor-based real-time monitoring system for ice thickness on power transmission lines according to claim 2 is characterized in that: The two groups of fiber Bragg gratings in the temperature monitoring and judgment module (100) separate the Stokes light from the anti-Stokes light according to the wavelength difference between the Stokes light and the anti-Stokes light: Setting the reflection wavelength of the first group of fiber Bragg gratings to be equal to the wavelength of the anti-Stokes light, so as to reflect only the anti-Stokes light and transmit other light. Specifically, when the backscattered light is incident on the first group of fiber Bragg gratings, the anti-Stokes light satisfies the Bragg condition, so that the first group of fiber Bragg gratings only reflects the anti-Stokes light and transmits the Stokes light and other light. The reflection wavelength of the second group of fiber Bragg gratings is set to be equal to the wavelength of the Stokes light, and only the Stokes light is reflected, while other light is transmitted. Specifically, when the Stokes light transmitted by the first group of fiber Bragg gratings and other light are incident on the second group of fiber Bragg gratings, the Stokes light meets the Bragg condition, so that the second group of fiber Bragg gratings only reflects the Stokes light and transmits the incident light.

6. The optical fiber sensor-based real-time monitoring system for ice thickness on power transmission lines according to claim 3 is characterized by: The image acquisition and processing module (200) comprises an image acquisition unit (210) and an edge pixel point extraction unit (220); The image acquisition unit (210) senses a location where ice is present on the surface of the transmission line, outputs the ice location to a drone, and uses a camera carried by the drone to acquire an image of the transmission line at the ice location, defining the acquired image as an ice-covered transmission line image. The image acquisition unit (210) also senses a location closest to the ice location where the temperature of the transmission line is greater than or equal to a temperature threshold, defines the location as a normal location, and the image acquired at the normal location is defined as a normal image of the transmission line.

7. The optical fiber sensor-based real-time monitoring system for ice thickness on power transmission lines according to claim 6 is characterized in that: The edge pixel point extraction unit (220) senses the ice-covered image of the transmission line and the normal image of the transmission line, and respectively extracts edge pixel points of the ice-covered image of the transmission line and the normal image of the transmission line using an image processing method: Grayscale conversion: Each pixel in the power line image is assigned a different channel coefficient based on the pixel values ​​of the red, green, and blue channels. Each channel pixel value is multiplied by the channel coefficient, and then the multiplication results are added together. The sum is the grayscale value of the pixel, thus grayscale conversion is performed; Gaussian smoothing denoising: A Gaussian kernel is used as a sliding window, moving pixel by pixel across the transmission line image. For each area covered by the window, each pixel value within the window is multiplied by the corresponding Gaussian kernel weight. All products are then added together, and the result is used as the new value of the center pixel. Calculate the gradient magnitude and direction: Use the horizontal gradient kernel and the vertical gradient kernel to convolve each pixel in the Gaussian smoothed and denoised power line image. Align the center of the convolution kernel with the pixel point in the Gaussian smoothed and denoised power line image, and multiply each element in the convolution kernel with the pixel value at the corresponding position. Finally, add all the products to obtain the horizontal and vertical gradient components of the pixel. Based on the horizontal and vertical gradient components, the two directional components are regarded as orthogonal components of a two-dimensional vector through the Euclidean norm. The gradient amplitude that comprehensively reflects the intensity of grayscale change is obtained by first summing the squares and then performing square root operations. The gradient direction is determined by the inverse tangent function operation based on the ratio of the vertical gradient component to the horizontal gradient component. Non-maximum suppression: For each pixel, compare the two adjacent pixels along its gradient direction: if the gradient amplitude of the pixel is greater than that of the adjacent pixels on both sides, then retain the pixel; otherwise, set the gradient amplitude of the pixel to 0; Dual-threshold detection and edge connection: set a high threshold Thigh and a low threshold Tlow; if the gradient amplitude M(x,y) of a pixel point is greater than the high threshold Thigh, the pixel point is judged to be the pixel point corresponding to the edge pixel point of the transmission line; if the low threshold Tlow is less than the gradient amplitude M(x,y) ≤ the high threshold Thigh, the pixel point is judged to be a weak edge point. If the weak edge point is adjacent to the determined edge pixel point, the pixel point is judged to be a transmission line edge pixel point, otherwise it is judged not to be a transmission line edge pixel point; if the gradient amplitude M(x,y) ≤ the low threshold Tlow, the pixel point is judged not to be an edge pixel point.

8. The optical fiber sensor-based real-time monitoring system for ice thickness on power transmission lines according to claim 7 is characterized in that: The ice thickness calculation module (300) comprises a circle center and radius fitting unit (310) and an ice thickness calculation unit (320); The circle center and radius fitting unit (310) defines edge pixel points of an iced transmission line image and a normal transmission line image as iced edge pixel points and normal edge pixel points, respectively; the circle center and radius of the iced edge pixel points and the normal edge pixel points are fitted using the least square method; and the ice thickness calculation unit (320) is used to calculate the difference between the edge pixel points when the transmission line is iced and the edge pixel points when the transmission line is normal, and determine the ice thickness on the transmission line surface.

9. The optical fiber sensor-based real-time monitoring system for ice thickness on power transmission lines according to claim 8, characterized in that: The least square method in the center and radius fitting unit (310) first converts the geometric equation of the standard circle into an algebraic equation form, and clarifies the relationship between the parameters in the algebraic equation and the coordinates of the center and the radius; the edge pixel points in the edge pixel point extraction unit (220) are sensed, the coordinates of the edge pixel points are substituted into the algebraic equation, an error function is constructed, the partial derivatives of the error function with respect to the parameters are respectively calculated, the partial derivatives are set to 0, a linear equation group is established, and the parameters are obtained by solving the linear equation group; the correspondence between the algebraic equation parameters and the coordinates of the center and the radius is used to calculate the coordinates of the center and the radius of the fitted circle from the parameters.

10. The optical fiber sensor-based real-time monitoring system for ice thickness on power transmission lines according to claim 9, characterized in that: The ice thickness calculation unit (320) selects a plurality of pixel points on the edge pixel points of the ice fitting circle, calculates the distance from each pixel point to the center of the normal fitting circle, and then calculates the average value of the distances from the plurality of pixel points to the center of the normal fitting circle, and then subtracts the radius of the normal transmission line edge pixel point fitting circle to obtain the ice thickness.

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