Transmission conductor galloping displacement abnormity monitoring method based on Beidou RTK

Abstract

The invention discloses a transmission conductor galloping displacement abnormity monitoring method based on Beidou RTK, relates to the technical field of conductor displacement monitoring, solves the technical problems of low reliability of galloping parameter calculation and abnormity judgment and lack of continuous over-limit trend prediction capability, and provides a transmission conductor galloping displacement abnormity monitoring method based on Beidou RTK by realizing accurate coordinate system conversion and eliminating projection deformation influence. Two form reconstruction methods are provided, flexible selection can be performed according to requirements, meanwhile, environmental parameters such as real-time icing and wind speed are fused into a three-dimensional kinetic equation, the goodness of fit between a model and an actual galloping state is improved, an LSTM-ARIMA mixed time sequence model is constructed for a continuous overrun scene, and the abnormal trend is predicted in combination with meteorological data in the future 72 hours, so that the prediction accuracy is improved. And meanwhile, a three-dimensional composite risk index is introduced, three types of trends of slow remission, stable maintenance and rapid deterioration can be pre-judged in advance, and operation and maintenance personnel can deploy resources in advance according to the trends, so that the conversion from passive alarm to active prevention and control is realized, and the fault response time is shortened.

Description

Technical Field

[0001] This invention relates to the field of conductor displacement monitoring technology, specifically a method for monitoring abnormal galloping displacement of transmission conductors based on BeiDou RTK. Background Technology

[0002] Conductor galloping is a common and highly dangerous phenomenon during the operation of power transmission lines. Conductor galloping refers to a low-frequency, large-amplitude self-excited vibration of a non-circular cross-section conductor caused by wind. Due to its large amplitude, oscillation, and long duration, conductor galloping can easily cause phase-to-phase flashovers, hardware damage, line tripping and power outages, or serious accidents such as conductor burns, tower collapses, and conductor breakage. It poses a significant threat to the operational safety of transmission lines and results in substantial economic losses.

[0003] Currently, the monitoring of conductor galloping displacement mainly involves the following methods and problems: Monitoring methods based on video surveillance involve installing cameras along power transmission lines to observe conductor galloping. However, this method is greatly affected by weather and environmental factors. In severe weather conditions, such as heavy rain, fog, or snow, the clarity of the video images deteriorates significantly, sometimes making it impossible to see the conductor's condition, leading to monitoring failure. Moreover, video surveillance can only provide a visual observation of conductor galloping; it is difficult to accurately measure the amount of displacement from the video images, and it is even more difficult for video surveillance to capture minute changes in displacement.

[0004] Traditional sensor monitoring methods utilize accelerometers, angle sensors, and other conventional sensors to monitor conductor galloping. While these sensors can acquire some parameters related to conductor galloping, such as acceleration and angle, their measurements are often relative, leading to data divergence over time and a gradual decrease in monitoring accuracy. Furthermore, these traditional sensors typically only measure one or a few parameters individually, failing to comprehensively and accurately obtain information on the displacement of the conductor galloping, making it difficult to accurately assess the overall situation of conductor galloping. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for monitoring abnormal galloping displacement of transmission lines based on BeiDou RTK, which solves the problems of low reliability in galloping parameter calculation and anomaly judgment, and lack of ability to predict continuous over-limit trends.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for monitoring abnormal displacement of transmission line galloping based on BeiDou RTK, which specifically includes the following steps: Step 1: Receive BeiDou satellite signals to obtain the precise position information of the smart spacer, use sensors to collect acceleration and displacement data of the conductor, simultaneously collect micro-meteorological data, and filter the collected data to obtain pre-processed data. Step 2: Based on the obtained preprocessed data, establish a three-dimensional model using the reference station of the transmission tower as the coordinate origin and a right-handed Cartesian coordinate system. Based on the finite element method and d'Alembert's principle, establish the three-dimensional dynamic equations of conductor galloping to obtain the three-dimensional dynamic equations. Step 3: Based on the established three-dimensional model and three-dimensional dynamic equations, and combined with various data collected in real time, the dynamic equations are solved by numerical calculation methods to obtain the three-dimensional model analysis results of the conductor at different times, and compared with the preset threshold to generate abnormal information. Step 4: Based on the anomaly information, collect images of the conductor in the corresponding galloping area. By comparing and analyzing multiple consecutive frames of images, obtain the corresponding conductor morphology information. Compare the conductor morphology information and the anomaly information and determine the anomaly level to generate anomaly level information.

[0007] As a further aspect of the present invention, the three-dimensional model is established as follows: Real-time coordinate data of the conductor in the horizontal X, vertical Y, and horizontal Z axes are collected. Wind speed, wind direction, ice thickness, and temperature environmental parameters are collected simultaneously. A right-handed Cartesian coordinate system is established with the base station of the transmission tower as the coordinate origin. The original coordinate data collected by BeiDou RTK is transformed. First, the geodetic coordinate system is transformed to the engineering plane coordinate system through the seven-parameter Bursa model. Then, the coordinates of the mobile station are transformed to the right-hand Cartesian coordinate system through the relative positional relationship between the base station and the mobile station. The transmission line is divided into several equal-length micro-segments. The midpoint of each micro-segment is used as a node. Based on the node coordinates collected by BeiDou RTK, the three-dimensional spatial morphology of the transmission line is reconstructed by B-spline curve fitting or finite element discretization method to generate a three-dimensional model.

[0008] As a further aspect of the present invention, the three-dimensional dynamic equations are obtained as follows: Based on the finite element method and d'Alembert's principle, a three-dimensional dynamic equation for conductor galloping is established. Where M, C, and K are the mass matrix, damping matrix, and stiffness matrix of the conductor, respectively. , and These are the acceleration, velocity, and displacement vectors of the conductor, respectively. This is the external load vector.

[0009] As a further aspect of the present invention, the three-dimensional model analysis results include gobling displacement, velocity, and acceleration, wherein the gobling displacement is calculated as follows: Using the initial reference coordinates P0(x0, y0, z0) of the conductor node under no galloping and no wind load, the real-time coordinates P of a certain node in the model at time t are obtained. t (x) t y t , z t Then the displacement components of the node in the X, Y, and Z directions are: , , According to the formula The resultant displacement in three-dimensional space was calculated. ; The velocity is calculated based on the time-series displacement data collected by BeiDou RTK, using the central difference method to calculate the instantaneous velocity, according to the formula... The instantaneous velocity v was calculated. t ,in , They are respectively and The resultant displacement at time , The sampling time interval; Simultaneously based on the three-dimensional dynamic equations The velocity matrix is ​​solved directly using the Newmark-β method. The velocity components of each axis are obtained. , , And according to the formula Calculate the resultant velocity v, and calculate the instantaneous velocity v. t The average value of the resultant velocity v p And use it as the standard; The acceleration is calculated by using the central difference method again based on the known velocity time-series data, according to the formula... The acceleration a was calculated. t .

[0010] As a further aspect of the present invention, the method for generating abnormal information is as follows: If any of the calculated galloping displacement, velocity, or acceleration parameters exceeds a preset threshold, the galloping displacement of the conductor is determined to be abnormal, and abnormal information is generated. The abnormal information includes the conductor number, abnormal parameter type, abnormal occurrence time, and specific value. At the same time, the system automatically generates an electronic map marker containing the three-dimensional coordinates of the abnormal location and alerts the maintenance personnel in the form of an audible and visual alarm on the monitoring terminal. If all parameters do not exceed the preset threshold, the conductor galloping is determined to be in a normal state, and the system continues to monitor and collect data in real time.

[0011] As a further aspect of the present invention, the method for obtaining conductor morphology information is as follows: Based on the obtained anomaly information, images of the conductor corresponding to the galloping area are acquired, and the obtained conductor images are processed by image enhancement and feature extraction to obtain preprocessed images. The image enhancement operations include histogram equalization, contrast stretching, and sharpening. Feature extraction is carried out by using an edge detection algorithm to accurately identify the edge contour of the conductor. At the same time, image segmentation technology is used to separate the conductor area from the background. The preprocessed images are periodically monitored with time T as the period. Through comparative analysis of multiple consecutive frames of images, the corresponding conductor morphology information is obtained. The conductor morphology information specifically includes the degree of bending, the amplitude of galloping, and the icing condition of the conductor.

[0012] As a further aspect of the present invention, the specific method for generating anomaly level information is as follows: Compare the conductor morphology information with the anomaly information. If the conductor morphology information matches the anomaly information, further confirm the abnormality of the conductor galloping displacement. Determine the anomaly level based on the anomaly information, and the anomaly level determination method is as follows: If the displacement amplitude reaches 80%-100% of the dynamic threshold, and there are no extreme environments such as icing or strong winds, and the conductor does not touch the safe distance, it is considered a general anomaly. If the displacement amplitude exceeds the dynamic threshold, or the galloping frequency is greater than 1.5 Hz, accompanied by 5-10 mm of icing / 10-15 m / s wind speed, and the conductor is close to the safe distance, it is considered a moderate anomaly. If the displacement amplitude exceeds 10% of the safe distance, or the peak acceleration is greater than 5 m / s², accompanied by ≥10 mm of icing / ≥15 m / s gusts, the conductor has shown signs of local vibration fatigue, which is a serious anomaly, and anomaly level information is generated.

[0013] As a further aspect of the present invention, for cases where the same parameter exceeds the limit multiple times consecutively, and the exceeding of the limit means that the same dynamic parameter exceeds the limit ≥ 3 times within a set monitoring period, or remains in an exceeding state for 2 consecutive hours, a time series model is established to predict the development trend of conductor galloping anomalies. Based on the prediction results, the anomaly development trend is divided into three levels, and the specific division method is as follows: If the parameter is predicted to fall back to the safe threshold within 24 hours and the composite risk index is <0.3, the corresponding trend is slow easing. If the parameter is predicted to continue to exceed the limit within 48 hours but without significant deterioration and the composite risk index is 0.3-0.6, the corresponding trend is stable maintenance. If the parameter exceeds the limit by more than 20% within 72 hours and the composite risk index is >0.6, the corresponding trend is rapid deterioration, and corresponding trend prediction information is generated.

[0014] As a further aspect of the present invention, the composite risk index is calculated as follows: The composite risk index comprehensively analyzes three dimensions: parameter exceedance risk, environmentally induced risk, and line vulnerability risk. Parameter exceedance risk is analyzed based on the exceedance rates of displacement amplitude, galloping frequency, and peak acceleration. Environmentally induced risk is analyzed based on the influence coefficients of icing thickness and wind speed. Line vulnerability risk is analyzed based on the line load factor and fitting aging coefficient. To address the characteristics of different indicators, a linear normalization method is used to eliminate dimensional differences, transforming the original data into standardized 0-1 values. The linear normalization formula is as follows: The standardized value is calculated. , where x i x is the original value. max The limit value is the indicator limit; values ​​exceeding the limit are calculated as 1. According to standardized values The risk value r of each indicator is calculated using a piecewise function. i Highlighting the sensitivity of high-risk areas: when ≤0.8, r i =0.5 When 0.8 < ≤1.0, r i =2 -1.1, the risk value r of each indicator i With corresponding weight w i Multiply, according to the formula R= The summation yields the composite risk index, where 7 represents the number of corresponding indicators.

[0015] This invention provides a method for monitoring abnormal galloping displacement of power transmission lines based on BeiDou RTK. Compared with existing technologies, it has the following advantages: This invention achieves time synchronization of BeiDou RTK location data, acceleration data, and micro-meteorological data by employing a BeiDou time synchronization module, ensuring spatiotemporal consistency of the data. At the same time, it introduces a Kalman filter algorithm to replace the traditional simple filter, which can effectively eliminate signal obstruction and sensor noise interference, reduce preprocessing data errors, and provide a high-quality data foundation for subsequent modeling and analysis.

[0016] This invention achieves precise coordinate system transformation, eliminates the influence of projection deformation, and provides two morphological reconstruction methods: B-spline curve fitting and finite element discretization, which can be flexibly selected according to needs. At the same time, it integrates real-time environmental parameters such as icing and wind speed into the three-dimensional dynamic equations, improving the consistency between the model and the actual galloping state, and solving the defect of existing models being out of touch with the working conditions.

[0017] This invention employs a central difference method combined with a Newmark-β method for velocity calculation, and combines explicit solution of the dynamic equation with numerical differential verification for acceleration calculation to reduce parameter calculation errors. Anomaly detection incorporates conductor image feature verification and combines displacement, frequency, acceleration, and environmental parameters to achieve a three-level graded early warning system, avoiding misjudgment problems caused by single threshold determination and improving the accuracy of anomaly detection.

[0018] This invention addresses continuous over-limit scenarios by constructing an LSTM-ARIMA hybrid time series model. It combines meteorological data for the next 72 hours to predict abnormal trends and introduces a three-dimensional composite risk index. This index can predict three types of trends: slow mitigation, stable maintenance, and rapid deterioration. Based on this, maintenance personnel can allocate resources in advance, realizing a shift from passive alarm to proactive prevention and control, shortening fault response time, and reducing the risk of line outages. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the steps of the method for monitoring abnormal conductor galloping displacement according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] First Embodiment Please see Figure 1 This application provides a method for monitoring abnormal galloping displacement of transmission lines based on BeiDou RTK, which specifically includes the following steps: Step 1: Receive BeiDou satellite signals in real time through the BeiDou RTK positioning module to obtain the precise position information of the smart spacer. Accelerometer and displacement sensor collect acceleration and displacement data of the conductor respectively, and simultaneously collect micro-meteorological data. Filter and denoise the collected data to obtain preprocessed data. The filtering operation is performed using the Kalman filter algorithm to remove noise from the data.

[0022] Step 2: Based on the obtained preprocessed data, a 3D model of the conductor galloping is established, and the specific establishment method is as follows: Real-time coordinate data of the transmission line along three axes (X-axis, Y-axis, and Z-axis) are collected. Simultaneously, environmental parameters such as wind speed, wind direction, icing thickness, and temperature are also collected. A right-handed Cartesian coordinate system is established with the transmission tower's base station as the origin. The X-axis represents the horizontal direction along the transmission line, the Y-axis represents the vertical direction opposite to gravity, and the Z-axis represents the horizontal direction perpendicular to the X and Y axes. The raw coordinate data collected by BeiDou RTK is transformed. First, a seven-parameter Bursa model is used to convert the geodetic coordinate system to the engineering plane coordinate system, eliminating the influence of projection distortion. Then, based on the relative positions of the base station and the rover station, the rover station coordinates are transformed to the aforementioned custom Cartesian coordinate system. Next, the transmission line is divided into several equal-length micro-segment units. Taking the midpoint of each micro-segment as a node, the node coordinates are collected by Beidou RTK. The three-dimensional spatial shape of the line is reconstructed using B-spline curve fitting or finite element discretization. Specifically, if the focus is on the smooth visualization of the overall spatial shape of the line, a third-order B-spline curve fitting method is used. The curve curvature is optimized by adjusting the node weights, while ensuring the continuity of the first and second derivatives of the curve, thus restoring the smooth trajectory of the line's movement. If dynamic simulation analysis is required, a finite element discretization method is used, and each micro-segment is equivalent to a spatial beam element, giving the line real material properties. The mechanical properties and three-dimensional geometric shape of the line are reconstructed through the coupled calculation of the element stiffness matrix and the node coordinates. Simultaneously, based on the finite element method and d'Alembert's principle, a three-dimensional dynamic equation for conductor galloping was established. Where M, C, and K are the mass matrix, damping matrix, and stiffness matrix of the conductor, respectively. , and These are the acceleration, velocity, and displacement vectors of the conductor, respectively. This represents the vector of external loads such as wind load, icing load, and tension.

[0023] Step 3: Based on the established 3D model and 3D dynamic equations, the conductor galloping state is monitored and analyzed in real time. Using the 3D model and 3D dynamic equations, combined with various real-time collected data, the dynamic equations are solved using numerical calculation methods to obtain the key parameters of the conductor's galloping displacement, velocity, and acceleration at different times. The specific calculation method is as follows: For the calculation of galloping displacement, the initial reference coordinates P0(x0, y0, z0) of the conductor node under no galloping and no wind load are used as the reference coordinates. The real-time coordinates P of a certain node in the model at time t are then obtained. t (x) t y t , z t Then the displacement components of the node in the X, Y, and Z directions are: , , According to the formula The resultant displacement in three-dimensional space was calculated. ; For velocity calculation, based on the time-series displacement data acquired by BeiDou RTK, the instantaneous velocity is calculated using the central difference method, according to the formula... The instantaneous velocity v was calculated. t ,in , They are respectively and The resultant displacement at time , The sampling time interval; Simultaneously based on the three-dimensional dynamic equations The velocity matrix is ​​solved directly using the Newmark-β method. The velocity components of each axis are obtained. , , And according to the formula Calculate the resultant velocity v, and calculate the instantaneous velocity v. t The average value of the resultant velocity v p And use it as the standard; For acceleration calculation, based on the known velocity time-series data, the central difference method is used again to calculate the acceleration according to the formula. The acceleration a was calculated. t ; Next, the calculated 3D model analysis results are compared with preset thresholds. The 3D model analysis results include galloping displacement, velocity, and acceleration parameters. If any of the calculated galloping displacement, velocity, or acceleration parameters exceeds the preset threshold, it is determined that the galloping displacement of the conductor is abnormal, and abnormal information is generated. The abnormal information includes the conductor number, abnormal parameter type, abnormal occurrence time, and specific value. At the same time, the system automatically generates an electronic map marker containing the 3D coordinates of the abnormal location and alerts the operation and maintenance personnel in the form of an audible and visual alarm on the monitoring terminal. If all parameters do not exceed the preset threshold, the conductor galloping is determined to be in a normal state, and the system continues to monitor and collect data in real time.

[0024] Step 4: Based on the obtained anomaly information, acquire images of the conductor corresponding to the galloping area, and perform image enhancement and feature extraction processing on the obtained conductor images to obtain preprocessed images. Image enhancement operations include histogram equalization, contrast stretching, and sharpening to improve image clarity and contrast, facilitating subsequent feature extraction. Feature extraction uses edge detection algorithms, such as Canny edge detection, to accurately identify the conductor's edge contour. Simultaneously, image segmentation techniques, such as threshold-based segmentation, are used to separate the conductor region from the background to obtain precise conductor morphology information. The preprocessed images are periodically monitored at a time interval of T. Through comparative analysis of multiple consecutive frames, the corresponding conductor morphology information is obtained. This conductor morphology information specifically includes the conductor's bending degree, galloping amplitude, and icing condition. The conductor morphology information is compared with the anomaly information. If the conductor morphology information matches the anomaly information, the abnormality of the conductor's galloping displacement is further confirmed. Anomaly level determination is performed on the anomaly information, and the anomaly level determination method is as follows: If the displacement amplitude reaches 80%-100% of the dynamic threshold, and there are no extreme environments such as icing or strong winds, and the conductor does not touch the safe distance, it is considered a general anomaly. If the displacement amplitude exceeds the dynamic threshold, or the galloping frequency is greater than 1.5 Hz, accompanied by 5-10 mm of icing / 10-15 m / s wind speed, and the conductor is close to the safe distance, it is considered a moderate anomaly. If the displacement amplitude exceeds 10% of the safe distance, or the peak acceleration is greater than 5 m / s², accompanied by ≥10 mm of icing / ≥15 m / s gusts, the conductor has shown local vibration fatigue, which is a serious anomaly, and anomaly level information is generated at the same time. If there is a discrepancy between the conductor morphology information and the anomaly information, the anomaly determination process should be reviewed again. Errors or omissions should be checked in the data acquisition, processing, and analysis stages. The BeiDou RTK positioning module, accelerometer, displacement sensor, and micro-meteorological data acquisition equipment should be calibrated and checked to ensure the accuracy of data acquisition. The filtering and denoising algorithms, 3D model establishment methods, 3D dynamic equation solving process, and anomaly level determination criteria should be reviewed to optimize algorithm and model parameters and improve analysis accuracy. After confirming that the data acquisition and analysis process is error-free, the conductor galloping state should be reassessed by combining the conductor morphology information and the anomaly information, and a new anomaly report should be generated or the conductor galloping should be confirmed to be in a normal state.

[0025] Second Embodiment As a second embodiment of the present invention, it is implemented based on the first embodiment, and the difference from the first embodiment is as follows: For cases where the same parameter exceeds the limit multiple times consecutively, and the exceedance indicates that the cumulative number of times the same dynamic parameter exceeds the limit is ≥3 times within the set monitoring period, or that it is continuously in an exceedance state for 2 consecutive hours, trend analysis is required. The development trend of conductor galloping anomaly is predicted by establishing a time series model. The time series data of the parameter that is currently continuously exceeding the limit and the meteorological forecast data for the next 72 hours are input. The LSTM network is used to capture the complex nonlinear relationship between the galloping parameter and multiple environmental covariates. Based on the prediction results, the anomaly development trend is divided into three levels. If the parameter is predicted to fall back to the safe threshold within 24 hours and the composite risk index is <0.3, the corresponding trend is slow easing. If the parameter is predicted to continue to exceed the limit within 48 hours but without significant deterioration and the composite risk index is 0.3-0.6, the corresponding trend is stable maintenance. If the parameter exceeds the limit by more than 20% within 72 hours and the composite risk index is >0.6, the corresponding trend is rapid deterioration. At the same time, the corresponding trend prediction information is generated. The composite risk index comprehensively analyzes three dimensions: parameter exceedance risk, environmentally induced risk, and line vulnerability risk. Parameter exceedance risk is analyzed based on the exceedance rates of displacement amplitude, galloping frequency, and peak acceleration. Environmentally induced risk is analyzed based on the influence coefficients of icing thickness and wind speed. Line vulnerability risk is analyzed based on the line load factor and fitting aging coefficient. To address the characteristics of different indicators, a linear normalization method is used to eliminate dimensional differences, transforming the original data into standardized 0-1 values. The linear normalization formula is as follows: The standardized value is calculated. , where x i x is the original value. max is the limit value of the indicator, and values ​​exceeding the limit are calculated as 1, where i represents the type of indicator; According to standardized values The risk value r of each indicator is calculated using a piecewise function. i Highlighting the sensitivity of high-risk areas: when ≤0.8, r i =0.5 When 0.8 < ≤1.0, r i =2 -1.1, the risk value r of each indicator i With corresponding weight w i Multiply, according to the formula R= The summation yields the composite risk index, where 7 represents the number of corresponding indicators.

[0026] Third Embodiment As a third embodiment of the present invention, the focus is on combining the implementation processes of the first and second embodiments.

[0027] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0028] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for monitoring abnormal displacement of transmission line galloping based on BeiDou RTK, characterized in that, The method specifically includes the following steps: Step 1: Receive BeiDou satellite signals to obtain the precise position information of the smart spacer, use sensors to collect acceleration and displacement data of the conductor, simultaneously collect micro-meteorological data, and filter the collected data to obtain pre-processed data. Step 2: Based on the obtained preprocessed data, a right-handed Cartesian coordinate system is established with the reference station of the transmission tower as the coordinate origin to obtain a three-dimensional model. Based on the finite element method and d'Alembert's principle, a three-dimensional dynamic equation for conductor galloping is established to obtain the three-dimensional dynamic equation. Step 3: Based on the established three-dimensional model and three-dimensional dynamic equations, and combined with various data collected in real time, the three-dimensional dynamic equations are solved by numerical calculation methods to obtain the three-dimensional model analysis results of the conductor at different times, and compared with the preset threshold to generate abnormal information. Step 4: Based on the anomaly information, collect images of the conductor in the corresponding galloping area. By comparing and analyzing multiple consecutive frames of images, obtain the corresponding conductor morphology information. Compare the conductor morphology information and the anomaly information and determine the anomaly level to generate anomaly level information.

2. The method for monitoring abnormal galloping displacement of transmission lines based on BeiDou RTK according to claim 1, characterized in that, The method for creating a 3D model is as follows: Real-time coordinate data of the conductor in the horizontal X, vertical Y, and horizontal Z axes are collected. Wind speed, wind direction, ice thickness, and temperature environmental parameters are collected simultaneously. A right-handed Cartesian coordinate system is established with the base station of the transmission tower as the coordinate origin. The original coordinate data collected by BeiDou RTK is transformed. First, the geodetic coordinate system is transformed to the engineering plane coordinate system through the seven-parameter Bursa model. Then, the coordinates of the mobile station are transformed to the right-hand Cartesian coordinate system through the relative positional relationship between the base station and the mobile station. The transmission line is divided into several equal-length micro-segments. The midpoint of each micro-segment is taken as a node. Based on the node coordinates collected by BeiDou RTK, the three-dimensional spatial morphology of the transmission line is reconstructed by B-spline curve fitting or finite element discretization method to obtain a three-dimensional model.

3. The method for monitoring abnormal galloping displacement of transmission lines based on BeiDou RTK according to claim 1, characterized in that, The three-dimensional dynamic equations are obtained as follows: Based on the finite element method and d'Alembert's principle, a three-dimensional dynamic equation for conductor galloping is established. Where M, C, and K are the mass matrix, damping matrix, and stiffness matrix of the conductor, respectively. , and These are the acceleration, velocity, and displacement vectors of the conductor, respectively. This is the external load vector.

4. The method for monitoring abnormal galloping displacement of transmission lines based on BeiDou RTK according to claim 1, characterized in that, The 3D model analysis results include gobling displacement, velocity, and acceleration. The gobling displacement is calculated as follows: Using the initial reference coordinates P0(x0, y0, z0) of the conductor node under no galloping and no wind load, the real-time coordinates P of a certain node in the model at time t are obtained. t (x) t y t , z t Then, the displacement components of the node in the X, Y, and Z directions are: , , According to the formula The resultant displacement in three-dimensional space was calculated. ; The velocity is calculated based on the time-series displacement data collected by BeiDou RTK, using the central difference method to calculate the instantaneous velocity, according to the formula... The instantaneous velocity v was calculated. t ,in , They are respectively and The resultant displacement at time , The sampling time interval; Simultaneously based on the three-dimensional dynamic equations The velocity matrix is ​​solved directly using the Newmark-β method. The velocity components of each axis are obtained. , , And according to the formula Calculate the resultant velocity v, and calculate the instantaneous velocity v. t The average value of the resultant velocity v p And use it as the standard; The acceleration is calculated by using the central difference method again based on the known velocity time-series data, according to the formula... The acceleration a was calculated. t .

5. The method for monitoring abnormal galloping displacement of transmission lines based on BeiDou RTK according to claim 1, characterized in that, The method for generating exception information is as follows: If any of the calculated galloping displacement, velocity, or acceleration parameters exceeds a preset threshold, the galloping displacement of the conductor is determined to be abnormal, and abnormal information is generated. The abnormal information includes the conductor number, abnormal parameter type, abnormal occurrence time, and specific value. At the same time, the system automatically generates an electronic map marker containing the three-dimensional coordinates of the abnormal location and alerts the maintenance personnel in the form of an audible and visual alarm on the monitoring terminal. If all parameters do not exceed the preset threshold, the conductor galloping is determined to be in a normal state, and the system continues to monitor and collect data in real time.

6. The method for monitoring abnormal galloping displacement of transmission lines based on BeiDou RTK according to claim 1, characterized in that, Methods for obtaining conductor morphology information: Based on the obtained anomaly information, images of the conductor corresponding to the galloping area are acquired, and the obtained conductor images are processed by image enhancement and feature extraction to obtain preprocessed images. The image enhancement operations include histogram equalization, contrast stretching, and sharpening. Feature extraction is carried out by using an edge detection algorithm to accurately identify the edge contour of the conductor. At the same time, image segmentation technology is used to separate the conductor area from the background. The preprocessed images are periodically monitored with time T as the period. Through comparative analysis of multiple consecutive frames of images, the corresponding conductor morphology information is obtained. The conductor morphology information specifically includes the degree of bending, the amplitude of galloping, and the icing condition of the conductor.

7. The method for monitoring abnormal galloping displacement of transmission lines based on BeiDou RTK according to claim 1, characterized in that, The specific method for generating anomaly level information is as follows: Compare the conductor morphology information with the anomaly information. If the conductor morphology information matches the anomaly information, further confirm the abnormality of the conductor galloping displacement, and determine the anomaly level based on the anomaly information. The anomaly level determination method is as follows: If the displacement amplitude reaches 80%-100% of the dynamic threshold, and there are no extreme environments such as icing or strong winds, and the conductor does not touch the safe distance, it is considered a general anomaly. If the displacement amplitude exceeds the dynamic threshold, or the galloping frequency is greater than 1.5 Hz, accompanied by 5-10 mm of icing / 10-15 m / s wind speed, and the conductor is close to the safe distance, it is considered a moderate anomaly. If the displacement amplitude exceeds 10% of the safe distance, or the peak acceleration is greater than 5 m / s², accompanied by ≥10 mm of icing / ≥15 m / s gusts, the conductor has shown signs of local vibration fatigue, which is a serious anomaly, and anomaly level information is generated.

8. The method for monitoring abnormal galloping displacement of transmission lines based on BeiDou RTK according to claim 5, characterized in that, For cases where the same parameter exceeds the limit multiple times consecutively, and the exceedance is defined as ≥3 times within a set monitoring period, or continuously exceeding the limit for 2 consecutive hours, a time series model is established to predict the development trend of conductor galloping anomalies. Based on the prediction results, the anomaly development trend is divided into three levels, and the specific division method is as follows: If the parameter is predicted to fall back to the safe threshold within 24 hours and the composite risk index is <0.3, the corresponding trend is slow easing. If the parameter is predicted to continue to exceed the limit within 48 hours but without significant deterioration and the composite risk index is 0.3-0.6, the corresponding trend is stable maintenance. If the parameter exceeds the limit by more than 20% within 72 hours and the composite risk index is >0.6, the corresponding trend is rapid deterioration, and corresponding trend prediction information is generated.

9. The method for monitoring abnormal galloping displacement of transmission lines based on BeiDou RTK according to claim 8, characterized in that, The composite risk index is calculated as follows: The composite risk index comprehensively analyzes three dimensions: parameter exceedance risk, environmentally induced risk, and line vulnerability risk. Parameter exceedance risk is analyzed based on the exceedance rates of displacement amplitude, galloping frequency, and peak acceleration. Environmentally induced risk is analyzed based on the influence coefficients of icing thickness and wind speed. Line vulnerability risk is analyzed based on the line load factor and fitting aging coefficient. To address the characteristics of different indicators, a linear normalization method is used to eliminate dimensional differences, transforming the original data into standardized 0-1 values. The linear normalization formula is as follows: The standardized value was calculated. , where x i x is the original value. max is the limit value of the indicator, and values ​​exceeding the limit are calculated as 1, where i represents the type of indicator; According to standardized values The risk value r of each indicator is calculated using a piecewise function. i Highlighting the sensitivity of high-risk areas: when ≤0.8, r i =0.5 When 0.8 < ≤1.0, r i =2 -1.1, the risk value r of each indicator i With corresponding weight w i Multiply, according to the formula R= The summation yields the composite risk index, where 7 represents the number of corresponding indicators.

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