Power transmission line disaster toughness digital design method

Abstract

The invention relates to the technical field of power transmission lines, in particular to a power transmission line disaster toughness digital design method, which comprises the following steps: acquiring real-time environment parameters of a power transmission line, and calculating a comprehensive risk index based on the real-time environment parameters; calculating a ratio of an icing thickness change rate to a historical same-period average change rate based on the icing thickness sequence to obtain an icing risk index; obtaining a difference value sequence of galloping amplitudes of the power transmission line in adjacent monitoring time periods, and calculating a ratio of a variance of the difference value sequence to a historical galloping variance mean value to obtain a galloping risk index; and generating a comprehensive disaster early warning level based on the icing risk index and the galloping risk index, and adjusting a current load limit value of the power transmission line based on the comprehensive disaster early warning level. The unmanned aerial vehicle is used for monitoring the icing thickness, the vibration sensor is used for monitoring the galloping amplitude, different factors possibly influencing the safety of the power transmission line are comprehensively captured, and the comprehensiveness and accuracy of disaster early warning are improved through multi-dimensional monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission lines, and in particular to a digital design method for disaster resilience of power transmission lines. Background Art

[0002] Traditional methods often rely on regular manual inspections or preset static risk assessment models, which cannot obtain environmental parameters in real time, resulting in the inability to timely detect line safety issues or potential risks; and traditional methods rely on historical data for risk assessment, but cannot reflect the dynamic changes in the current environment, which makes disaster warnings not timely and accurate enough; and traditional methods usually rely on a single environmental monitoring factor, such as meteorological data, temperature, humidity, etc., or a single sensor (such as a temperature sensor or wind speed sensor) for monitoring. This single-dimensional monitoring easily ignores other influencing factors, such as changes in ice and snow thickness, vibration amplitude, etc., and it is difficult to fully capture complex disaster risks; and traditional methods usually do not have the ability to dynamically adjust the current load of the transmission line after risk assessment, especially for preventive measures before a disaster occurs. Once a disaster occurs, it may cause equipment damage or system collapse due to current overload or insufficient line load. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a digital design method for disaster resilience of transmission lines.

[0004] The technical solution adopted to solve the above technical problems is: a digital design method for disaster resilience of transmission lines, including: Acquiring real-time environmental parameters of the transmission line, and calculating a comprehensive risk index based on the real-time environmental parameters; When the comprehensive risk index exceeds a preset risk threshold, a primary disaster warning is executed, and the ice thickness of the transmission line is simultaneously monitored using a drone to obtain an ice thickness sequence; based on the ice thickness sequence, the ratio of the ice thickness change rate to the historical average change rate over the same period is calculated to obtain an ice risk index; When the icing risk index exceeds a preset icing risk threshold, an icing warning is executed, and the dancing amplitude of the transmission line is monitored based on a vibration sensor to obtain the dancing amplitude; Obtaining a difference sequence of the galloping amplitudes of the transmission line in adjacent monitoring periods, and calculating a ratio of the variance of the difference sequence to a mean of historical galloping variances to obtain a galloping risk index; When the dancing risk index exceeds a preset dancing risk threshold, a dancing warning is executed, a comprehensive disaster warning level is generated based on the icing risk index and the dancing risk index, and the current load limit value of the transmission line is adjusted based on the comprehensive disaster warning level.

[0005] Preferably, calculating the comprehensive risk index based on the real-time environmental parameters includes: Extracting the current air humidity, ambient temperature and wind speed vector based on the real-time environmental parameters; Calculate the ratio of the air humidity to the historical average humidity during the same period to obtain a humidity risk value; Calculating the absolute value of the difference between the ambient temperature and the critical embrittlement temperature of the transmission line material to obtain a low temperature risk value; Decomposing the wind speed vector into a perpendicular line direction component and a parallel line direction component, and calculating the ratios of the perpendicular line direction component and the parallel line direction component to the mean of the historical wind speed components, respectively, to obtain a transverse risk value and a longitudinal risk value; A weighted sum is performed on the humidity risk value, the low temperature risk value, the transverse risk value, and the longitudinal risk value to obtain a comprehensive risk index.

[0006] Preferably, monitoring the ice thickness of the transmission line based on a drone to obtain an ice thickness sequence includes: Obtaining point cloud data of the power transmission line collected by the drone, and generating a three-dimensional line model based on the point cloud data; determining potential ice growth areas of the transmission line during multiple monitoring periods based on conductor sag parameters and tower spacing data in the three-dimensional line model; Obtaining point cloud cross-sectional data of the potential ice growth area based on a laser radar scan carried by an unmanned aerial vehicle, and calculating the ice thickness increment based on the change in point cloud cross-sectional data between adjacent monitoring periods; The ice thickness increments during each monitoring period are integrated in time series to obtain the ice thickness sequence.

[0007] Preferably, generating a three-dimensional line model based on the point cloud data includes: Performing wire feature extraction on the point cloud data to identify point cloud clusters with continuous linear distribution as initial wire trajectories; The initial conductor trajectory is corrected in sections based on the tower design spacing parameters to determine the spatial positioning points of each tower; Between adjacent tower positioning points, the catenary equation is fitted based on the conductor tension parameters to generate the conductor space curve; The conductor space curve is offset based on a typical installation height of the ground wire to obtain a three-dimensional line model.

[0008] Preferably, determining potential ice growth areas of the transmission line during multiple monitoring periods based on the conductor sag parameters and tower spacing data in the three-dimensional line model includes: Extracting the coordinates of the lowest sag point of each span of the conductor from the three-dimensional line model; A correlation model between the lowest sag point and ice load was established based on historical ice cover data; Based on the snowfall amount and freezing rain probability in the weather forecast data and the correlation model, an icing risk level assessment is performed on each of the lowest points of the sag; The conductor span sections corresponding to the high risk levels are marked as potential ice growth areas.

[0009] Preferably, calculating the ice thickness increment based on the change in point cloud cross-section data between adjacent monitoring periods includes: Perform spatial registration on the point cloud cross-section data obtained during adjacent monitoring periods to establish an alignment mapping relationship of the conductor surface point cloud; Extracting an outer envelope contour line of the conductor surface point cloud based on the alignment mapping relationship, and calculating a maximum offset of the outer envelope contour line in a direction perpendicular to the conductor axis; The maximum offset is corrected based on the wire reference diameter parameter to obtain a standardized ice thickness increment.

[0010] Preferably, monitoring the dancing amplitude of the transmission line based on a vibration sensor to obtain the dancing amplitude includes: Real-time monitoring of the vibration signals of the transmission line in the horizontal and vertical directions at all times; dividing the vibration signals in the horizontal and vertical directions according to time periods, respectively, to obtain a horizontal original vibration sequence and a vertical original vibration sequence corresponding to each period; Performing multi-scale wavelet decomposition on the horizontal original vibration sequence of each period using a wavelet function to obtain a detail coefficient sequence of each scale, performing threshold processing on the detail coefficients in the detail coefficient sequence of each scale and reconstructing them through inverse wavelet transform to obtain a horizontal denoised vibration sequence of each period; Extracting the horizontal main amplitude and horizontal vibration frequency of each period based on the horizontal denoised vibration sequence of each period; calculating the absolute value of the difference between the horizontal main amplitudes of adjacent periods and the absolute value of the difference between the horizontal vibration frequencies of adjacent periods, and using the ratio of the two absolute values ​​of the difference as the horizontal galloping adjustment factor of the adjacent periods; The horizontal dancing amplitude of each period is calculated by weighted average based on the horizontal main amplitude, the horizontal vibration frequency and the horizontal dancing adjustment factor of each period.

[0011] Preferably, the method further comprises: monitoring the dancing amplitude of the power transmission line based on a vibration sensor to obtain the dancing amplitude; Performing multi-scale wavelet decomposition on the vertical original vibration sequence of each period using a wavelet function to obtain a detail coefficient sequence of each scale, performing threshold processing on the detail coefficients in the detail coefficient sequence of each scale and reconstructing them through inverse wavelet transform to obtain a vertical denoised vibration sequence of each period; Extracting the vertical main amplitude and vertical vibration frequency of each period based on the vertical denoised vibration sequence of each period; calculating the absolute value of the difference between the vertical main amplitudes of adjacent periods and the absolute value of the difference between the vertical vibration frequencies of adjacent periods, and using the ratio of the two absolute values ​​of the difference as the vertical galloping adjustment factor of the adjacent periods; Calculating the vertical dancing amplitude of each period by weighted average based on the vertical main amplitude, the vertical vibration frequency and the vertical dancing adjustment factor of each period; The horizontal dancing amplitude and the vertical dancing amplitude of each cycle are vector-synthesized to obtain the dancing amplitude of each cycle.

[0012] Preferably, vector synthesis of the horizontal dancing amplitude and the vertical dancing amplitude of each cycle to obtain the dancing amplitude of each cycle includes: The horizontal dancing amplitude is used as the abscissa component of a vector, and the vertical dancing amplitude is used as the ordinate component of a vector. The square root of the sum of the squares of the abscissa component and the ordinate component is calculated by the Pythagorean theorem to obtain the dancing amplitude.

[0013] Preferably, extracting the horizontal main amplitude and horizontal vibration frequency of each period based on the horizontal denoised vibration sequence of each period includes: Performing a fast Fourier transform on the horizontal denoised vibration sequence of each period to obtain a frequency-amplitude spectrum; The frequency with the largest amplitude in the frequency-amplitude spectrum is selected as the horizontal vibration frequency of the cycle, and the corresponding amplitude is selected as the horizontal main amplitude of the cycle.

[0014] The beneficial effects of the present invention are as follows: (1) The present invention provides a dynamic, data-based risk monitoring system for power transmission lines by acquiring environmental parameters in real time and calculating a comprehensive risk index. This system can promptly reflect the possible impact of the external environment on line safety, thereby providing more accurate and timely disaster warnings. Furthermore, by using drones to monitor ice thickness and vibration sensors to monitor ice vibration amplitude, the system comprehensively captures different factors that may affect the safety of power transmission lines. Multi-dimensional monitoring improves the comprehensiveness and accuracy of disaster warnings, ensuring that different types of disasters (such as ice disasters, wind disasters, etc.) can be effectively monitored. (2) By calculating the ratio of the ice thickness change rate and the variance of the dancing amplitude difference sequence to historical data, the present invention can identify abnormal change trends, such as drastic changes in ice and snow thickness or abnormal dancing amplitude caused by wind vibration. The change in the ratio can reflect the impending disaster risk, which is more timely and accurate. (3) The present invention dynamically adjusts the current load limit value of the transmission line based on the risk index, which helps to take preventive measures before a disaster occurs and avoid equipment damage caused by overload. It can effectively improve the resilience of the transmission line in extreme weather and natural disasters, reduce downtime and economic losses, and enhance the ability to respond to sudden disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flowchart of the steps of the overall method in one embodiment of the present invention. DETAILED DESCRIPTION

[0016] Example 1, as Figure 1 As shown, the present invention proposes a digital design method for disaster resilience of transmission lines, comprising: S1. Obtain real-time environmental parameters of the transmission line and calculate a comprehensive risk index based on the real-time environmental parameters; S2. When the comprehensive risk index exceeds the preset risk threshold, a primary disaster warning is issued, and the ice thickness of the transmission lines is monitored simultaneously using drones to obtain an ice thickness sequence. Based on the ice thickness sequence, the ratio of the ice thickness change rate to the historical average change rate over the same period is calculated to obtain an ice risk index. S3. When the icing risk index exceeds a preset icing risk threshold, an icing warning is executed, and the dancing amplitude of the transmission line is monitored using a vibration sensor to obtain the dancing amplitude; S4. Obtain a difference sequence of the galloping amplitudes of the transmission line in adjacent monitoring periods, and calculate the ratio of the variance of the difference sequence to the mean of the historical galloping variance to obtain a galloping risk index; S5. When the dancing risk index exceeds the preset dancing risk threshold, a dancing warning is executed, a comprehensive disaster warning level is generated based on the icing risk index and the dancing risk index, and the current load limit value of the transmission line is adjusted based on the comprehensive disaster warning level.

[0017] In the present invention, real-time environmental parameters refer to real-time data about the environment in which the transmission line is located, collected by sensors or monitoring equipment, such as temperature, humidity, wind speed, snowfall, ice thickness, etc. These parameters affect the safety of the line; based on these real-time environmental parameters, a comprehensive risk index is calculated; this index reflects the risks that the transmission line may face under current environmental conditions, such as icing, dancing and other risks; the comprehensive risk index is usually a weighted average of multiple risk factors or a comprehensive result based on a certain algorithm; a predefined threshold is set, and when the comprehensive risk index exceeds this threshold, the system will consider that the transmission line is facing a greater risk and activate the disaster warning mechanism, which is an early warning to remind possible risks that require further monitoring and analysis; the system will enable drones to monitor the ice thickness of the transmission line; the data collected by the drone forms a sequence of ice thickness, representing the ice condition of the line at different time points; calculate the ice thickness The rate at which the thickness changes over time reflects whether the ice layer is thickening rapidly; the rate of change can be calculated based on the difference in thickness at adjacent moments; compared with historical data, the average rate of change of ice thickness during the same period (for example, the same time period in the past few years) is obtained; the ratio of the current rate of change of ice thickness to the historical average rate of change during the same period is calculated; this ratio reflects whether the current rate of change of ice is abnormal. If the ratio is large, it means that the risk of icing is high; the icing risk index is calculated based on the ratio. When the index exceeds the preset threshold, the system triggers an icing warning to warn of possible icing disasters; a comprehensive disaster warning level is generated based on the icing risk index and the dancing risk index; this is a risk assessment that takes all factors into consideration to help decision makers understand the overall risk of transmission lines; the current load limit of the transmission line is adjusted according to the comprehensive disaster warning level; when the risk is high, the current load can be reduced to prevent the line from being damaged or malfunctioning due to overload.

[0018] In an optional embodiment, calculating a comprehensive risk index based on real-time environmental parameters includes: A1. Extract the current air humidity, ambient temperature, and wind speed vector based on real-time environmental parameters; A2. Calculate the ratio of air humidity to the historical average humidity for the same period to obtain the humidity risk value. A3. Calculate the absolute value of the difference between the ambient temperature and the critical embrittlement temperature of the transmission line material to obtain the low-temperature risk value; A4. Decompose the wind speed vector into a component perpendicular to the line direction and a component parallel to the line direction. Calculate the ratio of the perpendicular to the line direction component and the parallel to the line direction component to the mean of the historical wind speed component to obtain the lateral risk value and the longitudinal risk value. A5. Take the weighted sum of the humidity risk value, low temperature risk value, horizontal risk value and vertical risk value to obtain the comprehensive risk index.

[0019] It should be noted that the system extracts the current air humidity (i.e., the moisture content in the air), calculates the historical average humidity within the same time period (e.g., the past year, the past month, etc.), and calculates the humidity risk value by calculating the ratio of the current humidity to the historical average humidity for the same period. If the current humidity is high, it may increase the impact on the transmission line, such as the risk of moisture and corrosion. The system also obtains the current ambient temperature and compares it with the critical brittle temperature of the materials used in the transmission line. The absolute value of the difference between the ambient temperature and the critical brittle temperature (i.e., the temperature difference) is calculated. When the temperature difference is large, it indicates that the temperature is too low, which may cause brittleness or damage to the line material, so low temperature risks need to be considered. Wind speed is a vector and has directionality. The system decomposes the current wind speed vector into two components: The wind speed component perpendicular to the direction of the transmission line and the wind speed component parallel to the transmission line are used to calculate the ratio of the vertical wind speed component to the historical average wind speed component to obtain the lateral risk value; excessive lateral wind speed may cause the line to bend, swing or break; the longitudinal risk value is calculated by calculating the ratio of the parallel wind speed component to the historical average wind speed component; an increase in longitudinal wind speed may cause an increase in the tension of the line, which may increase the risk of line breakage; the humidity risk value, low temperature risk value, lateral risk value and longitudinal risk value are weighted and summed; the purpose of weighting is to assign different weights according to the degree of influence of each risk, and finally obtain a comprehensive risk index; this index will comprehensively consider the influence of all environmental factors and give a comprehensive risk assessment result. A high comprehensive risk index means that the transmission line faces greater risks.

[0020] In an optional embodiment, monitoring the ice thickness of a transmission line using a drone to obtain an ice thickness sequence includes: B1. Obtain point cloud data of the power transmission line collected by the drone and generate a 3D line model based on the point cloud data; B2. Determine potential ice growth areas along the transmission line during multiple monitoring periods based on conductor sag parameters and tower spacing data from the 3D line model. B3. Obtain point cloud cross-sectional data of potential ice growth areas based on drone-mounted LiDAR scanning, and calculate ice thickness increments based on the change in point cloud cross-sectional data between adjacent monitoring periods; B4. Integrate the ice thickness increments during each monitoring period in a time series to obtain an ice thickness sequence.

[0021] It should be noted that the transmission lines are scanned using drones equipped with equipment such as LiDAR to obtain a series of point cloud data. The point cloud data is composed of multiple three-dimensional coordinate points collected by the LiDAR, which can accurately depict the three-dimensional shape of the ground and objects. Based on these point cloud data, special algorithms and software are used to generate a three-dimensional model of the transmission line. This model includes information such as the structure of the line, the distribution of towers, and the shape of the conductors. The conductor sag (i.e. the drooping shape of the conductor under the action of gravity) and the spacing between towers in the three-dimensional model are used to determine the morphological changes of the transmission line in different monitoring periods. These factors affect whether the conductor is susceptible to ice accumulation. Using these data, combined with climate and weather factors, it is possible to identify areas where ice growth may exist on the transmission line in different time periods. Icing occurs when the weather is cold and the humidity is high. In an environment that causes ice to form on the surface of conductors and facilities; the drone uses lidar to conduct a detailed scan of potential icing areas and obtains point cloud data sections of the area; these data show the actual position and shape of each point on the transmission line, and can reflect the specific status of the conductors and towers; in continuous monitoring periods, by comparing the point cloud section data of adjacent periods, the changes in the surface of the conductors and towers (i.e., the growth of ice) can be seen; specifically, the changes in the point cloud section data of different monitoring periods are compared, and the incremental ice thickness of each period is calculated; the corresponding incremental ice thickness is calculated for each monitoring period, indicating how much the ice layer on the conductors or towers has thickened during that time period; the incremental ice thickness calculated for each monitoring period is arranged in chronological order to form a continuous time series; this series represents the accumulation of ice on the transmission line at different time points.

[0022] In an optional embodiment, generating a three-dimensional line model based on point cloud data includes: C1. Extract wire features from point cloud data to identify continuous linearly distributed point cloud clusters as initial wire trajectories; C2. Based on the tower design spacing parameters, the initial conductor trajectory is segmented and corrected to determine the spatial positioning points of each tower; C3. Between adjacent tower positioning points, the catenary equation is fitted based on the conductor tension parameters to generate the conductor space curve; C4. Offset the conductor space curve based on the typical installation height of the ground wire to obtain a three-dimensional line model.

[0023] It should be noted that the point cloud data collected by drones are analyzed by computer algorithms; through specific algorithms, point cloud clusters that are continuously arranged in space and have a linear shape are identified, which represent the initial trajectory of the transmission line; these linearly distributed point cloud clusters provide the basis for the preliminary conductor position; the initial conductor trajectory is corrected according to the design specifications and the actual tower spacing (i.e., the distance between adjacent towers); the design of transmission lines usually follows a certain tower spacing to ensure the safety and structural stability of the conductor; by segmenting the initial conductor trajectory according to the tower spacing and correcting the shape of each segment to make it conform to the actual design and constraints; this can more accurately determine the spatial position of each tower and the true shape of the conductor; after the segment correction, the position of each tower is determined according to the design spacing of each segment; each tower usually has a clear spatial positioning point, which is used to define the support point of the transmission line; the conductors on the transmission line will form a curved shape due to factors such as gravity and tension when hanging; its shape is calculated according to the tension of the conductor (i.e., the force applied to the conductor between different towers); based on the tension of the conductor, a suitable mathematical model is selected to simulate The morphology of the conductor is typically approximated by a catenary (i.e., a curve formed under the influence of gravity). By fitting the catenary equation, the spatial curve of the conductor between different towers can be accurately calculated. After determining the catenary equation between each two adjacent towers, a three-dimensional spatial curve of the conductor is generated. This curve reflects the precise shape of the conductor under load in reality and represents the connection path from one tower to another. In actual installation, the installation height of the ground wire is usually specified by regulations and is set based on a standard value for ground height. To ensure that the generated conductor spatial curve matches the actual installation height of the ground wire, the conductor spatial curve is offset, raising or lowering the conductor trajectory as a whole to match the typical installation height of the ground wire. This process creates a three-dimensional line model that reflects the actual spatial position and shape of the transmission line. After the offset process, the conductor spatial curve and the tower positioning points are finally combined to form a complete three-dimensional transmission line model. This model accurately represents all characteristics of the transmission line in three dimensions, including the conductor shape and tower distribution.

[0024] In an optional embodiment, determining potential ice growth areas of the transmission line during multiple monitoring periods based on conductor sag parameters and tower spacing data in the three-dimensional line model includes: D1. Extract the coordinates of the lowest sag point of each span of the conductor from the 3D line model; establish a correlation model between the lowest sag point and ice load based on historical icing data; D2. Based on the snowfall amount, freezing rain probability and correlation model in the meteorological forecast data, the icing risk level of each sag lowest point is assessed; D3. Mark the conductor span sections corresponding to high risk levels as potential ice growth areas.

[0025] It should be noted that due to factors such as gravity and conductor tension, the conductor will bend in the span section between each tower; the lowest point of the sag refers to the lowest point of the conductor within a certain span section, that is, the place where the conductor curve is lowest; it is very important to extract the coordinates of these lowest points because they are key locations for evaluating the impact of icing on the conductor; the transmission line consists of multiple towers, and the distance between the towers is called the span; the position of the lowest point of the sag between each span section can be used as a reference for subsequent analysis; historical icing data usually includes the occurrence of icing on the line and the load caused by it under different meteorological conditions; by analyzing historical icing data, a mathematical model can be established to relate the height, position and degree of icing of the lowest point of the sag; alternatively, the model can help predict how much icing load the lowest point of the conductor will be subjected to under certain meteorological conditions; this model is established to subsequently evaluate the icing risk of the conductor under different meteorological conditions; the weather forecast provides upcoming weather conditions, especially the amount of snowfall and the probability of freezing rain; the amount of snowfall and the probability of freezing rain are key factors affecting the occurrence of icing, and a higher snowfall or freezing rain probability means it is more likely Icing occurs; by inputting the snowfall and freezing rain probability from the weather forecast into the previously established correlation model between the lowest sag point and the icing load, the icing load that each lowest sag point may bear under the upcoming weather conditions can be obtained; based on the icing load at the lowest sag point of each span of the conductor, combined with weather forecast data, the icing risk of each lowest sag point can be assessed; for example, the following levels can be used to represent the risk: low, medium, and high risk levels; the goal of this assessment process is to identify which areas of the conductor may suffer severe icing due to the upcoming weather conditions; in the icing risk level assessment, if the lowest sag point of certain spans faces a higher icing risk, then these spans will be marked as potential icing growth areas; this means that the conductors in these areas may experience large ice accumulation under the upcoming weather conditions, which may cause the conductors to be overloaded, thereby increasing the risk of conductor breakage, tower collapse, etc.; therefore, marking these areas as potential icing growth areas can help relevant departments take preventive measures, such as strengthening inspections, reinforcing conductors, and clearing ice and snow in advance.

[0026] In an optional embodiment, calculating the ice thickness increment based on the change in point cloud cross-sectional data between adjacent monitoring periods includes: E1. Perform spatial registration on the point cloud cross-section data obtained during adjacent monitoring periods to establish an alignment mapping relationship of the conductor surface point cloud; E2. Extract the outer envelope contour line of the conductor surface point cloud based on the alignment mapping relationship, and calculate the maximum offset of the outer envelope contour line in the direction perpendicular to the conductor axis; E3. Correct the maximum offset based on the wire reference diameter parameter to obtain the standardized ice thickness increment.

[0027] It should be noted that since point cloud data are usually collected at different time periods and different perspectives, it is necessary to spatially align these data, that is, to align the point cloud data of different time periods to the same reference coordinate system for subsequent analysis; this is achieved by establishing a mapping relationship between point clouds, so that the data of each time period can correctly correspond to the same spatial position; the goal of registration is to establish a mathematical model or transformation relationship so that the point cloud data of the wire surface at different time periods can be accurately corresponded and aligned, thereby ensuring the accuracy of subsequent analysis; the outer envelope refers to the outermost contour in the point cloud data, which is usually used to represent the boundary of an object; for the point cloud data of a wire, the outer envelope represents the outermost curve of the wire surface, which can help analyze the shape of the wire; the outer envelope of the wire surface may be offset due to factors such as icing and temperature changes in different time periods; calculating the maximum offset of the outer envelope in the direction perpendicular to the wire axis can quantify the degree of deformation of the wire due to icing or other factors; this offset is the outermost layer of the wire surface point cloud and the reference position The maximum deviation represents the deformation of the conductor surface due to ice accumulation, but the actual ice thickness should be related to the conductor diameter. To standardize the deviation, the conductor's baseline diameter should be considered. By correcting the maximum deviation, a correction value related to the conductor size can be obtained, ensuring the universality and accuracy of the ice thickness assessment. The resulting value after correction represents the normalized ice thickness increase of the conductor over a specific time period, that is, the increase in the conductor surface due to ice and snow accumulation. This increase can be used as an indicator to assess the ice condition of the conductor and to calculate the conductor load increase and risk assessment.

[0028] In an optional embodiment, monitoring the galloping amplitude of the transmission line based on a vibration sensor to obtain the galloping amplitude includes: F1. Real-time monitoring of the horizontal and vertical vibration signals of the transmission line at all times; dividing the horizontal and vertical vibration signals by time period to obtain the horizontal original vibration sequence and vertical original vibration sequence corresponding to each period; F2. Perform multi-scale wavelet decomposition on the original horizontal vibration sequence of each period using a wavelet function to obtain a detail coefficient sequence of each scale. Threshold processing is performed on the detail coefficients in the detail coefficient sequence of each scale and then reconstructed using an inverse wavelet transform to obtain a horizontal denoised vibration sequence of each period. F3. Extract the horizontal main amplitude and horizontal vibration frequency of each period based on the horizontal denoised vibration sequence of each period; calculate the absolute value of the difference between the horizontal main amplitudes of adjacent periods and the absolute value of the difference between the horizontal vibration frequencies of adjacent periods, and use the ratio of the two absolute values ​​of the difference as the horizontal dance adjustment factor of the adjacent periods; F4. Calculate the horizontal dancing amplitude of each cycle by weighted average based on the horizontal main amplitude, horizontal vibration frequency and horizontal dancing adjustment factor of each cycle.

[0029] It should be noted that transmission lines are affected by various factors such as wind, ice and snow, and earthquakes, generating horizontal (transverse) and vertical (longitudinal) vibration signals. Real-time monitoring of these signals is to obtain information on line status and vibration changes. The obtained horizontal and vertical vibration signals are divided into time segments, usually by segmenting the vibration signals at fixed time intervals (such as every minute or every hour), to facilitate the analysis of vibration characteristics in different time periods. Wavelet transform is a technology commonly used in signal processing. It decomposes the signal into components of different frequencies through multi-scale analysis. Here, the horizontal original vibration sequence is decomposed by wavelet to obtain detail coefficients of different scales. The detail coefficients of each scale represent the signal characteristics of that frequency band. Threshold processing is a common method of wavelet denoising. By setting a threshold, smaller noise components are removed and only larger detail coefficients are retained to reduce the interference of noise on signal analysis. After threshold processing, the detail coefficients are reconstructed using inverse wavelet transform to obtain the horizontal vibration sequence without noise. This process helps to clearly reflect the true fluctuations of the vibration signal. The amplitude represents the intensity of the vibration, and the main amplitude refers to the most significant vibration amplitude in the signal. The denoised horizontal vibration sequence is analyzed to extract its maximum amplitude. Frequency represents the periodicity of vibration. The frequency of the dominant vibration in the signal is extracted using methods such as Fourier transform. These two parameters (amplitude and frequency) are key to evaluating line vibration characteristics. The absolute difference between the dominant horizontal amplitude and frequency of two adjacent time periods is calculated. This difference represents the change in vibration intensity and frequency between the two time periods. An adjustment factor, called the "horizontal galloping adjustment factor," is calculated by calculating the ratio of the absolute value of the horizontal dominant amplitude difference to the absolute value of the frequency difference. This factor can be used to measure the severity of vibration changes between adjacent time periods and reflect the "galloping" behavior of the line. The horizontal galloping amplitude of each cycle is calculated using a weighted average method based on the dominant horizontal amplitude, horizontal vibration frequency, and horizontal galloping adjustment factor of each cycle. The purpose of weighted averaging is to assign different weights to different parameters based on their importance, so that more important parameters have a greater impact on the final result. The resulting horizontal galloping amplitude represents the horizontal vibration intensity of the transmission line over a period of time. Combined with the dominant horizontal amplitude, frequency, and adjustment factor, it can comprehensively reflect the dynamic state of the line.

[0030] In an optional embodiment, the galloping amplitude of the power transmission line is monitored based on a vibration sensor to obtain the galloping amplitude, further comprising: F5. Perform multi-scale wavelet decomposition on the original vertical vibration sequence of each period using a wavelet function to obtain a detail coefficient sequence of each scale. Threshold processing is performed on the detail coefficients in the detail coefficient sequence of each scale and then reconstructed using an inverse wavelet transform to obtain a vertical denoised vibration sequence of each period. F6. Extract the vertical main amplitude and vertical vibration frequency of each cycle based on the vertical denoised vibration sequence of each cycle; calculate the absolute value of the difference between the vertical main amplitudes of adjacent cycles and the absolute value of the difference between the vertical vibration frequencies of adjacent cycles, and use the ratio of the two absolute values ​​of the difference as the vertical galloping adjustment factor of the adjacent cycles; F7, based on the vertical main amplitude, vertical vibration frequency and vertical dancing adjustment factor of each cycle, calculate the vertical dancing amplitude of each cycle by weighted average; F8. Perform vector synthesis on the horizontal dancing amplitude and the vertical dancing amplitude of each cycle to obtain the dancing amplitude of each cycle.

[0031] It should be noted that the vertical original vibration sequence is first decomposed into multi-scale wavelets using wavelet transform; for two adjacent periods, the absolute value of the difference between the vertical main amplitude and the absolute value of the difference between the vertical vibration frequencies are calculated; these two differences reflect the magnitude of the amplitude and frequency changes in time; the difference ratio uses the absolute value ratio of the two differences as the "vertical dance adjustment factor", which reflects the severity of the vertical vibration changes between adjacent periods; based on the vertical main amplitude, vertical vibration frequency and vertical dance adjustment factor of each period, the vertical dance amplitude of each period is calculated by weighted averaging; the weighted averaging method usually assigns different weights to these parameters according to their importance, and then comprehensively considers the influence of various factors to obtain a more accurate vertical dance amplitude; finally The vertical dancing amplitude is a comprehensive reflection of the vertical vibration intensity of the cycle, which combines the amplitude, frequency and severity of vibration changes; the previous section described how to calculate the dancing amplitude in the horizontal direction, which represents the vibration characteristics in the horizontal direction; through the above process, we obtain the dancing amplitude in the vertical direction, which represents the vibration characteristics in the vertical direction; the horizontal and vertical dancing amplitudes represent the vibration amplitudes in the horizontal and vertical directions respectively, but the real vibration is a two-dimensional dynamic process, which requires the integration of the influences of these two directions; therefore, the horizontal and vertical dancing amplitudes are vector synthesized; the principle of vector synthesis is to use the Pythagorean theorem to synthesize the horizontal and vertical amplitudes into a total vibration amplitude, and the total dancing amplitude reflects the comprehensive vibration intensity of the transmission line in each cycle.

[0032] In an optional embodiment, the horizontal dancing amplitude and the vertical dancing amplitude of each cycle are vector-synthesized to obtain the dancing amplitude of each cycle, including: G1. Use the horizontal dance amplitude as the abscissa component of the vector and the vertical dance amplitude as the ordinate component of the vector. Use the Pythagorean theorem to calculate the square root of the sum of the squares of the abscissa component and the ordinate component to obtain the dance amplitude.

[0033] In an optional embodiment, extracting the horizontal main amplitude and horizontal vibration frequency of each period based on the horizontal denoised vibration sequence of each period includes: H1. Perform fast Fourier transform on the horizontal denoised vibration sequence of each period to obtain the frequency-amplitude spectrum; H2. In the frequency-amplitude spectrum, select the frequency with the largest amplitude as the horizontal vibration frequency of the cycle, and the corresponding amplitude as the horizontal main amplitude of the cycle.

[0034] It should be noted that the horizontal denoised vibration sequence refers to a signal that has been denoised to remove background noise or unnecessary interference; the signal may come from a certain vibration sensor, and in the signal processing process, filtering, smoothing and other methods are used to remove noise, leaving the main vibration signal; Fast Fourier Transform (FFT) is a method of efficiently calculating Fourier transform, which can convert the signal from the time domain to the frequency domain; through FFT, the frequency components in the signal can be analyzed, and the intensity (amplitude) of different frequency components can be understood; for each cycle of the vibration sequence, the vibration intensity of the sequence at different frequencies can be obtained through FFT; this means that we can know which frequency components are contained in the signal and their relative intensity; the results obtained by FFT are usually presented as a graph of frequency versus amplitude; the horizontal axis is the frequency (usually in Hertz Hz), and the vertical axis is the amplitude of the corresponding frequency, which is expressed as follows: Indicates the vibration intensity at that frequency; at some frequencies, the amplitude may be very large, indicating that the frequency component dominates the signal; at other frequencies, the amplitude is smaller, indicating that the components of these frequencies have a weaker effect on the signal; in the resulting frequency-amplitude spectrum, there is usually a frequency point with the largest amplitude, which means that this frequency is the main frequency of the signal, that is, the strongest vibration component in the signal; by selecting the frequency with the largest amplitude, we can determine the main vibration frequency of the signal, that is, the main frequency component of the periodic signal; this frequency is usually considered to be the "core" frequency of the signal, reflecting the main vibration mode of the signal; the amplitude corresponding to the main vibration frequency in the spectrum is the vibration intensity (or amplitude) at that frequency, which can be regarded as the horizontal main amplitude, that is, the vibration amplitude of the signal at that frequency; the main amplitude describes the vibration intensity at that frequency and represents the proportion of this frequency component in the signal.

[0035] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A digital design method for disaster resilience of transmission lines, characterized by: include: Acquiring real-time environmental parameters of the transmission line, and calculating a comprehensive risk index based on the real-time environmental parameters; When the comprehensive risk index exceeds a preset risk threshold, a primary disaster warning is executed, and the ice thickness of the transmission line is simultaneously monitored using a drone to obtain an ice thickness sequence; based on the ice thickness sequence, the ratio of the ice thickness change rate to the historical average change rate over the same period is calculated to obtain an ice risk index; When the icing risk index exceeds a preset icing risk threshold, an icing warning is executed, and the dancing amplitude of the transmission line is monitored based on a vibration sensor to obtain the dancing amplitude; Obtaining a difference sequence of the galloping amplitudes of the transmission line in adjacent monitoring periods, and calculating a ratio of the variance of the difference sequence to a mean of historical galloping variances to obtain a galloping risk index; When the dancing risk index exceeds a preset dancing risk threshold, a dancing warning is executed, a comprehensive disaster warning level is generated based on the icing risk index and the dancing risk index, and the current load limit value of the transmission line is adjusted based on the comprehensive disaster warning level.

2. A digital design method for disaster resilience of transmission lines according to claim 1, characterized in that: Calculating a comprehensive risk index based on the real-time environmental parameters includes: Extracting the current air humidity, ambient temperature and wind speed vector based on the real-time environmental parameters; Calculate the ratio of the air humidity to the historical average humidity during the same period to obtain a humidity risk value; Calculating the absolute value of the difference between the ambient temperature and the critical embrittlement temperature of the transmission line material to obtain a low temperature risk value; Decomposing the wind speed vector into a perpendicular line direction component and a parallel line direction component, and calculating the ratios of the perpendicular line direction component and the parallel line direction component to the mean of the historical wind speed components, respectively, to obtain a transverse risk value and a longitudinal risk value; A weighted sum is performed on the humidity risk value, the low temperature risk value, the transverse risk value, and the longitudinal risk value to obtain a comprehensive risk index.

3. A digital design method for disaster resilience of transmission lines according to claim 2, characterized in that: The ice thickness of the transmission line is monitored based on a UAV to obtain an ice thickness sequence, including: Obtaining point cloud data of the power transmission line collected by the drone, and generating a three-dimensional line model based on the point cloud data; determining potential ice growth areas of the transmission line during multiple monitoring periods based on conductor sag parameters and tower spacing data in the three-dimensional line model; Obtaining point cloud cross-sectional data of the potential ice growth area based on a laser radar scan carried by an unmanned aerial vehicle, and calculating the ice thickness increment based on the change in point cloud cross-sectional data between adjacent monitoring periods; The ice thickness increments during each monitoring period are integrated in time series to obtain the ice thickness sequence.

4. A digital design method for disaster resilience of transmission lines according to claim 3, characterized in that: Generating a three-dimensional line model based on the point cloud data includes: Performing wire feature extraction on the point cloud data to identify point cloud clusters with continuous linear distribution as initial wire trajectories; The initial conductor trajectory is corrected in sections based on the tower design spacing parameters to determine the spatial positioning points of each tower; Between adjacent tower positioning points, the catenary equation is fitted based on the conductor tension parameters to generate the conductor space curve; The conductor space curve is offset based on a typical installation height of the ground wire to obtain a three-dimensional line model.

5. A digital design method for disaster resilience of transmission lines according to claim 4, characterized in that: Determine potential ice growth areas of the transmission line during multiple monitoring periods based on the conductor sag parameters and tower spacing data in the three-dimensional line model, including: Extracting the coordinates of the lowest sag point of each span of the conductor from the three-dimensional line model; A correlation model between the lowest sag point and ice load was established based on historical ice cover data; Based on the snowfall amount and freezing rain probability in the weather forecast data and the correlation model, an icing risk level assessment is performed on each of the lowest points of the sag; The conductor span sections corresponding to the high risk levels are marked as potential ice growth areas.

6. A digital design method for disaster resilience of transmission lines according to claim 5, characterized in that: Calculate the ice thickness increment based on the change in point cloud cross-section data between adjacent monitoring periods, including: Perform spatial registration on the point cloud cross-section data obtained during adjacent monitoring periods to establish an alignment mapping relationship of the conductor surface point cloud; Extracting an outer envelope contour line of the conductor surface point cloud based on the alignment mapping relationship, and calculating a maximum offset of the outer envelope contour line in a direction perpendicular to the conductor axis; The maximum offset is corrected based on the wire reference diameter parameter to obtain a standardized ice thickness increment.

7. A digital design method for disaster resilience of transmission lines according to claim 6, characterized in that: The galloping amplitude of the transmission line is monitored based on a vibration sensor to obtain the galloping amplitude, including: Real-time monitoring of the vibration signals of the transmission line in the horizontal and vertical directions at all times; dividing the vibration signals in the horizontal and vertical directions according to time periods, respectively, to obtain a horizontal original vibration sequence and a vertical original vibration sequence corresponding to each period; Performing multi-scale wavelet decomposition on the horizontal original vibration sequence of each period using a wavelet function to obtain a detail coefficient sequence of each scale, performing threshold processing on the detail coefficients in the detail coefficient sequence of each scale and reconstructing them through inverse wavelet transform to obtain a horizontal denoised vibration sequence of each period; Extracting the horizontal main amplitude and horizontal vibration frequency of each period based on the horizontal denoised vibration sequence of each period; calculating the absolute value of the difference between the horizontal main amplitudes of adjacent periods and the absolute value of the difference between the horizontal vibration frequencies of adjacent periods, and using the ratio of the two absolute values ​​of the difference as the horizontal galloping adjustment factor of the adjacent periods; The horizontal dancing amplitude of each period is calculated by weighted average based on the horizontal main amplitude, the horizontal vibration frequency and the horizontal dancing adjustment factor of each period.

8. A digital design method for disaster resilience of transmission lines according to claim 7, characterized in that: The method further comprises: monitoring the dancing amplitude of the transmission line based on a vibration sensor to obtain the dancing amplitude; Performing multi-scale wavelet decomposition on the vertical original vibration sequence of each period using a wavelet function to obtain a detail coefficient sequence of each scale, performing threshold processing on the detail coefficients in the detail coefficient sequence of each scale and reconstructing them through inverse wavelet transform to obtain a vertical denoised vibration sequence of each period; Extracting the vertical main amplitude and vertical vibration frequency of each period based on the vertical denoised vibration sequence of each period; calculating the absolute value of the difference between the vertical main amplitudes of adjacent periods and the absolute value of the difference between the vertical vibration frequencies of adjacent periods, and using the ratio of the two absolute values ​​of the difference as the vertical galloping adjustment factor of the adjacent periods; Calculating the vertical dancing amplitude of each period by weighted average based on the vertical main amplitude, the vertical vibration frequency and the vertical dancing adjustment factor of each period; The horizontal dancing amplitude and the vertical dancing amplitude of each cycle are vector-synthesized to obtain the dancing amplitude of each cycle.

9. A digital design method for disaster resilience of transmission lines according to claim 8, characterized in that: Performing vector synthesis on the horizontal dancing amplitude and the vertical dancing amplitude of each cycle to obtain the dancing amplitude of each cycle includes: The horizontal dancing amplitude is used as the abscissa component of a vector, and the vertical dancing amplitude is used as the ordinate component of a vector. The square root of the sum of the squares of the abscissa component and the ordinate component is calculated by the Pythagorean theorem to obtain the dancing amplitude.

10. A digital design method for disaster resilience of transmission lines according to claim 9, characterized in that: Extracting the horizontal main amplitude and horizontal vibration frequency of each period based on the horizontal denoised vibration sequence of each period includes: Performing a fast Fourier transform on the horizontal denoised vibration sequence of each period to obtain a frequency-amplitude spectrum; The frequency with the largest amplitude in the frequency-amplitude spectrum is selected as the horizontal vibration frequency of the cycle, and the corresponding amplitude is selected as the horizontal main amplitude of the cycle.

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