A power transmission line disaster resilience digital design method

Abstract

The present application relates to the technical field of power transmission line, specifically to a kind of power transmission line disaster resilience digital design method, including obtaining the real-time environmental parameter of power transmission line, based on real-time environmental parameter calculation comprehensive risk index;Based on the ratio of ice thickness sequence calculation ice thickness change rate and historical average change rate of same period, to obtain icing risk index;Obtain the difference sequence of galloping amplitude of power transmission line in adjacent monitoring period, calculate the ratio of variance of difference sequence and historical galloping variance mean, to obtain galloping risk index;Based on icing risk index and galloping risk index generate comprehensive disaster warning level, based on comprehensive disaster warning level adjust the current load limit value of power transmission line.The present application monitors ice thickness by unmanned aerial vehicle, monitors galloping amplitude by vibration sensor, comprehensively captures different factors that may affect the safety of power transmission line, and multidimensional monitoring improves the comprehensiveness and accuracy of disaster warning.

Description

Technical Field

[0001] This invention relates to the field of power transmission line technology, and more specifically to a digital design method for the disaster resilience of power transmission lines. Background Technology

[0002] Traditional methods often rely on periodic manual inspections or pre-set static risk assessment models, which cannot obtain environmental parameters in real time, resulting in the inability to detect line safety issues or potential risks in a timely manner. Furthermore, traditional methods rely on historical data for risk assessment, but cannot reflect the dynamic changes in the current environment, making disaster warnings untimely and inaccurate. In addition, 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 is prone to ignoring other influencing factors, such as changes in ice and snow thickness, vibration amplitude, etc., making it difficult to comprehensively capture complex disaster risks. Moreover, traditional methods usually do not have the ability to dynamically adjust the current load of transmission lines after risk assessment, especially in pre-disaster prevention measures. This means that once a disaster occurs, equipment damage or system collapse may occur 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 prior art and provide a digital design method for the disaster resilience of transmission lines.

[0004] The technical solution adopted to solve the above-mentioned technical problems is: a digital design method for the disaster resilience of transmission lines, including:

[0005] Obtain real-time environmental parameters of the transmission line and calculate a comprehensive risk index based on the real-time environmental parameters;

[0006] When the comprehensive risk index exceeds the preset risk threshold, a primary disaster warning is issued, and the ice thickness of the transmission line is monitored simultaneously using drones to obtain an ice thickness sequence; the ratio of the ice thickness change rate to the historical average change rate for the same period is calculated based on the ice thickness sequence to obtain the ice risk index.

[0007] When the icing risk index exceeds the preset icing risk threshold, an icing warning is issued, and the galloping amplitude of the transmission line is monitored simultaneously based on the vibration sensor to obtain the galloping amplitude.

[0008] Obtain the difference sequence of the galloping amplitude of the transmission line within adjacent monitoring periods, and calculate the ratio of the variance of the difference sequence to the mean of the historical galloping variance to obtain the galloping risk index;

[0009] When the galloping risk index exceeds the preset galloping risk threshold, a galloping warning is issued. A comprehensive disaster warning level is generated based on the icing risk index and the galloping risk index. The current load limit value of the transmission line is adjusted based on the comprehensive disaster warning level.

[0010] Preferably, the comprehensive risk index is calculated based on the real-time environmental parameters, including:

[0011] Based on the real-time environmental parameters, extract the current air humidity, ambient temperature, and wind speed vectors;

[0012] Calculate the ratio of the air humidity to the historical average humidity for the same period to obtain the humidity risk value;

[0013] 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;

[0014] The wind speed vector is decomposed into a vertical line direction component and a parallel line direction component. The ratios of the vertical line direction component and the parallel line direction component to the average historical wind speed component are calculated to obtain the lateral risk value and the longitudinal risk value.

[0015] The humidity risk value, the low temperature risk value, the lateral risk value, and the longitudinal risk value are weighted and summed to obtain a comprehensive risk index.

[0016] Preferably, the icing thickness of the transmission line is monitored by a drone to obtain an icing thickness sequence, including:

[0017] Acquire point cloud data of power transmission lines collected by drones, and generate a three-dimensional line model based on the point cloud data;

[0018] Based on the conductor sag parameters and tower spacing data in the three-dimensional line model, potential icing growth areas of the transmission line were determined during multiple monitoring periods.

[0019] The point cloud cross-sectional data of the potential icing growth area is obtained by scanning with a lidar mounted on a drone, and the icing thickness increment is calculated based on the change in point cloud cross-sectional data in adjacent monitoring periods.

[0020] The ice thickness increments for each monitoring period are integrated into a time series to obtain an ice thickness sequence.

[0021] Preferably, generating a three-dimensional route model based on the point cloud data includes:

[0022] Conductor features are extracted from the point cloud data to identify continuous linearly distributed point cloud clusters as initial conductor trajectories.

[0023] Based on the tower design spacing parameters, the initial conductor trajectory is segmented and corrected to determine the spatial positioning point of each tower.

[0024] Between adjacent tower positioning points, the catenary equation is fitted based on the conductor tension parameters to generate the conductor space curve;

[0025] The spatial curve of the conductor is offset based on the typical installation height of the ground wire to obtain a three-dimensional line model.

[0026] Preferably, based on the conductor sag parameters and tower spacing data in the three-dimensional line model, potential icing growth areas of the transmission line are determined within multiple monitoring periods, including:

[0027] The coordinates of the lowest sag point of each span segment of the conductor are extracted from the three-dimensional line model.

[0028] A correlation model between the lowest point of the sag and the icing load was established based on historical icing data;

[0029] Based on the snowfall and freezing rain probability in the meteorological forecast data and the aforementioned correlation model, the icing risk level of each of the lowest points of the sag is assessed.

[0030] The conductor spans corresponding to high-risk levels are marked as potential areas for icing growth.

[0031] Preferably, the calculation of the icing thickness increment based on the change in point cloud cross-sectional data between adjacent monitoring periods includes:

[0032] Spatial registration is performed on point cloud cross-sectional data acquired in adjacent monitoring periods to establish an alignment mapping relationship for the point cloud on the conductor surface;

[0033] Based on the alignment mapping relationship, the outer envelope contour of the point cloud on the conductor surface is extracted, and the maximum offset of the outer envelope contour in the direction perpendicular to the conductor axis is calculated.

[0034] The maximum offset is corrected based on the conductor reference diameter parameter to obtain the standardized icing thickness increment.

[0035] Preferably, the galloping amplitude of the transmission line is monitored based on vibration sensors to obtain the galloping amplitude, including:

[0036] The vibration signals of the transmission line in the horizontal and vertical directions are monitored in real time at various moments; the vibration signals in the horizontal and vertical directions are divided according to time periods to obtain the original horizontal vibration sequence and the original vertical vibration sequence corresponding to each period.

[0037] Multi-scale wavelet decomposition is performed on the original horizontal vibration sequence of each period using wavelet function to obtain the detail coefficient sequence of each scale. After thresholding the detail coefficients in the detail coefficient sequence of each scale, the horizontal denoised vibration sequence of each period is reconstructed by inverse wavelet transform to obtain the denoised horizontal vibration sequence of each period.

[0038] Based on the horizontal denoised vibration sequence of each period, the horizontal principal amplitude and horizontal vibration frequency of each period are extracted; the absolute value of the difference between the horizontal principal amplitude and the absolute value of the difference between the horizontal vibration frequency of adjacent periods are calculated, and the ratio of the two absolute values ​​of the difference is used as the horizontal dancing adjustment factor of adjacent periods.

[0039] The horizontal dance amplitude of each cycle is calculated by weighted averaging based on the horizontal principal amplitude, the horizontal vibration frequency, and the horizontal dance adjustment factor for each cycle.

[0040] Preferably, the method of monitoring the galloping amplitude of the transmission line based on vibration sensors to obtain the galloping amplitude further includes:

[0041] The original vertical vibration sequence of each period is decomposed into multi-scale wavelet using wavelet function to obtain the detail coefficient sequence of each scale. After thresholding the detail coefficients in the detail coefficient sequence of each scale, the vertical denoised vibration sequence of each period is reconstructed by inverse wavelet transform to obtain the vertical denoised vibration sequence of each period.

[0042] Based on the vertical denoised vibration sequence of each period, the vertical principal amplitude and vertical vibration frequency of each period are extracted; the absolute value of the difference between the vertical principal amplitude and the absolute value of the difference between the vertical vibration frequency of adjacent periods are calculated, and the ratio of the two absolute values ​​of the difference is used as the vertical dancing adjustment factor of adjacent periods.

[0043] Based on the vertical principal amplitude, the vertical vibration frequency, and the vertical galloping adjustment factor for each cycle, the vertical galloping amplitude for each cycle is calculated by weighted average.

[0044] The horizontal and vertical waving amplitudes of each cycle are vector-synthesized to obtain the waving amplitude of each cycle.

[0045] Preferably, the horizontal and vertical waving amplitudes of each cycle are vector-synthesized to obtain the waving amplitude of each cycle, including:

[0046] The horizontal waving amplitude is taken as the abscissa component of the vector, and the vertical waving amplitude is taken as the ordinate component of the vector. The square root of the sum of the squares of the abscissa and ordinate components is calculated using the Pythagorean theorem to obtain the waving amplitude.

[0047] Preferably, the extraction of the horizontal principal amplitude and horizontal vibration frequency of each period based on the horizontal denoised vibration sequence of each period includes:

[0048] A fast Fourier transform is performed on the horizontally denoised vibration sequence for each period to obtain the frequency-amplitude spectrum.

[0049] In the frequency-amplitude spectrum, the frequency with the largest amplitude is selected as the horizontal vibration frequency of the period, and the corresponding amplitude is selected as the horizontal principal amplitude of the period.

[0050] The beneficial effects of this invention are as follows:

[0051] (1) This invention provides a dynamic, data-based risk monitoring system for transmission lines by acquiring environmental parameters in real time and calculating a comprehensive risk index. It can reflect the potential impact of the external environment on the safety of the lines in a timely manner, thereby providing more accurate and timely disaster warnings. Furthermore, by monitoring ice thickness with drones and monitoring galloping amplitude with vibration sensors, it comprehensively captures different factors that may affect the safety of transmission lines. Multi-dimensional monitoring improves the comprehensiveness and accuracy of disaster warnings, ensuring that different types of disasters (such as ice storms and wind storms) can be effectively monitored.

[0052] (2) By calculating the ratio of the variance of the ice thickness change rate and the difference sequence of gazing amplitude to historical data, this invention can identify abnormal change trends, such as drastic changes in ice and snow thickness or abnormal gazing amplitude caused by wind vibration. It can reflect the risk of impending disasters through changes in the ratio, and is more timely and accurate.

[0053] (3) By dynamically adjusting the current load limit value of the transmission line based on the risk index, the present invention helps to take preventive measures before disasters occur, avoid equipment damage caused by overload, effectively improve the resilience of the transmission line in extreme weather and natural disasters, reduce downtime and economic losses, and enhance the ability to cope with sudden disasters. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the overall method steps in one embodiment of the present invention. Detailed Implementation

[0055] Example 1, as Figure 1 As shown, the present invention proposes a digital design method for the disaster resilience of transmission lines, comprising:

[0056] S1. Obtain real-time environmental parameters of the transmission line and calculate the comprehensive risk index based on the real-time environmental parameters;

[0057] S2. When the comprehensive risk index exceeds the preset risk threshold, a primary disaster warning is issued, and the ice thickness of the transmission line is monitored simultaneously using drones to obtain an ice thickness sequence. The ratio of the ice thickness change rate to the historical average change rate for the same period is calculated based on the ice thickness sequence to obtain the ice risk index.

[0058] S3. When the icing risk index exceeds the preset icing risk threshold, an icing warning is issued, and the galloping amplitude of the transmission line is monitored simultaneously based on the vibration sensor to obtain the galloping amplitude.

[0059] S4. Obtain the difference sequence of the galloping amplitude of the transmission line within adjacent monitoring periods, calculate the ratio of the variance of the difference sequence to the mean of the historical galloping variance, and obtain the galloping risk index.

[0060] S5. When the galloping risk index exceeds the preset galloping risk threshold, a galloping warning is issued. A comprehensive disaster warning level is generated based on the icing risk index and the galloping risk index. The current load limit value of the transmission line is adjusted based on the comprehensive disaster warning level.

[0061] In this invention, real-time environmental parameters refer to real-time data collected by sensors or monitoring equipment regarding the environment in which the transmission line is located, such as temperature, humidity, wind speed, snowfall, and ice thickness. 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 and galloping. 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. When the comprehensive risk index exceeds this threshold, the system considers the transmission line to be facing a significant risk and activates the disaster early warning mechanism. This is an early warning, alerting to potential risks that require further monitoring and analysis. The system will use drones to monitor the icing thickness of the transmission line. The data collected by the drones forms a sequence of icing thickness, representing the icing situation of the line at different time points. Icing is calculated. The rate of change of ice thickness over time reflects whether the ice layer is thickening rapidly; the rate of change can be calculated based on the thickness difference between adjacent moments; by comparing with historical data, the average rate of change of ice thickness during the same period (e.g., the same period over the past few years) is obtained; the ratio of the current rate of change of ice thickness to the historical average rate of change for the same period is calculated; this ratio reflects whether the current rate of change of ice thickness is abnormal, and a larger ratio means a higher risk of ice accumulation; an ice accumulation risk index is calculated based on the ratio, and when the index exceeds a preset threshold, the system triggers an ice accumulation warning, alerting to possible ice accumulation disasters; a comprehensive disaster warning level is generated based on the ice accumulation risk index and the galloping risk index; this is a risk assessment that comprehensively considers all factors, helping decision-makers understand the overall risk of transmission lines; the current load limit value 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 damage or failure of the line due to overload.

[0062] In an optional embodiment, a comprehensive risk index is calculated based on real-time environmental parameters, including:

[0063] A1. Extract the air humidity, ambient temperature, and wind speed vectors at the current moment based on real-time environmental parameters;

[0064] A2. Calculate the ratio of air humidity to the historical average humidity for the same period to obtain the humidity risk value;

[0065] 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;

[0066] A4. Decompose the wind speed vector into components perpendicular to the line and components parallel to the line. Calculate the ratios of the vertical and parallel wind speed components to the average historical wind speed components to obtain the lateral and longitudinal risk values.

[0067] A5. The humidity risk value, low temperature risk value, horizontal risk value, and vertical risk value are weighted and summed to obtain the comprehensive risk index.

[0068] 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 over the same time period (e.g., the past year, the past month, etc.), and obtains 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 lines, such as the risk of line dampness and corrosion. The system also obtains the current ambient temperature and compares it with the embrittlement critical temperature of the materials used in the transmission lines. It calculates the absolute value of the difference between the ambient temperature and the embrittlement critical temperature (i.e., temperature difference). When the temperature difference is large, it indicates that the temperature is too low, which may lead to embrittlement or damage to the line materials, so the risk of low temperature needs to be considered. Wind speed is a vector with directionality; the system decomposes the current wind speed vector into two components: The lateral risk value is obtained by calculating the ratio of the vertical wind speed component to the historical average wind speed component, both perpendicular and parallel to the transmission line. Excessive lateral wind speed may cause the line to bend, sway, or break. The longitudinal risk value is obtained by calculating the ratio of the parallel wind speed component to the historical average wind speed component. Increased longitudinal wind speed may increase the tension on the line, potentially increasing the risk of line breakage. The humidity risk value, low temperature risk value, lateral risk value, and longitudinal risk value are then weighted and summed. The purpose of weighting is to assign different weights according to the degree of impact of each risk, finally obtaining a comprehensive risk index. This index will take into account 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.

[0069] In an optional embodiment, the icing thickness of the transmission line is monitored using a drone to obtain an icing thickness sequence, including:

[0070] B1. Acquire point cloud data of power transmission lines collected by drones, and generate a 3D line model based on the point cloud data;

[0071] B2. Based on conductor sag parameters and tower spacing data in the three-dimensional line model, potential icing growth areas of transmission lines were determined in multiple monitoring periods.

[0072] B3. Obtain point cloud cross-sectional data of potential icing growth areas by scanning with lidar mounted on a drone, and calculate the icing thickness increment based on the change in point cloud cross-sectional data between adjacent monitoring periods.

[0073] B4. Integrate the ice thickness increments for each monitoring period according to the time series to obtain the ice thickness series.

[0074] It should be noted that drones equipped with LiDAR (Light Detection and Ranging) and other devices are used to scan power transmission lines and acquire a series of point cloud data. Point cloud data consists of multiple three-dimensional coordinate points collected by the LiDAR, accurately depicting the three-dimensional shape of the ground and objects. Based on this point cloud data, specialized algorithms and software are used to generate a three-dimensional model of the power transmission line. This model includes information such as the line structure, tower distribution, and conductor morphology. By analyzing the conductor sag (i.e., the downward droop of the conductor under gravity) and the spacing between towers in the three-dimensional model, the morphological changes of the power transmission line at different monitoring periods are determined. These factors influence whether the conductors are susceptible to icing. Using this data, combined with climate and weather factors, areas where icing growth may occur on the power transmission line at different times can be identified. Icing occurs in cold weather with high humidity. In the environment, ice forms on the surfaces of power lines and facilities. Unmanned aerial vehicles (UAVs) use lidar to perform detailed scans of potential icing areas, acquiring point cloud data cross-sections of the region. This data shows the actual location and shape of each point on the transmission line, reflecting the specific condition of the power lines and towers. During continuous monitoring periods, by comparing the point cloud cross-section data from adjacent periods, changes in the surface of the power lines and towers (i.e., the increase in ice accumulation) can be observed. Specifically, by comparing the changes in point cloud cross-section data from different monitoring periods, the increase in ice thickness for each period is calculated. The corresponding ice thickness increase for each monitoring period indicates how much the ice layer on the power lines or towers has thickened during that period. The ice thickness increases calculated for each monitoring period are arranged chronologically to form a continuous time series. This series represents the accumulation of ice on the transmission line at different points in time.

[0075] In an optional embodiment, generating a 3D route model based on point cloud data includes:

[0076] C1. Extract traverse features from the point cloud data to identify continuous linearly distributed point cloud clusters as the initial traverse trajectory;

[0077] C2. Based on the tower design spacing parameters, the initial conductor trajectory is segmented and corrected to determine the spatial positioning point of each tower;

[0078] C3. Between adjacent tower positioning points, the catenary equation is fitted based on the conductor tension parameters to generate the conductor space curve;

[0079] C4. The spatial curve of the conductor is offset based on the typical installation height of the ground wire to obtain a three-dimensional line model.

[0080] It should be noted that point cloud data collected by drones is analyzed using computer algorithms. Specific algorithms identify point cloud clusters arranged continuously in space, resembling lines; these clusters represent the initial trajectory of the transmission line. These linearly distributed point cloud clusters provide the basis for the initial conductor position. The initial conductor trajectory is corrected according to design specifications and the actual tower spacing (i.e., the distance between adjacent towers). Transmission line designs typically follow specific tower spacing to ensure conductor safety and structural stability. By segmenting the initial conductor trajectory according to tower spacing and correcting the shape of each segment to conform to actual design and constraints, the spatial position of each tower and the true shape of the conductor can be determined more accurately. After segmentation and correction, the position of each tower is determined based on the design spacing of each segment. Each tower usually has a clearly defined spatial positioning point, which is used to define the support points of the transmission line. The conductors on the transmission line will bend due to gravity, tension, and other factors during suspension. The shape is calculated based on the conductor tension (i.e., the stress on the conductor between different towers). Based on the conductor tension, an appropriate mathematical model is selected to simulate... The shape of the conductor is typically approximated by a catenary (i.e., a curve under gravity). By fitting the catenary equation, the spatial curve shape of the conductor between different towers can be accurately calculated. After determining the catenary equation between every two adjacent towers, the three-dimensional spatial curve of the conductor is generated. This curve reflects the precise shape of the conductor under stress in reality, representing the connection line from one tower to another. In actual installation, the installation height of the ground wire is usually subject to specifications, and this height is set based on a standard value of ground height. To ensure that the generated conductor spatial curve conforms to the actual installation height of the ground wire, the conductor spatial curve needs to be offset, that is, the conductor trajectory is raised or lowered as a whole to conform to the typical installation height of the ground wire. Through this processing, the resulting three-dimensional line model can reflect the true spatial location and shape of the transmission line. After offset processing, the spatial curve of the conductor and the positioning points of the towers are finally combined to form a complete three-dimensional transmission line model. This model can accurately represent all the characteristics of the transmission line in three-dimensional space, including the shape of the conductor, the distribution of towers, etc.

[0081] In an optional embodiment, potential icing growth areas of the transmission line during multiple monitoring periods are determined based on conductor sag parameters and tower spacing data in a three-dimensional line model, including:

[0082] D1. Extract the coordinates of the lowest sag point of each span of the conductor from the three-dimensional line model; establish a correlation model between the lowest sag point and the icing load based on historical icing data.

[0083] D2. Based on the snowfall, freezing rain probability and correlation model in the meteorological forecast data, assess the icing risk level of each sag's lowest point.

[0084] D3. Mark the conductor spans corresponding to high-risk levels as potential areas for icing growth.

[0085] It should be noted that due to factors such as gravity and conductor tension, the conductor will bend in the span between the various towers. The lowest point of sag refers to the lowest point of the conductor within a certain span, i.e., the lowest point of the conductor curve. Extracting the coordinates of these lowest points is crucial because they are key locations for assessing the impact of icing on the conductor. Transmission lines consist of multiple towers, and the distance between the towers is called the span. The location of the lowest point of sag between each span can serve as a reference for subsequent analysis. Historical icing data typically includes the occurrence of icing on the line under different weather conditions and the resulting load. By analyzing historical icing data, a mathematical model can be established to correlate the height, location, and degree of icing at the lowest point of sag. Alternatively, the model can help predict the amount of icing load the lowest point of the conductor will experience under certain weather conditions. This model is established to subsequently assess the icing risk of the conductor under different weather conditions. Weather forecasts provide upcoming weather conditions, especially snowfall and the probability of freezing rain. Snowfall and the probability of freezing rain are key factors affecting icing occurrence; higher snowfall or the probability of freezing rain means a greater likelihood of icing. Icing occurs; by inputting the snowfall amount 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 determined. Based on the icing load of the lowest sag points 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 impact due to the upcoming weather conditions. In the icing risk level assessment, if the lowest sag points of certain spans face a high risk of icing, then these spans will be marked as potential icing growth areas. This means that the conductors in these areas may experience significant icing accumulation under the upcoming weather conditions, which may lead to excessive conductor load, 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.

[0086] In an optional embodiment, calculating the ice thickness increment based on the change in point cloud cross-sectional data between adjacent monitoring periods includes:

[0087] E1. Spatial registration is performed on the point cloud cross-sectional data acquired in adjacent monitoring periods to establish the alignment mapping relationship of the point cloud on the conductor surface;

[0088] E2. Extract the outer envelope contour of the point cloud on the conductor surface based on the alignment mapping relationship, and calculate the maximum offset of the outer envelope contour in the direction perpendicular to the conductor axis.

[0089] E3. The maximum offset is corrected based on the conductor reference diameter parameter to obtain the standardized ice thickness increment.

[0090] It should be noted that since point cloud data is usually collected at different time periods and from different perspectives, spatial registration of this data is necessary. This involves aligning point cloud data from different time periods to the same reference coordinate system for subsequent analysis. This is achieved by establishing a mapping relationship between point clouds, ensuring that data from different time periods can correctly correspond to the same spatial location. The goal of registration is to establish a mathematical model or transformation relationship so that the point cloud data of the conductor surface at different time periods can accurately correspond and align, thereby ensuring the accuracy of subsequent analysis. The outer envelope refers to the outermost contour of the point cloud data, usually used to represent the boundary of an object. For conductor point cloud data, the outer envelope represents the outermost curve of the conductor surface, which helps in analyzing the conductor's morphology. The outer envelope of the conductor surface may shift at different time periods due to factors such as icing and temperature changes. Calculating the maximum offset of the outer envelope in the direction perpendicular to the conductor axis can quantify the degree of deformation of the conductor due to icing or other factors. This offset is the distance between the outermost layer of the conductor surface point cloud and the reference position. The maximum difference between the conductors is as follows: Since conductors are typically curved, a suitable direction must be chosen when calculating the offset, usually perpendicular to the conductor's main axis. This ensures that the offset calculation reflects the changes that occur during icing. The conductor's reference diameter refers to its standard diameter, typically the diameter under normal operating conditions or without icing. This parameter is important in icing assessment because the conductor's shape and size affect ice adhesion and load. The maximum offset represents the deformation of the conductor surface due to icing, but the actual icing thickness should be related to the conductor's diameter. To standardize the offset, the conductor's reference diameter needs to be considered. By correcting the maximum offset, a correction value related to the conductor size can be obtained, ensuring the universality and accuracy of the icing thickness assessment. After correction, the resulting value represents the standardized icing thickness increment of the conductor over a specific time period, i.e., the increment on the conductor surface due to ice and snow accumulation. This increment can serve as an indicator for assessing conductor icing conditions, and can then be used to calculate conductor load increases, risk assessments, etc.

[0091] In an optional embodiment, the galloping amplitude of the transmission line is monitored based on vibration sensors to obtain the galloping amplitude, including:

[0092] F1. Real-time monitoring of vibration signals in the horizontal and vertical directions of the transmission line at various times; dividing the horizontal and vertical vibration signals according to time periods to obtain the original horizontal vibration sequence and the original vertical vibration sequence corresponding to each period;

[0093] F2. Multi-scale wavelet decomposition is performed on the original horizontal vibration sequence of each period using wavelet function to obtain the detail coefficient sequence of each scale. After thresholding the detail coefficients in the detail coefficient sequence of each scale, the horizontal denoised vibration sequence of each period is reconstructed by inverse wavelet transform.

[0094] F3. Extract the horizontal principal 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 principal amplitude and the absolute value of the difference between the horizontal vibration frequency of adjacent periods, and use the ratio of the two absolute values ​​of the difference as the horizontal dancing adjustment factor of adjacent periods.

[0095] F4. Based on the horizontal principal amplitude, horizontal vibration frequency and horizontal galloping adjustment factor of each period, the horizontal galloping amplitude of each period is calculated by weighted average.

[0096] It should be noted that transmission lines are affected by various factors such as wind, snow, and earthquakes, generating horizontal (lateral) and vertical (longitudinal) vibration signals. Real-time monitoring of these signals is necessary to obtain information on line status and vibration changes. The acquired horizontal and vertical vibration signals are divided into time segments, typically at fixed time intervals (e.g., per minute, per hour), to facilitate analysis of vibration characteristics across different time periods. Wavelet transform is a commonly used signal processing technique that decomposes a signal into components of different frequencies through multi-scale analysis. Here, the original horizontal vibration sequence is decomposed using wavelet transform to obtain detail coefficients at different scales. Each detail coefficient represents the signal characteristics of that frequency band. Thresholding is a common wavelet denoising method. By setting a threshold, smaller noise components are removed, retaining only larger detail coefficients to reduce noise interference with signal analysis. After thresholding, inverse wavelet transform is used to reconstruct the detail coefficients, obtaining a noise-free horizontal vibration sequence. This process helps to clearly reflect the true fluctuations of the vibration signal. Amplitude represents the intensity of vibration, with the principal amplitude referring 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 principal vibration in the signal is extracted using methods such as Fourier transform; these two parameters (amplitude and frequency) are key to evaluating the line vibration characteristics; by comparing the horizontal principal amplitude and horizontal vibration frequency of two adjacent time periods, the absolute difference between them is calculated; this difference represents the change in vibration intensity and frequency between the two time periods; by calculating the ratio of the absolute value of the difference in horizontal principal amplitude to the absolute value of the difference in frequency, an adjustment factor is obtained, called the "horizontal galloping adjustment factor"; this factor can be used to measure the severity of vibration changes between adjacent time periods, reflecting the "galloping" behavior of the line; based on the horizontal principal amplitude, horizontal vibration frequency, and horizontal galloping adjustment factor of each period, the horizontal galloping amplitude of each period is calculated by weighted averaging; the purpose of weighted averaging is to assign different weights to different parameters according to their importance, so that more important parameters have a greater impact on the final result; the finally calculated horizontal galloping amplitude represents the horizontal vibration intensity of the transmission line over a period of time, and combined with the horizontal principal amplitude, frequency, and adjustment factor, it can comprehensively reflect the dynamic state of the line.

[0097] In an optional embodiment, monitoring the galloping amplitude of the transmission line based on vibration sensors to obtain the galloping amplitude further includes:

[0098] F5. Perform multi-scale wavelet decomposition on the original vertical vibration sequence of each period using wavelet function to obtain the detail coefficient sequence of each scale. After thresholding the detail coefficients in the detail coefficient sequence of each scale, reconstruct them through inverse wavelet transform to obtain the vertical denoised vibration sequence of each period.

[0099] F6. Based on the vertical denoised vibration sequence of each period, extract the vertical principal amplitude and vertical vibration frequency of each period; calculate the absolute value of the difference between the vertical principal amplitude and the absolute value of the difference between the vertical vibration frequency of adjacent periods, and use the ratio of the two absolute values ​​of the difference as the vertical dancing adjustment factor of adjacent periods.

[0100] F7. Based on the vertical principal amplitude, vertical vibration frequency and vertical galloping adjustment factor of each period, the vertical galloping amplitude of each period is calculated by weighted average.

[0101] F8. Vector synthesize the horizontal and vertical waving amplitudes of each cycle to obtain the waving amplitude of each cycle.

[0102] It should be noted that the original vertical vibration sequence is first decomposed into multi-scale wavelet transforms. For two adjacent cycles, the absolute values ​​of the differences in the vertical principal amplitude and the vertical vibration frequency are calculated. These two differences reflect the magnitude of amplitude and frequency changes over time. The ratio of the absolute values ​​of the two differences is used as the "vertical gobling adjustment factor," which reflects the severity of vertical vibration changes between adjacent cycles. Based on the vertical principal amplitude, vertical vibration frequency, and vertical gobling adjustment factor for each cycle, the vertical gobling amplitude for each cycle is calculated using a weighted average method. The weighted average method typically assigns different weights to these parameters according to their importance, thereby comprehensively considering the influence of various factors to obtain a more accurate vertical gobling amplitude. Finally... The vertical galloping amplitude is a comprehensive reflection of the vertical vibration intensity of that cycle, combining amplitude, frequency, and the severity of vibration changes. The calculation of the horizontal galloping amplitude, representing the horizontal vibration characteristics, was described earlier. Through this process, we obtain the vertical galloping amplitude, representing the vertical vibration characteristics. The horizontal and vertical galloping amplitudes represent the vibration amplitudes in the horizontal and vertical directions, respectively. However, actual vibration is a two-dimensional dynamic process, requiring a comprehensive consideration of the influence of both directions. Therefore, the horizontal and vertical galloping amplitudes are vector-synthesized. The principle of vector synthesis is based on the Pythagorean theorem, combining the horizontal and vertical amplitudes into a total vibration amplitude. This total galloping amplitude reflects the comprehensive vibration intensity of the transmission line in each cycle.

[0103] In an optional embodiment, the horizontal and vertical waving amplitudes of each cycle are vector-synthesized to obtain the waving amplitude of each cycle, including:

[0104] G1. Take the horizontal waving amplitude as the abscissa component of the vector and the vertical waving amplitude as the ordinate component of the vector. Calculate the square root of the sum of the squares of the abscissa and ordinate components using the Pythagorean theorem to obtain the waving amplitude.

[0105] In an optional embodiment, the horizontal principal amplitude and horizontal vibration frequency of each period are extracted based on the horizontal denoised vibration sequence of each period, including:

[0106] H1. Perform a fast Fourier transform on the horizontal denoised vibration sequence of each period to obtain the frequency-amplitude spectrum.

[0107] H2. Select the frequency with the largest amplitude in the frequency-amplitude spectrum as the horizontal vibration frequency of the period, and the corresponding amplitude as the horizontal principal amplitude of the period.

[0108] It should be noted that a horizontally denoised vibration sequence refers to a signal that has undergone denoising processing to remove background noise or unnecessary interference. The signal may originate from a vibration sensor, and during signal processing, methods such as filtering and smoothing are used to remove noise, leaving only the main vibration signal. Fast Fourier Transform (FFT) is an efficient method for calculating Fourier transforms, converting a 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 determined. For each cycle of the vibration sequence, FFT can obtain the vibration intensity at different frequencies. This means that we can know which frequency components the signal contains and their relative intensities. The results obtained through FFT are usually represented as a graph of frequency versus amplitude; the horizontal axis represents frequency (usually in Hertz, Hz), and the vertical axis represents the amplitude at the corresponding frequency. The amplitude represents the vibration intensity at a given frequency. At some frequencies, the amplitude may be large, indicating that the frequency component dominates the signal; while at other frequencies, the amplitude is smaller, indicating that these frequency components have a weaker impact 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 principal amplitude, that is, the vibration amplitude of the signal at that frequency. The principal amplitude describes the vibration intensity at that frequency and represents the proportion of that frequency component in the signal.

[0109] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, 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 the disaster resilience of transmission lines, characterized in that, include: Obtain real-time environmental parameters of the transmission line and calculate a comprehensive risk index based on the real-time environmental parameters; The calculation of the comprehensive risk index based on the real-time environmental parameters includes: Based on the real-time environmental parameters, extract the current air humidity, ambient temperature, and wind speed vectors; calculate the ratio of the air humidity to the historical average humidity for the same period to obtain the humidity risk value; 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. The wind speed vector is decomposed into a vertical line direction component and a parallel line direction component. The ratios of the vertical line direction component and the parallel line direction component to the average historical wind speed component are calculated to obtain the lateral risk value and the longitudinal risk value. The humidity risk value, the low temperature risk value, the lateral risk value, and the longitudinal risk value are weighted and summed to obtain a comprehensive risk index; When the comprehensive risk index exceeds the preset risk threshold, a primary disaster warning is issued, and the ice thickness of the transmission line is monitored simultaneously using drones to obtain an ice thickness sequence; the ratio of the ice thickness change rate to the historical average change rate for the same period is calculated based on the ice thickness sequence to obtain the ice risk index. When the icing risk index exceeds the preset icing risk threshold, an icing warning is issued, and the galloping amplitude of the transmission line is monitored simultaneously based on the vibration sensor to obtain the galloping amplitude. The method of monitoring the galloping amplitude of the transmission line based on vibration sensors to obtain the galloping amplitude includes: The vibration signals of the transmission line in the horizontal and vertical directions are monitored in real time at various moments; the vibration signals in the horizontal and vertical directions are divided according to time periods to obtain the original horizontal vibration sequence and the original vertical vibration sequence corresponding to each period. Multi-scale wavelet decomposition is performed on the original horizontal vibration sequence of each period using wavelet function to obtain the detail coefficient sequence of each scale. After thresholding the detail coefficients in the detail coefficient sequence of each scale, the horizontal denoised vibration sequence of each period is reconstructed by inverse wavelet transform to obtain the denoised horizontal vibration sequence of each period. Based on the horizontal denoised vibration sequence of each period, the horizontal principal amplitude and horizontal vibration frequency of each period are extracted; the absolute value of the difference between the horizontal principal amplitude and the absolute value of the difference between the horizontal vibration frequency of adjacent periods are calculated, and the ratio of the two absolute values ​​of the difference is used as the horizontal dancing adjustment factor of adjacent periods. Based on the horizontal principal amplitude, the horizontal vibration frequency, and the horizontal gobling adjustment factor for each cycle, the horizontal gobling amplitude for each cycle is calculated by weighted average. Obtain the difference sequence of the galloping amplitude of the transmission line within adjacent monitoring periods, and calculate the ratio of the variance of the difference sequence to the mean of the historical galloping variance to obtain the galloping risk index; When the galloping risk index exceeds the preset galloping risk threshold, a galloping warning is issued. A comprehensive disaster warning level is generated based on the icing risk index and the galloping risk index. The current load limit value of the transmission line is adjusted based on the comprehensive disaster warning level.

2. The digital design method for the disaster resilience of transmission lines according to claim 1, characterized in that, Based on the monitoring of icing thickness on the transmission line by drones, an icing thickness sequence is obtained, including: Acquire point cloud data of power transmission lines collected by drones, and generate a three-dimensional line model based on the point cloud data; Based on the conductor sag parameters and tower spacing data in the three-dimensional line model, potential icing growth areas of the transmission line were determined during multiple monitoring periods. The point cloud cross-sectional data of the potential icing growth area is obtained by scanning with a lidar mounted on a drone, and the icing thickness increment is calculated based on the change in point cloud cross-sectional data in adjacent monitoring periods. The ice thickness increments for each monitoring period are integrated into a time series to obtain an ice thickness sequence.

3. The digital design method for the disaster resilience of transmission lines according to claim 2, characterized in that, Generating a 3D route model based on the point cloud data includes: Conductor features are extracted from the point cloud data to identify continuous linearly distributed point cloud clusters as initial conductor trajectories. Based on the tower design spacing parameters, the initial conductor trajectory is segmented and corrected to determine the spatial positioning point 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 spatial curve of the conductor is offset based on the typical installation height of the ground wire to obtain a three-dimensional line model.

4. The digital design method for the disaster resilience of transmission lines according to claim 3, characterized in that, Based on the conductor sag parameters and tower spacing data in the three-dimensional line model, potential icing growth areas for transmission lines were identified over multiple monitoring periods, including: The coordinates of the lowest sag point of each span segment of the conductor are extracted from the three-dimensional line model. A correlation model between the lowest point of the sag and the icing load was established based on historical icing data; Based on the snowfall and freezing rain probability in the meteorological forecast data and the aforementioned correlation model, the icing risk level of each of the lowest points of the sag is assessed. The conductor spans corresponding to high-risk levels are marked as potential areas for icing growth.

5. The digital design method for the disaster resilience of transmission lines according to claim 4, characterized in that, The increase in icing thickness is calculated based on the changes in point cloud cross-sectional data between adjacent monitoring periods, including: Spatial registration is performed on point cloud cross-sectional data acquired in adjacent monitoring periods to establish an alignment mapping relationship for the point cloud on the conductor surface; Based on the alignment mapping relationship, the outer envelope contour of the point cloud on the conductor surface is extracted, and the maximum offset of the outer envelope contour in the direction perpendicular to the conductor axis is calculated. The maximum offset is corrected based on the conductor reference diameter parameter to obtain the standardized icing thickness increment.

6. The digital design method for the disaster resilience of transmission lines according to claim 5, characterized in that, Monitoring the galloping amplitude of the transmission line based on vibration sensors to obtain the galloping amplitude also includes: The original vertical vibration sequence of each period is decomposed into multi-scale wavelet using wavelet function to obtain the detail coefficient sequence of each scale. After thresholding the detail coefficients in the detail coefficient sequence of each scale, the vertical denoised vibration sequence of each period is reconstructed by inverse wavelet transform to obtain the vertical denoised vibration sequence of each period. Based on the vertical denoised vibration sequence of each period, the vertical principal amplitude and vertical vibration frequency of each period are extracted; the absolute value of the difference between the vertical principal amplitude and the absolute value of the difference between the vertical vibration frequency of adjacent periods are calculated, and the ratio of the two absolute values ​​of the difference is used as the vertical dancing adjustment factor of adjacent periods. Based on the vertical principal amplitude, the vertical vibration frequency, and the vertical galloping adjustment factor for each cycle, the vertical galloping amplitude for each cycle is calculated by weighted average. The horizontal and vertical waving amplitudes of each cycle are vector-synthesized to obtain the waving amplitude of each cycle.

7. The digital design method for the disaster resilience of transmission lines according to claim 6, characterized in that, The horizontal and vertical waving amplitudes of each cycle are vector-synthesized to obtain the waving amplitude of each cycle, including: The horizontal waving amplitude is taken as the abscissa component of the vector, and the vertical waving amplitude is taken as the ordinate component of the vector. The square root of the sum of the squares of the abscissa and ordinate components is calculated using the Pythagorean theorem to obtain the waving amplitude.

8. The digital design method for the disaster resilience of transmission lines according to claim 7, characterized in that, Based on the horizontal denoised vibration sequence of each period, the horizontal principal amplitude and horizontal vibration frequency of each period are extracted, including: A fast Fourier transform is performed on the horizontally denoised vibration sequence for each period to obtain the frequency-amplitude spectrum; In the frequency-amplitude spectrum, the frequency with the largest amplitude is selected as the horizontal vibration frequency of the period, and the corresponding amplitude is selected as the horizontal principal amplitude of the period.

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