Ice melting intermittent deicing risk assessment method and system for ultra-high voltage transmission line

Abstract

The invention relates to the technical field of ice melting intermittent deicing risk assessment, and provides an ultra-high voltage transmission line ice melting intermittent deicing risk assessment method and system. Performing icing form evolution analysis according to the split conductor icing point cloud data and the environmental meteorological data to obtain an icing form change trend of regional icing form change trends including different attachment regions of each sub-conductor; and according to the icing form change trend of each sub-conductor, performing icing shedding analysis to obtain a corresponding conductor icing shedding risk value, and summarizing the conductor icing shedding risk values of the sub-conductors to obtain a corresponding risk assessment result. According to the invention, a dynamic evolution analysis mechanism for the ice coating form of the ice-melting intermittent split conductor based on the coupling characteristics of the environmental meteorological data and the ice coating form change can effectively improve the accuracy and efficiency of the ice coating falling risk assessment of the split conductor.

Description

Technical Field

[0001] The present invention relates to the technical field of intermittent de-icing risk assessment for ice melting, and in particular to a method and system for assessing the intermittent de-icing risk for ice melting of ultra-high voltage transmission lines. Background Art

[0002] De-icing on UHV transmission lines typically involves rotating the ice on each sub-conductor within a split conductor, or by rotating the ice on each sub-conductor in pairs. However, during the intermittent periods of pulsed de-icing, unmelted ice may remain on the conductor surface. This loose structure is more susceptible to secondary reshaping (such as wind erosion or localized recrystallization) due to environmental meteorological influences. This change in ice morphology directly reshapes the aerodynamic properties of the ice, inevitably weakening the local adhesion between the ice and the conductor. This makes the ice extremely susceptible to detachment when encountering sudden airflows such as canyon gusts, which in turn induces violent abnormal vibrations and large oscillations in the split conductors, seriously threatening the mechanical stability of the sub-conductors and the overall safety of the line. Assessing the de-icing risk during intermittent de-icing on UHV transmission lines is of great significance for ensuring the stability and reliability of the power grid in extreme weather conditions.

[0003] Although the existing method of monitoring the aerodynamic parameters of ice-covered conductors based on fixed sensors can provide a reference for the risk assessment of intermittent de-icing during ice melting to a certain extent, this method can only monitor and evaluate the conductor's dancing state, and does not consider the dynamic evolution characteristics of the icing morphology affected by the environmental meteorological environment. There is insufficient analysis of the coupling mechanism of the interaction between complex environmental meteorological conditions and icing morphology, and it is impossible to accurately obtain the three-dimensional geometric characteristics of the ice covering on the split conductors, resulting in limited accuracy in de-icing risk prediction. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for assessing the risk of ice shedding during ice melting intervals of ultra-high voltage transmission lines. Based on the coupling characteristics of environmental meteorological data and changes in ice morphology, a dynamic evolution analysis mechanism of the ice morphology of split conductors during ice melting intervals is analyzed, which effectively improves the accuracy and efficiency of the risk assessment of ice shedding of split conductors, and provides reliable technical support for the safe operation of the power system.

[0005] In order to achieve the above objectives, it is necessary to provide a method and system for assessing the risk of intermittent de-icing during ice melting of ultra-high voltage transmission lines in response to the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a method for assessing intermittent de-icing risk of ice melting on a UHV transmission line, the method comprising the following steps: Obtaining ice point cloud data and environmental meteorological data of the split conductor at the initial moment of the ice melting interval; the environmental meteorological data includes wind speed, wind direction, temperature and terrain; the ice point cloud data of the split conductor includes initial ice point cloud data of each sub-conductor; performing an ice morphology evolution analysis based on the split conductor ice point cloud data and the environmental meteorological data to obtain an ice morphology change trend of each sub-conductor; the ice morphology change trend includes a regional ice morphology change trend of different attachment areas; An ice shedding analysis is performed based on the ice morphology change trend of each sub-conductor to obtain the corresponding conductor ice shedding risk value. The conductor ice shedding risk values ​​of each sub-conductor are then summarized to obtain the corresponding risk assessment result.

[0007] Furthermore, the step of performing ice morphology evolution analysis based on the split conductor ice point cloud data and the environmental meteorological data to obtain the ice morphology change trend of each sub-conductor includes: Performing ice morphology analysis based on the initial ice point cloud data of each sub-conductor to obtain ice geometric features of each sub-conductor; the ice geometric features include regional ice geometric features of different attachment areas; Based on the ice morphology of different attachment areas in the ice geometric morphology characteristics of each sub-conductor, a corresponding ice morphology feature prediction model is obtained, and based on the ice morphology feature prediction model and the ambient meteorological data, the ice morphology evolution of different attachment areas is predicted to obtain the ice morphology change trend of each sub-conductor; the ice morphology feature prediction model is a neural network model that predicts the ice geometric morphology characteristics based on the ambient meteorological data.

[0008] Furthermore, the geometric features of ice coating in the region include ice coating shape, ice coating thickness and surface roughness; The step of performing ice morphology analysis based on the initial ice point cloud data of each sub-conductor to obtain the ice geometric features of each sub-conductor includes: Using voxel grid-based statistical filtering, denoising the ice point cloud data of each sub-conductor in the split conductor ice point cloud data is performed to obtain corresponding sub-conductor smoothed point cloud data; Determine whether there are any missing points in the smoothed point cloud data of each sub-conductor; if so, fill in the missing points using a preset interpolation algorithm to generate the corresponding complete ice-covered point cloud data of the sub-conductor; According to the complete ice-covered point cloud data of each sub-conductor, a corresponding sub-conductor ice-covered morphology grid model is generated based on surface reconstruction technology; Based on a preset skeleton extraction algorithm, an ice morphology analysis is performed on the ice morphology grid model of each sub-conductor to obtain corresponding ice geometric morphology features.

[0009] Furthermore, the step of performing ice morphology analysis on the ice morphology grid model of each sub-conductor based on a preset skeleton extraction algorithm to obtain corresponding ice geometric features includes: Using a skeletonization algorithm based on Thiessen polygons, the central axis of the ice-covered conductor of the ice-covered morphology grid model of each sub-conductor is obtained; Calculating the distance between each grid point in the sub-conductor ice morphology grid model and the central axis of the ice-covered conductor to obtain corresponding ice thickness distribution data; Obtaining an attachment area set of each sub-conductor according to the ice coating thickness distribution data of each sub-conductor and a preset thickness threshold; Based on the iced conductor centerline of each sub-conductor, the deviation distribution of mesh vertices on both sides of the conductor corresponding to different attachment areas in the attachment area set is calculated respectively, and the ice morphology of the different attachment areas is obtained based on the deviation distribution of mesh vertices on both sides of the conductor; the ice morphology includes eccentric ice morphology and uniform ice morphology; Calculating the average ice thickness of each attachment area in the corresponding attachment area set according to the ice thickness distribution data of each sub-conductor, and using the average ice thickness as the ice thickness of the corresponding attachment area; According to the complete ice-covered point cloud data of each sub-conductor, the regional ice-covered surface corresponding to each attachment area is fitted based on the least squares method to obtain the corresponding fitting residual distribution, and the corresponding surface roughness is obtained according to the fitting residual distribution.

[0010] Furthermore, the step of performing ice shedding analysis based on the ice morphology change trend of each sub-conductor to obtain the corresponding conductor ice shedding risk value includes: According to the preset ice thickness threshold and the preset surface roughness threshold, the regional ice morphology change trend of different attachment areas on each sub-conductor is traversed and analyzed to obtain the corresponding attachment area to be analyzed; Based on the finite element analysis method, the regional adhesion force of different attachment areas to be analyzed on each sub-conductor is identified to obtain the corresponding regional adhesion force strength; Comparing the regional adhesion strength of different attachment areas to be analyzed on each sub-conductor with a preset adhesion strength threshold to obtain a weakened adhesion area on each sub-conductor; the weakened adhesion area is an attachment area to be analyzed where the regional adhesion strength is less than the preset adhesion strength threshold; Obtain wind field aerodynamic parameters and vibration frequency data for each sub-conductor, and predict regional ice shedding probabilities based on a pre-built logistic regression model based on the wind field aerodynamic parameters, the vibration frequency data, and the ice thickness and surface roughness of different adhesion-weakened areas on the corresponding sub-conductors to obtain the corresponding regional ice shedding probabilities; the wind field aerodynamic parameters include the aerodynamic drag coefficient, the aerodynamic lift coefficient, and the aerodynamic torsional moment coefficient; Based on the regional ice shedding probability of all adhesion weakened areas on each sub-conductor, the sub-conductor ice shedding areas are randomly sampled using the Monte Carlo simulation method to generate the conductor ice shedding event probability distribution. Based on the probability distribution of the conductor ice-coating and falling-off events, a corresponding conductor ice-coating and falling-off risk value is obtained.

[0011] Furthermore, the probability distribution of the conductor ice shedding event includes multiple groups of regional ice shedding combination events and corresponding event occurrence probabilities; The step of obtaining a corresponding conductor ice-falling risk value based on the conductor ice-falling event probability distribution includes: Obtaining a corresponding regional ice shedding risk value according to the ice thickness and regional location of each ice shedding region in each group of regional ice shedding combination events in the conductor ice shedding event probability distribution; The regional icing risk values ​​of all icing areas in each group of regional icing combination events are comprehensively analyzed based on the corresponding regional icing probability to obtain the corresponding icing combination event risk value; The ice-covered and shedding risk values ​​of the combined deicing events in each group of regions are weightedly analyzed based on the corresponding event occurrence probabilities to obtain the conductor ice-covered and shedding risk values.

[0012] Furthermore, the step of performing regional adhesion identification on different attachment areas to be analyzed on each sub-conductor based on the finite element analysis method to obtain the corresponding regional adhesion strength includes: According to the initial ice point cloud data of each sub-conductor, ice point cloud data of different attachment areas to be analyzed on each sub-conductor are obtained; According to the ice point cloud data of different attachment areas to be analyzed on each sub-conductor, the initial ice thickness and initial surface roughness of the corresponding attachment area to be analyzed are obtained based on the preset skeleton extraction algorithm; According to the initial ice thickness and initial surface roughness of different attachment areas to be analyzed on each sub-conductor, as well as the corresponding current ice thickness and current surface roughness, the corresponding regional ice thickness deviation and regional roughness deviation are obtained, and according to the regional ice thickness deviation and the regional roughness deviation, the coordinates of each ice point in the corresponding ice point cloud data are adjusted to obtain the ice point cloud data to be analyzed; According to the ice point cloud data of different attachment areas to be analyzed on each sub-conductor, the corresponding regional conductor ice point cloud model is generated based on the Poisson reconstruction algorithm, and the ice line interface adhesion force analysis of each regional conductor ice point cloud model is performed based on the finite element analysis method to obtain the regional adhesion force strength.

[0013] Furthermore, the method further comprises: According to the ranking result of the conductor ice shedding risk value of each sub-conductor in the risk assessment result, the de-icing priority of each sub-conductor is obtained, and the sub-conductor de-icing grouping strategy is adjusted according to the de-icing priority of each sub-conductor.

[0014] In a second aspect, an embodiment of the present invention provides a system for assessing intermittent de-icing risk of ultra-high voltage transmission lines, the system comprising: A data acquisition module is used to obtain ice point cloud data of the split conductor and environmental meteorological data at the initial moment of the ice melting interval; the ice point cloud data of the split conductor includes the initial ice point cloud data of each sub-conductor; the environmental meteorological data includes wind speed, wind direction, temperature and terrain; an evolution analysis module, configured to perform an ice morphology evolution analysis based on the split conductor ice point cloud data and the environmental meteorological data to obtain an ice morphology change trend of each sub-conductor; the ice morphology change trend includes a regional ice morphology change trend of different attachment areas; The risk assessment module is used to perform ice shedding analysis based on the ice morphology change trend of each sub-conductor, obtain the corresponding conductor ice shedding risk value, and summarize the conductor ice shedding risk values ​​of each sub-conductor to obtain the corresponding risk assessment result.

[0015] Furthermore, the system further comprises: The strategy adjustment module is used to obtain the de-icing priority of each sub-conductor according to the ranking result of the conductor ice shedding risk value of each sub-conductor in the risk assessment result, and adjust the sub-conductor de-icing grouping strategy according to the de-icing priority of each sub-conductor.

[0016] The present invention provides a method and system for assessing de-icing risks during ice melting intervals of ultra-high voltage transmission lines. The method realizes obtaining split conductor icing point cloud data including initial icing point cloud data of each sub-conductor at the initial moment of the ice melting interval and environmental meteorological data including wind speed, wind direction, temperature and terrain, performing icing morphological evolution analysis based on the split conductor icing point cloud data and the environmental meteorological data, obtaining icing morphological change trends of each sub-conductor including regional icing morphological change trends of different attachment areas, performing icing shedding analysis based on the icing morphological change trends of each sub-conductor to obtain corresponding conductor icing shedding risk values, and summarizing the conductor icing shedding risk values ​​of each sub-conductor to obtain corresponding risk assessment results. Compared with the existing technology, this ice shedding risk assessment method for ultra-high voltage transmission lines during ice melting intervals is based on the dynamic evolution analysis mechanism of the ice morphology of split conductors during ice melting intervals based on the coupling characteristics of environmental meteorological data and ice morphology changes. It can effectively improve the accuracy and efficiency of ice shedding risk assessment for split conductors during ice melting intervals, provide reliable guidance for subsequent ice melting strategy optimization, and thus provide reliable technical support for the safe and stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 1 is a flow chart of a method for assessing intermittent de-icing risk of ice melting on ultra-high voltage transmission lines according to an embodiment of the present invention; Figure 2 This is another flow chart of the method for assessing intermittent de-icing risk of melting ice on ultra-high voltage transmission lines according to an embodiment of the present invention; Figure 3 2 is a schematic structural diagram of an intermittent de-icing risk assessment system for ultra-high voltage transmission lines according to an embodiment of the present invention; Figure 4 2 is another structural schematic diagram of the intermittent de-icing risk assessment system for ultra-high voltage transmission lines according to an embodiment of the present invention; Description of the drawings: Among them, 1. Data acquisition module; 2. Evolution analysis module; 3. Risk assessment module; 4. Strategy adjustment module. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and beneficial effects of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are part of the embodiments of the present invention and are only used to illustrate the present invention, but are not used to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] In one embodiment, Figure 1 As shown, a method for assessing intermittent de-icing risk of ultra-high voltage transmission lines is provided, comprising the following steps: S11. Obtaining split conductor ice coverage point cloud data and environmental meteorological data at the initial moment of the ice melting interval; wherein, the initial moment of the ice melting interval can be understood as the sampling moment after the end of a round of ice melting operation during the ice melting process of the ultra-high voltage transmission line, which can be set according to actual application requirements and is not specifically limited here.

[0020] The split conductor ice point cloud data can be understood as three-dimensional point cloud data containing millions of points generated by flying a drone equipped with a lidar device along the split conductor to scan the ice surface of each sub-conductor. The data records the three-dimensional coordinate set of the ice surface of each sub-conductor, such as the coordinates of a point in the three-dimensional point cloud data are (10.2, 5.3, 2.1) meters, to reflect the spatial position of the ice surface of each sub-conductor, including the initial ice point cloud data of each sub-conductor.

[0021] Environmental meteorological data can be understood as data on relevant factors that may affect the changes in the ice morphology of the split conductor in the operating environment of the split conductor. In order to ensure the comprehensiveness and reliability of the subsequent analysis of the evolution of ice morphology, this embodiment preferably sets the environmental meteorological data to include wind speed, wind direction, temperature, and terrain. Among them, the wind speed, wind direction, and temperature can be obtained respectively through anemometers, wind vanes, and temperature sensors pre-deployed on the transmission tower. The terrain is a terrain feature obtained by identifying the surrounding terrain data of the split conductor obtained by scanning with a drone equipped with a lidar device, such as a canyon or a plain.

[0022] S12. Performing an ice morphology evolution analysis based on the split conductor ice point cloud data and the environmental meteorological data to obtain an ice morphology change trend of each sub-conductor; the ice morphology change trend includes a regional ice morphology change trend of different attachment areas.

[0023] The different attachment areas on each sub-conductor can be understood as the various conductor areas that may be at risk of ice shedding based on ice thickness identification. Considering that different areas on the same sub-conductor have different ice states due to different environmental influences, in order to better quantify the ice conditions of each sub-conductor, this embodiment preferably performs ice morphology evolution analysis on each attachment area of ​​each sub-conductor. The corresponding regional ice morphology change trend can be understood as the change trend of the ice geometric morphology parameters of the attachment area, mainly including the ice morphology, ice thickness, and surface roughness at each predicted moment. Specifically, the steps of performing ice morphology evolution analysis based on the split conductor ice point cloud data and the environmental meteorological data to obtain the ice morphology change trend of each sub-conductor include: Ice morphology analysis is performed based on the initial ice point cloud data of each sub-conductor to obtain the ice geometric features of each sub-conductor; the ice geometric features include regional ice geometric features of different attachment areas, and the regional ice geometric features include ice morphology, ice thickness and surface roughness.

[0024] Considering that the initial icing point cloud data actually collected may contain noise or some data is missing, in order to ensure the reliability of the regional icing geometric morphological feature analysis, this embodiment preferably performs relevant cleaning processing on the initial icing point cloud data before extracting the icing geometric morphological features of each sub-conductor, and then combines surface reconstruction technology and skeleton extraction algorithm to perform in-depth analysis of the icing geometric morphology. Specifically, the steps of performing icing morphological analysis based on the initial icing point cloud data of each sub-conductor to obtain the icing geometric morphological features of each sub-conductor include: The icing point cloud data of each sub-conductor in the split conductor icing point cloud data is denoised by using voxel grid-based statistical filtering to obtain the corresponding sub-conductor smoothed point cloud data; the process of obtaining the sub-conductor smoothed point cloud data can refer to the relevant existing technology of using voxel grid-based statistical filtering to remove outlier noise points from three-dimensional point cloud data, which will not be described in detail here.

[0025] It is determined whether there are missing data points in the smoothed point cloud data of each sub-conductor. If so, a preset interpolation algorithm is used to fill in the missing data points to generate the corresponding sub-conductor complete ice-covered point cloud data; wherein, the preset interpolation algorithm can be selected according to actual application requirements, such as the nearest neighbor interpolation method, linear interpolation method or spline interpolation method, which can meet the corresponding interpolation function.

[0026] According to the complete icing point cloud data of each sub-conductor, a corresponding sub-conductor icing morphology grid model is generated based on surface reconstruction technology; wherein, surface reconstruction technology can be understood as dividing the three-dimensional point cloud data into regular three-dimensional grids, and reconstructing the surface in each grid by estimating the surface normal and curvature. For example, the Poisson reconstruction method is used to generate a grid model containing triangular facets. The specific process of obtaining the sub-conductor icing morphology grid model based on the complete icing point cloud data of the sub-conductor can be achieved by referring to the existing surface reconstruction technology to clearly display the icing geometry.

[0027] Based on the preset skeleton extraction algorithm, the ice morphology of each sub-conductor ice morphology grid model is analyzed to obtain the corresponding ice geometric morphological features. The preset skeleton extraction algorithm can be understood as a skeletonization algorithm that can analyze the sub-conductor ice morphology grid model to extract the corresponding ice-covered conductor centerline, such as a distance transformation skeletonization algorithm, a Voronoi diagram-based skeletonization algorithm, etc. In order to ensure the accuracy of the ice-covered conductor centerline extraction, this embodiment preferably uses a Voronoi diagram (Thyssen polygon)-based skeletonization algorithm (with strict central axis) with excellent topology retention ability and geometric accuracy to extract the ice-covered conductor centerline, and based on this, analyze the ice thickness and ice shape of each attachment area; specifically, the steps of performing ice morphology analysis on each sub-conductor ice morphology grid model based on the preset skeleton extraction algorithm to obtain the corresponding ice geometric morphological features include: A skeletonization algorithm based on Thiessen polygons is used to obtain the central axis of the ice-covered conductor of the grid model of the ice-covered morphology of each sub-conductor; wherein, the central axis of the ice-covered conductor can be understood as the central axis of each sub-conductor, which serves as the basis for subsequent analysis of the conductor ice thickness and ice morphology. The specific acquisition process refers to the existing implementation technology of the skeletonization algorithm based on Thiessen polygons and will not be described in detail here.

[0028] Calculating the distance between each grid point in the sub-conductor ice morphology grid model and the central axis of the ice-covered conductor to obtain corresponding ice thickness distribution data, wherein the ice thickness distribution data includes ice thickness data at different positions on the sub-conductor. The thickness distribution data can reveal ice unevenness and intuitively reflect ice adhesion patterns; Based on the ice thickness distribution data and a preset thickness threshold for each sub-conductor, an attachment area set for each sub-conductor is obtained. The preset thickness threshold can be understood as the lower limit of ice thickness used to identify iced areas at risk of de-icing. In practical applications, each sub-conductor can be divided into conductor areas based on preset segment lengths, and the ice thickness distribution data for each conductor area is compared with the preset thickness threshold. If the ice thickness in each conductor area exceeds the preset thickness threshold (for example, if the preset thickness threshold is 0.15 meters), and the ice thickness in a conductor area is concentrated between 0.2 and 0.3 meters, it can be determined as an area requiring attachment. This process is repeated until all attachment areas of the entire sub-conductor are generated into a corresponding attachment area set.

[0029] Based on the iced conductor centerline of each sub-conductor, the deviation distribution of the wire mesh vertices on both sides of the wire is calculated for each attachment area within the set of attachment areas. The ice morphology of each attachment area is then determined based on the deviation distribution of the wire mesh vertices on both sides of the wire. The deviation distribution of the wire mesh vertices on both sides of the wire includes the deviation of the upper and lower mesh vertex coordinates of each conductor position within the attachment area. In practical applications, the mean deviation of the wire mesh vertices on both sides of the wire can be calculated based on the deviation distribution of the wire mesh vertices on both sides of the wire. If the mean deviation of the wire mesh vertices on both sides of the wire is less than a preset deviation threshold, the ice symmetry of the attachment area is considered to be strong, and the corresponding ice morphology is determined to be uniform. Otherwise, the ice morphology of the corresponding attachment area is determined to be eccentric.

[0030] According to the ice coating thickness distribution data of each sub-conductor, the average ice coating thickness of each attachment area in the corresponding attachment area set is calculated respectively, and the average ice coating thickness is used as the ice coating thickness of the corresponding attachment area.

[0031] According to the complete ice-covered point cloud data of each sub-conductor, the regional ice-covered surface corresponding to each attachment area is fitted based on the least squares method to obtain a corresponding fitting residual distribution, and the corresponding surface roughness is obtained based on the fitting residual distribution; wherein, the surface roughness refers to the microscopic and macroscopic unevenness of the outer surface of the ice layer, and the corresponding acquisition process is: first, based on the ice-covered point cloud data of each attachment area, the corresponding regional quadratic surface is fitted using the least squares method; the distance from each point in the ice-covered point cloud data to the regional quadratic surface is calculated to obtain the fitting residual corresponding to each point; the fitting residual distribution is obtained based on the fitting residuals of all points in the ice-covered point cloud data; after obtaining the fitting residual distribution, the corresponding quantitative value of the surface roughness can be obtained based on the fitting residual mean of the area, and based on the fitting residual mean and the preset fitting residual and surface roughness mapping relationship; it should be noted that the preset fitting residual and surface roughness mapping relationship can be based on empirical settings or based on fitting of relevant historical data, and is not specifically limited here.

[0032] This embodiment extracts the geometric features of ice coating on each conductor by combining the three-dimensional point cloud data of each sub-conductor with surface reconstruction technology and skeleton extraction technology. This not only ensures the reliability and accuracy of the geometric features of conductor ice coating, but also can preliminarily identify conductor areas that may be at risk of ice shedding. By analyzing the ice coating morphological features of different conductor areas, the analysis of the geometric features of conductor ice coating is refined, thereby providing reliable data support for subsequent analysis of ice coating morphological evolution.

[0033] Based on the ice morphology of different attachment areas in the ice geometric morphology characteristics of each sub-conductor, a corresponding ice morphology characteristic prediction model is obtained, and based on the ice morphology characteristic prediction model and the environmental meteorological data, the ice morphology evolution of different attachment areas is predicted to obtain the ice morphology change trend of each sub-conductor; wherein, the ice morphology characteristic prediction model is a neural network model that predicts the ice geometric morphology characteristics based on the environmental meteorological data.

[0034] Taking into account that the evolution of ice morphology under the same environmental meteorological data may be different for different ice morphologies, the ice morphology feature prediction model in this embodiment is set to one prediction model for each ice morphology, but the training and construction process of the ice morphology feature prediction models corresponding to different ice morphologies is the same: different ice morphology training data sets can be constructed in advance based on actual collection, or by obtaining different environmental meteorological data and corresponding different ice morphology feature data through fluid mechanics simulation models, and then the preset neural network models are optimized and trained based on the training data sets of different ice morphologies until the preset convergence conditions are reached to obtain the corresponding ice morphology feature prediction models. The type and structure of the preset neural network model used for training can be selected according to actual application requirements, and the corresponding training process can be implemented using existing relevant training technologies.

[0035] This embodiment captures the coupling characteristics of environmental meteorological data and ice morphological changes based on a machine learning model, and predicts the evolution of ice morphology based on this coupling characteristic, which can ensure the scientificity and rationality of various ice morphological evolution analyses and effectively improve the accuracy of the acquisition of the three-dimensional geometric features of the split conductor ice. It should be noted that the process of predicting the evolution of ice morphology for different attachment areas based on the ice morphological feature prediction model and environmental meteorological data and obtaining the ice morphological change trend of each sub-conductor can be understood as a rolling prediction of the regional ice geometric morphological features of different attachment areas. After each time the regional ice geometric morphological feature prediction value of different attachment areas is obtained, it is necessary to use the following method and steps to perform ice shedding analysis to timely discover potential ice shedding risks and improve the efficiency of risk assessment.

[0036] S13. Perform ice shedding analysis based on the ice morphology change trend of each sub-conductor to obtain a corresponding conductor ice shedding risk value, and summarize the conductor ice shedding risk values ​​of each sub-conductor to obtain a corresponding risk assessment result.

[0037] The conductor ice shedding risk value can be understood as the risk value of abnormal vibration and large dancing caused by conductor ice shedding. Taking into account that the conductor ice shedding risk value of the actual sub-conductor will vary depending on the actual amount of ice shedding, the location of ice shedding and the geometric characteristics of the ice shedding, in order to improve the accuracy and reliability of the conductor ice shedding risk value analysis as much as possible, this embodiment preferably identifies the attachment area with a higher risk of shedding based on the trend of ice morphological changes, and performs a shedding risk assessment on each attachment area with a higher risk of shedding based on the adhesion analysis combined with the aerodynamic characteristics of the wind field conductor. Specifically, the steps of performing ice shedding analysis based on the trend of ice morphological changes of each sub-conductor to obtain the corresponding conductor ice shedding risk value include: Based on the preset ice thickness threshold and the preset surface roughness threshold, the regional ice morphology change trend of different attachment areas on each sub-conductor is traversed and analyzed to obtain the corresponding attachment area to be analyzed. The preset ice thickness threshold and the preset surface roughness threshold can be determined according to the actual analysis requirements and are not specifically limited here. Considering that ice thickness and ice surface roughness are two key factors affecting the risk of ice shedding, they affect the adhesion between ice and conductors and the strength of ice itself through different physical mechanisms, thus jointly determining the possibility of shedding: ice thickness is significantly positively correlated with shedding risk. When the ice thickness exceeds a certain critical value (which varies with the structure, material, and meteorological conditions, usually in the range of several millimeters to more than ten millimeters), the shedding risk will increase sharply. Ice surface roughness will increase wind loads, causing stress concentration on sharp protrusions (such as the roots of ice ridges) or crack edges, which can easily lead to localized breakage or overall detachment of the ice layer, also increasing the shedding risk. This embodiment preferably screens the de-icing possibility of each attachment area on the sub-conductor after the ice morphology changes based on these two factors to obtain each attachment area to be analyzed; the corresponding attachment area to be analyzed can be understood as the attachment area where the ice thickness and surface roughness obtained by the latest prediction of the ice morphology change trend of the area on each sub-conductor meet the conditions that both are greater than the corresponding preset ice thickness threshold and preset surface roughness threshold.

[0038] Based on the finite element analysis method, regional adhesion force identification is performed on different attachment areas to be analyzed on each sub-conductor to obtain corresponding regional adhesion force strengths. The regional adhesion force strength can be understood as the magnitude of the adhesion force in the attachment area to be analyzed, and is used to evaluate the adhesion between ice and the conductor. To improve the reliability of the regional adhesion force strength analysis, this embodiment preferably performs finite element analysis based on the latest regional point cloud model corresponding to each attachment area to be analyzed. Specifically, the steps of performing regional adhesion force identification on different attachment areas to be analyzed on each sub-conductor based on the finite element analysis method to obtain corresponding regional adhesion force strengths include: According to the initial icing point cloud data of each sub-conductor, the icing point cloud data of different attachment areas to be analyzed on each sub-conductor are obtained; wherein, the icing point cloud data of different attachment areas to be analyzed can be understood as the regional point cloud data obtained after denoising and missing value filling of the original icing point cloud data of the corresponding area in the initial icing point cloud data of the sub-conductor; the specific denoising and missing value filling processing can refer to the aforementioned implementation process of obtaining the corresponding complete icing point cloud data of the sub-conductor based on the icing point cloud data of each sub-conductor, which will not be described in detail here.

[0039] According to the ice point cloud data of different attachment areas to be analyzed on each sub-conductor, the initial ice thickness and initial surface roughness of the corresponding attachment area to be analyzed are obtained based on the preset skeleton extraction algorithm; among them, the process of obtaining the initial ice thickness and initial surface roughness can refer to the aforementioned implementation description of the ice morphology analysis of the ice morphology grid model of each sub-conductor to obtain the ice geometric morphological characteristics, which will not be repeated here.

[0040] According to the initial ice thickness and initial surface roughness of different attachment areas to be analyzed on each sub-conductor, as well as the corresponding current ice thickness and current surface roughness, the corresponding regional ice thickness deviation and regional roughness deviation are obtained, and according to the regional ice thickness deviation and the regional roughness deviation, the coordinates of each ice point in the corresponding ice point cloud data are adjusted to obtain the ice point cloud data to be analyzed; the specific process of obtaining the three-dimensional coordinates of each ice point in the ice point cloud data to be analyzed is as follows: according to the ice point cloud data of different attachment areas to be analyzed, the unit normal vector at each base point (the point on the conductor corresponding to each ice point) is determined (which can be used to approximate the direction of the ice point perpendicular to the central axis of the ice-covered conductor); a thickness scaling factor is generated according to the ratio of the current surface roughness to the initial surface roughness; according to the thickness scaling factor, the ice thickness of each ice point in the ice point cloud data to be analyzed is calculated based on the following formula: Where, Indicates the ice thickness of the i-th ice point in the ice point cloud data to be analyzed; Indicates the average ice thickness (current surface roughness) of the ice point cloud data to be analyzed; represents the ice thickness of the i-th ice point in the ice point cloud data of the attachment area to be analyzed corresponding to the ice point cloud data to be analyzed; Indicates the average ice thickness (initial ice thickness) of the ice point cloud data of the attachment area to be analyzed corresponding to the ice point cloud data to be analyzed; Represents the thickness scaling factor, the ratio of the current surface roughness to the initial surface roughness.

[0041] According to the ice thickness of each ice point in the ice point cloud data to be analyzed, the corresponding base point position and the unit normal vector at the corresponding base point, the three-dimensional coordinates of each ice point in the ice point cloud data to be analyzed are obtained: Where, Represents the three-dimensional coordinates of the i-th ice point in the ice point cloud data to be analyzed; Represents the three-dimensional coordinates of the corresponding i-th point in the base point cloud (the point cloud data corresponding to the centerline of the ice-covered conductor); Indicates the ice thickness of the i-th ice point in the ice point cloud data to be analyzed; Represents the unit normal vector at the i-th point in the base point cloud.

[0042] Based on the ice point cloud data of different attachment areas to be analyzed on each sub-conductor, a corresponding regional conductor ice point cloud model is generated based on the Poisson reconstruction algorithm, and the ice line interface adhesion force analysis of each regional conductor ice point cloud model is performed based on the finite element analysis method to obtain the regional adhesion force strength; among which, the generation process of the regional conductor ice point cloud model can refer to the existing relevant technical implementation of constructing a three-dimensional point cloud model based on the Poisson reconstruction algorithm, which will not be described in detail here.

[0043] The process of obtaining the regional adhesion strength can refer to the existing related technologies for analyzing the deicing of conductors based on finite element analysis, including: meshing the regional conductor ice point cloud model; setting preset boundary conditions (fixed supports at both ends of the conductor) and gravity load application (vertical downward gravity is applied according to the ice thickness to simulate the shear force caused by the ice layer's own weight); using the outer surface of the conductor and the inner surface of the ice layer as the master and slave surfaces respectively, using hard contact to define the normal contact behavior of the master and slave surfaces, and using the Coulomb friction model to define the tangential contact behavior of the master and slave surfaces, and introducing the interface adhesion stress through a user-defined field and setting the initial value (1×10 4 Pa); static nonlinear analysis is performed under the condition of considering contact nonlinearity. After obtaining the normal stress and tangential stress, the difference between the tangential stress and the product of the normal stress and the friction coefficient is used as the integrand. The contact area is used as the integral variable. The interfacial tangential stress is integrated to obtain the total adhesion force. Then, the adhesion strength is obtained based on the ratio of the total adhesion force to the contact area.

[0044] The regional adhesion strength of different attachment areas to be analyzed on each sub-conductor is compared with a preset adhesion strength threshold to obtain a weakened adhesion area on each sub-conductor; wherein the preset adhesion strength threshold can be determined according to actual analysis requirements. When the regional adhesion strength is less than the preset adhesion strength threshold, the attachment area to be analyzed is determined to be a weakened adhesion area.

[0045] The wind field aerodynamic parameters and vibration frequency data of each sub-conductor are obtained. Based on the wind field aerodynamic parameters, the vibration frequency data, and the ice thickness and surface roughness of different adhesion-weakened areas on the corresponding sub-conductor, a pre-built logistic regression model is used to predict the regional ice shedding probability to obtain the corresponding regional ice shedding probability. The wind field aerodynamic parameters include the aerodynamic drag coefficient, the aerodynamic lift coefficient, and the aerodynamic torsional moment coefficient. The wind field aerodynamic parameters of the sub-conductor can be calculated using the following formula: Where, 、 and represent the aerodynamic lift coefficient, aerodynamic drag coefficient, and aerodynamic torsional moment coefficient, respectively; 、 and They represent the dancing lift, dancing drag and dancing torque of the ice-covered conductor respectively, which can be obtained by using relevant existing technologies and will not be described in detail here; Indicates the air density; Indicates the velocity component of the wind speed along the direction perpendicular to the conductor; and represent the effective length and diameter of the ice-covered conductor respectively.

[0046] Vibration frequency data can be understood as the dominant vibration frequency extracted from the frequency domain signal after Fourier transforming the conductor's mechanical vibration acceleration signal (digital signal) acquired by the accelerometer installed on the sub-conductor. Considering that the wind field aerodynamic characteristics and vibration frequency of the sub-conductor also affect conductor ice shedding, to ensure the comprehensiveness and reliability of regional ice shedding probability prediction, this embodiment preferably incorporates wind field aerodynamic parameters and vibration frequency data to perform logistic regression prediction of regional ice shedding probability, taking into account ice thickness and surface roughness. The logistic regression model in this embodiment can be understood as an LR (Logistic Regression) model trained based on relevant historical data, using wind field aerodynamic parameters, vibration frequency data, ice thickness, and surface roughness as independent variables, and ice shedding probability as the dependent variable.

[0047] Based on the regional ice shedding probabilities of all adhesion-weakened areas on each sub-conductor, the sub-conductor ice shedding areas are randomly sampled using a Monte Carlo simulation method to generate a conductor ice shedding event probability distribution. The conductor ice shedding event probability distribution can be understood as treating each adhesion-weakened area as a potential shedding source, simulating the likelihood of shedding in these areas using random sampling at an event scale where at least one area experiences shedding, and then statistically obtaining the overall shedding event probability distribution through a large number of repeated experiments. The conductor ice shedding event probability distribution includes multiple groups of regional ice shedding combination events and corresponding event occurrence probabilities, and each group of regional ice shedding combination events includes at least one ice shedding in the adhesion-weakened area. The event occurrence probability can be understood as the occurrence probability of the corresponding regional ice shedding combination event in the random sampling. Under the conditions of a complete event space and unbiased sampling, the sum of the event occurrence probabilities of all regional ice shedding combination events is 1 when randomly sampling. Using the Monte Carlo method to simulate the probability distribution of ice shedding based on the adhesion weakening area is an effective means to combine physical mechanisms with probability statistics. It can effectively quantify the uncertainty and risk level of conductor ice shedding events and provide a reliable analytical basis for ice shedding risk assessment.

[0048] Based on the probability distribution of the conductor ice shedding event, a corresponding conductor ice shedding risk value is obtained; wherein the conductor ice shedding risk value can be understood as a risk indicator of abnormal vibration and large dancing of the conductor caused by the conductor ice shedding. Specifically, the step of obtaining the corresponding conductor ice shedding risk value based on the probability distribution of the conductor ice shedding event includes: According to the ice thickness and regional position of each ice shedding area in each group of regional icing combination events in the probability distribution of the conductor ice shedding event, the corresponding regional ice shedding risk value is obtained; wherein, the regional position can be understood as the position of the ice shedding area on the sub-conductor, such as the center of the span, near the spacer, the center of the sub-span, one-quarter or three-quarters of the span, near the tower, etc.; the regional ice shedding risk value can be obtained by taking a weighted average of the risk quantification score values ​​corresponding to the ice thickness and regional position, and the risk quantification score values ​​for different regional positions and different ice thicknesses can be set based on experience and application requirements, and are not specifically limited here.

[0049] The regional ice shedding risk values ​​of all ice-covered detachment areas in each group of regional deicing combination events are comprehensively analyzed based on the corresponding regional ice shedding probabilities to obtain the corresponding risk value of the ice-covered detachment combination event. The process of obtaining the risk value of the ice-covered detachment combination event is as follows: first, the regional ice-covered detachment probability of each ice-covered detachment area in the regional deicing combination event is converted into a corresponding weight coefficient (the regional ice-covered detachment probability is divided by the sum of the regional ice-covered detachment probabilities of all ice-covered detachment areas in the regional deicing combination event); then, the corresponding regional ice-covered detachment risk values ​​are weightedly averaged using the weight coefficients of each ice-covered detachment area in the regional deicing combination event to obtain the risk value of the ice-covered detachment combination event.

[0050] The risk values ​​of the ice-covered and shedding combined events of each group of regional deicing combined events are weightedly analyzed based on the corresponding event occurrence probability to obtain the conductor ice-covered and shedding risk value; that is, the conductor ice-covered and shedding risk value is the result of weighted summation of the ice-covered and shedding combined event risk values ​​of each group of regional deicing combined events with the event occurrence probability as the weight coefficient.

[0051] This embodiment provides a technical solution for obtaining split conductor ice point cloud data, including initial ice point cloud data for each sub-conductor, and environmental meteorological data, including wind speed, wind direction, temperature, and terrain, at the initial moment of an ice-melting interval. Then, an ice morphological evolution analysis is performed based on the split conductor ice point cloud data and the environmental meteorological data to obtain regional ice morphological change trends for each sub-conductor, including trends in different attachment areas. Ice shedding analysis is then performed based on the ice morphological change trends for each sub-conductor to obtain a corresponding conductor ice shedding risk value. Furthermore, the conductor ice shedding risk values ​​for each sub-conductor are aggregated to obtain a corresponding risk assessment result. This dynamic evolution analysis mechanism for ice morphology on split conductors during ice-melting intervals, based on the coupling characteristics of environmental meteorological data and ice morphological changes, can effectively improve the accuracy and efficiency of ice shedding risk assessments for split conductors during ice-melting intervals, thereby providing reliable guidance for subsequent ice-melting strategy optimization.

[0052] In order to minimize the risk of ice shedding during the ice-melting interval of UHV transmission lines and ensure the stability of power system operation, this embodiment preferably optimizes and adjusts the ice-melting conductor combination strategy for the next ice-melting cycle based on the risk assessment results of the ice-melting interval. Figure 2 As shown, a method for assessing intermittent de-icing risk of ice melting on a UHV transmission line is provided, the method further comprising: S14. Determine the de-icing priority of each sub-conductor according to the ranking result of the conductor ice shedding risk value of each sub-conductor in the risk assessment result, and adjust the sub-conductor de-icing grouping strategy according to the de-icing priority of each sub-conductor.

[0053] In practical applications, the greater the conductor's ice shedding risk, the greater the need to reduce the risk of abnormal deicing through effective deicing, and the higher the corresponding deicing priority can be set. After obtaining the deicing priority of each sub-conductor, the sub-conductor deicing grouping strategy can be adjusted according to the order of deicing priority. Sub-conductors with similar deicing priorities are grouped together, and the group with the higher deicing priority is prioritized for deicing in the next round of pulse deicing to reduce the risk of instantaneous load imbalance caused by the asynchronous deicing time of different sub-conductors. In addition, by extending the time interval between the deicing of the high-risk group and the start of deicing of the next group, the split conductors can have more time to recover balance, further controlling the spatiotemporal distribution of the split conductors. This reduces the possibility and severity of mechanical losses such as dancing, jumping, or tension imbalance caused by asynchronous deicing of different sub-conductors during the deicing process, thereby reducing the occurrence of abnormal deicing accidents on transmission lines.

[0054] The embodiment of the present invention not only analyzes the dynamic evolution of the ice morphology of split conductors during ice melting intervals based on the coupling characteristics of environmental meteorological data and ice morphology changes, but also effectively improves the accuracy and efficiency of ice shedding risk assessment of split conductors during ice melting intervals. At the same time, it can also timely adjust the ice melting strategy optimization mechanism of the ice melting conductor combination strategy for the next ice melting cycle based on the risk assessment results of the ice melting intervals. It can scientifically and rationally optimize the ice melting process, minimize the overall ice melting risk, improve ice melting efficiency and ensure ice melting safety, effectively reduce the occurrence of abnormal ice shedding accidents, reduce the probability of ice shedding risk during the ice melting process, and provide reliable technical support for the safe and stable operation of the power system.

[0055] It should be noted that although the steps in the above flowchart are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders.

[0056] In one embodiment, Figure 3 As shown, a system for assessing the risk of intermittent de-icing of ultra-high voltage transmission lines is provided, the system comprising: Data acquisition module 1 is used to obtain ice point cloud data of the split conductor and environmental meteorological data at the initial moment of the ice melting interval; the ice point cloud data of the split conductor includes the initial ice point cloud data of each sub-conductor; the environmental meteorological data includes wind speed, wind direction, temperature and terrain; Evolution analysis module 2, configured to perform icing morphology evolution analysis based on the split conductor icing point cloud data and the environmental meteorological data to obtain icing morphology change trends of each sub-conductor; the icing morphology change trends include regional icing morphology change trends of different attachment areas; The risk assessment module 3 is used to perform ice shedding analysis based on the ice morphology change trend of each sub-conductor to obtain the corresponding conductor ice shedding risk value, and summarize the conductor ice shedding risk values ​​of each sub-conductor to obtain the corresponding risk assessment result.

[0057] In one embodiment, Figure 4 As shown, a system for assessing the risk of intermittent de-icing of ultra-high voltage transmission lines is provided, the system further comprising: The strategy adjustment module 4 is configured to obtain the de-icing priority of each sub-conductor according to the ranking result of the conductor ice shedding risk value of each sub-conductor in the risk assessment result, and adjust the sub-conductor de-icing grouping strategy according to the de-icing priority of each sub-conductor.

[0058] Regarding the specific definition of the intermittent de-icing risk assessment system for ice melting of ultra-high voltage transmission lines, please refer to the definition of the intermittent de-icing risk assessment method for ice melting of ultra-high voltage transmission lines mentioned above. The corresponding technical effects can also be obtained equivalently, which will not be repeated here. Each module in the above-mentioned intermittent de-icing risk assessment system for ice melting of ultra-high voltage transmission lines can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0059] In summary, the embodiment of the present invention provides a method and system for assessing the risk of ice shedding during ice melting intervals of ultra-high voltage transmission lines. Based on the coupling characteristics of environmental meteorological data and changes in ice morphology, the system analyzes the dynamic evolution of the ice morphology of split conductors during ice melting intervals. This can effectively improve the accuracy and efficiency of ice shedding risk assessment for split conductors during ice melting intervals, provide reliable guidance for subsequent ice melting strategy optimization, and provide reliable technical support for the safe and stable operation of the power system.

[0060] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0061] The above-described embodiments merely represent several preferred implementations of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art can make several improvements and substitutions without departing from the technical principles of the present invention, and such improvements and substitutions should also be considered within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the scope of protection of the claims.

Claims

1. A method for assessing intermittent de-icing risk of ultra-high voltage transmission lines, characterized in that: The method comprises: Acquire ice point cloud data and environmental meteorological data of the split conductor at the initial moment of the ice melting interval; the ice point cloud data of the split conductor includes initial ice point cloud data of each sub-conductor; the environmental meteorological data includes wind speed, wind direction, temperature and terrain; performing an ice morphology evolution analysis based on the split conductor ice point cloud data and the environmental meteorological data to obtain an ice morphology change trend of each sub-conductor; the ice morphology change trend includes a regional ice morphology change trend of different attachment areas; An ice shedding analysis is performed based on the ice morphology change trend of each sub-conductor to obtain the corresponding conductor ice shedding risk value. The conductor ice shedding risk values ​​of each sub-conductor are then summarized to obtain the corresponding risk assessment result.

2. The method for assessing intermittent de-icing risk of ultra-high voltage transmission lines according to claim 1, wherein: The step of performing ice morphology evolution analysis based on the split conductor ice point cloud data and the environmental meteorological data to obtain the ice morphology change trend of each sub-conductor includes: Performing ice morphology analysis based on the initial ice point cloud data of each sub-conductor to obtain ice geometric features of each sub-conductor; the ice geometric features include regional ice geometric features of different attachment areas; Based on the ice morphology of different attachment areas in the ice geometric morphology characteristics of each sub-conductor, a corresponding ice morphology feature prediction model is obtained, and based on the ice morphology feature prediction model and the ambient meteorological data, the ice morphology evolution of different attachment areas is predicted to obtain the ice morphology change trend of each sub-conductor; the ice morphology feature prediction model is a neural network model that predicts the ice geometric morphology characteristics based on the ambient meteorological data.

3. The method for assessing intermittent de-icing risk of ultra-high voltage transmission lines according to claim 2, wherein: The ice cover geometric features of the region include ice cover shape, ice cover thickness and surface roughness; The step of performing ice morphology analysis based on the initial ice point cloud data of each sub-conductor to obtain the ice geometric features of each sub-conductor includes: Using voxel grid-based statistical filtering, denoising the ice point cloud data of each sub-conductor in the split conductor ice point cloud data is performed to obtain corresponding sub-conductor smoothed point cloud data; Determine whether there are any missing points in the smoothed point cloud data of each sub-conductor; if so, fill in the missing points using a preset interpolation algorithm to generate the corresponding complete ice-covered point cloud data of the sub-conductor; According to the complete ice-covered point cloud data of each sub-conductor, a corresponding sub-conductor ice-covered morphology grid model is generated based on surface reconstruction technology; Based on a preset skeleton extraction algorithm, an ice morphology analysis is performed on the ice morphology grid model of each sub-conductor to obtain corresponding ice geometric morphology features.

4. The method for assessing intermittent de-icing risk of ultra-high voltage transmission lines according to claim 3, wherein: The step of performing ice morphology analysis on the ice morphology grid model of each sub-conductor based on a preset skeleton extraction algorithm to obtain corresponding ice geometric morphological features includes: Using a skeletonization algorithm based on Thiessen polygons, the central axis of the ice-covered conductor of the ice-covered morphology grid model of each sub-conductor is obtained; Calculating the distance between each grid point in the sub-conductor ice morphology grid model and the central axis of the ice-covered conductor to obtain corresponding ice thickness distribution data; Obtaining an attachment area set of each sub-conductor according to the ice coating thickness distribution data of each sub-conductor and a preset thickness threshold; Based on the iced conductor centerline of each sub-conductor, the deviation distribution of mesh vertices on both sides of the conductor corresponding to different attachment areas in the attachment area set is calculated respectively, and the ice morphology of the different attachment areas is obtained based on the deviation distribution of mesh vertices on both sides of the conductor; the ice morphology includes eccentric ice morphology and uniform ice morphology; Calculating the average ice thickness of each attachment area in the corresponding attachment area set according to the ice thickness distribution data of each sub-conductor, and using the average ice thickness as the ice thickness of the corresponding attachment area; According to the complete ice-covered point cloud data of each sub-conductor, the regional ice-covered surface corresponding to each attachment area is fitted based on the least squares method to obtain the corresponding fitting residual distribution, and the corresponding surface roughness is obtained according to the fitting residual distribution.

5. The method for assessing intermittent de-icing risk of ultra-high voltage transmission lines according to claim 1, wherein: The step of performing ice shedding analysis based on the ice morphology change trend of each sub-conductor to obtain the corresponding conductor ice shedding risk value includes: According to the preset ice thickness threshold and the preset surface roughness threshold, the regional ice morphology change trend of different attachment areas on each sub-conductor is traversed and analyzed to obtain the corresponding attachment area to be analyzed; Based on the finite element analysis method, the regional adhesion force of different attachment areas to be analyzed on each sub-conductor is identified to obtain the corresponding regional adhesion force strength; Comparing the regional adhesion strength of different attachment areas to be analyzed on each sub-conductor with a preset adhesion strength threshold to obtain a weakened adhesion area on each sub-conductor; the weakened adhesion area is an attachment area to be analyzed where the regional adhesion strength is less than the preset adhesion strength threshold; Obtain wind field aerodynamic parameters and vibration frequency data for each sub-conductor, and predict regional ice shedding probabilities based on a pre-built logistic regression model based on the wind field aerodynamic parameters, the vibration frequency data, and the ice thickness and surface roughness of different adhesion-weakened areas on the corresponding sub-conductors to obtain the corresponding regional ice shedding probabilities; the wind field aerodynamic parameters include the aerodynamic drag coefficient, the aerodynamic lift coefficient, and the aerodynamic torsional moment coefficient; Based on the regional ice shedding probability of all adhesion weakened areas on each sub-conductor, the sub-conductor ice shedding areas are randomly sampled using the Monte Carlo simulation method to generate the conductor ice shedding event probability distribution. Based on the probability distribution of the conductor ice-coating and falling-off events, a corresponding conductor ice-coating and falling-off risk value is obtained.

6. The method for assessing intermittent de-icing risk of ultra-high voltage transmission lines according to claim 5, wherein: The step of performing regional adhesion force identification on different attachment areas to be analyzed on each sub-conductor based on the finite element analysis method to obtain the corresponding regional adhesion force strength includes: According to the initial ice point cloud data of each sub-conductor, ice point cloud data of different attachment areas to be analyzed on each sub-conductor are obtained; According to the ice point cloud data of different attachment areas to be analyzed on each sub-conductor, the initial ice thickness and initial surface roughness of the corresponding attachment area to be analyzed are obtained based on the preset skeleton extraction algorithm; According to the initial ice thickness and initial surface roughness of different attachment areas to be analyzed on each sub-conductor, as well as the corresponding current ice thickness and current surface roughness, the corresponding regional ice thickness deviation and regional roughness deviation are obtained, and according to the regional ice thickness deviation and the regional roughness deviation, the coordinates of each ice point in the corresponding ice point cloud data are adjusted to obtain the ice point cloud data to be analyzed; According to the ice point cloud data of different attachment areas to be analyzed on each sub-conductor, the corresponding regional conductor ice point cloud model is generated based on the Poisson reconstruction algorithm, and the ice line interface adhesion force analysis of each regional conductor ice point cloud model is performed based on the finite element analysis method to obtain the regional adhesion force strength.

7. The method for assessing intermittent de-icing risk of ultra-high voltage transmission lines according to claim 5, wherein: The probability distribution of conductor ice shedding events includes multiple groups of regional ice shedding combination events and corresponding event occurrence probabilities; The step of obtaining a corresponding conductor ice-falling risk value based on the conductor ice-falling event probability distribution includes: Obtaining a corresponding regional ice shedding risk value according to the ice thickness and regional location of each ice shedding region in each group of regional ice shedding combination events in the conductor ice shedding event probability distribution; The regional icing risk values ​​of all icing areas in each group of regional icing combination events are comprehensively analyzed based on the corresponding regional icing probability to obtain the corresponding icing combination event risk value; The ice-covered and shedding risk values ​​of the combined deicing events in each group of regions are weightedly analyzed based on the corresponding event occurrence probabilities to obtain the conductor ice-covered and shedding risk values.

8. The method for assessing intermittent de-icing risk of ultra-high voltage transmission lines according to claim 1, wherein: The method further comprises: According to the ranking result of the conductor ice shedding risk value of each sub-conductor in the risk assessment result, the de-icing priority of each sub-conductor is obtained, and the sub-conductor de-icing grouping strategy is adjusted according to the de-icing priority of each sub-conductor.

9. A risk assessment system for intermittent de-icing of ultra-high voltage transmission lines, characterized in that: The system comprises: A data acquisition module is used to obtain ice point cloud data of the split conductor and environmental meteorological data at the initial moment of the ice melting interval; the environmental meteorological data includes wind speed, wind direction, temperature and terrain; the ice point cloud data of the split conductor includes the initial ice point cloud data of each sub-conductor; an evolution analysis module, configured to perform an ice morphology evolution analysis based on the split conductor ice point cloud data and the environmental meteorological data to obtain an ice morphology change trend of each sub-conductor; the ice morphology change trend includes a regional ice morphology change trend of different attachment areas; The risk assessment module is used to perform ice shedding analysis based on the ice morphology change trend of each sub-conductor, obtain the corresponding conductor ice shedding risk value, and summarize the conductor ice shedding risk values ​​of each sub-conductor to obtain the corresponding risk assessment result.

10. The intermittent de-icing risk assessment system for ultra-high voltage transmission lines according to claim 9, characterized in that: The system further comprises: The strategy adjustment module is used to obtain the de-icing priority of each sub-conductor according to the ranking result of the conductor ice shedding risk value of each sub-conductor in the risk assessment result, and adjust the sub-conductor de-icing grouping strategy according to the de-icing priority of each sub-conductor.

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