An ice-melting intermittent de-icing risk assessment method and system for an extra-high voltage transmission line

Abstract

The present application relates to the technical field of ice melting intermittent ice shedding risk assessment, and provides an ice melting intermittent ice shedding risk assessment method and system for an extra-high voltage transmission line, which comprises the following steps: obtaining ice point cloud data and environmental meteorological data of a split conductor at an initial moment of ice melting intermittence; performing ice shape evolution analysis according to the ice point cloud data and the environmental meteorological data to obtain an ice shape change trend of each sub-conductor, including the ice shape change trend of different attachment regions; performing ice shedding analysis according to the ice shape change trend of each sub-conductor to obtain a corresponding conductor ice shedding risk value; and summarizing the conductor ice shedding risk values of each sub-conductor to obtain a corresponding risk assessment result. The present application can effectively improve the accuracy and efficiency of the split conductor ice shedding risk assessment based on the coupling characteristics of the environmental meteorological data and the ice shape change.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ice-melting intermittent ice-shedding risk assessment, in particular to an ice-melting intermittent ice-shedding risk assessment method and system for an ultra-high voltage power transmission line. BACKGROUND

[0002] The ice-melting of the ultra-high voltage power transmission line usually adopts the method of rotating ice-melting of each sub-conductor in the split conductor or the method of rotating ice-melting of two-by-two sub-conductors in the split conductor. However, in the intermittent period of pulse ice-melting, the surface of the conductor may be covered with ice layers that are not completely melted, and the loose structure is more susceptible to secondary shaping (such as wind erosion or local recrystallization) under the influence of the environment and meteorology. The change of this ice-coating form will directly reshape the aerodynamic characteristics of the ice-coating, and inevitably lead to the weakening of the local adhesion between the ice and the conductor, so that the ice-coating is extremely prone to shedding when encountering sudden air flows such as canyon gusts, and then inducing severe abnormal vibration and large amplitude oscillation of the split conductor, which seriously threatens the mechanical stability between the sub-conductors and the overall safety of the line. The ice-melting intermittent ice-shedding risk assessment of the ultra-high voltage power transmission line is of great significance to the stability and reliability of the power grid under extreme weather conditions.

[0003] Although the existing method of monitoring the aerodynamic parameters of the ice-coated conductor oscillation based on fixed sensors can provide a reference basis for ice-melting intermittent ice-shedding risk assessment to a certain extent, this method can only monitor and evaluate the state of conductor oscillation, and does not consider the dynamic evolution characteristics of the ice-coating form under the influence of the environment and meteorology. The coupling mechanism analysis of the complex environment and meteorology and the ice-coating form is insufficient, and the three-dimensional geometric characteristics of the ice-coating of the split conductor cannot be accurately obtained, which limits the prediction accuracy of the ice-shedding risk. SUMMARY

[0004] The purpose of the present application is to provide an ice-melting intermittent ice-shedding risk assessment method for an ultra-high voltage power transmission line, which effectively improves the accuracy and efficiency of the ice-shedding risk assessment of the split conductor based on the coupling characteristics of the environmental meteorological data and the dynamic evolution analysis mechanism of the ice-coating form of the ice-melting intermittent split conductor, and provides reliable technical support for the safe operation of the power system.

[0005] In order to achieve the above-mentioned purpose, it is necessary to provide an ice-melting intermittent ice-shedding risk assessment method and system for an ultra-high voltage power transmission line in view of the above-mentioned technical problems.

[0006] In a first aspect, the embodiments of the present application provide an ice-melting intermittent ice-shedding risk assessment method for an ultra-high voltage power transmission line, which comprises the following steps:

[0007] Obtain the split conductor ice-coating point cloud data and environmental meteorological data at the initial time of ice-melting intermittence; the environmental meteorological data includes wind speed, wind direction, temperature and terrain; the split conductor ice-coating point cloud data includes the initial ice-coating point cloud data of each sub-conductor;

[0008] According to the ice-coated conductor point cloud data and the environmental meteorological data, ice-coated conductor shape evolution analysis is performed to obtain an ice-coated conductor shape change trend of each sub-conductor; the ice-coated conductor shape change trend includes a regional ice-coated conductor shape change trend of different attachment regions;

[0009] According to the ice-coated conductor shape change trend of each sub-conductor, ice-coated conductor shedding analysis is performed to obtain a corresponding ice-coated conductor shedding risk value, and the ice-coated conductor shedding risk values of each sub-conductor are summarized to obtain a corresponding risk assessment result.

[0010] Further, the step of performing ice-coated conductor shape evolution analysis according to the ice-coated conductor point cloud data and the environmental meteorological data to obtain an ice-coated conductor shape change trend of each sub-conductor includes:

[0011] According to the initial ice-coated conductor point cloud data of each sub-conductor, ice-coated conductor shape analysis is performed to obtain ice-coated conductor geometric shape characteristics of each sub-conductor; the ice-coated conductor geometric shape characteristics include regional ice-coated conductor geometric shape characteristics of different attachment regions;

[0012] According to the ice-coated conductor geometric shape characteristics of each sub-conductor, an ice-coated conductor shape feature prediction model is obtained according to the ice-coated conductor shape of different attachment regions, and the ice-coated conductor shape evolution of different attachment regions is predicted according to the ice-coated conductor shape feature prediction model and the environmental meteorological data to obtain an ice-coated conductor shape change trend of each sub-conductor; the ice-coated conductor shape feature prediction model is a neural network model for predicting ice-coated conductor geometric shape characteristics based on environmental meteorological data.

[0013] Further, the regional ice-coated conductor geometric shape characteristics include ice-coated conductor shape, ice-coated conductor thickness, and surface roughness;

[0014] The step of performing ice-coated conductor shape analysis according to the initial ice-coated conductor point cloud data of each sub-conductor to obtain ice-coated conductor geometric shape characteristics of each sub-conductor includes:

[0015] Statistical filtering based on a voxel grid is adopted to perform denoising processing on the ice-coated conductor point cloud data of each sub-conductor in the ice-coated conductor point cloud data to obtain corresponding sub-conductor smooth point cloud data;

[0016] It is judged whether each of the sub-conductor smooth point cloud data has a data missing point or not, and if so, a preset interpolation algorithm is adopted to fill each of the data missing points to generate corresponding sub-conductor complete ice-coated conductor point cloud data;

[0017] According to each of the sub-conductor complete ice-coated conductor point cloud data, a corresponding sub-conductor ice-coated conductor shape grid model is generated based on surface reconstruction technology;

[0018] The preset skeleton extraction algorithm is used to analyze the icing shape of each sub-conductor icing shape grid model to obtain corresponding icing geometric shape features.

[0019] Further, the preset skeleton extraction algorithm is used to analyze the icing shape of each sub-conductor icing shape grid model to obtain corresponding icing geometric shape features, and the step includes:

[0020] A skeleton algorithm based on a Voronoi polygon is used to obtain the icing conductor centerline of each sub-conductor icing shape grid model.

[0021] The distance between each grid point in the sub-conductor icing shape grid model and the icing conductor centerline is calculated to obtain corresponding icing thickness distribution data.

[0022] According to the icing thickness distribution data of each sub-conductor and a preset thickness threshold, an attachment region set of each sub-conductor is obtained.

[0023] According to the icing conductor centerline of each sub-conductor, the grid vertex deviation distribution on both sides of the conductor in different attachment regions in the corresponding attachment region set is calculated respectively, and according to the grid vertex deviation distribution on both sides of the conductor, the icing shape of different attachment regions is obtained. The icing shape includes eccentric icing and uniform icing.

[0024] According to the icing thickness distribution data of each sub-conductor, the average icing thickness of each attachment region in the corresponding attachment region set is calculated, and the average icing thickness is taken as the icing thickness of the corresponding attachment region.

[0025] According to the complete icing point cloud data of each sub-conductor, the regional icing surface corresponding to each attachment region is fitted based on the least square method to obtain the corresponding fitting residual distribution, and according to the fitting residual distribution, the surface roughness is obtained.

[0026] Further, the step of analyzing the icing shedding of each sub-conductor according to the icing shape change trend to obtain the corresponding conductor icing shedding risk value includes:

[0027] According to the preset icing thickness threshold and the preset surface roughness threshold, the regional icing shape change trend of different attachment regions on each sub-conductor is analyzed to obtain the corresponding attachment region to be analyzed.

[0028] Based on the finite element analysis method, the regional adhesion force of different attachment regions to be analyzed on each sub-conductor is identified to obtain the corresponding regional adhesion force strength.

[0029] The region adhesion strength of the different to-be-analyzed adhesion regions on each sub-conductor is compared with a preset adhesion strength threshold to obtain an adhesion-weakened region on each sub-conductor; the adhesion-weakened region is a to-be-analyzed adhesion region with a region adhesion strength less than the preset adhesion strength threshold;

[0030] Wind field aerodynamic parameters and vibration frequency data of each sub-conductor are obtained, and based on the wind field aerodynamic parameters, the vibration frequency data, and the ice thickness and surface roughness of different adhesion-weakened regions on the corresponding sub-conductor, a region ice shedding probability prediction is performed based on a pre-constructed logistic regression model to obtain a corresponding region ice shedding probability; the wind field aerodynamic parameters include aerodynamic drag coefficient, aerodynamic lift coefficient, and aerodynamic torsional moment coefficient;

[0031] Based on the region ice shedding probability of all adhesion-weakened regions on each sub-conductor, a sub-conductor ice shedding region is randomly sampled based on a Monte Carlo simulation method to generate a conductor ice shedding event probability distribution;

[0032] Based on the conductor ice shedding event probability distribution, a corresponding conductor ice shedding risk value is obtained.

[0033] Further, the conductor ice shedding event probability distribution includes a plurality of region ice shedding combination events and corresponding event occurrence probabilities;

[0034] The step of obtaining a corresponding conductor ice shedding risk value based on the conductor ice shedding event probability distribution includes:

[0035] Based on the ice thickness and region position of each ice shedding region in each group of region ice shedding combination events in the conductor ice shedding event probability distribution, a corresponding region ice shedding risk value is obtained;

[0036] The region ice shedding risk values of all ice shedding regions in each group of region ice shedding combination events are comprehensively analyzed based on the corresponding region ice shedding probabilities to obtain a corresponding ice shedding combination event risk value;

[0037] The ice shedding combination event risk values of each group of region ice shedding combination events are weighted analyzed based on the corresponding event occurrence probabilities to obtain the conductor ice shedding risk value.

[0038] Further, the step of identifying the region adhesion of different to-be-analyzed adhesion regions on each sub-conductor based on the finite element analysis method to obtain a corresponding region adhesion strength includes:

[0039] Based on the initial ice point cloud data of each sub-conductor, ice point cloud data of different to-be-analyzed adhesion regions on each sub-conductor is obtained;

[0040] According to the icing point cloud data of different attachment areas to be analyzed on each sub-conductor, initial icing thickness and initial surface roughness of the corresponding attachment area to be analyzed are obtained based on a preset skeleton extraction algorithm;

[0041] According to the initial icing thickness and initial surface roughness of different attachment areas to be analyzed on each sub-conductor, and the corresponding current icing thickness and current surface roughness, corresponding regional icing thickness deviation and regional roughness deviation are obtained, and each icing point in the corresponding icing point cloud data is adjusted in coordinates according to the regional icing thickness deviation and the regional roughness deviation, to obtain the analyzed icing point cloud data;

[0042] According to the analyzed icing point cloud data of different attachment areas to be analyzed on each sub-conductor, a corresponding regional conductor icing point cloud model is generated based on a Poisson reconstruction algorithm, and ice line interface adhesion force analysis is performed on each regional conductor icing point cloud model based on a finite element analysis method, to obtain the regional adhesion force strength.

[0043] Further, the method further comprises:

[0044] According to the sorting result of the conductor icing shedding risk value of each sub-conductor in the risk assessment result, the ice-melting priority of each sub-conductor is obtained, and the sub-conductor ice-melting grouping strategy is adjusted according to the ice-melting priority of each sub-conductor.

[0045] In a second aspect, an embodiment of the present application provides a melting intermittent ice shedding risk assessment system for an ultra-high voltage power transmission line, the system comprising:

[0046] A data acquisition module is configured to acquire split conductor icing point cloud data and environmental meteorological data at an initial melting intermittent time; the split conductor icing point cloud data comprises initial icing point cloud data of each sub-conductor; and the environmental meteorological data comprises wind speed, wind direction, temperature and terrain.

[0047] An evolution analysis module is configured to perform icing morphology evolution analysis according to the split conductor icing point cloud data and the environmental meteorological data, to obtain an icing morphology change trend of each sub-conductor; the icing morphology change trend comprises a regional icing morphology change trend of different attachment areas.

[0048] A risk assessment module is configured to perform ice shedding analysis according to the icing morphology change trend of each sub-conductor, to obtain a corresponding conductor icing shedding risk value, and to aggregate the conductor icing shedding risk values of each sub-conductor, to obtain a corresponding risk assessment result.

[0049] Further, the system further comprises:

[0050] The strategy adjustment module is used to obtain the de-icing priority of each sub-conductor based on the ranking results of the conductor icing and shedding risk values ​​of each sub-conductor in the risk assessment results, and to adjust the de-icing grouping strategy of each sub-conductor according to the de-icing priority of each sub-conductor.

[0051] This invention provides a method and system for assessing the risk of de-icing during ice melting intervals in ultra-high voltage (UHV) transmission lines. The method acquires initial icing point cloud data for each sub-conductor at the beginning of the ice melting interval, along with environmental meteorological data including wind speed, wind direction, temperature, and topography. Based on the icing point cloud data and environmental meteorological data, it performs icing morphology evolution analysis to obtain the icing morphology change trend of each sub-conductor, including different attachment areas. Then, based on the icing morphology change trend of each sub-conductor, it performs icing detachment analysis to obtain the corresponding conductor icing detachment risk value. Finally, it summarizes the conductor icing detachment risk values ​​of each sub-conductor to obtain the corresponding risk assessment result. Compared with existing technologies, the risk assessment method for intermittent de-icing of ultra-high voltage transmission lines based on the coupling characteristics of environmental meteorological data and changes in icing morphology can effectively improve the accuracy and efficiency of risk assessment for ice detachment from intermittent split conductors, provide reliable guidance for subsequent optimization of de-icing strategies, and thus provide reliable technical support for the safe and stable operation of the power system. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating the risk assessment method for intermittent de-icing of ultra-high voltage transmission lines in an embodiment of the present invention.

[0053] Figure 2 This is another flowchart illustrating the risk assessment method for intermittent de-icing of ultra-high voltage transmission lines in this invention embodiment;

[0054] Figure 3 This is a schematic diagram of the structure of the intermittent de-icing risk assessment system for ultra-high voltage transmission lines in this embodiment of the invention;

[0055] Figure 4 This is another structural schematic diagram of the risk assessment system for intermittent de-icing of ultra-high voltage transmission lines in this embodiment of the invention; Attached image description:

[0057] The module consists of: 1. Data acquisition module; 2. Evolutionary analysis module; 3. Risk assessment module; and 4. Strategy adjustment module. Detailed Implementation

[0058] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. Obviously, the embodiments described below are only part of the embodiments of this invention and are used to illustrate the invention, but are not intended to limit the scope of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0059] In one embodiment, such as Figure 1 As shown, a method for assessing the risk of intermittent de-icing during ice melting in ultra-high voltage transmission lines is provided, including the following steps:

[0060] S11. Obtain the ice-covered point cloud data and environmental meteorological data of the split conductor at the initial moment of the ice-melting interval; where the initial moment of the ice-melting interval can be understood as the sampling time after the end of one round of ice-melting operation in the ice-melting process of the UHV transmission line, which can be set according to the actual application requirements, and is not specifically limited here.

[0061] The icing point cloud data of the split conductor can be understood as a three-dimensional point cloud data containing millions of points generated by a drone equipped with lidar flying along the split conductor and scanning the icing surface of each sub-conductor. It records the three-dimensional coordinate set of the icing surface of each sub-conductor, such as the coordinates of a point in the three-dimensional point cloud data being (10.2, 5.3, 2.1) meters, to reflect the spatial position of the icing surface of each sub-conductor, including the initial icing point cloud data of each sub-conductor.

[0062] Environmental meteorological data can be understood as data on relevant factors in the operating environment of the split conductor that affect the changes in the icing morphology of the conductor. In order to ensure the comprehensiveness and reliability of the subsequent analysis of the icing morphology evolution, this embodiment preferably sets the environmental meteorological data to include wind speed, wind direction, temperature and topography. Among them, wind speed, wind direction and temperature can be obtained by anemometers, wind direction instruments and temperature sensors that are pre-deployed on the transmission tower, respectively. The topography is the topographic features identified based on the topographic data of the surrounding terrain of the split conductor obtained by UAVs equipped with lidar, such as canyons or plains.

[0063] S12. Based on the icing point cloud data of the split conductor and the environmental meteorological data, perform icing morphology evolution analysis to obtain the icing morphology change trend of each sub-conductor; the icing morphology change trend includes the regional icing morphology change trend of different attachment areas.

[0064] The different attachment regions on each sub-conductor can be understood as regions on each conductor that may be at risk of icing detachment, identified based on icing thickness. Considering that different regions on the same sub-conductor may have different icing states due to varying environmental influences, this embodiment preferably performs icing morphology evolution analysis on each attachment region of each sub-conductor to better quantify the icing situation. The corresponding regional icing morphology change trend can be understood as the change trend of icing geometric morphology parameters of the attachment region, mainly including icing morphology, icing thickness, and surface roughness at each prediction time. Specifically, the step of performing icing morphology evolution analysis based on the split conductor icing point cloud data and the environmental meteorological data to obtain the icing morphology change trend of each sub-conductor includes:

[0065] Based on the initial icing point cloud data of each sub-conductor, icing morphology analysis is performed to obtain the icing geometric morphology characteristics of each sub-conductor; the icing geometric morphology characteristics include the regional icing geometric morphology characteristics of different attachment areas, and the regional icing geometric morphology characteristics include icing morphology, icing thickness and surface roughness.

[0066] Considering that the initial icing point cloud data collected may contain noise or missing data, in order to ensure the reliability of the regional icing geometric morphology feature analysis, this embodiment preferably performs relevant cleaning processing on the initial icing point cloud data before extracting the icing geometric morphology features of each sub-traverse, and then combines surface reconstruction technology and skeleton extraction algorithm to perform icing geometric morphology depth analysis; specifically, the step of performing icing morphology analysis based on the initial icing point cloud data of each sub-traverse to obtain the icing geometric morphology features of each sub-traverse includes:

[0067] A voxel-based statistical filter is used to denoise the icing point cloud data of each sub-traverse in the split traverse icing point cloud data to obtain the corresponding smooth point cloud data of the sub-traverse. The process of obtaining the smooth point cloud data of the sub-traverse can refer to the existing technology of removing outlier noise points from three-dimensional point cloud data using voxel-based statistical filtering, which will not be described in detail here.

[0068] Each sub-traverse smoothed point cloud data is checked for missing data points. If missing data points are found, a preset interpolation algorithm is used to fill in the missing data points and generate the corresponding complete icing point cloud data for the sub-traverse. The preset interpolation algorithm can be selected according to the actual application requirements. For example, the nearest neighbor interpolation method, linear interpolation method, or spline interpolation method can all meet the corresponding interpolation function.

[0069] Based on the complete icing point cloud data of each sub-traverse, a corresponding sub-traverse icing morphology mesh model is generated using surface reconstruction technology. Surface reconstruction technology can be understood as dividing the three-dimensional point cloud data into regular three-dimensional meshes and reconstructing the surface by estimating the surface normals and curvature in each mesh. For example, the Poisson reconstruction method can be used to generate a mesh model containing triangular facets. The specific process of obtaining the sub-traverse icing morphology mesh model from the complete icing point cloud data can be implemented with reference to existing surface reconstruction technologies to clearly show the icing geometry.

[0070] The icing morphology of each sub-conductor icing morphology mesh model is analyzed based on a preset skeleton extraction algorithm to obtain the corresponding icing geometric morphology features. The preset skeleton extraction algorithm can be understood as a skeletonization algorithm capable of analyzing and extracting the corresponding icing conductor centerline from the sub-conductor icing morphology mesh model. Examples include distance transform skeletonization algorithms and Voronoi diagram-based skeletonization algorithms. To ensure the accuracy of the icing conductor centerline extraction, this embodiment preferably uses a Voronoi diagram (Thieson polygon)-based skeletonization algorithm (with strict centerline integrity) with excellent topology preservation and geometric accuracy to extract the icing conductor centerline, and analyzes the icing thickness and shape of each attachment region based on this. Specifically, the step of analyzing the icing morphology of each sub-conductor icing morphology mesh model based on the preset skeleton extraction algorithm to obtain the corresponding icing geometric morphology features includes:

[0071] A skeletonization algorithm based on Thiessen polygons is used to obtain the central axis of the icing conductor in the mesh model of the icing morphology of each sub-conductor. The central axis of the icing conductor can be understood as the central axis of each sub-conductor, which serves as the basis for subsequent analysis of the icing thickness and icing morphology of the conductor. The specific acquisition process refers to the existing implementation technology of skeletonization algorithm based on Thiessen polygons, which will not be described in detail here.

[0072] The distance between each grid point in the sub-conductor icing morphology grid model and the central axis of the icing conductor is calculated to obtain the corresponding icing thickness distribution data. The icing thickness distribution data includes icing thickness data at different locations on the sub-conductor. The thickness distribution data can reveal the unevenness of icing and intuitively reflect the icing adhesion pattern.

[0073] Based on the icing thickness distribution data of each sub-conductor and a preset thickness threshold, an attachment region set for each sub-conductor is obtained. The preset thickness threshold can be understood as a lower limit for icing thickness used to identify icing areas that may have a risk of de-icing. In practical applications, each sub-conductor can be divided into conductor regions according to a preset segment length, and the icing thickness distribution data of each conductor region can be compared with the preset thickness threshold. If the icing thickness of each conductor region is greater than the preset thickness threshold (for example, if the preset thickness threshold is set to 0.15 meters, and the icing thickness of a certain conductor region is concentrated between 0.2 and 0.3 meters), then it can be determined as an area requiring attachment. This process is repeated until all attachment regions of the entire sub-conductor are generated, resulting in a corresponding attachment region set.

[0074] Based on the centerline of the icing conductor of each sub-conductor, the deviation distribution of grid vertices on both sides of the conductor in different attachment regions within the corresponding attachment region set is calculated. The icing morphology of different attachment regions is then obtained based on this deviation distribution. The deviation distribution includes the deviation of the grid vertices coordinates on the upper and lower sides of each conductor position within the corresponding attachment region. In practical applications, the average deviation of the grid vertices on both sides of the conductor can be calculated based on the deviation distribution of the grid vertices on both sides of the conductor in different attachment regions. When the average deviation is less than a preset deviation threshold, the icing symmetry of the attachment region is considered strong, and the corresponding icing morphology is determined to be uniform icing. Conversely, if the average deviation is greater than a preset threshold, the icing morphology of the corresponding attachment region is determined to be eccentric icing.

[0075] Based on the ice thickness distribution data of each of the sub-conductors, the average ice thickness of each attachment region in the corresponding attachment region set is calculated, and the average ice thickness is taken as the ice thickness of the corresponding attachment region.

[0076] Based on the complete ice-covered point cloud data of each sub-conductor, the ice-covered surface of each attachment region is fitted using the least squares method to obtain the corresponding fitting residual distribution, and the corresponding surface roughness is obtained based on the fitting residual distribution. Surface roughness refers to the degree of microscopic and macroscopic unevenness of the outer surface of the ice layer. The acquisition process is as follows: First, based on the ice-covered point cloud data of each attachment region, the corresponding quadratic surface of the region is fitted using the least squares method; the distance from each point in the ice-covered point cloud data to the quadratic surface of the region 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 quantified value of the corresponding surface roughness can be obtained based on the mean of the fitting residuals of the region, and based on the mean of the fitting residuals and the preset mapping relationship between the fitting residuals and surface roughness; it should be noted that the preset mapping relationship between the fitting residuals and surface roughness can be based on empirical settings or fitted based on relevant historical data, and is not specifically limited here.

[0077] This embodiment extracts the icing geometry features of each conductor by combining the three-dimensional point cloud data of each sub-conductor with surface reconstruction and skeleton extraction techniques. This not only ensures the reliability and accuracy of conductor icing geometry identification, but also preliminarily identifies conductor areas that may be at risk of icing detachment. Furthermore, by analyzing the icing geometry features of different conductor areas, the analysis of conductor icing geometry features is refined, thereby providing reliable data support for subsequent icing evolution analysis.

[0078] Based on the icing morphology of different attachment areas in the icing geometry of each sub-conductor, a corresponding icing morphology feature prediction model is obtained. Based on the icing morphology feature prediction model and the environmental meteorological data, the evolution of icing morphology in different attachment areas is predicted to obtain the icing morphology change trend of each sub-conductor. The icing morphology feature prediction model is a neural network model that predicts the icing geometry based on environmental meteorological data.

[0079] Considering that the evolution of icing morphology under the same environmental meteorological data may differ, the icing morphology feature prediction model in this embodiment is set to one prediction model for each icing morphology. However, the training and construction process of the icing morphology feature prediction models corresponding to different icing morphologies is the same: training datasets for different icing morphologies can be constructed in advance based on actual data collection or by obtaining different environmental meteorological data and corresponding icing morphology feature data through fluid dynamics simulation models, etc. Then, the preset neural network model is optimized and trained based on the training datasets for different icing morphologies until the preset convergence condition is reached, thus obtaining the corresponding icing morphology feature prediction model. The type and structure of the preset neural network model used for training can be selected according to the actual application requirements, and the corresponding training process can be implemented using existing related training techniques.

[0080] This embodiment uses a machine learning model to capture the coupling characteristics between environmental meteorological data and icing morphology changes, and predicts icing morphology evolution based on these coupling characteristics. This ensures the scientific validity and rationality of various icing morphology evolution analyses and effectively improves the accuracy of obtaining the three-dimensional geometric features of icing on split conductors. It should be noted that the process of predicting icing morphology evolution for different attachment areas based on the icing morphology feature prediction model and environmental meteorological data, and obtaining the icing morphology change trend of each sub-conductor, can be understood as a rolling prediction of the regional icing geometric morphology features of different attachment areas. After obtaining the predicted values ​​of the regional icing geometric morphology features for each attachment area, the following steps are required for icing detachment analysis to promptly identify potential detachment risks and improve the efficiency of risk assessment.

[0081] S13. Based on the icing morphology change trend of each sub-conductor, conduct icing detachment analysis to obtain the corresponding conductor icing detachment risk value, and summarize the conductor icing detachment risk values ​​of each sub-conductor to obtain the corresponding risk assessment results.

[0082] The risk value of conductor icing detachment can be understood as the risk value of abnormal vibration and large-scale galloping caused by conductor icing detachment. Considering that the actual conductor icing detachment risk value of sub-conductors may vary depending on the actual amount of icing detachment, the location of detachment, and the geometric morphology of the detached icing, in order to improve the accuracy and reliability of conductor icing detachment risk value analysis as much as possible, this embodiment preferably identifies the attachment areas with high detachment risk based on the trend of icing morphology change, and assesses the detachment risk of each attachment area with high detachment risk based on adhesion force analysis combined with the aerodynamic characteristics of the wind field conductor. Specifically, the step of analyzing icing detachment based on the trend of icing morphology change of each sub-conductor to obtain the corresponding conductor icing detachment risk value includes:

[0083] Based on preset icing thickness and surface roughness thresholds, a comprehensive analysis of the regional icing morphology changes in different attachment areas on each sub-conductor is performed to obtain the corresponding attachment areas to be analyzed. The preset icing thickness and surface roughness thresholds can be determined according to actual analysis needs and are not specifically limited here. Considering that icing thickness and surface roughness are two key factors affecting the risk of icing detachment, they influence the adhesion between ice and the conductor and the strength of the ice itself through different physical mechanisms, thus jointly determining the likelihood of detachment: icing thickness is significantly positively correlated with detachment risk; when the ice thickness exceeds a certain critical value (which varies depending on the structure, material, and meteorological conditions, typically ranging from a few millimeters to tens of millimeters), the detachment risk increases sharply. Surface roughness increases wind load, causing stress concentration at sharp protrusions (such as the root of ice ridges) or crack edges, which can easily lead to localized ice breakage or overall peeling, also increasing the detachment risk. In this embodiment, the de-icing probability of each attachment region on the sub-conductor after the icing morphology change is preferably screened based on these two factors to obtain each attachment region to be analyzed. The corresponding attachment region to be analyzed can be understood as the attachment region whose latest predicted icing thickness and surface roughness in the icing morphology change trend of each region on the sub-conductor meet the condition that both are greater than the corresponding preset icing thickness threshold and preset surface roughness threshold.

[0084] Based on the finite element method (FEM), regional adhesion forces are identified for different attachment regions on each sub-conductor to obtain the corresponding regional adhesion force intensities. The regional adhesion force intensities can be understood as the magnitude of the adhesion force in the attachment region to be analyzed, used to assess the adhesion between icing and the conductor. To improve the reliability of the regional adhesion force intensities analysis, this embodiment preferably performs FEM analysis based on the latest regional point cloud model corresponding to each attachment region to be analyzed. Specifically, the steps of identifying regional adhesion forces for different attachment regions on each sub-conductor based on the FEM to obtain the corresponding regional adhesion force intensities include:

[0085] Based on the initial icing point cloud data of each sub-traverse, icing point cloud data of different attachment regions to be analyzed on each sub-traverse are obtained. The icing point cloud data of different attachment regions to be analyzed can be understood as the region point cloud data obtained after denoising and imputing missing values ​​on the original icing point cloud data of the corresponding region in the initial icing point cloud data of the sub-traverse. The specific denoising and imputation of missing values ​​can be referred to the above implementation process of obtaining the corresponding complete icing point cloud data of the sub-traverse based on the icing point cloud data of each sub-traverse, which will not be detailed here.

[0086] Based on the ice point cloud data of different attachment regions to be analyzed on each sub-conductor, the initial ice thickness and initial surface roughness of the corresponding attachment regions to be analyzed are obtained based on the preset skeleton extraction algorithm. The process of obtaining the initial ice thickness and initial surface roughness can refer to the aforementioned implementation description of obtaining the ice geometric features by performing ice morphology analysis on the ice morphology mesh model of each sub-conductor, which will not be repeated here.

[0087] Based on the initial icing thickness and initial surface roughness of different attachment regions on each sub-conductor, and the corresponding current icing thickness and current surface roughness, the corresponding regional icing thickness deviation and regional roughness deviation are obtained. Then, based on these deviations, the coordinates of each icing point in the corresponding icing point cloud data are adjusted to obtain the icing point cloud data to be analyzed. Specifically, the process of obtaining the three-dimensional coordinates of each icing point in the icing point cloud data is as follows: The unit normal vector at each base point (the point on the conductor corresponding to each icing point) is determined based on the icing point cloud data of different attachment regions (this can be used to approximate the direction perpendicular to the central axis of the icing conductor); a thickness scaling factor is generated based on the ratio of the current surface roughness to the initial surface roughness; based on the thickness scaling factor, the icing thickness of each icing point in the icing point cloud data to be analyzed is calculated using the following formula:

[0088]

[0089] In the formula, This represents the ice thickness at the i-th ice point in the ice cloud data to be analyzed. This represents the average ice thickness (current surface roughness) of the ice point cloud data to be analyzed. This represents the ice thickness at the i-th ice point in the ice cloud data of the area to be analyzed, corresponding to the ice cloud data to be analyzed. This represents the average ice thickness (initial ice thickness) of the ice point cloud data corresponding to the ice point cloud data to be analyzed in the area to be analyzed. This represents the thickness scaling factor, which is the ratio of the current surface roughness to the initial surface roughness.

[0090] Based on the ice thickness, corresponding base point position, and corresponding unit normal vector at each ice point in the ice cloud data to be analyzed, the three-dimensional coordinates of each ice point in the ice cloud data to be analyzed are obtained:

[0091]

[0092] In the formula, This represents the three-dimensional coordinates of the i-th icing point in the icing point cloud data to be analyzed. This represents the three-dimensional coordinates of the i-th point in the base point cloud (the point cloud data corresponding to the central axis of the icing guide); This represents the ice thickness at the i-th ice point in the ice cloud data to be analyzed. This represents the unit normal vector at the i-th point in the base point cloud.

[0093] Based on the ice-covered point cloud data of different attachment regions on each sub-traverse, the corresponding regional traverse ice-covered point cloud model is generated based on the Poisson reconstruction algorithm. Then, the ice-line interface adhesion force of each regional traverse ice-covered point cloud model is analyzed based on the finite element analysis method to obtain the regional adhesion force intensity. The generation process of the regional traverse ice-covered point cloud model can refer to the existing related technologies for constructing three-dimensional point cloud models based on the Poisson reconstruction algorithm, which will not be detailed here.

[0094] The process of obtaining the regional adhesion strength can refer to existing technologies based on finite element analysis simulation to analyze conductor de-icing, including: meshing the regional conductor ice-covered point cloud model; setting preset boundary conditions (applying fixed supports at both ends of the conductor) and applying gravity loads (applying vertically downward gravity based on the ice thickness to simulate the shear force caused by the weight of the ice layer); using the outer surface of the conductor and the inner surface of the ice layer as the master and slave surfaces respectively, defining the normal contact behavior of the master and slave surfaces using hard contact, and defining the tangential contact behavior of the master and slave surfaces using the Coulomb friction model; and introducing interface adhesion stress through a user-defined field and setting an initial value (1×10). 4(Pa) Considering contact nonlinearity, static nonlinear analysis is performed to obtain the normal stress and tangential stress. The difference between the product of the tangential stress and the normal stress and the friction coefficient is used as the integrand, and the contact area is used as the integration variable to calculate the interface tangential stress and obtain the total adhesion force. Then, based on the ratio of the total adhesion force to the contact area, the adhesion force strength is obtained.

[0095] The adhesion strength of different attachment regions to be analyzed on each sub-conductor is compared with a preset adhesion strength threshold to obtain the adhesion weakening region on each sub-conductor. The preset adhesion strength threshold can be determined according to the actual analysis requirements. When the adhesion strength of a region is less than the preset adhesion strength threshold, the attachment region to be analyzed is judged as the adhesion weakening region.

[0096] The aerodynamic parameters and vibration frequency data of each sub-conductor are acquired. Based on the aerodynamic parameters, vibration frequency data, and icing thickness and surface roughness of different adhesion weakening regions on the corresponding sub-conductor, the probability of regional icing detachment is predicted using a pre-built logistic regression model to obtain the corresponding regional icing detachment probability. The aerodynamic parameters include aerodynamic drag coefficient, aerodynamic lift coefficient, and aerodynamic torsional moment coefficient, and the aerodynamic parameters of the sub-conductors can all be calculated using the following formula:

[0097]

[0098] In the formula, , and These represent the aerodynamic lift coefficient, aerodynamic drag coefficient, and aerodynamic torsional moment coefficient, respectively. , and These represent the galloping lift, galloping resistance, and galloping torque of the icy conductor, respectively, which can be obtained using relevant existing technologies and will not be detailed here. Indicates air density; This represents the velocity component of the wind speed along the perpendicular direction of the conductor. and These represent the effective length and diameter of the icing conductor, respectively.

[0099] Vibration frequency data can be understood as the dominant vibration frequency extracted from the frequency domain signal after performing a Fourier transform on the mechanical vibration acceleration signal (digital signal) of the conductor acquired by an accelerometer installed on the sub-conductor. Considering that the wind field aerodynamic characteristics and vibration frequency of the sub-conductor also affect the icing detachment of the conductor, in order to ensure the comprehensiveness and reliability of the regional icing detachment probability prediction, this embodiment preferably introduces wind field aerodynamic parameters and vibration frequency data to perform logistic regression prediction of the regional icing detachment probability, taking into account icing 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, with wind field aerodynamic parameters, vibration frequency data, icing thickness, and surface roughness as independent variables, and icing detachment probability as the dependent variable.

[0100] Based on the regional ice detachment probability of all weak adhesion regions on each sub-conductor, the ice detachment regions of the sub-conductors are randomly sampled using the Monte Carlo simulation method to generate a probability distribution of conductor ice detachment events. This probability distribution can be understood as treating each weak adhesion region as a potential detachment source, simulating the probability of detachment in these regions using random sampling at the event scale of at least one region detaching, and obtaining the overall detachment event probability distribution through numerous repeated experiments. The conductor ice detachment event probability distribution includes multiple sets of regional detachment combination events and their corresponding event probabilities, with each set of regional detachment combination events including at least one weak adhesion region detaching. The event probability can be understood as the probability of occurrence of the corresponding regional detachment combination event in random sampling, and under the conditions of complete event space and unbiased sampling, the sum of the event probabilities of all regional detachment combination events is 1. Using the Monte Carlo method to simulate the probability distribution of ice detachment in regions with weakened adhesion is an effective means of combining physical mechanisms with probability statistics. It can effectively quantify the uncertainty and risk level of conductor ice detachment events and provide a reliable analytical basis for ice detachment risk assessment.

[0101] Based on the probability distribution of conductor icing and detachment events, the corresponding conductor icing and detachment risk value is obtained; wherein, the conductor icing and detachment risk value can be understood as a risk indicator that conductor icing and detachment will lead to abnormal vibration and large-scale galloping of the conductor; specifically, the step of obtaining the corresponding conductor icing and detachment risk value based on the probability distribution of conductor icing and detachment events includes:

[0102] Based on the ice thickness and location of each ice-shedding area in each group of regional ice-shedding combination events in the probability distribution of conductor ice shedding events, the corresponding regional ice shedding risk value is obtained. Here, the regional location 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 bar, the center of the second span, one-quarter or three-quarters of the span, or near the tower. The regional ice shedding risk value can be obtained by weighted averaging of the risk quantification score values ​​corresponding to the ice thickness and the regional location. The risk quantification score values ​​for different regional locations and different ice thicknesses can be set based on experience and application requirements, and are not specifically limited here.

[0103] The risk values ​​of regional ice shedding in all ice-shedding areas within each group of regional de-icing events are comprehensively analyzed based on the corresponding regional ice shedding probabilities to obtain the corresponding risk value of the ice shedding combined event. The process for obtaining the risk value of the ice shedding combined event is as follows: First, the regional ice shedding probabilities of each ice-shedding area in the regional de-icing combined event are converted into corresponding weight coefficients (regional ice shedding probability divided by the sum of the regional ice shedding probabilities of all ice-shedding areas in the regional de-icing combined event); then, the corresponding regional ice shedding risk values ​​are weighted and averaged using the weight coefficients of each ice-shedding area in the regional de-icing combined event to obtain the risk value of the ice shedding combined event.

[0104] The risk value of the ice shedding combination event of each group of regional de-icing combination events is obtained by weighting the risk values ​​of the ice shedding combination event of each group of regional de-icing combination events based on the corresponding event occurrence probability; that is, the risk value of the conductor ice shedding is the result of weighted summation of the risk values ​​of the ice shedding combination event of each group of regional de-icing combination events with the event occurrence probability as the weighting coefficient.

[0105] This embodiment provides a technical solution for acquiring split conductor icing point cloud data (including initial icing point cloud data for each sub-conductor) and environmental meteorological data (including wind speed, wind direction, temperature, and topography) at the initial moment of the melting interval. Based on the split conductor icing point cloud data and environmental meteorological data, an icing morphology evolution analysis is performed to obtain the icing morphology change trend of each sub-conductor, including regional icing morphology changes in different attachment areas. Then, based on the icing morphology change trend of each sub-conductor, an icing detachment analysis is performed to obtain the corresponding conductor icing detachment risk value. Finally, the technical solution summarizes the conductor icing detachment risk values ​​of each sub-conductor to obtain the corresponding risk assessment result. This dynamic evolution analysis mechanism for the icing morphology of split conductors during melting intervals, based on the coupling characteristics of environmental meteorological data and icing morphology changes, can effectively improve the accuracy and efficiency of icing detachment risk assessment for split conductors during melting intervals, thereby providing reliable guidance for subsequent melting strategy optimization.

[0106] To minimize the risk of de-icing during ice-melting intervals on ultra-high-voltage 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 ice-melting intervals. Specifically, such as... Figure 2 As shown, a method for assessing the risk of intermittent de-icing during ice melting in ultra-high voltage transmission lines is provided, the method further includes:

[0107] S14. Based on the ranking of the risk values ​​of conductor icing and detachment of each sub-conductor in the risk assessment results, obtain the de-icing priority of each sub-conductor, and adjust the de-icing grouping strategy of each sub-conductor according to the de-icing priority of each sub-conductor.

[0108] In practical applications, the higher the risk value of ice shedding from the conductor, the greater the need to reduce the risk of abnormal ice shedding through effective ice melting, and the higher the corresponding ice melting priority can be set. After obtaining the ice melting priority of each sub-conductor, the sub-conductor ice melting grouping strategy can be adjusted according to the order of ice melting priority. By grouping sub-conductors with similar ice melting priorities into one group, and prioritizing the group with higher ice melting priority in the next round of pulse ice melting, the risk of instantaneous load imbalance caused by the asynchronous ice shedding time of different sub-conductors can be reduced. In addition, by extending the time interval between the ice shedding of the high-risk group and the start of ice melting 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 galloping, jumping, or tension imbalance caused by asynchronous ice shedding of different sub-conductors during the ice melting process, and reduces the occurrence of abnormal ice shedding accidents of transmission lines.

[0109] This invention, through its dynamic evolution analysis mechanism for the icing morphology of intermittent split conductors based on the coupling characteristics of environmental meteorological data and icing morphology changes, effectively improves the accuracy and efficiency of icing detachment risk assessment for intermittent split conductors. Furthermore, it possesses an icing strategy optimization mechanism that adjusts the conductor combination strategy for the next icing cycle based on the risk assessment results of intermittent icing. This mechanism scientifically and rationally optimizes the icing process, minimizes overall icing risk, improves icing efficiency, ensures icing safety, effectively reduces the occurrence of abnormal icing detachment accidents, lowers the probability of icing detachment risks during the icing process, and provides reliable technical support for the safe and stable operation of the power system.

[0110] It should be noted that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order requirement for the execution of these steps, and they can be executed in other orders.

[0111] In one embodiment, such as Figure 3As shown, a risk assessment system for intermittent de-icing of ultra-high voltage transmission lines is provided. The system includes:

[0112] Data acquisition module 1 is used to acquire ice cloud data and environmental meteorological data of the split conductor at the initial moment of the ice melting interval; the ice cloud data of the split conductor includes the initial ice cloud data of each sub-conductor; the environmental meteorological data includes wind speed, wind direction, temperature and topography;

[0113] Evolution analysis module 2 is used to perform icing morphology evolution analysis based on the icing point cloud data of the split conductor and the environmental meteorological data to obtain the icing morphology change trend of each sub-conductor; the icing morphology change trend includes the regional icing morphology change trend of different attachment areas.

[0114] Risk assessment module 3 is used to analyze the icing detachment based on the icing morphology change trend of each sub-conductor, obtain the corresponding conductor icing detachment risk value, and summarize the conductor icing detachment risk values ​​of each sub-conductor to obtain the corresponding risk assessment result.

[0115] In one embodiment, such as Figure 4 As shown, a risk assessment system for intermittent de-icing of ultra-high voltage transmission lines is provided, the system further includes:

[0116] The strategy adjustment module 4 is used to obtain the de-icing priority of each sub-conductor based on the ranking result of the conductor icing and shedding risk value of each sub-conductor in the risk assessment result, and to adjust the de-icing grouping strategy of each sub-conductor according to the de-icing priority of each sub-conductor.

[0117] Specific limitations regarding the risk assessment system for intermittent de-icing of UHV transmission lines can be found in the above description of the risk assessment method for intermittent de-icing of UHV transmission lines; the corresponding technical effects are equivalent and will not be repeated here. Each module in the aforementioned risk assessment system for intermittent de-icing of UHV transmission lines can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0118] In summary, the method and system for assessing the risk of intermittent de-icing of ultra-high voltage transmission lines provided by this invention, based on the coupling characteristics of environmental meteorological data and changes in icing morphology, provides a dynamic evolution analysis mechanism for the icing morphology of split conductors during intermittent de-icing. This effectively improves the accuracy and efficiency of assessing the risk of icing detachment from split conductors during intermittent de-icing, provides reliable guidance for subsequent optimization of de-icing strategies, and ultimately provides reliable technical support for the safe and stable operation of the power system.

[0119] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0120] The above-described embodiments are merely preferred embodiments of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention. Therefore, the scope of protection of this invention should be determined by the scope of the claims.

Claims

1. A method for assessing the risk of intermittent de-icing during ice melting in ultra-high voltage transmission lines, characterized in that, The method includes: Acquire icing point cloud data and environmental meteorological data of the split conductor at the initial moment of the ice melting interval; the icing point cloud data of the split conductor includes the initial icing point cloud data of each sub-conductor; the environmental meteorological data includes wind speed, wind direction, temperature and topography; Based on the icing point cloud data of the split conductor and the environmental meteorological data, an icing morphology evolution analysis was performed to obtain the icing morphology change trend of each sub-conductor; the icing morphology change trend includes the regional icing morphology change trend of different attachment areas. Based on the trend of icing morphology changes of each sub-conductor, an icing detachment analysis is conducted to obtain the corresponding conductor icing detachment risk value. The conductor icing detachment risk values ​​of each sub-conductor are then summarized to obtain the corresponding risk assessment results. The step of performing ice detachment analysis based on the ice morphology change trend of each sub-conductor to obtain the corresponding conductor ice detachment risk value includes: Based on the preset icing thickness threshold and the preset surface roughness threshold, the variation trend of regional icing morphology in different attachment areas on each sub-conductor is analyzed to obtain the corresponding attachment areas to be analyzed. Based on the finite element analysis method, the regional adhesion force of different attachment areas on each sub-conductor is identified, and the corresponding regional adhesion force intensity is obtained. The adhesion strength of different adhesion regions to be analyzed on each sub-conductor is compared with a preset adhesion strength threshold to obtain the adhesion weakening region on each sub-conductor; the adhesion weakening region is the adhesion region to be analyzed where the regional adhesion strength is less than the preset adhesion strength threshold. The wind field aerodynamic parameters and vibration frequency data of each sub-conductor are acquired. Based on the wind field aerodynamic parameters, the vibration frequency data, and the icing thickness and surface roughness of different adhesion weakening areas on the corresponding sub-conductor, the probability of regional icing detachment is predicted based on a pre-built logistic regression model to obtain the corresponding regional icing detachment probability. The wind field aerodynamic parameters include aerodynamic drag coefficient, aerodynamic lift coefficient, and aerodynamic torsional moment coefficient. Based on the regional ice detachment probability of all areas with weakened adhesion on each sub-conductor, the ice detachment area of ​​the sub-conductor is randomly sampled using the Monte Carlo simulation method to generate the probability distribution of ice detachment events. Based on the probability distribution of the conductor icing and detachment event, the corresponding conductor icing and detachment risk value is obtained.

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

3. The method for assessing the risk of intermittent de-icing during ice melting in ultra-high voltage transmission lines as described in claim 2, characterized in that, The geometric features of the ice accretion in the region include ice accretion morphology, ice accretion thickness, and surface roughness; The step of performing icing morphology analysis based on the initial icing point cloud data of each sub-traverse to obtain the icing geometric morphology characteristics of each sub-traverse includes: A statistical filter based on voxel grids is used to denoise the icing point cloud data of each sub-traverse in the split conductor icing point cloud data to obtain the corresponding smooth point cloud data of the sub-traverse. Determine whether there are missing data points in the smoothed point cloud data of each sub-traverse. If so, use a preset interpolation algorithm to fill in the missing data points and generate the corresponding complete icing point cloud data of the sub-traverse. Based on the complete ice-covered point cloud data of each sub-conductor, a corresponding sub-conductor ice-covered morphology mesh model is generated based on surface reconstruction technology. Based on the preset skeleton extraction algorithm, the icing morphology of each of the sub-conductors is analyzed to obtain the corresponding icing geometric morphology features.

4. The method for assessing the risk of intermittent de-icing during ice melting in ultra-high voltage transmission lines as described in claim 3, characterized in that, The step of performing ice morphology analysis on the ice morphology mesh model of each of the sub-conductors based on a preset skeleton extraction algorithm to obtain the corresponding ice morphology features includes: The skeletalization algorithm based on Thiessen polygons is used to obtain the centerline of the icing conductor of each of the sub-conductors in the icing morphology mesh model; The distance between each grid point in the sub-conductor icing morphology grid model and the central axis of the icing conductor is calculated to obtain the corresponding icing thickness distribution data; Based on the ice thickness distribution data of each sub-conductor and the preset thickness threshold, the attachment region set of each sub-conductor is obtained; Based on the centerline of the icing conductor of each sub-conductor, the deviation distribution of the grid vertices on both sides of the conductor in different attachment regions within the corresponding attachment region set is calculated, and the icing morphology of different attachment regions is obtained based on the deviation distribution of the grid vertices on both sides of the conductor; the icing morphology includes eccentric icing and uniform icing. Based on the ice thickness distribution data of each sub-conductor, the average ice thickness of each attachment region in the corresponding attachment region set is calculated, and the average ice thickness is taken as the ice thickness of the corresponding attachment region. Based on the complete ice-covered point cloud data of each sub-conductor, the ice-covered surface of each attachment region is fitted using the least squares method to obtain the corresponding fitting residual distribution, and the corresponding surface roughness is obtained based on the fitting residual distribution.

5. The method for assessing the risk of intermittent de-icing during ice melting in ultra-high voltage transmission lines as described in claim 1, characterized in that, The step of identifying the regional adhesion force of different attachment regions on each sub-conductor based on the finite element analysis method and obtaining the corresponding regional adhesion force intensity includes: Based on the initial icing point cloud data of each sub-conductor, obtain the icing point cloud data of different attachment areas to be analyzed on each sub-conductor. Based on 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 areas to be analyzed are obtained based on the preset skeleton extraction algorithm. Based on 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. Based on 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. Based on the ice-covered point cloud data of different attachment regions on each sub-conductor, the corresponding ice-covered point cloud model of the regional conductor is generated based on the Poisson reconstruction algorithm, and the ice-line interface adhesion force of each regional conductor ice-covered point cloud model is analyzed based on the finite element analysis method to obtain the regional adhesion force intensity.

6. The method for assessing the risk of intermittent de-icing during ice melting in ultra-high voltage transmission lines as described in claim 1, characterized in that, The probability distribution of conductor icing detachment events includes multiple sets of regional icing detachment combination events and their corresponding event occurrence probabilities; The step of obtaining the corresponding conductor icing and detachment risk value based on the probability distribution of the conductor icing and detachment event includes: Based on the ice thickness and location of each ice-shedding area in each group of regional de-icing combination events in the probability distribution of conductor ice shedding events, the corresponding regional ice shedding risk value is obtained. The risk values ​​of regional icing loss in all icing loss areas in each group of regional de-icing combination events are comprehensively analyzed based on the corresponding regional icing loss probabilities to obtain the corresponding risk values ​​of the icing loss combination events. The risk values ​​of ice detachment combinations in each group of regional de-icing events are weighted and analyzed based on the corresponding event occurrence probabilities to obtain the ice detachment risk value of the conductor.

7. The method for assessing the risk of intermittent de-icing during ice melting in ultra-high voltage transmission lines as described in claim 1, characterized in that, The method further includes: Based on the ranking of the risk values ​​of conductor icing and shedding in the risk assessment results, the de-icing priority of each sub-conductor is obtained, and the de-icing grouping strategy of each sub-conductor is adjusted according to the de-icing priority of each sub-conductor.

8. A risk assessment system for intermittent de-icing of ultra-high voltage transmission lines, characterized in that, The system employing the intermittent de-icing risk assessment method for ultra-high voltage transmission lines as described in claim 1 includes: The data acquisition module is used to acquire the ice 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 topography; the ice cloud data of the split conductor includes the initial ice cloud data of each sub-conductor. The evolution analysis module is used to perform icing morphology evolution analysis based on the icing point cloud data of the split conductor and the environmental meteorological data to obtain the icing morphology change trend of each sub-conductor; the icing morphology change trend includes the regional icing morphology change trend of different attachment areas. The risk assessment module is used to analyze the icing detachment based on the icing morphology change trend of each sub-conductor, obtain the corresponding conductor icing detachment risk value, and summarize the conductor icing detachment risk values ​​of each sub-conductor to obtain the corresponding risk assessment result.

9. The risk assessment system for intermittent de-icing of ultra-high voltage transmission lines as described in claim 8, characterized in that, The system also includes: The strategy adjustment module is used to obtain the de-icing priority of each sub-conductor based on the ranking results of the conductor icing and shedding risk values ​​of each sub-conductor in the risk assessment results, and to adjust the de-icing grouping strategy of each sub-conductor according to the de-icing priority of each sub-conductor.

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