Power transmission line icing real-time monitoring method and system based on multi-parameter fusion

Abstract

The invention discloses a power transmission line icing real-time monitoring method and system based on multi-parameter fusion, and relates to the field of data processing, and the method comprises the steps: receiving conductor physical parameters at the same moment, and simulating an icing track of a target power transmission line section in combination with current environment data; determining a windward side of the icing track based on the current wind direction, and simulating a windward icing layer of the icing track by taking the windward side as a starting surface, taking the current wind speed as a reference wind speed and adopting an icing layer simulation strategy; based on the topographic data of the target power transmission line section, determining a high-risk icing area in the icing track, and determining icing layer data of the high-risk icing area; determining an icing galloping area in the icing track based on the vibration parameter and the strain parameter; and fitting the windward icing layer, the high-risk icing area and the icing galloping area with the icing track, and optimizing the fitted icing track according to the icing layer data to generate a visual interface. According to the invention, the accuracy of icing monitoring of the power transmission line can be effectively improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data processing, and in particular, to a real-time monitoring method and system for icing on transmission lines based on multi-parameter fusion. Background Art

[0002] As an important part of the power system, the operating state of overhead transmission lines is crucial for the safe and stable operation of the power system. Icing on overhead transmission lines is one of the main natural disasters faced by the power system. Especially in cold, high-altitude or high-humidity areas, icing may lead to serious accidents such as line breakage, tower collapse, and insulator flashover. With the intensification of climate change and the expansion of the power grid scale, the importance of icing monitoring has become increasingly prominent.

[0003] Traditional icing monitoring technologies mainly perform linear modeling based on fixed meteorological parameters such as temperature and humidity and historical icing data, and predict the icing thickness by inputting real-time meteorological parameters into a single static model to monitor the icing condition.

[0004] However, for transmission lines in high-altitude and / or complex terrain areas, the icing condition needs to consider not only the influence of fixed meteorological parameters on icing, but also the influence of the windward slopes, special geographical environment areas within the layout range of the transmission line, and the areas where the lines are prone to icing and galloping with an angle greater than 45° with the winter dominant wind direction on the icing of the lines. In addition to being affected by fixed meteorological parameters, the icing conditions in the above areas also need to consider the influence of geographical environment and conductor galloping on icing. Moreover, in the icing monitoring of overhead transmission lines, geographical environment, conductor galloping, and meteorological parameters are not independent variables, but act on the icing process through complex non-linear coupling relationships. This mutual influence makes it difficult for traditional single static models to comprehensively characterize the icing condition.

[0005] In summary, the accuracy of current icing monitoring technologies for icing monitoring is relatively low. Summary of the Invention

[0006] The embodiments of the present application provide a real-time monitoring method and system for icing on transmission lines based on multi-parameter fusion, which is used to effectively improve the accuracy of icing monitoring for transmission lines.

[0007] To achieve the above object, the embodiments of the present application adopt the following technical solutions:

[0008] In a first aspect, a method for icing monitoring for a transmission line is provided, which is applied to an icing monitoring system. The method includes:

[0009] In response to receiving the physical parameters of the conductor at the same moment, combining with the current environmental data, simulating the icing trajectory of the target transmission line segment, where the current environmental data includes the current wind speed and the current wind direction, and the physical parameters of the conductor include the vibration parameter and the strain parameter;

[0010] Determine the windward side of the ice accretion trajectory based on the current wind direction, and take the windward side as the starting surface. Using the current wind speed as the reference wind speed, adopt an ice layer simulation strategy to simulate the windward ice layer of the ice accretion trajectory, where the ice layer simulation strategy is implemented based on a preset ice thickness prediction model;

[0011] Based on the terrain data of the target transmission line segment, determine the high-risk ice accretion areas in the ice accretion trajectory, and determine the ice layer data of the high-risk ice accretion areas based on a preset big data model;

[0012] Determine the ice galloping areas in the ice accretion trajectory based on vibration parameters and strain parameters;

[0013] Fit the windward ice layer, high-risk ice accretion areas and ice galloping areas with the ice accretion trajectory, and optimize the fitted ice accretion trajectory according to the ice layer data;

[0014] Generate a visualization interface according to the optimized ice accretion trajectory.

[0015] In a possible implementation manner of the first aspect, simulating the ice accretion trajectory of the target transmission line segment includes:

[0016] According to the current wind speed and current wind direction, use a preset hydrodynamic model to calculate the air flow distribution on the surface of the conductors of the target transmission line segment;

[0017] Based on the air flow distribution and conductor physical parameters, use the finite element analysis method to simulate the ice formation process on the surface of the conductors;

[0018] Divide the ice formation process into multiple time steps, and obtain the ice layer morphology parameters of each time step, where the ice layer morphology parameters include ice layer density, surface roughness and attachment angle;

[0019] Based on the ice layer morphology parameters of each time step, use a three-dimensional point cloud reconstruction algorithm to generate the ice accretion cross-section contour of each time step;

[0020] Perform spatial interpolation on the ice accretion cross-section contours of multiple time steps along the conductor extension direction to construct a three-dimensional space model of the ice accretion trajectory.

[0021] In another possible implementation manner of the first aspect, based on the air flow distribution and conductor physical parameters, use a preset finite element analysis strategy to simulate the ice formation process on the surface of the conductors, including:

[0022] Discretize the surface of the conductors into multiple non-uniform triangular meshes, where the mesh density of the windward side is higher than that of the leeward side;

[0023] Apply dynamic boundary conditions at the nodes of the triangular grid, and adopt a preset finite element analysis strategy to solve the preset coupled equations in each time iteration step to output the ice accretion morphology data, which is used to simulate the ice accretion formation process on the conductor surface;

[0024] Among them, the dynamic boundary conditions include the air flow distribution, the conductor surface displacement constraint conditions calculated according to the vibration parameters, and the conductor material elastic modulus correction coefficient calculated based on the strain parameters.

[0025] In another possible implementation manner of the first aspect, the conductor physical parameters include the temperature parameter on the conductor surface, and the current environmental data further includes temperature and humidity. The ice layer simulation strategy includes:

[0026] Taking the conductor axis as the reference, establish a polar coordinate system, and divide the windward surface into multiple sector grids;

[0027] In each grid, calculate the aerodynamic load according to the current wind speed, and combine the temperature parameter on the conductor surface to solve the ice layer growth rate through the thermodynamic equation;

[0028] Adopt the finite element analysis method to simulate the stress distribution of the ice layer on the conductor surface, and screen out the grid areas where the stress concentration exceeds the preset threshold;

[0029] In the grid area, taking the reference wind speed, temperature and humidity as input parameters, according to the pre-constructed ice accretion thickness prediction model, output the ice accretion thickness distribution data of the windward ice layer;

[0030] Generate a three-dimensional model of the windward ice layer according to the ice accretion thickness distribution data.

[0031] In another possible implementation manner of the first aspect, the current environmental data includes humidity parameters. Based on the terrain data of the target transmission line section, determine the high-risk ice accretion areas in the ice accretion trajectory, including:

[0032] Obtain the terrain data of the target transmission line section, and the terrain data includes altitude, slope and aspect;

[0033] Extract the three-dimensional terrain feature vector of the terrain data;

[0034] Identify the potential wind acceleration areas in the three-dimensional terrain feature vector, where the potential wind acceleration areas include canyon areas, ridge areas and windward slope areas;

[0035] Determine the average ice accretion thickness corresponding to each potential wind acceleration area in the preset historical ice accretion database;

[0036] Take the potential wind acceleration areas corresponding to the average ice accretion thickness greater than the preset ice accretion thickness threshold as the target ice accretion areas;

[0037] Input the humidity parameter into a pre - constructed big data model to predict the ice accretion growth rate of each target ice - covered area in a preset future time period;

[0038] Regard the target ice - covered area corresponding to the ice accretion growth rate greater than the preset growth rate as a high - risk ice - covered area.

[0039] In another possible implementation manner of the first aspect, determining the ice - covered dancing area in the ice accretion trajectory based on vibration parameters and strain parameters includes:

[0040] Perform FFT transformation on the vibration parameters, extract the proportion of vibration energy in a preset frequency band, and calculate its deviation value from the natural frequency of the wire;

[0041] Construct a wire deformation field model according to the strain parameters to identify the area where the deformation gradient exceeds the limit, where the wire deformation field model is constructed based on the spatio - temporal distribution of the strain parameters;

[0042] Determine the dancing value of the area where the deviation value is greater than the first preset value and the deformation gradient exceeds the limit;

[0043] If the dancing value exceeds the preset critical dancing coefficient, then determine the area where the deviation value is greater than the first preset value and the deformation gradient exceeds the limit as the ice - covered dancing area.

[0044] In another possible implementation manner of the first aspect, fitting the wind - facing ice layer, high - risk ice - covered area, and ice - covered dancing area with the ice accretion trajectory includes:

[0045] Convert the coordinate system of the ice accretion trajectory into a projection coordinate system in a preset format, and divide the ice accretion trajectory into multiple spatial grids;

[0046] In the projection coordinate system, perform spatial superposition of the three - dimensional model of the wind - facing ice layer and the spatial grids of the ice accretion trajectory to generate a first ice accretion trajectory, where the first ice accretion trajectory contains the ice accretion thickness distribution data of the wind - facing ice layer;

[0047] Align the coordinates of the high - risk ice - covered area and the ice - covered dancing area, and interpolate the coordinate - aligned high - risk ice - covered area and ice - covered dancing area into the projection coordinate system of the first ice accretion trajectory to generate the fitted ice accretion trajectory.

[0048] In another possible implementation manner of the first aspect, in the projection coordinate system, performing spatial superposition of the three - dimensional model of the wind - facing ice layer and the spatial grids of the ice accretion trajectory to generate a first ice accretion trajectory includes:

[0049] Perform voxelization processing on the three - dimensional model of the wind - facing ice layer to generate a voxel data set with the same resolution as the spatial grid of the ice accretion trajectory;

[0050] Using the bilinear interpolation algorithm, map the ice thickness distribution data in the voxel dataset to the grid vertices of the ice accretion trajectory;

[0051] According to the wire curvature parameter of the ice accretion trajectory, use a preset compensation formula to perform curvature compensation calculation on the mapped ice thickness data to obtain the ice accretion trajectory after compensation calculation.

[0052] In another possible implementation manner of the first aspect, optimizing the fitted ice accretion trajectory according to the ice layer data includes:

[0053] In the projection coordinate system of the fitted ice accretion trajectory, interpolate the ice layer data to the corresponding high-risk ice accretion area on the fitted ice accretion trajectory to optimize the fitted ice accretion trajectory.

[0054] In a second aspect, the present application provides an ice accretion monitoring system, including:

[0055] A solar panel / battery for powering the ice accretion monitoring system;

[0056] A memory configured to store instructions; and

[0057] A processor configured to call the instructions from the memory and capable of implementing the above-mentioned ice accretion monitoring method for transmission lines when executing the instructions.

[0058] Through the above technical solution, by receiving the physical parameters of the wire at the same moment and combining with the current environmental data, the icing trajectory of the target transmission line segment can be simulated in real time. It not only considers traditional meteorological parameters but also introduces the dynamic response of the wire, making the icing prediction closer to the actual situation. The icing layer simulation strategy can effectively characterize the icing formation process on the windward side through a preset icing thickness prediction model, thus effectively increasing the accuracy of icing prediction on the windward side. In addition, based on the terrain data of the target transmission line segment, the high-risk icing areas in the icing trajectory are determined, and the icing layer data of the high-risk icing areas are determined through a preset big data model, fully considering the influence of the geographical environment of complex terrain on icing, making the monitoring results more targeted and reliable. At the same time, the icing dancing areas in the icing trajectory are determined through vibration parameters and strain parameters, which can identify the promoting effect of wire dancing on icing. Compared with single meteorological parameters, considering multiple parameters further improves the accuracy of icing monitoring. The windward icing layer, high-risk icing areas, and icing dancing areas are fitted with the icing trajectory, and the fitted icing trajectory is optimized according to the icing layer data, ensuring the comprehensiveness and accuracy of the icing monitoring results. Finally, a visualization interface is generated based on the optimized icing trajectory, which can not only intuitively display the icing distribution and thickness but also display the high-risk areas and icing dancing areas, facilitating the monitoring of icing, effectively solving the deficiencies of traditional methods in icing monitoring in high-altitude and complex terrain areas, providing a strong guarantee for the safe and stable operation of the power system, and significantly improving the accuracy and comprehensiveness of transmission line icing monitoring compared with single static models.

[0059] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a schematic flow chart of an icing monitoring method for a transmission line provided by an embodiment of the present application;

[0061] Figure 2 is a schematic diagram of an icing growth trajectory simulation provided by an embodiment of the present application;

[0062] Figure 3 is a schematic diagram of an icing feature reconstruction simulation provided by an embodiment of the present application;

[0063] Figure 4 is a schematic diagram of an ice layer parameter time evolution curve provided by an embodiment of the present application;

[0064] Figure 5 is a schematic diagram of the structure of an icing monitoring system provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will clearly and completely describe the technical solutions in the embodiments of this application with reference to the accompanying drawings in the embodiments of this application. It should be understood that the specific implementation manners described herein are only used to illustrate and explain the embodiments of this application, and are not used to limit the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts shall fall within the scope of protection of this application.

[0066] It should be noted that if there are directional indications (such as up, down, left, right, front, back,...) involved in the embodiments of this application, the directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0067] In addition, if there are descriptions such as "first", "second", etc. involved in the embodiments of this application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between various embodiments may be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0068] Figure 1 A schematic flow diagram of a method for monitoring ice accretion on a transmission line according to an embodiment of this application is schematically shown. As Figure 1 shown, an embodiment of this application provides a method for monitoring ice accretion on a transmission line, which is applied to an ice accretion monitoring system. The method may include the following steps.

[0069] S110. In response to receiving the physical parameters of the wire at the same moment, and in combination with the current environmental data, simulate the ice accretion trajectory of the target transmission line segment. The current environmental data includes the current wind speed and the current wind direction, and the physical parameters of the wire include vibration parameters and strain parameters;

[0070] S120. Determine the windward surface of the ice accretion trajectory based on the current wind direction, and starting from the windward surface and taking the current wind speed as the reference wind speed, adopt an ice layer simulation strategy to simulate the windward ice layer of the ice accretion trajectory, where the ice layer simulation strategy is implemented based on a preset ice accretion thickness prediction model;

[0071] S130. Determine the high-risk icing areas in the icing trajectory based on the terrain data of the target transmission line section, and determine the icing layer data of the high-risk icing areas based on a preset big data model;

[0072] S140. Determine the ice galloping areas in the icing trajectory based on the vibration parameters and strain parameters;

[0073] S150. Fit the windward icing layer, high-risk icing areas and ice galloping areas with the icing trajectory, and optimize the fitted icing trajectory according to the icing layer data;

[0074] S160. Generate a visualization interface according to the optimized icing trajectory.

[0075] During the icing monitoring process, the physical parameters of the wire include vibration parameters and strain parameters, which can be collected in real time by sensors installed on the transmission line. The vibration parameters reflect the vibration state of the wire under the action of wind and icing, while the strain parameters describe the deformation of the wire under the action of external loads. The current environmental data includes the current wind speed, current wind direction, temperature, humidity, etc., which can be obtained through a weather station or a wind speed and wind direction sensor. In order to simulate the icing trajectory of the target transmission line section, the physical parameters of the wire are combined with the current environmental data, and the hydrodynamic model and the finite element analysis method are used for calculation.

[0076] The hydrodynamic model is used to simulate the influence of wind speed and wind direction on the air flow distribution on the wire surface, while the finite element analysis method is used to simulate the icing formation process on the wire surface. Specifically, during implementation, first calculate the air flow distribution on the wire surface according to the current wind speed and current wind direction, and then based on the air flow distribution and the physical parameters of the wire, use the finite element analysis method to simulate the icing formation process on the wire surface. The icing formation process is divided into multiple time steps, and the ice layer morphology parameters at each time step are obtained, including ice layer density, surface roughness and attachment angle. Based on the ice layer morphology parameters at each time step, a three-dimensional point cloud reconstruction algorithm is used to generate the icing cross-section contour at each time step. The icing cross-section contours of multiple time steps are spatially interpolated along the wire extension direction to construct a three-dimensional space model of the icing trajectory.

[0077] After determining the windward side of the ice accretion trajectory, taking the windward side as the starting surface and the current wind speed as the reference wind speed, an ice accretion layer simulation strategy is adopted to simulate the windward ice accretion layer. The windward side is the main area where ice accretion forms because the airflow directly contacts the wire surface here, resulting in rapid ice accumulation. The ice accretion layer simulation strategy is implemented based on a preset ice accretion thickness prediction model, which comprehensively considers the influence of factors such as wind speed, wind direction, temperature, humidity, and wire surface characteristics on the ice accretion thickness. Specifically, during implementation, first, a polar coordinate system is established with the wire axis as the reference, and the windward side is divided into multiple sector grids. Within each grid, the aerodynamic load is calculated based on the current wind speed, and the aerodynamic load determines the adhesion and freezing speed of water droplets on the wire surface. The temperature parameter is collected in real-time through a temperature sensor. A low-temperature environment accelerates ice accretion formation. Therefore, combined with the temperature parameter on the wire surface, the ice layer growth rate is solved through a thermodynamic equation.

[0078] The ice accretion thickness prediction model uses the finite element analysis method to simulate the stress distribution of the ice layer on the wire surface. The stress distribution reflects the adhesion strength and stability of the ice layer on the wire surface. By screening out the areas where the stress concentration exceeds the preset threshold, the areas with the fastest ice accretion growth can be identified. In these areas, taking the reference wind speed, temperature, and humidity as input parameters, according to the pre-constructed ice accretion thickness prediction model, the ice accretion thickness distribution data of the windward ice accretion layer is output. Based on the ice accretion thickness distribution data, a three-dimensional model of the windward ice accretion layer is generated, effectively characterizing the ice accretion formation process on the windward side, thereby significantly improving the accuracy of ice accretion prediction.

[0079] To determine the high-risk ice accretion areas in the ice accretion trajectory, first, the terrain data of the target transmission line section is obtained. The terrain data includes altitude, slope, and aspect, which can be obtained through geographic information system (GIS) or lidar scanning technology. Specifically, during implementation, first, the three-dimensional terrain feature vectors of the terrain data are extracted, and the potential wind acceleration areas in the three-dimensional terrain feature vectors are identified, including canyon areas, ridge areas, and windward slope areas. Due to terrain effects, the wind speed in these areas is significantly higher than that in the surrounding areas, resulting in a higher ice accretion risk. In the preset historical ice accretion database, the average ice accretion thickness corresponding to each potential wind acceleration area is determined. The historical ice accretion database contains ice accretion observation data over the years and can reflect the ice accretion laws under different terrain conditions. The potential wind acceleration areas corresponding to the average ice accretion thickness greater than the preset ice accretion thickness threshold are taken as the target ice accretion areas. Then, the humidity parameter is input into the pre-constructed big data model to predict the ice accretion growth rate of each target ice accretion area in a preset future time period. The big data model is based on machine learning and statistical analysis and can comprehensively consider the influence of multiple factors on ice accretion growth. The target ice accretion areas corresponding to the ice accretion growth rate greater than the preset growth rate are taken as the high-risk ice accretion areas.

[0080] The ice galloping area is the area where the conductor vibrates violently under the action of ice coating and wind force, posing a serious threat to the safe operation of the transmission line. To determine the ice galloping area, first perform a fast Fourier transform (FFT) on the vibration parameters to extract the proportion of vibration energy in the preset frequency band. The proportion of vibration energy reflects the vibration intensity of the conductor at different frequencies. Calculate the deviation value between the proportion of vibration energy and the natural frequency of the conductor. The larger the deviation value, the more violent the conductor vibration. Then, based on the strain parameters, construct a conductor deformation field model. The conductor deformation field model is based on the spatio-temporal distribution of the strain parameters and can identify the areas where the deformation gradient exceeds the limit. The areas where the deformation gradient exceeds the limit are the areas with the highest surface stress concentration of the conductor and are prone to ice galloping. Determine the galloping value for the areas where the deviation value is greater than the first preset value and the deformation gradient exceeds the limit. The galloping value is a comprehensive index that reflects the violent degree of conductor vibration and the distribution of ice coating. If the galloping value exceeds the preset critical galloping coefficient, the areas where the deviation value is greater than the first preset value and the deformation gradient exceeds the limit are determined as the ice galloping areas.

[0081] After determining the windward ice layer, high-risk ice-covered areas, and ice galloping areas, fit these data with the ice coating trajectory. Specifically, convert the coordinate system of the ice coating trajectory into a projected coordinate system in a preset format and divide the ice coating trajectory into multiple spatial grids. In the projected coordinate system, perform a spatial overlay of the three-dimensional model of the windward ice layer and the spatial grids of the ice coating trajectory to generate the first ice coating trajectory. The first ice coating trajectory contains the ice thickness distribution data of the windward ice layer. Then, align the coordinates of the high-risk ice-covered areas and the ice galloping areas and interpolate the coordinate-aligned high-risk ice-covered areas and ice galloping areas into the projected coordinate system of the first ice coating trajectory to generate the fitted ice coating trajectory. In the projected coordinate system of the fitted ice coating trajectory, interpolate the ice layer data to the corresponding high-risk ice-covered areas on the fitted ice coating trajectory to optimize the fitted ice coating trajectory. In this way, a comprehensive and accurate ice coating trajectory model can be generated.

[0082] When generating the visualization interface, perform visualization processing based on the optimized ice coating trajectory. Specifically, first convert the optimized ice coating trajectory into a visualization data format, including a three-dimensional model, color coding, and annotation information. Using graphics rendering technology, render the three-dimensional model of the ice coating trajectory into the visualization interface, where different colors and annotation information represent different ice thicknesses, high-risk ice-covered areas, and ice galloping areas. Through interactive operations, users can view the detailed information of the ice coating trajectory, including the ice thickness distribution, the positions and ranges of the high-risk ice-covered areas and the ice galloping areas. Through this step, the ice coating situation of the target transmission line segment can be intuitively displayed, providing important support for ice coating monitoring and decision-making.

[0083] This embodiment comprehensively considers environmental data, wire physical parameters, and terrain data, and can real-time simulate the icing trajectory of the target transmission line segment, significantly improving the accuracy of icing prediction. The simulation strategy for the windward icing layer effectively characterizes the icing formation process on the windward side. The identification of high-risk icing areas fully considers the influence of the geographical environment of complex terrain on icing. The determination of the icing galloping area identifies the promoting effect of wire galloping on icing. Fitting the windward icing layer, high-risk icing areas, and icing galloping areas with the icing trajectory, and optimizing the icing trajectory after fitting according to the icing layer data, ensures the comprehensiveness and accuracy of the icing monitoring results. The finally generated visualization interface can intuitively display the icing distribution and thickness, facilitating the monitoring of icing, effectively solving the deficiencies of traditional methods in icing monitoring in high-altitude and complex terrain areas, and providing a strong guarantee for the safe and stable operation of the power system.

[0084] By combining wire physical parameters and current environmental data to simulate the icing trajectory of the target transmission line segment, the accuracy and comprehensiveness of icing monitoring are significantly improved. Traditional methods mainly rely on fixed meteorological parameters and historical data for linear modeling, and it is difficult to comprehensively consider the non-linear coupling relationship among the geographical environment, wire galloping, and meteorological parameters, resulting in a low accuracy rate of icing monitoring. This solution effectively solves the problem of insufficient prediction of windward icing by traditional methods by introducing an icing layer simulation strategy, determining the windward side of the icing trajectory based on the current wind speed and direction, and simulating the windward icing layer. At the same time, by combining the terrain data of the target transmission line segment, using a big data model to identify high-risk icing areas, and determining the icing galloping area through vibration parameters and strain parameters, the influence of the geographical environment and wire galloping on icing is comprehensively considered. In addition, by fitting the windward icing layer, high-risk icing areas, and icing galloping areas with the icing trajectory and optimizing the icing trajectory after fitting, the accuracy of icing monitoring is further improved. The finally generated visualization interface provides intuitive icing distribution information for operation and maintenance personnel, helping to take timely countermeasures to ensure the safe and stable operation of the transmission line. This solution not only overcomes the deficiencies of traditional methods in considering complex terrain and wire galloping inadequately, but also realizes the comprehensiveness and accuracy of icing monitoring through dynamic simulation and optimization strategies, providing strong support for the safe operation of the power system.

[0085] In one implementation manner of this embodiment, simulating the icing trajectory of the target transmission line segment includes the following steps:

[0086] S210. According to the current wind speed and current wind direction, use a preset hydrodynamic model to calculate the air flow distribution on the surface of the wire of the target transmission line segment;

[0087] S220. Based on the air flow distribution and wire physical parameters, use the finite element analysis method to simulate the icing formation process on the surface of the wire;

[0088] S230. Divide the ice formation process into multiple time steps, and obtain the ice layer morphology parameters for each time step, where the ice layer morphology parameters include ice layer density, surface roughness, and attachment angle;

[0089] S240. Based on the ice layer morphology parameters for each time step, use a three-dimensional point cloud reconstruction algorithm to generate the ice-covered cross-section profile for each time step;

[0090] S250. Perform spatial interpolation on the ice-covered cross-section profiles of multiple time steps along the wire extension direction to construct a three-dimensional spatial model of the ice-covered trajectory.

[0091] When simulating the ice-covered trajectory of the target transmission line segment, according to the current wind speed and current wind direction, use a preset hydrodynamic model to calculate the air flow distribution on the wire surface. The hydrodynamic model is based on the Navier-Stokes equations, which describe the motion laws of fluids. The Navier-Stokes equations include the continuity equation and the momentum equation. The continuity equation describes the mass conservation of fluids, and the momentum equation describes the momentum conservation of fluids. The continuity equation and the momentum equation are as follows:

[0092] Continuity equation:

[0093]

[0094] Momentum equation:

[0095]

[0096] Where ρ is the fluid density, u is the fluid velocity vector, p is the pressure, μ is the dynamic viscosity, and f is the external force (such as gravity). In specific implementation, the wire surface is regarded as a three-dimensional geometric body and discretized into multiple tiny grid cells. The air flow distribution of each grid cell is determined by solving the Navier-Stokes equations. The Navier-Stokes equations include the continuity equation and the momentum equation. The continuity equation describes the mass conservation of fluids, and the momentum equation describes the momentum conservation of fluids.

[0097] By solving the above equations using the finite volume method or the finite difference method, the air flow velocity, pressure, and temperature distributions within each grid cell can be obtained. For example, assuming the current wind speed is known and the current wind direction is north wind, through calculation using the hydrodynamic model, the air flow velocity distribution at each point on the wire surface can be obtained. The air flow velocity on the windward side is relatively high, and the air flow velocity on the leeward side is relatively low. The calculation results of the air flow distribution will be used as input parameters for subsequent ice-covering simulation to ensure the accuracy of the ice formation process.

[0098] After obtaining the airflow distribution on the conductor surface, based on the airflow distribution and conductor physical parameters, the finite element analysis method is used to simulate the icing formation process on the conductor surface. Finite element analysis is a numerical calculation method that solves complex continuum problems by discretizing them into a finite number of simple element problems. Specifically, when implementing, the conductor surface is discretized into multiple non-uniform triangular meshes, and the mesh density on the windward side is higher than that on the leeward side to capture the influence of airflow on icing formation. Dynamic boundary conditions, including airflow distribution, conductor surface displacement constraint conditions, and conductor material elastic modulus correction coefficients, are applied at the nodes of each mesh element. By solving the preset coupled equations (including the heat conduction equation and the ice layer growth equation), icing morphology data is output to simulate the icing formation process on the conductor surface. The heat conduction equation and the ice layer growth equation are as follows:

[0099] Heat conduction equation:

[0100]

[0101] Ice layer growth equation:

[0102]

[0103] Where, T is the temperature, α is the thermal diffusivity, h is the ice layer thickness, k is the ice layer growth coefficient, and T f is the freezing point temperature. For example, assuming the temperature on the conductor surface is -5°C and the humidity is 90%, through finite element analysis, the formation and growth process of the ice layer on the conductor surface can be simulated, and the simulation results of the icing formation process will be used as the basis for subsequent extraction of ice layer morphology parameters to ensure the accuracy of the icing trajectory.

[0104] After simulating the icing formation process on the conductor surface, the icing formation process is divided into multiple time steps, and the ice layer morphology parameters at each time step are obtained. The ice layer morphology parameters include ice layer density, surface roughness, and attachment angle, which are used to describe the physical properties of the ice layer and can be determined specifically through experimental measurement or empirical formulas. Specifically, when implementing, the icing formation process is divided into multiple time steps (such as each 10 minutes as a time step). Within each time step, the ice layer density, surface roughness, and attachment angle are calculated through experimental measurement or empirical formulas. For example, the ice layer density can be calculated by measuring the mass and volume of the ice layer, the surface roughness can be measured by a surface profilometer, and the attachment angle can be measured by the contact angle between the ice layer and the conductor surface. The calculation formulas for the ice layer density, surface roughness, and attachment angle are as follows:

[0105] Ice layer density:

[0106]

[0107] Surface roughness:

[0108]

[0109] Attachment angle:

[0110]

[0111] where ρ i is the ice layer density, m i is the ice layer mass, V i is the ice layer volume, R a is the surface roughness, L is the measurement length, z(x) is the surface profile height, θ is the attachment angle, γ sv is the solid-vapor surface tension, γ sl is the solid-liquid surface tension, γ lv is the liquid-vapor surface tension. The extraction results of the ice layer morphology parameters will be used as the basis for subsequent three-dimensional point cloud reconstruction to ensure the accuracy of the ice-covered cross-section profile.

[0112] Figure 2 shows a schematic diagram of the simulation of ice accretion growth trajectory provided by an embodiment of the present application. As Figure 2 shown, the time step is 1 h. It can be seen that as time goes on, the ice accretion growth shows an asymmetric growth characteristic.

[0113] After obtaining the ice layer morphology parameters for each time step, based on the ice layer morphology parameters for each time step, a three-dimensional point cloud reconstruction algorithm is used to generate the ice-covered cross-section profile for each time step. Figure 3 shows a schematic diagram of the simulation of ice accretion feature reconstruction provided by an embodiment of the present application. As Figure 3 shown, it can generate a typical ice accretion morphology through a parametric surface. The ice-covered cross-section presents an irregular shape, indicating that the formation process of ice accretion is affected by various factors, and intuitively shows the shape and distribution of the ice on the wire at a specific time step. Specifically, the three-dimensional point cloud reconstruction algorithm is based on point cloud data. The point cloud data includes the three-dimensional coordinates and attribute information (such as density, roughness, and attachment angle) of the ice layer surface. Through Figure 4From the time-evolution curves of the ice layer parameters shown, it can be seen that both the ice layer density and the surface roughness of the ice layer show a gradually increasing trend, and the attachment angle changes gradually with time. It can be understood that the ice layer density increases approximately linearly with time, indicating that as time goes by, the ice layer becomes denser. The surface roughness increases non-linearly with time, indicating that as time goes by, the surface of the ice layer becomes rougher. The attachment angle changes periodically with time, indicating that the attachment situation between the ice layer and the wire surface is constantly changing. In this embodiment, a three-dimensional model is generated through a point cloud reconstruction algorithm, and the point cloud reconstruction algorithm can be a Poisson reconstruction algorithm to convert the point cloud data into the three-dimensional geometry of the ice layer, that is, the cross-sectional profile of the ice-covered area. For example, assuming that the ice layer density at a certain time step is 900 kg / m 3 , the surface roughness is 0.1 mm, and the attachment angle is 30°, the cross-sectional profile of the ice-covered area at this time step can be generated through the three-dimensional point cloud reconstruction algorithm.

[0114] After generating the cross-sectional profiles of the ice-covered area for each time step, the cross-sectional profiles of the ice-covered area for multiple time steps are spatially interpolated along the wire extension direction to construct a three-dimensional spatial model of the ice-covered trajectory. Spatial interpolation is a numerical method that infers the data of unknown points through the data of known points and is used to convert the discrete cross-sectional profile data into a continuous three-dimensional model to describe the distribution and change of the ice coverage on the wire surface. Specifically, in implementation, the cross-sectional profiles of the ice-covered area for each time step are arranged along the wire extension direction, and the cross-sectional profiles of the ice-covered area for multiple time steps are spatially interpolated along the wire extension direction. The gaps between the cross-sectional profiles are filled through linear interpolation or spline interpolation to generate a three-dimensional spatial model of the ice-covered trajectory. The formulas are as follows:

[0115] Linear interpolation:

[0116]

[0117] Spline interpolation:

[0118] S(x) = a i + b i (x - x i ) + c i (x - x i )2 + d i (x - x i ) 3 ;

[0119] where f(x) is the interpolation function, x0 and x1 are known points, S(x) is the spline function, a i , b i , c i and d iare spline coefficients. For example, assume that the ice-covered cross-section profiles are generated at time steps t1, t2, and t3 respectively. Through spatial interpolation, the ice-covered trajectory between t1 and t3 can be generated.

[0120] This embodiment comprehensively considers the influences of air flow distribution, conductor physical parameters, and ice layer morphology parameters on ice formation. The simulation process of the ice-covered trajectory is divided into multiple time steps, and the ice growth on the conductor surface is calculated once for each time step. In this way, a three-dimensional model of the ice-covered trajectory varying with time can be generated, and this model can accurately reflect the distribution and thickness change of the ice on the conductor surface. The application of the hydrodynamic model and the finite element analysis method ensures the accuracy and reliability of the ice formation process. Dividing the ice formation process into multiple time steps and extracting the ice layer morphology parameters of each time step provide detailed data support for the construction of the ice-covered trajectory. The application of the three-dimensional point cloud reconstruction algorithm and the spatial interpolation algorithm ensures the accuracy and continuity of the three-dimensional space model of the ice-covered trajectory. The finally constructed three-dimensional space model of the ice-covered trajectory can visually display the distribution and change of the ice on the conductor surface, providing strong data support for ice-covered monitoring. Compared with the traditional method, it significantly improves the accuracy and comprehensiveness of ice-covered monitoring of transmission lines, providing a strong guarantee for the safe and stable operation of the power system.

[0121] In one implementation manner of this embodiment, based on the air flow distribution and conductor physical parameters, a preset finite element analysis strategy is used to simulate the ice formation process on the conductor surface, including the following steps:

[0122] S310. Discretize the conductor surface into multiple non-uniform triangular meshes, where the mesh density on the windward side is higher than that on the leeward side;

[0123] S320. Apply dynamic boundary conditions at the nodes of the triangular meshes, and use the preset finite element analysis strategy to solve the preset coupling equation in each time iteration step to output ice formation data, and the ice formation data is used to simulate the ice formation process on the conductor surface;

[0124] Among them, the dynamic boundary conditions include air flow distribution, the conductor surface displacement constraint conditions calculated according to vibration parameters, and the conductor material elastic modulus correction coefficient calculated based on strain parameters.

[0125] When simulating the ice formation process on the surface of a simulated wire, the wire surface is discretized into multiple non-uniform triangular meshes. Discretization is to divide the continuous wire surface into a finite number of small mesh elements for numerical calculation. Specifically, in implementation, the wire surface is regarded as a three-dimensional geometric body, and a non-uniform mesh division method is used to divide it into multiple triangular meshes. The mesh density on the windward side is higher than that on the leeward side. Since the windward side is the main area where ice formation occurs, the mesh density is higher on the windward side to capture the influence of the air flow on ice formation; the mesh density on the leeward side is lower to reduce the computational amount.

[0126] The mesh division method is based on the Delaunay triangulation algorithm, which can generate high-quality triangular meshes. The basic idea of the Delaunay triangulation algorithm is that for a given set of points, a triangular mesh is generated such that no other points are contained within the circumcircle of each triangle. Specifically, in implementation, first a set of points is generated on the wire surface, and then the Delaunay triangulation algorithm is applied to generate the triangular mesh. For example, assuming the length of the wire surface is 10 meters and the diameter is 2 centimeters, by generating 1000 points on the wire surface and applying the Delaunay triangulation algorithm, approximately 2000 triangular meshes can be generated, where the mesh density on the windward side is 100 per square meter and the mesh density on the leeward side is 50 per square meter.

[0127] After discretizing the wire surface into multiple non-uniform triangular meshes, dynamic boundary conditions are applied at the nodes of the triangular meshes, and a preset finite element analysis strategy is used to solve the preset coupled equations at each time iteration step to output ice formation morphology data. The dynamic boundary conditions include the air flow distribution, the wire surface displacement constraint conditions calculated according to the vibration parameters, and the wire material elastic modulus correction coefficient calculated based on the strain parameters. The air flow distribution is calculated through a hydrodynamic model, which describes the air flow velocity, pressure, and temperature distribution on the wire surface. The wire surface displacement constraint conditions are calculated according to the vibration parameters, which describe the vibration characteristics of the wire during the ice formation process. The wire material elastic modulus correction coefficient is calculated based on the strain parameters, which describes the elastic change of the wire material during the ice formation process.

[0128] Specifically, in implementation, first the air flow distribution is applied at the nodes of each triangular mesh, and the air flow distribution is calculated through a hydrodynamic model. Then, the wire surface displacement constraint conditions are calculated according to the vibration parameters, and the vibration parameters are collected in real time by sensors. Finally, the wire material elastic modulus correction coefficient is calculated based on the strain parameters, and the strain parameters are collected in real time by strain gauges. By solving the preset coupled equations (including the heat conduction equation and the ice layer growth equation), the ice formation morphology data is output to simulate the ice formation process on the wire surface. For the heat conduction equation and the ice layer growth equation, please refer to S220, which will not be elaborated in this application.

[0129] In this embodiment, the surface of the wire is discretized into a non-uniform triangular mesh, where the mesh density on the windward side is higher than that on the leeward side, effectively improving the accuracy of icing simulation on the windward side. At the same time, dynamic boundary conditions are applied at the mesh nodes, including the air flow distribution, the displacement constraint conditions on the wire surface, and the material elastic modulus correction coefficient, comprehensively considering the coupled effects of air flow, vibration, and strain on icing formation. In addition, by solving the preset coupled equations, the icing morphology data is output, achieving a high-precision simulation of the icing formation process. This solution provides more accurate data support for icing monitoring through dynamic boundary conditions and non-uniform grid technology, contributing to improving the accuracy and reliability of transmission line icing monitoring.

[0130] In one implementation of this embodiment, the physical parameters of the wire include the temperature parameter on the wire surface, and the current environmental data also includes temperature and humidity. The icing layer simulation strategy includes the following steps:

[0131] S410: Taking the wire axis as the reference, establish a polar coordinate system and divide the windward side into multiple sector grids;

[0132] S420: In each grid, calculate the aerodynamic load according to the current wind speed, and combine the temperature parameter on the wire surface to solve the ice layer growth rate through the thermodynamic equation;

[0133] S430: Use the finite element analysis method to simulate the stress distribution of the ice layer on the wire surface, and screen out the grid areas where the stress concentration exceeds the preset threshold;

[0134] S440: In the grid area, taking the reference wind speed, temperature, and humidity as input parameters, according to the pre-constructed icing thickness prediction model, output the icing thickness distribution data of the windward icing layer;

[0135] S450: Generate a three-dimensional model of the windward icing layer according to the icing thickness distribution data.

[0136] When simulating the icing layer, first, taking the wire axis as the reference, establish a polar coordinate system and divide the windward side into multiple sector grids. The polar coordinate system is a coordinate system with polar radius and polar angle as coordinates, suitable for describing the geometric shape of circular or axisymmetric objects. Specifically, when implementing, taking the wire axis as the pole and the normal direction of the wire surface as the polar axis, establish a polar coordinate system. Divide the windward side into multiple sector grids, where the polar angle range of each sector grid is 5° to 10°, and the polar radius range is the wire radius. For example, assuming the wire diameter is 2 cm and the polar angle range is 5°, then the arc length of each sector grid is 0.17 cm.

[0137] After dividing the sector grid, within each grid, calculate the aerodynamic load based on the current wind speed, and combine the temperature parameters on the surface of the wire to solve the ice layer growth rate through the thermodynamic equation. The aerodynamic load is the force exerted by the air flow on the surface of the wire and is related to the wind speed and the direction of the air flow. During specific implementation, calculate the aerodynamic load within each grid according to the current wind speed and the direction of the air flow. The calculation formula for the aerodynamic load is as follows:

[0138]

[0139] where F is the aerodynamic load, ρ is the air density, v is the wind speed, C d is the drag coefficient, and A is the grid area. Combine the temperature parameters on the surface of the wire to solve the ice layer growth rate through the thermodynamic equation. The thermodynamic equation is as follows:

[0140]

[0141] where h is the ice layer thickness, k is the ice layer growth coefficient, T f is the freezing point temperature, and T is the surface temperature of the wire. For example, assuming the current wind speed is 10 m / s and the surface temperature of the wire is -5°C, through the thermodynamic equation, the ice layer growth rate can be solved to be 0.1 mm / min.

[0142] After calculating the ice layer growth rate, adopt the finite element analysis method to simulate the stress distribution of the ice layer on the surface of the wire and screen out the grid areas where the stress concentration exceeds the preset threshold. The finite element analysis is a numerical calculation method that solves complex continuum problems by discretizing them into a finite number of simple element problems. During specific implementation, divide the ice layer into multiple finite element units, and determine the stress distribution within each unit by solving the elasticity equations. The elasticity equations are as follows:

[0143] σ = E∈;

[0144] where σ is the stress, E is the elastic modulus, and ∈ is the strain. By solving the elasticity equations, the stress distribution within each unit can be obtained. Screen out the grid areas where the stress concentration exceeds the preset threshold, and the preset threshold is determined according to the tensile strength of the ice layer. For example, assuming the tensile strength of the ice layer is 1 MPa and the preset threshold is 0.8 MPa, through the finite element analysis, the grid areas where the stress concentration exceeds 0.8 MPa can be screened out.

[0145] After screening out the grid areas where the stress concentration exceeds the preset threshold, in the grid areas, with the reference wind speed, temperature, and humidity as input parameters, according to the pre-constructed ice thickness prediction model, the ice thickness distribution data of the windward ice layer is output. The ice thickness prediction model is based on a machine learning algorithm and is obtained by training historical ice data. Specifically, when implemented, the reference wind speed, temperature, and humidity are used as input features and input into the ice thickness prediction model to output the ice thickness of each grid area. The formula of the ice thickness prediction model is as follows:

[0146] h = f(v, T, H);

[0147] where h is the ice thickness, v is the reference wind speed, T is the temperature, H is the humidity, and f is the ice thickness prediction model. Through the ice thickness prediction model, the ice thickness of each grid area can be output.

[0148] After obtaining the ice thickness distribution data, based on the ice thickness distribution data, a three-dimensional model of the windward ice layer is generated. The generation of the three-dimensional model is based on a three-dimensional reconstruction algorithm and is generated by interpolation and fitting methods. Specifically, when implemented, the ice thickness distribution data can be converted into point cloud data, and a three-dimensional model is generated by interpolation and fitting methods. The interpolation uses linear interpolation, and its formula can be referred to S250, which will not be elaborated here in this application. The fitting method uses the least squares method, and the formula is as follows:

[0149] Least squares method:

[0150]

[0151] where y i is the measured value, and f(x i ) is the fitted value. For example, assuming the ice thickness distribution data is from 0.1 mm to 0.5 mm, through the three-dimensional reconstruction algorithm, a three-dimensional model of the windward ice layer can be generated.

[0152] In this embodiment, by establishing a polar coordinate system and a sector grid, and combining the thermodynamic equation and the finite element analysis method, the accuracy and efficiency of the simulation of ice accretion on the windward side are significantly improved. Traditional methods rely on fixed parameters and static models, and it is difficult to accurately reflect the influence of wire surface temperature, wind speed, and humidity on ice formation. In this solution, a polar coordinate system is established with the wire axis as the reference, the windward side is divided into multiple sector grids, and the aerodynamic load is calculated according to the current wind speed in each grid. Combining the wire surface temperature parameter, the ice layer growth rate is solved through the thermodynamic equation, effectively improving the accuracy of the simulation of ice accretion on the windward side. At the same time, the finite element analysis method is used to simulate the stress distribution of the ice layer on the wire surface, and the grid areas with stress concentration exceeding the preset threshold are screened out, further optimizing the accuracy of the ice accretion thickness distribution data. In addition, through a pre-constructed ice accretion thickness prediction model, the ice accretion thickness distribution data of the windward ice layer is output, and a three-dimensional model is generated, providing more intuitive data support for ice accretion monitoring, realizing the comprehensiveness and accuracy of ice accretion monitoring, and providing strong support for the safe operation of the power system.

[0153] In one implementation of this embodiment, the current environmental data includes humidity parameters. Based on the terrain data of the target transmission line segment, to determine the high-risk ice accretion areas in the ice accretion trajectory, the following steps are included:

[0154] S510. Obtain the terrain data of the target transmission line segment, where the terrain data includes altitude, slope, and aspect;

[0155] S520. Extract the three-dimensional terrain feature vector of the terrain data;

[0156] S530. Identify the potential wind acceleration areas in the three-dimensional terrain feature vector, where the potential wind acceleration areas include canyon areas, ridge areas, and windward slope areas;

[0157] S540. Determine the average ice accretion thickness corresponding to each potential wind acceleration area in the preset historical ice accretion database;

[0158] S550. Take the potential wind acceleration areas corresponding to the average ice accretion thickness greater than the preset ice accretion thickness threshold as the target ice accretion areas;

[0159] S560. Input the humidity parameter into the pre-constructed big data model to predict the ice accretion growth rate of each target ice accretion area in the future preset time period;

[0160] S570. Take the target ice accretion areas corresponding to the ice accretion growth rate greater than the preset growth rate as the high-risk ice accretion areas.

[0161] When determining high-risk icing areas, terrain data of the target transmission line section is obtained, including altitude, slope, and aspect. The terrain data is obtained through Geographic Information System (GIS) or Light Detection and Ranging (LiDAR) technology. Specifically, when implementing, a LiDAR device is used to scan the target transmission line section to generate a high-precision three-dimensional terrain model. The LiDAR device measures the elevation information of the ground surface by emitting laser pulses and receiving the reflected signals. For example, assuming the length of the target transmission line section is 10 kilometers, using a LiDAR device for scanning, a three-dimensional terrain model with a resolution of 1 meter can be generated, including the altitude, slope, and aspect of each point. Altitude represents the vertical height of the point relative to sea level, slope represents the degree of inclination of the point, and aspect represents the direction of inclination of the point.

[0162] After obtaining the terrain data, the three-dimensional terrain feature vector of the terrain data is extracted. The three-dimensional terrain feature vector is a mathematical representation describing the terrain features, including the statistical features of altitude, slope, and aspect. Specifically, when implementing, the Principal Component Analysis (PCA) method is used to extract the three-dimensional terrain feature vector. PCA is a dimensionality reduction technique that transforms the original data into a new coordinate system through a linear transformation, such that the first dimension of the new coordinate system has the largest variance, the second dimension has the second largest variance, and so on. For example, assuming the terrain data includes the altitude, slope, and aspect of 1000 points, through the PCA method, three principal components can be extracted, respectively representing the main features of the terrain. The first principal component may represent the overall height of the terrain, the second principal component may represent the undulation degree of the terrain, and the third principal component may represent the directionality of the terrain.

[0163] After extracting the three-dimensional terrain feature vector, the potential wind acceleration areas in the three-dimensional terrain feature vector are identified. Potential wind acceleration areas refer to areas where terrain features may cause an increase in wind speed, including canyon areas, ridge areas, and windward slope areas.

[0164] Specifically, when implementing, a clustering analysis method is used to identify potential wind acceleration areas. Clustering analysis is an unsupervised learning method that identifies potential patterns by grouping similar data points. For example, assuming the three-dimensional terrain feature vector includes 1000 data points, through the clustering analysis method, these data points can be divided into several categories, where one category represents the canyon area, another category represents the ridge area, and the third category represents the windward slope area.

[0165] After identifying potential wind acceleration areas, determine the average ice accretion thickness corresponding to each potential wind acceleration area in a preset historical ice accretion database. The historical ice accretion database includes ice accretion thickness data for each area in the past few years. Specifically, when implementing, according to the geographical location of the potential wind acceleration area, query the historical ice accretion database to obtain the ice accretion thickness data for each area and calculate the average ice accretion thickness. For example, assume that the historical ice accretion database includes ice accretion thickness data for each month in the past five years. By querying the database, the ice accretion thickness data for each potential wind acceleration area can be obtained and the average ice accretion thickness can be calculated.

[0166] After determining the average ice accretion thickness, use the potential wind acceleration areas corresponding to the average ice accretion thickness greater than the preset ice accretion thickness threshold as target ice accretion areas. The preset ice accretion thickness threshold is determined based on historical data and practical experience. Specifically, when implementing, compare the average ice accretion thickness of each potential wind acceleration area with the preset ice accretion thickness threshold, and use the areas greater than the threshold as target ice accretion areas. For example, if the preset ice accretion thickness threshold is 10 mm, by comparing the average ice accretion thickness with the threshold, the potential wind acceleration areas with an average ice accretion thickness greater than 10 mm can be used as target ice accretion areas.

[0167] After screening the target ice accretion areas, input the humidity parameter into a pre-constructed big data model to predict the ice accretion growth rate of each target ice accretion area in a preset future time period. The big data model is based on machine learning algorithms and is obtained by training historical meteorological data and ice accretion data. Specifically, when implementing, use the humidity parameter as an input feature and input it into the big data model to output the ice accretion growth rate of each target ice accretion area. For example, assume that the humidity parameter is 90%. Through the big data model, the ice accretion growth rate of each target ice accretion area within the next 24 hours can be predicted.

[0168] After predicting the ice accretion growth rate, use the target ice accretion areas corresponding to the ice accretion growth rate greater than the preset growth rate as high-risk ice accretion areas. The preset growth rate is determined based on historical data and practical experience. Specifically, when implementing, compare the ice accretion growth rate of each target ice accretion area with the preset growth rate, and use the areas greater than the growth rate as high-risk ice accretion areas. For example, assume that the preset growth rate is 5%. By comparing the ice accretion growth rate with the growth rate, the target ice accretion areas with an ice accretion growth rate greater than 5% can be used as high-risk ice accretion areas.

[0169] In this embodiment, by obtaining the terrain data of the target transmission line segment, including altitude, slope, and aspect, and extracting the three-dimensional terrain feature vectors, potential wind acceleration areas such as canyon areas, ridge areas, and windward slope areas are identified, effectively improving the identification accuracy of high-risk icing areas. At the same time, by using the preset historical icing database to determine the average icing thickness of each potential wind acceleration area, and combining with humidity parameters, the icing growth rate in the preset future time period is predicted using a big data model, further optimizing the determination criteria for high-risk icing areas. This solution not only overcomes the problem of insufficient consideration of terrain and humidity in traditional methods, but also provides more accurate data support for icing monitoring through big data models and dynamic prediction technologies, helping to improve the accuracy and reliability of transmission line icing monitoring.

[0170] In one implementation of this embodiment, determining the icing galloping area in the icing trajectory based on vibration parameters and strain parameters includes the following steps:

[0171] S610. Perform FFT transformation on the vibration parameters, extract the proportion of vibration energy in the preset frequency band, and calculate its deviation value from the natural frequency of the conductor;

[0172] S620. According to the strain parameters, construct a conductor deformation field model to identify the areas where the deformation gradient exceeds the limit, where the conductor deformation field model is constructed based on the spatio-temporal distribution of the strain parameters;

[0173] S630. Determine the galloping value of the area where the deviation value is greater than the first preset value and the deformation gradient exceeds the limit;

[0174] S640. If the galloping value exceeds the preset critical galloping coefficient, then determine the area where the deviation value is greater than the first preset value and the deformation gradient exceeds the limit as the icing galloping area.

[0175] When determining the icing galloping area, first perform a fast Fourier transform (FFT) on the vibration parameters, extract the proportion of vibration energy in the preset frequency band, and calculate its deviation value from the natural frequency of the conductor. FFT is a tool for converting a time-domain signal into a frequency-domain signal, which can reveal the frequency components in the signal. Specifically, when implementing, collect the vibration signal of the conductor, use the FFT algorithm to convert the vibration signal from the time domain to the frequency domain to obtain a spectrogram. The spectrogram shows the vibration energy distribution of different frequency components. The preset frequency band is usually selected as the frequency range related to the conductor galloping, such as 0.1 Hz to 10 Hz. Extract the proportion of vibration energy in the preset frequency band, that is, the proportion of vibration energy in this frequency band to the total vibration energy. Then, calculate the deviation value between the proportion of vibration energy in this frequency band and the natural frequency of the conductor. The natural frequency of the conductor is the natural vibration frequency of the conductor without external force and can be obtained through experiments. The calculation formula for the deviation value is as follows:

[0176]

[0177] Where, Δf is the deviation value, E1 is the proportion of vibration energy in the preset frequency band, and E2 is the proportion of vibration energy corresponding to the natural frequency of the wire. For example, assuming that the proportion of vibration energy in the preset frequency band is 30%, and the proportion of vibration energy corresponding to the natural frequency of the wire is 20%, then the deviation value is 0.5.

[0178] After calculating the deviation value, a wire deformation field model is constructed based on the strain parameters to identify the regions where the deformation gradient exceeds the limit. The strain parameters are collected in real time by strain gauges, which reflect the deformation of the wire under the action of ice coating and wind force. Specifically, during implementation, the strain parameters of the wire are interpolated in time and space to construct a wire deformation field model. The deformation field model describes the deformation distribution of the wire at different positions and times. The deformation gradient is the rate of change of the deformation amount with respect to the spatial position in the deformation field model, and the calculation formula is as follows:

[0179]

[0180] Where, is the deformation gradient, ∈ is the strain parameter, and x, y, and z are spatial coordinates. Identifying the regions where the deformation gradient exceeds the limit means identifying the regions where the deformation gradient exceeds the preset threshold. The preset threshold is determined according to the material properties and safety standards of the wire. For example, assuming that the deformation gradient threshold of the wire is 0.01, by calculating the deformation gradient, the regions where the deformation gradient exceeds 0.01 can be identified.

[0181] After identifying the regions where the deformation gradient exceeds the limit, the galloping value of the regions where the deviation value is greater than the first preset value and the deformation gradient exceeds the limit is determined. The galloping value is an index to measure the galloping degree of the wire, comprehensively considering the vibration energy deviation and the deformation gradient. Specifically, during implementation, first, the regions where the deviation value is greater than the first preset value and the deformation gradient exceeds the limit are screened out. The first preset value is determined according to historical data and practical experience. Then, the galloping values of these regions are calculated. The calculation formula of the galloping value is as follows:

[0182]

[0183] Where, D is the galloping value, α and β are weight coefficients, Δf is the deviation value, is the deformation gradient. The weight coefficients are determined according to the influence degrees of vibration and deformation on galloping.

[0184] After calculating the galloping value, it is judged whether the galloping value exceeds the preset critical galloping coefficient. The critical galloping coefficient is a threshold determined according to the safety standards and historical data of the wire. Specifically, during implementation, the galloping value is compared with the critical galloping coefficient. If the galloping value exceeds the critical galloping coefficient, the regions where the deviation value is greater than the first preset value and the deformation gradient exceeds the limit are determined as the ice-coated galloping regions. For example, assuming that the critical galloping coefficient is 0.8, by comparing the galloping value with the critical galloping coefficient, the regions where the galloping value exceeds 0.8 can be determined as the ice-coated galloping regions.

[0185] In this embodiment, by performing FFT transformation on vibration parameters, extracting the proportion of vibration energy in a preset frequency band, and calculating the deviation value from the natural frequency of the wire, the recognition accuracy of the icing galloping area is effectively improved. At the same time, by constructing a wire deformation field model, identifying the areas where the deformation gradient exceeds the limit, and combining the deviation value and the deformation gradient to determine the galloping value, the determination criteria for the icing galloping area are further optimized, providing more accurate data support for icing monitoring, and helping to improve the accuracy and reliability of transmission line icing monitoring.

[0186] In one implementation of this embodiment, fitting the windward icing layer, high-risk icing area, and icing galloping area to the icing trajectory includes the following steps:

[0187] S710. Convert the coordinate system of the icing trajectory to a projected coordinate system in a preset format, and divide the icing trajectory into multiple spatial grids;

[0188] S720. In the projected coordinate system, perform spatial superposition of the three-dimensional model of the windward icing layer and the spatial grids of the icing trajectory to generate a first icing trajectory, where the first icing trajectory contains the icing thickness distribution data of the windward icing layer;

[0189] S730. Align the coordinates of the high-risk icing area and the icing galloping area, and interpolate the coordinate-aligned high-risk icing area and icing galloping area into the projected coordinate system of the first icing trajectory to generate a fitted icing trajectory.

[0190] When fitting the windward icing layer, high-risk icing area, and icing galloping area, first convert the coordinate system of the icing trajectory to a projected coordinate system in a preset format, and divide the icing trajectory into multiple spatial grids. The coordinate system conversion is to unify the coordinate reference systems of different data sources, facilitating subsequent spatial superposition and interpolation operations. Specifically, in implementation, map projection technology is used to convert the coordinate system of the icing trajectory to a projected coordinate system in a preset format, such as the Universal Transverse Mercator (UTM) projection. The UTM projection is a commonly used map projection method that can convert the longitude and latitude coordinates on the earth's surface into plane rectangular coordinates, reducing projection distortion. For example, assuming the original coordinate system of the icing trajectory is the WGS84 longitude and latitude coordinate system, through the UTM projection, it can be converted into the UTM plane rectangular coordinate system. Then, divide the icing trajectory into multiple spatial grids, and the size of the grids is determined according to the resolution and accuracy requirements of the icing trajectory.

[0191] After converting the coordinate system and dividing the spatial grids, in the projected coordinate system, perform spatial superposition of the three-dimensional model of the windward icing layer and the spatial grids of the icing trajectory to generate a first icing trajectory. Spatial superposition is to fuse the spatial information of different data sources to generate a comprehensive data set containing multiple types of information.

[0192] In specific implementation, a three-dimensional model of the windward ice-covered layer is spatially superimposed with the spatial grid of the ice accretion trajectory to generate a first ice accretion trajectory. The three-dimensional model of the windward ice-covered layer includes ice accretion thickness distribution data, and the ice accretion thickness distribution data is mapped to the spatial grid of the ice accretion trajectory by an interpolation method. The interpolation method can adopt bilinear interpolation or Kriging interpolation. For example, assuming that the three-dimensional model of the windward ice-covered layer includes ice accretion thickness data of 1000 points, through bilinear interpolation, these data can be mapped to 1000 spatial grids of the ice accretion trajectory to generate a first ice accretion trajectory. The first ice accretion trajectory contains the ice accretion thickness distribution data of the windward ice-covered layer, providing a basis for subsequent interpolation operations in high-risk ice-covered areas and ice galloping areas.

[0193] After generating the first ice accretion trajectory, the coordinates of the high-risk ice-covered area and the ice galloping area are aligned, and the high-risk ice-covered area and the ice galloping area after coordinate alignment are interpolated into the projection coordinate system of the first ice accretion trajectory to generate a fitted ice accretion trajectory. Coordinate alignment is to unify the coordinate systems of different data sources into a common reference system for subsequent interpolation operations. In specific implementation, a coordinate transformation method is used to convert the coordinate systems of the high-risk ice-covered area and the ice galloping area into the projection coordinate system of the first ice accretion trajectory. For example, assuming that the original coordinate system of the high-risk ice-covered area and the ice galloping area is the WGS84 longitude and latitude coordinate system, through UTM projection, it can be converted into the UTM plane rectangular coordinate system. Then, the high-risk ice-covered area and the ice galloping area after coordinate alignment are interpolated into the projection coordinate system of the first ice accretion trajectory. The interpolation method can adopt bilinear interpolation or Kriging interpolation. The fitted ice accretion trajectory contains the ice accretion thickness distribution data of the windward ice-covered layer, the ice accretion thickness data of the high-risk ice-covered area, and the galloping value data of the ice galloping area.

[0194] In this embodiment, by fitting the windward ice-covered layer, the high-risk ice-covered area, and the ice galloping area with the ice accretion trajectory, the comprehensiveness and accuracy of ice accretion monitoring are significantly improved. Traditional methods rely on a single data source and static models, and it is difficult to comprehensively reflect the multi-dimensional characteristics of ice accretion distribution. This solution effectively improves the modeling accuracy of the ice accretion trajectory by converting the coordinate system of the ice accretion trajectory into a projection coordinate system in a preset format and dividing the ice accretion trajectory into multiple spatial grids. At the same time, by spatially superimposing the three-dimensional model of the windward ice-covered layer with the spatial grid of the ice accretion trajectory to generate a first ice accretion trajectory, and combining the coordinate alignment and interpolation of the high-risk ice-covered area and the ice galloping area, the fitted ice accretion trajectory is further optimized, realizing the comprehensiveness and accuracy of ice accretion monitoring, and providing strong support for the safe operation of the power system.

[0195] In one implementation manner of this embodiment, in the projection coordinate system, spatially superimposing the three-dimensional model of the windward ice-covered layer with the spatial grid of the ice accretion trajectory to generate a first ice accretion trajectory includes the following steps:

[0196] S810. Voxelize the three-dimensional model of the windward ice layer to generate a voxel dataset with the same spatial grid resolution as the ice accretion trajectory;

[0197] S820. Use the bilinear interpolation algorithm to map the ice accretion thickness distribution data in the voxel dataset to the grid vertices of the ice accretion trajectory;

[0198] S830. According to the wire curvature parameter of the ice accretion trajectory, use a preset compensation formula to perform curvature compensation calculation on the mapped ice accretion thickness data to obtain the ice accretion trajectory after compensation calculation.

[0199] When performing spatial superposition of the three-dimensional model of the windward ice layer and the spatial grid of the ice accretion trajectory, first voxelize the three-dimensional model of the windward ice layer to generate a voxel dataset with the same spatial grid resolution as the ice accretion trajectory. Voxelization is the process of converting a three-dimensional model into a voxel dataset composed of voxels (three-dimensional pixels). A voxel is the smallest unit in three-dimensional space, similar to a pixel in a two-dimensional image. Specifically, when implementing, divide the three-dimensional model of the windward ice layer into multiple voxels, and the size of each voxel is the same as the resolution of the spatial grid of the ice accretion trajectory. For example, assume that the resolution of the spatial grid of the ice accretion trajectory is 10 m × 10 m × 10 m, then the size of each voxel is also 10 m × 10 m × 10 m. Voxelization generates a voxel dataset by mapping each point in the three-dimensional model to the nearest voxel. Each voxel in the voxel dataset contains ice accretion thickness distribution data. For example, assume that the three-dimensional model of the windward ice layer includes ice accretion thickness data of 1000 points. Through voxelization, 1000 voxels can be generated, and each voxel contains an ice accretion thickness value.

[0200] After generating the voxel dataset, use the bilinear interpolation algorithm to map the ice accretion thickness distribution data in the voxel dataset to the grid vertices of the ice accretion trajectory. The bilinear interpolation algorithm can infer the data of unknown points based on the data of known points. Specifically, when implementing, map the ice accretion thickness distribution data in the voxel dataset to the grid vertices of the ice accretion trajectory. The grid vertices of the ice accretion trajectory are the intersection points of the spatial grid, and each vertex contains an ice accretion thickness value. The formula of the bilinear interpolation algorithm is as follows:

[0201]

[0202] Among them, f(x, y) is the interpolation function, (x1, y1), (x2, y1), (x1, y2), and (x2, y2) are known points, and f(x1, y1), f(x2, y1), f(x1, y2), and f(x2, y2) are the ice accretion thickness values of the known points. For example, assuming that the voxel dataset includes 1000 voxels, through the bilinear interpolation algorithm, the ice accretion thickness distribution data in these voxels can be mapped to 1000 grid vertices of the ice accretion trajectory.

[0203] After mapping the ice accretion thickness distribution data to the grid vertices of the ice accretion trajectory, according to the wire curvature parameter of the ice accretion trajectory, a preset compensation formula is used to perform curvature compensation calculation on the mapped ice accretion thickness data to obtain the ice accretion trajectory after compensation calculation. The wire curvature parameter is a parameter describing the degree of wire bending and is obtained by measuring the curvature radius of the wire. Specifically, when implementing, a preset compensation formula is used to perform curvature compensation calculation on the mapped ice accretion thickness data. The preset compensation formula is as follows:

[0204]

[0205] Among them, h1 is the ice accretion thickness after compensation calculation, h2 is the ice accretion thickness after mapping, k is the compensation coefficient, and R is the curvature radius of the wire. The compensation coefficient is determined according to the material characteristics of the wire and the ice accretion conditions. For example, assuming that the ice accretion thickness after mapping is 10 mm, the curvature radius of the wire is 100 m, and the compensation coefficient is 0.1, through the preset compensation formula, the ice accretion thickness after compensation calculation can be calculated to be 11 mm.

[0206] In this embodiment, by performing voxelization processing on the three-dimensional model of the windward ice accretion layer, a voxel dataset consistent with the spatial grid resolution of the ice accretion trajectory is generated, and the ice accretion thickness distribution data is mapped to the grid vertices of the ice accretion trajectory through the bilinear interpolation algorithm, effectively improving the modeling accuracy of the ice accretion trajectory. At the same time, according to the wire curvature parameter of the ice accretion trajectory, a preset compensation formula is used to perform curvature compensation calculation on the mapped ice accretion thickness data, further optimizing the accuracy of the ice accretion trajectory, providing more accurate data support for ice accretion monitoring, and helping to improve the accuracy and reliability of transmission line ice accretion monitoring.

[0207] In one implementation manner of this embodiment, optimizing the ice accretion trajectory according to the ice accretion layer data after fitting includes the following steps:

[0208] S910. In the projection coordinate system of the fitted ice accretion trajectory, interpolate the ice accretion layer data to the corresponding high-risk ice accretion area on the fitted ice accretion trajectory to optimize the fitted ice accretion trajectory.

[0209] When optimizing the ice-covered trajectory after fitting, first, in the projection coordinate system of the ice-covered trajectory after fitting, interpolate the ice layer data to the corresponding high-risk ice-covered area on the ice-covered trajectory after fitting. The ice layer data includes parameters such as ice thickness, ice density, and ice temperature, and these data are obtained through sensors or historical databases. During specific implementation, an interpolation method is used to map the ice layer data to the corresponding high-risk ice-covered area on the ice-covered trajectory after fitting. The interpolation method can use bilinear interpolation. The bilinear interpolation formula refers to S820, which will not be elaborated in this application.

[0210] In this embodiment, by interpolating the ice layer data to the corresponding high-risk ice-covered area in the projection coordinate system of the ice-covered trajectory after fitting, the accuracy of the ice-covered trajectory is further optimized, realizing the comprehensiveness and accuracy of ice-covered monitoring, and providing strong support for the safe operation of the power system.

[0211] The embodiment of this application also provides an ice-covered monitoring system, as Figure 5 shown, including:

[0212] A solar panel / battery for powering the ice-covered monitoring system;

[0213] A memory configured to store instructions; and

[0214] A processor configured to call the instructions from the memory and be able to implement the above-mentioned ice-covered monitoring method for transmission lines when executing the instructions.

[0215] Those skilled in the art should understand that the embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program code.

[0216] This application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of this application. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0217] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one or more processes and / or one or more blocks in a process Figure 1 one or more processes and / or blocks Figure 1 specified in a block or blocks.

[0218] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more processes and / or one or more blocks Figure 1 one or more processes and / or blocks Figure 1 specified in a block or blocks.

[0219] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0220] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0221] Computer-readable media includes both permanent and non-permanent, removable and non-removable media implemented by any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0222] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent in such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0223] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A real-time monitoring method for ice coating on power transmission lines based on multi-parameter fusion, characterized in that: Applied to an ice cover monitoring system, the method comprises: In response to receiving the conductor physical parameters at the same time, combined with the current environmental data, simulating the ice-covered track of the target transmission line section, the current environmental data includes the current wind speed and the current wind direction, and the conductor physical parameters include the vibration parameters and the strain parameters; The windward side of the ice-covered track is determined based on the current wind direction, and the windward side is used as the starting side, the current wind speed is used as the reference wind speed, and an ice-covered layer simulation strategy is adopted to simulate the windward ice-covered layer of the ice-covered track, wherein the ice-covered layer simulation strategy is implemented based on a preset ice-covered thickness prediction model; Based on the terrain data of the target transmission line section, determine the high-risk icing area in the icing trajectory, and determine the ice layer data of the high-risk icing area based on the preset big data model; Determine the ice dancing area in the ice trajectory based on vibration parameters and strain parameters; Fit the windward ice layer, high-risk ice area and ice dancing area to the ice trajectory, and optimize the fitted ice trajectory based on the ice layer data; Generate a visualization interface based on the optimized ice-covering trajectory.

2. The method according to claim 1, characterized in that: Simulate the ice-covered trajectory of the target transmission line section, including: According to the current wind speed and current wind direction, the preset fluid dynamics model is used to calculate the airflow distribution on the conductor surface of the target transmission line section; Based on the airflow distribution and physical parameters of the conductor, the finite element analysis method is used to simulate the ice formation process on the conductor surface. The ice formation process is divided into multiple time steps, and the ice layer morphology parameters of each time step are obtained, wherein the ice layer morphology parameters include ice layer density, surface roughness and attachment angle; Based on the ice layer morphological parameters at each time step, a 3D point cloud reconstruction algorithm is used to generate the ice cross-sectional profile at each time step. The ice-covered cross-sectional profiles of multiple time steps are spatially interpolated along the extension direction of the wire to construct a three-dimensional spatial model of the ice-covered trajectory.

3. The method according to claim 2, characterized in that Based on the airflow distribution and physical parameters of the conductor, a preset finite element analysis strategy is used to simulate the ice formation process on the conductor surface, including: The conductor surface is discretized into multiple non-uniform triangular grids, wherein the grid density on the windward side is higher than that on the leeward side; Dynamic boundary conditions are applied at the nodes of the triangular mesh, and a preset finite element analysis strategy is used to solve the preset coupling equations in each time iteration step to output ice morphology data, which is used to simulate the ice formation process on the conductor surface; The dynamic boundary conditions include airflow distribution, conductor surface displacement constraint conditions calculated based on vibration parameters, and conductor material elastic modulus correction coefficient calculated based on strain parameters.

4. The method according to claim 1, characterized in that: The conductor physical parameters include the temperature parameters of the conductor surface. The current environmental data also includes temperature and humidity. The ice layer simulation strategy includes: Taking the conductor axis as the reference, a polar coordinate system is established to divide the windward surface into multiple sector-shaped grids; In each grid, the aerodynamic load is calculated according to the current wind speed, and the ice growth rate is solved by thermodynamic equations in combination with the temperature parameters of the conductor surface. Finite element analysis method is used to simulate the stress distribution of ice on the conductor surface, and mesh areas where stress concentration exceeds a preset threshold are screened out; In the grid area, the reference wind speed, temperature and humidity are used as input parameters, and the ice thickness distribution data of the windward ice layer is output according to the pre-built ice thickness prediction model; A three-dimensional model of the windward ice layer is generated based on the ice thickness distribution data.

5. The method according to claim 1, characterized in that Current environmental data including humidity parameters, based on the terrain data of the target transmission line section, identify high-risk icing areas in the icing trajectory, including: Obtaining terrain data of the target transmission line section, including altitude, slope and slope direction; Extract three-dimensional terrain feature vectors from terrain data; Identify potential wind acceleration areas in the three-dimensional terrain feature vector, wherein the potential wind acceleration areas include canyon areas, ridge areas, and windward slope areas; Determine the average ice thickness corresponding to each potential wind acceleration zone in a preset historical ice database; The potential wind acceleration area corresponding to the average ice thickness greater than the preset ice thickness threshold is taken as the target ice area; Input humidity parameters into the pre-built big data model to predict the ice growth rate of each target ice-covered area in a preset time period in the future; The target icing area corresponding to the icing growth rate greater than the preset growth rate is regarded as a high-risk icing area.

6. The method according to claim 1, characterized in that The ice dancing area in the ice trajectory is determined based on vibration parameters and strain parameters, including: Perform FFT transformation on the vibration parameters, extract the vibration energy ratio of the preset frequency band, and calculate its deviation value from the natural frequency of the conductor; According to the strain parameters, a wire deformation field model is constructed to identify the area where the deformation gradient exceeds the limit, wherein the wire deformation field model is constructed based on the spatiotemporal distribution of the strain parameters; Determine a dancing value for a region where the deviation value is greater than a first preset value and the deformation gradient exceeds a limit; If the dancing value exceeds a preset critical dancing coefficient, the region where the deviation value is greater than a first preset value and the deformation gradient exceeds the limit is determined as an ice-covered dancing region.

7. The method according to claim 4, characterized in that Fit the windward ice layer, high-risk ice area and ice dancing area to the ice trajectory, including: The coordinate system of the ice-covered track is converted into a projection coordinate system in a preset format, and the ice-covered track is divided into a plurality of spatial grids; In the projected coordinate system, the three-dimensional model of the windward ice layer is spatially superimposed with the spatial grid of the ice track to generate a first ice track, wherein the first ice track includes ice thickness distribution data of the windward ice layer; The high-risk icing area and the dancing icing area are aligned in coordinates, and the aligned high-risk icing area and the dancing icing area are interpolated into the projection coordinate system of the first icing trajectory to generate a fitted icing trajectory.

8. The method according to claim 7, characterized in that In the projected coordinate system, the three-dimensional model of the windward ice layer is spatially superimposed with the spatial grid of the ice track to generate the first ice track, including: The three-dimensional model of the windward ice layer is voxelized to generate a voxel dataset consistent with the spatial grid resolution of the ice track; The bilinear interpolation algorithm is used to map the ice thickness distribution data in the voxel data set to the grid vertices of the ice trajectory; According to the conductor curvature parameters of the ice-covered trajectory, a preset compensation formula is used to perform curvature compensation calculation on the mapped ice thickness data to obtain the ice-covered trajectory after compensation calculation.

9. The method according to claim 7, characterized in that: The ice cover trajectory after optimization and fitting based on the ice cover data includes: In the projection coordinate system of the fitted ice-covered trajectory, the ice-covered layer data is interpolated to the corresponding high-risk ice-covered area on the fitted ice-covered trajectory to optimize the fitted ice-covered trajectory.

10. An ice monitoring system, characterized in that: include: A solar panel / battery for supplying power to the ice monitoring system; a memory configured to store instructions; as well as A processor is configured to call the instructions from the memory and implement the ice coating monitoring method for a power transmission line according to any one of claims 1 to 9 when executing the instructions.

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