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

Abstract

The application discloses a kind of based on multi-parameter fusion transmission line icing real-time monitoring method and system, it is related to data processing field, its method includes: receiving the physical parameter of wire at the same time, combine current environmental data, simulate the icing track of target transmission line section;Determine the windward surface of icing track based on current wind direction, and take windward surface as starting surface, take current wind speed as reference wind speed, adopt icing layer simulation strategy, simulate the windward icing layer of icing track;Determine high-risk icing area in icing track based on the topographic data of target transmission line section, and determine the icing layer data of high-risk icing area;Determine icing dance area in icing track based on vibration parameter and strain parameter;Windward icing layer, high-risk icing area and icing dance area are fitted with icing track, and the fitted icing track is optimized according to icing layer data, to generate visual interface.The application can effectively improve the accuracy of icing monitoring on transmission line.

Description

Technical Field

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

[0002] Overhead transmission lines are a crucial component of the power system, and their operational status is vital to the safe and stable operation of the power system. Icing on overhead transmission lines is one of the major natural disasters facing the power system, especially in cold, high-altitude, or high-humidity regions, where icing can lead to serious accidents such as line breaks, tower collapses, and insulator flashovers. With the intensification of climate change and the expansion of power grids, the importance of icing monitoring is becoming increasingly prominent.

[0003] Traditional icing monitoring technology is mainly based on linear modeling of fixed meteorological parameters such as temperature and humidity and historical icing data. By inputting real-time meteorological parameters into a single static model, the icing thickness is predicted, thereby enabling the monitoring of icing conditions.

[0004] However, for transmission lines located at high altitudes and / or in complex terrain areas, the icing condition needs to consider not only the influence of fixed meteorological parameters on icing, but also the influence of windward slopes, special geographical environments, and line galloping areas with an angle greater than 45° with the prevailing winter wind direction on the line icing. In addition to the influence of fixed meteorological parameters, the icing condition in the above-mentioned areas also needs to consider the influence of geographical environment and conductor galloping on icing. Moreover, in the monitoring of icing of overhead transmission lines, geographical environment, conductor galloping and meteorological parameters are not independent variables, but act together on the icing process through complex nonlinear coupling relationships. This mutual influence makes it difficult for traditional single static models to fully characterize the icing condition.

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

[0006] This application provides a method and system for real-time monitoring of icing on transmission lines based on multi-parameter fusion, which can effectively improve the accuracy of icing monitoring on transmission lines.

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

[0008] Firstly, a method for monitoring icing on transmission lines is provided, applied to an icing monitoring system. This method includes:

[0009] In response to the received conductor physical parameters at the same time, combined with the current environmental data, the icing trajectory of the target transmission line segment is simulated. The current environmental data includes the current wind speed and current wind direction, and the conductor physical parameters include vibration parameters and strain parameters.

[0010] The windward side of the icing trajectory is determined based on the current wind direction. Using the windward side as the starting surface and the current wind speed as the reference wind speed, an icing layer simulation strategy is adopted to simulate the windward icing layer of the icing trajectory. The icing layer simulation strategy is implemented based on a preset icing thickness prediction model.

[0011] Based on the terrain data of the target transmission line segment, high-risk icing areas in the icing trajectory are identified, and the icing layer data of the high-risk icing areas are determined based on the pre-set big data model.

[0012] The icing dance zone in the icing trajectory is determined based on vibration and strain parameters;

[0013] The windward icing layer, high-risk icing area, and icing galloping area are fitted with the icing trajectory, and the fitted icing trajectory is optimized based on the icing layer data.

[0014] A visualization interface is generated based on the optimized icing trajectory.

[0015] In one possible implementation of the first aspect, simulating the icing trajectory of the target transmission line segment includes:

[0016] Based on the current wind speed and direction, the airflow distribution on the conductor surface of the target transmission line section is calculated using a preset fluid dynamics model;

[0017] Based on airflow distribution and conductor physical parameters, the finite element analysis method is used to simulate the icing formation process on the conductor surface;

[0018] The ice formation process was divided into multiple time steps, and ice morphology parameters were obtained for each time step, including ice density, surface roughness, and adhesion angle.

[0019] Based on the ice morphology parameters at each time step, a 3D point cloud reconstruction algorithm is used to generate the ice-covered cross-sectional profile at each time step.

[0020] By spatially interpolating the icing cross-sectional profiles at multiple time steps along the direction of the conductor extension, a three-dimensional spatial model of the icing trajectory is constructed.

[0021] In another possible implementation of the first aspect, based on airflow distribution and conductor physical parameters, a pre-defined finite element analysis strategy is used to simulate the icing formation process on the conductor surface, including:

[0022] The surface of the conductor is discretized into multiple non-uniform triangular grids, with the grid density on the windward side being higher than that on the leeward side.

[0023] 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 icing morphology data. The icing morphology data is used to simulate the icing formation process on the surface of the conductor.

[0024] 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.

[0025] In another possible implementation of the first aspect, the physical parameters of the conductor include the temperature parameters of the conductor surface, and the current environmental data also includes temperature and humidity. The icing simulation strategy includes:

[0026] A polar coordinate system is established with the conductor axis as the reference, and the windward surface is divided into multiple sector grids;

[0027] Within each grid, the aerodynamic load is calculated based on the current wind speed, and the ice growth rate is solved using thermodynamic equations in conjunction with the temperature parameters of the conductor surface.

[0028] The finite element method was used to simulate the stress distribution of ice on the surface of the conductor, and the mesh areas with stress concentration exceeding the preset threshold were screened out.

[0029] In the grid area, using the baseline wind speed, temperature and humidity as input parameters, the ice thickness distribution data of the windward ice layer is output based on the pre-built ice thickness prediction model.

[0030] A three-dimensional model of the windward ice layer is generated based on the ice thickness distribution data.

[0031] In another possible implementation of the first aspect, the current environmental data includes humidity parameters, and high-risk icing zones in the icing trajectory are identified based on topographic data of the target transmission line segment, including:

[0032] Acquire topographic data of the target transmission line segment, including elevation, slope, and aspect;

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

[0034] Identify potential wind acceleration zones in three-dimensional terrain feature vectors, including canyon areas, ridge areas, and windward slope areas;

[0035] The average icing thickness corresponding to each potential wind acceleration zone is determined from a pre-set historical icing database.

[0036] The potential wind acceleration zone corresponding to the average icing thickness that is greater than the preset icing thickness threshold is taken as the target icing zone.

[0037] Humidity parameters are input into a pre-built big data model to predict the icing growth rate of each target icing area in the future within a preset time period;

[0038] The target icing area corresponding to the icing growth rate that exceeds the preset growth rate is designated as a high-risk icing area.

[0039] In another possible implementation of the first aspect, the icing dance zone in the icing trajectory is determined based on vibration and strain parameters, including:

[0040] Perform FFT transformation on the vibration parameters, extract the proportion of vibration energy in the preset frequency band, and calculate the deviation value between it and the natural frequency of the conductor.

[0041] Based on the strain parameters, a conductor deformation field model is constructed to identify regions where the deformation gradient exceeds the limit. The conductor deformation field model is constructed based on the spatiotemporal distribution of the strain parameters.

[0042] Determine the swirl value for the region 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, the area with a deviation value greater than the first preset value and a deformation gradient exceeding the limit will be identified as the ice-covered dancing zone.

[0044] In another possible implementation of the first aspect, the windward icing layer, high-risk icing zone, and icing galloping zone are fitted with the icing trajectory, including:

[0045] The coordinate system of the icing trajectory is converted into a projected coordinate system of a preset format, and the icing trajectory is divided into multiple spatial grids;

[0046] In the projected coordinate system, the three-dimensional model of the windward icing layer is spatially superimposed with the spatial grid of the icing trajectory to generate the first icing trajectory, which contains the icing thickness distribution data of the windward icing layer.

[0047] The high-risk icing area and the icing galloping area are aligned by coordinates, and the aligned high-risk icing area and the icing galloping area are interpolated into the projected coordinate system of the first icing trajectory to generate the fitted icing trajectory.

[0048] In another possible implementation of the first aspect, in a projected coordinate system, the three-dimensional model of the windward icing layer is spatially superimposed with the spatial mesh of the icing trajectory to generate a first icing trajectory, including:

[0049] The three-dimensional model of the windward ice layer is voxelized to generate a voxel dataset with the same spatial grid resolution as the ice trajectory.

[0050] A bilinear interpolation algorithm is used to map the ice thickness distribution data in the voxel dataset to the grid vertices of the ice trajectory;

[0051] Based on the curvature parameters of the conductor in the icing trajectory, a preset compensation formula is used to calculate the curvature compensation of the mapped icing thickness data to obtain the compensated icing trajectory.

[0052] In another possible implementation of the first aspect, optimizing the fitted icing trajectory based on icing layer data includes:

[0053] In the projected coordinate system of the fitted icing trajectory, the icing layer data is interpolated to the corresponding high-risk icing area on the fitted icing trajectory to optimize the fitted icing trajectory.

[0054] Secondly, this application provides an icing monitoring system, comprising:

[0055] Solar panels / batteries are used to power the icing monitoring system.

[0056] The memory is configured to store instructions; and

[0057] The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the aforementioned method for monitoring icing on power transmission lines.

[0058] By receiving conductor physical parameters at the same time and combining them with current environmental data, the icing trajectory of the target transmission line segment can be simulated in real time. This approach not only considers traditional meteorological parameters but also incorporates conductor dynamic response, making icing prediction more realistic. The icing layer simulation strategy, through a pre-set icing thickness prediction model, effectively characterizes the icing formation process on the windward side, thus significantly increasing the accuracy of icing prediction on the windward side. Furthermore, based on the topographic data of the target transmission line segment, high-risk icing zones are identified in the icing trajectory, and icing layer data for these zones is determined using a pre-set big data model. This fully considers the impact of complex terrain on icing, making the monitoring results more targeted and reliable. Simultaneously, by determining the icing galloping zone in the icing trajectory using vibration and strain parameters, the promoting effect of conductor galloping on icing can be identified. Compared to a single meteorological parameter, considering multiple parameters further improves the accuracy of icing monitoring. The icing trajectory was fitted to the windward icing layer, high-risk icing areas, and icing galloping areas. The fitted icing trajectory was then optimized based on icing layer data, ensuring the comprehensiveness and accuracy of icing monitoring results. Finally, a visualization interface was generated based on the optimized icing trajectory, which not only visually displays the distribution and thickness of icing but also shows high-risk areas and icing galloping areas, facilitating icing monitoring. This effectively addresses the shortcomings of traditional methods in icing monitoring in high-altitude and complex terrain areas, providing strong support for the safe and stable operation of the power system. Compared to a single static model, this significantly improves the accuracy and comprehensiveness of icing monitoring for transmission lines.

[0059] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0060] Figure 1 A schematic flowchart illustrating a method for monitoring icing on power transmission lines, provided in an embodiment of this application;

[0061] Figure 2 A schematic diagram simulating the icing growth trajectory provided in this application embodiment;

[0062] Figure 3 This is a schematic diagram of an icing feature reconstruction simulation provided in an embodiment of this application;

[0063] Figure 4 A schematic diagram of the time evolution curve of ice layer parameters provided in this application embodiment;

[0064] Figure 5 This is a schematic diagram of the structure of an icing monitoring system provided in an embodiment of this application. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

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

[0067] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0068] Figure 1 The illustration schematically shows a flow chart of a method for monitoring icing on transmission lines according to an embodiment of this application. Figure 1 As shown in the figure, this application provides a method for monitoring icing on transmission lines, which is applied to an icing monitoring system. The method may include the following steps.

[0069] S110. In response to receiving the conductor physical parameters at the same time, and combining them with the current environmental data, simulate the icing trajectory of the target transmission line segment. The current environmental data includes the current wind speed and current wind direction, and the conductor physical parameters include vibration parameters and strain parameters.

[0070] S120. Based on the current wind direction, determine the windward side of the icing trajectory, and take the windward side as the starting surface and the current wind speed as the reference wind speed. Use the icing layer simulation strategy to simulate the windward icing layer of the icing trajectory. The icing layer simulation strategy is implemented based on a preset icing thickness prediction model.

[0071] S130. Based on the terrain data of the target transmission line segment, identify the high-risk icing area in the icing trajectory, and determine the icing layer data of the high-risk icing area based on the preset big data model.

[0072] S140. Determine the icing dance zone in the icing trajectory based on vibration parameters and strain parameters;

[0073] S150. Fit the windward icing layer, high-risk icing area, and icing galloping area to the icing trajectory, and optimize the fitted icing trajectory based on the icing layer data.

[0074] S160. Generate a visualization interface based on the optimized icing trajectory.

[0075] During icing monitoring, conductor physical parameters, including vibration and strain parameters, are collected in real time by sensors installed on the transmission line. Vibration parameters reflect the conductor's vibration state under wind and icing conditions, while strain parameters describe the conductor's deformation under external loads. Current environmental data, including current wind speed, wind direction, temperature, and humidity, can be obtained from weather stations or wind speed and direction sensors. To simulate the icing trajectory of the target transmission line segment, the conductor physical parameters are combined with current environmental data, and calculations are performed using a fluid dynamics model and finite element analysis.

[0076] A fluid dynamics model is used to simulate the influence of wind speed and direction on the airflow distribution on the conductor surface, while the finite element method (FEM) is used to simulate the icing formation process on the conductor surface. In practice, the airflow distribution on the conductor surface is first calculated based on the current wind speed and direction. Then, based on the airflow distribution and the conductor's physical parameters, the FEM is used to simulate the icing formation process. The icing formation process is divided into multiple time steps, and ice morphology parameters for each time step, including ice density, surface roughness, and adhesion angle, are obtained. Based on the ice morphology parameters for each time step, a 3D point cloud reconstruction algorithm is used to generate the icing cross-sectional profile for each time step. The icing cross-sectional profiles from multiple time steps are spatially interpolated along the conductor's extension direction to construct a 3D spatial model of the icing trajectory.

[0077] After determining the windward side of the icing trajectory, an icing layer simulation strategy is employed, using the windward side as the starting point and the current wind speed as the baseline, to simulate the windward icing layer. The windward side is the primary area for icing formation because the airflow directly contacts the conductor surface, leading to rapid ice accumulation. The icing layer simulation strategy is based on a pre-defined icing thickness prediction model, which comprehensively considers the influence of factors such as wind speed, wind direction, temperature, humidity, and conductor surface characteristics on icing thickness. In practice, a polar coordinate system is first established with the conductor axis as the reference, dividing the windward side into multiple sector-shaped grids. Within each grid, aerodynamic loads are calculated based on the current wind speed. These aerodynamic loads determine the adhesion force and freezing rate of water droplets on the conductor surface. Temperature parameters are collected in real-time by temperature sensors. Low temperatures accelerate icing formation; therefore, the ice growth rate is calculated using thermodynamic equations, taking into account the temperature parameters of the conductor surface.

[0078] The icing thickness prediction model employs the finite element method to simulate the stress distribution of ice on the conductor surface. This stress distribution reflects the adhesion strength and stability of the ice layer on the conductor surface. By screening areas where the stress concentration exceeds a preset threshold, the regions with the fastest icing growth can be identified. In these regions, using baseline wind speed, temperature, and humidity as input parameters, the pre-constructed icing thickness prediction model outputs icing thickness distribution data for the windward icing layer. Based on this data, a three-dimensional model of the windward icing layer is generated, effectively characterizing the icing formation process on the windward side and significantly improving the accuracy of icing prediction.

[0079] To identify high-risk icing zones within the icing trajectory, topographic data of the target transmission line segment is first acquired. This data, including elevation, slope, and aspect, can be obtained through Geographic Information System (GIS) or LiDAR scanning technology. Specifically, a three-dimensional topographic feature vector is extracted from the topographic data to identify potential wind acceleration zones, including canyon areas, ridge areas, and windward slopes. These areas experience significantly higher wind speeds due to topographic effects, leading to a higher risk of icing. The average icing thickness corresponding to each potential wind acceleration zone is determined from a pre-defined historical icing database. This database contains icing observation data from multiple years, reflecting icing patterns under different topographic conditions. Potential wind acceleration zones with average icing thicknesses exceeding a pre-defined threshold are designated as target icing zones. Next, humidity parameters are input into a pre-built big data model to predict the icing growth rate for each target icing zone over a pre-defined time period. This big data model, based on machine learning and statistical analysis, comprehensively considers the impact of multiple factors on icing growth. Target icing zones with icing growth rates exceeding a pre-defined growth rate are designated as high-risk icing zones.

[0080] Ice-covered galloping zones are areas where conductors vibrate violently under the influence of icing and wind, posing a serious threat to the safe operation of transmission lines. To determine these zones, a Fast Fourier Transform (FFT) is first performed on the vibration parameters to extract the proportion of vibration energy in a preset frequency band. This proportion reflects the vibration intensity of the conductor at different frequencies. The deviation between the vibration energy proportion and the conductor's natural frequency is calculated; a larger deviation indicates more severe conductor vibration. Next, a conductor deformation field model is constructed based on strain parameters. This model, based on the spatiotemporal distribution of strain parameters, can identify areas where the deformation gradient exceeds the limit. These areas exhibit the highest stress concentration on the conductor surface and are prone to ice-covered galloping. The galloping value is determined for areas where the deviation is greater than a first preset value and the deformation gradient exceeds the limit. The galloping value is a comprehensive indicator reflecting the severity of conductor vibration and the distribution of icing. If the galloping value exceeds a preset critical galloping coefficient, the area with a deviation greater than the first preset value and the deformation gradient exceeding the limit is identified as an ice-covered galloping zone.

[0081] After identifying the windward icing layer, high-risk icing areas, and icing galloping areas, this data is fitted to the icing trajectory. Specifically, the coordinate system of the icing trajectory is converted to a pre-formatted projected coordinate system, and the trajectory is divided into multiple spatial grids. In the projected coordinate system, the 3D model of the windward icing layer is spatially superimposed on the spatial grids of the icing trajectory to generate the first icing trajectory. This first icing trajectory includes the icing thickness distribution data of the windward icing layer. Next, the coordinates of the high-risk icing areas and icing galloping areas are aligned, and these aligned high-risk and galloping areas are interpolated into the projected coordinate system of the first icing trajectory to generate the fitted icing trajectory. In the projected coordinate system of the fitted icing trajectory, icing layer data is interpolated to the corresponding high-risk icing areas on the fitted trajectory to optimize it. In this way, a comprehensive and accurate icing trajectory model can be generated.

[0082] When generating the visualization interface, the optimized icing trajectory is visualized. Specifically, the optimized icing trajectory is first converted into a visualization data format, including a 3D model, color coding, and annotation information. Using graphics rendering technology, the 3D model of the icing trajectory is rendered into a visualization interface, where different colors and annotations represent different ice thicknesses, high-risk icing areas, and icing galloping areas. Through interactive operation, users can view detailed information about the icing trajectory, including ice thickness distribution, and the location and extent of high-risk icing areas and icing galloping areas. This step provides a clear visual representation of the icing situation on the target transmission line segment, offering crucial support for icing monitoring and decision-making.

[0083] This embodiment comprehensively considers environmental data, conductor physical parameters, and terrain data, enabling real-time simulation of the icing trajectory of target transmission line segments and 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 impact of complex terrain on icing, while the determination of icing galloping areas identifies the promoting effect of conductor galloping on icing. By fitting the windward icing layer, high-risk icing areas, and icing galloping areas to the icing trajectory, and optimizing the fitted icing trajectory based on icing layer data, the comprehensiveness and accuracy of icing monitoring results are ensured. The final generated visualization interface intuitively displays the icing distribution and thickness, facilitating icing monitoring and effectively addressing the shortcomings of traditional methods in icing monitoring in high-altitude and complex terrain areas, providing strong support for the safe and stable operation of the power system.

[0084] By combining conductor physical parameters and current environmental data, the icing trajectory of the target transmission line segment is simulated, significantly improving the accuracy and comprehensiveness of icing monitoring. Traditional methods mainly rely on fixed meteorological parameters and historical data for linear modeling, which makes it difficult to fully consider the nonlinear coupling relationship between geographical environment, conductor galloping, and meteorological parameters, resulting in low accuracy in icing monitoring. This solution introduces an icing layer simulation strategy, determines the windward side of the icing trajectory based on current wind speed and direction, and simulates the windward icing layer, effectively solving the problem of insufficient prediction of windward icing by traditional methods. Simultaneously, by combining topographic data of the target transmission line segment, a big data model is used to identify high-risk icing areas, and icing galloping areas are determined through vibration and strain parameters, comprehensively considering the impact of geographical environment and conductor galloping on icing. Furthermore, by fitting the windward icing layer, high-risk icing areas, and icing galloping areas to the icing trajectory and optimizing the fitted icing trajectory, the accuracy of icing monitoring is further improved. The final generated visualization interface provides maintenance personnel with intuitive information on icing distribution, helping to take timely countermeasures and ensure the safe and stable operation of the transmission line. This solution not only overcomes the shortcomings of traditional methods in failing to consider complex terrain and conductor galloping, but also achieves comprehensiveness and accuracy in icing monitoring through dynamic simulation and optimization strategies, providing strong support for the safe operation of the power system.

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

[0086] S210. Based on the current wind speed and direction, calculate the airflow distribution on the conductor surface of the target transmission line section using a preset fluid dynamics model;

[0087] S220. Based on airflow distribution and conductor physical parameters, the finite element analysis method is used to simulate the icing formation process on the conductor surface.

[0088] S230. Divide the ice formation process into multiple time steps and obtain the ice morphology parameters for each time step, including ice density, surface roughness and adhesion angle.

[0089] S240. Based on the ice morphology parameters of each time step, a three-dimensional point cloud reconstruction algorithm is used to generate the ice-covered cross-sectional profile of each time step.

[0090] S250. Spatial interpolation is performed on the icing cross-sectional profiles of multiple time steps along the extension direction of the conductor to construct a three-dimensional spatial model of the icing trajectory.

[0091] When simulating the icing trajectory of a target transmission line segment, the airflow distribution on the conductor surface is calculated using a pre-defined fluid dynamics model based on the current wind speed and direction. The fluid dynamics model is based on the Navier-Stokes equations, which describe the motion of the fluid. The Navier-Stokes equations include a continuity equation and a momentum equation. The continuity equation describes the conservation of mass in the fluid, and the momentum equation describes the conservation of momentum. The continuity equation and 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 practice, the surface of the conductor is considered as a three-dimensional geometric body and discretized into multiple tiny grid cells. The airflow distribution in 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 conservation of fluid mass, and the momentum equation describes the conservation of fluid momentum.

[0097] Solving the above equations using the finite volume method or the finite difference method yields the airflow velocity, pressure, and temperature distribution within each grid cell. For example, assuming the current wind speed and direction are northerly, the airflow velocity distribution at various points on the conductor surface can be calculated using a fluid dynamics model. The airflow velocity is higher on the windward side and lower on the leeward side. The calculated airflow distribution results will serve as input parameters for subsequent icing simulations, ensuring the accuracy of the icing formation process.

[0098] After obtaining the airflow distribution on the conductor surface, the finite element method (FEM) is used to simulate the icing formation process based on the airflow distribution and the conductor's physical parameters. FEM is a numerical computation method that solves a complex continuum problem by discretizing it into a finite number of simple element problems. In practice, the conductor surface is discretized into multiple non-uniform triangular meshes, with a higher mesh density on the windward side than on the leeward side, to capture the influence of airflow on icing formation. Dynamic boundary conditions are applied at the nodes of each mesh element, including airflow distribution, conductor surface displacement constraints, and correction coefficients for the conductor material's elastic modulus. By solving the pre-defined coupled equations (including the heat conduction equation and the ice growth equation), icing morphology data is output to simulate the icing formation process on the conductor surface. The heat conduction equation and the ice growth equation are as follows:

[0099] Heat conduction equation:

[0100]

[0101] Ice layer growth equation:

[0102]

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

[0104] After simulating the icing formation process on the conductor surface, the process is divided into multiple time steps, and ice morphology parameters are obtained for each time step. These parameters include ice density, surface roughness, and adhesion angle, which describe the physical properties of the ice layer and can be determined through experimental measurements or empirical formulas. In practice, the icing formation process is divided into multiple time steps (e.g., every 10 minutes). Within each time step, ice density, surface roughness, and adhesion angle are calculated through experimental measurements or empirical formulas. For example, ice density can be calculated by measuring the mass and volume of the ice layer, surface roughness can be measured using a surface profilometer, and the adhesion angle can be measured by measuring the contact angle between the ice layer and the conductor surface. The formulas for calculating ice density, surface roughness, and adhesion angle are as follows:

[0105] Ice density:

[0106]

[0107] Surface roughness:

[0108]

[0109] Attachment angle:

[0110]

[0111] Where, ρ i It is the density of the ice layer, m i It is the mass of the ice layer, V i It is the volume of the ice layer, R a Surface roughness, L is the measurement length, z(x) is the surface profile height, θ is the adhesion angle, and γ is the surface roughness. sv It is the solid-vapor surface tension, γ sl It is the solid-liquid surface tension, γ lv The extracted results of liquid-vapor surface tension and ice layer morphology parameters will serve as the basis for subsequent 3D point cloud reconstruction, ensuring the accuracy of the ice-covered cross-sectional profile.

[0112] Figure 2 This application provides a schematic diagram of an icing growth trajectory simulation, as illustrated in an embodiment of the present application. Figure 2 As shown, with a time step of 1 hour, it can be seen that the ice cover growth exhibits asymmetric growth characteristics as time extends.

[0113] After obtaining the ice morphology parameters for each time step, a 3D point cloud reconstruction algorithm is used to generate the ice-covered cross-sectional profile for each time step based on the ice morphology parameters for each time step. Figure 3 This illustration shows a schematic diagram of an icing feature reconstruction simulation provided in an embodiment of this application, such as... Figure 3 As shown, it can generate typical icing morphology through parametric surfaces. The icing cross-section exhibits an irregular shape, indicating that the icing formation process is influenced by multiple factors. It intuitively demonstrates the shape and distribution of icing on the conductor at a specific time step. Specifically, the 3D point cloud reconstruction algorithm is based on point cloud data, which includes the 3D coordinates and attribute information (such as density, roughness, and adhesion angle) of the ice surface. Figure 4The time evolution curves of the ice layer parameters shown indicate that both ice density and surface roughness gradually increase, while the adhesion angle changes gradually over time. This can be understood as follows: ice density increases approximately linearly with time, indicating that the ice layer becomes denser over time. Surface roughness increases non-linearly with time, indicating that the ice surface becomes rougher over time. The adhesion angle changes periodically with time, indicating that the adhesion between the ice layer and the conductor surface is constantly changing. This embodiment generates a 3D model using a point cloud reconstruction algorithm, which can be a Poisson reconstruction algorithm, to convert the point cloud data into the 3D geometry of the ice layer, i.e., the ice-covered cross-sectional profile. For example, assuming the ice density at a certain time step is 900 kg / m³... 3 With a surface roughness of 0.1 mm and an adhesion angle of 30°, the ice-covered cross-sectional profile at this time step can be generated using a three-dimensional point cloud reconstruction algorithm.

[0114] After generating the icing cross-sectional profile for each time step, spatial interpolation is performed on the icing cross-sectional profiles of multiple time steps along the conductor extension direction to construct a three-dimensional spatial model of the icing trajectory. Spatial interpolation is a numerical method that infers data for unknown points from data at known points. It is used to convert discrete cross-sectional profile data into a continuous three-dimensional model, describing the distribution and changes of icing on the conductor surface. In specific implementation, the icing cross-sectional profiles of each time step are arranged along the conductor extension direction, and spatial interpolation is performed on the icing cross-sectional profiles of multiple time steps along the conductor extension direction. The gaps between the cross-sectional profiles are filled by linear interpolation or spline interpolation to generate a three-dimensional spatial model of the icing trajectory. The formula is as follows:

[0115] Linear interpolation:

[0116]

[0117] Spline interpolation:

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

[0119] Where f(x) is the interpolation function, x0 and x1 are known points, S(x) is the spline function, and a i b i c i and d iThese are spline coefficients. For example, assuming the icing cross-sectional profile is generated at time steps t1, t2, and t3 respectively, the icing trajectory between t1 and t3 can be generated through spatial interpolation.

[0120] This implementation comprehensively considers the influence of airflow distribution, conductor physical parameters, and ice morphology parameters on icing formation. The simulation process of the icing trajectory is divided into multiple time steps, with the ice growth on the conductor surface calculated at each time step. In this way, a three-dimensional model of the icing trajectory that varies over time can be generated, accurately reflecting the distribution and thickness changes of ice on the conductor surface. The application of fluid dynamics models and finite element analysis methods ensures the accuracy and reliability of the icing formation process. Dividing the icing formation process into multiple time steps and extracting the ice morphology parameters for each time step provides detailed data support for the construction of the icing trajectory. The application of three-dimensional point cloud reconstruction algorithms and spatial interpolation algorithms ensures the accuracy and continuity of the three-dimensional spatial model of the icing trajectory. The final constructed three-dimensional spatial model of the icing trajectory can intuitively display the distribution and changes of ice on the conductor surface, providing strong data support for icing monitoring. Compared with traditional methods, this significantly improves the accuracy and comprehensiveness of transmission line icing monitoring, providing a strong guarantee for the safe and stable operation of the power system.

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

[0122] S310. Discretize the surface of the conductor into multiple non-uniform triangular grids, wherein the grid density on the windward side is higher than the grid density on the leeward side.

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

[0124] 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.

[0125] When simulating the icing formation process on the conductor surface, the conductor surface is discretized into multiple non-uniform triangular meshes. Discretization involves dividing the continuous conductor surface into a finite number of small mesh units to facilitate numerical computation. In practice, the conductor surface is treated as a three-dimensional geometry, and a non-uniform meshing 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, because the windward side is the main area for icing formation, and the higher mesh density on the windward side is to capture the influence of airflow on icing formation; the mesh density on the leeward side is lower to reduce computational load.

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

[0127] After discretizing the conductor surface into multiple non-uniform triangular meshes, dynamic boundary conditions are applied at the nodes of the triangular meshes. A pre-defined finite element analysis strategy is employed in each time iteration step to solve pre-defined coupled equations to output icing morphology data. The dynamic boundary conditions include airflow distribution, conductor surface displacement constraints calculated based on vibration parameters, and a correction factor for the conductor material's elastic modulus calculated based on strain parameters. The airflow distribution is calculated using a fluid dynamics model, describing the airflow velocity, pressure, and temperature distribution on the conductor surface. The conductor surface displacement constraints, calculated based on vibration parameters, describe the vibration characteristics of the conductor during the icing process. The correction factor for the conductor material's elastic modulus, calculated based on strain parameters, describes the elastic changes of the conductor material during the icing process.

[0128] In practice, firstly, an airflow distribution is applied at each node of the triangular mesh, calculated using a fluid dynamics model. Then, the surface displacement constraints of the conductor are calculated based on vibration parameters, which are acquired in real-time by sensors. Finally, the elastic modulus correction coefficient of the conductor material is calculated based on strain parameters, which are acquired in real-time by strain gauges. By solving pre-defined coupled equations (including heat conduction and ice growth equations), icing morphology data is output to simulate the ice formation process on the conductor surface. The heat conduction and ice growth equations are described in S220 and will not be repeated here.

[0129] This implementation discretizes the conductor surface into a non-uniform triangular mesh, with a higher mesh density on the windward side than on the leeward side, effectively improving the accuracy of icing simulation on the windward side. Simultaneously, dynamic boundary conditions are applied at the mesh nodes, including airflow distribution, conductor surface displacement constraints, and material elastic modulus correction coefficients, comprehensively considering the coupled effects of airflow, vibration, and strain on icing formation. Furthermore, by solving the preset coupling equations, icing morphology data is output, achieving high-precision simulation of the icing formation process. This scheme, through dynamic boundary conditions and non-uniform mesh technology, provides more accurate data support for icing monitoring, contributing to improved accuracy and reliability of transmission line icing monitoring.

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

[0131] S410. Using the conductor axis as a reference, establish a polar coordinate system and divide the windward surface into multiple sector grids;

[0132] S420. Within each grid, calculate the aerodynamic load based on the current wind speed, and combine it with the temperature parameters of the conductor surface to solve for the ice growth rate using thermodynamic equations.

[0133] S430. Using the finite element analysis method, the stress distribution of the ice layer on the surface of the conductor is simulated, and the mesh areas with stress concentration exceeding the preset threshold are screened out.

[0134] S440. In the grid area, using the baseline wind speed, temperature and humidity as input parameters, output the icing thickness distribution data of the windward icing layer according to the pre-built icing thickness prediction model.

[0135] S450. Based on the ice thickness distribution data, generate a three-dimensional model of the windward ice layer.

[0136] When simulating icing, a polar coordinate system is first established based on the conductor axis, and the windward surface is divided into multiple sector grids. A polar coordinate system is a coordinate system that uses polar radius and polar angle as coordinates, suitable for describing the geometry of circular or axisymmetric objects. In practice, the polar coordinate system is established with the conductor axis as the pole and the normal direction of the conductor surface as the polar axis. The windward surface is divided into multiple sector grids, each with a polar angle ranging from 5° to 10° and a polar radius equal to the conductor radius. For example, assuming the conductor diameter is 2 cm and the polar angle range is 5°, the arc length of each sector grid is 0.17 cm.

[0137] After dividing the grid into sector shapes, the aerodynamic load is calculated within each grid based on the current wind speed. Combined with the temperature parameters of the conductor surface, the ice growth rate is solved using thermodynamic equations. The aerodynamic load is the force exerted by the airflow on the conductor surface, and it is related to wind speed and airflow direction. In practice, the aerodynamic load within each grid is calculated based on the current wind speed and airflow direction. The formula for calculating the aerodynamic load is as follows:

[0138]

[0139] Where F is the aerodynamic load, ρ is the air density, v is the wind speed, and C is the wind speed. d Here, A is the drag coefficient, and A is the grid area. Combining the temperature parameters of the conductor surface, the ice growth rate is solved using thermodynamic equations. The thermodynamic equations are as follows:

[0140]

[0141] Where h is the ice thickness, k is the ice growth coefficient, and T f T is the freezing point temperature, and T is the surface temperature of the conductor. For example, assuming the current wind speed is 10 m / s and the surface temperature of the conductor is -5℃, the ice growth rate can be calculated to be 0.1 mm / min using the thermodynamic equation.

[0142] After calculating the ice growth rate, the finite element method (FEM) was used to simulate the stress distribution of the ice layer on the conductor surface, and mesh regions with stress concentrations exceeding a preset threshold were selected. FEM is a numerical computation method that solves a complex continuum problem by discretizing it into a finite number of simple element problems. In practice, the ice layer is divided into multiple finite element elements, and the stress distribution within each element is determined by solving the elasticity equations. The elasticity equations are as follows:

[0143] σ=E∈;

[0144] Where σ is stress, E is the elastic modulus, and ∈ is strain. The stress distribution within each element can be obtained by solving the equations of elasticity. Mesh regions with stress concentrations exceeding a preset threshold are selected; this threshold is determined based on 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, mesh regions with stress concentrations exceeding 0.8 MPa can be selected through finite element analysis.

[0145] After filtering out grid areas where stress concentration exceeds a preset threshold, the ice thickness distribution data of the windward ice layer is output within each grid area, using baseline wind speed, temperature, and humidity as input parameters, based on a pre-built ice thickness prediction model. The ice thickness prediction model is based on a machine learning algorithm, trained using historical ice data. In practice, baseline wind speed, temperature, and humidity are used as input features and fed into the ice thickness prediction model, outputting the ice thickness for each grid area. The formula for the ice thickness prediction model is as follows:

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

[0147] Where h is the icing thickness, v is the baseline wind speed, T is the temperature, H is the humidity, and f is the icing thickness prediction model. The icing thickness prediction model can output the icing thickness for each grid region.

[0148] After obtaining the ice thickness distribution data, a three-dimensional model of the windward ice layer is generated based on this data. The three-dimensional model generation is based on a three-dimensional reconstruction algorithm, using interpolation and fitting methods. In practice, the ice thickness distribution data can be converted into point cloud data, and the three-dimensional model can be generated using interpolation and fitting methods. Linear interpolation is used, and its formula is given in S250; it will not be elaborated here. The fitting method uses the least squares method, and the formula is as follows:

[0149] Least squares method:

[0150]

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

[0152] This implementation significantly improves the accuracy and efficiency of windward icing simulation by establishing a polar coordinate system and sector grids, combined with thermodynamic equations and finite element analysis. Traditional methods rely on fixed parameters and static models, making it difficult to accurately reflect the influence of conductor surface temperature, wind speed, and humidity on icing formation. This scheme establishes a polar coordinate system based on the conductor axis, divides the windward side into multiple sector grids, and calculates the aerodynamic load within each grid based on the current wind speed. Combined with conductor surface temperature parameters, the ice growth rate is solved using thermodynamic equations, effectively improving the accuracy of windward icing simulation. Simultaneously, the finite element analysis method is used to simulate the stress distribution of the ice layer on the conductor surface, and grid areas with stress concentration exceeding a preset threshold are selected, further optimizing the accuracy of icing thickness distribution data. Furthermore, a pre-constructed icing thickness prediction model outputs windward icing thickness distribution data and generates a 3D model, providing more intuitive data support for icing monitoring, achieving comprehensiveness and accuracy in icing monitoring, and providing strong support for the safe operation of the power system.

[0153] In one embodiment of this example, the current environmental data includes humidity parameters. Based on the terrain data of the target transmission line segment, high-risk icing areas in the icing trajectory are determined, including the following steps:

[0154] S510. Obtain the terrain data of the target transmission line section, including elevation, slope and aspect.

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

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

[0157] S540. Determine the average icing thickness corresponding to each potential wind acceleration zone in the preset historical icing database.

[0158] S550, The potential wind acceleration zone corresponding to the average icing thickness that is greater than the preset icing thickness threshold is taken as the target icing zone.

[0159] S560: Input humidity parameters into a pre-built big data model to predict the icing growth rate of each target icing area in the future within a preset time period.

[0160] S570. Target icing areas with icing growth rates exceeding the preset growth rate are designated as high-risk icing areas.

[0161] When identifying high-risk icing areas, topographic data of the target transmission line segment is acquired, including elevation, slope, and aspect. This topographic data is obtained using Geographic Information System (GIS) or LiDAR technology. Specifically, a LiDAR device is used to scan the target transmission line segment, generating a high-precision 3D topographic model. The LiDAR device measures the elevation information of the earth's surface by emitting laser pulses and receiving reflected signals. For example, assuming the target transmission line segment is 10 kilometers long, scanning with a LiDAR device can generate a 3D topographic model with a resolution of 1 meter, including the elevation, slope, and aspect of each point. Elevation 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 acquiring the terrain data, a three-dimensional terrain feature vector is extracted. This three-dimensional feature vector is a mathematical representation describing terrain features, including statistical characteristics of elevation, slope, and aspect. Specifically, Principal Component Analysis (PCA) 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 linear transformation, ensuring 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 elevation, slope, and aspect data for 1000 points, PCA can extract three principal components, each representing a key feature of the terrain. The first principal component might represent the overall elevation of the terrain, the second principal component might represent the degree of undulation, and the third principal component might represent the directionality of the terrain.

[0163] After extracting the 3D terrain feature vector, potential wind acceleration zones are identified within the 3D terrain feature vector. Potential wind acceleration zones refer to areas where terrain features may lead to increased wind speeds, including canyon areas, ridge areas, and windward slope areas.

[0164] In practice, cluster analysis is used to identify potential wind acceleration zones. Cluster analysis is an unsupervised learning method that identifies potential patterns by grouping similar data points. For example, assuming a 3D terrain feature vector contains 1000 data points, cluster analysis can divide these data points into several classes, one representing canyon areas, another representing ridge areas, and a third representing windward slope areas.

[0165] After identifying potential wind acceleration zones, the average icing thickness corresponding to each zone is determined from a pre-defined historical icing database. This database includes icing thickness data for each region over the past few years. In practice, based on the geographical location of the potential wind acceleration zone, the historical icing database is queried to obtain icing thickness data for each region, and the average icing thickness is calculated. For example, assuming the historical icing database includes monthly icing thickness data for the past five years, querying the database allows for the acquisition of icing thickness data for each potential wind acceleration zone, and the calculation of the average icing thickness.

[0166] After determining the average icing thickness, potential wind acceleration zones corresponding to average icing thicknesses greater than a preset icing thickness threshold are designated as target icing zones. The preset icing thickness threshold is determined based on historical data and practical experience. In practice, the average icing thickness of each potential wind acceleration zone is compared with the preset icing thickness threshold, and areas exceeding the threshold are designated as target icing zones. For example, if the preset icing thickness threshold is 10 mm, by comparing the average icing thickness with the threshold, potential wind acceleration zones with an average icing thickness greater than 10 mm can be designated as target icing zones.

[0167] After selecting target icing areas, humidity parameters are input into a pre-built big data model to predict the icing growth rate of each target icing area over a preset time period. The big data model is based on machine learning algorithms and is trained using historical meteorological and icing data. In practice, humidity parameters are used as input features into the big data model, which outputs the icing growth rate for each target icing area. For example, assuming a humidity parameter of 90%, the big data model can predict the icing growth rate of each target icing area over the next 24 hours.

[0168] After predicting the icing growth rate, target icing areas with icing growth rates exceeding the preset growth rate are designated as high-risk icing areas. The preset growth rate is determined based on historical data and practical experience. In practice, the icing growth rate of each target icing area is compared with the preset growth rate, and areas exceeding the preset growth rate are designated as high-risk icing areas. For example, assuming a preset growth rate of 5%, by comparing the icing growth rate with the preset growth rate, target icing areas with icing growth rates greater than 5% can be designated as high-risk icing areas.

[0169] This implementation method acquires topographic data of the target transmission line segment, including altitude, slope, and aspect, and extracts three-dimensional topographic feature vectors to identify potential wind acceleration zones, such as canyon areas, ridge areas, and windward slope areas, effectively improving the accuracy of identifying high-risk icing areas. Simultaneously, it determines the average icing thickness of each potential wind acceleration zone using a pre-set historical icing database and, combined with humidity parameters, uses a big data model to predict the icing growth rate over a pre-set time period, further optimizing the criteria for identifying high-risk icing areas. This solution not only overcomes the shortcomings of traditional methods in considering terrain and humidity but also provides more accurate data support for icing monitoring through big data models and dynamic prediction technology, contributing to improving the accuracy and reliability of transmission line icing monitoring.

[0170] In one embodiment of this invention, determining the icing galloping zone in the icing trajectory based on vibration 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 the deviation value between it and the natural frequency of the conductor.

[0172] S620. Based on the strain parameters, construct a conductor deformation field model to identify regions where the deformation gradient exceeds the limit. The conductor deformation field model is constructed based on the spatiotemporal distribution of the strain parameters.

[0173] S630. Determine the dancing value for the region where the deviation value is greater than the first preset value and the deformation gradient exceeds the limit;

[0174] S640. If the dancing value exceeds the preset critical dancing coefficient, the area with a deviation value greater than the first preset value and a deformation gradient exceeding the limit is determined as the ice-covered dancing zone.

[0175] When determining the icing galloping zone, the vibration parameters are first subjected to a Fast Fourier Transform (FFT) to extract the proportion of vibration energy in a preset frequency band and calculate its deviation from the conductor's natural frequency. FFT is a tool that converts a time-domain signal to a frequency-domain signal, revealing the frequency components of the signal. In practice, the vibration signal of the conductor is acquired, and the FFT algorithm is used to convert the vibration signal from the time domain to the frequency domain, obtaining a spectrum. The spectrum shows the distribution of vibration energy at different frequency components. The preset frequency band is typically selected from the frequency range related to conductor galloping, such as 0.1Hz to 10Hz. The proportion of vibration energy in the preset frequency band is extracted, that is, the proportion of vibration energy in that frequency band to the total vibration energy. Then, the deviation between the proportion of vibration energy in that frequency band and the conductor's natural frequency is calculated. The conductor's natural frequency is the natural vibration frequency of the conductor under no external force, which can be obtained experimentally. The formula for calculating the deviation 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 conductor's natural frequency. For example, assuming the proportion of vibration energy in the preset frequency band is 30% and the proportion of vibration energy corresponding to the conductor's natural frequency is 20%, then the deviation value is 0.5.

[0178] After calculating the deviation value, a conductor deformation field model is constructed based on the strain parameters to identify regions where the deformation gradient exceeds the limit. The strain parameters are acquired in real time using strain gauges, reflecting the conductor's deformation under icing and wind conditions. In practice, the conductor's strain parameters are interpolated in time and space to construct the conductor deformation field model. The deformation field model describes the deformation distribution of the conductor at different locations and times. The deformation gradient is the rate of change of deformation with spatial location in the deformation field model, calculated using the following formula:

[0179]

[0180] in, Here, ∈ represents the deformation gradient, x, y, and z are the spatial coordinates. The function identifies regions where the deformation gradient exceeds the limit, specifically areas where the deformation gradient surpasses a preset threshold. This preset threshold is determined based on the conductor's material properties and safety standards. For example, assuming the conductor's deformation gradient threshold is 0.01, calculating the deformation gradient can identify regions where the deformation gradient exceeds 0.01.

[0181] After identifying regions where the deformation gradient exceeds the limit, the galloping value is determined for these regions where the deviation value is greater than a first preset value and the deformation gradient exceeds the limit. The galloping value is an indicator of the degree of conductor galloping, comprehensively considering both vibration energy deviation and deformation gradient. In practice, regions where the deviation value is greater than the first preset value and the deformation gradient exceeds the limit are first screened out. The first preset value is determined based on historical data and practical experience. Then, the galloping value for these regions is calculated. The formula for calculating the galloping value is as follows:

[0182]

[0183] Where D is the dancing value, α and β are weighting coefficients, and Δf is the deviation value. It is the deformation gradient. The weighting coefficients are determined based on the degree of influence of vibration and deformation on the galloping.

[0184] After calculating the galloping value, it is determined whether the galloping value exceeds a preset critical galloping coefficient. The critical galloping coefficient is a threshold determined based on conductor safety standards and historical data. In practice, the galloping value is compared with the critical galloping coefficient. If the galloping value exceeds the critical galloping coefficient, the area with a deviation value greater than a first preset value and an excessive deformation gradient is identified as an icing galloping zone. For example, assuming the critical galloping coefficient is 0.8, by comparing the galloping value with the critical galloping coefficient, the area with a galloping value exceeding 0.8 can be identified as an icing galloping zone.

[0185] This implementation method effectively improves the accuracy of identifying icing galloping zones by performing FFT transformation on vibration parameters, extracting the proportion of vibration energy in a preset frequency band, and calculating its deviation from the conductor's natural frequency. Simultaneously, by constructing a conductor deformation field model, it identifies areas where the deformation gradient exceeds limits, and combines the deviation value and deformation gradient to determine the galloping value, further optimizing the criteria for determining icing galloping zones. This provides more accurate data support for icing monitoring and helps improve the accuracy and reliability of transmission line icing monitoring.

[0186] In one embodiment of this invention, 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 preset format projection coordinate system, and divide the icing trajectory into multiple spatial grids;

[0188] S720. In the projected coordinate system, the three-dimensional model of the windward icing layer is spatially superimposed with the spatial grid of the icing trajectory to generate the first icing trajectory, wherein 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 dancing area, and interpolate the aligned high-risk icing area and the icing dancing area into the projection coordinate system of the first icing trajectory to generate the fitted icing trajectory.

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

[0191] After transforming the coordinate system and dividing the space into grids, the 3D model of the windward ice layer is spatially superimposed with the spatial grid of the ice trajectory in the projected coordinate system to generate the first ice trajectory. Spatial superposition integrates spatial information from different data sources to generate a comprehensive dataset containing multiple types of information.

[0192] In practice, the 3D model of the windward icing layer is spatially overlaid with the spatial grid of the icing trajectory to generate the first icing trajectory. The 3D model of the windward icing layer includes icing thickness distribution data, which is mapped onto the spatial grid of the icing trajectory using interpolation methods. Bilinear interpolation or Kriging interpolation can be used. For example, assuming the 3D model of the windward icing layer includes 1000 points of icing thickness data, bilinear interpolation can map this data onto 1000 spatial grids of the icing trajectory to generate the first icing trajectory. The first icing trajectory contains the icing thickness distribution data of the windward icing layer, providing a foundation for subsequent interpolation operations on high-risk icing areas and icing galloping areas.

[0193] After generating the first icing trajectory, the high-risk icing area and the icing-galloping area are coordinate-aligned, and then interpolated into the projected coordinate system of the first icing trajectory to generate the fitted icing trajectory. Coordinate alignment unifies the coordinate systems of different data sources into a common reference system, facilitating subsequent interpolation operations. Specifically, a coordinate transformation method is used to convert the coordinate systems of the high-risk icing area and the icing-galloping area into the projected coordinate system of the first icing trajectory. For example, assuming the original coordinate system of the high-risk icing area and the icing-galloping area is the WGS84 latitude and longitude coordinate system, it can be converted into the UTM Cartesian coordinate system through UTM projection. Then, the coordinate-aligned high-risk icing area and the icing-galloping area are interpolated into the projected coordinate system of the first icing trajectory. Bilinear interpolation or Kriging interpolation can be used. The fitted icing trajectory includes icing thickness distribution data of the windward icing layer, icing thickness data of the high-risk icing area, and galloping value data of the icing-galloping area.

[0194] This implementation significantly improves the comprehensiveness and accuracy of icing monitoring by fitting the windward icing layer, high-risk icing area, and icing galloping area to the icing trajectory. Traditional methods rely on a single data source and static model, making it difficult to fully reflect the multi-dimensional characteristics of icing distribution. This scheme effectively improves the modeling accuracy of the icing trajectory by converting the coordinate system of the icing trajectory to a preset format projected coordinate system and dividing the icing trajectory into multiple spatial grids. Simultaneously, by spatially overlaying the 3D model of the windward icing layer with the spatial grid of the icing trajectory to generate the first icing trajectory, and combining coordinate alignment and interpolation of the high-risk icing area and icing galloping area, the fitted icing trajectory is further optimized, achieving comprehensiveness and accuracy in icing monitoring and providing strong support for the safe operation of the power system.

[0195] In one embodiment of this invention, in the projected coordinate system, the three-dimensional model of the windward ice layer is spatially superimposed with the spatial grid of the ice trajectory to generate a first ice trajectory, including the following steps:

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

[0197] S820: The bilinear interpolation algorithm is used to map the ice thickness distribution data in the voxel dataset to the grid vertices of the ice trajectory.

[0198] S830. Based on the curvature parameters of the conductor of the icing trajectory, a preset compensation formula is used to perform curvature compensation calculation on the mapped icing thickness data to obtain the compensated icing trajectory.

[0199] When spatially overlaying a 3D model of the windward icing layer with a spatial mesh of the icing trajectory, the first step is to voxelize the 3D model of the windward icing layer to generate a voxel dataset with the same resolution as the spatial mesh of the icing trajectory. Voxelization is the process of converting a 3D model into a voxel dataset composed of voxels (3D pixels). A voxel is the smallest unit in 3D space, similar to a pixel in a 2D image. Specifically, the 3D model of the windward icing layer is divided into multiple voxels, each with a size consistent with the resolution of the spatial mesh of the icing trajectory. For example, assuming the resolution of the spatial mesh of the icing trajectory is 10m × 10m × 10m, then each voxel is also 10m × 10m × 10m in size. Voxelization generates the voxel dataset by mapping each point in the 3D model to the nearest voxel. Each voxel in the voxel dataset contains icing thickness distribution data. For example, assuming a three-dimensional model of the windward icing layer includes 1,000 points of icing thickness data, voxelization can generate 1,000 voxels, each containing an icing thickness value.

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

[0201]

[0202] Here, 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 thickness values ​​at the known points. For example, assuming a voxel dataset contains 1000 voxels, the bilinear interpolation algorithm can map the ice thickness distribution data of these voxels onto 1000 grid vertices of the ice trajectory.

[0203] After mapping the icing thickness distribution data to the grid vertices of the icing trajectory, a preset compensation formula is used to calculate the curvature compensation of the mapped icing thickness data based on the conductor curvature parameters of the icing trajectory, thus obtaining the compensated icing trajectory. The conductor curvature parameter describes the degree of conductor bending and is obtained by measuring the conductor's radius of curvature. In practice, the preset compensation formula is used to calculate the curvature compensation of the mapped icing thickness data. The preset compensation formula is as follows:

[0204]

[0205] Where h1 is the compensated icing thickness, h2 is the mapped icing thickness, k is the compensation coefficient, and R is the radius of curvature of the conductor. The compensation coefficient is determined based on the material properties of the conductor and the icing conditions. For example, assuming the mapped icing thickness is 10 mm, the conductor's radius of curvature is 100 m, and the compensation coefficient is 0.1, the compensated icing thickness can be calculated to be 11 mm using a preset compensation formula.

[0206] This implementation method voxelizes the 3D model of the windward icing layer to generate a voxel dataset with the same spatial grid resolution as the icing trajectory. A bilinear interpolation algorithm is then used to map the icing thickness distribution data to the grid vertices of the icing trajectory, effectively improving the modeling accuracy of the icing trajectory. Simultaneously, based on the conductor curvature parameters of the icing trajectory, a preset compensation formula is used to calculate curvature compensation for the mapped icing thickness data, further optimizing the accuracy of the icing trajectory. This provides more precise data support for icing monitoring and helps improve the accuracy and reliability of icing monitoring for transmission lines.

[0207] In one embodiment of this invention, optimizing the fitted icing trajectory based on icing layer data includes the following steps:

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

[0209] When optimizing the fitted icing trajectory, the icing layer data is first interpolated to the corresponding high-risk icing area on the fitted icing trajectory within its projected coordinate system. The icing layer data includes parameters such as icing thickness, ice density, and ice temperature, which are obtained through sensors or historical databases. Specifically, an interpolation method is used to map the icing layer data to the corresponding high-risk icing area on the fitted icing trajectory. Bilinear interpolation can be used. The bilinear interpolation formula is referenced in S820 and will not be repeated here.

[0210] This implementation method further optimizes the accuracy of the icing trajectory by interpolating the icing layer data to the corresponding high-risk icing area in the projected coordinate system of the fitted icing trajectory. This achieves comprehensiveness and accuracy in icing monitoring and provides strong support for the safe operation of the power system.

[0211] This application also provides an icing monitoring system, such as... Figure 5 As shown, it includes:

[0212] Solar panels / batteries are used to power the icing monitoring system.

[0213] The memory is configured to store instructions; and

[0214] The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the aforementioned method for monitoring icing on power transmission lines.

[0215] Those skilled in the art will understand that 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. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0216] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0217] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0218] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

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

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

[0221] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, 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, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0222] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0223] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for real-time monitoring of icing on power transmission lines based on multi-parameter fusion, characterized in that, The method is applied to an icing monitoring system, and the method comprises: In response to receiving the conductor physical parameters at the same time, the current environmental data is combined to simulate the icing 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; Based on the current wind direction, the windward surface of the icing track is determined, and the windward surface is taken as the starting surface, the current wind speed is taken as the reference wind speed, and the icing layer simulation strategy is used to simulate the windward icing layer of the icing track, wherein the icing layer simulation strategy is realized based on a preset icing thickness prediction model; Based on the terrain data of the target transmission line section, the high-risk icing area in the icing track is determined, and the icing layer data of the high-risk icing area is determined based on a preset big data model; The vibration parameters are subjected to FFT transformation, the vibration energy proportion of a preset frequency band is extracted, and the deviation value of the vibration energy proportion from the natural frequency of the conductor is calculated; According to the strain parameters, a conductor deformation field model is constructed to identify a region with a deformation gradient exceeding a limit, wherein the conductor deformation field model is constructed based on the time and space distribution of the strain parameters; The flutter value of the region with the deviation value greater than a first preset value and the deformation gradient exceeding the limit is determined; If the flutter value exceeds a preset critical flutter coefficient, the region with the deviation value greater than the first preset value and the deformation gradient exceeding the limit is determined as an icing flutter area; The windward icing layer, the high-risk icing area, and the icing flutter area are fitted with the icing track, and the fitted icing track is optimized according to the icing layer data; A visual interface is generated according to the optimized icing track.

2. The method of claim 1, wherein, Simulating the icing track of the target transmission line section comprises: According to the current wind speed and the current wind direction, a preset fluid dynamics model is used to calculate the airflow distribution on the surface of the conductor of the target transmission line section; Based on the airflow distribution and the conductor physical parameters, a finite element analysis method is used to simulate the icing formation process on the surface of the conductor; The icing 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 adhesion angle; Based on the ice layer morphology parameters of each time step, a three-dimensional point cloud reconstruction algorithm is used to generate the icing cross-sectional profile of each time step; The icing cross-sectional profiles of multiple time steps are spatially interpolated along the extension direction of the conductor to construct a three-dimensional space model of the icing track.

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

4. The method of claim 1, wherein, The conductor physical parameters include the temperature parameters of the conductor surface, the current environmental data further includes the temperature and humidity, and the icing layer simulation strategy comprises: A polar coordinate system is established with the wire axis as the reference, and the windward surface is divided into multiple fan-shaped grids; In each grid, the aerodynamic load is calculated according to the current wind speed, and the ice layer growth rate is solved through the thermodynamic equation combined with the temperature parameters of the wire surface; The finite element analysis method is used to simulate the stress distribution of the ice layer on the wire surface, and the grid area with a stress concentration degree exceeding a preset threshold is screened out; In the grid area, the reference wind speed, temperature and humidity are input parameters, and the ice thickness distribution data of the windward ice layer is output according to the pre-constructed ice thickness prediction model; According to the ice thickness distribution data, a three-dimensional model of the windward ice layer is generated.

5. The method of claim 1, wherein, The current environmental data includes humidity parameters, and based on the terrain data of the target power transmission line section, the high-risk icing area in the icing track is determined, including: Obtain the terrain data of the target power transmission line section, which includes elevation, slope and slope direction; Extract the three-dimensional terrain feature vector of the 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 area in the preset historical icing database; The potential wind acceleration area corresponding to the average ice thickness greater than the preset ice thickness threshold is taken as the target icing area; Input the humidity parameters into the pre-constructed big data model to predict the ice growth rate of each target icing area in a future preset time period; The target icing area corresponding to the ice growth rate greater than the preset growth rate is taken as the high-risk icing area.

6. The method of claim 4, wherein, The windward ice layer, the high-risk icing area and the ice dance area are fitted with the icing track, including: Convert the coordinate system of the icing track into a projection coordinate system in a preset format, and divide the icing track into multiple spatial grids; In the projection coordinate system, the three-dimensional model of the windward ice layer is spatially superimposed with the spatial grid of the icing track to generate a first icing track, wherein the first icing track contains the ice thickness distribution data of the windward ice layer; Coordinate alignment is performed on the high-risk icing area and the ice dance area, and the coordinate-aligned high-risk icing area and the ice dance area are interpolated into the projection coordinate system of the first icing track to generate a fitted icing track.

7. The method of claim 6, wherein, In the projection coordinate system, the three-dimensional model of the windward ice layer is spatially superimposed with the spatial grid of the icing track to generate a first icing track, including: The three-dimensional model of the windward ice layer is voxelized to generate a voxel dataset consistent with the resolution of the icing track spatial grid; The bilinear interpolation algorithm is used to map the ice thickness distribution data in the voxel dataset to the grid vertices of the icing track; According to the wire curvature parameters of the icing track, a preset compensation formula is used to perform curvature compensation calculation on the mapped ice thickness data to obtain a compensated icing track.

8. The method of claim 6, wherein, Optimize the fitted icing track according to the ice layer data, including: In the projection coordinate system of the fitted icing track, the ice layer data is interpolated into the corresponding high-risk icing area of the fitted icing track to optimize the fitted icing track.

9. An ice monitoring system, characterized by It includes: Solar panels / batteries for powering the icing monitoring system; a memory configured to store instructions; and a processor configured to call the instructions from the memory and, upon execution of the instructions, enable the method for ice monitoring of a power line according to any one of claims 1 to 8.

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