Method and system for analyzing icing of power transmission line under influence of microtopography and micrometeorology

Abstract

The invention discloses a power transmission line icing analysis method and system influenced by microtopography and micrometeorology, and relates to the technical field of power transmission line risk prediction.The method comprises the steps that original meteorological data of an area where a power transmission line is located is collected and fused, and micrometeorological field variables are generated based on topographic parameters extracted by a DEM; on the basis of the fusion data and the micrometeorological field variables, three-dimensional micrometeorological field data are obtained through a fluid mechanics model, the three-dimensional micrometeorological field data are corrected through terrain parameters obtained through a DEM model, an exponential weighted moving average method is used for adjustment, and accurate three-dimensional micrometeorological field data are obtained; based on the accurate micrometeorological field data, the icing thickness is obtained by adopting a physical-empirical hybrid icing growth model, the output icing thickness is adjusted in real time through a dynamic correction algorithm, and a final icing simulation result is obtained. The accuracy of power transmission line icing disaster prediction and the flexibility of emergency response are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power transmission line risk prediction, in particular to a power transmission line icing analysis method and system affected by micro-topography and micro-meteorology. BACKGROUND

[0002] With the continuous growth of power demand and the continuous expansion of power transmission lines, power transmission lines are facing more and more environmental risks, among which icing disaster is one of the main factors leading to power interruption and equipment damage. The occurrence of icing disaster is usually closely related to meteorological conditions, topographic features and the operating state of the power transmission line. In the prior art, many studies have explored how to predict icing risk through meteorological data, topographic data and wind speed and other variables, but most of the technologies still face the problem of insufficient prediction of complex topography and micro-meteorological effects. The existing power transmission line icing prediction method usually relies on relatively simple meteorological parameters (such as temperature, humidity, wind speed, etc.), and ignores the profound influence of topographic micro-meteorological effects on the icing process. For example, the topography of mountainous and valley areas has an important influence on air flow, temperature and radiant energy. If the topographic effect is not fully considered, the accuracy of the icing risk assessment will be reduced. Current icing prediction techniques mostly focus on using static meteorological data and topographic models, but lack sufficient attention to real-time effects of dynamic meteorological changes and topographic corrections. Traditional methods often respond to emergencies after disasters occur, lacking real-time monitoring and early warning mechanisms for power transmission lines. This delayed response not only fails to reduce the risk of power grid damage in a timely manner, but also can cause serious equipment damage and power outages, resulting in huge losses to the power grid. Most existing technologies usually use relatively simple topographic correction methods when simulating and analyzing micro-meteorological effects, failing to accurately consider the influence of complex topography on wind speed, radiation and temperature distribution. For example, many models rely only on relatively rough topographic height data, ignoring the influence of micro-topographic features such as ridges and valleys. Existing icing growth models mostly do not fully consider the dynamic changes of the micro-meteorological field, especially in the case of rapid changes in actual weather conditions, lacking real-time adjustment mechanisms, resulting in low accuracy of model prediction results under rapidly changing climate conditions, affecting the prediction and prevention of icing disasters. SUMMARY

[0003] In view of the above existing problems, the present application is proposed.

[0004] Therefore, the present application provides a power transmission line icing analysis method and system affected by micro-topography and micro-meteorology, which solves the problem that the prior art fails to effectively combine real-time data of topographic micro-meteorology, resulting in low prediction accuracy and lack of rapid response mechanism to real-time changes.

[0005] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present invention provides a method for analyzing the icing of transmission lines under the influence of micro-topography and micro-meteorology, which includes collecting and fusing raw meteorological data of the area where the transmission line is located, and generating micro-meteorological field variables based on the topographic parameters extracted from the DEM model. Based on fused data and micro-meteorological field variables, three-dimensional micro-meteorological field data are obtained through a fluid dynamics model. The terrain parameters obtained from the DEM model are used to correct the three-dimensional micro-meteorological field data, and the exponential weighted moving average method is used for adjustment to obtain accurate three-dimensional micro-meteorological field data. Based on precise micro-meteorological field data, the ice thickness is obtained by using a physical-empirical hybrid ice growth model. The output ice thickness is then adjusted in real time using a dynamic correction algorithm to obtain the final ice simulation result. Based on the final icing simulation results, a risk assessment model is used to classify the icing risk of different areas, and different levels of early warning measures are triggered based on the classification results.

[0006] As a preferred embodiment of the transmission line icing analysis method based on micro-topography and micro-meteorological influences described in this invention, the following steps are included: collecting and fusing raw meteorological data of the area where the transmission line is located; generating micro-meteorological field variables based on topographic parameters extracted from the DEM model; integrating collected remote sensing data, UAV monitoring data, meteorological station data, and historical icing data into a unified micro-meteorological dataset; and fusing multi-source data using a weighted regression method to generate a fused micro-meteorological dataset. And calculate the actual solar shortwave radiation energy received per unit area of ​​the Earth's surface. .

[0007] As a preferred embodiment of the transmission line icing analysis method based on micro-topography and micro-meteorological influences described in this invention, the method involves: obtaining three-dimensional micro-meteorological field data through a CFD model based on fused data and micro-meteorological field variables; correcting the three-dimensional micro-meteorological field data using topographic parameters obtained from a DEM model; and adjusting the data using an exponentially weighted moving average method to obtain accurate three-dimensional micro-meteorological field data indicating the solar shortwave radiation energy. The data is input into the CFD model as thermal boundary conditions. The built-in steady-state RANS solver in the CFD model is then used to numerically solve the governing equations, yielding the wind speed field. and temperature and relative moisture Three-dimensional micro-meteorological field obtained based on CFD solution For each transmission tower location According to the slope of the location and slope and background wind direction To determine whether it is in a valley area, if preset threshold Q and located on the ridge line and slope towards with the background wind direction less than 90°, a ridge acceleration factor is applied to the wind speed output by CFD , and the temporary wind speed after the ridge terrain correction is updated ; If located in a closed valley, the radiation heating difference between the sunny slope and the shady slope is calculated for each tower site , if (that is, in the sunshine area), the temperature value after the terrain radiation effect correction is obtained , if , , the original value of CFD is retained; The terrain-corrected microclimate field is used as the prior estimate value, combined with the wind speed and temperature returned by the online monitoring equipment installed at the tower foundation in real time, the exponential weighted moving average method (EWMA) is used for dynamic deviation correction to obtain the optimal estimated wind speed value ; The final temperature value is calculated , and the corrected and and specific humidity are used as the high-precision observation-simulation fusion results at the tower site, and the bilinear interpolation method is used to output the high-precision three-dimensional microclimate field data set.

[0008] As a preferred scheme of the micro-terrain microclimate influence transmission line icing analysis method described in the application, wherein: the high-precision three-dimensional microclimate field data set is used as input data to drive the icing growth model to obtain the change rate of icing mass with time , the change rate of conductor icing mass is , the change rate is numerically integrated in each time step, the increments of each period are accumulated to obtain the total icing mass from the start of icing to the current time, the cross-sectional area is calculated using the original radius of the conductor, and it is assumed that the icing uniformly wraps the conductor to form a concentric cylinder, combined with the density of ice, the ice volume is obtained from the total mass, and the equivalent outer diameter after icing is obtained according to the cylindrical geometric relationship. The icing thickness is half the difference between the outer diameter and the original diameter of the conductor.

[0009] As a preferred scheme of the power transmission line icing analysis method under the influence of micro-topography and micro-meteorology, wherein: the real-time adjustment of the output icing thickness by the dynamic correction algorithm obtains an initial icing simulation result based on a high-precision three-dimensional micro-meteorological field driving, automatically accesses the wind speed, air temperature, humidity, precipitation intensity and remotely sensed supercooled water droplet content observation data measured at the power transmission tower base every hour, and compares the actual observed icing growth rate at the current time with the model predicted value, if the deviation exceeds the preset threshold V, the dynamic correction mechanism is started, including regional adaptive adjustment of the key parameters in the icing growth model according to the deviation direction and the local real-time meteorological conditions, the parameters are smoothed and updated by using the exponential weighted moving average method, and finally the icing simulation result corrected by real-time observation is output.

[0010] As a preferred scheme of the power transmission line icing analysis method under the influence of micro-topography and micro-meteorology, wherein: based on the final icing simulation result, a risk assessment model is used to grade the icing risk in different regions, the corrected icing thickness data is obtained, the historical fault records of the power grid and the carrying capacity of each device are combined as input data, and the risk assessment model is used based on the input data to quantitatively assess the icing risk in different regions to obtain the risk value of each region , if the risk value is less than the preset threshold W, it is classified as a low-risk area, if the risk value is greater than the preset threshold O, it is classified as a medium-risk area, and if the risk value is greater than the preset threshold O, it is classified as a high-risk area.

[0011] As a preferred scheme of the power transmission line icing analysis method under the influence of micro-topography and micro-meteorology, wherein: based on the grading result, different levels of early warning measures are triggered, according to the risk assessment level, the corresponding early warning measures are triggered through the decision system, and the specific early warning measures include automatically triggering the heating device, load adjustment and strengthening the patrol measures when the icing thickness reaches the set threshold, the high-risk area can start the power grid protection strategy in advance to implement line load distribution and reduce the pressure of the power grid, and the warning level setting includes notifying the power grid operator to perform routine monitoring and patrol for light risk, performing equipment inspection and maintenance in advance for medium risk, starting the emergency power supply system for severe risk, and immediately enabling the emergency scheme.

[0012] In a second aspect, the application provides a power transmission line icing analysis system under the influence of micro-topography and micro-meteorology, comprising a data acquisition and fusion module for acquiring and fusing meteorological data from different sources to generate accurate micro-meteorological data sets. A microclimate field simulation and correction module is used to calculate three-dimensional microclimate field data through a CFD model and provide accurate microclimate information according to terrain correction; An ice growth simulation module is used to simulate the growth rate and type of ice based on microclimate data, and calculate the ice thickness of the conductor; A dynamic correction and real-time correction module is used to dynamically adjust the ice growth model according to real-time meteorological observation data, to ensure the accuracy of the simulation results; A risk assessment grading and early warning system module is used to assess and grade the ice risk of each region based on the ice thickness and the carrying capacity of the power grid, and trigger different levels of early warning measures according to the risk assessment results.

[0013] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and wherein the computer program is executed by the processor to implement any step of the method for analyzing the ice on the transmission line affected by the microtopography and microclimate according to the first aspect of the present application.

[0014] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement any step of the method for analyzing the ice on the transmission line affected by the microtopography and microclimate according to the first aspect of the present application.

[0015] The present application has the following advantages: the data fusion process is optimized by the weighted regression method, the weights of each data source are dynamically adjusted by the principal component analysis (PCA) and cross-validation method, the accuracy of the microclimate data set is improved, the three-dimensional microclimate field is simulated by the CFD model, and the meteorological variables are accurately corrected by the terrain correction technology, so that the present application can respond to complex terrain and weather changes in real time, and provide more accurate ice simulation results; the dynamic correction algorithm is introduced, the key parameters of the ice growth model are optimized by comparing the deviation between the real-time observation data and the model prediction value, so that the model can be automatically adjusted under sudden weather conditions, the accuracy and timeliness of the simulation results are ensured, and the accuracy of the risk assessment of the ice disaster of the transmission line can be greatly improved, to provide more scientific and timely early warning and response measures for the operation of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Fig. 1A flow chart of a microtopography microclimate-influenced transmission line icing analysis method in Example 1.

[0018] Fig. 2 A structural schematic diagram of a microtopography microclimate-influenced transmission line icing analysis system in Example 1.

[0019] Fig. 3 A flow chart of icing growth and dynamic correction in Example 1. DETAILED DESCRIPTION

[0020] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0021] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited by the specific embodiments disclosed below.

[0022] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is separate or alternative to other embodiments.

[0023] Example 1, refer to Figs. 1 to 3 , for the first embodiment of the present application, the embodiment provides a microtopography microclimate-influenced transmission line icing analysis method, comprising the following steps: S1, collecting original meteorological data of the area where the transmission line is located and fusing, generating microclimate field variables based on the terrain parameters extracted by the DEM model; Based on the fused data and the microclimate field variables, obtain the three-dimensional microclimate field data through the fluid mechanics model, correct the three-dimensional microclimate field data using the terrain parameters obtained by the DEM model, and adjust using the exponential weighted moving average method to obtain accurate three-dimensional microclimate field data; Specifically, collecting original meteorological data of the area where the transmission line is located and fusing, generating microclimate field variables based on the terrain parameters extracted by the DEM model means integrating the collected remote sensing data, unmanned aerial vehicle monitoring data, meteorological station data and historical icing data into a unified microclimate data set, the specific operation steps are: removing missing values and abnormal values that do not meet the standard, spatially registering all data sources to ensure that the geographic coordinates of different data sources are consistent, time aligning the remote sensing data and unmanned aerial vehicle data according to the time stamp of the meteorological station to ensure that the timeliness of each data source is consistent; The multi-source data is fused by a weighted regression method to generate a fused microclimate dataset , and the formula is: , wherein, is the final fused microclimate dataset, is remote sensing data (such as DEM, terrain features), is unmanned aerial vehicle monitoring data (such as high-precision laser radar data), is weather station data (temperature, humidity, wind speed), is historical icing data, and , , is the weight of each data source, and the size of the weight is set according to the reliability, timeliness and accuracy of the data, which is determined by principal component analysis (PCA) combined with cross-validation method. The specific steps are as follows: first, the information contribution of each data source is evaluated by PCA, then the fusion error minimization optimization is carried out based on historical icing events, the weight is adjusted through cross-validation iteration, and finally the optimal weight combination that makes the fusion result most consistent with the measured icing is obtained; The solar elevation angle a and the azimuth angle g of the current time are calculated according to the current date, time and latitude and longitude of each grid point by using astronomical algorithm (such as SPA model), and the solar elevation angle a and the azimuth angle g of each DEM grid point is analyzed along the direction of the sun's incidence (determined by the solar elevation angle a and the azimuth angle g), and if the line of sight is blocked by the adjacent terrain, it is determined that the grid point is in the shadow, and the highest elevation angle of the terrain in each direction is recorded by scanning 360 degrees around the grid point , and it is judged whether it is surrounded by mountains (affecting long-wave radiation heat dissipation). The shadow mask is output to mark whether each grid point is blocked, and the ground points located at the grid point at time t are generated. The proportion of the actual received solar direct radiation due to terrain blocking (1 for no blocking, 0 for complete blocking, and intermediate values for partial blocking) quantifies the shielding effect of mountains and valleys on solar radiation; The actual received solar short-wave radiation energy per unit area of the ground , that is, the microclimate field variable driven by terrain-meteorological coupling, more accurately simulates the local ground temperature, such as in the shady slope or valley area, to avoid overestimating the radiation energy and causing the temperature to be too high. The formula is: , wherein, represents the actual received net short-wave solar radiation flux of the ground at position at time t, is the atmospheric top solar irradiance after the geodetic distance correction (i.e. the actual value of the space solar constant on the day, used to correct the seasonal radiation difference), is the solar zenith angle, which refers to the angle between the solar ray and the normal line of the ground surface, the range The larger the angle, the lower the sun, the weaker the radiation, is the projection attenuation factor of the solar ray incident to the horizontal ground, which is used to convert the radiation perpendicular to the ray into the effective radiation on the horizontal plane; The remote sensing data refers to collecting DEM (Digital Elevation Model) data of the area where the power transmission line is located using high-resolution satellite remote sensing, including terrain information (DEM), land cover type, vegetation distribution; The unmanned aerial vehicle monitoring data refers to deploying unmanned aerial vehicles (UAVs) for real-time monitoring, and the unmanned aerial vehicles are equipped with high-precision laser radar (LiDAR) equipment for local three-dimensional modeling, providing more detailed terrain data than satellite data, such as complex terrain of ridges and valleys; The weather station data refers to collecting weather station data along the power transmission line, including real-time temperature, humidity, wind speed, wind direction, and precipitation, and the data interval of each weather station is 10 minutes to ensure the timeliness of the data; The historical icing data refers to collecting past icing disaster history data, including icing thickness, type (such as rime, glaze), time and location of occurrence.

[0024] By fusing remote sensing data, unmanned aerial vehicle monitoring data, weather station data and historical icing data, the problem of data isolation and insufficient analysis accuracy in the prior art is solved, and the data fusion process is optimized by weighted regression method and principal component analysis (PCA), so that the information contribution of each data source is reasonably balanced, thereby improving the accuracy of the micro-meteorological data set. The shading effect of solar radiation is calculated using astronomical algorithms, and the actual received solar radiation on the ground surface is accurately simulated in combination with the terrain parameters, avoiding the overestimation of radiation energy in the traditional method under complex terrain, and further more accurately simulating the local temperature change; With the help of high-precision unmanned aerial vehicle laser radar data and dynamic integration of multi-source weather data, the changes of the micro-meteorological field can be corrected in real time, especially under complex weather conditions, to ensure real-time adjustment of the icing thickness and type. Based on the scheme of multi-dimensional data fusion and dynamic correction, the precision and response speed of the icing risk assessment are improved, and the prediction lag and inaccuracy of the prior art under complex terrain and weather conditions are solved, providing more scientific and accurate support for the icing prevention and control of the power transmission line.

[0025] ​Further, based on the fused data and the micro-meteorological field variables, three-dimensional micro-meteorological field data are obtained through a CFD model, the three-dimensional micro-meteorological field data are corrected by using the terrain parameters obtained by the DEM model, and are adjusted by using an exponential weighted moving average method, to obtain accurate three-dimensional micro-meteorological field data based on the fused micro-meteorological data set and the surface net shortwave solar radiation flux The terrain parameters obtained by the DEM are imported into OpenFOAM to construct a three-dimensional calculation domain, and the solar shortwave radiation energy Data are input into a CFD model (computational fluid dynamics model) as a thermal boundary condition, a built-in steady-state RANS (Reynolds-averaged Navier-Stokes) solver in the CFD model is called, and a numerical solution is performed on a control equation set (including a momentum equation, an energy equation, and a water vapor transport equation), the momentum equation is used to simulate a three-dimensional flow structure of air under the joint action of a pressure gradient, a viscous stress, and gravity (including buoyancy), and a wind speed field (x, y, z) is output, and the momentum equation formula is as follows: , , wherein, is a three-dimensional wind speed vector, is air density, that is, the mass of unit volume of air, is effective viscosity, is calculated by a standard k-ε turbulence model, is a transpose, is a gravity acceleration vector, is gravity (including buoyancy), indicating the acceleration of air caused by gravity, wherein the density change introduces the buoyancy effect (such as the upward movement of warm air), and ∇ is a gradient operator, indicates divergence, is a mass conservation (continuity equation), divergence of momentum flux, and a non-conservative quantity, is a pressure gradient force, indicating the accelerated movement of air due to the difference in air pressure, and the direction is from high pressure to low pressure; The energy equation describes the spatial distribution of air temperature under the action of air flow transport and turbulent heat diffusion, and reflects the influence of terrain thermal forcing (such as solar radiation heating) on the near-surface layer temperature, and the formula is as follows: , wherein, is the air temperature of any point in a three-dimensional space, is a turbulent Prandtl number, used to represent the turbulent heat diffusion efficiency in the energy equation, such as = 0.85; The water vapor transport equation describes specific humidity The evolution process of advection and turbulent diffusion in the wind field is expressed by the following formula: , in, For any point in three-dimensional space The specific moisture content, The Schmitt number is used to control the intensity of turbulent water vapor diffusion in the water vapor transport equation, such as... =0.7; Three-dimensional micrometeorological field obtained based on CFD solution The topographic parameters extracted from the DEM model were used to perform physical consistency correction on near-surface meteorological variables. Specific steps included: for each transmission tower location... According to the slope of the location and slope and background wind direction ,(slope and slope It was obtained through terrain analysis using the second-order difference method on a GIS platform (such as ArcGIS or QGIS) based on high-resolution DEM data, with background wind direction. The wind direction observation at a height of 10 meters (obtained through weighted fusion of ground automatic stations, radiosondes, and numerical models) is used to determine whether the location is in a typical ridge or valley area. Measured wind speed data from multiple typical mountain tower locations are collected, and slope is extracted using the DEM of the location. A correlation curve between wind speed and slope is plotted. When the slope exceeds Q, a significant wind speed acceleration is commonly observed. Q is set as the threshold for ridge acceleration correction. >Preset threshold Q and located on the ridgeline (identified by DEM curvature analysis) and slope aspect With background wind direction If the included angle is less than 90°, it indicates that the slope is directly facing the incoming flow, and a ridge acceleration factor is applied to the wind speed output by the CFD. The formula is:

[0026] in, Indicates slope The sine value is used to quantify the nonlinear effect of terrain steepness on airflow acceleration (the steeper the slope, the higher the velocity). The larger ( This is an empirical coefficient, representing the relative increase in wind speed caused by a unit slope sine value, which is obtained through statistical calibration of measured data from mountain wind fields; Updated temporary wind speeds after ridge topography correction The formula is:

[0027] in, It is the wind speed output from the original CFD model; If located in a closed valley (the Sky View Factor (SVF) of each tower location is calculated using a DEM; a threshold E is set based on comprehensive analysis and experience; if SVF < the preset threshold E, it is determined to be a closed valley; SVF refers to the proportion of the sky visible from a surface point within a 360° range, obtained by the "Sky View Factor" tool in GIS based on a 3D line-of-sight analysis of the DEM), then utilize... Calculate the difference in radiant heating between sunny and shady slopes for each tower location. ,like (i.e., in a sunlit area), the temperature value obtained is corrected for the topographic radiation effect. The formula is: , , in, The radiation warming coefficient represents the unit net shortwave radiation. The resulting increase in near-surface temperature ranges from 0.005 to 0.012℃·m. 2 / W, the range was determined through regression analysis of measured air temperature and net shortwave radiation data at multiple transmission tower sites in the mountainous areas of southern China during different seasons, such as the surface temperature of sunny slopes as... The temperature increases with increasing temperature, and the average value obtained through statistical fitting is approximately 0.008℃·m. 2 / W, This is the original temperature field output by the CFD model. This is the correction for radiative warming at time t; like (Completely obscured), then =0, retain the original CFD value; The terrain-corrected micro-meteorological field was used as a priori estimate, combined with the real-time wind speed data transmitted from the online monitoring equipment installed at the tower base. and temperature The optimal estimated wind speed value was obtained by using the exponentially weighted moving average (EWMA) method for dynamic deviation correction. The calculation formula is: , In the formula, The weights for terrain-corrected wind speeds, ranging from 0.6 to 0.8, reflect the confidence level in the spatial details of the CFD model. These weights are determined through back-substitution optimization using multi-station measured and simulated data from historical icing events. This refers to the total wind speed after terrain correction, including the ridge acceleration factor. and the corrected temporary wind speed ; The final temperature value fused with the terrain correction and the measured data is calculated , and the formula is: , wherein β is the weight of the terrain correction temperature, and the value range is, for example, 0.5-0.7, which reflects the correction requirement for the simulation of the ground thermal process, and is determined through the back substitution optimization of the measured and simulated data in the historical icing events; The corrected and and the specific humidity adjusted for humidity consistency are taken as the high-precision observation-simulation fusion result at the tower site, the spatial extrapolation is performed to the original 5m*5m horizontal grid by using the bilinear interpolation method, the interpolation reconstruction is performed along the vertical direction according to the CFD layering structure (for example, 20 layers), and finally the three-dimensional wind speed, temperature and humidity spatial distribution field covering the power transmission line area, with the resolution of 5m and the time resolution of the hourly level, is formed, and the high-precision three-dimensional microclimate field data set is output.

[0028] By introducing the three-dimensional microclimate field simulation based on the CFD model, combining the high-precision remote sensing data, unmanned aerial vehicle monitoring data and meteorological station data, the problem of insufficient simulation in the complex terrain area in the prior art is solved, the terrain parameters extracted from the DEM model are introduced into OpenFOAM to construct a three-dimensional calculation domain, and the solar radiation energy is taken as the thermal boundary condition to input the CFD model, the influence of the solar radiation and the terrain on the local temperature is accurately simulated, the exponential weighted moving average method (EWMA) is used for dynamic deviation correction of the microclimate field data, and the accurate tracking and real-time adjustment of the meteorological variables such as wind speed and temperature in the actual environment are ensured; through the fine processing of the ridge acceleration effect and the terrain radiation correction, the prediction error caused by the complexity of the terrain in the traditional method is effectively reduced, especially in the special areas of the mountain and the closed valley, the accuracy of the microclimate field data is significantly improved, the three-dimensional wind speed, temperature and humidity data calculated by the CFD model provide more accurate and real-time microclimate data support for the icing risk assessment of the power transmission line, improve the accuracy and real-time of the icing simulation, can more scientifically predict the icing disaster and take corresponding prevention and control measures, and break through the problem that the existing method cannot fully simulate the complex terrain and the microclimate effect.

[0029] S2, based on the accurate microclimate field data, an icing thickness is obtained by using a physical-empirical hybrid icing growth model, real-time adjustment is performed on the output icing thickness by using a dynamic correction algorithm, and finally an icing simulation result is obtained; Specifically, based on the accurate microclimate field data, an icing thickness is obtained by using a physical-empirical hybrid icing growth model, that is, the air temperature T and the relative humidity RH corresponding to each spatial point , according to the empirical relationship defined in the international general icing model (such as Makkonen model), temperature and humidity are substituted into the preset function for calculation, is the freezing coefficient, which reflects the relationship between temperature and water droplet freezing, and the specific operation is: is a piecewise function, The value range of is 0 to 1, such as when the air temperature T <-10℃ and RH≥90%, =1.0 (water droplets are completely frozen), in the range of-15℃~0℃, the value is 0.3~1.0, the higher the relative humidity, the easier the water droplets are completely frozen, C tends to 1, and if the air temperature >0℃, =0 (no freezing), wherein in the range of-15℃~0℃, the value is 0.3~1.0, which is set by calibrating the value range close to 0℃ (such as-2℃ to 0℃) through Makkonen icing theory model and IEC60826 standard recommended value, combined with the measured temperature and humidity conditions and icing density inversion results in typical icing events in the southern mountainous areas of China. Some water droplets are not completely frozen and are blown away by the wind due to short residence time or high conductor temperature, so the freezing efficiency is low, and therefore The lower limit is about 0.3; The high-precision three-dimensional microclimate field data set is used as input data to drive the icing growth model to obtain the change rate of icing mass with time , which reflects the instantaneous rate of icing growth, wherein represents the increase in conductor icing mass per unit time, is the time microelement, that is, the time step of calculation, and the formula is: , , , is the collision velocity, which represents the collision velocity of supercooled water droplets with the conductor, is the wind speed, and the wind speed value of each time step and geographical position is obtained from the microclimate field data set for calculating the water droplet collision velocity, is the freezing coefficient, which reflects the relationship between temperature and water droplet freezing, such as , which is calculated from the real-time microclimate field temperature T(x,y,z,t) and relative humidity RH(x,y,z,t), is the density of water, is the temperature difference, that is, the difference between the conductor surface temperature and the supercooled water droplet temperature, and the temperature difference between the conductor surface temperature and the supercooled water droplet temperature is determined by using the temperature data of different positions and time points in the microclimate field data set, is the cross-sectional area of the conductor; The change rate of conductor icing mass is The numerical integral (for example, Euler method) is performed on the change rate in each time step (for example, 1 hour), the increment of each period is accumulated to obtain the total ice mass from the ice coating to the current time, the cross-sectional area is calculated by using the original radius r of the conductor, and it is assumed that the ice uniformly wraps the conductor to form a concentric cylinder, combined with the density of ice (for example, 900 kg / m 3 ), the ice layer volume (i.e., volume = mass ÷ density) is obtained from the total mass, and the equivalent outer diameter after ice coating is obtained according to the cylindrical geometric relationship, and finally the ice coating thickness is half of the difference between the outer diameter and the original diameter of the conductor.

[0030] By introducing the physical-experience hybrid ice growth model based on accurate microclimate field data, the problem of ignoring the influence of microclimate field in the prior art is solved. By extracting real-time temperature, humidity and wind speed and other information from high-precision three-dimensional microclimate field data set, the key parameters such as collision speed, freezing coefficient and temperature difference are dynamically calculated, the ice growth process after the collision of supercooled water droplets and conductor can be more accurately simulated, the freezing coefficient is calculated according to the real-time temperature and relative humidity, so that the model can flexibly respond to the changes of ice growth under different meteorological conditions. The numerical integral method (such as Euler method) is used to accumulate the ice mass in each time step, so as to calculate the ice thickness in real time, improve the spatio-temporal resolution and prediction accuracy of the model, and make the prediction of ice thickness more in line with the actual situation. Through the combination of fine microclimate field data and physical-experience hybrid model, the problems of insufficient response to complex weather changes and inaccurate ice growth simulation in the prior art are solved, which can provide more accurate and real-time ice risk assessment for power transmission lines, ensure more scientific disaster warning and prevention measures, and improve the accuracy and practicality of ice simulation.

[0031] Further, the output ice thickness is adjusted in real time by a dynamic correction algorithm to obtain the final ice simulation result. The initial ice simulation result is based on a high-precision three-dimensional micro-meteorological field. The wind speed, air temperature, humidity, precipitation intensity, and remotely sensed supercooled water droplet content (LWC) observation data measured at the power tower base are automatically accessed every hour. The actual observed ice growth rate (which can be indirectly estimated by changes in conductor load or image recognition) at the current time is compared with the model prediction value. Through statistical analysis of the measured and simulated results in historical ice events, the critical value that optimizes the model correction effect and avoids false triggering is determined. Typically, a preset threshold V is set based on the error distribution fitting of multiple stations and multiple cases. If the deviation exceeds the preset threshold V, the dynamic correction mechanism is started, including regionalized adaptive adjustment of key parameters in the ice growth model according to the deviation direction and local real-time meteorological conditions. For example, by combining typical ice field measurement data in southern mountainous areas of China with international general ice criteria (such as IEC 60826) to invert and calibrate the threshold v, in the case of high wind speed (> 10 m / s, based on engineering threshold > 10 m / s derived from international general standards (such as IEC 60826) combined with typical ice field measurement data in southern mountainous areas of China) and liquid water content exceeding the preset threshold v, the airflow carries a large amount of supercooled water droplets and impacts the conductor with high kinetic energy, increasing the water droplet collision frequency and adhesion efficiency, improving the water droplet collision efficiency to accelerate ice growth, and when the air temperature approaches C, the freezing coefficient is reduced to reflect the phenomenon that part of the water droplets are not frozen but blown away; To avoid drastic fluctuations in the model caused by single abnormal observations, the exponential weighted moving average (EWMA) method is used to update the parameters smoothly, including weighting and fusing the new parameter value calculated at the current time with the historical parameter value, where recent observations are given a high weight (such as 80%) and historical information is given a low weight (such as 20%) to respond quickly to weather changes while maintaining model stability. After updating the parameters, the ice growth equation is re-integrated to update the ice mass increment and cumulative thickness from the current time. The final output is a high-spatial and temporal resolution ice simulation result dynamically corrected by real-time observations.

[0032] By introducing a dynamic correction algorithm, the output ice thickness and type are adjusted in real time, significantly improving the accuracy of ice simulation. By automatically accessing the measured wind speed, air temperature, humidity, and precipitation intensity data of the transmission tower foundation every hour, the initial ice simulation results are dynamically compared and corrected. When there is a significant deviation between the actual observed ice growth rate and the model prediction value, based on the statistical analysis of the measured data and simulation results of historical ice events, the correction threshold can be determined and the dynamic correction mechanism is started. Through this mechanism, the model can adjust key parameters (such as freezing coefficient, collision speed, etc.) according to real-time weather conditions, ensuring the adaptability and accuracy of the model under extreme weather conditions such as high wind speed and low temperature. The Exponential Weighted Moving Average (EWMA) method is used to smooth the update of the correction parameters, avoiding the model's drastic fluctuations caused by single abnormal observations, ensuring that the model can quickly respond and maintain stability under rapidly changing climate conditions. This ice simulation method based on real-time observation data and dynamic adjustment mechanism solves the problem of insufficient response to sudden weather changes in the prior art, providing high spatiotemporal resolution ice prediction results, greatly improving the risk assessment accuracy and emergency response efficiency of transmission line ice disasters.

[0033] S3, based on the final ice simulation results, a risk assessment model is used to classify the ice risk of different regions, and different levels of warning measures are triggered based on the classification results; Specifically, based on the final ice simulation results, a multi-dimensional risk assessment model is used to classify the ice risk of different regions. The corrected ice thickness data is obtained, combined with the historical failure records of the power grid and the carrying capacity of each device as input data, where the ice thickness data comes from the microclimate field inversion and ice growth simulation, which can reflect the ice accumulation of each region. The historical failure data includes the records of power outages and equipment damage events caused by ice in the past, which is used to assess the vulnerability of the power grid. The carrying capacity of the power grid equipment is evaluated through the design standards, historical performance, and environmental impact of each line tower to ensure that it will not overload under extreme weather conditions. Based on the input data, a multi-dimensional risk assessment model is used to quantitatively evaluate the ice risk of different regions, obtaining the risk value of each region , the formula is as follows: , Where, is the ice inhomogeneity (i.e. the difference in ice thickness in different regions), is the ice density, reflecting the mass of the ice layer, is the carrying capacity of the tower, indicating the tolerance of the power grid structure, The historical failure frequency reflects the number of failures caused by icing in the past, A is a risk assessment model based on random forest, and the architecture is that: the input layer receives four key factors, including icing unevenness, icing density, tower bearing capacity and historical failure frequency, the middle layer is composed of multiple decision trees, each tree randomly selects a subset of features and samples for splitting during training to avoid overfitting, and the output layer generates the final risk value by averaging the prediction results of each tree , operation steps: extract four-dimensional input variables and corresponding actual failure event labels of each region from power grid historical data, construct a training data set, use the data set to train a random forest model, automatically learn the nonlinear contribution weight of each factor to the risk, input the input parameters of the new region into the trained model, and output the risk value of each region , realizing the quantification and objective evaluation of icing risk; After obtaining the icing risk values of different regions from historical icing data and risk assessment models, use unsupervised machine learning methods (such as K-means clustering or Gaussian mixture model) to model the distribution of risk values, automatically identify the natural division point of risk levels, and set threshold values W and O based on the optimal cluster number based on the contour coefficient to determine the objective statistical criteria; If the risk value < the preset threshold W, it is divided into a low-risk area; If the preset threshold W is less than or equal to the risk value < the preset threshold O, it is divided into a medium-risk area; If the risk value ≥ the preset threshold O, it is divided into a high-risk area, which seriously affects the power grid.

[0034] By using a multi-dimensional risk assessment model, the problem of ignoring the comprehensive influence of multiple factors in the prior art is solved. Based on the corrected icing thickness data, historical failure records and equipment bearing capacity, the unevenness of icing, the quality of ice layer, the tolerance of power grid structure and the failure frequency caused by icing in the past are considered, and the comprehensive evaluation can more accurately reflect the icing risk of different regions, especially in the face of complex terrain and weather conditions, to provide more predictable risk assessment results for the power grid. Through statistical analysis method, the distribution of risk values in different regions is analyzed, and reasonable risk division threshold is set, so as to provide specific emergency response strategies for regions with different risk levels. By quantifying the risk value and correlating it with the historical failure data of the power grid and the equipment bearing capacity, high-risk areas are accurately identified, and practical prevention and control suggestions are provided for power grid managers, avoiding the early warning lag caused by incomplete risk assessment in the prior art.

[0035] Further, triggering different levels of early warning measures based on the classification results includes triggering corresponding early warning measures through the decision system according to the risk assessment level, and the specific early warning measures include automatically triggering the heating device, load adjustment and strengthening the patrol measures when the ice thickness reaches the set critical value, starting the power grid protection strategy in advance in the high-risk area, implementing line load distribution, reducing the pressure of the power grid, or increasing the standby power; The early warning level setting includes notifying the power grid operator to perform routine monitoring and patrol, and starting the heating device if necessary, performing equipment inspection and maintenance in advance in the moderate risk, and starting the emergency power supply system in the severe risk, and immediately starting the emergency scheme, such as shutting down the power supply in the high-risk area, starting the standby line, and fully opening the heating device. Through risk level division and early warning measure implementation, different risk levels are flexibly responded to, and the influence of ice disaster on power grid operation is effectively reduced.

[0036] By classifying the icing disasters in different areas based on the risk assessment results, the problem of inaccurate and inflexible early warning measures in the prior art is solved. According to the ice thickness and the bearing capacity of the power grid, a three-level early warning strategy (light, moderate and severe risk) is adopted to divide different risk areas, so as to ensure accurate response to different risk levels, ensure stable operation of the power grid under extreme weather conditions, avoid large-scale failure, and through the grading early warning mechanism, the emergency response strategy of the power grid is flexibly adjusted according to the actual icing risk situation, the use efficiency of resources is improved, and the influence of icing disaster on the operation of the power grid is reduced. The implementation of risk assessment and emergency response is more refined and intelligent.

[0037] The embodiment also provides a power transmission line icing analysis system affected by microtopography and microclimate, which comprises: A data acquisition and fusion module is configured to acquire and fuse meteorological data from different sources to generate a precise microclimate data set; A microclimate field simulation and correction module is configured to calculate three-dimensional microclimate field data through a CFD model and provide accurate microclimate information by correcting the terrain; An icing growth simulation module is configured to simulate the growth rate and type of icing based on microclimate data and calculate the ice thickness of the conductor; A dynamic correction and real-time correction module is configured to dynamically adjust the icing growth model according to real-time meteorological observation data to ensure the accuracy of the simulation results; A risk assessment classification and early warning system module is configured to assess and classify the icing risk of each area based on the ice thickness and the bearing capacity of the power grid, and trigger different levels of early warning measures according to the risk assessment results.

[0038] The embodiment also provides a computer device suitable for the microtopography and microclimate influenced transmission line icing analysis method, and the computer device comprises a memory and a processor.

[0039] The computer device can be a terminal, and the computer device comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved through WIFI, an operator network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse.

[0040] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to implement the microtopography and microclimate influenced transmission line icing analysis method and system. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0041] To sum up, the application optimizes the data fusion process by the weighted regression method, uses the principal component analysis (PCA) and the cross-validation method to dynamically adjust the weight of each data source, improves the accuracy of the micro-meteorological data set, uses the CFD model to simulate the three-dimensional micro-meteorological field, and combines the terrain correction technology to accurately correct the meteorological variables, so that the application can respond to the complex terrain and meteorological changes in real time, provide more accurate icing simulation results, introduce the dynamic correction algorithm, compare the deviation of the real-time observation data and the model prediction value, optimize the key parameters of the icing growth model, so that under the sudden weather conditions, the model can be automatically adjusted, ensure the accuracy and timeliness of the simulation results, and can greatly improve the accuracy of the transmission line icing disaster risk assessment, provide more scientific and timely early warning and response measures for the operation of the power grid.

[0042] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for analyzing icing on transmission lines under the influence of micro-topography and micro-meteorology, characterized in that: include, Raw meteorological data of the area where the transmission line is located are collected and fused, and micro-meteorological field variables are generated based on the terrain parameters extracted from the DEM model; Based on fused data and micro-meteorological field variables, three-dimensional micro-meteorological field data are obtained through a fluid dynamics model. The terrain parameters obtained from the DEM model are used to correct the three-dimensional micro-meteorological field data, and the exponential weighted moving average method is used for adjustment to obtain accurate three-dimensional micro-meteorological field data. Based on precise micro-meteorological field data, the ice thickness is obtained by adopting a physical-empirical hybrid ice growth model. The output ice thickness is then adjusted in real time using a dynamic correction algorithm to obtain the final ice simulation result. Based on the final icing simulation results, a risk assessment model is used to classify the icing risk of different areas, and different levels of early warning measures are triggered based on the classification results.

2. The method for analyzing icing of transmission lines under the influence of micro-topography and micro-meteorology as described in claim 1, characterized in that: The process involves collecting and fusing raw meteorological data from the area where the transmission lines are located. Based on terrain parameters extracted from the DEM model, micro-meteorological field variables are generated by integrating collected remote sensing data, UAV monitoring data, meteorological station data, and historical icing data into a unified micro-meteorological dataset. A weighted regression method is then used to fuse the multi-source data, generating the fused micro-meteorological dataset. And calculate the actual solar shortwave radiation energy received per unit area of ​​the Earth's surface. .

3. The method for analyzing icing of transmission lines under the influence of micro-topography and micro-meteorology as described in claim 2, characterized in that: Based on fused data and micro-meteorological field variables, three-dimensional micro-meteorological field data is obtained through a CFD model. Topographic parameters obtained from a DEM model are used to correct the three-dimensional micro-meteorological field data, and an exponentially weighted moving average method is used for adjustment to obtain accurate three-dimensional micro-meteorological field data indicating the solar shortwave radiation energy. The data is input into the CFD model as thermal boundary conditions. The built-in steady-state RANS solver in the CFD model is then used to numerically solve the governing equations, yielding the wind speed field. and temperature and relative moisture Three-dimensional micro-meteorological field obtained based on CFD solution For each transmission tower location According to the slope of the location and slope and background wind direction To determine whether it is in a valley area, if >Preset threshold Q and located on the ridgeline and slope aspect With background wind direction If the included angle is less than 90°, then a ridge acceleration factor is applied to the wind speed output by the CFD. And update the temporary wind speed after correction based on ridge topography. ; If located in a closed valley, then utilize Calculate the difference in radiant heating between sunny and shady slopes for each tower location. ,like (i.e., in a sunlit area), the temperature value obtained is corrected for the topographic radiation effect. ,like ,but =0, retain the original CFD value; The terrain-corrected micro-meteorological field was used as a priori estimate, combined with the real-time wind speed data transmitted from the online monitoring equipment installed at the tower base. and temperature The optimal estimated wind speed value was obtained by using the exponentially weighted moving average (EWMA) method for dynamic deviation correction. ; Calculate the final temperature value , will be corrected and and relative moisture As a high-precision observation-simulation fusion result at the tower location, a high-precision three-dimensional micro-meteorological field dataset is output using the bilinear interpolation method.

4. The method for analyzing icing of transmission lines under the influence of micro-topography and micro-meteorology as described in claim 3, characterized in that: Based on precise micro-meteorological field data, a physical-empirical hybrid icing growth model is used to obtain the icing thickness index. A high-precision three-dimensional micro-meteorological field dataset is used as input data to drive the icing growth model, yielding the rate of change of icing mass over time. The rate of change of the mass of ice covering the conductor The rate of change is numerically integrated over each time step, and the increments of each time period are accumulated to obtain the total icing mass from the start of icing to the current time. The cross-sectional area is calculated using the original radius of the conductor, and it is assumed that the ice uniformly wraps the conductor to form a concentric cylinder. Combining the density of the ice, the volume of the ice layer is obtained from the total mass. Then, the equivalent outer diameter after icing is obtained according to the geometric relationship of the cylinder. The icing thickness is half the difference between the outer diameter and the original diameter of the conductor.

5. The method for analyzing icing of transmission lines under the influence of micro-topography and micro-meteorology as described in claim 4, characterized in that: The process involves adjusting the output icing thickness in real time using a dynamic correction algorithm to obtain the final icing simulation result. This result is based on the initial icing simulation result driven by a high-precision three-dimensional micro-meteorological field. Every hour, the system automatically accesses the measured wind speed, air temperature, humidity, precipitation intensity, and supercooled water droplet content observation data retrieved from remote sensing at the transmission tower base. The system compares the actual observed icing growth rate at the current moment with the model prediction value. If the deviation exceeds a preset threshold V, the dynamic correction mechanism is activated. This includes regional adaptive adjustment of key parameters in the icing growth model based on the deviation direction and local real-time meteorological conditions, and smoothing the parameter updates using an exponentially weighted moving average method. Finally, the system outputs the icing simulation result that has been dynamically corrected through real-time observation.

6. The method for analyzing icing of transmission lines under the influence of micro-topography and micro-meteorology as described in claim 5, characterized in that: Based on the final icing simulation results, a risk assessment model is used to classify the icing risk of different regions, obtaining corrected icing thickness data. This data, combined with historical fault records of the power grid and the load-bearing capacity of each device, is used as input data. Based on this input data, the risk assessment model is used to quantitatively assess the icing risk of different regions, obtaining a risk value for each region. If the risk value If the risk level is less than or equal to a preset threshold W, the area is classified as low-risk; if the preset threshold W is less than or equal to the risk value, the area is classified as low-risk. If the risk value is less than the preset threshold of 0, it is classified as a medium-risk area. If the value is greater than or equal to the preset threshold O, it is classified as a high-risk area.

7. The method for analyzing icing of transmission lines under the influence of micro-topography and micro-meteorology as described in claim 6, characterized in that: The aforementioned early warning measures triggered based on the graded results refer to triggering corresponding early warning measures through the decision-making system according to the risk assessment level. Specific early warning measures include automatically triggering heating devices, load regulation, and enhanced inspection measures when the icing thickness reaches a set critical value. In high-risk areas, power grid protection strategies can be activated in advance to implement line load distribution and reduce power grid pressure. The early warning level settings include: for mild risk, notifying power grid operators to conduct routine monitoring and inspections; for moderate risk, conducting equipment inspections and maintenance in advance and activating the emergency power supply system; and for severe risk, immediately activating the emergency plan.

8. A transmission line icing analysis system based on the micro-topography and micro-meteorological influences analysis method for transmission lines according to any one of claims 1 to 7, characterized in that: include, The data acquisition and fusion module is used to collect and fuse meteorological data from different sources to generate accurate micro-meteorological datasets; The micro-meteorological field simulation and correction module is used to calculate three-dimensional micro-meteorological field data through CFD models and correct it according to the terrain, providing accurate micro-meteorological information; The icing growth simulation module is used to simulate the growth rate and type of icing based on micrometeorological data and to calculate the icing thickness of the conductor. The dynamic correction and real-time calibration module is used to dynamically adjust the icing growth model based on real-time meteorological observation data to ensure the accuracy of the simulation results. The risk assessment and early warning system module is used to assess and classify the icing risk of each region based on the icing thickness and power grid carrying capacity, and trigger different levels of early warning measures according to the risk assessment results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the method for analyzing icing of transmission lines under the influence of micro-topography and micro-meteorology as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the method for analyzing icing of transmission lines under the influence of micro-topography and micro-meteorology as described in any one of claims 1 to 7.

Images