Icing forecast condition gridding optimization method and system

Abstract

The invention discloses an icing forecast condition gridding optimization method and system, and relates to the technical field of power system freezing disaster prediction, and the method comprises the steps: obtaining an icing terminal of a target region and the monitoring data of the icing terminal; acquiring historical meteorological elements, and interpolating the historical meteorological elements to the icing terminal to obtain a first data set; establishing a physical icing growth model based on the first data set; changing the icing condition of the physical icing growth model, and determining the optimal solution of the icing forecast condition parameters; according to the method, the rmse of the icing forecast can be relatively accurately reduced, the TS score of the icing forecast is improved, a scientific support is provided for the icing forecast of the power transmission line, and the method has important scientific significance and application value.

Description

Technical Field

[0001] The present invention relates to the technical field of power system ice disaster prediction, and particularly to a method and system for grid optimization of icing forecast conditions. Background Art

[0002] Under the background of global climate change, extreme weather events occur frequently, posing a serious threat to the national economy, especially energy infrastructure. Icing disasters on transmission lines are one of the most serious meteorological disasters in the power system. Especially in high-altitude and rainy and snowy areas, icing on conductors may lead to serious consequences such as line short circuits, wire breaks, and tower collapses, directly affecting the safe and stable operation of the power grid and even triggering large-scale power outages. In recent years, the economic losses caused by icing disasters have reached tens of billions of yuan, and disaster prevention and response have become one of the key issues in power system operation.

[0003] In order to reduce the losses caused by icing disasters, accurately predicting the icing thickness of conductors is a key link. The occurrence and development of conductor icing are closely related to changes in meteorological elements, mainly including meteorological conditions such as temperature, humidity, and wind speed. Existing icing prediction methods mainly rely on numerical prediction models. However, the atmosphere is a highly nonlinear complex system, and the accuracy of icing forecast results (measured by TS score) is extremely sensitive to the setting of icing forecast conditions. Although existing methods have improved the forecast accuracy to a certain extent, existing icing forecast models often rely on fixed forecast conditions (such as thresholds of temperature, humidity, etc.), but the setting of these conditions is not verified based on actual icing data. Due to the lack of dynamic adjustment of icing forecast conditions, the false alarm rate of forecast results is relatively high, especially in the case of complex changes in icing conditions; the error of icing forecast will gradually accumulate over time, especially in long-term sequence forecasts, small initial errors may be amplified, resulting in distorted final forecast results and unable to accurately reflect the actual icing situation; existing methods usually use fixed meteorological parameter thresholds for icing forecast, and do not perform grid-based parameter optimization for different regions and different meteorological conditions. This makes the adaptability of forecast results in different regions poor and it is difficult to achieve refined forecasting; due to the existence of forecast errors, existing icing forecast results cannot provide accurate guidance for anti-icing and de-icing work, which may lead to over-allocation or under-allocation of de-icing resources, increasing the de-icing cost and reducing the de-icing efficiency. At the same time, inaccurate resource scheduling will also affect the safe operation of the power grid. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed.

[0005] Therefore, the technical problem to be solved by the present invention is: how to solve the problems existing in the prior art, such as low accuracy of icing prediction, high false alarm rate, serious error accumulation, low resource utilization efficiency, and insufficient refinement of prediction, so as to provide more accurate and reliable prediction support for the anti-icing and de-icing work of the power system.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, an embodiment of the present invention provides a method for grid optimization of icing prediction conditions, including:

[0008] Obtain icing terminals in the target area and their monitoring data;

[0009] Obtain historical meteorological elements and interpolate them to the icing terminals to obtain a first data set;

[0010] Based on the first data set, establish a physical icing growth model;

[0011] Change the icing conditions of the physical icing growth model and determine the optimal solution of the icing prediction condition parameters.

[0012] As a preferred solution of the method for grid optimization of icing prediction conditions, wherein:

[0013] The changing of the icing conditions of the physical icing growth model and determining the optimal solution of the icing prediction condition parameters includes:

[0014] Adjust the icing condition parameters of the physical icing growth model, simulate different icing conditions, and search for the optimal parameter combination.

[0015] As a preferred solution of the method for grid optimization of icing prediction conditions, wherein:

[0016] The adjusting of the icing condition parameters of the physical icing growth model includes:

[0017] Set a first threshold, a second threshold, and a first fixed step size for the first parameter and divide the interval;

[0018] Set a third threshold, a fourth threshold, and a second fixed step size for the second parameter and divide the interval;

[0019] Set a fifth threshold, a sixth threshold, and a third fixed step size for the third parameter and divide the interval;

[0020] Generate a series of parameter combinations by combining different threshold intervals.

[0021] As a preferred solution of the method for grid optimization of icing prediction conditions, wherein:

[0022] The icing conditions for changing the physical icing growth model and determining the optimal solution of the icing forecast condition parameters further include:

[0023] For each parameter combination, use the physical icing growth model to predict the icing thickness at the icing end, and use weighted scoring to evaluate the accuracy of the prediction; determine the icing condition parameters with the highest weighted score as the optimal solution.

[0024] As an optimal scheme of the grid search optimization method for icing forecast conditions, wherein:

[0025] The historical meteorological elements include ground temperature, humidity, wind speed, liquid water content, air pressure, wet bulb temperature at a preset resolution, and wet bulb temperature and relative humidity at high altitude.

[0026] As an optimal scheme of the grid search optimization method for icing forecast conditions, wherein:

[0027] Establishing a physical icing growth model based on the first data set includes:

[0028] The calculation model of glaze icing thickness is expressed as:

[0029]

[0030] Wherein, is the ice thickness of glaze growth, Γ I and Γ T to R eq are weight coefficients;

[0031]

[0032]

[0033] Wherein, I u and I l are the upper and lower bounds of the ice content rate respectively, T u and T l are the upper and lower bounds of the wet bulb temperature.

[0034] As an optimal scheme of the grid search optimization method for icing forecast conditions, wherein:

[0035] Establishing a physical icing growth model based on the first data set further includes:

[0036] The calculation of rime icing thickness is expressed as:

[0037]

[0038] Wherein, is the mass growth rate of rime icing per unit time, a 1 is the collision rate, a2 is the capture rate, a 3 is the freezing rate, v is the effective particle velocity, w is the liquid water content, and S is the effective icing cross-section.

[0039] In a second aspect, an embodiment of the present invention provides an icing prediction condition grid optimization system, including:

[0040] An icing data acquisition module, configured to obtain icing terminals in a target area and their monitoring data;

[0041] A meteorological data interpolation module, configured to obtain historical meteorological elements and interpolate them to the icing terminals to obtain a first data set;

[0042] An icing model construction module, configured to establish a physical icing growth model based on the first data set;

[0043] A parameter optimization module, configured to change the icing conditions of the physical icing growth model and determine the optimal solution of the icing prediction condition parameters.

[0044] In a third aspect, an embodiment of the present invention provides a computing device, including:

[0045] A memory and a processor;

[0046] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the one or more programs are executed by the one or more processors, the one or more processors implement the icing prediction condition grid optimization method as described in any embodiment of the present invention.

[0047] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores computer-executable instructions that implement the icing prediction condition grid optimization method when executed by a processor.

[0048] Advantages of the present invention: By obtaining the cumulative icing value data of 1341 icing terminals during four cold snaps in the winter of 2023 and meteorological elements such as ground temperature, humidity, wind speed, liquid water content, air pressure, wet bulb temperature at a resolution of 2.5 km in the southern region provided by the China Public Service Center, and by changing the icing conditions of the physical icing growth model, the predicted icing thickness at the longitudes and latitudes of 1341 icing terminals can be predicted using the physical icing growth models of glaze and rime, which can more accurately reduce the RMSE of icing prediction, improve the TS score of icing prediction, provide scientific and technological support for the prediction of transmission line icing, and have important scientific significance and application value. Description of the Drawings

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0050] Figure 1 is the overall flowchart of the grid optimization method for icing prediction conditions of the present invention;

[0051] Figure 2 is the three-dimensional schematic diagram of CSI and RMSE under different icing prediction condition parameter settings in the simulation example of the grid optimization method for icing prediction conditions of the present invention;

[0052] Figure 3 is the schematic diagram of the change of CSI and RMSE of icing thickness with time in winter 2023 under the best icing prediction condition parameter settings in the simulation example of the grid optimization method for icing prediction conditions of the present invention. Specific Embodiments

[0053] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0055] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or selectively exclusive embodiment from other embodiments.

[0056] Embodiment 1

[0057] Refer to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a grid optimization method for icing prediction conditions, including:

[0058] S1: Obtain the icing terminals in the target area and their monitoring data;

[0059] S2: Obtain historical meteorological elements and interpolate them to the icing terminal to obtain the first data set;

[0060] S3: Based on the first data set, establish a physical icing growth model;

[0061] S4: Change the icing conditions of the physical icing growth model and determine the optimal solution of the icing forecast condition parameters.

[0062] It should be noted that by implementing steps S1 to S4, by obtaining high-resolution meteorological data and interpolating it to the specific location of the icing terminal, the local meteorological conditions can be more accurately reflected; establish a physical icing growth model, and improve the accuracy of icing thickness prediction by optimizing parameters; by adjusting and optimizing icing condition parameters such as rainfall, relative humidity, and liquid water content, the most suitable icing forecast conditions for the current environment can be found. Use a variety of evaluation indicators (such as TS score and RMSE) to comprehensively evaluate the performance of the model, ensuring that the model is not only accurate in prediction but also stable and reliable. Provide accurate icing forecasts for industries such as electricity and transportation, which helps to take anti-icing measures in advance, reduce losses, and ensure safety and efficiency.

[0063] Embodiment 2

[0064] Refer to Figure 1 , which is an embodiment of the present invention. Based on the previous embodiment, a method for grid optimization of icing forecast conditions is provided, including:

[0065] In the embodiment of the present application, obtaining the icing terminal and its monitoring data in the above step S1 includes:

[0066] Obtain the cumulative icing value data of 1341 icing terminals during four cold snaps in winter 2023, with the times being from 8:00 on December 16, 2023 to 8:00 on December 26, 2023, from 8:00 on January 20, 2024 to 8:00 on January 30, 2024, from 8:00 on February 2, 2024 to 8:00 on February 16, 2024, and from 8:00 on February 21, 2024 to 8:00 on March 2, 2024.

[0067] In another possible implementation manner, in addition to the data of the four cold snaps in winter 2023, icing data in other seasons (such as spring, autumn) can also be obtained to verify the applicability of the model under different climate conditions.

[0068] In another possible implementation manner, in addition to the cumulative icing value, the following data can also be collected:

[0069] Icing rate: The growth amount of icing thickness per unit time, used to analyze the dynamic changes of the icing process.

[0070] Ice type: Distinguish different types of icing such as glaze ice and rime ice to provide more detailed input for the model.

[0071] Icing duration: The start and end times of icing, used to analyze the time characteristics of icing.

[0072] In the embodiment of the present application, in the above step S2, historical meteorological elements are obtained and interpolated to the icing terminal to obtain the first data set including:

[0073] Obtain meteorological elements such as ground temperature, humidity, wind speed, liquid water content, air pressure, wet bulb temperature at high altitude, and relative humidity at high altitude with a resolution of 2.5 km in the southern region provided by the China Public Service Center, and interpolate them to the longitude and latitude of the above 1341 icing terminals using the bilinear interpolation method.

[0074] Specifically, bilinear interpolation represents the linear interpolation extension of an interpolation function containing two variables. The core idea of this method is to perform linear interpolation in two directions respectively.

[0075] To obtain the value of the unknown function f at the point P=(x, y), assume that the known function f is at Q 11 =(x 1 , y 1 ), Q 12 =(x 1 , y 2 ), Q 21 =(x 2 , y 1 ) and Q 22 =(x 2 , y 2 ) at four points.

[0076] First, perform linear interpolation in the x direction to obtain Equation (1) and Equation (2), and then perform linear interpolation in the y direction to obtain Equation (3), so as to obtain the result of the function f(x, y), as shown in Equation (4).

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] It should be noted that the first data set is a data set containing detailed meteorological element data of 1341 ice-covered terminals, and these data are obtained by bilinear interpolation from the original meteorological data with a resolution of 2.5 km to the specific locations of each ice-covered terminal.

[0083] In another possible implementation, the first data set can not only be data interpolated based on historical meteorological elements, but also include the following possible data sources:

[0084] Real-time meteorological data: Meteorological data collected in real time through meteorological sensors or meteorological satellites, such as temperature, humidity, wind speed, wind direction, rainfall, etc.

[0085] Remote sensing data: High-resolution image data of the ice-covered area obtained by using remote sensing technologies (such as satellite remote sensing, UAV remote sensing), and the ice thickness and distribution information are extracted by combining image processing technologies.

[0086] Numerical weather prediction (NWP) data:

[0087] Future meteorological element data generated by numerical weather prediction models, used to predict meteorological conditions in the future for a period of time.

[0088] In another possible implementation, meteorological data with a higher resolution (such as 1 km resolution) can also be obtained to improve the accuracy of interpolation.

[0089] In another possible implementation, in addition to bilinear interpolation, the following methods can also be tried:

[0090] Kriging interpolation: A statistical-based interpolation method suitable for data with large spatial variability.

[0091] Spline Interpolation: Interpolation by fitting a smooth curve, suitable for data with high continuity requirements.

[0092] Inverse Distance Weighted (IDW) interpolation: Weighted according to the reciprocal of the distance, suitable for data with large local variations.

[0093] In the embodiment of the present application, establishing a physical ice accretion growth model based on the first data set in step S3 above includes:

[0094] Specifically, the calculation model of glaze ice accretion thickness is expressed as:

[0095]

[0096] Wherein, is the ice thickness of glaze ice growth, Γ I and Γ T to Req is the weight coefficient.

[0097]

[0098]

[0099] Where, I u and I l are the upper and lower bounds of the ice content, which are 0.85 and 0.04 respectively, and T u and T l are the upper and lower bounds of the wet-bulb temperature, which are 1°C and 0°C respectively.

[0100] The calculation of the rime icing thickness is expressed as:

[0101]

[0102] Where, is the mass growth rate of rime icing per unit time, a 1 is the collision rate, a 2 is the capture rate, a 3 is the freezing rate, v is the effective particle velocity, which is the wind speed, w is the liquid water content (LWC), and S is the effective ice accumulation cross-section.

[0103] The collision rate a 1 is expressed as:

[0104] a 1 = A - 0.028 - C(B - 0.0454)(10)

[0105] Where:

[0106] A = 1.066K -0.00616 exp(-1.103K -0.688 )(11)

[0107] B = 3.614K 0.498 exp(-1.497K -0.694 )(12)

[0108]

[0109] Where, is a dimensionless parameter, expressed as:

[0110] K = ρ w MVD 2 / 9μD(14)

[0111]

[0112] Re = ρ a MVDv / μ(16)

[0113] where Re is the Reynolds number, D is the wire diameter, and ρ w is the density of water (1000 kg / m 3 ), ρ a is the air density, v is the wind speed, and the absolute viscosity μ of air is 1.7984×10 -5 kg / (m·s). The collection rate a 2 and the freezing rate a 3 are both 1.

[0114] In the embodiment of the present application, changing the icing conditions of the physical icing growth model and determining the optimal solution of the icing forecast condition parameters in the above step S4 includes:

[0115] Adjusting the icing condition parameters of the physical icing growth model, specifically including rainfall, relative humidity, liquid water content, etc., to find the optimal parameter combination. By changing the thresholds of these parameters, different icing conditions are simulated.

[0116] Specifically, set the first threshold, the second threshold, and the first fixed step size for the first parameter and divide the interval;

[0117] Set the third threshold, the fourth threshold, and the second fixed step size for the second parameter and divide the interval;

[0118] Set the fifth threshold, the sixth threshold, and the third fixed step size for the third parameter and divide the interval;

[0119] By combining different threshold intervals, a series of parameter combinations are generated.

[0120] Exemplarily, define the following thresholds:

[0121] Rainfall: the first threshold = 0.1 mm, the second threshold = 100 mm, and the step size = 10 mm.

[0122] Relative humidity: the third threshold = 30%, the fourth threshold = 90%, and the step size = 10%.

[0123] Liquid water content: the fifth threshold = 0.005, the sixth threshold = 0.04, and the step size = 0.005.

[0124] The generated parameter combinations may include:

[0125] Rainfall = 0.1 mm, relative humidity = 30%, liquid water content = 0.005.

[0126] Rainfall = 0.1 mm, relative humidity = 40%, liquid water content = 0.010.

[0127] Rainfall = 10 mm, relative humidity = 50%, liquid water content = 0.015.

[0128] For each parameter combination, the physical ice accretion growth model is used to predict the ice thickness at the ice-covered terminal, and the TS (Threat Score) scoring and RMSE (Root Mean Square Error) scoring are used to evaluate the accuracy of the prediction.

[0129] To comprehensively consider the two indicators of TS and RMSE, they are standardized and weighted and summed to obtain a comprehensive weighted score.

[0130] Specifically, first standardize the TS score to the range of 0-1, then take the reciprocal of RMSE and also standardize it to the range of 0-1, and finally multiply each of them by a weight of 0.5 and add them to obtain the final weighted score. The formula is expressed as:

[0131] Best Weighted Score = 0.5 * scale(TS) + 0.5 * scale(1 / RMSE)

[0132] Among them, the scale() function represents the standardization process of mapping the original score to the range of 0-1.

[0133] By comparing the weighted scores under different parameter combinations, determine which set of ice accretion condition parameters can give the highest weighted score, and thus consider this set of parameters as the optimal solution.

[0134] In another possible implementation, based on TS and RMSE, the following evaluation indicators can also be added:

[0135] Correlation Coefficient: Measures the linear correlation between the predicted value and the actual value.

[0136] Mean Absolute Error (MAE): Measures the average absolute deviation between the predicted value and the actual value.

[0137] Coverage Rate: Measures the proportion of the predicted value falling within a certain range of the actual value.

[0138] Example 3

[0139] The above is a schematic solution of the grid optimization method for ice accretion prediction conditions in this embodiment. It should be noted that the technical solution of the grid optimization system for ice accretion prediction conditions belongs to the same concept as the technical solution of the above grid optimization method for ice accretion prediction conditions. For the details not described in detail in the technical solution of the grid optimization system for ice accretion prediction conditions in this embodiment, reference can be made to the description of the technical solution of the above grid optimization method for ice accretion prediction conditions.

[0140] This embodiment also provides a system based on the grid optimization method for icing prediction conditions, including:

[0141] An icing data acquisition module, configured to obtain icing terminals and their monitoring data in a target area;

[0142] A meteorological data interpolation module, configured to obtain historical meteorological elements and interpolate them to the icing terminals to obtain a first data set;

[0143] An icing model construction module, configured to establish a physical icing growth model based on the first data set;

[0144] A parameter optimization module, configured to change the icing conditions of the physical icing growth model and determine the optimal solution of the icing prediction condition parameters.

[0145] This embodiment also provides a computing device applicable to the grid optimization method for icing prediction conditions, including:

[0146] A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the grid optimization method for icing prediction conditions as proposed in the above embodiment.

[0147] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the grid optimization method for icing prediction conditions as proposed in the above embodiment.

[0148] The storage medium proposed in this embodiment and the grid optimization method for icing prediction conditions proposed in the above embodiment belong to the same inventive concept. Technical details not described in detail in this embodiment can be referred to in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0149] Embodiment 4

[0150] Referring to Figures 1 - 3 , as an embodiment of the present invention, a grid optimization method for icing prediction conditions is provided. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0151] Obtain the cumulative ice accretion value data of 1341 ice-covered terminals during four cold snaps in winter 2023, with the time periods being from 8:00 on December 16, 2023 to 8:00 on December 26, 2023, from 8:00 on January 20, 2024 to 8:00 on January 30, 2024, from 8:00 on February 2, 2024 to 8:00 on February 16, 2024, and from 8:00 on February 21, 2024 to 8:00 on March 2, 2024; Obtain the meteorological elements of ground temperature, humidity, wind speed, liquid water content, air pressure, wet bulb temperature on the ground and wet bulb temperature and relative humidity at high altitude with a 2.5 km resolution in the southern region provided by the China Public Service Center, and interpolate them to the latitudes and longitudes of the above 1341 ice-covered terminals using the bilinear interpolation method; Change the ice accretion conditions of the physical ice accretion growth model, adjust the rainfall (less than 0.1 mm, less than 100 mm), relative humidity (greater than 30% - greater than 90%, every 10%), and liquid water content (greater than 0.005 - greater than 0.04, every 0.005), and find the optimal parameters. Use the above different ice accretion conditions to predict the predicted ice accretion thickness at the latitudes and longitudes of 1341 ice-covered terminals using the physical ice accretion growth models of glaze ice and rime ice; Use the weighted normalization of the TS (CSI) score and the RMSE score to obtain a weighted score, and finally evaluate which composition of ice accretion conditions has the highest weighted score value.

[0152] The specific steps are as follows:

[0153] Step A: Obtain the cumulative ice accretion value data of 1341 ice-covered terminals during four cold snaps in winter 2023, with the time periods being from 8:00 on December 16, 2023 to 8:00 on December 26, 2023, from 8:00 on January 20, 2024 to 8:00 on January 30, 2024, from 8:00 on February 2, 2024 to 8:00 on February 16, 2024, and from 8:00 on February 21, 2024 to 8:00 on March 2, 2024;

[0154] Step B: Obtain the meteorological elements of ground temperature, humidity, wind speed, liquid water content, air pressure, wet bulb temperature on the ground and wet bulb temperature and relative humidity at high altitude with a 2.5 km resolution in the southern region provided by the China Public Service Center, and interpolate them to the latitudes and longitudes of the above 1341 ice-covered terminals using the bilinear interpolation method;

[0155] Step C: Change the ice accretion conditions of the physical ice accretion growth model, adjust the rainfall (less than 0.1 mm, less than 100 mm), relative humidity (greater than 30% - greater than 90%, every 10%), and liquid water content (greater than 0.005 - greater than 0.04, every 0.005), and find the optimal parameters;

[0156] Step D: Use the above different ice accretion conditions to predict the predicted ice accretion thickness at the latitudes and longitudes of 1341 ice-covered terminals using the physical ice accretion growth models of glaze ice and rime ice;

[0157] Step E: Use the weighted normalization of the TS(CSI) score and the RMSE score to obtain a weighted score, and finally evaluate which composition of icing conditions has the highest weighted score.

[0158] Figure 2 The three-dimensional schematic diagrams of CSI and RMSE under different icing condition parameter settings are shown. It can be clearly seen from the figure that as the rainfall, relative humidity, and liquid water content change, the values of CSI and RMSE show different trends. By observing these trends, it can be preliminarily judged which parameter combinations can result in a higher CSI score and a lower RMSE score, that is, a better prediction effect.

[0159] Figure 3 The changes of CSI and RMSE of the icing thickness over time in winter 2023 under the optimal icing condition parameter settings are shown. It can be seen from the figure that under the optimal parameter combination, the CSI score always remains at a high level, while the RMSE score remains at a low level, indicating that the icing prediction under this parameter combination has high accuracy and small errors.

[0160] In summary, the method provided by the embodiments of the present invention can relatively accurately reduce the RMSE of the icing prediction, improve the TS score of the icing prediction, provide scientific and technological support for the prediction of transmission line icing, and has important scientific significance and application value.

[0161] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A grid optimization method for ice cover forecast conditions, characterized in that: include: Obtain ice-covered terminals and their monitoring data in the target area; Acquire historical meteorological elements and interpolate them to the ice-covered terminal to obtain a first data set; Based on the first data set, a physical ice growth model is established; Change the icing conditions of the physical icing growth model and determine the optimal solution for the icing forecast condition parameters.

2. The grid optimization method for ice cover forecast conditions according to claim 1, characterized in that: The step of changing the icing condition of the physical icing growth model and determining the optimal solution of the icing forecast condition parameters includes: Adjust the icing condition parameters of the physical icing growth model and simulate different icing conditions to find the optimal parameter combination.

3. The grid optimization method for ice cover forecast conditions according to claim 2, characterized in that: The icing condition parameters for adjusting the physical icing growth model include: Setting a first threshold, a second threshold, and a first fixed step size for the first parameter and dividing the intervals; Setting a third threshold, a fourth threshold and a second fixed step size for the second parameter and dividing the intervals; Setting a fifth threshold, a sixth threshold and a third fixed step length for the third parameter and dividing the intervals; By combining different threshold intervals, a series of parameter combinations are generated.

4. The grid optimization method for ice cover forecast conditions according to claim 3, characterized in that: The step of changing the icing conditions of the physical icing growth model and determining the optimal solution of the icing forecast condition parameters also includes: For each parameter combination, the physical icing growth model is used to predict the ice thickness at the icing terminal, and the weighted score is used to evaluate the accuracy of the forecast; the icing condition parameters with the highest weighted score are determined to be the optimal solution.

5. The grid optimization method for ice cover forecast conditions according to claim 4, characterized in that: The historical meteorological elements include ground temperature, humidity, wind speed, liquid water content, air pressure, wet-bulb temperature, and high-altitude wet-bulb temperature and relative humidity at a preset resolution.

6. The grid optimization method for ice cover forecast conditions according to claim 5, characterized in that: The establishing of a physical ice growth model based on the first data set includes: The calculation model of rime ice thickness is expressed as: in, The thickness of ice increased by rime, Γ I and Γ T R eq is the weight coefficient; Among them, I u and I l are the upper and lower bounds of ice content, T u and T l The upper and lower bounds of the wet bulb temperature.

7. The grid optimization method for ice cover forecast conditions according to claim 6, characterized in that: The establishing of the physical ice growth model based on the first data set further comprises: The calculation of rime ice thickness is expressed as: in, is the mass growth of rime ice per unit time, a1 is the collision rate, a2 is the capture rate, a3 is the freezing rate, v is the effective particle velocity, w is the liquid water content, and S is the effective ice accumulation cross section.

8. A system using the gridding optimization method for ice cover forecast conditions as claimed in any one of claims 1 to 7, characterized in that: include: Ice cover data collection module, used to obtain ice cover terminals and monitoring data in the target area; A meteorological data interpolation module, used for acquiring historical meteorological elements and interpolating them to the ice-covered terminal to obtain a first data set; An ice cover model building module, used to build a physical ice cover growth model based on the first data set; The parameter optimization module is used to change the icing conditions of the physical icing growth model and determine the optimal solution of the icing forecast condition parameters.

9. A computing device comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the gridding optimization method for ice forecast conditions described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the gridding optimization method for ice cover forecast conditions as described in any one of claims 1 to 7.

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