Method and system for predicting wind turbine blade icing thickness from freezing fog

Abstract

The embodiment of the application provides a kind of wind turbine blade frozen fog icing thickness prediction method and system.The method comprises: obtaining the environmental parameter of the wind turbine to be predicted, according to the environmental parameter, the wind farm where the wind turbine to be predicted is located is divided into equidistance grid, to determine the target meteorological boundary field condition and target initial field condition of equidistance grid;Target meteorological boundary field condition and target initial field condition are input to weather prediction model, and the meteorological prediction data of each preset time point in future preset time period is output;According to meteorological prediction data, determine the frozen fog icing thickness change parameter of wind turbine to be predicted at each preset time point in future preset time period;The current frozen fog icing thickness of the blade of wind turbine to be predicted is obtained;According to current frozen fog icing thickness and the frozen fog icing thickness change parameter of each preset time point, determine the frozen fog icing thickness of each preset time point in future preset time period.

Description

Technical Field

[0001] This application relates to the field of wind farm icing monitoring, specifically to a method, system, processor, and storage medium for predicting the thickness of freezing fog icing on wind turbine blades. Background Technology

[0002] As the proportion of wind power generation increases, widespread grid disconnection and shutdown of wind turbines due to blade icing often leads to power supply crises. In complex terrains such as high mountains, wind turbine shutdowns caused by freezing fog and icing are frequent occurrences.

[0003] Current technologies for predicting icing conditions on wind turbine blades rely on real-time data, providing predictions within a few hours. This often leaves insufficient time for power supply systems to adjust, leading to power shortages. Therefore, it is necessary to improve existing methods for predicting wind turbine icing conditions to provide longer-term predictions of freezing fog and icing thickness, thus addressing power supply crises caused by large-scale wind turbine icing shutdowns. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, processor, and storage medium for predicting the thickness of freezing fog icing on wind turbine blades.

[0005] To achieve the above objectives, the first aspect of this application provides a method for predicting the thickness of freezing fog icing on wind turbine blades, comprising:

[0006] The environmental parameters of the wind turbine generator to be predicted are obtained. The environmental parameters include the location information of the wind turbine generator to be predicted, the spacing between adjacent units, the terrain elevation data and the land use data.

[0007] Based on location information and spacing, the wind farm where the wind turbine generator to be predicted is located is divided into equidistant grids;

[0008] Topographic elevation data and land use data are interpolated to an equidistant grid to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grid.

[0009] The target meteorological boundary field conditions and target initial field conditions are input into the weather prediction model so that the weather prediction model can output meteorological prediction data for each preset time point within a future preset time period.

[0010] Based on meteorological forecast data, determine the parameters of freezing fog and ice thickness variation at each preset time point within a future preset time period for the wind turbine generator to be predicted.

[0011] Obtain the current freezing fog and icing thickness of the wind turbine blades to be predicted;

[0012] The freezing fog and ice thickness at each preset time point within the future preset time period is determined based on the current freezing fog and ice thickness and the freezing fog and ice thickness change parameters at each preset time point.

[0013] In this embodiment of the application, topographic elevation data and land use data are interpolated to an equidistant grid to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grid. This includes: interpolating topographic elevation data and land use data to an equidistant grid to determine the lower boundary conditions of the equidistant grid for predicting the thickness of freezing fog and ice; obtaining initial field data for predicting the thickness of freezing fog and ice to determine the initial meteorological boundary field conditions and initial values ​​of the initial field conditions for the numerical calculation model of freezing fog and ice thickness based on the initial field data; interpolating the initial field data to an equidistant grid and adjusting the interpolation according to the lower boundary conditions to obtain the target meteorological boundary field conditions and target initial field conditions driving the numerical calculation.

[0014] In this embodiment of the application, the method further includes: determining the hub temperature, relative humidity, and liquid water content of the wind turbine generator set to be predicted at a future prediction time point; determining that the freezing fog icing thickness is in an increasing state when the hub temperature is lower than a first temperature, the relative humidity is higher than a reference relative humidity, and the liquid water content is higher than a reference liquid water content; and determining that the freezing fog icing thickness is in a non-increasing state when the hub temperature is higher than the first temperature, and / or the relative humidity is lower than the reference relative humidity, and / or the liquid water content is lower than the reference liquid water content.

[0015] In this embodiment of the application, the method further includes: when the thickness of the freezing fog icing at a future predicted time point is increasing, calculating the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period according to formula (1):

[0016]

[0017] in, ε is the rate of increase in the thickness of the frozen fog icing per unit time, β is the freezing efficiency coefficient of the frozen fog icing, q is the liquid water content, and ρ is the freezing efficiency coefficient of the frozen fog icing. i Let P be the standard ice density, P be the air pressure at the hub height of the wind turbine generator to be predicted, R be the ideal gas constant, T be the hub temperature of the wind turbine generator to be predicted, and U be the standard ice density. ∞ Let α be the inflow wind speed of the wind turbine generator to be predicted, and α be the angle between the inflow wind speed and the blade of the wind turbine generator to be predicted.

[0018] In this embodiment, the non-growth state includes a reduction state and a zero-change state. The reduction state includes a melting state, a sublimation state, and a detachment state. The method further includes: determining that the freezing fog icing thickness is in a melting state when the wheel hub temperature is greater than a first temperature and less than a second temperature, and the relative humidity is less than a reference relative humidity; determining that the freezing fog icing thickness is in a sublimation state when the wheel hub temperature is less than or equal to the first temperature, and the relative humidity is less than a reference relative humidity; determining that the freezing fog icing thickness is in a detachment state when the wheel hub temperature is greater than the second temperature; determining the freezing fog icing thickness at each preset time point within a future preset time period based on the current freezing fog icing thickness and the freezing fog icing thickness change parameters at each preset time point includes: determining the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period using a calculation formula corresponding to the freezing fog icing thickness change parameters; and determining the freezing fog icing thickness at each preset time point within a future preset time period based on the current freezing fog icing thickness and the growth rate.

[0019] In this embodiment of the application, determining the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period using a calculation formula corresponding to the freezing fog icing thickness change parameter includes: when it is determined that the freezing fog icing thickness is in a melting state, calculating the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period according to formula (2):

[0020]

[0021] in, η1 is the growth rate of the freezing fog icing thickness per unit time, E1 is the efficiency coefficient determined based on the airfoil characteristics of the wind turbine blades to be predicted, and E1 is the melting rate. Assuming the freezing fog icing thickness is in a sublimation state, the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period is calculated according to formula (3):

[0022]

[0023] Where D is the diffusion coefficient of water vapor in air, f v ρ is the ventilation coefficient for water vapor. s (T) represents the saturated vapor density at ambient air temperature T, ρ i For standard ice density, L S The latent heat of sublimation is given by k, where k is the thermal conductivity of air, and f is the thermal conductivity of air. h The ventilation coefficient for heat, ρ′ s For ρ s The derivative of (T). Given that the freezing fog icing thickness is in a state of detachment, the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period is calculated according to formula (4):

[0024]

[0025] Where η2 is the efficiency coefficient determined based on the airfoil characteristics of the wind turbine blades to be predicted, and E2 is the shedding rate.

[0026] In this embodiment of the application, the calculation formula corresponding to the change parameter of the freezing fog icing thickness is used to determine the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period, including: when the freezing fog icing thickness is determined to be in a non-growing state based on the hub temperature, relative humidity, and liquid water content, and is not in any of the melting, sublimation, or shedding states, the freezing fog icing thickness is determined to be in a zero-change state; when the freezing fog icing thickness is determined to be in a zero-change state, the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period is calculated according to formula (5):

[0027]

[0028] in, This represents the rate of increase in the thickness of the freezing fog icing layer per unit time.

[0029] A second aspect of this application provides a processor configured to perform the above-described method for predicting the thickness of freezing fog icing on wind turbine blades.

[0030] A third aspect of this application provides a system for predicting the thickness of freezing fog icing on wind turbine blades, comprising:

[0031] The data collection module is used to acquire environmental parameters of the wind turbine generator to be predicted. These environmental parameters include the location information of the wind turbine generator, the distance between adjacent generators, terrain elevation data, and land use data.

[0032] The data grid partitioning module is used to divide the wind farm where the wind turbine generator to be predicted is located into equidistant grids based on location information and interval distance.

[0033] The data preprocessing module is used to interpolate topographic elevation data and land use data to an equidistant grid to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grid.

[0034] The numerical calculation module is used to input the target meteorological boundary field conditions and the target initial field conditions into the weather prediction model, so as to output the meteorological prediction data for each preset time point within the future preset time period through the weather prediction model.

[0035] The freezing fog and icing thickness calculation module is used to determine the freezing fog and icing thickness change parameters of the wind turbine generator to be predicted at each preset time point within a future preset time period based on meteorological forecast data; and to determine the freezing fog and icing thickness at each preset time point within a future preset time period based on the current freezing fog and icing thickness and the freezing fog and icing thickness change parameters at each preset time point.

[0036] A fourth aspect of this application provides a machine-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the aforementioned method for predicting the thickness of freezing fog accretion on wind turbine blades.

[0037] Using the aforementioned method, system, processor, and storage medium for predicting the thickness of freezing fog and icing on wind turbine blades, the environmental parameters of the wind turbine to be predicted are obtained. These parameters include the location information of the wind turbine, the distance between adjacent turbines, terrain elevation data, and land use data. Based on the location information and distance, the wind farm where the wind turbine is located is divided into equidistant grids. The terrain elevation data and land use data are interpolated to the equidistant grids to determine the target meteorological boundary field conditions and target initial field conditions. The target meteorological boundary field conditions and target initial field conditions are input into a weather prediction model to output meteorological prediction data for each preset time point within a future preset time period. Based on the meteorological prediction data, the parameters for the change in freezing fog and icing thickness of the wind turbine blades within the future preset time period are determined. The current freezing fog and icing thickness of the wind turbine blades is obtained. Based on the current freezing fog and icing thickness and the parameters for the change in freezing fog and icing thickness at each preset time point, the freezing fog and icing thickness at each preset time point within the future preset time period is determined. Using the above method, the thickness of freezing fog and ice on the blades of wind turbine generators can be obtained for a preset number of days in the future. When the thickness of freezing fog and ice on the blades exceeds the warning value at a certain point in the future, the power supply system can make timely adjustments to ensure a stable power supply.

[0038] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0039] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0040] Figure 1 The schematic diagram illustrates a flowchart of a method for predicting the thickness of freezing fog icing on wind turbine blades according to an embodiment of this application;

[0041] Figure 2 The diagram illustrates the variation of freezing fog icing thickness over time according to an embodiment of this application.

[0042] Figure 3 This schematic diagram illustrates the structural block diagram of a system for predicting the thickness of freezing fog icing on wind turbine blades according to an embodiment of this application.

[0043] Figure 4 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0045] Figure 1 The illustration schematically shows a flowchart of a method for predicting the thickness of freezing fog icing on wind turbine blades according to an embodiment of this application. Figure 1 As shown in one embodiment of this application, a method for predicting the thickness of freezing fog icing on wind turbine blades is provided, comprising the following steps:

[0046] Step 102: Obtain the environmental parameters of the wind turbine generator to be predicted. The environmental parameters include the location information of the wind turbine generator to be predicted, the distance between adjacent generators, the terrain elevation data, and the land use data.

[0047] Step 104: Divide the wind farm where the wind turbine generator to be predicted is located into an equidistant grid according to the location information and the interval distance.

[0048] Step 106: Interpolate the topographic elevation data and land use data to an equidistant grid to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grid.

[0049] Step 108: Input the target meteorological boundary field conditions and the target initial field conditions into the weather prediction model, so as to output the meteorological prediction data for each preset time point within the future preset time period through the weather prediction model.

[0050] Step 110: Determine the parameters of the freezing fog and ice thickness change of the wind turbine generator set to be predicted at each preset time point in the future preset time period based on meteorological forecast data.

[0051] Step 112: Obtain the current freezing fog and icing thickness of the wind turbine blades to be predicted.

[0052] Step 114: Determine the freezing fog thickness at each preset time point within the future preset time period based on the current freezing fog thickness and the freezing fog thickness change parameters at each preset time point.

[0053] Frost fog icing refers to the phenomenon where small-diameter supercooled water droplets float with airflow and freeze on objects, forming frost or a mixture of frost and fog. To obtain the frost fog icing thickness on wind turbine blades at a predetermined time point within a future timeframe, the following steps are taken: First, the current environmental parameters of the wind turbines are acquired. These parameters include the location of the wind turbines, the distance between adjacent turbines, terrain elevation data, and land use data. Location information refers to the latitude and longitude coordinates of the wind turbines; terrain elevation is the altitude of the wind turbines to be predicted; and land use data indicates the land use of the area where the wind turbines are located (whether it is sandy, vegetated, or urban), which affects the air humidity at the location of the wind turbines. Then, based on the location information and distance, the wind farm containing the wind turbines is divided into equidistant grids. For example, in a wind farm where the distance between adjacent wind turbines is 900 meters, the equidistant grid is set to a 30m x 30m square grid to ensure that each wind turbine is in an independent grid. Subsequently, topographic elevation data and land use data are interpolated to an equidistant grid to determine the target meteorological boundary field conditions and target initial field conditions for the equidistant grid. Further, the target meteorological boundary field conditions and target initial field conditions are input into a weather prediction model to output meteorological prediction data for each preset time point within a future preset time period. This meteorological prediction data includes the temperature, humidity, liquid water content, wind speed, and wind direction at the location of the wind turbine to be predicted at future time points. Then, based on the meteorological prediction data, the parameters for the variation of freezing fog and icing thickness of the wind turbine to be predicted at each preset time point within the future preset time period are determined. After knowing the meteorological prediction data for each time point within the future preset time period, the variation of freezing fog and icing thickness on the wind turbine blades can be obtained, including whether the freezing fog and icing thickness increases, decreases, or remains unchanged, and the rate of change. Subsequently, the current freezing fog and icing thickness of the wind turbine blades to be predicted is obtained. Finally, based on the current freezing fog and icing thickness and the parameters for the variation of freezing fog and icing thickness at each preset time point, the freezing fog and icing thickness at each preset time point within the future preset time period is determined. After obtaining the variation parameters of the freezing fog icing thickness of the wind turbine blades at a preset time point within a future preset time period, the variation parameters are integrated to obtain the amount of freezing fog icing increase within the future preset time period. This amount is then added to the current freezing fog icing thickness of the wind turbine blades to obtain the freezing fog icing thickness of the wind turbine blades at the preset time point within the future preset time period.

[0054] Using the above method, the thickness of freezing fog and ice on wind turbine blades can be obtained over a relatively long period of time. When the thickness of freezing fog and ice on the blades exceeds the warning value at a certain point in the future, the power supply system can make timely adjustments to ensure a stable power supply.

[0055] In one embodiment, topographic elevation data and land use data are interpolated to an equidistant grid to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grid. This includes: interpolating topographic elevation data and land use data to an equidistant grid to determine the lower boundary conditions of the equidistant grid for predicting freezing fog and icing thickness; acquiring initial field data for predicting freezing fog and icing thickness to determine the initial meteorological boundary field conditions and initial values ​​of the initial field conditions for the numerical calculation model of freezing fog and icing thickness based on the initial field data; interpolating the initial field data to an equidistant grid and adjusting the interpolation according to the lower boundary conditions to obtain the target meteorological boundary field conditions and target initial field conditions driving the numerical calculation; and interpolating topographic elevation data into the value grid, adjusting the topographic elevation data within the grid when there is a difference between the topographic elevation data within the grid and the actual measured data to ensure prediction accuracy.

[0056] In one embodiment, the method further includes: determining the hub temperature, relative humidity, and liquid water content of the wind turbine generator at a future prediction time point; determining that the freezing fog icing thickness is increasing when the hub temperature is lower than a first temperature, the relative humidity is higher than a reference relative humidity, and the liquid water content is higher than a reference liquid water content; and determining that the freezing fog icing thickness is not increasing when the hub temperature is higher than the first temperature, and / or the relative humidity is lower than the reference relative humidity, and / or the liquid water content is lower than the reference liquid water content. Relative humidity refers to the percentage of water vapor pressure in the air to the saturated water vapor pressure at the same temperature. Liquid water content refers to the density of liquid water in the air, i.e., the mass of liquid water in 1 cubic meter of air. The first temperature refers to the reference temperature for freezing fog icing. When the hub temperature, relative humidity, and liquid water content are the three standard parameters that cause freezing fog to transform into icing, the freezing fog icing thickness is determined to be increasing when the hub temperature is lower than the first temperature, the relative humidity is higher than the reference relative humidity, and the liquid water content is higher than the reference liquid water content. Among the three standard parameters, the freezing fog icing thickness is determined to be in a non-growing state when the hub temperature is greater than the first temperature, and / or the relative humidity is less than the reference relative humidity, and / or the liquid water content is less than the reference liquid water content. The growing and non-growing states are used to measure the change in freezing fog icing.

[0057] In one embodiment, the method further includes: if the thickness of the freezing fog icing at a future predicted time point is increasing, calculating the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period according to formula (1):

[0058]

[0059] in, ε is the rate of increase in the thickness of the frozen fog icing per unit time, β is the freezing efficiency coefficient of the frozen fog icing, q is the liquid water content, and ρ is the freezing efficiency coefficient of the frozen fog icing. iLet P be the standard ice density, P be the air pressure at the hub height of the wind turbine generator to be predicted, R be the ideal gas constant, T be the hub temperature of the wind turbine generator to be predicted, and U be the standard ice density. ∞ Let α be the inflow wind speed of the wind turbine generator to be predicted, and α be the angle between the inflow wind speed and the blade of the wind turbine generator to be predicted.

[0060] In one embodiment, the non-growth state includes a reduction state and a zero-change state. The reduction state includes a melting state, a sublimation state, and a detachment state. The method further includes: determining that the freezing fog icing thickness is in a melting state when the hub temperature is greater than a first temperature and less than a second temperature, and the relative humidity is less than a reference relative humidity; determining that the freezing fog icing thickness is in a sublimation state when the hub temperature is less than or equal to the first temperature, and the relative humidity is less than a reference relative humidity; and determining that the freezing fog icing thickness is in a detachment state when the hub temperature is greater than the second temperature. Determining the freezing fog icing thickness at each preset time point within a future preset time period based on the current freezing fog icing thickness and the freezing fog icing thickness change parameters at each preset time point includes: determining the growth rate of the freezing fog icing thickness at each preset time point within the future preset time period using a calculation formula corresponding to the freezing fog icing thickness change parameters; and determining the freezing fog icing thickness at each preset time point within the future preset time period based on the current freezing fog icing thickness and the growth rate. The second temperature refers to the preset temperature value at which the freezing fog icing will detach. In one embodiment, the second temperature is equal to 274.15K. After obtaining the formula for calculating the thickness of freezing fog icing at preset time points within a future preset time period, the formula can be integrated to determine the increase in freezing fog icing thickness within each preset time interval. The sum of these increases represents the cumulative increase in freezing fog icing thickness at that preset time point relative to the initial freezing fog icing thickness. This cumulative increase is then added to the initial freezing fog icing thickness to obtain the freezing fog icing thickness at the preset time points within the future preset time period.

[0061] In one embodiment, determining the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period using a calculation formula corresponding to the freezing fog icing thickness variation parameter includes: if it is determined that the freezing fog icing thickness is in a melting state, calculating the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period according to formula (2):

[0062]

[0063] in, η1 is the growth rate of the freezing fog icing thickness per unit time, E1 is the efficiency coefficient determined based on the airfoil characteristics of the wind turbine blades to be predicted, and E1 is the melting rate. Assuming the freezing fog icing thickness is in a sublimation state, the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period is calculated according to formula (3):

[0064]

[0065] Where D is the diffusion coefficient of water vapor in air, f v ρ is the ventilation coefficient for water vapor. s (T) represents the saturated vapor density at ambient air temperature T, ρ i For standard ice density, L S The latent heat of sublimation is given by k, where k is the thermal conductivity of air, and f is the thermal conductivity of air. h The ventilation coefficient for heat, ρ′ s For ρ s The derivative of (T). Given that the freezing fog icing thickness is in a state of detachment, the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period is calculated according to formula (4):

[0066]

[0067] Where η2 is the efficiency coefficient determined based on the airfoil characteristics of the wind turbine blades to be predicted, and E2 is the shedding rate.

[0068] In one embodiment, determining the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period using a calculation formula corresponding to the freezing fog icing thickness change parameter includes: determining that the freezing fog icing thickness is in a non-growing state and not in any of the melting, sublimation, or shedding states, based on the hub temperature, relative humidity, and liquid water content; and determining that the freezing fog icing thickness is in a zero-change state, based on the zero-change state, calculating the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period according to formula (5):

[0069]

[0070] in, This represents the rate of increase in the thickness of the freezing fog icing layer per unit time.

[0071] In one specific embodiment, the location of a wind power plant in Hunan Province was determined to be between 111.7° and 112.5° east longitude and 26.3° and 27.4° north latitude. The wind turbine blade length was 45m and the hub height was 88.5m. Topographic elevation data and land use data for this area at a resolution of 30m were collected. Using the area where the wind farm is located as the calculation region, it was divided into equidistant grids with a horizontal distance of 30m in both east-west and north-south directions. The collected topographic elevation and land use data of the wind farm area were interpolated and transformed into the aforementioned equidistant grids. The elevation of the grid points was corrected based on the location of the wind turbine generators to obtain the target meteorological boundary field conditions and target initial field conditions for the equidistant grids. Subsequently, a meteorological prediction model was obtained. The meteorological boundary field conditions and target initial field conditions were interpolated into the meteorological prediction model to obtain meteorological prediction data for the wind turbine generators every 15 minutes for the next 3 days. Based on the meteorological forecast data, the change in freezing fog icing thickness every 15 minutes was analyzed, resulting in a calculation formula for the change in freezing fog icing thickness. For example, at time t1, the thickness is in the growth phase; at time t2, it is in the sublimation phase; at time t3, it is in the melting phase; and at time t4, it is in the shedding phase. The change parameter at time t1 is 0.9, at time t2 it is -0.1, at time t3 it is -0.3, and at time t4 it is -5.0. Integrating these parameters and adding the result to the initial freezing fog icing thickness yields the freezing fog icing thickness on the wind turbine blades three days later. The output results are as follows. Figure 2 The graph shown represents the change in freezing fog icing thickness over time. The vertical axis represents the freezing fog icing thickness, and the horizontal axis represents the time points. In this embodiment, each time point is spaced 15 minutes apart.

[0072] The above method allows for the acquisition of future freezing fog and icing thickness forecasts for wind turbine blades over a longer period. The prediction data is accurate, and the calculation results are visualized. After obtaining the prediction results, the power supply system can easily adjust its operations based on the freezing fog and icing thickness of the wind turbines, such as increasing or decreasing thermal power generation, or shutting down wind turbines whose freezing fog and icing thickness exceeds the warning value for maintenance. This effectively prevents malfunctions and ensures a stable power supply.

[0073] Figure 1 This is a flowchart illustrating a method for predicting the thickness of freezing fog icing on wind turbine blades in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0074] In one embodiment, such as Figure 3 As shown, a prediction system 300 for the thickness of freezing fog icing on wind turbine blades is provided, comprising:

[0075] The data collection module 301 is used to acquire environmental parameters of the wind turbine generator to be predicted. The environmental parameters include the location information of the wind turbine generator to be predicted, the distance between adjacent generators, terrain elevation data, and land use data.

[0076] The data grid partitioning module 302 is used to divide the wind farm where the wind turbine generator to be predicted is located into an equidistant grid according to the location information and the interval distance.

[0077] The data preprocessing module 303 is used to interpolate topographic elevation data and land use data to an equidistant grid to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grid.

[0078] The numerical calculation module 304 is used to input the target meteorological boundary field conditions and the target initial field conditions into the weather prediction model, so as to output the meteorological prediction data for each preset time point within the future preset time period through the weather prediction model.

[0079] The freezing fog and icing thickness calculation module 305 is used to determine the freezing fog and icing thickness variation parameters of the wind turbine generator to be predicted at each preset time point within a future preset time period based on meteorological forecast data. It determines the freezing fog and icing thickness at each preset time point within the future preset time period based on the current freezing fog and icing thickness and the freezing fog and icing thickness variation parameters at each preset time point.

[0080] The prediction system for the thickness of freezing fog and ice on wind turbine blades includes a processor and a memory. The data collection module, data grid partitioning module, data preprocessing module, numerical calculation module, and freezing fog and ice thickness calculation module are all stored as program units in the memory. The processor executes the program modules stored in the memory to implement the corresponding functions.

[0081] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and methods for predicting the thickness of freezing fog accretion on wind turbine blades can be implemented by adjusting kernel parameters.

[0082] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0083] This application provides a storage medium storing a program that, when executed by a processor, implements the above-described method for predicting the thickness of freezing fog icing on wind turbine blades.

[0084] This application provides a processor for running a program, wherein the program executes the above-described method for predicting the thickness of freezing fog and icing on wind turbine blades.

[0085] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown in the figure, the computer device includes a processor A01, a network interface A02, a display screen A04, an input device A05, and a memory (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A06. The non-volatile storage medium A06 stores an operating system B01 and a computer program B02. The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A06. The network interface A02 is used for communication with external terminals via a network connection. When the computer program is executed by the processor A01, it implements a method for predicting the thickness of freezing fog icing on wind turbine blades. The display screen A04 can be a liquid crystal display (LCD) or an e-ink display. The input device A05 can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0086] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0087] In one embodiment, the wind turbine blade freezing fog icing thickness prediction system provided in this application can be implemented as a computer program, which can be implemented in, for example... Figure 4The computer device shown runs on this system. The computer device's memory can store the various program modules that make up the prediction system for the freezing fog and icing thickness of the wind turbine blades, for example... Figure 3 The data collection module, data grid partitioning module, and data preprocessing module are shown. The computer program, comprised of these modules, enables the processor to execute the steps in the methods for predicting the thickness of freezing fog icing on wind turbine blades according to the various embodiments of this application described in this specification.

[0088] This application provides an apparatus, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: acquiring environmental parameters of a wind turbine generator to be predicted, including location information of the wind turbine generator, the distance between adjacent generators, terrain elevation data, and land use data; dividing the wind farm where the wind turbine generator is located into an equidistant grid based on the location information and the distance; interpolating the terrain elevation data and land use data to the equidistant grid to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grid; inputting the target meteorological boundary field conditions and target initial field conditions into a weather prediction model to output meteorological prediction data for each preset time point within a future preset time period; determining the freezing fog and icing thickness variation parameters of the wind turbine generator to be predicted at each preset time point within a future preset time period based on the meteorological prediction data; acquiring the current freezing fog and icing thickness of the blades of the wind turbine generator to be predicted; and determining the freezing fog and icing thickness at each preset time point within a future preset time period based on the current freezing fog and icing thickness and the freezing fog and icing thickness variation parameters at each preset time point.

[0089] In one embodiment, interpolating topographic elevation data and land use data to an equidistant grid to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grid includes: interpolating topographic elevation data and land use data to an equidistant grid to determine the lower boundary conditions of the equidistant grid for predicting freezing fog and ice thickness; obtaining initial field data for predicting freezing fog and ice thickness to determine the initial meteorological boundary field conditions and initial values ​​of the initial field conditions for the numerical calculation model of freezing fog and ice thickness based on the initial field data; interpolating the initial field data to an equidistant grid and adjusting the interpolation according to the lower boundary conditions to obtain the target meteorological boundary field conditions and target initial field conditions driving the numerical calculation.

[0090] In one embodiment, the method further includes: determining the hub temperature, relative humidity, and liquid water content of the wind turbine generator set to be predicted at a future prediction time point; determining that the freezing fog icing thickness is in an increasing state when the hub temperature is lower than a first temperature, the relative humidity is higher than a reference relative humidity, and the liquid water content is higher than a reference liquid water content; and determining that the freezing fog icing thickness is in a non-increasing state when the hub temperature is higher than the first temperature, and / or the relative humidity is lower than the reference relative humidity, and / or the liquid water content is lower than the reference liquid water content.

[0091] In one embodiment, the method further includes: if the thickness of the freezing fog icing at a future predicted time point is increasing, calculating the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period according to formula (1):

[0092]

[0093] in, ε is the rate of increase in the thickness of the frozen fog icing per unit time, β is the freezing efficiency coefficient of the frozen fog icing, q is the liquid water content, and ρ is the freezing efficiency coefficient of the frozen fog icing. i Let P be the standard ice density, P be the air pressure at the hub height of the wind turbine generator to be predicted, R be the ideal gas constant, T be the hub temperature of the wind turbine generator to be predicted, and U be the standard ice density. ∞ Let α be the inflow wind speed of the wind turbine generator to be predicted, and α be the angle between the inflow wind speed and the blade of the wind turbine generator to be predicted.

[0094] In one embodiment, the non-growth state includes a reduction state and a zero-change state. The reduction state includes a melting state, a sublimation state, and a detachment state. The method further includes: determining that the freezing fog icing thickness is in a melting state when the wheel hub temperature is greater than a first temperature and less than a second temperature, and the relative humidity is less than a reference relative humidity; determining that the freezing fog icing thickness is in a sublimation state when the wheel hub temperature is less than or equal to the first temperature, and the relative humidity is less than a reference relative humidity; determining that the freezing fog icing thickness is in a detachment state when the wheel hub temperature is greater than the second temperature; determining the freezing fog icing thickness at each preset time point within a future preset time period based on the current freezing fog icing thickness and the freezing fog icing thickness change parameters at each preset time point includes: determining the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period using a calculation formula corresponding to the freezing fog icing thickness change parameters; and determining the freezing fog icing thickness at each preset time point within a future preset time period based on the current freezing fog icing thickness and the growth rate.

[0095] In one embodiment, determining the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period using a calculation formula corresponding to the freezing fog icing thickness variation parameter includes: if it is determined that the freezing fog icing thickness is in a melting state, calculating the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period according to formula (2):

[0096]

[0097] in, η1 is the growth rate of the freezing fog icing thickness per unit time, E1 is the efficiency coefficient determined based on the airfoil characteristics of the wind turbine blades to be predicted, and E1 is the melting rate. Assuming the freezing fog icing thickness is in a sublimation state, the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period is calculated according to formula (3):

[0098]

[0099] Where D is the diffusion coefficient of water vapor in air, f v ρ is the ventilation coefficient for water vapor. s (T) represents the saturated vapor density at ambient air temperature T, ρ i For standard ice density, L S The latent heat of sublimation is given by k, where k is the thermal conductivity of air, and f is the thermal conductivity of air. h The ventilation coefficient for heat, ρ′ s For ρ s The derivative of (T). Given that the freezing fog icing thickness is in a state of detachment, the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period is calculated according to formula (4):

[0100]

[0101] Where η2 is the efficiency coefficient determined based on the airfoil characteristics of the wind turbine blades to be predicted, and E2 is the shedding rate.

[0102] In one embodiment, determining the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period using a calculation formula corresponding to the freezing fog icing thickness change parameter includes: determining that the freezing fog icing thickness is in a non-growing state and not in any of the melting, sublimation, or shedding states, based on the hub temperature, relative humidity, and liquid water content; and determining that the freezing fog icing thickness is in a zero-change state, based on the zero-change state, calculating the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period according to formula (5):

[0103]

[0104] in, This represents the rate of increase in the thickness of the freezing fog icing layer per unit time.

[0105] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following method steps: acquiring environmental parameters of the wind turbine generator to be predicted, including location information of the wind turbine generator, the distance between adjacent generators, terrain elevation data, and land use data; dividing the wind farm where the wind turbine generator is located into an equidistant grid according to the location information and the distance; interpolating the terrain elevation data and land use data to the equidistant grid to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grid; inputting the target meteorological boundary field conditions and target initial field conditions into a weather prediction model to output meteorological prediction data for each preset time point within a future preset time period through the weather prediction model; determining the freezing fog and icing thickness variation parameters of the wind turbine generator to be predicted at each preset time point within a future preset time period based on the meteorological prediction data; acquiring the current freezing fog and icing thickness of the blades of the wind turbine generator to be predicted; and determining the freezing fog and icing thickness at each preset time point within a future preset time period based on the current freezing fog and icing thickness and the freezing fog and icing thickness variation parameters at each preset time point.

[0106] In one embodiment, interpolating topographic elevation data and land use data to an equidistant grid to determine the target meteorological boundary field conditions and target initial field conditions of the equidistant grid includes: interpolating topographic elevation data and land use data to an equidistant grid to determine the lower boundary conditions of the equidistant grid for predicting freezing fog and ice thickness; obtaining initial field data for predicting freezing fog and ice thickness to determine the initial meteorological boundary field conditions and initial values ​​of the initial field conditions for the numerical calculation model of freezing fog and ice thickness based on the initial field data; interpolating the initial field data to an equidistant grid and adjusting the interpolation according to the lower boundary conditions to obtain the target meteorological boundary field conditions and target initial field conditions driving the numerical calculation.

[0107] In one embodiment, the method further includes: determining the hub temperature, relative humidity, and liquid water content of the wind turbine generator set to be predicted at a future prediction time point; determining that the freezing fog icing thickness is in an increasing state when the hub temperature is lower than a first temperature, the relative humidity is higher than a reference relative humidity, and the liquid water content is higher than a reference liquid water content; and determining that the freezing fog icing thickness is in a non-increasing state when the hub temperature is higher than the first temperature, and / or the relative humidity is lower than the reference relative humidity, and / or the liquid water content is lower than the reference liquid water content.

[0108] In one embodiment, the method further includes: if the thickness of the freezing fog icing at a future predicted time point is increasing, calculating the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period according to formula (1):

[0109]

[0110] in, ε is the rate of increase in the thickness of the frozen fog icing per unit time, β is the freezing efficiency coefficient of the frozen fog icing, q is the liquid water content, and ρ is the freezing efficiency coefficient of the frozen fog icing. i Where is the standard ice density, P is the air pressure at the hub height of the wind turbine generator to be predicted, R is the ideal gas constant, T is the hub temperature of the wind turbine generator to be predicted, U∞ is the inflow wind speed of the wind turbine generator to be predicted, and α is the angle between the inflow wind speed and the blade of the wind turbine generator to be predicted.

[0111] In one embodiment, the non-growth state includes a reduction state and a zero-change state. The reduction state includes a melting state, a sublimation state, and a detachment state. The method further includes: determining that the freezing fog icing thickness is in a melting state when the wheel hub temperature is greater than a first temperature and less than a second temperature, and the relative humidity is less than a reference relative humidity; determining that the freezing fog icing thickness is in a sublimation state when the wheel hub temperature is less than or equal to the first temperature, and the relative humidity is less than a reference relative humidity; determining that the freezing fog icing thickness is in a detachment state when the wheel hub temperature is greater than the second temperature; determining the freezing fog icing thickness at each preset time point within a future preset time period based on the current freezing fog icing thickness and the freezing fog icing thickness change parameters at each preset time point includes: determining the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period using a calculation formula corresponding to the freezing fog icing thickness change parameters; and determining the freezing fog icing thickness at each preset time point within a future preset time period based on the current freezing fog icing thickness and the growth rate.

[0112] In one embodiment, determining the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period using a calculation formula corresponding to the freezing fog icing thickness variation parameter includes: if it is determined that the freezing fog icing thickness is in a melting state, calculating the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period according to formula (2):

[0113]

[0114] in, η1 is the growth rate of the freezing fog icing thickness per unit time, E1 is the efficiency coefficient determined based on the airfoil characteristics of the wind turbine blades to be predicted, and E1 is the melting rate. Assuming the freezing fog icing thickness is in a sublimation state, the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period is calculated according to formula (3):

[0115]

[0116] Where D is the diffusion coefficient of water vapor in air, f v ρ is the ventilation coefficient for water vapor. s (T) represents the saturated vapor density at ambient air temperature T, ρ i For standard ice density, L S The latent heat of sublimation is given by k, where k is the thermal conductivity of air, and f is the thermal conductivity of air. h The ventilation coefficient for heat, ρ′ s For ρ s The derivative of (T). Given that the freezing fog icing thickness is in a state of detachment, the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period is calculated according to formula (4):

[0117]

[0118] Where η2 is the efficiency coefficient determined based on the airfoil characteristics of the wind turbine blades to be predicted, and E2 is the shedding rate.

[0119] In one embodiment, determining the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period using a calculation formula corresponding to the freezing fog icing thickness change parameter includes: determining that the freezing fog icing thickness is in a non-growing state and not in any of the melting, sublimation, or shedding states, based on the hub temperature, relative humidity, and liquid water content; and determining that the freezing fog icing thickness is in a zero-change state, based on the zero-change state, calculating the growth rate of the freezing fog icing thickness at each preset time point within a future preset time period according to formula (5):

[0120]

[0121] in, This represents the rate of increase in the thickness of the freezing fog icing layer per unit time.

[0122] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0127] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0128] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0129] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0130] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method of predicting a wind turbine blade icing thickness of a freezing fog, characterized by, The prediction method comprises: obtaining environmental parameters of a wind turbine to be predicted, the environmental parameters comprising position information of the wind turbine to be predicted, interval distances between adjacent wind turbines, terrain elevation data and land use data; dividing a wind farm where the wind turbine to be predicted is located into equidistance grids according to the position information and the interval distances; interpolating the terrain elevation data and the land use data to the equidistance grids to determine target meteorological boundary field conditions and target initial field conditions of the equidistance grids; inputting the target meteorological boundary field conditions and the target initial field conditions into a weather prediction model to output meteorological prediction data of each preset time point in a future preset time period through the weather prediction model; determining a rime icing thickness variation parameter of the wind turbine to be predicted at each preset time point in the future preset time period according to the meteorological prediction data; obtaining a current rime icing thickness of a blade of the wind turbine to be predicted; determining a hub temperature, a relative humidity and a liquid water content of the wind turbine to be predicted at a future prediction time point; in a case where the hub temperature is less than a first temperature, the relative humidity is greater than a reference relative humidity and the liquid water content is greater than a reference liquid water content, determining that the rime icing thickness is in a growth state; in a case where the hub temperature is greater than the first temperature, and / or the relative humidity is less than a reference relative humidity, and / or the liquid water content is less than the reference liquid water content, determining that the rime icing thickness is in a non-growth state, wherein the non-growth state comprises a melting state, a sublimation state and a shedding state; in a case where the hub temperature is greater than a first temperature and less than a second temperature, and the relative humidity is less than a reference relative humidity, determining that the rime icing thickness is in the melting state; in a case where the hub temperature is less than or equal to the first temperature, and the relative humidity is less than the reference relative humidity, determining that the rime icing thickness is in the sublimation state; in a case where the hub temperature is greater than the second temperature, determining that the rime icing thickness is in the shedding state; determining a growth speed of the rime icing thickness at each preset time point in the future preset time period by using a calculation formula corresponding to the rime icing thickness variation parameter; determining the rime icing thickness at each preset time point in the future preset time period according to the current rime icing thickness and the growth speed.

2. The method of claim 1, wherein, The interpolation of the terrain elevation data and the land use data to the equidistance grids to determine the target meteorological boundary field conditions and the target initial field conditions of the equidistance grids comprises: interpolating the terrain elevation data and the land use data to the equidistance grids to determine lower boundary conditions of the equidistance grids for rime icing thickness prediction; obtaining initial field data for rime icing thickness prediction to determine initial meteorological boundary field conditions and initial field condition initial values of a rime icing thickness numerical calculation mode according to the initial field data; The initial field data is interpolated to the equidistant grid and adjusted according to the lower boundary condition to obtain target meteorological boundary field conditions and target initial field conditions for driving numerical calculation.

3. The method of claim 1, wherein, The method further comprises: In a case where the rime icing thickness at the future prediction time point is in a growth state, a growth rate of the rime icing thickness at each preset time point in the future preset time period is calculated according to formula (1): （1）； wherein, is the growth rate of the rime icing thickness in a unit of time, is the rime icing freezing efficiency coefficient, is the conversion coefficient, q is the liquid water content, is the standard ice density, is the atmospheric pressure at the hub height of the wind turbine to be predicted, R is the ideal gas constant, T is the hub temperature of the wind turbine to be predicted, is the inflow wind speed of the wind turbine to be predicted, is the angle between the inflow wind speed and the blade of the wind turbine to be predicted.

4. The method of claim 1, wherein, The growth rate of the rime icing thickness at each preset time point in the future preset time period is determined by using a calculation formula corresponding to the rime icing thickness change parameter, which comprises: In a case where it is determined that the rime icing thickness is in the melting state, a growth rate of the rime icing thickness at each preset time point in the future preset time period is calculated according to formula (2): （2）； wherein, is the growth rate of the rime icing thickness per unit time, is the efficiency coefficient determined according to the blade airfoil characteristics of the wind turbine generator set to be predicted, is the melting rate; In a case where it is determined that the rime icing thickness is in the sublimation state, a growth rate of the rime icing thickness at each preset time point in the future preset time period is calculated according to formula (3): （3）； wherein D is the diffusion coefficient of water vapor in air, is the ventilation coefficient of water vapor, is the ambient air temperature T is the saturated vapor density at the ambient air temperature, is the standard ice density, is the latent heat of sublimation, k is the thermal conductivity of air, is the thermal ventilation coefficient, is the differential of the ambient air temperature. In a case where it is determined that the rime icing thickness is in the shedding state, a growth rate of the rime icing thickness at each preset time point in the future preset time period is calculated according to formula (4): （4）； wherein, is the efficiency factor identified from the airfoil characteristics of the wind turbine rotor blade to be predicted, is the shedding rate.

5. The method of claim 1, wherein, The growth rate of the rime icing thickness at each preset time point in the future preset time period is determined by using a calculation formula corresponding to the rime icing thickness change parameter, which comprises: In a case where it is determined that the rime icing thickness is in a non-growth state and is not in any one of the melting state, the sublimation state and the shedding state according to the hub temperature, the relative humidity and the liquid water content, it is determined that the rime icing thickness is in the zero change state; In a case where it is determined that the rime icing thickness is in the zero change state, a growth rate of the rime icing thickness at each preset time point in the future preset time period is calculated according to formula (5): （5）； wherein, is the growth rate of the rime icing thickness per unit time.

6. A processor, comprising: configured to perform the method according to any one of claims 1 to 5.

7. A system for predicting the thickness of a wind turbine blade icing due to freezing fog, characterized in that, comprises: a data collection module configured to acquire environmental parameters of a wind turbine generator to be predicted, the environmental parameters comprising location information of the wind turbine generator to be predicted, interval distances between adjacent wind turbine generators, terrain elevation data and land use data, and a current rime icing thickness of a blade of the wind turbine generator to be predicted; a data grid division module configured to divide a wind farm where the wind turbine generator to be predicted is located into equidistant grids according to the location information and the interval distances; a data preprocessing module configured to interpolate the terrain elevation data and the land use data to the equidistant grids to determine target meteorological boundary field conditions and target initial field conditions of the equidistant grids; a numerical calculation module configured to input the target meteorological boundary field conditions and the target initial field conditions into a weather prediction model to output meteorological prediction data at each preset time point in a future preset time period through the weather prediction model; and and The frozen fog icing thickness calculation module is configured to determine a frozen fog icing thickness change parameter of the wind turbine to be predicted at each preset time point in the future preset time period according to the meteorological prediction data; The frozen fog icing thickness at each preset time point in the future preset time period is determined according to the current frozen fog icing thickness and the frozen fog icing thickness change parameter at each preset time point. The hub temperature, relative humidity and liquid water content of the wind turbine to be predicted at the future prediction time point are determined; in a case where the hub temperature is less than a first temperature, the relative humidity is greater than a reference relative humidity, and the liquid water content is greater than a reference liquid water content, it is determined that the frozen fog icing thickness is in a growth state; In a case where the hub temperature is greater than the first temperature, and / or the relative humidity is less than a reference relative humidity, and / or the liquid water content is less than the reference liquid water content, it is determined that the frozen fog icing thickness is in a non-growth state, wherein the non-growth state includes a reduction state and a zero change state, the reduction state includes a melting state, a sublimation state and a shedding state; in a case where the hub temperature is greater than the first temperature and less than a second temperature, and the relative humidity is less than a reference relative humidity, it is determined that the frozen fog icing thickness is in the melting state; in a case where the hub temperature is less than or equal to the first temperature, and the relative humidity is less than the reference relative humidity, it is determined that the frozen fog icing thickness is in the sublimation state; in a case where the hub temperature is greater than the second temperature, it is determined that the frozen fog icing thickness is in the shedding state; a calculation formula corresponding to the frozen fog icing thickness change parameter is used to determine a growth rate of the frozen fog icing thickness at each preset time point in the future preset time period; the frozen fog icing thickness at each preset time point in the future preset time period is determined according to the current frozen fog icing thickness and the growth rate.

8. A machine-readable storage medium having stored thereon instructions, the instructions being executable by a machine to cause the machine to: The instructions, when executed by a processor, cause the processor to be configured to perform the method according to any one of claims 1 to 5. The instructions, when executed by a processor, cause the processor to be configured to perform the method according to any one of claims 1 to 5.

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