Method and device for predicting icing thickness of fan, storage medium and processor

Abstract

Embodiments of the present application provide a method and device for predicting icing thickness of a wind turbine, a storage medium and a processor. The method comprises: determining predicted meteorological data of a target region in a preset time period before a target time point; determining a predicted icing state of each wind turbine in the target region at the target time point according to the predicted meteorological data; obtaining blade parameters of each wind turbine in the target region; determining a predicted linear speed of a blade tip of each wind turbine at the target time point according to the predicted meteorological data and the blade parameters of each wind turbine; determining an icing increment prediction value of each wind turbine in the preset time period according to the predicted icing state, the predicted linear speed and the predicted meteorological data of each wind turbine; and determining an icing thickness prediction value of each wind turbine at the target time point according to the icing increment prediction value. The icing thickness of the wind turbine at a future time can be accurately predicted. Therefore, the operation mode can be scientifically adjusted in advance by the dispatching department, the prediction of the standby capacity of the wind turbine and the advance scheduling plan can be guided, and the winter power supply can be ensured.

Description

Technical Field

[0001] This application relates to the field of wind power risk assessment technology, specifically to a method, device, storage medium, and processor for predicting the icing thickness of wind turbines. Background Technology

[0002] Wind energy is a renewable energy source, and wind power generation has become an important choice for countries worldwide to develop clean energy. However, in cold weather conditions, wind turbine blades are prone to icing, which severely affects the performance and normal operation of the turbines, leading to turbine shutdown and reduced wind power output, exacerbating the power shortage in winter. Therefore, predicting the icing thickness of wind turbine blades is crucial, as it can guide the on-site response and dispatching plans in advance, ensuring power supply. Current research methods for predicting wind turbine blade icing often use empirical or simplified models. These models are mainly based on statistical analysis and historical data, and cannot account for the impact of changes in meteorological conditions on wind turbine icing. Therefore, their prediction accuracy is low and cannot meet the needs of practical applications. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, storage medium, and processor for predicting the icing thickness of wind turbines.

[0004] To achieve the above objectives, the first aspect of this application provides a method for predicting the icing thickness of wind turbines, the method comprising:

[0005] Determine the predicted meteorological data for the target area during a preset time period prior to the target time point;

[0006] Determine the predicted icing status of each wind turbine in the target area at the target time point based on forecast meteorological data;

[0007] Obtain the blade parameters of each wind turbine in the target area;

[0008] The predicted linear velocity of the blade tip of each wind turbine at the target time point is determined based on the predicted meteorological data and the blade parameters of each wind turbine.

[0009] The predicted icing increment for each wind turbine within a preset time period is determined based on the predicted icing status, predicted linear velocity, and predicted meteorological data for each wind turbine.

[0010] The predicted icing thickness for each wind turbine at the target time point is determined based on the predicted icing increment.

[0011] In the embodiments of this application, the predicted meteorological data includes atmospheric temperature, ground temperature, and relative humidity. Determining the predicted icing state of each wind turbine in the target area at a target time point based on the predicted meteorological data includes: determining the ice content of precipitation particles falling to the ground within a preset time period based on the atmospheric temperature; determining the predicted icing state of each wind turbine at the target time point as freezing rain icing state when the ice content is less than a preset threshold and the ground temperature is less than a preset temperature; and determining the predicted icing state of each wind turbine at the target time point as freezing fog icing state when the relative humidity is greater than a preset relative humidity and the ground temperature is less than a preset temperature.

[0012] In the embodiments of this application, determining the ice content of precipitation particles falling to the ground within a preset time period based on atmospheric temperature includes calculating the ice content according to formula (1):

[0013]

[0014] Where I refers to the ice content of precipitation particles falling to the ground within a preset time period, W refers to the atmospheric thermal conductivity of the target area within a preset time period, z refers to the descent altitude of precipitation particles falling to the ground within a preset time period, T refers to the atmospheric temperature of the target area within a preset time period, and p i "r" refers to the density of ice, and "r" refers to the radius of the precipitation particles.

[0015] In embodiments of this application, the predicted meteorological data further includes precipitation and supercooled water content. Determining the predicted icing increment for each wind turbine within a preset time period based on the predicted icing state, predicted linear velocity, and predicted meteorological data includes: for each wind turbine, if the predicted icing state is freezing rain icing, determining a first predicted icing increment for the wind turbine within a preset time period based on precipitation, supercooled water content, and the predicted linear velocity of the wind turbine blades; for each wind turbine, if the predicted icing state is freezing fog icing, determining a second predicted icing increment for the wind turbine within a preset time period based on the supercooled water content and the predicted linear velocity of the wind turbine blades; and for each wind turbine, determining the predicted icing increment for the wind turbine within a preset time period based on the first and / or second predicted icing increments.

[0016] In the embodiments of this application, for each wind turbine, when the predicted icing state of the wind turbine is freezing rain icing, determining the first incremental predicted value of icing of the wind turbine in a preset time period based on precipitation, supercooled water content and predicted linear velocity of the wind turbine blades includes determining the first incremental predicted value of icing of each wind turbine in a preset time period according to formula (2):

[0017]

[0018] Among them, E frzThis refers to the first incremental prediction value, p, for each wind turbine within a preset time period. i ρ0 refers to the density of ice, p is the precipitation in the target area during the preset time period, U is the predicted linear velocity of the blade tip of each wind turbine at the target time point, and M is the subcooled water content in the target area during the preset time period.

[0019] In the embodiments of this application, for each wind turbine, when the predicted icing state of the wind turbine is freezing fog icing state, the second incremental predicted value of the icing of the wind turbine in the preset time period is determined according to the supercooled water content and the predicted linear velocity of the wind turbine blades, including determining the second incremental predicted value of the icing of each wind turbine in the preset time period according to formula (3).

[0020] E fog =M×U×Δt×p 2ir ×β (3)

[0021] Among them, E fog This refers to the second incremental predicted value of icing for each wind turbine within a preset time period; M refers to the supercooled water content in the target area within the preset time period; U refers to the predicted linear velocity of the blade tip of each wind turbine at the target time point; Δt refers to the preset time period; and ρ... air β refers to the atmospheric density of the target area during a preset time period, while β refers to the freezing coefficient of each wind turbine during the preset time period.

[0022] In the embodiments of this application, the predicted meteorological data includes wind speed, and the blade parameters include blade rotation radius and blade rotation number. Determining the predicted linear velocity of the blade tip of each wind turbine at the target time point based on the predicted meteorological data and the blade parameters of each wind turbine includes: determining the blade tip speed ratio of each wind turbine based on the blade rotation radius and blade rotation number of each wind turbine; determining the blade tip linear velocity of each wind turbine based on the blade tip speed ratio of each wind turbine; and determining the predicted linear velocity of the blade tip of each wind turbine at the target time point based on the wind speed and the blade tip linear velocity of each wind turbine.

[0023] A second aspect of this application provides a processor configured to perform the above-described method for predicting wind turbine icing thickness.

[0024] A third aspect of this application provides a device for predicting the icing thickness of a wind turbine, comprising:

[0025] The weather forecasting module is used to determine the predicted weather data for the target area during a preset time period before the target time point;

[0026] The icing status prediction module is used to determine the predicted icing status of each wind turbine in the target area at the target time point based on the predicted meteorological data.

[0027] The wind turbine parameter prediction module is used to obtain the blade parameters of each wind turbine in the target area, and determine the predicted linear velocity of the blade tip of each wind turbine at the target time point based on the predicted meteorological data and the blade parameters of each wind turbine.

[0028] The icing thickness prediction module is used to determine the predicted icing increment value of each wind turbine within a preset time period based on the predicted icing status, predicted linear velocity, and predicted meteorological data of each wind turbine, and to determine the predicted icing thickness value of each wind turbine at the target time point based on the predicted icing increment value.

[0029] 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 wind turbine icing thickness.

[0030] The above technical solution involves: determining the predicted meteorological data for a preset time period before the target time point for the target area; determining the predicted icing state of each wind turbine in the target area at the target time point based on the predicted meteorological data; acquiring the blade parameters of each wind turbine in the target area; determining the predicted linear velocity of the blade tip of each wind turbine at the target time point based on the predicted meteorological data and the blade parameters of each wind turbine; determining the predicted icing increment value of each wind turbine within the preset time period based on the predicted icing increment value; and determining the predicted icing thickness value of each wind turbine at the target time point based on the predicted icing increment value. Based on the impact of meteorological conditions on wind turbine icing, the icing thickness of wind turbines in the future can be accurately predicted. This allows for scientific guidance to dispatch departments to adjust operating modes in advance, protecting the service life and normal operation of wind turbine generators. It enables the prediction of wind turbine blade icing thickness, guiding the prediction of wind turbine reserve capacity and advance dispatch planning, and ensuring winter power supply.

[0031] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0032] 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:

[0033] Figure 1 A schematic flowchart of a method for predicting wind turbine icing thickness according to an embodiment of this application is shown.

[0034] Figure 2 The illustration shows a graph of actual meteorological data and forecast meteorological data according to an embodiment of this application;

[0035] Figure 3The diagram illustrates the predicted icing thickness and actual operating capacity according to an embodiment of this application.

[0036] Figure 4 A schematic diagram illustrating the structure of a wind turbine icing thickness prediction device according to an embodiment of this application is shown.

[0037] Figure 5 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0038] 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.

[0039] Figure 1 A schematic flowchart illustrating a method for predicting wind turbine icing thickness according to an embodiment of this application is shown. Figure 1 As shown in one embodiment of this application, a method for predicting the icing thickness of a wind turbine is provided, comprising the following steps:

[0040] S102, determine the predicted meteorological data for the target area during a preset time period before the target time point.

[0041] The target area refers to the geographical region where the wind farm is located. The target time point refers to any future time point, which can be customized. The preset time period is a period of time before and including the target time point, and can also be customized. For example, if the target time point is 4 PM on the 15th day after the current time, then the preset time period could be the period between 4 PM on the 14th day and 4 PM on the 15th day. Forecast meteorological data refers to the meteorological elements predicted for the wind farm within the preset time period. Meteorological elements include at least one of the following: ambient temperature, precipitation, wind speed, wind direction, relative humidity, and air pressure. Through the meteorological numerical forecasting model, the processor can select the model calculation area based on the target area where the wind farm is located, and drive the numerical forecasting model based on static data such as topographic elevation of the target area and global large-scale meteorological data to provide predicted meteorological data such as precipitation, temperature, relative humidity, and wind speed for the target area where the wind farm is located within the preset time period.

[0042] S104, determine the predicted icing status of each wind turbine in the target area at the target time point based on the predicted meteorological data.

[0043] The processor can determine the predicted icing status of each wind turbine in the target area at a target time point based on predicted meteorological data such as precipitation, temperature, relative humidity, and wind speed for a preset time period in the future. The predicted icing status refers to the predicted icing state of the turbine blades at a future time, determining whether the turbine will become icy. The predicted icing status can be caused by freezing fog or freezing rain.

[0044] S106, Obtain the blade parameters of each wind turbine in the target area.

[0045] S108 determines the predicted linear velocity of the blade tip of each wind turbine at the target time point based on the predicted meteorological data and the blade parameters of each wind turbine.

[0046] The blade parameters of a wind turbine include the blade rotation radius and the number of revolutions per second. The processor can calculate the predicted linear velocity of each wind turbine blade tip at a target time point based on the wind speed, blade rotation radius, and blade revolutions from forecast meteorological data within a preset time period. The predicted linear velocity refers to the predicted relative velocity between the blade tip of the rotating wind turbine blade and water vapor within the preset time period.

[0047] S110, based on the predicted icing status, predicted linear velocity, and predicted meteorological data of each wind turbine, determine the predicted icing increment value of each wind turbine within a preset time period.

[0048] The processor can determine the specific icing situation of each wind turbine based on the predicted icing state. If the predicted meteorological environment at the target time point will not cause icing of the turbine blades, the predicted icing increment value is zero. If the predicted meteorological environment at the target time point will cause icing of the turbine blades, then for different predicted icing states, the processor can predict the predicted icing increment value for each wind turbine within a preset time period based on the predicted linear velocity and predicted meteorological data for that wind turbine within that preset time period. The predicted icing increment value refers to the predicted increase in the thickness of the icing on the turbine blades.

[0049] S112, determine the predicted icing thickness of each wind turbine at the target time point based on the predicted icing increment.

[0050] The current icing thickness of each wind turbine can be obtained through direct measurement. Therefore, for a target time point in the future, the processor can add the cumulative predicted icing increment to the current icing thickness to obtain the predicted icing thickness for each turbine at that target time. Meteorological data significantly impacts wind power generation, especially for wind turbines located in mountainous areas with strong winds, where frequent winter cold waves and freezing disasters can cause turbine blades to become icy and shut down. Therefore, monitoring meteorological data can scientifically guide dispatching departments to adjust operating modes in advance, protecting the lifespan and normal operation of wind turbine generators.

[0051] In one embodiment, the predicted meteorological data includes wind speed, the blade parameters include blade rotation radius and blade rotation number, and determining the predicted linear velocity of the blade tip of each wind turbine at the target time point based on the predicted meteorological data and the blade parameters of each wind turbine includes: determining the blade tip speed ratio of each wind turbine based on the blade rotation radius and blade rotation number of each wind turbine; determining the blade tip linear velocity of each wind turbine based on the blade tip speed ratio of each wind turbine; and determining the predicted linear velocity of the blade tip of each wind turbine at the target time point based on the wind speed and the blade tip linear velocity of each wind turbine.

[0052] Tip linear velocity refers to the theoretical linear velocity of the fan blade tip, which is related to the parameters of the fan blade itself. The ambient wind speed affects the tip linear velocity of the fan during rotation. Therefore, the predicted linear velocity is calculated using the following formula (4):

[0053]

[0054] Where U refers to the predicted linear velocity of the blade tip of each wind turbine at the target time point, and v x This refers to the tip linear velocity of each wind turbine blade at the target time point, v. c This refers to the wind speed in the target area where each wind turbine is located during a preset time period.

[0055] In one embodiment, the predicted meteorological data includes atmospheric temperature, ground temperature, and relative humidity. Determining the predicted icing state of each wind turbine in the target area at a target time point based on the predicted meteorological data includes: determining the ice content of precipitation particles falling to the ground within a preset time period based on the atmospheric temperature; determining the predicted icing state of each wind turbine at the target time point as freezing rain icing state when the ice content is less than a preset threshold and the ground temperature is less than a preset temperature; and determining the predicted icing state of each wind turbine at the target time point as freezing fog icing state when the relative humidity is greater than a preset relative humidity and the ground temperature is less than a preset temperature.

[0056] Wind turbine blade icing is typically caused by freezing fog and / or freezing rain. The processor can determine the ice content of precipitation particles falling to the ground within a preset time period based on the predicted atmospheric temperature. The preset threshold is the maximum ice content of precipitation particles falling to the ground during freezing rain. The preset temperature is the highest ground temperature during freezing rain and / or freezing fog. The preset relative humidity refers to the maximum relative humidity of the atmosphere during freezing fog. Only when the predicted ice content is less than the preset threshold and the ground temperature is less than the preset temperature can the weather during the preset time period be determined to be freezing rain, and the wind turbine may experience icing due to freezing rain at the target time. In this case, the processor can determine the predicted icing state of the wind turbine at the target time as freezing rain icing. For freezing fog prediction, the weather during the target time period can only be predicted to be freezing fog if the predicted relative humidity is greater than the preset relative humidity and the ground temperature is less than the preset temperature, and the predicted icing state of the wind turbine at the target time is freezing fog icing.

[0057] In one embodiment, the processor can determine the ice content of precipitation particles falling to the ground within a preset time period based on atmospheric temperature. The cloud top temperature of the solid precipitation particle formation layer is less than -6.6℃, and the precipitation particles in the formation layer are in a completely frozen state. During the precipitation particle descent, the melting rate is calculated layer by layer by performing vertical integration from the cloud top height downwards. Then, the ice content of the precipitation particles when they fall to the ground can be calculated according to formula (1):

[0058]

[0059] Where I refers to the ice content of precipitation particles falling to the ground within a preset time period, W refers to the atmospheric thermal conductivity of the target area within a preset time period, z refers to the descent altitude of precipitation particles falling to the ground within a preset time period, T refers to the atmospheric temperature of the target area within a preset time period, and p i "r" refers to the density of ice, and "r" refers to the radius of the precipitation particles.

[0060] In one embodiment, the predicted meteorological data further includes precipitation and supercooled water content. Determining the predicted icing increment for each wind turbine within a preset time period based on the predicted icing state, predicted linear velocity, and predicted meteorological data for each wind turbine includes: for each wind turbine, if the predicted icing state is freezing rain icing, determining a first predicted icing increment for the wind turbine within a preset time period based on precipitation, supercooled water content, and the predicted linear velocity of the wind turbine blades; for each wind turbine, if the predicted icing state is freezing fog icing, determining a second predicted icing increment for the wind turbine within a preset time period based on the supercooled water content and the predicted linear velocity of the wind turbine blades; and for each wind turbine, determining the predicted icing increment for the wind turbine within a preset time period based on the first and / or second predicted icing increments.

[0061] When the predicted icing condition for a wind turbine is freezing rain icing, precipitation, supercooled water content, and the predicted linear velocity of the turbine blades will affect the increase in icing thickness. Therefore, the processor can calculate the first incremental predicted value of icing caused by freezing rain for each wind turbine within a preset time period based on the above data. When the predicted icing condition for a wind turbine is freezing fog icing, supercooled water content and the predicted linear velocity of the turbine blades will affect the increase in icing thickness. Therefore, the processor can calculate the second incremental predicted value of icing caused by freezing fog for each wind turbine within a preset time period based on the above data. Within the preset time period, there may be a period of freezing rain, a period of freezing fog, or both. Therefore, for each wind turbine, the processor can predict the incremental icing value for the wind turbine within the preset time period based on the first incremental predicted value and / or the second incremental predicted value.

[0062] In one embodiment, for each wind turbine, when the predicted icing state of the wind turbine is freezing rain icing, determining the first incremental predicted value of icing for the wind turbine in a preset time period based on precipitation, supercooled water content, and predicted linear velocity of the wind turbine blades includes determining the first incremental predicted value of icing for each wind turbine in a preset time period according to formula (2):

[0063]

[0064] Among them, E frz This refers to the first incremental prediction value, p, for each wind turbine within a preset time period. i ρ0 refers to the density of ice, p is the precipitation in the target area during the preset time period, U is the predicted linear velocity of the blade tip of each wind turbine at the target time point, and M is the subcooled water content in the target area during the preset time period.

[0065] In one embodiment, for each wind turbine, when the predicted icing state of the wind turbine is freezing fog icing state, the second incremental predicted value of the icing of the wind turbine in the preset time period is determined according to the supercooled water content and the predicted linear velocity of the wind turbine blades, including determining the second incremental predicted value of the icing of each wind turbine in the preset time period according to formula (3).

[0066] E fog =M×U×Δt×p air ×β (3)

[0067] Among them, E fog This refers to the second incremental predicted value of icing for each wind turbine within a preset time period; M refers to the supercooled water content in the target area within the preset time period; U refers to the predicted linear velocity of the blade tip of each wind turbine at the target time point; Δt refers to the preset time period; and ρ... air β refers to the atmospheric density of the target area during a preset time period, while β refers to the freezing coefficient of each wind turbine during the preset time period.

[0068] In one specific embodiment, a method for predicting the icing thickness of a wind turbine is provided, comprising the following steps:

[0069] Step 1: Select the Weather Research and Forecasting (WRF) model developed by the National Center for Atmospheric Research (NCAR) as the meteorological numerical forecasting model to predict meteorological data for a wind farm in Hunan Province from February 13th to 15th, 2023. The wind farm has 25 wind turbines, each with an installed capacity of 2MW. A two-layer nested simulation area is used, with horizontal resolutions of 9km and 3km respectively. A 6-hour interval is used... ° ×1 ° Spatially resolved GFS (Global Forecast System) weather forecast products drive the WRF model to simulate and obtain predicted meteorological data such as precipitation, temperature, relative humidity, and wind speed. Figure 2 As shown, the predicted temperature and relative humidity of the numerical weather prediction model are very close to the actual temperature and relative humidity, which simulates the atmospheric cold layer structure and the near-saturated air relative humidity in the lower layer of the wind farm area during freezing fog.

[0070] Step 2: Using the predicted wind speed and the tip linear velocity of each wind turbine, calculate the predicted linear velocity according to the following formula (4):

[0071]

[0072] Where U refers to the predicted linear velocity of the blade tip of each wind turbine at the target time point, and v x This refers to the tip linear velocity of each wind turbine blade at the target time point, v. c This refers to the wind speed in the target area where each wind turbine is located during a preset time period.

[0073] Step 3: Determine the ice content of precipitation particles falling to the ground within a preset time period based on atmospheric temperature. The cloud top temperature of the solid precipitation particle formation layer is less than -6.6℃, and the precipitation particles in the formation layer are in a completely frozen state. During the precipitation particle descent, the melting rate is calculated layer by layer by performing vertical integration from the cloud top height downwards. Therefore, the ice content of precipitation particles when they fall to the ground can be calculated according to formula (1):

[0074]

[0075] Where I refers to the ice content of precipitation particles falling to the ground within a preset time period, W refers to the atmospheric thermal conductivity of the target area within a preset time period, z refers to the descent altitude of precipitation particles falling to the ground within a preset time period, T refers to the atmospheric temperature of the target area within a preset time period, and p i "r" refers to the density of ice, and "r" refers to the radius of the precipitation particles.

[0076] Step 4: If the ice content is less than 0.8 and the ground temperature is less than zero degrees Celsius, freezing rain is considered to be present. At this time, the first incremental prediction value of icing for each wind turbine within a preset time period is determined according to formula (2):

[0077]

[0078] Among them, E frz This refers to the first incremental prediction value, p, for each wind turbine within a preset time period. i ρ0 refers to the density of ice, p is the precipitation in the target area during the preset time period, U is the predicted linear velocity of the blade tip of each wind turbine at the target time point, and M is the subcooled water content in the target area during the preset time period.

[0079] When the ground temperature is below zero degrees Celsius and the relative humidity is above 90%, freezing fog is considered to exist. In this case, the second incremental prediction value of icing for each wind turbine within a preset time period is determined according to formula (3).

[0080] E fog = ×U×Δt×p air ×β (3)

[0081] Among them, E fogThis refers to the second incremental predicted value of icing for each wind turbine within a preset time period; M refers to the supercooled water content in the target area within the preset time period; U refers to the predicted linear velocity of the blade tip of each wind turbine at the target time point; Δt refers to the preset time period; and ρ... air β refers to the atmospheric density of the target area during a preset time period, while β refers to the freezing coefficient of each wind turbine during the preset time period.

[0082] Step 5: Combining the first incremental forecast values ​​of wind turbines during freezing rain and freezing fog, calculate the predicted icing thickness during the numerical weather prediction model integration process. The calculation results are as follows: Figure 3 As shown, it was predicted that the wind turbines at this wind farm would begin icing at 00:00 on February 13th, and the icing would continue to increase until reaching its thickest point that evening. In reality, the turbines began to shut down gradually on the morning of February 13th, and all turbine capacity was taken out of standby by the afternoon of the same day. It was predicted that the icing would rapidly weaken from the early morning of February 15th, and the turbines were gradually restarted in the early morning of the 15th, with all turbines resuming operation by the morning of the same day, thus concluding the turbine icing and standby process. In summary, the predicted turbine icing situation is highly consistent with the actual turbine operation. This method has good predictive value for wind turbine blade icing and can provide important guidance for wind farm power forecasting, station operation and maintenance, and scheduling planning.

[0083] Using the aforementioned method, device, storage medium, and processor for predicting wind turbine icing thickness, and employing a numerical weather prediction model, predicted meteorological data for a predetermined time period prior to the target time point can be determined for the target area. Based on predicted meteorological data such as atmospheric temperature, ground temperature, precipitation, and supercooled water content, the predicted icing state of each wind turbine in the target area at the target time point is determined. The blade parameters of each wind turbine in the target area are acquired, and the predicted linear velocity of the blade tip at the target time point is determined based on the predicted meteorological data and the blade parameters. Based on the predicted icing state, predicted linear velocity, and predicted meteorological data for each wind turbine, the predicted icing increment for freezing rain and freezing fog during the predetermined time period is determined for each wind turbine. The predicted icing thickness at the target time point is then determined based on the predicted icing increment. By accurately predicting the future icing thickness of wind turbines based on the impact of meteorological conditions on wind turbine icing, dispatching departments can scientifically guide the adjustment of operating modes in advance, protecting the service life and normal operation of wind turbine generators. It can predict the icing thickness of wind turbine blades, guide the prediction of wind turbine reserve capacity and advance scheduling plans, and ensure power supply in winter.

[0084] Figure 1 This is a flowchart illustrating a method for predicting wind turbine icing thickness in one embodiment. It should be understood that, although... Figure 1The 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 1 At 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.

[0085] In one embodiment, such as Figure 4 As shown, a device for predicting wind turbine icing thickness is provided, including a weather forecasting module, an icing state prediction module, a wind turbine parameter prediction module, and an icing thickness prediction module, wherein:

[0086] The weather forecast module 402 is used to determine the forecast weather data for the target area during a preset time period before the target time point;

[0087] The icing state prediction module 404 is used to determine the predicted icing state of each wind turbine in the target area at the target time point based on the predicted meteorological data.

[0088] The wind turbine parameter prediction module 406 is used to acquire the blade parameters of each wind turbine in the target area and determine the predicted linear velocity of the blade tip of each wind turbine at the target time point based on the predicted meteorological data and the blade parameters of each wind turbine.

[0089] The icing thickness prediction module 408 is used to determine the predicted icing increment value of each wind turbine in a preset time period based on the predicted icing status, predicted linear velocity and predicted meteorological data of each wind turbine, and to determine the predicted icing thickness value of each wind turbine at the target time point based on the predicted icing increment value.

[0090] In one embodiment, the predicted meteorological data includes atmospheric temperature, ground temperature, and relative humidity. The icing state prediction module 404 is further configured to: determine the ice content of precipitation particles falling to the ground within a preset time period based on the atmospheric temperature; determine the predicted icing state of each wind turbine at the target time point as freezing rain icing state when the ice content is less than a preset threshold and the ground temperature is less than a preset temperature; and determine the predicted icing state of each wind turbine at the target time point as freezing fog icing state when the relative humidity is greater than a preset relative humidity and the ground temperature is less than a preset temperature.

[0091] In one embodiment, the ice thickness prediction module 408 is further configured to calculate the ice content according to formula (1):

[0092]

[0093] Where I refers to the ice content of precipitation particles falling to the ground within a preset time period, W refers to the atmospheric thermal conductivity of the target area within a preset time period, z refers to the descent altitude of precipitation particles falling to the ground within a preset time period, T refers to the atmospheric temperature of the target area within a preset time period, and p i "r" refers to the density of ice, and "r" refers to the radius of the precipitation particles.

[0094] In one embodiment, the predicted meteorological data further includes precipitation and supercooled water content, and the icing thickness prediction module 408 is further configured to: for each wind turbine, if the predicted icing state of the wind turbine is freezing rain icing, determine a first incremental predicted value of icing of the wind turbine within a preset time period based on precipitation, supercooled water content, and the predicted linear velocity of the wind turbine blades; for each wind turbine, if the predicted icing state of the wind turbine is freezing fog icing, determine a second incremental predicted value of icing of the wind turbine within a preset time period based on supercooled water content and the predicted linear velocity of the wind turbine blades; and for each wind turbine, determine an incremental predicted value of icing of the wind turbine within a preset time period based on the first incremental predicted value and / or the second incremental predicted value.

[0095] In one embodiment, the icing thickness prediction module 408 is further configured to: determine a first incremental prediction value of icing for each wind turbine within a preset time period according to formula (2):

[0096]

[0097] Among them, E frz This refers to the first incremental prediction value, p, for each wind turbine within a preset time period. i ρ0 refers to the density of ice, p is the precipitation in the target area during the preset time period, U is the predicted linear velocity of the blade tip of each wind turbine at the target time point, and M is the subcooled water content in the target area during the preset time period.

[0098] In one embodiment, the icing thickness prediction module 408 is further configured to: determine a second incremental prediction value of the icing of each wind turbine in a preset time period according to formula (3);

[0099] E fog = ×U×Δt×p air ×β (3)

[0100] Among them, E fog This refers to the second incremental predicted value of icing for each wind turbine within a preset time period; M refers to the supercooled water content in the target area within the preset time period; U refers to the predicted linear velocity of the blade tip of each wind turbine at the target time point; Δt refers to the preset time period; and ρ... airβ refers to the atmospheric density of the target area during a preset time period, while β refers to the freezing coefficient of each wind turbine during the preset time period.

[0101] In one implementation, the meteorological data includes wind speed, and the blade parameters include blade rotation radius and blade rotation number. The wind turbine parameter prediction module 406 is also used to: determine the tip speed ratio of each wind turbine based on the blade rotation radius and blade rotation number of each wind turbine; determine the tip linear velocity of each wind turbine based on the tip speed ratio of each wind turbine; and determine the predicted tip linear velocity of each wind turbine at the target time point based on the wind speed and the tip linear velocity of each wind turbine.

[0102] The wind turbine icing thickness prediction device includes a processor and a memory. The meteorological prediction module, icing state prediction module, wind turbine parameter prediction module, and icing thickness prediction 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.

[0103] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and methods for predicting wind turbine icing thickness can be implemented by adjusting kernel parameters.

[0104] 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.

[0105] This application provides a storage medium storing a program that, when executed by a processor, implements the aforementioned method for predicting wind turbine icing thickness.

[0106] This application provides a processor for running a program, wherein the program executes the above-described method for predicting wind turbine icing thickness.

[0107] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5As shown, the computer device includes a processor A01, a network interface A02, memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computational and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). 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 A04. The database stores data for a method to predict wind turbine icing thickness. The network interface A02 communicates with external terminals via a network connection. When the processor A01 executes the computer program B02, it implements a method for predicting wind turbine icing thickness.

[0108] Those skilled in the art will understand that Figure 5 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.

[0109] This application provides an apparatus including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of a method for predicting the thickness of icing on a wind turbine.

[0110] This application also provides a computer program product that, when executed on a data processing device, is suitable for performing the steps of a method for initializing the prediction of wind turbine icing thickness.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0116] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0117] 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.

[0118] 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.

[0119] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for predicting the thickness of icing on wind turbines, characterized in that, The prediction method includes: Determine the predicted meteorological data for the target area during a preset time period before the target time point, wherein the predicted meteorological data includes precipitation and supercooled water content; Based on the predicted meteorological data, determine the predicted icing status of each wind turbine in the target area at the target time point; Obtain the blade parameters of each wind turbine in the target area; The predicted linear velocity of the blade tip of each wind turbine at the target time point is determined based on the predicted meteorological data and the blade parameters of each wind turbine. The predicted icing increment for each wind turbine during the preset time period is determined based on the predicted icing status, predicted linear velocity, and the predicted meteorological data for each wind turbine. The predicted icing thickness for each wind turbine at the target time point is determined based on the predicted icing increment value. The step of determining the predicted icing increment for each wind turbine within a preset time period based on the predicted icing state, predicted linear velocity, and predicted meteorological data includes: for each wind turbine, if the predicted icing state is freezing rain icing, determining a first predicted icing increment for the wind turbine within the preset time period based on the precipitation, the supercooled water content, and the predicted linear velocity of the wind turbine blades; for each wind turbine, if the predicted icing state is freezing fog icing, determining a second predicted icing increment for the wind turbine within the preset time period based on the supercooled water content and the predicted linear velocity of the wind turbine blades; and for each wind turbine, determining the predicted icing increment for the wind turbine within the preset time period based on the first and / or the second predicted icing increment. For each wind turbine, when the predicted icing state of the wind turbine is freezing rain icing, determining the first incremental predicted value of icing for the wind turbine in the preset time period based on the precipitation, the supercooled water content, and the predicted linear velocity of the wind turbine blades includes determining the first incremental predicted value of icing for each wind turbine in the preset time period according to formula (2): （2） in, This refers to the first incremental predicted value for each wind turbine within the preset time period. This refers to the density of ice. ρ refers to the density of water, p is the precipitation in the target area during the preset time period, U is the predicted linear velocity of the blade tip of each wind turbine at the target time point, and M is the subcooled water content in the target area during the preset time period. For each wind turbine, when the predicted icing state of the wind turbine is freezing fog icing state, determining the second incremental predicted value of the icing of the wind turbine in the preset time period based on the supercooled water content and the predicted linear velocity of the wind turbine blades includes determining the second incremental predicted value of the icing of each wind turbine in the preset time period according to formula (3). （3） in, This refers to the second incremental predicted value of icing for each wind turbine within the preset time period, M refers to the subcooled water content in the target area within the preset time period, and U refers to the predicted linear velocity of the blade tip of each wind turbine at the target time point. This refers to the preset time period. This refers to the atmospheric density of the target area during the preset time period. This refers to the freezing coefficient of each wind turbine during the preset time period.

2. The method for predicting wind turbine icing thickness according to claim 1, characterized in that, The predicted meteorological data also includes atmospheric temperature, ground temperature, and relative humidity. Determining the predicted icing status of each wind turbine in the target area at the target time point based on the predicted meteorological data includes: The ice content of precipitation particles falling to the ground during the preset time period is determined based on the atmospheric temperature. If the ice content is less than a preset threshold and the ground temperature is less than a preset temperature, the predicted icing state of each wind turbine at the target time point is determined to be a freezing rain icing state. When the relative humidity is greater than the preset relative humidity and the ground temperature is less than the preset temperature, the predicted icing state of each wind turbine at the target time point is determined to be a freezing fog icing state.

3. The method for predicting wind turbine icing thickness according to claim 2, characterized in that, The step of determining the ice content of precipitation particles falling to the ground during the preset time period based on the atmospheric temperature includes calculating the ice content according to formula (1): （1） in, This refers to the ice content of precipitation particles that fall to the ground within a preset time period. z ... This refers to the density of ice. This refers to the radius of the precipitation particles.

4. The method for predicting wind turbine icing thickness according to claim 1, characterized in that, The predicted meteorological data includes wind speed, and the blade parameters include blade rotation radius and blade rotation number. Determining the predicted linear velocity of the blade tip of each wind turbine at the target time point based on the predicted meteorological data and the blade parameters of each wind turbine includes: The tip speed ratio of each fan is determined based on the blade rotation radius and blade rotation speed of each fan. The tip linear velocity of each fan is determined based on the tip speed ratio of each fan. The predicted linear velocity of each wind turbine tip at the target time point is determined based on the wind speed and the tip linear velocity of each wind turbine.

5. A processor, characterized in that, It is configured to perform the wind turbine icing thickness prediction method according to any one of claims 1 to 4.

6. A device for predicting the thickness of icing on wind turbines, characterized in that, include: The weather forecasting module is used to determine the predicted weather data for the target area during a preset time period before the target time point; An icing state prediction module is used to determine the predicted icing state of each wind turbine in the target area at the target time point based on the predicted meteorological data. The wind turbine parameter prediction module is used to acquire the blade parameters of each wind turbine in the target area, and determine the predicted linear velocity of the blade tip of each wind turbine at the target time point based on the predicted meteorological data and the blade parameters of each wind turbine. The icing thickness prediction module is used to determine the predicted icing increment value of each wind turbine in the preset time period based on the predicted icing status, predicted linear velocity and the predicted meteorological data of each wind turbine, and to determine the predicted icing thickness value of each wind turbine at the target time point based on the predicted icing increment value. The predicted meteorological data includes precipitation and supercooled water content. Determining the predicted icing increment for each wind turbine within a preset time period based on its predicted icing state, predicted linear velocity, and the predicted meteorological data includes: for each wind turbine, if its predicted icing state is freezing rain icing, determining a first predicted icing increment for the wind turbine within the preset time period based on the precipitation, the supercooled water content, and the predicted linear velocity of the wind turbine blades; for each wind turbine, if its predicted icing state is freezing fog icing, determining a second predicted icing increment for the wind turbine within the preset time period based on the supercooled water content and the predicted linear velocity of the wind turbine blades; and for each wind turbine, determining the predicted icing increment for the wind turbine within the preset time period based on the first and / or the second predicted icing increment. For each wind turbine, when the predicted icing state of the wind turbine is freezing rain icing, determining the first incremental predicted value of icing for the wind turbine in the preset time period based on the precipitation, the supercooled water content, and the predicted linear velocity of the wind turbine blades includes determining the first incremental predicted value of icing for each wind turbine in the preset time period according to formula (2): （2） in, This refers to the first incremental predicted value for each wind turbine within the preset time period. This refers to the density of ice. ρ refers to the density of water, p is the precipitation in the target area during the preset time period, U is the predicted linear velocity of the blade tip of each wind turbine at the target time point, and M is the subcooled water content in the target area during the preset time period. For each wind turbine, when the predicted icing state of the wind turbine is freezing fog icing state, determining the second incremental predicted value of the icing of the wind turbine in the preset time period based on the supercooled water content and the predicted linear velocity of the wind turbine blades includes determining the second incremental predicted value of the icing of each wind turbine in the preset time period according to formula (3). （3） in, This refers to the second incremental predicted value of icing for each wind turbine within the preset time period, M refers to the subcooled water content in the target area within the preset time period, and U refers to the predicted linear velocity of the blade tip of each wind turbine at the target time point. This refers to the preset time period. This refers to the atmospheric density of the target area during the preset time period. This refers to the freezing coefficient of each wind turbine during the preset time period.

7. A machine-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform a method for predicting wind turbine icing thickness according to any one of claims 1 to 4.

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