Method, device, computer equipment and storage medium for predicting wind turbine blade status

Abstract

The present application discloses a method, apparatus, computer device, and storage medium for predicting the status of wind turbine blades, relating to the field of wind power generation technology. The method comprises: obtaining an nth meteorological parameter corresponding to a target wind turbine blade at the nth moment, and an nth ice accumulation mass corresponding to the nth moment, where n is a positive integer; determining a target determination method corresponding to the nth ice accumulation mass at the nth moment based on the nth meteorological parameter and the nth ice accumulation mass; determining an nth ice accumulation mass based on the target determination method and the nth ice accumulation mass; and predicting the icing status of the target wind turbine blade at the nth moment based on the nth ice accumulation mass. This method provides a method for predicting the icing status of wind turbine blades, providing a more scientific basis for wind turbine equipment maintenance.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of wind power generation technology, and in particular to a method, apparatus, computer equipment, and storage medium for predicting the status of wind turbine blades. Background Art

[0002] In response to global warming, the clean energy industry has developed rapidly in recent years. Among them, wind power generation is one of the main technologies for new energy power generation.

[0003] Under low temperature and high humidity conditions, wind turbine blades may freeze (ice accumulate), which may cause power loss. In severe cases, it may lead to wind turbine blade breakage, resulting in serious economic losses and safety hazards. Obviously, in the wind power generation scenario, how to predict future freezing of the wind farm in advance is the basis for improving the operation and maintenance efficiency of wind turbines. Summary of the Invention

[0004] The present invention provides a method, apparatus, computer device, and storage medium for predicting the status of wind turbine blades. The technical solution is as follows:

[0005] In one aspect, a method for predicting a state of a wind turbine blade is provided, the method comprising:

[0006] Obtain the nth meteorological element parameter of the target wind turbine blade at the nth moment, and the n-1th ice accumulation mass corresponding to the n-1th moment, where n is a positive integer;

[0007] determining a target determination method for the nth ice accumulation mass corresponding to the nth moment based on the nth meteorological element parameter and the n-1th ice accumulation mass;

[0008] determining the nth ice accretion mass based on the target determination method and the n-1th ice accretion mass;

[0009] Based on the nth ice accumulation mass, an icing state of the target wind turbine blade at the nth moment is predicted.

[0010] In another aspect, a device for predicting a state of a wind turbine blade is provided, the device comprising:

[0011] An acquisition module is used to obtain the nth meteorological element parameter of the target wind turbine blade at the nth moment and the n-1th ice accumulation mass corresponding to the n-1th moment, where n is a positive integer;

[0012] a first determining module, configured to determine a target determination method for an nth ice accretion mass corresponding to the nth moment based on the nth meteorological element parameter and the n-1th ice accretion mass;

[0013] a second determining module, configured to determine the nth ice accretion mass based on the target determination method and the n-1th ice accretion mass;

[0014] The third determining module is configured to predict an icing state of the target wind turbine blade at the nth moment based on the nth ice accumulation mass.

[0015] On the other hand, a computing device is provided, which includes a processor and a memory; the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the method for predicting the state of a wind turbine blade as described in the above aspect.

[0016] On the other hand, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, and the at least one instruction is configured to be executed by a processor to implement the method for predicting a status of a wind turbine blade as described in the above aspect.

[0017] In another aspect, a computer program product or computer program is provided. The computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method for predicting a wind turbine blade state provided in any of the aforementioned optional implementations.

[0018] The technical solution provided by this application may have the following beneficial effects:

[0019] In an embodiment of the present application, a method for predicting the icing state of a wind turbine blade is provided. By analyzing the meteorological element parameters corresponding to the target wind turbine blade at the nth moment and the ice accumulation mass corresponding to the n-1th moment, a target determination method for the nth ice accumulation mass corresponding to the nth moment is determined. Then, based on the target determination method and the n-1th ice accumulation mass, the nth ice accumulation mass corresponding to the nth moment is predicted. By analogy, the corresponding icing state of the wind turbine equipment within a preset time period in the future can be predicted, providing a more scientific basis for the maintenance of the wind turbine equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0021] Figure 1 A schematic structural diagram of a system corresponding to a method for predicting a wind turbine blade state provided by an exemplary embodiment of the present application is shown;

[0022] Figure 2A flow chart showing a method for predicting a status of a wind turbine blade provided by an exemplary embodiment of the present application is shown;

[0023] Figure 3 A flow chart showing a method for predicting a status of a wind turbine blade provided by another exemplary embodiment of the present application is shown;

[0024] Figure 4 A flowchart for obtaining the nth meteorological element parameter is shown in an exemplary embodiment of the present application;

[0025] Figure 5 A flow chart of a method for determining ice accretion mass provided by an exemplary embodiment of the present application is shown;

[0026] Figure 6 A block diagram of an apparatus for predicting a wind turbine blade state provided by an exemplary embodiment of the present application is shown;

[0027] Figure 7 It is a structural block diagram of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0028] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0029] It should be understood that the term "several" in this document refers to one or more, and "multiple" refers to two or more. "And / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exists simultaneously, or B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0030] Wind turbines operate in cold regions. In such environments, they are subject to meteorological conditions such as frost, sleet, and wet snow, making blades susceptible to icing. This can lead to a series of consequences, including the following hazards:

[0031] 1) The airfoil of wind turbine blades changes after ice formation, resulting in a decrease in wind energy capture capability. In addition, the ice layer attached to the blades increases the energy required for blade rotation, ultimately leading to power loss of wind power generation.

[0032] 2) When wind turbine blades are frozen, the structural parameters of some blades are changed, which in turn affects their inherent modal parameters and induces blade fracture.

[0033] 3) When ice accumulates to a certain extent on the wind turbine blades, the ice layer breaks and flies out due to its own weight, which can easily hit the wind farm inspection personnel and cause personal accidents.

[0034] Therefore, timely detection and elimination of blade icing failures are of great significance for extending the service life of wind power equipment and preventing major safety accidents. During the operation and maintenance of wind turbine power stations in cold areas, it is necessary to check whether the wind turbine blades of the wind turbine are in an icing state. The present application provides a method for predicting the state of wind turbine blades, which can predict the icing state of wind turbine blades in a future preset period in the wind farm, such as the next 72 hours, so that the operation and maintenance personnel can make corresponding operation and maintenance preparations in advance based on the icing state, thereby improving the operation and maintenance efficiency of the wind farm. For ease of understanding, the terms involved in the embodiments of the present application are explained below.

[0035] The Weather and Research Forecasting Model (WRF): The WRF model obtains forecasts of future meteorological elements by numerically solving a set of governing equations consisting of Newton's laws of motion and the laws of thermodynamics.

[0036] Figure 1 FIG. 1 shows a schematic diagram of a system structure corresponding to a method for predicting a wind turbine blade state provided by an exemplary embodiment of the present application. Figure 1 As shown, the system includes: a wind turbine power station 101 and a monitoring platform 102.

[0037] Wind turbine station 101 includes multiple wind turbines, each of which includes wind turbine blades and a nacelle. In an embodiment of the present application, wind turbine station 101 may be equipped with multiple sensors for collecting data from the hierarchical power station. For example, these sensors may include temperature sensors, wind speed sensors, and the like, for collecting parameters such as ambient temperature and wind speed in wind turbine station 101 and transmitting the collected sensor data to monitoring platform 102.

[0038] The wind turbine power station 101 and the monitoring platform 102 are connected via a wired or wireless network.

[0039] The monitoring platform 102 is a computer device with functions such as storing data sent by the wind turbine power station 101, processing the data, and generating alarm records. The computer device can be a server or a server cluster or cloud server composed of several servers, or the computer device can also be implemented as a terminal. This application does not limit the implementation form of the computer device.

[0040] For ease of description, in the following method embodiment, the monitoring platform 102 is described as a computer device to illustrate the method for predicting the status of wind turbine blades provided in this application.

[0041] Figure 2 A flowchart of a method for predicting the status of a wind turbine blade provided by an exemplary embodiment of the present application is shown. The method for predicting the status of a wind turbine blade can be executed by a computer device, which can be implemented as Figure 1 The monitoring platform shown in FIG. 1 includes:

[0042] Step 201 : Obtain the nth meteorological parameter corresponding to the target wind turbine blade at the nth moment, and the n-1th ice accumulation mass corresponding to the n-1th moment, where n is a positive integer.

[0043] In the related art, in order to determine the icing status of each wind turbine blade in a wind farm, manual inspection may be required, and the mass of ice accumulation on the wind turbine blades cannot be measured by sensors or other measuring equipment, and manual inspection is difficult, time-consuming and labor-intensive. In order to improve the operation and maintenance efficiency of wind turbine equipment, in one possible implementation method, the icing status of the wind turbine blades is predicted based on the meteorological element parameters of the environment in which the wind turbine equipment is located, so that the operation and maintenance personnel can perform targeted maintenance on certain wind turbine equipment based on the predicted results of the icing status, thereby improving the operation and maintenance efficiency.

[0044] Meteorological parameters are obtained through the WRF model forecast, and primarily include temperature, air pressure, humidity, wind speed, water vapor content, liquid water content, and downward shortwave radiation to the surface. In one possible application scenario, the WRF model can be used to predict meteorological parameters for a preset time period in the future based on initial field data. The preset time period can be the next 72 hours.

[0045] In principle, before running the WRF model, you need to configure the WRF model, set the horizontal resolution to 2 km, 35 vertical layers, use the BouLac scheme for the boundary layer, use Noah-MP for the land surface model, use RRTMG for the radiation scheme (used to calculate the atmospheric radiation flux), and use the WSM6 scheme for microphysics.

[0046] It should be noted that before actually running the WRF mode, you can select configuration parameters according to the actual wind farm requirements and configure the WRF mode. The embodiment of this application does not limit the parameters configured during mode configuration.

[0047] Since the actual positions of each wind turbine blade or wind turbine device in the wind farm are different, when analyzing the icing status of the wind turbine blades, it is necessary to analyze the individual wind turbine device or wind blade. Therefore, in a possible implementation, after the meteorological element parameters are output through the WRF mode, it is also necessary to perform data analysis and processing on the meteorological element parameters. The wrf_user_vert_interp function in the meteorological data processing (NCAR Command Language, NCL) can be used to interpolate the meteorological element parameters predicted by the WRF model to obtain the meteorological element parameters corresponding to the wind turbine hub height. Then, based on the location of each wind turbine device, the meteorological element parameters corresponding to each wind turbine device are extracted, that is, the meteorological element parameters corresponding to different wind blades at each moment are obtained, wherein the meteorological element parameters corresponding to the nth moment are the nth meteorological element parameters.

[0048] Regarding the method of predicting the icing state corresponding to the target fan blade at the nth moment, since the icing state of the target fan blade is related to the environmental factors in which the target fan blade is located, and whether the target fan blade has ice accumulation at the previous moment, therefore, in a possible implementation method, it is necessary to obtain the nth meteorological element parameter corresponding to the environment in which the target fan blade is located at the nth moment, and the nth meteorological element parameter is used to determine whether the target fan blade will be frozen at the nth moment, and the ice accumulation rate under the icing state, and obtain the ice accumulation mass of the target fan blade at the n-1th moment, and then predict the nth ice accumulation mass of the target fan blade at the nth moment based on the nth meteorological element parameter and the n-1th ice accumulation mass.

[0049] When n is 1, n-1 is 0, and the n-1th ice accretion mass is the ice accretion mass corresponding to the initial moment. The n-1th ice accretion mass can be determined in the following ways: when the wind farm is initially operating, the n-1th ice accretion mass can be 0; when the wind farm is not initially operating, the n-1th ice accretion mass is the ice accretion mass predicted at the n-1th moment.

[0050] Step 202 : Based on the nth meteorological element parameter and the n-1th ice accumulation mass, a target determination method corresponding to the nth ice accumulation mass at the nth moment is determined.

[0051] When the nth meteorological element parameter indicates that the target wind turbine blades at the nth moment do not have the icing conditions, for example, when the temperature is high, the wind turbine blades will not freeze. Then, if ice is accumulated on the target wind turbine blades themselves, the nth ice accumulation mass needs to be calculated using the ice melting physical model. Conversely, if the nth meteorological element parameter indicates that the target wind turbine blades at the nth moment have the icing conditions, the nth ice accumulation mass needs to be calculated using the ice accumulation physical model. Therefore, in one possible implementation, in order to predict the nth ice accumulation mass corresponding to the nth moment, it is necessary to determine a target determination method for the nth ice accumulation mass based on the nth meteorological element parameter and the n-1th ice accumulation mass, and then use this target determination method to predict the nth ice accumulation mass.

[0052] Step 203 : Determine the nth ice accretion mass based on the target determination method and the n-1th ice accretion mass.

[0053] In a possible implementation, after the target determination method corresponding to the nth ice accretion mass at the nth moment is determined, the nth ice accretion mass may be calculated based on the target determination method and the (n-1)th ice accretion mass.

[0054] Step 204 : predicting the icing state of the target wind turbine blade at the nth moment based on the nth ice accumulation mass.

[0055] In one possible implementation, after predicting the nth ice accretion mass corresponding to the target wind blade at time n, the icing state of the target wind blade at time n can be predicted based on the nth ice accretion mass. For example, if the nth ice accretion mass is greater than 0, it indicates that ice accretion is present on the target wind blade; if the nth ice accretion mass is less than 0, it indicates that ice accretion is not present on the target wind blade.

[0056] Optionally, since the WRF mode can provide meteorological element parameters within a future preset time period, the ice accumulation mass corresponding to each moment within the future preset time period can be predicted based on the meteorological element parameters within the preset time period and the initial ice accumulation mass, and then the icing state of each wind turbine equipment in the wind farm within the future preset time period can be determined based on the ice accumulation mass, so as to judge whether the wind turbine equipment needs to be maintained based on the icing state, and which wind turbine equipment with more serious icing conditions should be maintained in advance, providing a certain data basis for operation and maintenance personnel to perform wind turbine maintenance.

[0057] In summary, an embodiment of the present application provides a method for predicting the icing state of wind turbine blades. By analyzing the meteorological element parameters corresponding to the target wind turbine blade at the nth moment and the ice accumulation mass corresponding to the n-1th moment, a target determination method corresponding to the nth ice accumulation mass at the nth moment is determined. Then, based on the target determination method and the n-1th ice accumulation mass, the nth ice accumulation mass corresponding to the nth moment is predicted. By analogy, the corresponding icing state of the wind turbine equipment within a preset time period in the future can be predicted, providing a more scientific basis for the maintenance of the wind turbine equipment.

[0058] In order to predict the mass of ice accumulation, an embodiment of the present application provides a freezing model, which is composed of an ice accumulation physical model and an ice melting physical model, so that the nth ice accumulation mass can be calculated by analyzing the nth meteorological element parameters and the n-1th ice accumulation mass using a suitable physical model.

[0059] Please refer to Figure 3 , which shows a flow chart of a method for predicting the status of a wind turbine blade provided by another exemplary embodiment of the present application. The method for predicting the status of a wind turbine blade can be executed by a computer device, which can be implemented as Figure 1 The monitoring platform shown in FIG. 1 includes:

[0060] Step 301 : Obtain the nth meteorological parameter corresponding to the target wind turbine blade at the nth moment, and the n-1th ice accumulation mass corresponding to the n-1th moment, where n is a positive integer.

[0061] When determining the nth meteorological element parameter corresponding to the target wind turbine blade at the nth moment based on the meteorological element parameters predicted by the WRF model, since the WRF model has a certain spatial resolution when forecasting, when obtaining the meteorological element information corresponding to the target wind turbine blade, the meteorological element information corresponding to the grid point closest to the target wind turbine blade is obtained, which is somewhat different from the actual terrain height where the wind turbine equipment is located. The terrain height will affect the air temperature where the wind turbine equipment is located, and thus affect the accuracy of the subsequent prediction of ice accumulation mass. Therefore, in one possible implementation, when extracting the predicted meteorological element parameter corresponding to the grid point where the target wind turbine blade (target wind equipment) is located from the meteorological element parameters predicted by the WRF model, it is also necessary to perform terrain correction on its temperature to obtain the corrected nth meteorological element parameter.

[0062] In an exemplary example, the process of determining the nth temperature value in the nth meteorological element parameter may include step 301A and step 301B.

[0063] Step 301A: Obtain the predicted temperature value corresponding to the target wind blade obtained through the WRF model forecast, the actual terrain height where the target wind blade is located, and the model terrain height corresponding to the target wind blade in the WRF model.

[0064] Since the WRF model has a certain spatial resolution, there is a certain difference between the actual terrain height and the model terrain height, which leads to a certain difference between the actual air temperature of the target fan blade and the predicted temperature value. The accuracy of the predicted air temperature of the target fan blade will affect the accuracy of the subsequent prediction of ice accumulation mass. Therefore, in one possible implementation, it is necessary to perform terrain correction on the predicted temperature value output by the WRF model. In this case, it is necessary to obtain the predicted temperature value corresponding to the target fan blade predicted by the WRF model, the actual terrain height where the target fan blade is located, and the model terrain height corresponding to the target fan blade in the WRF model, and then correct the predicted temperature value based on the difference between the model terrain height and the actual terrain height.

[0065] Step 301B: correct the predicted temperature value based on the actual terrain height, the model terrain height, and the humidity and heat lapse rate to obtain a corrected nth temperature value.

[0066] In an exemplary example, the relationship between the actual terrain height, the model terrain height, the predicted temperature value, and the nth temperature value (corrected temperature value) can be expressed as:

[0067] T a =T0-(H real -H model )×γ d (1)

[0068] Among them, T a represents the corrected temperature value (nth temperature value), T0 represents the predicted temperature value predicted by the WRF model, and H real Indicates the actual terrain height where the target wind turbine blades are located, H model represents the model terrain height of the target wind turbine blade in the WRF mode, γ d It represents the moist adiabatic lapse rate, where the moist adiabatic lapse rate is 0.65℃ / 100m.

[0069] In a possible implementation, based on the acquired actual terrain height, model terrain height, and wet adiabatic lapse rate of the target wind turbine blade, a terrain correction is performed on the predicted temperature value corresponding to the nth moment to obtain a corrected nth temperature value.

[0070] It should be noted that, in the embodiment of the present application, terrain correction is only performed on the predicted temperature value predicted by the WRF model, and other meteorological element parameters still use the parameter values ​​predicted by the WRF model.

[0071] like Figure 4 As shown, a flowchart for obtaining the nth meteorological parameter is shown in an exemplary embodiment of the present application. Meteorological parameters such as temperature, air pressure, humidity, wind speed, water vapor content, liquid water content in clouds, and downward shortwave radiation are obtained from the WRF model forecast. These three-dimensional meteorological parameters are interpolated to the two-dimensional meteorological parameters corresponding to the height of the wind turbine hub, and the two-dimensional meteorological parameters corresponding to the wind turbine position are extracted from them. Simultaneously, the model terrain height corresponding to the wind turbine is extracted, and the wind turbine's true altitude is obtained. Based on the true altitude, the temperature in the meteorological parameters is then topographically corrected to obtain the corrected temperature. This corrected temperature and other meteorological parameters are then input into the freezing model to predict ice accumulation mass.

[0072] Step 302: Obtain the nth temperature value and the nth liquid water content in the nth meteorological element parameter.

[0073] The freezing model in this embodiment includes an ice accumulation physics model and an ice melting physics model. Whether ice accumulation will occur at the current moment is related to the temperature and liquid water content of the current environment. Therefore, in one possible implementation, it is necessary to obtain the nth temperature value and the nth liquid water content in the nth meteorological element parameter to determine the target determination method of the nth ice accumulation mass.

[0074] Step 303 : When the nth temperature value is less than the temperature threshold and the nth liquid water content is greater than the water content threshold, the nth ice accumulation mass is determined using the ice accumulation physical model.

[0075] Among them, the ice accretion physical model is used to calculate the ice accretion rate of the target wind turbine blade. If the ice accretion physical model is required, the nth meteorological element parameter corresponding to the environment in which the target wind turbine blade is located needs to meet the icing conditions, corresponding to the air temperature being below 0°C and the presence of liquid water in the air. Therefore, in one possible implementation, when it is determined that the nth temperature value is less than the temperature threshold and the nth liquid water content is greater than the water content threshold, it indicates that the target wind turbine blade meets the icing conditions, and the ice accretion physical model can be used to determine the nth ice accumulation mass.

[0076] Schematically, the temperature threshold is 0°C and the water content threshold is 0 kg / m 3 .

[0077] In step 304 , when the nth ice accretion mass is determined by the ice accretion physical model, the nth ice accretion rate corresponding to the nth moment is calculated by the ice accretion physical model.

[0078] In one possible implementation, when an ice accretion physical model is used to determine the nth ice accretion mass, it is necessary to calculate the nth ice accretion rate corresponding to the nth moment through the ice accretion physical model, and then determine the nth ice accretion mass based on the nth ice accretion rate, the n-1th ice accretion mass, and the interval duration. Since the interval duration in this embodiment is the time difference between the n-1th moment and the nth moment, with a unit time of 1 hour, it can be equivalent to determining the nth ice accretion mass based on the nth ice accretion rate and the n-1th ice accretion mass.

[0079] In one illustrative example, the process of determining the nth ice accretion rate may include step 304A.

[0080] Step 304A: Calculate the nth ice accretion rate based on the collision coefficient, the adhesion coefficient, the icing coefficient, the nth wind speed, the nth liquid water content, and the cross-sectional area.

[0081] In one possible implementation, it can be seen from the ice accumulation physical model that the ice accumulation rate is calculated by the collision coefficient, adhesion coefficient, freezing coefficient, liquid water content, particle velocity vector, and the cross-sectional area of ​​the object relative to the direction of motion of the colliding particles. When applied to the scenario of calculating the ice accumulation rate of wind turbine blades in the embodiment of the present application, the particle velocity vector is equivalent to the wind speed in the meteorological element parameters, and the cross-sectional area of ​​the object relative to the direction of motion of the colliding particles is the cross-sectional area of ​​the target wind turbine blade relative to the direction of motion of the water droplets, which can be simplified to unit area.

[0082] In an exemplary example, the ice accretion physical model can be expressed as follows (that is, the ice accretion rate calculation process can be expressed as follows):

[0083] dM / dt=α1α2α3LWCυΛ (2)

[0084] Where dM / dt represents the ice accretion rate, α1 represents the collision coefficient, α2 represents the adhesion coefficient, α3 represents the icing coefficient, LWC represents the liquid water content, υ represents the velocity vector of the particle, which is approximately equal to the wind speed, and Λ represents the cross-sectional area of ​​the target wind turbine blade relative to the direction of water droplet movement.

[0085] It should be noted that α1 is closely related to the size of the particles. For freezing rain weather, α1 is approximately 1, and for small raindrops, α1 = A-0.028-C (B-0.0454).

[0086] A=1.066K -0.00616 exp(-1.103K -0.688 ) (3)

[0087] Where A can be calculated by formula (3), and K is a dimensionless parameter, and the calculation formula is as follows:

[0088] K=ρw d 2 / 9μD (4)

[0089] Among them, ρ w represents the water droplet density, d represents the median volume diameter of the droplet, D represents the blade diameter corresponding to the target fan blade, which can be set to unit length, and μ represents the air viscosity, which is related to the air temperature T, that is, it is determined by the nth temperature value. The calculation formula is as follows:

[0090] μ=1.458*10 -6 *T 1.5 / (T+110.4) (5)

[0091] The calculation formulas for B and C are shown in formula (6) and formula (7) respectively:

[0092] B=3.641K -0.498 exp(-1.497 K -0.694 ) (6)

[0093]

[0094] in, Re is the Reynolds number, Re=ρ a dv / μ, v represents wind speed, ρ a represents the air density, which is calculated from the temperature, water vapor content, and air pressure:

[0095] ρ a =10000 / (287*T(1+0.61Qvapor)) (8)

[0096] α2 is approximately 1, and α3 is approximately 1 in dry growth. In wet growth, it is related to latent heat. The calculation formula is as follows:

[0097] α3=[(h+6a)(T s -T a )+hεL e (e s -e a ) / (C p p)-hrv 2 / (2C p )+FC w (T s -T d )] / [F(1-λ)L f ](9)

[0098] Among them, λ=0.3, r=0.79, ε=0.622, e s =6.17mbar, a=8.1*(10 7), F=α1α2LWC*v,C p is the specific heat of air, C w is the specific heat of water, L e is the latent heat of vaporization, L f is the latent heat of fusion, h is the thermal convection coefficient, T d is the temperature of the droplet when it impacts, which is approximately the ambient temperature, t s represents the surface temperature of ice, which is a constant of 273K, t a Indicates the air temperature (nth temperature value).

[0099] It can be seen from formula (2) that in one possible implementation, if the nth ice accretion rate needs to be calculated, it is necessary to obtain the nth air pressure, nth liquid water content, nth temperature value, and nth wind speed among the nth meteorological element parameters, and calculate them in combination with parameters such as the collision coefficient, adhesion coefficient, icing coefficient, and the diameter of the target wind turbine blade.

[0100] Step 305 : Determine the nth ice accretion mass based on the nth ice accretion rate and the n−1th ice accretion mass.

[0101] In one possible implementation, since the environment in which the target wind turbine blade is located at the nth moment is an icing environment, the nth ice accumulation mass corresponding to the target wind turbine blade at the nth moment needs to increase the ice accumulation mass per unit time on the basis of the original ice accumulation mass. That is, the nth ice accumulation mass is determined by the sum of the nth ice accumulation rate and the (n-1)th ice accumulation mass.

[0102] In an illustrative example, the relationship between the nth ice accretion mass, the nth ice accretion rate, and the n-1th ice accretion mass can be expressed as:

[0103] MICE(t)=MICE(t-1)+dM / dt (10)

[0104] Wherein, MICE(t) represents the nth ice accretion mass, MICE(t-1) represents the n-1th ice accretion mass, and dM / dt represents the nth ice accretion rate.

[0105] It should be noted that, since the embodiment of the present application predicts the ice accretion mass by using the n-1th ice accretion mass at the n-1th moment to predict the n-th ice accretion mass at the n-1th moment, and the time interval between the n-1th moment and the n-th moment is exactly the unit time interval of 1 hour, when calculating the n-th ice accretion mass, the unit time interval of 1 hour can be omitted, and the n-th ice accretion mass can be directly determined by adding the n-1th ice accretion mass and the n-th ice accretion rate.

[0106] Step 306 : When the nth temperature value is greater than the temperature threshold and the n-1th ice accumulation mass is greater than the mass threshold, the nth ice accumulation mass is determined using the ice melting physical model.

[0107] Among them, the ice melting physical model is used to calculate the ice melting rate of the target wind turbine blade. If the ice melting physical model is required, the nth meteorological element parameter corresponding to the environment in which the target wind turbine blade is located needs to meet the ice melting conditions, the corresponding air temperature is above 0°C, and there is ice accumulation in the target wind turbine blade itself. Therefore, in a possible implementation method, when it is determined that the nth temperature value is greater than the temperature threshold and the n-1th ice accumulation mass is greater than the mass threshold, it indicates that the target wind turbine blade meets the ice melting conditions, and the ice melting physical model can be used to determine the nth ice accumulation mass.

[0108] Schematically, the temperature threshold is 0°C and the mass threshold is 0kg.

[0109] Step 307 : When the nth ice accumulation mass is determined by the ice melting physical model, the nth ice melting rate corresponding to the nth moment is calculated by the ice melting physical model.

[0110] In one possible implementation, when an ice melting physical model is used to determine the nth ice accumulation mass, it is necessary to calculate the nth ice melting rate corresponding to the nth moment through the ice melting physical model, and then determine the nth ice accumulation mass based on the nth ice melting rate and the (n-1)th ice accumulation mass.

[0111] In an exemplary embodiment, the process of determining the nth ice melting rate may include steps 307A to 307D.

[0112] Step 307A: Calculate the nth sensible heat value corresponding to the nth moment based on the heat convection coefficient, the ice surface temperature, and the nth temperature value.

[0113] According to the ice melting physics model, the ice melting rate needs to be calculated based on the sensible heat between the air and the melting layer, the heat loss due to evaporation, and the net radiation flux. In an exemplary example, the calculation process of the sensible heat value can be expressed as:

[0114] Q h =h(t s -t a ) (11)

[0115] Among them, Q h represents the sensible heat value (nth sensible heat value), h represents the heat convection coefficient, t s represents the surface temperature of ice, which is a constant of 273K, t a Indicates the air temperature (nth temperature value).

[0116] It can be seen from formula (5) that in the process of calculating the nth sensible heat value, the nth temperature value can be obtained from the nth meteorological element parameter. The nth temperature value is substituted into formula (11) to calculate the nth sensible heat value.

[0117] Step 307B: Calculate the nth latent heat value corresponding to the nth moment based on the heat convection coefficient, latent heat of evaporation, specific heat capacity of air, nth air pressure value, saturated water vapor pressure, and ambient water vapor pressure.

[0118] In an illustrative example, the calculation process of latent heat value (heat loss due to evaporation) can be expressed as:

[0119] Q e =hεL e (e s -e a ) / c p P (12)

[0120] Among them, Q e represents latent heat (nth latent heat value), h represents the heat convection coefficient, ε is a constant, ε=0.622, L e represents the latent heat of vaporization, c p represents the specific heat at constant pressure, P represents the air pressure, e s represents the saturated water vapor pressure on the ice surface, which is a constant, 6.17 mbar, e a represents the ambient water vapor pressure as a function of temperature and relative humidity rh, e a＝ 6.108*e^[17.269*(T-273.15) / (T-35.86)]*rh.

[0121] Based on formula (12), in order to calculate the nth latent heat value corresponding to the nth moment, it is necessary to obtain the nth air pressure from the nth meteorological element parameter. By substituting the nth air pressure into formula (12), the nth latent heat value can be calculated.

[0122] Step 307C: Based on the nth temperature value, the nth downward surface shortwave radiation value, and the reflectivity, calculate and obtain the nth radiation flux corresponding to the nth moment.

[0123] In an exemplary example, the calculation process of the radiation flux (net radiation flux) can be expressed as:

[0124] Q n =Q L -σT s 4 +Q s (1-a) (13)

[0125] Among them, Q n represents the radiation flux, the long-wave term Q L -σT s 4 It can be written as σ(T a 4 -T0 4 ), σ is the Stephan-Bolzman constant, Ta Indicates air temperature, T0 = 273K, Q s represents the downward shortwave radiation value of the surface, and a represents the reflectivity.

[0126] It can be seen from formula (13) that in order to calculate the nth radiation flux corresponding to the nth moment, it is necessary to obtain the nth downward surface shortwave radiation value and the nth temperature value from the nth meteorological element parameters, and substitute them into formula (13) to calculate the nth radiation flux corresponding to the nth moment.

[0127] Step 307D: Calculate the nth ice melting rate based on the nth sensible heat value, the nth latent heat value, and the nth radiation flux.

[0128] In an exemplary example, the ice melting physical model can be expressed as (the calculation formula for the ice melting rate can be):

[0129] L f dMelt / dt=Q h +Q e +Q n (14)

[0130] Among them, L f represents the melting heat of ice, dMelt / dt represents the melting rate, Q h Indicates sensible heat value, Q e Indicates the latent heat value, Q n Represents the radiation flux.

[0131] In a possible implementation, after the nth sensible heat value, the nth latent heat value, and the nth radiation flux corresponding to the nth moment are calculated, they can be substituted into formula (14) to calculate the nth ice melting rate.

[0132] Step 308 : Determine the nth ice accumulation mass based on the nth ice melting rate and the n−1th ice accumulation mass.

[0133] In one possible implementation, when the ice melting conditions are met, the nth ice accumulation mass corresponding to the nth moment is determined by subtracting the ice melting mass per unit time from the original ice accumulation mass. That is, the difference between the (n-1)th ice accumulation mass and the nth ice melting rate is determined as the nth ice accumulation mass corresponding to the nth moment.

[0134] In an exemplary example, the relationship between the nth ice accumulation mass, the n-1th ice accumulation mass, and the nth ice melting rate can be expressed as:

[0135] MICE(t)=MICE(t-1)-dMelt / dt (15)

[0136] Where MICE(t) represents the nth ice accretion mass corresponding to the nth moment, MICE(t-1) represents the n-1th ice accretion mass corresponding to the n-1th moment, and dMelt / dt represents the nth ice melting rate corresponding to the nth moment.

[0137] Similar to the calculation of the nth ice accretion mass based on the n-1th ice accretion mass and the nth ice accretion rate above, when calculating the nth ice accretion mass based on the n-1th ice accretion mass and the nth ice melting rate, since the time difference between the n-1th moment and the nth moment is 1 hour per unit time, the unit time can be ignored when calculating the nth ice accretion mass, and the mass can be determined by subtracting the n-1th ice accretion mass from the nth ice melting rate.

[0138] Optionally, in another possible implementation, when it is determined that the nth temperature value is less than the temperature threshold and the liquid water content is less than or equal to the water content threshold, it means that although the temperature conditions for freezing are currently met, there is no liquid water content in the air and the physical material for freezing is not available. Accordingly, even if the target wind turbine blades are under low temperature conditions, they will not freeze or the ice will not grow. Accordingly, the nth ice accumulation mass corresponding to the nth moment is equal to the n-1th ice accumulation mass corresponding to the n-1th moment. That is, the n-1th ice accumulation mass can be directly determined as the nth ice accumulation mass.

[0139] Optionally, when it is determined that the nth temperature value is greater than the temperature threshold and the n-1th ice accumulation mass is less than or equal to the mass threshold, it means that although the temperature condition for melting ice is met, there is no ice accumulation in the target wind turbine blade. Accordingly, there is no need to calculate the ice melting rate, and the nth ice accumulation mass is 0.

[0140] Step 309 : predicting the icing state of the target wind turbine blade at the nth moment based on the nth ice accumulation mass.

[0141] Optionally, an embodiment of the present application provides a method for predicting the mass of ice accumulation, which can predict in advance the mass of ice accumulation in each wind turbine blade in a future preset time period in the wind farm, and then determine the icing status of the wind turbine blades within the preset time period, for example, whether the accumulated ice will melt automatically or whether the ice accumulation will become more and more serious, so that based on the icing status, necessary de-icing treatment can be performed on the wind turbine equipment in advance to avoid affecting the power generation power of the wind turbine equipment.

[0142] In this embodiment, by judging the temperature, liquid water content and the n-1th ice accumulation mass, it can be determined whether to use the ice accumulation physical model or the ice melting physical model in the freezing model to predict the nth ice accumulation mass at the next moment (nth moment), so that the prediction of the icing state is consistent with the changes in the environment in which the target wind turbine equipment is located, thereby improving the accuracy of predicting the icing state of the wind turbine blades.

[0143] Figure 5A flowchart of an ice accumulation mass prediction method provided by an exemplary embodiment of the present application is shown. The method includes the following steps:

[0144] Step 501: Obtain meteorological element parameters output by the WRF model and the ice mass MICE(t-1) at the previous moment.

[0145] Step 502: Correct the temperature in the meteorological element parameter based on the actual terrain height.

[0146] Step 503: Calculate the ice accretion rate dM / dt.

[0147] When the temperature is less than 0℃ and the liquid water content is greater than 0kg / m 3 When it is determined that the icing condition is met, the process proceeds to step 503 to calculate the ice accretion rate, and the mass of ice at the current moment is the sum of the ice accretion rate and the mass of ice at the previous moment.

[0148] Step 504: The mass of ice at the current moment MICE(t)=MICE(t-1)+dM / dt.

[0149] Step 505: Calculate the ice melting rate dMelt / dt.

[0150] When the temperature is greater than 0°C and the mass of ice at the previous moment is greater than 0, the ice melting condition is met, and the process proceeds to step 505 to calculate the ice melting rate. The mass of ice at the current moment is the difference between the mass of ice at the previous moment and the ice melting rate.

[0151] Step 506: The mass of ice at the current moment MICE(t)=MICE(t-1)-dMelt / dt.

[0152] Step 507, MICE(t)=0.

[0153] When the temperature is greater than 0° C. and the mass of ice at the previous moment is less than or equal to 0, it means that there is no ice accumulation on the target wind turbine blade, and the process goes to step 507 . The mass of ice at the current moment is 0.

[0154] Step 508, MICE(t)=MICE(t-1).

[0155] When the temperature is less than 0° C. and the liquid water content is less than or equal to 0, it indicates that the target wind turbine blade will not be frozen, and the process proceeds to step 508 . The mass of ice accumulated on the target wind turbine blade remains unchanged, and the mass of ice at the current moment is the same as the mass of ice at the previous moment.

[0156] Step 509: Output and save the mass of ice MICE(t).

[0157] Figure 6 FIG. 1 shows a block diagram of a device for predicting the status of a wind turbine blade provided by an exemplary embodiment of the present application. Figure 6 As shown, the device includes:

[0158] An acquisition module 601 is configured to acquire an nth meteorological parameter corresponding to the target wind turbine blade at the nth moment, and an n-1th ice accumulation mass corresponding to the n-1th moment, where n is a positive integer;

[0159] A first determining module 602 is configured to determine a target determination method for an nth ice accretion mass corresponding to the nth moment based on the nth meteorological element parameter and the n-1th ice accretion mass;

[0160] A second determining module 603 is configured to determine the nth ice accretion mass based on the target determination method and the n-1th ice accretion mass;

[0161] The third determining module 604 is configured to predict an icing state of the target wind turbine blade at the nth moment based on the nth ice accumulation mass.

[0162] Optionally, the first determining module 602 includes:

[0163] A first acquiring unit is configured to acquire an nth temperature value and an nth liquid water content in the nth meteorological element parameter;

[0164] a first determining unit, configured to determine the nth ice accumulation mass by using an ice accumulation physical model when the nth temperature value is less than a temperature threshold and the nth liquid water content is greater than a water content threshold;

[0165] The second determining unit is configured to determine the nth ice accumulation mass by using an ice melting physical model when the nth temperature value is greater than the temperature threshold and the (n-1)th ice accumulation mass is greater than a mass threshold.

[0166] Optionally, the second determining module 603 includes:

[0167] a third determining unit configured to, when determining the nth ice accretion mass by using the ice accretion physical model, calculate an nth ice accretion rate corresponding to the nth moment by using the ice accretion physical model; and determine the nth ice accretion mass based on the nth ice accretion rate and the (n-1)th ice accretion mass;

[0168] a fourth determining unit, configured to, when determining the nth ice accumulation mass through the ice melting physical model, calculate an nth ice melting rate corresponding to the nth moment through the ice melting physical model; and determine the nth ice accumulation mass based on the nth ice melting rate and the (n-1)th ice accumulation mass.

[0169] Optionally, the third determining unit is further configured to:

[0170] The nth ice accretion rate is calculated based on the collision coefficient, the adhesion coefficient, the icing coefficient, the nth wind speed, the nth liquid water content, and the cross-sectional area, where the cross-sectional area is the cross-sectional area of ​​the target wind turbine blade relative to the direction of water droplet movement.

[0171] Optionally, the fourth determining unit is further configured to:

[0172] Calculating an nth sensible heat value corresponding to the nth moment based on the heat convection coefficient, the ice surface temperature, and the nth temperature value;

[0173] Calculating an nth latent heat value corresponding to the nth moment based on the heat convection coefficient, latent heat of evaporation, specific heat capacity of air, nth air pressure value, saturated water vapor pressure, and ambient water vapor pressure;

[0174] Calculating an nth radiation flux corresponding to the nth moment based on the nth temperature value, the nth downward surface shortwave radiation value, and the reflectivity;

[0175] The nth ice melting rate is calculated based on the nth sensible heat value, the nth latent heat value, and the nth radiation flux.

[0176] Optionally, the device further includes:

[0177] a fourth determining module, configured to determine the (n-1)th ice accretion mass as the nth ice accretion mass when the nth temperature value is less than the temperature threshold and the nth liquid water content is less than or equal to the liquid water content threshold;

[0178] The fifth determining module is configured to determine that the nth ice accumulation mass is 0 when the nth temperature value is greater than the temperature threshold and the (n-1)th ice accumulation mass is less than or equal to the mass threshold.

[0179] Optionally, the acquisition module 601 includes:

[0180] The second acquisition unit is further configured to acquire a predicted temperature value corresponding to the target wind turbine blade obtained through a WRF model forecast, an actual terrain height at which the target wind turbine blade is located, and a model terrain height corresponding to the target wind turbine blade under the WRF model;

[0181] The fifth determining unit is configured to correct the predicted temperature value based on the actual terrain height, the model terrain height, and the moisture and heat lapse rate to obtain the corrected nth temperature value.

[0182] In summary, an embodiment of the present application provides a method for predicting the icing state of wind turbine blades. By analyzing the meteorological element parameters corresponding to the target wind turbine blade at the nth moment and the ice accumulation mass corresponding to the n-1th moment, a target determination method corresponding to the nth ice accumulation mass at the nth moment is determined. Then, based on the target determination method and the n-1th ice accumulation mass, the nth ice accumulation mass corresponding to the nth moment is predicted. By analogy, the corresponding icing state of the wind turbine equipment within a preset time period in the future can be predicted, providing a more scientific basis for the maintenance of the wind turbine equipment.

[0183] Figure 7 7 is a block diagram of a computer device 700 according to an exemplary embodiment. The computer device can be implemented as the monitoring platform in the above-mentioned solution of the present application. The computer device 700 includes a central processing unit (CPU) 701, a system memory 704 including a random access memory (RAM) 702 and a read-only memory (ROM) 703, and a system bus 705 connecting the system memory 704 and the central processing unit 701. The computer device 700 also includes a basic input / output system (I / O system) 706 that helps transmit information between various devices in the computer, and a large-capacity storage device 707 for storing an operating system 713, application programs 714 and other program modules 715.

[0184] The basic input / output system 706 includes a display 708 for displaying information and an input device 709 such as a mouse and keyboard for user input. The display 708 and the input device 709 are both connected to the central processing unit 701 via an input / output controller 710 connected to the system bus 705. The basic input / output system 706 may also include an input / output controller 710 for receiving and processing input from a variety of other devices such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller 710 also provides output to a display screen, printer, or other types of output devices.

[0185] The mass storage device 707 is connected to the central processing unit 701 via a mass storage controller (not shown) connected to the system bus 705. The mass storage device 707 and its associated computer-readable media provide non-volatile storage for the computer device 700. In other words, the mass storage device 707 may include a computer-readable medium (not shown) such as a hard disk or a Compact Disc Read-Only Memory (CD-ROM) drive.

[0186] Without loss of generality, the computer-readable medium may include computer storage media and communication media. Computer storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules or other data. Computer storage media include RAM, ROM, Erasable Programmable Read Only Memory (EPROM), Electronically Erasable Programmable Read-Only Memory (EEPROM), flash memory or other solid-state storage technologies, CD-ROM, Digital Versatile Disc (DVD) or other optical storage, tape cassettes, magnetic tape, disk storage or other magnetic storage devices. Of course, those skilled in the art will appreciate that the computer storage media are not limited to the above-mentioned ones. The above-mentioned system memory 704 and mass storage device 707 can be collectively referred to as memory.

[0187] According to various embodiments of the present application, the computer device 700 may also be connected to a remote computer on a network such as the Internet for operation. That is, the computer device 700 may be connected to a network 712 via a network interface unit 711 connected to the system bus 705, or the network interface unit 711 may be used to connect to other types of networks or remote computer systems (not shown).

[0188] The memory also includes one or more programs, which are stored in the memory. The CPU 701 executes the one or more programs to implement Figure 1 ,or Figure 3 All or part of the steps of the method shown.

[0189] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any media that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0190] The present application also provides a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set. The at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the above-described method for predicting the status of a wind turbine blade. For example, the computer-readable storage medium may be a ROM, RAM, CD-ROM, magnetic tape, floppy disk, or optical data storage device.

[0191] The present application also provides a computer program product comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method for predicting a wind turbine blade state provided in any of the aforementioned optional implementations.

[0192] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0193] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for predicting the status of a wind turbine blade, characterized in that: The method comprises: Obtain the nth meteorological parameter corresponding to the target wind turbine blade at the nth moment, and the n-1th ice accumulation mass corresponding to the n-1th moment, where n is a positive integer; Obtaining an nth temperature value and an nth liquid water content in the nth meteorological element parameter; When the nth temperature value is less than a temperature threshold and the nth liquid water content is greater than a liquid water content threshold, determining an nth ice accretion rate corresponding to the nth moment; and determining an nth ice accretion mass corresponding to the nth moment based on the nth ice accretion rate and the (n-1)th ice accretion mass. When the nth temperature value is greater than the temperature threshold and the n-1th ice accumulation mass is greater than the mass threshold, determining an nth ice melting rate corresponding to the nth moment; and determining an nth ice accumulation mass corresponding to the nth moment based on the nth ice melting rate and the n-1th ice accumulation mass. Based on the nth ice accumulation mass, an icing state of the target wind turbine blade at the nth moment is predicted.

2. The method according to claim 1, characterized in that Determining the nth ice accretion rate corresponding to the nth moment includes: The nth ice accretion rate is calculated based on the collision coefficient, the adhesion coefficient, the icing coefficient, the nth wind speed, the nth liquid water content, and the cross-sectional area, where the cross-sectional area is the cross-sectional area of ​​the target wind turbine blade relative to the direction of water droplet movement.

3. The method according to claim 1, characterized in that Determining the nth ice melting rate corresponding to the nth moment includes: Calculating an nth sensible heat value corresponding to the nth moment based on the heat convection coefficient, the ice surface temperature, and the nth temperature value; Calculating an nth latent heat value corresponding to the nth moment based on the heat convection coefficient, latent heat of evaporation, specific heat capacity of air, nth air pressure value, saturated water vapor pressure, and ambient water vapor pressure; Calculating an nth radiation flux corresponding to the nth moment based on the nth temperature value, the nth downward surface shortwave radiation value, and the reflectivity; The nth ice melting rate is calculated based on the nth sensible heat value, the nth latent heat value, and the nth radiation flux.

4. The method according to claim 1, wherein The method further comprises: When the nth temperature value is less than the temperature threshold, and the nth liquid water content is less than or equal to the liquid water content threshold, determining the (n-1)th ice accumulation mass as the nth ice accumulation mass; When the nth temperature value is greater than the temperature threshold and the (n-1)th ice accumulation mass is less than or equal to the mass threshold, it is determined that the nth ice accumulation mass is 0.

5. The method according to any one of claims 1 to 4, characterized in that: The obtaining of the nth meteorological element parameter corresponding to the target wind turbine blade at the nth moment includes: Obtaining a predicted temperature value corresponding to the target wind turbine blade obtained through a WRF model forecast, an actual terrain height at which the target wind turbine blade is located, and a model terrain height corresponding to the target wind turbine blade under the WRF model; The predicted temperature value is corrected based on the actual terrain height, the model terrain height, and the moisture and heat lapse rate to obtain a corrected nth temperature value.

6. A device for predicting the status of a fan blade, characterized in that: The device comprises: An acquisition module is used to obtain the nth meteorological element parameter corresponding to the target wind turbine blade at the nth moment, and the n-1th ice accumulation mass corresponding to the n-1th moment, where n is a positive integer; The first determining module and the second determining module are configured to obtain an nth temperature value and an nth liquid water content in the nth meteorological element parameter; determine an nth ice accretion rate corresponding to the nth moment when the nth temperature value is less than a temperature threshold and the nth liquid water content is greater than a liquid water content threshold; determine an nth ice accretion mass corresponding to the nth moment based on the nth ice accretion rate and the n-1th ice accretion mass; determine an nth ice melting rate corresponding to the nth moment when the nth temperature value is greater than the temperature threshold and the n-1th ice accretion mass is greater than a mass threshold; and determine an nth ice accretion mass corresponding to the nth moment based on the nth ice melting rate and the n-1th ice accretion mass; The third determining module is configured to predict an icing state of the target wind turbine blade at the nth moment based on the nth ice accumulation mass.

7. A computer device, characterized in that: The computer device includes a processor and a memory; the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the method for predicting the state of a wind turbine blade as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the method for predicting the state of a wind turbine blade as described in any one of claims 1 to 5.

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