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

Abstract

The embodiments of the present application provide a method, device, processor, and storage medium for predicting the thickness of ice on a fan. The method includes: obtaining the three-dimensional coordinates of the fan, which are the longitude, latitude, and altitude of the center of the fan impeller plane; establishing a three-dimensional computational grid based on the three-dimensional coordinates; interpolating the three-dimensional computational grid using the WRF numerical model to obtain the meteorological parameters of the three-dimensional computational grid within a preset future time period; using CFD calculations to determine the environmental parameters of each grid point within the three-dimensional computational grid within a preset future time period; and determining the ice thickness of the fan within a preset future time period based on the environmental parameters, the relative velocity of the inflow of supercooled water particles, the tangential relative velocity of the fan blade rotation, the velocity of the supercooled water particles relative to the rotating fan blades, and the effective collision coefficient of the cold water particles effectively impacting the fan blades. This solution solves the problem of deviation in the prediction of future fan ice thickness and achieves high-precision calculation of the ice thickness of the fan blades.

Description

Technical Field

[0001] The present application relates to the field of new energy power generation technology, and specifically to a method, device, processor and storage medium for predicting ice thickness of a wind turbine. Background Art

[0002] In recent years, winter cold snaps have been frequent. Southward-moving cold air has caused wind turbines to freeze and shut down, leading to widespread grid disconnections. Currently, wind turbine icing predictions rely primarily on machine learning to calculate future wind turbine icing conditions. This method, based on statistical models, lacks the physical meaning of wind turbine icing and can lead to inaccurate predictions of extreme icing conditions. Summary of the Invention

[0003] The purpose of the embodiments of the present application is to provide a method, device, processor and storage medium for predicting the ice thickness of a wind turbine.

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

[0005] Obtaining the three-dimensional coordinates of the fan, the three-dimensional coordinates including the longitude, latitude, and altitude of the center of the impeller plane of the fan, wherein the three-dimensional coordinates are determined with the center of the impeller plane as a reference point;

[0006] Establish a three-dimensional computational grid based on three-dimensional coordinates;

[0007] Interpolate the three-dimensional computational grid using the WRF numerical model to obtain the meteorological parameters within the three-dimensional computational grid within a preset future time period;

[0008] Using CFD calculation method, the environmental parameters of each grid point in the three-dimensional calculation grid in the preset future time period are determined according to meteorological parameters;

[0009] According to the environmental parameters, the relative velocity V of supercooled water particles c And the fan blade rotation tangential relative speed V x Determine the velocity U of the supercooled water particles relative to the rotating fan blades;

[0010] The ice thickness of the fan within a preset future time period is determined based on the effective collision coefficient, velocity U, and environmental parameters of the supercooled water particles effectively impacting the fan blades.

[0011] In the embodiment of the present application, the relative velocity V of the supercooled water particles inflow is calculated according to formula (1): c :

[0012] V c =(1-a)V (1)

[0013] Where a is the axial induction factor and V is the wind speed perpendicular to the turbine plane.

[0014] In the embodiment of the present application, the fan blade rotation tangential speed V is calculated according to formula (2): x :

[0015] V x =(1+a′)λ r V (2)

[0016] Where a′ is the tangential induction factor, λ r is the tip speed ratio of the fan blades, and V is the wind speed perpendicular to the fan plane.

[0017] In the embodiment of the present application, the speed U of the supercooled water particles relative to the rotating fan blades is calculated according to formula (3):

[0018]

[0019] In an embodiment of the present application, the method further includes: determining an effective collision coefficient based on the icing condition of the fan blades; wherein, when the fan blades are not frozen, the effective collision coefficient is determined to be c1; when the fan blades are frozen, the effective collision coefficient is determined to be c2.

[0020] In the embodiment of the present application, determining the ice thickness of the fan within a preset future time period based on the effective collision coefficient of supercooled water particles effectively impacting the fan blades, the velocity U, and the environmental parameters includes: when the fan is not frozen, calculating the ice thickness of the fan within the preset future time period according to formula (4):

[0021]

[0022] In the case of icing on the fan, the ice thickness of the fan in the preset future time period is calculated according to formula (5):

[0023]

[0024] Among them, I r is the fan ice thickness, ρ i is the density of ice, ρ0 is the density of water, R is the amount of supercooled freezing rain, Q is the concentration of supercooled freezing fog, c1 is the effective collision coefficient when the fan is not frozen, c2 is the effective collision coefficient when the fan is frozen, T is the time value of the preset future time period, and π is the pi.

[0025] In an embodiment of the present application, establishing a three-dimensional computational grid based on three-dimensional coordinates includes: using the center of the impeller plane of the fan as a reference point, setting the horizontal grid resolution, and determining the horizontal number of grids; using the highest point of the impeller plane as the upper boundary and the ground as the lower boundary to determine the vertical number of grid layers; and establishing a three-dimensional computational grid based on the horizontal number and the vertical number of layers.

[0026] A second aspect of the present application provides a processor configured to execute the above-mentioned method for predicting ice thickness of a wind turbine.

[0027] A third aspect of the present application provides a device for predicting icing thickness of a wind turbine, comprising:

[0028] A data acquisition module is used to obtain the three-dimensional coordinates of the wind turbine, wherein the three-dimensional coordinates include the longitude, latitude and altitude of the center of the impeller plane of the wind turbine, wherein the three-dimensional coordinates are determined with the center of the impeller plane as a reference point;

[0029] A grid building module is used to build a three-dimensional calculation grid according to three-dimensional coordinates;

[0030] A grid processing module is used to perform interpolation processing on the three-dimensional computational grid using the WRF numerical model to obtain meteorological parameters within the three-dimensional computational grid within a preset future time period;

[0031] The data processing module is used to use CFD calculation method to determine the environmental parameters of each grid point in the three-dimensional calculation grid in the preset future time period according to meteorological parameters, and to calculate the relative velocity V of supercooled water particles in the inflow according to the environmental parameters. c And the fan blade rotation tangential relative speed V x Determine the velocity U of the supercooled water particles relative to the rotating fan blades;

[0032] The ice thickness prediction module is used to determine the ice thickness of the fan within a preset future time period based on the effective collision coefficient, velocity U and environmental parameters of supercooled water particles effectively impacting the fan blades.

[0033] A fourth aspect of the present application provides a machine-readable storage medium having instructions stored thereon. When the instructions are executed by a processor, the processor is configured to execute the above-mentioned method for predicting ice thickness of a wind turbine.

[0034] Through the above technical solution, the three-dimensional coordinates of the center of the fan's impeller plane are obtained, and a three-dimensional computational grid centered on the fan is established. The three-dimensional computational grid is interpolated using the WRF numerical model to obtain the meteorological parameters within the three-dimensional computational grid within a preset future time period. CFD calculations are then used to determine the environmental parameters of each grid within the preset future time period. The speed of the supercooled water particles relative to the rotating fan blades is determined based on the obtained environmental parameters, the relative velocity of the supercooled water particles entering the fan, and the relative tangential velocity of the fan blades. The effective collision coefficient of the supercooled water immediately and effectively hitting the fan blades is finally determined, and the above parameters are used to calculate the ice thickness of the fan within the preset future time period. The above solution can achieve high-precision and rapid calculation of the ice thickness on the fan blades.

[0035] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the following detailed description, they are used to explain the embodiments of the present application but do not constitute a limitation on the embodiments of the present application. In the accompanying drawings:

[0037] Figure 1 The following schematically shows a flow chart of a method for predicting icing thickness of a wind turbine according to an embodiment of the present application;

[0038] Figure 2 The structural block diagram of the device for predicting icing thickness of a wind turbine according to an embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0039] The following describes the specific implementation of the embodiment of the present application in detail with reference to the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the embodiment of the present application and is not intended to limit the embodiment of the present application.

[0040] It should be noted that if the implementation methods of this application involve directional indications (such as up, down, left, right, front, back, etc.), such directional indications are only used to explain the relative positions, relationships, movements, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0041] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of this application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features specified as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0042] Figure 1 The flowchart of the method for predicting the thickness of icing on a wind turbine according to an embodiment of the present application is shown schematically. Figure 1 As shown, in one embodiment of the present application, a method for predicting ice thickness of a wind turbine is provided, comprising the following steps:

[0043] Step 101 : Acquire the three-dimensional coordinates of the wind turbine. The three-dimensional coordinates include the longitude, latitude, and altitude of the center of the impeller plane of the wind turbine. The three-dimensional coordinates are determined with the center of the impeller plane as a reference point.

[0044] Step 102: Create a three-dimensional computational grid based on the three-dimensional coordinates.

[0045] Step 103 : interpolating the three-dimensional computational grid using the WRF numerical model to obtain meteorological parameters within the three-dimensional computational grid within a preset future time period.

[0046] Step 104 : Using CFD calculation method, determine the environmental parameters of each grid point in the three-dimensional calculation grid in a preset future time period according to the meteorological parameters.

[0047] Step 105: Based on the environmental parameters, the relative velocity V of the supercooled water particles c And the fan blade rotation tangential relative speed V x Determine the velocity U of the supercooled water particles relative to the rotating fan blades.

[0048] Step 106 : determining the ice thickness of the fan within a preset future time period based on the effective collision coefficient of the supercooled water particles effectively impacting the fan blades, the velocity U, and the environmental parameters.

[0049] Specifically, the three-dimensional coordinates of the fan can be determined by taking the center of the fan's impeller plane as the reference point and obtaining its longitude, latitude, and altitude. A three-dimensional computational grid is then established based on the obtained three-dimensional coordinates of the fan. A computational grid refers to an integrated, integrated, and collaborative computing environment over a wide area. Specifically, it aggregates various homogeneous and heterogeneous computers, workstations, clusters, databases, advanced instruments, and storage devices distributed across a network to form a virtual, high-performance computing environment that is relatively transparent to users. The use of a computational grid enables the effective aggregation of high-performance computing, supports widely distributed, high-performance collaborative computing, and solves large-scale computing problems.

[0050] Interpolation of the resulting three-dimensional computational grid using the WRF numerical model can yield meteorological parameters for the three-dimensional computational grid within a preset future time period. The WRF (Weather Research and Forecast Model) numerical model is a new-generation, high-resolution, non-hydrostatic equilibrium, mesoscale numerical model jointly developed by the National Center for Environmental Forecasting (NCEF), the National Center for Atmospheric Research (NCAR), and several major research institutes and business units. It consists of four components: the WRF Standard Initialization Module (WRFSI), an assimilation system (including three-dimensional variational assimilation), a dynamical core, and a post-processing module. The dynamical core is divided into two modules: the ARM (for scientific research) and the NMM (for operational forecasting). The post-processing module primarily analyzes and processes the WRF model output, converting model surface physical quantities to standard isobaric surfaces, performing diagnostic analysis of the physical fields, and converting graphical data. The preset future time period can be customized by the operator based on actual needs, for example, a fixed time period of one hour, one day, one week, or one month. Meteorological parameters can include one or more of wind speed, wind direction, temperature, air pressure, humidity, and water vapor content.

[0051] Using CFD calculations, based on acquired meteorological parameters, the environmental parameters for each grid point within a three-dimensional computational grid can be determined for a predetermined future time period. CFD (Computational Fluid Dynamics) refers to computational fluid dynamics, a field that combines numerical calculations with computer science. Specifically, the integral and differential terms in the governing equations of fluid dynamics are approximately expressed as discrete algebraic equations. These discrete systems of algebraic equations are then solved by a computer to obtain numerical solutions in discrete time or space. For example, using CFD calculations within a three-dimensional computational grid can yield specific numerical values ​​for each grid point within the grid. Environmental parameters can include wind speed, supercooled freezing rain, and supercooled freezing fog concentration at each grid point within the grid. Supercooled water particles have an inflow relative velocity when impacting wind turbine blades, and the rotation of the blades themselves has a tangential relative velocity. Supercooled water particles are water particles that remain liquid at temperatures below 0°C. The effective collision coefficient for supercooled water particles effectively impacting wind turbine blades can be a value determined through multiple experiments. Through actual testing, researchers have determined that the effective collision coefficient of supercooled water particles striking fan blades differs between unfrozen and frozen conditions. For example, when the fan is unfrozen, the effective collision coefficient is 1; when the fan is frozen, the effective collision coefficient is 0.8.

[0052] In one embodiment, the center of the wind turbine's impeller plane can be used as a reference point to obtain the longitude, latitude, and altitude of the wind turbine's impeller plane. The horizontal grid resolution is then set to determine the number of horizontal grid levels. The highest point of the impeller plane is used as the upper boundary, and the ground is used as the lower boundary to determine the number of vertical grid layers. A three-dimensional computational grid is established based on the horizontal and vertical number of grid layers. For example, a wind farm in Hunan can be selected as a target to obtain the three-dimensional coordinates of the wind turbines therein. Using the center of the impeller plane of the wind turbine in the wind farm as the reference point, the longitude of the wind turbine is determined to be 113°, the latitude is 28°, the horizontal grid resolution is 300m, and the horizontal number of grid layers is 5×5. Using the highest point of the wind turbine's impeller plane as the upper boundary, and the ground as the lower boundary to determine the number of vertical grid layers to be 10, a 5×5×10 three-dimensional computational grid is established. The future preset time period can be set to two hours, and the WRF numerical model can be used to interpolate the 5×5×10 three-dimensional computational grid to obtain the meteorological parameters for the next two hours within the grid range. Among them, meteorological parameters can be wind speed, wind direction, temperature, air pressure, humidity, and water vapor content. Based on the meteorological parameters within the obtained grid range, CFD calculation is used to obtain the wind speed of 5m / s, the supercooled freezing rain amount, and the supercooled freezing fog concentration of 2g / m3 at each grid point in the 5×5×10 three-dimensional calculation grid in the next two hours. 3 .

[0053] In one embodiment, when the supercooled water particles are blown toward the fan blades, they will have an inflow relative velocity. The inflow relative velocity V of the supercooled water particles can be calculated using formula (1): c :

[0054] V c =(1-a)V (1)

[0055] Where a is the axial induction factor and V is the wind speed perpendicular to the turbine plane.

[0056] In one embodiment, the rotation of the fan blade itself will have a tangential relative speed. The fan blade rotation tangential speed V can be calculated using formula (2): x :

[0057] V x =(1+a′)λ r V (2)

[0058] Where a′ is the tangential induction factor, λ r is the tip speed ratio of the fan blades, and V is the wind speed perpendicular to the fan plane.

[0059] In one embodiment, the relative velocity V of the supercooled water particles can be calculated based on the environmental parameters, c And the fan blade rotation tangential relative speed V xDetermine the speed U of the supercooled water particles relative to the rotating fan blades. The square of the speed U of the supercooled water particles relative to the rotating fan blades is the relative speed V of the supercooled water particles in the inflow. c and the fan blade rotation tangential speed V x The square sum of , the speed U of the supercooled water particles relative to the rotating fan blades can be calculated using formula (3):

[0060]

[0061] In one embodiment, the ice thickness of the fan within a preset future time period can be determined based on the effective collision coefficient of the supercooled water particles effectively hitting the fan blades, the speed U of the supercooled water particles relative to the rotating fan blades, and environmental parameters. Specifically, the staff can determine whether the fan is frozen based on observation. If the fan is not frozen, the ice thickness of the fan within the preset future time period can be calculated according to formula (4):

[0062]

[0063] In the case of icing on the fan, the ice thickness of the fan in a preset future time period can be calculated according to formula (5):

[0064]

[0065] Among them, I r is the fan ice thickness, ρ i is the density of ice, ρ0 is the density of water, R is the amount of supercooled freezing rain, Q is the concentration of supercooled freezing fog, c1 is the effective collision coefficient when the fan is not frozen, c2 is the effective collision coefficient when the fan is frozen, T is the time value of the preset future time period, and π is the pi.

[0066] In one embodiment, when the fan is not frozen, the effective collision coefficient can be determined to be c1. When the fan is frozen, the effective collision coefficient can be determined to be c2. For example, based on actual experiments, the staff can determine that the value of the effective collision coefficient c1 when the fan is not frozen is 1, and the value of the effective collision coefficient c2 when the fan is frozen is 0.8. The preset time period is two hours. When the three-dimensional calculation grid is 5×5×10, the 5×5×10 three-dimensional calculation grid is interpolated using the WRF numerical model to obtain the meteorological parameters within the above three-dimensional calculation grid in two hours. Then, the CFD calculation method can be used to determine the environmental parameters of each grid point in the three-dimensional calculation grid in the next two hours based on the meteorological parameters obtained above. Specifically, the environmental parameters include a wind speed of 5m / s vertical to the fan plane, a supercooled freezing rain amount of 2mm / h, and a supercooled freezing fog concentration of 2g / m 3According to the selected fan, the axial induction factor a can be 0.1, the tangential induction factor a′ can be 0.1, and the fan blade tip speed ratio can be 8. The relative velocity V of the supercooled water particles inflow is calculated using formula (1): c :

[0067] V c =(1-a)V=(1-0.1)×10=4.5m / s (1)

[0068] Use formula (2) to calculate the fan blade rotation tangential speed V x :

[0069] V x =(1+a′)λ r V=(1+0.1)×8×10=39.6m / s (2)

[0070] The speed U of the supercooled water particles relative to the rotating fan blades is calculated using formula (3):

[0071]

[0072] In one embodiment, after determining the effective collision coefficient of supercooled water particles effectively hitting the fan blades, the speed of the supercooled water particles relative to the rotating fan blades, and the environmental parameters of each grid point in the three-dimensional calculation grid in a preset future time period, the ice thickness of the fan blades in the preset future time period can be calculated. For example, the preset future time period is two hours, and the speed U of the supercooled water particles relative to the rotating fan blades is 39.8 m / s. The value of the effective collision coefficient c1 when the fan is not frozen is 1, and the value of the effective collision coefficient c2 when the fan is frozen is 0.8. The wind speed of each grid point in the three-dimensional calculation grid in the vertical fan plane in the next two hours is 5 m / s, the supercooled freezing rain is 2 mm / h, and the supercooled freezing fog concentration is 2 g / m 3 Calculate the ice thickness of the fan for two hours. In the first hour, the staff observed that the fan had not yet been frozen. Using the effective collision coefficient of 1, the ice thickness of the fan blades was calculated according to formula (4):

[0073]

[0074] Among them, ρ i The density of ice is 0.9×10 3 kg / m 3 , ρ0 is the density of water 1×10 3 kg / m 3 , R is the supercooled freezing rain 2mm / h, Q is the supercooled freezing fog concentration 2g / m 3 , c1 is the effective collision coefficient 1 when the fan is not iced, V cis the relative velocity of supercooled water particles entering the flow, 4.5 m / s, V x is the tangential speed of the fan blades, 39.6 m / s, T is the time value of the preset future time period, 2 h, and the calculated fan ice thickness I r In the second hour, when the staff observed that the fan was icing, they used the effective collision coefficient of 0.8 and calculated the ice thickness of the fan blade according to formula (5):

[0075]

[0076] Among them, ρ i The density of ice is 0.9×10 3 kg / m 3 , ρ0 is the density of water 1×10 3 kg / m 3 , R is the supercooled freezing rain 2mm / h, Q is the supercooled freezing fog concentration 2g / m 3 , c1 is the effective collision coefficient of 0.8 when the fan is not iced, V c is the relative velocity of supercooled water particles entering the flow, 4.5 m / s, V x is the tangential speed of the fan blades, 39.6 m / s, T is the time value of the preset future time period, 2 h, and the calculated fan ice thickness I r It is 18.2mm.

[0077] The above scheme first establishes a three-dimensional computational grid centered on the wind turbine. Then, the WRF data model is used to obtain the meteorological parameters within the three-dimensional computational grid within a preset future time period. Then, the environmental parameters of each grid point within the three-dimensional computational grid within the preset future time period can be obtained through CFD calculation. Different effective collision coefficients are set according to whether the wind turbine blades are frozen. Finally, the ice thickness of the wind turbine within the preset future time period is determined based on the environmental parameters, the relative velocity of the inflow of supercooled water particles, the relative tangential velocity of the fan blade rotation, the speed of the supercooled water particles relative to the rotating fan blades, and the effective collision coefficient. This scheme can achieve accurate and rapid calculation of the ice thickness of the fan blades.

[0078] In one embodiment, Figure 2 As shown, Figure 2 The following schematically illustrates a structural block diagram of a wind turbine icing thickness prediction device 200 according to an embodiment of the present application. The device comprises a data acquisition module 201, a grid establishment module 202, a grid processing module 203, a data processing module 204, and an icing thickness prediction module 205, wherein:

[0079] The data acquisition module 201 is used to acquire the three-dimensional coordinates of the wind turbine, which include the longitude, latitude and altitude of the center of the impeller plane of the wind turbine, wherein the three-dimensional coordinates are determined with the center of the impeller plane as the reference point.

[0080] The grid establishment module 202 is used to establish a three-dimensional calculation grid according to three-dimensional coordinates.

[0081] The grid processing module 203 is used to perform interpolation processing on the three-dimensional calculation grid using the WRF numerical model to obtain meteorological parameters within the three-dimensional calculation grid within a preset future time period.

[0082] The data processing module 204 is used to determine the environmental parameters of each grid point in the three-dimensional calculation grid in a preset future time period based on the meteorological parameters using the CFD calculation method, and calculate the relative velocity V of the supercooled water particles in the inflow according to the environmental parameters. c And the fan blade rotation tangential relative speed V x Determine the velocity U of the supercooled water particles relative to the rotating fan blades.

[0083] The ice thickness prediction module 205 is used to determine the ice thickness of the fan within a preset future time period based on the effective collision coefficient of supercooled water particles effectively impacting the fan blades, the speed U and environmental parameters.

[0084] In one embodiment, the ice thickness prediction module 205 is further configured to determine an effective collision coefficient according to the icing condition of the wind turbine blades, wherein when the wind turbine blades are not iced, the effective collision coefficient is determined to be c1, and when the wind turbine blades are iced, the effective collision coefficient is determined to be c2.

[0085] In one embodiment, the grid establishment module 202 is also used to set the horizontal grid resolution with the center of the impeller plane of the fan as the reference point, determine the horizontal number of grids, determine the vertical number of grid layers with the highest point of the impeller plane as the upper boundary and the ground as the lower boundary, and establish a three-dimensional calculation grid based on the horizontal number and the vertical number of layers.

[0086] The device for predicting the ice thickness of a wind turbine includes a processor and a memory. The above-mentioned data acquisition module, grid establishment module, grid processing module, data processing module and ice thickness prediction module are all stored in the memory as program units, and the processor executes the above-mentioned program modules stored in the memory to implement corresponding functions.

[0087] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be provided, and the method for predicting the ice thickness of the wind turbine can be implemented by adjusting the kernel parameters.

[0088] The memory may include non-permanent memory in a computer-readable medium, 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.

[0089] An embodiment of the present application provides a processor, which is used to run a program, wherein the program executes the above-mentioned method for predicting the ice thickness of a wind turbine when running.

[0090] An embodiment of the present application provides a storage medium having a program stored thereon, which, when executed by a processor, implements the above-mentioned method for predicting ice thickness of a wind turbine.

[0091] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0092] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0095] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0096] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0097] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules 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 technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0098] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0099] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A method for predicting the thickness of icing on a wind turbine, characterized in that: The method comprises: Obtaining the three-dimensional coordinates of the wind turbine, the three-dimensional coordinates including the longitude, latitude, and altitude of the center of the impeller plane of the wind turbine, wherein the three-dimensional coordinates are determined with the center of the impeller plane as a reference point; Establishing a three-dimensional computational grid according to the three-dimensional coordinates; Interpolating the three-dimensional computational grid using a WRF numerical model to obtain meteorological parameters within a preset future time period within the three-dimensional computational grid; Determining, using a CFD calculation method, environmental parameters of each grid point in the three-dimensional calculation grid within the preset future time period according to the meteorological parameters; According to the environmental parameters, the relative velocity V of supercooled water particles c And the fan blade rotation tangential relative speed V x Determine the velocity U of the supercooled water particles relative to the rotating fan blades; When the fan is not frozen, the ice thickness of the fan in the preset future time period is calculated according to formula (4): In the case where the fan is frozen, the ice thickness of the fan in the preset future time period is calculated according to formula (5): Among them, I r is the fan ice thickness, ρ i is the density of ice, ρ0 is the density of water, R is the amount of supercooled freezing rain, Q is the concentration of supercooled freezing fog, c1 is the effective collision coefficient when the fan is not frozen, c2 is the effective collision coefficient when the fan is frozen, T is the time value of the preset future time period, π is the pi, and t is the time variable.

2. The method according to claim 1, characterized in that The relative velocity V of the supercooled water particles inflow is calculated according to formula (1): c : In c =(1-a)V (1) Where a is the axial induction factor and V is the wind speed perpendicular to the turbine plane.

3. The method according to claim 1, characterized in that According to formula (2), the fan blade rotation tangential velocity V is calculated as x : IN x =(1+a′)λ r In (2) Where a′ is the tangential induction factor, λ r is the tip speed ratio of the fan blades, and V is the wind speed perpendicular to the fan plane.

4. The method according to claim 1, wherein The speed U of the supercooled water particles relative to the rotating fan blades is calculated according to formula (3): Among them, V c is the relative velocity of the supercooled water particles inflow, V x is the tangential relative speed of the fan blades.

5. The method according to claim 1, wherein The method further comprises: Determining the effective collision coefficient according to the icing condition of the fan blades; Wherein, when the fan blades are not iced, the effective collision coefficient is determined to be c1; In the case where the wind turbine blades are iced, the effective collision coefficient is determined to be c2.

6. The method according to claim 1, characterized in that The establishing of a three-dimensional computational grid according to the three-dimensional coordinates comprises: Taking the center of the impeller plane of the fan as a reference point, setting the horizontal grid resolution and determining the horizontal number of grids; The number of vertical layers of the grid is determined with the highest point of the impeller plane as the upper boundary and the ground as the lower boundary; The three-dimensional computational grid is established according to the number of horizontal layers and the number of vertical layers.

7. A processor, characterized in that: The method is configured to execute the method for predicting icing thickness of a wind turbine according to any one of claims 1 to 6.

8. A device for predicting the thickness of icing on a fan, characterized in that: include: a data acquisition module, configured to acquire the three-dimensional coordinates of the wind turbine, wherein the three-dimensional coordinates include the longitude, latitude, and altitude of the center of the impeller plane of the wind turbine, wherein the three-dimensional coordinates are determined with the center of the impeller plane as a reference point; A grid establishment module, configured to establish a three-dimensional computational grid according to the three-dimensional coordinates; A grid processing module is used to perform interpolation processing on the three-dimensional calculation grid using a WRF numerical model to obtain meteorological parameters within a preset future time period within the three-dimensional calculation grid; The data processing module is used to use CFD calculation to determine the environmental parameters of each grid point in the three-dimensional calculation grid in the preset future time period according to the meteorological parameters, and to calculate the relative velocity V of the supercooled water particles in the inflow according to the environmental parameters. c And the fan blade rotation tangential relative speed V x Determine the velocity U of the supercooled water particles relative to the rotating fan blades; The ice thickness prediction module is used to calculate the ice thickness of the fan in the preset future time period according to formula (4) when the fan is not frozen: In the case where the fan is frozen, the ice thickness of the fan in the preset future time period is calculated according to formula (5): Among them, I r is the fan ice thickness, ρ i is the density of ice, ρ0 is the density of water, R is the amount of supercooled freezing rain, Q is the concentration of supercooled freezing fog, c1 is the effective collision coefficient when the fan is not frozen, c2 is the effective collision coefficient when the fan is frozen, T is the time value of the preset future time period, π is the pi, and t is the time variable.

9. A machine-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by a processor, the processor is configured to execute the method for predicting icing thickness of a wind turbine according to any one of claims 1 to 6.

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