Method for identifying icing difference of wind power transmission system and electronic device

Abstract

Embodiments of the present application provide a method for identifying icing differences of a wind power transmission system and an electronic device. The method comprises: predicting icing states of a power transmission conductor and a wind turbine blade based on target environmental parameters, to obtain an icing prediction result of the power transmission conductor and an icing prediction result of the wind turbine blade; and displaying icing differentiation information of the power transmission conductor and the wind turbine blade based on the icing prediction result of the power transmission conductor and the icing prediction result of the wind turbine blade. In this way, the icing differences between the wind turbine blade and the power transmission conductor under the same external environment can be effectively identified.

Description

Technical Field

[0001] This disclosure relates to the field of wind power generation technology, and in particular to a method and electronic device for identifying differences in icing in wind power transmission systems. Background Technology

[0002] With the continuous expansion of wind power generation, the number of wind farms being built in high-altitude and cold mountainous areas and regions with low temperature and high humidity is constantly increasing. Affected by factors such as low winter temperatures, high humidity, significant wind speed variations, and the effect of supercooling water droplets, both the wind turbine blades at the generation end and the transmission lines at the transmission end are prone to icing. Icing on wind turbine blades can cause a decrease in the aerodynamic performance of the wind turbine, output loss, increased vibration, and even shutdown; icing on transmission lines may lead to operational risks such as conductor galloping, line breakage, and tower collapse, seriously affecting the safe and stable operation of wind farms and transmission systems.

[0003] Currently, research on wind turbine blades mainly focuses on icing wind tunnel tests, analysis of factors affecting blade icing, and icing judgment based on statistics or machine learning; research on power transmission lines mainly focuses on mathematical models of icing growth, sensitivity analysis of environmental parameters, and experimental studies.

[0004] However, while the above studies can obtain the icing patterns of wind turbine blades and power transmission lines respectively, they are difficult to effectively identify the differences in icing between wind turbine blades and power transmission lines under the same external environmental conditions. Summary of the Invention

[0005] To address the aforementioned technical issues, this application provides a method and electronic device for identifying differences in icing in wind power transmission systems, which can effectively identify differences in icing between wind turbine blades and transmission lines under the same external environment.

[0006] This application provides a method for identifying differences in icing in wind power transmission systems, the method comprising: Based on the target environmental parameters, the icing state of transmission lines and wind turbine blades is predicted respectively, and the icing prediction results of transmission lines and wind turbine blades are obtained. Based on the icing prediction results of power transmission lines and wind turbine blades, the differential information on icing of power transmission lines and wind turbine blades is shown.

[0007] In a second aspect, an electronic device is provided, the electronic device comprising: The processing unit is used to predict the icing state of the power transmission line and the wind turbine blade based on the target environmental parameters, and obtain the icing prediction results of the power transmission line and the wind turbine blade. The processing unit is also used to display differential information on icing of power transmission lines and wind turbine blades based on the icing prediction results of power transmission lines and wind turbine blades.

[0008] Thirdly, an electronic device is provided, comprising: a processor and a memory for storing a computer program, the processor for calling and running the computer program stored in the memory, and performing the methods as described in the first aspect or its various implementations.

[0009] Fourthly, a computer-readable storage medium is provided for storing a computer program that causes a computer to perform the methods described in the first aspect or its various implementations.

[0010] Fifthly, a computer program product is provided, including computer program instructions that cause a computer to perform the methods as described in the first aspect or its various implementations.

[0011] Sixthly, a computer program is provided that causes a computer to perform the methods described in the first aspect or its various implementations.

[0012] In this embodiment, the icing state of transmission lines and wind turbine blades can be predicted based on target environmental parameters, respectively, to obtain icing prediction results for transmission lines and wind turbine blades, thereby displaying the differences in icing information between transmission lines and wind turbine blades. This effectively identifies the differences in icing between wind turbine blades and transmission lines under the same external environment. Attached Figure Description

[0013] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A flowchart illustrating a method for identifying differences in icing in a wind power transmission system, provided in an embodiment of this application; Figure 2 A flowchart illustrating another method for identifying differences in icing in a wind power transmission system provided in this application embodiment; Figure 3 A schematic block diagram of an electronic device provided in an embodiment of this application; Figure 4 This is a schematic structural diagram of another electronic device provided in an embodiment of this application. Detailed Implementation

[0016] To better understand the above-mentioned objectives, features, and advantages of this application, the solution of this application will be further described below. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0017] Many specific details are set forth in the following description in order to provide a full understanding of this application, but this application may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only some embodiments of this application, and not all embodiments.

[0018] Figure 1 This is a flowchart illustrating a method 100 for identifying differences in icing in a wind power transmission system, as provided in an embodiment of this application. Figure 1 As shown, the method includes: In step 110, the icing state of the transmission line and the wind turbine blades are predicted based on the target environmental parameters, and the icing prediction results of the transmission line and the wind turbine blades are obtained.

[0019] In this application, the target environmental parameters can also be referred to as uniform environmental parameters, such as the liquid water content in the air (represented by LWC) and the incoming wind speed (represented by LWC). The parameters are: ambient temperature, median volume diameter of supercooled water droplets, wind direction, relative humidity, and air pressure, and are not limited to any one or more of these.

[0020] The target environmental parameters serve as the external boundary conditions for predicting icing on transmission lines and wind turbine blades, enabling the identification of icing differences between transmission lines and wind turbine blades under the same environment.

[0021] Optionally, before predicting the icing status of transmission lines and wind turbine blades based on target environmental parameters, for example, [further steps can be taken]. Figure 2 Steps 201 to 204 of the wind power transmission system icing difference identification method 200 shown.

[0022] In step 201, the target environment parameters are input.

[0023] The target environmental parameters have been described in detail in step 110 and will not be repeated here.

[0024] In step 202, the transmission line is parametrically modeled.

[0025] That is, inputting the parameters of the transmission line into the first geometric model corresponding to the transmission line to obtain the geometric characteristics of the transmission line.

[0026] The parameters of the transmission conductor may include one or more of the following: outer diameter of the transmission conductor, inner diameter of the transmission conductor, strand roughness structure, segment length of the transmission conductor, material of the transmission conductor, and torsional stiffness.

[0027] The geometric characteristics of power transmission lines may include, for example, their specific dimensions and shape.

[0028] In step 203, the wind turbine blades are parametrically modeled.

[0029] That is, inputting the parameters of the wind turbine blade into the second geometric model corresponding to the wind turbine blade to obtain the geometric features of the wind turbine blade.

[0030] The parameters of wind turbine blades include one or more of the following: total blade length, number of spanwise segments, chord length of each segment, airfoil thickness, installation angle, and local angle of attack.

[0031] The geometric features of wind turbine blades may include, for example, the overall dimensions of the wind turbine blades, spanwise segment information, and the airfoil geometry of each segment.

[0032] In step 204, differentiated meshing is performed on the power transmission lines and wind turbine blades respectively.

[0033] Specifically, the geometric features of the transmission line are input into the first mesh partitioning model corresponding to the transmission line. The first mesh partitioning model performs mesh partitioning on the transmission line and its external airflow field space, obtaining the mesh partitioning results of the transmission line and the external airflow field space. Specifically, in the mesh partitioning result of the transmission line, the surface of the transmission line is divided into multiple discrete elements, and in the mesh partitioning result of the external airflow field space, the external airflow field space is divided into multiple computational domain meshes.

[0034] It should be understood that, for the first mesh partitioning model, a two-dimensional or three-dimensional mesh structure adapted to the flow around the cylinder and the local icing growth on the surface is adopted.

[0035] The geometric features of the wind turbine blades are input into the corresponding second mesh generation model. This model then meshes the wind turbine blades and their external airflow field space, resulting in mesh generation results for both the wind turbine blades and their external airflow field space. Specifically, the wind turbine blade surface is divided into multiple discrete elements in the mesh generation results, while the external airflow field space is divided into multiple computational domain meshes.

[0036] It should be understood that for the second mesh partitioning model, a multi-section mesh structure is adopted to adapt to the complex airfoil boundary and spanwise segmentation changes.

[0037] In some implementations, software such as finite element method (FEM) or computational fluid dynamics (CFD) software can be used to generate meshes, and the boundary layer region on the surface of the power transmission line or wind turbine blade can be ensured to have sufficient mesh resolution to guarantee the accuracy of subsequent calculations (such as water droplet collisions, ice thickness, etc.).

[0038] For example, the process of predicting the icing state of transmission lines and wind turbine blades based on target environmental parameters includes, for example,... Figure 2 Steps 205 to 210 of the method 200 for identifying differences in icing in wind power transmission systems.

[0039] It should be noted that when predicting the icing state of transmission lines based on target environmental parameters, this can include: predicting the icing state of transmission lines based on target environmental parameters according to a preset time step (which can be used as a time step). Indicates, such as The prediction process is performed at least once (i.e., one prediction process corresponds to one time step) until a preset condition is met, at which point the prediction process ends. In the case of predicting the icing state of wind turbine blades based on target environmental parameters, this may include: predicting the icing state of wind turbine blades based on target environmental parameters according to a preset time step (e.g., ...). Perform at least one prediction (i.e., one prediction process corresponds to one time step) until the prediction process ends when the preset conditions are met.

[0040] The prediction conditions can be, for example, the number of predictions for the icing state reaching a preset threshold, or the prediction duration for the icing state reaching a preset duration, which can include multiple time steps.

[0041] For example, if the threshold for the number of predictions is 100, the prediction process will end after 100 prediction cycles; or, if the preset duration is 2 hours, the time step Δt = 10 seconds, and the prediction process will end after 720 prediction cycles.

[0042] For example, taking the m-th prediction process (e.g., m=1, 2, ..., n-1 or n) as an example, the prediction process of icing status of transmission lines and the prediction process of icing of wind turbine blades are described respectively, as shown in steps 205 to 210.

[0043] In step 205, the external airflow field is solved.

[0044] In the m-th prediction process, for multiple computational domain grids of the external airflow field space of the transmission line, based on the target environmental parameters, methods such as the boundary element method, finite volume method, or finite element method are used to solve for the external airflow field distribution of the transmission line, thus obtaining the external airflow field distribution of the transmission line. Similarly, in the m-th prediction process, for multiple computational domain grids of the external airflow field space of the wind turbine blade, based on the target environmental parameters, corresponding numerical solution methods are used to solve for the external airflow field distribution of the wind turbine blade, thus obtaining the external airflow field distribution of the wind turbine blade, i.e., the incoming flow distribution at different spanwise segments of the wind turbine blade.

[0045] The external airflow field distribution can include the distribution of physical quantities such as velocity, pressure, and optional temperature at each point in the computational domain. The velocity field and pressure field serve as direct inputs for subsequent calculations of water droplet trajectory, collision coefficient, and icing.

[0046] The trajectory of the water droplet satisfies the following formula (1): (1) in, For the mass of the water droplet, Let be the velocity vector of the water droplet. It is the airflow velocity vector. The air drag coefficient, air density, This represents the maximum windward cross-sectional area of ​​the water droplet. It is a neutral acceleration vector.

[0047] It should be noted that the trajectory of the water droplet can be tracked by equation (1), thereby obtaining the local collision position and collision flux of the surface of the wire object and the blade object.

[0048] In step 206, the local collision coefficient is calculated.

[0049] In the m-th prediction process, based on the collision flux of each discrete unit (denoted as i) on the surface of the transmission line, the local collision coefficient and the freezing coefficient of each discrete unit are determined; and based on the collision flux of each discrete unit on the surface of the wind turbine blade, the local collision coefficient and the freezing coefficient of each discrete unit are determined.

[0050] It should be understood that a discrete element is a wall boundary element that is discretized into the surface of a power transmission line or a wind turbine blade. Discrete elements can also be called surface discrete elements, without limitation.

[0051] Specifically, based on the impact position and flux distribution of water droplets on the surfaces of power transmission lines and wind turbine blades, local water droplet collision coefficient models for different discrete units on the surfaces of power transmission lines and wind turbine blades are constructed. For the i-th discrete unit (on the surface of the power transmission line or wind turbine blade), its local water droplet collision coefficient can be calculated based on the following formula (2): (2) in, Let be the local droplet collision coefficient of the i-th discrete unit. unit time step The mass of the water droplet that collides with the surface of the unit. LWC is the density of water, and LWC is the liquid water content in the air. For the incoming wind speed, This represents the characteristic windward area of ​​the corresponding discrete unit.

[0052] In step 207, the freezing coefficient is calculated.

[0053] In the m-th prediction process, after obtaining the local collision coefficient, a freezing heat balance equation is further established. Taking into account the heat release, convective heat transfer, evaporation and phase change processes after the water droplet impact, the freezing ratio of each position on the surface of the transmission line and the surface of the wind turbine blade is obtained respectively. For the i-th discrete unit (on the surface of the transmission line or the wind turbine blade), its freezing coefficient can be calculated based on the following formula (3): (3) in, is the freezing coefficient (or local freezing coefficient) of the i-th discrete unit. For the i-th discrete unit at time step The actual mass of the water droplets that freeze into ice inside. Let be the mass of the water droplet that collides with the i-th discrete unit.

[0054] In summary, for all discrete elements on the surface of the power transmission line and the wind turbine blade, the local collision coefficient can be calculated according to equations (2) and (3), respectively. With local freezing coefficient By mapping the coefficient values ​​of discrete elements on the surface of a power transmission line to a geometric surface according to their spatial coordinates, the distribution of collision and freezing capabilities along the length or circumference of the power transmission line can be obtained; similarly, by mapping the coefficient values ​​of discrete elements on the surface of a wind turbine blade to a geometric surface according to their spatial coordinates, the distribution of collision and freezing capabilities along the spanwise or chordal direction of the wind turbine blade can be obtained.

[0055] In step 208, the icing growth is calculated.

[0056] In the m-th prediction process, the ice thickness of each discrete unit is determined based on the local collision coefficient and the freezing coefficient of each discrete unit on the surface of the transmission line; and the ice thickness of each discrete unit is determined based on the local collision coefficient and the freezing coefficient of each discrete unit on the surface of the wind turbine blade.

[0057] Specifically, after obtaining the local collision coefficient and freezing coefficient, the ice layer growth of each discrete unit on the surface of the transmission line and the wind turbine blade within the current time step is calculated. For the i-th discrete unit (on the surface of the transmission line or the wind turbine blade), its ice thickness can be calculated based on the following formula (4): (4) in, For the i-th discrete unit at the k-th time step The thickness of the ice layer, For the i-th discrete unit at the (k+1)-th time step The thickness of the ice layer, The density of ice, This is a geometric correction factor used to characterize the effects of curvature, angle of attack, avoidance of roughness, or boundary discretization on local icing growth.

[0058] According to equation (4), the ice shape of the transmission line cross section and the ice shape of each segment of the wind turbine blade cross section can be calculated (or updated).

[0059] In step 209, the transmission line torsion feedback is updated.

[0060] In the m-th prediction process, the torsion angle of each discrete position (denoted as j) on the surface of the transmission conductor is determined based on the ice thickness of all discrete units on the conductor surface. Here, a discrete position is an axial discrete point or discrete segment along the length of the transmission conductor, and each discrete position includes at least one discrete unit.

[0061] Specifically, after the ice layer growth calculation for the current time step is completed, the position of the ice-covered centroid and the eccentric moment are calculated based on the ice layer distribution in the cross section. Then, combined with the conductor material parameters, polar moment of inertia, and torsional stiffness, the torsional angle at each discrete position of the conductor is obtained. The torsional load per unit length of the transmission conductor at position z and time t can be expressed by the following formula (5): (5) in, Let Z be the torsional load on the transmission line at position z and time k. Let g be the mass of ice per unit length, and g be the acceleration due to gravity. This represents the eccentric distance between the center of gravity of the current icing layer and the center of the transmission line. The coordinate angle of the centroid of the ice cover is given.

[0062] Furthermore, the torsion angle of the transmission line can be updated based on the following formula (6): (6) in, Let the torsion angle at the j-th discrete position at the k-th time step be denoted as . Let the torsion angle of the j-th discrete position at the (k+1)-th time step be denoted as . For the distance between the transmission line and the runway, This represents the torsional stiffness at the corresponding location.

[0063] After the transmission line twists, its windward side and main collision area will change. Therefore, the twist angle will be fed back to the flow field boundary and water droplet collision area correction in the next time step, forming a dynamic coupling process from icing growth to twisting change and then to icing growth.

[0064] In step 210, the wind turbine blade segment boundaries are updated.

[0065] In the m-th prediction process, the boundary of each segment (denoted as x) of the wind turbine blade is updated based on the ice thickness of all discrete elements on the surface of the wind turbine blade. Each segment includes at least one discrete element.

[0066] Specifically, for wind turbine blades, after the local icing growth of the current time step is completed, the leading edge boundary, suction surface boundary, pressure surface boundary, and local angle of attack of the corresponding segment are updated based on the ice thickness and ice shape profile of each spanwise segment. For the x-th segment of the wind turbine blade divided along the spanwise direction, its segment boundary can be updated based on the following formula (7): (7) in, For the x-th segment at the k-th time step, Let x be the local icing increment of the x-th segment at the current time step, denoted by [symbol]. This indicates that the boundary is updated along the normal direction of the x-th segment surface.

[0067] Correspondingly, the local equivalent angle of attack of the x-th segment can be updated based on the following formula (8): (8) in, and These are the local equivalent angles of attack for the x-th segment at two adjacent time steps. The geometric sensitivity coefficient is used to characterize the influence of the airfoil thickness, chord length, and ice growth on the local inflow angle in the x-th segment. The updated geometric boundary re-participates in the external flow field solution and droplet collision calculation for the next time step, thus forming an iterative mechanism of airfoil change, droplet trapping change, freezing change, and re-icing growth.

[0068] In step 120, based on the icing prediction results of the power transmission line and the wind turbine blade, the differential information on icing of the power transmission line and the wind turbine blade is displayed.

[0069] In this application, the icing prediction results for transmission lines may include one or more of the following: icing thickness distribution, icing morphology changes (i.e., ice shape profiles that change over time), icing location evolution (i.e., spatial distribution changes of water droplet collision locations over time), icing type (such as rime, hoarfrost, or mixed rime), and icing growth rate curve.

[0070] The prediction results for wind turbine blade icing can include one or more of the following: icing thickness distribution, icing morphology changes (i.e., the ice shape profile changes over time), icing location evolution (i.e., the spatial distribution of water droplet collision locations changes over time), icing type (such as rime, hard rime, soft rime, or mixed rime), and icing growth rate curve.

[0071] The icing growth rate curve is related to the icing growth in each prediction process. That is, the icing growth rate curve is the rate of change of icing thickness over time. Specifically, the vertical axis of the icing growth rate curve is the increment of icing thickness per unit time step, and the horizontal axis is the simulation time.

[0072] For example, step 120 may include, as Figure 2 Steps 211 to 212 in the method 200 for identifying differences in icing in wind power transmission systems.

[0073] In step 211, the judgment is iteratively made.

[0074] If the preset conditions are not met, steps 205 to 210 can be repeated until the preset conditions are met.

[0075] In step 212, icing differentiation information is displayed.

[0076] By comparing the icing prediction results of power transmission lines with the icing prediction results of wind turbine blades, icing difference information can be displayed (or obtained). This icing difference information may include differences in the starting location of icing, differences in the rate of icing growth, differences in the area covered by icing, and differences in icing morphology.

[0077] In this embodiment, the icing state of transmission lines and wind turbine blades can be predicted based on target environmental parameters, respectively, to obtain icing prediction results for transmission lines and wind turbine blades, thereby displaying the differences in icing information between transmission lines and wind turbine blades. This effectively identifies the differences in icing between wind turbine blades and transmission lines under the same external environment.

[0078] The above text combined Figure 1 and Figure 2 The method embodiments of this application are described in detail below, in conjunction with... Figure 3 and Figure 4 The present application describes the device embodiments in detail. It should be understood that the device embodiments correspond to the method embodiments, and similar descriptions can be referred to the method embodiments.

[0079] Figure 3 This is a schematic block diagram of an electronic device 300 provided in an embodiment of this application. Figure 3 As shown, the electronic device 300 includes: The processing unit 330 is used to predict the icing state of the power transmission line and the wind turbine blade based on the target environmental parameters, and obtain the icing prediction results of the power transmission line and the wind turbine blade. The processing unit 330 is also used to display differential information on icing of power transmission lines and wind turbine blades based on the icing prediction results of the power transmission lines and wind turbine blades.

[0080] In some embodiments, the target environmental parameters include at least one of the following: Liquid water content in the air; Incoming air velocity; Ambient temperature; Median volume diameter of a supercooled water droplet; wind direction; Relative humidity; Air pressure.

[0081] In some embodiments, the processing unit 330 is further configured to input the parameters of the transmission line into the first geometric model corresponding to the transmission line to obtain the geometric features of the transmission line; The processing unit 330 is also used to input the geometric features of the transmission line into the first mesh partitioning model corresponding to the transmission line, and obtain the mesh partitioning result of the transmission line and the mesh partitioning result of the external airflow field space of the transmission line. In the mesh partitioning results of the transmission line, the surface of the transmission line is divided into multiple discrete units, and in the mesh partitioning results of the external airflow field space of the transmission line, the external airflow field space of the transmission line is divided into multiple computational domain meshes. The parameters of the transmission lines include at least one of the following: Outer diameter of the conductor; Inner diameter of the conductor; The stranded wire has a rough structure. Wire segment length; Conductor material; Torsional stiffness.

[0082] In some embodiments, the processing unit 330 is further configured to perform at least one prediction process on the icing state of the transmission line based on the target environmental parameters according to a preset time step, until the prediction process ends when the preset conditions are met. The m-th prediction process includes: For multiple computational domain grids, the external airflow field distribution of the transmission line is obtained based on the target environmental parameters; Based on the external airflow field distribution, the collision flux of each discrete cell in multiple discrete cells on the surface of the transmission conductor is determined; Based on the collision flux of each discrete element, the local collision coefficient and the freezing coefficient of each discrete element are determined. The ice thickness of each discrete unit is determined based on the local collision coefficient and the freezing coefficient of each discrete unit. Based on the ice thickness of all discrete units on the surface of the transmission conductor, the torsion angle at each discrete location on the surface of the transmission conductor is determined; wherein each discrete location includes at least one discrete unit.

[0083] In some embodiments, the processing unit 330 is further configured to input the parameters of the wind turbine blade into the second geometric model corresponding to the wind turbine blade to obtain the geometric features of the wind turbine blade. The processing unit 330 is also used to input the geometric features of the wind turbine blade into the second mesh partitioning model corresponding to the wind turbine blade, and obtain the mesh partitioning result of the wind turbine blade and the mesh partitioning result of the external airflow field space of the wind turbine blade. In the mesh partitioning results of the wind turbine blades, the surface of the wind turbine blades is divided into multiple discrete units, and in the mesh partitioning results of the external airflow field space of the wind turbine blades, the external airflow field space of the wind turbine blades is divided into multiple computational domain meshes. The parameters of the wind turbine blades include at least one of the following: Total blade length; Number of spanwise segments; chord lengths of each segment; Airfoil thickness; Installation angle; Local angle of attack.

[0084] In some embodiments, the processing unit 330 is further configured to perform at least one prediction process on the icing state of the wind turbine blades based on the target environmental parameters according to a preset time step, until the prediction process ends when the preset conditions are met. The m-th prediction process includes: For multiple computational domain grids, the external airflow field distribution of the wind turbine blades is obtained based on the target environmental parameters; Based on the external airflow field distribution, the collision flux of each discrete unit in multiple discrete units on the surface of the wind turbine blade is determined. Based on the collision flux of each discrete element, the local collision coefficient and the freezing coefficient of each discrete element are determined. The ice thickness of each discrete unit is determined based on the local collision coefficient and the freezing coefficient of each discrete unit. The boundary of each segment of the wind turbine blade is updated based on the ice thickness of all discrete units on the surface of the wind turbine blade; wherein each segment includes at least one discrete unit.

[0085] In some embodiments, the preset conditions include: The number of predictions for icing conditions reaches a preset threshold; or, The predicted duration of the icing state has reached the preset duration.

[0086] In some embodiments, icing prediction results include at least one of the following: Ice thickness distribution; Changes in the morphology of ice covering; Evolution of icing location; Icing type identification; Ice growth rate curve.

[0087] The electronic devices provided in the above embodiments can execute the technical solutions of the above method embodiments, and their implementation principles and technical effects are similar, so they will not be described again here.

[0088] Figure 4 This is a schematic structural diagram of an electronic device 400 provided in an embodiment of this application. Figure 4 The illustrated electronic device includes a processor 410, which can call and run computer programs from memory to implement the methods in the embodiments of this application.

[0089] Optionally, such as Figure 4 As shown, the electronic device 400 may further include a memory 420. The processor 410 can retrieve and run computer programs from the memory 420 to implement the methods described in the embodiments of this application.

[0090] The memory 420 can be a separate device independent of the processor 410, or it can be integrated into the processor 410.

[0091] Optionally, such as Figure 4 As shown, the electronic device 400 may also include a transceiver 430, which the processor 410 can control to communicate with other devices. Specifically, it can send information or data to other devices or receive information or data sent by other devices.

[0092] The transceiver 430 may include a transmitter and a receiver. The transceiver 430 may further include an antenna, and the number of antennas may be one or more.

[0093] Optionally, the electronic device 400 can implement the corresponding process of the terminal device in the various methods of the embodiments of this application, which will not be described in detail here for the sake of brevity.

[0094] It should be understood that the processor in the embodiments of this application may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0095] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0096] It should be understood that the above-described memory is exemplary and not a limiting description. For example, the memory in the embodiments of this application may also be static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DR RAM), etc. That is to say, the memory in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0097] This application also provides a computer-readable storage medium for storing computer programs.

[0098] Optionally, the computer-readable storage medium can be applied to the terminal device or server in the embodiments of this application, and the computer program causes the computer to execute the corresponding processes in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0099] This application also provides a computer program product, including computer program instructions.

[0100] Optionally, the computer program product can be applied to the terminal device or server in the embodiments of this application, and the computer program instructions cause the computer to execute the corresponding processes in the various methods of the embodiments of this application. For the sake of brevity, they will not be described in detail here.

[0101] This application also provides a computer program.

[0102] Optionally, the computer program can be applied to the terminal device or server in the embodiments of this application. When the computer program is run on the computer, it causes the computer to execute the corresponding processes in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.

[0103] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0104] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0105] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0108] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0109] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for identifying differences in icing in a wind power transmission system, characterized in that, The method includes: The icing state of power transmission lines and wind turbine blades is predicted based on target environmental parameters, and the icing prediction results of the power transmission lines and the wind turbine blades are obtained. Based on the icing prediction results of the power transmission line and the wind turbine blade, the differential information on icing of the power transmission line and the wind turbine blade is displayed.

2. The method according to claim 1, characterized in that, The target environmental parameters include at least one of the following: Liquid water content in the air; Incoming air velocity; Ambient temperature; Median volume diameter of a supercooled water droplet; wind direction; Relative humidity; Air pressure.

3. The method according to claim 1, characterized in that, The method further includes: The parameters of the transmission line are input into the first geometric model corresponding to the transmission line to obtain the geometric features of the transmission line; The geometric features of the transmission line are input into the first mesh partitioning model corresponding to the transmission line to obtain the mesh partitioning result of the transmission line and the mesh partitioning result of the external airflow field space of the transmission line. In the mesh partitioning result of the transmission line, the surface of the transmission line is divided into multiple discrete units, and in the mesh partitioning result of the external airflow field space of the transmission line, the external airflow field space of the transmission line is divided into multiple computational domain meshes. The parameters of the transmission line include at least one of the following: Outer diameter of the conductor; Inner diameter of the conductor; The stranded wire has a rough structure. Wire segment length; Conductor material; Torsional stiffness.

4. The method according to claim 3, characterized in that, When predicting the icing state of transmission lines based on target environmental parameters, the method includes: Based on the target environmental parameters, the icing state of the transmission line is predicted at least once according to a preset time step until the preset conditions are met and the prediction process ends. The m-th prediction process includes: For the multiple computational domain grids, the external airflow field distribution of the transmission line is obtained based on the target environmental parameters; Based on the external airflow field distribution, the collision flux of each discrete unit in multiple discrete units on the surface of the transmission conductor is determined; Based on the collision flux of each discrete unit, the local collision coefficient and the freezing coefficient of each discrete unit are determined. The ice layer thickness of each discrete unit is determined based on the local collision coefficient and the freezing coefficient of each discrete unit. Based on the ice thickness of all discrete units on the surface of the transmission conductor, the torsion angle at each discrete location on the surface of the transmission conductor is determined; wherein each discrete location includes at least one discrete unit.

5. The method according to claim 1, characterized in that, The method further includes: The parameters of the wind turbine blade are input into the second geometric model corresponding to the wind turbine blade to obtain the geometric features of the wind turbine blade. The geometric features of the wind turbine blade are input into the second mesh partitioning model corresponding to the wind turbine blade to obtain the mesh partitioning result of the wind turbine blade and the mesh partitioning result of the external airflow field space of the wind turbine blade. In the mesh partitioning result of the wind turbine blade, the surface of the wind turbine blade is divided into multiple discrete units, and in the mesh partitioning result of the external airflow field space of the wind turbine blade, the external airflow field space of the wind turbine blade is divided into multiple computational domain meshes. The parameters of the wind turbine blades include at least one of the following: Total blade length; Number of spanwise segments; chord lengths of each segment; Airfoil thickness; Installation angle; Local angle of attack.

6. The method according to claim 5, characterized in that, When predicting the icing state of wind turbine blades based on target environmental parameters, the method includes: Based on the target environmental parameters, the icing state of the wind turbine blades is predicted at least once according to a preset time step until the preset conditions are met and the prediction process ends. The m-th prediction process includes: For the multiple computational domain grids, based on the target environmental parameters, the external airflow field distribution of the wind turbine blades is obtained; Based on the external airflow field distribution, the collision flux of each discrete unit in the multiple discrete units on the surface of the wind turbine blade is determined; Based on the collision flux of each discrete unit, the local collision coefficient and the freezing coefficient of each discrete unit are determined. The ice layer thickness of each discrete unit is determined based on the local collision coefficient and the freezing coefficient of each discrete unit. The boundary of each segment of the wind turbine blade is updated based on the ice thickness of all discrete units on the surface of the wind turbine blade; wherein each segment includes at least one discrete unit.

7. The method according to claim 4 or 6, characterized in that, The preset conditions include: The number of predictions for icing conditions reaches a preset threshold; or, The predicted duration of the icing state has reached the preset duration.

8. The method according to claim 1, characterized in that, The icing prediction results include at least one of the following: Ice thickness distribution; Changes in the morphology of ice covering; Evolution of icing location; Icing type identification; Ice growth rate curve.

9. An electronic device, characterized in that, include: Processor and memory; The memory is used to store computer programs, and the processor is used to call and run the computer programs stored in the memory to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, Used to store a computer program that causes a computer to perform the method as described in any one of claims 1 to 8.

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