Fan blade de-icing method and apparatus, electronic device, and storage medium

Abstract

The application relates to a fan blade vibration deicing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a multi-modal state parameter of an ice layer on a fan blade surface; calculating an inherent frequency of a current ice layer-blade coupling structure of the fan blade based on the multi-modal state parameter; generating a vibration excitation parameter according to the inherent frequency of the current ice layer-blade coupling structure and the multi-modal state parameter; and controlling a vibration exciter arranged on the fan blade to apply a vibration excitation based on the vibration excitation parameter, so as to perform a deicing operation on the fan blade. Thus, the multi-modal state parameter of the ice layer is sensed in real time, the inherent frequency of the ice layer-blade coupling structure is dynamically calculated based on the multi-modal state parameter, and then a vibration excitation parameter matched with the inherent frequency is generated, so that the problems of poor frequency adaptability, the inability to adapt to inherent frequency drift caused by ice layer state changes and low deicing efficiency in the related art are solved, and an intelligent vibration deicing effect with high efficiency, low energy consumption and strong environmental adaptability is achieved.

Description

Technical Field

[0001] This application relates to the field of wind power equipment maintenance technology, and in particular to a method, device, electronic equipment and storage medium for de-icing wind turbine blades by vibration, which is especially suitable for removing ice from wind turbine blades under extreme environments such as low temperature and high humidity. Background Technology

[0002] In cold climates, wind turbine blades are highly susceptible to icing. Accumulated ice significantly alters the blade's aerodynamic shape, leading to decreased power generation efficiency and potentially causing safety incidents such as blade dynamic imbalance, overload shutdowns, and ice ejection, seriously threatening the operational safety and economic benefits of wind turbines. Therefore, efficient and reliable blade de-icing technology has become a critical issue urgently needing to be addressed in the wind power industry.

[0003] In related technologies, wind turbine blade de-icing technology is mainly divided into two categories: passive de-icing and active de-icing. Passive de-icing mainly reduces the adhesion between the ice layer and the blade surface through an anti-icing coating, while active de-icing technologies include electrothermal de-icing, hot gas de-icing, and mechanical vibration de-icing. However, all of the above methods suffer from poor frequency adaptability and cannot adapt to the natural frequency drift caused by changes in the ice layer state, resulting in low de-icing efficiency, which urgently needs to be improved. Summary of the Invention

[0004] This application provides a method, apparatus, electronic device, and storage medium for wind turbine blade vibration de-icing, which solves the problems of poor frequency adaptability and inability to adapt to the natural frequency drift caused by changes in ice layer state in related technologies, resulting in low de-icing efficiency. It achieves a highly efficient, low-energy-consumption, and environmentally adaptable intelligent vibration de-icing effect.

[0005] To achieve the above objectives, the first aspect of this application proposes a method for de-icing wind turbine blades using vibration, comprising the following steps: Obtain multimodal state parameters of ice layer on wind turbine blade surface; Based on the multimodal state parameters, the natural frequency of the current ice layer-blade coupling structure of the wind turbine blade is calculated; Based on the natural frequency of the current ice-blade coupling structure and the multimodal state parameters, the vibration excitation parameters are optimized and generated. Based on the vibration excitation parameters, the vibrators arranged on the wind turbine blades are controlled to apply vibration excitation in order to perform de-icing operation on the wind turbine blades.

[0006] According to one embodiment of this application, calculating the natural frequency of the current ice-blade coupling structure of the wind turbine blade based on the multimodal state parameters includes: Based on the ice temperature in the multimodal state parameters, the Young's modulus of the ice in the current ice-blade coupling structure is temperature compensated to obtain the temperature-compensated Young's modulus. The fundamental frequency of the current ice-blade coupling structure is calculated based on the temperature-compensated Young's modulus, the ice thickness and ice density in the multimodal state parameters. The boundary condition correction factor is calculated based on the bonding strength between the ice layer and the wind turbine blade surface in the multimodal state parameters. The natural frequencies of the current ice-blade coupling structure are calculated based on the fundamental frequency of the current ice-blade coupling structure and the boundary condition correction factor.

[0007] According to one embodiment of this application, the natural frequency of the current ice-blade coupling structure is:

[0008] in, The natural frequency of the current ice-blade coupling structure is... This refers to the temperature-compensated Young's modulus. The temperature of the ice layer, The thickness of the ice layer is [missing information]. The density of the ice layer is... For the Poisson's ratio of ice, The characteristic length of the wind turbine blade. This is the boundary condition correction factor. The bonding strength between the ice layer and the surface of the wind turbine blades. Let be the radius of curvature of the wind turbine blade.

[0009] According to one embodiment of this application, the vibration excitation parameters include vibration frequency, vibration amplitude, and vibration duration. Generating the vibration excitation parameters based on the natural frequency of the current ice-blade coupling structure and the multimodal state parameters includes: The vibration frequency is determined based on the natural frequency of the current ice-blade coupling structure. The vibration amplitude is determined based on the ice thickness and the adhesion strength between the ice layer and the wind turbine blade surface in the multimodal state parameters. The duration of the vibration is determined based on the thickness of the ice layer.

[0010] According to one embodiment of this application, the vibration frequency is:

[0011]

[0012] in, The vibration frequency is... This represents the offset of the vibration frequency; The vibration amplitude is:

[0013] in, The vibration amplitude, It is the amplitude coefficient; The duration of the vibration is:

[0014] in, The duration of the vibration is [missing information]. Based on the duration of action, As a reference ice thickness, This is the time compensation coefficient.

[0015] The wind turbine blade vibration de-icing method proposed in this application obtains the multimodal state parameters of the ice layer on the wind turbine blade surface, and calculates the natural frequency of the current ice layer-blade coupling structure based on the multimodal state parameters. Vibration excitation parameters are generated based on the natural frequency of the current ice layer-blade coupling structure and the multimodal state parameters. Vibration excitation is then applied to a vibrator arranged on the wind turbine blade based on the vibration excitation parameters to perform de-icing operations on the wind turbine blade. Therefore, by sensing the multimodal state parameters of the ice layer in real time and dynamically calculating the natural frequency of the ice layer-blade coupling structure based on these parameters, and then generating matching vibration excitation parameters, the method solves the problems of poor frequency adaptability and inability to adapt to natural frequency drift caused by changes in ice layer state, resulting in low de-icing efficiency in related technologies. This achieves a highly efficient, low-energy-consumption, and environmentally adaptable intelligent vibration de-icing effect.

[0016] To achieve the above objectives, a second aspect of this application provides a wind turbine blade vibration de-icing device, comprising: The acquisition module is used to acquire multimodal state parameters of the ice layer on the surface of the wind turbine blades; The calculation module is used to calculate the natural frequency of the current ice layer-blade coupling structure of the wind turbine blade based on the multimodal state parameters. The generation module is used to optimize and generate vibration excitation parameters based on the natural frequency of the current ice-blade coupling structure and the multimodal state parameters; The control module is used to control the vibrator arranged on the wind turbine blade to apply vibration excitation based on the vibration excitation parameters, so as to perform de-icing operation on the wind turbine blade.

[0017] According to one embodiment of this application, the computing module is specifically used for: Based on the ice temperature in the multimodal state parameters, the Young's modulus of the ice in the current ice-blade coupling structure is temperature compensated to obtain the temperature-compensated Young's modulus. The fundamental frequency of the current ice-blade coupling structure is calculated based on the temperature-compensated Young's modulus, the ice thickness and ice density in the multimodal state parameters. The boundary condition correction factor is calculated based on the bonding strength between the ice layer and the wind turbine blade surface in the multimodal state parameters. The natural frequencies of the current ice-blade coupling structure are calculated based on the fundamental frequency of the current ice-blade coupling structure and the boundary condition correction factor.

[0018] According to one embodiment of this application, the natural frequency of the current ice-blade coupling structure is:

[0019] in, The natural frequency of the current ice-blade coupling structure is... This refers to the temperature-compensated Young's modulus. The temperature of the ice layer, The thickness of the ice layer is [missing information]. The density of the ice layer is... For the Poisson's ratio of ice, The characteristic length of the wind turbine blade. This is the boundary condition correction factor. The bonding strength between the ice layer and the surface of the wind turbine blades. Let be the radius of curvature of the wind turbine blade.

[0020] According to one embodiment of this application, the vibration excitation parameters include vibration frequency, vibration amplitude, and vibration duration, and the generation module is specifically used for: The vibration frequency is determined based on the natural frequency of the current ice-blade coupling structure. The vibration amplitude is determined based on the ice thickness and the adhesion strength between the ice layer and the wind turbine blade surface in the multimodal state parameters. The duration of the vibration is determined based on the thickness of the ice layer.

[0021] According to one embodiment of this application, the vibration frequency is:

[0022]

[0023] in, The vibration frequency is... This represents the offset of the vibration frequency; The vibration amplitude is:

[0024] in, The vibration amplitude, It is the amplitude coefficient; The duration of the vibration is:

[0025] in, The duration of the vibration is [missing information]. Based on the duration of action, As a reference ice thickness, This is the time compensation coefficient.

[0026] The wind turbine blade vibration de-icing device proposed in this application obtains multimodal state parameters of the ice layer on the wind turbine blade surface and calculates the natural frequency of the current ice layer-blade coupling structure based on these parameters. Vibration excitation parameters are generated based on the natural frequency of the current ice layer-blade coupling structure and the multimodal state parameters. Vibration excitation parameters are then applied by a vibrator arranged on the wind turbine blade to perform de-icing. Thus, by sensing the multimodal state parameters of the ice layer in real time and dynamically calculating the natural frequency of the ice layer-blade coupling structure, and generating matching vibration excitation parameters, the device solves the problems of poor frequency adaptability and inability to adapt to natural frequency drift caused by changes in ice layer state, resulting in low de-icing efficiency in related technologies. This achieves a highly efficient, low-energy-consumption, and environmentally adaptable intelligent vibration de-icing effect.

[0027] To achieve the above objectives, a third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the wind turbine blade vibration de-icing method as described in the above embodiments.

[0028] To achieve the above objectives, a fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the wind turbine blade vibration de-icing method as described in the above embodiments.

[0029] To achieve the above objectives, a fifth aspect of this application provides a computer program product comprising a computer program that, when executed by a processor, is used to implement the wind turbine blade vibration de-icing method as described in the above embodiments.

[0030] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a wind turbine blade vibration de-icing method according to an embodiment of this application; Figure 2 This is a flowchart of natural frequency calculation and vibration parameter optimization according to an embodiment of this application; Figure 3 This is a flowchart of another wind turbine blade vibration de-icing method provided according to an embodiment of this application; Figure 4 This is a block diagram of a wind turbine blade vibration de-icing device provided according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0032] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0033] The following describes, with reference to the accompanying drawings, a method, apparatus, electronic device, and storage medium for de-icing wind turbine blades based on embodiments of this application. First, the method for de-icing wind turbine blades based on embodiments of this application will be described with reference to the accompanying drawings.

[0034] Figure 1 This is a flowchart of a wind turbine blade vibration de-icing method according to an embodiment of this application.

[0035] For example, such as Figure 1 As shown, the wind turbine blade vibration de-icing method includes the following steps: In step S101, the multimodal state parameters of the ice layer on the surface of the wind turbine blades are obtained.

[0036] It is understandable that multimodal state parameters refer to state variables that characterize the physical properties of ice layers in multiple dimensions, which may include at least ice layer thickness, ice layer temperature, ice layer density, and the bonding strength between the ice layer and the surface of the wind turbine blades.

[0037] Specifically, the multimodal state parameters of the ice layer on the wind turbine blade surface can be acquired in real time by a multimodal monitoring unit. For example, a millimeter-wave radar array can be used to measure the ice layer thickness with an accuracy of ±0.1 mm and a refresh rate of 10 Hz; a distributed PT1000 platinum resistance network can be used to measure the ice layer temperature, covering a range of -50 to 50℃ with an accuracy of ±0.3℃; and a microwave dielectric constant analyzer can be used to invert the ice layer density by measuring the dielectric constant of the ice with an accuracy of ±20 kg / m³. 3 Furthermore, a piezoresistive impedance spectrometer can be used to indirectly assess the bonding strength between the ice layer and the wind turbine blade surface based on the shear wave velocity method, with an accuracy of ±0.2 MPa. These multi-dimensional state parameters form the data foundation for subsequent natural frequency calculations and vibration parameter optimization, enabling the system to perceive in real time all-round changes in the ice layer from thin to thick, from soft to hard, and from weak to strong bonding, providing accurate input information for achieving adaptive resonant de-icing.

[0038] In step S102, the natural frequency of the current ice layer-blade coupling structure of the wind turbine blade is calculated based on the multimodal state parameters.

[0039] It is understandable that an ice-blade coupled structure refers to a composite structural system formed when ice forms on the blade surface, and the ice layer bonds with the blade body through interfacial adhesion, sharing the same vibration and deformation. The natural frequency refers to the characteristic frequency of the structural system when it vibrates freely after being subjected to an initial disturbance. For an ice-blade coupled structure, its natural frequency changes dynamically with the state of the ice layer.

[0040] Specifically, by collecting key parameters (i.e., multimodal state parameters) that affect the mechanical properties and vibration response of the ice layer in real time through the multimodal monitoring unit, the natural frequency of the current ice-blade coupling structure of the wind turbine blade can be calculated based on these multimodal state parameters.

[0041] To facilitate understanding, the following details how to calculate the natural frequency of the current ice layer-blade coupling structure of the wind turbine blade.

[0042] In one possible implementation, in some embodiments, the natural frequencies of the current ice-blade coupling structure of the wind turbine blade are calculated based on multimodal state parameters, including: performing temperature compensation on the Young's modulus of the ice in the current ice-blade coupling structure based on the ice temperature in the multimodal state parameters to obtain the temperature-compensated Young's modulus; calculating the fundamental frequency of the current ice-blade coupling structure based on the temperature-compensated Young's modulus, the ice thickness and ice density in the multimodal state parameters; calculating the boundary condition correction factor based on the bonding strength between the ice layer and the wind turbine blade surface in the multimodal state parameters; and calculating the natural frequencies of the current ice-blade coupling structure based on the fundamental frequency and the boundary condition correction factor.

[0043] It is understandable that Young's modulus refers to a physical quantity characterizing a material's resistance to elastic deformation, i.e., the material's "rigidity." The Young's modulus of ice varies significantly with temperature; the lower the temperature, the "harder" the ice, and the larger the Young's modulus; conversely, the higher the temperature, the "softer" the ice, and the smaller the Young's modulus. Temperature compensation is the process of correcting the material's mechanical property parameters based on the actual temperature. Since the Young's modulus of ice varies significantly with temperature, by substituting the measured temperature into the temperature-Young's modulus relationship model, the reference value at the standard temperature can be corrected to the actual value at the current temperature, ensuring the accuracy of subsequent calculations. The fundamental frequency refers to the natural frequency of the ice-blade coupling structure under ideal boundary conditions (i.e., without considering the effects of bond strength and curvature), determined only by the ice material properties (such as Young's modulus, density, and Poisson's ratio) and geometric dimensions (such as thickness and blade characteristic length). It is an intermediate reference value for calculating the actual natural frequency. The boundary condition correction factor refers to a coefficient used to correct the deviation between the ideal model and the actual situation. In this embodiment, the influence of interfacial bond strength and blade surface curvature on the natural frequency is mainly considered. The higher the bonding strength and the greater the curvature, the greater the equivalent stiffness of the coupled structure, and the higher the natural frequency.

[0044] Specifically, such as Figure 2 As shown, firstly, based on the ice temperature in the multimodal state parameters, the Young's modulus of the ice in the current ice-blade coupling structure can be temperature-compensated to obtain the temperature-compensated Young's modulus. This process involves substituting the measured temperature into a pre-established temperature-Young's modulus relationship model to correct the standard Young's modulus at the reference temperature to the actual value at the current temperature, thus reflecting the physical law of the change in ice material stiffness with temperature. Secondly, based on the temperature-compensated Young's modulus, the ice thickness, and the ice density in the multimodal state parameters, the fundamental frequency of the current ice-blade coupling structure is calculated. This fundamental frequency reflects the vibration characteristics determined by the material properties and geometry of the ice layer itself under ideal boundary conditions. Then, based on the bonding strength between the ice layer and the wind turbine blade surface in the multimodal state parameters, a boundary condition correction factor is calculated. This correction factor quantifies the enhancing effect of the interface bonding state on the stiffness of the coupling structure; the higher the bonding strength, the greater the overall stiffness of the coupling structure. Finally, multiplying the fundamental frequency by the boundary condition correction factor yields the actual natural frequency of the current ice-blade coupling structure. Through this series of progressive calculations, a precise mapping from the original monitoring parameters to the target's natural frequency is achieved, providing a reliable frequency reference for the subsequent generation of matching vibration excitation parameters.

[0045] Optionally, in some embodiments, the natural frequency of the current ice-blade coupling structure is:

[0046] in, This represents the natural frequency (in Hz) of the current ice-blade coupling structure. This is the temperature-compensated Young's modulus (in GPa). Ice temperature ( ), The thickness of the ice layer (in mm). Ice density (unit: kg / m³) 3 ), Poisson's ratio for ice ( Take 0.33). The characteristic length of the wind turbine blade (in meters). This is the boundary condition correction factor. The bonding strength between the ice layer and the surface of the wind turbine blade (unit: MPa). The radius of curvature of the wind turbine blade (in meters).

[0047] The temperature-compensated Young's modulus is:

[0048] in, This is the Young's modulus after temperature compensation. Young's modulus reference value ( (This represents the reference value for the Young's modulus of ice at -20℃). Temperature decay coefficient ( ); The fundamental frequency of the current ice-blade coupling structure is:

[0049] in, This is the fundamental frequency (in Hz) of the current ice-blade coupling structure. The boundary condition correction factor is:

[0050] in, This is the boundary condition correction factor. For reference bond strength ( ), For reference radius of curvature ( ), , , For empirical coefficients calibrated through finite element simulation and experiments, preferably, 0.1, , .

[0051] It should be noted that, for the wet snow icing conditions that offshore wind turbines may encounter, the embodiments of this application can also extend the salinity compensation function. Specifically, ice salinity parameters (which can be obtained through a salinity sensor, etc.) are added to the multimodal state parameters collected in step S101. When the ice salinity is detected to be greater than a preset threshold (e.g., 3%), salinity correction is introduced based on the calculation of the natural frequency of the current ice-blade coupling structure. The correction formula is as follows:

[0052] in, The corrected natural frequency. For ice layer salinity, Salinity compensation coefficient ( ).

[0053] In step S103, vibration excitation parameters are optimized and generated based on the natural frequency and multimodal state parameters of the current ice-blade coupling structure.

[0054] It is understandable that vibration excitation parameters refer to the set of instructions that the control system applies to the execution layer (such as a vibrator) to generate specific vibrations.

[0055] In other words, based on the calculated natural frequencies of the current ice-blade coupling structure, and combined with multimodal state parameters, vibration excitation parameters can be optimized and generated.

[0056] The following details how to obtain the vibration excitation parameters.

[0057] As one possible implementation, in some embodiments, the vibration excitation parameters include vibration frequency, vibration amplitude, and vibration duration. The vibration excitation parameters are generated based on the natural frequency and multimodal state parameters of the current ice-blade coupling structure, including: determining the vibration frequency based on the natural frequency of the current ice-blade coupling structure; determining the vibration amplitude based on the ice thickness and the bonding strength between the ice layer and the wind turbine blade surface in the multimodal state parameters; and determining the vibration duration based on the ice thickness.

[0058] As can be understood, vibration frequency refers to the number of vibrations per second (in Hz) of the exciter, which determines the rate of change of alternating stress applied to the ice-blade coupling structure. When the vibration frequency approaches or equals the natural frequency of the ice-blade coupling structure, the system resonates, and the stress amplitude at the ice interface is significantly amplified. Vibration amplitude refers to the maximum value of the exciter's vibration displacement (in mm), characterizing the intensity of the vibration. The larger the amplitude, the greater the amplitude of alternating stress generated at the ice interface, and the stronger the destructive effect on the ice adhesion. Vibration duration refers to the length of time the exciter continuously applies vibration excitation (in seconds). The longer the duration, the greater the fatigue damage accumulated by the alternating stress at the ice interface, ultimately leading to interface failure and ice detachment.

[0059] Specifically, firstly, the vibration frequency is determined using the natural frequency of the current ice-blade coupling structure calculated in step S102 as the benchmark for setting the vibration frequency, ensuring that the applied excitation frequency matches the resonant frequency of the coupling structure. Secondly, the vibration amplitude is determined based on the ice thickness and the adhesion strength between the ice layer and the wind turbine blade surface from the multimodal state parameters. The thicker the ice layer and the greater the adhesion strength, the larger the required vibration amplitude is to generate sufficient alternating stress at the ice layer interface to disrupt the adhesion. Finally, the vibration duration is determined based on the ice thickness. The thicker the ice layer, the longer the required excitation time is to ensure that the alternating stress fully accumulates at the interface, causing fatigue damage and ultimately leading to ice detachment. Through this parameter generation method, the vibration frequency, vibration amplitude, and vibration duration are all precisely matched with the current ice layer state, laying the foundation for subsequent efficient de-icing.

[0060] Optionally, in some embodiments, the vibration frequency is:

[0061]

[0062] in, The vibration frequency, This represents the offset of the vibration frequency; The amplitude of vibration is:

[0063] in, The amplitude of vibration. For amplitude coefficient, preferably, ; The duration of vibration is:

[0064] in, The duration of vibration. Basic action time ( ), The baseline ice thickness ( ), Time compensation coefficient ( ).

[0065] Understandably, to avoid excitation failure due to frequency calculation errors or dynamic changes in the ice layer state, the vibration frequency can be set using a frequency sweep method, that is, using the natural frequency calculated in step S102. Centered on the preset bandwidth range (e.g.) The scanning is performed within the range of [missing information]. This frequency sweeping strategy can effectively cover the resonant frequency shift that may be caused by model errors, parameter fluctuations, or environmental disturbances, ensuring that the excitation frequency always matches the actual resonance point of the ice-blade coupling structure, thereby reliably exciting the resonance effect.

[0066] The vibration amplitude can be dynamically adjusted according to the ice thickness and the bonding strength between the ice and the wind turbine blade surface to ensure that alternating stress sufficient to break the bond is generated at the ice interface. The thicker the ice or the greater the bonding strength, the larger the amplitude required. This allows for adaptive matching of the vibration energy requirements of different icing conditions, ensuring the de-icing effect while avoiding over-excitation.

[0067] The vibration duration can be dynamically set according to the ice thickness (i.e., there is a linear positive correlation between the vibration duration and the ice thickness). The thicker the ice, the longer the excitation time is required to ensure that the alternating stress fully accumulates fatigue damage at the interface. For example, a base duration of 10 seconds is used when the ice thickness is 5 mm. For every 1 mm increase in ice thickness, the duration is extended by 4 seconds, thus adaptively matching the excitation duration requirements of different ice thicknesses, ensuring sufficient de-icing while avoiding unnecessary energy consumption.

[0068] In step S104, the vibrator arranged on the wind turbine blades is controlled to apply vibration excitation based on the vibration excitation parameters in order to perform de-icing operation on the wind turbine blades.

[0069] It is understood that a vibrator is an actuator that converts electrical energy into mechanical vibration energy, used to apply controllable vibration excitation to wind turbine blades. In the embodiments of this application, the vibrator includes two types: piezoelectric ceramic stacks (such as the PZT-8 type, with a frequency range of 20~2000Hz) distributed along the blade span, and electromagnetic vibrators (such as those with a peak force of 1.5kN and a response time of <50ms), which are suitable for the vibration requirements of different parts of the blade. For example, a low-frequency, large-amplitude electromagnetic vibrator can be arranged in the blade root region, while a high-frequency response piezoelectric ceramic array can be arranged in the blade middle and tip regions.

[0070] Specifically, the control system (such as an FPGA (Field-Programmable Gate Array)-ARM (Advanced RISC Machines) dual-core architecture) can convert the vibration excitation parameters calculated in step S103 into control commands, which are then sent to the exciter array distributed along the blade span. The exciter array starts working according to the commands. Specifically, the electromagnetic exciter in the blade root region responds to the low-frequency, high-amplitude excitation requirement, generating strong vibration force to drive the stiffer blade root region. The piezoelectric ceramic array in the blade mid-section and tip region responds to the high-frequency excitation requirement, generating precise high-frequency vibration to act on the more flexible blade tip region. The entire excitation process follows a preset frequency sweep range (i.e., a preset bandwidth range, such as...). The amplitude and duration of the vibration are continuously adjusted, allowing the vibration energy to be efficiently transferred to the ice interface through the blade structure, generating alternating stress at the interface. When the amplitude of the alternating stress exceeds the bonding strength between the ice layer and the blade surface, the ice layer gradually develops microcracks, expands, and eventually falls off as a whole, thereby achieving the de-icing operation on the blade surface.

[0071] During the application of vibration excitation, the strain state of the wind turbine blades can be monitored in real time. When the wind turbine blade strain exceeds the preset safety threshold (such as 200με), the system can determine that the current excitation may cause damage to the blade structure and immediately stop the vibration excitation to protect the blade structure safety. Under the premise of ensuring the de-icing effect, the risk of fatigue damage to the blades caused by vibration is minimized.

[0072] Furthermore, such as Figure 3 As shown, after applying vibration excitation, the system evaluates the ice removal rate and executes closed-loop feedback control. Specifically, by comparing the ice thickness changes collected by the multimodal monitoring unit before and after excitation, the current ice removal rate can be calculated. (For example, determined based on the ratio of the reduction in ice thickness to the initial ice thickness), if If this is the case, then de-icing can be determined to be complete, and the system can automatically stop vibration excitation and return to step S101 to continuously monitor the ice layer status on the wind turbine blade surface; if And if the current number of adjustments is less than the preset maximum number of adjustments (e.g., 3 times) (the number of adjustments refers to the number of rounds in which the system automatically optimizes the vibration excitation parameters (e.g., expands the sweep frequency range and / or increases the amplitude) and reapplies excitation because the ice removal rate is not up to standard in a complete de-icing task), then the system can automatically optimize and adjust the vibration excitation parameters and return to step S104 to reapply excitation to the exciter; if after 3 consecutive adjustments If the value remains below 95%, an alarm will be triggered, prompting manual intervention to ensure the reliability and safety of the system under abnormal operating conditions.

[0073] In addition, in this embodiment, the system can also monitor the bonding strength between the ice layer and the surface of the wind turbine blades over a long period of time. When the rate of increase of the bonding strength per unit time exceeds a preset threshold (such as 0.5 MPa / h), it is determined that there is a risk of rapid icing on the blades, and an icing warning signal is issued to prompt the wind farm operation and maintenance personnel to start preventive de-icing measures or take shutdown protection measures to avoid safety accidents caused by excessive icing of the blades.

[0074] To facilitate those skilled in the art to further understand the wind turbine blade vibration de-icing method proposed in the embodiments of this application, further supplements are provided below with reference to specific embodiments.

[0075] Example 1: De-icing of a 3MW onshore wind turbine under freezing rain conditions During freezing rain at a 3MW onshore wind farm, ice formed on the surface of the wind turbine blades. The system, through a multi-modal monitoring unit, collected the following data in real time: ice thickness 8.2mm, ice temperature -12℃, and ice density 910kg / m³. 3 The bonding strength between the ice layer and the wind turbine blade surface is 1.9 MPa, and the leading edge curvature radius of the blade is 0.45 m. Based on the aforementioned data, the natural frequency and vibration excitation parameters of the current ice-blade coupling structure can be optimized (taking the characteristic length of the blade L = 5.6 m), and the natural frequency of the current ice-blade coupling structure can be obtained. Vibration frequency in vibration excitation parameters vibration amplitude Vibration duration .

[0076] Therefore, the control system can start the exciter according to the above parameters. After 3.2s, the resonance feedback signal is detected to be enhanced. After 18.5s, the ice layer is completely detached, and the multi-modal monitoring unit reports the removal rate. =98%, de-icing process complete. Energy consumption for this de-icing operation was 0.34 kWh, a 60% reduction compared to the fixed-frequency method (e.g., 0.85 kWh).

[0077] Example 2: De-icing of 5MW offshore wind turbines under wet snow conditions To address the icing conditions that offshore wind turbines may encounter in wet snow and ice weather, this application further expands the salinity compensation function to improve adaptability and de-icing accuracy in marine environments. In this embodiment, a salinity sensor is added to the multimodal monitoring unit to collect salinity parameters (in %) in the ice layer in real time. When the salinity is detected to be higher than 3%, it indicates that the ice layer is affected by seawater salinity, and its mechanical properties differ from pure ice, requiring salinity correction to the calculated natural frequency. Subsequently, the system can use the corrected natural frequency as a basis to optimize and generate excitation parameters such as vibration frequency, amplitude, and vibration duration according to the same method as in Embodiment 1, and control the exciter to perform de-icing operations. By introducing a salinity compensation mechanism, this application can effectively adapt to the changes in the mechanical properties of the ice layer under wet snow and ice conditions of offshore wind turbines, ensuring the accuracy of the resonant frequency calculation, thereby maintaining high-efficiency de-icing performance in marine environments. In summary, the wind turbine blade vibration de-icing method proposed in this application has at least the following beneficial effects: (1) This application uses a multimodal monitoring unit to sense the ice thickness, temperature, density, and bonding strength between the ice layer and the wind turbine blade surface in real time, and constructs a dynamic model of the natural frequency of the ice-blade coupling structure to achieve precise matching between the vibration frequency and the ice state. Experimental results show that under ice thickness of 8 mm or more, the de-icing rate of the embodiment of this application can reach more than 92%, which is 142% higher than the fixed frequency vibration de-icing method (38%) in related technologies, effectively solving the technical problem of the difficulty in removing thick ice layers.

[0078] (2) This application adopts adaptive resonance technology, which only requires applying a small amplitude that matches the natural frequency of the ice layer to induce resonance, causing the ice layer to fall off quickly and avoiding energy waste caused by ineffective vibration. Taking a single de-icing of a 3MW wind turbine as an example, the energy consumption of this application is only 0.34kWh, which is 60% lower than the 0.85kWh of the fixed frequency method and more than 80% lower than the 2-3kWh of the electric heating de-icing method. Based on 30 days of icing per year, the annual electricity saving of a single wind turbine can reach 15-20kWh, significantly reducing the operation and maintenance cost of wind farms.

[0079] (3) This invention achieves dynamic adaptation to complex working conditions through multi-dimensional parameter fusion: by establishing an exponential decay model of Young's modulus with temperature, the influence of ice mechanical property changes on natural frequency in a wide temperature range of -40℃ to 0℃ is effectively compensated; by density monitoring, frost ice (low density), clear ice (high density) and mixed ice are distinguished, and the frequency response characteristics corresponding to different ice types are differentiated for excitation; by introducing the bonding strength correction factor between ice layer and wind turbine blade surface, the amplitude and sweep frequency range are automatically increased for high-strength bonded ice layer, ensuring the de-icing effect under various working conditions.

[0080] (4) After applying vibration excitation, the system provides real-time feedback on ice shedding through a multi-modal monitoring unit. Vibration automatically stops when the ice removal rate reaches 95% or higher. Simultaneously, it is equipped with amplitude threshold protection (≤1.2mm) and blade strain monitoring (automatic shutdown when >200με) to minimize fatigue damage to the blade structure while ensuring effective de-icing. Fatigue life assessment shows that compared to vibration de-icing methods in related technologies, fatigue damage at the blade root is reduced by approximately 40%.

[0081] (5) An electromagnetic exciter is arranged in the blade root region with greater stiffness to provide low-frequency, large-amplitude excitation; a piezoelectric ceramic array is arranged in the blade mid-section and blade tip regions with greater flexibility to achieve high-frequency response excitation. This layout scheme can transfer vibration energy to the ice layer interface more efficiently, and the energy utilization rate is increased by more than 35% compared with a single type of exciter layout.

[0082] (6) The core algorithm of this application embodiment is based on a physical model and can be ported to wind turbines of different power levels (1.5MW-10MW) and different types (onshore and offshore). For offshore wind turbines, a salinity compensation module can be added to correct the influence of seawater ice crystals on ice layer characteristics; for high-altitude wind turbines, a pressure compensation module can be added. The system adopts a modular design and standardized hardware interfaces, which facilitates the retrofitting and upgrading of in-service units.

[0083] (7) By monitoring the trend of ice layer bonding strength over a long period of time, when the bonding strength is detected to rise rapidly in a short period of time, the risk of icing can be warned in advance, providing a basis for decision-making for wind farms to start preventive de-icing or shutdown protection, and effectively avoiding safety accidents caused by excessive blade icing.

[0084] The wind turbine blade vibration de-icing method proposed in this application obtains the multimodal state parameters of the ice layer on the wind turbine blade surface, and calculates the natural frequency of the current ice layer-blade coupling structure based on the multimodal state parameters. Vibration excitation parameters are generated based on the natural frequency of the current ice layer-blade coupling structure and the multimodal state parameters. Vibration excitation is then applied to a vibrator arranged on the wind turbine blade based on the vibration excitation parameters to perform de-icing operations on the wind turbine blade. Therefore, by sensing the multimodal state parameters of the ice layer in real time and dynamically calculating the natural frequency of the ice layer-blade coupling structure based on these parameters, and then generating matching vibration excitation parameters, the method solves the problems of poor frequency adaptability and inability to adapt to natural frequency drift caused by changes in ice layer state, resulting in low de-icing efficiency in related technologies. This achieves a highly efficient, low-energy-consumption, and environmentally adaptable intelligent vibration de-icing effect.

[0085] Next, the wind turbine blade vibration de-icing device according to the embodiments of this application is described with reference to the accompanying drawings.

[0086] Figure 4This is a block diagram of a wind turbine blade vibration de-icing device according to an embodiment of this application.

[0087] like Figure 4 As shown, the wind turbine blade vibration de-icing device 10 includes: an acquisition module 100, a calculation module 200, a generation module 300, and a control module 400.

[0088] The acquisition module 100 is used to acquire the multimodal state parameters of the ice layer on the surface of the wind turbine blades. Calculation module 200 is used to calculate the natural frequency of the current ice layer-blade coupling structure of the wind turbine blade based on multimodal state parameters; The generation module 300 is used to optimize and generate vibration excitation parameters based on the natural frequency and multimodal state parameters of the current ice-blade coupling structure. The control module 400 is used to control the vibrator arranged on the wind turbine blades to apply vibration excitation based on the vibration excitation parameters in order to perform de-icing operation on the wind turbine blades.

[0089] Optionally, in some embodiments, the calculation module 200 is specifically used for: Based on the ice temperature in the multimodal state parameters, the Young's modulus of the ice in the current ice-blade coupling structure is temperature compensated to obtain the temperature-compensated Young's modulus. The fundamental frequency of the current ice-blade coupling structure is calculated based on the temperature-compensated Young's modulus, ice thickness, and ice density in the multimodal state parameters. The boundary condition correction factor is calculated based on the bonding strength between the ice layer and the wind turbine blade surface in the multimodal state parameters. Based on the fundamental frequency and boundary condition correction factor of the current ice-blade coupling structure, the natural frequency of the current ice-blade coupling structure is calculated.

[0090] Optionally, in some embodiments, the natural frequency of the current ice-blade coupling structure is:

[0091] in, The natural frequency of the current ice-blade coupling structure. This is the Young's modulus after temperature compensation. The temperature of the ice layer. For ice thickness, The density of the ice layer, For the Poisson's ratio of ice, The characteristic length of the wind turbine blade. This is the boundary condition correction factor. The bonding strength between the ice layer and the surface of the wind turbine blades. Let be the radius of curvature of the wind turbine blade.

[0092] Optionally, in some embodiments, the vibration excitation parameters include vibration frequency, vibration amplitude, and vibration duration, and the generation module 300 is specifically used for: The vibration frequency is determined based on the natural frequency of the current ice-blade coupling structure. The vibration amplitude is determined based on the ice thickness and the bonding strength between the ice layer and the wind turbine blade surface in the multimodal state parameters. The duration of vibration is determined based on the thickness of the ice layer.

[0093] Optionally, in some embodiments, the vibration frequency is:

[0094]

[0095] in, The vibration frequency, This represents the offset of the vibration frequency; The amplitude of vibration is:

[0096] in, The amplitude of vibration. It is the amplitude coefficient; The duration of vibration is:

[0097] in, The duration of vibration. Based on the duration of action, As a reference ice thickness, This is the time compensation coefficient.

[0098] It should be noted that the foregoing explanation of the wind turbine blade vibration de-icing method embodiment also applies to the wind turbine blade vibration de-icing device of this embodiment, and will not be repeated here.

[0099] The wind turbine blade vibration de-icing device proposed in this application obtains multimodal state parameters of the ice layer on the wind turbine blade surface and calculates the natural frequency of the current ice layer-blade coupling structure based on these parameters. Vibration excitation parameters are generated based on the natural frequency of the current ice layer-blade coupling structure and the multimodal state parameters. Vibration excitation parameters are then applied by a vibrator arranged on the wind turbine blade to perform de-icing. Thus, by sensing the multimodal state parameters of the ice layer in real time and dynamically calculating the natural frequency of the ice layer-blade coupling structure, and generating matching vibration excitation parameters, the device solves the problems of poor frequency adaptability and inability to adapt to natural frequency drift caused by changes in ice layer state, resulting in low de-icing efficiency in related technologies. This achieves a highly efficient, low-energy-consumption, and environmentally adaptable intelligent vibration de-icing effect.

[0100] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0101] When the processor 502 executes the program, it implements the wind turbine blade vibration de-icing method provided in the above embodiments.

[0102] Furthermore, electronic devices also include: Communication interface 503 is used for communication between memory 501 and processor 502.

[0103] The memory 501 is used to store computer programs that can run on the processor 502.

[0104] The memory 501 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0105] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0106] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0107] The processor 502 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0108] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described wind turbine blade vibration de-icing method.

[0109] This application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described wind turbine blade vibration de-icing method.

[0110] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0111] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0112] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for de-icing wind turbine blades using vibration, characterized in that, Includes the following steps: Obtain multimodal state parameters of ice layer on wind turbine blade surface; Based on the multimodal state parameters, the natural frequency of the current ice layer-blade coupling structure of the wind turbine blade is calculated; Based on the natural frequency of the current ice-blade coupling structure and the multimodal state parameters, the vibration excitation parameters are optimized and generated. Based on the vibration excitation parameters, the vibrators arranged on the wind turbine blades are controlled to apply vibration excitation in order to perform de-icing operation on the wind turbine blades.

2. The method according to claim 1, characterized in that, The calculation of the natural frequency of the current ice layer-blade coupling structure of the wind turbine blade based on the multimodal state parameters includes: Based on the ice temperature in the multimodal state parameters, the Young's modulus of the ice in the current ice-blade coupling structure is temperature compensated to obtain the temperature-compensated Young's modulus. The fundamental frequency of the current ice-blade coupling structure is calculated based on the temperature-compensated Young's modulus, the ice thickness and ice density in the multimodal state parameters. The boundary condition correction factor is calculated based on the bonding strength between the ice layer and the wind turbine blade surface in the multimodal state parameters. The natural frequencies of the current ice-blade coupling structure are calculated based on the fundamental frequency of the current ice-blade coupling structure and the boundary condition correction factor.

3. The method according to claim 2, characterized in that, The natural frequency of the current ice-blade coupling structure is: in, The natural frequency of the current ice-blade coupling structure is... This refers to the temperature-compensated Young's modulus. The temperature of the ice layer, The thickness of the ice layer is [missing information]. The density of the ice layer is... For the Poisson's ratio of ice, The characteristic length of the wind turbine blade. This is the boundary condition correction factor. The bonding strength between the ice layer and the surface of the wind turbine blades. Let be the radius of curvature of the wind turbine blade.

4. The method according to claim 1, characterized in that, The vibration excitation parameters include vibration frequency, vibration amplitude, and vibration duration. Generating the vibration excitation parameters based on the natural frequency of the current ice-blade coupling structure and the multimodal state parameters includes: The vibration frequency is determined based on the natural frequency of the current ice-blade coupling structure. The vibration amplitude is determined based on the ice thickness and the adhesion strength between the ice layer and the wind turbine blade surface in the multimodal state parameters. The duration of the vibration is determined based on the thickness of the ice layer.

5. The method according to claim 4, characterized in that, The vibration frequency is: in, The vibration frequency is... This represents the offset of the vibration frequency; The vibration amplitude is: in, The vibration amplitude, It is the amplitude coefficient; The duration of the vibration is: in, The duration of the vibration is [duration]. Based on the duration of action, As a reference ice thickness, This is the time compensation coefficient.

6. A wind turbine blade vibration de-icing device, characterized in that, include: The acquisition module is used to acquire multimodal state parameters of the ice layer on the surface of the wind turbine blades; The calculation module is used to calculate the natural frequency of the current ice layer-blade coupling structure of the wind turbine blade based on the multimodal state parameters. The generation module is used to optimize and generate vibration excitation parameters based on the natural frequency of the current ice-blade coupling structure and the multimodal state parameters; The control module is used to control the vibrator arranged on the wind turbine blade to apply vibration excitation based on the vibration excitation parameters, so as to perform de-icing operation on the wind turbine blade.

7. The apparatus according to claim 6, characterized in that, The computing module is specifically used for: Based on the ice temperature in the multimodal state parameters, the Young's modulus of the ice in the current ice-blade coupling structure is temperature compensated to obtain the temperature-compensated Young's modulus. The fundamental frequency of the current ice-blade coupling structure is calculated based on the temperature-compensated Young's modulus, the ice thickness and ice density in the multimodal state parameters. The boundary condition correction factor is calculated based on the bonding strength between the ice layer and the wind turbine blade surface in the multimodal state parameters. The natural frequencies of the current ice-blade coupling structure are calculated based on the fundamental frequency of the current ice-blade coupling structure and the boundary condition correction factor.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, the processor executing the program to implement the wind turbine blade vibration de-icing method as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the wind turbine blade vibration de-icing method as described in any one of claims 1-5.

10. A computer program product, characterized in that, The method includes a computer program, which, when executed by a processor, is used to implement the wind turbine blade vibration de-icing method according to any one of claims 1-5.

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