Pneumatic self-cleaning method and device based on blade pitch

Abstract

The application discloses a kind of based on blade variable pitch aerodynamic self-cleaning method and device, it is related to wind power generation technical field, the method includes: after self-cleaning mode triggers, first, determine the dirt degree and distribution atlas according to blade surface multi-sensor monitoring data, then calculate the aerodynamic disturbance required for stripping pollutant;Second, in combination with current wind condition parameters, the best frequency and amplitude combination of blade variable pitch action is determined by gradient descent optimization or multi-scenario optimization model;Then, to avoid resonance with blade inherent mode, the frequency combination is adjusted to avoid frequency, and the blade modal shape synthesis instruction sequence is integrated;Finally, control variable pitch system executes the instruction sequence, so that blade produces specific aerodynamic disturbance to remove surface pollutant.By the above mode, the method realizes intelligent, low-energy consumption online blade self-cleaning, effectively improves the power generation efficiency and operation safety of wind turbine.

Description

Technical Field

[0001] This application relates to the field of wind power generation technology, and in particular to an aerodynamic self-cleaning method and apparatus based on blade pitch control. Background Technology

[0002] Currently, blade cleaning often relies on periodic manual inspections and cleaning, a method that is significantly delayed and passive. Cleaning typically only begins when pollutants have accumulated to the point of causing significant damage to aerodynamic performance and power generation, failing to achieve timely preventative cleaning and resulting in wind energy capture efficiency remaining suboptimal for extended periods.

[0003] Furthermore, manual cleaning operations are inherently challenging and risky. They require professionals to operate on massive blades at high altitudes using suspended platforms or ropes, which is not only costly and time-consuming but also severely dependent on inclement weather conditions, placing extremely high demands on personnel safety and further exacerbating the inefficiency and high risk of cleaning operations.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a pneumatic self-cleaning method and device based on blade pitch, which aims to solve the technical problems of blade cleaning relying on manual labor, low efficiency and unsafety in the prior art.

[0006] To achieve the above objectives, this application provides an aerodynamic self-cleaning method based on blade pitch, the method comprising: When the self-cleaning mode is triggered, the aerodynamic disturbance requirement is determined based on the monitored degree of dirt on the blade surface. Based on the current wind conditions and the aerodynamic disturbance requirements, determine the combination of the target frequency and target amplitude of the blade pitching action; Based on the combination of the target frequency and target amplitude, and combined with the blade mode shape, a blade pitching command sequence is generated; The blades of the wind turbine generator are controlled to perform blade pitching actions based on the blade pitching command sequence.

[0007] In one embodiment, the step of determining the aerodynamic disturbance requirement based on the monitored degree of dirt on the blade surface when the self-cleaning mode is triggered includes: When the self-cleaning mode is triggered, the degree of dirt on the blade surface is obtained by fusing data returned by multiple sensors set on the leading edge and pressure surface of the blade. Based on the degree of dirt on the blade surface, dirt distribution maps of different spanwise positions were obtained; Determine the flow field stall critical point and the expected boundary layer thickening in the aforementioned fouling distribution map; The minimum local aerodynamic shear force for stripping contaminants is determined based on the flow field stall critical point and the expected boundary layer thickening, and this minimum local aerodynamic shear force is used as the aerodynamic disturbance requirement.

[0008] In one embodiment, the step of determining the flow field stall critical point and the desired boundary layer thickening in the fouling distribution map includes: The key contamination areas were identified based on the contamination distribution map, and the positions of the key contamination areas in the spanwise and tangential directions of the blades were determined. Based on the airfoil of the blade and the position of the critical contamination area in the spanwise and chordwise directions of the blade, determine the aerodynamic performance degradation caused by the critical contamination area to the blade; The flow field stall critical point and the expected boundary layer thickening are determined based on the aerodynamic performance degradation.

[0009] In one embodiment, the step of determining the combination of the target frequency and target amplitude of the blade pitching action based on the current wind condition parameters and the aerodynamic disturbance requirements includes: The real-time wind speed and turbulence intensity in the wind direction are determined based on the current wind condition parameters, and the wind conditions are classified into steady conditions, gradual change conditions and turbulence conditions based on the real-time wind speed and the turbulence intensity in the wind direction. Under stable operating conditions, gradient descent optimization is performed on a preset frequency and preset amplitude based on the aerodynamic disturbance requirements to obtain a combination of target frequency and target amplitude for blade pitching action. In gradual or turbulent conditions, the combination of target frequency and target amplitude of blade pitching action is determined based on historical wind data, short-term predicted wind sequences, and the aerodynamic disturbance requirements.

[0010] In one embodiment, the step of determining the combination of the target frequency and target amplitude of blade pitching action based on historical wind data, short-term forecast wind sequences, and the aerodynamic disturbance requirements under gradually changing or turbulent conditions includes: In the case of gradual or turbulent conditions, a multi-scenario optimization model is constructed based on historical wind data, short-term predicted wind sequences, and the aerodynamic disturbance requirements. The real-time wind speed and wind direction turbulence intensity are input into the multi-scenario optimization model, and the target scenario is output. Determine the feasible search space of the target scenario, take the target frequency and target amplitude of the blade pitching action as variables to be optimized, and randomly initialize a set of particles in the feasible search space, each particle representing a combination of frequency and amplitude. In each iteration, the cleanliness index that each particle can achieve in the target scene is calculated, and the minimum value of the cleanliness index is used as the fitness value. Based on the fitness value, the historical best position, and the population's historical best position, update the velocity and position of each particle to determine the change in the population's optimal fitness. When the number of iterations reaches the maximum value or the change value of the optimal fitness of the population is within a preset range, the iteration is terminated, and the combination of frequency and amplitude corresponding to the historical best particle of the population is determined as the target frequency and target amplitude of the blade pitching action.

[0011] In one embodiment, the step of generating a blade pitching command sequence based on the combination of the target frequency and the target amplitude, combined with the blade mode shape, includes: Obtain the finite element model of the blade, and extract the flapping mode frequency, oscillation mode frequency, and blade mode shape based on the finite element model; When the target frequency is the same as the waving mode frequency or the swing mode frequency, the target frequency is adjusted based on the waving mode frequency and the swing mode frequency to obtain the adjusted target frequency; The adjusted target frequency, the target amplitude, and the blade mode shape are combined into a blade pitching command sequence.

[0012] In one embodiment, the step of adjusting the target frequency based on the waving mode frequency and the oscillating mode frequency to obtain the adjusted target frequency when the target frequency is the same as the waving mode frequency or the oscillating mode frequency includes: When the target frequency is the same as the waving mode frequency or the swing mode frequency, the target frequency is multiplied by an integer within the frequency threshold range to obtain the harmonic frequency; The harmonic frequency is compared with the waving mode frequency or the swing mode frequency to determine whether there is a frequency conflict; When the frequency conflict occurs, the target frequency is adjusted to the frequency point with the maximum safety margin in the frequency threshold range to obtain the adjusted target frequency; When there is no frequency conflict, the target frequency is adjusted to the harmonic frequency to obtain the target frequency.

[0013] In one embodiment, the step of synthesizing the adjusted target frequency, the target amplitude, and the blade mode shape into a blade pitch command sequence includes: For any single blade, the adjusted target frequency, the target amplitude, and the blade mode shape are synthesized into a single blade pitch command sequence; By associating all the single-blade pitch command sequences, the resultant torque fluctuation formed at the hub by the aerodynamic excitation force of each blade is determined. The blade pitch command sequence is obtained by adjusting the single blade pitch command sequence based on the resultant torque fluctuation.

[0014] In one embodiment, the step of synthesizing the adjusted target frequency, the target amplitude, and the mode shape into a blade pitch command sequence includes: The blades are subjected to icing detection, and the ice desorption strength threshold is determined when the blades are in icing condition. The auxiliary vibration command is determined based on the ice desorption strength threshold, and the auxiliary vibration command, the adjusted target frequency, the target amplitude, and the mode shape are combined into a blade pitching command sequence.

[0015] Furthermore, to achieve the above objectives, this application also proposes a pneumatic self-cleaning device based on blade pitch, which includes: The demand determination module is used to determine the aerodynamic disturbance demand based on the monitored degree of dirt on the blade surface when the self-cleaning mode is triggered. The parameter optimization module is used to determine the combination of the target frequency and target amplitude of the blade pitching action based on the current wind condition parameters and the aerodynamic disturbance requirements. The instruction generation module is used to generate a blade pitching instruction sequence based on the combination of the target frequency and the target amplitude, combined with the blade mode shape; The pitch control module is used to control the blades of the wind turbine generator to perform blade pitching actions based on the blade pitching command sequence.

[0016] In addition, to achieve the above objectives, this application also proposes a pneumatic self-cleaning device based on blade pitch, the pneumatic self-cleaning device based on blade pitch includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the pneumatic self-cleaning method based on blade pitch as described above.

[0017] In addition, to achieve the above objectives, the present invention also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the aerodynamic self-cleaning method based on blade pitch as described above.

[0018] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the aerodynamic self-cleaning method based on blade pitch as described above.

[0019] This application provides an aerodynamic self-cleaning method based on blade pitch control. After the self-cleaning mode is triggered, the degree and distribution of dirt on the blade surface are first determined based on multi-sensor monitoring data, and then the aerodynamic disturbance required to remove contaminants is calculated. Next, combined with current wind parameters, the optimal combination of frequency and amplitude for blade pitch control is determined through gradient descent optimization or a multi-scenario optimization model. Then, to avoid resonance with the blade's inherent modes, frequency avoidance adjustments are made to this frequency combination, and a command sequence is synthesized by integrating blade mode shapes. Finally, the pitch control system executes this command sequence, causing specific aerodynamic disturbances on the blade to remove surface contaminants. Through this method, intelligent, low-energy-consumption online blade self-cleaning is achieved, effectively improving the power generation efficiency and operational safety of wind turbine units. Attached Figure Description

[0020] 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.

[0021] 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.

[0022] Figure 1 This is a schematic flowchart of an embodiment of the aerodynamic self-cleaning method based on blade pitch in this application; Figure 2 This is a schematic diagram of the blade spanwise contamination level in an embodiment of the aerodynamic self-cleaning method based on blade pitch according to this application; Figure 3 This is a schematic diagram of the radial fouling degree of a blade in an embodiment of the aerodynamic self-cleaning method based on blade pitch according to this application; Figure 4 This is a schematic diagram of the module structure of the aerodynamic self-cleaning device based on blade pitch according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the aerodynamic self-cleaning method based on blade pitch in the embodiments of this application.

[0023] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0024] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0025] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0026] The main solution of this application embodiment is: when the self-cleaning mode is triggered, the aerodynamic disturbance requirement is determined based on the monitored degree of dirt on the blade surface; Based on the current wind conditions and the aerodynamic disturbance requirements, determine the combination of the target frequency and target amplitude of the blade pitching action; Based on the combination of the target frequency and target amplitude, and combined with the blade mode shape, a blade pitching command sequence is generated; The blades of the wind turbine generator are controlled to perform blade pitching actions based on the blade pitching command sequence.

[0027] Currently, blade cleaning often relies on periodic manual inspections and cleaning, a method that is significantly delayed and passive. Cleaning typically only begins when pollutants have accumulated to the point of causing significant damage to aerodynamic performance and power generation, failing to achieve timely preventative cleaning and resulting in wind energy capture efficiency remaining suboptimal for extended periods.

[0028] Furthermore, manual cleaning operations are inherently challenging and risky. They require professionals to operate on massive blades at high altitudes using suspended platforms or ropes, which is not only costly and time-consuming but also severely dependent on inclement weather conditions, placing extremely high demands on personnel safety and further exacerbating the inefficiency and high risk of cleaning operations.

[0029] This application provides a solution that, upon triggering the self-cleaning mode, firstly determines the degree and distribution of dirt on the blade surface based on multi-sensor monitoring data, and then calculates the aerodynamic disturbance required to remove contaminants. Secondly, combining current wind parameters, it determines the optimal frequency and amplitude combination of blade pitch control through gradient descent optimization or a multi-scenario optimization model. Then, to avoid resonance with the blade's inherent modes, frequency avoidance adjustments are made to this frequency combination, and a command sequence is synthesized by integrating blade mode shapes. Finally, the pitch system is controlled to execute this command sequence, causing specific aerodynamic disturbances on the blade to remove surface contaminants. Through this method, intelligent, low-energy-consumption online blade self-cleaning is achieved, effectively improving the power generation efficiency and operational safety of wind turbine units.

[0030] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a pneumatic self-cleaning device based on blade pitch. This embodiment does not specifically limit it in this regard. The following uses a pneumatic self-cleaning device based on blade pitch as an example to describe this embodiment and the following embodiments.

[0031] All actions involving the acquisition of signals, information, or data in this application are carried out in accordance with the relevant data protection laws and policies of the country where the application is located, and with the authorization of the owner of the relevant device.

[0032] This application provides an aerodynamic self-cleaning method based on blade pitch control, referring to... Figure 1 , Figure 1 This is a schematic flowchart of the first embodiment of the aerodynamic self-cleaning method based on blade pitch in this application.

[0033] In this embodiment, the aerodynamic self-cleaning method based on blade pitch includes steps S10~S40: Step S10: When the self-cleaning mode is triggered, determine the aerodynamic disturbance requirement based on the monitored degree of dirt on the blade surface. It should be noted that aerodynamic disturbance requirements are a quantitative indicator of the minimum aerodynamic load required to effectively remove contaminants adhering to the blade surface. The core principle is to induce specific flow field instabilities within the blade boundary layer, thereby generating sufficient wall shear stress to strip away the contaminants.

[0034] Understandably, determining the aerodynamic disturbance requirements by analyzing the degree of fouling on the blade surface relies on data collected by a multi-sensor array deployed on the blade leading edge and pressure surface. Data fusion algorithms process these heterogeneous sensor signals to generate a high-precision map of the blade surface fouling distribution. Subsequently, computational fluid dynamics models or query mechanisms based on airfoil aerodynamic databases are invoked. Based on the thickness, distribution location, and characteristics of the fouling, the critical local shear stress required for contaminant desorption is calculated, and this mechanical quantity is then transformed into preliminary requirements parameters for the amplitude and frequency of the pitch control system.

[0035] It should be understood that the triggering conditions for self-cleaning mode include at least the following: Active triggering based on direct dirt monitoring: By installing online real-time monitoring sensors at key locations on the blade (such as the leading edge and pressure surface), the system can continuously acquire the thickness or adhesion of contaminants on the blade surface. When the sensor reading exceeds a preset cleaning threshold, the system automatically triggers a self-cleaning mode.

[0036] Predictive triggering based on performance data analysis: This approach indirectly determines the degree of contamination by analyzing the operating performance data of wind turbine generators, thereby triggering cleaning. Key monitoring parameters include: power curve deviation, decrease in wind energy utilization coefficient, and / or blade stress imbalance. When the deterioration trend of these performance indicators exceeds a set threshold, the system determines that significant contamination exists and triggers cleaning.

[0037] Intelligent triggering based on environmental conditions and operational strategies: Taking into account both the external environment and grid demand, the system selects the optimal triggering time to maximize efficiency and ensure safety. For example, triggering during low wind speeds or periods of power curtailment. To avoid power generation losses caused by cleaning during periods of high wind speeds and high power generation, the system will prioritize automatically initiating cleaning during idle windows when wind speeds are lower than the cut-in wind speed or when grid dispatch requires power curtailment, thereby minimizing power generation losses.

[0038] In one feasible implementation, the step of determining the aerodynamic disturbance requirement based on the monitored degree of dirt on the blade surface when the self-cleaning mode is triggered includes: When the self-cleaning mode is triggered, the degree of dirt on the blade surface is obtained by fusing data returned by multiple sensors set on the leading edge and pressure surface of the blade. Based on the degree of dirt on the blade surface, dirt distribution maps of different spanwise positions were obtained; Determine the flow field stall critical point and the expected boundary layer thickening in the aforementioned fouling distribution map; The minimum local aerodynamic shear force for stripping contaminants is determined based on the flow field stall critical point and the expected boundary layer thickening, and this minimum local aerodynamic shear force is used as the aerodynamic disturbance requirement.

[0039] It should be noted that the flow field stall critical point refers to the critical position where significant flow separation begins to occur on the local airfoil surface of the blade under a specific fouling profile, usually represented by the formation of the leading-edge separation bubble or the laminar separation point. The expected boundary layer thickening refers to the degree of blade thickening caused by fouling within the blade region.

[0040] In practical implementation, when the dirt distribution map is obtained... Subsequently, its impact on aerodynamic shape needs to be quantified. Furthermore, the desired boundary layer thickening... This refers to the change in geometric morphology directly caused by the physical accumulation of dirt on the blade surface, i.e., the equivalent profile thickening. This thickening can be obtained by integrating dirt distribution data or using a pre-calibrated dirt thickness-equivalent height mapping relationship. For example, for a micro-element region, its equivalent aerodynamic roughness height... It can be compared with the dirt monitoring signal value Correlation through empirical formulas: ,in and This is the calibration coefficient. Furthermore, this local roughness significantly alters the nominal thickness of the boundary layer.

[0041] After determining that the geometric thickening was caused by dirt... After obtaining the stall critical point through flow field analysis, the minimum local aerodynamic shear force required for cleaning can be calculated. According to Prandtl's boundary layer theory, wall shear stress It is closely related to the boundary layer velocity profile. For turbulent boundary layers, It can be approximated as follows:

[0042] in air density, For the outer edge velocity of the boundary layer, The viscosity of air motion. This represents the total thickness of the boundary layer. (Dirty layer thickening) This will lead to a total thickness Increase( , (for clean state thickness), thus in its natural state It will decrease. Therefore, the aerodynamic disturbance requirement necessitates active control (such as dynamic pitch control) to locally accelerate the airflow and improve the effective flow. To compensate for Increased The stress is attenuated until it reaches the critical shear stress threshold required to strip away contaminants. The system uses iterative simulation or table lookup methods to inversely determine the conditions that satisfy... The required flow field conditions are determined and quantified into a set of specific actuator commands.

[0043] In one feasible implementation, the step of determining the flow field stall critical point and the desired boundary layer thickening in the fouling distribution map includes: The key contamination areas were identified based on the contamination distribution map, and the positions of the key contamination areas in the spanwise and tangential directions of the blades were determined. Based on the airfoil of the blade and the position of the critical contamination area in the spanwise and chordwise directions of the blade, determine the aerodynamic performance degradation caused by the critical contamination area to the blade; The flow field stall critical point and the expected boundary layer thickening are determined based on the aerodynamic performance degradation.

[0044] It should be noted that critical contamination areas refer to those locally highly contaminated areas that have a nonlinear and significant impact on aerodynamic performance. Aerodynamic performance degradation is a quantified performance loss relative to the clean airfoil baseline.

[0045] In the specific implementation, refer to Figure 2 Figure 3 First, analyze the distribution map of the contaminants. Gradient analysis and region clustering are performed to identify key contaminated areas. The algorithm sets a dynamic threshold based on location weights. The threshold value is even lower in sensitive areas such as the leading edge, thus filtering out those that meet the high level of contamination. It is located in an aerodynamically sensitive region, and its spanwise coordinates of the center of mass are accurately recorded. Chord coordinates .

[0046] Subsequently, using the geometric information of the key contaminated areas as input, a lightweight aerodynamic reduced-order model (ROM) or a pre-trained neural network surrogate model is invoked to quickly evaluate the local aerodynamic performance degradation. This model outputs the local lift loss caused by the key contaminated areas. and pressure difference resistance increase The determination of the critical stall point in the flow field is based on a modified critical stall angle of attack. Stall angle of attack of clean airfoil It decreases as the roughness of the leading edge increases, and its empirical relationship can be expressed as follows: ,in This is an empirical coefficient. For the local chord length. The system uses... The equivalent angle-of-attack loss is derived by reverse calculation, thereby determining the new stall critical point.

[0047] For the identified key areas It can be calculated directly from high-resolution dirt distribution maps through geometric relationships. For example, assuming the pollutant density is uniform, then... .

[0048] Step S20: Determine the combination of target frequency and target amplitude of blade pitching action based on the current wind condition parameters and the aerodynamic disturbance requirements; It should be noted that the target frequency refers to the number of complete oscillation cycles that the blade completes per unit time when performing periodic pitch control, and the unit is Hz. The target amplitude refers to the angular amplitude of the pitch angle as it periodically changes around its equilibrium position, and the unit is degrees.

[0049] Understandably, this involves constructing an optimized mapping relationship that takes the current wind conditions and aerodynamic disturbance requirements as input and the target frequency and target amplitude as output. This is typically based on a pre-trained lookup table or lightweight regression model, which implicitly incorporates knowledge obtained from aerodynamic simulations or field test data. Specifically, it identifies the most effective frequency-amplitude pairs required to generate specific areas of the blade (determined by the distribution of dirt) at different wind speeds.

[0050] In one feasible implementation, the step of determining the combination of the target frequency and target amplitude of the blade pitching action based on the current wind condition parameters and the aerodynamic disturbance requirements includes: The real-time wind speed and turbulence intensity in the wind direction are determined based on the current wind condition parameters, and the wind conditions are classified into steady conditions, gradual change conditions and turbulence conditions based on the real-time wind speed and the turbulence intensity in the wind direction. Under stable operating conditions, gradient descent optimization is performed on a preset frequency and preset amplitude based on the aerodynamic disturbance requirements to obtain a combination of target frequency and target amplitude for blade pitching action. In gradual or turbulent conditions, the combination of target frequency and target amplitude of blade pitching action is determined based on historical wind data, short-term predicted wind sequences, and the aerodynamic disturbance requirements.

[0051] It should be noted that stable operating conditions refer to operating conditions where the rate of change of wind speed and wind direction are both below the set threshold, and the flow field is relatively stable. Gradual changing operating conditions refer to operating conditions where wind speed or wind direction exhibits slow, continuous, and trend-like changes; turbulent operating conditions refer to operating conditions where wind speed or wind direction experiences high-frequency, irregular, and violent fluctuations.

[0052] Understandably, this is first achieved through real-time monitoring of the rate of change of wind speed. and wind direction change rate With preset threshold and Compare and combine turbulence intensity The size of the wind determines the classification of wind conditions. The classification logic can be formalized as: if... and and If it is stable, then it is a stable operating condition; otherwise, if If the turbulence intensity is low but the rate of change exceeds the limit, it is a turbulent condition; if the turbulence intensity is not high but the rate of change exceeds the limit, it is a gradual condition.

[0053] Under stable operating conditions, due to the high predictability of external excitations, the system employs a model-based gradient descent method for online optimization. It defines a cleaning efficiency function:

[0054] in The dirt removal efficiency is estimated based on an aerodynamic model. It is the pitch actuator power. These are weighting coefficients. The algorithm starts from a preset initial frequency. and amplitude Begin iterative calculation right and The partial derivative (gradient) along Update parameters in the opposite direction Until the solution is found Maximize the optimal combination .

[0055] For gradually changing or turbulent conditions, online optimization convergence is difficult and may fail due to the drastic dynamic changes in wind conditions. Therefore, the system adopts a data-driven predictive control strategy. It queries the historical wind database to find historical scenarios similar to the current wind conditions and short-term predicted wind sequences (such as the average wind speed and turbulence intensity for the next 2 minutes). Through pattern matching (such as the k-nearest neighbor algorithm), it extracts frequency-amplitude combinations that have been verified as effective under these similar historical scenarios and can meet the current aerodynamic disturbance requirements, and uses these as the target command for the current control cycle.

[0056] In one feasible implementation, the step of determining the combination of the target frequency and target amplitude of the blade pitching action based on historical wind data, short-term predicted wind sequences, and the aerodynamic disturbance requirements under gradually changing or turbulent conditions includes: In the case of gradual or turbulent conditions, a multi-scenario optimization model is constructed based on historical wind data, short-term predicted wind sequences, and the aerodynamic disturbance requirements. The real-time wind speed and wind direction turbulence intensity are input into the multi-scenario optimization model, and the target scenario is output. Determine the feasible search space of the target scenario, take the target frequency and target amplitude of the blade pitching action as variables to be optimized, and randomly initialize a set of particles in the feasible search space, each particle representing a combination of frequency and amplitude. In each iteration, the cleanliness index that each particle can achieve in the target scene is calculated, and the minimum value of the cleanliness index is used as the fitness value. Based on the fitness value, the historical best position, and the population's historical best position, update the velocity and position of each particle to determine the change in the population's optimal fitness. When the number of iterations reaches the maximum value or the change value of the optimal fitness of the population is within a preset range, the iteration is terminated, and the combination of frequency and amplitude corresponding to the historical best particle of the population is determined as the target frequency and target amplitude of the blade pitching action.

[0057] In its implementation, once the system determines whether it is in a gradual or turbulent operating condition, it first activates a multi-scenario optimization model. The core of this model is a pre-built scenario library, where each scenario... Defined by a set of eigenvectors, in the form of .in This represents statistical features related to the scenario extracted from historical wind data; The characteristics representing the short-term predicted wind sequence corresponding to that historical moment; The corresponding historical best control performance record. The current real-time wind conditions are also recorded. and predictive features Input the model and calculate the Euclidean distance between it and each scene in the scene library:

[0058] Then select the scene with the smallest distance to determine the current target scene.

[0059] Subsequently, the system determines the feasible search space, i.e., the frequency range, based on the wind characteristics of the target scenario. and amplitude range Within this space, a particle swarm optimization algorithm is used for rapid optimization. The algorithm initializes a swarm of particles, with each particle's position... This represents a frequency-amplitude combination. In each iteration, the fitness value of each particle is evaluated, which is defined as the cleanliness index that the combination can achieve under the target scene's wind conditions. negative values ​​( (This is because PSO typically solves minimum problems.) Cleanliness Index It is a comprehensive indicator that is directly proportional to the degree to which aerodynamic disturbance requirements are met and inversely proportional to the energy consumption of pitch control.

[0060] The particle updates its velocity according to the following formula and location :

[0061]

[0062] in, It is the historical optimal position of particle k. It is the best historical position for the entire group. It is inertial weight. , It is a learning factor. , It is a random number. The algorithm continuously monitors the changes in the optimal fitness of the population. or several consecutive iterations The absolute value is less than a very small threshold. The iteration terminates at that point. Ultimately, it will... The corresponding frequency and amplitude combination output serves as the optimal control command under the current dynamic wind conditions.

[0063] Step S30: Based on the combination of the target frequency and target amplitude, and combined with the blade mode shape, generate a blade pitching command sequence.

[0064] It should be noted that the blade mode shape describes the distribution of relative amplitude and phase at various points on the blade. The blade pitch command sequence contains the target trajectory of each blade pitch angle changing over time.

[0065] It is understandable that generating a blade pitching command sequence based on the combination of the target frequency and target amplitude, combined with the blade mode shape, transforms the macroscopic aerodynamic control objective into control commands that are operable by specific actuators and can avoid inducing harmful structural vibrations in the blade. First, the blade's finite element analysis model or experimental modal parameter database is queried to obtain its mode shapes in the first few major modes (such as first-order flapping and first-order tessellation), thereby determining the blade's sensitive regions to different frequency excitations. Then, a reference sine signal is generated based on the target frequency f. Next, the reference signal is spatially modulated in terms of phase and amplitude using the modal mode information. For example, the pitch amplitude is appropriately increased near the modal node to enhance the disturbance effect, while the amplitude is reduced at the inverse modal node to suppress the risk of resonance, ultimately generating a time-varying blade pitch command sequence that may be non-uniform along the blade span.

[0066] In one feasible implementation, the step of generating a blade pitching command sequence based on the combination of the target frequency and the target amplitude, combined with the blade mode shape, includes: Obtain the finite element model of the blade, and extract the flapping mode frequency, oscillation mode frequency, and blade mode shape based on the finite element model; When the target frequency is the same as the waving mode frequency or the swing mode frequency, the target frequency is adjusted based on the waving mode frequency and the swing mode frequency to obtain the adjusted target frequency; The adjusted target frequency, the target amplitude, and the blade mode shape are combined into a blade pitching command sequence.

[0067] In the specific implementation, a pre-established finite element model of the blade is first invoked, and the eigenvalue problem is solved. (in It is a quality matrix. It is the stiffness matrix. It is the angular frequency. (It is a mode shape), and the main order waving mode frequencies are extracted. and oscillation mode frequency and The system will compare the target frequency with all extracted modal frequencies in real time. If the conditions are met... , A small tolerance percentage, typically 5%, is considered to indicate a risk of resonance. In this case, it will be determined according to... The rules fine-tune the target frequency, among which, It is a preset safety offset to ensure the adjusted target frequency. Avoid all major modal frequencies.

[0068] Subsequently, the time-domain command is modulated based on the spatial characteristics of the blades. The fundamental time signal is a sine wave. However, in order to achieve differentiated but synergistic aerodynamic disturbances at different spanwise positions of the blades and to avoid local stress concentration, a first-order flapping mode will be utilized. This serves as the weight distribution function. Ultimately, it generates a sequence of instructions for multiple control segments on the blade (if independent pitch control is supported) or for the entire blade. For the j-th control segment, the instruction is:

[0069] in It is the amplitude of the first-order swing mode at the corresponding position of this segment. This is a phase shift introduced to optimize the perturbation effect.

[0070] In one feasible implementation, the step of adjusting the target frequency based on the waving mode frequency and the oscillating mode frequency to obtain the adjusted target frequency when the target frequency is the same as the waving mode frequency or the oscillating mode frequency includes: When the target frequency is the same as the waving mode frequency or the swing mode frequency, the target frequency is multiplied by an integer within the frequency threshold range to obtain the harmonic frequency; The harmonic frequency is compared with the waving mode frequency or the swing mode frequency to determine whether there is a frequency conflict; When the frequency conflict occurs, the target frequency is adjusted to the frequency point with the maximum safety margin in the frequency threshold range to obtain the adjusted target frequency; When there is no frequency conflict, the target frequency is adjusted to the harmonic frequency to obtain the target frequency.

[0071] In practical implementation, when the system detects the target frequency With any waving mode frequency or oscillation mode frequency The difference is less than the safety threshold. (i.e. |) ∣< When |), it means there is a risk of resonance, and adjustments must be made. To avoid simple frequency shifts potentially causing conflicts with other modes or poor perturbation effects at the new frequency, the original frequency can be used. The frequency threshold range centered Within, calculate all possible candidate points for harmonic frequencies, i.e. ,in It is a positive integer and satisfies:

[0072] For example, if , Then the possible harmonics are The Hz frequency is 0.5Hz (for itself) when n=2 and 1.0Hz when n=2.

[0073] Next, the system will use each multiplier frequency. With all modal frequencies that need to be avoided A comparison is performed to detect frequency collisions. The collision condition is: The system iterates through all candidate points and records those without conflicts. If a conflict-free candidate point exists (usually, points with smaller multiples n are preferred because they cause less change to the original control objective), the system directly adjusts the target frequency to that multiple. If all candidate points conflict, the system activates an alternative strategy: within the frequency threshold range, calculate the minimum distance from each frequency point f to all modal frequencies. Then select to make The frequency with the highest frequency, i.e., the frequency with the largest safety margin, is used as the final frequency. .

[0074] In one feasible implementation, the step of synthesizing the adjusted target frequency, the target amplitude, and the blade mode shape into a blade pitch command sequence includes: For any single blade, the adjusted target frequency, the target amplitude, and the blade mode shape are synthesized into a single blade pitch command sequence; By associating all the single-blade pitch command sequences, the resultant torque fluctuation formed at the hub by the aerodynamic excitation force of each blade is determined. The blade pitch command sequence is obtained by adjusting the single blade pitch command sequence based on the resultant torque fluctuation.

[0075] In practical implementation, based on the adjusted target frequency And the target amplitude A, combined with the flapping mode shape of the blade. (in (where the spanwise position is the basis) is the single-blade pitch command sequence that forms the basis for generating a single blade. Its mathematical model can be expressed as:

[0076] in This is the initial phase. Subsequently, the system uses a multibody dynamics model or aerodynamic load model to calculate the resultant torque fluctuation generated at the wind turbine hub center by the aerodynamic excitation force when all blades operate according to this basic command. To avoid excessive fatigue load on the entire wind turbine structure, the system aims to minimize the resultant torque fluctuation and uses optimization algorithms (such as the least squares method) to optimize the phase of each individual blade command. Even slight adjustments to its amplitude are made for coordinated fine-tuning, ultimately outputting a set of blade pitch control command sequences that can achieve aerodynamic goals while ensuring overall machine stability.

[0077] In one feasible implementation, the step of synthesizing the adjusted target frequency, the target amplitude, and the mode shape into a blade pitch command sequence includes: The blades are subjected to icing detection, and the ice desorption strength threshold is determined when the blades are in icing condition. The auxiliary vibration command is determined based on the ice desorption strength threshold, and the auxiliary vibration command, the adjusted target frequency, the target amplitude, and the mode shape are combined into a blade pitching command sequence.

[0078] In practical implementation, when the icing detection unit (such as based on image recognition or vibration spectrum analysis) determines that the blade is in an icing condition, the system first looks up a table or uses an empirical formula based on the ice layer characteristics (such as type and thickness, usually estimated through meteorological models or historical data). Determine the threshold of ice desorption strength Subsequently, the system reverse-engineers the vibration level required to generate alternating aerodynamic loads on the blade surface sufficient to overcome this threshold, and generates a high-frequency, small-amplitude auxiliary vibration command accordingly. ,in Typically higher than the main target frequency to avoid coupling. Ultimately, this is achieved through linear superposition. In this way, the auxiliary de-icing command is combined with the main control command based on the adjusted target frequency and target amplitude into a composite blade pitch command sequence for use in icing conditions.

[0079] Step S40: Control the blades of the wind turbine generator to perform blade pitching actions based on the blade pitching command sequence.

[0080] Understandably, the main controller of a wind turbine generator translates the calculated blade pitch command sequence into actual physical actions. This process is achieved through a control loop: the controller sends the command sequence to the pitch driver of each blade, typically a servo motor or hydraulic system. The driver rotates the pitch bearing, thereby precisely adjusting the blade pitch action, i.e., changing the blade's angle of attack relative to the incoming wind. This ensures that the blades can perform periodic pitching movements according to preset specific modes and frequencies, achieving the purpose of blade self-cleaning.

[0081] This embodiment provides an aerodynamic self-cleaning method based on blade pitch control. After the self-cleaning mode is triggered, the degree and distribution of dirt on the blade surface are first determined based on multi-sensor monitoring data, and then the aerodynamic disturbance required to remove contaminants is calculated. Next, combined with current wind parameters, the optimal combination of frequency and amplitude for blade pitch control is determined through gradient descent optimization or a multi-scenario optimization model. Then, to avoid resonance with the blade's inherent modes, frequency avoidance adjustments are made to this frequency combination, and a command sequence is synthesized by integrating blade mode shapes. Finally, the pitch control system executes this command sequence, causing the blade to generate specific aerodynamic disturbances to remove surface contaminants. Through this method, intelligent, low-energy-consumption online blade self-cleaning is achieved, effectively improving the power generation efficiency and operational safety of wind turbine units.

[0082] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the aerodynamic self-cleaning method based on blade pitch in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0083] This application also provides a pneumatic self-cleaning device based on blade pitch control; please refer to [reference needed]. Figure 4 The pneumatic self-cleaning device based on blade pitch includes: The demand determination module 10 is used to determine the aerodynamic disturbance demand based on the monitored degree of dirt on the blade surface when the self-cleaning mode is triggered. The parameter optimization module 20 is used to determine the combination of the target frequency and target amplitude of the blade pitching action based on the current wind condition parameters and the aerodynamic disturbance requirements. The instruction generation module 30 is used to generate a blade pitching instruction sequence based on the combination of the target frequency and the target amplitude, combined with the blade mode shape; The pitch control module 40 is used to control the blades of the wind turbine generator to perform blade pitching actions based on the blade pitching command sequence.

[0084] In one feasible implementation, the demand determination module 10 is further configured to perform data fusion based on the dirt monitoring data returned by multiple sensors set on the leading edge and pressure surface of the blade when the self-cleaning mode is triggered, to obtain the degree of dirt on the blade surface. Based on the degree of dirt on the blade surface, dirt distribution maps of different spanwise positions were obtained; Determine the flow field stall critical point and the expected boundary layer thickening in the aforementioned fouling distribution map; The minimum local aerodynamic shear force for stripping contaminants is determined based on the flow field stall critical point and the expected boundary layer thickening, and this minimum local aerodynamic shear force is used as the aerodynamic disturbance requirement.

[0085] In one feasible implementation, the demand determination module 10 is further configured to identify key dirty areas based on the dirt distribution map, and determine the positions of the key dirty areas in the spanwise and tangential directions of the blade. Based on the airfoil of the blade and the position of the critical contamination area in the spanwise and chordwise directions of the blade, determine the aerodynamic performance degradation caused by the critical contamination area to the blade; The flow field stall critical point and the expected boundary layer thickening are determined based on the aerodynamic performance degradation.

[0086] In one feasible implementation, the parameter optimization module 20 is further configured to determine the real-time wind speed and wind direction turbulence intensity based on the current wind condition parameters, and classify the wind condition into stable operating conditions, gradual operating conditions and turbulent operating conditions based on the real-time wind speed and the wind direction turbulence intensity. Under stable operating conditions, gradient descent optimization is performed on a preset frequency and preset amplitude based on the aerodynamic disturbance requirements to obtain a combination of target frequency and target amplitude for blade pitching action. In gradual or turbulent conditions, the combination of target frequency and target amplitude of blade pitching action is determined based on historical wind data, short-term predicted wind sequences, and the aerodynamic disturbance requirements.

[0087] In one feasible implementation, the parameter optimization module 20 is further used to construct a multi-scenario optimization model based on historical wind data, short-term predicted wind sequences and the aerodynamic disturbance requirements under gradual or turbulent conditions. The real-time wind speed and wind direction turbulence intensity are input into the multi-scenario optimization model, and the target scenario is output. Determine the feasible search space of the target scenario, take the target frequency and target amplitude of the blade pitching action as variables to be optimized, and randomly initialize a set of particles in the feasible search space, each particle representing a combination of frequency and amplitude. In each iteration, the cleanliness index that each particle can achieve in the target scene is calculated, and the minimum value of the cleanliness index is used as the fitness value. Based on the fitness value, the historical best position, and the population's historical best position, update the velocity and position of each particle to determine the change in the population's optimal fitness. When the number of iterations reaches the maximum value or the change value of the optimal fitness of the population is within a preset range, the iteration is terminated, and the combination of frequency and amplitude corresponding to the historical best particle of the population is determined as the target frequency and target amplitude of the blade pitching action.

[0088] In one feasible implementation, the instruction generation module 30 is further configured to acquire a finite element model of the blade and extract flapping mode frequency, oscillation mode frequency and blade mode shape based on the finite element model. When the target frequency is the same as the waving mode frequency or the swing mode frequency, the target frequency is adjusted based on the waving mode frequency and the swing mode frequency to obtain the adjusted target frequency; The adjusted target frequency, the target amplitude, and the blade mode shape are combined into a blade pitching command sequence.

[0089] In one feasible implementation, the instruction generation module 30 is further configured to perform an integer multiple of the target frequency within a frequency threshold range to obtain a multiple frequency when the target frequency is the same as the waving mode frequency or the swing mode frequency. The harmonic frequency is compared with the waving mode frequency or the swing mode frequency to determine whether there is a frequency conflict; When the frequency conflict occurs, the target frequency is adjusted to the frequency point with the maximum safety margin in the frequency threshold range to obtain the adjusted target frequency; When there is no frequency conflict, the target frequency is adjusted to the harmonic frequency to obtain the target frequency.

[0090] In one feasible implementation, the instruction generation module 30 is further configured to, for any single blade, synthesize the adjusted target frequency, the target amplitude, and the blade mode shape into a single blade pitching instruction sequence. By associating all the single-blade pitch command sequences, the resultant torque fluctuation formed at the hub by the aerodynamic excitation force of each blade is determined. The blade pitch command sequence is obtained by adjusting the single blade pitch command sequence based on the resultant torque fluctuation.

[0091] In one feasible implementation, the instruction generation module 30 is further used to detect icing on the blade and determine the ice desorption strength threshold when the blade is in an icing condition. The auxiliary vibration command is determined based on the ice desorption strength threshold, and the auxiliary vibration command, the adjusted target frequency, the target amplitude, and the mode shape are combined into a blade pitching command sequence.

[0092] The pneumatic self-cleaning device based on blade pitch provided in this application employs the pneumatic self-cleaning method based on blade pitch in the above embodiments, which can solve the technical problems of blade cleaning relying on manual labor, low efficiency, and safety hazards. Compared with the prior art, the beneficial effects of the pneumatic self-cleaning device based on blade pitch provided in this application are the same as those of the pneumatic self-cleaning method based on blade pitch provided in the above embodiments, and other technical features in the pneumatic self-cleaning device based on blade pitch are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0093] This application provides a pneumatic self-cleaning device based on blade pitch, the pneumatic self-cleaning device based on blade pitch includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the pneumatic self-cleaning method based on blade pitch in the first embodiment described above.

[0094] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a pneumatic self-cleaning device based on blade pitch, suitable for implementing embodiments of this application. The pneumatic self-cleaning device based on blade pitch in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The pneumatic self-cleaning device based on blade pitch shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0095] like Figure 5As shown, the blade pitch-based aerodynamic self-cleaning device may include a processing unit 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the blade pitch-based aerodynamic self-cleaning device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the blade-pitch-based pneumatic self-cleaning device to exchange data wirelessly or via wired communication with other devices. Although blade-pitch-based pneumatic self-cleaning devices with various systems are shown in the figures, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0096] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0097] The pneumatic self-cleaning device based on blade pitch provided in this application employs the pneumatic self-cleaning method based on blade pitch in the above embodiments, and can solve the technical problems of pneumatic self-cleaning based on blade pitch. Compared with the prior art, the beneficial effects of the pneumatic self-cleaning device based on blade pitch provided in this application are the same as the beneficial effects of the pneumatic self-cleaning method based on blade pitch provided in the above embodiments, and other technical features in the pneumatic self-cleaning device based on blade pitch are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0098] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0099] The above are merely specific embodiments 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 scope of the technology 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.

[0100] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the aerodynamic self-cleaning method based on blade pitch in the above embodiments.

[0101] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0102] The aforementioned computer-readable storage medium may be included in a blade-based pneumatic self-cleaning device; or it may exist independently and not assembled into a blade-based pneumatic self-cleaning device.

[0103] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the blade pitch-based aerodynamic self-cleaning device, cause the blade pitch-based aerodynamic self-cleaning device to: determine aerodynamic disturbance requirements based on the monitored degree of dirt on the blade surface when the self-cleaning mode is triggered. Based on the current wind conditions and the aerodynamic disturbance requirements, determine the combination of the target frequency and target amplitude of the blade pitching action; Based on the combination of the target frequency and target amplitude, and combined with the blade mode shape, a blade pitching command sequence is generated; The blades of the wind turbine generator are controlled to perform blade pitching actions based on the blade pitching command sequence.

[0104] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0106] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0107] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described aerodynamic self-cleaning method based on blade pitch, and is capable of solving the technical problem of aerodynamic self-cleaning based on blade pitch. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the aerodynamic self-cleaning method based on blade pitch provided in the above embodiments, and will not be repeated here.

[0108] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the aerodynamic self-cleaning method based on blade pitch as described above.

[0109] The computer program product provided in this application can solve the technical problem of aerodynamic self-cleaning based on blade pitch. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the aerodynamic self-cleaning method based on blade pitch provided in the above embodiments, and will not be repeated here.

[0110] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method of aerodynamic self-cleaning based on blade pitch, characterized in that, The aerodynamic self-cleaning method based on blade pitch control includes: When the self-cleaning mode is triggered, the aerodynamic disturbance requirement is determined based on the monitored degree of dirt on the blade surface. Based on the current wind conditions and the aerodynamic disturbance requirements, determine the combination of the target frequency and target amplitude of the blade pitching action; Based on the combination of the target frequency and target amplitude, and combined with the blade mode shape, a blade pitching command sequence is generated; Based on the blade pitch command sequence, the blades of the wind turbine generator are controlled to perform blade pitch actions. The step of determining the aerodynamic disturbance requirement based on the monitored degree of dirt on the blade surface when the self-cleaning mode is triggered includes: When the self-cleaning mode is triggered, the degree of dirt on the blade surface is obtained by fusing data returned by multiple sensors set on the leading edge and pressure surface of the blade. Based on the degree of dirt on the blade surface, dirt distribution maps of different spanwise positions were obtained; Determine the flow field stall critical point and the expected boundary layer thickening in the aforementioned fouling distribution map; The minimum local aerodynamic shear force for stripping contaminants is determined based on the flow field stall critical point and the expected boundary layer thickening, and this minimum local aerodynamic shear force is used as the aerodynamic disturbance requirement.

2. The method of claim 1, wherein, The steps for determining the flow field stall critical point and the desired boundary layer thickening in the fouling distribution map include: The key contamination areas were identified based on the contamination distribution map, and the positions of the key contamination areas in the spanwise and tangential directions of the blades were determined. Based on the airfoil of the blade and the position of the critical contamination area in the spanwise and chordwise directions of the blade, determine the aerodynamic performance degradation caused by the critical contamination area to the blade; The flow field stall critical point and the expected boundary layer thickening are determined based on the aerodynamic performance degradation.

3. The method of claim 1, wherein, The step of determining the combination of the target frequency and target amplitude of the blade pitching action based on the current wind condition parameters and the aerodynamic disturbance requirements includes: The real-time wind speed and turbulence intensity in the wind direction are determined based on the current wind condition parameters, and the wind conditions are classified into steady conditions, gradual change conditions and turbulence conditions based on the real-time wind speed and the turbulence intensity in the wind direction. Under stable operating conditions, gradient descent optimization is performed on a preset frequency and preset amplitude based on the aerodynamic disturbance requirements to obtain a combination of target frequency and target amplitude for blade pitching action. In gradual or turbulent conditions, the combination of target frequency and target amplitude of blade pitching action is determined based on historical wind data, short-term predicted wind sequences, and the aerodynamic disturbance requirements.

4. The method of claim 3, wherein, The step of determining the combination of target frequency and target amplitude of blade pitching action based on historical wind data, short-term predicted wind sequences, and the aerodynamic disturbance requirements under gradually changing or turbulent conditions includes: In the case of gradual or turbulent conditions, a multi-scenario optimization model is constructed based on historical wind data, short-term predicted wind sequences, and the aerodynamic disturbance requirements. The real-time wind speed and wind direction turbulence intensity are input into the multi-scenario optimization model, and the target scenario is output. Determine the feasible search space of the target scenario, take the target frequency and target amplitude of the blade pitching action as variables to be optimized, and randomly initialize a set of particles in the feasible search space, each particle representing a combination of frequency and amplitude. In each iteration, the cleanliness index that each particle can achieve in the target scene is calculated, and the minimum value of the cleanliness index is used as the fitness value. Based on the fitness value, the historical best position, and the population's historical best position, update the velocity and position of each particle to determine the change in the population's optimal fitness. When the number of iterations reaches the maximum value or the change value of the optimal fitness of the population is within a preset range, the iteration is terminated, and the combination of frequency and amplitude corresponding to the historical best particle of the population is determined as the target frequency and target amplitude of the blade pitching action.

5. The method as described in claim 1, characterized in that, The step of generating the blade pitch command sequence based on the combination of the target frequency and target amplitude, combined with the blade mode shape, includes: Obtain the finite element model of the blade, and extract the flapping mode frequency, oscillation mode frequency, and blade mode shape based on the finite element model; When the target frequency is the same as the waving mode frequency or the swing mode frequency, the target frequency is adjusted based on the waving mode frequency and the swing mode frequency to obtain the adjusted target frequency; The adjusted target frequency, the target amplitude, and the blade mode shape are combined into a blade pitching command sequence.

6. The method as described in claim 5, characterized in that, The step of adjusting the target frequency based on the waving mode frequency and the oscillating mode frequency to obtain the adjusted target frequency when the target frequency is the same as the waving mode frequency or the oscillating mode frequency includes: When the target frequency is the same as the waving mode frequency or the swing mode frequency, the target frequency is multiplied by an integer within the frequency threshold range to obtain the harmonic frequency; The harmonic frequency is compared with the waving mode frequency or the swing mode frequency to determine whether there is a frequency conflict; When the frequency conflict occurs, the target frequency is adjusted to the frequency point with the maximum safety margin in the frequency threshold range to obtain the adjusted target frequency; When there is no frequency conflict, the target frequency is adjusted to the harmonic frequency to obtain the target frequency.

7. The method as described in claim 5, characterized in that, The step of synthesizing the adjusted target frequency, the target amplitude, and the blade mode shape into a blade pitch command sequence includes: For any single blade, the adjusted target frequency, the target amplitude, and the blade mode shape are synthesized into a single blade pitch command sequence; By associating all the single-blade pitch command sequences, the resultant torque fluctuation formed at the hub by the aerodynamic excitation force of each blade is determined. The blade pitch command sequence is obtained by adjusting the single blade pitch command sequence based on the resultant torque fluctuation.

8. The method as described in claim 5, characterized in that, The step of synthesizing the adjusted target frequency, the target amplitude, and the mode shape into a blade pitch command sequence includes: The blades are subjected to icing detection, and the ice desorption strength threshold is determined when the blades are in icing condition. The auxiliary vibration command is determined based on the ice desorption strength threshold, and the auxiliary vibration command, the adjusted target frequency, the target amplitude, and the mode shape are combined into a blade pitching command sequence.

9. A pneumatic self-cleaning device based on blade pitch control, characterized in that, The pneumatic self-cleaning device based on blade pitch control includes: The demand determination module is used to determine the aerodynamic disturbance demand based on the monitored degree of dirt on the blade surface when the self-cleaning mode is triggered. The parameter optimization module is used to determine the combination of the target frequency and target amplitude of the blade pitching action based on the current wind condition parameters and the aerodynamic disturbance requirements. The instruction generation module is used to generate a blade pitching instruction sequence based on the combination of the target frequency and the target amplitude, combined with the blade mode shape; The pitch control module is used to control the blades of the wind turbine generator to perform blade pitch actions based on the blade pitch command sequence. The requirement determination module is further configured to perform data fusion based on the fouling monitoring data returned by multiple sensors located at the leading edge and pressure surface of the blade to obtain the degree of fouling on the blade surface; obtain a fouling distribution map of different spanwise positions based on the degree of fouling on the blade surface; determine the flow field stall critical point and boundary layer thickening expectation corresponding to the fouling distribution map; determine the minimum local aerodynamic shear force for stripping pollutants based on the flow field stall critical point and boundary layer thickening expectation, and use the minimum local aerodynamic shear force as the aerodynamic disturbance requirement.

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