Wind turbine critical flutter speed prediction method, system, equipment, medium and product
By setting up a time series of continuous velocity changes and a gas-elastic coupling model, combined with damping ratio calculation, the problem of difficult prediction of the critical fluctuation speed of the wind turbine blades is solved, and the accurate prediction of the critical fluctuation speed of the wind turbine blades is achieved, the risk of failure is reduced, and the stable and safe operation of the wind power system is ensured.
Patent Information
- Application Number
- CN202510107688.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-23
- Publication Date
- 2025-05-30
AI Technical Summary
The prior art is difficult to accurately predict the critical flutter speed of wind turbine blades, which leads to fatigue and damage of the blades, shorten their lifespan, and even causes catastrophic failure of the wind turbine.
By setting the time series of continuous velocity changes, combining the gas-elastic coupling model for gas-elastic simulation, extracting the time domain data of the blade tip deformation, fitting the envelope line and calculating the damping ratio, determining whether the blade has critical flutter, and determining the critical flutter speed.
The accurate prediction of the critical flutter speed of wind turbine blades is achieved, the risk of failure caused by flutter is reduced, and the stable and safe operation of the wind power system is ensured.
Smart Images
Figure CN120068607A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of wind power generation, and particularly relates to a method, a system, a device, a medium and a product for predicting the critical flutter speed of a wind turbine. Background Art
[0002] Under the global trend of pursuing clean energy, wind power generation has developed vigorously. When a wind turbine operates, the aerodynamic force on the blade is complex and variable. The traditional steady-state aerodynamic analysis assumes that the airflow parameters are constant or slowly changing, which is contrary to the reality that the high-speed rotation of the blade causes periodic drastic changes in the airflow. Using it to estimate the blade force has a large error and cannot accurately grasp the dynamics of the wind turbine.
[0003] In the small deformation stage of blade deformation, a linear model can be used to describe it. However, when facing strong winds or gusts, the deformation amplitude of the blade increases significantly. The blade stiffness changes with the deformation, and the changed surrounding airflow distribution reacts on the aerodynamic force. The two influence each other nonlinearly. If the aeroelastic model ignores this nonlinearity and simply uses a linear model, it is difficult to simulate the true deformation of the blade under complex working conditions, and it is also impossible to accurately predict the change of the aerodynamic force, which brings deviations to the evaluation of the performance, stability and safety of the wind turbine.
[0004] In addition, when the blade is close to or in the critical flutter state, the blade is prone to fatigue damage, its life is greatly reduced, and even catastrophic failure of the wind turbine may be caused, resulting in economic losses and safety hazards. Therefore, accurately predicting the critical flutter speed of the wind turbine blade and taking preventive measures in advance are of great significance for ensuring the reliable operation of the wind turbine and promoting the development of the wind power industry.
[0005] In view of the above problems, there is an urgent need for a method for predicting the critical flutter speed of a wind turbine that can comprehensively and accurately consider these factors to meet the strict requirements of the modern wind power generation industry for the efficient, stable and safe operation of the wind turbine. Summary of the Invention
[0006] Aiming at the deficiencies of the existing technology, the present invention provides a method for predicting the critical flutter speed of a wind turbine, including:
[0007] Setting a time series with continuously stepped changes in speed and clarifying each stepped segment;
[0008] Based on the time series with continuously stepped changes in speed and the aeroelastic coupling model, performing aeroelastic simulation and extracting the time-domain data of the tip deformation;
[0009] Fitting the envelope of the tip deformation within each stepped segment and calculating the damping ratio within each stepped segment;
[0010] Determining whether the blade undergoes critical flutter according to the damping ratio within each stepped segment and determining the critical flutter speed when critical flutter occurs.
[0011] Further, the time length of each step segment is determined according to the following formula:
[0012]
[0013] where Δt is the time length of each time segment; f rot is the blade rotation frequency.
[0014] Further, the aeroelastic coupling model includes:
[0015] Unsteady aerodynamic characteristic features and large-amplitude nonlinear deformation features.
[0016] Further, the damping ratio within each step segment is calculated according to the following formula:
[0017]
[0018] where δ is the logarithmic decrement of vibration; u i and u i+j are the amplitudes within the i-th and (i + j)-th vibration periods respectively, i is the vibration period index, j is the vibration period interval; ζ is the damping ratio.
[0019] Further, determining whether the blade undergoes critical flutter according to the damping ratio within each step segment includes:
[0020] When the damping ratio is positive, the blade does not undergo critical flutter and the aeroelasticity is stable at this time; when the damping ratio is negative, the blade undergoes critical flutter and the aeroelasticity is unstable at this time.
[0021] The present invention provides a critical flutter speed prediction system for a wind turbine, including:
[0022] Step setting module: used to set a time series with continuous step changes in speed and clarify each step segment;
[0023] Time domain data extraction module: used to perform aeroelastic simulation based on the time series with continuous step changes in speed and the aeroelastic coupling model, and extract the time domain data of the tip deformation;
[0024] Damping ratio calculation module: used to fit the envelope of the tip deformation within each step segment and calculate the damping ratio within each step segment;
[0025] Critical flutter judgment module: used to determine whether the blade undergoes critical flutter according to the damping ratio within each step segment and determine the critical flutter speed.
[0026] Further, the time length of each step segment is determined according to the following formula:
[0027]
[0028] where Δt is the time length of each time segment; f rot is the blade rotation frequency.
[0029] Further, the aeroelastic coupling model includes:
[0030] Unsteady aerodynamic characteristic features and large-amplitude nonlinear deformation features.
[0031] Further, the damping ratio within each step segment is calculated as follows:
[0032]
[0033] where δ is the logarithmic decrement of vibration; u i and u i+j are the amplitudes within the i-th and (i + j)-th vibration cycles respectively, i is the vibration cycle index, j is the vibration cycle interval; ζ is the damping ratio.
[0034] Further, determining whether the blade undergoes critical flutter according to the damping ratio within each step segment includes:
[0035] When the damping ratio is positive, the blade does not undergo critical flutter and the aeroelasticity is stable at this time; when the damping ratio is negative, the blade undergoes critical flutter and the aeroelasticity is unstable at this time.
[0036] The present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the wind turbine critical flutter speed prediction method.
[0037] The present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by the processor, the processor executes the steps of the wind turbine critical flutter speed prediction method.
[0038] The present invention provides a computer program product including a computer program, and the computer program is executed by a processor to implement the wind turbine critical flutter speed prediction method.
[0039] Compared with the prior art, the present invention has the following advantages:
[0040] 1. By reasonably setting the speed continuous step change time series, the present invention ensures the comprehensiveness and accuracy of the monitoring of the wind turbine operation state.
[0041] 2. By introducing the aeroelastic coupling model, the present invention greatly improves the prediction accuracy of the actual operation behavior of the blade.
[0042] 3. The present invention directly measures the risk boundary of blade operation, effectively reducing the fault risk of wind turbines caused by blade flutter, ensuring the stable and efficient operation of the wind power generation system, and comprehensively promoting the development of the wind power generation industry towards a more mature and reliable direction.
[0043] Other features and advantages of the present invention will be described in the following specification, and some of them will be obvious from the specification or understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures pointed out in the specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0045] Figure 1 The flowchart of a method for predicting the critical flutter speed of a wind turbine provided in an embodiment of the present invention is shown;
[0046] Figure 2 The schematic diagram of the continuous step change time series of a method for predicting the critical flutter speed of a wind turbine provided in an embodiment of the present invention is shown;
[0047] Figure 3 The schematic diagram of the time domain curve of a method for predicting the critical flutter speed of a wind turbine provided in an embodiment of the present invention is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.
[0049] In this embodiment, a certain large-scale megawatt-class wind turbine is taken as an example, and the rotational main frequency of the wind turbine blade is 0.125 Hz. Refer to Figure 1 As shown, the application embodiment of the present invention provides a method for predicting the critical flutter speed of a wind turbine, including:
[0050] S1: Set the continuous step change time series of speed and clarify each step segment.
[0051] The time length of each step segment is determined by the following formula:
[0052]
[0053] where Δt is the time length of each time segment; f rot is the blade rotation frequency.
[0054] See Figure 2 As shown, preferably, the time length of each step segment in this embodiment is taken as 20 s.
[0055] S2: Based on the time series of the continuous step change of speed and the aeroelastic coupling model, conduct aeroelastic simulation and extract the time-domain data of the tip deformation.
[0056] The aeroelastic coupling model needs to consider the unsteady aerodynamic characteristics and the characteristics of large-amplitude nonlinear deformation.
[0057] For traditional steady aerodynamic analysis, it is assumed that parameters such as the air flow velocity and pressure do not change with time or change very slowly. However, in actual situations, the rotation of the wind turbine blade will cause the air flow velocity and direction around the blade to change periodically. The aeroelastic coupling model considering the unsteady aerodynamic characteristics can more accurately simulate the aerodynamic force on the blade. The aerodynamic force is one of the important driving factors for the vibration and deformation of the wind turbine blade. If the unsteady aerodynamic characteristics cannot be correctly considered, it will lead to an incorrect estimation of the actual force on the blade, thereby affecting the prediction of the dynamic behavior of the entire wind turbine.
[0058] In the case of small deformation, according to Hooke's law, the stress and strain of the material are proportional, and at this time, a linear model can be used to describe the deformation. However, the stiffness of the blade will change with the increase of the deformation, and the deformation of the blade will change the air flow distribution around it, which in turn affects the aerodynamic force. This interaction is nonlinear. If the aeroelastic coupling model ignores the nonlinear factors and only uses a simple linear model, it cannot accurately simulate the true deformation of the blade under complex working conditions (such as strong wind, gust, etc.), nor can it well predict the change of the aerodynamic force caused by the blade deformation, thus affecting the evaluation of the performance, stability and safety of the wind turbine.
[0059] In this embodiment, the aero-elastic coupling model adopts a dynamic blade element momentum model coupled with a B-L dynamic stall model, and the structural model adopts a geometrically exact beam model. Among them, the B-L dynamic stall model can well describe the stall phenomenon and unsteady aerodynamic conditions such as airflow separation; the geometrically exact beam model can accurately describe the geometric nonlinear deformation of the beam structure (the wind turbine blade can be regarded as a cantilever beam structure); the dynamic blade element momentum model divides the blade along the span into multiple blade elements, considers the aerodynamic force of each blade element, and obtains the aerodynamic force of the entire blade through spanwise integration. The coupling of the three models accurately simulates the blade deformation, takes into account the influence of the aerodynamic force on the blade deformation, and truly reflects the deformation behavior of the blade in actual operation.
[0060] S3: Fit the envelope of the tip deformation within each step segment and calculate the damping ratio within each step segment.
[0061] The envelope of the tip deformation is the maximum deformation range of the tip within each step segment. By fitting it, the complex and discrete tip deformation data is converted into a relatively smooth and continuous function form. Through this fitted function, the overall trend of the tip deformation can be analyzed more intuitively. The damping ratio is an important parameter to measure the degree of vibration attenuation of the system. The fitted envelope function can be used to analyze the attenuation between adjacent vibration peaks, thereby calculating the damping ratio.
[0062] The damping ratio within each step segment is calculated by the following formula:
[0063]
[0064] In the formula, δ is the logarithmic decrement of vibration; u i and u i+j are the amplitudes within the i-th and (i + j)-th vibration periods respectively, i is the vibration period index, j is the period interval; ζ is the damping ratio.
[0065] Preferably, the tip deformation selects the time-domain data of the blade torsional deformation for the calculation of the damping ratio. Compared with other deformations, the torsional deformation is more sensitive to the change of the aerodynamic force. A small change in the blade twist angle can cause a significant change in the aerodynamic force. From the perspective of energy, the work done by the aerodynamic force during the torsional deformation process can well reflect the energy input and dissipation of the system, which is closely related to the concept of the damping ratio. For example, when the blade twist causes the aerodynamic force to do positive work on the blade, the system energy increases, and the damping is a factor to measure the system energy dissipation. Therefore, the torsional deformation data can more effectively reflect the damping characteristics of the system and is more representative than other deformation data.
[0066] S4: Determine whether the blade undergoes critical flutter according to the damping ratio within each step segment, and determine the critical flutter speed when critical flutter occurs.
[0067] When the damping ratio is positive, the blade vibration is suppressed and the aeroelasticity is stable at this time; when the damping ratio is negative, the blade vibration diverges and aeroelastic instability occurs at this time.
[0068] See Figure 3 As shown, in this embodiment, the damping ratio is solved according to the time-domain curve of the tip torsion deformation, and the aeroelastic stability and critical speed of the blade are judged according to the positive or negative of the damping ratio. It can be seen that the damping ratio changes from positive to negative in the time interval of 400 s to 440 s. At this time, the blade undergoes aeroelastic instability, and the blade speed is 10 rpm. Therefore, it can be determined that the critical flutter speed corresponding to the wind turbine blade is around 10 rpm.
[0069] The application embodiment of the present invention provides a wind turbine critical flutter speed prediction system, including:
[0070] Step setting module: used to set a time series of continuously changing speeds in steps and clarify each step segment.
[0071] Time-domain data extraction module: used to perform aeroelastic simulation based on the time series of continuously changing speeds in steps and the aeroelastic coupling model, and extract the time-domain data of the tip deformation.
[0072] Damping ratio calculation module: used to fit the envelope of the tip deformation within each step segment and calculate the damping ratio within each step segment.
[0073] Critical flutter judgment module: used to judge whether the blade undergoes critical flutter according to the damping ratio within each step segment, and determine the critical flutter speed when critical flutter occurs.
[0074] The application embodiment of the present invention provides a computer device. The computer device includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of a wind turbine critical flutter speed prediction method.
[0075] The application embodiment of the present invention provides a computer-readable storage medium, storing a computer program. When the computer program is executed by the processor, the processor executes the steps of a wind turbine critical flutter speed prediction method.
[0076] The embodiment of the present invention also provides a computer program product corresponding to the wind turbine critical flutter speed prediction method provided by the foregoing embodiment. The computer program product includes a computer program, and the computer program is executed by the processor to implement the wind turbine critical flutter speed prediction method provided by the foregoing embodiment.
[0077] The above description and the drawings sufficiently illustrate embodiments of the present invention so that those skilled in the art can practice them. Other embodiments may include structural and other changes. The embodiments represent only possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or substituted for parts and features of other embodiments. The embodiments of the present invention are not limited to the structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is limited only by the appended claims.
Claims
1. A method for predicting critical flutter speed of a wind turbine, characterized in that: include: Set the time sequence of continuous step-by-step speed changes and clearly define the steps; Based on the time series of continuous step-change velocity and the aeroelastic coupling model, aeroelastic simulation is performed and the time domain data of blade tip deformation is extracted; Fitting the envelope of blade tip deformation in each step segment and calculating the damping ratio in each step segment; Whether the blade has critical flutter is determined based on the damping ratio in each step segment, and the critical flutter speed when critical flutter occurs is determined.
2. The method for predicting critical flutter speed of a wind turbine according to claim 1, characterized in that: The time length of each step segment is determined according to the following formula: In the formula, Δt is the length of each time segment; f rot is the blade rotation frequency.
3. The method for predicting critical flutter speed of a wind turbine according to claim 1, characterized in that: The gas-elastic coupling model includes: Unsteady aerodynamic characteristics and large-amplitude nonlinear deformation characteristics.
4. The method for predicting critical flutter speed of a wind turbine according to claim 1, characterized in that: The damping ratio in each step segment is calculated as follows: Where, δ is the logarithmic attenuation rate of vibration; u i and u i+j are the amplitudes in the i-th and i+j-th vibration cycles respectively, i is the vibration cycle index, j is the vibration cycle interval; ζ is the damping ratio.
5. The method for predicting critical flutter speed of a wind turbine according to claim 1, characterized in that: The step of determining whether critical flutter occurs in the blade according to the damping ratio in each step segment comprises: When the damping ratio is positive, the blade does not experience critical flutter, and the aeroelastic is stable at this time; when the damping ratio is negative, the blade experiences critical flutter, and the aeroelastic is unstable at this time.
6. A wind turbine critical flutter speed prediction system, characterized in that: include: Step setting module: used to set the time sequence of continuous step change of speed and clarify the segmentation of each step; Time domain data extraction module: used to perform aeroelastic simulation based on the time series of continuous step-change velocity and the aeroelastic coupling model, and extract the time domain data of blade tip deformation; Damping ratio calculation module: used to fit the envelope of blade tip deformation in each step segment and calculate the damping ratio in each step segment; Critical flutter judgment module: used to judge whether the blade has critical flutter according to the damping ratio in each step segment, and determine the critical flutter speed when critical flutter occurs.
7. The wind turbine critical flutter speed prediction system according to claim 6, characterized in that: The time length of each step segment is determined according to the following formula: In the formula, Δt is the length of each time segment; f rot is the blade rotation frequency.
8. The wind turbine critical flutter speed prediction system according to claim 6, characterized in that: The gas-elastic coupling model includes: Unsteady aerodynamic characteristics and large-amplitude nonlinear deformation characteristics.
9. The wind turbine critical flutter speed prediction system according to claim 6, characterized in that: The damping ratio in each step segment is calculated as follows: Where, δ is the logarithmic attenuation rate of vibration; u i and u i+j are the amplitudes in the i-th and i+j-th vibration periods respectively, j is the period interval; ζ is the damping ratio.
10. The wind turbine critical flutter speed prediction system according to claim 6, characterized in that: The step of determining whether critical flutter occurs in the blade according to the damping ratio in each step segment comprises: When the damping ratio is positive, the blade does not experience critical flutter, and the aeroelastic is stable at this time; when the damping ratio is negative, the blade experiences critical flutter, and the aeroelastic is unstable at this time.
11. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the computer program is executed by a processor, the processor executes the steps of the method for predicting the critical flutter speed of a wind turbine as described in any one of claims 1 to 5.
12. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor executes the steps of the method for predicting the critical flutter speed of a wind turbine as described in any one of claims 1 to 5.
13. A computer program product, comprising a computer program, characterized in that The computer program is executed by a processor to implement the method for predicting critical flutter speed of a wind turbine according to any one of claims 1 to 5.