A method, device, equipment and medium for predicting the remaining fatigue life of wind turbine blades

Abstract

The present application relates to the technical field of wind turbine blades, and specifically provides a method, device, equipment and medium for predicting the remaining fatigue life of wind turbine blades, aiming to solve the problem that the remaining fatigue damage of wind turbine blades cannot be predicted. To this end, the present application includes: obtaining environmental data related to the blades; inputting the environmental data into an aeroelastic model to obtain displacement data of the blades and the forces and moments corresponding to the displacement data; wherein the aeroelastic model is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model; according to the forces and moments corresponding to the displacement data, a stress set and a strain set are obtained; according to the stress set and the strain set, the remaining fatigue life of the blades is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine blades, and specifically provides a method, device, equipment and medium for predicting the remaining fatigue life of a wind turbine blade. Background Art

[0002] Wind turbine blades are made of composite materials, and their fatigue damage is driven by environmental flow and wake operation. Fatigue damage threatens the safety and economy of wind turbine structures, thereby reducing the efficiency and economy of power generation systems. In recent years, as wind turbines continue to grow in size, the length of blades has become longer and longer, and their structural safety margin has become lower and lower, which is also the bottleneck encountered in increasing the length of turbine blades to achieve higher power generation efficiency at this stage.

[0003] In the prior art, active control and passive control are usually used to reduce fatigue loads. Among them, active control usually involves using sensors to monitor the real-time status of wind turbines, such as wind speed, wind direction, blade angle, etc., and making real-time adjustments based on this information. Passive control usually designs the structure and parameters of the wind turbine to automatically adjust and optimize performance under specific circumstances. Active control and passive control can only reduce fatigue loads to extend the life of blades, but cannot predict remaining fatigue damage, and thus cannot replace blades in time, resulting in technical problems such as low operating efficiency of wind turbines.

[0004] Accordingly, the art needs a new method for predicting the remaining fatigue life of wind turbine blades to solve the above problems. Summary of the invention

[0005] In order to overcome the above-mentioned defects, the present application is proposed to provide a solution or at least partially solve the technical problem that the prior art cannot accurately predict the remaining fatigue life of the wind turbine blades.

[0006] In a first aspect, the present application provides a method for predicting the remaining fatigue life of a wind turbine blade, comprising:

[0007] Obtain environmental data related to the blade;

[0008] Inputting the environmental data into an aeroelastic model to obtain displacement data of the blade and the force and moment corresponding to the displacement data; wherein the aeroelastic model is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model;

[0009] Obtaining a stress set and a strain set according to the force and moment corresponding to the displacement data;

[0010] The remaining fatigue life of the blade is obtained according to the stress set and the strain set.

[0011] In a technical solution of the above-mentioned method for predicting the remaining fatigue life of a wind turbine blade, the step of obtaining the one-dimensional anisotropic beam model includes:

[0012] Analyzing a two-dimensional cross section of the blade to obtain a stiffness matrix;

[0013] According to the stiffness matrix, the one-dimensional anisotropic beam model is obtained.

[0014] In a technical solution of the above-mentioned method for predicting the remaining fatigue life of a wind turbine blade, the step of analyzing the two-dimensional cross section of the blade to obtain a stiffness matrix includes:

[0015] Performing finite element decomposition on the blade to obtain the relationship between the node warping displacement, generalized strain, and external six-direction degrees of freedom;

[0016] According to the relationship among the node warping displacement, generalized strain, and external six-direction degrees of freedom, a stiffness matrix is ​​obtained.

[0017] In a technical solution of the above-mentioned method for predicting the remaining fatigue life of a wind turbine blade, the one-dimensional anisotropic beam model is obtained according to the stiffness matrix, including:

[0018] According to the stiffness matrix, kinetic energy, potential energy and work done by external force are obtained;

[0019] According to the kinetic energy, potential energy and work done by external force, a local form is obtained;

[0020] Integrate local forms to obtain global forms;

[0021] The global form is solved to obtain the one-dimensional anisotropic beam model.

[0022] In a technical solution of the above-mentioned method for predicting the remaining fatigue life of a wind turbine blade, obtaining the aerodynamic model includes:

[0023] Obtaining axial induction factor and tangential induction factor;

[0024] In the nth round, an inflow angle is obtained according to the axial induction factor and the tangential induction factor;

[0025] According to the inflow angle and the pitch angle, an angle of attack is obtained;

[0026] According to the angle of attack and the inflow angle, local lift and drag coefficients are obtained;

[0027] According to the local lift and drag coefficients, a tangential force coefficient is obtained, and according to the magnitude of the tangential force coefficient and a preset threshold, an updated axial induction factor and a tangential induction factor are obtained;

[0028] As shown in the figure, 1≤n≤N, N corresponds to the residual amount being less than a threshold after the Nth round, and the aerodynamic model is obtained.

[0029] In a technical solution of the above-mentioned method for predicting the remaining fatigue life of a wind turbine blade, the aeroelastic model is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model, and includes:

[0030] In the i-th round, obtain the displacement vector;

[0031] According to the displacement vector, a one-dimensional time derivative of the displacement vector and a two-dimensional time derivative of the displacement vector are obtained;

[0032] Obtaining force and torque according to the displacement vector, the one-dimensional time derivative of the displacement vector and the two-dimensional time derivative of the displacement vector;

[0033] Among them, as shown in 1≤i≤I, where I corresponds to the final time step after the Ith round, and the aeroelastic model is obtained.

[0034] In a technical solution of the above-mentioned method for predicting the remaining fatigue life of a wind turbine blade, obtaining the remaining fatigue life of the blade according to the stress set and the strain set includes:

[0035] Counting the stress set and the strain set to obtain the number of failure cycles;

[0036] According to the number of failure cycles, fatigue damage is obtained;

[0037] The remaining fatigue life is obtained according to the fatigue damage and time.

[0038] In a second aspect, the present application provides a device, comprising:

[0039] An acquisition module, used for acquiring environmental data related to the blade;

[0040] An analysis module, used for inputting the environmental data into an aeroelastic model to obtain displacement data of the blade and the force and moment corresponding to the displacement data; wherein the aeroelastic model is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model;

[0041] A processing module, used for obtaining a stress set and a strain set according to the force and moment corresponding to the displacement data;

[0042] The prediction module is used to obtain the remaining fatigue life of the blade according to the stress set and the strain set.

[0043] In a third aspect, the present application provides an electronic device, comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, and the program codes are suitable for being loaded and run by the processor to execute a method as described in any one of the first aspects.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium storing a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute any of the methods described in the first aspect.

[0045] The present application provides a method, device, equipment and medium for predicting the remaining fatigue life of a wind turbine blade. The method specifically comprises: acquiring environmental data related to the blade; inputting the environmental data into an aeroelastic model to obtain displacement data of the blade and forces and moments corresponding to the displacement data; wherein the aeroelastic model is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model; obtaining a stress set and a strain set according to the forces and moments corresponding to the displacement data; obtaining the remaining fatigue life of the blade according to the stress set and the strain set, and then obtaining an accurate remaining fatigue life of the wind turbine blade. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The disclosure of the present application will become more easily understood with reference to the accompanying drawings. It is easy for those skilled in the art to understand that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present invention. In addition, similar numbers in the figures are used to represent similar components, among which:

[0047] Figure 1 A schematic diagram of a flow chart of a first embodiment of a method for predicting the remaining fatigue life of a wind turbine blade provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of a flow chart of a second embodiment of a method for predicting the remaining fatigue life of a wind turbine blade provided in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of a flow chart of a third embodiment of a method for predicting the remaining fatigue life of a wind turbine blade provided in an embodiment of the present application;

[0050] Figure 4 A schematic diagram of a flow chart of a fourth embodiment of a method for predicting the remaining fatigue life of a wind turbine blade provided in an embodiment of the present application;

[0051] Figure 5 A schematic diagram of a flow chart of a fifth embodiment of a method for predicting the remaining fatigue life of a wind turbine blade provided in an embodiment of the present application;

[0052] Figure 6 A schematic diagram of a flow chart of a sixth embodiment of a method for predicting the remaining fatigue life of a wind turbine blade provided in an embodiment of the present application;

[0053] Figure 7 A schematic flow chart of Embodiment 7 of a method for predicting the remaining fatigue life of a wind turbine blade provided in an embodiment of the present application;

[0054] Figure 8 A schematic diagram of the structure of a first embodiment of a device provided in an embodiment of the present application;

[0055] Fig. 9 A schematic diagram of the structure of an electronic device embodiment 1 provided in an embodiment of the present application.

[0056] Reference numerals list :

[0057] 11: acquisition module; 12: analysis module; 13: processing module; 14: prediction module; 21: processor; 22: memory. DETAILED DESCRIPTION

[0058] Some embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0059] In the description of the present invention, "module" and "processor" may include hardware, software or a combination of the two. A module may include hardware circuits, various suitable sensors, communication ports, and memories, and may also include software parts, such as program codes, or a combination of software and hardware. The processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. The processor has data and / or signal processing functions. The processor may be implemented in software, hardware, or a combination of the two. Non-temporary computer-readable storage media include any suitable medium that can store program codes, such as a magnetic disk, a hard disk, an optical disk, a flash memory, a read-only memory, a random access memory, and the like. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The term "at least one A or B" or "at least one of A and B" has a similar meaning to "A and / or B", and may include only A, only B, or A and B. The singular terms "one" and "the" may also include plural forms.

[0060] In the prior art, active control and passive control are used to reduce the fatigue load of wind turbine blades. Active control usually involves using sensors to monitor the real-time status of wind turbines, such as wind speed, wind direction, blade angle, etc., and making real-time adjustments based on this information. Passive control usually designs the structure and parameters of the wind turbine to automatically adjust and optimize performance under specific circumstances. Active control and passive control can only reduce fatigue loads to extend the life of blades, but cannot predict remaining fatigue damage, and thus cannot replace blades in a timely manner, resulting in technical problems such as low operating efficiency of wind turbines.

[0061] Based on this, in order to solve the above technical problems, the present application provides a method for predicting the remaining fatigue life of wind turbine blades, so as to predict the remaining fatigue life of wind turbine blades.

[0062] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0063] Figure 1 The following is a flow chart of a method for predicting the remaining fatigue life of a wind turbine blade according to an embodiment of the present application. Figure 1 As shown, specifically, the method includes:

[0064] Step S101: Acquire environmental data related to the blade.

[0065] In this embodiment, the environmental data includes wind force, wind speed, wind direction, temperature, humidity, and light intensity.

[0066] In this embodiment, for example, wind force is collected by a wind sensor, temperature and humidity are collected by a temperature and humidity sensor, and light intensity is collected by a light sensor.

[0067] Step S102: inputting environmental data into an aeroelastic model to obtain displacement data of the blade and force and torque corresponding to the displacement data; wherein the aeroelastic model is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model.

[0068] In this embodiment, the aeroelastic model is a model of the elastic deformation of the structure of the wind blade under the current environment.

[0069] Step S103: Obtain a stress set and a strain set according to the force and moment corresponding to the displacement data.

[0070] In this embodiment, under the current environment, the wind blade obtains stress and strain at each position of the wind blade according to the force and moment corresponding to the displacement data obtained by the aeroelastic model.

[0071] Step S104: Obtain the remaining fatigue life of the blade according to the stress set and the strain set.

[0072] In this embodiment, the total stress and total strain of the blade are obtained by superimposing the stress set and strain set of the blade in the current environment with the historical stress set and strain set, and the remaining fatigue life of the blade is obtained based on the total stress and total strain.

[0073] In this embodiment, the remaining fatigue life of the blade can be obtained through the stress set and strain set generated by the wind turbine blade in the current environment, and the remaining fatigue life of the current wind turbine blade is sent to the maintenance personnel terminal for the maintenance personnel to check the remaining fatigue life of the blade.

[0074] Optionally, after the remaining fatigue life of the blades of the current wind turbine generator set is obtained, the remaining fatigue life is compared with a preset threshold value, and if the remaining fatigue life is less than the preset threshold value, an alarm is triggered.

[0075] In this embodiment, environmental data related to the blades are obtained; the environmental data are input into an aeroelastic model to obtain displacement data of the blades and forces and moments corresponding to the displacement data; a stress set and a strain set are obtained based on the forces and moments corresponding to the displacement data; and the remaining fatigue life of the blades is obtained based on the stress set and the strain set. Compared with the prior art, the fatigue load of the wind turbine blades is reduced by taking active control and passive control, and thus the fatigue load of the wind turbine cannot be predicted. In this application, environmental data related to the blades are obtained, and the environmental data are input into an aeroelastic model to obtain displacement data of the blades and forces and moments corresponding to the displacement data, and then a stress set and a strain set are obtained based on the forces and moments corresponding to the displacement data, and finally the remaining fatigue life of the blades is obtained based on the stress set and the strain set, so that the remaining fatigue life of the wind turbine blades can be predicted.

[0076] Figure 2 The present invention provides a flow chart of a second embodiment of a method for predicting the remaining fatigue life of a wind turbine blade. Based on the above embodiment, Figure 2 As shown, specifically, obtaining the one-dimensional anisotropic beam model in step S102 includes:

[0077] Step S201: Analyze the two-dimensional cross section of the blade to obtain a stiffness matrix.

[0078] In this embodiment, the blade is cut into a finite number of times to obtain a plurality of two-dimensional cross sections, and a stiffness matrix is ​​obtained by analyzing each point in the cross section.

[0079] Step S202: Obtain a one-dimensional anisotropic beam model according to the stiffness matrix.

[0080] In this embodiment, the stiffness matrix of each two-dimensional section is combined to obtain a one-dimensional anisotropic beam model.

[0081] In this embodiment, the two-dimensional cross section of the blade is analyzed to obtain a stiffness matrix; based on the stiffness matrix, a one-dimensional anisotropic beam model is obtained, and then a model of the blade cross section can be obtained.

[0082] Figure 3 The present invention provides a flow chart of a third embodiment of a method for predicting the remaining fatigue life of a wind turbine blade. Based on the above embodiment, Figure 3 As shown, specifically, a specific implementation of step S201 is:

[0083] Step S301: Perform finite element decomposition on the blade to obtain the relationship between the node warping displacement, generalized strain, and external six-direction degrees of freedom.

[0084] In this embodiment, assuming that the control equation of the cross-sectional displacement is a linear homogeneous differential equation, the relationship between the node warping displacement U, the generalized strain Φ, and the external six-direction degree of freedom θ is obtained by performing finite element calculation on the blade as Formula 1 and Formula 2:

[0085]

[0086]

[0087] Where X represents the warping displacement under unit stress, Y represents the generalized strain under unit stress, and Z represents the z-axis in the coordinate system.

[0088] Step S302: Obtain a stiffness matrix based on the relationship between the node warping displacement, generalized strain, and external six-direction degrees of freedom.

[0089] In this embodiment, according to Formula 3:

[0090]

[0091] Get the stiffness matrix F s .in,

[0092] In the above formula, Z e =[I3 n e], I3 is a unitary matrix, Q e is the material constitutive matrix in Hooke’s law, N 2d,e It is the shape parameter of a finite element unit on the blade cross section.

[0093] In this embodiment, the blade is decomposed by finite element method to obtain the relationship among the node warping displacement, generalized strain and the external six-direction degrees of freedom; and the stiffness matrix is ​​obtained according to the relationship among the node warping displacement, generalized strain and the external six-direction degrees of freedom.

[0094] Figure 4 The present invention provides a flow chart of a fourth embodiment of a method for predicting the remaining fatigue life of a wind turbine blade. Based on the above embodiment, Figure 4 As shown, specifically, a specific implementation of step S202 is:

[0095] Step S401: According to the stiffness matrix, kinetic energy, potential energy and work done by external force are obtained.

[0096] In this embodiment, according to Formula 4, Formula 5 and Formula 6:

[0097]

[0098] Get kinetic energy T, potential energy Π, and work done by external force W f Among them, B b is the first-order derivative of the shape function N, L e is the length of the beam element, and the matrix E s Expressed as a surface integral of the cross section, f is the force acting on the beam element.

[0099] Step S402: Obtain the local form according to the kinetic energy, potential energy and work done by the external force.

[0100] In this embodiment, the dynamic model is constructed according to Formula 7.

[0101] L=T-Π+W f (7)

[0102] In this embodiment, according to Formula 8, the beam displacement vector d in the finite element formula can be obtained by the node displacement vector d n To express:

[0103] d = NN α d n (8)

[0104] Among them, N α It is realized in each term of Hamilton's principle equation, N = [I zI z 2 I 3 I].

[0105] The relevant parameter matrix is ​​shown below:

[0106]

[0107]

[0108] in,

[0109]

[0110] Among them, B s =T r N(z′)+I6N′(z).

[0111] N α =Y1+Y2Q -1 P.

[0112] Step S403: Integrate the local forms to obtain the global form.

[0113] In this embodiment, the relevant parameter matrices in step S402 are integrated to obtain a global form according to Formula 9.

[0114]

[0115] Among them, K G is the global stiffness matrix, D is the global displacement matrix, M G is the global mass matrix, F G is the global external force matrix.

[0116] Step S404: Solve the global form to obtain a one-dimensional anisotropic beam model.

[0117] In this embodiment, for example, the implicit Newmark method is used to solve Formula 9 to obtain a one-dimensional anisotropic beam model.

[0118] In this embodiment, kinetic energy, potential energy and work done by external forces are obtained according to the stiffness matrix; local forms are obtained according to the kinetic energy, potential energy and work done by external forces; the local forms are integrated to obtain the global form; the global form is solved to obtain a one-dimensional anisotropic beam model.

[0119] Figure 5 The flowchart of Embodiment 5 of a method for predicting the remaining fatigue life of a wind turbine blade provided in the present application is based on the above embodiments. Figure 5 Specifically, obtaining the aerodynamic model in step S102 includes:

[0120] Step S501: obtaining an axial induction factor and a tangential induction factor.

[0121] In this embodiment, if it is the first round, the axial induction factor and the tangential induction factor are initialized.

[0122] Step S502: In the nth round, the inflow angle is obtained according to the axial induction factor and the tangential induction factor.

[0123] In this embodiment, 1≤n≤N, N corresponds to the residual amount after the Nth round being less than the threshold, and the aerodynamic model is obtained.

[0124] In this embodiment, according to formula 10:

[0125]

[0126] Get the inflow angle Φ.

[0127] Step S503: Obtain the angle of attack according to the inflow angle and the pitch angle.

[0128] In this embodiment, according to Formula 11:

[0129] α=Φ-β (11)

[0130] The angle of attack α is obtained. Where β is the pitch angle.

[0131] Step S504: Obtain the local lift and drag coefficients according to the angle of attack and the inflow angle.

[0132] In this embodiment, for example, the local lift and drag coefficients are obtained by looking up a table according to the angle of attack and the inflow angle.

[0133] Step S505: Obtain the tangential force coefficient according to the local lift and drag coefficients, and obtain the updated axial induction factor and tangential induction factor according to the magnitude of the tangential force coefficient and a preset threshold.

[0134] In this embodiment, according to Formula 12:

[0135] C t =C l sinΦ-C d cosΦ (12)

[0136] The tangential force coefficient C is obtained t Among them, C l is the local lift, C d is the tangential force coefficient.

[0137] In this embodiment, if the tangential force coefficient is less than the preset threshold, according to Formula 13:

[0138]

[0139] Get the updated axial induction factor a. Among them, C n =C l cosΦ+C d sinΦ,F is the loss correction factor of the blade.

[0140] According to formula 14:

[0141]

[0142] The updated tangential induction factor a′ is obtained.

[0143] In this embodiment, if the tangential force coefficient is greater than or equal to the preset threshold, according to Formula 15:

[0144]

[0145] Get the updated axial induction factor a.

[0146] According to formula 16:

[0147]

[0148] The updated tangential induction factor a′ is obtained.

[0149] In this embodiment, for example, the preset threshold may be 0.96F.

[0150] In this embodiment, an axial induction factor and a tangential induction factor are obtained; in the nth round, the inflow angle is obtained according to the axial induction factor and the tangential induction factor; the angle of attack is obtained according to the inflow angle and the pitch angle; the local lift and drag coefficients are obtained according to the angle of attack and the inflow angle; the tangential force coefficient is obtained according to the local lift and drag coefficients, and the updated axial induction factor and tangential induction factor are obtained according to the size of the tangential force coefficient and a preset threshold.

[0151] Figure 6 The flowchart of the sixth embodiment of the method for predicting the remaining fatigue life of a wind turbine blade provided in the present application is based on the above embodiment. Figure 6 As shown, specifically, the aeroelastic model in step S102 is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model, including:

[0152] Step S601: In the i-th round, obtain a displacement vector.

[0153] In this embodiment, 1≤i≤I, where I corresponds to the aeroelastic model obtained after the Ith round when the final time step is met.

[0154] In this embodiment, if it is the first round, the displacement vector needs to be initialized.

[0155] Step S602: According to the displacement vector, obtain a one-dimensional time derivative of the displacement vector and a two-dimensional time derivative of the displacement vector.

[0156] Step S603: Obtain force and torque according to the displacement vector, the one-dimensional time derivative of the displacement vector and the two-dimensional time derivative of the displacement vector.

[0157] In this embodiment, the displacement vector, the one-dimensional time derivative of the displacement vector and the two-dimensional time derivative of the displacement vector are input into the aerodynamic model to obtain the power angle and the inflow angle, and the local lift and drag coefficients are obtained by looking up the table, and the tangential and normal forces and moments are obtained based on the local lift and drag coefficients.

[0158] In this embodiment, in the i-th round, a displacement vector is obtained; based on the displacement vector, a one-dimensional time derivative of the displacement vector and a two-dimensional time derivative of the displacement vector are obtained; based on the displacement vector, the one-dimensional time derivative of the displacement vector and the two-dimensional time derivative of the displacement vector, force and torque are obtained.

[0159] Figure 7 The flowchart of Embodiment 7 of a method for predicting the remaining fatigue life of a wind turbine blade provided in the present application is based on the above embodiments. Figure 7 As shown, specifically, one implementation of step S104 includes:

[0160] Step S701: Statistics are collected on the stress set and the strain set to obtain the number of failure cycles.

[0161] In this embodiment, for example, the rain flow counting method is used to count the stress set and the strain set to obtain the number of failure cycles.

[0162] Step S702: Obtain fatigue damage according to the number of failure cycles.

[0163] Step S703: Obtain the remaining fatigue life according to the fatigue damage and time.

[0164] In this embodiment, the remaining fatigue life is obtained according to the Goodman curve, fatigue damage and time.

[0165] In this embodiment, the stress set and the strain set are statistically analyzed to obtain the number of failure cycles; fatigue damage is obtained based on the number of failure cycles; and the remaining fatigue life is obtained based on the fatigue damage and time, thereby obtaining the remaining fatigue life of the wind turbine blade.

[0166] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art can understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0167] Furthermore, the present invention also provides a device.

[0168] Figure 8 This is a schematic diagram of the structure of a device provided in an embodiment of the present application. Figure 8 As shown, the device in the embodiment of the present application mainly includes an acquisition module 11, an analysis module 12, a processing module 13 and a prediction module 14. In some embodiments, one or more of the acquisition module 11, the analysis module 12, the processing module 13 and the prediction module 14 can be combined into one module. In some embodiments, the acquisition module 11 can be configured to acquire environmental data related to the blade. The analysis module 12 can be configured to input the environmental data into an aeroelastic model to obtain the displacement data of the blade and the force and moment corresponding to the displacement data; wherein the aeroelastic model is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model. The processing module 13 can be configured to obtain a stress set and a strain set according to the force and moment corresponding to the displacement data. The prediction module 14 can be configured to obtain the remaining fatigue life of the blade according to the stress set and the strain set.

[0169] The above device is used to perform Figures 1 to 7 The embodiment of a method for predicting the remaining fatigue life of a wind turbine blade is shown. The technical principles, technical problems solved and technical effects produced by the two are similar. Technical personnel in this technical field can clearly understand that for the convenience and conciseness of description, the specific working process and related instructions of the device can refer to the contents described in an embodiment of a method for predicting the remaining fatigue life of a wind turbine blade, and will not be repeated here.

[0170] It is understood by those skilled in the art that all or part of the processes in the method for implementing the above-mentioned embodiment of the present application can also be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium that can carry the computer program code. It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0171] Furthermore, the present application also provides an electronic device.

[0172] Fig. 9 This is a schematic diagram of the structure of an electronic device embodiment 1 provided in the present application. Fig. 9 As shown, the electronic device includes at least one processor 21 and a memory 22, and the memory 22 can be configured to store and execute the above Figures 1 to 7 The program of the method for predicting the remaining fatigue life of a wind turbine blade in the embodiment shown, the processor 21 can be configured to execute the program in the memory 22, the program including but not limited to executing a program of a method for predicting the remaining fatigue life of a wind turbine blade in the above method embodiment. For ease of explanation, only the part related to the embodiment of the present application is shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The electronic device can be a control device device formed by various electronic devices.

[0173] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing a method for predicting the remaining fatigue life of a wind turbine blade according to the above-mentioned method embodiment, and the program can be loaded and run by a processor to implement the above-mentioned method for predicting the remaining fatigue life of a wind turbine blade. For ease of explanation, only the parts related to the embodiment of the present application are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present application. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present application is a non-temporary computer-readable storage medium.

[0174] Further, it should be understood that since the setting of each module is only for illustrating the functional units of the device of the present application, the physical devices corresponding to these modules may be the processor itself, or a part of the software in the processor, a part of the hardware, or a part of the combination of software and hardware. Therefore, the number of each module in the figure is only schematic.

[0175] It is understood by those skilled in the art that each module in the device can be adaptively split or merged. Such splitting or merging of specific modules will not cause the technical solution to deviate from the principle of the present application, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present invention.

[0176] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Without departing from the principles of the present application, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present application.

Claims

1. A method for predicting the remaining fatigue life of a wind turbine blade, characterized in that: include: Obtain environmental data related to the blade; Inputting the environmental data into an aeroelastic model to obtain displacement data of the blade and the force and moment corresponding to the displacement data; The aeroelastic model is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model; Obtaining a stress set and a strain set according to the force and moment corresponding to the displacement data; According to the stress set and strain set, the remaining fatigue life of the blade is obtained. Wherein, obtaining the one-dimensional anisotropic beam model includes: Analyzing a two-dimensional cross section of the blade to obtain a stiffness matrix; According to the stiffness matrix, the one-dimensional anisotropic beam model is obtained; Wherein, analyzing the two-dimensional cross section of the blade to obtain a stiffness matrix includes: Performing finite element decomposition on the blade to obtain the relationship between the node warping displacement, generalized strain, and external six-direction degrees of freedom; According to the relationship among the node warping displacement, generalized strain, and external six-direction degrees of freedom, a stiffness matrix is ​​obtained; Wherein, obtaining the one-dimensional anisotropic beam model according to the stiffness matrix includes: According to the stiffness matrix, kinetic energy, potential energy and work done by external force are obtained; According to the kinetic energy, potential energy and work done by external force, a local form is obtained; Integrate local forms to obtain global forms; Solving the global form to obtain the one-dimensional anisotropic beam model; Wherein, obtaining the aerodynamic model includes: Obtaining axial induction factor and tangential induction factor; In the nth round, an inflow angle is obtained according to the axial induction factor and the tangential induction factor; According to the inflow angle and the pitch angle, an angle of attack is obtained; According to the angle of attack and the inflow angle, local lift and drag coefficients are obtained; According to the local lift and drag coefficients, a tangential force coefficient is obtained, and according to the magnitude of the tangential force coefficient and a preset threshold, an updated axial induction factor and a tangential induction factor are obtained; Wherein 1≤n≤N, N corresponds to the residual amount after the Nth round being less than the threshold value, and the aerodynamic model is obtained; The aeroelastic model is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model, and includes: In the i-th round, obtain the displacement vector; According to the displacement vector, a one-dimensional time derivative of the displacement vector and a two-dimensional time derivative of the displacement vector are obtained; Obtaining force and torque according to the displacement vector, the one-dimensional time derivative of the displacement vector and the two-dimensional time derivative of the displacement vector; Wherein, 1≤i≤I, where I corresponds to the aeroelastic model obtained after the Ith round when the final time step is satisfied.

2. The method according to claim 1, characterized in that The obtaining the remaining fatigue life of the blade according to the stress set and the strain set comprises: Counting the stress set and the strain set to obtain the number of failure cycles; According to the number of failure cycles, fatigue damage is obtained; The remaining fatigue life is obtained according to the fatigue damage and time.

3. A wind turbine blade residual fatigue life prediction device, characterized in that: include: An acquisition module, used for acquiring environmental data related to the blade; An analysis module, used for inputting the environmental data into an aeroelastic model to obtain displacement data of the blade and the force and moment corresponding to the displacement data; The aeroelastic model is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model; A processing module, used for obtaining a stress set and a strain set according to the force and moment corresponding to the displacement data; A prediction module, used for obtaining the remaining fatigue life of the blade according to the stress set and the strain set; Wherein, obtaining the one-dimensional anisotropic beam model includes: Analyzing a two-dimensional cross section of the blade to obtain a stiffness matrix; According to the stiffness matrix, the one-dimensional anisotropic beam model is obtained; Wherein, analyzing the two-dimensional cross section of the blade to obtain a stiffness matrix includes: Performing finite element decomposition on the blade to obtain the relationship between the node warping displacement, generalized strain, and external six-direction degrees of freedom; According to the relationship among the node warping displacement, generalized strain, and external six-direction degrees of freedom, a stiffness matrix is ​​obtained; Wherein, obtaining the one-dimensional anisotropic beam model according to the stiffness matrix includes: According to the stiffness matrix, kinetic energy, potential energy and work done by external force are obtained; According to the kinetic energy, potential energy and work done by external force, a local form is obtained; Integrate local forms to obtain global forms; Solving the global form to obtain the one-dimensional anisotropic beam model; Wherein, obtaining the aerodynamic model includes: Obtaining axial induction factor and tangential induction factor; In the nth round, an inflow angle is obtained according to the axial induction factor and the tangential induction factor; According to the inflow angle and the pitch angle, an angle of attack is obtained; According to the angle of attack and the inflow angle, local lift and drag coefficients are obtained; According to the local lift and drag coefficients, a tangential force coefficient is obtained, and according to the magnitude of the tangential force coefficient and a preset threshold, an updated axial induction factor and a tangential induction factor are obtained; Wherein 1≤n≤N, N corresponds to the residual amount after the Nth round being less than the threshold value, and the aerodynamic model is obtained; The aeroelastic model is obtained based on a one-dimensional anisotropic beam model and an aerodynamic model, and includes: In the i-th round, obtain the displacement vector; According to the displacement vector, a one-dimensional time derivative of the displacement vector and a two-dimensional time derivative of the displacement vector are obtained; Obtaining force and torque according to the displacement vector, the one-dimensional time derivative of the displacement vector and the two-dimensional time derivative of the displacement vector; Wherein, 1≤i≤I, where I corresponds to the aeroelastic model obtained after the Ith round when the final time step is satisfied.

4. An electronic device comprising a processor and a storage device, wherein the storage device is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and executed by the processor to perform the method according to any one of claims 1 to 2.

5. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and executed by a processor to execute the method according to any one of claims 1 to 2.

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