Blade instability prediction method, device and equipment of wind turbine generator and medium

Abstract

The invention relates to the technical field of wind turbine generators, particularly provides a blade instability prediction method, device and equipment of a wind turbine generator and a medium, and aims to solve the technical problem that blade instability cannot be accurately predicted due to the fact that the calculation error is large when an existing method is used for predicting blade instability. In order to achieve the purpose, the method comprises the steps of obtaining blade vibration data and wind turbine generator operation data; inputting the blade vibration data and the wind turbine generator operation data into a preset blade vibration prediction model to obtain a vibration frequency and a vibration frequency growth rate; and obtaining a blade instability prediction result according to the vibration frequency and the vibration frequency growth rate.

Description

Technical Field

[0001] The present application relates to the technical field of wind turbines, and specifically provides a method, device, equipment and medium for predicting blade instability of a wind turbine. Background Art

[0002] At present, the aerodynamic response of the fluid-structure coupling of large flexible blades in wind turbines is complex and changeable, which makes it difficult to clearly understand the instability mechanism of blades, causing instability of blades during operation. When blades become unstable, serious accidents such as blade failure, blade damage, and even tower sweeping may occur. Therefore, it is necessary to predict the instability of wind turbine blades.

[0003] In the prior art, blade instability is predicted based on the modified blade element momentum theory and the geometrically precise beam theory, that is, the aerodynamic force is obtained according to the modified blade element momentum theory, and the structural dynamic characteristics of the blade are obtained according to the geometrically precise beam theory, and the aerodynamic force and structural dynamic characteristics of the blade are coupled to analyze the aeroelastic properties of the blade under the consideration of factors such as the geometric nonlinearity and large deformation of the blade, and the flutter critical wind speed, aerodynamic force distribution and displacement spectrum characteristics are obtained, and the blade instability is predicted accordingly. However, this blade instability prediction method belongs to an engineering calculation method, and its calculation process contains many errors, resulting in a technical problem that the blade instability cannot be accurately predicted.

[0004] Accordingly, the art needs a new method for predicting blade instability of a wind turbine to solve the above problem. 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 in the prior art that when the load on the blade root is too large, the operating parameters of the wind turbine cannot be adjusted in time, causing the blade root to overload and thus causing blade damage.

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

[0007] Obtain blade vibration data and wind turbine operation data;

[0008] Inputting the blade vibration data and the wind turbine operation data into a preset blade vibration prediction model to obtain the vibration frequency and the vibration frequency growth rate;

[0009] A blade instability prediction result is obtained according to the vibration frequency and the vibration frequency growth rate.

[0010] In a technical solution of the above-mentioned method for predicting blade instability of a wind turbine generator set, obtaining the preset blade vibration prediction model includes:

[0011] Based on the blade vibration data and the wind turbine operation data, an unanalyzed blade vibration model is obtained;

[0012] Based on the unanalyzed blade vibration model, a blade vibration function is obtained;

[0013] According to a preset optimization algorithm, the blade vibration function is optimized to obtain parameters to be estimated;

[0014] Based on the parameters to be estimated, the unanalyzed blade vibration model is analyzed to obtain the preset blade vibration prediction model.

[0015] In a technical solution of the above method for predicting blade instability of a wind turbine, the step of optimizing the blade vibration function according to a preset optimization algorithm to obtain parameters to be estimated includes:

[0016] In any round, when it is determined that the output value of the blade vibration function is not the maximum value, the gradient value of the i-th parameter to be estimated is obtained according to the blade vibration function;

[0017] Based on the gradient value and the gradient value of the i-th parameter to be estimated, the parameter to be estimated is updated to obtain the (i + 1)-th parameter to be estimated;

[0018] where 0 < i ≤ I, and I is the number of iterations when it is determined that the output value of the blade vibration function is the maximum value.

[0019] In a technical solution of the above method for predicting blade instability of a wind turbine, the step of obtaining the blade instability prediction result based on the vibration frequency and the vibration frequency growth rate includes:

[0020] Obtain the average frequency of the blade within a preset time;

[0021] Based on the average frequency and the vibration frequency, a frequency deviation value is obtained;

[0022] Based on the frequency deviation value and the vibration frequency growth rate, a blade instability prediction result is obtained.

[0023] In a technical solution of the above method for predicting blade instability of a wind turbine, the step of obtaining the blade instability prediction result based on the frequency deviation value and the vibration frequency growth rate includes:

[0024] Determine that the frequency deviation value is less than a preset frequency threshold and / or the vibration frequency growth rate is greater than a preset growth rate to obtain the blade instability prediction result.

[0025] In a technical solution of the above method for predicting blade instability of a wind turbine, the method further includes:

[0026] Obtain the true result of blade instability;

[0027] If it is determined that the true result of blade instability is different from the predicted result of blade instability, obtain blade structure data and blade aerodynamic data;

[0028] Train the preset blade vibration prediction model according to the blade structure data and the blade aerodynamic data to obtain a preset updated blade vibration prediction model.

[0029] In one technical solution of the above-mentioned blade instability prediction method for a wind turbine, the method further includes:

[0030] Determine that the predicted result of blade instability is that aeroelastic instability may occur;

[0031] Generate an alarm message and send the alarm message to the maintenance personnel terminal for the maintenance personnel to perform maintenance on the wind turbine according to the alarm message.

[0032] In a second aspect, the present application provides a blade instability prediction device for a wind turbine, including:

[0033] An acquisition module, configured to acquire blade vibration data and wind turbine operation data;

[0034] A processing module, configured to input the blade vibration data and the wind turbine operation data into a preset blade vibration prediction model to obtain a vibration frequency and a vibration frequency growth rate;

[0035] An analysis module, configured to obtain a blade instability prediction result according to the vibration frequency and the vibration frequency growth rate.

[0036] In a third aspect, the present application provides an electronic device, including a processor and a storage device, the storage device being adapted to store multiple program codes, and the program codes being adapted to be loaded and run by the processor to execute the method according to any one of the first aspects.

[0037] In a fourth aspect, the present application provides a computer-readable storage medium, in which multiple program codes are stored, and the program codes are adapted to be loaded and run by a processor to execute the method according to any one of the first aspects.

[0038] The present application provides a blade instability prediction method, device, equipment and medium for a wind turbine. The method specifically includes: acquiring blade vibration data and wind turbine operation data; inputting the blade vibration data and the wind turbine operation data into a preset blade vibration prediction model to obtain a vibration frequency and a vibration frequency growth rate; obtaining a blade instability prediction result according to the vibration frequency and the vibration frequency growth rate, thereby improving the accuracy of blade instability prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Referring to the accompanying drawings, the disclosure of the present application will become more readily understandable. It is easily understood by those skilled in the art that these drawings are only for illustrative purposes and are not intended to limit the scope of protection of the present application. In addition, similar numbers in the drawings are used to represent similar components, where:

[0040] Figure 1 is a schematic flow chart of the first embodiment of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application;

[0041] Figure 2 is a schematic flow chart of the second embodiment of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application;

[0042] Figure 3 is a schematic flow chart of the third embodiment of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application;

[0043] Figure 4 is a schematic flow chart of the fourth embodiment of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application;

[0044] Figure 5 is a schematic flow chart of the fifth embodiment of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application;

[0045] Figure 6 is a schematic flow chart of the sixth embodiment of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application.

[0046] Figure 7 is a schematic flow chart of the seventh embodiment of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application;

[0047] Figure 8 is a schematic structural diagram of the first embodiment of a device for predicting blade instability of a wind turbine provided by an embodiment of the present application;

[0048] Figure 9 is a schematic structural diagram of the first embodiment of an electronic device provided by an embodiment of the present application.

[0049] List of Reference Signs :

[0050] 11: Acquisition module; 12: Processing module; 13: Analysis module; 21: Processor; 22: Memory. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] Some embodiments of the present application will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present application and are not intended to limit the protection scope of the present application.

[0052] In the description of the present application, "module" and "processor" may include hardware, software, or a combination of both. A module may include a hardware circuit, various suitable sensors, communication ports, memories, and may also include a software part, such as program code, or a combination of software and hardware. A 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, in hardware, or in a combination of both. A non-transitory computer-readable storage medium includes any suitable medium for storing program code, such as magnetic disks, hard disks, optical disks, flash memories, read-only memories, random access memories, and so on. The term "A and / or B" represents 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 meaning similar to "A and / or B" and may include only A, only B, or A and B. The singular terms "a" and "the" may also include the plural form.

[0053] In the prior art, the blade instability is predicted based on the modified blade element momentum theory and the geometrically exact beam theory. This method predicts the blade instability through theoretical means. However, during the actual operation of the blade, various unpredictable situations may occur. Therefore, this method cannot predict the instability in all situations, which leads to the technical problem of low accuracy in instability prediction.

[0054] Based on this, to solve the above technical problems, the present application provides a method for predicting blade instability of a wind turbine to improve the accuracy of blade instability prediction.

[0055] The technical solution of the present application and how the technical solution of the present application solves the above technical problems will be described in detail below with specific embodiments. These several specific embodiments below 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 with reference to the accompanying drawings.

[0056] Figure 1 It is a schematic flowchart of the first embodiment of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application. As Figure 1 shown, specifically, the method includes:

[0057] Step S101: Obtain blade vibration data and wind turbine operation data.

[0058] In this embodiment, the blade vibration data contains rich information. Before the blade becomes unstable, the blade vibrations in the directions of flapping, swinging and twisting are all based on the rotor rotation frequency. After the blade becomes unstable, the vibration state changes, the blade is in a multi-modal frequency coupling state, and the disorder of the vibration is significantly enhanced, resulting in an increase in the complexity of each frequency component in the vibration frequency spectrum and a significant increase in the volatility of the speed data.

[0059] In this embodiment, the wind turbine operating data may include wind speed and rotor speed.

[0060] Step S102: input the blade vibration data and the wind turbine operation data into a preset blade vibration prediction model to obtain the vibration frequency and the vibration frequency growth rate.

[0061] In this embodiment, the preset blade vibration prediction model can be an artificial intelligence model, which is trained based on a large amount of blade vibration data, wind turbine operation data and blade instability data. The obtained artificial intelligence model has high robustness and generalization ability. After the acquired blade vibration data and wind turbine operation data are input into the artificial intelligence model, accurate vibration frequency and vibration frequency growth rate are obtained.

[0062] Step S103: Obtaining a blade instability prediction result according to the vibration frequency and the vibration frequency growth rate.

[0063] In this embodiment, the vibration condition of the blade is obtained by analyzing the vibration frequency and the vibration frequency growth rate, and the blade instability prediction result is obtained based on the blade vibration condition.

[0064] In this embodiment, blade vibration data and wind turbine operation data are obtained; the blade vibration data and wind turbine operation data are input into a preset blade vibration prediction model to obtain the vibration frequency and the vibration frequency growth rate; based on the vibration frequency and the vibration frequency growth rate, a blade instability prediction result is obtained. Compared with the prior art, the process of predicting blade instability based on the modified blade element momentum theory and the geometric precise beam theory contains more errors, resulting in the inability to accurately predict blade instability. In this application, the obtained blade vibration data and wind turbine operation data are input into a preset blade vibration prediction model to obtain the vibration frequency and the vibration frequency growth rate, and the blade instability prediction result is obtained based on the vibration frequency and the vibration frequency growth rate, thereby improving the accuracy of blade instability prediction.

[0065] Figure 2 The flowchart of the second embodiment of the method for predicting blade instability of a wind turbine provided in the embodiment of the present application is shown in FIG. Figure 2 Specifically, obtaining the preset blade vibration prediction model in step S102 includes:

[0066] Step S201: Obtain the blade vibration model to be analyzed according to the blade vibration data and the wind turbine operation data.

[0067] In this embodiment, a polynomial nonlinear model describing the blade vibration frequency is constructed according to the blade vibration data and the wind turbine operation data.

[0068] Step S202: Obtain the blade vibration function according to the blade vibration model to be analyzed.

[0069] In this embodiment, the blade vibration function is obtained according to the blade vibration model to be analyzed and the preset adjustment value.

[0070] Step S203: Optimize the blade vibration function according to the preset optimization algorithm to obtain the parameter to be estimated.

[0071] In this embodiment, the parameter to be estimated is included in the blade vibration function. After optimizing the blade vibration function according to the preset optimization algorithm, the parameter to be estimated is obtained.

[0072] Step S204: Analyze the blade vibration model to be analyzed according to the parameter to be estimated to obtain the preset blade vibration prediction model.

[0073] In this embodiment, substitute the parameter to be estimated into the blade vibration function to obtain the analyzed blade vibration function, and obtain the preset blade vibration prediction model according to the analyzed blade vibration function, the blade vibration data and the wind turbine operation data.

[0074] In this embodiment, obtain the blade vibration model to be analyzed according to the blade vibration data and the wind turbine operation data; obtain the blade vibration function according to the blade vibration model to be analyzed; optimize the blade vibration function according to the preset optimization algorithm to obtain the parameter to be estimated; analyze the blade vibration model to be analyzed according to the parameter to be estimated to obtain the preset blade vibration prediction model.

[0075] Figure 3 This is a schematic flowchart of the third embodiment of the blade instability prediction method for a wind turbine provided by the embodiment of the present application. On the basis of the above embodiment, as Figure 3 shown, specifically, a specific implementation manner of step S203 includes:

[0076] Step S301: In any round, when it is determined that the output value of the blade vibration function is not the maximum value, obtain the gradient value of the i-th parameter to be estimated according to the blade vibration function.

[0077] In this embodiment, when it is determined that the output value of the blade vibration function is not the maximum value, according to the formula

[0078] Formula 1:

[0079]

[0080] Obtain the blade vibration function L(θ). Among them, N is the number of data groups; f(x 1i , x 2i , x 3i , θ) is the blade vibration model of the i-th group of data; θ is the parameter to be estimated.

[0081] In this embodiment, calculate the gradient of the blade vibration function with respect to the parameter θ, and obtain the gradient value as ▽ θ lnL(θ).

[0082] Step S302: Update the parameter to be estimated according to the gradient value and the gradient value of the i-th parameter to be estimated, and obtain the (i + 1)-th parameter to be estimated.

[0083] In this embodiment, according to Formula 2:

[0084] θ i+1 = θ i + α▽ θ lnL(θ) (2)

[0085] Obtain the (i + 1)-th parameter to be estimated θ i+1 . Among them, θ i is the i-th parameter to be estimated; α is the learning rate.

[0086] In this embodiment, 0 < i ≤ I, where I is the number of iterations when the output value of the blade vibration function is the maximum value.

[0087] In this embodiment, in any round, when it is determined that the output value of the blade vibration function is not the maximum value, according to the blade vibration function, obtain the gradient value of the i-th parameter to be estimated; update the parameter to be estimated according to the gradient value and the gradient value of the i-th parameter to be estimated, and obtain the (i + 1)-th parameter to be estimated.

[0088] Figure 4 This is a schematic flowchart of the fourth embodiment of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application. As Figure 4 shown, specifically, a specific implementation manner of step S103 includes:

[0089] Step S401: Obtain the average frequency of the blade within a preset time.

[0090] In this embodiment, obtain the frequency values of the blade within n consecutive cycles within a preset time, and average the frequency values within n consecutive cycles to obtain the average frequency of the blade within a preset time.

[0091] Step S402: Obtain a frequency deviation value based on the average frequency and the vibration frequency.

[0092] In this embodiment, the vibration frequency includes a first-order vibration frequency and a second-order vibration frequency. The absolute value of the difference between the average frequency and the first-order vibration frequency is the first frequency deviation value, and the absolute value of the difference between the average frequency and the second-order vibration frequency is the second frequency deviation value.

[0093] In this embodiment, when the vibration frequency of the blade approaches its inherent first-order vibration frequency or second-order vibration frequency, the blade may enter a resonance state, thereby increasing the risk of instability.

[0094] Step S403: Obtain a blade instability prediction result based on the frequency deviation value and the vibration frequency growth rate.

[0095] In this embodiment, the frequency deviation value is compared with a first preset threshold to obtain a first comparison result, and the vibration frequency growth rate is compared with a second preset threshold to obtain a second comparison result. By analyzing the first comparison result and the second result, a blade instability prediction result is obtained.

[0096] In this embodiment, according to Formula 3:

[0097]

[0098] the vibration frequency growth rate is obtained. Where f current is the current vibration frequency; f previous is the vibration frequency of the previous cycle.

[0099] In this embodiment, the average frequency of the blade within a preset time is obtained; a frequency deviation value is obtained based on the average frequency and the vibration frequency; and a blade instability prediction result is obtained based on the frequency deviation value and the vibration frequency growth rate.

[0100] Figure 5 is a schematic flowchart of Embodiment 5 of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application. As Figure 5 shown, specifically, a specific implementation manner of step S403 includes:

[0101] Step S501: Determine that the frequency deviation value is less than a preset frequency threshold and / or the vibration frequency growth rate is greater than a preset growth rate to obtain a blade instability prediction result.

[0102] In this embodiment, when it is determined that the frequency deviation value is less than the preset frequency threshold and the vibration frequency growth rate is greater than the preset growth rate, the blade instability prediction result is that instability may occur.

[0103] In this embodiment, when it is determined that the frequency deviation value is less than the preset frequency threshold or the vibration frequency growth rate is greater than the preset growth rate, the blade instability prediction result is that instability may occur.

[0104] In this embodiment, when it is determined that the frequency deviation value is greater than the preset frequency threshold and the vibration frequency growth rate is less than the preset growth rate, the blade instability prediction result is that instability is unlikely to occur.

[0105] In this embodiment, for example, the preset frequency threshold may be 0.05 times the vibration frequency, and the preset growth rate is 1.1.

[0106] In this embodiment, the average frequency of the blade within a preset time is obtained; according to the average frequency and the vibration frequency, the frequency deviation value is obtained; it is determined that the frequency deviation value is less than the preset frequency threshold and / or the vibration frequency growth rate is greater than the preset growth rate, and the blade instability prediction result is obtained.

[0107] Figure 6 It is a schematic flowchart of Embodiment 6 of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application. As Figure 6 shown, specifically, after step S103, the method further includes:

[0108] Step S601: Obtain the true result of blade instability.

[0109] Step S602: If it is determined that the true result of blade instability is different from the blade instability prediction result, obtain the blade structure data and the blade aerodynamic data.

[0110] In this embodiment, the blade structure data includes blade stiffness, damping, and mass distribution.

[0111] Step S603: Perform training processing on the preset blade vibration prediction model according to the blade structure data and the blade aerodynamic data to obtain a preset updated blade vibration prediction model.

[0112] In this embodiment, according to the blade structure data and the blade aerodynamic data, an updated vibration model of the blade to be analyzed is obtained, and according to the updated vibration model of the blade to be analyzed, a blade updated vibration function is obtained, so as to optimize the blade updated vibration function according to a preset algorithm to obtain the parameters to be estimated for update, and finally, according to the parameters to be estimated for update, the preset blade vibration prediction model is analyzed to obtain a preset blade vibration prediction model.

[0113] In this embodiment, the preset updated blade vibration prediction model is deployed to the server, and the collected blade structure data and blade aerodynamic data are input into the preset updated blade vibration prediction model to obtain the blade instability prediction result.

[0114] In this embodiment, the true result of blade instability is obtained; if it is determined that the true result of blade instability is different from the predicted result of blade instability, the blade structure data and the blade aerodynamic data are obtained; according to the blade structure data and the blade aerodynamic data, the preset blade vibration prediction model is trained to obtain a preset updated blade vibration prediction model, thereby improving the accuracy of the blade instability prediction result.

[0115] Figure 7 It is a schematic flow chart of the seventh embodiment of a method for predicting blade instability of a wind turbine provided by an embodiment of the present application. As Figure 7 shown, specifically, after step S103, the method further includes:

[0116] Step S701: Determine that the predicted result of blade instability is that aeroelastic instability may occur.

[0117] Step S702: Generate an alarm message and send the alarm message to the maintenance personnel terminal for the maintenance personnel to perform maintenance on the wind turbine according to the alarm message.

[0118] In this embodiment, the preset blade vibration model is deployed to the monitoring system of the wind turbine to analyze the real-time monitored data. When the predicted result of blade instability is that aeroelastic instability may occur, an alarm message is generated according to the result generation time and the wind turbine location information, and the alarm message is sent to the terminal of the maintenance personnel. The maintenance personnel check and maintain the wind turbine according to the alarm time and location.

[0119] In this embodiment, it is determined that the predicted result of blade instability is that aeroelastic instability may occur; an alarm message is generated and sent to the maintenance personnel terminal for the maintenance personnel to perform maintenance on the wind turbine according to the alarm message, thereby improving the operation stability of the wind turbine.

[0120] It should be noted that although the above steps are described in a specific order in the above embodiments, those skilled in the art can understand that in order to achieve the effects of the present application, different steps do not necessarily have to be executed in such an order, and they can be executed simultaneously (in parallel) or in other orders, and these changes are all within the protection scope of the present application.

[0121] Furthermore, the present application also provides a device for predicting blade instability of a wind turbine.

[0122] Figure 8 It is a schematic structural diagram of the first embodiment of a device for predicting blade instability of a wind turbine provided by an embodiment of the present application. As Figure 8As shown in the figure, the device in the embodiment of the present application mainly includes an acquisition module 11, a processing module 12, and an analysis module 13. In some embodiments, one or more of the acquisition module 11, the processing module 12, and the analysis module 13 may be combined into one module. In some embodiments, the acquisition module 11 may be configured to acquire blade vibration data and wind turbine operation data. The processing module 12 may be configured to input the blade vibration data and the wind turbine operation data into a preset blade vibration prediction model to obtain a vibration frequency and a vibration frequency growth rate. The analysis module 13 may be configured to obtain a blade instability prediction result based on the vibration frequency and the vibration frequency growth rate.

[0123] The above blade instability prediction device of the wind turbine is used to execute Figure 1 the blade instability prediction method embodiment of the wind turbine shown in the figure. The technical principles, the technical problems solved, and the technical effects produced by the two are similar. Those skilled in the art of this technology can clearly understand that for the convenience and conciseness of description, the specific working process and related descriptions of the blade instability prediction device of the wind turbine can refer to the content described in the embodiment of the blade instability prediction method of the wind turbine, which will not be elaborated here.

[0124] Those skilled in the art can understand that all or part of the processes of the methods in the above embodiments of the present application can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium that can carry the computer program code. It should be noted that the content included 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, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0125] Furthermore, the present application also provides an electronic device.

[0126] Figure 9 It is a schematic structural diagram of Embodiment 1 of an electronic device provided by an embodiment of the present application. As Figure 9 shown, the electronic device includes at least one processor 21 and a memory 22. The memory 22 may be configured to store and execute the above Figures 1 to 7The program of the blade instability prediction method for a wind turbine unit in the illustrated embodiment. The processor 21 can be configured to execute the program in the memory 22, and the program includes, but is not limited to, the program of a blade instability prediction method for a wind turbine unit that executes the above method embodiment. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments of the present application. The root load prediction device can be a control device formed by various electronic devices.

[0127] Furthermore, the present application also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present application, the computer-readable storage medium can be configured to store a program for executing the blade instability prediction method for a wind turbine unit in the above method embodiment. The program can be loaded and run by a processor to implement the above blade instability prediction method for a wind turbine unit. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown. For the specific technical details not disclosed, please refer to the method part of the embodiments 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 embodiments of the present application is a non-transitory computer-readable storage medium.

[0128] Furthermore, it should be understood that since the setting of each module is only for explaining the functional units of the device of the present application, the corresponding physical devices of these modules can 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 illustrative.

[0129] Those skilled in the art can understand that the various modules in the device can be adaptively split or combined. Such splitting or combination of specific modules will not cause the technical solution to deviate from the principle of the present application. Therefore, the technical solutions after splitting or combination will all fall within the protection scope of the present application.

[0130] So far, the technical solutions of the present application have been described in conjunction with the preferred embodiments shown in the 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 principle 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 all fall within the protection scope of the present application.

Claims

1. A method for predicting blade instability of a wind turbine, characterized in that: include: Obtain blade vibration data and wind turbine operation data; Inputting the blade vibration data and the wind turbine operation data into a preset blade vibration prediction model to obtain the vibration frequency and the vibration frequency growth rate; A blade instability prediction result is obtained according to the vibration frequency and the vibration frequency growth rate.

2. The method according to claim 1, characterized in that Acquiring the preset blade vibration prediction model includes: Obtaining a blade vibration model to be analyzed according to the blade vibration data and the wind turbine operating data; According to the blade vibration model to be analyzed, a blade vibration function is obtained; According to a preset optimization algorithm, the blade vibration function is optimized to obtain parameters to be estimated; The blade vibration model to be analyzed is analyzed according to the parameters to be estimated to obtain the preset blade vibration prediction model.

3. The method according to claim 2, characterized in that The step of optimizing the blade vibration function according to a preset optimization algorithm to obtain the parameters to be estimated includes: In any round, when it is determined that the output value of the blade vibration function is not a maximum value, a gradient value of the i-th parameter to be estimated is obtained according to the blade vibration function; According to the gradient value and the gradient value of the i-th parameter to be estimated, the parameter to be estimated is updated to obtain the i+1-th parameter to be estimated; Wherein, 0<i≤I, I is the number of iterations when determining that the output value of the blade vibration function is the maximum value.

4. The method according to claim 1, characterized in that: The obtaining of a blade instability prediction result according to the vibration frequency and the vibration frequency growth rate includes: Get the average frequency of the blade within a preset time; Obtaining a frequency deviation value according to the average frequency and the vibration frequency; A blade instability prediction result is obtained according to the frequency deviation value and the vibration frequency growth rate.

5. The method according to claim 4, characterized in that The step of obtaining a blade instability prediction result according to the frequency deviation value and the vibration frequency growth rate includes: It is determined that the frequency deviation value is less than a preset frequency threshold and / or the vibration frequency growth rate is greater than a preset growth rate, and a blade instability prediction result is obtained.

6. The method according to claim 1, characterized in that The method further comprises: Get the real result of blade instability; If it is determined that the actual result of the blade instability is different from the predicted result of the blade instability, then obtaining blade structure data and blade aerodynamic data; The preset blade vibration prediction model is trained according to the blade structure data and the blade aerodynamic data to obtain a preset updated blade vibration prediction model.

7. The method according to claim 1, characterized in that The method further comprises: Determining that the blade instability prediction result is that aeroelastic instability may occur; Generate alarm information, and send the alarm information to a maintenance personnel terminal, so that the maintenance personnel can perform maintenance on the wind turbine according to the alarm information.

8. A blade instability prediction device for a wind turbine, characterized in that: include: An acquisition module, used to acquire blade vibration data and wind turbine operation data; A processing module, used for inputting the blade vibration data and the wind turbine operation data into a preset blade vibration prediction model to obtain a vibration frequency and a vibration frequency growth rate; The analysis module is used to obtain a blade instability prediction result according to the vibration frequency and the vibration frequency growth rate.

9. 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 of any one of claims 1 to 7.

10. 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 7.

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