Vibration suppression method and device of wind driven generator

Abstract

The invention discloses a vibration suppression method and device of a wind driven generator. The vibration suppression method comprises the steps that vibration acceleration signals (including historical moment vibration acceleration signals and current moment vibration acceleration signals) related to a fan, and operation data and operation environment data of the fan related to a preset operation condition are obtained; performing feature extraction on the historical moment vibration acceleration signal and the current moment vibration acceleration signal to obtain a historical moment vibration feature signal and a current moment vibration feature signal associated with the fan; predicting a vibration characteristic signal at a future moment of the current moment based on the historical moment vibration characteristic signal, the current moment vibration characteristic signal, the operation data and the operation environment data; based on the vibration characteristic signal at the current moment and the vibration characteristic signal at the future moment, determining whether a vibration suppression condition associated with a preset operation condition is met or not; and in response to determining that the vibration suppression condition is met, executing a vibration suppression action associated with the preset operation condition.

Description

Technical Field

[0001] The present disclosure generally relates to the field of wind power generation technology, and more specifically, to a vibration suppression method and device for a wind turbine generator. Background Art

[0002] In the wind power industry, with the development of wind power technology, many wind turbines (referred to as "units") are used in different environments for wind power generation. Since abnormal vibration of wind turbines (referred to as "fans") may cause serious problems to the fans and even affect the operation of the entire unit, the control of fan vibration has become one of the important issues that attract much attention in the industry.

[0003] There are many factors that can cause abnormal vibration of the wind turbine. For example, different climatic conditions may cause abnormal vibration of the wind turbine. For example, multiple factors such as multi-system coupled vibration of the unit / wind turbine, insufficient blade aeroelastic damping, negative shear, large turbulence, icing, etc. can cause abnormal vibrations in different modes. In the related field, the general protection method for abnormal vibration is to detect the vibration amplitude related to the wind turbine, and directly trigger a fault shutdown when the vibration amplitude reaches a specific threshold. However, the existing vibration protection methods may have the disadvantages of causing a large loss of power generation and may lead to an increase in the failure rate of the unit.

[0004] Therefore, in order to solve the above problems, it is necessary to provide an improved protection method for fan vibration. Summary of the invention

[0005] The embodiments of the present disclosure provide a method and device for suppressing vibration of a wind turbine, thereby effectively suppressing the vibration of the wind turbine while reducing power generation loss, and significantly improving the operating safety and stability of the wind turbine.

[0006] In one general aspect, a vibration suppression method for a wind turbine is provided, the vibration suppression method comprising: acquiring a vibration acceleration signal associated with the wind turbine and operating data and operating environment data of the wind turbine associated with a preset operating condition, the vibration acceleration signal comprising a vibration acceleration signal at a historical moment and a vibration acceleration signal at a current moment; performing feature extraction on the vibration acceleration signal at a historical moment and the vibration acceleration signal at a current moment to obtain a vibration characteristic signal at a historical moment and a vibration characteristic signal at a current moment associated with the wind turbine; predicting a vibration characteristic signal at at least one future moment of the current moment based on the vibration characteristic signal at a historical moment and the vibration characteristic signal at a current moment as well as the operating data and the operating environment data; determining whether a vibration suppression condition associated with the preset operating condition is satisfied based on the vibration characteristic signal at the current moment and the vibration characteristic signal at at least one future moment; and executing a vibration suppression action associated with the preset operating condition in response to determining that the vibration suppression condition is satisfied.

[0007] Optionally, the step of determining whether the vibration suppression condition associated with the preset operating condition is met based on the vibration characteristic signal at the current moment and the vibration characteristic signal at at least one future moment may include: determining whether the vibration suppression condition associated with the preset operating condition is met by comparing the amplitude of the vibration characteristic signal at the current moment with a first preset threshold and comparing the amplitude of the vibration characteristic signal at at least one future moment with a second preset threshold.

[0008] Optionally, in response to determining that the vibration suppression condition is met, the step of executing the vibration suppression action associated with the preset operating condition may include: in response to the amplitude of the vibration characteristic signal at the current moment being less than the first preset threshold and the amplitude of the vibration characteristic signal at at least one future moment being greater than or equal to the second preset threshold, executing the vibration suppression action associated with the preset operating condition.

[0009] Optionally, the step of predicting the vibration characteristic signal of at least one future moment of the current moment based on the vibration characteristic signal of the historical moment, the vibration characteristic signal of the current moment, the operating data and the operating environment data may include: using the vibration characteristic signal of the historical moment, the operating data and the operating environment data to train a feature prediction model based on a neural network, and inputting the vibration characteristic signal of the current moment into the trained feature prediction model based on the neural network to obtain the vibration characteristic signal of the at least one future moment.

[0010] Optionally, the step of using the vibration characteristic signal at the historical moment, the operating data and the operating environment data to train the neural network-based feature prediction model may include: inputting the vibration characteristic signal at the historical moment, the operating data and the operating environment data into the neural network-based feature prediction model to obtain a predicted amplitude corresponding to the actual amplitude of the vibration characteristic signal at the historical moment; calculating a prediction model loss function based on the actual amplitude and the predicted amplitude of the vibration characteristic signal at the historical moment; and adjusting model parameters of the neural network-based feature prediction model according to the prediction model loss function to obtain a trained neural network-based feature prediction model.

[0011] Optionally, the type of vibration suppressed by the vibration suppression action may include at least one of the following items: resonant vibration of the impeller speed of the wind turbine when the impeller speed is close to a preset speed and the natural frequency of the wind turbine, negative damping vibration of the blades of the wind turbine when the blades are within a preset pitch angle range, generator vibration of the wind turbine when the wind turbine is within a preset power range, first-order vibration of the tower of the wind turbine under a first preset wind speed condition, and first-order vortex-induced vibration of the blades of the wind turbine under a second preset wind speed condition.

[0012] In another general aspect, a vibration suppression device for a wind turbine is provided, the vibration suppression device comprising: a data acquisition module, configured to acquire a vibration acceleration signal associated with the wind turbine and operating data and operating environment data of the wind turbine associated with a preset operating condition, the vibration acceleration signal comprising a vibration acceleration signal at a historical moment and a vibration acceleration signal at a current moment; a feature extraction module, configured to perform feature extraction on the vibration acceleration signal at a historical moment and the vibration acceleration signal at a current moment to obtain a vibration characteristic signal at a historical moment and a vibration characteristic signal at a current moment associated with the wind turbine; a data prediction module, configured to predict a vibration characteristic signal at at least one future moment of the current moment based on the vibration characteristic signal at a historical moment and the vibration characteristic signal at a current moment as well as the operating data and the operating environment data; a vibration suppression determination module, configured to determine whether a vibration suppression condition associated with the preset operating condition is satisfied based on the vibration characteristic signal at the current moment and the vibration characteristic signal at at least one future moment, and in response to determining that the vibration suppression condition is satisfied, execute a vibration suppression action associated with the preset operating condition.

[0013] Optionally, the size relationship among a first task cycle in which the data acquisition module performs data acquisition operations, a second task cycle in which the acquired data is stored, a third task cycle in which the feature extraction module performs feature extraction, a fourth task cycle in which the data prediction module performs prediction operations, and a fifth task cycle in which the vibration suppression determination module determines to perform vibration suppression actions may be as follows: the third task cycle is equal to the fourth task cycle, the first task cycle is equal to the fifth task cycle, the first task cycle is smaller than the third task cycle, and the third task cycle is smaller than the second task cycle.

[0014] Optionally, the vibration suppression device may include a first data processing routine and a second data processing routine, the first data processing routine being used to execute data processing tasks associated with the first task cycle and the fifth task cycle, the second data processing routine being used to execute data processing tasks associated with the third task cycle and the fourth task cycle, and the first data processing routine and the second data processing routine may be respectively deployed in different controllers or may be deployed in the same controller.

[0015] Optionally, the vibration suppression determination module may include determining whether the vibration suppression condition associated with the preset operating condition is met based on the vibration characteristic signal at the current moment and the vibration characteristic signal at at least one future moment by comparing the amplitude of the vibration characteristic signal at the current moment with a first preset threshold and comparing the amplitude of the vibration characteristic signal at at least one future moment with a second preset threshold to determine whether the vibration suppression condition associated with the preset operating condition is met.

[0016] Optionally, in response to determining that the vibration suppression condition is met, the vibration suppression determination module may perform an operation of performing a vibration suppression action associated with the preset operating condition, which may include: in response to the amplitude of the vibration characteristic signal at the current moment being less than the first preset threshold and the amplitude of the vibration characteristic signal at at least one future moment being greater than or equal to the second preset threshold, performing the vibration suppression action associated with the preset operating condition.

[0017] Optionally, the data prediction module may predict the vibration characteristic signal of at least one future moment of the current moment based on the vibration characteristic signal of the historical moment, the vibration characteristic signal of the current moment, the operating data and the operating environment data, and the operation may include: using the vibration characteristic signal of the historical moment, the operating data and the operating environment data to train a neural network-based feature prediction model, and inputting the vibration characteristic signal of the current moment into the trained neural network-based feature prediction model to obtain the vibration characteristic signal of the at least one future moment.

[0018] Optionally, the operation of the data prediction module using the vibration characteristic signal at the historical moment, the operating data and the operating environment data to train the feature prediction model based on the neural network may include: inputting the vibration characteristic signal at the historical moment, the operating data and the operating environment data into the feature prediction model based on the neural network to obtain a predicted amplitude corresponding to the actual amplitude of the vibration characteristic signal at the historical moment; calculating a prediction model loss function based on the actual amplitude and the predicted amplitude of the vibration characteristic signal at the historical moment; and adjusting model parameters of the feature prediction model based on the neural network according to the prediction model loss function to obtain a trained feature prediction model based on the neural network.

[0019] Optionally, the type of vibration suppressed by the vibration suppression action may include at least one of the following items: resonant vibration of the impeller speed of the wind turbine when the impeller speed is close to a preset speed and the natural frequency of the wind turbine, negative damping vibration of the blades of the wind turbine when the blades are within a preset pitch angle range, generator vibration of the wind turbine when the wind turbine is within a preset power range, first-order vibration of the tower of the wind turbine under a first preset wind speed condition, and first-order vortex-induced vibration of the blades of the wind turbine under a second preset wind speed condition.

[0020] In another general aspect, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor is prompted to perform the vibration suppression method of a wind turbine as described above.

[0021] In another general aspect, a computer program product is provided, comprising computer instructions, which, when executed by at least one processor, cause the at least one processor to perform the vibration suppression method for a wind turbine as described above.

[0022] In another general aspect, a computing device is provided, comprising: at least one processor; and at least one memory storing computer executable instructions, wherein the computer executable instructions, when executed by the at least one processor, cause the at least one processor to perform the vibration suppression method for a wind turbine as described above.

[0023] In another general aspect, a wind turbine generator set is provided, the wind turbine generator set comprising: the vibration suppression device of the wind turbine generator as described above, or the computing device as described above.

[0024] According to the vibration suppression method and device of the wind turbine according to the embodiment of the present disclosure, the fine control of the wind turbine vibration is achieved through vibration feature prediction and optimization control strategy, which effectively suppresses the vibration of the wind turbine while reducing the power generation loss, and significantly improves the operating safety and stability of the wind turbine. In addition, by deeply combining the artificial intelligence method, the wind turbine vibration stability mechanism and the wind turbine edge side real-time control decision method, the vibration suppression method and device of the wind turbine according to the present disclosure can be used for the suppression of wind turbine vibration related to the working conditions, thereby making the vibration suppression method and device of the wind turbine highly scalable. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other objects and features of the embodiments of the present disclosure will become more apparent through the following description in conjunction with the accompanying drawings showing the embodiments, in which:

[0026] Figure 1 is a flow chart showing a vibration suppression method of a wind turbine generator according to an embodiment of the present disclosure;

[0027] Figure 2 is a flowchart showing an example of a vibration suppression method of a wind turbine generator according to an embodiment of the present disclosure;

[0028] Figure 3 is a schematic diagram showing abnormal vibration of an exemplary wind turbine;

[0029] Figure 4 is a timing diagram illustrating a feature extraction process according to an embodiment of the present disclosure;

[0030] Figure 5 is a block diagram showing a vibration suppression device of a wind turbine generator according to an embodiment of the present disclosure;

[0031] Figure 6 is a block diagram showing a vibration suppression system of a wind turbine generator according to one embodiment of the present disclosure;

[0032] Figure 7 is a block diagram showing a vibration suppression system of a wind turbine generator according to another embodiment of the present disclosure;

[0033] Figure 8 is a block diagram illustrating a computing device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0034] The following specific embodiments are provided to help the reader obtain a comprehensive understanding of the methods, devices and / or systems described herein. However, after understanding the disclosure of the present application, various changes, modifications and equivalents of the methods, devices and / or systems described herein will be clear. For example, the order of operations described herein is only an example and is not limited to those orders set forth herein, but can be changed as will be clear after understanding the disclosure of the present application, except for operations that must occur in a specific order. In addition, for greater clarity and simplicity, the description of features known in the art may be omitted.

[0035] The features described herein can be implemented in different forms and should not be construed as being limited to the examples described herein. Rather, the examples described herein have been provided to illustrate only some of the many possible ways to implement the methods, devices, and / or systems described herein, which will be clear after understanding the disclosure of the present application.

[0036] As used herein, the term "and / or" includes any one of the associated listed items and any combination of any two or more.

[0037] The terms used herein are only used to describe various examples and are not intended to limit the disclosure. Unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. The terms "comprise", "include" and "have" indicate the presence of the described features, quantities, operations, components, elements and / or combinations thereof, but do not exclude the presence or addition of one or more other features, quantities, operations, components, elements and / or combinations thereof.

[0038] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by a person of ordinary skill in the art to which the present disclosure belongs after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in a general dictionary) should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal manner.

[0039] Furthermore, in the description of examples, when it is considered that a detailed description of a well-known related structure or function would cause vague interpretation of the present disclosure, such a detailed description will be omitted.

[0040] Reference will now be made in detail to the embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like parts throughout. The embodiments will be described below with reference to the drawings in order to explain the present disclosure.

[0041] In the related art, as mentioned in the background art, the general protection method for abnormal vibration is to detect the vibration amplitude related to the fan (i.e., the amplitude that characterizes the vibration level of large components or the whole machine), and directly trigger a fault shutdown when the vibration amplitude reaches a specific threshold. The execution condition of this vibration protection method is only the detected vibration amplitude, and the fault shutdown of the fan is triggered when the amplitude reaches a specific threshold. However, this fault shutdown will bring about a large loss of power generation, increase the failure rate of the fan, and thus reduce the availability of the fan.

[0042] In order to solve the above problems, the present disclosure proposes a vibration suppression method and device for a wind turbine. Specifically, in the present disclosure, a vibration characteristic value prediction model is established with the wind turbine vibration signal and operating conditions (for example, operating conditions) as inputs; the output of the prediction model and the real-time vibration characteristic value are both used as inputs of the vibration suppression decision model to determine the control decision; and vibration suppression is ultimately achieved by adjusting the operating state of the wind turbine. The present disclosure achieves fine control of wind turbine vibration (for example, by controlling and adjusting in advance and actively avoiding vibration-sensitive operating conditions) through vibration characteristic value prediction and optimized control decisions, thereby effectively suppressing wind turbine vibration with minimal power generation loss, and significantly improving the safety and stability of wind turbine operation.

[0043] Refer to the following Figures 1 to 8 A method and apparatus for suppressing vibration of a wind turbine according to an embodiment of the present disclosure are described in detail.

[0044] Figure 1 is a flowchart illustrating a vibration suppression method 100 of a wind turbine according to an embodiment of the present disclosure.

[0045] Reference Figure 1 In step S101, a vibration acceleration signal associated with a wind turbine and operating data and operating environment data of the wind turbine associated with a preset operating condition are acquired.

[0046] Here, the vibration acceleration signal includes the vibration acceleration signal at the historical moment and the vibration acceleration signal at the current moment. Further, the vibration acceleration signal can be a signal of the large component or cabin acceleration signal of the wind turbine that represents the vibration acceleration level of the large component or the whole machine.

[0047] According to an embodiment of the present disclosure, in step S102, feature extraction is performed on the vibration acceleration signal at the historical moment and the vibration acceleration signal at the current moment to obtain the vibration characteristic signal at the historical moment and the vibration characteristic signal at the current moment associated with the wind turbine.

[0048] According to an embodiment of the present disclosure, in step S103, based on the vibration characteristic signals at historical moments and the vibration characteristic signals at the current moment as well as the operation data and the operation environment data, a vibration characteristic signal at at least one future moment of the current moment is predicted.

[0049] Here, the at least one future time moment may be, for example, at least one future adjacent time moment, and further may be, for example, the next time moment or a combination of future adjacent or non-adjacent time moments.

[0050] As an example, step S103 may further include: using vibration characteristic signals at historical moments, operating data and operating environment data to train a neural network-based feature prediction model, and inputting the vibration characteristic signal at the current moment into the trained neural network-based feature prediction model to obtain a vibration characteristic signal at at least one future moment.

[0051] According to an embodiment of the present disclosure, by using a feature prediction model, a method for predicting vibration characteristic values ​​using the correlation between vibration characteristic signals and wind turbine operating conditions is implemented, thereby realizing the prediction of vibration characteristic signals at at least one future moment.

[0052] Here, the method for training a neural network-based feature prediction model may include the following steps S31 to S33:

[0053] In step S31, the vibration characteristic signal at the historical moment, the operating data and the operating environment data are input into a feature prediction model based on a neural network to obtain a predicted amplitude corresponding to the actual amplitude of the vibration characteristic signal at the historical moment.

[0054] In step S32, the prediction model loss function is calculated based on the actual amplitude and predicted amplitude of the vibration characteristic signal at the historical moment.

[0055] In step S33, the model parameters of the neural network-based feature prediction model are adjusted according to the prediction model loss function to obtain a trained neural network-based feature prediction model.

[0056] According to an embodiment of the present disclosure, a feature prediction model based on a neural network is adopted to predict the amplitude of the vibration characteristic signal. From the perspective of the control framework, the comparison between the predicted value and the actual value is considered to reflect the consideration of the safety of the fan operation, thereby ensuring the safety of the fan operation.

[0057] According to an embodiment of the present disclosure, in step S104, based on the vibration characteristic signal at the current moment and at least one vibration characteristic signal at a future moment, it is determined whether a vibration suppression condition associated with a preset operating condition is satisfied.

[0058] As an example, step S104 may further include: determining whether the vibration suppression condition associated with the preset operating condition is met by comparing the amplitude of the vibration characteristic signal at the current moment with a first preset threshold and comparing the amplitude of the vibration characteristic signal at at least one future moment with a second preset threshold.

[0059] According to an embodiment of the present disclosure, the fan vibration suppression condition is determined by comparing the amplitude of the vibration characteristic signal at the current moment and the amplitude of the vibration characteristic signal at at least one future moment with corresponding conditions respectively.

[0060] According to an embodiment of the present disclosure, in step S105 , in response to determining that the vibration suppression condition is satisfied, a vibration suppression action associated with a preset operating condition is performed.

[0061] As an example, step S105 may further include: in response to the amplitude of the vibration characteristic signal at the current moment being less than a first preset threshold and the amplitude of the vibration characteristic signal at at least one future moment being greater than or equal to a second preset threshold, performing a vibration suppression action associated with a preset operating condition.

[0062] According to an embodiment of the present disclosure, corresponding control decisions are executed by respectively satisfying corresponding conditions by the amplitude of the vibration characteristic signal at the current moment and the amplitude of the vibration characteristic signal at at least one future moment, thereby realizing the determination of the fan vibration suppression action that needs to be executed, thereby facilitating the realization of proactive vibration suppression processing in advance.

[0063] For example, the types of vibrations suppressed by the vibration suppression action may include at least one of the following items: the resonant vibration of the impeller speed of the wind turbine when the impeller speed is close to a preset speed and the natural frequency of the wind turbine, the negative damping vibration of the blades of the wind turbine when the blades are within a preset pitch angle range, the generator vibration of the wind turbine when the wind turbine is within a preset power range, the first-order vibration of the tower of the wind turbine under a first preset wind speed condition, and the first-order vortex-induced vibration of the blades of the wind turbine under a second preset wind speed condition.

[0064] It should be noted that the vibration types that can be suppressed by the method of the present disclosure are not limited to the above types, but can also include other vibration types that can reflect a certain degree of correlation between the vibration amplitude of the entire machine or specific components of the wind turbine and a specific condition (for example, operating condition). In other words, the method of the present disclosure can achieve the suppression of abnormal vibrations of units / wind turbines in different modes.

[0065] According to the embodiments of the present disclosure, by establishing a vibration characteristic value prediction model with the fan vibration signal and operating conditions as input, the above-mentioned various types of vibrations are suppressed, the vibration prediction and control related to the fan operating conditions are effectively solved, and the wide applicability of the solution of the present disclosure is improved.

[0066] Below, by referring to Figures 2 to 4 The vibration suppression method 100 of the wind turbine generator described above is described as an example. Figure 2 is a flowchart showing an example of a vibration suppression method of a wind turbine generator according to an embodiment of the present disclosure, Figure 3 is a schematic diagram showing abnormal vibration of an exemplary wind turbine generator, and Figure 4 is a timing diagram illustrating a feature extraction process according to an embodiment of the present disclosure.

[0067] As an example, see Figure 2 An example of a vibration suppression method for a wind turbine may include the following processes: signal reading 101, signal processing 102, model M0 processing 103, model M1 processing 104, and control decision 105, which may be referred to as modules 101 to 105 hereinafter.

[0068] Specifically, module 101 can realize the acquisition of the fan vibration acceleration signal and the acquisition of the fan operation data and operating environment data associated with the preset operating conditions, modules 102 to 104 can realize the signal processing and predictive analysis of the fan vibration acceleration signal, and module 105 can realize the decision control for determining the need to perform vibration suppression actions.

[0069] Here, in Figure 2 The task cycle for performing data processing by modules 101 and 105 shown in FIG. 1 is T0, and the task cycle for performing data processing by modules 102 to 104 is T1. For example, the task cycle T1 may be greater than the task cycle T0. For example, the value of T0 may be 10 ms or 20 ms, but the present disclosure is not limited thereto. In addition, for example, the value of T1 may be 2 s or 4 s, but the present disclosure is not limited thereto.

[0070] In addition, regarding the task cycle of each processing process, as an example, the task cycle of the data reading process of the signal reading module may be equal to the task cycle of the decision-making process performed by the control decision module. In addition, for example, the task cycle of performing feature extraction processing may be greater than the task cycle of performing data storage processing. In addition, for example, the task cycle of performing data prediction processing may be equal to the task cycle of performing feature extraction processing. In addition, for example, the task cycle of performing data reading processing may be less than the task cycle of feature extraction processing.

[0071] By limiting these task cycles, a stable, reliable and real-time response effect can be achieved.

[0072] According to the vibration suppression method of the wind turbine generator of the above example of the present disclosure, it is possible to suppress abnormal vibrations of wind turbines / units of different modes. For example, typical vibration modes include but are not limited to at least one of the following items: resonance between the frequency multiple of the rotational frequency near a specific rotational speed and the natural frequency of the generator, negative damping vibration of the blades that may occur when the blades are in a specific pitch angle range, generator vibration within a specific power range, first-order vibration of the tower within a specific wind speed range, etc. Since the common feature of the above-mentioned vibrations is that there is a predetermined degree of correlation between the vibration amplitude of the whole machine or large components and the specific working conditions, the vibration suppression method of the present disclosure can achieve vibration prediction and control.

[0073] The following is an example of a vibration suppression method under different vibration situations.

[0074] As an example, the vibration situation to be solved in this example is: at a specific speed, the rotation frequency of the impeller speed is close to the natural frequency of the generator, causing the generator to resonate. Here, the rotation frequency refers to the impeller speed of the unit / fan or the inverse of the time it takes for the generator to rotate one circle.

[0075] Here, refer to Figure 3 To explain the abnormal vibration in this example. Figure 3 As shown, the current moment is shown as k, the moment of the previous unit time (for example, its typical value can be 2s) of the current moment is shown as k-1, the moment of the next unit time of the current moment is shown as k+1, the sensitive speed is shown as n0, and the actual speeds of the current moment, the previous moment and the next moment are shown as n respectively. k 、n k-1 and n k+1 The area difference between the actual speed and the sensitive speed is shown as d k The acceleration frequency domain amplitudes at the current moment, the previous moment, and the next moment are shown as x k 、x k-1 and x k+1 .

[0076] Specifically, the sensitive speed is the speed at which resonance is more likely to occur. When the actual speed of the impeller is close to the sensitive speed, the acceleration frequency domain amplitude, which characterizes the magnitude of the vibration, will continue to increase. In other words, the acceleration frequency domain amplitude is proportional to the area difference between the actual speed and the sensitive speed per unit time. Here, the acceleration frequency domain amplitude (Amplitude ofAcceleration Frequency Domain) represents the amplitude of the maximum point in a specific frequency range in the acceleration spectrum.

[0077] Refer again Figure 2, first, regarding module 101, module 101 reads a vibration acceleration signal at a predetermined task cycle (eg, T0), for example, a vibration acceleration signal v(t) as an analog signal read from a vibration acceleration sensor installed in the generator.

[0078] It should be noted that the vibration acceleration sensor is installed at the generator as an example here, but the present disclosure is not limited thereto, and can also be installed at other locations as long as the relevant vibration signal can be detected, for example, it can be installed at other locations such as the middle of the cabin base. Similarly, the vibration sensing sensor used in detecting other vibrations can be installed at the corresponding location as long as the corresponding vibration signal for identifying the vibration mode can be detected.

[0079] Then, regarding module 102, module 102 performs feature extraction on the vibration acceleration signal v(t) at a predetermined task period (e.g., T1). For example, the extracted feature may be at least one of the acceleration frequency domain amplitude, acceleration effective value, acceleration original value standard deviation, acceleration effective value standard deviation, frequency domain amplitude standard deviation, etc.

[0080] In addition, it should be noted that when extracting features from vibration signals of other vibration modes / situations, other feature extraction methods may be used, not limited to Fourier transform, and the extracted features may be features of other dimensions in addition to the above-mentioned feature examples, which is not particularly limited in the present disclosure.

[0081] Here, refer to Figure 4 The timing of executing the feature extraction process is briefly described. Module 102 stores the acceleration value v(t) array of the time period length T2 (for example but not limited to 5.12s) in real time. At the same time, the feature extraction process is performed once every T1 task cycle. Here, regarding the storage of the acceleration value received from module 101 by module 102, for example, real-time storage can be adopted. For example, data is received from module 101 in real time, that is, whenever module 101 reads data at a predetermined time interval, the read data is sent to module 102 for real-time storage. In addition, the storage of the received data by module 102 is stored in sequence according to the order of reception. For example, the data stored in each T2 time period length can be recorded so that relevant data processing needs to be performed according to the data of the T2 time period length when performing feature extraction.

[0082] That is to say, feature extraction is to perform corresponding processing on at least one set of data stored in the most recent T2 time period before the current T1. For example, the processed data can be a single set of data, or it can be multiple sets of data, depending on whether the features processed by several sets of data can represent the target vibration mode. For example, in the case where the cabin acceleration direction includes the two directions of x and y, if the x direction itself is sufficient to effectively represent the target vibration mode, a single set of data can be used; otherwise, it is necessary to extract feature data of acceleration in the two directions of x and y, in which case multiple sets of data can be used. In addition, for example, in the case where the direction of the blade vibration or load signal includes the two directions of edgewise (swing) and flapwise (flapping), if it is necessary to represent the vibration within the impeller surface, a single set of data indicating the edgewise direction can be used.

[0083] In this example, the steps of performing feature extraction processing in module 102 include:

[0084] Step 1), in the current task cycle, the stored vibration acceleration value array is subjected to, for example, a fast Fourier transform and an acceleration spectrum (ie, acceleration frequency domain data) is obtained.

[0085] Step 2), assuming that the natural frequency of the generator at the resonance point is f, take the amplitude x(k) of the maximum point in the frequency range [f-f0, f+f0] in the spectrum.

[0086] Here, the frequency f0 used to limit the above frequency range is typically 1 Hz, but the present disclosure is not limited thereto.

[0087] In addition, module 102 can also output the acceleration frequency domain amplitudes x(k-1), x(k-2)... of several cycles before the current cycle in addition to the acceleration frequency domain amplitude x(k) of the current cycle, so as to input x(k) into module 103, and input x(k-1), x(k-2)... into module 104, so as to be used by model M1 to predict the future acceleration frequency domain amplitude (reflecting the vibration trend) using the acceleration frequency domain amplitude over a period of time in the past.

[0088] Next, refer to Figure 2 , regarding module 103, the input of model M0 is the acceleration frequency domain amplitude x(k) of the current cycle, and the output of model M0 is u0(k). The relationship between the input and output of model M0 is shown in the following equation (1):

[0089]

[0090] Where a represents the acceleration amplitude exceeding the limit shutdown threshold.

[0091] In addition, regarding module 104, model M1 uses a recurrent neural network prediction model to predict the acceleration frequency domain amplitude. It should be noted that the method according to the present disclosure is not limited to the recurrent neural network prediction model in terms of the selection of the prediction model, and other types of prediction models can also be used, for example, a perceptron neural network containing one or more hidden layers, a multivariate linear regression, and other different prediction models.

[0092] Specifically, the training process of the prediction model can be as follows:

[0093] (1) The acceleration frequency domain amplitude x(k), x(k-1)... of the past j T1 cycles and the rotation speed n(k-1), n(k-2)... which has a strong correlation with vibration, and / or at least one of the fan operating power data, wind speed data, etc. are used as inputs of the prediction model as the actual test set data.

[0094] (2) Using the test set data, a prediction model is constructed, for example, with a single hidden layer recurrent neural network layer having 256 hidden units, a batch size of 32, and 300 training iterations.

[0095] (3) Predicting the test set data through the prediction model to obtain the test set prediction value, and obtaining the loss function based on the predicted value and the actual value. The parameters of the prediction model are adjusted by making the loss function meet the preset conditions. For example, the root mean square error between the predicted value and the actual value can reach an acceptable level as the goal of adjusting the model parameters, but the present disclosure is not limited to this.

[0096] After the prediction model training is completed, the trained model M1 is used to predict the acceleration frequency domain amplitudes x(k+1), x(k+2), ..., x(k+i) at the time k+1, k+2, ..., k+i. When the predicted value x(k+1) exceeds the fault shutdown threshold a, the output u1(k) of M1 takes the value of 1. In other words, the relationship between the value of u1(k) and x(k+1) is similar to the relationship in formula (1).

[0097] Finally, regarding module 105, module 105 performs control decision processing at a predetermined task cycle (e.g., T0). Module 105 judges its inputs u0(k) and u1(k), and determines the corresponding fan control decision action according to the judgment result. Here, the rules for module 105 to perform judgment are shown in Table 1 below. In addition, Figure 2 In the equation, z(k) represents the preset system disturbance.

[0098] Table 1

[0099]

[0100] Referring to Table 1, when u0(k) is 1, u(k) takes the value of 1, and the corresponding control decision is: directly execute the shutdown action. Here, the shutdown state is maintained for a period of time of T3, which can be, for example, 10 minutes, but the present disclosure is not limited to this. When only u1(k) is 1 (that is, u0(k) is 0 and u1(k) is 1), u(k) takes the value of 2, and the corresponding control decision is: limit the speed and maintain the period of time of T4 and the speed limit value is n. Here, T4 can take the value of, for example, 10 minutes, but the present disclosure is not limited to this.

[0101] In addition, the speed limit value n can be taken as shown in the following formula (2):

[0102] n=no-Δn (2)

[0103] The value of Δn may be, for example, 1.5 rpm, so that after the speed limit operation, the actual speed is far away from the sensitive speed (this also reflects the effect of power reduction), thereby achieving the effect of vibration suppression.

[0104] In addition, when u0(k) and u1(k) are both 0, u(k) takes the value of 0, and the corresponding control decision is: the fan continues to run.

[0105] For the above example, a deep learning vibration frequency domain amplitude prediction model is established with the fan vibration signal and operating conditions as input; the output of the prediction model and the real-time vibration amplitude are used as the input of the vibration control decision model to make a control decision; and the vibration control is achieved by adjusting the operating state of the fan. According to the above example of the present disclosure, fine control of fan vibration is achieved through vibration amplitude prediction and optimization control based on the control decision model, which can control and adjust in advance to avoid vibration-sensitive conditions, thereby effectively suppressing fan vibration with minimal power generation loss, and significantly improving the safety and stability of fan operation.

[0106] In addition, as another example, the vibration situation to be solved by this example is: negatively damped vibration of blades that may occur when a unit of a specific blade model (e.g., a large unit) is under specific wind conditions and within a specific pitch angle range. For this vibration situation, the pitch angle (e.g., which can be used to replace the characteristics of the rotational speed in the above example), and / or variables such as power and wind speed, and the corresponding characteristic values ​​and blade vibration characteristic values ​​(e.g., which can be used to replace the characteristics of the acceleration frequency domain amplitude in the above example) can be used to predict blade vibration, and the minimum pitch angle is actively increased based on the blade vibration prediction value (which also reflects the effect of power reduction), thereby achieving vibration suppression in this abnormal vibration mode.

[0107] In this example, the relevant rule that can be used to replace the execution of the speed limit judgment in the above example can be, for example: if the vibration amplitude is greater than the threshold value b, the minimum pitch angle is increased by 3 degrees, otherwise no action is taken.

[0108] In addition, as another example, the vibration situation to be solved in this example is: when the wind turbine is in a shutdown state, the first-order vibration of the tower and the first-order vortex-induced vibration of the blades may occur under certain wind speed and wind deviation conditions. For this vibration situation, the variables such as wind speed and wind deviation and the corresponding characteristic values ​​and the tower and blade vibration characteristic values ​​(for example, which can be used to replace the characteristics of the acceleration frequency domain amplitude in the above example) can be used to predict the tower and blade vibrations respectively, and active minimum pitch angle adjustment or yaw action is performed based on the tower and blade vibration prediction values, respectively, so as to achieve vibration suppression of the first-order vibration of the tower and the vortex-induced vibration of the blades in the shutdown state of the wind turbine.

[0109] In addition, as another example, for the vibration of the generator within a specific power range, this situation may be that due to the structural problem of the generator of some units, the amplitude corresponding to the natural frequency of the generator in the acceleration spectrum of the nacelle under the rated power condition will continue to increase. For this vibration situation, after identifying this situation, the power limit operation can be performed and the relevant operators can be prompted to conduct investigations through alarms.

[0110] Additionally, as another specific example, under conditions of deep power limiting (eg, 5% of rated power), most vibrations (eg, the examples of typical vibration types / modes described above) may be eliminated.

[0111] That is to say, in control scenarios for different vibration types / modes, different suppression methods can be used, such as power limiting, increasing the minimum pitch angle, torque reverse resistance (as a suppression method of "tower lateral resistance"), or a combination of different suppression methods can be used to achieve targeted control of specific vibration types, but the present disclosure is not limited to this.

[0112] According to an embodiment of the present disclosure, the vibration suppression method of the wind turbine disclosed in the present disclosure can be deployed in a wind turbine edge side control system, or can also be deployed in a wind farm controller with real-time computing capabilities. Here, the wind turbine edge side control system (Wind Turbine Edge Side Control System) is an on-site control system located at the edge side of the wind turbine, which is a carrier for realizing wind turbine edge computing (Wind Turbine Edge Computing, a computing architecture for data processing, control, and storage), and it has the advantages of low latency (high real-time performance), high efficiency (for example, on-site online perception, analysis, and control), data optimization, and high security. By being deployed in a system capable of realizing real-time online signal processing, the vibration suppression method disclosed in the present disclosure has the advantages of high real-time performance, high efficiency, and high security.

[0113] Figure 5 is a block diagram showing a vibration suppression apparatus 500 of a wind turbine generator according to an embodiment of the present disclosure.

[0114] Reference Figure 5 The vibration suppression device 500 of a wind turbine according to an embodiment of the present disclosure may include a data acquisition module 510 , a feature extraction module 520 , a data prediction module 530 and a vibration suppression determination module 540 .

[0115] According to an embodiment of the present disclosure, the data acquisition module 510 may execute: acquiring a vibration acceleration signal associated with the wind turbine and operating data and operating environment data of the wind turbine associated with a preset operating condition.

[0116] Here, the vibration acceleration signal includes a vibration acceleration signal at a historical moment and a vibration acceleration signal at a current moment.

[0117] As an example, the size relationship of the first task cycle in which the data acquisition module 510 performs data acquisition operations, the second task cycle in which the acquired data is stored, the third task cycle in which the feature extraction module performs feature extraction, the fourth task cycle in which the data prediction module performs prediction operations, and the fifth task cycle in which the vibration suppression determination module determines to perform vibration suppression actions can be as follows: the third task cycle is equal to the fourth task cycle, the first task cycle is equal to the fifth task cycle, the first task cycle is less than the third task cycle, and the third task cycle is less than the second task cycle.

[0118] According to the embodiments of the present disclosure, by setting the task cycles corresponding to different operations, the data processing efficiency can be improved, the time spent on data processing can be saved, and the data processing process can be optimized while ensuring the accuracy and precision of data processing.

[0119] According to an embodiment of the present disclosure, the feature extraction module 520 may perform: performing feature extraction on the vibration acceleration signal at the historical moment and the vibration acceleration signal at the current moment to obtain the vibration feature signal at the historical moment and the vibration feature signal at the current moment associated with the wind turbine.

[0120] According to an embodiment of the present disclosure, the data prediction module 530 may execute: based on the vibration characteristic signals at historical moments and the vibration characteristic signals at the current moment, as well as the operation data and the operation environment data, predict the vibration characteristic signals at at least one future moment of the current moment.

[0121] Here, the at least one future time moment may be, for example, at least one future adjacent time moment, and further may be, for example, the next time moment or a combination of future adjacent or non-adjacent time moments.

[0122] As an example, the data prediction module 530 may predict the vibration characteristic signal of at least one future moment of the current moment based on the vibration characteristic signal of the historical moment, the vibration characteristic signal of the current moment, the operating data, and the operating environment data, and the operation may include: using the vibration characteristic signal of the historical moment, the operating data, and the operating environment data to train a neural network-based feature prediction model, and inputting the vibration characteristic signal of the current moment into the trained neural network-based feature prediction model to obtain at least one vibration characteristic signal of the future moment.

[0123] For example, the operation of the data prediction module 530 using the historical vibration characteristic signal, operation data and operation environment data to train the neural network-based feature prediction model may include the following operations 5301) to 5303):

[0124] In operation 5301), the vibration characteristic signal at the historical moment, the operating data and the operating environment data are input into a feature prediction model based on a neural network to obtain a predicted amplitude corresponding to the actual amplitude of the vibration characteristic signal at the historical moment.

[0125] In operation 5302), a prediction model loss function is calculated based on the actual amplitude and predicted amplitude of the vibration characteristic signal at the historical moment.

[0126] In operation 5303), according to the prediction model loss function, the model parameters of the neural network-based feature prediction model are adjusted to obtain a trained neural network-based feature prediction model.

[0127] According to an embodiment of the present disclosure, the vibration suppression determination module 540 can execute: based on the vibration characteristic signal at the current moment and the vibration characteristic signal at at least one future moment, determine whether the vibration suppression condition associated with the preset operating condition is met, and in response to determining that the vibration suppression condition is met, perform a vibration suppression action associated with the preset operating condition.

[0128] Here, the type of vibration suppressed by the vibration suppression action may include, for example, at least one of the following items: the resonant vibration of the impeller speed of the wind turbine when the impeller speed is close to the preset speed and the natural frequency of the wind turbine, the negative damping vibration of the blades of the wind turbine when the blades are within the preset pitch angle range, the generator vibration of the wind turbine when the wind turbine is within the preset power range, the first-order vibration of the tower of the wind turbine under the first preset wind speed condition, the first-order vortex-induced vibration of the blades of the wind turbine under the second preset wind speed condition, etc.

[0129] As an example, the vibration suppression determination module 540 may include determining whether the vibration suppression condition associated with the preset operating condition is met based on the vibration characteristic signal at the current moment and the vibration characteristic signal at at least one future moment by comparing the amplitude of the vibration characteristic signal at the current moment with a first preset threshold and comparing the amplitude of the vibration characteristic signal at at least one future moment with a second preset threshold to determine whether the vibration suppression condition associated with the preset operating condition is met.

[0130] In addition, for example, in response to determining that the vibration suppression conditions are met, the vibration suppression determination module 540 may perform an operation of performing a vibration suppression action associated with a preset operating condition, which may include: in response to the amplitude of the vibration characteristic signal at the current moment being less than a first preset threshold and the amplitude of the vibration characteristic signal at at least one future moment being greater than or equal to a second preset threshold, performing the vibration suppression action associated with the preset operating condition.

[0131] In addition, the vibration suppression device 500 may include a first data processing routine and a second data processing routine. Specifically, the first data processing routine is used to perform data processing tasks associated with the first task cycle and the fifth task cycle, and the second data processing routine is used to perform data processing tasks associated with the third task cycle and the fourth task cycle.

[0132] For example, the first data processing routine and the second data processing routine may be respectively deployed in different controllers or may be deployed in the same controller.

[0133] According to the embodiments of the present disclosure, data processing of different task cycles can be assigned to different routines according to different data processing requirements (for example, the amount of data to be processed and the real-time requirements, etc.), thereby achieving an improvement in processing speed and processing efficiency, and further ensuring the timely application of prediction results, thereby improving the effectiveness of vibration suppression.

[0134] It should be noted that the operations performed by the above modules can be referred to in Figure 1 The related contents described are similar and will not be repeated here.

[0135] Next, refer to Figure 6 and Figure 7 A vibration suppression system for a wind turbine according to an embodiment of the present disclosure is described by way of example. Figure 6 is a block diagram showing a vibration suppression system of a wind turbine generator according to an embodiment of the present disclosure, and Figure 7 is a block diagram illustrating a vibration suppression system of a wind turbine generator according to another embodiment of the present disclosure.

[0136] Reference Figure 6According to an embodiment of the present disclosure, a vibration suppression system for a wind turbine generator may include a vibration sensor 601 , a data acquisition device 602 , a controller 604 , a working condition sensor and a data acquisition device 603 , and an actuator 605 .

[0137] Specifically, the vibration sensor 601 is used to realize the vibration perception of the whole machine or large components for a specific vibration mode. The data acquisition device 602 is used to collect vibration signals and transmit the signals to the controller 604. The working condition sensor and data acquisition device 603 include sensors for collecting working condition information such as rotation speed and corresponding data acquisition devices, and are used to transmit working condition signals to the controller 604. The controller 604 is used to send control decision module instructions to the actuator 605. The actuator 605 includes a wind turbine pitch control system, a variable flow system, a yaw system, etc., which are used to execute the actions determined by the control decision in the method disclosed in the present invention, and finally realize the active vibration suppression of the target vibration mode.

[0138] As an example, the controller 604 includes two processor cores, namely core 1 and core 2. As an example, core 1 is used to perform various processes in the method of the present disclosure with an execution period of T0 task cycle, and core 2 is used to perform various processes in the method of the present disclosure with an execution period of T1 task cycle. In addition, core 1 and core 2 communicate with each other in the T0 task cycle.

[0139] Reference Figure 7 According to another embodiment of the present disclosure, a vibration suppression system for a wind turbine generator may include a vibration sensor 701, a data acquisition device 702, two controllers (shown as controller 1 and controller 2) 704 and 705, an operating condition sensor and a data acquisition device 703, and an actuator 706.

[0140] Specifically, the vibration sensor 701 is used to realize the vibration perception of the whole machine or large components for a specific vibration mode. The data acquisition device 702 is used to collect vibration signals and transmit the signals to the controller 704. The working condition sensor and data acquisition device 703 include sensors and corresponding data acquisition devices for collecting working condition information such as rotation speed, and are used to transmit working condition signals to the controller 705. The controller 704 can communicate with the controller 705 in real time. The controller 705 is used to send the control decision module instructions to the actuator 706. The actuator 706 includes a wind turbine pitch system, a variable flow system, a yaw system, etc., which are used to execute the actions determined by the control decision in the method disclosed in the present invention, and finally realize the active vibration suppression of the target vibration mode.

[0141] As an example, the controller 704 is used to execute the processing tasks corresponding to the signal reading module, signal processing module, model M1 module, and model M0 module in the method of the present disclosure, and its functions are similar to those of Figure 6The controller 705 is used to execute the processing tasks corresponding to the control decision module with the execution cycle of T0 task cycle in the method disclosed in the present invention, and its functions are similar to those of the core 2 in the embodiment of the present invention. Figure 6 The function of kernel 1 in is similar.

[0142] By adopting Figure 6 Kernel 2 in and Figure 7 The controller 704 in the embodiment can improve the computing power of the system and process the operations related to the prediction model separately from the system input and control commands, thereby effectively improving the computing efficiency of the system while meeting the prediction method based on complex machine learning / deep learning algorithms.

[0143] Figure 8 is a block diagram illustrating a computing device 800 according to an embodiment of the present disclosure.

[0144] Reference Figure 8 According to an embodiment of the present disclosure, the computing device 800 may include at least one processor 810 and a memory 820. The at least one processor 810 may include (but not limited to) a central processing unit (CPU), a digital signal processor (DSP), a microcomputer, a field programmable gate array (FPGA), a system on a chip (SoC), a microprocessor, an application-specific integrated circuit (ASIC), etc. The memory 820 may store computer executable instructions to be executed by the at least one processor 810. The memory 820 includes a high-speed random access memory and / or a non-volatile computer-readable storage medium. When the at least one processor 810 executes the computer executable instructions stored in the memory 820, the vibration suppression method of the wind turbine generator as described above may be implemented.

[0145] The vibration suppression method of the wind turbine according to the embodiment of the present disclosure can be written as a computer program / instruction to form a computer program product and stored on a computer-readable storage medium. When the computer program / instruction is executed by a processor, the vibration suppression method of the wind turbine as described above can be implemented. When the instructions in the computer-readable storage medium are executed by at least one processor (for example, a processor of an electronic device / server), at least one processor is enabled to execute the vibration suppression method of the wind turbine as described above. Examples of computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), card storage (such as, multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk and any other device, any other device is configured to store computer program and any associated data, data files and data structures in a non-transitory manner and provide the computer program and any associated data, data files and data structures to a processor or computer so that the processor or computer can execute the computer program. In one example, the computer program and any associated data, data files and data structures are distributed on a networked computer system so that the computer program and any associated data, data files and data structures are stored, accessed and executed in a distributed manner by one or more processors or computers.

[0146] In addition, according to an embodiment of the present disclosure, a wind turbine generator set may be provided, the wind turbine generator set comprising: the vibration suppression device of the wind turbine generator as described above and / or the computing device as described above.

[0147] According to the vibration suppression method and device of the wind turbine according to the embodiment of the present disclosure, fine control of wind turbine vibration is achieved through vibration characteristic prediction and optimization control strategy, which effectively suppresses the vibration of the wind turbine while reducing power generation loss and significantly improves the operating safety and stability of the wind turbine.

[0148] On the other hand, by deeply combining artificial intelligence methods, wind turbine vibration stability mechanisms and real-time control decision methods on the wind turbine edge side, the vibration suppression method and device for wind turbines disclosed herein can be used to suppress wind turbine vibrations related to working conditions, thereby making the vibration suppression method and device for wind turbines highly scalable.

[0149] Although some embodiments of the present disclosure have been disclosed and described, it will be appreciated by those skilled in the art that modifications and variations may be made to the embodiments without departing from the concept and spirit of the present disclosure, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for suppressing vibration of a wind turbine, characterized in that: The vibration suppression method comprises: Acquire a vibration acceleration signal associated with the wind turbine and operation data and operation environment data of the wind turbine associated with a preset operation status, wherein the vibration acceleration signal includes a vibration acceleration signal at a historical moment and a vibration acceleration signal at a current moment; Performing feature extraction on the vibration acceleration signal at the historical moment and the vibration acceleration signal at the current moment to obtain the vibration feature signal at the historical moment and the vibration feature signal at the current moment associated with the wind turbine; Based on the vibration characteristic signal at the historical moment and the vibration characteristic signal at the current moment, as well as the operation data and the operation environment data, predict the vibration characteristic signal at at least one future moment of the current moment; Determining whether a vibration suppression condition associated with the preset operating condition is satisfied based on the vibration characteristic signal at the current moment and the vibration characteristic signal at the at least one future moment; In response to determining that the vibration suppression condition is satisfied, a vibration suppression action associated with the preset operating condition is performed.

2. The vibration suppression method according to claim 1, characterized in that: The step of determining whether the vibration suppression condition associated with the preset operating condition is met based on the vibration characteristic signal at the current moment and the vibration characteristic signal at the at least one future moment comprises: By comparing the amplitude of the vibration characteristic signal at the current moment with a first preset threshold and comparing the amplitude of the vibration characteristic signal at at least one future moment with a second preset threshold, it is determined whether the vibration suppression condition associated with the preset operating condition is met.

3. The vibration suppression method according to claim 2, characterized in that: In response to determining that the vibration suppression condition is satisfied, the step of executing the vibration suppression action associated with the preset operating condition comprises: In response to the amplitude of the vibration characteristic signal at the current moment being less than the first preset threshold and the amplitude of the vibration characteristic signal at at least one future moment being greater than or equal to the second preset threshold, a vibration suppression action associated with the preset operating condition is performed.

4. The vibration suppression method according to claim 1, wherein: The step of predicting the vibration characteristic signal at at least one future moment of the current moment based on the vibration characteristic signal at the historical moment and the vibration characteristic signal at the current moment as well as the operation data and the operation environment data comprises: The vibration characteristic signal at the historical moment, the operating data and the operating environment data are used to train a feature prediction model based on a neural network, and the vibration characteristic signal at the current moment is input into the trained feature prediction model based on the neural network to obtain the vibration characteristic signal at at least one future moment.

5. The vibration suppression method according to claim 4, characterized in that: The step of using the vibration characteristic signal at the historical moment, the operation data and the operation environment data to train the feature prediction model based on the neural network comprises: Inputting the vibration characteristic signal at the historical moment, the operation data and the operation environment data into the feature prediction model based on the neural network to obtain a predicted amplitude corresponding to the actual amplitude of the vibration characteristic signal at the historical moment; Calculating a prediction model loss function based on the actual amplitude and the predicted amplitude of the vibration characteristic signal at the historical moment; According to the prediction model loss function, the model parameters of the neural network-based feature prediction model are adjusted to obtain a trained neural network-based feature prediction model.

6. The vibration suppression method according to claim 1, wherein: The types of vibrations suppressed by the vibration suppression action include at least one of the following items: resonant vibration of the impeller speed of the wind generator when the impeller speed is close to a preset speed and the natural frequency of the wind generator, negative damping vibration of the blades of the wind generator when the blades are within a preset pitch angle range, generator vibration of the wind generator when the wind generator is within a preset power range, first-order vibration of the tower of the wind generator under a first preset wind speed condition, and first-order vortex-induced vibration of the blades of the wind generator under a second preset wind speed condition.

7. A vibration suppression device for a wind turbine, characterized in that: The vibration suppression device comprises: The data acquisition module is configured to: acquire a vibration acceleration signal associated with the wind turbine and operation data and operation environment data of the wind turbine associated with a preset operation status, wherein the vibration acceleration signal includes a vibration acceleration signal at a historical moment and a vibration acceleration signal at a current moment; The feature extraction module is configured to: perform feature extraction on the vibration acceleration signal at the historical moment and the vibration acceleration signal at the current moment to obtain the vibration feature signal at the historical moment and the vibration feature signal at the current moment associated with the wind turbine; A data prediction module is configured to: predict a vibration characteristic signal at at least one future moment of the current moment based on the vibration characteristic signal at the historical moment and the vibration characteristic signal at the current moment, the operation data, and the operation environment data; The vibration suppression determination module is configured to: determine whether a vibration suppression condition associated with the preset operating condition is met based on the vibration characteristic signal at the current moment and the vibration characteristic signal at at least one future moment, and in response to determining that the vibration suppression condition is met, perform a vibration suppression action associated with the preset operating condition.

8. The vibration suppression device according to claim 7, characterized in that: The magnitude relationship of the first task cycle of the data acquisition module performing data acquisition operation, the second task cycle of storing acquired data, the third task cycle of the feature extraction module performing feature extraction, the fourth task cycle of the data prediction module performing prediction operation, and the fifth task cycle of the vibration suppression determination module determining to perform vibration suppression action is as follows: The third task cycle is equal to the fourth task cycle, the first task cycle is equal to the fifth task cycle, the first task cycle is smaller than the third task cycle, and the third task cycle is smaller than the second task cycle.

9. The vibration suppression device according to claim 8, characterized in that: The vibration suppression device includes a first data processing routine and a second data processing routine, the first data processing routine is used to execute data processing tasks associated with the first task cycle and the fifth task cycle, the second data processing routine is used to execute data processing tasks associated with the third task cycle and the fourth task cycle, and the first data processing routine and the second data processing routine are respectively deployed in different controllers or deployed in the same controller.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor is prompted to perform the vibration suppression method for a wind turbine according to any one of claims 1 to 6.

11. A computer program product comprising computer instructions, characterized in that: When the computer instructions are executed by at least one processor, the at least one processor is prompted to perform the vibration suppression method for a wind turbine as claimed in any one of claims 1 to 6.

12. A computing device, characterized in that: The computing device comprises: at least one processor; and at least one memory storing computer executable instructions, wherein when the computer executable instructions are executed by the at least one processor, the at least one processor is prompted to execute the vibration suppression method for a wind turbine according to any one of claims 1 to 6.

13. A wind turbine generator set, characterized in that: The wind turbine generator set comprises: a vibration suppression device for a wind turbine generator as claimed in any one of claims 7 to 9, or The computing device of claim 12.

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