A control method, device and equipment of a wind turbine generator

Abstract

The application discloses a kind of control method, device and equipment of wind turbine, the method includes: based on the measured wind parameter on the impeller face of wind turbine and the simulation wind parameter of the impeller face of wind turbine, determine the impeller face wind parameter of wind turbine;Determine the fault identification value corresponding to each fault type in at least one fault type based on the impeller face wind parameter, and the fault identification value is used to indicate the possibility of the fault of target fault type of wind turbine;The operating state of wind turbine is controlled based on at least one fault identification value.This way, the impeller face wind parameter is calculated in combination with the simulation wind parameter of impeller face and the measured wind parameter of a small amount of points on impeller face, the fault identification value corresponding to multiple fault types is determined by accurate and abundant impeller face wind parameter, and the control strategy for responding to the fault of possible fault type is determined based on fault identification value, so that wind turbine can be safely operated in running stage.

Description

Technical Field

[0001] This application relates to the field of wind power generation technology, and in particular to a control method, device and equipment for a wind turbine. Background Technology

[0002] With the adjustment of the energy structure, the proportion of wind power in the energy structure is gradually increasing. Wind turbines are typically deployed in environments with complex and variable wind conditions, which can lead to various types of failures, resulting in power generation losses and increased operation and maintenance costs.

[0003] Currently, wind farm simulations based on computational fluid dynamics (CFD) methods are typically used to assess wind resources and calculate power generation. However, in controlling wind turbines to cope with potential faults caused by wind conditions, simply relying on wind parameters collected at the hub height is insufficient to provide a reasonable response to the wind conditions affecting the wind turbine.

[0004] Therefore, there is an urgent need to provide a technical solution that can accurately control wind turbines based on the wind conditions they are exposed to, so as to reduce the occurrence of faults in wind turbines during operation, thereby reducing power generation losses and lowering operation and maintenance costs. Summary of the Invention

[0005] This application provides a control method, device, and equipment for wind turbine units. By determining the fault identification values ​​corresponding to various fault types through rotor surface wind parameters, and determining the fault control strategy to deal with possible fault types based on the fault identification values, the wind turbine units can operate safely during the operation phase.

[0006] In a first aspect, this application provides a control method for a wind turbine generator, the method comprising:

[0007] Based on the measured wind parameters of the wind turbine impeller surface and the simulated wind parameters of the wind turbine impeller surface, the wind parameters of the wind turbine impeller surface are determined.

[0008] Based on the impeller surface wind parameters, a fault identification value is determined for each of at least one fault type, and the fault identification value is used to indicate the probability of the wind turbine unit experiencing the target fault type.

[0009] The operating status of the wind turbine is controlled based on at least one fault identification value.

[0010] Optionally, determining the wind parameters of the wind turbine rotor surface based on the measured wind parameters and the simulated wind parameters of the wind turbine rotor surface may include:

[0011] Based on the measured wind parameters at at least one point on the rotor surface of the wind turbine and the simulated wind parameters at at least one point on the rotor surface of the wind turbine, determine the correlation between the measured wind parameters on the rotor surface and the simulated wind parameters.

[0012] Based on the aforementioned correlation and the simulated wind parameters of the wind turbine's rotor surface, the wind parameters of the wind turbine's rotor surface are determined.

[0013] Optionally, the simulated wind parameters of the impeller surface include the simulated wind parameters of multiple simulation points on the impeller surface corresponding to each of multiple sectors.

[0014] Determining the correlation between the measured wind parameters at at least one point on the rotor surface of the wind turbine and the simulated wind parameters at the same point on the rotor surface of the wind turbine, based on the measured wind parameters at at least one point and the simulated wind parameters at the same point on the rotor surface of the wind turbine, may include:

[0015] Based on the measured wind parameters of the at least one point, the simulated wind parameters of multiple simulated points on the impeller surface corresponding to the target sector are selected from the simulated wind parameters of the impeller surface, and the target sector matches the wind direction in the measured wind parameters;

[0016] Based on the simulated wind parameters and the measured wind parameters of at least one point on the impeller surface corresponding to the target sector, the estimation coefficients are determined.

[0017] The determination of the wind turbine rotor surface wind parameters based on the correlation and the simulated wind parameters of the wind turbine rotor surface may include:

[0018] Based on the estimated coefficients and the simulated wind parameters of multiple simulation points on the impeller surface corresponding to the target sector, the estimated wind parameters of multiple simulation points on the impeller surface are calculated.

[0019] Based on the estimated wind parameters of multiple simulation points on the impeller surface corresponding to the target sector, the wind parameters of the impeller surface are obtained.

[0020] Optionally, determining the fault identification value corresponding to each fault type among at least one fault type based on the impeller surface wind parameters may include:

[0021] The impeller surface wind parameters are input into the fault identification model to obtain the fault identification value corresponding to each fault type among the at least one fault types. The fault identification model includes a fault identification sub-model corresponding to each fault type among the at least one fault types, and each fault identification sub-model is used to characterize the correspondence between the impeller surface wind parameters and the fault identification value.

[0022] Optionally, the at least one fault type includes tower sweeping fault, and determining the fault identification value corresponding to each fault type among the at least one fault type based on the impeller surface wind parameters may include:

[0023] The key parameter set at the lower blade tip position and the average wind speed at the hub center position in the impeller surface parameters are input into the first fault identification sub-model. The first fault identification sub-model outputs the fault identification value corresponding to the tower sweeping fault. The fault identification value corresponding to the tower sweeping fault is used to indicate the probability of the wind turbine experiencing a tower sweeping fault. The key parameter set includes inflow angle, wind deflection, turbulence intensity and average wind speed. The first fault identification sub-model is the fault identification sub-model corresponding to the tower sweeping fault in the fault identification model.

[0024] Optionally, the at least one fault type includes a yaw trip fault, and determining the fault identification value corresponding to each fault type among the at least one fault types based on the impeller surface wind parameters may include:

[0025] The key parameter set at each position of the impeller surface parameters and the average wind speed at the hub center position are input into the second fault identification sub-model. The second fault identification sub-model outputs the fault identification value corresponding to the yaw trip fault. The fault identification value corresponding to the yaw trip fault is used to indicate the possibility of the wind turbine experiencing a yaw trip fault. The key parameter set includes inflow angle, wind deflection, turbulence intensity and average wind speed. The second fault identification sub-model is the fault identification sub-model corresponding to the yaw trip fault in the fault identification model.

[0026] Optionally, before controlling the operating state of the wind turbine based on at least one fault identification value, the method may further include:

[0027] Obtain a control strategy library, which includes control strategies corresponding to different fault levels under each fault type in different fault types;

[0028] The at least one fault type includes a first fault type and a second fault type, and controlling the operating status of the wind turbine based on at least one fault identification value includes:

[0029] A first fault level is determined based on a first fault identification value corresponding to the first fault type, and a second fault level is determined based on a second fault identification value corresponding to the second fault type.

[0030] Based on the control strategy library, the first fault level, and the second fault level, a target control strategy is determined.

[0031] The operating status of the wind turbine is controlled based on the target control strategy.

[0032] Optionally, the first fault level indicates the possibility that the wind turbine may experience a fault of the first fault type, and the second fault level indicates the possibility that the wind turbine may experience a fault of the second fault type. Determining the target control strategy based on the control strategy library, the first fault level, and the second fault level may include:

[0033] From the control strategies in the control strategy library that correspond to the first fault type, match the first control strategy corresponding to the first fault level.

[0034] From the control strategies in the control strategy library that correspond to the second fault type, match the second control strategy corresponding to the second fault level;

[0035] Based on the first control strategy and the second control strategy, a target control strategy is determined.

[0036] Optionally, the first fault level indicates the possibility that the wind turbine may experience a fault of the first fault type, and the second fault level indicates the possibility that the wind turbine may not experience a fault of the second fault type. Determining the target control strategy based on the control strategy library, the first fault level, and the second fault level may include:

[0037] The target control strategy that matches the first fault level is obtained from the control strategy library corresponding to the first fault type.

[0038] Optionally, if the first fault type includes tower sweeping fault, then...

[0039] If the first fault level corresponding to the first fault type of the wind turbine is high risk within a first preset time period, the target control strategy includes shutdown.

[0040] If the first fault level corresponding to the first fault type of the wind turbine is medium risk within the second preset time period, the target control strategy includes adjusting the blade pitch angle.

[0041] If the first fault level corresponding to the first fault type of the wind turbine is low risk within a third preset time period, the target control strategy includes keeping it unchanged.

[0042] Optionally, if the first fault type includes a yaw trip fault, then...

[0043] If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type is high-risk within the fourth preset time period, the target control strategy includes stopping the yaw.

[0044] If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type is medium risk within the fifth preset time period, the target control strategy includes reducing the yaw torque and reducing the yaw speed.

[0045] If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type is low risk within a sixth preset time period, the target control strategy includes keeping it unchanged.

[0046] Secondly, this application also provides a control device for a wind turbine generator, comprising:

[0047] The first determining unit is used to determine the wind parameters of the impeller surface of the wind turbine based on the measured wind parameters of the impeller surface and the simulated wind parameters of the impeller surface of the wind turbine.

[0048] The second determining unit is used to determine a fault identification value corresponding to each fault type in at least one fault type based on the impeller surface wind parameters. The fault identification value is used to indicate the probability that the wind turbine will experience a target fault type.

[0049] A control unit is used to control the operating status of the wind turbine based on at least one fault identification value.

[0050] Optionally, the first determining unit may include: a first determining subunit and a second determining subunit;

[0051] The first determining subunit is used to determine the correlation between the measured wind parameters on the impeller surface and the simulated wind parameters of the impeller surface of the wind turbine based on the measured wind parameters at at least one point on the impeller surface of the wind turbine and the simulated wind parameters of the impeller surface of the wind turbine.

[0052] The second determining subunit is used to determine the wind parameters of the wind turbine rotor surface based on the correlation and the simulated wind parameters of the wind turbine rotor surface.

[0053] Optionally, the simulated wind parameters of the impeller surface include the simulated wind parameters of multiple simulation points on the impeller surface corresponding to each of multiple sectors.

[0054] The first determining subunit is specifically used for:

[0055] Based on the measured wind parameters of the at least one point, the simulated wind parameters of multiple simulated points on the impeller surface corresponding to the target sector are selected from the simulated wind parameters of the impeller surface, and the target sector matches the wind direction in the measured wind parameters;

[0056] Based on the simulated wind parameters and the measured wind parameters of at least one point on the impeller surface corresponding to the target sector, the estimation coefficients are determined.

[0057] The second determining subunit is specifically used for:

[0058] Based on the estimated coefficients and the simulated wind parameters of multiple simulation points on the impeller surface corresponding to the target sector, the estimated wind parameters of multiple simulation points on the impeller surface are calculated.

[0059] Based on the estimated wind parameters of multiple simulation points on the impeller surface corresponding to the target sector, the wind parameters of the impeller surface are obtained.

[0060] Optionally, the second determining unit is specifically used for:

[0061] The impeller surface wind parameters are input into the fault identification model to obtain the fault identification value corresponding to each fault type among the at least one fault types. The fault identification model includes a fault identification sub-model corresponding to each fault type among the at least one fault types, and each fault identification sub-model is used to characterize the correspondence between the impeller surface wind parameters and the fault identification value.

[0062] Optionally, the at least one fault type includes tower sweeping fault, and the second determining unit may include: a third determining subunit;

[0063] The third determining sub-unit is used to input the set of key parameters of the lower blade tip position and the average wind speed of the hub center position in the impeller surface parameters into the first fault identification sub-model. The first fault identification sub-model outputs the fault identification value corresponding to the tower sweeping fault. The fault identification value corresponding to the tower sweeping fault is used to indicate the possibility of the wind turbine experiencing a tower sweeping fault. The set of key parameters includes inflow angle, wind deflection, turbulence intensity and average wind speed. The first fault identification sub-model is the fault identification sub-model corresponding to the tower sweeping fault in the fault identification model.

[0064] Optionally, the at least one fault type includes a yaw trip fault, and the second determining unit may include: a fourth determining subunit;

[0065] The fourth determining sub-unit is used to input the set of key parameters at each position of the impeller surface parameters and the average wind speed at the hub center position into the second fault identification sub-model. The second fault identification sub-model outputs the fault identification value corresponding to the yaw trip fault. The fault identification value corresponding to the yaw trip fault is used to indicate the possibility of the wind turbine unit experiencing a yaw trip fault. The set of key parameters includes inflow angle, wind deflection, turbulence intensity and average wind speed. The second fault identification sub-model is the fault identification sub-model corresponding to the yaw trip fault in the fault identification model.

[0066] Optionally, the device may further include: an acquisition unit;

[0067] The acquisition unit is used to acquire a control strategy library before controlling the operating state of the wind turbine based on at least one fault identification value. The control strategy library includes control strategies corresponding to different fault levels under each fault type in different fault types.

[0068] The at least one fault type includes a first fault type and a second fault type, and the control unit may include: a fifth determining subunit, a sixth determining subunit, and a control subunit;

[0069] The fifth determining subunit determines the first fault level based on the first fault identification value corresponding to the first fault type, and determines the second fault level based on the second fault identification value corresponding to the second fault type.

[0070] The sixth determining subunit is used to determine the target control strategy based on the control strategy library, the first fault level, and the second fault level;

[0071] The control subunit is used to control the operating state of the wind turbine based on the target control strategy.

[0072] Optionally, the first fault level indicates the possibility that the wind turbine may experience a fault of the first fault type, and the second fault level indicates the possibility that the wind turbine may experience a fault of the second fault type. The sixth determining subunit is specifically used for:

[0073] From the control strategies in the control strategy library that correspond to the first fault type, match the first control strategy corresponding to the first fault level.

[0074] From the control strategies in the control strategy library that correspond to the second fault type, match the second control strategy corresponding to the second fault level;

[0075] Based on the first control strategy and the second control strategy, a target control strategy is determined.

[0076] Optionally, the first fault level indicates the possibility that the wind turbine may experience a fault of the first fault type, and the second fault level indicates that the wind turbine may not experience a fault of the second fault type. The sixth determining subunit is specifically used for:

[0077] The target control strategy that matches the first fault level is obtained from the control strategy library corresponding to the first fault type.

[0078] Optionally, if the first fault type includes tower sweeping fault, then...

[0079] If the first fault level corresponding to the first fault type of the wind turbine is high risk within a first preset time period, the target control strategy includes shutdown.

[0080] If the first fault level corresponding to the first fault type of the wind turbine is medium risk within the second preset time period, the target control strategy includes adjusting the blade pitch angle.

[0081] If the first fault level corresponding to the first fault type of the wind turbine is low risk within a third preset time period, the target control strategy includes keeping it unchanged.

[0082] Optionally, if the first fault type includes a yaw trip fault, then...

[0083] If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type is high-risk within the fourth preset time period, the target control strategy includes stopping the yaw.

[0084] If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type is medium risk within the fifth preset time period, the target control strategy includes reducing the yaw torque and reducing the yaw speed.

[0085] If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type is low risk within a sixth preset time period, the target control strategy includes keeping it unchanged.

[0086] Thirdly, this application also provides an electronic device, which includes a processor and a memory:

[0087] The memory is used to store computer programs;

[0088] The processor is configured to execute the method provided in the first aspect according to the computer program.

[0089] Fourthly, this application also provides a computer-readable storage medium for storing a computer program for performing the method described in the first aspect.

[0090] Therefore, this application has the following beneficial effects:

[0091] This application provides a control method for a wind turbine generator set. The method may include: determining the wind parameters of the wind turbine generator set's rotor surface based on measured wind parameters and simulated wind parameters of the rotor surface; determining a fault identification value corresponding to each of at least one fault type based on the rotor surface wind parameters, wherein the fault identification value is used to indicate the probability of the wind turbine generator set experiencing a fault of the target fault type; and controlling the operating state of the wind turbine generator set based on the at least one fault identification value. Therefore, considering that wind parameters at a single height cannot reflect the probability of a fault occurring, this application calculates the impeller surface wind parameters at various times by combining simulated wind parameters of the impeller surface and measured wind parameters at a small number of points on the impeller surface. This provides accurate and comprehensive evidence for calculating the fault identification value for each fault type in at least one fault type. Moreover, by considering the characteristics of various fault types, this application defines fault identification indicators for each fault type. Thus, the values ​​of the corresponding fault identification indicators (i.e., fault identification values) for each fault type can be determined using the impeller surface wind parameters at various times, making it possible to accurately and comprehensively predict possible faults during the operation of wind turbines. Consequently, based on at least one fault identification value, more accurate and reasonable control of the wind turbine's operating status can be achieved, enabling the wind turbine to cope with the possible faults indicated by the corresponding fault identification value. This not only reduces the failure rate of wind turbines and increases their lifespan but also reduces the operation and maintenance costs of wind turbines and minimizes power generation losses caused by wind turbine failures. Attached Figure Description

[0092] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0093] Figure 1 This is a schematic diagram illustrating a scenario to which an embodiment of this application applies;

[0094] Figure 2 This is a schematic diagram illustrating another scenario to which the embodiments of this application are applicable;

[0095] Figure 3 A flowchart illustrating a wind turbine control method provided in an embodiment of this application;

[0096] Figure 4 This is a flowchart illustrating a method for obtaining simulation parameters of an impeller surface according to an embodiment of this application.

[0097] Figure 5 A schematic diagram of the structure of a control device for a wind turbine provided in an embodiment of this application;

[0098] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0099] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the embodiments of this application will be further described in detail below with reference to the accompanying drawings and specific implementation methods. It should be understood that the specific embodiments described herein are merely for explaining this application and are not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to this application are shown in the accompanying drawings, not the entire structure.

[0100] In the fault protection of wind power generation, wind turbines are usually protected based on data collected by the Supervisory Control and Data Acquisition (SCADA) system (hereinafter referred to as SCADA data) or data collected by the nacelle radar (hereinafter referred to as nacelle radar data). SCADA data or nacelle radar data usually only indicates the wind parameters at the hub height of the wind turbine.

[0101] Research has shown that some types of wind turbine faults cannot be reflected by wind parameters at a single height. For example, for tower sweep faults (i.e., blades colliding with the tower), wind parameters at hub height cannot reflect the likelihood of such faults. Wind parameters at multiple points on the turbine rotor surface must be considered to predict the possibility of a tower sweep fault. Similarly, for yaw trip faults (i.e., tripping due to wind conditions during yaw), wind parameters at hub height cannot reflect the likelihood of such faults. Wind parameters at multiple points on the turbine rotor surface must be considered to predict the possibility of a yaw trip. Therefore, in wind turbine fault protection, obtaining accurate wind parameters at as many points as possible on the rotor surface allows for more accurate prediction of at least one possible fault. This makes it possible to rationally control the wind turbine for at least one possible fault, improving the intelligence level of wind turbine control.

[0102] Based on this, embodiments of this application provide a control method for a wind turbine. In this method, firstly, the wind parameters of the wind turbine's rotor surface are determined based on measured wind parameters and simulated wind parameters of the wind turbine's rotor surface; then, based on the rotor surface wind parameters, a fault identification value is determined for each of at least one fault type, the fault identification value indicating the probability of the wind turbine experiencing a target fault type; and the operating state of the wind turbine is controlled based on at least one fault identification value.

[0103] Therefore, considering that wind parameters at a single height cannot reflect the probability of many fault types occurring, this embodiment of the application calculates the impeller surface wind parameters at each moment by combining simulated wind parameters of the impeller surface and measured wind parameters at a small number of points on the impeller surface (such as at the hub height). The impeller surface wind parameters include relatively accurate wind parameters at multiple points on the impeller surface (relatively accurate wind parameters can be understood as close to actual wind parameters), providing accurate and abundant basis for calculating the fault identification value of each fault type in at least one fault type. Moreover, this embodiment of the application considers the characteristics of various fault types and defines fault identification indicators for various fault types. In this way, the value of the fault identification indicator (i.e., the fault identification value) corresponding to each fault type can be determined by the impeller surface wind parameters at each moment, making it possible to accurately and comprehensively predict the faults that may occur during the operation of the wind turbine. Thus, based on at least one fault identification value, more accurate and reasonable control of the operating status of the wind turbine can be achieved to deal with the possible faults indicated by the corresponding fault identification value and avoid the faults that might have originally occurred. As can be seen, the method provided in this application embodiment can not only reduce the failure rate of wind turbine units and increase the life of wind turbines, but also reduce the operation and maintenance costs of wind turbine units and reduce the power generation loss caused by wind turbine unit failures.

[0104] The wind turbine control method provided in this application embodiment can be applied to the online fault warning and intelligent control of wind turbines during operation.

[0105] The wind turbine in this application embodiment can be, for example, an upwind three-bladed horizontal axis wind turbine, a dual-rotor wind turbine, a two-bladed wind turbine, or a floating offshore wind turbine. This application embodiment does not limit the type of wind turbine.

[0106] It should be noted that the main body implementing the wind turbine control method can be the wind turbine control device provided in the embodiments of this application. This wind turbine control device can be housed in an electronic device or a functional module of an electronic device. The aforementioned electronic device can be any device capable of implementing the wind turbine control method in the embodiments of this application. For example, the electronic device can be the main control system of the wind turbine. See the following field diagram for a schematic diagram. Figure 1 For example, electronic devices can also be the control system of a wind farm, as shown in the following diagram. Figure 2 .

[0107] For example, the method provided in the embodiments of this application can be applied to Figure 1 The scene shown. (As shown) Figure 1As shown, this scenario corresponds to wind farm 10, which may include wind turbine generators 11 to 1N, where N is an integer greater than or equal to 1. Each wind turbine generator corresponds to a control system 2i and a data acquisition module 3i (i = 1, 2, ..., N). The control system 2i may refer to the main control system of wind turbine generator 1i. This application embodiment only mentions the parts of the main control system of wind turbine generator 1i that are relevant to this application embodiment. In this application embodiment, the control system 2i of wind turbine generator 1i may, for example, include: a CFD simulation module 2i1, a fault identification module 2i2, and a control module 2i3. Therefore, the method provided in this application embodiment may include, for example, the following: the control system 2i receives simulation parameters of the rotor surface of the wind turbine 1i from the wind farm 10 and stores them in the CFD simulation module 2i1; and the control system 2i of the wind turbine 1i receives measured wind parameters of the rotor surface of the wind turbine 1i from the acquisition module 3i; thereby, the fault identification module 2i2 of the control system 2i determines the rotor surface wind parameters of the wind turbine 1i based on the simulation parameters of the rotor surface of the wind turbine 1i and the measured wind parameters of the rotor surface of the wind turbine 1i, and thus determines the fault identification value of each fault type in at least one fault type based on the rotor surface wind parameters of the wind turbine 1i; then, the control module 2i3 of the control system 2i controls the operating state of the wind turbine 1i based on at least one fault identification value. For example, the control module 2i3 determines the control command corresponding to the target control strategy of the wind turbine 1i based on at least one fault identification value, and controls the operating state of the wind turbine 1i based on the determined control command. Figure 1 The internal structure of the control system 2i is shown using the control system 21 of the wind turbine 11 as an example. The control systems of other wind turbines can refer to the control system 21.

[0108] For example, the method provided in the embodiments of this application can be applied to Figure 2 The scene shown. (As shown) Figure 2As shown, the scenario may include a wind farm 100 and a control system 200. The wind farm 100 may include wind turbine 11, wind turbine 12, ..., wind turbine 1N, where N is an integer greater than or equal to 1. The control system 200 is used to control the wind farm 100. For example, the control system 200 can control the corresponding wind turbine through the control module of each wind turbine in the wind farm 100. In this embodiment, the control system 200 may include, for example, a CFD simulation module 201, a fault identification module 202, and a control module 203. Therefore, the method provided in this application embodiment may include, for example, the following: a CFD simulation module 201 obtains simulation parameters of the rotor surface of wind turbine 1i (i = 1, 2, ..., N) in wind farm 100; a control system 200 receives measured wind parameters of the rotor surface of wind turbine 1i from the acquisition module 3i of wind turbine 1i; thereby, a fault identification module 202 determines the rotor surface wind parameters of wind turbine 1i based on the simulation parameters and the measured wind parameters, and thus determines the fault identification value for each fault type in at least one fault type based on the rotor surface wind parameters of wind turbine 1i; then, the control module... Block 203 controls the operating state of the wind turbine 1i based on at least one fault identification value. For example, control module 203 determines the control command corresponding to the target control strategy of wind turbine 1i based on at least one fault identification value of wind turbine 1i, and controls the operating state of wind turbine 1i based on the determined control command. This control of the operating state of wind turbine 1i based on the determined control command may include, for example, the control module 203 sending the control command to the corresponding control module 2i of wind turbine 1i, and the control module 2i controlling the operating state of wind turbine 1i based on the received control command. Here, control module 2i can be understood as the main control system of wind turbine 1i.

[0109] To facilitate understanding of the specific implementation of the wind turbine control method provided in the embodiments of this application, the following description will be provided in conjunction with the accompanying drawings.

[0110] Figure 3 This is a schematic flowchart illustrating a control method for a wind turbine generator provided in an embodiment of this application. This method can be applied to a control device for a wind turbine generator. The control device for the wind turbine generator can be... Figure 1 The control system 2i shown can also be Figure 1 The part of the control system 2i shown that is relevant to the embodiments of this application; or, the control device of the wind turbine may be Figure 2 The control system 200 shown can also be Figure 2 The control system 200 shown is the part related to the embodiments of this application; or, the control device of the wind turbine may be... Figure 4The control device 400 of the wind turbine generator shown (i.e., the method can be applied to the control device 400 of the wind turbine generator); or, the control device of the wind turbine generator can also be integrated into Figure 5 In the electronic device 500 shown (i.e., the method can also be applied to electronic device 500).

[0111] like Figure 3 As shown, the method may include, for example, S101 to S103:

[0112] S101, Based on the measured wind parameters of the wind turbine impeller surface and the simulated wind parameters of the wind turbine impeller surface, determine the wind parameters of the wind turbine impeller surface.

[0113] Wind parameters refer to parameters that describe wind conditions.

[0114] Measured wind parameters refer to the wind parameters actually measured. In the embodiments of this application, measured wind parameters can refer to the actual wind parameters at a target point on the rotor surface of a wind turbine. The target point can be one or more locations where the actual wind parameters on the rotor surface can be obtained. For example, if a sensor for collecting wind parameters is installed at at least one location on the wind turbine, then the target point can be understood as the location on the rotor surface of the wind turbine where the sensor is installed, and the data collected by at least one sensor is the measured wind parameters in S101. For another example, the measured wind parameters can be the actual wind parameters at the hub height in the SCADA data of the wind turbine, then the target point can be understood as the location of the hub center of the wind turbine. For yet another example, the measured wind parameters can be the actual wind parameters at the hub height in the nacelle radar data of the wind turbine, then the target point can be understood as the location of the hub center of the wind turbine. Since the measured wind parameters obtained from SCADA data may be affected by the rotation of the blades, the measured wind parameters obtained from the nacelle radar data are more accurate than those obtained from SCADA data.

[0115] The simulated wind parameters for the rotor surface of a wind turbine can be obtained using any method currently used to simulate wind resources in a wind farm. For example, if the method for simulating wind resources in a wind farm is a CFD method for detailed simulation of wind resources, then the simulated wind parameters for the rotor surface of the wind turbine will be obtained by simulating the rotor surface of the wind turbine using a CFD method.

[0116] Impeller surface wind parameters can refer to the estimated wind parameters at multiple points on the impeller surface based on the measured wind parameters at a small number of points on the impeller surface and the simulated wind parameters of the impeller surface. These estimated wind parameters at multiple points constitute the impeller surface wind parameters. The estimated wind parameters at each point in the impeller surface wind parameters are close to the actual wind parameters at that point.

[0117] Taking the simulation of wind parameters of a wind turbine rotor surface using the CFD method as an example, before S101, this method may also include: obtaining the simulation wind parameters of the wind turbine rotor surface based on the CFD method. As an example, such as... Figure 4 As shown, the simulation wind parameters of the wind turbine rotor surface obtained based on the CFD method may include, for example, the following S21 to S24:

[0118] S21, obtain information about the wind farm site.

[0119] The site information may include, but is not limited to, wind farm topographic information, roughness information, and wind turbine location information. Wind farm topographic information describes the terrain of the wind farm, such as a topographic map including the elevation of various points within the wind farm. Roughness information describes objects at different locations within the wind farm that may affect wind flow; these objects may include, but are not limited to, trees, grass, and rocks. Wind turbine location information describes the location of the wind turbines within the wind farm, such as their coordinates. Optionally, the site information may also include wind farm atmospheric thermal stability information, which indicates the atmospheric thermal stability of the wind farm. For example, nine layers may be preset, each corresponding to a region with a specific atmospheric thermal stability characteristic; for instance, each layer corresponds to a Monin-Obukhov length scale. Different wind farms select different atmospheric thermal stability levels based on wind measurement data.

[0120] S22, using CFD methods and wind farm area information, multi-sector simulation of the wind farm is performed to obtain the CFD simulation flow field of the wind farm.

[0121] As an example, the simulation process of S22 may include: determining the simulation region, setting boundary conditions, mesh generation, and selecting the turbulence model. Specifically, determining the simulation region involves defining its length and width based on the wind farm's footprint, and defining the height of the simulation region as the distance between the lowest and highest points of the wind farm. Boundaries may include inlet boundaries and ground boundaries, and setting boundary conditions includes setting inlet boundary conditions and ground boundary conditions. For example, the inlet boundary condition could be a three-layer wind profile inflow condition that considers meteorological laws (such as atmospheric thermal stability). In mesh generation, both the mesh resolution and the number of meshes should be sufficient to ensure refined simulation results using the CFD method. The number of meshes can be appropriately increased to meet the requirements of refined simulation; the specific increase can be adjusted based on simulation results and experience. In selecting a turbulence model, one can combine information on atmospheric thermal stability and choose a model that considers atmospheric thermal stability, such as the Reynolds averaged 25 Navier–Stokes (RANS) model or the Detached-Eddy Simulation (DES) model. Alternatively, a suitable model can be selected based on the different terrains of the wind farm. For example, when the wind farm is in mountainous terrain, a two-equation model can be selected, such as the k-ε model, the Shear Stress Transfer (SST) model, or the k-ω model. When the wind farm is in plains and hills, a DES or Large Eddy Simulation (LES) model can be selected. It should be noted that all models in this section are models that take into account atmospheric thermal stability. The influence of thermal stability is added to the model in the form of volume forces. For example, the RANS model mentioned above is a RANS model that takes into account atmospheric thermal stability, the DES model is a DES model that takes into account atmospheric thermal stability, and the LES model is a LES model that takes into account atmospheric thermal stability.

[0122] To adapt to the complex wind conditions in wind farms, it is necessary to simulate the impact of wind on the wind farm when the wind direction is different. Therefore, the wind farm is divided into multiple sectors, each sector corresponding to a wind direction. The CFD simulation flow field of each sector is a set of parameters that characterize the wind conditions of the simulation area when the wind direction corresponding to that sector is encountered. For example, the CFD simulation flow field of 16 sectors can be determined according to the wind direction. The CFD simulation flow field of each sector corresponds to the wind conditions of a specific wind direction in the wind farm.

[0123] S23, obtain the unit information of each wind turbine in the wind farm.

[0124] Unit information may include, but is not limited to: wind turbine number, tower height, hub diameter, or power curve.

[0125] It should be noted that there is no fixed order for the execution of S23 and S21 to S22. You can execute S23 first and then S21 to S22, or you can execute S21 to S22 first and then S23, or you can execute S23 at the same time as S21 or S22.

[0126] S24. Based on the CFD simulation flow field of the wind farm and the unit information of each wind turbine, determine the simulation wind parameters of the impeller surface of each wind turbine.

[0127] As an example, S24 can construct a mesh of the rotor surface of the wind turbine at the corresponding wind turbine location based on the tower height and hub diameter; and obtain the simulated wind parameters of the rotor surface of the wind turbine in each sector from the CFD simulated flow field using the spatial volume difference method. The simulated wind parameters include, but are not limited to: wind speed, turbulence, or wind direction.

[0128] For example, S22 obtains the CFD simulation flow field 1-16 corresponding to sectors 1-16. Assuming the wind farm includes 3 wind turbine units A, and the impeller surface mesh is divided into 13*13 grids, then after processing in S24, the simulation wind parameters of the impeller surface of wind turbine unit A can include: in the case of sector 1, the simulation wind parameter 11 of the impeller surface of wind turbine unit A, which includes the simulation wind parameter corresponding to each grid in the 13*13 grid as a point (e.g., the center point of the grid); in the case of sector 2, the simulation wind parameter 12 of the impeller surface of wind turbine unit A, which includes the simulation wind parameter corresponding to each grid in the 13*13 grid as a point; ..., in the case of sector 16, the simulation wind parameter 16 of the impeller surface of wind turbine unit A, which includes the simulation wind parameter corresponding to each grid in the 13*13 grid as a point.

[0129] It can be seen that, through Figure 4 The method shown obtains the simulated wind parameters of the rotor surfaces of each wind turbine in the wind farm. The wind farm can send the simulated wind parameters of the rotor surfaces of each wind turbine to the control system of the corresponding wind turbine, providing a data basis for executing S101.

[0130] In some implementations, S101 may include, for example, S1011, determining the correlation between the measured wind parameters on the impeller surface and the simulated wind parameters on the impeller surface of the wind turbine, based on the measured wind parameters at least one point on the impeller surface and the simulated wind parameters at least one point on the impeller surface of the wind turbine; S1012, determining the impeller surface wind parameters of the wind turbine based on the correlation and the simulated wind parameters on the impeller surface of the wind turbine.

[0131] As an example, the simulated wind parameters of the impeller surface include the simulated wind parameters of multiple simulated points on the impeller surface corresponding to each of multiple sectors. Then, step S1011 may include, for example: First, based on the measured wind parameters of at least one point, select the simulated wind parameters of multiple simulated points on the impeller surface corresponding to the target sector from the simulated wind parameters of the impeller surface, where the target sector matches the wind direction in the measured wind parameters; then, determine the estimation coefficients based on the simulated wind parameters of at least one point on the impeller surface corresponding to the target sector and the measured wind parameters of at least one point. Step S1012 may include, for example: calculating the estimated wind parameters of multiple simulated points on the impeller surface based on the estimation coefficients and the simulated wind parameters of multiple simulated points on the impeller surface corresponding to the target sector; obtaining the impeller surface wind parameters based on the estimated wind parameters of multiple simulated points on the impeller surface corresponding to the target sector. For example, if the wind direction in the measured wind parameters is 15 degrees east of south, then a sector with a wind direction that is the same as or close to 15 degrees east of south can be selected from multiple sectors as the target sector. The simulated wind parameters of the impeller surface corresponding to the target sector can then be used for accurate wind parameter estimation when the measured wind parameters are generated. The estimation coefficient can be the quotient of the measured wind parameters at a point with known measured wind parameters and the simulated wind parameters at that point in the simulated wind parameters of the impeller surface corresponding to the target sector. In this case, in S1012, the simulated wind parameters at that point in the simulated wind parameters of the impeller surface corresponding to the target sector can be multiplied by the estimation coefficient to obtain the estimated wind parameters at the corresponding point. Alternatively, the estimation coefficient can also be the quotient of the simulated wind parameters at that point in the simulated wind parameters of the impeller surface corresponding to the target sector and the measured wind parameters at that point. In this case, in S1012, the simulated wind parameters at that point in the simulated wind parameters of the impeller surface corresponding to the target sector can be divided by the estimation coefficient to obtain the estimated wind parameters at the corresponding point. The impeller surface wind parameters can include the estimated wind parameters corresponding to each of the 13*13 grids on the impeller surface of the wind turbine as a point. The estimated wind parameters of a point with measured wind parameters are the same as the measured wind parameters of that point. For example, if there is a measured wind parameter of 1 point, then the estimated wind parameters need to be calculated for (13*13-1)=168 points. The impeller surface wind parameters are composed of the estimated wind parameters of 168 points and the measured wind parameters of 1 point.

[0132] It should be noted that, in order to reduce the amount of computation, the number of meshes can be reduced during mesh generation. Alternatively, while keeping the number of meshes unchanged, the estimated wind parameters of a portion of the meshes can be calculated based on sampling or a pre-defined method, and the estimated wind parameters of the portion of the meshes can be used as the wind parameters of the impeller surface.

[0133] It is understandable that the wind parameters of the impeller surface can also be called the wind parameters of the time sequence wind of the impeller surface, which may include, but are not limited to: wind speed, turbulence, wind direction, inflow angle, deflection or turbulence.

[0134] In this way, by combining the measured wind speed at a small number of points (such as the center of the hub) and the simulated wind parameters of the impeller surface in real time, accurate impeller surface wind parameters can be calculated, providing an accurate and comprehensive data basis for fault prediction of various fault types, making it possible to predict at least one possible fault of the wind turbine and carry out adaptive control.

[0135] S102, based on the impeller surface wind parameters, determine the fault identification value corresponding to each fault type in at least one fault type, the fault identification value is used to indicate the probability of the wind turbine unit experiencing the target fault type.

[0136] In some implementations, S102 can be implemented, for example, by a fault identification model, which can be a mathematical model (such as a formula) or a machine learning model. The specific implementation of the fault identification model does not affect the implementation of the embodiments of this application.

[0137] As an example, S102 may include: inputting impeller surface wind parameters into a fault identification model to obtain a fault identification value corresponding to each fault type in at least one fault type, wherein the fault identification model includes a fault identification sub-model corresponding to each fault type in at least one fault type, and each fault identification sub-model is used to characterize the correspondence between impeller surface wind parameters and fault identification values.

[0138] Fault types may include, but are not limited to, tower sweeping faults and yaw trip faults.

[0139] For example, if at least one of the above-mentioned fault types includes a tower sweeping fault, then S102 may include: inputting the set of key parameters of the lower blade tip position and the average wind speed of the hub center position in the impeller surface parameters into the first fault identification sub-model; the first fault identification sub-model outputs the fault identification value corresponding to the tower sweeping fault; the fault identification value corresponding to the tower sweeping fault is used to indicate the probability of the wind turbine experiencing a tower sweeping fault; the set of key parameters includes the inflow angle, wind deflection, turbulence intensity and average wind speed; and the first fault identification sub-model is the fault identification sub-model corresponding to the tower sweeping fault in the fault identification model.

[0140] Tower sweeping faults refer to the danger of blades approaching or even hitting the tower due to complex wind conditions encountered by the wind turbine during power generation, causing blade damage or even tower collapse. Considering the imbalance of wind energy on the rotor surface, the hub center is the stress point of the wind turbine. Analyzing the energy difference between the hub center and the lower blade tip, if the energy difference exceeds a preset threshold, indicates a risk of tower sweeping faults. For example, a tower sweeping identification index I can be constructed by measuring wind speed, turbulence, and wind deflection at the hub center and lower blade tip at each moment. sweep I sweep For example, it can be calculated based on the following formula (1):

[0141]

[0142] In formula (1), θ inc_low_tip It is the inflow angle at the tip of the lower blade, θ veer_low_tip It is the wind deflection at the tip of the lower blade, ti low_tip It is the turbulence intensity at the tip of the lower blade, U low_tip and U hub These are the average wind speeds at the tip of the lower blade and the center of the hub, respectively. Formula (1) can be understood as the first fault identification sub-model in the fault identification model. I is calculated based on formula (1). sweep This can be understood as the fault identification value corresponding to the tower sweeping fault.

[0143] For example, if at least one of the above-mentioned fault types includes yaw trip fault, S102 may include: inputting the set of key parameters at each position in the rotor surface parameters and the average wind speed at the hub center position into the second fault identification sub-model; the second fault identification sub-model outputs the fault identification value corresponding to the yaw trip fault; the fault identification value corresponding to the yaw trip fault is used to indicate the probability of the wind turbine unit experiencing a yaw trip fault; the set of key parameters includes inflow angle, wind deflection, turbulence intensity and average wind speed; and the second fault identification sub-model is the fault identification sub-model corresponding to the yaw trip fault in the fault identification model.

[0144] Yaw tripping refers to the risk of a wind turbine tripping due to excessive yaw caused by complex wind conditions during yaw. Considering the wind energy imbalance on the rotor surface, with the hub center as the stress point, the maximum energy difference between all points on the rotor surface and the hub center is analyzed. If this maximum value exceeds a preset threshold, it indicates a risk of yaw tripping. For example, a yaw tripping identification index I can be constructed by using wind deflection as the primary factor and correcting for wind deflection through wind speed and turbulence. yaw I yaw For example, it can be calculated based on the following formula (2):

[0145]

[0146] In formula (2), θ inc_k θ is the inflow angle at point k on the impeller surface. veep_k It is the wind deflection at point k on the impeller surface, ti k U is the turbulence intensity at point k on the impeller surface. low_k and U hub These are the average wind speeds at point k on the impeller surface and at the center of the hub, respectively. Formula (2) can be understood as the second fault identification sub-model in the fault identification model. I is calculated based on formula (2).yaw This can be understood as the fault identification value corresponding to the yaw trip fault.

[0147] In this way, the fault identification values ​​corresponding to various fault types can be accurately determined based on the impeller surface wind parameters, providing an accurate data foundation for subsequent control of the wind turbine based on the fault identification values.

[0148] S103, control the operating status of the wind turbine based on at least one fault identification value.

[0149] It should be noted that the control device for the wind turbine implemented in this application embodiment can obtain and maintain a control strategy library, preparing for the implementation of S103 in this application embodiment. This control strategy library can include control strategies corresponding to different fault levels under each fault type. Therefore, S103 may include, for example: first, determining the fault level of each fault type based on the fault identification value; then, searching the control strategy corresponding to the determined fault level under each fault type from the control strategy library; thereby, comprehensively determining the target control strategy for the wind turbine by integrating the control strategies for different fault types; and controlling the wind turbine based on the target control strategy.

[0150] Taking at least one fault type including a first fault type and a second fault type as an example, S103 may include: S1031, determining a first fault level based on a first fault identification value corresponding to the first fault type, and determining a second fault level based on a second fault identification value corresponding to the second fault type; S1032, determining a target control strategy based on a control strategy library, the first fault level, and the second fault level; S1033, controlling the operating state of the wind turbine unit based on the target control strategy. Specifically, different ranges of fault identification values ​​corresponding to different fault levels can be maintained for different fault types, so that in S1031, the range of fault identification values ​​for the corresponding fault type can be matched based on the fault identification values, thereby determining the corresponding fault level based on the matched range of fault identification values.

[0151] As an example, if a first fault level indicates the possibility of a first fault type occurring in the wind turbine, and a second fault level indicates the possibility of a second fault type occurring in the wind turbine, then S1032 may include: matching a first control strategy corresponding to the first fault level from the control strategies corresponding to the first fault type in the control strategy library; matching a second control strategy corresponding to the second fault level from the control strategies corresponding to the second fault type in the control strategy library; and determining a target control strategy based on the first and second control strategies. It is evident that in this embodiment, if multiple fault types are possible, the target control strategy can be determined by comprehensively considering the control strategies used to address each possible fault, thereby enabling proactive responses to all possible faults and improving the intelligence level of the method in this embodiment.

[0152] As another example, if a first fault level indicates the possibility of a first fault type occurring in the wind turbine, and a second fault level indicates the possibility of a second fault type occurring in the wind turbine, then S1032 may include: obtaining a target control strategy matching the first fault level from the control strategies corresponding to the first fault type in the control strategy library. Therefore, in this embodiment, if only one fault type is possible, then only the control strategy for dealing with that possible fault can be searched as the target control strategy, enabling early response to that possible fault and reducing the workload of searching and processing operations when determining the target control strategy.

[0153] For S1033, if the first fault type includes tower sweeping fault, the first fault level can be divided into: high risk, medium risk, and low risk. If the first fault level corresponding to the first fault type of the wind turbine remains high risk for a first preset time period (e.g., 10 seconds), the target control strategy includes shutdown; if the first fault level corresponding to the first fault type of the wind turbine remains medium risk for a second preset time period, the target control strategy includes adjusting the blade pitch angle; if the first fault level corresponding to the first fault type of the wind turbine remains low risk for a third preset time period, the target control strategy includes keeping it unchanged. The first, second, and third preset time periods can be the same or different, and can be flexibly designed according to requirements and experience. This application embodiment does not impose specific limitations.

[0154] For S1033, if the first fault type includes a yaw trip fault, the first fault level can be divided into: high risk, medium risk, and low risk. If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type remains high risk for a fourth preset duration (e.g., 5 seconds), then the target control strategy includes stopping the yaw. If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type remains medium risk for a fifth preset duration, then the target control strategy includes reducing the yaw torque and decreasing the yaw speed. If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type remains low risk for a sixth preset duration, then the target control strategy includes keeping it unchanged. The fourth, fifth, and sixth preset durations can be the same or different, and can be flexibly designed according to requirements and experience; this application embodiment does not impose specific limitations.

[0155] It should be noted that, in order to facilitate the monitoring of wind turbine units, the fault levels corresponding to various fault types can also be displayed to the wind farm staff. For fault types with high-risk fault levels, they can be highlighted or notified to the staff in a preset specific way (such as voice broadcast or SMS notification), so as to realize more intelligent and humanized fault prediction and monitoring of wind turbine units and improve the level of intelligence in wind turbine unit control.

[0156] As can be seen, this method has several advantages. First, considering that wind parameters at a single height cannot reflect the probability of a fault occurring, it calculates the impeller surface wind parameters at each moment by combining simulated wind parameters of the impeller surface and measured wind parameters at a small number of points on the impeller surface. This provides accurate and comprehensive evidence for calculating the fault identification value for each fault type among at least one fault type. Second, by considering the characteristics of various fault types, this embodiment defines fault identification indicators for each fault type. Thus, the fault identification value corresponding to each fault type can be determined using the impeller surface wind parameters at each moment, making it possible to accurately and comprehensively predict possible faults during the operation of the wind turbine. Moreover, this embodiment achieves more accurate and reasonable control of the wind turbine's operating status based on at least one fault identification value, enabling the wind turbine to cope with the possible faults indicated by the corresponding fault identification value. This not only reduces the failure rate of the wind turbine and increases its lifespan but also reduces the operation and maintenance costs of the wind turbine and minimizes the power generation loss caused by wind turbine failures.

[0157] In this embodiment, the hub height and the hub center point refer to the same meaning and can be understood and used interchangeably.

[0158] Accordingly, this application also provides a control device for a wind turbine generator, see [link to relevant documentation]. Figure 5 As shown, the control device 500 of the wind turbine may include, for example:

[0159] The first determining unit 501 is used to determine the wind parameters of the impeller surface of the wind turbine based on the measured wind parameters of the impeller surface and the simulated wind parameters of the impeller surface of the wind turbine.

[0160] The second determining unit 502 is used to determine a fault identification value corresponding to each fault type in at least one fault type based on the impeller surface wind parameters. The fault identification value is used to indicate the probability that the wind turbine will experience a target fault type.

[0161] Control unit 503 is used to control the operating status of the wind turbine based on at least one fault identification value.

[0162] Optionally, the first determining unit 501 may include: a first determining subunit and a second determining subunit;

[0163] The first determining subunit is used to determine the correlation between the measured wind parameters on the impeller surface and the simulated wind parameters of the impeller surface of the wind turbine based on the measured wind parameters at at least one point on the impeller surface of the wind turbine and the simulated wind parameters of the impeller surface of the wind turbine.

[0164] The second determining subunit is used to determine the wind parameters of the wind turbine rotor surface based on the correlation and the simulated wind parameters of the wind turbine rotor surface.

[0165] Optionally, the simulated wind parameters of the impeller surface include the simulated wind parameters of multiple simulation points on the impeller surface corresponding to each of multiple sectors.

[0166] The first determining subunit is specifically used for:

[0167] Based on the measured wind parameters of the at least one point, the simulated wind parameters of multiple simulated points on the impeller surface corresponding to the target sector are selected from the simulated wind parameters of the impeller surface, and the target sector matches the wind direction in the measured wind parameters;

[0168] Based on the simulated wind parameters and the measured wind parameters of at least one point on the impeller surface corresponding to the target sector, the estimation coefficients are determined.

[0169] The second determining subunit is specifically used for:

[0170] Based on the estimated coefficients and the simulated wind parameters of multiple simulation points on the impeller surface corresponding to the target sector, the estimated wind parameters of multiple simulation points on the impeller surface are calculated.

[0171] Based on the estimated wind parameters of multiple simulation points on the impeller surface corresponding to the target sector, the wind parameters of the impeller surface are obtained.

[0172] Optionally, the second determining unit 502 is specifically used for:

[0173] The impeller surface wind parameters are input into the fault identification model to obtain the fault identification value corresponding to each fault type among the at least one fault types. The fault identification model includes a fault identification sub-model corresponding to each fault type among the at least one fault types, and each fault identification sub-model is used to characterize the correspondence between the impeller surface wind parameters and the fault identification value.

[0174] Optionally, the at least one fault type includes tower sweeping fault, and the second determining unit 502 may include: a third determining subunit;

[0175] The third determining sub-unit is used to input the set of key parameters of the lower blade tip position and the average wind speed of the hub center position in the impeller surface parameters into the first fault identification sub-model. The first fault identification sub-model outputs the fault identification value corresponding to the tower sweeping fault. The fault identification value corresponding to the tower sweeping fault is used to indicate the possibility of the wind turbine experiencing a tower sweeping fault. The set of key parameters includes inflow angle, wind deflection, turbulence intensity and average wind speed. The first fault identification sub-model is the fault identification sub-model corresponding to the tower sweeping fault in the fault identification model.

[0176] Optionally, the at least one fault type includes a yaw trip fault, and the second determining unit 502 may include: a fourth determining subunit;

[0177] The fourth determining sub-unit is used to input the set of key parameters at each position of the impeller surface parameters and the average wind speed at the hub center position into the second fault identification sub-model. The second fault identification sub-model outputs the fault identification value corresponding to the yaw trip fault. The fault identification value corresponding to the yaw trip fault is used to indicate the possibility of the wind turbine unit experiencing a yaw trip fault. The set of key parameters includes inflow angle, wind deflection, turbulence intensity and average wind speed. The second fault identification sub-model is the fault identification sub-model corresponding to the yaw trip fault in the fault identification model.

[0178] Optionally, the device 500 may further include: an acquisition unit;

[0179] The acquisition unit is used to acquire a control strategy library before controlling the operating state of the wind turbine based on at least one fault identification value. The control strategy library includes control strategies corresponding to different fault levels under each fault type in different fault types.

[0180] The at least one fault type includes a first fault type and a second fault type. The control unit 503 may include: a fifth determining subunit, a sixth determining subunit, and a control subunit.

[0181] The fifth determining subunit determines the first fault level based on the first fault identification value corresponding to the first fault type, and determines the second fault level based on the second fault identification value corresponding to the second fault type.

[0182] The sixth determining subunit is used to determine the target control strategy based on the control strategy library, the first fault level, and the second fault level;

[0183] The control subunit is used to control the operating state of the wind turbine based on the target control strategy.

[0184] Optionally, the first fault level indicates the possibility that the wind turbine may experience a fault of the first fault type, and the second fault level indicates the possibility that the wind turbine may experience a fault of the second fault type. The sixth determining subunit is specifically used for:

[0185] From the control strategies in the control strategy library that correspond to the first fault type, match the first control strategy corresponding to the first fault level.

[0186] From the control strategies in the control strategy library that correspond to the second fault type, match the second control strategy corresponding to the second fault level;

[0187] Based on the first control strategy and the second control strategy, a target control strategy is determined.

[0188] Optionally, the first fault level indicates the possibility that the wind turbine may experience a fault of the first fault type, and the second fault level indicates that the wind turbine may not experience a fault of the second fault type. The sixth determining subunit is specifically used for:

[0189] The target control strategy that matches the first fault level is obtained from the control strategy library corresponding to the first fault type.

[0190] Optionally, if the first fault type includes tower sweeping fault, then...

[0191] If the first fault level corresponding to the first fault type of the wind turbine is high risk within a first preset time period, the target control strategy includes shutdown.

[0192] If the first fault level corresponding to the first fault type of the wind turbine is medium risk within the second preset time period, the target control strategy includes adjusting the blade pitch angle.

[0193] If the first fault level corresponding to the first fault type of the wind turbine is low risk within a third preset time period, the target control strategy includes keeping it unchanged.

[0194] Optionally, if the first fault type includes a yaw trip fault, then...

[0195] If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type is high-risk within the fourth preset time period, the target control strategy includes stopping the yaw.

[0196] If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type is medium risk within the fifth preset time period, the target control strategy includes reducing the yaw torque and reducing the yaw speed.

[0197] If, during the yaw process of the wind turbine, the first fault level corresponding to the first fault type is low risk within a sixth preset time period, the target control strategy includes keeping it unchanged.

[0198] It should be noted that the specific implementation method of the device 500 and the technical effects it achieves can be found in the relevant descriptions in the methods provided in the embodiments of this application.

[0199] Furthermore, embodiments of this application also provide an electronic device 600, such as... Figure 6 As shown, the electronic device 600 includes a processor 601 and a memory 602:

[0200] The memory 602 is used to store computer programs;

[0201] The processor 601 is used to execute the method provided in the embodiments of this application according to the computer program.

[0202] Furthermore, embodiments of this application also provide a computer-readable storage medium for storing a computer program for executing the method provided in embodiments of this application.

[0203] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the methods of the above embodiments can be implemented by means of software plus a general-purpose hardware platform. Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as a read-only memory (ROM) / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network communication device such as a router) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0204] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system and device embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The device and system embodiments described above are merely illustrative. Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0205] The above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method of controlling a wind turbine, characterized by, The method comprises: determining a blade surface wind parameter of the wind turbine based on a measured wind parameter of a blade surface of the wind turbine and a simulated wind parameter of the blade surface of the wind turbine, the blade surface wind parameter comprising wind parameters of multiple points on the blade surface; determining a fault identification value corresponding to each fault type in at least one fault type based on the blade surface wind parameter, the fault identification value being used to indicate a possibility of the wind turbine occurring a target fault type; controlling an operating state of the wind turbine based on at least one fault identification value.

2. The method of claim 1, wherein, The method of determining a blade surface wind parameter of the wind turbine based on a measured wind parameter of a blade surface of the wind turbine and a simulated wind parameter of the blade surface of the wind turbine comprises: determining a correlation between the measured wind parameter and the simulated wind parameter at the at least one point on the blade surface of the wind turbine based on the measured wind parameter of the at least one point on the blade surface of the wind turbine and the simulated wind parameter of the at least one point in the simulated wind parameter of the blade surface of the wind turbine; determining the blade surface wind parameter of the wind turbine based on the correlation and the simulated wind parameter of the blade surface of the wind turbine.

3. The method of claim 2, wherein, The simulated wind parameter of the blade surface comprises simulated wind parameters of multiple simulated points on the blade surface corresponding to each sector in multiple sectors, The method of determining a correlation between the measured wind parameter and the simulated wind parameter at the at least one point on the blade surface of the wind turbine based on the measured wind parameter of the at least one point on the blade surface of the wind turbine and the simulated wind parameter of the at least one point in the simulated wind parameter of the blade surface of the wind turbine comprises: selecting simulated wind parameters of multiple simulated points on the blade surface corresponding to a target sector from the simulated wind parameter of the blade surface based on the measured wind parameter of the at least one point, the target sector matching a wind direction in the measured wind parameter; determining an estimation coefficient based on the simulated wind parameter of the at least one point on the blade surface corresponding to the target sector and the measured wind parameter of the at least one point; The method of determining the blade surface wind parameter of the wind turbine based on the correlation and the simulated wind parameter of the blade surface of the wind turbine comprises: calculating estimated wind parameters of multiple simulated points on the blade surface based on the estimation coefficient and the simulated wind parameters of the multiple simulated points on the blade surface corresponding to the target sector; obtaining the blade surface wind parameter based on the estimated wind parameters of the multiple simulated points on the blade surface corresponding to the target sector.

4. The method of claim 1, wherein, The method of determining a fault identification value corresponding to each fault type in at least one fault type based on the blade surface wind parameter comprises: inputting the blade surface wind parameter into a fault identification model to obtain the fault identification value corresponding to each fault type in the at least one fault type, wherein the fault identification model comprises a fault identification sub-model corresponding to each fault type in the at least one fault type, and each fault identification sub-model is used to represent a corresponding relationship between a blade surface wind parameter and a fault identification value.

5. The method of claim 4, wherein, The at least one fault type comprises a tower-scanning fault, and the method of determining a fault identification value corresponding to each fault type in at least one fault type based on the blade surface wind parameter comprises: The key parameter set of the lower blade tip position in the blade surface parameter and the average wind speed of the hub center position are input into a first fault identification sub-model, the first fault identification sub-model outputs a fault identification value corresponding to a tower-swept fault, the fault identification value corresponding to the tower-swept fault is used to indicate the possibility of the wind turbine generator occurring the tower-swept fault, and the key parameter set includes an inflow angle, a wind deflection, a turbulence intensity, and an average wind speed. The first fault identification sub-model is a fault identification sub-model corresponding to the tower-swept fault in the fault identification model.

6. The method of claim 4, wherein, The at least one fault type includes a yaw tripping fault, and the determination of the fault identification value corresponding to each fault type in the at least one fault type based on the blade surface wind parameter includes: The key parameter set of each position in the blade surface parameter and the average wind speed of the hub center position are input into a second fault identification sub-model, the second fault identification sub-model outputs a fault identification value corresponding to a yaw tripping fault, the fault identification value corresponding to the yaw tripping fault is used to indicate the possibility of the wind turbine generator occurring the yaw tripping fault, and the key parameter set includes an inflow angle, a wind deflection, a turbulence intensity, and an average wind speed. The second fault identification sub-model is a fault identification sub-model corresponding to the yaw tripping fault in the fault identification model.

7. The method of claim 1, wherein, Before the control of the operating state of the wind turbine generator based on the at least one fault identification value, the method further includes: obtaining a control strategy library, the control strategy library including a control strategy corresponding to each fault level in each fault type in different fault types; The at least one fault type includes a first fault type and a second fault type, and the control of the operating state of the wind turbine generator based on the at least one fault identification value includes: determining a first fault level based on a first fault identification value corresponding to the first fault type and determining a second fault level based on a second fault identification value corresponding to the second fault type; determining a target control strategy based on the control strategy library, the first fault level, and the second fault level; controlling the operating state of the wind turbine generator based on the target control strategy.

8. The method of claim 7, wherein, The first fault level indicates the possibility of the wind turbine generator occurring the fault of the first fault type, the second fault level indicates the possibility of the wind turbine generator occurring the fault of the second fault type, and the determination of the target control strategy based on the control strategy library, the first fault level, and the second fault level includes: matching a first control strategy corresponding to the first fault level from the control strategy corresponding to the first fault type in the control strategy library; matching a second control strategy corresponding to the second fault level from the control strategy corresponding to the second fault type in the control strategy library; determining a target control strategy based on the first control strategy and the second control strategy.

9. The method of claim 7, wherein, The first failure level indicates a possibility of the wind turbine having a failure of the first failure type, the second failure level indicates a possibility of the wind turbine not having a failure of the second failure type, and the determining the target control strategy based on the control strategy library, the first failure level and the second failure level comprises: obtaining the target control strategy matching the first failure level from the control strategies corresponding to the first failure type in the control strategy library.

10. The method of claim 7, wherein, If the first failure type includes a tower scanning failure, then if the first failure level corresponding to the first failure type of the wind turbine within a first preset time length is high risk, the target control strategy includes shutdown; if the first failure level corresponding to the first failure type of the wind turbine within a second preset time length is medium risk, the target control strategy includes adjusting a blade pitch angle; if the first failure level corresponding to the first failure type of the wind turbine within a third preset time length is low risk, the target control strategy includes keeping unchanged.

11. The method of claim 7, wherein, If the first failure type includes a yaw tripping failure, then if the first failure level corresponding to the first failure type within a fourth preset time length during a yaw process of the wind turbine is high risk, the target control strategy includes stopping yaw; if the first failure level corresponding to the first failure type within a fifth preset time length during the yaw process of the wind turbine is medium risk, the target control strategy includes reducing a yaw torque and lowering a yaw speed; if the first failure level corresponding to the first failure type within a sixth preset time length during the yaw process of the wind turbine is low risk, the target control strategy includes keeping unchanged.

12. A control device for a wind turbine generator characterized by comprising: comprise: a first determination unit configured to determine a blade surface wind parameter of a wind turbine based on an actually measured wind parameter of a blade surface of the wind turbine and a simulated wind parameter of the blade surface of the wind turbine, the blade surface wind parameter comprising wind parameters of a plurality of points on the blade surface; a second determination unit configured to determine a failure identification value corresponding to each of at least one failure type based on the blade surface wind parameter, the failure identification value being used to indicate a possibility of the wind turbine having a target failure type; a control unit configured to control an operating state of the wind turbine based on the at least one failure identification value.

13. An electronic device, comprising: The electronic device comprises a processor and a memory: The memory is configured to store a computer program; The processor is configured to execute the method according to any one of claims 1-11 according to the computer program.

14. A computer-readable storage medium, characterized in that, The computer readable storage medium is configured to store a computer program, and the computer program is configured to execute the method according to any one of claims 1-11.

Images