Method and control device for controlling a wind turbine

Abstract

The present disclosure provides a control method and a control device of a wind turbine. The control method comprises: obtaining working parameter data of the wind turbine in a predetermined time period; identifying, based on the working parameter data, a parameter estimation model satisfying a predetermined condition from a plurality of parameter estimation models as a similar estimation model, wherein the plurality of parameter estimation models correspond to a plurality of predetermined blade contamination levels respectively; determining a control parameter of the wind turbine based on the similar estimation model; adjusting a pitch angle control instruction and / or a torque control instruction of the wind turbine according to the control parameter, and controlling the wind turbine according to the pitch angle control instruction and / or the torque control instruction. The operation of the wind turbine for the blade contamination condition can be improved by the control method.

Description

Technical Field

[0001] This disclosure relates to the field of wind power generation, specifically to a control method and control equipment for a wind turbine. Background Technology

[0002] When wind turbine blades become contaminated, such as through sand accumulation or corrosion, the turbine may experience malfunctions such as stalling or controller failure. Blade contamination can have numerous adverse effects on the normal operation of a wind turbine.

[0003] Therefore, accurately assessing the contamination status of the blades and implementing corresponding control strategies is crucial for controlling the operation of wind turbines. Summary of the Invention

[0004] The purpose of this disclosure is to provide a control method and control device for a wind turbine, so as to overcome the shortcomings of the prior art and improve the operation of the wind turbine in response to blade contamination.

[0005] According to embodiments of this disclosure, a control method for a wind turbine is provided. The control method includes: acquiring operating parameter data of the wind turbine within a predetermined time period; identifying, based on the operating parameter data, a parameter estimation model that meets predetermined conditions from multiple parameter estimation models as a similar estimation model, wherein the multiple parameter estimation models correspond to multiple predetermined blade pollution levels; determining control parameters of the wind turbine based on the similar estimation model; adjusting the pitch angle control command and / or torque control command of the wind turbine according to the control parameters, and controlling the wind turbine according to the pitch angle control command and / or torque control command.

[0006] According to embodiments of this disclosure, a control device is provided, the control device comprising: one or more sensors configured to acquire operating parameter data of a wind turbine; and a controller communicatively coupled to the wind turbine and the sensors, the controller including at least one processor communicating with at least one memory device, the at least one processor being configured to: identify, based on the operating parameter data, a parameter estimation model satisfying predetermined conditions from a plurality of parameter estimation models as a similarity estimation model, wherein the plurality of parameter estimation models respectively correspond to a plurality of predetermined blade contamination levels; determine control parameters of the wind turbine based on the similarity estimation model; adjust the pitch angle control command and / or torque control command of the wind turbine according to the control parameters, and control the wind turbine according to the pitch angle control command and / or torque control command.

[0007] According to embodiments of this disclosure, a computer program product is provided that can be downloaded from a communication network and / or stored on a computer-readable storage medium, the computer program product including program code instructions for implementing the control method described above.

[0008] According to embodiments of the present disclosure, a computer-readable storage medium storing a computer program is provided, which, when executed by a processor, implements the control method described above.

[0009] According to an embodiment of the present disclosure, a computing device is provided, the computing device comprising: a processor; and a memory storing a computer program, wherein when the computer program is executed by the processor, the control method described above is implemented.

[0010] According to an embodiment of this disclosure, a wind turbine is provided, the wind turbine including: the control device as described above.

[0011] By employing the wind turbine control method and control device, computer program product, computer-readable storage medium, computing device, and wind turbine according to embodiments of the present disclosure, at least one of the following technical effects can be achieved: by identifying similar estimation models to determine control parameters under corresponding blade contamination levels, and adjusting the control commands of the wind turbine accordingly, the performance changes of the wind turbine caused by blade contamination can be handled in a timely manner, improving the operation of the wind turbine under different blade contamination levels, and enabling the wind turbine to maintain good working efficiency under blade contamination conditions. Attached Figure Description

[0012] The above and other objects and features of this disclosure will become clearer from the following description taken in conjunction with the accompanying drawings.

[0013] Figure 1 This is a flowchart of a control method for a wind turbine according to an embodiment of the present disclosure.

[0014] Figure 2 This is a flowchart of a control method for a wind turbine according to an embodiment of the present disclosure.

[0015] Figure 3 This is a flowchart of a control method for a wind turbine according to an embodiment of the present disclosure.

[0016] Figure 4 This is a flowchart of a control method for a wind turbine according to an embodiment of the present disclosure.

[0017] Figure 5 This is a schematic diagram illustrating the application of a similarity estimation model for a wind turbine according to an embodiment of the present disclosure.

[0018] Figure 6This is a block diagram of a control device according to an embodiment of the present disclosure.

[0019] Figure 7 This is another block diagram of a control device according to an embodiment of the present disclosure. Detailed Implementation

[0020] During long-term operation, wind turbine blades are frequently contaminated, such as by dust accumulation and corrosion. Blade contamination can lead to reduced turbine efficiency, stalling, or other adverse conditions, affecting the normal operation of the wind turbine. Achieving optimal control under blade contamination conditions has become a challenging problem to solve.

[0021] This disclosure proposes a control method and control system for wind turbines, which can identify the blade contamination level of wind turbines, determine the control parameters or optimal operating conditions under the identified blade contamination level, and adjust the control commands according to the control parameters or optimal operating conditions, thereby promptly addressing the performance changes of wind turbines caused by blade contamination and enabling the wind turbines to maintain good operating efficiency under blade contamination conditions.

[0022] The control strategy of a wind turbine according to embodiments of the present disclosure is described in detail below with reference to the accompanying drawings. A detailed description of specific implementations is provided to help the reader gain a comprehensive understanding of the methods, apparatus, and / or systems described herein. However, after understanding this disclosure, various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become clear. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but may be changed as will become clear after understanding this disclosure, except for operations that must occur in a specific order. Furthermore, for clarity and conciseness, descriptions of features known in the art may be omitted.

[0023] The features described herein may be implemented in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein are provided only to illustrate some of the many feasible ways of implementing the methods, apparatus, and / or systems described herein, which will become clear upon understanding the disclosure of this application.

[0024] As used herein, the term “and / or” includes any one of the associated listed items and any combination of any two or more.

[0025] Although terms such as “first,” “second,” and “third” may be used herein to describe various components, assemblies, regions, layers, or parts, these components, assemblies, regions, layers, or parts should not be limited by these terms. Rather, these terms are used only to distinguish one component, assembly, region, layer, or part from another. Thus, without departing from the teaching of the examples described herein, the first component, first assembly, first region, first layer, or first part referred to as the first component, first assembly, first region, first layer, or first part may also be referred to as the second component, second assembly, second region, second layer, or second part.

[0026] The terminology used herein is for the purpose of describing various examples only and is not intended to limit disclosure. Unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. The terms “comprising,” “including,” and “having” indicate the presence of the described features, quantities, operations, components, elements, and / or combinations thereof, but do not preclude the presence or addition of one or more other features, quantities, operations, components, elements, and / or combinations thereof.

[0027] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains upon understanding this disclosure. Unless expressly defined herein, terms (such as those defined in a general dictionary) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and in this disclosure, and shall not be interpreted in an idealized or overly formalistic manner.

[0028] Furthermore, in the description of the examples, detailed descriptions of well-known related structures or functions will be omitted when it is believed that such detailed descriptions would lead to a vague interpretation of this disclosure.

[0029] Figure 1 This is a flowchart of a control method for a wind turbine according to an embodiment of the present disclosure. In embodiments of the present disclosure, the control method can be executed by a controller disposed in the wind turbine or by a control device external to the wind turbine.

[0030] like Figure 1 As shown, operating parameter data of the wind turbine can be acquired within a predetermined time period (S11). For example, operating parameter data can be acquired through various parameter measuring devices (e.g., sensors). Operating parameter data may include: rotational speed data and non-rotational speed data. In one embodiment, non-rotational speed data may include pitch angle data, torque data, and wind speed data, but is not limited to these; it may also include other types of non-rotational speed data associated with rotational speed data. Pitch angle data can be acquired using a pitch angle sensor, torque data can be acquired using a torque sensor, wind speed data can be acquired using an anemometer, and so on.

[0031] In one embodiment of this disclosure, a predetermined time period (e.g., 10 minutes) can be set according to the performance parameters of the wind turbine (e.g., operating frequency, etc.). Furthermore, the operating parameter data within the predetermined time period can be processed, for example, by averaging. For instance, the average values ​​of the pitch angle measurement, torque measurement, and wind speed measurement within the predetermined time period can be obtained.

[0032] Then, based on the working parameter data, a parameter estimation model that meets predetermined conditions can be identified from multiple parameter estimation models as a similarity estimation model (S12). The multiple parameter estimation models correspond to various predetermined leaf pollution levels. These multiple predetermined leaf pollution levels can be sequentially increasing in pollution level, and may include different levels such as no pollution, slight pollution, moderate pollution, and severe pollution. The predetermined conditions can be set according to the parameter estimation requirements.

[0033] In one embodiment of this disclosure, the plurality of parameter estimation models may be generated by: determining the normal operating state of the wind turbine at different wind speeds under various predetermined blade pollution levels using an aeroelastic model, thereby generating the plurality of parameter estimation models corresponding to the various predetermined blade pollution levels. The plurality of parameter estimation models may each include estimated rotational speed and non-rotational speed estimates (e.g., pitch angle estimates, torque estimates, and wind speed estimates) at different wind speeds under the corresponding predetermined blade pollution levels, as well as optimal control parameters (e.g., optimal gain and optimal pitch angle).

[0034] In one embodiment of this disclosure, the optimal power curve (C) of the wind turbine can be used as a basis. p The aeroelastic model is established using aeroelastic simulation codes (curves) to simulate the normal operation of the wind turbine under various predetermined blade contamination levels and different wind speeds, thereby generating multiple parameter estimation models. Based on the expected operation of the wind turbine under the various predetermined blade contamination levels, combined with the controller operation relative to the rated rotor speed and the optimal power curve, the static mapping relationship under the various predetermined blade contamination levels can be determined to generate multiple parameter estimation models. The static mapping relationship includes the mapping relationship between the speed estimate, non-speed estimate, and optimal control parameters at different wind speeds under the corresponding predetermined blade contamination levels.

[0035] The following reference Figure 2 , Figure 3 , Figure 5 This describes how to identify parameter estimation models that meet predetermined conditions from multiple parameter estimation models.

[0036] like Figure 2As shown, based on non-speed data, multiple speed estimates are generated using multiple parameter estimation models (S21).

[0037] According to embodiments of this disclosure, the acquired non-rotational speed data of a wind turbine within a predetermined time period can be used as input to various parameter estimation models, enabling each parameter estimation model to generate multiple rotational speed estimates based on the non-rotational speed data. For example, each parameter estimation model can generate rotational speed estimates according to the following mapping relationship:

[0038]

[0039] in, This represents the average value of the pitch angle data over a predetermined time period. This represents the average torque data over a predetermined time period. f represents the average value of the rotational speed data over a predetermined time period. n (·) represents the mapping function of the nth parameter estimation model. This represents the speed estimate generated by the nth parameter estimation model.

[0040] Then, the multiple speed estimates are compared with the speed data (S22). In embodiments of this disclosure, similar estimation models can be selected by comparing multiple speed estimates with speed data.

[0041] In mathematical statistics, residuals, as the difference between actual observed values ​​and estimated values, contain important information about the basic assumptions of a model and can be considered as observed values ​​of error. Residuals should meet the model's assumptions and possess the properties of error. Using the information provided by residuals, the rationality of the model's assumptions can be examined. For example, the differences between multiple speed estimates and speed data can be calculated separately; these are the speed residuals. Thus, similar estimation models can be identified based on residual theory. This explanation uses the comparison of speed estimates and speed data as an example, but this disclosure is not limited to this; similar estimation models can also be screened based on comparisons of other types of data.

[0042] By comparing multiple speed estimates with speed data, parameter estimation models whose speed similarity meets predetermined conditions can be identified from multiple parameter estimation models and designated as similar estimation models (S23). For example, the similarity of each parameter estimation model can be measured based on the difference between each speed estimate and the speed data, so as to identify parameter estimation models whose speed similarity meets predetermined conditions from multiple parameter estimation models, i.e., similar estimation models.

[0043] The following is combined Figure 3 and Figure 5 Describe an example of a similarity estimation model.

[0044] like Figure 3 As shown, multiple speed differences between multiple speed estimates and speed data are calculated (S31).

[0045] For example, by averaging non-speed data (e.g., pitch angle data over a predetermined time period). Average value of torque data Average value of rotational speed data Input to multiple parameter estimation models to generate multiple speed estimates (e.g., the speed estimate generated by the first parameter estimation model). The speed estimate generated by the second parameter estimation model The speed estimate generated by the (n-1)th parameter estimation model The speed estimate generated by the nth parameter estimation model ).exist Figure 5 In the example shown, the multiple parameter estimation models may include a first parameter estimation model 51a, a second parameter estimation model 51b, a (n-1)th parameter estimation model 51c, and an nth parameter estimation model 51d, where n is a natural number greater than 2. The predetermined leaf pollution levels corresponding to the multiple parameter estimation models may increase sequentially.

[0046] Each parameter estimation model can generate a corresponding speed estimate based on the input non-speed data and output the speed estimate to the corresponding difference calculator (also known as the residual module) to calculate the corresponding residual. For example, the first parameter estimation model 51a, the second parameter estimation model 51b, the (n-1)th parameter estimation model 51c, and the nth parameter estimation model 51d respectively output the speed estimates to the first difference calculator 52a, the second difference calculator 52b, the (n-1)th difference calculator 52c, and the nth difference calculator 52d to calculate multiple speed estimates and speed data (e.g., the average speed data within a predetermined time period). Multiple speed differences between ( ). For example, the first speed difference output by the first difference calculator 52a. The second difference calculator 52b outputs the second speed difference. The (n-1)th difference calculator 52c outputs the (n-1)th speed difference. The nth difference calculator 52d outputs the nth speed difference.

[0047] Then, as Figure 3 As shown, the rotational speed similarity is calculated for each of the multiple parameter estimation models based on multiple rotational speed differences (S32). For example, the rotational speed similarity of the multiple parameter estimation models can be measured based on the comparison between multiple rotational speed differences.

[0048] In one embodiment of this disclosure, a counter can be used to calculate the rotational speed similarity. For example... Figure 5 As shown, multiple speed difference values ​​can be input to the counter module 53, which counts multiple parameter estimation models based on comparisons between these differences. The count value of each parameter estimation model can be used as the speed similarity calculation value for that model. The counter module 53 may include n counters, corresponding to n difference calculators and n parameter estimation models respectively, and can output the speed similarity calculation value for the corresponding parameter estimation model. For example, the i-th counter can output the speed similarity calculation value C of the i-th parameter estimation model. i , where 1≤i≤n.

[0049] In one embodiment of this disclosure, the speed similarity calculation value of the parameter estimation model corresponding to the smallest absolute value among the plurality of speed differences can be incremented, while the speed similarity calculation values ​​of the remaining parameter estimation models can be decremented. For example, in response to the speed differences (unit: r / min) output by the first difference calculator 52a, the second difference calculator 52b, the (n-1)th difference calculator 52c, and the nth difference calculator 52d being 0.1, 0.2, 0.6, and 0.8 respectively, the counter module 53 can increment the speed similarity calculation value of the first parameter estimation model 51a corresponding to the smallest absolute value among the plurality of speed differences by one (i.e., C1 = C1 + 1), while decrementing the speed similarity calculation values ​​of the second parameter estimation model 51b, the (n-1)th parameter estimation model 51c, and the nth parameter estimation model 51d by one (i.e., C2 = C2 - 1, C1 ... n-1 =C n-1 -1, C n =C n -1). Thus, the counter module 53 can accumulate the rotational speed similarity calculation values ​​of multiple parameter estimation models and output the rotational speed similarity calculation value of each parameter estimation model.

[0050] In embodiments of this disclosure, the rotational speed similarity calculation value of each parameter estimation model may have a predetermined upper limit calculation value and a predetermined lower limit calculation value. The predetermined upper limit calculation value and the predetermined lower limit calculation value can be set according to a predetermined time period and a predetermined counting period used to acquire the working parameter data. For example, if the predetermined time period is 10 minutes and the predetermined counting period is 1 day, the predetermined upper limit calculation value can be set to 144, and the predetermined lower limit calculation value can be set to zero. Thus, the counter module 53 can accumulate the rotational speed similarity calculation value of each parameter estimation model within a predetermined range between the predetermined upper limit calculation value and the predetermined lower limit calculation value.

[0051] Refer again Figure 3Based on multiple rotational speed similarity calculation values ​​from multiple parameter estimation models, the parameter estimation model whose rotational speed similarity calculation values ​​meet predetermined conditions is identified from the multiple parameter estimation models and used as the similarity estimation model (S33).

[0052] Predefined conditions can be set based on the relative magnitudes of multiple rotational speed similarity calculation values. For example, the predetermined condition can be a first predetermined condition. The first predetermined condition indicates that the rotational speed similarity count value is the highest among the multiple rotational speed similarity count values. Therefore, the parameter estimation model with the highest rotational speed similarity count value among multiple parameter estimation models can be identified as a similar estimation model. For example, if the rotational speed similarity count values ​​of the current first parameter estimation model 51a, second parameter estimation model 51b, (n-1)th parameter estimation model 51c, and nth parameter estimation model 51d are 50, 47, 30, and 20 respectively, then the first parameter estimation model 51a can be identified as a similar estimation model.

[0053] In another example, the predetermined condition can be a second predetermined condition. The second predetermined condition indicates that the rotational speed similarity count value is the highest and second highest among the plurality of rotational speed similarity count values. Therefore, the parameter estimation model with the highest and second highest rotational speed similarity count value among the plurality of parameter estimation models can be identified as a similar estimation model. For example, if the rotational speed similarity count values ​​of the current first parameter estimation model 51a, second parameter estimation model 51b, (n-1)th parameter estimation model 51c, and nth parameter estimation model 51d are 50, 47, 30, and 20, respectively, then the first parameter estimation model 51a and the second parameter estimation model 51b can be identified as similar estimation models.

[0054] When the predetermined condition is the second predetermined condition, the similarity estimation model may include: a first similarity estimation model and a second similarity estimation model, wherein the rotational speed similarity count value of the first similarity estimation model is the highest value among the plurality of rotational speed similarity count values, and the rotational speed similarity count value of the second similarity estimation model is the second highest value among the plurality of rotational speed similarity count values.

[0055] As mentioned above, similar estimation models can be identified by calculating the residuals between multiple estimated rotational speeds and actual rotational speed data. Since the residuals reflect the estimation errors predicted by the parameter estimation model, they implicitly contain the characteristics of the relevant assumptions of the parameter estimation model. Therefore, based on residual theory, the parameter estimation model can be evaluated by analyzing the residuals between actual and estimated data, thereby identifying similar estimation models. After determining the similar estimation model, the control parameters of the wind turbine can be determined.

[0056] Refer again Figure 1 Based on the similarity estimation model, the control parameters of the wind turbine are determined (S13).

[0057] In embodiments of this disclosure, the control parameters of a wind turbine can be determined based on the optimal control parameters of a parameter estimation model that serves as a similarity estimation model.

[0058] For example, when the predetermined condition is the first predetermined condition, the optimal control parameters (e.g., optimal gain and optimal pitch angle) of the parameter estimation model with the highest rotational speed similarity count value can be determined as the control parameters of the wind turbine.

[0059] For example, when the predetermined condition is the second predetermined condition, the control parameters of the wind turbine can be determined based on the optimal control parameters of the first similarity estimation model with the highest rotational speed similarity count value and the optimal control parameters of the second similarity estimation model with the second highest rotational speed similarity count value.

[0060] like Figure 4 As shown, a first weight applied to the first similarity estimation model and a second weight applied to the second similarity estimation model can be determined based on the first speed difference between the speed estimate of the first similarity estimation model and the speed data at the last count, and the second speed difference between the speed estimate of the second similarity estimation model and the speed data (S41). The first and second weights can be determined based on the ratio of the first speed difference to the second speed difference, where the ratio of the second weight to the first weight can be equal to the ratio of the first speed difference to the second speed difference. For example, at the last count, the first speed difference corresponding to the first similarity estimation model is 0.7, and the second speed difference corresponding to the first similarity estimation model is 0.3. Accordingly, the first weight can be determined to be 30%, and the second weight to be 70%. The first and second weights can be applied to the first and second similarity estimation models, respectively.

[0061] Based on a first similarity estimation model applying a first weight and a second similarity estimation model applying a second weight, the control parameters of the wind turbine are determined (S42). For example, the control parameters may include: the optimal gain of the wind turbine (K... opt ) and optimal pitch angle (β) opt ).

[0062] In embodiments of this disclosure, a first weight and a second weight can be applied to the first optimal control parameters of the first similarity estimation model and the second optimal control parameters of the second similarity estimation model, respectively. The first optimal control parameters and the second optimal control parameters can correspond to the non-speed data input to the model during the last counting process. For example, the sum of the first optimal gain with the first weight and the second optimal gain with the second weight can be used as the optimal gain of the wind turbine, and the sum of the first optimal pitch angle with the first weight and the second optimal pitch angle with the second weight can be used as the optimal pitch angle of the wind turbine.

[0063] According to embodiments of this disclosure, the blade contamination level of a wind turbine can also be determined based on a similarity estimation model. For example, when the predetermined condition is a first predetermined condition, the predetermined blade contamination level corresponding to the parameter estimation model with the highest rotational speed similarity count value can be determined as the blade contamination level of the wind turbine. When the predetermined condition is a second predetermined condition, a first weight and a second weight can be applied to the predetermined blade contamination levels corresponding to the first similarity estimation model and the second similarity estimation model, respectively, and the sum of the predetermined blade contamination levels after applying the weights can be determined as the blade contamination level of the wind turbine.

[0064] Once the control parameters of the wind turbine are determined, the control commands for the wind turbine can be adjusted accordingly.

[0065] like Figure 1 As shown, the pitch angle control command and / or torque control command of the wind turbine can be adjusted according to the control parameters (S14). For example, the pitch angle control command and / or torque control command of the wind turbine can be adjusted according to the optimal gain and optimal pitch angle determined as described above.

[0066] like Figure 6 As shown, the controller 61 can be used to control the wind turbine 62, and may include a control parameter determination module 611 and a control command adjustment module 612. Operations S11 to S13 as described above can be performed by the control parameter determination module 611, and operation S14 can be performed by the control command adjustment module 612. Figure 6 In the example shown, the control parameter determination module 611 can generate control parameters based on residual theory, and can act as a residual-based performance mitigator. The control command adjustment module 612 can output control commands to the wind turbine.

[0067] For example, the control parameter determination module 611 can acquire the operating parameter data of the wind turbine within a predetermined time period (e.g., the average value of the pitch angle data within the predetermined time period). Average value of torque data Average value of rotational speed data The average value of rotational speed data within the predetermined time period This generates and outputs control parameters (e.g., optimal gain K). opt and optimal pitch angle β opt (See reference) Figures 1 to 5 The functions of the control parameter determination module 611 are explained by the related operations shown. The control command adjustment module 612 can acquire real-time data D, such as generator speed data ω, pitch angle data β, power generation data P, and nacelle acceleration data a. tt Blade load data MB Wind speed data v W The control command adjustment module 612, by combining the acquired real-time data D and the control parameters received from the control parameter determination module 611, adjusts the pitch angle control command and / or torque control command of the wind turbine. The control command adjustment module 612 can acquire the real-time data D through detection devices such as sensors in the wind turbine 62 or the wind farm; for example, it can acquire the generator speed data ω through a speed sensor.

[0068] like Figure 1 As shown, the wind turbine (S15) can be controlled according to pitch angle control commands and / or torque control commands. For example, it can be controlled via... Figure 6 The control command adjustment module 612 shown outputs adjusted pitch angle control commands and / or torque control commands to the wind turbine 62. For example, the pitch angle control command may include a reference pitch angle β. ref Torque control commands may include a reference torque τ ref .

[0069] In embodiments of this disclosure, pitch control of the wind turbine can be performed via a pitch mechanism according to pitch angle control commands. Optionally, torque control of the wind turbine can be performed via a converter according to torque control commands.

[0070] As mentioned above, refer to Figures 1 to 6 A control method for a wind turbine according to embodiments of the present disclosure is described. By adjusting the control commands of the wind turbine based on control parameters determined by a similarity estimation model, the effects of blade contamination on the wind turbine can be addressed, and the operation of the wind turbine under different blade contamination levels can be improved.

[0071] Figure 7 A control apparatus for controlling a wind turbine is illustrated according to an embodiment of the present disclosure. The control apparatus may include a controller 71 and one or more sensors 72. The one or more sensors 72 are configured to acquire operating parameter data of the wind turbine. The controller 71 is communicatively coupled to the wind turbine 73 and the sensors 72.

[0072] The controller 71 may include at least one processor 711 communicating with at least one memory device 712. The at least one processor 711 is configured to: identify, based on operating parameter data, a parameter estimation model satisfying predetermined conditions from a plurality of parameter estimation models as a similar estimation model, wherein the plurality of parameter estimation models correspond to a plurality of predetermined blade contamination levels; determine control parameters of the wind turbine based on the similar estimation model; and adjust the pitch angle control command and / or torque control command of the wind turbine according to the control parameters and control the wind turbine according to the pitch angle control command and / or torque control command.

[0073] The controller may also be implemented as a computing device including a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, implements the control method according to embodiments of the present disclosure.

[0074] For reference Figures 1 to 6 The operations described are used to understand the corresponding operations of each component of the control device 7, and will not be elaborated further here for the sake of brevity.

[0075] What should be understood is: Figure 6 and Figure 7 The various modules, units, or components shown can be configured as software, hardware, firmware, or any combination thereof to perform specific functions, and are not limited to including the components shown. Some components can be added or removed, or components can be combined, as needed.

[0076] According to embodiments of this disclosure, a computer program product downloadable from a communication network and / or stored on a computer-readable storage medium is also provided. The computer program product includes program code instructions for implementing the control methods as described in embodiments of this disclosure. See also... Figures 1 to 6 The operations described are used to understand the operations implemented by the program code instructions, which will not be elaborated further here.

[0077] According to embodiments of this disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed, implements the control method according to embodiments of this disclosure. See also... Figures 1 to 6 The operations described are used to understand the operations performed by computer programs, which will not be elaborated upon here.

[0078] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In embodiments of this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a computer program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof. A computer-readable storage medium can be included in any apparatus; it can also exist independently without being assembled into that apparatus.

[0079] By employing the wind turbine control method and control device, computer program product, computer-readable storage medium, computing device and wind turbine according to the embodiments of the present disclosure, at least one of the following technical effects can be achieved: timely handling of wind turbine performance changes caused by blade contamination, improving the operation of wind turbines under different blade contamination levels, and enabling wind turbines to maintain good working efficiency under blade contamination conditions.

[0080] Control logic or functions performed by various components or controllers in a control device can be represented by flowcharts or similar diagrams in one or more accompanying figures. These figures provide representative control strategies and / or logic, which can be implemented using one or more processing strategies (such as event-driven, interrupt-driven, multitasking, multithreading, etc.). Therefore, the individual steps or functions shown may be performed in the order shown, in parallel, or in some cases omitted. Although not always explicitly shown, those skilled in the art will recognize that one or more steps or functions shown may be repeatedly performed depending on the specific processing strategy used.

[0081] Although this disclosure has been shown and described with reference to preferred embodiments, those skilled in the art will understand that various modifications and variations may be made to these embodiments without departing from the spirit and scope of this disclosure as defined by the claims.

Claims

1. A control method for a wind turbine, characterized in that, The control method includes: Acquire operating parameter data of a wind turbine within a predetermined time period; Based on the working parameter data, a parameter estimation model that meets predetermined conditions is identified from multiple parameter estimation models and used as a similar estimation model, wherein the multiple parameter estimation models correspond to multiple predetermined leaf pollution levels respectively; Based on the similarity estimation model, the control parameters of the wind turbine are determined; Based on the aforementioned control parameters, adjust the pitch angle control command and / or torque control command of the wind turbine, and control the wind turbine according to the pitch angle control command and / or torque control command. The operating parameter data includes: rotational speed data and non-rotational speed data. The step of identifying parameter estimation models that meet predetermined conditions from multiple parameter estimation models based on the working parameter data, and using them as similar estimation models, includes: Based on the non-speed data, multiple speed estimates are generated using the multiple parameter estimation models; By calculating the speed residuals between the multiple speed estimates and the speed data, parameter estimation models that satisfy predetermined conditions for speed similarity are identified from the multiple parameter estimation models and used as similarity estimation models.

2. The control method according to claim 1, characterized in that, The non-rotational speed data includes pitch angle data, torque data, and wind speed data.

3. The control method according to claim 1, characterized in that, The step of identifying parameter estimation models whose speed similarity satisfies the predetermined conditions from the multiple parameter estimation models by calculating the speed residuals between the multiple speed estimates and the speed data, and using these models as similarity estimation models, includes: Calculate the multiple speed differences between the multiple speed estimates and the speed data respectively; Based on the multiple speed differences, the speed similarity of the multiple parameter estimation models is calculated respectively. Based on the multiple rotational speed similarity calculation values ​​of the multiple parameter estimation models, the parameter estimation model whose rotational speed similarity calculation values ​​satisfy the predetermined conditions is identified from the multiple parameter estimation models and used as the similarity estimation model.

4. The control method according to claim 3, characterized in that, The step of calculating the rotational speed similarity of the multiple parameter estimation models based on the multiple rotational speed differences includes: The rotational speed similarity calculation value of the parameter estimation model corresponding to the smallest absolute value among the multiple rotational speed differences is increased, while the rotational speed similarity calculation value of the remaining parameter estimation models is decreased.

5. The control method according to claim 3, characterized in that, The predetermined condition is either a first predetermined condition or a second predetermined condition. The first predetermined condition indicates that the rotational speed similarity count value is the highest among the plurality of rotational speed similarity count values. The second predetermined condition means that the rotational speed similarity count value is the highest and second highest value among the plurality of rotational speed similarity count values.

6. The control method according to claim 5, characterized in that, When the predetermined condition is the second predetermined condition, the similarity estimation model includes: a first similarity estimation model and a second similarity estimation model, wherein the rotational speed similarity count value of the first similarity estimation model is the highest value among the plurality of rotational speed similarity count values, and the rotational speed similarity count value of the second similarity estimation model is the second highest value among the plurality of rotational speed similarity count values. The determination of control parameters for the wind turbine based on the similarity estimation model includes: Based on the first speed difference between the speed estimate of the first similarity estimation model and the speed data at the last count, and the second speed difference between the speed estimate of the second similarity estimation model and the speed data, determine the first weight applied to the first similarity estimation model and the second weight applied to the second similarity estimation model. The control parameters of the wind turbine are determined based on the first similarity estimation model with the first weight and the second similarity estimation model with the second weight.

7. The control method according to claim 1, characterized in that, The multiple parameter estimation models are generated through the following operations: By using an aeroelastic model, the normal operating state of the wind turbine under various predetermined blade pollution levels and different wind speeds is determined, so as to generate the multiple parameter estimation models corresponding to the various predetermined blade pollution levels.

8. The control method according to claim 1, characterized in that, The control method further includes: determining the blade contamination level of the wind turbine based on the similarity estimation model.

9. The control method according to claim 1, characterized in that, The control parameters include the optimal gain and optimal pitch angle of the wind turbine.

10. The control method according to claim 3, characterized in that, The rotational speed similarity calculation value for each parameter estimation model has a predetermined upper limit and a predetermined lower limit.

11. The control method according to claim 1, characterized in that, The control of the wind turbine according to the pitch angle control command and / or torque control command includes: According to the pitch angle control command, pitch control is performed on the wind turbine through the pitch mechanism; and / or Torque control is performed on the wind turbine via a converter according to the torque control command.

12. A control device, characterized in that, The control device includes: One or more sensors configured to acquire operating parameter data of a wind turbine; as well as A controller communicatively coupled to the wind turbine and the sensor, the controller including at least one processor communicating with at least one memory device, the at least one processor being configured to: Based on the working parameter data, a parameter estimation model that meets predetermined conditions is identified from multiple parameter estimation models and used as a similar estimation model, wherein the multiple parameter estimation models correspond to multiple predetermined leaf pollution levels respectively; Based on the similarity estimation model, the control parameters of the wind turbine are determined; Based on the control parameters, the pitch angle control command and / or torque control command of the wind turbine are adjusted, and the wind turbine is controlled according to the pitch angle control command and / or torque control command. The operating parameter data includes: rotational speed data and non-rotational speed data. The step of identifying parameter estimation models that meet predetermined conditions from multiple parameter estimation models based on the working parameter data, and using them as similar estimation models, includes: Based on the non-speed data, multiple speed estimates are generated using the multiple parameter estimation models; By calculating the speed residuals between the multiple speed estimates and the speed data, parameter estimation models that satisfy predetermined conditions for speed similarity are identified from the multiple parameter estimation models and used as similarity estimation models.

13. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the control method as described in any one of claims 1 to 11.

14. A computing device, characterized in that, The computing device includes: processor; A memory storing a computer program that, when executed by a processor, implements the control method according to any one of claims 1 to 11.

15. A wind turbine, characterized in that, The wind turbine includes the control device as described in claim 12.

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