Wind turbine generator self-adaptive control method and device based on hub load turbulence evaluation

Abstract

The invention discloses a wind turbine generator self-adaptive control method and device based on hub load turbulence evaluation. The method comprises the steps that S01, real-time wind speed data of the environment where a controlled fan is located and real-time load data of the controlled fan are obtained; s02, converting the real-time load data of the controlled fan to obtain a real-time Myz hub load value; s03, the current turbulence intensity state is evaluated according to the real-time Myz hub load value; and step S04, controlling the operation mode of the controlled fan according to the current turbulence intensity state evaluation result and the real-time wind speed data, and if the current turbulence intensity state is evaluated to be an abnormal state and the wind speed reaches a specified wind speed condition, controlling the controlled fan to perform power reduction and speed reduction actions. Self-adaptive control of the wind turbine generator is achieved through the turbulence intensity state based on hub load real-time evaluation, safe, stable and efficient operation of a fan can be guaranteed, the hub fatigue load of the generator set is reduced, and the service life of the generator set is prolonged.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind turbine generator sets, and in particular to a wind turbine generator set adaptive control method and device based on hub load turbulence evaluation. BACKGROUND

[0002] During the operation of a wind farm, the actual turbulence intensity at some sites may be greater than the design turbulence intensity. With the rapid development of the wind power industry, the scale of wind farm construction is continuously expanding, and the site selection is becoming increasingly diversified, which increases the complexity of the turbulence conditions of different wind turbines. When the actual turbulence intensity of a wind turbine is greater than the design turbulence intensity, the overall load of the wind turbine during operation is prone to exceed the design load, resulting in abnormal wind turbine load, affecting the normal operation of the wind turbine, increasing maintenance costs and downtime, and thus reducing power generation efficiency and economic benefits.

[0003] To solve the above problems, the existing technology usually adopts a sector management method, which reduces the equivalent turbulence by closing a certain sector. However, closing the sector will result in a large loss of power generation, and this method can only simply reduce a certain amount of equivalent turbulence and cannot adaptively control the wind turbine based on the real-time state of the turbulence. The real-time turbulence intensity state of the wind turbine is difficult to detect and identify directly by sensors, making it difficult to accurately evaluate the turbulence intensity state of the wind turbine. In addition, the existing adaptive control technology lacks robustness, and when multiple factors are superimposed in complex working conditions, the preset control strategy is prone to failure and cannot be stably adapted. Moreover, most of the adaptive technologies in the existing technology rely on fixed control models and cannot dynamically adjust parameters according to the characteristics of different models and external environments, which has poor universality.

[0004] For turbulence intensity estimation, the existing technology usually adopts the following two methods: 1. Estimation method based on wind speed measurement This method installs a wind measurement tower at a fixed position in the wind farm and estimates the turbulence intensity of the wind farm by processing the wind speed measured by the wind measurement tower. However, this method has high implementation cost and complexity, and requires offline data processing based on the measurement data of the wind measurement tower, which cannot provide real-time turbulence information for wind turbines at different positions, making it impossible to control the wind turbine in real time according to the turbulence intensity state and perform adaptive actions in a timely manner.

[0005] 2. Estimation method based on wind turbine response This way is to monitor the response information of the unit (such as speed fluctuation, tower vibration), and indirectly estimate the turbulence intensity by using the response information of the unit. However, the accuracy of this method is not high, and since the response information of the unit is needed for estimation, and the response information of the unit has a lag, the real-time evaluation of the turbulence intensity state cannot be realized, and the fan action cannot be controlled in time when the turbulence intensity exceeds the design turbulence intensity, which affects the safe operation of the fan.

[0006] Therefore, it is urgent to provide a wind turbine generator adaptive control method to accurately evaluate the turbulence intensity state of the environment where the wind turbine generator is located, and to accurately control the wind turbine generator according to the real-time turbulence intensity state, so as to ensure the safe, stable and efficient operation of the wind turbine generator, and to improve the safety performance and service life of the unit. SUMMARY

[0007] The technical problem to be solved by the present application is that in view of the technical problems existing in the prior art, the present application provides a wind turbine generator adaptive control method and device based on hub load turbulence evaluation, which realizes adaptive control of the operation mode of the wind turbine generator based on real-time evaluation of the turbulence intensity state based on the hub load, can ensure the safe, stable and efficient operation of the wind turbine generator, reduce the hub fatigue load of the unit, and improve the service life of the unit.

[0008] To solve the above technical problems, the technical solution provided by the present application is: A wind turbine adaptive control method based on hub load turbulence evaluation, comprising the following steps: Step S01. Obtain real-time wind speed data of the environment where the controlled wind turbine is located and real-time load data of the controlled wind turbine; Step S02. Convert the real-time load data of the controlled wind turbine to obtain a real-time Myz hub load value; Step S03. Evaluate the current turbulence intensity state of the environment where the controlled wind turbine is located according to the real-time Myz hub load value; Step S04. Control the operation mode of the controlled wind turbine according to the current turbulence intensity state evaluation result obtained in step S03 and the real-time wind speed data, wherein if the current turbulence intensity state is abnormal and the real-time wind speed data reaches a specified wind speed condition, the controlled wind turbine is controlled to perform a power reduction and speed reduction action.

[0009] Further, in step S02, the real-time Myz hub load value is converted according to the real-time load data of the controlled wind turbine, comprising: Step S201. Obtain the load My1, My2 and My3 of the three blades measured by the blade root sensor of the controlled wind turbine; Step S202. The obtained loads My1, My2, and My3 are converted into hub center loads My and Mz through coordinate transformation; Step S203. Combine the loads My and Mz at the hub center to obtain the Myz hub load value at the hub center.

[0010] Furthermore, step S02 also includes calculating the statistical value of the real-time Myz hub load value to assess the turbulence intensity state of the environment in which the controlled wind turbine is currently located based on the statistical value of the real-time Myz hub load value. In step S03, it is determined whether the specified turbulence conditions have been reached based on the statistical value of the real-time Myz load.

[0011] Furthermore, the statistical values ​​include std value, mean, variance, range, peak value, MAD, and IQR, or any one or more of these.

[0012] Further, step S03 includes: Step S301. Find the Myz hub load threshold corresponding to the current wind speed from the pre-constructed first Myz load boundary scheduling table and use it as the first load threshold. The first Myz load boundary scheduling table stores the Myz hub load thresholds that keep the wind turbine in normal power generation under different wind speed conditions. Step S302. Determine whether the statistical value of the current real-time Myz load exceeds the first load threshold. If it does, determine that the current turbulence intensity state is an abnormal turbulence intensity state; otherwise, determine that it is a normal turbulence intensity state.

[0013] Further, step S04 includes: Step S401. Determine whether the current turbulence intensity state evaluated based on the real-time Myz hub load value is an abnormal state. If it is, proceed to step S402; otherwise, return to step S01. Step S402. Determine whether the current real-time wind speed data exceeds the specified wind speed condition. If yes, proceed to step S403; otherwise, return to step S01. Step S403. Control the controlled fan to reduce power and speed.

[0014] Furthermore, after step S04, there is also a step S05 to control the controlled fan to exit the power reduction and speed reduction action. Step S05 includes: Step S501. Count the duration of the current power reduction and speed reduction action. If the duration of the power reduction and speed reduction action reaches the preset time threshold, proceed to step S502. Step S502. Find the Myz hub load threshold corresponding to the current wind speed from the pre-built second Myz load boundary scheduling table as the second load threshold. The second Myz load boundary scheduling table stores the Myz hub load thresholds that keep the wind turbine in normal operating condition under different wind speed conditions after power reduction and speed reduction. Step S503. Determine whether the current real-time Myz load value exceeds the second load threshold. If so, continue to maintain the power reduction and speed reduction action and return to step S501; otherwise, restore the normal power generation state and return to step S01.

[0015] Furthermore, when obtaining the real-time wind speed data of the environment where the controlled fan is located in step S01 and / or obtaining the statistical value of the real-time Myz hub load in step S02, the obtained data is also subjected to sliding window filtering processing.

[0016] An adaptive control device for wind turbines based on hub load turbulence assessment includes: The load acquisition module is used to acquire real-time wind speed data of the environment where the controlled wind turbine is located and real-time load data of the controlled wind turbine. The hub load value calculation module is used to convert the real-time load data of the controlled wind turbine into the real-time Myz hub load value; The turbulence state assessment module is used to assess the current turbulence intensity state in the environment where the controlled wind turbine is located based on the real-time Myz hub load value. The adaptive control module is used to control the operating mode of the controlled fan based on the current turbulence intensity state assessment result obtained by the turbulence state assessment module and the real-time wind speed data. If the current turbulence intensity state is assessed as abnormal and the real-time wind speed data reaches the specified wind speed condition, the controlled fan is controlled to reduce power and speed.

[0017] An electronic device includes a processor and a memory, the memory being used to store a computer program, and the processor being used to execute the computer program to perform the method described above.

[0018] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention characterizes the turbulence intensity state of the environment in which the controlled wind turbine is located based on the real-time Myz hub load value. Then, based on the evaluation result of the turbulence intensity state and the real-time wind speed data, the operating mode of the controlled wind turbine is controlled. It can make full use of the hub load data of the controlled wind turbine to realize the real-time evaluation of the turbulence intensity state of the environment in which the wind turbine is located. It can realize turbulence adaptive control in real time. When the turbulence intensity state is abnormal and the wind speed reaches the specified conditions, it can promptly reduce power and speed. This ensures that the hub fatigue load of the wind turbine is reduced in the case of large turbulence that exceeds the design turbulence and in the case of severe wind resources, thus protecting the safe operation of the unit, improving the reliability of the wind turbine and extending the service life of the unit.

[0020] 2. This invention utilizes the Myz hub load value to characterize the turbulence intensity of the environment in which the wind turbine is located. It can directly capture the influence of turbulence on the hub load, and the Myz hub load value is less affected by other factors, thus more accurately reflecting the influence of turbulence intensity on the hub load. Based on the Myz hub load value, it is also easy to capture small changes in turbulence intensity in a timely manner, thereby more accurately assessing the turbulence intensity. At the same time, during unit operation, the real-time changes in hub load can be used to quickly assess the real-time situation of turbulence intensity, improving the real-time performance of turbulence identification and solving the problem that traditional turbulence assessment methods are difficult to achieve in real time. Attached Figure Description

[0021] Figure 1 This is a schematic diagram illustrating the implementation process of the wind turbine adaptive control method based on hub load turbulence assessment in this embodiment.

[0022] Figure 2 This is a detailed flowchart illustrating the adaptive control of a wind turbine in a specific application embodiment of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0024] As disclosed in this invention, unless the context clearly indicates otherwise, words such as "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. The terms "first," "second," and similar terms used in this invention disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, words such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0025] like Figure 1As shown, the steps of the wind turbine adaptive control method based on hub load turbulence assessment in this embodiment include: Step S01. Obtain real-time wind speed data of the environment where the controlled fan is located and real-time load data of the controlled fan; Step S02. Obtain the real-time Myz hub load value based on the real-time load data of the controlled wind turbine; Step S03. Evaluate the current turbulence intensity state in the environment where the controlled wind turbine is located based on the real-time Myz hub load value; Step S04. Based on the current turbulence intensity state assessment result obtained in step S03 and the real-time wind speed data, control the operating mode of the controlled fan. If the current turbulence intensity state is assessed as abnormal and the real-time wind speed data reaches the specified wind speed condition, control the controlled fan to perform power reduction and speed reduction actions.

[0026] This embodiment acquires real-time load data of the controlled wind turbine and converts it into a real-time Myz hub load value. This real-time Myz hub load value characterizes the turbulence intensity state of the environment in which the controlled wind turbine is currently located. Based on the evaluation result of the turbulence intensity state and real-time wind speed data, the operating mode of the controlled wind turbine is controlled. This fully utilizes the hub load data of the controlled wind turbine to achieve real-time evaluation of the turbulence intensity state of the environment in which the wind turbine is located. Then, the operation of the controlled wind turbine can be adaptively controlled using the evaluation result of the real-time turbulence intensity state and real-time wind speed data, realizing turbulence adaptive control. When the turbulence intensity state is abnormal and the wind speed reaches the specified conditions, the power reduction and speed reduction actions can be performed in time. This ensures that the hub fatigue load of the wind turbine is reduced in the case of large turbulence exceeding the design turbulence and severe wind resources in the actual environment, protecting the safe operation of the unit, thereby improving the reliability of the wind turbine and extending the service life of the unit.

[0027] As an optional implementation, in step S01 of this embodiment, when acquiring the real-time wind speed data of the environment where the controlled fan is located, the acquired data is further subjected to sliding window filtering to improve the reliability of control. For example, after acquiring wind speed data from the anemometer, the acquired wind speed data can be subjected to 600s sliding window filtering.

[0028] As an optional implementation, in step S02 of this embodiment, obtaining the real-time Myz hub load value based on the real-time load data of the controlled wind turbine includes: Step S201. Obtain the loads My1, My2, and My3 of the three blades measured by the blade root sensor of the controlled fan; Step S202. The acquired loads My1, My2, and My3 are converted into hub center loads My and Mz through coordinate transformation; Step S203. Combine the loads My and Mz at the hub center to obtain the Myz hub load value at the hub center.

[0029] The Myz hub load value directly reflects the magnitude of the load borne at the hub, and changes in turbulence intensity directly affect the hub load. The equivalent fatigue load of the hub increases linearly with the increase of turbulence intensity, meaning that the Myz hub load value is highly sensitive to changes in turbulence intensity. In this embodiment, the blade root load measured by the blade root load sensor is converted into My and Mz loads at the hub center to synthesize the hub Myz load. This Myz hub load value is used to characterize the turbulence intensity state of the wind turbine's environment, directly capturing the influence of turbulence on the hub load. Furthermore, the Myz hub load value is less affected by other factors and can more accurately reflect the influence of turbulence intensity on the hub load. Based on the Myz hub load value, it is also convenient to capture small changes in turbulence intensity in a timely manner, thereby more accurately assessing the turbulence intensity. At the same time, during unit operation, the real-time changes in hub load can be used to quickly assess the real-time situation of turbulence intensity, improving the real-time performance of turbulence identification and solving the problem that traditional turbulence assessment methods are difficult to achieve in real time.

[0030] As an optional implementation, step S02 of this embodiment further includes calculating the statistical value of the real-time Myz hub load value to assess the turbulence intensity state of the environment in which the controlled wind turbine is currently located. That is, by using the statistical value of the real-time Myz hub load value to characterize the turbulence intensity state of the environment in which the controlled wind turbine is currently located, compared to a single Myz hub load value, the statistical value of the Myz hub load value can more accurately identify the turbulence intensity state and facilitate the analysis of the changing trend of the turbulence intensity state. The statistical value can specifically be any one of the following: std (standard deviation), mean, variance, range, peak value, MAD, and IQR. Of course, two or more statistical values ​​can also be used for comprehensive evaluation to further improve the reliability of the evaluation.

[0031] Taking the statistical value as an example, after calculating the real-time Myz hub load value, the std value of the hub Myz load over a period of time can be further calculated. The std value is used to characterize the turbulence intensity state experienced by the wind turbine. Subsequently, the std value is used to determine whether an abnormal state of high turbulence has been reached, so that the unit can be controlled to reduce power and speed in a timely manner to protect the unit's safety.

[0032] As an optional implementation, this embodiment further includes performing sliding window filtering on the statistical value of the real-time Myz wheel hub load when obtaining the statistical value, so as to further improve the accuracy of the statistical value and thus improve the reliability of the control.

[0033] As an optional implementation, step S03 of this embodiment includes the following specific steps: Step S301. Find the Myz hub load threshold corresponding to the current wind speed from the pre-constructed first Myz load boundary scheduling table and use it as the first load threshold. The first Myz load boundary scheduling table stores the Myz hub load thresholds that keep the wind turbine in normal power generation under different wind speed conditions. Step S302. Determine whether the statistical value of the current real-time Myz load exceeds the first load threshold. If it does, determine that the current turbulence intensity state is an abnormal turbulence intensity state; otherwise, determine that it is a normal turbulence intensity state. The abnormal turbulence intensity state can be the state corresponding to the current turbulence intensity being greater than the design turbulence intensity.

[0034] This embodiment pre-constructs a first Myz load boundary scheduling table, which stores the Myz hub load thresholds that maintain the wind turbine in normal power generation conditions under different wind speeds. Exceeding these Myz hub load thresholds indicates that the turbulence intensity exceeds the design turbulence intensity, i.e., a high-turbulence condition. During the operation of the controlled wind turbine, after calculating the statistical value of the real-time Myz load, the corresponding Myz hub load threshold is retrieved from the first Myz load boundary scheduling table based on the current wind speed data of the controlled wind turbine. Then, this Myz hub load threshold is used as the first load threshold and compared with the current statistical value of the real-time Myz load to assess whether the current turbulence intensity state is abnormal. If the current statistical value of the real-time Myz load exceeds the first load threshold, it indicates that the current turbulence intensity state is an abnormal turbulence intensity state exceeding the design turbulence intensity; otherwise, it is a normal turbulence intensity state. Through the above method, the statistical value of the real-time Myz load can be fully utilized to accurately assess the turbulence intensity state in real time.

[0035] As an optional implementation, step S04 in this embodiment includes: Step S401. Determine whether the current turbulence intensity state evaluated based on the real-time Myz hub load value is an abnormal state. If it is, proceed to step S402; otherwise, return to step S01. Step S402. Determine whether the current real-time wind speed data exceeds the specified wind speed condition. If yes, proceed to step S403; otherwise, return to step S01. Step S403. Control the controlled fan to reduce power and speed.

[0036] Specifically, taking the use of the std value of the hub Myz load to characterize the turbulence intensity state experienced by the wind turbine as an example, during the operation of the unit, the loads My1, My2, and My3 measured by the blade root sensor are first input. After coordinate transformation, the blade root loads are converted into hub center My and Mz loads. Then, the hub center Myz load is synthesized from the hub center My and Mz loads, and the std value of the Myz hub load over a period of time is calculated. The calculated Myz_std value is subjected to sliding window filtering to obtain the processed data Myz_std2. The processed data Myz_std2 is compared with the first load threshold found in the first Myz load boundary scheduling table. If Myz_std2 exceeds the first load threshold, the current turbulence intensity state is determined to be an abnormal state. The current real-time wind speed data is further judged. If the current real-time wind speed data exceeds the specified wind speed condition, it indicates that the current situation is under high turbulence and severe wind resource conditions. The controlled wind turbine is controlled to reduce power and speed to protect the safe operation of the unit. In other cases, no action is taken, and the initial judgment logic of step S01 is returned.

[0037] In Myz load boundary scheduling table 1, the load threshold is the 600s filtered boundary value of the hub Myz load std value under simulated normal power generation conditions. In Myz load boundary scheduling table 2, the load threshold is the 600s filtered boundary value of the hub Myz load std value after power and speed reduction in simulation. Load threshold 1 and load threshold 2 are obtained by looking up the table using a 600s sliding window filtered wind speed. The power and speed reduction duration threshold t1 is set.

[0038] In this embodiment, after step S04, there is also a step S05 to control the controlled fan to exit the power reduction and speed reduction action. Step S05 includes: Step S501. Count the duration of the current power reduction and speed reduction action. If the duration of the power reduction and speed reduction action reaches the preset time threshold, proceed to step S502. Step S502. Find the Myz hub load threshold corresponding to the current wind speed from the pre-built second Myz load boundary scheduling table as the second load threshold. The second Myz load boundary scheduling table stores the Myz hub load thresholds that keep the wind turbine in normal operating condition under different wind speed conditions after power reduction and speed reduction. Step S503. Determine whether the current real-time Myz load value exceeds the second load threshold. If so, continue to maintain the power reduction and speed reduction action and return to step S501; otherwise, restore the normal power generation state and return to step S01.

[0039] By adopting the above-mentioned exit logic, this embodiment can control the unit to exit control in a timely manner while realizing turbulence adaptive control, thus avoiding premature exit of the protection logic when the unit has not completely avoided severe turbulence conditions.

[0040] Taking the std value of the Myz load value to characterize the turbulence intensity state as an example, the Myz_std threshold that can maintain the normal operation of the unit under different wind speed conditions after power reduction and speed reduction is obtained in advance, and a second Myz load boundary scheduling table is established. After the controlled wind turbine performs power reduction and speed reduction, the duration of power reduction and speed reduction is counted. If the preset duration threshold is reached, the Myz_std threshold corresponding to the current wind speed is found from the second Myz load boundary scheduling table. The Myz_std threshold is used as the second load threshold. The data Myz_std2 after sliding window filtering is compared with the second load threshold. If Myz_std2 exceeds the second load threshold, it indicates that the current condition is still under high turbulence, and the power reduction and speed reduction action continues. At the same time, the power reduction and speed reduction duration t is reset and the timing is restarted. If Myz_std2 is less than the second load threshold, the unit exits the power reduction and speed reduction action, restores the normal power generation state, and returns to the initial judgment logic.

[0041] The following example, using the method described above to implement adaptive control of a wind turbine in a specific application embodiment, further illustrates the present invention. Figure 2 As shown, the detailed steps are as follows: Step 1. Measure and obtain real-time wind speed data, and perform sliding window filtering on the real-time wind speed data; Step 2. Measure and obtain real-time blade root load data, convert it to obtain the hub center Myz load value, and then calculate the std value of Myz. Use Myz_std to characterize the magnitude of turbulence currently experienced by the unit. Step 3. Construct a first Myz load boundary scheduling table T_HubMyzThreshold1 and a second Myz load boundary scheduling table T_HubMyzThreshold2 based on the sliding window filtered wind speed. Each element in the first Myz load boundary scheduling table T_HubMyzThreshold1 corresponds to the load threshold for maintaining normal operation of the wind turbine under different filtered wind speeds. Each element in the second Myz load boundary scheduling table T_HubMyzThreshold2 corresponds to the load threshold for maintaining normal operation of the wind turbine under different filtered wind speeds after power reduction and speed reduction actions, as shown in Tables 1 and 2.

[0042] Table 1 Myz Load Boundary Scheduling Table 1 (T_HubMyzThreshold1)

[0043] Table 2 Myz Load Boundary Scheduling Table 2 (T_HubMyzThreshold2)

[0044] Step 4. Set the power reduction and speed reduction duration threshold t1. During unit operation, the first load threshold 1 and the second load threshold 2 are obtained by looking up the table according to the sliding window filter wind speed. The measured and converted Myz_std value is processed by sliding window filtering to obtain Myz_std2. When Myz_std2 < threshold 1, return to the initial judgment. When Myz_std2 > threshold 1, if the filtered wind speed is greater than the preset wind speed threshold (e.g., 6m / s), the power reduction and speed reduction action is performed and the timing starts synchronously. If the filtered wind speed is not greater than the preset wind speed threshold, return to the initial judgment. When the power reduction and speed reduction duration reaches the time threshold t1, it is determined whether the current Myz_std2 is greater than the second load threshold 2. If it is greater than the second load threshold 2, the power reduction and speed reduction action continues and the timing restarts. When Myz_std2 is less than the second load threshold 2, the power reduction and speed reduction state is exited and the initial judgment is returned.

[0045] This embodiment of the wind turbine adaptive control device based on hub load turbulence assessment includes: The load acquisition module is used to acquire real-time wind speed data of the environment where the controlled wind turbine is located and real-time load data of the controlled wind turbine. The hub load value calculation module is used to convert the real-time load data of the controlled wind turbine into the real-time Myz hub load value; The turbulence state assessment module is used to assess the current turbulence intensity state in the environment where the controlled wind turbine is located based on the real-time Myz hub load value. The adaptive control module is used to control the operating mode of the controlled fan based on the current turbulence intensity state assessment result obtained by the turbulence state assessment module and the real-time wind speed data. If the current turbulence intensity state is assessed as abnormal and the real-time wind speed data reaches the specified wind speed condition, the controlled fan is controlled to reduce power and speed.

[0046] In this embodiment, the turbulence state assessment module includes: The load judgment unit is used to determine whether the current turbulence intensity state evaluated based on the real-time Myz hub load value is an abnormal state. If it is, it will proceed to the wind speed judgment unit; otherwise, it will return to the load acquisition module. The wind speed judgment unit is used to determine whether the current real-time wind speed data exceeds the specified wind speed condition. If it does, proceed to step S403; otherwise, return to the execution load acquisition module. The action unit is used to control the controlled fan to reduce power and speed.

[0047] In this embodiment, an exit control module is also included to control the controlled fan to exit the power reduction and speed reduction action. The exit control module includes: The timing unit is used to count the duration of the current power reduction and speed reduction action. If the duration of the power reduction and speed reduction action reaches the preset time threshold, it will be transferred to the judgment unit. The judgment unit is used to look up the Myz hub load threshold corresponding to the current wind speed from the pre-built second Myz load boundary scheduling table as the second load threshold. The second Myz load boundary scheduling table stores the Myz hub load thresholds that keep the wind turbine in normal operating condition under different wind speed conditions after power reduction and speed reduction. Exit the control unit to determine whether the current real-time Myz load value exceeds the second load threshold. If it does, continue to maintain the power reduction and speed reduction action and return to the timing unit; otherwise, restore the normal power generation state and return to the execution load acquisition module.

[0048] The wind turbine adaptive control device based on hub load turbulence assessment in this embodiment corresponds one-to-one with the wind turbine adaptive control method based on hub load turbulence assessment described above, and will not be described in detail here.

[0049] This embodiment further provides an electronic device, including a processor and a memory, wherein the memory is used to store a computer program and the processor is used to execute the computer program to perform the method as described above.

[0050] It is understood that the method described in this embodiment can be executed by a single device, such as a computer or server, or it can be applied to a distributed scenario where multiple devices cooperate to complete the task. In a distributed scenario, one of the multiple devices may execute only one or more steps of the method described in this embodiment, and the multiple devices interact to complete the method. The processor can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the method described in this embodiment. The memory can be implemented using read-only memory (ROM), random access memory (RAM), static storage devices, and dynamic storage devices. The memory can store the operating system and other applications. When the method described in this embodiment is implemented through software or firmware, the relevant program code is stored in the memory and called and executed by the processor.

[0051] This embodiment further provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0052] Those skilled in the art will understand that the above embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce implementations of the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A fan adaptive control method based on wheel load turbulence assessment, characterized by the steps of The application comprises the following steps: Step S01. Obtain real-time wind speed data of the environment where the controlled wind turbine is located and real-time load data of the controlled wind turbine; Step S02. Convert the real-time load data of the controlled wind turbine to obtain real-time Myz hub load value; Step S03. Evaluate the current turbulence intensity state in the environment where the controlled wind turbine is located according to the real-time Myz hub load value; Step S04. Control the operation mode of the controlled wind turbine according to the current turbulence intensity state evaluation result obtained in step S03 and the real-time wind speed data, wherein if the current turbulence intensity state is an abnormal state and the real-time wind speed data reaches a specified wind speed condition, the controlled wind turbine is controlled to perform power reduction and speed reduction action.

2. The fan adaptive control method based on wheel load turbulence assessment of claim 1, wherein, In step S02, the real-time Myz hub load value is converted from the real-time load data of the controlled wind turbine, which comprises the following steps: Step S201. Obtain the loads My1, My2 and My3 of the three blades measured by the blade root sensor of the controlled wind turbine; Step S202. Convert the obtained loads My1, My2 and My3 to hub center My and Mz loads through coordinate transformation; Step S203. Synthesize the hub center My and Mz loads to obtain the Myz hub load value of the hub center.

3. The fan adaptive control method based on wheel load turbulence assessment of claim 1, wherein, In step S02, the statistical value of the real-time Myz hub load value is also calculated to evaluate the turbulence intensity state in the current environment of the controlled wind turbine according to the statistical value of the real-time Myz hub load value. In step S03, whether the specified turbulence condition is reached is determined according to the statistical value of the real-time Myz load.

4. The fan adaptive control method based on wheel load turbulence assessment of claim 3, wherein, The statistical value includes any one or more of std value, mean value, variance, range, peak value, MAD and IQR.

5. The fan adaptive control method based on wheel load turbulence assessment of claim 3, wherein, Step S03 comprises the following steps: Step S301. Find the Myz hub load threshold corresponding to the current wind speed from the first Myz load boundary scheduling table pre-constructed and take it as the first load threshold, wherein the first Myz load boundary scheduling table stores the Myz hub load threshold under different wind speed conditions to keep the wind turbine in normal power generation condition; Step S302. Determine whether the statistical value of the current real-time Myz load value exceeds the first load threshold, if yes, determine that the current turbulence intensity state is an abnormal turbulence intensity state, otherwise, determine that it is a normal turbulence intensity state.

6. The fan adaptive control method based on wheel load turbulence assessment according to any one of claims 1-5, wherein, Step S04 comprises the following steps: Step S401. Determine whether the current turbulence intensity state evaluated according to the real-time Myz hub load value is an abnormal state, if yes, go to step S402, otherwise, return to step S01; Step S402. Determine whether the current real-time wind speed data exceeds the specified wind speed condition, if yes, go to step S403, otherwise, return to step S01; Step S403. Control the controlled wind turbine to perform power reduction and speed reduction action.

7. The fan adaptive control method based on wheel load turbulence assessment according to any one of claims 1-5, wherein, Step S04 further comprises step S05 of controlling the controlled wind turbine to exit the power reduction and speed reduction action, and step S05 comprises the following steps: Step S501. Statistically determine the duration of the current power reduction and speed reduction action, if the duration of the power reduction and speed reduction action reaches a preset time threshold, go to step S502; Step S502. Find the Myz hub load threshold corresponding to the current wind speed from a pre-constructed second Myz load boundary schedule table, in which the Myz hub load thresholds for keeping the wind turbine in normal operating condition under different wind speed conditions after power and speed reduction are stored. Step S503. Determine whether the statistical value of the real-time Myz load value exceeds the second load threshold. If yes, continue to maintain the power and speed reduction action and return to step S501; otherwise, restore the normal power generation state and return to step S01.

8. The fan adaptive control method based on wheel load turbulence assessment according to any one of claims 1-5, wherein, In step S01, when obtaining the real-time wind speed data of the environment where the controlled wind turbine is located, and / or in step S02, when obtaining the statistical value of the real-time Myz hub load value, the obtained data is further subjected to sliding window filtering processing.

9. A fan adaptive control device based on wheel load turbulence assessment, characterized by, Comprise: A load acquisition module for acquiring real-time wind speed data of the environment where the controlled wind turbine is located and real-time load data of the controlled wind turbine; A hub load value calculation module for converting the real-time load data of the controlled wind turbine to obtain a real-time Myz hub load value; A turbulence state evaluation module for evaluating the current turbulence intensity state in the environment where the controlled wind turbine is located according to the real-time Myz hub load value; An adaptive control module for controlling the operating mode of the controlled wind turbine according to the current turbulence intensity state evaluation result obtained by the turbulence state evaluation module and the real-time wind speed data, wherein if the current turbulence intensity state is evaluated as an abnormal state and the real-time wind speed data reaches a specified wind speed condition, the controlled wind turbine is controlled to perform a power and speed reduction action.

10. An electronic device comprising a processor and a memory for storing a computer program, characterized in that The processor is configured to execute the computer program to perform the method of any one of claims 1-8.

11. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the method of any one of claims 1-8.

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