A control method based on acceleration guaranteeing safety and wind driven generator

Abstract

The application discloses a control method based on acceleration guarantee safety and a wind driven generator, and the method comprises the following steps: calculating current turbulence data corresponding to current acceleration data by using a turbulence prediction mapping function, determining that the current turbulence data is in a certain region of a weak turbulence region, a medium turbulence region and a strong turbulence region; and adopting different strategies. The method has no error in space, is more accurate in turbulence data calculation, reduces the steps of waiting for a weather station or a wind measurement tower test and transmitting turbulence data compared with the related art, and improves the efficiency of obtaining turbulence data. Meanwhile, the intensity of turbulence encountered by the wind driven generator is judged based on the turbulence data, and reasonable strategies are made according to the judgment result, so that the safety of the wind driven generator and the power grid downstream of the wind driven generator is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind power generation, and particularly relates to a control method based on acceleration for guaranteeing safety and a wind power generator. BACKGROUND

[0002] The wind power generator is an electric power device for converting wind energy into mechanical energy, and the mechanical energy drives the rotor to rotate, and finally outputs alternating current. The turbulence refers to irregular changes of wind speed in space and time, which is caused by the turbulent flow state of wind flowing through the ground or obstacles such as buildings. In the running process of the wind power generator, the turbulence will affect the movement of the wind power generator blade, and further affect the stability of the wind power generator. If the intensity of the turbulence is large, the power output by the wind power generator will be significantly increased, the frequency of the power grid connected with the wind power generator will be increased, and further the stable operation of the power grid will be affected, and in severe cases, the power supply or damage of the power equipment downstream of the power grid will be caused, and serious economic losses will be caused.

[0003] In the related art, the turbulence data is obtained through the meteorological station or the wind measurement tower, and then the wind power generator is controlled according to the turbulence data. However, the meteorological station or the wind measurement tower is far away from some wind power generators, and the turbulence data tested by the meteorological station or the wind measurement tower is quite different from the turbulence data suffered by the wind power generator. At the same time, the meteorological station or the wind measurement tower has a delay in testing and transmitting the turbulence data, and the wind power generator cannot receive the turbulence data in time, which may cause damage to the wind power generator group, and even after the wind power generator reacts, the power output by the wind power generator is still large, which also has an impact on the power grid downstream of the wind power generator. SUMMARY

[0004] The present application provides a control method based on acceleration for guaranteeing safety and a wind power generator, which guarantees the safety of the wind power generator and the power grid downstream of the wind power generator in the case of encountering turbulence.

[0005] In a first aspect, the application provides a control method for ensuring safety based on acceleration, comprising: calculating current turbulence data corresponding to current acceleration data of a wind turbine cabin using a turbulence prediction mapping function, the current acceleration data being current detected acceleration data of the wind turbine cabin, and the turbulence prediction mapping function being determined by a corresponding relationship between historical acceleration data of a blade of the wind turbine and recorded turbulence data; determining that the current turbulence data is in a certain region of a weak turbulence region, a medium turbulence region or a strong turbulence region; if the current turbulence data is in the weak turbulence region, adopting a normal operation strategy; if the current turbulence data is in the medium turbulence region, adopting any one or more of an adjusting blade angle strategy, an adjusting wind wheel rotating speed strategy, an adjusting wind wheel direction strategy and an adjusting wind wheel position strategy; and if the current turbulence data is in the strong turbulence region, adopting a grid disengaging strategy or simultaneously adopting the adjusting blade angle strategy, the adjusting wind wheel rotating speed strategy, the adjusting wind wheel direction strategy and the adjusting wind wheel position strategy.

[0006] In the above embodiment, acceleration data is obtained by the wind turbine, and turbulence data is obtained by a meteorological station or a wind measurement tower, and a corresponding relationship between the acceleration data and the turbulence data, i.e., a turbulence prediction mapping function, is established. In a subsequent case where the wind turbine encounters turbulence, the acceleration data tested by the wind turbine is calculated by the turbulence prediction mapping function, so that the intensity of the turbulence encountered by the wind turbine can be determined. This method has no error in space, and thus is more accurate in calculating turbulence data. Compared with related technologies, the method reduces the steps of waiting for the meteorological station or the wind measurement tower to test and transmit the turbulence data, and improves the efficiency of obtaining the turbulence data. Meanwhile, the intensity of the turbulence encountered by the wind turbine is determined based on the turbulence data, and a reasonable strategy is made according to the determination result, so as to ensure the safety of the wind turbine and a power grid downstream of the wind turbine.

[0007] In combination with some embodiments of the first aspect, in some embodiments, if the current turbulence data is in the medium turbulence region, after any one or more of the adjusting blade angle strategy, the adjusting wind wheel rotating speed strategy, the adjusting wind wheel direction strategy and the adjusting wind wheel position strategy is adopted, the method further comprises: monitoring a change trend of output power in real time, if the change trend of the output power is downward, stopping any one or more of the corresponding adjusting blade angle strategy, the adjusting wind wheel rotating speed strategy, the adjusting wind wheel direction strategy and the adjusting wind wheel position strategy; and if the change trend of the output power is upward, maintaining any one or more of the corresponding adjusting blade angle strategy, the adjusting wind wheel rotating speed strategy, the adjusting wind wheel direction strategy and the adjusting wind wheel position strategy.

[0008] In the above embodiment, in the case that the wind turbine encounters medium-intensity turbulence, the output power of the wind turbine is monitored. In the case that the output power of the wind turbine is too high, the output power of the wind turbine is reduced by the above strategies to ensure the safe operation of the wind turbine and the stability of the downstream power grid. In the case that the output power of the wind turbine is too low, the above strategies are stopped, and the output power of the wind turbine is kept in a stable range to ensure the stability and reliability of the wind turbine and improve economic benefits.

[0009] In combination with some embodiments of the first aspect, in some embodiments, if the current turbulence data is in the strong turbulence region, the off-grid strategy is adopted or the blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy and the wind wheel position adjustment strategy are simultaneously adopted, specifically including: if the current turbulence data is in the strong turbulence region, the blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy and the wind wheel position adjustment strategy are simultaneously adopted; the change trend of the output power is monitored in real time, if the change trend of the output power is downward, the blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy and the wind wheel position adjustment strategy are stopped; if the change trend of the output power is upward, the off-grid strategy is adopted.

[0010] In the above embodiment, in the case that the wind turbine encounters high-intensity turbulence, the strategy of protecting the safety of the wind turbine is adopted to quickly reduce the output power of the wind turbine. At the same time, the output power of the wind turbine is monitored to determine whether the strategies can control the output power of the wind turbine in a reasonable range. When the strategies cannot control the output power of the wind turbine in a reasonable range, the wind turbine is disconnected from the grid to minimize economic losses.

[0011] In combination with some embodiments of the first aspect, in some embodiments, the data structure of the acceleration data is: acceleration value, corresponding time value, corresponding region representative value, and the data structure of the turbulence data is: turbulence value, corresponding time value, corresponding region representative value.

[0012] In the above embodiment, the subsequent acceleration data and turbulence data are trained more efficiently and quickly, and the time complexity and space complexity of the trained algorithm are more excellent.

[0013] In some embodiments of the first aspect, in some embodiments, the turbulence prediction mapping function is determined by a correspondence between historical acceleration data of the nacelle of the wind turbine and recorded turbulence data, specifically comprising: taking time values in the historical acceleration data and the recorded turbulence data as scale points in one dimension, and taking region representative values in the historical acceleration data and the recorded turbulence data as scale points in another dimension to respectively construct an acceleration data matrix and a turbulence data matrix; filling all the historical acceleration data into the acceleration data matrix according to the time values and the region representative values thereof, and filling all the recorded turbulence data into the turbulence data matrix according to the time values and the region representative values thereof; traversing each element in the acceleration data matrix to determine whether the element contains historical acceleration data; filling the element not containing historical acceleration data with historical acceleration data; traversing each element in the turbulence data matrix to determine whether the element contains recorded turbulence data; filling the element not containing recorded turbulence data with recorded turbulence data; and training the acceleration data matrix and the turbulence data matrix to determine the turbulence prediction mapping function.

[0014] In the above embodiment, by taking time values in the historical acceleration data and the recorded turbulence data as scale points in one dimension, and taking region representative values in the historical acceleration data and the recorded turbulence data as scale points in another dimension to respectively construct an acceleration data matrix and a turbulence data matrix, the formats of the historical acceleration data and the recorded turbulence data are unified, facilitating subsequent comparison and analysis of the historical acceleration data and the recorded turbulence data. Meanwhile, the missing historical acceleration data or recorded turbulence data is filled with corresponding data, so that the historical acceleration data and the recorded turbulence data that can be used are expanded, and thus the turbulence prediction mapping function obtained subsequently is more accurate.

[0015] In some embodiments of the first aspect, in some embodiments, the element without historical acceleration data is filled with historical acceleration data, specifically comprising: setting a first preset range; taking the element without historical acceleration data as a midpoint of the first preset range, finding all elements within the first preset range in the acceleration data matrix, and judging whether the relationship of all elements within the first preset range is linear; if the relationship of all elements within the first preset range is linear, using linear interpolation to assign values to the element without historical acceleration data; if the relationship of all elements within the first preset range is nonlinear, using spline interpolation to assign values to the element without historical acceleration data; the element without recorded turbulent flow data is filled with recorded turbulent flow data, specifically comprising: setting a second preset range; taking the element without recorded turbulent flow data as a midpoint of the second preset range, finding all elements within the second preset range in the turbulent flow data matrix, and judging whether the relationship of all elements within the second preset range is linear; if the relationship of all elements within the second preset range is linear, using linear interpolation to assign values to the element without recorded turbulent flow data; if the relationship of all elements within the second preset range is nonlinear, using spline interpolation to assign values to the element without recorded turbulent flow data.

[0016] In the above embodiments, by finding a certain rule or functional relationship between the element without historical acceleration data or recorded turbulent flow data and the element with historical acceleration data or recorded turbulent flow data. Then use this relationship to estimate the data, as much as possible to use the information of the element with historical acceleration data or recorded turbulent flow data to estimate the value of the missing historical acceleration data or recorded turbulent flow data of the element without historical acceleration data or recorded turbulent flow data, thereby reducing the error of assignment.

[0017] In some embodiments of the first aspect, in some embodiments, after filling all historical acceleration data into the acceleration data matrix according to its time value and regional representative value, and filling all recorded turbulent flow data into the turbulent flow data matrix according to its time value and regional representative value, before traversing each element in the acceleration data matrix to determine whether it contains historical acceleration data, it further comprises: determining whether each historical acceleration data in the acceleration data matrix is acceleration abnormal data by box plot; if the historical acceleration data is acceleration abnormal data, removing the historical acceleration data from the acceleration data matrix; determining whether each recorded turbulent flow data in the turbulent flow data matrix is turbulent flow abnormal data by box plot; if the recorded turbulent flow data is turbulent flow abnormal data, removing the recorded turbulent flow data from the turbulent flow data matrix.

[0018] In the above embodiments, whether each historical acceleration data or recorded turbulence data in the acceleration data matrix or the turbulence data matrix is abnormal data is judged, and the abnormal data is removed from the acceleration data matrix or the turbulence data matrix, so as to remove the interference of the abnormal data on subsequent training, and improve the accuracy of the subsequent turbulence prediction mapping function.

[0019] In a second aspect, the application further provides a wind turbine based on acceleration to ensure safety, comprising:

[0020] a data calculation module, configured to calculate current turbulence data corresponding to current acceleration data by using a turbulence prediction mapping function, the current acceleration data being acceleration data of a nacelle of the wind turbine detected currently, and the turbulence prediction mapping function being determined by a corresponding relationship between historical acceleration data of the nacelle of the wind turbine and recorded turbulence data;

[0021] a turbulence judgment module, configured to determine that the current turbulence data is in a certain region of a weak turbulence region, a medium turbulence region or a strong turbulence region;

[0022] a weak turbulence execution module, configured to adopt a normal operation strategy if the current turbulence data is in the weak turbulence region;

[0023] a medium turbulence execution module, configured to adopt any one or more of an adjustment blade angle strategy, an adjustment wind wheel rotation speed strategy, an adjustment wind wheel direction strategy and an adjustment wind wheel position strategy if the current turbulence data is in the medium turbulence region;

[0024] a strong turbulence execution module, configured to adopt a disengagement grid strategy or simultaneously adopt the adjustment blade angle strategy, the adjustment wind wheel rotation speed strategy, the adjustment wind wheel direction strategy and the adjustment wind wheel position strategy if the current turbulence data is in the strong turbulence region.

[0025] In combination with some embodiments of the second aspect, in some embodiments, the medium turbulence execution module further comprises:

[0026] a first medium turbulence execution submodule, configured to monitor a change trend of output power in real time;

[0027] a second medium turbulence execution submodule, configured to stop any one or more of the adjustment blade angle strategy, the adjustment wind wheel rotation speed strategy, the adjustment wind wheel direction strategy and the adjustment wind wheel position strategy if the change trend of the output power is downward;

[0028] a third medium turbulence execution submodule, configured to maintain any one or more of the adjustment blade angle strategy, the adjustment wind wheel rotation speed strategy, the adjustment wind wheel direction strategy and the adjustment wind wheel position strategy if the change trend of the output power is upward.

[0029] In some embodiments in combination with the second aspect, in some embodiments, the strong turbulence execution module further comprises:

[0030] a first strong turbulence execution submodule for simultaneously adopting the adjusting blade angle strategy, the adjusting wind wheel rotating speed strategy, the adjusting wind wheel direction strategy and the adjusting wind wheel position strategy;

[0031] a second strong turbulence execution submodule for monitoring the variation trend of the output power in real time;

[0032] a third strong turbulence execution submodule for stopping the adjusting blade angle strategy, the adjusting wind wheel rotating speed strategy, the adjusting wind wheel direction strategy and the adjusting wind wheel position strategy if the variation trend of the output power is a decrease;

[0033] a fourth strong turbulence execution submodule for adopting the disengaging grid strategy if the variation trend of the output power is an increase.

[0034] In some embodiments in combination with the second aspect, in some embodiments, the data structure of the acceleration data is: acceleration value, corresponding time value, corresponding region representative value, and the data structure of the turbulence data is: turbulence value, corresponding time value, corresponding region representative value.

[0035] In some embodiments in combination with the second aspect, in some embodiments, the wind power generator further comprises:

[0036] a data matrix construction module for constructing an acceleration data matrix and a turbulence data matrix respectively by taking the time values in the whole historical acceleration data and the recorded turbulence data as the scale points in one dimension and taking the region representative values in the whole historical acceleration data and the recorded turbulence data as the scale points in another dimension;

[0037] a data matrix filling module for filling the whole historical acceleration data into the acceleration data matrix according to the time values and the region representative values thereof, and filling the whole recorded turbulence data into the turbulence data matrix according to the time values and the region representative values thereof;

[0038] a data matrix scanning module for scanning whether each element in the acceleration data matrix contains historical acceleration data, and scanning whether each element in the turbulence data matrix contains recorded turbulence data;

[0039] a data matrix filling module for filling elements not containing historical acceleration data into the historical acceleration data, and filling elements not containing recorded turbulence data into the recorded turbulence data.

[0040] a training module for determining a turbulence prediction mapping function by training the acceleration data matrix and the turbulence data matrix.

[0041] In some embodiments of the second aspect, in some embodiments, the data matrix filling module further comprises:

[0042] a first data matrix filling submodule, configured to set the first preset range or set the second preset range;

[0043] a second data matrix filling submodule, configured to find all elements in the first preset range in the acceleration data matrix by taking an element without historical acceleration data as a midpoint of the first preset range, and find all elements in the second preset range in the turbulence data matrix by taking an element without recorded turbulence data as a midpoint of the second preset range;

[0044] a third data matrix filling submodule, configured to determine whether the relationship between all elements in the first preset range is linear, and determine whether the relationship between all elements in the second preset range is linear if the relationship between all elements in the first preset range is linear;

[0045] a fourth data matrix filling submodule, configured to assign values to the element without historical acceleration data by using a spline interpolation method if the relationship between all elements in the first preset range is nonlinear, and assign values to the element without recorded turbulence data by using a linear interpolation method if the relationship between all elements in the second preset range is linear;

[0046] a fifth data matrix filling submodule, configured to assign values to the element without historical acceleration data by using a spline interpolation method if the relationship between all elements in the first preset range is nonlinear, and assign values to the element without recorded turbulence data by using a spline interpolation method if the relationship between all elements in the second preset range is nonlinear.

[0047] In some embodiments of the second aspect, in some embodiments, the wind turbine further comprises:

[0048] an abnormal data determining module, configured to determine whether each historical acceleration data in the acceleration data matrix is acceleration abnormal data by using a box plot, and determine whether each recorded turbulence data in the turbulence data matrix is turbulence abnormal data by using a box plot;

[0049] an abnormal data removing module, configured to remove the historical acceleration data from the acceleration data matrix if the historical acceleration data is acceleration abnormal data, and remove the recorded turbulence data from the turbulence data matrix if the recorded turbulence data is turbulence abnormal data.

[0050] In a third aspect, an electronic device is provided, which comprises one or more processors and a memory.

[0051] The memory is coupled with the one or more processors, and is configured to store computer program code including computer instructions, which are invoked by the one or more processors to cause the electronic device to perform the method as described in the first aspect and any possible implementation manner of the first aspect.

[0052] In a fourth aspect, an embodiment of the present application provides a computer program product containing instructions, which, when executed on an electronic device, cause the electronic device to perform the method as described in the first aspect and any possible implementation manner of the first aspect.

[0053] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, including instructions, which, when executed on an electronic device, cause the electronic device to perform the method as described in the first aspect and any possible implementation manner of the first aspect.

[0054] It can be understood that the ground penetrating generator provided in the second aspect, the electronic device provided in the third aspect, the computer program product provided in the fourth aspect, and the computer storage medium provided in the fifth aspect are all used to perform the wireless hotspot connection method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved are referred to the beneficial effects in the corresponding method, which will not be described here.

[0055] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0056] 1. The control method based on acceleration to ensure safety provided in the present application obtains acceleration data through the wind turbine, obtains turbulence data through the weather station or wind measurement tower, and then establishes a corresponding relationship between the acceleration data and the turbulence data, i.e., a turbulence prediction mapping function. In the case that the wind turbine encounters turbulence subsequently, the acceleration data tested by the wind turbine can be calculated through the turbulence prediction mapping function, so as to determine the intensity of the turbulence encountered by the wind turbine. This method has no error in space, and thus the calculation of the turbulence data is more accurate. Compared with the related art, the method reduces the steps of waiting for the weather station or wind measurement tower to test and transmit the turbulence data, and improves the efficiency of obtaining the turbulence data. At the same time, the intensity of the turbulence encountered by the wind turbine is determined based on the turbulence data, and a reasonable strategy is made according to the determination result, so as to ensure the safety of the wind turbine and the power grid downstream of the wind turbine.

[0057] 2、The control method based on acceleration to ensure safety provided in the application, in the case of wind turbine encountering medium intensity turbulence, the output power of the wind turbine is monitored. In the case of the output power of the wind turbine being too high, the output power of the wind turbine is reduced by the above-mentioned various strategies to ensure the safe operation of the wind turbine and the stability of the downstream power grid. In the case of the output power of the wind turbine being too low, the above-mentioned various strategies are stopped, and the output power of the wind turbine is kept in a stable range to ensure the stability and reliability of the wind turbine and improve economic efficiency.

[0058] 3、The control method based on acceleration to ensure safety provided in the application, in the case of wind turbine encountering high intensity turbulence, the strategy of protecting the safety of the wind turbine is adopted to make the output power of the wind turbine decrease rapidly. At the same time, the output power of the wind turbine is monitored to determine whether these strategies can control the output power of the wind turbine within a reasonable range. When these strategies cannot control the output power of the wind turbine within a reasonable range, the wind turbine is disconnected from the power grid to minimize economic loss. BRIEF DESCRIPTION OF DRAWINGS

[0059] Figure 1 An information interaction scene diagram of the control method based on acceleration to ensure safety provided in the application.

[0060] Figure 2 An exemplary scene diagram of the control of the wind turbine in the related art.

[0061] Figure 3 An exemplary scene diagram of the control method based on acceleration to ensure safety provided in the application.

[0062] Figure 4 A flow diagram of the control method based on acceleration to ensure safety provided in the application.

[0063] Figure 5 An effect comparison diagram of the control method based on acceleration to ensure safety provided in the application.

[0064] Figure 6 Another exemplary scene diagram of the control method based on acceleration to ensure safety provided in the application.

[0065] Figure 7 Another flow diagram of the control method based on acceleration to ensure safety provided in the application.

[0066] Figure 8 A schematic diagram of the modular virtual device of the wind turbine based on acceleration to ensure safety provided in the application.

[0067] Figure 9 A schematic diagram of the physical device of the wind turbine generator with acceleration-based safety provided in this application. Detailed Implementation

[0068] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0069] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0070] It should be noted that the data structures of acceleration data and turbulence data are similar, and the data structures of acceleration data matrices and turbulence data matrices are similar. However, for the sake of simplicity, after processing the acceleration data or acceleration data matrix below, the turbulence data or turbulence data matrix should also be processed in the same way.

[0071] In some embodiments, for ease of description, the historical acceleration data in this application may also be referred to as acceleration data, and the recorded turbulence data may be referred to as turbulence data.

[0072] like Figure 1 As shown, Figure 1 This is a schematic diagram of an information interaction scenario for the acceleration-based safety control method provided in this application.

[0073] This includes wind turbines and the acceleration sensors and small computing terminal devices within them.

[0074] Accelerometers detect the movement of the nacelle in a wind turbine and calculate the acceleration based on this movement. The acceleration is then converted into an electrical signal, amplified, and processed to output a voltage signal proportional to the acceleration, reflecting the turbine's rotational speed.

[0075] The small computing terminal device receives the electric signal and converts it into acceleration data, and determines the intensity of the turbulence encountered by the wind turbine according to the acceleration data, and adjusts the blade angle, the rotating speed of the wind wheel, the direction of the wind wheel, the position of the wind wheel or the disconnection from the power grid according to the intensity of the turbulence.

[0076] As shown in Figure 2 , Figure 2 , it is an exemplary scene diagram of the control of the wind turbine in the related art.

[0077] When the wind turbine encounters turbulence, the wind measurement tower in the same area as the wind turbine detects the intensity of the turbulence and transmits it to the wind turbine, and the wind turbine adopts different strategies according to the intensity of the turbulence.

[0078] In some other embodiments, the intensity of the turbulence is detected by a weather station, and other devices for testing the intensity of the turbulence are not limited here.

[0079] However, not all wind turbines are close to the wind measurement tower, and for these wind turbines, the turbulence data tested by the wind measurement tower is quite different from the turbulence data suffered by the wind turbine, and the different strategies adopted by the wind turbine according to the intensity of the turbulence often do not conform to the actual situation. In addition, the testing and transmission of turbulence data by the wind measurement tower have a delay, and the wind turbine cannot receive the turbulence data in time, and the output power of the wind turbine is still large, which also has an impact on the power grid downstream of the wind turbine.

[0080] The control method based on acceleration to ensure safety in the embodiments of the present application will be described in detail in combination with the embodiments shown in Figure 3 .

[0081] Referring to Figure 3 , Figure 3 , it is an exemplary scene diagram of the control method based on acceleration to ensure safety provided by the present application.

[0082] As shown in (a) of Figure 3 , the small computing terminal device of the wind turbine obtains historical turbulence data in the wind measurement tower and obtains historical acceleration data in the acceleration sensor of the wind turbine, and obtains the turbulence prediction mapping function according to the historical acceleration data and the historical turbulence data.

[0083] It should be noted that in some embodiments, wind turbines close to the wind measurement tower are selected to minimize the error in space.

[0084] In some other embodiments, the weather station is used to test the intensity of the turbulence, and other devices for testing the intensity of the turbulence are not limited here.

[0085] As shown in Figure 3 , the small computing terminal device of the wind turbine obtains historical turbulence data in the wind measurement tower and obtains historical acceleration data in the acceleration sensor of the wind turbine, and obtains the turbulence prediction mapping function according to the historical acceleration data and the historical turbulence data.As shown in (b) of FIG. 1, when the wind turbine encounters turbulence, the acceleration sensor in the wind turbine detects acceleration data, and the small computing terminal device obtains the intensity of the turbulence encountered by the wind turbine through the acceleration data and the turbulence prediction mapping function, and adopts different strategies according to the intensity of the turbulence.

[0086] As shown in (c) of FIG. 1, when the calculated turbulence intensity is too large, the wind turbine is disconnected from the power grid. Figure 3

[0087] It can be seen that in the case that the wind turbine encounters turbulence, the intensity of the turbulence encountered by the wind turbine can be directly judged, compared with the related art, the steps of waiting for the meteorological station or the wind tower test and transmitting the turbulence data are reduced, and the efficiency of obtaining the turbulence data is improved. At the same time, the safety of the wind turbine and the power grid downstream of the wind turbine is ensured.

[0088] It can be understood that the above scenario is only an exemplary scenario, and in actual application, the control method can be other content or form, which is not limited here.

[0089] The control method based on acceleration to ensure safety in the embodiment is described as follows:

[0090] Reference is made to Figure 4 , Figure 4 A flowchart of the control method based on acceleration to ensure safety provided by the present application is shown in FIG. 2.

[0091] S401: Using a turbulence prediction mapping function to calculate current turbulence data corresponding to current acceleration data, the current acceleration data being the acceleration data of the nacelle of the wind turbine currently detected, and the turbulence prediction mapping function being determined by the corresponding relationship between the historical acceleration data of the blades of the wind turbine and the recorded turbulence data.

[0092] In actual application, the recorded turbulence data is the turbulence data detected by multiple wind towers or meteorological stations within a time range, and the historical acceleration data is the acceleration data detected by multiple wind turbines when they are subjected to turbulence within a time range.

[0093] In some embodiments, the machine learning model is trained. The acceleration data is taken as input, and the turbulence data is taken as output. Through training the model, the relationship between the acceleration data and the turbulence data, i.e., the turbulence prediction mapping function, can be obtained, and used for prediction and judgment. Common machine learning methods include neural network, support vector machine, random forest, etc. Of course, other training methods can also be used, which are not limited here.

[0094] ​In actual application, when the wind turbine encounters turbulence, the acceleration sensor detects abnormal acceleration data, based on which, it is determined whether the wind turbine encounters turbulence, at this time, the turbulence prediction mapping function is used to calculate the current turbulence data corresponding to the current acceleration data.

[0095] S402: Determine that the current turbulence data is in a certain region of the weak turbulence region, the medium turbulence region, or the strong turbulence region.

[0096] Among them, the flow field TI of the weak turbulence region is less than 5%, the turbulence meeting this region is weak or strong turbulence, the flow field TI of the medium turbulence region is between 5% and 15%, the turbulence meeting this region is medium intensity turbulence, and the flow field TI of the strong turbulence region is greater than 15%, the turbulence meeting this region is high intensity turbulence. Of course, other division methods can also be used, which are not limited here.

[0097] S403: If the current turbulence data is in the weak turbulence region, a normal operation strategy is adopted.

[0098] The normal operation strategy is that the wind turbine operates normally.

[0099] In actual use, the wind turbine can take various ways to prevent turbulence, for example, using longer blades can increase the moment of inertia of the wind turbine, thereby reducing the fluctuation caused by turbulence. The variable pitch body technology can make the inflow angle of the blade more stable, and reduce the vibration caused by turbulence. Therefore, the weak or strong turbulence has little effect on the wind turbine, based on which, when the wind turbine is subjected to weak or strong turbulence, the wind turbine can be operated normally to improve economic benefits.

[0100] S404: If the current turbulence data is in the medium turbulence region, any one or more of the blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy, and the wind wheel position adjustment strategy is adopted.

[0101] The blade angle adjustment strategy is to adjust the blade angle, the wind wheel rotation speed adjustment strategy is to adjust the wind wheel rotation speed, the wind wheel direction adjustment strategy is to adjust the wind wheel direction, and the wind wheel position adjustment strategy is to adjust the wind wheel position.

[0102] In actual use, for medium intensity turbulence, any one or more of the blade angle, the wind wheel rotation speed, the wind wheel direction, and the wind wheel position can reduce the influence of medium intensity turbulence on the wind turbine.

[0103] S405: If the current turbulence data is in the strong turbulence region, the off-grid strategy is adopted or the blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy, and the wind wheel position adjustment strategy are adopted at the same time.

[0104] The off-grid strategy refers to wind turbines being disconnected from the power grid.

[0105] In practical applications, for high-intensity turbulence, the impact of high-intensity turbulence on wind turbines can be reduced by adjusting all of the following: blade angle, rotor rotation speed, rotor direction, and rotor position. For extremely high-intensity turbulence, economic losses can be minimized by disconnecting the wind turbine from the power grid.

[0106] As can be seen, by acquiring acceleration data from wind turbines and turbulence data from meteorological stations or anemometer towers, and then establishing a correspondence between the acceleration and turbulence data—that is, a turbulence prediction mapping function—the intensity of the turbulence encountered by the wind turbine can be determined by calculating its own measured acceleration data using the turbulence prediction mapping function when the wind turbine subsequently encounters turbulence. This method has no spatial error, thus providing more accurate calculations of turbulence data. Furthermore, compared to related technologies, it reduces the steps of waiting for meteorological stations or anemometer towers to test and transmit turbulence data, improving the efficiency of turbulence data acquisition. Simultaneously, based on the turbulence data, the intensity of the turbulence encountered by the wind turbine is assessed, and reasonable strategies are implemented based on the assessment results to ensure the safety of the wind turbine and the downstream power grid.

[0107] In the above embodiments, the intensity of turbulence encountered by the wind turbine can be determined simply by using the turbulence prediction mapping function, allowing for the development of appropriate strategies to ensure the safety of the wind turbine and the downstream power grid. However, in practical applications, the scarcity of wind turbines close to the meteorological tower results in insufficient acceleration and turbulence data. Furthermore, the lack of a one-to-one correspondence between acceleration and turbulence data further reduces the usable data. The following section will combine... Figure 5 and Figure 6 The illustrated embodiment, taking one method of expanding data usage as an example, provides a detailed description of the acceleration-based safety control method in this application:

[0108] refer to Figure 5 , Figure 5 A comparative diagram illustrating the effects of the acceleration-based safety control method provided in this application.

[0109] refer to Figure 6 , Figure 6 This is another exemplary scenario diagram of the acceleration-based safety control method provided in this application.

[0110] Because the acceleration data and the turbulence data can have some data missing, for example, at a certain time point, only the acceleration data has data, and the turbulence data has no data. Because the acceleration data and the turbulence data can have different data sampling frequencies, i.e., different sampling time intervals, for example, the acceleration data is sampled once per second, and the turbulence data is sampled once every few seconds. As shown in (a) of FIG. 1, Figure 5 The acceleration data and the turbulence data cannot be one-to-one corresponding, so only the common part of the acceleration data and the turbulence data can be used as usable data. The usable data is too little, and the turbulence prediction mapping function obtained subsequently is not accurate enough.

[0111] As shown in (a) of FIG. 1, Figure 6 In some embodiments, the acceleration data is:

[0112] Acceleration data 1, acceleration value x1, time value a, corresponding area represents value B;

[0113] Acceleration data 2, acceleration value x2, time value b, corresponding area represents value C;

[0114] Acceleration data 3, acceleration value x3, time value d, corresponding area represents value F;

[0115] ...

[0116] Acceleration data n, acceleration value xn, time value z, corresponding area represents value Z;

[0117] The turbulence data is:

[0118] Turbulence data 1, turbulence value y1, time value b, corresponding area represents value A;

[0119] Turbulence data 2, turbulence value y2, time value c, corresponding area represents value C;

[0120] Turbulence data 3, turbulence value y3, time value d, corresponding area represents value D;

[0121] ...

[0122] Turbulence data n, turbulence value yn, time value z, corresponding area represents value Z.

[0123] Of course, in other embodiments, the acceleration data and the turbulence data can be in other forms, which are not limited herein.

[0124] As shown in (a) of FIG. 1, Figure 6As shown in (b) of FIG. 1, the acceleration data matrix and the turbulence data matrix are respectively constructed by taking the time values (a, b, c…z) in all the acceleration data and the turbulence data as the scale points in one dimension and taking the region representative values (A, B, C…Z) in all the acceleration data and the turbulence data as the scale points in another dimension.

[0125] Of course, in other embodiments, the acceleration data matrix and the turbulence data matrix are in other forms, which are not limited here.

[0126] As shown in (c) of FIG. 1, all the acceleration data is filled into the acceleration data matrix according to the time values and the region representative values thereof, and all the turbulence data is filled into the turbulence data matrix according to the time values and the region representative values thereof. Figure 6

[0127] As shown in the figure, in some embodiments, the acceleration data usable data can only use the acceleration values x1, x6, …, x86, and the current turbulence data usable data can only use the turbulence values y1, y7, …, y97.

[0128] Of course, in other embodiments, the acceleration data usable data and the current turbulence data usable data are in other forms, which are not limited here.

[0129] The elements without acceleration data are filled into the acceleration data, and the elements without turbulence data are filled into the turbulence data.

[0130] As shown in the figure, in some embodiments, the acceleration data usable data can be the velocity values x1, the acceleration values x2, …, xn, and the current turbulence data usable data can only be the turbulence values y1, y2, …, yn.

[0131] As shown in (b) of FIG. 1, the acceleration data usable data and the current turbulence data usable data can be converted from the common part of the original acceleration data and the turbulence data. Figure 5

[0132] It can be seen that, by filling the missing acceleration data or turbulence data into the acceleration data or the turbulence data, the usable acceleration data and turbulence data are expanded, and the subsequent turbulence prediction mapping function is more accurate.

[0133] In the above embodiments, various use scenarios of the control method based on acceleration to ensure safety are described, and the control method based on acceleration to ensure safety in the embodiments of the present application is described in detail in combination with the embodiment shown in Figure 7

[0134] Reference​​​Figure 7 , Figure 7 Another flowchart of the control method based on acceleration to ensure safety provided in the present application.

[0135] S701: Obtain acceleration data and turbulence data.

[0136] The acceleration data is historical acceleration data of the blades of the wind turbine, and the turbulence data is recorded turbulence data.

[0137] In some embodiments, the recorded turbulence data is turbulence data detected by multiple wind measurement towers or weather stations within a time range, and the historical acceleration data is acceleration data detected by multiple wind turbines when subjected to turbulence within a time range.

[0138] S702: Take the time values in all acceleration data and turbulence data as the scale points in one dimension, and take the region representative values in all acceleration data and turbulence data as the scale points in another dimension to respectively construct an acceleration data matrix and a turbulence data matrix.

[0139] In some embodiments, the row number of the acceleration data matrix and the turbulence data matrix represents the time value, and the column number represents the region representative value, and each element can be distinguished by the time value and the region representative value it is in. At the same time, since the scale points of the speed data matrix and the scale points of the turbulence data matrix are consistent, the elements in the acceleration data matrix one-to-one correspond to the elements in the turbulence data matrix.

[0140] S703: Fill all acceleration data into the acceleration data matrix according to its time value and region representative value.

[0141] In some embodiments, the acceleration data finds the element in the acceleration data matrix according to its time value and region representative value, and fills the acceleration value into the element. Similarly, the turbulence data finds the element in the current turbulence data matrix according to its time value and region representative value, and fills the turbulence value into the element.

[0142] In other embodiments, due to instrument failure, data input error, human operation error, etc., several acceleration data in the acceleration data are quite different from other acceleration data, and their acceleration values deviate significantly from the acceleration values. These acceleration data may lead to inaccurate turbulence prediction mapping functions in the subsequent process.

[0143] S703: Determine whether each acceleration data in the acceleration data matrix is acceleration abnormal data through the box plot. If the acceleration data is acceleration abnormal data, remove the acceleration data from the acceleration data matrix. Determine whether each turbulence data in the turbulence data matrix is turbulence abnormal data through the box plot. If the turbulence data is turbulence abnormal data, remove the turbulence data from the turbulence data matrix.

[0144] In some embodiments, the method of drawing the box plot is as follows: sort the acceleration values, and then calculate the quartiles. The lower quartile is the value of 25% of the acceleration values, and the upper quartile is the value of 75% of the acceleration values.

[0145] According to the quartiles, calculate the positions of the box and the line. The length of the box represents the range of the middle 50% of the acceleration values, and the line represents the maximum and minimum values of the acceleration values.

[0146] Determine the range of the acceleration values with the box and the line, and then find the acceleration values that are out of the range. The acceleration data represented by the acceleration values is acceleration abnormal data.

[0147] S704: Traverse each element in the acceleration data matrix to determine whether it contains acceleration data.

[0148] In some embodiments, determine whether each element in the acceleration data matrix contains acceleration data by judging whether the element is filled with an acceleration value.

[0149] In another case, an element in the acceleration data matrix is filled with an acceleration value at first, but is deleted as acceleration abnormal data later. In this case, the element is determined to not contain acceleration data.

[0150] Of course, other judgment methods can also be used, which are not limited herein.

[0151] S705: Fill the element that does not contain acceleration data with acceleration data, and use the acceleration data in the turbulence data matrix as the acceleration data for training.

[0152] In some embodiments, specifically including

[0153] Set a first preset range;

[0154] Fill the element that does not contain acceleration data as the midpoint of the first preset range, find all elements in the acceleration data matrix within the first preset range, and determine whether the relationship of all elements within the first preset range is linear;

[0155] If the relationship of all elements within the first preset range is linear, use linear interpolation to assign values to the element that does not contain acceleration data;

[0156] If the relationship between all elements in the first preset range is nonlinear, the spline interpolation method is used to assign values to the elements without acceleration data.

[0157] Of course, other assignment methods can also be used, which are not limited here.

[0158] In some embodiments, the linear interpolation method calculates the interpolation of the elements without acceleration data through the coordinates and slopes of these elements.

[0159] In some embodiments, the spline interpolation divides these elements into several small intervals, and a low-order polynomial is used to fit in each small interval. The coefficients of these low-order polynomials can be obtained by a certain calculation method, so as to obtain the entire interpolation function, and the interpolation of the elements without acceleration data is calculated through the interpolation function.

[0160] S706: Fill all turbulence data into the turbulence data matrix according to its time value and region representative value.

[0161] S707: Traverse each element in the turbulence data matrix to determine whether it contains turbulence data.

[0162] S708: Fill the elements without turbulence data into the turbulence data, and use the turbulence data matrix as the turbulence data for training.

[0163] To simplify the description, after processing the acceleration data matrix, the turbulence data matrix should also refer to the above operations, i.e., steps S706, S707 and S708, and the specific implementation process is detailed in S703, S704 and S705, which will not be repeated here.

[0164] S709: Train to obtain the turbulence prediction mapping function according to the acceleration data matrix and the turbulence data matrix.

[0165] The steps used in this embodiment belong to the same concept as the steps used in the above embodiments, and the specific implementation process is detailed in Figure 1 The embodiments and steps S402, which will not be repeated here.

[0166] S710: Calculate the current turbulence data corresponding to the current acceleration data using the turbulence prediction mapping function.

[0167] In some embodiments, the training is performed by establishing a machine learning model. The acceleration data is used as input, and the turbulence data is used as output. Through the training model, the relationship between the acceleration data and the turbulence data, i.e., the turbulence prediction mapping function, can be obtained, and is used for prediction and judgment. Common machine learning methods include neural networks, support vector machines, random forests, etc. Of course, other training methods can also be used, which are not limited here.

[0168] S711: Determine whether the current turbulence data is in a weak turbulence region, a medium turbulence region, or a strong turbulence region.

[0169] The steps adopted by this embodiment belong to the same concept as the steps adopted by the above-mentioned embodiments, and the specific implementation process is described in detail in the above-mentioned embodiments. Figure 1 The steps S402 of the above-mentioned embodiments are not described here.

[0170] S712: If the current turbulence data is in a weak turbulence region, a normal operation strategy is adopted.

[0171] The normal operation strategy means that the wind turbine is in normal operation. The above-mentioned embodiments mention that the wind turbine can adopt various ways to prevent turbulence. Therefore, the weak and strong turbulence cannot have a great impact on the wind turbine, and the wind turbine can be in normal operation to ensure economic benefits.

[0172] S713: If the current turbulence data is in a medium turbulence region, any one or more of the blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy, and the wind wheel position adjustment strategy is adopted.

[0173] The blade angle adjustment strategy is to adjust the blade angle, the wind wheel rotation speed adjustment strategy is to adjust the wind wheel rotation speed, the wind wheel direction adjustment strategy is to adjust the wind wheel direction, and the wind wheel position adjustment strategy is to adjust the wind wheel position.

[0174] In actual use, any one or more of the blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy, and the wind wheel position adjustment strategy can make the impact of medium-intensity turbulence on the wind turbine within an acceptable range. However, if these strategies continue, the output power of the wind turbine will decrease, causing economic losses.

[0175] S714: Real-time monitor the change trend of the output power, and if the change trend of the output power is downward, stop any one or more of the corresponding blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy, and the wind wheel position adjustment strategy.

[0176] When the change trend of the output power is downward, it can be determined that any one or more of the blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy, and the wind wheel position adjustment strategy can make the impact of turbulence on the wind turbine within an acceptable range. Therefore, these strategies are stopped to allow the wind turbine to operate normally, avoiding the decrease of the output power of the wind turbine caused by these strategies and economic losses.

[0177] S715: If the trend of the output power is increasing, then keep any one or more of the adjusting blade angle strategy, the adjusting rotor rotation speed strategy, the adjusting rotor direction strategy, and the adjusting rotor position strategy.

[0178] When the trend of the output power is increasing, it can be determined that the current use of any one or more of the adjusting blade angle strategy, the adjusting rotor rotation speed strategy, the adjusting rotor direction strategy, and the adjusting rotor position strategy cannot make the influence of the moderate intensity turbulence on the wind turbine within an acceptable range, so these strategies are continued. The influence of the moderate intensity turbulence on the wind turbine is within an acceptable range.

[0179] In other embodiments, the adopted strategy can be increased according to the actual situation.

[0180] S716: If the current turbulence data is in the strong turbulence region, and any one or more of the adjusting blade angle strategy, the adjusting rotor rotation speed strategy, the adjusting rotor direction strategy, and the adjusting rotor position strategy are adopted.

[0181] In actual use, in order to avoid the damage of the high intensity turbulence to the wind turbine, the adjusting blade angle strategy, the adjusting rotor rotation speed strategy, the adjusting rotor direction strategy, and the adjusting rotor position strategy are adopted at the same time to make the influence of the high intensity turbulence on the wind turbine within an acceptable range.

[0182] S717: Real-time monitor the trend of the output power, if the trend of the output power is decreasing, then stop the adjusting blade angle strategy, the adjusting rotor rotation speed strategy, the adjusting rotor direction strategy, and the adjusting rotor position strategy.

[0183] In actual use, by real-time monitoring the trend of the output power, it is determined whether these strategies will make the influence of the high intensity turbulence on the wind turbine within an acceptable range, if within an acceptable range, then stop the adjusting blade angle strategy, the adjusting rotor rotation speed strategy, the adjusting rotor direction strategy, and the adjusting rotor position strategy, to avoid the decrease of the output power of the wind turbine caused by these strategies, and to avoid economic losses.

[0184] S718: If the trend of the output power is increasing, then adopt the off-grid strategy.

[0185] If not within an acceptable range, the wind turbine is disconnected from the grid to avoid affecting the downstream power grid, to protect the downstream power equipment, and to minimize economic losses. At the same time, the adjusting blade angle strategy, the adjusting rotor rotation speed strategy, the adjusting rotor direction strategy, and the adjusting rotor position strategy are continued to make the influence of the high intensity turbulence on the wind turbine within a minimum range.

[0186] According to the technical solution, the control method based on acceleration to ensure safety provided by the application obtains acceleration data through the wind turbine, obtains turbulence data through the weather station or the wind measurement tower, and establishes a corresponding relationship between the acceleration data and the turbulence data, i.e., a turbulence prediction mapping function. In the case that the wind turbine encounters turbulence, the acceleration data tested by the wind turbine is calculated through the turbulence prediction mapping function, and the strength of the turbulence encountered by the wind turbine can be determined. This method has no error in space, and thus the calculation of the turbulence data is more accurate. Compared with related technologies, the method reduces the steps of waiting for the weather station or the wind measurement tower to test and transmit the turbulence data, and improves the efficiency of obtaining the turbulence data. At the same time, the strength of the turbulence encountered by the wind turbine is determined based on the turbulence data, and a reasonable strategy is made according to the determination result to ensure the safety of the wind turbine and the power grid downstream of the wind turbine.

[0187] The control method based on acceleration to ensure safety provided by the application monitors the output power of the wind turbine in the case that the wind turbine encounters medium-strength turbulence. In the case that the output power of the wind turbine is too high, the output power of the wind turbine is reduced through various strategies to ensure the safe operation of the wind turbine and the stability of the downstream power grid. In the case that the output power of the wind turbine is too low, the various strategies are stopped, and the output power of the wind turbine is kept in a stable range to ensure the stability and reliability of the wind turbine and improve economic benefits.

[0188] In the case that the wind turbine encounters high-strength turbulence, the control method based on acceleration to ensure safety provided by the application adopts a strategy to protect the safety of the wind turbine, so that the output power of the wind turbine is rapidly reduced. At the same time, the output power of the wind turbine is monitored to determine whether the strategy can control the output power of the wind turbine in a reasonable range. When the strategy cannot control the output power of the wind turbine in a reasonable range, the wind turbine is disconnected from the power grid to minimize economic losses.

[0189] The control method based on acceleration to ensure safety provided by the application makes the training of subsequent acceleration data and turbulence data more efficient and fast, and makes the time complexity and space complexity of the trained algorithm more excellent.

[0190] The control method based on acceleration to ensure safety provided in the application unifies the formats of the acceleration data and the turbulence data by taking the time values in the acceleration data and the turbulence data as the scale points in one dimension and taking the region representative values in the acceleration data and the turbulence data as the scale points in another dimension to construct the acceleration data matrix and the turbulence data matrix, respectively, so that the acceleration data and the turbulence data can be directly compared and analyzed subsequently. Meanwhile, the missing acceleration data or turbulence data is filled in the acceleration data or the turbulence data, so that the acceleration data and the turbulence data that can be used are expanded, and the turbulence prediction mapping function obtained subsequently is more accurate.

[0191] The control method based on acceleration to ensure safety provided in the application finds a certain rule or function relationship between the elements without acceleration data or turbulence data and the elements with acceleration data or turbulence data. Then, the data estimation is performed by using the relationship, and the values of the missing acceleration data or turbulence data of the elements without acceleration data or turbulence data are estimated by using the information of the elements with acceleration data or turbulence data as much as possible, so that the error of the assignment is reduced.

[0192] The control method based on acceleration to ensure safety provided in the application judges whether each acceleration data or turbulence data in the acceleration data matrix or the turbulence data matrix is abnormal data, and removes the abnormal data from the acceleration data matrix or the turbulence data matrix, so as to remove the interference of the abnormal data on the subsequent training and improve the accuracy of the turbulence prediction mapping function subsequently.

[0193] The following is an apparatus embodiment of the application, which can be used to execute the method embodiments of the application. For details not disclosed in the apparatus embodiments of the application, please refer to the method embodiments of the application.

[0194] Please refer to Figure 8 which shows a schematic diagram of a modular virtual device of a wind turbine based on acceleration to ensure safety provided in an example embodiment of the application. The wind turbine can be realized by software, hardware or a combination of the two to become all or part of the wind turbine.

[0195] The wind turbine comprises:

[0196] The data calculation module 801 is configured to calculate current turbulence data corresponding to current acceleration data by using a turbulence prediction mapping function, the current acceleration data being acceleration data of a nacelle of the wind turbine detected currently, and the turbulence prediction mapping function being determined according to the corresponding relationship between the historical acceleration data of the nacelle of the wind turbine and the recorded turbulence data;

[0197] The turbulence judgment module 802 is configured to determine that the current turbulence data is in a weak turbulence region, a medium turbulence region, or a strong turbulence region.

[0198] The weak turbulence execution module 803 is configured to adopt a normal operation strategy if the current turbulence data is in the weak turbulence region.

[0199] The medium turbulence execution module 804 is configured to adopt any one or more of a blade angle adjustment strategy, a wind wheel rotation speed adjustment strategy, a wind wheel direction adjustment strategy, and a wind wheel position adjustment strategy if the current turbulence data is in the medium turbulence region.

[0200] The strong turbulence execution module 805 is configured to adopt a grid disengagement strategy or simultaneously adopt the blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy, and the wind wheel position adjustment strategy if the current turbulence data is in the strong turbulence region.

[0201] In some embodiments, the medium turbulence execution module 804 further comprises:

[0202] A first medium turbulence execution submodule is configured to monitor a change trend of the output power in real time.

[0203] A second medium turbulence execution submodule is configured to stop any one or more of the corresponding blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy, and the wind wheel position adjustment strategy if the change trend of the output power is downward.

[0204] A third medium turbulence execution submodule is configured to maintain any one or more of the corresponding blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy, and the wind wheel position adjustment strategy if the change trend of the output power is upward.

[0205] In some embodiments, the strong turbulence execution module 805 further comprises:

[0206] A first strong turbulence execution submodule is configured to simultaneously adopt the blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy, and the wind wheel position adjustment strategy.

[0207] A second strong turbulence execution submodule is configured to monitor a change trend of the output power in real time.

[0208] A third strong turbulence execution submodule is configured to stop the blade angle adjustment strategy, the wind wheel rotation speed adjustment strategy, the wind wheel direction adjustment strategy, and the wind wheel position adjustment strategy if the change trend of the output power is downward.

[0209] A fourth strong turbulence execution submodule is configured to adopt the grid disengagement strategy if the change trend of the output power is upward.

[0210] In some embodiments, the data structure of the acceleration data is: acceleration value, corresponding time value, corresponding region representative value, and the data structure of the turbulence data is: turbulence value, corresponding time value, corresponding region representative value.

[0211] In some embodiments, the wind turbine further comprises:

[0212] a data matrix construction module, configured to construct an acceleration data matrix and a turbulence data matrix respectively by taking the time values in the historical acceleration data and the recorded turbulence data as the scale points in one dimension and taking the region representative values in the historical acceleration data and the recorded turbulence data as the scale points in another dimension;

[0213] a data matrix filling module, configured to fill the historical acceleration data into the acceleration data matrix according to the time values and the region representative values of the historical acceleration data, and fill the recorded turbulence data into the turbulence data matrix according to the time values and the region representative values of the recorded turbulence data;

[0214] a data matrix scanning module, configured to scan whether each element in the acceleration data matrix contains historical acceleration data, and scan whether each element in the turbulence data matrix contains recorded turbulence data;

[0215] a data matrix filling module, configured to fill elements not containing historical acceleration data with historical acceleration data, and fill elements not containing recorded turbulence data with recorded turbulence data.

[0216] a training module, configured to determine a turbulence prediction mapping function by training the acceleration data matrix and the turbulence data matrix.

[0217] In some embodiments, the data matrix filling module further comprises:

[0218] a first data matrix filling submodule, configured to set a first preset range or set a second preset range;

[0219] a second data matrix filling submodule, configured to take elements not containing historical acceleration data as the midpoint of the first preset range, find all elements in the acceleration data matrix within the first preset range, and take elements not containing recorded turbulence data as the midpoint of the second preset range, find all elements in the turbulence data matrix within the second preset range;

[0220] a third data matrix filling submodule, configured to determine whether the relationship between all elements in the first preset range is linear, and if the relationship between all elements in the first preset range is linear, determine whether the relationship between all elements in the second preset range is linear;

[0221] The fourth data matrix filling sub-module is configured to assign values to elements without historical acceleration data by using a spline interpolation method if the relationship between all elements in the first preset range is nonlinear, and to assign values to elements without recorded turbulence data by using a linear interpolation method if the relationship between all elements in the second preset range is linear.

[0222] The fifth data matrix filling sub-module is configured to assign values to elements without historical acceleration data by using a spline interpolation method if the relationship between all elements in the first preset range is nonlinear, and to assign values to elements without recorded turbulence data by using a spline interpolation method if the relationship between all elements in the second preset range is nonlinear.

[0223] In some embodiments, the wind power generator further comprises:

[0224] The abnormal data judging module is configured to determine whether each historical acceleration data in the acceleration data matrix is acceleration abnormal data by using a box plot, and to determine whether each recorded turbulence data in the turbulence data matrix is turbulence abnormal data by using a box plot.

[0225] The abnormal data removing module is configured to remove the historical acceleration data from the acceleration data matrix if the historical acceleration data is acceleration abnormal data, and to remove the recorded turbulence data from the turbulence data matrix if the recorded turbulence data is turbulence abnormal data.

[0226] It should be noted that the apparatus provided in the above embodiments is only used as an example to divide the above functional modules, and in actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is described in detail in the method embodiments, which will not be described here.

[0227] The embodiment of the application further provides a computer storage medium, which can store a plurality of instructions, the instructions being suitable for being loaded and executed by a processor to implement the control method based on acceleration guaranteeing safety as shown in the above Figures 1-7 The specific implementation process can refer to the specific description of the embodiment of the control method based on acceleration guaranteeing safety as shown in the above Figures 1-7 The specific implementation process can refer to the specific description of the embodiment of the control method based on acceleration guaranteeing safety as shown in the above

[0228] The application further discloses an electronic device. Referring to Figure 9 , Figure 9The schematic diagram of the entity device of the wind driven generator based on acceleration guarantee safety provided in the present application. The electronic device 900 can include: at least one processor 901, at least one network interface 904, a user interface 903, a memory 905, at least one communication bus 902.

[0229] The communication bus 902 is used to realize the connection communication between the components.

[0230] The user interface 903 can include a display screen (Display), a camera (Camera), and the optional user interface 903 can further include a standard wired interface, a wireless interface.

[0231] The network interface 908 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface).

[0232] The processor 901 can include one or more processing cores. The processor 901 connects various parts in the server through various interfaces and lines, executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 905, and calling data stored in the memory 905. Optionally, the processor 901 can be realized in at least one of the hardware forms of digital signal processing (DSP), field-programmable gate array (FPGA), programmable logic array (PLA). The processor 901 can integrate a combination of one or several of central processing unit (CPU), graphics processing unit (GPU) and modem. Among them, the CPU mainly processes operating systems, user interfaces and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 901, but be realized by a separate chip.

[0233] The memory 905 may include random access memory (RAM) or read-only memory. Optionally, the memory 905 may include a non-transitory computer-readable storage medium. The memory 905 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 905 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 905 may also be at least one storage device located remotely from the aforementioned processor 901. (Refer to...) Figure 9 The memory 405, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for acceleration-based security control.

[0234] exist Figure 9 In the illustrated electronic device 900, the user interface 903 is mainly used to provide an input interface for the user and acquire user input data; while the processor 901 can be used to call the application program stored in the memory 905 for acceleration-based safety control. When executed by one or more processors 901, the electronic device 900 performs one or more of the methods described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0235] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0236] In several embodiments provided in the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments is merely illustrative, and the division of units can be changed according to actual conditions, such as a combination or integration of some units, or a distribution of some features to other systems, or some features can be ignored, or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units can be indirect coupling or communication connection through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0237] The units described as separate components may or can not be physically separate, and the components shown as units may or can not be physical units, i.e. may be located in one place or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0238] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0239] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or the whole or part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, magnetic disk or optical disk, and various program code storage media.

[0240] The above is only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will easily think of other embodiments of the present disclosure after considering the specification and the true disclosure.

[0241] The present application is intended to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A control method for ensuring safety based on acceleration, characterized in that, include: The current turbulence data corresponding to the current acceleration data is calculated using a turbulence prediction mapping function. The current acceleration data refers to the acceleration data of the currently detected wind turbine nacelle. The turbulence prediction mapping function is determined by the correspondence between the historical acceleration data of the wind turbine nacelle and the recorded turbulence data. Wherein: Using the time values ​​in all the historical acceleration data and the recorded turbulence data as a scale point in one dimension, and using the region representative values ​​in all the historical acceleration data and the recorded turbulence data as scale points in another dimension, an acceleration data matrix and a turbulence data matrix are constructed respectively. All historical acceleration data are filled into the acceleration data matrix according to their time values ​​and region representative values, and all recorded turbulence data are filled into the turbulence data matrix according to their time values ​​and region representative values. Iterate through each element in the acceleration data matrix to determine if it contains the historical acceleration data. Fill in historical acceleration data for elements that do not contain historical acceleration data; And to iterate through each element in the turbulence data matrix to see if it contains the recorded turbulence data; Fill the recorded turbulence data with elements that do not contain recorded turbulence data; The acceleration data matrix and the turbulence data matrix are trained to determine the turbulence prediction mapping function; The current turbulence data is determined to be located in a region of weak turbulence, moderate turbulence, or strong turbulence. If the current turbulence data is in the weak turbulence region, a strategy to maintain normal operation is adopted; If the current turbulence data is in the medium turbulence region, then any one or more of the following strategies are adopted: adjusting blade angle, adjusting rotor rotation speed, adjusting rotor direction, and adjusting rotor position. If the current turbulence data is in the strong turbulence region, then the disconnection from the power grid strategy or the simultaneous use of the blade angle adjustment strategy, the wind turbine rotation speed adjustment strategy, the wind turbine direction adjustment strategy, and the wind turbine position adjustment strategy shall be adopted.

2. The control method for ensuring safety based on acceleration according to claim 1, characterized in that, If the current turbulence data is in the intermediate turbulence region, after employing any one or more of the following strategies—adjusting the blade angle, adjusting the rotor rotation speed, adjusting the rotor direction, and adjusting the rotor position—the method further includes: The output power is monitored in real time. If the output power is decreasing, then any one or more of the following strategies are stopped: adjusting blade angle, adjusting rotor rotation speed, adjusting rotor direction, and adjusting rotor position. If the output power changes in an upward trend, then maintain any one or more of the following strategies: adjusting blade angle, adjusting rotor rotation speed, adjusting rotor direction, and adjusting rotor position.

3. The control method for ensuring safety based on acceleration according to claim 1, characterized in that, If the current turbulence data is in a strong turbulence region, a strategy of disconnecting from the power grid or a strategy of simultaneously adjusting the blade angle, the rotor rotation speed, the rotor direction, and the rotor position is adopted, specifically including: If the current turbulence data is in a strong turbulence region, the following strategies are employed simultaneously: adjusting the blade angle, adjusting the wind turbine rotation speed, adjusting the wind turbine direction, and adjusting the wind turbine position. The output power is monitored in real time. If the output power is decreasing, the strategies for adjusting the blade angle, adjusting the rotor rotation speed, adjusting the rotor direction, and adjusting the rotor position are stopped. If the output power shows an upward trend, then the grid disconnection strategy is adopted.

4. The control method for ensuring safety based on acceleration according to claim 1, characterized in that, The data structure for acceleration data is: acceleration value, corresponding time value, and corresponding region representative value. The data structure for turbulence data is: turbulence value, corresponding time value, and corresponding region representative value.

5. The control method for ensuring safety based on acceleration according to claim 1, characterized in that, The step of filling historical acceleration data with elements that do not contain historical acceleration data specifically includes: Set the first preset range; The element that does not contain historical acceleration data is taken as the midpoint of the first preset range. All elements within the first preset range are found in the acceleration data matrix, and it is determined whether the relationship between all elements within the first preset range is linear. If the relationship between all elements within the first preset range is linear, then the linear interpolation method is used to assign values ​​to the elements that do not contain historical acceleration data. If the relationship between all elements within the first preset range is nonlinear, then the spline interpolation method is used to assign values ​​to the elements that do not contain historical acceleration data. The step of filling the recorded turbulence data with elements that do not contain recorded turbulence data specifically includes: Set a second preset range; Using the turbulence data elements that do not contain records as the midpoint of the second preset range, find all elements within the second preset range in the turbulence data matrix, and determine whether the relationship between all elements within the second preset range is linear. If the relationship between all elements within the second preset range is linear, then the linear interpolation method is used to assign values ​​to the elements that do not contain recorded turbulence data. If the relationship between all elements within the second preset range is non-linear, then spline interpolation is used to assign values ​​to the elements that do not contain recorded turbulence data.

6. The control method for ensuring safety based on acceleration according to claim 1, characterized in that, After filling the acceleration data matrix with all historical acceleration data according to its time value and region representative value, and after filling the turbulence data matrix with all recorded turbulence data according to its time value and region representative value, before iterating through each element of the acceleration data matrix to check if it contains historical acceleration data, the process further includes: The box plot is used to determine whether each historical acceleration data point in the acceleration data matrix is ​​an abnormal acceleration data point. If the historical acceleration data is abnormal acceleration data, then the historical acceleration data is removed from the acceleration data matrix; The box plot is used to determine whether the turbulence data in each record of the turbulence data matrix is ​​turbulence anomaly data; If the recorded turbulence data is abnormal turbulence data, then the turbulence data of that record is removed from the turbulence data matrix.

7. A wind turbine generator with safety guaranteed by acceleration, characterized in that, For performing the method as described in any one of claims 1-6, comprising: The data calculation module is used to calculate the current turbulence data corresponding to the current acceleration data using the turbulence prediction mapping function. The current acceleration data is the acceleration data of the nacelle of the wind turbine that is currently detected. The turbulence prediction mapping function is determined by the correspondence between the historical acceleration data of the nacelle of the wind turbine and the recorded turbulence data. The turbulence determination module is used to determine whether the current turbulence data is in a weak turbulence region, a moderate turbulence region, or a strong turbulence region. The weak turbulence execution module is used to adopt a strategy of maintaining normal operation if the current turbulence data is in the weak turbulence region. The medium turbulence execution module is used to employ any one or more of the following strategies if the current turbulence data is in the medium turbulence region: adjusting blade angle, adjusting wind turbine rotation speed, adjusting wind turbine direction, and adjusting wind turbine position. The strong turbulence execution module is used to employ a grid disconnection strategy or simultaneously employ the blade angle adjustment strategy, the wind turbine rotation speed adjustment strategy, the wind turbine direction adjustment strategy, and the wind turbine position adjustment strategy if the current turbulence data is in the strong turbulence region.

8. An electronic device, characterized in that, include: One or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1-6.

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