A method and system for correcting north based on a beidou satellite for a wind turbine nacelle

Abstract

The present invention discloses a method and system for north alignment correction of a wind turbine nacelle based on Beidou satellites. The method comprises: S1, dividing a wind farm into sub-wind farms; S2, selecting a reference turbine; S3, introducing the Beidou satellite system to perform north alignment correction on the reference turbine; S4, collecting operating data of all wind turbines in the wind farm; S5, preprocessing and screening the operating data; S6, calculating the optimal correction value of each wind turbine according to the operating data and based on a kernel probability density estimation method; S7, performing north alignment correction on the wind turbine according to the obtained optimal correction value. The present invention uses the Beidou satellite system and a method based on kernel probability density estimation of operating data to perform north alignment correction on the nacelles of each turbine in the wind farm, thereby reducing errors and improving the efficiency of wake control in the wind farm. Compared with traditional methods, the present invention can shorten construction period and reduce maintenance costs, and has greater application advantages.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind turbine north correction, in particular to a wind turbine nacelle north correction method and system based on Beidou satellite. BACKGROUND

[0002] Clean energy has developed rapidly in recent years, and wind power as a relatively mature new energy power generation has been rapidly applied. With the increase in the number of wind farm construction and the expansion of the scale, the wake effect in the wind farm is highlighted. The wake effect not only affects the power generation of the whole field, but also causes the load of the downstream unit to increase. The widely used wake optimization control is to reduce the influence of the wake effect by deflecting the wake, but this control method needs to obtain the accurate wind field inflow and the accurate nacelle position of each wind turbine in the wind farm. The accurate position of each wind turbine nacelle has a certain calibration error caused by artificial calibration in the initial debugging, and this error becomes the main error source of the nacelle north calibration, thereby affecting the efficiency of the wake control execution. SUMMARY

[0003] The present application aims to overcome the shortcomings of the prior art and provides a wind turbine nacelle north correction method and system based on Beidou satellite. The Beidou satellite is used to correct the nacelle north of part of the wind turbines, and these wind turbines are used as reference units. According to the wind direction north consistency principle of each wind turbine in the sub-wind field, the historical operation data of the remaining wind turbines are compared and analyzed with the historical data of the reference units, and the nacelle north error of the remaining wind turbines is obtained by using the kernel probability density estimation method.

[0004] The purpose of the present application is achieved by the following technical scheme: a wind turbine nacelle north correction method based on Beidou satellite, comprising the following steps:

[0005] S1, dividing the wind farm into sub-wind fields according to the arrangement of wind turbines;

[0006] S2, for each sub-wind field, selecting a wind turbine close to the center of the sub-wind field as a reference unit;

[0007] S3, introducing the Beidou satellite system to correct the north of the reference unit selected in step S2;

[0008] S4, collecting the operation data of all wind turbines in the wind farm within a preset period;

[0009] S5, preprocessing the operation data by conversion, and then screening according to the preset condition to obtain the processed operation data;

[0010] S6, calculating the optimal correction value of each wind turbine according to the processed operation data in step S5 and based on the kernel probability density estimation method;

[0011] S7. Correcting northward of the wind turbine according to the optimal correction value.

[0012] Further, the step S1 comprises:

[0013] In the wind farm, the wind farm is divided into multiple sub wind farms according to the area where the wind turbine set is concentrated, and the distance between the wind turbines in each sub wind farm is not greater than a preset threshold.

[0014] Further, the step S3 comprises:

[0015] The north correction of the cabin of the reference machine set is measured by using the Beidou satellite measurement equipment on the top of the cabin, the signals from multiple satellites are received by using the triangulation method of the Beidou satellite system, the position of the satellite is calculated to determine the north direction, and finally the cabin of the reference machine set is corrected northward.

[0016] Further, the step S4 comprises:

[0017] Collecting the operation data of all wind turbines in the wind farm within 2 months; the operation data are all second-level data, including time, wind speed, wind direction north data and cabin north data; at the same time, all wind turbines do not perform wind direction correction operation during the data collection process.

[0018] Further, the step S5 comprises:

[0019] S5.1, converting the collected data from second-level data to 10 min average data;

[0020] S5.2, screening out data with wind speed greater than 6 m / s;

[0021] S5.3, comparing the wind measurement data of the remaining wind turbines with the reference machine set at the same period to obtain the deviation sequence of each wind turbine.

[0022] Further, the step S6 comprises:

[0023] According to the operation data processed in step S5 and based on the gaussian_kde function of the scipy library of python, the optimal correction value of each wind turbine is calculated, including the following steps:

[0024] S6.1, importing the gaussian_kde function, and assuming that angle_deviation is an array containing the deviation angle;

[0025] S6.2, using gaussian_kde to create a kernel density estimation object kde, which is used to calculate the probability density of the given data set;

[0026] S6.3, generating a range of deviation angles angle_deviation_range, the range of deviation angles from -180 degrees to 180 degrees, representing possible deviation angles;

[0027] S6.4, calling the kde object to calculate the probability density of each deviation angle in the range of deviation angles, obtaining the density array;

[0028] S6.5, using the argmax method to find the index corresponding to the maximum value of the probability density, and using the index to obtain opt_angle, the optimal correction value of the deviation angle, to obtain the optimal correction value of the single wind turbine generator;

[0029] Repeating steps S6.1 to S6.5 can obtain the optimal correction value of the remaining wind turbine generators.

[0030] A wind turbine nacelle north correction system based on Beidou satellite, for implementing the above-mentioned wind turbine nacelle north correction method based on Beidou satellite, comprising:

[0031] Beidou satellite system, for north correction of selected reference units;

[0032] Operation data acquisition module, for collecting operation data of all wind turbine generators in the wind farm within a predetermined period;

[0033] Data processing module, for pre-processing of the converted operation data, and then filtering according to the preset conditions to obtain the processed operation data;

[0034] Optimal correction value calculation module, for calculating the optimal correction value of each wind turbine generator according to the operation data and based on the kernel probability density estimation method;

[0035] North correction module, for north correction of each wind turbine generator according to the obtained optimal correction value.

[0036] Further, the optimal correction value calculation module comprises:

[0037] According to the processed operation data and based on the gaussian_kde function of the scipy library of python, the optimal correction value of each wind turbine generator is calculated, including the following steps:

[0038] a, importing the gaussian_kde function and assuming that angle_deviation is an array containing deviation angles;

[0039] b, using gaussian_kde to create a kernel density estimation object kde, which is used to calculate the probability density of a given data set;

[0040] c. generating an angle deviation range angle_deviation_range, the angle deviation range ranging from -180 degrees to 180 degrees, representing possible deviation angles;

[0041] d. calling the kde object to calculate the probability density of each deviation angle in the angle deviation range, obtaining a density array;

[0042] e. finding the index corresponding to the maximum value of the probability density using the argmax method, and obtaining the optimal correction value opt_angle of the deviation angle by using the index, to obtain the optimal correction value of the single wind turbine generator;

[0043] Repeating steps a to e can obtain the optimal correction value of the remaining wind turbine generators.

[0044] A non-transitory computer readable medium storing instructions, when the instructions are executed by a processor, perform the steps of the Beidou satellite-based wind turbine nacelle north correction method according to the above.

[0045] A computing device comprising a processor and a memory for storing processor-executable programs, when the processor executes the programs stored in the memory, the above-mentioned Beidou satellite-based wind turbine nacelle north correction method is realized.

[0046] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0047] The present application uses the Beidou satellite system, and the method of kernel probability density estimation based on operation data to correct the nacelle north of each unit of the wind farm, reduce the error, and improve the efficiency of the wind farm wake control. Compared with the traditional method, the present application can shorten the construction period, reduce the maintenance cost, and has great application advantages. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 It is a schematic diagram of a wind farm without sub-wind field division.

[0049] Figure 2 It is a schematic diagram of a wind farm after sub-wind field division.

[0050] Figure 3 It is a schematic diagram of a selected reference unit in a sub-wind field.

[0051] Figure 4 It is a schematic diagram of the implementation code of step S6.

[0052] Figure 5 It is a deviation angle distribution diagram of the wind direction north of a certain wind turbine and a reference unit.

[0053] Figure 6This is the kernel probability density distribution diagram of a wind turbine unit facing north and the reference unit.

[0054] Figure 7 Coordinate distribution map of wind farms.

[0055] Figure 8 The coordinate distribution diagram of the wind farm divided into four sub-wind farms.

[0056] Figure 9 for Figure 8 Coordinate distribution diagram of reference units in the four sub-wind farms.

[0057] Figure 10 The figure is a flow chart of the north alignment method for the wind turbine nacelle. DETAILED DESCRIPTION

[0058] The present invention will be further described below with reference to specific embodiments.

[0059] Example 1

[0060] See also Figure 10 As shown, the method for north alignment correction of a wind turbine nacelle based on BeiDou satellites provided in this embodiment includes the following steps:

[0061] S1, see Figures 1 to 2 As shown, in a wind farm, the wind farm is divided into multiple sub-wind farms according to the areas where wind turbines are concentrated, and the distance between wind turbines in each sub-wind farm is not greater than a preset threshold;

[0062] S2, see Figure 3 As shown, for each sub-wind farm, a wind turbine unit close to the center of the sub-wind farm is selected as the reference unit;

[0063] S3, introducing the BeiDou satellite system to perform north correction on the reference unit selected in step S2;

[0064] Beidou satellite measurement equipment is used to measure the north direction on the top of the reference unit cabin. The Beidou satellite system uses triangulation method to receive signals from multiple satellites, and then calculates the position of the satellites to determine the true north direction. Finally, the north direction of the reference unit cabin is corrected.

[0065] S4. Collecting operating data of all wind turbines in the wind farm within a preset period;

[0066] The operating data of all wind turbines in the wind farm are collected within 2 months. The operating data are all second-level data, including time, wind speed, wind direction data relative to the north, and nacelle data relative to the north. At the same time, all wind turbines do not perform wind direction correction operations during the data collection process.

[0067] S5, preprocessing the operation data, and then screening according to the preset conditions to obtain the processed operation data, including:

[0068] S5.1, converting the collected data from second-level data to 10min average data;

[0069] S5.2, screening out data with wind speed greater than 6m / s;

[0070] S5.3, comparing the wind measurement data of the remaining wind turbines and the reference turbine in the same period to obtain the deviation sequence of each remaining wind turbine.

[0071] S6, according to the processed operation data of step S5 and based on the kernel probability density estimation method, calculating the optimal correction value of each wind turbine, including:

[0072] According to the processed operation data of step S5 and based on the gaussian_kde function of the scipy library of python, the optimal correction value of each wind turbine is calculated, including the following steps:

[0073] S6.1, import the gaussian_kde function, and assume that angle_deviation is an array containing the deviation angle;

[0074] S6.2, use gaussian_kde to create a kernel density estimation object kde, which is used to calculate the probability density of a given data set;

[0075] S6.3, generate a range of deviation angles angle_deviation_range, the range of deviation angles from -180 degrees to 180 degrees, representing the possible deviation angles;

[0076] S6.4, call the kde object to calculate the probability density of each deviation angle in the range of deviation angles, and obtain the density array;

[0077] S6.5, use the argmax method to find the index corresponding to the maximum value of the probability density, and use the index to obtain opt_angle, the optimal correction value of the deviation angle, to obtain the optimal correction value of a single wind turbine;

[0078] Repeat steps S6.1 to S6.5 to obtain the optimal correction value of the remaining wind turbines.

[0079] The code of step S6 is shown in Figure 4 .

[0080] See Figures 5 to 6As shown, the scatter points in the deviation distribution diagram correspond to angle_deviation, the points on the curve in the kernel probability density estimation diagram correspond to density, and the dashed line in the deviation distribution diagram and the highest point of the kernel probability density correspond to the deviation angle opt_angle.

[0081] By using kernel probability density estimation, a smooth distribution diagram of the deviation angle data is obtained, which helps to more accurately perform the nacelle-to-north correction. In the case of sparse data or the presence of noise, kernel probability density estimation (KDE) provides an effective way to obtain the potential structure of the data.

[0082] S7, according to the obtained optimal correction value, performing the nacelle-to-north correction on the wind turbine.

[0083] Embodiment 2

[0084] Taking a certain offshore wind farm as an example, the wind farm has a total of 47 wind turbines, and the arrangement coordinates of each wind turbine in the wind farm are as shown in Table 1:

[0085]

[0086]

[0087] Table 1 arrangement coordinates of wind turbines

[0088] The nacelle-to-north correction method for wind turbines based on Beidou satellites provided in this embodiment includes the following steps:

[0089] S1, referring to Figures 7 to 8 As shown, in the wind farm, the wind farm is divided into 4 sub-wind farms according to the areas where wind turbines are concentrated, and the distance between wind turbines in each sub-wind farm is not greater than a preset threshold;

[0090] S2, for each sub-wind farm, selecting a wind turbine close to the center of the sub-wind farm as a reference machine, referring to Figure 9 As shown, the wind turbines contained in the 4 sub-wind farms and their corresponding reference machines are as shown in Table 2:

[0091] Sub-wind farm Reference unit Remaining units 1 31 24，25，31，32，37，38，39，40，41，42 2 6 1，2，3，4，5，6，7，8，14，15，16，17，18 3 45 33，34，35，36，43，44，45，46，47 4 21 9，10，11，12，13，14，19，20，21，22，23，26，27，28，29，30

[0092] Table 2 reference machine and remaining machine division in sub-wind farm

[0093] S3, introducing the Beidou satellite system to perform the nacelle-to-north correction on the reference machine selected in step S2;

[0094] The operator uses the Beidou satellite measurement device to measure north at the top of the reference unit cabin, uses a triangulation method through the Beidou satellite system, receives signals from multiple satellites, calculates the positions of the satellites to determine the north direction, and finally corrects the cabin of the reference unit north.

[0095] S4, collect the operation data of all wind turbines in the wind farm in a preset period;

[0096] Collect the operation data of all wind turbines in the wind farm in 2 months; the operation data are all second-level data, including time, wind speed, wind direction north data and cabin north data; at the same time, all wind turbines do not perform wind direction correction operation in the data collection process.

[0097] S5, pre-process the operation data, and then screen according to a preset condition to obtain processed operation data, including:

[0098] S5.1, convert the collected data from second-level data to 10min average data;

[0099] S5.2, screen out data with wind speed greater than 6m / s;

[0100] S5.3, subtract the wind direction north of the same time end of each wind turbine and its corresponding reference unit to obtain the deviation angle sequence of each wind turbine.

[0101] S6, according to the processed operation data of step S5 and based on the kernel probability density estimation method, calculate the optimal correction value of each wind turbine, use the gaussian_kde function of the scipy library of python and the data obtained in the previous step, calculate the optimal correction value of all non-reference units, and the optimal correction value of all units is as shown in Table 3, wherein 31, 6, 45 and 21 are reference units, and the optimal correction value is 0 degrees.

[0102]

[0103]

[0104] Table 3 optimal correction value statistics table of all wind turbines

[0105] S7, according to the optimal correction value obtained in step S6, correct the wind turbine north.

[0106] Example 3

[0107] The Beidou satellite-based wind turbine cabin north correction system provided in this embodiment is used to implement the Beidou satellite-based wind turbine cabin north correction method of embodiments 1 or 2, and includes:

[0108] The Beidou satellite system is used to correct the north of the selected reference unit group;

[0109] The operation data acquisition module is used to collect the operation data of all wind turbines in the wind farm within a preset period.

[0110] The data processing module is used to convert the operation data for preprocessing, and then filter according to the preset condition to obtain the processed operation data.

[0111] The optimal correction value calculation module calculates the optimal correction value of each wind turbine based on the operation data and the kernel probability density estimation method, including:

[0112] According to the processed operation data and the gaussian_kde function of the scipy library of python, the optimal correction value of each wind turbine is calculated, including the following steps:

[0113] a. Import the gaussian_kde function and assume that angle_deviation is an array containing the deviation angle;

[0114] b. Use gaussian_kde to create a kernel density estimation object kde, which is used to calculate the probability density of the given data set;

[0115] c. Generate a range of deviation angles angle_deviation_range, which ranges from -180 degrees to 180 degrees, representing the possible deviation angles;

[0116] d. Call the kde object to calculate the probability density of each deviation angle in the range of deviation angles to obtain the density array;

[0117] e. Use the argmax method to find the index corresponding to the maximum value of the probability density, and use the index to obtain opt_angle, which is the optimal correction value of the deviation angle, to obtain the optimal correction value of a single wind turbine;

[0118] Repeat steps a to e to obtain the optimal correction value of the remaining wind turbines.

[0119] The north correction module corrects the north of each wind turbine according to the obtained optimal correction value.

[0120] Embodiment 4

[0121] The embodiment discloses a non-transitory computer readable medium storing instructions, when the instructions are executed by a processor, the steps of the Beidou satellite-based wind turbine nacelle north correction method according to embodiment 1 or 2 are executed.

[0122] The non-transitory computer readable medium in the embodiment can be a disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a U disk, a mobile hard disk, and the like.

[0123] Embodiment 5

[0124] The embodiment discloses a computing device, comprising a processor and a memory for storing a processor-executable program, when the processor executes the program stored in the memory, the method for correcting north based on the Beidou satellite is realized.

[0125] The computing device in the embodiment can be a desktop computer, a notebook computer, a smart phone, a PDA handheld terminal, a tablet computer, a programmable logic controller (PLC), or other terminal devices with processor functions.

[0126] The above-mentioned embodiments are only the preferred embodiments of the present application, and are not intended to limit the scope of the present application, and any changes made according to the shape and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A method for north alignment correction of a wind turbine nacelle based on BeiDou satellites, characterized in that: The following steps are involved: S1. Divide the wind farm into sub-wind farms according to the arrangement of wind turbines; S2. For each wind farm, select a wind turbine unit close to the center of the wind farm as the reference unit; S3, introducing the BeiDou satellite system to perform north correction on the reference unit selected in step S2; S4. Collecting operating data of all wind turbines in the wind farm within a preset period; S5. Pre-process the operation data for conversion, and then filter it according to preset conditions to obtain processed operation data; S6. Calculating the optimal correction value of each wind turbine generator set based on the operating data processed in step S5 and using a kernel probability density estimation method, including: According to the operating data processed in step S5 and based on the gaussian_kde function of the scipy library of Python, the optimal correction value of each wind turbine is calculated, including the following steps: S6.

1. Import the gaussian_kde function and assume that angle_deviation is an array containing the deviation angles. S6.

2. Use gaussian_kde to create a kernel density estimator object kde, which is used to calculate the probability density of a given data set; S6.

3. Generate a deviation angle range angle_deviation_range, where the deviation angle range is from -180 degrees to 180 degrees, representing possible deviation angles; S6.

4. Call the kde object to calculate the probability density of each deviation angle in the range of deviation angles and obtain the density array; S6.

5. Use the argmax method to find the index corresponding to the maximum value of the probability density, and use this index to obtain opt_angle, that is, the optimal correction value of the deviation angle, to obtain the optimal correction value for a single wind turbine. Repeat steps S6.1 to S6.5 to obtain the optimal correction values ​​for the remaining wind turbines; S7. Perform north correction on the wind turbine generator set according to the obtained optimal correction value.

2. The method for north alignment correction of a wind turbine nacelle based on BeiDou satellite according to claim 1, characterized in that: The step S1 comprises: In a wind farm, the wind farm is divided into multiple sub-wind farms according to the areas where wind turbines are concentrated, and the distance between wind turbines in each sub-wind farm is not greater than a preset threshold.

3. The method for north alignment correction of a wind turbine nacelle based on BeiDou satellite according to claim 1, characterized in that: The step S3 comprises: Beidou satellite measurement equipment is used to measure the north direction on the top of the reference unit cabin. The Beidou satellite system uses triangulation method to receive signals from multiple satellites, and then calculates the position of the satellites to determine the true north direction. Finally, the north direction of the reference unit cabin is corrected.

4. The method for north alignment correction of a wind turbine nacelle based on BeiDou satellite according to claim 1, characterized in that: The step S4 comprises: The operating data of all wind turbines in the wind farm are collected within 2 months. The operating data are all second-level data, including time, wind speed, wind direction data relative to the north, and nacelle data relative to the north. At the same time, all wind turbines do not perform wind direction correction operations during the data collection process.

5. The method for north alignment correction of a wind turbine nacelle based on BeiDou satellite according to claim 1, characterized in that: The step S5 comprises: S5.

1. Convert the collected data from second-level data to 10-minute average data; S5.

2. Filter out data with wind speed greater than 6 m / s; S5.

3. Compare the wind measurement data of the remaining wind turbines with the wind measurement data of the reference turbine during the same period to obtain the deviation sequence of the remaining wind turbines.

6. A wind turbine nacelle north alignment correction system based on BeiDou satellite, characterized in that: The method for implementing the north alignment correction of a wind turbine nacelle based on BeiDou satellites as described in any one of claims 1 to 5 comprises: BeiDou satellite system, used to perform north alignment corrections on the selected reference units; An operation data acquisition module is used to collect the operation data of all wind turbines in the wind farm within a preset period; The data processing module is used to pre-process the operation data for conversion and then filter it according to preset conditions to obtain the processed operation data; The optimal correction value calculation module calculates the optimal correction value of each wind turbine according to the operating data and based on the kernel probability density estimation method; The north correction module performs north correction on each wind turbine generator set according to the obtained optimal correction value.

7. The wind turbine nacelle north alignment correction system based on BeiDou satellite according to claim 6, characterized in that: The optimal correction value calculation module includes: According to the processed operating data and based on the gaussian_kde function of the Python scipy library, the optimal correction value of each wind turbine is calculated, including the following steps: a. Import the gaussian_kde function and assume that angle_deviation is an array containing the deviation angles. b. Use gaussian_kde to create a kernel density estimation object kde, which is used to calculate the probability density of a given data set; c. Generate a deviation angle range angle_deviation_range, which ranges from -180 degrees to 180 degrees, representing the possible deviation angles; d. Call the kde object to calculate the probability density of each deviation angle in the range of deviation angles and obtain the density array; e. Use the argmax method to find the index corresponding to the maximum value of the probability density, and use this index to obtain opt_angle, that is, the optimal correction value of the deviation angle, to obtain the optimal correction value of a single wind turbine; Repeat steps a to e to obtain the optimal correction values ​​for the remaining wind turbines.

8. A non-transitory computer-readable medium storing instructions, characterized in that: When the instruction is executed by the processor, the steps of the method for north alignment correction of a wind turbine nacelle based on Beidou satellites according to any one of claims 1 to 5 are performed.

9. A computing device comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the method for north alignment correction of a wind turbine nacelle based on Beidou satellites as described in any one of claims 1 to 5 is implemented.

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