Wind turbine generator cabin north correction method and system based on Beidou satellite

Abstract

The invention discloses a Beidou satellite-based wind turbine generator cabin north correction method and system. The method comprises the following steps: S1, dividing sub-wind fields; s2, selecting a reference unit; s3, introducing a Beidou satellite system to perform north correction on the reference unit; s4, collecting operation data of all wind turbine generators in the wind power plant; s5, preprocessing and screening the operation data; s6, according to the operation data and based on a kernel probability density estimation method, calculating an optimal correction value of each wind turbine generator set; s7, performing north correction on the wind turbine generator according to the obtained optimal correction value; according to the method, a Beidou satellite system and a kernel probability density estimation method based on operation data are used, the cabin of each unit of the wind power plant is corrected to the north, the error is reduced, the wake flow control efficiency of the wind power plant is improved, and compared with a traditional method, the method can shorten the construction period and reduce the maintenance cost, and has great application advantages.

Description

Technical Field

[0001] The present invention relates to the technical field of north alignment of a wind turbine generator set, and in particular to a method and system for north alignment of a wind turbine generator set cabin based on a Beidou satellite. Background Art

[0002] Clean energy has developed rapidly in recent years, and wind power has been rapidly applied as a relatively mature new energy source. With the increase in the number of wind farms and the expansion of their scale, the wake effect in wind farms has become prominent. The wake effect not only affects the power generation of the entire farm, but also increases the load on downstream units. The widely used wake optimization control currently reduces the impact of wake by deflecting the wake, but this control method requires accurate wind farm flow and the accurate nacelle position of each wind turbine in the wind farm. The accurate position of the nacelle of each wind turbine has a certain calibration error due to manual calibration in the early stage of commissioning. This error has become the main error source for the nacelle's north calibration, which in turn affects the efficiency of wake control execution. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art. A method and system for north alignment correction of a wind turbine cabin based on Beidou satellite is proposed. Beidou satellite is used to perform north alignment correction on the cabins of some wind turbines. These wind turbines are used as reference units. According to the principle of consistency of wind direction to north of each wind turbine in a sub-wind farm, the historical operating data of the remaining wind turbines are compared and analyzed with the historical data of the reference units. The kernel probability density estimation method is used to obtain the north alignment errors of the cabins of the remaining wind turbines.

[0004] The object of the present invention is achieved by the following technical solution: A method for north alignment of a wind turbine cabin based on Beidou satellite, comprising the following steps:

[0005] S1. Divide the wind farm into sub-wind farms according to the arrangement of wind turbines;

[0006] S2. For each wind farm, select a wind turbine unit close to the center of the wind farm as a reference unit;

[0007] S3, introducing the Beidou satellite system to perform north correction on the reference unit selected in step S2;

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

[0009] S5, pre-processing the operation data for conversion, and then screening according to preset conditions to obtain processed operation data;

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

[0011] S7. According to the obtained optimal correction value, the wind turbine generator set is corrected to the north.

[0012] Further, the step S1 comprises:

[0013] In a wind farm, the wind farm is divided into a plurality of sub-wind farms according to the areas where the wind turbines are 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] Beidou satellite measuring equipment is used to measure the north direction on top of the reference unit cabin. The Beidou satellite system uses triangulation methods to receive signals from multiple satellites, and then the positions of the satellites are calculated to determine the true north direction. Finally, the reference unit cabin is corrected for north direction.

[0016] Further, the step S4 comprises:

[0017] 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.

[0018] Further, the step S5 comprises:

[0019] S5.1. Convert the collected data from second-level data to 10-minute average data;

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

[0021] S5.3. Compare the wind measurement data of the remaining wind turbines with those of the reference turbine during the same period to obtain the deviation sequence of the remaining wind turbines.

[0022] Further, the step S6 comprises:

[0023] 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:

[0024] S6.1. Import the gaussian_kde function and assume that angle_deviation is an array containing the deviation angles.

[0025] 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;

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

[0027] 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;

[0028] 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, that is, the optimal correction value of the deviation angle, to obtain the optimal correction value of a single wind turbine;

[0029] Repeat steps S6.1 to S6.5 to obtain the optimal correction values ​​of the remaining wind turbines.

[0030] A wind turbine cabin north alignment correction system based on Beidou satellite, used to implement the above-mentioned wind turbine cabin north alignment correction method based on Beidou satellite, comprising:

[0031] BeiDou satellite system, used to make north alignment corrections for the selected reference units;

[0032] An operation data collection module is used to collect the operation data of all wind turbines in the wind farm within a preset period;

[0033] 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;

[0034] 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;

[0035] The north correction module performs north correction on each wind turbine according to the obtained optimal correction value.

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

[0037] According to the processed operating data 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:

[0038] a. Import the gaussian_kde function and assume that angle_deviation is an array containing the deviation angles;

[0039] 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;

[0040] c. Generate a deviation angle range angle_deviation_range, the deviation angle range is from -180 degrees to 180 degrees, representing the possible deviation angles;

[0041] 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;

[0042] 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, that is, the optimal correction value of the deviation angle, to obtain the optimal correction value of a single wind turbine;

[0043] Repeat steps a to e to obtain the optimal correction values ​​of the remaining wind turbines.

[0044] A non-temporary computer-readable medium storing instructions, when the instructions are executed by a processor, executes the steps of the above-mentioned method for north alignment correction of a wind turbine nacelle based on Beidou satellites.

[0045] A computing device comprises a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the above-mentioned method for north alignment correction of a wind turbine cabin based on Beidou satellite is implemented.

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

[0047] The present invention uses the BeiDou satellite system and a method based on kernel probability density estimation of operating data to correct the north orientation of the cabins of each unit in the wind farm, thereby reducing the error and improving the efficiency of the wind farm wake control. Compared with traditional methods, the present invention can shorten the construction period and reduce maintenance costs, and has greater application advantages. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of a wind farm without sub-wind farm division.

[0049] Figure 2 Schematic diagram of the wind farm after being divided into sub-wind farms.

[0050] Figure 3 Schematic diagram of the reference unit selected in the sub-wind farm.

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

[0052] Figure 5 This is the distribution diagram of the deviation angle of a wind turbine unit from the reference unit in terms of wind direction to the north.

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

[0054] Figure 7 This is the coordinate distribution map of the wind farm.

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

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

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

[0058] The present invention will be further described below in conjunction with specific embodiments.

[0059] Example 1

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

[0061] S1. See Figure 1 to Figure 2 As shown, in a wind farm, the wind farm is divided into a plurality of sub-wind farms according to the areas where the wind turbines are concentrated, and the distance between the 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 measuring equipment is used to measure the north direction on top of the reference unit cabin. The Beidou satellite system uses triangulation methods to receive signals from multiple satellites, and then the positions of the satellites are calculated to determine the true north direction. Finally, the reference unit cabin is corrected for north direction.

[0065] S4, collecting the 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. Pre-process the operation data for conversion, and then filter it according to preset conditions to obtain the processed operation data, including:

[0068] S5.1. Convert the collected data from second-level data to 10-minute average data;

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

[0070] S5.3. Compare the wind measurement data of the remaining wind turbines with those of the reference turbine during the same period to obtain the deviation sequence of the remaining wind turbines.

[0071] S6. Calculate the optimal correction value of each wind turbine generator set according to the operating data processed in step S5 and based on the kernel probability density estimation method, including:

[0072] 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:

[0073] S6.1. Import the gaussian_kde function and assume that angle_deviation is an array containing the deviation angles.

[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, generating a deviation angle range angle_deviation_range, the deviation angle range is from -180 degrees to 180 degrees, representing 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, that is, 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 values ​​of the remaining wind turbines.

[0079] Step S6 implementation code see Figure 4 shown.

[0080] See also Figures 5 and 6As shown, the scattered points in the deviation distribution graph correspond to angle_deviation, the points on the curve in the kernel probability density estimation graph correspond to density, and the deviation angle corresponding to the dotted line in the deviation distribution graph and the highest point of the kernel probability density corresponds to opt_angle.

[0081] By using kernel probability density estimation, a smooth distribution of the deviation angle data is obtained, which helps to make more accurate cabin north correction. In the case of sparse data or noise, kernel probability density estimation (KDE) provides an effective way to obtain the underlying structure of the data.

[0082] S7. According to the obtained optimal correction value, the wind turbine generator set is corrected to the north.

[0083] Example 2

[0084] Taking an offshore wind farm as an example, there are 47 wind turbines in the wind farm. The layout coordinates of each wind turbine in the wind farm are shown in Table 1:

[0085]

[0086]

[0087] Table 1 Wind turbine layout coordinates

[0088] The method for calibrating the wind turbine nacelle to the north based on Beidou satellite provided in this embodiment includes the following steps:

[0089] S1. See Figures 7 and 8 As shown, in a wind farm, the wind farm is divided into four 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 wind farm, select a wind turbine unit near the center of the wind farm as the reference unit. Fig. 9 As shown in Table 2, the wind turbines included in the four sub-wind farms and their corresponding reference turbines are as follows:

[0091] Sub-wind farm Reference unit Other 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 Division of reference units and other units in a sub-wind farm

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

[0094] The operator uses Beidou satellite measuring equipment to measure the north direction on the top of the reference unit cabin. Using the Beidou satellite system, the operator receives signals from multiple satellites using triangulation methods, calculates the positions of the satellites to determine the direction of true north, and finally corrects the north direction of the reference unit cabin.

[0095] S4, collecting the operating data of all wind turbines in the wind farm within a preset period;

[0096] 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.

[0097] S5. Pre-process the operation data for conversion, and then filter it according to preset conditions to obtain the processed operation data, including:

[0098] S5.1. Convert the collected data from second-level data to 10-minute average data;

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

[0100] S5.3. Subtract the wind direction to the north of each wind turbine set from that of its corresponding reference turbine set at the same time end to obtain the deviation angle sequence of each wind turbine set.

[0101] S6. According to the operating data processed in step S5 and based on the kernel probability density estimation method, the optimal correction value of each wind turbine is calculated. The optimal correction values ​​of all non-reference units are obtained by calculation using the gaussian_kde function of the scipy library of Python and the data obtained in the previous step. The optimal correction values ​​of all units are shown in Table 3 below, where 31, 6, 45, and 21 are reference units, and their optimal correction values ​​are 0 degrees.

[0102]

[0103]

[0104] Table 3 Statistics of optimal correction values ​​for all wind turbines

[0105] S7. According to the optimal correction value obtained in step S6, the wind turbine generator set is corrected for north.

[0106] Example 3

[0107] The wind turbine nacelle north alignment correction system based on Beidou satellite provided in this embodiment is used to implement the wind turbine nacelle north alignment correction method based on Beidou satellite described in Embodiment 1 or 2, including:

[0108] BeiDou satellite system, used to make north alignment corrections for the selected reference units;

[0109] An operation data collection 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 pre-process the operation data for conversion and then filter it according to preset conditions to obtain the processed operation data;

[0111] 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, including:

[0112] According to the processed operating data 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:

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

[0114] 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;

[0115] c. Generate a deviation angle range angle_deviation_range, the deviation angle range is 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 and 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, that 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 values ​​of the remaining wind turbines.

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

[0120] Example 4

[0121] This embodiment discloses a non-transitory computer-readable medium storing instructions. When the instructions are executed by a processor, the steps of the method for north alignment of a wind turbine cabin based on Beidou satellites according to Embodiment 1 or 2 are performed.

[0122] The non-temporary computer-readable medium in this embodiment may be a disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a USB flash drive, a mobile hard disk, or other media.

[0123] Example 5

[0124] This embodiment discloses a computing device, including a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the method for north alignment correction of a wind turbine cabin based on Beidou satellite described in Embodiment 1 or 2 is implemented.

[0125] The computing device described in this embodiment may be a desktop computer, a laptop computer, a smart phone, a PDA handheld terminal, a tablet computer, a programmable logic controller (PLC), or other terminal devices with a processor function.

[0126] The embodiments described above are only preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Therefore, all changes made according to the shape and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for north alignment correction of a wind turbine nacelle based on Beidou satellite, 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 a reference unit; S3, introducing the Beidou satellite system to perform north correction on the reference unit selected in step S2; S4, collecting the operating data of all wind turbines in the wind farm within a preset period; S5, pre-processing the operation data for conversion, and then screening according to preset conditions to obtain processed operation data; S6, calculating the optimal correction value of each wind turbine generator set according to the operating data processed in step S5 and based on the kernel probability density estimation method; S7. According to the obtained optimal correction value, the wind turbine generator set is corrected to the north.

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 a plurality of sub-wind farms according to the areas where the wind turbines are concentrated, and the distance between the 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 measuring equipment is used to measure the north direction on top of the reference unit cabin. The Beidou satellite system uses triangulation method to receive signals from multiple satellites, and then the positions of the satellites are calculated to determine the true north direction. Finally, the reference unit cabin is corrected for north direction.

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 6m / s; S5.

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

6. The method for north alignment correction of a wind turbine nacelle based on BeiDou satellite according to claim 1, characterized in that: The step S6 comprises: 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 estimation object kde, which is used to calculate the probability density of a given data set; S6.3, generating a deviation angle range angle_deviation_range, 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 the 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 S6.1 to S6.5 to obtain the optimal correction values ​​of the remaining wind turbines.

7. A wind turbine cabin north alignment correction system based on Beidou satellite, characterized in that: The method for realizing the north alignment correction of a wind turbine nacelle based on Beidou satellite as described in any one of claims 1 to 6 comprises: BeiDou satellite system, used to make north alignment corrections for the selected reference units; An operation data collection 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 according to the obtained optimal correction value.

8. The north alignment correction system for a wind turbine nacelle based on Beidou satellite according to claim 7, 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 scipy library of Python, 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, the deviation angle range is 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 the 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 ​​of the remaining wind turbines.

9. 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 cabin based on Beidou satellite according to any one of claims 1 to 6 are performed.

10. 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 cabin based on Beidou satellite as described in any one of claims 1 to 6 is implemented.

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