A method, device and equipment for determining sway parameters of wind turbine tower

Abstract

The present invention provides a method, device and equipment for determining the sway parameters of a wind turbine tower, and relates to the technical field of wind power generation. The method comprises: obtaining observation point data on the wind turbine tower; preprocessing the observation point data to obtain a characteristic data set; obtaining the maximum tower sway distance and corresponding characteristic points according to the characteristic data set; determining the wind turbine tower sway amplitude and sway period according to the maximum tower sway distance and corresponding characteristic points. In the scheme of the present invention, the acquisition of tower sway amplitude data is more convenient and faster, making monitoring more accurate and comprehensive, ensuring the accuracy of wind turbine tower sway amplitude monitoring, and high reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a method, a device and equipment for determining sway parameters of a wind turbine tower. Background Art

[0002] Wind power generation plays a key role in energy structure adjustment and emission reduction. In recent years, with the rapid increase in installed capacity of wind farms, the operation monitoring and fault troubleshooting of wind turbines have become increasingly complex, and the losses caused by various accidents are unbearable. Therefore, it is crucial to establish reliable condition monitoring and fault analysis technology to ensure the normal operation of wind turbines.

[0003] Wind turbine tower verticality detection is an important step to ensure the normal operation of wind turbines and prevent safety accidents. Excessive wind turbine tower shaking is a threat to the safe operation of the unit. Existing wind turbine tower shaking monitoring mostly uses technologies such as tilt sensors, spectrum analysis, acceleration sensors, Doppler laser radar, etc., and achieves online or remote monitoring by installing sensor equipment. The installation and maintenance are expensive, which increases the investment cost of the monitoring system. The sensitivity and accuracy of the sensor are affected by external conditions, and the monitoring results are easily affected by extreme weather conditions. The implementation of wind turbine tower shaking monitoring is difficult, the method is complex, and the reliability is difficult to guarantee, and the accuracy of monitoring cannot be guaranteed. Summary of the invention

[0004] The present invention provides a method, device and equipment for determining the sway parameters of a wind turbine tower, so as to solve the problem that the existing method cannot ensure the accuracy of monitoring the sway amplitude of the wind turbine tower.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A parameter method for the sway amplitude of a wind turbine tower, comprising:

[0007] Obtain observation point data on the wind turbine tower;

[0008] Preprocessing the observation point data to obtain a feature data set;

[0009] According to the characteristic data set, the maximum tower shaking distance and the corresponding characteristic points are obtained;

[0010] The sway amplitude and sway period of the wind turbine tower are determined according to the maximum sway distance of the tower and the corresponding characteristic points.

[0011] Optionally, the observation point data is preprocessed to obtain a feature data set, including:

[0012] Removing or correcting missing values ​​and abnormal values ​​in the observation point data to obtain feature point data;

[0013] The feature point data is converted into a preset data structure to obtain a feature data set, wherein the preset data structure includes the three-dimensional coordinates and observation time of the feature points.

[0014] Optionally, according to the characteristic data set, obtaining the maximum tower sway distance and corresponding characteristic points includes:

[0015] Traversing and calculating the distances between the feature points in the feature data set to obtain a set of spatial distance solution vectors;

[0016] The spatial distance solution vector set is traversed to obtain the maximum tower sway distance and the corresponding feature point position.

[0017] Optionally, the distances between the feature points in the feature data set are calculated traversally to obtain a set of spatial distance solution vectors, including:

[0018] By formula:

[0019]

[0020] Calculate the distance between any two observation points in the feature data set to obtain a set of spatial distance solution vectors;

[0021] Among them, (x1, y1, z1, y1) is the first feature point data, x1, y1, z1 are the first feature point spatial coordinate data, t1 is the observation time of the first feature point, (x2, y2, z2, t2) is the second feature point data, x2, y2, z2 are the second feature point spatial coordinate data, t2 is the observation time of the second feature point, s 12 is the spatial distance solution vector.

[0022] Optionally, the distance between any two observation points in the feature data set is calculated to obtain a set of spatial distance solution vectors, including:

[0023] According to the distance between the first height observation points and the observation height angle in the characteristic data set, a set of spatial distance solution vectors of the tower top observation points is obtained.

[0024] Optionally, traversing the spatial distance solution vector set to obtain the maximum tower sway distance and corresponding feature points includes:

[0025] Obtain the maximum deviation of the spatial distance of the feature points in the set of spatial distance solution vectors;

[0026] According to the maximum deviation of the spatial distance of the feature points, the maximum shaking distance is determined, and the two feature point position data corresponding to the maximum shaking distance are recorded.

[0027] Optionally, determining the sway amplitude and sway period of the wind turbine tower according to the maximum sway distance of the tower and the corresponding characteristic points includes:

[0028] Determining the sway amplitude of the wind turbine tower according to the maximum sway distance;

[0029] The shaking period is determined according to the observation time of the two characteristic point data corresponding to the maximum shaking distance.

[0030] The present invention also provides a device for determining the sway parameters of a wind turbine tower, comprising:

[0031] An acquisition module is used to obtain observation point data on the wind turbine tower;

[0032] A determination module is used to preprocess the observation point data to obtain a feature data set; based on the feature data set, the maximum tower sway distance and the corresponding feature points are obtained; based on the maximum tower sway distance and the corresponding feature points, the sway amplitude and sway period of the wind turbine tower are determined.

[0033] The present invention also provides a computing device, comprising: a processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed.

[0034] The present invention also provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above.

[0035] The above solution of the present invention includes at least the following beneficial effects:

[0036] The above scheme of the present invention obtains observation point data on the wind turbine tower; pre-processes the observation point data to obtain a feature data set; obtains the maximum tower sway distance and corresponding feature points based on the feature data set; and determines the wind turbine tower sway amplitude and sway period based on the maximum tower sway distance and corresponding feature points. In the scheme of the present invention, the acquisition of tower sway amplitude data is more convenient and rapid, making monitoring more accurate and comprehensive, ensuring the accuracy of wind turbine tower sway amplitude monitoring and high reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of a method for determining wind turbine tower sway parameters provided by an embodiment of the present invention;

[0038] Figure 2 It is a schematic diagram of the installation position of a high-precision prism-free total station and a wind turbine tower provided by an embodiment of the present invention;

[0039] Figure 3 It is a technical principle diagram of a method for determining sway parameters of a wind turbine tower provided by an embodiment of the present invention;

[0040] Figure 4 is a calculation flow chart of a method for determining a wind turbine tower sway parameter provided by an embodiment of the present invention;

[0041] Figure 5 It is a module schematic diagram of a device for determining wind turbine tower sway parameters provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0043] like Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a method for determining a tower sway parameter of a wind turbine generator, comprising:

[0044] Step 11, obtaining observation point data on the wind turbine tower;

[0045] Step 12, preprocessing the observation point data to obtain a feature data set;

[0046] Step 13, obtaining the maximum tower shaking distance and the corresponding characteristic points according to the characteristic data set;

[0047] Step 14, determining the sway amplitude and sway period of the wind turbine tower according to the maximum sway distance of the tower and the corresponding characteristic points.

[0048] In this embodiment, the wind turbine tower mostly adopts a conical column structure, and its surface is smooth and tidy, which is very suitable for prism-free measurement using a total station. Therefore, a high-precision prism-free total station is used to continuously observe the wind turbine tower at a fixed position. The faster the measurement speed of the high-precision prism-free total station, the higher the accuracy of capturing the sway amplitude and period. The high-precision prism-free total station is set 30 to 60 meters below the line connecting the main wind direction and the machine position. The meteorological parameters are input and recorded. After setting the observation direction, continuous observation is performed to obtain the observation point data on the wind turbine tower, including the location coordinates and observation time of the observation point; when the continuous observation time is long enough to cover the tower sway period, the collection is terminated, and the collection time can also be extended according to the actual situation. The collection time is generally set to 3 to 8 minutes.

[0049] The data is then exported and preprocessed to obtain the tower sway characteristic data, including three-dimensional coordinates and time. For the tower sway characteristic data, the feature point spatial distance traversal algorithm is used to calculate the maximum distance corresponding to the tower sway and its corresponding feature point and observation time. The sway amplitude is determined according to the maximum distance of the tower sway, and the sway period is determined according to the observation time of the corresponding feature point.

[0050] The method for determining the tower sway parameters of the wind turbine in this embodiment does not require the installation of sensors and other monitoring equipment. The observation is simple to implement and can be measured on the spot without affecting the normal operation of the wind turbine. There is no need to consider factors that limit installation; measurements can be performed under various environmental conditions without considering equipment power supply, communication and other issues, and it has stronger applicability; when assisting the verticality detection and analysis of the wind turbine tower, the acquisition of the tower sway amplitude data is more convenient and faster due to the synchronization of the measurement time; through a set of observation equipment under the condition of synchronization of the observation time, the monitoring of the bidirectional sway amplitude of the wind turbine tower is realized, making the monitoring more accurate and comprehensive.

[0051] In an optional embodiment of the present invention, step 12 may include:

[0052] Step 121, removing or correcting missing values ​​and abnormal values ​​in the observation point data to obtain feature point data;

[0053] Step 122: convert the feature point data into a preset data structure to obtain a feature data set, wherein the preset data structure includes the three-dimensional coordinates and observation time of the feature points.

[0054] In this embodiment, the acquired observation point data is first preprocessed to remove or correct missing values ​​and data with obvious abnormal values ​​in the observation point data to obtain feature point data that meets the calculation conditions;

[0055] Then, the feature point data is converted into a feature data set (x n ,y n , z n , t n ), as shown in Table 1 below:

[0056] Xn(m) Yn(m) Zn(m) Tn(s) -37.343 6.773 40.096 28.0 -37.345 6.773 40.099 28.4 -37.347 6.773 40.101 28.8 -37.349 6.774 40.103 29.2 -37.347 6.773 40.102 29.7 -37.347 6.773 40.101 30.0 … … … …

[0057] Table 1

[0058] In an optional embodiment of the present invention, step 13 may include:

[0059] Step 131, traverse and calculate the distances between the feature points in the feature data set to obtain a set of spatial distance solution vectors;

[0060] Step 132, traverse the set of spatial distance solution vectors to obtain the maximum tower shaking distance and the corresponding feature points.

[0061] In an optional embodiment of the present invention, step 131 may include:

[0062] Step 1311, by formula:

[0063]

[0064] Calculate the distance between any two observation points in the feature data set to obtain a set of spatial distance solution vectors;

[0065] Among them, (x1, y1, z1, t1) is the first feature point data, x1, y1, z1 are the spatial coordinate data of the first feature point, t1 is the observation time of the first feature point, (x2, y2, z2, t2) is the second feature point data, x2, y2, z2 are the spatial coordinate data of the second feature point, t2 is the observation time of the second feature point, s 12 is the spatial distance solution vector.

[0066] In the above embodiment, the feature point data in the feature data set are traversed, the coordinate distances between the feature point data are calculated, all the coordinate distances are taken as elements in the set, and a set of spatial distance solution vectors is obtained;

[0067] Specifically, any two points (x1, y1, z1, t1) and (x2, y2, z2, t2) in the feature data set are traversed and calculated according to the following formula:

[0068]

[0069] Through the above traversal calculation, multiple sets of spatial distance solution vectors can be obtained;

[0070] Traverse the set of spatial distance solution vectors, sort the coordinate distances in the set of spatial distance solution vectors from large to small according to the numerical value, extract the maximum coordinate distance and the current two coordinate values, the maximum coordinate distance is determined as the maximum tower sway distance, and the current two coordinate values ​​are determined as the characteristic points corresponding to the maximum tower sway distance.

[0071] In an optional embodiment of the present invention, step 1311 may include:

[0072] According to the distance between the first height observation points and the observation height angle in the characteristic data set, a set of spatial distance solution vectors of the tower top observation points is obtained.

[0073] like Figure 3As shown, in this embodiment, M, N, P, and Q are observation points on the tower, M and N are located in the middle of the tower body, and P and Q are located at the top of the tower. The distance between M and N is used instead of calculating the distance between P and Q.

[0074] Specifically, let the tower sway amplitude be f, then f = 0.5|PQ|. Since |PQ|-|MN| = tanq·|Zn-Zp| = tanq·tanv·|MN|, where q is the tower verticality. Therefore, the error δ of using |MN| instead of |PQ| depends on the tower verticality q, the instrument observation altitude angle v and the tower sway amplitude |MN|.

[0075] If q=4‰, tanv=80 / 30 (i.e., the tower height is 80m, and the instrument station is 30m away from the tower), the tower sway amplitude |MN|=0.8m, then δ=0.009m, i.e., the error is at the millimeter level, which meets the use requirements. Therefore, this embodiment uses |MN| instead of |PQ|, i.e., f=0.5|PQ|≈0.5|MN|.

[0076] Therefore, the feature point space distance traversal algorithm is used to calculate the maximum distance PN corresponding to the tower sway and its corresponding feature points P (Xp, Yp, Zp, Tp) and N (Xn, Yn, Zn, Tn).

[0077] Then we have:

[0078]

[0079] Where v is the instrument observation altitude angle.

[0080] Through this approximate substitution method, the precision prism-free total station can be set 30 to 60 meters below the tower, without adjusting the position of the total station according to the height of the tower. The maximum distance of the tower sway at the top of the tower can be calculated by simply detecting the middle of the tower body, which can effectively reduce the distance between the total station and the tower, without considering the problem of installation restrictions due to environmental factors.

[0081] In an optional embodiment of the present invention, step 132 may include:

[0082] Step 1321, obtaining the maximum deviation of the spatial distance of the feature points in the spatial distance solution vector set;

[0083] Step 1322, determining the maximum shaking distance according to the maximum deviation of the spatial distance of the feature points, and recording the two feature point position data corresponding to the maximum shaking distance.

[0084] In this embodiment, the spatial distance solution vector set is traversed, the coordinate distances in the spatial distance solution vector set are sorted from large to small according to the numerical value, the maximum value and the minimum value of the coordinate distance are extracted, the difference between the maximum value and the minimum value is recorded as the maximum deviation of the spatial distance of the feature point, and the maximum deviation value is determined as the maximum shaking distance;

[0085] At the same time, the coordinates and observation time of the two characteristic points corresponding to the maximum shaking distance are recorded. The tower shaking period is twice the observation time difference of the two characteristic points.

[0086] In an optional embodiment of the present invention, step 14 may include:

[0087] Step 141, determining the sway amplitude of the wind turbine tower according to the maximum sway distance;

[0088] Step 142, determining the shaking period according to the observation time of the two feature point data corresponding to the maximum shaking distance.

[0089] In this embodiment, according to the maximum shaking distance calculated in step 13, the corresponding observation points are (x1, y1, z1, t1) and (x2, y2, z2, t2), and the shaking amplitude of the wind turbine tower is:

[0090]

[0091] The tower sway period is t=2|T1-T2|.

[0092] In an optional embodiment of the present invention, step 14 further includes:

[0093] Step 143, after obtaining the sway amplitude and sway period of the wind turbine tower, performing a result check on the sway amplitude data;

[0094] Step 144: when the shaking amplitude data meets the threshold range, output a report; when the shaking amplitude data does not meet the threshold range, adjust the output and recalculate the shaking amplitude.

[0095] In this embodiment, the shaking amplitude data is checked to determine whether it meets the threshold range. In this embodiment, when the shaking amplitude is less than 50cm, the data is correct and the achievement report can be output. Otherwise, it is considered that there are still erroneous data in the collected data, and the data needs to be readjusted and recalculated.

[0096] The method for determining the sway parameters of the wind turbine tower described in the above embodiment of the present invention uses a high-precision prism-free total station to continuously observe the wind turbine tower in a fixed position, obtain tower sway characteristic data, and calculate the tower sway amplitude and period, so as to measure the sway amplitude and period of the tower under actual operating conditions, and can achieve millimeter-level precision measurement of the unidirectional sway amplitude of the wind turbine tower, without the need to install monitoring equipment such as sensors, and the observation is simple to implement, and can be measured on the ground without affecting the normal operation of the wind turbine. There is no need to consider factors that limit installation; measurements can be performed under various environmental conditions, without considering equipment power supply, communication and other issues, and it has stronger applicability; when assisting the verticality detection and analysis of the wind turbine tower, due to the synchronization of the measurement time, the acquisition and use of the tower sway amplitude data is more convenient and faster; by adding a set of observation equipment under the condition of synchronization of the observation time, the monitoring of the bidirectional sway amplitude of the wind turbine tower is achieved, so that the monitoring is more accurate and comprehensive, and reliable data support is provided for the evaluation of the tower verticality detection results and the safe operation of the wind turbine.

[0097] The embodiment of the present invention further provides a device 50 for determining a tower sway parameter of a wind turbine generator, comprising:

[0098] An acquisition module 51 is used to acquire observation point data on a wind turbine tower;

[0099] The determination module 52 is used to pre-process the observation point data to obtain a feature data set; obtain the maximum tower sway distance and the corresponding feature points based on the feature data set; and determine the wind turbine tower sway amplitude and sway period based on the maximum tower sway distance and the corresponding feature points.

[0100] Optionally, the observation point data is preprocessed to obtain a feature data set, including:

[0101] Removing or correcting missing values ​​and abnormal values ​​in the observation point data to obtain feature point data;

[0102] The feature point data is converted into a preset data structure to obtain a feature data set, wherein the preset data structure includes the three-dimensional coordinates and observation time of the feature points.

[0103] Optionally, according to the characteristic data set, obtaining the maximum tower sway distance and corresponding characteristic points includes:

[0104] Traversing and calculating the distances between the feature points in the feature data set to obtain a set of spatial distance solution vectors;

[0105] The spatial distance solution vector set is traversed to obtain the maximum tower sway distance and the corresponding feature point position.

[0106] Optionally, the distances between the feature points in the feature data set are calculated traversally to obtain a set of spatial distance solution vectors, including:

[0107] By formula:

[0108]

[0109] Calculate the distance between any two observation points in the feature data set to obtain a set of spatial distance solution vectors;

[0110] Among them, (x1, y1, z1, y1) is the first feature point data, x1, y1, z1 are the first feature point spatial coordinate data, t1 is the observation time of the first feature point, (x2, y2, z2, t2) is the second feature point data, x2, y2, z2 are the second feature point spatial coordinate data, t2 is the observation time of the second feature point, s 12 is the spatial distance solution vector.

[0111] Optionally, the distance between any two observation points in the feature data set is calculated to obtain a set of spatial distance solution vectors, including:

[0112] According to the distance between the first height observation points and the observation height angle in the characteristic data set, a set of spatial distance solution vectors of the tower top observation points is obtained.

[0113] Optionally, traversing the spatial distance solution vector set to obtain the maximum tower sway distance and corresponding feature points includes:

[0114] Obtain the maximum deviation of the spatial distance of the feature points in the set of spatial distance solution vectors;

[0115] According to the maximum deviation of the spatial distance of the feature points, the maximum shaking distance is determined, and the two feature point position data corresponding to the maximum shaking distance are recorded.

[0116] Optionally, determining the sway amplitude and sway period of the wind turbine tower according to the maximum sway distance of the tower and the corresponding characteristic points includes:

[0117] Determining the sway amplitude of the wind turbine tower according to the maximum sway distance;

[0118] The shaking period is determined according to the observation time of the two characteristic point data corresponding to the maximum shaking distance.

[0119] It should be noted that the device is a device corresponding to the above method, and all implementation methods in the above method embodiments are applicable to the embodiments of the device and can achieve the same technical effects.

[0120] The embodiment of the present invention further provides a computing device, including: a processor, a memory storing a computer program, and when the computer program is executed by the processor, the method described in the above embodiment is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0121] In an embodiment of the present invention, a computer-readable storage medium is further provided, which stores instructions, and when the instructions are executed on a computer, the computer executes the method described in the above embodiment. All implementations in the above method embodiment are applicable to this embodiment, and can also achieve the same technical effect.

[0122] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0123] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0124] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0125] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0126] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0127] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0128] In addition, it should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0129] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0130] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining the sway parameters of a wind turbine tower, characterized in that: include: Obtain observation point data on the wind turbine tower; Preprocessing the observation point data to obtain a feature data set; According to the characteristic data set, the maximum tower shaking distance and the corresponding characteristic points are obtained; Determine the sway amplitude and sway period of the wind turbine tower according to the maximum sway distance of the tower and the corresponding characteristic points; Wherein, according to the characteristic data set, the maximum tower sway distance and the corresponding characteristic points are obtained, including: Traversing and calculating the distances between the feature points in the feature data set to obtain a set of spatial distance solution vectors; Traversing the spatial distance solution vector set to obtain the maximum tower sway distance and the corresponding characteristic point position; The distances between the feature points in the feature data set are traversed and calculated to obtain a set of spatial distance solution vectors, including: By formula: The distance between any two observation points in the feature data set is obtained by traversal calculation to obtain a set of spatial distance solution vectors; Among them, (x1, y1, z1, t1) is the first feature point data, x1, y1, z1 are the spatial coordinate data of the first feature point, t1 is the observation time of the first feature point, (x2, y2, z2, t2) is the second feature point data, x2, y2, z2 are the spatial coordinate data of the second feature point, t2 is the observation time of the second feature point, s 12 is the spatial distance solution vector; Traverse the set of spatial distance solution vectors, sort the coordinate distances in the set of spatial distance solution vectors from large to small according to the numerical value, extract the maximum coordinate distance and the current two coordinate values, the maximum coordinate distance is determined as the maximum tower sway distance, and the current two coordinate values ​​are determined as the characteristic point corresponding to the maximum tower sway distance; The distance between any two observation points in the feature data set is calculated to obtain a set of spatial distance solution vectors, including: According to the distance between the first height observation points in the feature data set and the observation height angle, a set of spatial distance solution vectors of the tower top observation points is obtained; The feature point space distance traversal algorithm is used to calculate the maximum distance corresponding to the tower sway: |PQ|-|MN|=tanq·|Zn-Zp|=tanq·tanv·|MN|; Among them, q is the verticality of the tower, v is the instrument observation altitude angle, |MN| is the maximum sway distance of the tower, M, N, P, Q are observation points on the tower, M and N are located in the middle of the tower body, P and Q are located at the top of the tower, the coordinates of the feature point P are (Xp, Yp, Zp, Tp), and the coordinates of the feature point N are (Xn, Yn, Zn, Tn); Wherein, according to the maximum tower sway distance and the corresponding characteristic points, determining the sway amplitude and sway period of the wind turbine tower includes: Determining the sway amplitude of the wind turbine tower according to the maximum sway distance; Determine the shaking period according to the observation time of the two characteristic point data corresponding to the maximum shaking distance; The shaking period t=2|T1-T2|, where T1 and T2 are the observation time values ​​of the characteristic points corresponding to the maximum shaking distance of the tower.

2. The method for determining the sway parameters of a wind turbine tower according to claim 1, characterized in that: The observation point data is preprocessed to obtain a feature data set, including: Removing or correcting missing values ​​and abnormal values ​​in the observation point data to obtain feature point data; The feature point data is converted into a preset data structure to obtain a feature data set, wherein the preset data structure includes the three-dimensional coordinates and observation time of the feature points.

3. A device for determining the sway parameters of a wind turbine tower, characterized in that: include: An acquisition module is used to obtain observation point data on the wind turbine tower; A determination module is used to pre-process the observation point data to obtain a characteristic data set; obtain the maximum tower sway distance and the corresponding characteristic points according to the characteristic data set; determine the wind turbine tower sway amplitude and sway period according to the maximum tower sway distance and the corresponding characteristic points; Wherein, according to the characteristic data set, the maximum tower sway distance and the corresponding characteristic points are obtained, including: Traversing and calculating the distances between the feature points in the feature data set to obtain a set of spatial distance solution vectors; Traversing the spatial distance solution vector set to obtain the maximum tower sway distance and the corresponding characteristic point position; The distances between the feature points in the feature data set are traversed and calculated to obtain a set of spatial distance solution vectors, including: By formula: The distance between any two observation points in the feature data set is obtained by traversal calculation to obtain a set of spatial distance solution vectors; Among them, (x1, y1, z1, t1) is the first feature point data, x1, y1, z1 are the spatial coordinate data of the first feature point, t1 is the observation time of the first feature point, (x2, y2, z2, t2) is the second feature point data, x2, y2, z2 are the spatial coordinate data of the second feature point, t2 is the observation time of the second feature point, s 12 is the spatial distance solution vector; Traverse the set of spatial distance solution vectors, sort the coordinate distances in the set of spatial distance solution vectors from large to small according to the numerical value, extract the maximum coordinate distance and the current two coordinate values, the maximum coordinate distance is determined as the maximum tower sway distance, and the current two coordinate values ​​are determined as the characteristic point corresponding to the maximum tower sway distance; The distance between any two observation points in the feature data set is calculated to obtain a set of spatial distance solution vectors, including: According to the distance between the first height observation points in the feature data set and the observation height angle, a set of spatial distance solution vectors of the tower top observation points is obtained; The feature point space distance traversal algorithm is used to calculate the maximum distance corresponding to the tower sway: |PQ|-|MN|=tanq·|Zn-Zp|=tanq·tanv·|MN|; Among them, q is the verticality of the tower, v is the instrument observation altitude angle, |MN| is the maximum distance of the tower shaking, M, N, P, Q are the observation points on the tower, M and N are located in the middle of the tower body, P and Q are located at the top of the tower, the coordinates of the feature point P are (Xp, Yp, Zp, Tp), and the coordinates of the feature point N are (Xn, Yn, Zn, Tn); Wherein, according to the maximum tower sway distance and the corresponding characteristic points, determining the sway amplitude and sway period of the wind turbine tower includes: Determining the sway amplitude of the wind turbine tower according to the maximum sway distance; Determine the shaking period according to the observation time of the two characteristic point data corresponding to the maximum shaking distance; The shaking period t=2|T1-T2|, where T1 and T2 are the observation time values ​​of the characteristic points corresponding to the maximum shaking distance of the tower.

4. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 2 is performed.

5. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 2.

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