A method and device for quantitatively screening sensitive points of a wind farm

Abstract

The present application relates to the technical field of power transmission line disaster prevention and reduction, and particularly provides a quantitative screening method and device for wind field sensitive points, comprising: performing flow field simulation analysis on a region to obtain wind speed vector data at each grid center point in the wind field of the whole flow region; removing bad data in the wind speed vector data, and performing smoothing processing on the wind speed vector data after removing the bad data; and screening wind field sensitive points based on the wind speed vector data after smoothing processing. The technical scheme provided by the present application can be used for quickly screening massive wind field simulation analysis data, recommending sheltered area tower positions suitable for arranging power transmission towers, and warning acceleration area tower positions unsuitable for arranging power transmission towers.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power transmission line disaster prevention and mitigation, and particularly relates to a quantitative screening method and device for wind field sensitive points. BACKGROUND

[0002] With the development of computer and fluid dynamics simulation analysis technology, complex mountainous wind field simulation analysis technology is becoming mature, and simulation analysis cloud map and corresponding massive analysis data of complex mountainous wind field can be obtained through simulation analysis.

[0003] The wind field sensitive point refers to a point with significant wind speed acceleration effect or wind speed shielding effect, which has a large difference with the inflow wind speed in the plain area. The wind field sensitive point in the complex mountainous area is usually determined by researchers according to their own feelings by observing the simulation analysis cloud map, and cannot be accurately quantitatively analyzed. This greatly affects the objectivity of the wind field simulation analysis result, and cannot quickly and accurately judge the massive data, which is not conducive to accurately grasping the distribution rule of the complex mountainous wind field. SUMMARY

[0004] In order to overcome the above defects, the present application provides a quantitative screening method and device for wind field sensitive points.

[0005] In a first aspect, a quantitative screening method for wind field sensitive points is provided, which comprises:

[0006] Performing flow field simulation analysis on the region to obtain wind speed vector data at each grid center point in the whole flow field;

[0007] Removing bad data in the wind speed vector data, and performing smoothing processing on the wind speed vector data after removing the bad data;

[0008] Screening the wind field sensitive points based on the wind speed vector data after smoothing processing.

[0009] Preferably, the calculation formula of the wind speed vector data at each grid center point in the whole flow field is as follows:

[0010]

[0011] In the above formula, U i is the wind speed vector data at the i-th grid center point in the whole flow field, u x,i is the horizontal wind speed at the i-th grid center point in the whole flow field, u y,i is the vertical wind speed at the i-th grid center point in the whole flow field, i∈[1,n], and n is the total number of grids in the whole flow field.

[0012] Further, the removing the bad data in the wind speed vector data comprises:

[0013] If the wind speed vector data U i does not satisfy the following calculation formula:

[0014]

[0015] then the U i is removed, otherwise, the U i is not removed.

[0016] Further, the smoothing the wind speed vector data after the bad data is removed comprises:

[0017] When the wind speed vector data at the kth grid center point in the whole basin wind field is removed, the wind speed vector data at the kth grid center point in the whole basin wind field after the smoothing is obtained according to the following formula: k

[0018] U k = spline(U k-j , U k+j )

[0019] In the formula, spline is a cubic spline interpolation function, U k-j is the wind speed vector data at the k-jth grid center point in the whole basin wind field, and U k+j is the wind speed vector data at the k+jth grid center point in the whole basin wind field, k, j ∈ [1, n].

[0020] Preferably, the screening the wind field sensitive point based on the wind speed vector data after the smoothing comprises:

[0021] arranging the wind speed vector data after the smoothing in descending order to obtain a wind speed vector data descending sequence;

[0022] arranging the wind speed vector data after the smoothing in ascending order to obtain a wind speed vector data ascending sequence;

[0023] screening the wind field sensitive point by using the wind speed vector data descending sequence and the wind speed vector data ascending sequence.

[0024] Further, the wind field sensitive point at least comprises one of the following: a wind field acceleration effect sensitive point, a wind field shielding effect sensitive point.

[0025] Further, the screening the wind field sensitive point by using the wind speed vector data descending sequence and the wind speed vector data ascending sequence comprises: ​

[0026] When the xth wind speed vector data in the descending sequence of wind speed vector data satisfies: or x < P1, the corresponding grid of the xth wind speed vector data in the whole-basin wind field is a wind field acceleration effect sensitive point, where x ∈ [1, n], n is the total number of grids in the whole-basin wind field, and P1 is a first preset value.

[0027] When the xth wind speed vector data in the ascending sequence of wind speed vector data satisfies: or x < P2, the corresponding grid of the xth wind speed vector data in the whole-basin wind field is a wind field shielding effect sensitive point, where P2 is a second preset value.

[0028] In a second aspect, a wind field sensitive point screening device is provided, which comprises:

[0029] An acquisition module is configured to perform flow field simulation analysis on a region, and acquire wind speed vector data at each grid center point in a whole-basin wind field.

[0030] A smoothing processing module is configured to remove bad data in the wind speed vector data, and perform smoothing processing on the wind speed vector data after removing the bad data.

[0031] A screening module is configured to screen wind field sensitive points based on the wind speed vector data after smoothing processing.

[0032] In a third aspect, a storage medium is provided, which comprises a stored program. When the program is run, the device where the storage medium is located is controlled to perform the wind field sensitive point screening method.

[0033] In a fourth aspect, a processor is provided, which is configured to run a program. When the program is run, the wind field sensitive point screening method is performed.

[0034] The above one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0035] The present application relates to power transmission line disaster prevention and mitigation technology field, specifically provides a kind of wind field sensitive point screening method and device, comprising: to region flow field simulation analysis is carried out, and the wind speed vector data at each grid center point in whole-basin wind field is acquired;Remove bad data in the wind speed vector data, and the wind speed vector data after removing bad data is carried out smoothing processing;Wind field sensitive point is screened based on the wind speed vector data after smoothing processing.The technical scheme provided by the present application can quickly, objectively and accurately screen wind field sensitive point based on massive simulation analysis data, realize the automatic batch processing of complex mountain mass wind speed data, and provide data reference for power transmission line route selection and tower location work. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the main steps of the quantitative screening method for wind field sensitive points according to an embodiment of the present invention;

[0037] Figure 2 This is a main structural block diagram of the quantitative screening device for wind field sensitive points according to an embodiment of the present invention. Detailed Implementation

[0038] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] See appendix Figure 1 , Figure 1 This is a schematic flowchart illustrating the main steps of a quantitative screening method for wind field sensitive locations according to an embodiment of the present invention. Figure 1 As shown, the quantitative screening method for wind field sensitive points in this embodiment of the invention mainly includes the following steps:

[0041] Step S101: Perform flow field simulation analysis on the region to obtain wind speed vector data at the center point of each grid within the wind field of the entire watershed;

[0042] Step S102: Remove bad data from the wind speed vector data and smooth the wind speed vector data after removing bad data;

[0043] Step S103: Filter wind field sensitive points based on the smoothed wind speed vector data.

[0044] In this embodiment, the formula for calculating the wind speed vector data at the center point of each grid within the entire watershed wind field is as follows:

[0045]

[0046] In the above formula, U i Let u be the wind speed vector data at the center point of the i-th grid within the wind field of the entire watershed. x,i Let u be the horizontal wind speed at the center point of the i-th grid within the entire watershed wind field. y,i Let be the vertical wind speed at the center point of the i-th grid in the wind field of the entire basin, i∈[1,n], and n be the total number of grids in the wind field of the entire basin.

[0047] In one embodiment, the removing the bad data in the wind speed vector data comprises:

[0048] If the wind speed vector data U i does not satisfy the following calculation formula:

[0049]

[0050] then the U i is removed, otherwise, the U i is not removed.

[0051] In one embodiment, the smoothing the wind speed vector data after the bad data is removed comprises:

[0052] When the wind speed vector data at the kth grid center point in the whole basin wind field is removed, the wind speed vector data at the kth grid center point in the whole basin wind field after smoothing is obtained according to the following formula: k

[0053] U k = spline(U k-j , U k+j )

[0054] In the above formula, spline is a cubic spline interpolation function, U k-j is the wind speed vector data at the k-jth grid center point in the whole basin wind field, and U k+j is the wind speed vector data at the k+jth grid center point in the whole basin wind field, k, j ∈ [1, n].

[0055] In the embodiment, the filtering the wind field sensitive point based on the wind speed vector data after smoothing comprises:

[0056] arranging the wind speed vector data after smoothing in descending order to obtain a wind speed vector data descending sequence;

[0057] arranging the wind speed vector data after smoothing in ascending order to obtain a wind speed vector data ascending sequence;

[0058] filtering the wind field sensitive point by using the wind speed vector data descending sequence and the wind speed vector data ascending sequence.

[0059] The wind field sensitive point at least includes one of the following: a wind field acceleration effect sensitive point, a wind field shielding effect sensitive point.

[0060] ​In one embodiment, the filtering of the wind field sensitive point by using the wind speed vector data descending sequence and the wind speed vector data ascending sequence comprises:

[0061] When the xth wind speed vector data in the wind speed vector data descending sequence satisfies: or x

[0062] When the xth wind speed vector data in the wind speed vector data ascending sequence satisfies: or x

[0063] Based on the above scheme, the application provides an optimal embodiment, which is specifically based on the filtering of the complex mountainous wind field sensitive point, and a patent example application is introduced:

[0064] (1) Extract the surface elevation data of the complex mountainous area, generate a rigid surface boundary model in the wind field simulation analysis software, perform grid division on the wind field on the upper layer of the rigid boundary of the complex mountainous area in the wind field simulation analysis software, set the parameters for the flow field simulation analysis, give the inflow wind speed U=10 m / s, and perform flow field simulation analysis.

[0065] (2) Through the data export function in the post-processor of the fluid simulation analysis software, the horizontal wind speeds {10.31, 11.23, 14.50, 12.11, …, u xi ,…,u x,99 ,u x,100} and {0.03, 0.12, -1.33, 0.45, …, u yi ,…u y,99 ,u y,100} of the total 100 grid center points in the 10*10 grid size full-flow wind field are batch exported, and the wind speed vector {10.31, 11.23, 14.56, 12.12, …, U i ,…U 99 ,U 100} at any point i in the full-flow field is calculated according to formula (1).

[0066] (3) Let n=50, take 50 numbers as a group, as the adjacent area wind speed vector U. The average of the group of wind speed vectors is 14.3216, and the standard deviation is 10.3669, when the wind speed U iWhen in the range of [-21.9627, 50.61], all can be determined as normal numerical results, it is found through search that when i=10, U i =84.14, which belongs to abnormal numerical solution, wherein:

[0067]

[0068] (4) By means of the wind speed U9 and U 10 at the point positions near the recording position, the abnormal numerical solution at the recording position k=10 in the mutation area is replaced according to formula (3), and the wind speed vector after replacement is:

[0069]

[0070] (5) Based on the descending order sorting function, the wind speed vector after smoothing processing is sorted to form a new wind speed vector

[0071]

[0072] According to the need, the threshold value 5% is set, and the position relationship satisfies or x

[0073] Based on the ascending order sorting function, the wind speed vector after smoothing processing is sorted to form a new wind speed vector

[0074]

[0075] According to the need, the threshold value 5% is set, and the position relationship satisfies or x

[0076] Based on the same inventive concept, the present application provides a quantitative screening device for wind field sensitive point positions, as shown in Figure 2 , the quantitative screening device for wind field sensitive point positions comprises:

[0077] The acquisition module is used for performing flow field simulation analysis on the region to acquire wind speed vector data at each grid center point in the whole flow field;

[0078] The smoothing processing module is used for eliminating bad data in the wind speed vector data and performing smoothing processing on the wind speed vector data after eliminating the bad data;

[0079] The screening module is configured to screen the wind field sensitive point based on the smoothed wind speed vector data.

[0080] Preferably, the wind speed vector data at each grid center point in the whole basin wind field is calculated according to the following formula:

[0081]

[0082] In the above formula, U i is the wind speed vector data at the i-th grid center point in the whole basin wind field, u x,i is the horizontal wind speed at the i-th grid center point in the whole basin wind field, u y,i is the vertical wind speed at the i-th grid center point in the whole basin wind field, i∈[1,n], and n is the total number of grids in the whole basin wind field.

[0083] Further, the bad data in the wind speed vector data is removed, including:

[0084] If the wind speed vector data U i at the i-th grid center point in the whole basin wind field does not satisfy the following calculation formula:

[0085]

[0086] then the U i is removed, otherwise, the U i is not removed, wherein std is the standard deviation of the wind speed vector data at all grid center points in the whole basin wind field.

[0087] Further, the wind speed vector data after removing the bad data is smoothed, including:

[0088] When the wind speed vector data at the k-th grid center point in the whole basin wind field is removed, the wind speed vector data U k at the k-th grid center point in the whole basin wind field after smoothing is obtained according to the following formula:

[0089] U k =spline(U k-j ,U k+j )

[0090] In the above formula, spline is a cubic spline interpolation function, U k-j is the wind speed vector data at the k-j-th grid center point in the whole basin wind field, U k+j is the wind speed vector data at the k+j-th grid center point in the whole basin wind field, k,j∈[1,n].

[0091] Preferably, the wind field sensitive point is screened based on the smoothed wind speed vector data, including:

[0092] arranging the wind speed vector data after smoothing in descending order to obtain a descending sequence of wind speed vector data;

[0093] arranging the wind speed vector data after smoothing in ascending order to obtain an ascending sequence of wind speed vector data;

[0094] screening the wind field sensitive point from the descending sequence of wind speed vector data and the ascending sequence of wind speed vector data.

[0095] Further, the wind field sensitive point at least includes one of wind field acceleration effect sensitive point and wind field shielding effect sensitive point.

[0096] Further, the screening the wind field sensitive point from the descending sequence of wind speed vector data and the ascending sequence of wind speed vector data includes:

[0097] when the xth wind speed vector data in the descending sequence of wind speed vector data satisfies: or x < P1, the corresponding grid of the xth wind speed vector data in the whole flow field wind field is the wind field acceleration effect sensitive point, wherein x ∈ [1, n], n is the total number of grids in the whole flow field wind field, and P1 is a first preset value.

[0098] when the xth wind speed vector data in the ascending sequence of wind speed vector data satisfies: or x < P2, the corresponding grid of the xth wind speed vector data in the whole flow field wind field is the wind field shielding effect sensitive point, wherein P2 is a second preset value.

[0099] Further, the present application provides a storage medium, the storage medium includes a stored program, wherein, when the program runs, the device where the storage medium is located executes the quantitative screening method of the wind field sensitive point.

[0100] Further, the present application provides a processor, the processor is used for running a program, wherein, when the program runs, the quantitative screening method of the wind field sensitive point is executed.

[0101] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0102] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart Figure 1 one or more flows and / or blocks. Figure 1 one or more flows and / or blocks.

[0103] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart Figure 1 one or more flows and / or blocks. Figure 1 one or more flows and / or blocks.

[0104] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart Figure 1 one or more flows and / or blocks. Figure 1 one or more flows and / or blocks.

[0105] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the present application. Although the present application is described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or equivalent replacements without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A quantitative screening method for wind field sensitive locations, characterized in that, The method includes: Flow field simulation analysis was performed in the region to obtain wind speed vector data at the center point of each grid within the wind field of the entire watershed; Remove bad data from the wind speed vector data, and smooth the wind speed vector data after removing bad data; Screening of wind field sensitive points based on smoothed wind speed vector data; The process of removing bad data from the wind speed vector data includes: If the wind speed vector data U at the center point of the i-th grid in the wind field of the entire watershed is... i The following calculation formula is not satisfied: Then remove U i Otherwise, do not remove U. i , where std is the standard deviation of the wind speed vector data at all grid center points within the wind field of the entire watershed; The method of filtering wind-sensitive points based on smoothed wind speed vector data includes: The smoothed wind speed vector data is sorted in descending order to obtain a descending sequence of wind speed vector data. The smoothed wind speed vector data is sorted in ascending order to obtain the ascending sequence of wind speed vector data. The wind field sensitive points are screened using the descending sequence and ascending sequence of the wind speed vector data. The wind field sensitive points include at least one of the following: wind field acceleration effect sensitive points, wind field shading effect sensitive points; The process of filtering wind-sensitive locations using the descending and ascending sequences of wind speed vector data includes: When the x-th wind speed vector data in the descending order sequence of the wind speed vector data satisfies: or x < P1, the grid corresponding to the x-th wind speed vector data in the whole basin wind field is a sensitive point for the wind field acceleration effect, where x ∈ [1, n], n is the total number of grids in the whole basin wind field, and P1 is the first preset value; When the x-th wind speed vector data in the ascending sequence of the wind speed vector data satisfies: or x < P2, the grid corresponding to the x-th wind speed vector data in the whole-basin wind field is a sensitive point for the wind field occlusion effect, where P2 is a second preset value.

2. The method as described in claim 1, characterized in that, The formula for calculating the wind speed vector data at the center point of each grid within the entire watershed wind field is as follows: In the above formula, U i Let u be the wind speed vector data at the center point of the i-th grid within the wind field of the entire watershed. x,i Let u be the horizontal wind speed at the center point of the i-th grid within the entire watershed wind field. y,i Let be the vertical wind speed at the center point of the i-th grid within the wind field of the entire basin, where i∈[1,n] and n is the total number of grids within the wind field of the entire basin.

3. The method as described in claim 2, characterized in that, The smoothing process for the wind speed vector data after removing bad data includes: When the wind speed vector data at the center point of the kth grid within the entire watershed wind field is removed, the smoothed wind speed vector data U at the center point of the kth grid within the entire watershed wind field is obtained using the following formula. k : IN k =spline(U k-j ,IN k+j ) In the above formula, spline is the cubic spline interpolation function, U k-j U represents the wind speed vector data at the center point of the kj-th grid within the entire watershed wind field. k+j The wind speed vector data is located at the center point of the (k+j)th grid within the wind field of the entire watershed, where k,j∈[1,n].

4. An apparatus for quantitative screening of wind field sensitive points based on any one of claims 1-3, characterized in that, The device includes: The acquisition module is used to perform flow field simulation analysis on the region and acquire wind speed vector data at the center point of each grid within the wind field of the entire watershed. The smoothing module is used to remove bad data from the wind speed vector data and smooth the wind speed vector data after removing bad data. The filtering module is used to filter wind field sensitive points based on smoothed wind speed vector data.

5. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the method described in any one of claims 1 to 3.

6. A processor, characterized in that, The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 3 when it runs.

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