CN114692521A - Optimized layout method for wind measuring tower of wind power plant - Google Patents
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Abstract
The invention provides an optimized layout method of a wind measuring tower of a wind power plant, which comprises the following steps: determining the area where the wind power plant is located and the arrangement positions of the fans; based on the arrangement positions of the fans, dividing a wind measuring area in the area where the wind power plant is located through a DBSCAN algorithm; establishing a wind tower site selection quantization standard according to topographic data and historical wind measurement data, and screening wind measurement points meeting a preset site selection standard in a wind measurement area by combining a gridding screening method; and establishing a layout evaluation index of the anemometer tower, and determining the position of the anemometer tower in the candidate anemometer points according to the layout evaluation index. According to the method, the wind power plant anemometer tower site selection precision is improved through reasonable point distribution of the wind power plant anemometer tower, wind measurement data are guaranteed to be more representative and relatively reliable, and the method has important significance on post-evaluation of generated energy and ultra-short-term power prediction and prediction precision. Meanwhile, the provided partitioning method also provides a basis for determining the number of the anemometer towers, and reduces the fund waste caused by excessive construction of the anemometer towers.
Description
Technical Field
The invention belongs to the field of layout of wind resource monitoring devices, and particularly relates to an optimized layout method of a wind measuring tower of a wind power plant.
Background
The wind measuring tower is generally set up in the wind farm to reasonably monitor and accurately reflect the wind resource condition in the farm, so that data support is provided for wind resource evaluation and fan site selection of the wind farm. In the early design of the optimized site selection of the wind measuring tower, a wind power plant is not built, a wind generating set does not start to operate, and a flow field in the wind power plant is mainly influenced by site topographic features and barrier distribution in the plant, so that the condition of the flow field in the site can be preliminarily judged by analyzing the topographic features and the barrier distribution of the wind power plant, and then a position which can represent the wind resource condition of the whole wind power plant is selected as a wind measuring point according to the condition of the flow field. And the problem of site selection of the wind measuring tower in later-stage production and operation is complex, so that the wind generating set in the built wind power plant starts to operate, and the wake effect generated by the wind generating set in operation can seriously disturb the flow field condition in the wind power plant. Therefore, after the construction of the wind power plant is completed, the wind measuring tower constructed in the field area at the early stage is influenced by the wake flow of the wind generating set, so that the existing wind measuring data cannot represent the real wind resource condition of the wind power plant, the accuracy of the later generated energy post-evaluation and ultra-short-term power prediction result is negatively influenced, and meanwhile, the data show that the error of 10% of the wind measuring data may cause the error of about 30% of the annual capacity of the wind power plant.
Therefore, the anemometer tower in the early design stage is difficult to continue to be used in the operation of the wind power plant. In the later stage of wind power plant construction, the wind measuring tower position which is more representative and is prevented from being influenced by the wake flow of the fan as much as possible is selected, the regional wind energy condition can be objectively and accurately reflected, more effective evaluation activities after the running condition of the wind power plant are realized, and the method has important significance for wind energy development of the wind power plant.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an optimized layout method of a wind measuring tower of a wind power plant, which comprises the following steps:
s100: determining the area where the wind power plant is located and the arrangement positions of the fans;
s200: based on the arrangement positions of the fans, dividing a wind measuring area in the area where the wind power plant is located through a DBSCAN algorithm;
s300: establishing a wind tower site selection quantization standard according to topographic data and historical wind measurement data, and screening wind measurement points meeting a preset site selection standard in a wind measurement area by combining a gridding screening method;
s400: and establishing a layout evaluation index of the anemometer tower, and determining the position of the anemometer tower in the candidate anemometer points according to the layout evaluation index.
Optionally, the determining the area where the wind farm is located includes:
determining a floor area of a wind power plant;
the distance between the Jixi and the Jidong of the occupied area is prolonged according to a preset proportion, and the length of the area where the wind power plant is located is obtained;
the distance between the polar south and the polar north of the occupied place is extended according to a preset proportion, and the width of the area where the wind power plant is located is obtained;
and based on the obtained length and width, taking a rectangular area containing the occupied area as the area where the wind power plant is located.
Optionally, the S200 includes:
s210: estimating the quantity of the wind measuring areas which can be divided by the wind power plant according to the topographic change of the arrangement positions of the fans, the historical arrangement distances among the fans and the preset basic principle of wind measuring tower distribution points;
s220: determining an estimated range of the clustering density threshold according to the distribution density of the fan arrangement positions, and taking values of the clustering density threshold in the estimated range;
s230: determining the value of the clustering radius according to the value of the clustering density threshold, and dividing the fan arrangement position into a plurality of clusters through a DBSCAN algorithm based on the values of the clustering density threshold and the clustering radius;
s240: and judging whether the number of the clusters accords with the estimated result of the number of the wind measuring areas, if so, determining the wind measuring areas according to the divided clusters, otherwise, adjusting the value of the clustering density threshold value in the estimated range, and repeating the step S230.
Optionally, the fan arrangement positions are divided into a plurality of clusters through a DBSCAN algorithm, including:
s231: processing the arrangement position of the fan into a three-dimensional coordinate, and performing normalization processing on the three-dimensional coordinate;
s232: randomly selecting a coordinate point from the three-dimensional coordinates after normalization processing;
s233: judging whether the selected coordinate point is a core point or not according to the field radius and the clustering density threshold, and if the selected coordinate point is the core point, forming a cluster by the coordinate point with the density of the core point;
s234: if the selected coordinate point is not the core point, reselecting another coordinate point, and repeating S233;
s235: and selecting other coordinate points which are not clustered, and repeating the steps S233 and S234 until no selectable coordinate points exist.
Optionally, the S231 includes:
acquiring geodetic coordinates and altitude in the arrangement position of the fans, taking the geodetic coordinates as horizontal coordinates and vertical coordinates of the arrangement position of the fans, and taking the altitude as vertical coordinates of the arrangement position of the fans;
respectively carrying out normalization processing on an abscissa, an ordinate and a vertical coordinate in the three-dimensional coordinate, wherein the calculation formula of the normalization processing is as follows:
in the formula, a represents a three-dimensional coordinate before normalization processing of a certain fan, min (a) represents a minimum value in the three-dimensional coordinates before normalization processing of all the fans, max (a) represents a maximum value in the three-dimensional coordinates before normalization processing of all the fans, and B is the three-dimensional coordinate after normalization processing of the certain fan.
Optionally, the determining a wind measuring region according to the divided clusters includes:
marking coordinate points which do not belong to any cluster as noise points to be removed;
and generating a rectangular envelope of each cluster along the east, south, west and north directions, extending the southwest vertex of the rectangular envelope in the southwest direction by a preset proportion, and extending the northeast vertex of the rectangular envelope in the northeast direction by a preset proportion to obtain a wind measuring area.
Optionally, the S300 includes:
dividing grids in the wind measuring area at fixed intervals, and taking intersection points of the grids as alternative wind measuring points;
calculating wind flow parameters, horizontal distance with a fan, altitude difference with the fan and wake effect reduction rate according to topographic data and historical wind measuring data at alternative wind measuring points, and screening out alternative wind measuring points of which the calculation results do not accord with site selection quantization standards of a wind measuring tower;
and taking the candidate wind measuring points left after screening out as finally determined wind measuring points.
Optionally, the wind flow parameters include a wind acceleration factor, a turbulence intensity, and an inflow angle.
Optionally, the S500 includes:
establishing a layout evaluation index of a wind measuring point on a wind speed dimension, a shape dimension and a scale dimension, wherein a calculation formula of the layout evaluation index is as follows:
wherein X is a layout evaluation index, X1Is a layout evaluation index in the wind speed dimension, X2Is a layout evaluation index in the dimension of the shape, X3Is a layout evaluation index in the dimension of scale, vtIs the average wind speed, v, of all fan pointsmWind speed, v, being the point of anemometry to be evaluated1、v2、...、vnThe wind speed, k, of each fan pointtIs the average shape parameter, k, of all fan pointsmAs a shape parameter of the point of wind measurement to be evaluated, k1、k2、...、knThe shape parameters of each fan point location, ctIs the average scale parameter of all fan point locations, cmAs a scale parameter of the wind measurement point to be evaluated, c1、c2、...、cnSequentially setting the scale parameters of each fan point location, wherein n is the total number of the wind turbine generators;
and selecting the wind measuring point with the maximum layout evaluation index in each wind measuring area as the position of the wind measuring tower.
The technical scheme provided by the invention has the beneficial effects that:
according to the method, the wind power plant anemometer tower is reasonably distributed, so that the site selection precision of the wind power plant anemometer tower is improved, the wind measurement data are more representative and relatively reliable, and the method has important significance on post-evaluation of generated energy and ultra-short-term power prediction precision. Meanwhile, the partition method also provides a basis for determining the number of the anemometer towers, and reduces the fund waste caused by excessive construction of the anemometer towers.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an optimized layout method of a wind measuring tower of a wind farm according to an embodiment of the present invention;
FIG. 2 is a wind turbine cluster calculation process based on DBSCAN algorithm;
FIG. 3 is a result of zoning of a wind sensing zone of a wind farm;
FIG. 4 shows the position of the optimum anemometer tower selected after the grid screening and the stationing evaluation.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present application, "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that, in the present invention, "a plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that A, B, C all comprise, "comprises A, B or C" means comprise one of A, B, C, "comprises A, B and/or C" means comprise any 1 or any 2 or 3 of A, B, C.
It should be understood that in the present invention, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, and B can be determined from a. Determining B from a does not mean determining B from a alone, but may also be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
As shown in fig. 1, the present embodiment provides an optimized layout method for a wind measuring tower of a wind farm, including:
s100: determining the area where the wind power plant is located and the arrangement positions of the fans;
s200: based on the arrangement positions of the fans, dividing a wind measuring area in the area where the wind power plant is located through a DBSCAN algorithm;
s300: establishing a wind tower site selection quantization standard according to topographic data and historical wind measurement data, and screening wind measurement points meeting a preset site selection standard in a wind measurement area by combining a gridding screening method;
s400: and establishing a layout evaluation index of the anemometer tower, and determining the position of the anemometer tower in the candidate anemometer points according to the layout evaluation index.
In order to solve the defect that the position of a wind measuring tower in the early stage of wind power plant construction is difficult to use in the later stage, the embodiment provides an optimized layout method of the wind measuring tower of the wind power plant, and the method comprises the steps of firstly obtaining the STRM DEM altitude elevation data, the Global Land30 ground surface coverage data, the historical wind measuring data and the fan arrangement coordinate information of the area where the wind power plant is located; secondly, based on historical arrangement of the fans, adopting a DBSCAN algorithm to perform macroscopic region division, and determining the number of the anemometer towers; then, a gridding screening method is adopted, and CFD software is used for calculating the micro-scale wind flow parameters of the wind power plant, so that a microscopic site selection quantization standard of the wind measuring tower is established by considering the wake flow effect of a flow field and the wind condition parameters of the prevailing wind direction, and the screening of wind measuring points is realized; and finally, establishing an optimized layout quantitative evaluation index of the anemometer tower, and determining the final position of the anemometer tower in the anemometer point. According to the wind power plant anemometer tower, wind measurement data are more representative and relatively reliable through reasonable point distribution of the wind power plant anemometer tower, and the method has important significance on post-evaluation of generated energy and ultra-short-term power prediction and prediction accuracy.
In this embodiment, the process of determining the area where the wind farm is located includes:
determining a floor area of a wind power plant;
the distance between the Jixi and the Jidong of the occupied area is prolonged according to a preset proportion, and the length of the area where the wind power plant is located is obtained; the distance between the polar south and the polar north of the occupied place is extended according to a preset proportion, and the width of the area where the wind power plant is located is obtained; and based on the obtained length and width, taking a rectangular area containing the occupied area as the area where the wind power plant is located.
Specifically, in this embodiment, the distance between the north and south of the wind farm site is taken as the length of the wind farm site, the distance between the south and north of the wind farm site is taken as the width of the wind farm site, and the length and the width of the wind farm site are respectively extended by 5% of the length and the width of the wind farm site in the four directions of south, east, west and north, so that the obtained rectangular area is taken as the area where the wind farm is located.
The wind power station area is divided into rectangles by the embodiment, so that the subsequent wind measuring area division and the determination of the coordinate position are facilitated.
In this embodiment, from the perspective of the distribution Density of the wind turbines in the area of the wind farm, a dbss algorithm is used to divide the wind measuring area, and the dbss (Density-Based Spatial Clustering of Applications with Noise) algorithm is a Spatial Clustering algorithm Based on Density. The algorithm divides the area with sufficient density into clusters and finds arbitrarily shaped clusters in a spatial database with noise, which defines clusters as the largest set of density-connected points. The basic idea of the algorithm is as follows: randomly selecting a certain data object in the data set, inquiring the neighborhood density of a given radius of the data object, defining the neighborhood density as a cluster if the neighborhood density exceeds a given threshold, carrying out the same density calculation on the neighborhood data object, and then carrying out cluster expansion and merging. The general algorithm flow of the DBSCAN algorithm in this embodiment is shown in fig. 2, the three-dimensional coordinate data of the fans is input at the beginning of the algorithm, then the algorithm parameters are defined, the types of the data points are marked according to the distance between the two fans, and the area is divided into clusters by judging whether the density of the data points and the core points is up to. And finally, processing the noise data, drawing a cluster map and then finishing the algorithm.
In this embodiment, the S200 includes:
s210: estimating the quantity of the wind measuring areas which can be divided by the wind power plant according to the topographic change of the arrangement positions of the fans, the historical arrangement distances among the fans and the preset basic principle of wind measuring tower distribution points;
s220: determining an estimated range of the clustering density threshold according to the distribution density of the fan arrangement positions, and taking values of the clustering density threshold in the estimated range;
s230: determining the value of the clustering radius according to the value of the clustering density threshold, and dividing the fan arrangement position into a plurality of clusters through a DBSCAN algorithm based on the values of the clustering density threshold and the clustering radius;
s240: and judging whether the number of the clusters accords with the estimated result of the number of the wind measuring areas, if so, determining the wind measuring areas according to the divided clusters, otherwise, adjusting the value of the clustering density threshold value in the estimated range, and repeating the step S230.
The DBSCAN algorithm-related definition includes:
(1) from the epsilon neighborhood of the object: the neighborhood of any one data object p in space refers to: a set of all data objects q contained in a region with data object p as the center of the circle and epsilon as the radius. Recording as follows:
Nε(p)＝{q∈C|dist(p,q)≤ε}；
where C is a spatial data set.
(2) Three types of data points:
core point: if the epsilon neighborhood of data object p contains at least d data objects, i.e. | NsAnd (p) | is more than or equal to d, the data object p is called as a core point.
Boundary points are as follows: data object p is said to be a boundary point if its epsilon neighborhood is less than d data objects, but it is within the neighborhood of other core points.
Noise point: points that are neither core points nor boundary points.
(3) The density is up to: given a spatial data set C, for any two data objects p, q ∈ C, if:
|Nε(q)|≥d；
p∈Nε(q)；
then, p is said to be direct from q with respect to ε and d density.
(4) The density can reach: if there is a chain of objects p1，p2，...，pnWherein
p1＝q,pn＝p；
And satisfies the condition that for all i ═ 1,2, …, n-1, pi+1Is from piWith respect to the direct of the epsilon and d densities, p is said to be reachable from q with respect to the epsilon and d densities.
(5) Cluster and noise: and (3) taking an object p from the data set C, searching the data set C for all points which satisfy the conditions of epsilon and d and have reachable density from the object p to form a cluster, and marking the objects which do not belong to any cluster as noise points.
Based on the definition, the value range of an important parameter in the DBSCAN algorithm, namely the clustering density threshold value d, is estimated, and the clustering radius epsilon is determined according to the value of d. Specifically, a topographic map of a wind power plant site and a wind generating set arrangement mode are analyzed, and the range of the number of the wind measuring areas which can be divided by the wind farm is estimated according to the topographic change of the wind generating set arrangement positions in the site and the distance between the wind generating sets and some basic principles of wind tower arrangement points. And then, estimating the value range of d in the DBSCAN algorithm by observing the distribution density of the wind turbine generator. And in the subsequent area dividing process, taking values in the estimated value range of d, comparing area dividing results obtained under different values of d with the estimated results of the number of the wind measuring areas, and taking the result which accords with the estimation as a proper wind measuring area dividing result.
As shown in fig. 2, the dividing of the fan arrangement positions into a plurality of clusters through the DBSCAN algorithm includes:
s231: processing the arrangement positions of the fans into three-dimensional coordinates, and performing normalization processing on the three-dimensional coordinates;
s232: randomly selecting a coordinate point from the three-dimensional coordinates after normalization processing;
s233: judging whether the selected coordinate point is a core point or not according to the field radius and the clustering density threshold, and if the selected coordinate point is the core point, forming a cluster by the coordinate point with the density of the core point;
s234: if the selected coordinate point is not the core point, reselecting another coordinate point, and repeating S233;
s235: and selecting other coordinate points which are not clustered, and repeating the steps S233 and S234 until no selectable coordinate points exist.
Specifically, the altitude of the fan is smaller than the geodetic coordinate subjected to longitude and latitude conversion by several orders of magnitude, and the influence of the altitude on the anemometer tower is larger than the distance, so that the altitude coordinate and the position coordinate of the fan can be subjected to normalization processing, and the influence weight of the altitude is amplified. The normalization process specifically includes:
acquiring geodetic coordinates and altitude in the arrangement position of the fans, taking the geodetic coordinates as horizontal coordinates and vertical coordinates of the arrangement position of the fans, and taking the altitude as vertical coordinates of the arrangement position of the fans;
respectively carrying out normalization processing on an abscissa, an ordinate and a vertical coordinate in the three-dimensional coordinate, wherein the calculation formula of the normalization processing is as follows:
in the formula, a represents a three-dimensional coordinate before normalization processing of a certain fan, min (a) represents a minimum value in the three-dimensional coordinates before normalization processing of all the fans, max (a) represents a maximum value in the three-dimensional coordinates before normalization processing of all the fans, and B is the three-dimensional coordinate after normalization processing of the certain fan.
Finally, determining a wind measuring area according to the divided clusters, comprising:
marking coordinate points which do not belong to any cluster as noise points to be removed;
and generating a rectangular envelope of each cluster along the east, south, west and north directions, extending the southwest vertex of the rectangular envelope in the southwest direction by a preset proportion, and extending the northeast vertex of the rectangular envelope in the northeast direction by a preset proportion to obtain a wind measuring area.
In this embodiment, after clustering is performed on the wind turbine generator by using the DBSCAN algorithm, the position of the wind measurement area is calculated according to the obtained positions of the wind turbine clusters. For the convenience of calculation, the shape of each wind measurement area is taken as a rectangle. In this embodiment, the coordinates of the southwest point and the northeast point of each fan cluster are respectively extended to the west direction, the south direction, the east direction and the north direction by 5% of the difference value between the east direction and the south direction, and then the specific position of each rectangular wind measuring area can be obtained. Finally, as shown in fig. 3, the area where the wind farm is located is divided into 2 wind measuring areas according to the present embodiment.
In this embodiment, the number of the anemometry regions is the number of the finally determined anemometry tower positions, that is, 1 anemometry tower is arranged in each anemometry region.
In this embodiment, in order to determine the position of the wind measurement point in each wind measurement area, a grid screening method is used to obtain a series of candidate wind measurement points, and then candidate wind measurement points that do not meet the requirement are screened out according to a preset wind measurement tower site selection quantization standard, which specifically includes:
and dividing grids in the wind measuring area at fixed intervals, and taking the intersection points of the grids as alternative wind measuring points. In this embodiment, each wind measurement region is a grid division region, and the grid shape is determined to be a square. In order to not miss every possible position suitable for building the anemometer tower as much as possible, the grid is divided according to the length l in the following formula as the side length of a square:
L＝min{L1，L2，...Ln}
in the formula, L1、L2、...LnThe distance (m) between every two fans in the wind power plant; n is a wind turbine generator in a wind farmThe number of the cells.
Secondly, according to a space consistency principle, a prevailing wind direction wind condition parameter representative principle and a screening principle considering wake effect reduction, six indexes, namely horizontal distance from a fan, altitude difference with the fan, a prevailing wind direction wind acceleration factor, prevailing wind direction turbulence intensity, a prevailing wind direction inflow angle and a wake effect reduction rate are considered, a candidate wind measuring point quantitative screening strategy is proposed, and a wind measuring tower quantitative site selection standard is established.
In this embodiment, altitude elevation data, surface roughness data, and anemometry data of the wind farm are input into computational fluid dynamics software WindSim or Meteodyn WT, and micro-scale wind flow parameters such as wind acceleration factor, turbulence intensity, inflow angle, and the like are calculated. The elevation data is STRM DEM elevation data, the surface roughness data is data converted from Global Land30 surface coverage data to 30m resolution, and the corresponding relation between the Global Land30 surface coverage data and the surface roughness is shown in Table 1.
TABLE 1
In this embodiment, the historical anemometry data may be obtained from an anemometry tower established early in the wind farm area, or from 3Tier reanalysis data. The data comprises a wind speed and direction sequence which is more than 3 years and corresponds to a position with the vertical height of 80m of the earth surface of a certain candidate wind measuring point in the wind power plant. The method for acquiring the wind speed and direction sequence of more than 3 years through 3Tier reanalysis data specifically comprises the following steps: acquiring by using a numerical weather forecast model (NWP) of 3Tier, wherein input data used in the model is global weather data of the past 50 years; high resolution terrain, soil and vegetation data; and field measured data; by adopting climate change analysis, long-term historical data and a spatial distribution map, the long-term change of the wind energy condition in the project area can be obtained.
In this embodiment, the wind tower quantitative site selection standard established based on the six indexes is as follows:
removing alternative wind measuring points within 2 times of the diameter of the wind wheel from the wind turbine:
r＜2D
in the formula, x is the abscissa of the fan coordinate; y the ordinate of the fan coordinate; x is a radical of a fluorine atom0The horizontal coordinate of the alternative wind measuring point is used; y is0The vertical coordinate of the candidate wind measuring point is used; d is the diameter (m) of the wind wheel; and r is the distance (m) between the alternative wind measuring point and the wind turbine generator.
Eliminating alternative wind measuring points with the altitude difference larger than 100 meters from the altitude of the wind turbine generator:
ΔH＝|H-H0|
ΔH＞100
in the formula, H is the altitude of the wind turbine generator; h0Is the altitude of the alternative point of anemometry.
And thirdly, in the prevailing wind direction, reserving alternative wind measuring points of the wind acceleration factors within the range of plus or minus 5 percent of the average value of the wind acceleration factors at all wind turbines in the wind power plant:
a1＝-a*5％
a2＝a*5％
ai∈(a1,a2)(i＝1,2,...,m)
in the formula, A1,A2,...,AnThe wind acceleration factor of the wind turbine generator in the prevailing wind direction; n is the number of wind generating sets in the wind power plant; and m is the number of alternative wind measuring points reserved by the previous screening work.
And keeping alternative wind measuring points with the turbulence intensity below the average level of the turbulence intensity of all the alternative wind measuring points in the prevailing wind direction:
Ri＜R(i＝1,2,...,m)
in the formula, R1,R2,...,RmThe turbulence intensity of the alternative wind measuring points reserved for the prevailing wind upwards.
In the prevailing wind direction, reserving the alternative wind measuring points with inflow angles below the average level of inflow angles of all the alternative wind measuring points:
λa1＝|λ1|,λa2＝|λ2|,...
λai＜λav(i＝1,2,...,m)
in the formula, λ1,λ2,...,λmAnd reserving the inflow angle of the alternative wind measuring point for the prevailing wind upwards.
Sixthly, reserving the alternative wind measuring points with the average reduction rate caused by the wake effect lower than the average level of all the alternative wind measuring points:
wi＜w(i＝1,2,...,m)
in the formula, w1,w2,...wm-average discount rate due to wake effect of alternative wind points.
And finally, calculating wind flow parameters, horizontal distance to the fan, altitude difference to the fan and wake effect reduction rate according to topographic data and historical wind measuring data at the alternative wind measuring points, screening out the alternative wind measuring points of which the calculation results do not accord with the site selection quantization standard of the wind measuring tower, and taking the remaining alternative wind measuring points after screening out as finally determined wind measuring points.
Considering that the average wind speed distribution is an important parameter for monitoring wind resources, establishing a wind measuring tower quantitative evaluation index based on the average wind speeds of a fan and alternative wind measuring points and data such as shape parameters k and scale parameters c of wind speed Weibull distribution, and determining the position of the wind measuring tower in each partition in the alternative wind measuring points finally screened and reserved according to an evaluation result. The weibull distribution is represented by the formula:
wherein P (v) represents a wind speed distribution.
Therefore, a layout evaluation index of the wind measuring point on the wind speed dimension, the shape dimension and the scale dimension is established, and a calculation formula of the layout evaluation index is as follows:
wherein X is a layout evaluation index, X1For layout evaluation in wind speed dimensionLabel, X2Is a layout evaluation index in the dimension of the shape, X3Is a layout evaluation index in a dimension, vtIs the average wind speed, v, of all fan pointsmWind speed, v, being the point of anemometry to be evaluated1、v2、...、vnThe wind speed, k, of each fan pointtIs the average shape parameter, k, of all fan pointsmAs a shape parameter of the point of wind measurement to be evaluated, k1、k2、...、knThe shape parameters of each fan point location, ctIs the average scale parameter of all fan point locations, cmAs a scale parameter of the wind measurement point to be evaluated, c1、c2、...、cnSequentially setting the scale parameters of each fan point location, wherein n is the total number of the wind turbine generators; the wind turbine point location is the location of each wind turbine.
And selecting the wind measuring point with the maximum layout evaluation index in each wind measuring area as the position of the wind measuring tower.
The value of the index X is in the range of [0,1], and the closer the value of X of the candidate anemometry point is to 1, the more suitable the position is for establishing the anemometry tower. The optimal distribution point of the anemometer tower selected through gridding screening and quantitative evaluation is shown in figure 4.
The sequence numbers in the above embodiments are merely for description, and do not represent the sequence of the assembly or the use of the components.
The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An optimized layout method for wind measuring towers of a wind power plant is characterized by comprising the following steps:
s100: determining the area where the wind power plant is located and the arrangement positions of the fans;
s200: based on the arrangement positions of the fans, dividing a wind measuring area in the area where the wind power plant is located through a DBSCAN algorithm;
s300: establishing a wind tower site selection quantization standard according to topographic data and historical wind measurement data, and screening wind measurement points meeting a preset site selection standard in a wind measurement area by combining a gridding screening method;
s400: and establishing a layout evaluation index of the anemometer tower, and determining the position of the anemometer tower in the candidate anemometer points according to the layout evaluation index.
2. The method for optimizing layout of wind measuring towers of wind power plant according to claim 1, wherein the step of determining the area where the wind power plant is located comprises the following steps:
determining a floor area of a wind power plant;
the distance between the Jixi and the Jidong of the occupied area is prolonged according to a preset proportion, and the length of the area where the wind power plant is located is obtained;
the distance between the polar south and the polar north of the occupied place is extended according to a preset proportion, and the width of the area where the wind power plant is located is obtained;
and based on the obtained length and width, taking a rectangular area containing the occupied area as the area where the wind power plant is located.
3. The method for optimizing layout of wind farm anemometer towers according to claim 1, wherein the step S200 comprises:
s210: estimating the quantity of the wind measuring areas which can be divided by the wind power plant according to the topographic change of the arrangement positions of the fans, the historical arrangement distances among the fans and the preset basic principle of wind measuring tower distribution points;
s220: determining an estimation range of a clustering density threshold value according to the distribution density of the fan arrangement positions, and taking values of the clustering density threshold value in the estimation range;
s230: determining the value of the clustering radius according to the value of the clustering density threshold, and dividing the fan arrangement position into a plurality of clusters through a DBSCAN algorithm based on the values of the clustering density threshold and the clustering radius;
s240: and judging whether the number of the clusters accords with the estimated result of the number of the wind measuring areas, if so, determining the wind measuring areas according to the divided clusters, otherwise, adjusting the value of the clustering density threshold value in the estimated range, and repeating the step S230.
4. The optimal layout method of the wind measuring tower of the wind power plant according to claim 3, wherein the wind turbine arrangement positions are divided into a plurality of clusters through a DBSCAN algorithm, and the method comprises the following steps:
s231: processing the arrangement position of the fan into a three-dimensional coordinate, and performing normalization processing on the three-dimensional coordinate;
s232: randomly selecting a coordinate point from the three-dimensional coordinates after normalization processing;
s233: judging whether the selected coordinate point is a core point or not according to the field radius and the clustering density threshold, and if the selected coordinate point is the core point, forming a cluster by the coordinate point with the density of the core point;
s234: if the selected coordinate point is not the core point, reselecting another coordinate point, and repeating S233;
s235: and selecting other coordinate points which are not clustered, and repeating the steps S233 and S234 until no selectable coordinate points exist.
5. The optimized layout method for wind power plant anemometer towers according to claim 4, wherein the step S231 comprises the following steps:
acquiring geodetic coordinates and altitude in the arrangement position of the fans, taking the geodetic coordinates as horizontal coordinates and vertical coordinates of the arrangement position of the fans, and taking the altitude as vertical coordinates of the arrangement position of the fans;
respectively carrying out normalization processing on an abscissa, an ordinate and a vertical coordinate in the three-dimensional coordinate, wherein the calculation formula of the normalization processing is as follows:
in the formula, a represents a three-dimensional coordinate before normalization processing of a certain fan, min (a) represents a minimum value in the three-dimensional coordinates before normalization processing of all the fans, max (a) represents a maximum value in the three-dimensional coordinates before normalization processing of all the fans, and B is the three-dimensional coordinate after normalization processing of the certain fan.
6. The method for optimizing layout of wind farm anemometer towers according to claim 3, wherein the determining a anemometer area according to the divided clusters comprises:
marking coordinate points which do not belong to any cluster as noise points to be removed;
and generating a rectangular envelope of each cluster along the east, south, west and north directions, extending the southwest vertex of the rectangular envelope in the southwest direction by a preset proportion, and extending the northeast vertex of the rectangular envelope in the northeast direction by a preset proportion to obtain a wind measuring area.
7. The method for optimizing layout of wind farm anemometer towers according to claim 1, wherein the step S300 comprises:
dividing grids in the wind measuring area at fixed intervals, and taking intersection points of the grids as alternative wind measuring points;
calculating wind flow parameters, horizontal distance with a fan, altitude difference with the fan and wake effect reduction rate according to topographic data and historical wind measuring data at alternative wind measuring points, and screening out alternative wind measuring points of which the calculation results do not accord with site selection quantization standards of a wind measuring tower;
and taking the remaining alternative wind measuring points after screening out as finally determined wind measuring points.
8. The method for optimizing the layout of the wind measuring tower of the wind power plant according to claim 7, wherein the wind flow parameters comprise a wind acceleration factor, a turbulence intensity and an inflow angle.
9. The method for optimizing layout of wind farm anemometer towers according to claim 1, wherein the step S500 comprises:
establishing a layout evaluation index of a wind measuring point on a wind speed dimension, a shape dimension and a scale dimension, wherein a calculation formula of the layout evaluation index is as follows:
wherein X is a layout evaluation index, X1Is a layout evaluation index in the wind speed dimension, X2Is a layout evaluation index in the dimension of the shape, X3Is a layout evaluation index in the dimension of scale, vtIs the average wind speed, v, of all fan pointsmWind speed, v, being the point of anemometry to be evaluated1、v2、...、vnThe wind speed, k, of each fan pointtIs the average shape parameter, k, of all fan pointsmAs a shape parameter of the point of wind measurement to be evaluated, k1、k2、...、knThe shape parameters of each fan point location, ctIs the average scale parameter of all fan point locations, cmIs to be treatedScale parameter of wind measurement point evaluated, C1、C2、...、CnSequentially setting the scale parameters of each fan point location, wherein n is the total number of the wind turbine generators;
and selecting the wind measuring point with the maximum layout evaluation index in each wind measuring area as the position of the wind measuring tower.
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