CN108932554A - The method for optimizing configuration and device of a kind of wind power plant flow field measuring point - Google Patents
The method for optimizing configuration and device of a kind of wind power plant flow field measuring point Download PDFInfo
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Abstract
The embodiment of the present application discloses the method for optimizing configuration and device of a kind of wind power plant flow field measuring point, including constructing initial sample space, initial sample space includes the sample of at least one wind power plant flow field measuring point position deployment decision, wherein the sample of wind power plant flow field measuring point position deployment decision includes the position of at least one wind power plant flow field measuring point, and wind power plant flow field measuring point is configured on blower and/or anemometer tower；N number of sample of wind power plant flow field measuring point position deployment decision is obtained from initial sample space；It according to the linear approximation error result of the flow model curve to wind power plant, sorts to N number of sample, and chooses sample of the linear approximation error result less than preset value from N number of sample, constitute ideal sample space S；The accurate simulation evaluation that Power Output for Wind Power Field precision of prediction is carried out to the sample of ideal sample space S, obtains the position optimal solution Ω of wind power plant flow field measuring point.
Description
Technical field
This application involves wind power generation field more particularly to the method for optimizing configuration and dress of a kind of wind power plant flow field measuring point
It sets.
Background technique
Wind Power In China installation installed capacity occupies first place in the world.At the same time, annual abandonment electricity rapid development, average abandonment
Rate reaches 21% at present, and abandonment is rationed the power supply to normalization, malignization development.Larger wind power uncertainty is to influence wind-powered electricity generation simultaneously
A key factor of the net to cause abandonment electricity to increase.And optimize wind farm power prediction effect for promoting wind-electricity integration
Stability to reduce abandonment electricity it is most important." ultra-short term predicts 4h predicted value moon root mean square to energy industry standard regulation
Error should be less than 0.15 ".Mainstream wind electric field power prediction method (including physical method and statistical method) prediction result at present
Root-mean-square error is substantially between 0.1-2.0.A low main problem of precision of prediction is：The deployment of wind power plant flow field measuring point
Position is not reasonable, and the characteristic that will lead to the sensor of wind power plant flow field measuring point in this way cannot utilize well.
The acquisition and analysis of the multi-source informations such as wind power plant geography, meteorology, environment, energy are that wind power plant environment accurately perceives
The basis known.Wherein, the direct perception as the premise of wind-power electricity generation, to near-earth flow field dynamic (wind speed, wind direction) in wind power plant
It is the core of wind power analysis of uncertainty.To wind power plant flow field in ten square kilometres of large-scale wind power number of fields of region
The quantity of measuring point and position optimize configuration, and the perception efficiency and precision of prediction of wind power can be improved, and reduce
The construction cost and construction period of flow field measuring point and its information system.
Currently, the flow field measuring point in Construction of Wind Power standard is mainly disposed at the cabin or anemometer tower of blower, in wind
In electric field, the position of flow field measuring point concrete configuration is mainly configured according to multi fan similarity classification configuration method；For example, base
The similitude of the history weather information at multiple blowers in wind field, by the multiple blower in the wind field be divided into
A few grouping；Flow field measuring point is configured from a grouping, such as：In the grouping selection represent blower sensors configured or
Suitable position of the person in the grouping arranges anemometer tower, and obtains flow field measuring point to the measured value of the grouping.Based in wind-powered electricity generation
The measured value of the flow field measuring point configured in multiple groupings for being divided in analyzes the output powers of multiple blowers in wind field.
It certainly, can also include the distance between blower in grouping less than the wind in preset distance, grouping to the rule of classification of blower
The model of machine is consistent；Adjusting the rule that blower is grouped can be：Whether the blower in grouping meets rule of classification, based on through adjusting
Multiple blowers in the wind field are divided into new grouping etc. by whole similitude.Sensor packet on blower or anemometer tower
It includes：Meteorological sensor, fan condition sensor and blower output power sensor.After being grouped to blower, wind field is utilized
In the similitude of weather information at each blower can to reduce the type and quantity of required sensor in power prediction
Only to dispose sensor at the representative blower in multiple blowers with similitude, and then deployment sensor can be substantially reduced
Every cost of Shi Suoxu.
However, the program is dedicated on existing sensor integration, the kind of required sensor in power prediction is reduced
Class and quantity, i.e., the configuration of flow field measuring point relies primarily on blower grouping in the prior art, adjustment rule of classification is realized, and in order to
The precision of prediction of wind power is further increased, specifically how the position of stream field measuring point, which optimizes, is not directed to, example
The cabin which blower flow field measuring point is specifically configured at such as in wind power plant or in each grouping or configuration are at which
On a anemometer tower.
Summary of the invention
Embodiments herein provides the method for optimizing configuration and device of a kind of wind power plant flow field measuring point, can be realized pair
The position of flow field measuring point optimizes, and improves the precision of prediction of wind power.
In order to achieve the above objectives, embodiments herein adopts the following technical scheme that：
In a first aspect, providing a kind of method for optimizing configuration of wind power plant flow field measuring point, initial sample space is constructed, this is first
Beginning sample space includes the sample of wind power plant flow field measuring point position deployment decision, wind power plant flow field measuring point position deployment decision
Sample include at least one wind power plant flow field measuring point position, wind power plant flow field measuring point be configured at blower and/or survey wind
On tower；N number of sample of wind power plant flow field measuring point position deployment decision is obtained from initial sample space；According to wind power plant
The linear approximation error result of flow model curve sorts to N number of sample, and linear approximation error knot is chosen from N number of sample
Fruit is less than the sample of preset value, constitutes ideal sample space S；It include S sample in illustrative ideal sample space S, S≤N,
The accurate simulation evaluation that Power Output for Wind Power Field precision of prediction is carried out to the sample of ideal sample space S, obtains wind power plant flow field
The position optimal solution Ω of measuring point.In the above scheme, be made of wind power plant flow field measuring point position deployment decision it is initial
N number of sample is obtained in sample space, and to N number of sample according to the linear approximation error knot of the flow model curve to wind power plant
Fruit sequence, the accurate simulation for then carrying out Power Output for Wind Power Field precision of prediction to the sample of ideal sample space S are evaluated, are obtained
The position optimal solution Ω of wind power plant flow field measuring point, due to the configuration in the application to the specific location of wind power plant flow field measuring point
Further optimization has been carried out, can be improved the precision of prediction of wind power.
A kind of example is that the accurate simulation of Power Output for Wind Power Field precision of prediction is carried out to the sample of ideal sample space S
Evaluation obtains the position optimal solution Ω of wind power plant flow field measuring point, including：According to root-mean-square error RMSE (root-mean-
Square error) accurate simulation of the sample progress Power Output for Wind Power Field precision of prediction of the ideal sample space S is commented
Valence obtains the position optimal solution Ω of wind power plant flow field measuring point.Wherein, according to the one dimensional flow curvature of field to wind power plant in above scheme
The linear approximation error result of line sorts to N number of sample, then and from N number of sample obtains ideal sample space S, thus
The sample size for Power Output for Wind Power Field prediction is reduced, computing cost is reduced, in this step, by according to root mean square
The accurate simulation evaluation that error RMSE carries out Power Output for Wind Power Field precision of prediction to the sample of ideal sample space S can be into one
Step reduces the sample size for Power Output for Wind Power Field prediction, reduces computing cost.
In addition, initial sample space can be two dimensional sample space or one-dimensional sample space, but in order to realize according to
Optimum linearity approximate error to the flow model curve of wind power plant is as a result, sort to N number of sample, if initial sample space is two
Sample space is tieed up, then is needed first to the dimension-reduction treatment of two dimensional sample space, a kind of example is：The position of wind power plant flow field measuring point
For the two-dimensional coordinate of wind power plant flow field measuring point；Constructing initial sample space includes：According to being uniformly distributed random function in two dimension
Generation wind power plant flow field measuring point in space is uniformly distributed random two-dimensional coordinate, constitutes initial sample space；It is empty from initial sample
Between it is middle obtain wind power plant flow field measuring point position deployment decision N number of sample, including：N number of sample is chosen in initial sample space
This；Wind field data are obtained, wind field data include at least：The wind direction of wind power plant flow field measuring point, wind power plant flow field measuring point are in wind
Position in；According to wind field data, the prevailing wind direction in predetermined amount of time is obtained；According to the coordinate of wind power plant flow field measuring point with
And the prevailing wind direction, determine projector distance of the wind power plant flow field measuring point on the prevailing wind direction；Traverse the N of initial sample space
A sample, obtains one-dimensional sample space, and one-dimensional sample space includes N number of sample.
If initial sample space is two dimensional sample space, another example is：Initial sample space is two dimensional sample space,
The position of wind power plant flow field measuring point is the two-dimensional coordinate of wind power plant flow field measuring point；Constructing initial sample space includes：
It is uniformly distributed random two-dimensional coordinate in two-dimensional space generation wind power plant flow field measuring point according to random function is uniformly distributed, is constituted
Initial sample space；N number of sample of wind power plant flow field measuring point position deployment decision is obtained from initial sample space, including：
Wind field data are obtained, the wind field data include at least：The wind direction of wind power plant flow field measuring point, wind power plant flow field measuring point are in wind
Position in；According to the wind field data, the prevailing wind direction in predetermined amount of time is obtained；According to the seat of wind power plant flow field measuring point
It is marked with and the prevailing wind direction, determines projector distance of the wind power plant flow field measuring point on the prevailing wind direction；Traverse the initial sample
The wind power plant flow field measuring point in this space, obtains one-dimensional sample space；N number of sample is chosen in the one-dimensional sample space.
More than, initial sample space be two dimensional sample space two examples in, first example and second it is exemplary
It is distinguished as：N number of sample first is chosen in two-dimensional initial sample space in first example, then to the sample of N number of sample composition
This space carries out dimension-reduction treatment, forms the one-dimensional sample space comprising N number of sample, is selected in initial sample space with realizing
Take the process of N number of sample；In second example, two-dimensional initial sample space dimension-reduction treatment is first formed into one-dimensional sample space,
Then N number of sample is chosen in the one-dimensional sample space again, to realize the process for choosing N number of sample in initial sample space.
If initial sample space is one-dimensional sample space, a kind of example is：Initial sample space is constructed, including：According to equal
Even distribution random function generates the random two-dimensional coordinate that is uniformly distributed of wind power plant flow field measuring point in two-dimensional space, constitutes with press proof
This space；Wind field data are obtained, wind field data include at least：Wind direction, the wind power plant flow field measuring point of wind power plant flow field measuring point
Position in wind field；According to wind field data, the prevailing wind direction in predetermined amount of time is obtained；According to the seat of wind power plant flow field measuring point
It is marked with and prevailing wind direction, determines projector distance of the wind power plant flow field measuring point on prevailing wind direction；Traverse the wind-powered electricity generation of initial sample space
Field flow field measuring point, obtains one-dimensional sample space；The deployment of wind power plant flow field measuring point position is obtained from random sample space to determine
N number of sample of plan, including：N number of sample is chosen in one-dimensional sample space.
A kind of example is to carry out Power Output for Wind Power Field according to sample of the root-mean-square error RMSE to ideal sample space S
The accurate simulation of precision of prediction is evaluated, and before the position optimal solution Ω for obtaining wind power plant flow field measuring point, further includes：By ideal sample
This space S is divided into depth-first search (depth-first search, SD) subset and wide according to the characteristic of one-dimensional sample space
Spend first search (breadth-first search, SB) subset；According to the linear approximation of the flow model curve to wind power plant
Error result respectively sorts to the sample in depth-first search subset and breadth first search subset；It is missed according to root mean square
The accurate simulation that poor RMSE carries out Power Output for Wind Power Field precision of prediction to the sample of ideal sample space S is evaluated, and wind-powered electricity generation is obtained
The position optimal solution Ω of field flow field measuring point, including：According to predetermined order, depth-first search subset and breadth First are searched
The sample that large rope is concentrated carries out the accurate simulation evaluation of Power Output for Wind Power Field precision of prediction, obtains wind power plant flow field measuring point
Position optimal solution Ω, the sample in sample and breadth first search subset wherein in predetermined order, in depth-first search subset
This interspersed setting, and linearly the forcing according to the flow model curve to the wind power plant of the sample in depth-first search subset
Nearly error result ascending order arranges, and the sample in breadth first search subset linearly being forced according to the flow model curve to wind power plant
Nearly error result ascending order arrangement.Wherein, due in the above scheme, sample space being reduced to processing to the one-dimensional sample on prevailing wind direction
This space, therefore the sample in breadth first search subset can be according to the wind power plant flow field measuring point density prediction on prevailing wind direction
Wind power, the sample in depth-first search subset can be according to throwing of the wind power plant flow field measuring point on prevailing wind direction
The quantitative forecast wind power of wind power plant flow field measuring point near shadow, thus when improving wind farm power prediction precision,
The distribution density and quantity of wind power plant flow field measuring point can be taken into account.
Second aspect provides a kind of configuration optimization device of wind power plant flow field measuring point, including：Initialization module is used for
Initial sample space is constructed, the initial sample space includes wind power plant flow field measuring point position deployment decision, wherein the wind
Electric field flow field measuring point position deployment decision includes the position of wind power plant flow field measuring point, wind power plant flow field measuring point
It is configured on blower and/or anemometer tower；Sampling module, for from the initial sample space that the initialization module constructs
Obtain N number of sample of wind power plant flow field measuring point position deployment decision；Computing module, for according to the one-dimensional of the wind power plant
The optimum linearity approximate error of flow field curve is as a result, sort to N number of sample that the sampling module obtains, and from the N
The sample that approximate error result is less than preset value is chosen in a sample, constitutes ideal sample space S；Module is solved, for institute
The accurate simulation for stating the sample progress Power Output for Wind Power Field precision of prediction of the ideal sample space S of computing module acquisition is commented
Valence obtains the position optimal solution Ω of wind power plant flow field measuring point.
The third aspect provides the configuration optimization device of wind power plant flow field measuring point, including：Processor, memory and bus；
For storing computer executed instructions, the processor is connect with the memory by the bus memory, works as institute
When stating the configuration optimization device operation of wind power plant flow field measuring point, the processor executes the calculating of the memory storage
Machine executes instruction, so that the control device executes method as described in relation to the first aspect.
Fourth aspect provides computer storage medium, including instruction, when run on a computer, so that the meter
Calculation machine executes method as described in relation to the first aspect.
5th aspect, present invention also provides a kind of computer program products, when run on a computer, make to succeed in one's scheme
Calculation machine executes method described in above-mentioned first aspect.
It is to be appreciated that the configuration optimization device or computer of any wind power plant flow field measuring point of above-mentioned offer store
Medium or computer program product are used to execute the configuration optimization side of corresponding wind power plant flow field measuring point presented above
Method, therefore, attainable beneficial effect can refer to the method for optimizing configuration and hereafter of wind power plant flow field above measuring point
The beneficial effect of corresponding scheme in specific embodiment, details are not described herein again.
Detailed description of the invention
Fig. 1 is a kind of method for optimizing configuration flow diagram of wind power plant flow field measuring point provided by the embodiments of the present application；
Fig. 2 is that ideal sample space S provided by the embodiments of the present application is that line taking approximate error result is most in N number of sample
Small preceding k% sample；
Fig. 3 is the position of wind power plant flow field measuring point in three scenes provided by the embodiments of the present application to flow model curve
The schematic diagram of linear approximation；
Fig. 4 is that prevailing wind direction provided by the embodiments of the present application obtains schematic diagram；
Fig. 5 is the projection provided by the embodiments of the present application being mapped as wind power plant flow field measuring point spatial position along prevailing wind direction
Schematic diagram；
Fig. 6 is two-dimentional wind power plant space provided by the embodiments of the present application dimensionality reduction flow diagram；
Fig. 7 is that the sample progress Power Output for Wind Power Field of a kind of pair of ideal sample space S provided by the embodiments of the present application is pre-
Survey the flow diagram of the accurate simulation evaluation of precision；
Fig. 8 is that any three wind power plant flow field measuring points of prior art arrangement (mention Power Output for Wind Power Field ultra-short term
Previous hour) prognostic chart；
Fig. 9 is any three wind power plant flow field measuring points provided by the embodiments of the present application to Power Output for Wind Power Field ultra-short term
(in advance one hour) prognostic chart；
Figure 10 is a kind of structural representation of the configuration optimization device of wind power plant flow field measuring point provided by the embodiments of the present application
Figure；
Figure 11 is a kind of structure of the configuration optimization device for wind power plant flow field measuring point that another embodiment of the application provides
Schematic diagram；
Figure 12 is a kind of structure of the configuration optimization device for wind power plant flow field measuring point that the another embodiment of the application provides
Schematic diagram.
Specific embodiment
With reference to the accompanying drawing, embodiments herein is described.
Firstly, simply being introduced the technical background being referred to herein, understood with helping reader：
The embodiment of the present invention is applied to the configuration optimization of wind power plant flow field measuring point, and wherein wind power plant flow field measuring point refers to
For acquiring the position of the sensor of the data of wind farm power prediction, wherein sensing implement body may be mounted at the cabin of blower
Or on anemometer tower, the configuration of wind power plant flow field measuring point refers in the application：It specifically will sensing implement body arrangement in wind power plant
In in the cabin of which blower or anemometer tower；Sensor may include：Meteorological sensor, fan condition sensor and blower
Output power sensor.
When the cabin that sensor is installed on to blower, a kind of typical sensor is：Nacelle wind speed instrument, every Fans are necessary
A nacelle wind speed instrument is configured, is generally installed at the top of the rear of fan engine room.For controlling blower real-time monitoring, yaw,
And real-time data source is provided for wind farm power prediction.And it is by the carrying scheme of anemometer tower realization sensor：Survey wind
The position of tower preferably configures outside wind power plant within the scope of 1km~5km, and in order to avoid wind power plant wake effect shadow as far as possible
It rings, preferably builds in the representative position of the upwind of wind power plant cardinal wind.It is microcosmic early period that anemometer tower is mainly used in wind power plant
In addressing, but its collected real time data is also the significant data source of wind farm power prediction.Wherein, nacelle wind speed instrument
Advantage：Due to nacelle top position and wind-driven generator group wind-wheel center relative close, measuring value and the practical wind-engaging of wind wheel when
Between postpone very little, influenced by surrounding terrain smaller.The advantage of anemometer tower：Due to just considering to subtract as far as possible in the design addressing stage
The influence of few wind power plant wake effect, anemometer tower sensor are higher for the accuracy in measurement of free wind.But nacelle wind speed instrument：
Anemobiagraph is very big by blower wake effect and wind wheel disturbing influence, can not characterize the free flow wind really captured before wind wheel
Speed, and measurement has biggish randomness.Uncertainty ± 1.5m/s caused by wake effect；Anemometer tower：Wind is surveyed at present
For tower position configuration specification multi-panel to the microcosmic structure of early period, measuring point is less, hypertelorism of the measuring point away from most of blower, empty
Between density it is inadequate.Accuracy in measurement：± 0.5m/s (3m/s~30m/s).
Mathematical model for the predicted exactitude evaluation function of wind power is following formula：
Wherein, minJ is evaluation goal, and J is evaluation scene number, fj(Ωn)=RMSETFor the prediction essence of wind power
Spend evaluation function, Ωn={ (xi,yi)}I=1,2 ..., nPoint set, RMSE (root-mean- are measured for wind power plant flow field in scene j
Square error, root-mean-square error).
Wherein：
PmfFor the actual average power of i period wind power plant；
PpiFor the prediction power of i period wind power plant；
CiFor i period wind power plant booting total capacity；
N is all number of samples in wind power plant.
Technical solution provided by the present application is introduced with reference to the accompanying drawing.
Shown in referring to Fig.1, the embodiment of the present invention provides a kind of method for optimizing configuration of wind power plant flow field measuring point, including
Following steps：
101, initial sample space is constructed, initial sample space is disposed comprising at least one wind power plant flow field measuring point position
The sample of decision, wherein the sample of wind power plant flow field measuring point position deployment decision includes at least one described wind power plant flow field amount
The position of measuring point, wind power plant flow field measuring point are configured on blower and/or anemometer tower.
102, N number of sample of wind power plant flow field measuring point position deployment decision is obtained from initial sample space.
103, it according to the linear approximation error result of the flow model curve to wind power plant, sorts to N number of sample, and from N
The sample that linear approximation error result is less than preset value is chosen in a sample, constitutes ideal sample space S.
The smallest several samples of approximate error result in N number of sample can be chosen in step 103 constitutes ideal sample sky
Between S further reduced accurate as the input of the accurate simulation evaluation of Power Output for Wind Power Field precision of prediction in step 104
Spend the computation complexity of simulation evaluation.Step 103 can be described as in given segmentation number (wind power plant flow field measuring point configuration
Number) under the premise of, the waypoint position (position of wind power plant flow field measuring point) of function is determined, so that in given scenario collection (one
Tie up flow field curve) under, linear approximation error result is minimum.Its solving model is as follows：
s.t.-εj≤αP(j)+β-C(j)≤εj, 0≤εj≤εM+1, wherein α, β are linear approximation undetermined coefficient, εjTo approach
Error, P(j)Indicate the first order of linear approximation function, C(j)Indicate the constant term of linear approximation function, M refers to point of linear approximation
Number of segment.
As shown in Fig. 2, ideal sample space S the smallest preceding k% of line taking approximate error result preferably in N number of sample,
I.e. as shown in figure 3, the position for the wind power plant flow field measuring point that sample is included in ideal sample space S in scene one, two, three
D1, d2, d3, linear approximation error result are minimum.
104, the accurate simulation for carrying out Power Output for Wind Power Field precision of prediction to the sample of ideal sample space S is evaluated, and is obtained
Take the position optimal solution Ω of wind power plant flow field measuring point.
In the above scheme, it is obtained in the initial sample space being made of wind power plant flow field measuring point position deployment decision
N number of sample, and sort to N number of sample according to the linear approximation error result of the flow model curve to wind power plant, it is then right
The sample of ideal sample space S carries out the accurate simulation evaluation of Power Output for Wind Power Field precision of prediction, obtains wind power plant flow field amount
The position optimal solution Ω of measuring point, due to having been carried out further in the application to the configuration of the specific location of wind power plant flow field measuring point
Optimization, can be improved the precision of prediction of wind power.
It is approached due to needing the flow model curve using wind power plant to do optimum linearity to N number of sample in step 103, in the hope of
Take error result, thus before the step of in firstly the need of to two-dimentional wind power plant space carry out dimension-reduction treatment；Concrete principle is：
In wind power plant, the fluctuation of wind energy is one, and there are the dynamic communication process of time lag, the output works of blower and blower
There are the time and space usage relationships of upstream and downstream between rate.Microcosmic structure and design phase in wind power plant, the arrangement of blower mainly according to
It is optimized according to prevailing wind direction and vertical prevailing wind direction.In the case where determining prevailing wind direction, the fluctuation of blower output power presents autonomous
The situation that wind direction upstream toward downstream is propagated.In the communication process for considering wind energy, same or similar wind-powered electricity generation is projected with prevailing wind direction
Measuring point its measuring value in field flow field has biggish similitude, therefore wind power plant flow field measuring point is affixed one's name to inside wind-powered electricity generation field areas
Two-dimensional problems can be converted to the one-dimensional problem arranged according to prevailing wind direction, as shown in figure 4, two-dimentional wind power plant space is by two axis of X, Y
The cross facet of coordinate is constituted, and shows prevailing wind direction with arrow in figure, wind power plant flow field measuring point (being shown with circle) is in random point
Cloth.Dimensionality reduction is carried out to two-dimentional wind power plant space, as shown in figure 5, passing using the dynamic that one-dimensional advective equation describes wind energy variation
Process is broadcast, by the decision variable (x of wind power plant flow field measuring point spatial positioni,yi) it is mapped as projection d along prevailing wind directioni, realize
The dimensionality reduction of decision space.Following conversion is realized to which point set will be measured to wind power plant flow field in scene j：
Specific two dimension wind power plant space carries out dimensionality reduction and includes the following steps, as shown in Figure 6：
S1：Wind field data are obtained, wind field data include at least：The wind direction of wind power plant flow field measuring point, wind power plant flow field amount
Position of the measuring point in wind field；Its Wind Field data can be by GIS (Geographic Information System, geography information
System), EMS (Energy Management System, energy management system) read.
S2, according to wind field data, obtain the prevailing wind direction in predetermined amount of time.
S3, according to the coordinate and prevailing wind direction of wind power plant flow field measuring point, determine wind power plant flow field measuring point in prevailing wind direction
On projector distance.
Basic demand is disposed according to GIS data and measuring point, if the projector distance can configure wind power plant flow field measuring point,
Projector distance of the wind power plant flow field measuring point on prevailing wind direction is then recorded, it is infeasible for otherwise recording wind power plant flow field measuring point
Point.According to all measuring points in step S3 traversal wind power plant.
According to above-mentioned dimension reduc-ing principle, initial sample space can be two dimensional sample space or one-dimensional sample in step 101
This space, but in order to realize according to the optimum linearity approximate error of the flow model curve to wind power plant as a result, to N number of sample
Sequence needs first if initial sample space is two dimensional sample space to the dimension-reduction treatment of two dimensional sample space.
In example one, the position of wind power plant flow field measuring point is the two-dimensional coordinate of wind power plant flow field measuring point；
Step 101 constructs initial sample space：Wind power plant is generated in two-dimensional space according to random function is uniformly distributed
Flow field measuring point is uniformly distributed random two-dimensional coordinate, constitutes initial sample space；Step 102 is obtained from initial sample space
N number of sample of wind power plant flow field measuring point position deployment decision, including：S1：N number of sample is chosen in initial sample space；Tool
Body chooses N number of sample in initial sample space according to probability equal principle；S2：Wind field data are obtained, wind field data include at least：
Position of the wind direction, wind power plant flow field measuring point of wind power plant flow field measuring point in wind field；According to wind field data, pre- timing is obtained
Between prevailing wind direction in section；According to the coordinate and the prevailing wind direction of wind power plant flow field measuring point, wind power plant flow field measuring point is determined
Projector distance on prevailing wind direction；The N number of sample for traversing initial sample space obtains one-dimensional sample space, one-dimensional sample space
Include N number of sample.
In example two, if initial sample space is two dimensional sample space, initial sample space is two dimensional sample space, wind
The position of electric field flow field measuring point is the two-dimensional coordinate of wind power plant flow field measuring point；Step 101 constructs initial sample space
Including：It is sat according to random function is uniformly distributed in the random two dimension that is uniformly distributed that two-dimensional space generates wind power plant flow field measuring point
Mark, constitutes initial sample space；Step 102 obtains wind power plant flow field measuring point position deployment decision from initial sample space
N number of sample, including：Wind field data are obtained, the wind field data include at least：The wind direction of wind power plant flow field measuring point, wind power plant
Position of the flow field measuring point in wind field；According to the wind field data, the prevailing wind direction in predetermined amount of time is obtained；According to wind power plant
The coordinate of flow field measuring point and the prevailing wind direction determine projector distance of the wind power plant flow field measuring point on the prevailing wind direction；
The wind power plant flow field measuring point for traversing the initial sample space, obtains one-dimensional sample space；It is chosen in one-dimensional sample space N number of
Sample, concrete foundation probability equal principle choose N number of sample in one-dimensional sample space.
More than, initial sample space be two dimensional sample space two examples in, first example and second it is exemplary
It is distinguished as：N number of sample first is chosen in two-dimensional initial sample space in first example, then to the sample of N number of sample composition
This space carries out dimension-reduction treatment, forms the one-dimensional sample space comprising N number of sample, is selected in initial sample space with realizing
Take the process of N number of sample；In second example, two-dimensional initial sample space dimension-reduction treatment is first formed into one-dimensional sample space,
Then N number of sample is chosen in the one-dimensional sample space again, to realize the process for choosing N number of sample in initial sample space.
In example three：If initial sample space is one-dimensional sample space, step 101 constructs initial sample space, including：
It is uniformly distributed random two-dimensional coordinate in two-dimensional space generation wind power plant flow field measuring point according to random function is uniformly distributed, is constituted
Random sample space；Wind field data are obtained, wind field data include at least：The wind direction of wind power plant flow field measuring point, wind power plant flow field
Position of the measuring point in wind field；According to wind field data, the prevailing wind direction in predetermined amount of time is obtained；It is measured according to wind power plant flow field
The coordinate and prevailing wind direction of point, determine projector distance of the wind power plant flow field measuring point on prevailing wind direction；Traverse initial sample space
Wind power plant flow field measuring point, obtain one-dimensional sample space；Step 102 obtains the measurement of wind power plant flow field from random sample space
N number of sample of point position deployment decision, including：N number of sample is chosen in one-dimensional sample space.
Using the similitude of prevailing wind direction vertical direction wind power plant flow field measuring point, wind power plant will be affixed one's name to inside wind-powered electricity generation field areas
The two-dimensional problems of flow field measuring point are converted to the one-dimensional problem arranged according to prevailing wind direction.Illustratively：For ten kilometer ranges
Ideal wind power plant measure region, decision space compressible 85%.
Wherein step 103 is：It is defeated that wind power plant is carried out according to sample of the root-mean-square error RMSE to the ideal sample space S
The accurate simulation evaluation of power prediction precision out, obtains the position optimal solution Ω of wind power plant flow field measuring point.Wherein, above scheme
According to the optimum linearity approximate error of the flow model curve to wind power plant as a result, sorting to N number of sample, then and from N number of
Ideal sample space S is obtained in sample, to reduce the sample size for Power Output for Wind Power Field prediction, reduces calculating
Expense, in this step, by carrying out Power Output for Wind Power Field according to sample of the root-mean-square error RMSE to ideal sample space S
The accurate simulation evaluation of precision of prediction can further reduce the sample size for Power Output for Wind Power Field prediction, reduce meter
Calculate expense.
Due in the above scheme, sample space is reduced to processing to the one-dimensional sample space on prevailing wind direction, therefore can be with
Ideal sample space S is divided into breadth first search subset and depth-first search subset, due in breadth first search subset
Sample can be according to the wind power plant flow field measuring point density prediction wind power on prevailing wind direction, in depth-first search subset
Sample can be according to the number of wind power plant flow field measuring point of the wind power plant flow field measuring point near the projection on prevailing wind direction
Amount prediction wind power, so that point of wind power plant flow field measuring point can be taken into account when improving wind farm power prediction precision
Cloth density and quantity.It specifically include following processing step after obtaining ideal sample space S, referring to shown in Fig. 7：
S11：Ideal sample space S is divided into depth-first search subset according to the characteristic of one-dimensional sample space and range is excellent
First search subset.
S12：According to the linear approximation error result of the flow model curve to wind power plant, respectively to depth-first search
Sample sequence in collection and breadth first search subset.
S13：According to predetermined order, wind is carried out to the sample in depth-first search subset and breadth first search subset
The accurate simulation of electric field output power precision of prediction is evaluated, and the position optimal solution Ω of wind power plant flow field measuring point is obtained.It is wherein pre-
Determine in sequence, the sample in depth-first search subset and the interspersed setting of the sample in breadth first search subset, and depth
Sample in first search subset according to the flow model curve to the wind power plant optimum linearity approximate error result ascending order
Arrangement, sample in breadth first search subset according to the flow model curve to wind power plant optimum linearity approximate error result
Ascending order arrangement.
It is equal according to [0,1] in step s 13 to the arbitrary sample s in ideal sample space S, such as first sample s=1
Even distribution generates random number r, to the sample s=1 of SB subset if r < 0.6, carries out the essence of Power Output for Wind Power Field precision of prediction
True simulation evaluation；Otherwise the sample s=1 of the SD subset accurate simulation for carrying out Power Output for Wind Power Field precision of prediction is evaluated；Time
It goes through SB subset and SD subset obtains the position optimal solution Ω of wind power plant flow field measuring point.Wherein, in SB subset, accurate simulation
The sequence of evaluation sorts according to root-mean-square error RMSE, similar, and in SD subset, the sequence of accurate simulation evaluation is according to square
Root error RMSE sequence.
Any three wind power plant flow fields measuring point that embodiments herein provides prior art arrangement is defeated to wind power plant
Power ultra-short term (in advance one hour) prognostic chart out, as shown in Figure 8；Wherein, three wind power plant flow field measuring point coordinates and wind
The RMSE of electric field output power prediction result and real output is as shown in Table 1：
Table one
| Wind power plant flow field measuring point coordinate | RMSE |
| (3678,541), (3898,6258), (1525,520) | 0.12 |
| (2382,4521), (596,4916), (1666,314) | 0.15 |
| (6515,6397), (2987,1748), (4993,4351) | 0.11 |
| (296,3973), (1406,2427), (6190,5091) | 0.11 |
After the measuring point configuration optimization of scheme electric field provided by the present application flow field, any three wind power plant flow fields measuring point
To Power Output for Wind Power Field ultra-short term (in advance one hour) prognostic chart, as shown in Figure 9；Wherein, three wind power plant flow field measuring points
The RMSE of coordinate and Power Output for Wind Power Field prediction result and real output is as shown in Table 2：
Table two
| Wind power plant flow field measuring point coordinate | RMSE |
| (296,3973)(1406,2427)(6190,5091) | 0.08 |
Contrast table one and two is as can be seen that the RMSE of Power Output for Wind Power Field prediction is reduced to 0.08.
Embodiments herein provides a kind of configuration optimization device of wind power plant flow field measuring point, for executing above-mentioned wind-powered electricity generation
The method for optimizing configuration of field flow field measuring point.The embodiment of the present application can be according to above method example to wind power plant flow field measuring point
Configuration optimization device carry out the division of functional module, can also be with for example, each functional module of each function division can be corresponded to
Two or more functions are integrated in a processing module.Above-mentioned integrated module both can take the form of hardware
It realizes, can also be realized in the form of software function module.It should be noted that the division of module in the embodiment of the present application
It is schematically, only a kind of logical function partition, there may be another division manner in actual implementation.
In the case where each function division of use correspondence each functional module, Figure 10 shows involved in above-described embodiment
And wind power plant flow field measuring point configuration optimization device 100 a kind of possible structural schematic diagram, wind power plant flow field measuring point
Configuration optimization device 100 include：Initialization module 101, computing module 103, solves module 104 at sampling module 102.Initially
Changing module 101 is used to support the configuration optimization device of wind power plant flow field measuring point to execute step 101；Sampling module 102 is for branch
The configuration optimization device for holding wind power plant flow field measuring point executes step 102；Computing module 103 is for supporting wind power plant flow field to measure
The configuration optimization device of point executes step 103；Solve the configuration optimization device that module 104 is used to support wind power plant flow field measuring point
Execute step 104；In addition, computing module 103 is used to support the configuration optimization device of wind power plant flow field measuring point to execute step
S11,S12；Solving module 104 is used to support the configuration optimization device of wind power plant flow field measuring point to execute step S13；Wherein, on
All related contents for stating each step that embodiment of the method is related to can quote the function description of corresponding function module, herein
It repeats no more.
Using integrated unit, Figure 11 shows the measurement of wind power plant flow field involved in above-described embodiment
A kind of possible structural schematic diagram of the configuration optimization device 110 of point.The configuration optimization device 110 of wind power plant flow field measuring point wraps
It includes：Communication unit 111, processing unit 112 and storage unit 113.Processing unit 112 is for matching wind power plant flow field measuring point
The movement for setting optimization device carries out control management, for example, processing unit 112 is for supporting the configuration of wind power plant flow field measuring point excellent
The process 101,102,103,104 executed in the above method is set in makeup；Communication unit 111 is for supporting wind power plant flow field measuring point
Configuration optimization device connect with external device carry out data interaction, for executing the partial function of above-mentioned sampling module 102, example
Such as obtain wind field data.Storage unit 2413, the program code of the configuration optimization device for storing wind power plant flow field measuring point
And data.
Wherein, processing unit 112 can be processor or controller, such as can be central processing unit (central
Processing unit, CPU), general processor, digital signal processor (digital signal processor, DSP),
Specific integrated circuit (application-specific integrated circuit, ASIC), field programmable gate array
It is (field programmable gate array, FPGA) or other programmable logic device, transistor logic, hard
Part component or any combination thereof.It may be implemented or execute to combine and various illustratively patrol described in present disclosure
Collect box, module and circuit.The processor is also possible to realize the combination of computing function, such as includes one or more micro- places
Manage device combination, DSP and the combination of microprocessor etc..Communication unit 111 can be communication interface etc..Storage unit 113 can be with
It is memory.
When processing unit 112 is processor, communication unit 111 is interface circuit, when storage unit 113 is memory, this
Apply for that the configuration optimization device of wind power plant flow field measuring point involved in embodiment can be measured for wind power plant flow field as described below
The configuration optimization device of measuring point.
Referring to Fig.1 shown in 2, the configuration optimization device 120 of the wind power plant flow field measuring point, including：Processor 121, storage
Device 122, bus 123 and interface circuit 124；Memory 122 is for storing computer executed instructions, interface circuit 124, processor
121 are connect with memory 122 by bus 123, when the configuration optimization device 120 of wind power plant flow field measuring point is run, processing
Device 121 executes the computer executed instructions that memory 122 stores, so that the control device of optical switch chip executes such as above-mentioned wind
The method for optimizing configuration of electric field flow field measuring point.Bus 123 can be Peripheral Component Interconnect standard (peripheral
Component interconnect, PCI) bus or expanding the industrial standard structure (extended industry standard
Architecture, EISA) bus etc..Bus 2503 can be divided into address bus, data/address bus, control bus etc..For convenient for
It indicates, is only indicated with a thick line in Figure 12, it is not intended that an only bus or a type of bus.
The embodiment of the present application also provides a kind of storage medium, which may include memory 122.
Since the configuration optimization device of wind power plant flow field provided by the embodiments of the present application measuring point can be used for executing above-mentioned wind
The method for optimizing configuration of electric field flow field measuring point, therefore it can be obtained technical effect can refer to above method embodiment, this
Apply for that details are not described herein for embodiment.
In the above-described embodiments, can come wholly or partly by software, hardware, firmware or any combination thereof real
It is existing.When being realized using software program, can entirely or partly realize in the form of a computer program product.The computer
Program product includes one or more computer instructions.On computers load and execute computer program instructions when, all or
It partly generates according to process or function described in the embodiment of the present application.The computer can be general purpose computer, dedicated meter
Calculation machine, computer network or other programmable devices.The computer instruction can store in computer readable storage medium
In, or from a computer readable storage medium to the transmission of another computer readable storage medium, for example, the computer
Instruction can pass through wired (such as coaxial cable, optical fiber, number from a web-site, computer, server or data center
Word user line (digital subscriber line, DSL)) or wireless (such as infrared, wireless, microwave etc.) mode to another
A web-site, computer, server or data center are transmitted.The computer readable storage medium can be computer
Any usable medium that can be accessed either includes the numbers such as one or more server, data centers that medium can be used to integrate
According to storage equipment.The usable medium can be magnetic medium (for example, floppy disk, hard disk, tape), optical medium (for example, DVD),
Or semiconductor medium (such as solid state hard disk (solid state disk, SSD)) etc..
Although the application is described in conjunction with each embodiment herein, however, implementing the application claimed
In the process, those skilled in the art are by checking the attached drawing, disclosure and the appended claims, it will be appreciated that and it is real
Other variations of the existing open embodiment.In the claims, " comprising " (comprising) word is not excluded for other compositions
Part or step, "a" or "an" are not excluded for multiple situations.Claim may be implemented in single processor or other units
In several functions enumerating.Mutually different has been recited in mutually different dependent certain measures, it is not intended that these are arranged
It applies to combine and generates good effect.
Although the application is described in conjunction with specific features and embodiment, it is clear that, do not departing from this Shen
In the case where spirit and scope please, it can be carry out various modifications and is combined.Correspondingly, the specification and drawings are only institute
The exemplary illustration for the application that attached claim is defined, and be considered as covered within the scope of the application any and all and repair
Change, change, combining or equivalent.Obviously, those skilled in the art the application can be carried out various modification and variations without
It is detached from spirit and scope.If in this way, these modifications and variations of the application belong to the claim of this application and its
Within the scope of equivalent technologies, then the application is also intended to include these modifications and variations.
Claims (14)
1. a kind of method for optimizing configuration of wind power plant flow field measuring point, which is characterized in that
Initial sample space is constructed, the initial sample space includes at least one wind power plant flow field measuring point position deployment decision
Sample, wherein the sample of wind power plant flow field measuring point position deployment decision includes at least one described wind power plant flow field amount
The position of measuring point, wind power plant flow field measuring point are configured on blower and/or anemometer tower；
N number of sample of wind power plant flow field measuring point position deployment decision is obtained from the initial sample space；
It according to the linear approximation error result of the flow model curve to the wind power plant, sorts to N number of sample, and from institute
It states and chooses the sample that linear approximation error result is less than preset value in N number of sample, constitute ideal sample space S；
The accurate simulation evaluation that Power Output for Wind Power Field precision of prediction is carried out to the sample of the ideal sample space S, obtains wind
The position optimal solution Ω of electric field flow field measuring point.
2. the method according to claim 1, wherein the sample to the ideal sample space S carries out wind power plant
The accurate simulation of output power precision of prediction is evaluated, and the position optimal solution Ω of wind power plant flow field measuring point is obtained, including：
The essence of Power Output for Wind Power Field precision of prediction is carried out to the sample of the ideal sample space S according to root-mean-square error RMSE
True simulation evaluation obtains the position optimal solution Ω of wind power plant flow field measuring point.
3. the method according to claim 1, wherein the initial sample space be two dimensional sample space, it is described
The position of wind power plant flow field measuring point is the two-dimensional coordinate of wind power plant flow field measuring point；
The initial sample space of the building includes：The measurement of wind power plant flow field is generated in two-dimensional space according to random function is uniformly distributed
Point is uniformly distributed random two-dimensional coordinate, constitutes the initial sample space；
N number of sample that wind power plant flow field measuring point position deployment decision is obtained from the initial sample space, including：
N number of sample is chosen in the initial sample space；
Wind field data are obtained, the wind field data include at least：Wind direction, the wind power plant flow field measuring point of wind power plant flow field measuring point
Position in wind field；
According to the wind field data, the prevailing wind direction in predetermined amount of time is obtained；
According to the coordinate and the prevailing wind direction of wind power plant flow field measuring point, determine wind power plant flow field measuring point in the prevailing wind direction
On projector distance；
The N number of sample for traversing the initial sample space, obtains one-dimensional sample space, and the one-dimensional sample space includes N number of sample
This.
4. the method according to claim 1, wherein the initial sample space be two dimensional sample space, it is described
The position of wind power plant flow field measuring point is the two-dimensional coordinate of wind power plant flow field measuring point；
The initial sample space of the building includes：The measurement of wind power plant flow field is generated in two-dimensional space according to random function is uniformly distributed
Point is uniformly distributed random two-dimensional coordinate, constitutes the initial sample space；
N number of sample that wind power plant flow field measuring point position deployment decision is obtained from the initial sample space, including：
Wind field data are obtained, the wind field data include at least：Wind direction, the wind power plant flow field measuring point of wind power plant flow field measuring point
Position in wind field；
According to the wind field data, the prevailing wind direction in predetermined amount of time is obtained；
According to the coordinate and the prevailing wind direction of wind power plant flow field measuring point, determine wind power plant flow field measuring point in the prevailing wind direction
On projector distance；
The wind power plant flow field measuring point for traversing the initial sample space, obtains one-dimensional sample space；
N number of sample is chosen in the one-dimensional sample space.
5. the method according to claim 1, wherein the initial sample space be one-dimensional sample space, it is described
Initial sample space is constructed, including：
It is uniformly distributed random two-dimensional coordinate in two-dimensional space generation wind power plant flow field measuring point according to random function is uniformly distributed,
Constitute random sample space；
Wind field data are obtained, the wind field data include at least：Wind direction, the wind power plant flow field measuring point of wind power plant flow field measuring point
Position in wind field；
According to the wind field data, the prevailing wind direction in predetermined amount of time is obtained；
According to the coordinate and the prevailing wind direction of wind power plant flow field measuring point, determine wind power plant flow field measuring point in the prevailing wind direction
On projector distance；
The wind power plant flow field measuring point for traversing the random sample space, obtains one-dimensional sample space；
N number of sample of wind power plant flow field measuring point position deployment decision is obtained from the initial sample space, including：Described
One-dimensional sample space chooses N number of sample.
6. according to the described in any item methods of claim 3-5, which is characterized in that the foundation root-mean-square error RMSE is to described
The sample of ideal sample space S carries out the accurate simulation evaluation of Power Output for Wind Power Field precision of prediction, obtains wind power plant flow field amount
Before the position optimal solution Ω of measuring point, the method also includes：
The ideal sample space S is divided into depth-first search subset according to the characteristic of the one-dimensional sample space and range is excellent
First search subset；
According to the linear approximation error result of the flow model curve to the wind power plant, respectively to depth-first search
Sample sequence in collection and breadth first search subset；
It is described to carry out Power Output for Wind Power Field precision of prediction according to sample of the root-mean-square error RMSE to the ideal sample space S
Accurate simulation evaluation, obtain wind power plant flow field measuring point position optimal solution Ω, including：According to predetermined order, to the depth
The sample spent in first search subset and breadth first search subset carries out the accurate imitative of Power Output for Wind Power Field precision of prediction
True evaluation, obtains the position optimal solution Ω of wind power plant flow field measuring point, wherein in the predetermined order, the depth-first search
The interspersed setting of the sample in sample and the breadth first search subset in subset, and in the depth-first search subset
Sample arranged according to the linear approximation error result ascending order of the flow model curve to the wind power plant, the breadth First searches
The sample that large rope is concentrated is arranged according to the optimum linearity approximate error result ascending order of the flow model curve to the wind power plant.
7. a kind of configuration optimization device of wind power plant flow field measuring point, which is characterized in that
Initialization module, for constructing initial sample space, the initial sample space is measured comprising at least one wind power plant flow field
The sample of point position deployment decision, wherein the sample of wind power plant flow field measuring point position deployment decision includes at least one
The position of wind power plant flow field measuring point, wind power plant flow field measuring point are configured on blower and/or anemometer tower；
Sampling module, for obtaining wind power plant flow field measuring point from the initial sample space that the initialization module constructs
N number of sample of position deployment decision；
Computing module, for the linear approximation error result according to the flow model curve to the wind power plant, to the sampling
N number of sample sequence that module obtains, and sample of the linear approximation error result less than preset value is chosen from N number of sample
This, constitutes ideal sample space S；
Module is solved, the sample of the ideal sample space S for obtaining to the computing module carries out wind power plant output work
The accurate simulation of rate precision of prediction is evaluated, and the position optimal solution Ω of wind power plant flow field measuring point is obtained.
8. device according to claim 7, which is characterized in that the solution module is specifically used for according to root-mean-square error
The accurate simulation that RMSE carries out Power Output for Wind Power Field precision of prediction to the sample of the ideal sample space S is evaluated, and wind is obtained
The position optimal solution Ω of electric field flow field measuring point.
9. device according to claim 7, which is characterized in that the initial sample space is two dimensional sample space, described
The position of wind power plant flow field measuring point is the two-dimensional coordinate of wind power plant flow field measuring point；
It is described initially draw unit be specifically used for according to be uniformly distributed random function two-dimensional space generate wind power plant flow field measuring point
Be uniformly distributed random two-dimensional coordinate, constitute the initial sample space；
The sampling module is specifically used for choosing N number of sample in the initial sample space；Obtain wind field data, the wind field
Data include at least：Position of the wind direction, wind power plant flow field measuring point of wind power plant flow field measuring point in wind field；According to the wind
Field data obtains the prevailing wind direction in predetermined amount of time；According to the coordinate and the prevailing wind direction of wind power plant flow field measuring point, determine
Projector distance of the wind power plant flow field measuring point on the prevailing wind direction；N number of sample of the initial sample space is traversed, obtains one
Sample space is tieed up, the one-dimensional sample space includes N number of sample.
10. device according to claim 7, which is characterized in that the initial sample space is two dimensional sample space, described
The position of wind power plant flow field measuring point is the two-dimensional coordinate of wind power plant flow field measuring point；
It is described initially draw unit be specifically used for according to be uniformly distributed random function two-dimensional space generate wind power plant flow field measuring point
Be uniformly distributed random two-dimensional coordinate, constitute the initial sample space；
The sampling module is specifically used for obtaining wind field data, and the wind field data include at least：Wind power plant flow field measuring point
The position of wind direction, wind power plant flow field measuring point in wind field；According to the wind field data, the main wind in predetermined amount of time is obtained
To；According to the coordinate and the prevailing wind direction of wind power plant flow field measuring point, determine wind power plant flow field measuring point in the prevailing wind direction
On projector distance；The wind power plant flow field measuring point for traversing the initial sample space, obtains one-dimensional sample space；Described one
It ties up sample space and chooses N number of sample.
11. device according to claim 7, which is characterized in that
It is described initially draw unit be specifically used for according to be uniformly distributed random function two-dimensional space generate wind power plant flow field measuring point
Be uniformly distributed random two-dimensional coordinate, constitute random sample space；Wind field data are obtained, the wind field data include at least：Wind
Position of the wind direction, wind power plant flow field measuring point of electric field flow field measuring point in wind field；According to the wind field data, obtain predetermined
Prevailing wind direction in period；According to the coordinate and the prevailing wind direction of wind power plant flow field measuring point, determine that wind power plant flow field measures
Projector distance of the point on the prevailing wind direction；The wind power plant flow field measuring point for traversing the random sample space, obtains one-dimensional sample
This space；
The sampling module is specifically used for choosing N number of sample in the one-dimensional sample space.
12. according to the described in any item devices of claim 7-11, which is characterized in that the computing module is also used to the reason
Think that sample space S is divided into depth-first search subset and breadth first search subset according to the characteristic of the one-dimensional sample space；
According to the linear approximation error result of the flow model curve to the wind power plant, respectively to the depth-first search subset with
And the sample sequence in breadth first search subset；
The solution module is specifically used for according to predetermined order, to the depth-first search subset and breadth first search
The solution of the sample of concentration carries out the accurate simulation evaluation of Power Output for Wind Power Field precision of prediction, obtains wind power plant flow field measuring point
Position optimal solution Ω；Wherein in the predetermined order, the sample in the depth-first search subset is searched with the breadth First
The interspersed setting of the sample that large rope is concentrated, and the sample in the depth-first search subset is according to the one-dimensional of the wind power plant
The linear approximation error result ascending order of flow field curve arranges, and the sample in the breadth first search subset is according to the wind-powered electricity generation
The linear approximation error result ascending order arrangement of the flow model curve of field.
13. a kind of configuration optimization device of wind power plant flow field measuring point, which is characterized in that including：Processor, memory and total
Line；For storing computer executed instructions, the processor is connect with the memory by the bus memory, when
When the configuration optimization device operation of wind power plant flow field measuring point, the processor executes the meter of the memory storage
Calculation machine executes instruction, so that the control device executes as the method according to claim 1 to 6.
14. a kind of computer storage medium, which is characterized in that including instruction, when run on a computer, so that the meter
Calculation machine executes as the method according to claim 1 to 6.
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| CN110619160A (en) * | 2019-09-02 | 2019-12-27 | 四川腾盾科技有限公司 | Implicit solution method based on accompanying residual sorting |
| CN110619160B (en) * | 2019-09-02 | 2022-12-02 | 四川腾盾科技有限公司 | Implicit solution method based on accompanying residual sorting |
| CN110705029A (en) * | 2019-09-05 | 2020-01-17 | 西安交通大学 | Flow field prediction method of oscillating flapping wing energy acquisition system based on transfer learning |
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