CN105956318A - Improved splitting H-K clustering method-based wind power plant fleet division method - Google Patents
Improved splitting H-K clustering method-based wind power plant fleet division method Download PDFInfo
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Abstract
The invention discloses an improved splitting H-K clustering method-based wind power plant fleet division method. The method comprises the following steps: dividing parameters of a draught fan into k clusters by adopting a divisive hierarchical clustering algorithm, wherein k is a wind power plant division number; finding a cluster C with the maximum diameter in all the clusters; finding a point p having the maximum average dissimilarity degree with the other points in the cluster C, and putting the point p in a new cluster; finding a target point in the cluster C, and putting the target point in the new cluster, wherein the distance from the target point to the point in the new cluster is not greater than the distance from the target point to the nearest point in the cluster C; calculating silhouette coefficients at different k values, and determining an optimum cluster number according to the silhouette coefficients; under the state of the optimum cluster number, calculating a clustering center to replace a random clustering center in a k mean value clustering algorithm to carry out clustering; calculating the distances of remained points in the cluster C to each clustering center, distributing the remained points into the nearest class, and calculating a new clustering center after the distribution; and establishing a new wind power plant model by being subject to the clustering result, so as to carry out simulation.
Description
Technical field
The present invention relates to wind generator system field, it is more particularly related to one is divided based on improving
Splitting the wind energy turbine set group of planes division methods of H-K (Hierarchical K-means) clustering method, it can be applicable to
Set up Large Scale Wind Farm Integration Equivalent Model, use silhouette coefficient and in addition suitably criterion solve group of planes division number
Problem, the clustering method utilizing the present invention to propose completes wind energy turbine set cluster, finally sets up Equivalent Model.
Background technology
Development and the continuous reduction of cost of electricity-generating, the pass that wind generating technology is subject to along with wind power technology
Note degree is more and more higher, but for traditional energy, wind-powered electricity generation can be described as " power supply inferior " because of wind
Electricity is to have the intermittent and particular power source of stochastic volatility, and therefore its output also has the strongest the most true
Qualitative.When can stability and the quality of power supply of electrical network be brought huge after large-scale wind farm grid-connected operation
Impact.Analyzing the grid-connected rear impact produced in large-scale wind power field at present, it is necessary first to the problem of solution is big
Scale wind energy turbine set models.Research worker is to model the every Fans in wind energy turbine set in detail in early days, but
This method, along with the continuous expansion of wind energy turbine set scale, gradually starts to become more and more difficult, and therefore research is built
Vertical one relatively easy and the model of wind energy turbine set can be portrayed become critically important, i.e. set up wind energy turbine set to electrical network
External characteristics model.The Equivalent Model of simplification is mainly used mainly to have two kinds of sides for wind energy turbine set dynamic model at present
Formula i.e. unit Equivalent Model and multimachine Equivalent Model.The accuracy of multimachine Equivalent Model is higher comparatively speaking.
But at present, the foundation of Equivalent Model all exists corresponding problem, number is divided for a group of planes and does not has
Having clear and definite standard, employing is all that empirical method is in the majority.Accordingly, it is desirable to provide a kind of need meeting reality
The dividing mode asked and simple and clear Classification Index, it is ensured that set up the accuracy of model.
Summary of the invention
The technical problem to be solved is for there is drawbacks described above in prior art, it is provided that Yi Zhongji
Model in the large-scale wind power field of clustering algorithm, determined the clusters number of optimum by silhouette coefficient, and in detail
Analyze fan operation mechanism and select suitable clustering target, ensureing that model reduces multimachine model the most simultaneously
Complexity.It is not described later in detail in terms of wind energy turbine set equivalent modeling about silhouette coefficient at present,
The most deeply probe in related fields.
In order to realize above-mentioned technical purpose, according to the present invention, it is provided that a kind of based on improving division H-K cluster
The wind energy turbine set group of planes division methods of method, including:
First step: use split-merge algorithm algorithm, is divided into k bunch, wherein k by the parameter of blower fan
It is that wind energy turbine set divides number；
Second step: find bunch C with maximum gauge in all bunches for the division of data；
Third step: find out a some p maximum with other average dissimilarity in bunch C, and put it into one
In individual new bunch；
4th step: find impact point in bunch C, the distance of the point in this impact point to new bunch is not more than
Point nearest in bunch C, and impact point is put in new bunch；
5th step: calculate silhouette coefficient during different value of K, determine optimum clustering number according to silhouette coefficient；
6th step: when optimum clustering number, calculates cluster centre to replace k-means to cluster
Stochastic clustering center in (k mean cluster) algorithm is to cluster；
7th step: each left point to the distance of each cluster centre and is divided by the left point calculated in bunch C
Join and enter in nearest classification, after being assigned, calculate new cluster centre；
8th step: be then as the criterion with cluster result and set up new farm model and carry out analogue simulation.
Preferably, in the 5th step, the computing formula of silhouette coefficient is:
Wherein niElement x in being bunchiAverage distance between other element in this bunch, miElement in being bunch
xiThe minima of element average distance in other bunch.
Preferably, the codomain of S (i) is between [-1,1].
Preferably, k is the integer not less than 2.
Preferably, in all bunches, bunch C exists two points that Euclidean distance is maximum.
Preferably, terminate the 4th step and perform during the impact point being not allocated in bunch C in new bunch
Five steps.
Can be suitable for based on the wind energy turbine set group of planes division methods improving division H-K clustering method according to the present invention
Large-scale wind power field Equivalent Model, its wind energy turbine set based on H-K clustering algorithm models, is to be gathered by k-means
Class method and hierarchy clustering method pluses and minuses on data process combine, and first use hierarchical clustering to calculate
Method obtains initial information, then uses k-means clustering algorithm to improve cluster process further.In the present invention
Middle by H-K clustering method and use silhouette coefficient optimize both clusters number combine, clearly divide
The number of a group of planes so can make the farm model of foundation more conform to practical situation.
Present invention wind energy turbine set based on H-K clustering algorithm models, and gives a group of planes and divides one clear and definite mark of establishment
Standard, defines large-scale wind power field based on this and carries out the number of equivalent type required during multimachine equivalent modeling
Mesh, selects suitable clustering target the most afterwards, uses H-K clustering algorithm to carry out wind energy turbine set cluster modeling.
Guarantee modeling efficiently and accurately.
Accompanying drawing explanation
In conjunction with accompanying drawing, and by with reference to detailed description below, it will more easily the present invention is had more complete
Understand and its adjoint advantage and feature is more easily understood, wherein:
Fig. 1 schematically shows according to the preferred embodiment of the invention based on improving division H-K clustering method
The flow chart of wind energy turbine set group of planes division methods.
Fig. 2 schematically shows profile value according to Variable cluster according to the preferred embodiment of the invention.
It should be noted that accompanying drawing is used for illustrating the present invention, and the unrestricted present invention.Note, represent structure
Accompanying drawing may be not necessarily drawn to scale.Further, in accompanying drawing, same or like element indicate identical or
The label that person is similar to.
Detailed description of the invention
In order to make present disclosure more clear and understandable, below in conjunction with specific embodiments and the drawings to this
Bright content is described in detail.
Holistic approach is described in detail, in MATLAB, first builds phantom, set up a wind-powered electricity generation
The detailed model of field.Under different wind velocity condition, carry out the analogue simulation of wind field, and preserve what wind energy turbine set was correlated with
Curve of output, selects suitable index to calculate as primary data input after the operation mechanism analysing in depth blower fan
Carrying out detailed calculating in method, accompanying drawing 1 is the flow chart of algorithm part in the present invention, and algorithm is according to following step
Suddenly process:
First step S1: use split-merge algorithm algorithm, is divided into k bunch, wherein by the parameter of blower fan
K is that wind energy turbine set divides number.Wherein in order to fit reality reduce operand, k value can start to select from 2
According to wind field scale, the upper limit is set；That is, k is the integer not less than 2.
Second step S2: in all bunches find have maximum gauge bunch C (that is, in all bunches, bunch C
Middle there are two points that Euclidean distance is maximum) for the division of data.
Third step S3: find out a some p maximum with other average dissimilarity in bunch C, and put
Enter in one new bunch.
4th step S4: find impact point in bunch C, the distance of the point in this impact point to new bunch is little
In to point nearest in bunch C, and impact point is put in new bunch.New bunch it is not allocated in bunch C
In impact point time terminate the 4th step S4 and perform the 5th step S5.
5th step S5: calculate silhouette coefficient during different value of K, determine optimum clustering number according to silhouette coefficient,
Wherein the computing formula of silhouette coefficient is:
Wherein niElement x in being bunchiAverage distance between other element in this bunch, miElement in being bunch
xiThe minima of element average distance in other bunch, and the codomain of S (i) is between [-1,1]；
Silhouette coefficient is closer to this element of 1 explanation in a tight independent collective；It is closer to-1
Illustrate that element is not belonging to this bunch；If silhouette coefficient is 0, show sample set does not exist nature clustering architecture, and
It it is equally distributed structure.
Such as, under different value of K state, calculate the wind turbine parameter profile value relative to this bunch of center, and
And a profile value curve chart can be set up, so that profile value sum is maximum and opposing curves all compares tendency 1
K value be optimum clustering number.
6th step S6: when optimum clustering number, the optimum wind energy turbine set i.e. determined divides under number,
Calculate cluster centre to replace the stochastic clustering center in k-means clustering algorithm to cluster.
7th step S7: calculate the left point in bunch C to the distance of each cluster centre and by each left point
Distribute in nearest classification, after being assigned, calculate new cluster centre.Preferably, if the 7th step
Changing of the cluster centre that S7 calculates, recalculates cluster centre and again carries out left point distribution until receiving
Hold back.
8th step S8: be then as the criterion with cluster result and set up new farm model and carry out analogue simulation, and
And preferably can also be analyzed with the curve of output of the detailed model preserved before.
Below in conjunction with concrete example, the present invention is described further, is constituted a wind energy turbine set with 24 Fans
Being analyzed, blower fan primary data sets and is shown in Table 1 (data are perunit value), and subduing the wind syndrome speed refers at 12m/s
Being blower fan output, at two kinds of wind speed mode operations, the first is basic wind and the superposition of progressive wind；The
Two kinds is that fluctuation wind speed is with constant wind speed and white Gaussian noise signal syntheses.Clustering target is selected to cluster,
Profile value under different cluster numbers is as shown in Figure 2.
Table 1 blower fan initial parameter
Show according to the result of calculation shown in Fig. 2, be polymerized to result during 3 class optimum, then set equivalent blower fan
It is 3 machine Equivalent Model that model has, and Equivalent Model parameter calculates according to waiting binarization mode to carry out.
It should be noted that unless stated otherwise or point out, otherwise the term in description " first ",
The description such as " second ", " the 3rd " is used only for each assembly in differentiation description, element, step etc.,
Rather than for representing the logical relation between each assembly, element, step or ordering relation etc..
Although it is understood that the present invention discloses as above with preferred embodiment, but above-described embodiment is also
It is not used to limit the present invention.For any those of ordinary skill in the art, without departing from skill of the present invention
In the case of art aspects, technical solution of the present invention is made many by the technology contents that all may utilize the disclosure above
Possible variation and modification, or it is revised as the Equivalent embodiments of equivalent variations.Therefore, every without departing from this
The content of bright technical scheme, according to the present invention technical spirit to any simple modification made for any of the above embodiments,
Equivalent variations and modification, all still fall within the range of technical solution of the present invention protection.
Claims (6)
1. a wind energy turbine set group of planes division methods based on improvement division H-K clustering method, it is characterised in that bag
Include:
First step: use split-merge algorithm algorithm, is divided into k bunch, wherein k by the parameter of blower fan
It is that wind energy turbine set divides number；
Second step: find bunch C with maximum gauge in all bunches for the division of data；
Third step: find out a some p maximum with other average dissimilarity in bunch C, and put it into one
In individual new bunch；
4th step: find impact point in bunch C, the distance of the point in this impact point to new bunch is not more than
Point nearest in bunch C, and impact point is put in new bunch；
5th step: calculate silhouette coefficient during different value of K, determine optimum clustering number according to silhouette coefficient；
6th step: when optimum clustering number, calculates cluster centre to replace k mean cluster to calculate
Stochastic clustering center in method is to cluster；
7th step: each left point to the distance of each cluster centre and is divided by the left point calculated in bunch C
Join and enter in nearest classification, after being assigned, calculate new cluster centre；
8th step: be then as the criterion with cluster result and set up new farm model and carry out analogue simulation.
The most according to claim 1 based on the wind energy turbine set group of planes division side improving division H-K clustering method
Method, it is characterised in that in the 5th step, the computing formula of silhouette coefficient is:
Wherein niElement x in being bunchiAverage distance between other element in this bunch, miElement in being bunch
xiThe minima of element average distance in other bunch.
The most according to claim 2 based on the wind energy turbine set group of planes division side improving division H-K clustering method
Method, it is characterised in that the codomain of S (i) is between [-1,1].
A wind energy turbine set group of planes based on improvement division H-K clustering method the most according to claim 1 and 2 is drawn
Divide method, it is characterised in that k is the integer not less than 2.
A wind energy turbine set group of planes based on improvement division H-K clustering method the most according to claim 1 and 2 is drawn
Divide method, it is characterised in that in all bunches, bunch C exists two points that Euclidean distance is maximum.
A wind energy turbine set group of planes based on improvement division H-K clustering method the most according to claim 1 and 2 is drawn
Point method, it is characterised in that be not allocated to during the impact point in new bunch terminate the 4th step in bunch C
And perform the 5th step.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109685139A (en) * | 2018-12-25 | 2019-04-26 | 刘万里 | Based on the gastroesophageal reflux disease risk factor extracting method precisely clustered and system |
| CN113408579A (en) * | 2021-05-13 | 2021-09-17 | 桂林电子科技大学 | Internal threat early warning method based on user portrait |
| CN117476165A (en) * | 2023-12-26 | 2024-01-30 | 贵州维康子帆药业股份有限公司 | Intelligent management method and system for Chinese patent medicine medicinal materials |
| CN117708613A (en) * | 2023-12-25 | 2024-03-15 | 北京中微盛鼎科技有限公司 | Industrial chain collaborative operation-oriented digital resource matching method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103401236A (en) * | 2013-07-30 | 2013-11-20 | 华北电力大学 | Wind power farm generator unit grouping method based on flow correlation of wind power farm |
| CN103558768A (en) * | 2013-08-12 | 2014-02-05 | 清华大学 | Equivalent modeling method based on wind speed distribution characteristics in wind power plant |
| CN103955521A (en) * | 2014-05-08 | 2014-07-30 | 华北电力大学 | Cluster classification method for wind power plant |
| US20150095004A1 (en) * | 2013-09-27 | 2015-04-02 | Korea Electric Power Corporation | Apparatus for simulating wind power farm |
-
2016
- 2016-05-19 CN CN201610334655.1A patent/CN105956318B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103401236A (en) * | 2013-07-30 | 2013-11-20 | 华北电力大学 | Wind power farm generator unit grouping method based on flow correlation of wind power farm |
| CN103558768A (en) * | 2013-08-12 | 2014-02-05 | 清华大学 | Equivalent modeling method based on wind speed distribution characteristics in wind power plant |
| US20150095004A1 (en) * | 2013-09-27 | 2015-04-02 | Korea Electric Power Corporation | Apparatus for simulating wind power farm |
| CN103955521A (en) * | 2014-05-08 | 2014-07-30 | 华北电力大学 | Cluster classification method for wind power plant |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109685139A (en) * | 2018-12-25 | 2019-04-26 | 刘万里 | Based on the gastroesophageal reflux disease risk factor extracting method precisely clustered and system |
| CN113408579A (en) * | 2021-05-13 | 2021-09-17 | 桂林电子科技大学 | Internal threat early warning method based on user portrait |
| CN117708613A (en) * | 2023-12-25 | 2024-03-15 | 北京中微盛鼎科技有限公司 | Industrial chain collaborative operation-oriented digital resource matching method |
| CN117708613B (en) * | 2023-12-25 | 2024-05-14 | 北京中微盛鼎科技有限公司 | Industrial chain collaborative operation-oriented digital resource matching method |
| CN117476165A (en) * | 2023-12-26 | 2024-01-30 | 贵州维康子帆药业股份有限公司 | Intelligent management method and system for Chinese patent medicine medicinal materials |
| CN117476165B (en) * | 2023-12-26 | 2024-03-12 | 贵州维康子帆药业股份有限公司 | Intelligent management method and system for Chinese patent medicine medicinal materials |
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