CN106844422A - It is a kind of to be based on three wind power plant unit polymerizations of increment cluster - Google Patents
It is a kind of to be based on three wind power plant unit polymerizations of increment cluster Download PDFInfo
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Abstract
The invention discloses a kind of wind power plant unit polymerization that cluster is overlapped based on tree increment, it includes the standardization to all data first；Then overlap clustering algorithm using static state first carries out a point clustering class to wind power plant unit to be measured original data, and the class cluster for being overlapped is created and receives Suo Shu；Then incremental clustering algorithm is created；The representative point of incremental data set is contrasted, finally newly-increased data sample is carried out by point clustering class and original search tree is updated；Point is represented based on newly-increased data to be iterated, complete new wind power plant machine group cluster.
Description
Technical field
The present invention relates to wind power plant field, specifically, a kind of three increments cluster based on tree construction is related specifically to
Wind power plant unit polymerization.
Background technology
Wind-powered electricity generation is very important clean energy resource, can solve the problem that China's energy supply now and the emission reduction of carbon dioxide
Problem, realizes the sustainable development of the energy, is that the clean energy resource of China and environmental protection contribute.The advantage of wind-power electricity generation has very
It is many：It is renewable, without the fossil fuel of consumption pollution environment, does not produce greenhouse gases.The Northwest is built to, was both not take up
Arable land, does not pollute the environment again.And wind power plant operation is simple, short construction period.
For large-scale wind power plant, proportion is larger in systems for blower fan installed capacity, exports the random of energy
Fluctuation will cause to compare large effect to mains frequency.General, the most of power network in the Northwest of China's wind-power electricity generation
End, the characteristics of change the one-way flow of end power grid energy, Wind turbines can absorb largely while being powered to power network
It is idle, when system voltage is reduced may result in line voltage is further reduced, and other generating set solutions can be made when serious
Row, cause to have a power failure.And due to the randomness of wind energy, Wind turbines can operationally produce harmonic wave, and power network is impacted.Institute
With, it is necessary to the running status according to Wind turbines carries out a point group to unit, and control is relatively stablized to wind power output power to reach
System.
In the prior art, for point group of Wind turbines, the hard cluster for also mainly using, such as k-means etc..They
Have a disadvantage that：First, they are mostly that static data collection is clustered, and the data set for increasing newly is needed new data set
Rerun as an entirety with legacy data collection, use duration；Secondly, the K values of these algorithms are difficult to estimate；Finally, they
Influenceed huge by initial cluster center, bad initial value is chosen and may effectively be clustered.On based on tree construction three
The wind power plant unit polymerization of Zhi Zengliang clusters, not yet finds related record in the prior art.
The content of the invention
It is an object of the invention to be directed to deficiency of the prior art, there is provided a kind of three increments cluster based on tree construction
Wind power plant unit polymerization, to solve problems of the prior art.
Technical problem solved by the invention can be realized using following technical scheme：
A kind of wind power plant unit polymerization of the three increments cluster based on tree construction, comprises the following steps,
1) sample and newly-increased data to the wind power generating set of wind power plant are standardized；
2) overlap clustering algorithm using static state carries out a point clustering class, the class for being overlapped to wind power plant unit to be measured original data
Cluster, creates and receives Suo Shu；
3) incremental clustering algorithm is created；
4) the representative point of incremental data set is contrasted, newly-increased data sample is carried out by point clustering class and original search tree is carried out
Update；Need to represent point iteration cluster computing again by newly-increased, until the newly-increased point that represents no longer changes, obtain newly-increased blower fan
Divide group.
The step 2) detailed process it is as follows：
A) similitude of sample is calculated using Euclid's formula, and sample is initialized；
B) requirement according to real work and error setpoint distance threshold value, all data objects for meeting certain condition are added
It is added in corresponding cluster；
C) the representative point in data set is then looked for, and updates representative point sample data set；
D) the entropy desired value of each sample attribute is calculated, and attribute is arranged according to desired value descending order
Sequence；
E) search tree is created, when the child node for being formed is identical with father node major part, is stopped creating and is received Suo Shu.
The step 3) detailed process it is as follows：
B1 cluster analysis) is carried out to newly-increased data set；
B2 the corresponding representative point of the cluster of newly-increased data set) is found out；
) and then the closing on for representative point found out corresponding to newly-increased data set represents a little b3.
Compared with prior art, beneficial effects of the present invention are as follows：
The advantage of the wind power plant polymerization that the present invention is overlapped based on tree increment is the use of three decision-makings and clusters to data
Pre-processed, can be very good, by the positive domain of model, border and negative domain, to make the result of cluster more accurate.Also, increment weight
The cluster that algorithm can be more quickly and effectively for newly-increased unit is folded, the time is both saved, preferably newly-increased machine can be obtained again
The cluster result of group, so as to improve disposal ability of the prior art for dynamic data, can be such that power network more effectively stablizes
Operation.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the wind power plant unit polymerization of increment cluster of the present invention.
Specific embodiment
For technological means, creation characteristic, reached purpose and effect for making present invention realization are easy to understand, with reference to
Specific embodiment, is expanded on further the present invention.
Referring to Fig. 1, by taking generator as an example, the emulation that can be seen that wind-driven generator from the steady transient state equation of unit is main
By shapes such as generator unit stator electric current d axle q axles component, wind-force torque, unit revolutional slip, unit propeller pitch angle, generator electromagnetic torques
State variable decision, so, during wind power plant machine component group, static cluster can be carried out to unit according to these variables, so as to complete
The static cluster of Wind turbines primary data sample.
Cluster is the process that the set of multiple objects is divided into multiple classes, and each class is also called cluster.In same cluster
Attribute between each object is similar, and attribute has larger difference between the object in different clusters.And increment overlapping algorithm is
In original data basis, with less time to dynamic data set, that is, newly-increased data carry out the algorithm of quick clustering.
In a wind power plant, for the generator towards different directions, generating set windward will work, and blade
After the unit vertical with wind direction will not work, but wind direction changes, different unit participation work are just had, the unit of work may
Become to be likely to tail off more.If a wind power plant has n typhoon group of motors X1, X2 ..., Xn, if i-th unit Xi=[Xi1,
Xi2 ..., Xin] it is n dimension.When we are polymerized, mainly include the following steps that：
(1)：The standardization of data.
X=log10x
Xi=[log10xi1, log10xi2..., log10xin]
(2)：Data are clustered, its process is as follows：
First, the Euclidean distance of unit Xi and unit Xj is defined as：
Then a distance threshold δ is set, all unit data Xi for meeting Distance (Xi, Xj)≤δ is added to
In Neighbor (Xi).The representative point sample in data set is then looked for, will be with the data object at most closing on as first
It is individual to represent the geometric center of point, and it is deleted into first row of representative sample in distance matrix [Distance (Xi, Xj)],
Second is found in remaining distance matrix to represent a little, untill distance matrix is for sky, so just obtains institute in data set
Some representative points and clustering processing is carried out to data sample.
After the completion of this cluster, the entropy desired value of each sample attribute and right according to the descending order of desired value is calculated
Attribute is ranked up.Important attribute be preferentially used for construction tree node, using the data set attribute set after descending sort come
Create each layer of tree node of search tree.When the child node for being formed is identical with father node major part, stops creating and receive Suo Shu.If
Determine threshold value λ (according to actual conditions), Stop creating search tree.
(3)：Create incremental clustering algorithm process as follows：
The representative point in newly-increased data sample △ U is first looked for, if it be a little rwait that these is represented.For data prediction,
All data are clustered by the cluster result that i.e. static data cluster is generated out in the data of n dimensions, point
The geometric center of each cluster is not obtained, allows it as the representative point of newly-increased data set △ U, represent the new cluster that cluster is produced.So
Newly-increased closing on for representative point rwait is found out afterwards to represent a little.It is specific find represent point rwait close on representative sample method be by
Following relation principle determines：
Relation one, the only one of which tree node similar to newly-increased representative point rwait in each layer of search tree；Relation two, searches
Multiple tree nodes at least one layer of Suo Shu are similar to newly-increased representative point rwait；Relation three, search tree some layers do not exist with
Rwait similar tree node.Search search tree will merge tree node, relation make for a moment similarity tree node and
Rwait merges；Relation two can also merge child node on the basis of tree node merging；Relation three will make search tree node point
Split.
The closing on for representative point that the cluster that newly-increased unit is constituted can be just found out according to relation above principle represents a little.
(4) the representative point of incremental data set is contrasted, newly-increased data sample is carried out by point clustering class and original search tree is entered
Row updates.Rneighbor is set first as closing on for rwait represents point set.Three kinds of relation form more new search according to them
Tree represents point rwait with increment, and these three relation forms are as follows：
(find closing on for rwait to represent the representative region of point and rwait and closed on and represent region
All standing)
(find to close on representative point but only partly represent region and closed on and represent region overlay)
(can not find to close on and represent a little)
(5)：Judge that the newly-increased data for producing represent whether point rwait changes, and do not change and then increase blower fan point newly
Group completes.
General principle of the invention and principal character and advantages of the present invention has been shown and described above.The technology of the industry
Personnel it should be appreciated that the present invention is not limited to the above embodiments, simply explanation described in above-described embodiment and specification this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and modifications of the present invention are possible, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appending claims and its
Equivalent thereof.
Claims (3)
1. the wind power plant unit polymerization that a kind of three increments based on tree construction are clustered, it is characterised in that including following step
Suddenly,
1) sample and newly-increased data to the wind power generating set of wind power plant are standardized；
2) overlap clustering algorithm using static state carries out a point clustering class to wind power plant unit to be measured original data, the class cluster for being overlapped,
Create and receive Suo Shu；
3) incremental clustering algorithm is created；
4) the representative point of incremental data set is contrasted, newly-increased data sample is carried out by point clustering class and original search tree is carried out more
Newly；Need to represent point iteration cluster computing again by newly-increased, until the newly-increased point that represents no longer changes, obtain newly-increased blower fan point
Group.
2. the wind power plant unit polymerization that three increments based on tree construction according to claim 1 are clustered, its feature
Be, the step 2) detailed process it is as follows：
A) similitude of sample is calculated using Euclid's formula, and sample is initialized；
B) requirement according to real work and error setpoint distance threshold value, all data objects for meeting certain condition are added to
In corresponding cluster；
C) the representative point in data set is then looked for, and updates representative point sample data set；
D) the entropy desired value of each sample attribute is calculated, and attribute is ranked up according to desired value descending order；
E) search tree is created, when the child node for being formed is identical with father node major part, is stopped creating and is received Suo Shu.
3. the wind power plant unit polymerization that three increments based on tree construction according to claim 1 are clustered, its feature
Be, the step 2) detailed process it is as follows：
B1 cluster analysis) is carried out to newly-increased data set；
B2 the corresponding representative point of the cluster of newly-increased data set) is found out；
) and then the closing on for representative point found out corresponding to newly-increased data set represents a little b3.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109697452A (en) * | 2017-10-23 | 2019-04-30 | 北京京东尚科信息技术有限公司 | Processing method, processing unit and the processing system of data object |
| CN115048431A (en) * | 2022-07-14 | 2022-09-13 | 南京理工大学 | Clustering-based business process resource organization mining method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103823843A (en) * | 2014-01-24 | 2014-05-28 | 北京理工大学 | Gauss mixture model tree and incremental clustering method thereof |
| CN103870923A (en) * | 2014-03-03 | 2014-06-18 | 华北电力大学 | Information entropy condensation type hierarchical clustering algorithm-based wind power plant cluster aggregation method |
| CN103886181A (en) * | 2014-02-25 | 2014-06-25 | 国家电网公司 | Wind power plant aggregation method based on K-MEDOIDS aggregation |
| US20140277808A1 (en) * | 2013-03-15 | 2014-09-18 | Open Access Technology International, Inc. | Use of Demand Response (DR) and Distributed Energy Resources (DER) to mitigate the impact of Variable Energy Resources (VER) in Power System Operation |
| CN105023090A (en) * | 2015-05-15 | 2015-11-04 | 天津大学 | Power generator unit coherence grouping scheme based on wide area information |
| CN105468867A (en) * | 2015-12-21 | 2016-04-06 | 华北电力大学（北京） | Wind power plant clustering method based on CLARANS clustering |
-
2016
- 2016-12-02 CN CN201611097386.8A patent/CN106844422A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140277808A1 (en) * | 2013-03-15 | 2014-09-18 | Open Access Technology International, Inc. | Use of Demand Response (DR) and Distributed Energy Resources (DER) to mitigate the impact of Variable Energy Resources (VER) in Power System Operation |
| CN103823843A (en) * | 2014-01-24 | 2014-05-28 | 北京理工大学 | Gauss mixture model tree and incremental clustering method thereof |
| CN103886181A (en) * | 2014-02-25 | 2014-06-25 | 国家电网公司 | Wind power plant aggregation method based on K-MEDOIDS aggregation |
| CN103870923A (en) * | 2014-03-03 | 2014-06-18 | 华北电力大学 | Information entropy condensation type hierarchical clustering algorithm-based wind power plant cluster aggregation method |
| CN105023090A (en) * | 2015-05-15 | 2015-11-04 | 天津大学 | Power generator unit coherence grouping scheme based on wide area information |
| CN105468867A (en) * | 2015-12-21 | 2016-04-06 | 华北电力大学（北京） | Wind power plant clustering method based on CLARANS clustering |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109697452A (en) * | 2017-10-23 | 2019-04-30 | 北京京东尚科信息技术有限公司 | Processing method, processing unit and the processing system of data object |
| CN109697452B (en) * | 2017-10-23 | 2021-09-14 | 北京京东尚科信息技术有限公司 | Data object processing method, processing device and processing system |
| CN115048431A (en) * | 2022-07-14 | 2022-09-13 | 南京理工大学 | Clustering-based business process resource organization mining method |
| CN115048431B (en) * | 2022-07-14 | 2024-07-12 | 南京理工大学 | Clustering-based business process resource organization mining method |
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