CN103268573A - Wind power plant marker post draught fan selection method based on principal component analysis - Google Patents

Wind power plant marker post draught fan selection method based on principal component analysis Download PDF

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CN103268573A
CN103268573A CN201310168790XA CN201310168790A CN103268573A CN 103268573 A CN103268573 A CN 103268573A CN 201310168790X A CN201310168790X A CN 201310168790XA CN 201310168790 A CN201310168790 A CN 201310168790A CN 103268573 A CN103268573 A CN 103268573A
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blower fan
matrix
mark post
major component
wind energy
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CN103268573B (en
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汪宁渤
路亮
马彦宏
何世恩
刘光途
李剑楠
王小勇
赵龙
丁坤
王定美
周强
周识远
李津
马明
张金平
黄蓉
吕清泉
张建美
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State Grid Corp of China SGCC
State Grid Gansu Electric Power Co Ltd
Wind Power Technology Center of Gansu Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Gansu Electric Power Co Ltd
Wind Power Technology Center of Gansu Electric Power Co Ltd
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Abstract

The invention discloses a wind power plant marker post draught fan selection method based on principal component analysis. The method includes the steps of acquiring a historical output power curve of each draught fan in a wind power plant, establishing a draught fan output power matrix according to historical output power of each draught fan in the wind power plant, carrying out the principal component analysis on the draught fan output power matrix after preprocessing the draught fan output power matrix, and with principal components with class distinction degrees as a basis for marker post draught fan selection, selecting the marker post draught fans. The wind power plant marker post draught fan selection method based on the principal component analysis can overcome the shortcomings of low selection efficiency, poor selection effects and the like in the prior art, and is high in selection efficiency and good in selection effect.

Description

A kind of wind energy turbine set mark post blower fan system of selection based on principal component analysis (PCA)
Technical field
The present invention relates to technical field of wind power generation, particularly, relate to a kind of wind energy turbine set mark post blower fan system of selection based on principal component analysis (PCA).
Background technology
Exert oneself for the theory of statistics wind energy turbine set, generally need wind energy turbine set to select the mark post blower fan, under the situation that limit is exerted oneself, should guarantee as far as possible that the mark post blower fan is not limit to exert oneself, the mark post blower fan that wind energy turbine set therefore just occurred how carrying out is selected this brand-new problem.The mark post selection of fan is representative, can characterize the overall operation situation of wind energy turbine set, objectively responds the situations such as year theoretical generated energy of this wind energy turbine set.
At present, because China ten million multikilowatt wind-powered electricity generation base still in the construction period, does not therefore form complete and effective wind energy turbine set mark post blower fan choice criteria as yet.
In realizing process of the present invention, the inventor finds not occur at present correlative study or the technology of wind energy turbine set mark post blower fan system of selection.
Summary of the invention
The objective of the invention is to, at the problems referred to above, propose a kind of wind energy turbine set mark post blower fan system of selection based on principal component analysis (PCA), to realize that efficiency of selection is high and to select effective advantage.
For achieving the above object, the technical solution used in the present invention is: a kind of wind energy turbine set mark post blower fan system of selection based on principal component analysis (PCA) comprises:
A, obtain the historical power curve of every typhoon electric fan in the wind energy turbine set;
B, based on the historical power curve of every typhoon electric fan in the wind energy turbine set, set up the blower fan matrix of exerting oneself :
（1）；
M is wind energy turbine set inner blower platform number, and n is the power sample number of every typhoon machine,
Figure 202012DEST_PATH_IMAGE006
Represent actual the exerting oneself of i typhoon machine, a j moment point;
C, to the blower fan matrix of exerting oneself
Figure 477136DEST_PATH_IMAGE002
Carry out the square graduation and handle, the matrix after the square graduation is handled carries out principal component analysis (PCA);
The foundation that d, the major component that will have a class discrimination degree are selected as the mark post blower fan is carried out the mark post blower fan and is selected.
Further, described step c specifically comprises:
C1, data pre-service are about to matrix
Figure 120607DEST_PATH_IMAGE008
Deduct equal value matrix and be processed into the flat matrix of square
Figure 924615DEST_PATH_IMAGE010
:
Figure 653536DEST_PATH_IMAGE012
，
Wherein,
C2, based on above-mentioned data pre-service result, carry out covariance and calculate, obtain real symmetric matrix
Figure 230328DEST_PATH_IMAGE016
:
Figure 96915DEST_PATH_IMAGE018
,
Figure 47554DEST_PATH_IMAGE020
For
Figure 398901DEST_PATH_IMAGE022
The commentaries on classics order;
C3, realistic symmetrical matrix
Figure 751384DEST_PATH_IMAGE016
Proper vector And eigenwert
Figure 194184DEST_PATH_IMAGE026
, satisfy
Figure 614801DEST_PATH_IMAGE028
, wherein
Figure 680846DEST_PATH_IMAGE030
（ ） （3），
Matrix
Figure 188368DEST_PATH_IMAGE034
Be orthogonal matrix, matrix
Figure 201310168790X100002DEST_PATH_IMAGE037
Column element is exactly eigenwert
Figure 201310168790X100002DEST_PATH_IMAGE039
The characteristic of correspondence vector;
C4, according to above-mentioned real symmetric matrix
Figure 474993DEST_PATH_IMAGE016
Proper vector And eigenwert
Figure 820840DEST_PATH_IMAGE026
, obtain the variance contribution ratio of each proper vector and the accumulative total variance contribution ratio of preceding several proper vectors, obtain describing the major component of power of fan.
Further, in step c4, the operation of the major component of described calculating wind energy turbine set specifically comprises:
Get and add up preceding p the bigger eigenwert that variance contribution ratio reaches 85-95%
Figure 337097DEST_PATH_IMAGE042
Corresponding first, second ...,
Figure 365095DEST_PATH_IMAGE044
Individual proper vector is major component;
The variance contribution ratio of each proper vector is defined as:
Figure 546678DEST_PATH_IMAGE046
（4）；
The accumulative total variance contribution ratio of preceding p proper vector is defined as:
Figure DEST_PATH_IMAGE048
（5）。
Further, described steps d specifically comprises:
Descending by eigenwert, select to have the major component of class discrimination degree successively, in each classification of the major component with class discrimination degree, select corresponding blower fan as the mark post blower fan.
Further, described descending by eigenwert, select to have the major component of class discrimination degree successively, in each classification of the major component with class discrimination degree, select corresponding blower fan as the operation of mark post blower fan, specifically comprise:
It is descending to press eigenwert, checks the class discrimination degree of each major component successively;
If each component of a certain major component presents good class discrimination degree, then should in each classification, select 1-2 typhoon machine as the mark post blower fan of this wind energy turbine set;
Referring to Fig. 2, for second many major component of bag energy time, each blower fan shows different numerical value, should divide the mark post blower fans by two components, for the major component component greater than zero, approach zero blower fan less than zero-sum and all should select the 1-2 platform as the mark post blower fan.
The wind energy turbine set mark post blower fan system of selection based on principal component analysis (PCA) of various embodiments of the present invention is owing to comprise the historical power curve that obtains every typhoon machine in the wind energy turbine set; History based on every typhoon machine in the wind energy turbine set is exerted oneself, and sets up the blower fan matrix of exerting oneself To the blower fan matrix of exerting oneself
Figure DEST_PATH_IMAGE050
After the pre-service, it is carried out principal component analysis (PCA); To have the major component of class discrimination degree as the foundation of mark post blower fan selection, and carry out the mark post blower fan and select; By the operate power data of each blower fan of wind energy turbine set in the ten million multikilowatt wind-powered electricity generation base are carried out the analysis of dimensionality reduction degree, can obtain the most representative mark post blower fan; Thereby it is low and select the defective of weak effect to overcome efficiency of selection in the prior art, to realize that efficiency of selection is high and to select effective advantage.
Other features and advantages of the present invention will be set forth in the following description, and, partly from instructions, become apparent, perhaps understand by implementing the present invention.
Below by drawings and Examples, technical scheme of the present invention is described in further detail.
Description of drawings
Accompanying drawing is used to provide further understanding of the present invention, and constitutes the part of instructions, is used from explanation the present invention with embodiments of the invention one, is not construed as limiting the invention.In the accompanying drawings:
Fig. 1 is the schematic flow sheet that the present invention is based on the wind energy turbine set mark post blower fan system of selection of principal component analysis (PCA);
Fig. 2 is the EOF decomposition result synoptic diagram that the present invention is based on first three proper vector in the wind energy turbine set mark post blower fan system of selection of principal component analysis (PCA).
Embodiment
Below in conjunction with accompanying drawing the preferred embodiments of the present invention are described, should be appreciated that preferred embodiment described herein only is used for description and interpretation the present invention, and be not used in restriction the present invention.
At problems of the prior art, according to the embodiment of the invention, as depicted in figs. 1 and 2, proposed a kind of based on principal component analysis (PCA) (PCA, or claim empirical orthogonal to decompose, be EOF) the system of selection of wind energy turbine set mark post blower fan, by the operate power data of each blower fan of wind energy turbine set in the ten million multikilowatt wind-powered electricity generation base are carried out the analysis of dimensionality reduction degree, can obtain the most representative mark post blower fan.
Referring to Fig. 1, the wind energy turbine set mark post blower fan system of selection based on principal component analysis (PCA) of present embodiment specifically may further comprise the steps:
Step 1: obtain the historical power curve of every typhoon electric fan in the wind energy turbine set, advise per 5 minutes time points, time span was above 6 months.
Step 2: establishing has m typhoon machine in the wind energy turbine set, every typhoon machine has n power sample, the blower fan that then can constitute the capable n of the m row matrix of exerting oneself
Figure 205192DEST_PATH_IMAGE002
:
Figure 698491DEST_PATH_IMAGE004
（1）；
Figure 201310168790X100002DEST_PATH_IMAGE051
Represent i typhoon machine, the actual of j moment point exerted oneself.
Step 3: the data pre-service is about to matrix
Figure DEST_PATH_IMAGE052
Deduct equal value matrix and be processed into the flat matrix of square
Figure 733443DEST_PATH_IMAGE010
:
Figure DEST_PATH_IMAGE053
；
Wherein:
Figure 35111DEST_PATH_IMAGE014
。
Step 4: calculate covariance matrix:
Figure DEST_PATH_IMAGE054
；
(
Figure DEST_PATH_IMAGE055
For
Figure 989161DEST_PATH_IMAGE022
The commentaries on classics order) by matrix theory as can be known
Figure 376280DEST_PATH_IMAGE016
Be real symmetric matrix.
Step 5: realistic symmetrical matrix
Figure 214923DEST_PATH_IMAGE016
Proper vector
Figure 105518DEST_PATH_IMAGE040
And eigenwert , satisfy
Figure DEST_PATH_IMAGE056
, wherein
Figure 104884DEST_PATH_IMAGE030
（
Figure DEST_PATH_IMAGE057
） （3）；
Matrix
Figure DEST_PATH_IMAGE058
Be orthogonal matrix, matrix
Figure 747218DEST_PATH_IMAGE034
Column element is exactly eigenwert
Figure DEST_PATH_IMAGE059
The characteristic of correspondence vector.
Step 6: according to above-mentioned real symmetric matrix Proper vector
Figure 820796DEST_PATH_IMAGE040
And eigenwert
Figure 916928DEST_PATH_IMAGE026
, obtain the variance contribution ratio of each proper vector and the accumulative total variance contribution ratio of preceding several proper vectors, obtain describing the major component of power of fan;
Step 7: calculate the major component of describing power of fan: by eigenwert is descending proper vector is sorted, the accumulative total variance contribution ratio is major component greater than 95% preceding n proper vector;
Generally get and add up preceding p the bigger eigenwert that variance contribution ratio reaches 85-95%
Figure 97373DEST_PATH_IMAGE042
Corresponding first, second ...,
Figure 962561DEST_PATH_IMAGE044
Individual proper vector is major component.
The variance contribution ratio of each proper vector is defined as:
Figure 101418DEST_PATH_IMAGE046
（4）；
The accumulative total variance contribution ratio of preceding p proper vector is defined as:
Figure DEST_PATH_IMAGE061
（5）。
Step 8: descending by eigenwert, select to have the major component of class discrimination degree successively, in each classification of the major component with class discrimination degree, select corresponding blower fan as the mark post blower fan.
In step 8, need be descending by eigenwert, check the class discrimination degree of each major component successively.Specifically comprise following two aspects:
On the one hand, first few items proper vector (being major component) has characterized the distribution situation that wind electric field blower is exerted oneself to greatest extent, each component as proper vector is prosign, and this proper vector reflects so is each blower fan of this wind energy turbine set variation basically identical of exerting oneself; If each component of a certain major component presents good class discrimination degree, then this proper vector represents each wind-powered electricity generation blower fan of wind energy turbine set and show different characteristics in this projector space, therefore for guaranteeing the representativeness of mark post blower fan, should in each classification, select 1-2 typhoon machine as the mark post blower fan of this wind energy turbine set.
On the other hand, preceding 3 proper vectors after the descending ordering are extracted drafting pattern 2.Be not difficult to find that comprising on first maximum proper vector of energy, therefore each blower fan value corresponding basically identical, is characterized on this projecting direction, each blower fan of wind energy turbine set variation basically identical of exerting oneself.For second many major component of bag energy time, each blower fan shows different numerical value, therefore, should divide the mark post blower fans by two components, for the major component component greater than zero, approach zero blower fan less than zero-sum and all should select the 1-2 platform as the mark post blower fan.
The wind energy turbine set mark post blower fan system of selection based on principal component analysis (PCA) of the various embodiments described above of the present invention will be played directive function to the mark post of wind energy turbine set selection in the future blower fan.
It should be noted that at last: the above only is the preferred embodiments of the present invention, be not limited to the present invention, although with reference to previous embodiment the present invention is had been described in detail, for a person skilled in the art, it still can be made amendment to the technical scheme that aforementioned each embodiment puts down in writing, and perhaps part technical characterictic wherein is equal to replacement.Within the spirit and principles in the present invention all, any modification of doing, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (5)

1. the wind energy turbine set mark post blower fan system of selection based on principal component analysis (PCA) is characterized in that, comprising:
A, obtain the historical power curve of every typhoon electric fan in the wind energy turbine set;
B, based on the historical power curve of every typhoon electric fan in the wind energy turbine set, set up the blower fan matrix of exerting oneself
Figure 393DEST_PATH_IMAGE002
:
Figure 917534DEST_PATH_IMAGE004
（1）；
M is wind energy turbine set inner blower platform number, and n is the power sample number of every typhoon machine,
Figure 697271DEST_PATH_IMAGE006
Represent actual the exerting oneself of i typhoon machine, a j moment point;
C, to the blower fan matrix of exerting oneself
Figure 201310168790X100001DEST_PATH_IMAGE007
Carry out the square graduation and handle,
The covariance matrix of matrix after d, the computing;
E, the eigenwert of asking for covariance matrix and proper vector
F, by eigenwert is descending proper vector is sorted, getting the accumulative total variance contribution ratio, to reach 85-95% the corresponding proper vector of eigenwert be major component;
The foundation that g, the major component that will have a class discrimination degree are selected as the mark post blower fan is carried out the mark post blower fan and is selected.
2. the wind energy turbine set mark post blower fan system of selection based on principal component analysis (PCA) according to claim 1 is characterized in that described step c specifically comprises:
C1, data pre-service are about to matrix
Figure DEST_PATH_IMAGE009
Deduct equal value matrix and be processed into the flat matrix of square
Figure DEST_PATH_IMAGE011
:
，
Wherein,
Figure DEST_PATH_IMAGE015
C2, based on above-mentioned data pre-service result, carry out covariance and calculate, obtain real symmetric matrix
Figure DEST_PATH_IMAGE017
:
Figure DEST_PATH_IMAGE019
,
Figure DEST_PATH_IMAGE021
For
Figure DEST_PATH_IMAGE023
The commentaries on classics order;
C3, realistic symmetrical matrix
Figure 623638DEST_PATH_IMAGE024
Proper vector
Figure 31486DEST_PATH_IMAGE026
And eigenwert
Figure 853948DEST_PATH_IMAGE028
, satisfy
Figure 589823DEST_PATH_IMAGE030
, wherein
（
Figure 457602DEST_PATH_IMAGE034
） （3），
Matrix
Figure 545906DEST_PATH_IMAGE036
Be orthogonal matrix, matrix
Figure DEST_PATH_IMAGE037
Figure DEST_PATH_IMAGE039
Column element is exactly eigenwert
Figure DEST_PATH_IMAGE041
The characteristic of correspondence vector;
C4, according to above-mentioned real symmetric matrix
Figure 769077DEST_PATH_IMAGE042
Proper vector And eigenwert
Figure 506089DEST_PATH_IMAGE028
, obtain the variance contribution ratio of each proper vector and the accumulative total variance contribution ratio of preceding several proper vectors, obtain describing the major component of power of fan.
3. the wind energy turbine set mark post blower fan system of selection based on principal component analysis (PCA) according to claim 2 is characterized in that, in step c4, the described operation that obtains describing the major component of power of fan specifically comprises:
Get and add up preceding p the bigger eigenwert that variance contribution ratio reaches 85-95%
Figure DEST_PATH_IMAGE045
Corresponding first, second ...,
Figure DEST_PATH_IMAGE047
Individual proper vector is major component;
The variance contribution ratio of each proper vector is defined as:
Figure DEST_PATH_IMAGE049
（4）；
The accumulative total variance contribution ratio of preceding p proper vector is defined as:
Figure DEST_PATH_IMAGE051
（5）。
4. according to claim 2 or 3 described wind energy turbine set mark post blower fan systems of selection based on principal component analysis (PCA), it is characterized in that described steps d specifically comprises:
Descending by eigenwert, select to have the major component of class discrimination degree successively, in each classification of the major component with class discrimination degree, select corresponding blower fan as the mark post blower fan.
5. the wind energy turbine set mark post blower fan system of selection based on principal component analysis (PCA) according to claim 4, it is characterized in that, described descending by eigenwert, select to have the major component of class discrimination degree successively, in each classification of the major component with class discrimination degree, select corresponding blower fan as the mark post blower fan, specifically comprise:
It is descending to press eigenwert, checks the class discrimination degree of each major component successively;
If each component of a certain major component presents good class discrimination degree, then should in each classification, select 1-2 typhoon machine as the mark post blower fan of this wind energy turbine set;
For second many major component of bag energy time, each blower fan shows different numerical value, should divide the mark post blower fans by two components, for the major component component greater than zero, approach zero blower fan less than zero-sum and all should select the 1-2 platform as the mark post blower fan.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104112236A (en) * 2014-05-29 2014-10-22 国家电网公司 Calculating method of generating power of wind power field
CN104200001A (en) * 2014-07-23 2014-12-10 清华大学 Selection method of marker post fan
CN105978041A (en) * 2016-03-23 2016-09-28 三重型能源装备有限公司 Active power control method for wind power station configured with marker post draught fans
CN106447234A (en) * 2016-10-26 2017-02-22 国网电力科学研究院武汉南瑞有限责任公司 A wind power plant abandoned wind power assessment method based on a hierarchical clustering method
CN106780147A (en) * 2016-12-29 2017-05-31 南京天谷电气科技有限公司 A kind of wind-resources assessment anemometer tower addressing optimization device and method of facing area
CN106897771A (en) * 2017-01-03 2017-06-27 北京国能日新系统控制技术有限公司 A kind of new energy template processing machine site selecting method and system based on Chaos Genetic Algorithm

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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104112236A (en) * 2014-05-29 2014-10-22 国家电网公司 Calculating method of generating power of wind power field
CN104112236B (en) * 2014-05-29 2018-04-27 国家电网公司 The computational methods of wind power plant generated output
CN104200001A (en) * 2014-07-23 2014-12-10 清华大学 Selection method of marker post fan
CN104200001B (en) * 2014-07-23 2017-09-22 清华大学 The choosing method of mark post blower fan
CN105978041A (en) * 2016-03-23 2016-09-28 三重型能源装备有限公司 Active power control method for wind power station configured with marker post draught fans
CN105978041B (en) * 2016-03-23 2019-06-18 三一重型能源装备有限公司 A kind of wind power station active power control method configuring mark post blower
CN106447234A (en) * 2016-10-26 2017-02-22 国网电力科学研究院武汉南瑞有限责任公司 A wind power plant abandoned wind power assessment method based on a hierarchical clustering method
CN106780147A (en) * 2016-12-29 2017-05-31 南京天谷电气科技有限公司 A kind of wind-resources assessment anemometer tower addressing optimization device and method of facing area
CN106897771A (en) * 2017-01-03 2017-06-27 北京国能日新系统控制技术有限公司 A kind of new energy template processing machine site selecting method and system based on Chaos Genetic Algorithm

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