CN111075647B - ELM-based maximum wind energy capture method for variable-speed wind turbine generator - Google Patents
ELM-based maximum wind energy capture method for variable-speed wind turbine generator Download PDFInfo
- Publication number
- CN111075647B CN111075647B CN201911224529.0A CN201911224529A CN111075647B CN 111075647 B CN111075647 B CN 111075647B CN 201911224529 A CN201911224529 A CN 201911224529A CN 111075647 B CN111075647 B CN 111075647B
- Authority
- CN
- China
- Prior art keywords
- elm
- wind
- energy capture
- unit
- speed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000012549 training Methods 0.000 claims abstract description 30
- 238000010606 normalization Methods 0.000 claims abstract description 12
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 7
- 238000006073 displacement reaction Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000001133 acceleration Effects 0.000 claims description 3
- 230000004913 activation Effects 0.000 claims description 3
- 238000013016 damping Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 2
- 230000008901 benefit Effects 0.000 abstract description 5
- 230000005540 biological transmission Effects 0.000 abstract description 2
- 230000007547 defect Effects 0.000 abstract description 2
- 230000002035 prolonged effect Effects 0.000 abstract 1
- 230000006870 function Effects 0.000 description 8
- 238000007796 conventional method Methods 0.000 description 6
- 238000010248 power generation Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 101001095088 Homo sapiens Melanoma antigen preferentially expressed in tumors Proteins 0.000 description 1
- 102100037020 Melanoma antigen preferentially expressed in tumors Human genes 0.000 description 1
- 238000005299 abrasion Methods 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D7/00—Controlling wind motors
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2260/00—Function
- F05B2260/84—Modelling or simulation
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/32—Wind speeds
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/70—Type of control algorithm
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Abstract
The invention discloses a maximum wind energy capture method based on ELM. The method comprises the steps of obtaining effective wind speed information of a unit in a certain period of time and unit output data related to the effective wind speed in a corresponding period of time, removing correlation in the obtained unit output data, carrying out normalization operation, constructing an ELM training set, determining an ELM model by using the training set, obtaining a wind speed estimation model, giving an effective wind speed value on line by the model, further calculating a rotating speed tracking error, and giving a continuous maximum wind energy capture controller. The maximum wind energy capture controller obtained by the method can eliminate buffeting, so that the load of a transmission system is reduced, the service life of a unit is prolonged, the defect that the convergence speed of a traditional optimal torque algorithm is low is overcome, the wind energy capture efficiency is improved, the method is simple and easy to implement, the implementation cost is low, the number of parameters needing debugging is small, and compared with the traditional optimal torque control algorithm, the method can improve the productivity of the unit and increase the economic benefit of a wind power plant.
Description
Technical Field
The invention relates to the technical field of wind generating set control, in particular to a variable speed wind generating set maximum wind energy capture method based on ELM.
Background
Wind power generation has been rapidly developed worldwide over the past few decades. Wind in nature has strong randomness and intermittency, so that the unpredictability and fluctuation of wind power exist, and wind abandoning and electricity limiting exist commonly in the wind power industry, so that the commercial value of wind power generation is to be further promoted and mined.
The maximum wind energy capture is one of main control targets of a wind turbine generator and is an important guarantee for maximizing the economic benefit of a wind power plant, in order to achieve the target, an optimal torque control algorithm is generally adopted in the industry at present, the principle of the algorithm is very simple, namely under the condition that the wind speed is assumed to be a fixed value, only the steady state of a system is considered, and the control gain is multiplied by the square of the rotating speed of a generator to be used as a set value of the electromagnetic torque. However, there are two main problems with the optimal torque control algorithm. Firstly, the maximum power coefficient and the optimal tip speed ratio of the wind turbine generator are required to be known for calculating the control gain, although the two key quantities have a nominal value when the wind turbine generator leaves a factory, the maximum power coefficient and the optimal tip speed ratio of the wind turbine generator also change along with the operation of time and the wing shape of the blade changes due to the reasons of abrasion, waste accumulation, blade icing and the like, and the accurate value of the wing shape is difficult to determine, so that the original control gain continuously deviates from the theoretical optimal value, and the wind capturing efficiency of the wind turbine generator system is reduced; secondly, the optimal torque control algorithm does not use wind speed information, and the implementation form of the optimal torque control algorithm does not have an optimal rotating speed tracking error and adjustable parameters which can influence the convergence speed of the optimal rotating speed tracking error, so that the response speed of the algorithm is low under the condition of turbulent wind, and the productivity of a unit can be influenced.
Aiming at the problems existing in the optimal torque control algorithm, scholars provide a tip speed ratio method, and the core idea of the method is to convert the maximum wind energy capture problem of a wind turbine generator into the optimal rotating speed tracking problem. In addition, sliding mode control is widely used in the design of the optimal rotating speed controller of the wind turbine generator, and the methods have two problems. Firstly, an electromagnetic torque reference value expression partially based on a sliding mode control method contains discontinuous sign functions, which can bring buffeting of control signals to influence the service life of an actuator, even though a learner uses the continuous functions to replace the discontinuous sign functions, the method does not solve the discontinuous problem of sliding mode control from a theoretical level, so that the control performance is influenced by improper selection of related control parameters; secondly, many maximum wind energy capturing methods based on the sliding mode control theory assume that the effective wind speed of the unit can be accurately obtained, however, in practice, the effective wind speed obtained through a sensor has the problem of large measurement error or expensive acquisition cost, and the practicability of the methods is not strong.
Aiming at the problems of the existing maximum wind energy capture control method based on sliding mode control, the invention uses an effective wind speed estimation method based on an ELM (extreme learning machine) to replace an expensive radar wind measuring device, improves the estimation precision of the effective wind speed, reduces the acquisition cost of the effective wind speed, and further obtains an optimal rotating speed estimation value.
Disclosure of Invention
In order to improve the wind energy capturing efficiency of an optimal torque control algorithm and solve the problems of high implementation cost and difficult parameter selection of the conventional maximum wind energy capturing method, the invention provides the maximum wind energy capturing method which is low in implementation cost and simple in control parameter debugging, and the method can reduce the construction and operation and maintenance cost of a wind power plant, prolong the service life of a unit, improve the productivity of the unit and increase the economic benefit of the wind power plant.
The technical scheme adopted by the invention for solving the technical problems is as follows: an ELM-based maximum wind energy capture method, comprising the steps of:
(1) obtaining effective wind speed information of a unit in a certain period of time, recording the effective wind speed information as V, wherein the V is an ELM training target set, obtaining unit output data related to the effective wind speed information in a corresponding period of time, and removing the correlation in the obtained unit output data to obtain data with the correlation removed;
(2) carrying out normalization processing on the data obtained in the step (1) after the correlation is removed to obtain column components in a training feature set X of the ELM, constructing the training feature set of the ELM, wherein the training feature set X and a training target set V jointly form a training set of the ELM;
(3) constructing an ELM structure comprising an input layer, a hidden layer and an output layer, determining parameters of the ELM by using the training set in the step (2), and training to obtain an ELM model;
(4) when the wind turbine generator set is used on line, normalization processing is carried out on the output data of the generator set after the correlation is removed, the output data are input into the ELM trained in the step (3), and an effective wind speed estimation value is obtained through calculation;
(5) obtaining an optimal wind wheel rotating speed estimated value of the wind wheel of the unit according to the effective wind speed estimated value obtained in the step (4), and further calculating to obtain a wind wheel rotating speed tracking error e:
wherein, ω isrIs the rotational speed of the wind wheel,
is an optimal wind wheel speed estimate, λoptThe optimal tip speed ratio of the unit is shown, and R is the radius of the wind wheel. Further, the dynamic characteristic of the tracking error of the wind wheel rotating speed is obtained as follows:
wherein
Is an unknown that will be compensated using a subsequent maximum wind energy capture controller. Suppose that
F0Is the upper bound of the derivative of the unknown term F, F0Is known as TaIs the pneumatic torque, KtIs equivalent damping, TgIs an electromagnetic torque.
(6) Obtaining a tracking error e according to the step (5), and obtaining an expression of the maximum wind energy capture controller as follows:
wherein c and b are constant control parameters taken as:
b＝1.1F0sign (·) is a sign function.
Further, in step (1), effective wind speed information of the unit in a certain period of time is obtained by a lidar wind measuring device, and a SCADA system is used to record unit output data X ' ═ X ' (i, j) ], i ═ 1., l, j ═ 1., 8, which is associated with the effective wind speed information in a corresponding period of time, where X ' (i, j) is a sampled output of the SCADA system, and the expression is:
x'(i,:)＝[ωr,ωg,Tem,Pe,afa,vfa,xfa,Ra]
wherein, ω isrIs the rotational speed of the wind wheel, omegagIs the generator speed, TemIs an electromagnetic torque, PeIs the generated power, afaIs the tower fore-aft acceleration, vfaIs the tower fore-aft velocity, xfaIs a tower fore-and-aft displacement, RaIs the angular displacement of the wind wheel.
Further, in the step (1), a PCA algorithm is adopted to remove the correlation in the acquired unit output data, and the specific steps include: performing decentralized processing on the unit output data, namely subtracting respective mean values from each line of data of X'; calculating a covariance matrix; calculating an eigenvalue and an eigenvector of the covariance matrix; sorting the eigenvectors in columns according to the eigenvalues from big to small, and taking the first 4 columns to form a matrix P; the data X' is projected into the matrix P, resulting in decorrelated data X ═ X "(i:) ].
Further, in the step (2), the normalization processing specifically includes:
where X "(: j) represents the column component in X", μ (j) and σ (j) are the mean and standard deviation, respectively, of X "(: j), and X (: j) constitutes the column component in the training feature set X of the ELM.
Further, in the step (3), the constructed ELM includes an input layer with 4 nodes, an implicit layer with 12 nodes, and an output layer with 1 node; randomly initializing weights W of input layers to hidden layers1And bias B to obtain the output of the hidden layerH＝ψ(XW1+ B), the activation function psi of the hidden layer is taken as sigmoid function, and the weight from the hidden layer to the output layer is recorded as W2Then W is2Can be calculated as:
W2＝H+V
wherein H+Is the pseudo-inverse of H.
Further, in the step (4), the effective wind speed estimation value
The expression of (a) is:
wherein f isELMRepresenting the well-trained ELM model, xnewThe unit real-time output is processed by PCA decorrelation and normalization.
The invention has the beneficial effects that: the ELM is used for effective wind speed estimation, so that the use of a laser radar wind measuring device is avoided, the system cost is reduced, and the wind speed estimation precision is improved; the obtained expression of the maximum wind energy capture controller is continuous in nature, buffeting of the controller is eliminated, mechanical load of a unit is reduced, and the defect that a traditional optimal torque control algorithm is low in convergence speed is overcome. The ELM-based maximum wind energy capturing method is simple and easy to implement, low in implementation cost and few in parameter needing debugging, and compared with the traditional sliding mode control-based maximum wind energy capturing method for the wind turbine generator, the ELM-based maximum wind energy capturing method can prolong the service life of the wind turbine generator; compared with the traditional optimal torque control algorithm, the method can improve the unit productivity and increase the economic benefit of the wind power plant.
Drawings
FIG. 1 is a comparison graph of the real wind speed value and the estimated wind speed value according to the present invention;
FIG. 2 is a graph of wind speed estimation error according to the present invention;
FIG. 3 is a flow chart of the method of the present invention;
FIG. 4 is a graph comparing power generated by the proposed method with the conventional method;
FIG. 5 is a graph of electromagnetic torque comparison of the proposed method and the conventional method;
fig. 6 is a graph comparing rotor speed for the proposed method and the conventional method.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples.
The invention provides an ELM-based maximum wind energy capture method, which comprises the following steps:
x'(i,:)＝[ωr,ωg,Tem,Pe,afa,vfa,xfa,Ra]
wherein, ω isrIs the rotational speed of the wind wheel, omegagIs the generator speed, TemIs an electromagnetic torque, PeIs the generated power, afaIs the tower fore-aft acceleration, vfaIs the tower fore-aft velocity, xfaIs a tower fore-and-aft displacement, RaIs the angular displacement of the wind wheel.
Further, in order to remove the correlation in the unit output data X 'and improve the accuracy of effective wind speed estimation, a PCA algorithm is used to perform dimensionality reduction on the output data X', the data is subjected to decentralization (that is, each column of data of X 'is subtracted by the respective mean value), a covariance matrix is calculated, eigenvalues and eigenvectors of the covariance matrix are calculated, the eigenvectors are sorted from large to small according to the eigenvalues, the first 4 columns are taken to form a matrix P, and the data X' is projected into the matrix P, so that the data X ═ X "(i:) after the correlation is removed is obtained.
wherein X "(: j) represents the column component in X", μ (j) and σ (j) are the mean and standard deviation of X "(: j), X (: j) constitutes the column component in the training feature set X of the ELM, and the training feature set X and the training target set V together constitute the training set of the ELM.
And 3, constructing an ELM, and determining ElM parameters by using the training set obtained in the step (2). The constructed ELM includes an input layer (4 nodes), an implied layer (12 nodes), and an output layer (1 node). Randomly initializing weights W of input layers to hidden layers1And an offset B for obtaining the output of the hidden layer to obtain the output H ═ ψ (XW) of the hidden layer1+ B), the activation function psi of the hidden layer is taken as sigmoid function, and the weight from the hidden layer to the output layer is recorded as W2Then W is2Can be calculated as:
W2＝H+V
wherein H+Is the pseudo-inverse of H.
Wherein f isELMRepresenting the well-trained ELM model, xnewThe unit real-time output is processed by PCA decorrelation and normalization.
wherein, ω isrIs the rotational speed of the wind wheel, λoptIs the optimal tip speed ratio of the unit, R is the radius of the wind wheel,
and the estimated value of the optimal wind wheel rotating speed is obtained.
And 6, solving the dynamic characteristic of the tracking error e of the wind wheel rotating speed obtained in the step 5:
wherein
Is an unknown that will be compensated using a subsequent maximum wind energy capture controller. It is assumed here that
F0Is the upper bound of the derivative of the unknown term F, TaIs the pneumatic torque, KtIs equivalent damping, TgIs an electromagnetic torque.
And 7, designing an expression of the electromagnetic torque control signal according to a super-twisting algorithm in sliding mode control as follows:
where c and b are constant control parameters, here it is proposed to take:
b＝1.1F0. The expression of the magnetic torque control signal obtained from step 6 mayKnown as TgIs continuous because
And
are all continuous. According to the lyapunov principle, the system is stable under the action of the above-mentioned control signals. Therefore, the method essentially eliminates the buffeting phenomenon of the control signals, can reduce the load of a transmission system and prolong the service life of the unit.
Examples
In the embodiment, GH Bladed wind power development software is used for verifying the effectiveness of the method provided by the invention. To illustrate the inventive novelty, a comparison is made with the conventional optimal torque control method as follows
Wherein, TgOTCIs the electromagnetic torque value, k, given by the optimal torque control algorithmoptIs a control parameter, ωgIs the rotating speed of the generator, rho is 1.225Kg/m3Is the air density, R is 38.5m is the wind wheel radius, Cpmax0.482 is the maximum wind energy capture coefficient, λopt8.5 is the optimum tip speed ratio, ng104.494 is the gear ratio of the gearbox.
FIG. 1 is a graph showing the comparison between the true and estimated values of the effective wind speed according to the present invention. In order to reduce the load of the drive train of the unit, the wind speed estimation value is input into the control system after passing through a low-pass filter (the bandwidth of the filter can be selected according to the actual situation). The variation trend of the wind speed estimated value is consistent with the real wind speed value, and the dynamic performance of the MPPT control algorithm can be improved due to the variation condition of the wind speed trend. Through calculation, MAPE of the wind speed estimated value and the wind speed real value is 5.61%, and MSE is 0.1623m2/s2。
As shown in FIG. 2, it is a wind speed estimation error chart according to the present invention, and the wind speed estimation error is defined as the difference between the true wind speed value and the wind speed estimation value, and as can be seen from the chart, the value is small, which illustrates the effectiveness of the wind speed estimation method. FIG. 3 is a flow chart of the method design of the present invention. Firstly, acquiring relevant output data of a unit, performing data preprocessing including PCA decorrelation and normalization, and constructing an ELM training set; secondly, training the ELM by using a training set of the ELM to obtain an effective wind speed estimation model, and giving the size of a wind speed estimation value on line by using the wind speed estimation model; and finally, calculating a rotation speed tracking error, and further providing a maximum wind energy capture controller.
Fig. 4 is a comparison graph of power generation power of the method proposed by the present invention and a conventional method, fig. 5 is a comparison graph of electromagnetic torque of the method proposed by the present invention and a conventional method, and fig. 6 is a comparison graph of wind turbine rotation speed of the method proposed by the present invention and a conventional method. According to calculation, the method disclosed by the invention has the advantages that the productivity is improved by 0.44% compared with that of the traditional method, and the yield is improved by 0.44% due to the fact that the generating capacity base number of the actual wind power plant is very large. From the electromagnetic torque signal comparison of fig. 4, the method improves the unit capacity without increasing the buffeting (the control signal is continuous) of the electromagnetic torque signal.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the appended claims.
Claims (6)
1. An ELM-based maximum wind energy capture method, characterized in that the method comprises the following steps:
(1) obtaining effective wind speed information of a unit in a certain period of time, recording the effective wind speed information as V, wherein the V is an ELM training target set, obtaining unit output data related to the effective wind speed information in a corresponding period of time, and removing the correlation in the obtained unit output data to obtain data with the correlation removed;
(2) normalizing the data obtained in the step (1) after the correlation is removed to obtain column components in a training feature set X of the ELM, constructing the training feature set of the ELM, and forming a training set of the ELM by the training feature set X and a training target set V together;
(3) constructing an ELM structure comprising an input layer, a hidden layer and an output layer, determining parameters of the ELM by using the training set in the step (2), and training to obtain an ELM model;
(4) when the wind turbine generator set is used on line, normalization processing is carried out on the output data of the generator set after the correlation is removed, the output data are input into the ELM trained in the step (3), and an effective wind speed estimation value is obtained through calculation;
(5) obtaining an optimal wind wheel rotating speed estimated value of the wind wheel of the unit according to the effective wind speed estimated value obtained in the step (4), and further calculating to obtain a wind wheel rotating speed tracking error e:
wherein, ω isrIs the rotational speed of the wind wheel,
is an estimated value of the optimal rotating speed of the wind wheel,
lambda is an effective wind speed estimateoptThe optimal tip speed ratio of the unit is obtained, and R is the radius of the wind wheel; further, the dynamic characteristic of the tracking error of the wind wheel rotating speed is obtained as follows:
wherein
Is an unknown item that will be compensated using a subsequent maximum wind energy capture controller; suppose that
F0Is unknown item FUpper bound of derivative, F0Is known as TaIs the pneumatic torque, KtIs equivalent damping, TgIs an electromagnetic torque;
(6) obtaining a tracking error e according to the step (5), and obtaining an expression of the maximum wind energy capture controller as follows:
wherein c and b are constant control parameters taken as:
b＝1.1F0sign (·) is a sign function.
2. The maximum ELM-based wind energy capture method according to claim 1, wherein in step (1), the effective wind speed information of the wind turbine generator unit in a certain period of time is obtained by a lidar wind measuring device, and the SCADA system is used to record unit output data X ' X ' (i, j) ], i 1, j 1, 8 related to the effective wind speed information in a corresponding period of time, wherein X ' (i, j) is a sampled output of the SCADA system, and the expression is as follows:
x'(i,:)＝[ωr,ωg,Tem,Pe,afa,vfa,xfa,Ra]
wherein, ω isrIs the rotational speed of the wind wheel, omegagIs the generator speed, TemIs an electromagnetic torque, PeIs the generated power, afaIs the tower fore-aft acceleration, vfaIs the tower fore-aft velocity, xfaIs a tower fore-and-aft displacement, RaIs the angular displacement of the wind wheel.
3. The ELM-based maximum wind energy capture method according to claim 2, wherein in step (1), a PCA algorithm is used to remove correlation in the acquired unit output data, and the specific steps include: performing decentralized processing on the unit output data, namely subtracting respective mean values from each line of data of X'; calculating a covariance matrix; calculating an eigenvalue and an eigenvector of the covariance matrix; sorting the eigenvectors in columns according to the eigenvalues from big to small, and taking the first 4 columns to form a matrix P; the data X' is projected into the matrix P, resulting in decorrelated data X ═ X "(i:) ].
4. The ELM-based maximum wind energy capture method of claim 3, wherein in step (2), the specific operation of the normalization process is:
where X "(: j) represents the column component in X", μ (j) and σ (j) are the mean and standard deviation, respectively, of X "(: j), and X (: j) constitutes the column component in the training feature set X of the ELM.
5. The ELM-based maximum wind energy capture method of claim 1, wherein in step (3), the constructed ELM comprises an input layer with 4 nodes, an hidden layer with 12 nodes, an output layer with 1 node; randomly initializing weights W of input layers to hidden layers1And bias B, the output H ═ psi (XW) of the hidden layer is obtained1+ B), the activation function psi of the hidden layer is taken as sigmoid function, and the weight from the hidden layer to the output layer is recorded as W2Then W is2The calculation is as follows:
W2＝H+V
wherein H+Is the pseudo-inverse of H.
6. The ELM-based maximum wind energy capture method of claim 1, wherein in step (4), the estimate of the effective wind speed is
The expression of (a) is:
wherein f isELMRepresenting the well-trained ELM model, xnewThe unit real-time output is processed by PCA decorrelation and normalization.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911224529.0A CN111075647B (en) | 2019-12-04 | 2019-12-04 | ELM-based maximum wind energy capture method for variable-speed wind turbine generator |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911224529.0A CN111075647B (en) | 2019-12-04 | 2019-12-04 | ELM-based maximum wind energy capture method for variable-speed wind turbine generator |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111075647A CN111075647A (en) | 2020-04-28 |
| CN111075647B true CN111075647B (en) | 2021-06-22 |
Family
ID=70312654
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911224529.0A Active CN111075647B (en) | 2019-12-04 | 2019-12-04 | ELM-based maximum wind energy capture method for variable-speed wind turbine generator |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111075647B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111963372B (en) * | 2020-09-01 | 2021-12-17 | 北京石油化工学院 | Tracking control method for optimal rotating speed of wind driven generator |
| CN112343770B (en) * | 2020-11-17 | 2021-09-24 | 北京石油化工学院 | Observer-based wind driven generator optimal rotation speed finite time tracking control method |
| CN115825894B (en) * | 2022-11-17 | 2023-08-18 | 中国能源建设集团广东省电力设计研究院有限公司 | Method, device, terminal equipment and medium for determining wind energy capturing position |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003167603A (en) * | 2001-12-03 | 2003-06-13 | Eco Energy:Kk | Prediction type high speed maximum power follow-up control method using current/voltage characteristic of power generator |
| CN105354363A (en) * | 2015-10-09 | 2016-02-24 | 上海大学 | Fluctuation wind speed prediction method based on extreme learning machine |
| CN107220469A (en) * | 2017-07-14 | 2017-09-29 | 华北电力大学（保定） | A kind of method and system of fan condition estimation |
| CN107516150A (en) * | 2017-08-25 | 2017-12-26 | 广东工业大学 | A kind of Forecasting Methodology of short-term wind-electricity power, apparatus and system |
| CN108334672A (en) * | 2018-01-14 | 2018-07-27 | 浙江大学 | Variable Speed Wind Power Generator maximal wind-energy capture method based on effective wind speed estimation |
| CN110454329A (en) * | 2019-09-04 | 2019-11-15 | 风脉能源（武汉）股份有限公司 | A kind of Wind turbines award setting method |
-
2019
- 2019-12-04 CN CN201911224529.0A patent/CN111075647B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003167603A (en) * | 2001-12-03 | 2003-06-13 | Eco Energy:Kk | Prediction type high speed maximum power follow-up control method using current/voltage characteristic of power generator |
| CN105354363A (en) * | 2015-10-09 | 2016-02-24 | 上海大学 | Fluctuation wind speed prediction method based on extreme learning machine |
| CN107220469A (en) * | 2017-07-14 | 2017-09-29 | 华北电力大学（保定） | A kind of method and system of fan condition estimation |
| CN107516150A (en) * | 2017-08-25 | 2017-12-26 | 广东工业大学 | A kind of Forecasting Methodology of short-term wind-electricity power, apparatus and system |
| CN108334672A (en) * | 2018-01-14 | 2018-07-27 | 浙江大学 | Variable Speed Wind Power Generator maximal wind-energy capture method based on effective wind speed estimation |
| CN110454329A (en) * | 2019-09-04 | 2019-11-15 | 风脉能源（武汉）股份有限公司 | A kind of Wind turbines award setting method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111075647A (en) | 2020-04-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111075647B (en) | ELM-based maximum wind energy capture method for variable-speed wind turbine generator | |
| CN108334672B (en) | Maximum wind energy capturing method of variable-speed wind generating set based on effective wind speed estimation | |
| Li et al. | Using neural networks to estimate wind turbine power generation | |
| CN110454329B (en) | Pitch angle control method for wind turbine generator | |
| CN111079343B (en) | Wind turbine generator effective wind speed estimation method based on width learning | |
| CN106979126B (en) | Wind power generating set high wind speed section effective wind speed estimation method based on SVR | |
| CN111120205B (en) | Wind power plant regionalization control method based on laser radar | |
| CN113188794B (en) | Gearbox fault diagnosis method and device based on improved PSO-BP neural network | |
| CN111241748A (en) | Wind driven generator fault diagnosis based on long-short term memory model recurrent neural network | |
| Schlipf et al. | Systems engineering for lidar-assisted control: a sequential approach | |
| CN113612237A (en) | Method for positioning resonance-induced subsynchronous oscillation source in offshore wind farm | |
| US9658124B2 (en) | System and method for wind turbine operation | |
| CN116625686A (en) | On-line diagnosis method for bearing faults of aero-engine | |
| CN110966144B (en) | BLS-based intelligent constant power control method for wind turbine generator | |
| Xu et al. | Rotor dynamic balancing control method based on fuzzy auto-tuning single neuron PID | |
| CN108223274B (en) | Large Scale Variable Pitch Wind Turbine System discrimination method based on optimization RBF neural | |
| CN111997833B (en) | Wind turbine generator set composite intelligent pitch control method based on laser wind measurement feedforward | |
| CN110985287B (en) | Indirect rotating speed control method based on width learning | |
| CN111472930B (en) | Evolvable wind speed calculation method and feedforward unified variable pitch control method based on evolvable wind speed calculation method | |
| CN113252938A (en) | Method for determining the wind speed in the rotor plane of a wind turbine | |
| CN110889780B (en) | Wind turbine generator system performance-guaranteeing maximum wind energy capturing method based on BLS and double-layer SMC | |
| Cao et al. | A novel method for detection of wind turbine blade imbalance based on multi-variable spectrum imaging and convolutional neural network | |
| CN110985290B (en) | Optimal torque control method based on support vector regression | |
| Wu et al. | Fault diagnosis of pitch sensor bias for wind turbine based on the multi-innovation Kalman filter | |
| CN115977874A (en) | Wind turbine generator yaw self-adaptive calibration method and system based on laser wind finding radar |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |