CN113283200B - Wind turbine generator dynamic wake modeling method based on measurable parameters - Google Patents
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Abstract
The invention relates to a wind turbine generator dynamic wake modeling method based on measurable parameters, which comprises the following steps: acquiring a first wind turbine generator wake data set, a multipoint sampling data set in a first wake space, a first deterministic state parameter data set, a multipoint sampling data set in a second wake space and a second deterministic state parameter data set; decomposing by utilizing the characteristic values to obtain a first wind turbine generator wake data set and a left characteristic vector of a multipoint sampling data set in a first wake space; performing dimension reduction mapping on the wake data set of the first wind turbine generator, the multipoint sampling data set in the first wake space and the multipoint sampling data set in the second wake space; and determining a state updating matrix and a dimension reduction reconstruction matrix according to the result, so as to obtain a dimension reduction model of the wake flow of the wind turbine, and carrying out dimension increasing mapping on the model to obtain the estimation of the wake flow of the full-dimension wind turbine. The state quantity for constructing the dynamic model is a wake flow related parameter which has physical significance and can be measured.
Description
Technical Field
The invention relates to the field of wind turbine generator wake modeling, in particular to a wind turbine generator dynamic wake modeling method based on measurable parameters.
Background
The invention mainly relates to the following fields: modeling the wake flow of the wind turbine generator. Many studies have been conducted on this keyword, such as patent publications 202110237938.5, 202011109217.8, 201710021657.X, and related papers are represented by Frandsen: analytical modelling of wind speed deficit in large offshore wind farms, bastankhah M: experimental and theoretical study of wind turbine wakes in yawed conditions, etc.
The patent relates to a modeling method of dynamic wake flow of a wind turbine generator, wherein the main method in the field is a dynamic mode Decomposition Method (DMD), and the description is as follows: dynamic mode decomposition data-driven modeling of complex systems, the existing dynamic wake modeling application of the wind turbine generator set based on the DMD is as follows: data-driven reduced order model for prediction of wind turbine wakes.
The wind turbine generator wake modeling is characterized in that the wind turbine generator wake model represented by Frandsen and Gauss is obtained by extracting and approximating static characteristics such as wind turbine generator wake space distribution, speed distribution and the like, and on the basis, the DMD method is used for constructing a dynamic wake model with linear dimension reduction, and the dynamic wake model is essentially obtained by approximating linear finite dimension of nonlinear fluid. The patent provides a wind turbine generator dynamic wake modeling method based on a Koopman operator theory, wherein the form of a wake dynamic model obtained by construction is similar to that of a model obtained by DMD, and the model is a linear state space model. The difference is that the state quantity of the dynamic model constructed by the patent is a wake related parameter which has physical meaning and can be measured, and the state parameter of the DMD wake model is reduced from global wake data, has no physical meaning and is not measurable.
Disclosure of Invention
The invention aims to provide a wind turbine generator dynamic wake modeling method based on measurable parameters, wherein state quantity for constructing a dynamic model is a wake related parameter which has physical significance and can be measured.
In order to achieve the above object, the present invention provides the following solutions:
a wind turbine generator dynamic wake modeling method based on measurable parameters comprises the following steps:
acquiring wake data of a first wind turbine generator set, multipoint sampling data in a first wake space and first deterministic state parameter data in a certain time from a certain moment;
respectively constructing a first wind turbine generator wake data set, a multipoint sampling data set in a first wake space and a first deterministic state parameter data set according to the first wind turbine generator wake data, the multipoint sampling data in the first wake space and the first deterministic state parameter data;
acquiring multipoint sampling data and second deterministic state parameter data in a second wake space in a certain time from the next moment of the certain moment, and respectively constructing a multipoint sampling data set and a second deterministic state parameter data set in the second wake space;
respectively carrying out eigenvalue decomposition on the first wind turbine generator wake data set and the multipoint sampling data set in the first wake space to obtain a left eigenvector of the first wind turbine generator wake data set and a left eigenvector of the multipoint sampling data set in the first wake space;
performing dimension reduction mapping on the first wind turbine generator wake data set, the multipoint sampling data set in the first wake space and the multipoint sampling data set in the second wake space according to the left eigenvector of the first wind turbine generator wake data set and the left eigenvector of the multipoint sampling data set in the first wake space;
determining a state update matrix and a dimension reduction reconstruction matrix according to the mapping result, the first deterministic state parameter data set and the second deterministic state parameter data set;
determining a wake flow dimension reduction model of the wind turbine generator according to the state updating matrix and the dimension reduction reconstruction matrix;
and performing dimension-increasing mapping on the wind turbine generator wake dimension-decreasing model to obtain the estimation of the full-dimension wind turbine generator wake.
Optionally, the data size of the wake data of the first wind turbine generator is larger than the data size of the multipoint sampling data and the first deterministic state parameter data in the first wake space.
Optionally, the sampling step length of the wake data of the first wind turbine generator, the multipoint sampling data in the first wake space and the first deterministic state parameter data is smaller than one half of the wake bending time scale.
Optionally, the wake data of the first wind turbine generator, the multipoint sampling data in the first wake space and the first deterministic state parameter data have the same sampling step length and sampling interval, all start at the same time and end at the same time.
Optionally, an SVD algorithm is adopted to respectively decompose characteristic values of the wake data set of the first wind turbine generator and the multipoint sampling data set in the first wake space.
Optionally, the dimension-reducing mapping specifically adopts the following formula:
wherein X is a wake data set of the first wind turbine generator, R is a mapping matrix of X, U x Is the left eigenvector of X, Σ x Is a diagonal matrix formed by characteristic values of X, V x Is the right eigenvector of X prb For a multi-point sampling dataset in a first wake space, R prb Is X prb Mapping matrix, U p Is X prb Is a left eigenvector, Σ p Is X prb Diagonal matrix formed by eigenvalues of V p Is X prb Is used for the right feature vector of (c),for a multi-point sampling dataset in the second wake space +.>Is->The superscript T denotes a transpose of the matrix.
Optionally, the state update matrix and the dimension reduction reconstruction matrix adopt the following formulas:
wherein F is a state update matrix, H is a dimension reduction reconstruction matrix, and is marked with a superscriptIs Moore-Penrose generalized inverse operation,for a multipoint sampling dataset in the second wake space +.>Mapping matrix of->R is the second deterministic state parameter data set prb For multi-point sampling data set X in the first wake space prb Mapping matrix, X of (X) det And R is a mapping matrix of a wake data set X of the first wind turbine generator.
Optionally, the wake dimension reduction model of the wind turbine generator is as follows:
wherein ,Zrc Reducing the order of the linear mapping of the multi-point sample data Z in the first wake space,for sampling data z at multiple points in the second wake space + Is a linear mapping reduced order data, z d For the first deterministic state parameter data, +.>For the second deterministic state parameter data, x rc Dimension reduction mapping for wake flow of full-order wind turbine generator system>Representation of pair x rc Is a function of the estimate of (2).
Optionally, z rc and xrc The following formula is satisfied:
wherein ,zrc Reducing order of linear mapping of multi-point sampling data z in first wake space, U p For the left eigenvector, x, of the multi-point sampled dataset in the first wake space rc For the dimension-reducing mapping of the wake flow of the full-order wind turbine generator, U x And x is the wake data of the first wind turbine generator, and the superscript T represents transposition of the matrix.
Optionally, the step of performing dimension-increasing mapping on the wind turbine generator wake dimension-reducing model to obtain the estimate of the full-dimension wind turbine generator wake specifically adopts the following formula:
wherein ,for the estimation of the wake of the full-dimensional wind turbine generator, < > a>Representation of pair x rc Is estimated, U x Is the left eigenvector of the wake dataset of the first wind turbine.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
in the modeling method provided by the invention, deterministic state parameters and multipoint sampling measurement parameters are easy to obtain for an industrial field, and from the field application point of view, the measurement of the full-dimensional wake parameters in the modeling depends on a high-precision wind-finding radar.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a wind turbine generator dynamic wake modeling method based on measurable parameters.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a wind turbine generator dynamic wake modeling method based on measurable parameters, wherein state quantity for constructing a dynamic model is a wake related parameter which has physical significance and can be measured.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
The invention provides a wind turbine generator dynamic wake modeling method based on measurable parameters, which mainly comprises the following two parts: 1. and 2, carrying out multipoint sampling on data in the wake flow range, wherein the wake flow is related to measurable deterministic states such as regional average wind speed, wind turbine generator rotating speed, wind turbine generator power and the like. After the downscaling of the multi-point sampled data, the state parameters of the model are composed together with the deterministic state. The model comprises two parts, namely a dynamic part and a reconstruction part. The dynamic part describes the dynamic characteristics of wake flow of the wind turbine generator by low-order approximation based on the existing state quantity, the reconstruction part maps the existing state parameter into a reduced-order flow field, and the full-order flow field is obtained after dimension-lifting mapping.
FIG. 1 is a flowchart of a wind turbine generator dynamic wake modeling method based on measurable parameters, as shown in FIG. 1, comprising:
step 101: and acquiring wake data of the first wind turbine generator set, multipoint sampling data in a first wake space and first deterministic state parameter data in a certain time from a certain moment.
Step 102: and respectively constructing a first wind turbine generator wake data set, a multipoint sampling data set in the first wake space and a first deterministic state parameter data set according to the first wind turbine generator wake data, the multipoint sampling data in the first wake space and the first deterministic state parameter data.
Steps 101 to 102 are specifically steps for acquiring measurement data:
the measurement data includes the following three parts: the system comprises first wind turbine generator wake data, multipoint sampling data and first deterministic state parameter data in a first wake space. The deterministic state parameter can be directly or indirectly related parameters of wake flow which can be directly measured in the wind turbine, and the parameters comprise wind speed average value of a region, multipoint pressure or temperature data, wind turbine power or rotating speed and the like. Wind turbine generator wake flow measurement refers to grid measurement to obtain wind speed in wake flow areas, and the data source can be high-precision laser radar measurement data or computational fluid mechanics simulation data. Starting the measurement at a given moment, the total duration of k+1 steps is k+1, and the following records are made:
x k represents wake flow data x of the wind turbine generator measured in the kth step,
z k representing the sampled data z measured at the kth step,
deterministic state parameter data z representing the measurement of the kth step d 。
The data amount of the data measured for each step is defined as follows: the wake flow data of the wind turbine generator comprise m pieces of measurement point data, the sampling data comprise n pieces of measurement point data, and the deterministic state parameter comprises p pieces of measurement point data. Wherein, the values of n and p are all significantly smaller than m, considering that the sampling data is non-uniform/random sampling of the wake of the wind turbine. The measurement should satisfy the following conditions: the three measurement data sets have the same sampling step length and sampling interval, start at the same moment and end at the same moment; the sampling step size should be less than one half of the time scale of wake bending, such as 1 second or 2 seconds; the measurement process should be maintained for a sufficient length of time, the basic requirements of which are: k is greater than n and p.
The measurement data are then organized into measurement data sets as follows:
the basic three measurement data sets are defined as follows:
X＝[x 1 ，x 2 ，…，x k ]
X prb ＝[z 1 ，z 2 ，…，z k ]
wherein X is a wake data set of the first wind turbine generator, and X prb For a first multi-point sampled data set, X det Is a first deterministic state parameter data set.
Step 103: and acquiring the multipoint sampling data and the second deterministic state parameter data in the second wake space in a certain time from the next moment of the certain moment, and respectively constructing a multipoint sampling data set and a second deterministic state parameter data set in the second wake space.
Since each of the three measurement data sets has one data set with the same measurement parameters, but is delayed in time sequence by one step:
X + ＝[x 2 ，x 3 ，…，x k+1 ]
X + prb ＝[z 2 ，z 3 ，…，z k+1 ]
wherein X+ The data set of the wind turbine generator wake casting later step can be recorded as a second wind turbine generator wake flow data set; x is X + prb The data set can be a second multi-point sampling data set; x is X + det The one-step data set is postponed for the deterministic state parameter, and can also be noted as a second deterministic state parameter data set.
Step 104: and respectively carrying out eigenvalue decomposition on the first wind turbine generator wake data set and the multipoint sampling data set in the first wake space to obtain a left eigenvector of the first wind turbine generator wake data set and a left eigenvector of the multipoint sampling data set in the first wake space.
The method comprises the following steps: performing n-order eigenvalue decomposition (eigenvalue decomposition is a common data decomposition mode in the linear system field, which is named singular value decomposition and SVD) on the first wind turbine generator wake data set and the multipoint sampling data set in the first wake space, and obtaining the following decomposition result:
wherein X is a wake flow data set of the first wind turbine generator, U x Is the left eigenvector of the X dataset, Σ x Is a diagonal matrix formed by the eigenvalues of the X data set, V x Is the right eigenvector of the X dataset, X prb For a multi-point sampling dataset in a first wake space, U p Is X prb Left eigenvector, Σ, of the dataset p Is X prb Diagonal matrix formed by eigenvalues of dataset, V p Is X prb The right eigenvector of the dataset, superscript T, represents the transpose of the matrix (one of the basic operations of the matrix, not necessarily defined).
Step 105: and performing dimension reduction mapping on the first wind turbine generator wake data set, the multipoint sampling data set in the first wake space and the multipoint sampling data set in the second wake space according to the left eigenvector of the first wind turbine generator wake data set and the left eigenvector of the multipoint sampling data set in the first wake space.
And performing dimension reduction mapping on the original data set according to a result of the eigenvalue decomposition, wherein the dimension reduction mapping result is obtained according to a left eigenvector of a wake data set of the first wind turbine generator and a left eigenvector of a multipoint sampling data set in a first wake space, and a specific calculation formula is as follows:
wherein X is a wake data set of the first wind turbine generator, R is a mapping matrix of X, U x Is the left eigenvector of X, Σ x Is a diagonal matrix formed by characteristic values of X, V x Is the right eigenvector of X prb For a multi-point sampling dataset in a first wake space, R prb Is X prb Mapping matrix, U p Is X prb Is a left eigenvector, Σ p Is X prb Diagonal matrix formed by eigenvalues of V p Is X prb Is used for the right feature vector of (c),for a multi-point sampling dataset in the second wake space +.>Is->The superscript T denotes a transpose of the matrix.
Step 106: and determining a state updating matrix and a dimension-reducing reconstruction matrix according to the mapping result, the first deterministic state parameter data set and the second deterministic state parameter data set.
The calculation state update matrix and the dimension reduction reconstruction matrix adopt the following formulas:
wherein F is a state update matrix, H is a dimension reduction reconstruction matrix, and is marked with a superscriptGeneralized inverse operation (one of the matrix-based operations, not required to be defined) for Moore-Penrose, for>For a multipoint sampling dataset in the second wake space +.>Mapping matrix of->R is the second deterministic state parameter data set prb For multi-point sampling data set X in the first wake space prb Mapping matrix, X of (X) det And R is a mapping matrix of a wake data set X of the first wind turbine generator.
Step 107: and determining a wake flow dimension reduction model of the wind turbine generator according to the state updating matrix and the dimension reduction reconstruction matrix.
The wake flow dimension reduction model of the wind turbine generator is determined by the following formula:
wherein ,zrc The linear mapping of the multi-point sample data z in the first wake space is reduced,for sampling data z at multiple points in the second wake space + Is a linear mapping reduced order data, z d For the first deterministic state parameter data, +.>For the second deterministic state parameter data, x rc Dimension reduction mapping for wake x of full-order wind turbine generator set with superscript ++>Representation of pair x rc Is a function of the estimate of (2).
z rc The following relation exists between the first wake space and the multi-point sampling data Zx rc The following relationship exists between the full-order wake x: />
Step 108: and performing dimension-increasing mapping on the wind turbine generator wake dimension-decreasing model to obtain the estimation of the full-dimension wind turbine generator wake.
Estimation of wake flow of full-dimensional wind turbine generatorThe specific calculation formula is as follows:
wherein ,for the estimation of the wake of the full-dimensional wind turbine generator, < > a>Representation of pair x rc Is estimated, U x Is the left eigenvector of the wake dataset of the first wind turbine.
The dynamic wake modeling method and the obtained wake dynamic model provided by the invention are not found in the previous research, and comprise the following characteristics:
1. constructing a wake dynamic model of the wind turbine in the form of a linear state space model;
2. the sources of state quantities include two parts: multipoint sampling data, and measuring deterministic state;
3. the state quantity corresponding to the multi-point sampling data is obtained by the same-order linear mapping of the original sampling data;
4. the obtained dynamic model comprises a dynamic updating part which is a discrete model with state quantity updated along with time;
5. the state quantity is calculated through a reconstruction matrix to obtain a dimension-reduced flow field, and the dimension-increased mapping is carried out to obtain a full-dimension flow field.
Based on the above, the invention also discloses the following technical effects:
compared with the prior art represented by DMDs, the modeling method provided by the invention has the advantage that the state quantity comes from a physically significant and measurable parameter. In a dynamic wake model of a wind turbine generator represented by a DMD, a linear state space model is obtained, but the state parameters of the model are obtained by the reduced order mapping of the full-dimensional wake, and the model has no feasibility for an industrial field; in the modeling method provided by the invention, deterministic state parameters and multipoint sampling measurement parameters are easy to obtain for an industrial field. From the field application point of view, the measurement of the full-dimensional wake parameters in the modeling depends on the high-precision wind-finding radar, the model provided by the invention can be constructed on the basis of the measurement of the early-stage radar, and the field application can be still carried out according to the obtained model after the later-stage radar is removed. This advantage comes mainly from step 101, step 102, step 103 and step 107, it being seen that the model obtained in step 107 is based on three classes of data sets, whereas the construction of the DMD dynamic wake model does not require multi-point sampling and deterministic state parameter data sets.
Another advantage of the present invention is that it has a better suppression capability for measurement noise, which results from step 104 and step 105. The result of the eigenvalue decomposition of step 104 is used for the reduced order mapping of the data sets in step 105, and the first two data sets after the reduction are calculated from the result of the eigenvalue decomposition, which can filter the nonlinear non-modelable components in the flow field measurement and the main measurement noise. The filtering is not only aimed at a multi-point sampling data set, but also aimed at a wake flow measurement data set, so that the obtained model is ensured to have better robustness.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. The utility model provides a wind turbine generator system dynamic wake modeling method based on measurable parameters, which is characterized by comprising the following steps:
acquiring wake data of a first wind turbine generator set, multipoint sampling data in a first wake space and first deterministic state parameter data in a certain time from a certain moment;
respectively constructing a first wind turbine generator wake data set, a multipoint sampling data set in a first wake space and a first deterministic state parameter data set according to the first wind turbine generator wake data, the multipoint sampling data in the first wake space and the first deterministic state parameter data;
acquiring multipoint sampling data and second deterministic state parameter data in a second wake space in a certain time from the next moment of the certain moment, and respectively constructing a multipoint sampling data set and a second deterministic state parameter data set in the second wake space;
respectively carrying out eigenvalue decomposition on the first wind turbine generator wake data set and the multipoint sampling data set in the first wake space to obtain a left eigenvector of the first wind turbine generator wake data set and a left eigenvector of the multipoint sampling data set in the first wake space;
performing dimension reduction mapping on the first wind turbine generator wake data set, the multipoint sampling data set in the first wake space and the multipoint sampling data set in the second wake space according to the left eigenvector of the first wind turbine generator wake data set and the left eigenvector of the multipoint sampling data set in the first wake space;
determining a state update matrix and a dimension reduction reconstruction matrix according to the mapping result, the first deterministic state parameter data set and the second deterministic state parameter data set;
determining a wake flow dimension reduction model of the wind turbine generator according to the state updating matrix and the dimension reduction reconstruction matrix;
and performing dimension-increasing mapping on the wind turbine generator wake dimension-decreasing model to obtain the estimation of the full-dimension wind turbine generator wake.
2. The method for modeling a dynamic wake of a wind turbine generator based on a measurable parameter of claim 1, wherein the first wind turbine generator wake data has a data volume greater than the data volumes of the multi-point sampled data and the first deterministic state parameter data in the first wake space.
3. The method for modeling dynamic wake of wind turbine generator based on measurable parameters of claim 1, wherein the sampling step size of the first wind turbine generator wake data, the multi-point sampling data in the first wake space, and the first deterministic state parameter data is less than one half of the wake bending time scale.
4. The method for modeling a dynamic wake of a wind turbine generator based on a measurable parameter of claim 1, wherein the first wind turbine generator wake data, the multi-point sampling data in the first wake space, and the first deterministic state parameter data have the same sampling step size and sampling interval, all starting at the same time and ending at the same time.
5. The method for modeling the dynamic wake of the wind turbine generator based on the measurable parameters of claim 1, wherein the SVD algorithm is adopted to decompose characteristic values of the first wind turbine generator wake data set and the multipoint sampling data set in the first wake space respectively.
6. The method for modeling the dynamic wake of the wind turbine generator based on the measurable parameters according to claim 1, wherein the dimension-reducing mapping specifically adopts the following formula:
wherein X is a wake data set of the first wind turbine generator, R is a mapping matrix of X, U x Is the left eigenvector of X, Σ x Is a diagonal matrix formed by characteristic values of X, V x Is the right eigenvector of X prb For a multi-point sampling dataset in a first wake space, R prb Is X prb Mapping matrix, U p Is X prb Is a left eigenvector, Σ p Is X prb Diagonal matrix formed by eigenvalues of V p Is X prb Is used for the right feature vector of (c),for a multi-point sampling dataset in the second wake space +.>Is->The superscript T denotes a transpose of the matrix.
7. The method for modeling dynamic wake of wind turbine generator based on measurable parameters of claim 1, wherein the state update matrix and the dimension reduction reconstruction matrix are represented by the following formula:
wherein F is a state update matrix, H is a dimension reduction reconstruction matrix, and is marked with a superscriptGeneralized inverse operation for Moore-Penrose,/->For a multipoint sampling dataset in the second wake space +.>Mapping matrix of->R is the second deterministic state parameter data set prb For multi-point sampling data set X in the first wake space prb Mapping matrix, X of (X) det And R is a mapping matrix of a wake data set X of the first wind turbine generator.
8. The method for modeling a dynamic wake of a wind turbine generator based on measurable parameters of claim 1, wherein the wind turbine generator wake dimension reduction model is as follows:
wherein ,zrc The linear mapping of the multi-point sample data z in the first wake space is reduced,for sampling data z at multiple points in the second wake space + Is a linear mapping reduced order data, z d For the first deterministic state parameter data, +.>For the second deterministic state parameter data, x rc Dimension reduction mapping for wake x of full-order wind turbine generator set with superscript ++>Representation of pair x rc Is a function of the estimate of (2).
9. The method for modeling dynamic wake of wind turbine generator based on measurable parameters as defined in claim 8, wherein z rc and xrc The following formula is satisfied:
wherein ,zrc The linear mapping of the multi-point sample data z in the first wake space is reduced,U p for the left eigenvector, x, of the multi-point sampled dataset in the first wake space rc For the dimension-reducing mapping of the wake flow of the full-order wind turbine generator, U x And x is the wake data of the first wind turbine generator, and the superscript T represents transposition of the matrix.
10. The method for modeling the dynamic wake of the wind turbine generator based on the measurable parameters according to claim 1, wherein the step of performing dimension-increasing mapping on the wind turbine generator wake dimension-decreasing model to obtain the estimate of the wake of the full-dimensional wind turbine generator specifically adopts the following formula:
wherein ,for the estimation of the wake of the full-dimensional wind turbine generator, < > a>Representation of pair x rc Is estimated, U x Is the left eigenvector of the wake dataset of the first wind turbine.
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| CN110728066A (en) * | 2019-10-18 | 2020-01-24 | 华北电力大学 | Wind power plant sector optimization method and system |
| CN112199908A (en) * | 2020-10-16 | 2021-01-08 | 中国船舶重工集团海装风电股份有限公司 | Wind turbine generator yaw control wake model correction method based on hydrodynamics |
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| CN110728066A (en) * | 2019-10-18 | 2020-01-24 | 华北电力大学 | Wind power plant sector optimization method and system |
| CN112199908A (en) * | 2020-10-16 | 2021-01-08 | 中国船舶重工集团海装风电股份有限公司 | Wind turbine generator yaw control wake model correction method based on hydrodynamics |
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| New method to capture traveling waves in flow passing a wind turbine;Le Clainche, S等;《WIND ENERGY》;922-931 * |
| 大跨度变截面连续钢箱梁桥涡激振动计算方法研究;秦浩 等;《中国博士学位论文全文数据库》(第8期);1-122 * |
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