CN113486580A - High-precision numerical modeling method, server and storage medium for in-service wind turbine generator - Google Patents

High-precision numerical modeling method, server and storage medium for in-service wind turbine generator Download PDF

Info

Publication number
CN113486580A
CN113486580A CN202110749216.8A CN202110749216A CN113486580A CN 113486580 A CN113486580 A CN 113486580A CN 202110749216 A CN202110749216 A CN 202110749216A CN 113486580 A CN113486580 A CN 113486580A
Authority
CN
China
Prior art keywords
neural network
wind turbine
turbine generator
sample
material parameter
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.)
Granted
Application number
CN202110749216.8A
Other languages
Chinese (zh)
Other versions
CN113486580B (en
Inventor
韩旭
段书用
李雨乐
欧阳衡
刘晓明
贾冠龙
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hebei University of Technology
Original Assignee
Hebei University of Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Hebei University of Technology filed Critical Hebei University of Technology
Priority to CN202110749216.8A priority Critical patent/CN113486580B/en
Publication of CN113486580A publication Critical patent/CN113486580A/en
Application granted granted Critical
Publication of CN113486580B publication Critical patent/CN113486580B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/10—Numerical modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/06—Wind turbines or wind farms
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B10/00—Integration of renewable energy sources in buildings
    • Y02B10/30—Wind power

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • Artificial Intelligence (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computational Linguistics (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Computer Hardware Design (AREA)
  • Geometry (AREA)
  • Wind Motors (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application provides a high-precision numerical modeling method for an in-service wind turbine generator, a server and a storage medium, wherein the method comprises the following steps: acquiring sample data: acquiring a material parameter calibration value of the wind turbine generator, and sampling and processing in an error interval of the calibration value to obtain a material parameter sample; establishing a simulation model of the wind turbine generator, simulating and outputting a node displacement sample corresponding to each material parameter sample; dividing sample data into a training set and a test set; building a neural network, taking the material parameter samples in the training set as the input of the neural network, and taking the node displacement samples in the training set as the output of the neural network; training a neural network to obtain optimal weight and bias information; reversing the trained neural network to construct a reverse neural network; and inputting the node displacement samples concentrated in the test into a reverse neural network, and outputting the material parameters of the wind turbine generator. Through the steps, the material parameters of the wind turbine generator can be accurately and quickly determined.

Description

High-precision numerical modeling method, server and storage medium for in-service wind turbine generator
Technical Field
The disclosure relates generally to the technical field of wind turbines, and particularly relates to a high-precision numerical modeling method, a server and a storage medium for an in-service wind turbine.
Background
The wind turbine generator generates electricity through wind power, and has the advantages of reproducibility, good environmental benefit, short capital construction period, flexible installed scale and the like; in order to enable wind energy to be a reliable energy source, the influence of material parameters on a wind turbine needs to be considered;
after the wind turbine generator is in service for a period of time, material degradation occurs, and at the moment, the material parameter of the wind turbine generator is different from the material parameter value calibrated when the wind turbine generator leaves a factory; the material parameters are difficult to directly measure, the wind turbine generator needs to be disassembled and tested, the disassembling process is complex, and the normal use is influenced; and an indirect method is adopted: such as gradient iteration, which tends to fall into local optima for complex problems; for example, ant colony algorithm and genetic algorithm, which often require enormous workload, are inefficient.
Disclosure of Invention
In view of the above-mentioned defects or shortcomings in the prior art, it is desirable to provide a modeling method, a server and a storage medium for accurately and quickly determining a high-precision numerical value of an in-service wind turbine.
In a first aspect, the application provides a high-precision numerical modeling method for an in-service wind turbine generator, which comprises the following steps:
acquiring sample data: acquiring a material parameter calibration value of a wind turbine generator, and sampling and processing in an error interval of the calibration value to obtain a material parameter sample; establishing a simulation model of the wind turbine generator, simulating and outputting a node displacement sample corresponding to each material parameter sample; dividing the sample data into a training set and a testing set;
building a neural network, taking the material parameter sample in the training set as the input of the neural network, and taking the node displacement sample in the training set as the output of the neural network;
training a neural network to obtain optimal weight and bias information;
reversing the trained neural network to construct a reverse neural network;
and inputting the node displacement samples concentrated in the test into the reverse neural network, and outputting the material parameters of the wind turbine generator.
According to the technical scheme provided by the embodiment of the application, the material parameters comprise elastic modulus, shear modulus and poisson's ratio.
According to the technical scheme provided by the embodiment of the application, the number of the neurons in each layer of the neural network is set,
Figure BDA0003143886850000021
wherein the content of the first and second substances,
Figure BDA0003143886850000022
the number of the first layer neurons is defined, and k is a set value;
defining a loss function, and selecting an optimizer to form the neural network.
According to the technical scheme provided by the embodiment of the application, the neural network specifically comprises:
Figure BDA0003143886850000023
Figure BDA0003143886850000024
wherein the content of the first and second substances,
Figure BDA0003143886850000025
is an activation function;
Figure BDA0003143886850000026
representing the weight value of the ith neuron of the l layer connected with the jth neuron of the l-1 layer;
Figure BDA0003143886850000027
bias of ith neuron of l layer.
According to the technical scheme provided by the embodiment of the application, the reverse neural network specifically comprises:
Figure BDA0003143886850000028
wherein the content of the first and second substances,
Figure BDA0003143886850000029
is a weight matrix of the inverse neural network,
Figure BDA00031438868500000210
is a bias matrix of the inverse neural network.
According to the technical scheme provided by the embodiment of the application, the method further comprises the following steps after the material parameters of the wind turbine generator are output:
comparing the material parameter with the material parameter sample in the test set to obtain error data;
and analyzing the error data and verifying the accuracy of the algorithm.
According to the technical scheme provided by the embodiment of the application, the method for establishing the simulation model of the wind turbine generator specifically comprises the following steps:
drawing a three-dimensional part, correspondingly inputting the material parameter sample into the data of the three-dimensional part, and assembling all the three-dimensional parts to form the simulation model.
According to the technical scheme provided by the embodiment of the application, the method for simulating and outputting the node displacement sample corresponding to each material parameter sample specifically comprises the following steps:
performing dynamic analysis and static analysis on the simulation model;
determining a load and a boundary condition;
and drawing a grid and outputting the node displacement sample.
In a second aspect, the present application provides a server, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the in-service wind turbine high-precision numerical modeling method as described above.
In a third aspect, the present application provides a computer-readable storage medium having a computer program, where the computer program is executed by a processor to implement the steps of the in-service wind turbine high-precision numerical modeling method.
The beneficial effect of this application lies in: in the process of obtaining sample data, a material parameter sample is obtained by sampling and processing in an error interval of a calibration value, and a node displacement sample is obtained through a simulation model; the precision of the algorithm is improved by adopting sampling processing and simulation processing; constructing and training a neural network by taking the material parameter sample as input and the node displacement sample as output, so that optimal weight and bias information can be obtained; reversing the trained neural network to obtain a reverse neural network; at the moment, the node displacement samples concentrated in the test are input into the reverse neural network, and then the material parameters of the wind turbine generator can be output.
According to the method, the material parameters of the wind turbine generator are determined, and the optimal weight and the bias information can be obtained through sampling processing, simulation processing and neural network construction, so that the calculation accuracy is improved; compared with the calculation inverse method such as the genetic algorithm, the ant colony algorithm and the like, the method has the advantages of small calculation amount, high operation speed and capability of improving the calculation efficiency of the material parameters of the wind turbine generator.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of a high-precision numerical modeling method for an in-service wind turbine generator set provided by the application;
fig. 2 is a flowchart of algorithm verification after step S500 shown in fig. 1.
FIG. 3 is a schematic structural diagram of the neural network in step S200 shown in FIG. 1;
FIG. 4 is a schematic structural diagram of the inverse neural network in step S400 shown in FIG. 1;
fig. 5 is a server according to the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Example 1
Please refer to fig. 1, which is a flowchart of a high-precision numerical modeling method for an in-service wind turbine generator provided by the present application; the method comprises the following steps:
s100: acquiring sample data: acquiring a material parameter calibration value of a wind turbine generator, and sampling and processing in an error interval of the calibration value to obtain a material parameter sample; establishing a simulation model of the wind turbine generator, simulating and outputting a node displacement sample corresponding to each material parameter sample; dividing the sample data into a training set and a testing set;
specifically, a method for sampling in an error interval of the calibration value adopts Latin hypercube sampling, and the error interval is 90% -110% of the calibration value; specifically, after sampling, normalization processing is carried out on the data to obtain the material parameter sample.
Preferably, the material parameter samples include an elastic modulus sample, a shear modulus sample, and a poisson's ratio sample.
S200: building a neural network, taking the material parameter sample in the training set as the input of the neural network, and taking the node displacement sample in the training set as the output of the neural network;
s300: training a neural network to obtain optimal weight and bias information;
specifically, the method for training the neural network and obtaining the optimal weight and bias information specifically comprises the following steps: selecting an initial learning rate of the neural network of 0.01 or 0.001; after convergence, acquiring optimal weight and bias information;
s400: reversing the trained neural network to construct a reverse neural network;
s500: and inputting the node displacement samples concentrated in the test into the reverse neural network, and outputting the material parameters of the wind turbine generator.
The working principle is as follows: in the process of obtaining sample data, a material parameter sample is obtained by sampling and processing in an error interval of a calibration value, and a node displacement sample is obtained through a simulation model; the precision of the algorithm is improved by adopting sampling processing and simulation processing;
constructing and training a neural network by taking the material parameter sample as input and the node displacement sample as output, so that optimal weight and bias information can be obtained; reversing the trained neural network to obtain a reverse neural network; at the moment, the node displacement samples concentrated in the test are input into the reverse neural network, and then the material parameters of the wind turbine generator can be output.
According to the method, the material parameters of the wind turbine generator are determined, and the optimal weight and the bias information can be obtained through sampling processing, simulation processing and neural network construction, so that the calculation accuracy is improved; compared with the calculation inverse method such as the genetic algorithm, the ant colony algorithm and the like, the method has the advantages of small calculation amount, high operation speed and capability of improving the calculation efficiency of the material parameters of the wind turbine generator.
Preferably, the material parameters include elastic modulus, shear modulus, and poisson's ratio.
Preferably, the method for constructing the neural network specifically comprises the following steps:
setting the neural networkThe number of neurons in each layer is,
Figure BDA0003143886850000051
wherein the content of the first and second substances,
Figure BDA0003143886850000052
the number of the first layer neurons is defined, and k is a set value;
defining a loss function, and selecting an optimizer to form the neural network.
The specific loss can be the mean square error:
Figure BDA0003143886850000053
where mse (mean Square error) is an average of the sum of squares of the difference between the estimated value, which is the value output by the neural network, and the actual value, which is the value output by the training samples.
Specifically, the optimizer can adopt Adam;
specifically, k is a set constant. As shown by the formula and the figure 3, the neural network is a forward horn network, namely the number of neurons in each layer of the neural network is in an arithmetic progression, so that the precision of the neural network is ensured.
Since the weights and biases of the trained forward horn network reflect the correlation between variables in the inverse problem, the trained forward horn network is reversed, and the weights and biases of the reverse horn network are obtained by using an explicit formula, so that a reverse neural network can be obtained, as shown in fig. 4.
Preferably, the neural network is specifically:
Figure BDA0003143886850000061
Figure BDA0003143886850000062
wherein，
Figure BDA0003143886850000063
Is an activation function;
Figure BDA0003143886850000064
representing the weight value of the ith neuron of the l layer connected with the jth neuron of the l-1 layer;
Figure BDA0003143886850000065
bias of ith neuron of l layer.
Preferably, the inverse neural network specifically includes:
Figure BDA0003143886850000066
wherein the content of the first and second substances,
Figure BDA0003143886850000067
is a weight matrix of the inverse neural network,
Figure BDA0003143886850000068
is a bias matrix of the inverse neural network.
For convenience of explanation of the technical solutions provided in the present application, material parameters (elastic modulus, shear modulus, and poisson's ratio) are taken as examples.
S100: acquiring sample data: respectively obtaining calibration values of the elastic modulus, the shear modulus and the Poisson ratio, respectively sampling in an error interval (90% -110% of the calibration value) and carrying out normalization processing to obtain material parameter samples; establishing a simulation model of the wind turbine generator, simulating and outputting a node displacement sample corresponding to the elastic modulus, the shear modulus and the Poisson ratio; and dividing the sample data into a training set and a testing set.
S200: building a neural network, taking the material parameter samples in the training set as the input of the neural network, and taking the node displacement samples in the training set as the output of the neural network:
Figure BDA0003143886850000069
the matrix form is:
Figure BDA00031438868500000610
three material parameters are input into the neural network, i.e. the values of all neurons of the input layer (first layer) can be expressed as
Figure BDA00031438868500000611
Its value with the ith neuron of the second layer
Figure BDA00031438868500000612
Can be expressed as:
Figure BDA00031438868500000613
the value of each neuron of the second layer can be obtained through the formula, and the values of each neuron of the output layer, namely the values of node displacement corresponding to the elastic modulus, the shear modulus and the Poisson ratio, can be obtained by analogy.
S300: training a neural network to obtain optimal weight and bias information;
s400: reversing the trained neural network to construct a reverse neural network;
reversing the trained neural network:
Figure BDA0003143886850000071
the above formula is expanded in parentheses and expressed as:
Figure BDA0003143886850000072
the weight matrix of the reverse neural network can be obtained by the above formula
Figure BDA0003143886850000073
And a bias matrix
Figure BDA0003143886850000074
Thereby obtaining the reverse neural network.
S500: and inputting the node displacement samples concentrated in the test into the reverse neural network, and outputting material parameters (elastic modulus, shear modulus and Poisson ratio) of the wind turbine generator.
Preferably, the method further comprises the following steps after the material parameters of the wind turbine generator are output:
s601: comparing the output material parameters with the material parameter samples in the test set to obtain error data;
s602: and analyzing the error data and verifying the accuracy of the algorithm.
Preferably, the method for establishing the simulation model of the wind turbine generator specifically comprises the following steps:
drawing a three-dimensional part, correspondingly inputting the material parameter sample into the data of the three-dimensional part, and assembling all the three-dimensional parts to form the simulation model.
Preferably, the method for simulating and outputting the node displacement sample corresponding to each material parameter sample specifically comprises:
performing dynamic analysis and static analysis on the simulation model;
determining a load and a boundary condition;
and drawing a grid and outputting the node displacement sample.
Example 2
Referring to fig. 5, a schematic block diagram of a computer system 700 of a server or a server provided in the present application includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the in-service wind turbine high-precision numerical modeling method as described above.
As shown in fig. 5, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section into a Random Access Memory (RAM) 703. In the RAM703, various programs and data necessary for system operation are also stored. The CPU 701, the ROM 702, and the RAM703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. The drives are also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to an embodiment of the invention, the process described above with reference to the flowchart of fig. 1 may be implemented as a computer software program. For example, embodiment 1 of the invention comprises a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. The above-described functions defined in the system of the present application are executed when the computer program is executed by the Central Processing Unit (CPU) 601.
Example 3
The present application further provides a computer-readable storage medium having a computer program, which when executed by a processor, implements the steps of the modeling method for determining a high accuracy value of an in-service wind turbine generator as described above.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present invention may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves. The described units or modules may also be provided in a processor, and may be described as: a processor comprises an acquisition module, an initialization module and a data processing module.
The names of these units or modules do not in some cases constitute a limitation on the units or modules themselves, and for example, an acquisition module may also be described as an "acquisition module for acquiring sample data".
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the high-precision numerical modeling method for the in-service wind turbine generator set in the embodiment.
For example, the electronic device may implement the following as shown in fig. 1:
s100: acquiring sample data: acquiring a material parameter calibration value of a wind turbine generator, and sampling and processing in an error interval of the calibration value to obtain a material parameter sample; establishing a simulation model of the wind turbine generator, simulating and outputting a node displacement sample corresponding to each material parameter sample; dividing the sample data into a training set and a testing set;
s200: building a neural network, taking the material parameter sample in the training set as the input of the neural network, and taking the node displacement sample in the training set as the output of the neural network;
s300: training a neural network to obtain optimal weight and bias information;
s400: reversing the trained neural network to construct a reverse neural network;
s500: and inputting the node displacement samples concentrated in the test into the reverse neural network, and outputting the material parameters of the wind turbine generator.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention as referred to in the present application is not limited to the embodiments with a specific combination of the above-mentioned features, but also covers other embodiments with any combination of the above-mentioned features or their equivalents without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims (10)

1. A high-precision numerical modeling method for an in-service wind turbine generator is characterized by comprising the following steps: the method comprises the following steps:
acquiring sample data: acquiring a material parameter calibration value of a wind turbine generator, and sampling and processing in an error interval of the calibration value to obtain a material parameter sample; establishing a simulation model of the wind turbine generator, simulating and outputting a node displacement sample corresponding to each material parameter sample; dividing the sample data into a training set and a testing set;
building a neural network, taking the material parameter sample in the training set as the input of the neural network, and taking the node displacement sample in the training set as the output of the neural network;
training the neural network to obtain optimal weight and bias information;
reversing the trained neural network to construct a reverse neural network;
and inputting the node displacement samples concentrated in the test into the reverse neural network, and outputting the material parameters of the wind turbine generator.
2. The in-service wind turbine generator high-precision numerical modeling method according to claim 1, characterized in that: the material parameters include modulus of elasticity, shear modulus, and poisson's ratio.
3. The in-service wind turbine generator high-precision numerical modeling method according to claim 1, characterized in that: the method for building the neural network specifically comprises the following steps:
setting the number of neurons in each layer of the neural network,
Figure FDA0003143886840000011
wherein the content of the first and second substances,
Figure FDA0003143886840000012
the number of the first layer neurons is defined, and k is a set value;
defining a loss function, and selecting an optimizer to form the neural network.
4. The in-service wind turbine generator high-precision numerical modeling method according to claim 3, characterized in that: the neural network specifically comprises:
Figure FDA0003143886840000013
Figure FDA0003143886840000014
wherein the content of the first and second substances,
Figure FDA0003143886840000015
is an activation function;
Figure FDA0003143886840000016
representing the weight value of the ith neuron of the l layer connected with the jth neuron of the l-1 layer;
Figure FDA0003143886840000017
bias of ith neuron of l layer.
5. The in-service wind turbine generator high-precision numerical modeling method according to claim 4, characterized in that: the reverse neural network specifically comprises:
Figure FDA0003143886840000021
wherein the content of the first and second substances,
Figure FDA0003143886840000022
is a weight matrix of the inverse neural network,
Figure FDA0003143886840000023
is a bias matrix of the inverse neural network.
6. The in-service wind turbine generator high-precision numerical modeling method according to claim 1, characterized in that: the method also comprises the following steps after the material parameters of the wind turbine generator are output:
comparing the material parameter with the material parameter sample in the test set to obtain error data;
and analyzing the error data and verifying the accuracy of the algorithm.
7. The in-service wind turbine generator high-precision numerical modeling method according to claim 1, characterized in that: the method for establishing the simulation model of the wind turbine generator specifically comprises the following steps:
drawing a three-dimensional part, correspondingly inputting the material parameter sample into the data of the three-dimensional part, and assembling all the three-dimensional parts to form the simulation model.
8. The in-service wind turbine generator high-precision numerical modeling method according to claim 1, characterized in that: the method for simulating and outputting the node displacement sample corresponding to each material parameter sample specifically comprises the following steps:
performing dynamic analysis and static analysis on the simulation model;
determining a load and a boundary condition;
and drawing a grid and outputting the node displacement sample.
9. A server comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: the processor, when executing the computer program, implements the steps of the high-precision numerical modeling method for an active wind turbine according to any one of claims 1 to 8.
10. A computer-readable storage medium having a computer program, the computer-readable storage medium characterized by: the computer program, when being executed by a processor, implements the steps of the method for high-precision numerical modeling of an active wind turbine according to any of claims 1 to 8.
CN202110749216.8A 2021-07-01 2021-07-01 High-precision numerical modeling method, server and storage medium for in-service wind turbine generator Active CN113486580B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110749216.8A CN113486580B (en) 2021-07-01 2021-07-01 High-precision numerical modeling method, server and storage medium for in-service wind turbine generator

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110749216.8A CN113486580B (en) 2021-07-01 2021-07-01 High-precision numerical modeling method, server and storage medium for in-service wind turbine generator

Publications (2)

Publication Number Publication Date
CN113486580A true CN113486580A (en) 2021-10-08
CN113486580B CN113486580B (en) 2022-11-01

Family

ID=77939449

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110749216.8A Active CN113486580B (en) 2021-07-01 2021-07-01 High-precision numerical modeling method, server and storage medium for in-service wind turbine generator

Country Status (1)

Country Link
CN (1) CN113486580B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11480478B2 (en) * 2016-11-30 2022-10-25 Cummins Emission Solutions Inc. Temperature estimation for sensor
WO2023103927A1 (en) * 2021-12-08 2023-06-15 中兴通讯股份有限公司 Information transmission method, communication node, and storage medium

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106560848A (en) * 2016-10-09 2017-04-12 辽宁工程技术大学 Novel neural network model for simulating biological bidirectional cognition capability, and training method
CN111523713A (en) * 2020-04-14 2020-08-11 中国地质大学（北京） Method and device for predicting residual oil saturation distribution in oil field
CN112101530A (en) * 2020-11-10 2020-12-18 南京集成电路产业服务中心有限公司 Neural network training method, device, equipment and storage medium

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106560848A (en) * 2016-10-09 2017-04-12 辽宁工程技术大学 Novel neural network model for simulating biological bidirectional cognition capability, and training method
CN111523713A (en) * 2020-04-14 2020-08-11 中国地质大学（北京） Method and device for predicting residual oil saturation distribution in oil field
CN112101530A (en) * 2020-11-10 2020-12-18 南京集成电路产业服务中心有限公司 Neural network training method, device, equipment and storage medium

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
刘威 等: "互学习神经网络训练方法研究", 《计算机学报》 *
段书用 等: "一种新的复合材料参数精确逆向识别方法", 《2018年全国固体力学学术会议摘要集（下）》 *
练继建 等: "基于BP神经网络的李家峡拱坝材料参数反演", 《水力发电学报》 *
陈金富 等: "利用时空相关性的多位置多步风速预测模型", 《中国电机工程学报》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11480478B2 (en) * 2016-11-30 2022-10-25 Cummins Emission Solutions Inc. Temperature estimation for sensor
WO2023103927A1 (en) * 2021-12-08 2023-06-15 中兴通讯股份有限公司 Information transmission method, communication node, and storage medium

Also Published As

Publication number Publication date
CN113486580B (en) 2022-11-01

Similar Documents

Publication Publication Date Title
CN113486580B (en) High-precision numerical modeling method, server and storage medium for in-service wind turbine generator
Giannakoglou et al. Aerodynamic shape design using evolutionary algorithms and new gradient-assisted metamodels
Abdeljaber et al. Nonparametric structural damage detection algorithm for ambient vibration response: utilizing artificial neural networks and self-organizing maps
CN108594660B (en) Working modal parameter identification method and system of time invariant structure
CN116150897A (en) Machine tool spindle performance evaluation method and system based on digital twin
CN116450486B (en) Modeling method, device, equipment and medium for nodes in multi-element heterogeneous computing system
CN114583767B (en) Data-driven wind power plant frequency modulation response characteristic modeling method and system
CN115270239A (en) Bridge reliability prediction method based on dynamic characteristics and intelligent algorithm response surface method
CN113448821B (en) Method and device for identifying engineering defects
CN109187898B (en) Soft measurement method and device for ammonia nitrogen content of water in aquaculture environment
Zong et al. Embedded software fault prediction based on back propagation neural network
CN112949000B (en) Component residual stress inversion method based on convolutional neural network model
CN116227045A (en) Local stress strain field construction method and system for structural test piece
Chen et al. Control-oriented low-order approximation and reconstruction of yaw-excited wind turbine wake dynamics
CN115618709A (en) Method for establishing digital twinning model of textile surface quality under periodic frictional wear
Wang et al. Optimal sensor placement for digital twin based on mutual information and correlation with multi-fidelity data
Li et al. A comprehensive framework for model validation and reliability assessment of container crane structures
CN111241614B (en) Engineering structure load inversion method based on condition generation confrontation network model
CN111241725B (en) Structure response reconstruction method for generating countermeasure network based on conditions
US20230062268A1 (en) System and method for fatigue response prediction
CN111210409A (en) Condition-based generation confrontation network structure damage identification method
CN112818455A (en) Bridge structure response monitoring method and system
CN106709167A (en) Similarity theory-based assembling validity assessment method
CN111695989A (en) Modeling method and platform of wind-control credit model
Zhang et al. Towards ML-based Performance Estimation of Embedded Software: A RISC-V Case Study

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