CN113486580B - 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

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CN113486580B
CN113486580B CN202110749216.8A CN202110749216A CN113486580B CN 113486580 B CN113486580 B CN 113486580B CN 202110749216 A CN202110749216 A CN 202110749216A CN 113486580 B CN113486580 B CN 113486580B
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neural network
wind turbine
turbine generator
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material parameter
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CN113486580A (en
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韩旭
段书用
李雨乐
欧阳衡
刘晓明
贾冠龙
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Hebei University of Technology
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    • 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
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    • 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
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    • 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
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    • 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

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: obtaining 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: for example, gradient iterations, which tend 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:
obtaining 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 an elastic modulus, a shear modulus and a poisson 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 first and the second end of the pipe are connected with each other,
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 first and the second end of the pipe are connected with each other,
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, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the in-service wind turbine high-precision numerical modeling method when executing the computer program.
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 above-mentioned high-precision numerical modeling method for an active wind turbine.
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.
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Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
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, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
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: obtaining 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 to be 0.01 or 0.001; after convergence, acquiring an optimal weight and bias information;
s400: reversing the trained neural network to construct a reverse neural network;
s500: and inputting the node displacement samples in the test set 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 acquiring 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 acquired 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 number of neurons in each layer of the neural network,
Figure BDA0003143886850000051
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003143886850000052
the number of the first layer of 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 value obtained by summing squares of differences between an estimated value and a true value, where the estimated value is a value output by the neural network, and the actual value is a 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 reversed horn network are obtained by using an explicit formula, so that a reversed neural network can be obtained, as shown in fig. 4.
Preferably, the neural network is specifically:
Figure BDA0003143886850000061
Figure BDA0003143886850000062
wherein the content of the first and second substances,
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: obtaining 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 a material parameter sample; 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 using the display formula 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 in the test set 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, ROM 702, and 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 computer program performs the above-described functions defined in the system of the present application when 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 context of 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 contrast, in the present invention, 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: obtaining 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 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;
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 in the test set 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 (7)

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 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 the neural network to obtain optimal weight and bias information;
reversing the trained neural network to construct a reverse neural network;
inputting the node displacement samples in the test set into the reverse neural network, and outputting material parameters of the wind turbine generator;
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 FDA0003813675830000011
wherein the content of the first and second substances,
Figure FDA0003813675830000012
the number of the first layer of neurons is defined, and k is a set value;
defining a loss function, selecting an optimizer, and forming the neural network;
the neural network specifically comprises:
Figure FDA0003813675830000013
Figure FDA0003813675830000014
wherein the content of the first and second substances,
Figure FDA0003813675830000015
is an activation function;
Figure FDA0003813675830000016
representing the weight value of the ith neuron of the l layer connected with the jth neuron of the l-1 layer;
Figure FDA0003813675830000017
bias of ith neuron of l layer;
the reverse neural network specifically comprises:
Figure FDA0003813675830000018
wherein the content of the first and second substances,
Figure FDA0003813675830000019
is a weight matrix of the inverse neural network,
Figure FDA00038136758300000110
is a bias matrix of the inverse neural network.
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 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.
4. 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.
5. 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.
6. 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 5.
7. 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 high-precision numerical modeling method for an in-service wind turbine generator set according to any one of claims 1 to 5.
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