CN113821979A - Wind turbine generator fatigue damage and service life assessment method, computer equipment and storage medium - Google Patents

Wind turbine generator fatigue damage and service life assessment method, computer equipment and storage medium Download PDF

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CN113821979A
CN113821979A CN202111155954.6A CN202111155954A CN113821979A CN 113821979 A CN113821979 A CN 113821979A CN 202111155954 A CN202111155954 A CN 202111155954A CN 113821979 A CN113821979 A CN 113821979A
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张林伟
蔡安民
林伟荣
许扬
李媛
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Huaneng Clean Energy Research Institute
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Abstract

The invention relates to the field of safety calculation and evaluation of wind driven generators and discloses a fatigue damage and service life evaluation method for a wind turbine generator, which comprises the steps of measuring load and synchronous wind resource parameters of the generator through test equipment, evaluating the fatigue damage in a generator test period based on actually measured load data and wind resource parameters, establishing a mapping relation between key variables in SCADA operation data of the generator in the test period and the fatigue damage based on the test data by using a lightGBM machine learning method, and further evaluating the fatigue damage and the residual service life of the generator in service under historical SCADA data; after testing and evaluating a large number of in-service units, establishing a mapping relation between SCADA operation data evaluation fatigue based on a training model and fatigue damage evaluation based on a Palmgren-Miner criterion, and correcting the fatigue damage and service life evaluation of the computer units of the rest same type by a Palmgren-Miner criterion method.

Description

Wind turbine generator fatigue damage and service life assessment method, computer equipment and storage medium
Technical Field
The invention relates to the field of safety calculation and evaluation of wind driven generators, in particular to a fatigue damage and service life evaluation method of a wind turbine generator, computer equipment and a storage medium.
Background
In the upgrading and transforming process of the stock unit, the upgrading or the replacement of key parts is necessarily involved, the design life of the whole wind turbine generator is generally 20 years, different parts have different life spans, particularly, the service life of a support structure, a fastener and the like can be up to 30 years or even 50 years. Due to the lack and insufficiency of historical operating data, the evaluation of the service life of the components which are still used after the unit reaches the service life and can be used for a plurality of years is a problem to be solved at present.
In the process of wind turbine load calculation and safety evaluation, fatigue load is one of key indexes affecting the safety and service life of the wind turbine. The fatigue load evaluation method commonly used in the design stage of the wind turbine generator at present is based on Palmgren-Miner criterion, the equivalent fatigue load of each key section of the wind turbine generator is calculated and evaluated, the equivalent cycle number during calculation generally adopts 1E7, the value is only suitable for low-strength carbon steel, alloy steel, nodular cast iron, blade composite materials and the like in the wind turbine generator, and the method is not suitable for welding seams, high-strength steel such as bolts and the like. In addition, the fatigue load is positively correlated with key parameters such as turbulence intensity, wind frequency distribution, wind shear and the like in wind resource parameters, and wind resource parameter data of a certain year are adopted in the design stage of the wind turbine generator set for service life evaluation. Therefore, it is necessary to determine input parameters for calculating fatigue loads and a method for evaluating the rationality of the fatigue loads by using the unit operation characteristics and the wind resource parameter characteristics.
In addition, the current operating data of the unit only contains variables which basically have no direct relation with load and service life evaluation of the wind turbine generator, such as wind speed, pitch angle and power, and cannot be directly used for evaluation of historical fatigue damage of unit parts and service life management.
Disclosure of Invention
The invention aims to provide a fatigue damage and service life evaluation method for a wind turbine generator, which solves the problem that the operation data of the existing generator cannot be directly used for the evaluation of the historical fatigue damage and the service life extension management of the components of the generator.
The invention is realized by the following technical scheme:
a fatigue damage and service life evaluation method for a wind turbine generator comprises the following steps:
s1, measuring the load of the unit and the key wind resource parameters in the same period through test equipment to obtain load test data and anemometer tower anemometer data; obtaining SCADA operation data of the synchronous unit;
s2, fatigue damage of each test position in each SCADA operation data sampling period in the test period is calculated by the computer set through wind measurement data and load test data of the wind measuring tower;
s3, establishing a nonlinear mapping relation between key variables in the unit SCADA operation data in the test period and the fatigue damage of each test position in S2, and further evaluating the fatigue damage and the residual life of the in-service unit under the historical SCADA data;
s4, carrying out operations from S1 to S3 on a certain number of in-service units, and carrying out correlation nonlinear fitting on a fatigue evaluation result data set based on a training model and a fatigue damage result data set calculated based on Palmgren-Miner;
training, verifying and testing the model by adopting a lightGBM method, judging the accuracy of the model by taking regression evaluation indexes as evaluation standards, and correcting the fatigue damage and service life evaluation results of the rest of same type of units by using a Palmgren-Miner criterion to calculate the unit so as to accurately evaluate the service life and prolong the service life.
Further, in S1, installing load testing equipment at each key position of the testing unit to obtain load testing data;
and (3) installing wind measuring equipment near the test unit, measuring key wind resource parameters, and obtaining wind measuring data of the wind measuring tower within a test period of at least more than 3 months.
Further, in S1, the key wind resource parameters include wind speed, wind direction, ambient temperature, air pressure, wind shear, and turbulence intensity.
Further, in S2, when the equivalent fatigue load is calculated by a Palmgren _ Miner criterion method, different equivalent cycle times N are adopted for different materials in the wind turbine generator; the method specifically comprises the following steps:
if the parts are made of composite materials, low-strength steel, alloy steel or nodular cast iron, counting by using equivalent cycle number 1E7, and then N is 1E 7;
if the part is made of a material which does not adopt an infinite number of cycles, the equivalent number of cycles is counted according to the actual number of cycles N1, and N is N1.
Further, the calculation of the actual number of cycles N1 refers to the actual wind frequency of the unit representative of a certain wind measurement year, and is typically defined as representing the average level of the wind measurement data of years, and the effectiveness of the wind measurement data is more than 90%.
Further, the calculation formula of the actual cycle number is as follows:
Figure BDA0003288397380000031
wherein i represents different wind speeds; omegaave_iRepresenting the average rotational speed of the rotor; hi is the annual number of hours of occurrence at each wind speed calculated through actual wind frequency or fitted Weibull parameters; t islifeThe design life of a certain in-service unit.
Further, S3 includes the steps of:
s31, randomly disordering the fatigue damage of each test position in the unit SCADA data sampling period and the sequence of the variable data set in the SCADA operation data during the test period;
s32, dividing the disordered data into a training set, a verification set and a test set according to the ratio of 8:1: 1; training the lightGBM model by using a training set to obtain a prediction model based on SCADA data;
the training set is used to determine a training model and to use variables within the SCADA data; the verification set is used for determining the generalization ability of the model and adjusting the hyper-parameters of the model; the test set is used for controlling model overfitting;
s33, determining the weight coefficient and the hyper-parameter of the unit operation data variable in the prediction model through iterative training of the prediction model;
s34, substituting the test set into the trained model to obtain a fatigue damage result Ss based on the SCADA operation data, comparing the fatigue damage results St of each test position obtained in S2, and evaluating regression evaluation indexes MSE, RMSE, MAE and R-Squared of the model;
s35, calculating the equivalent fatigue damage SN _ N of each part of the in-service unit after the part has operated for N _ N years by using a rain flow counting method based on the actual cycle number N1 and the design life Tlife, and calculating the equivalent fatigue damage S of each part of the in-service unit in the design life Tlife by using a Palmgren-Miner ruleTlife；
All SCADA operation data before the unit test are substituted into the trained model to obtain the actual fatigue damage Sp；
By Sp+StComparison STlifeEvaluating the residual life of each part of the unit;
comparison SpEquivalent fatigue damage S calculated based on Palmgren-MinerN_nThe difference between them.
The invention discloses computer equipment which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the fatigue damage and service life evaluation method of a wind turbine generator.
The invention discloses a computer readable storage medium, which stores a computer program, wherein the computer program realizes the steps of the wind turbine fatigue damage and service life evaluation method when being executed by a processor.
Compared with the prior art, the invention has the following beneficial technical effects:
the invention provides a fatigue damage and service life evaluation method for a wind turbine generator, which comprises the steps of measuring load and synchronous wind resource parameters of the wind turbine generator through test equipment, evaluating the fatigue damage in a wind turbine generator test period based on actually measured load data and the wind resource parameters, establishing a nonlinear mapping relation between key variables in SCADA operation data of the wind turbine generator in the test period and the fatigue damage based on the test data by using a lightGBM machine learning method, and further evaluating the fatigue damage and the residual service life of the wind turbine generator under the historical SCADA data of the active wind turbine generator; after testing and evaluating a large number of in-service units, establishing a mapping relation between SCADA operation data evaluation fatigue based on a training model and fatigue damage evaluation based on a Palmgren-Miner criterion, and correcting the fatigue damage and service life evaluation of the computer unit by a Palmgren-Miner criterion method when no test data exists. The relation between the historical operation data of the unit and the damage of the parts is established, and the deviation between the actual damage and the equivalent fatigue damage is evaluated, so that the service life of the unit can be better evaluated, and the problem of prolonging the service life of the unit can be solved.
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FIG. 1 is a flow chart of a method for evaluating fatigue damage and life of a wind turbine generator according to the present invention.
Detailed Description
The present invention will now be described in further detail with reference to specific examples, which are intended to be illustrative, but not limiting, of the invention.
S1, carrying out load and actual wind resource parameter testing on the unit, wherein the unit is called a testing unit under the unit, and the method specifically comprises the following steps: special load testing equipment is pasted or installed at each key position of the testing unit, such as a blade root, the center of a hub, a yaw bearing and a tower bottom, the special load testing equipment is not limited to a strain gauge, and the same pasting or installing position can be at any position to obtain load testing data;
and (3) installing a wind measuring tower or other wind measuring equipment such as a laser radar near the test unit according to the IEC61400-13 standard requirements, measuring key wind resource parameters such as wind speed, wind direction, ambient temperature, air pressure, wind shear, turbulence intensity and the like, and obtaining wind measuring data of the wind measuring tower within a test period of at least more than 3 months.
And S2, through the anemometer tower anemometry data and the load test data, the computer set is based on fatigue damage in each SCADA sampling period of each test position of the load test data during the test.
2.1. Calculating the fatigue load working condition of the whole machine of a certain in-service unit, such as the design life Tlife of 20 years
2.2. When the equivalent fatigue load statistics is carried out on the load calculation result, different equivalent cycle times N are adopted for different materials in the wind turbine generator, and if parts made of composite materials, low-strength steel, alloy steel and nodular cast iron are used, the equivalent cycle times N of 1E7 can be used for statistics;
for high-strength steel such as bolts, welding seams and the like which are not suitable for materials with infinite cycle times, the equivalent cycle times should be counted according to the actual cycle times N which is N1.
The actual circulation times N1 should be calculated by referring to the wind frequency of all the years of the operated units or the wind resource effectiveness of a representative year of a certain year to reach over 90 percent of the actual wind frequency of the data.
The actual cycle number is calculated by the formula:
Figure BDA0003288397380000061
ωave_irepresenting the average rotating speed of the wind wheel under the conditions of various wind speeds, turbulence intensity, wind shear and the like, wherein i represents different wind speeds; the annual number of hours of occurrence Hi at each wind speed is calculated from the actual wind frequency or fitted Weibull parameters.
S3, establishing a nonlinear mapping relation between variables such as wind speed, pitch angle, rotating speed, power, tower displacement and acceleration, generator torque and the like in the SCADA operation data of the unit during the test and fatigue damage S1 based on load test data through a histogram-based LightGBM machine learning method;
3.1, randomly disordering the fatigue damage of each test position in the unit SCADA data sampling period and the data set sequence of variables in the SCADA operation data such as wind speed, pitch angle, rotating speed, power, tower displacement and acceleration, generator torque and the like during the test;
3.2, dividing the disordered data into a training set, a verification set and a test set according to the ratio of 8:1:1, and training the lightGBM model by using the training set to obtain a prediction model based on SCADA data; the training set is used for training the model and using variables in the SCADA data; the verification set is used for determining the generalization ability of the model and adjusting the hyper-parameters of the model; the test set is used to control model overfitting. Implementing a lightGBM model algorithm by using Python or R language;
3.3, determining the weight coefficient and the hyperparameter of the unit operation data variable in the model through iterative training of the prediction model;
and 3.4, substituting the test set into the trained model to obtain a fatigue damage result Ss based on the SCADA operation data, and evaluating regression evaluation indexes MSE, RMSE, MAE and R-Squared of the model by comparing the fatigue damage result St based on the load test data.
3.5, based on the actual cycle number N1 and the design life Tlife, calculating the equivalent fatigue damage SN _ N of each part of the active unit after the part has operated for N _ N years and the equivalent fatigue damage STlife in the design life Tlife by using a rain flow counting method and a Palmgren-Miner rule;
all SCADA operation data before the unit test are substituted into the trained model to obtain fatigue damage Sp;
comparing the Sp + St with the STlife, and evaluating the residual life of each part of the unit;
comparing the difference between Sp and SN _ n calculated based on Palmgren-Miner;
if the deviation is not large, the Palmgren-Miner method can be used for evaluating the actual fatigue damage of the unit and the first screen evaluation. If the deviation is large, more units need to be tested, the Sp calculation process is repeated, and then when a certain data volume is accumulated, the corresponding relation between Sp and Palmgren-Miner can be found out, a corrected model or coefficient is obtained, and the result obtained by the Palmgren-Miner is corrected.
S4, carrying out operations from S1 to S3 on a certain number of in-service units, carrying out correlation nonlinear fitting on a fatigue evaluation result Sp data set based on a training model and a fatigue damage result SN _ n data set based on Palmgren-Miner calculation, training, verifying and testing the model by adopting a lightGBM method, judging the accuracy of the model by taking regression evaluation indexes as evaluation standards, and correcting the results of fatigue damage calculation and service life evaluation of the units calculated by using Palmgren-Miner rules on the rest of the same type of units so as to accurately carry out service life evaluation and service life extension.
The fatigue damage and life evaluation method for the wind turbine generator can adopt the forms of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The method for evaluating the fatigue damage and the service life of the wind turbine generator can be stored in a computer readable storage medium if the method is realized in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. Computer-readable storage media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice. The computer storage medium may be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO), etc.), optical memory (e.g., CD, DVD, BD, HVD, etc.), and semiconductor memory (e.g., ROM, EPROM, EEPROM, nonvolatile memory (NANDFLASH), Solid State Disk (SSD)), etc.
In an exemplary embodiment, a computer device is also provided, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the wind turbine fatigue damage and life assessment method when executing the computer program. The processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc.

Claims (9)

1. A fatigue damage and service life evaluation method for a wind turbine generator is characterized by comprising the following steps:
s1, measuring the load of the unit and the key wind resource parameters in the same period through test equipment to obtain load test data and anemometer tower anemometer data; obtaining SCADA operation data of the synchronous unit;
s2, fatigue damage of each test position in each SCADA operation data sampling period in the test period is calculated by the computer set through wind measurement data and load test data of the wind measuring tower;
s3, establishing a nonlinear mapping relation between key variables in the unit SCADA operation data in the test period and the fatigue damage of each test position in S2, and further evaluating the fatigue damage and the residual life of the in-service unit under the historical SCADA data;
s4, carrying out operations from S1 to S3 on a certain number of in-service units, and carrying out correlation nonlinear fitting on a fatigue evaluation result data set based on a training model and a fatigue damage result data set calculated based on Palmgren-Miner;
training, verifying and testing the model by adopting a lightGBM method, judging the accuracy of the model by taking regression evaluation indexes as evaluation standards, and correcting the fatigue damage and service life evaluation results of the rest of same type of units by using a Palmgren-Miner criterion to calculate the unit so as to accurately evaluate the service life and prolong the service life.
2. The method for evaluating the fatigue damage and the service life of the wind turbine generator set according to claim 1, wherein in S1, load testing equipment is installed at each key position of the wind turbine generator set to obtain load testing data;
and (3) installing wind measuring equipment near the test unit, measuring key wind resource parameters, and obtaining wind measuring data of the wind measuring tower within a test period of at least more than 3 months.
3. The method of claim 1, wherein in step S1, the key wind resource parameters include wind speed, wind direction, ambient temperature, air pressure, wind shear, and turbulence intensity.
4. The method for evaluating the fatigue damage and the life of the wind turbine generator as claimed in claim 1, wherein in S2, when the equivalent fatigue load is calculated by a Palmgren _ Miner criterion method, different equivalent cycle times N are adopted for different materials in the wind turbine generator; the method specifically comprises the following steps:
if the parts are made of composite materials, low-strength steel, alloy steel or nodular cast iron, counting by using equivalent cycle number 1E7, and then N is 1E 7;
if the part is made of a material which does not adopt an infinite number of cycles, the equivalent number of cycles is counted according to the actual number of cycles N1, and N is N1.
5. The method for evaluating the fatigue damage and the service life of the wind turbine generator as claimed in claim 4, wherein the calculation of the actual cycle number N1 refers to the actual wind frequency of the reference unit representing a certain wind measurement year, and the representative definition is that the average level of the wind measurement data of a plurality of years is represented, and the effectiveness of the wind measurement data is more than 90%.
6. The wind turbine generator fatigue damage and life evaluation method according to claim 4, wherein the calculation formula of the actual cycle number is as follows:
Figure FDA0003288397370000021
wherein i represents different wind speeds; omegaave_iRepresenting the average rotational speed of the rotor; hi is the annual number of hours of occurrence at each wind speed calculated through actual wind frequency or fitted Weibull parameters; t islifeThe design life of a certain in-service unit.
7. The wind turbine generator fatigue damage and life evaluation method according to claim 1, wherein S3 comprises the following steps:
s31, randomly disordering the fatigue damage of each test position in the unit SCADA data sampling period and the sequence of the variable data set in the SCADA operation data during the test period;
s32, dividing the disordered data into a training set, a verification set and a test set according to the ratio of 8:1: 1; training the lightGBM model by using a training set to obtain a prediction model based on SCADA data;
the training set is used to determine a training model and to use variables within the SCADA data; the verification set is used for determining the generalization ability of the model and adjusting the hyper-parameters of the model; the test set is used for controlling model overfitting;
s33, determining the weight coefficient and the hyper-parameter of the unit operation data variable in the prediction model through iterative training of the prediction model;
s34, substituting the test set into the trained model to obtain a fatigue damage result Ss based on the SCADA operation data, comparing the fatigue damage results St of each test position obtained in S2, and evaluating regression evaluation indexes MSE, RMSE, MAE and R-Squared of the model;
s35, calculating the equivalent fatigue damage SN _ N of each part of the in-service unit after the part has operated for N _ N years by using a rain flow counting method based on the actual cycle number N1 and the design life Tlife, and calculating the equivalent fatigue damage S of each part of the in-service unit in the design life Tlife by using a Palmgren-Miner ruleTlife；
All SCADA operation data before the unit test are substituted into the trained model to obtain the actual fatigue damage Sp；
By Sp+StComparison STlifeEvaluating the residual life of each part of the unit;
comparison SpEquivalent fatigue damage S calculated based on Palmgren-MinerN_nThe difference between them.
8. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor when executing the computer program implements the steps of the wind turbine fatigue damage and life assessment method according to any of claims 1 to 7.
9. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for fatigue damage and lifetime assessment of a wind turbine generator according to any one of claims 1 to 7.
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CN116484751A (en) * 2023-06-21 2023-07-25 北京尚文汇通能源科技有限公司 Fatigue life assessment method and device for wind turbine generator components
CN116484751B (en) * 2023-06-21 2023-09-05 北京尚文汇通能源科技有限公司 Fatigue life assessment method and device for wind turbine generator components

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