CN119249766A - Wind turbine blade fatigue damage prediction method, computer device and readable storage medium - Google Patents
Wind turbine blade fatigue damage prediction method, computer device and readable storage medium Download PDFInfo
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Abstract
本申请提供一种风电叶片疲劳损伤预测方法、计算机设备和可读存储介质，涉及风力发电技术领域。本申请针对目标风电场内的每个待测风电机组，获取该待测风电机组在当前检测周期内的实际机组运行数据，而后在与目标风电场匹配的多种叶片疲劳损伤预测模型中，调用与该待测风电机组在当前检测周期内的实际工况类型对应的目标损伤预测模型，基于该待测风电机组的实际机组运行数据进行叶片疲劳损伤预测，得到该待测风电机组的叶片疲劳损伤集中区域在当前检测周期内的疲劳损伤预估值，从而在有效降低风电叶片健康状况监测成本的基础上，针对同一风电场内的各个风电机组分别实现高精准度的叶片疲劳损伤实时预测效果。
The present application provides a method for predicting fatigue damage of wind turbine blades, a computer device and a readable storage medium, and relates to the field of wind power generation technology. The present application obtains the actual unit operation data of each wind turbine to be tested in the target wind farm in the current detection cycle, and then calls the target damage prediction model corresponding to the actual working condition type of the wind turbine to be tested in the current detection cycle among the multiple blade fatigue damage prediction models matched with the target wind farm, and predicts blade fatigue damage based on the actual unit operation data of the wind turbine to be tested, and obtains the fatigue damage estimate of the blade fatigue damage concentration area of the wind turbine to be tested in the current detection cycle, thereby effectively reducing the cost of monitoring the health status of wind turbine blades, and achieving high-precision blade fatigue damage real-time prediction effects for each wind turbine in the same wind farm.
Description
Technical Field
The application relates to the technical field of wind power generation, in particular to a wind power blade fatigue damage prediction method, computer equipment and a readable storage medium.
Background
Along with the continuous development of scientific technology, the application of wind power generation technology is more extensive, often can erect wind power field in areas such as remote mountain area, plateau or coast, and in the actual production process of wind power field, wind power blade is the core component of wind turbine generator system capture wind energy, receive the coupling effect of load such as bending, torsion, shearing for a long time, the accumulated damage phenomenon of blade fatigue damage appears easily, the productivity benefit of corresponding wind turbine generator system is seriously influenced, therefore real-time monitoring wind power blade health status just is the important means of guaranteeing the normal steady operation of corresponding wind turbine generator system.
At present, a wind power blade health condition real-time monitoring scheme adopted by the main stream in the industry is to independently add sensing devices such as a load sensor and an audio sensor for each wind turbine in a wind power plant so as to determine the health condition of the wind power blade of the corresponding wind turbine by carrying out wind power blade state analysis on load data or audio data monitored in real time by the added sensing devices. However, it is worth noting that the scheme needs a large amount of expensive sensing devices, and the problem of high wind power blade health condition monitoring cost exists in the whole.
Disclosure of Invention
Accordingly, the application aims to provide a wind power blade fatigue damage prediction method, computer equipment and readable storage medium, which can predict blade fatigue damage based on wind turbine running data through a blade fatigue damage prediction model adapted to a wind power plant, and does not need to use a large amount of sensing devices with high cost for each wind turbine in the wind power plant, so that on the basis of effectively reducing the wind power blade health monitoring cost, high-precision real-time blade fatigue damage prediction effect is realized for each wind turbine in the same wind power plant.
In order to achieve the above object, the technical scheme adopted by the embodiment of the application is as follows:
In a first aspect, the application provides a wind turbine blade fatigue damage prediction method, which comprises the following steps:
Aiming at each wind turbine to be tested in a target wind power plant, acquiring actual turbine running data of the wind turbine to be tested in a current detection period;
According to the actual unit operation data of the wind turbine to be tested, the actual working condition type of the wind turbine to be tested in the current detection period is identified;
Screening target damage prediction models corresponding to the actual working condition types of the wind turbine to be tested from multiple blade fatigue damage prediction models matched with the target wind power plant, wherein each blade fatigue damage prediction model independently corresponds to one working condition type of the wind turbine;
And calling the target damage prediction model to predict the fatigue damage of the blade based on the actual unit operation data of the wind turbine to be tested, and obtaining a fatigue damage predicted value of the concentrated region of the fatigue damage of the blade of the wind turbine to be tested in the current detection period.
In an alternative embodiment, the method further comprises:
According to the wind speed frequency distribution data of the target wind power plant, calculating a load stress conversion coefficient of a blade fatigue damage concentration area of the sample wind turbine generator in the target wind power plant;
Acquiring actual measurement unit operation data of each of a plurality of historical detection periods of the sample wind turbine generator in different turbine working condition types in the target wind power plant and actual measurement load data related to a blade fatigue damage concentrated region;
Aiming at each historical detection period, carrying out blade fatigue damage calculation according to the load stress conversion coefficient and actual measurement load data of the historical detection period to obtain an actual fatigue damage value of a blade fatigue damage concentrated region of the sample wind turbine generator in the historical detection period;
And storing actual measured unit operation data and actual fatigue damage values corresponding to a plurality of historical detection periods corresponding to each unit working condition type into a fatigue damage database corresponding to the unit working condition type, and performing deep neural network model training based on the fatigue damage database corresponding to the unit working condition type to obtain a blade fatigue damage prediction model which is matched with the target wind power plant and corresponds to the unit working condition type.
In an optional embodiment, the step of calculating a load stress conversion coefficient of a blade fatigue damage concentration area of the sample wind turbine generator set in the target wind farm according to wind speed frequency distribution data of the target wind farm includes:
According to wind speed frequency distribution data of the target wind power plant, wind power generation simulation is carried out on the sample wind power generation set in a normal power generation working condition state, and load simulation data corresponding to a blade fatigue damage concentrated area of the sample wind power generation set in each simulation detection period are obtained;
carrying out rain flow counting processing on load simulation data of each simulation detection period, and calculating an equivalent load value of the simulation detection period according to the obtained load rain flow counting result;
And solving a load stress conversion coefficient based on a fatigue damage accumulation principle according to a theoretical average fatigue damage value of a blade fatigue damage concentrated region of the sample wind turbine generator in a normal power generation working condition in a single detection period and equivalent load values corresponding to the simulation detection periods, so as to obtain the load stress conversion coefficient of the sample wind turbine generator in the target wind power plant.
In an alternative embodiment, the solution of the load stress conversion coefficient is expressed by the following equation:
;
Wherein, The theoretical average fatigue damage value of the blade fatigue damage concentrated area of the sample wind turbine generator in a single detection period is used for representing the condition of normal power generation,For representing the total number of wind speed segments of the target wind farm,For representing the target wind farmThe occurrence probability value of the seed wind speed segment,The blade fatigue damage concentrated area for representing the sample wind turbine generator set in the normal power generation working condition state is in the first positionThe simulated average fatigue damage value in a single detection period under the action of the seed wind speed section,For indicating that the average wind speed is at the firstThe total number of simulated detection cycles of the seed wind speed segment,For indicating that the corresponding average wind speed is at the firstSeed wind speed segment NoThe simulated fatigue damage values of each simulated inspection cycle,For indicating that the corresponding average wind speed is at the firstSeed wind speed segment NoThe equivalent load value of each simulation test period,For representing the load stress conversion coefficient of the sample wind turbine generator in the target wind power plant,Is used for representing the minimum stress suffered by the fatigue damage concentrated area of the blade of the sample wind turbine generator when the fatigue damage is accumulated,Is used for representing the maximum stress suffered by the fatigue damage concentrated area of the blade of the sample wind turbine generator when the fatigue damage is accumulated,A blade fatigue damage concentrated region for representing sample wind turbine generator system is inThe number of stress cycles life times under action,For representing the period duration of a single detection period,For representing the load cycle frequency of the sample wind turbine,AndAre all constant.
In an optional embodiment, for each historical detection period, the step of calculating the blade fatigue damage according to the load stress conversion coefficient and the actually measured load data of the historical detection period to obtain an actual fatigue damage value of the blade fatigue damage concentration area of the sample wind turbine generator in the historical detection period includes:
performing stress conversion treatment on the actually measured load data of the historical detection period according to the load stress conversion coefficient to obtain actual stress data of the historical detection period;
Performing rain flow counting processing on actual stress data of the historical detection period, and performing Markov matrix conversion on an obtained stress rain flow counting result to obtain a target Markov matrix related to stress of the historical detection period;
carrying out average stress correction on the target Markov matrix according to the stress rain flow counting result to obtain effective stress values corresponding to all matrix elements in the target Markov matrix;
Aiming at each matrix element in the target Markov matrix, calculating the number of stress cycle life according to the effective stress value corresponding to the matrix element to obtain the number of target stress cycle life matched with the matrix element;
and carrying out accumulated damage calculation according to the actual stress cycle times and the target stress cycle life times which are respectively corresponding to all matrix elements in the target Markov matrix to obtain the actual fatigue damage value of the blade fatigue damage concentrated region of the sample wind turbine generator in the historical detection period.
In an alternative embodiment, the first of the target Markov matricesLine 1The effective stress value corresponding to the matrix element of the column is calculated by the following formula:
;
the first of the target Markov matrices Line 1The number of target stress cycle life times corresponding to the matrix elements of the columns is calculated by the following formula:
;
Wherein, For representing the first of the target Markov matricesLine 1The effective stress value corresponding to the matrix element of the column,For representing the first of the target Markov matricesThe stress amplitude of the row matrix element in the corresponding stress rain flow count result,For representing the first of the target Markov matricesThe column matrix element is the stress average value in the corresponding stress rain flow counting result,For the purpose of representing the value of the reference stress,Is used for representing the minimum stress suffered by the fatigue damage concentrated area of the blade of the sample wind turbine generator when the fatigue damage is accumulated,Is used for representing the maximum stress suffered by the fatigue damage concentrated area of the blade of the sample wind turbine generator when the fatigue damage is accumulated,For representing the first of the target Markov matricesLine 1The number of target stress cycles lifetimes corresponding to the matrix elements of the columns,AndAre all constant.
In an alternative embodiment, the actual fatigue damage value of the blade fatigue damage concentrated region of the sample wind turbine generator in a single historical detection period is calculated by adopting the formula:
;
Wherein, Is used for representing the actual fatigue damage value of the blade fatigue damage concentrated region of the sample wind turbine generator in a single historical detection period,Matrix orders representing the target markov matrix,For representing the first of the target Markov matricesLine 1The number of target stress cycles lifetimes corresponding to the matrix elements of the columns,For representing the first of the target Markov matricesLine 1The actual number of stress cycles corresponding to the matrix elements of the columns.
In an alternative embodiment, the method further comprises:
predicting the fatigue life of the current blade of the wind turbine to be tested according to the fatigue damage predicted value of the wind turbine to be tested in the current detection period and the fatigue damage predicted values of the wind turbine to be tested in all the historical detection periods before the current detection period;
Wherein, single wind turbine generator system to be tested is at the first The blade fatigue life of each detection period is calculated by the following formula:
;
Wherein, Is used for indicating that the wind turbine to be tested is at the first stageThe fatigue life of the blade for each test period,Used for representing the design life of the blade of the wind turbine to be tested,Is used for indicating that the wind turbine to be tested is at the first stageThe accumulated full hair length of each detection period,Is used for representing the total full-time length of the wind turbine to be tested in the whole life cycle,Is used for indicating that the wind turbine to be tested is at the first stageThe fatigue damage predicted value for each detection period,The method is used for representing the blade material strengthening coefficient of the wind turbine generator to be tested.
In a second aspect, the present application provides a computer device comprising a processor and a memory, the memory storing a computer program executable by the processor, the processor being executable to implement the wind power blade fatigue damage prediction method according to any of the previous embodiments.
In a third aspect, the present application provides a readable storage medium having stored thereon a computer program which, when executed by a computer device, implements the wind turbine blade fatigue damage prediction method of any of the previous embodiments.
In this case, the beneficial effects of the embodiments of the present application may include the following:
According to the method, for each wind turbine to be tested in the target wind power plant, actual turbine running data of the wind turbine to be tested in a current detection period is obtained, the actual working condition type of the wind turbine to be tested in the current detection period is identified based on the actual turbine running data of the wind turbine to be tested, then a target damage prediction model corresponding to the actual working condition type of the wind turbine to be tested is screened out from various blade fatigue damage prediction models matched with the target wind power plant, the screened target damage prediction model is called to conduct blade fatigue damage prediction based on the actual turbine running data of the wind turbine to be tested, and accordingly fatigue damage prediction of a blade fatigue damage concentrated region of the wind turbine to be tested in the current detection period is obtained.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a computer device according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a wind turbine blade fatigue damage prediction method according to an embodiment of the present application;
FIG. 3 is a second flow chart of a method for predicting fatigue damage of a wind turbine blade according to an embodiment of the present application;
FIG. 4 is a flow chart illustrating the sub-steps included in step S250 in FIG. 3;
fig. 5 is a schematic flow chart of the sub-steps included in step S270 in fig. 3;
FIG. 6 is a third flowchart of a method for predicting fatigue damage of a wind turbine blade according to an embodiment of the application.
The icons are 10-computer device, 11-memory, 12-processor, 13-communication unit.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present application, it should be understood that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," and the like indicate orientations or positional relationships based on those shown in the drawings, or those conventionally put in place when the product of the application is used, or those conventionally understood by those skilled in the art, merely for convenience in describing the present application and simplifying the description, and do not indicate or imply that the device or element to be referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
In the description of the present application, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, integrally connected, mechanically connected, electrically connected, directly connected, indirectly connected through an intermediary, or in communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Furthermore, in the description of the present application, it is understood that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The embodiments described below and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a schematic diagram illustrating a composition of a computer device 10 according to an embodiment of the application. In the embodiment of the present application, the computer device 10 may be respectively connected to all wind turbines in a certain target wind farm in a communication manner, so as to obtain the operational data of each wind turbine in different detection periods, identify the specific condition type (for example, the start-up condition, the shutdown condition, the standby condition, the normal power generation condition, etc.) of each wind turbine in the corresponding detection period according to the obtained operational data of each wind turbine, then call the fatigue damage prediction model of the blade adapted to the target wind farm and corresponding to the specific condition type of the wind turbine, and perform the fatigue damage prediction of the blade based on the operational data of the wind turbine corresponding to the wind turbine, thereby directly implementing the real-time prediction effect of the fatigue damage of the blade for each wind turbine in the same wind farm, without consuming a lot of expensive sensor devices for each wind turbine in the wind farm, so as to reduce the health condition monitoring cost of the wind turbine. The period duration of the detection period may be 10 minutes or 15 minutes or 20 minutes, and the specific period duration may be flexibly configured according to needs, the set operation data may be SCADA data collected by a SCADA (Supervisory Control And Data Acquisition, data collection and monitoring control) system corresponding to a wind turbine, the set operation data includes data such as an average set rotation speed, an average wind speed, an average pitch angle, an average power generation power and the like corresponding to the wind turbine in a certain detection period, and the computer device 10 may be, but is not limited to, a tablet computer, a notebook computer, a personal computer, a server and the like.
In the embodiment of the present application, the computer device 10 may include a memory 11, a processor 12 and a communication unit 13, where each element of the memory 11, the processor 12 and the communication unit 13 is directly or indirectly electrically connected to each other, so as to realize data transmission or interaction. For example, the memory 11, the processor 12 and the communication unit 13 may be electrically connected to each other through one or more communication buses or signal lines.
In this embodiment, the Memory 11 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), or the like. Wherein the memory 11 is configured to store a computer program, and the processor 12, upon receiving an execution instruction, can execute the computer program accordingly.
In addition, the memory 11 may be further configured to store a plurality of blade fatigue damage prediction models matched with a target wind farm, where each blade fatigue damage prediction model individually corresponds to one set working condition type, and each blade fatigue damage prediction model is configured to mine a potential mapping relationship between set operation data and a blade fatigue damage amount of any wind turbine in the target wind farm under the corresponding set working condition type so as to achieve a high-precision blade fatigue damage real-time prediction effect, and the memory 11 may be further configured to store a plurality of fatigue damage databases matched with the target wind farm, where each fatigue damage database individually corresponds to one set working condition type, and each fatigue damage database records a data association relationship between set operation data and a blade fatigue damage value of at least one wind turbine in at least one detection period under the corresponding set working condition type, so as to obtain the blade fatigue damage prediction model matched with the target wind farm and corresponding to the set working condition type by invoking the corresponding fatigue damage database to perform deep neural network model training.
In this embodiment, the processor 12 may be an integrated circuit chip with signal processing capabilities. The processor 12 may be a general purpose processor including at least one of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a network processor (Network Processor, NP), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present application.
In this embodiment, the communication unit 13 is configured to establish a communication connection between the computer device 10 and other electronic devices through a network, and send and receive data through the network, where the network includes a wired communication network and a wireless communication network. For example, the computer device 10 may obtain, from a certain wind turbine, turbine operation data of the wind turbine in different detection periods through the communication unit 13.
In this embodiment, the computer device 10 may store a specific computer program related to the wind turbine blade fatigue damage prediction function in advance at the memory 11, and call a blade fatigue damage prediction model adapted to the wind farm to perform blade fatigue damage prediction based on wind turbine operation data by driving the processor 12 to correspondingly execute the specific computer program, so that expensive sensing devices are not required to be added to each wind turbine in the wind farm at a great amount of cost, and thus, on the basis of effectively reducing the wind turbine blade health monitoring cost, high-precision real-time blade fatigue damage prediction effects are respectively implemented for each wind turbine in the same wind farm.
It will be appreciated that the block diagram shown in fig. 1 is merely a schematic diagram of one component of the computer device 10, and that the computer device 10 may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.
In the application, in order to ensure that the computer equipment 10 can respectively realize the real-time prediction effect of the high-precision blade fatigue damage aiming at each wind turbine generator set in the same wind power plant on the basis of effectively reducing the wind power blade health condition monitoring cost, the embodiment of the application realizes the aim by providing the wind power blade fatigue damage prediction method. The wind power blade fatigue damage prediction method provided by the application is described in detail below.
Referring to fig. 2, fig. 2 is a schematic flow chart of a wind turbine blade fatigue damage prediction method according to an embodiment of the application. In the embodiment of the application, the wind power blade fatigue damage prediction method shown in fig. 2 may include steps S210 to S240.
Step S210, acquiring actual unit operation data of each wind turbine to be tested in the current detection period of the wind turbine to be tested aiming at each wind turbine to be tested in the target wind power plant.
And step S220, identifying the type of the actual working condition of the wind turbine to be tested in the current detection period according to the actual turbine running data of the wind turbine to be tested.
In this embodiment, after obtaining the unit operation data of any wind turbine to be tested in the target wind farm in a certain detection period, the computer device 10 may perform numerical matching on the specific numerical distribution condition of each unit parameter included in the unit operation data and the unit parameter distribution standard corresponding to each of the pre-stored multiple unit operating condition types, so as to effectively determine which unit operating condition type of the unit operation data substantially accords with the unit parameter distribution standard of which unit operating condition type, and further determine the specific unit operating condition type of the corresponding wind turbine to be tested in the detection period.
And step S230, screening target damage prediction models corresponding to the actual working condition types of the wind turbine to be tested from multiple blade fatigue damage prediction models matched with the target wind power plant, wherein each blade fatigue damage prediction model independently corresponds to one working condition type of the wind turbine.
In this embodiment, the multiple blade fatigue damage prediction models are adapted to wind speed frequency distribution data of the target wind farm, where the wind speed frequency distribution data may be used to describe occurrence probability values of each of multiple wind speed segments (for example, 21 wind speed segments 3-4 m/s, 4-5 m/s, 5-6 m/s,...
And S240, calling a target damage prediction model to predict the fatigue damage of the blade based on the actual unit operation data of the wind turbine to be tested, and obtaining a fatigue damage predicted value of the concentrated region of the fatigue damage of the blade of the wind turbine to be tested in the current detection period.
In this embodiment, the blade fatigue damage concentration area is used to represent a main fatigue accumulation portion of the corresponding wind power blade, and is generally represented by a near blade root area of the corresponding wind power blade (for example, a local area 1 meter away from the blade root of the corresponding wind power blade), which may be determined by using expert experience and/or bladed simulation modes.
After determining a target damage prediction model corresponding to a current detection period of a certain wind turbine to be detected in the target wind power plant, the computer equipment 10 extracts a turbine characteristic parameter with highest correlation with the fatigue damage of the blade from actual turbine operation data of the wind turbine to be detected in the current detection period based on expert experience and Spearman correlation analysis, substitutes the extracted turbine characteristic parameter into the target damage prediction model, predicts a blade fatigue damage prediction value matched with the turbine characteristic parameter by using the target damage prediction model, and takes the blade fatigue damage prediction value as a fatigue damage prediction value corresponding to the wind turbine to be detected in the current detection period, so that a high-precision real-time blade fatigue damage prediction effect is realized for each wind turbine to be detected in the target wind power plant, and a large amount of expensive sensor devices are not required to be added for each wind turbine in the target wind power plant, thereby reducing wind power blade health condition monitoring cost.
Therefore, the method can be used for predicting the blade fatigue damage based on the running data of the wind turbines by executing the steps S210-S240 and calling the blade fatigue damage prediction model matched with the wind turbine, so that a great amount of expensive sensing devices are not required to be additionally arranged for each wind turbine in the wind turbine, and the high-precision real-time blade fatigue damage prediction effect is realized for each wind turbine in the same wind turbine on the basis of effectively reducing the monitoring cost of the health condition of the wind turbine.
Optionally, referring to fig. 3, fig. 3 is a second flowchart of a wind power blade fatigue damage prediction method according to an embodiment of the present application. In the embodiment of the application, compared with the wind power blade fatigue damage prediction method shown in fig. 2, the wind power blade fatigue damage prediction method shown in fig. 3 may further include steps S250 to S280 to train out a blade fatigue damage prediction model respectively adapted to different unit working condition types for a target wind power plant.
And S250, calculating a load stress conversion coefficient of the blade fatigue damage concentrated region of the sample wind turbine generator in the target wind power plant according to wind speed frequency distribution data of the target wind power plant.
In this embodiment, because the wind turbine blade design drawing and the load stress conversion tables of different constituent units of the wind turbine blade belong to the wind turbine generator design parameters, the overall acquisition difficulty is high, the load stress conversion coefficient of the blade fatigue damage concentration region of any wind turbine generator in the target wind turbine generator system cannot be directly acquired, and the load stress conversion coefficient of the blade fatigue damage concentration region of the sample wind turbine generator system (i.e. any wind turbine generator system in the target wind turbine generator system) in the target wind turbine generator system needs to be fitted by using a bladed simulation mode.
Optionally, referring to fig. 4, fig. 4 is a flowchart illustrating the sub-steps included in step S250 in fig. 3. In the embodiment of the present application, the step S250 may include sub-steps S251 to S253 to simulate and solve a load stress conversion coefficient when the blade fatigue damage concentrated area of the sample wind turbine generator is matched with wind speed frequency distribution data in the target wind farm.
And step S251, performing wind power generation simulation on the sample wind turbine generator in a normal power generation working condition state according to the wind speed frequency distribution data of the target wind power plant to obtain load simulation data corresponding to the blade fatigue damage concentrated region of the sample wind turbine generator in a plurality of simulation detection periods.
In this embodiment, the average wind speed simulated by the sample wind turbine generator in any simulation detection period is within any one of a plurality of wind speed sections related to the target wind power plant, and each wind speed section related to the target wind power plant corresponds to at least one simulation detection period independently.
Step S252, for each simulation detection period, carrying out rain flow counting processing on the load simulation data of the simulation detection period, and calculating an equivalent load value of the simulation detection period according to the obtained load rain flow counting result.
In this embodiment, for a single simulation test period, the load rain flow count result of the simulation test period may employ a load rain flow matrixThe representation is performed:
;
Wherein, For indicating the firstThe load average value of the seed load cycle,For indicating the firstThe load amplitude of the seed load cycle,For indicating the firstThe actual number of cycles of the load cycle.
For a single simulation test period, the equivalent load value of the simulation test period can be calculated by the following formula:
;
Wherein, Equivalent load values representing the simulated test period,For representing the first time in the simulation test periodThe actual number of cycles of the seed load cycle,For representing the first time in the simulation test periodThe load amplitude of the seed load cycle,The number of load cycle types used to represent the simulation test period,For representing the period duration of a single simulation test period,For representing the load cycle frequency of the sample wind turbine,And the blade material strengthening coefficient is used for representing the sample wind turbine generator.
And step 253, according to a theoretical average fatigue damage value of a blade fatigue damage concentrated region of the sample wind turbine in a normal power generation working condition state in a single detection period and equivalent load values corresponding to a plurality of simulation detection periods, carrying out load stress conversion coefficient solving based on a fatigue damage accumulation principle to obtain a load stress conversion coefficient of the sample wind turbine in a target wind power plant.
In this embodiment, the solution process of the load stress conversion coefficient is expressed by the following equation:
;
Wherein, The theoretical average fatigue damage value of the blade fatigue damage concentrated area of the sample wind turbine generator in a single detection period is used for representing the condition of normal power generation,For representing the total number of wind speed segments of the target wind farm,For representing the target wind farmThe occurrence probability value of the seed wind speed segment,The blade fatigue damage concentrated area for representing the sample wind turbine generator set in the normal power generation working condition state is in the first positionThe simulated average fatigue damage value in a single detection period under the action of the seed wind speed section,For indicating that the average wind speed is at the firstThe total number of simulated detection cycles of the seed wind speed segment,For indicating that the corresponding average wind speed is at the firstSeed wind speed segment NoThe simulated fatigue damage values of each simulated inspection cycle,For indicating that the corresponding average wind speed is at the firstSeed wind speed segment NoThe equivalent load value of each simulation test period,For representing the load stress conversion coefficient of the sample wind turbine generator in the target wind power plant,Is used for representing the minimum stress suffered by the fatigue damage concentrated area of the blade of the sample wind turbine generator when the fatigue damage is accumulated,Is used for representing the maximum stress suffered by the fatigue damage concentrated area of the blade of the sample wind turbine generator when the fatigue damage is accumulated,A blade fatigue damage concentrated region for representing sample wind turbine generator system is inThe number of stress cycles life times under action,For representing the period duration of a single detection period,For representing the load cycle frequency of the sample wind turbine,AndAre all constant.
In the course of this process, the process is carried out,WhereinUsed for representing the distribution proportion of the normal power generation working condition to all the working condition types of the unit,For representing the blade design life of the sample wind turbine,For representing the period duration of a single detection period.
Therefore, the load stress conversion coefficient of the blade fatigue damage concentrated region of the sample wind turbine generator in the target wind power plant when the blade fatigue damage concentrated region is matched with wind speed frequency distribution data can be obtained through simulation by executing the substeps S251-S253.
Step S260, obtaining actual measurement unit operation data of each of a plurality of historical detection periods of the sample wind turbine generator under different unit working condition types in a target wind power plant and actual measurement load data related to a blade fatigue damage concentrated region.
In this embodiment, multiple sets of actual measurement data corresponding to each sample wind turbine generator under different set of working condition types may be obtained by adding load sensors to the sample wind turbine generator, where each set of actual measurement data corresponds to one set of working condition types separately, and each set of actual measurement data corresponds to one history detection period separately, and each set of actual measurement data includes actual measurement unit operation data of the sample wind turbine generator in the corresponding history detection period, and actual measurement load data of a blade fatigue damage concentrated region of the sample wind turbine generator in the corresponding history detection period.
Step S270, for each historical detection period, carrying out blade fatigue damage calculation according to the load stress conversion coefficient and the actually measured load data of the historical detection period to obtain an actual fatigue damage value of the blade fatigue damage concentrated region of the sample wind turbine generator in the historical detection period.
Optionally, referring to fig. 5, fig. 5 is a flowchart illustrating the sub-steps included in step S270 in fig. 3. In an embodiment of the present application, the step S270 may include a sub-step S271 to a sub-step S275, so as to solve, for any historical detection period, an actual blade fatigue damage value that matches the actual measured load data thereof.
In sub-step S271, stress conversion processing is performed on the actually measured load data of the history detection period according to the load stress conversion coefficient, so as to obtain actual stress data of the history detection period.
The actual stress data of a single historical detection period is obtained by multiplying the actual load data of the historical detection period by a load stress conversion coefficient matched with the target wind power plant.
And step S272, performing rain flow counting processing on the actual stress data of the history detection period, and performing Markov matrix conversion on the obtained stress rain flow counting result to obtain a target Markov matrix related to the stress of the history detection period.
And S273, carrying out average stress correction on the target Markov matrix according to the stress rain flow counting result to obtain the effective stress values corresponding to all matrix elements in the target Markov matrix.
In this embodiment, the target Markov matrix is the first oneLine 1The effective stress value corresponding to the matrix element of the column is calculated by the following formula:
;
Wherein, For representing the first of the target Markov matricesLine 1The effective stress value corresponding to the matrix element of the column,For representing the first of the target Markov matricesThe stress amplitude of the row matrix element in the corresponding stress rain flow count result,For representing the first of the target Markov matricesThe column matrix element is the stress average value in the corresponding stress rain flow counting result,For representing the reference stress value.
In the substep S274, for each matrix element in the target markov matrix, the number of stress cycle lives is calculated according to the effective stress value corresponding to the matrix element, so as to obtain the number of target stress cycle lives matched with the matrix element.
In this embodiment, the target Markov matrix is the first oneLine 1The number of target stress cycle life times corresponding to the matrix elements of the columns is calculated by the following formula:
;
Wherein, For representing the first of the target Markov matricesLine 1The effective stress value corresponding to the matrix element of the column,Is used for representing the minimum stress suffered by the fatigue damage concentrated area of the blade of the sample wind turbine generator when the fatigue damage is accumulated,Is used for representing the maximum stress suffered by the fatigue damage concentrated area of the blade of the sample wind turbine generator when the fatigue damage is accumulated,For representing the first of the target Markov matricesLine 1The number of target stress cycles lifetimes corresponding to the matrix elements of the columns,AndAre all constant.
And S275, performing accumulated damage calculation according to the actual stress cycle times and the target stress cycle life times which are respectively corresponding to all matrix elements in the target Markov matrix to obtain the actual fatigue damage value of the blade fatigue damage concentrated region of the sample wind turbine generator in the historical detection period.
In this embodiment, the actual fatigue damage value of the blade fatigue damage concentrated region of the sample wind turbine generator in a single historical detection period is calculated by adopting the formula:
;
Wherein, Is used for representing the actual fatigue damage value of the blade fatigue damage concentrated region of the sample wind turbine generator in a single historical detection period,A matrix order (e.g., 128) representing the target markov matrix,For representing the first of the target Markov matricesLine 1The number of target stress cycles lifetimes corresponding to the matrix elements of the columns,For representing the first of the target Markov matricesLine 1The actual number of stress cycles corresponding to the matrix elements of the columns.
Therefore, the application can solve the actual blade fatigue damage value matched with the actual measured load data of the blade for any historical detection period by executing the substep S271 to the substep S275.
Step S280, for each unit working condition type, storing actual measured unit operation data and actual fatigue damage values corresponding to a plurality of historical detection periods corresponding to the unit working condition type into a fatigue damage database corresponding to the unit working condition type, and performing deep neural network model training based on the fatigue damage database corresponding to the unit working condition type to obtain a blade fatigue damage prediction model matched with a target wind farm and corresponding to the unit working condition type.
In this embodiment, for each unit working condition type, based on expert experience and Spearman correlation analysis, unit characteristic parameters with highest correlation degree to the fatigue damage of the blade are extracted from actual measurement unit operation data of each historical detection period recorded by a fatigue damage database corresponding to the unit working condition type, and then deep neural network model training is performed based on the unit characteristic parameters and actual fatigue damage values of each historical detection period, so that a finally trained prediction model of the fatigue damage of the blade can be ensured, and potential mapping relation between unit operation data and the fatigue damage amount of the blade of any wind turbine in a target wind power plant under the corresponding unit working condition type can be effectively excavated.
In addition, it may be understood that, after the computer device 10 finishes executing the step S240, the fatigue damage predicted value and the actual unit operation data corresponding to the wind turbine to be tested in the current detection period may be stored in the fatigue damage database adapted to the unit working condition type, so that the computer device 10 continuously calls the fatigue damage database to perform model optimization on the adapted target damage prediction model, so as to improve the blade fatigue damage prediction accuracy of the target damage prediction model.
Therefore, the method can train out the blade fatigue damage prediction model respectively adapted to different unit working condition types aiming at the target wind farm by executing the steps S250-S280.
Optionally, referring to fig. 6, fig. 6 is a third flowchart of a wind power blade fatigue damage prediction method according to an embodiment of the present application. In the embodiment of the application, compared with the wind power blade fatigue damage prediction method shown in fig. 2 or 3, the wind power blade fatigue damage prediction method shown in fig. 6 may further include step S290, so as to predict the current blade fatigue life of any wind turbine in the target wind power plant with high accuracy, so that the blade life early warning effect is conveniently realized.
And step S290, predicting the fatigue life of the current blade of the wind turbine to be tested according to the fatigue damage predicted value of the wind turbine to be tested in the current detection period and the fatigue damage predicted values of the wind turbine to be tested in all the historical detection periods before the current detection period.
In this embodiment, the single wind turbine to be tested is at the firstThe blade fatigue life of each detection period is calculated by the following formula:
;
Wherein, Is used for indicating that the wind turbine to be tested is at the first stageThe fatigue life of the blade for each test period,Used for representing the design life of the blade of the wind turbine to be tested,Is used for indicating that the wind turbine to be tested is at the first stageThe accumulated full hair length of each detection period,Is used for representing the total full-time length of the wind turbine to be tested in the whole life cycle,Is used for indicating that the wind turbine to be tested is at the first stageThe fatigue damage predicted value for each detection period,The method is used for representing the blade material strengthening coefficient of the wind turbine generator to be tested.
The computer device 10 may predict the fatigue life of the blade with high accuracy for any wind turbine in the target wind farm based on the above-mentioned calculation formula of the fatigue life of the blade, and then determine whether the wind turbine currently needs to perform early warning of the fatigue life of the blade by detecting whether the fatigue life of the blade corresponding to the wind turbine is less than or equal to a preset life threshold. When the fatigue life of a blade of a certain wind turbine generator is less than or equal to a preset life threshold, the computer device 10 may send a blade life alarm message about the wind turbine generator to a production management terminal of the target wind turbine generator, so that a wind turbine generator manager is informed of the need of timely blade replacement and maintenance of the wind turbine generator through the blade life alarm message, thereby avoiding serious safety accidents and productivity loss.
Therefore, the method can predict the fatigue life of the current blade of any wind turbine generator in the target wind power plant with high accuracy by executing the step S290, so that the early warning effect of the service life of the blade can be realized conveniently.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to embodiments of the present application. 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part. The functions provided by the present application, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solution of the present application, or the parts contributing to the prior art or the parts of the technical solution, may be embodied in the form of a software product stored in a readable storage medium comprising several instructions for causing the computer device 10 to perform all or part of the steps of the method described in the various embodiments of the present application. The readable storage medium includes a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The above description is merely illustrative of various embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the scope of the present application, and the application is intended to be covered by the scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
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