Wind turbine generator system service life prediction model construction method and wind turbine generator system service life prediction method

Abstract

The present application relates to the field of wind turbine generator systems. Provided are a wind turbine generator system service life prediction model construction method and a wind turbine generator system service life prediction method. The wind turbine generator system service life prediction model construction method comprises: determining the types of first wind condition parameters of wind turbine generator systems; establishing a first wind turbine generator system service life prediction model according to an association relationship between each type of the first wind condition parameters and a service life; acquiring the values of the plurality of first wind condition parameters; inputting the values of the first wind condition parameters into the first wind turbine generator system service life prediction model, so as to obtain a first service life; acquiring a second service life, wherein the second service life is obtained by means of whole-machine load calculation; and if there is a wind turbine generator system, a first service life and a second service life of which have a difference greater than a preset value, obtaining a second wind turbine generator system service life prediction model according to the first service life and the second service life. By means of the present application, when the service life of a wind turbine generator system is predicted, the prediction cost is reduced, and the prediction time is shortened.

Description

Wind turbine operating life prediction model construction method and operating life prediction method Technical Field

[0001] The present application relates to the field of wind turbines, and in particular to a method for constructing a wind turbine operating life prediction model and an operating life prediction method. Background Art

[0002] In the existing technology, the operating unit life of a wind turbine can be predicted and evaluated by calculating the whole-machine load. However, the whole-machine load calculation is expensive and time-consuming, and the unit model of the wind turbine needs to be obtained during the whole-machine load calculation process. As the core data, the unit model of the wind turbine is difficult to obtain. In addition, the operating life of a wind turbine can also be evaluated through on-site actual measurement by professionals, but this type of actual measurement evaluation method requires at least 6 months of testing time, which is very time-consuming. Therefore, there is an urgent need for a universal, fast and convenient method for predicting the operating life of a wind turbine.

[0003] Summary of the Invention

[0004] In order to reduce the prediction cost and shorten the prediction time while predicting the operating life of a wind turbine, the present application proposes a method for constructing a wind turbine operating life prediction model and an operating life prediction method.

[0005] In a first aspect, the present application provides a method for constructing a wind turbine operating life prediction model, the method comprising:

[0006] Determining the type of each first wind condition parameter of the wind turbine generator set;

[0007] Establishing a first wind turbine operating life prediction model based on the correlation between the type of each first wind condition parameter and the operating life of the wind turbine;

[0008] Obtaining values ​​of first wind condition parameters corresponding to types of first wind condition parameters of multiple wind turbines;

[0009] Inputting the value of each first wind condition parameter into the first wind turbine operating life prediction model to obtain the first operating life of each wind turbine;

[0010] Obtaining the second operating life of each wind turbine generator set, where each second operating life is obtained by calculating the entire machine load;

[0011] If the difference between the first operating life and the second operating life of the same wind turbine generator set is greater than a preset value, the first wind turbine generator set operating life prediction model is adjusted according to each first operating life and each second operating life to obtain a second wind turbine generator set operating life prediction model.

[0012] Taking into account the high cost and difficulty in obtaining the unit model in the related art when predicting the operating life of a wind turbine by calculating the whole machine load, or the time-consuming manual measurement and evaluation of the operating life, the method provided in this application first establishes a first wind turbine operating life prediction model based on the correlation between wind condition parameters and wind turbines, and then adjusts the first wind turbine operating life prediction model by the operating life obtained by the whole machine load calculation prediction, and finally obtains a second wind turbine operating life prediction model for predicting the operating life of the wind turbine. By using the model provided in this application, there is no need to use the whole machine load calculation or to obtain the unit model. The operating life of the wind turbine can be obtained by obtaining the unit reference life and reference wind condition parameters. While reducing the prediction cost, the prediction time is shortened and the complexity of the prediction process is reduced.

[0013] In an optional embodiment, if the difference between the first operating life and the second operating life of the same wind turbine generator set is greater than a preset value, the first wind turbine generator set operating life prediction model is adjusted according to each first operating life and each second operating life to obtain a second wind turbine generator set operating life prediction model, including:

[0014] If the difference between the first operating life and the second operating life of the same wind turbine generator set is greater than a preset value, the correlation between the value of each first wind condition parameter and the operating life is adjusted according to each first operating life and each second operating life until the difference between the first operating life and the second operating life of each wind turbine generator set is less than or equal to the preset value, thereby obtaining a second wind turbine generator set operating life prediction model.

[0015] In an optional implementation, determining the type of each first wind condition parameter includes:

[0016] Obtaining types of multiple second wind condition parameters;

[0017] Calculating a correlation between the type of each second wind condition parameter and the operating life of the wind turbine generator set, wherein the correlation is represented by a correlation coefficient;

[0018] According to the correlations, the types of the second wind condition parameters are screened to obtain the types of the first wind condition parameters.

[0019] In an optional embodiment, the types of the first wind condition parameters include turbulence, average wind speed within a preset time period, and average air density. In the second wind turbine operating life prediction model, turbulence, average wind speed, and average air density are all negatively correlated with the operating life.

[0020] In an optional implementation, the formula of the second wind turbine operating life prediction model is expressed as:

[0021] Among them, L Ois the predicted operating life, i.e. the first operating life; L D is the reference life; V D is the reference average wind speed; V O is the average wind speed; ρ D is the reference average air density; ρ O is the average air density; I D is the reference turbulence; I O is the turbulence, Cr1, Cr2 and Cr3 are adjustment coefficients.

[0022] In a second aspect, the present application further provides a method for predicting the operating life of a wind turbine generator set, the method comprising:

[0023] Obtaining values ​​of first wind condition parameters of the wind turbine generator set;

[0024] The operating life of the wind turbine is predicted based on the values ​​of each first wind condition parameter and the wind turbine operating life prediction model. The wind turbine operating life prediction model is a second wind turbine operating life model obtained by the wind turbine operating life prediction model construction method of the first aspect or any implementation method of the first aspect.

[0025] Taking into account the high cost and difficulty in obtaining the unit model in the related art when predicting the operating life of a wind turbine by calculating the whole machine load, or the time-consuming manual measurement and evaluation of the operating life, the method provided in this application uses the wind turbine operating life prediction model to predict the operating life of a wind turbine, without the need for whole machine load calculation, and without the need to obtain the corresponding unit model of the wind turbine. While reducing the prediction cost, it also shortens the prediction time and reduces the complexity of the prediction process.

[0026] In a third aspect, the present application further provides a device for predicting the operating life of a wind turbine generator set, the device comprising:

[0027] a determination module, configured to determine the type of each first wind condition parameter of the wind turbine generator set;

[0028] An establishment module is used to establish a first wind turbine operating life prediction model based on the correlation between the type of each first wind condition parameter and the operating life of the wind turbine;

[0029] A first acquisition module is used to obtain values ​​of first wind condition parameters corresponding to types of first wind condition parameters of multiple wind turbines;

[0030] A first prediction module is configured to input the value of each first wind condition parameter into a first wind turbine operating life prediction model to obtain a first operating life of each wind turbine;

[0031] A second acquisition module is used to obtain a second operating life of each wind turbine generator set, where each second operating life is obtained by calculating the entire machine load;

[0032] The adjustment module is used to adjust the first wind turbine operating life prediction model according to each first operating life and each second operating life to obtain a second wind turbine operating life prediction model if the difference between the first operating life and the second operating life of the same wind turbine is greater than a preset value.

[0033] Taking into account the high cost and difficulty in obtaining the unit model in the related art when predicting the operating life of a wind turbine by calculating the whole machine load, or the time-consuming manual measurement and evaluation of the operating life, the device provided in this application first establishes a first wind turbine operating life prediction model based on the correlation between wind condition parameters and wind turbines, and then adjusts the first wind turbine operating life prediction model by the operating life obtained by calculating the whole machine load, and finally obtains a second wind turbine operating life prediction model for predicting the operating life of the wind turbine. By using the model provided in this application, there is no need to use the whole machine load calculation or obtain the unit model. The operating life of the wind turbine can be obtained by obtaining the unit reference life and reference wind condition parameters. While reducing the prediction cost, the prediction time is shortened and the complexity of the prediction process is reduced.

[0034] In a fourth aspect, the present application further provides a device for predicting the operating life of a wind turbine generator set, the device comprising:

[0035] A third acquisition module is used to obtain the values ​​of each first wind condition parameter of the wind turbine generator set;

[0036] The second prediction module is used to predict the operating life of the wind turbine generator set based on the values ​​of each first wind condition parameter and the wind turbine generator set operating life prediction model. The wind turbine generator set operating life prediction model is a second wind turbine generator set operating life prediction model obtained by the wind turbine generator set operating life prediction model construction method of the first aspect or any implementation method of the first aspect.

[0037] Taking into account the high cost and difficulty in obtaining the unit model in the related art when predicting the operating life of a wind turbine by calculating the whole machine load, or the time-consuming manual measurement and evaluation of the operating life, the device provided in this application uses the wind turbine operating life prediction model to predict the operating life of the wind turbine, without the need to use the whole machine load calculation, and without the need to obtain the corresponding unit model of the wind turbine. While reducing the prediction cost, it also shortens the prediction time and reduces the complexity of the prediction process.

[0038] In a fifth aspect, the present application also provides a computer device comprising a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to thereby execute the steps of the method for constructing a wind turbine operating life prediction model of the first aspect or any embodiment of the first aspect, or the steps of the method for predicting the operating life of a wind turbine of the second aspect.

[0039] In the sixth aspect, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method for constructing a wind turbine operating life prediction model of the first aspect or any embodiment of the first aspect, and the steps of the wind turbine operating life prediction method of the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific implementation methods or the description of the prior art. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] FIG1 is a flow chart of a method for constructing a wind turbine operating life prediction model according to an exemplary embodiment;

[0042] FIG2 is a flow chart of a method for predicting the operating life of a wind turbine generator system according to an exemplary embodiment;

[0043] FIG3 is a schematic structural diagram of a device for constructing a wind turbine operating life prediction model according to an exemplary embodiment;

[0044] FIG4 is a schematic structural diagram of a device for predicting the operating life of a wind turbine generator set according to an exemplary embodiment;

[0045] FIG5 is a schematic diagram of a hardware structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0046] The following will clearly and completely describe the technical solution of this application in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0047] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0048] In order to reduce the prediction cost and shorten the prediction time while predicting the operating life of a wind turbine, the present application proposes a method for constructing a wind turbine operating life prediction model and an operating life prediction method.

[0049] Figure 1 is a flow chart of a method for constructing a wind turbine operating life prediction model according to an exemplary embodiment. As shown in Figure 1 , the method for constructing a wind turbine operating life prediction model includes the following steps S101 to S106 .

[0050] Step S101: determining the types of first wind condition parameters of a wind turbine.

[0051] In an optional embodiment, the type of the first wind condition parameter may be determined by the degree of correlation between the wind condition parameter and the operating life of the wind turbine generator set.

[0052] In an optional embodiment, the wind condition parameters include but are not limited to turbulence, average wind speed within a preset time period, and average air density.

[0053] Step S102: establishing a first wind turbine operating life prediction model based on the correlation between each first wind condition parameter and the operating life of the wind turbine.

[0054] In an optional embodiment, the correlation between the first wind condition parameter and the operating life of the wind turbine generator set is a negative correlation, and a first wind turbine generator set operating life prediction model can be established based on the negative correlation between the first wind condition parameter and the operating life of the wind turbine generator set.

[0055] Step S103: obtaining the value of the first wind condition parameter corresponding to each first wind condition parameter of a plurality of wind turbines.

[0056] In an optional embodiment, the value of the first wind condition parameter is the actual parameter value of the wind turbine generator set on site. The value of the first wind condition parameter of each wind turbine generator set can be obtained through a sensor or other equipment.

[0057] Step S104: inputting the value of each first wind condition parameter into the first wind turbine operating life prediction model to obtain the first operating life of each wind turbine.

[0058] Step S105: obtaining the second operating life of each wind turbine generator set, where each second operating life is obtained by calculating the entire machine load.

[0059] In an optional embodiment, the second operating life of each wind turbine is predicted by calculating the load of the entire machine. When performing the whole machine load calculation, it is necessary to first obtain the model of each wind turbine, and then obtain the values ​​of wind parameters such as the wind model, wind shear, and air density of the wind turbine. For example, the rain flow statistics method is used to calculate the damage load of the wind turbine in long-term operation, and then the second operating life is obtained.

[0060] Step S106: If the difference between the first operating life and the second operating life of the same wind turbine generator set is greater than a preset value, the first wind turbine generator set operating life prediction model is adjusted according to each first operating life and each second operating life to obtain a second wind turbine generator set operating life prediction model.

[0061] In an optional embodiment, the size of the preset value can be set according to actual needs and is not specifically limited here.

[0062] Taking into account the high cost and difficulty in obtaining the unit model, or the time-consuming manual measurement and evaluation of the operating life of a wind turbine in related technologies using the whole-machine load calculation to predict the operating life, the model construction method provided in the embodiment of the present application first establishes a first wind turbine operating life prediction model based on the correlation between the type of wind condition parameters and the wind turbine, and then adjusts the first wind turbine operating life prediction model based on the operating life obtained by the whole-machine load calculation prediction, and finally obtains a second wind turbine operating life prediction model for predicting the operating life of the wind turbine. By using the model provided in the present application, there is no need to calculate the whole-machine load or obtain the unit model. The operating life of the wind turbine can be obtained by obtaining the unit reference life and reference wind condition parameters. While reducing the prediction cost, the prediction time is shortened and the complexity of the prediction process is reduced.

[0063] In one example, in the above step S106, if the difference between the first operating life and the second operating life of the same wind turbine generator set is greater than a preset value, the negative correlation between each first wind condition parameter and the operating life is adjusted according to each first operating life and each second operating life until the difference between the first operating life and the second operating life of each wind turbine generator set is less than or equal to the preset value, thereby obtaining a second wind turbine generator set operating life prediction model.

[0064] In one example, in step S101 above, the type of each first wind condition parameter is determined in the following manner:

[0065] First, types of multiple second wind condition parameters are obtained.

[0066] In an optional embodiment, the type of the second wind condition parameter may be wind speed, wind shear, air density, or other wind condition parameters.

[0067] Then, the correlation between the type of each second wind condition parameter and the operating life of the wind turbine generator set is calculated.

[0068] In an optional embodiment, the correlation can be represented by a correlation coefficient. For example, the correlation coefficient between the average wind speed, air density, turbulence and operating life can be calculated based on the actual comparison results of the operating loads of units of different models using the whole machine load analysis software that is relatively recognized in the industry, such as Bladed. No specific restrictions are made here.

[0069] Finally, based on the correlations, the types of the second wind condition parameters are screened to obtain the types of the first wind condition parameters.

[0070] In an optional embodiment, a wind condition parameter having a correlation greater than a predetermined value is selected as the type of the first wind condition parameter.

[0071] In one example, in the above step S106, the types of the first wind condition parameters include turbulence, average wind speed within a preset time period, and average air density. In the second wind turbine operating life prediction model, turbulence, average wind speed, and average air density are all negatively correlated with the operating life. In one example, in the above step S106, since the second operating life can be calculated based on the load of the entire machine (for example, the damage load of the long-term operation of the wind turbine is calculated using the rain flow statistics method, and the second operating life is calculated accordingly), when the difference between the first operating life and the second operating life of the same wind turbine is greater than a preset value, based on the correlation coefficient between different second wind condition parameters and the operating life, the model parameters such as the reference average wind speed, reference air density, and reference turbulence can be adjusted until the difference between the first operating life and the second operating life of each wind turbine is less than or equal to the preset value, thereby obtaining the second wind turbine operating life prediction model. The formula of the second wind turbine operating life prediction model is expressed as:

[0072] Among them, L O is the predicted operating life, i.e. the first operating life; L D is the reference life, i.e. the design life of the unit; V D is the reference average wind speed, i.e. the designed average wind speed of the unit; V O is the actual average wind speed on site; ρ D is the reference average air density, i.e. the unit design average air density; ρ O is the actual average air density on site; I D is the reference turbulence, i.e. the unit design turbulence; I O is the actual turbulence on site, and Cr1, Cr2 and Cr3 are adjustment coefficients.

[0073] FIG2 is a flow chart of a method for predicting the operating life of a wind turbine generator system according to an exemplary embodiment. As shown in FIG2 , the method includes the following steps S201 to S202 .

[0074] Step S201: obtaining values ​​of first wind condition parameters of a wind turbine.

[0075] Step S202: predicting the service life of the wind turbine generator set according to the values ​​of the first wind condition parameters and the wind turbine generator set service life prediction model, wherein the wind turbine generator set service life prediction model is obtained by the wind turbine generator set service life prediction model construction method provided by the above embodiment.

[0076] Taking into account the high cost and difficulty in obtaining the unit model in the related art when predicting the operating life of a wind turbine by calculating the whole machine load, or the time-consuming manual measurement and evaluation of the operating life, the prediction method provided in this application uses the wind turbine operating life prediction model to predict the operating life of the wind turbine. There is no need to calculate the whole machine load, and there is no need to obtain the corresponding unit model of the wind turbine. While reducing the prediction cost, it shortens the prediction time and reduces the complexity of the prediction process.

[0077] Based on the same inventive concept, the present application also provides a device for predicting the operating life of a wind turbine generator set, as shown in FIG3 . The device includes:

[0078] The determination module 301 is used to determine the type of each first wind condition parameter of the wind turbine generator set; for details, please refer to the description of step S101 in the above embodiment, which will not be repeated here.

[0079] The establishment module 302 is used to establish a first wind turbine operating life prediction model based on the correlation between the type of each first wind condition parameter and the operating life of the wind turbine; for details, please refer to the description of step S102 in the above embodiment and will not be repeated here.

[0080] The first acquisition module 303 is used to obtain the value of the first wind condition parameter corresponding to each first wind condition parameter of the plurality of wind turbines; for details, please refer to the description of step S103 in the above embodiment, which will not be repeated here.

[0081] The first prediction module 304 is used to input the values ​​of each first wind condition parameter into the first wind turbine operating life prediction model to obtain the first operating life of each wind turbine; for details, please refer to the description of step S104 in the above embodiment and will not be repeated here.

[0082] The second acquisition module 305 is used to obtain the second operating life of each wind turbine generator set. Each second operating life is obtained by calculating the entire machine load. For details, please refer to the description of step S105 in the above embodiment and will not be repeated here.

[0083] Adjustment module 306 is configured to adjust the first wind turbine lifetime prediction model based on the first and second lifetimes to obtain a second wind turbine lifetime prediction model, if the difference between the first and second lifetimes of the same wind turbine is greater than a preset value. For details, see the description of step S106 in the above embodiment and will not be repeated here.

[0084] Taking into account the high cost and difficulty in obtaining the unit model when predicting the operating life of a wind turbine in related technologies using the whole-machine load calculation, or the long time required for manually measuring and evaluating the operating life, the device provided in this application first establishes a first wind turbine operating life prediction model based on the correlation between the type of wind condition parameters and the wind turbine, and then adjusts the first wind turbine operating life prediction model based on the operating life predicted by the whole-machine load calculation, and finally obtains a second wind turbine operating life prediction model for predicting the operating life of the wind turbine. By using the model provided in this application, there is no need to perform whole-machine load calculation or obtain the unit model. The operating life of the wind turbine can be obtained by obtaining the unit reference life and the type of reference wind condition parameters. While reducing the prediction cost, the prediction time is shortened and the complexity of the prediction process is reduced.

[0085] In one example, the adjustment module 306 includes:

[0086] The adjustment submodule is configured to, if the difference between the first and second operating lifespans of the same wind turbine generator set is greater than a preset value, adjust the negative correlation between the values ​​of the first wind condition parameters and the operating lifespans based on the first and second operating lifespans until the difference between the first and second operating lifespans of the wind turbine generator set is less than or equal to the preset value, thereby obtaining a second wind turbine generator operating life prediction model. For details, refer to the description in the above embodiment and will not be repeated here.

[0087] In one example, the determining module 301 includes:

[0088] The acquisition submodule is used to obtain the types of multiple second wind condition parameters; for details, please refer to the description in the above embodiment and will not be repeated here.

[0089] The calculation submodule is used to calculate the correlation between the type of each second wind condition parameter and the operating life of the wind turbine generator set; the details are described in the above embodiment and will not be repeated here.

[0090] The screening submodule is used to screen the types of the second wind condition parameters according to the correlations to obtain the types of the first wind condition parameters. For details, please refer to the description in the above embodiment and will not be repeated here.

[0091] In one example, in adjustment module 306, the types of first wind condition parameters include turbulence, average wind speed over a preset period, and average air density. In the second wind turbine operating life prediction model, turbulence, average wind speed, and average air density are all negatively correlated with operating life. For details, refer to the description in the above embodiment and are not repeated here.

[0092] In one example, in the adjustment module 306 , the formula of the second wind turbine operating life prediction model is expressed as:

[0093] Among them, L O is the predicted operating life, i.e., the first operating life; L D is the reference life; V D is the reference average wind speed; V O is the average wind speed; ρ D is the reference average air density; ρ O is the average air density; I D is the reference turbulence; I O is the turbulence, Cr1, Cr2 and Cr3 are adjustment coefficients. For details, please refer to the description in the above embodiment and will not be repeated here.

[0094] Based on the same inventive concept, the present application also provides a device for predicting the operating life of a wind turbine generator set, as shown in FIG4 , which includes:

[0095] The third acquisition module 401 is used to obtain the values ​​of the first wind condition parameters of the wind turbine generator set; for details, please refer to the description of step S201 in the above embodiment, which will not be repeated here.

[0096] The second prediction module 402 is configured to predict the operating life of the wind turbine based on the values ​​of the first wind condition parameters and a wind turbine operating life prediction model. The wind turbine operating life prediction model is obtained using the wind turbine operating life prediction model construction method in the above-described embodiment. For details, see the description of step S202 in the above-described embodiment and will not be repeated here.

[0097] Taking into account the high cost and difficulty in obtaining the unit model in the related art when predicting the operating life of a wind turbine by calculating the whole machine load, or the time-consuming manual measurement and evaluation of the operating life, the device provided in this application uses the wind turbine operating life prediction model to predict the operating life of the wind turbine, without the need to calculate the whole machine load, and even without the need to obtain the corresponding unit model of the wind turbine. While reducing the prediction cost, it also shortens the prediction time and reduces the complexity of the prediction process.

[0098] Figure 5 is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment. As shown in Figure 5 , the device includes one or more processors 510 and memory 520. Memory 520 includes persistent memory, volatile memory, and a hard disk. Figure 5 uses one processor 510 as an example. The device may also include an input device 530 and an output device 540.

[0099] The processor 510 , the memory 520 , the input device 530 and the output device 540 may be connected via a bus or other means. FIG. 5 takes the bus connection as an example.

[0100] The processor 510 may be a central processing unit (CPU). The processor 510 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips. The general-purpose processor may be a microprocessor or any conventional processor.

[0101] Memory 520, as a non-transient computer-readable storage medium, includes persistent memory, volatile memory, and a hard disk, and can be used to store non-transient software programs, non-transient computer executable programs, and modules, such as the program instructions / modules corresponding to the wind turbine operating life prediction model construction method and wind turbine operating life prediction method in the embodiments of the present application. Processor 510 executes the non-transient software programs, instructions, and modules stored in memory 520 to execute various server functional applications and data processing, thereby implementing any of the aforementioned wind turbine operating life prediction model construction methods and wind turbine operating life prediction methods.

[0102] The memory 520 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data required for use, etc. In addition, the memory 520 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 520 may optionally include a memory remotely located relative to the processor 510, and these remote memories may be connected to the data processing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0103] The input device 530 can receive input digital or character information and generate signal input related to user settings and function control. The output device 540 can include a display device such as a display screen.

[0104] One or more modules are stored in the memory 520 , and when executed by one or more processors 510 , the method shown in FIG. 1 or FIG. 2 is performed.

[0105] The above-mentioned product can execute the method provided in the embodiment of this application, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not fully described in this embodiment, please refer to the relevant description in the embodiment shown in Figure 1 or Figure 2.

[0106] The present application also provides a non-transitory computer storage medium storing computer-executable instructions that can execute the construction and prediction methods in any of the above method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); the storage medium can also include a combination of the above types of memory.

[0107] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device that includes the element.

[0108] The above are merely specific embodiments of the present application to enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but rather is intended to conform to the widest scope consistent with the principles and novel features of the present application.

Claims

1. A method for constructing a wind turbine operating life prediction model, characterized in that: The method comprises: Determining the type of each first wind condition parameter of the wind turbine generator set; Establishing a first wind turbine operating life prediction model according to the correlation between the type of each of the first wind condition parameters and the operating life of the wind turbine; Obtaining values ​​of first wind condition parameters corresponding to types of the first wind condition parameters of each of a plurality of wind turbine generator sets; Inputting the value of each of the first wind condition parameters into the first wind turbine operating life prediction model to obtain the first operating life of each of the wind turbines; Obtaining a second operating life of each of the wind turbine generator sets, wherein each of the second operating lifespans is obtained by calculating the entire machine load; If the difference between the first operating life and the second operating life of the same wind turbine set is greater than a preset value, the first wind turbine set operating life prediction model is adjusted according to each of the first operating life and each of the second operating life to obtain a second wind turbine set operating life prediction model.

2. The method according to claim 1, characterized in that If the difference between the first operating life and the second operating life of the same wind turbine generator set is greater than a preset value, the first wind turbine generator set operating life prediction model is adjusted according to each of the first operating life and each of the second operating life to obtain a second wind turbine generator set operating life prediction model, including: If the difference between the first operating life and the second operating life of the same wind turbine set is greater than a preset value, the correlation between the value of each of the first wind condition parameters and the operating life is adjusted according to each of the first operating life and each of the second operating life until the difference between the first operating life and the second operating life of each of the wind turbine sets is less than or equal to the preset value, thereby obtaining the operating life prediction model of the second wind turbine set.

3. The method according to claim 1, characterized in that Determining the type of each first wind condition parameter includes: Obtaining types of multiple second wind condition parameters; Calculating the correlation between the type of each of the second wind condition parameters and the operating life of the wind turbine generator set, wherein the correlation is represented by a correlation coefficient; According to each of the correlations, the type of each of the second wind condition parameters is screened to obtain the type of each of the first wind condition parameters.

4. The method according to claim 2, characterized in that: The types of the first wind condition parameters include turbulence, average wind speed within a preset time period and average air density. In the second wind turbine operating life prediction model, the turbulence, the average wind speed and the average air density are all negatively correlated with the operating life.

5. The method according to claim 4, characterized in that The formula of the second wind turbine operating life prediction model is expressed as: Among them, L O is the predicted operating life, i.e., the first operating life; L D is the reference life; V D is the reference average wind speed; V O is the average wind speed; ρ D is the reference average air density; ρ O is the average air density; I D is the reference turbulence; I O is the turbulence degree, Cr1, Cr2 and Cr3 are adjustment coefficients.

6. A method for predicting the operating life of a wind turbine generator set, characterized in that: The method comprises: Obtaining values ​​of first wind condition parameters of the wind turbine generator set; The operating life of the wind turbine is predicted based on the values ​​of each of the first wind condition parameters and a wind turbine operating life prediction model, wherein the wind turbine operating life prediction model is the second wind turbine operating life prediction model obtained by the wind turbine operating life prediction model construction method described in any one of claims 1 to 5.

7. A method for constructing a wind turbine operating life prediction model, characterized in that: The method comprises: A determination module, used to determine the type of each first wind condition parameter of the wind turbine generator set; An establishing module, used for establishing a first wind turbine operating life prediction model according to the correlation between the type of each of the first wind condition parameters and the operating life of the wind turbine; A first acquisition module, used to acquire values ​​of first wind condition parameters corresponding to types of the first wind condition parameters of a plurality of wind turbines; A first prediction module, used for inputting the value of each of the first wind condition parameters into the first wind turbine operating life prediction model to obtain a first operating life of each of the wind turbines; A second acquisition module is used to acquire a second operating life of each of the wind turbines, where each of the second operating life is obtained by calculating the whole machine load; The adjustment module is used to adjust the first wind turbine operating life prediction model according to each of the first operating life and each of the second operating life to obtain the second wind turbine operating life prediction model if the difference between the first operating life and the second operating life of the same wind turbine is greater than a preset value.

8. A device for predicting the operating life of a wind turbine generator set, characterized in that: The device comprises: A third acquisition module is used to obtain the values ​​of each first wind condition parameter of the wind turbine generator set; A second prediction module is used to predict the operating life of the wind turbine according to the values ​​of each of the first wind condition parameters and a wind turbine operating life prediction model, wherein the wind turbine operating life prediction model is the second wind turbine operating life prediction model obtained by the wind turbine operating life prediction model construction method described in any one of claims 1 to 5.

9. A computer device, characterized in that: It comprises a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the method for constructing a wind turbine operating life prediction model according to any one of claims 1 to 5, or the steps of the wind turbine operating life prediction method according to claim 6 by executing the computer instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for constructing a wind turbine operating life prediction model according to any one of claims 1 to 5, or the steps of the method for predicting the operating life of a wind turbine according to claim 6 is implemented.

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