Method for constructing service life prediction model for wind-force power generation unit and method for predicting service life of wind-force power generation unit

Abstract

To provide a method for constructing a service life prediction model for wind-force power generation unit and a method for predicting service life of wind force-power generation unit.SOLUTION: A method for constructing service life prediction model for wind-force power generation system includes the following steps of: determining the types of first wind condition parameters of wind-force power generation systems; establishing a first wind-force power generation system service life prediction model according to an association relationship between each type of 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-force power generation system service life prediction model 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 a first service life and a second service life of the same wind turbine generation unit have a difference greater than a preset value, obtaining a second wind turbine generator unit service life prediction model according to the first service life and the second service life.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to the field of wind power generating units, and in particular to a method for constructing an operating life prediction model and an operating life prediction method for wind power generating units. [Background technology]

[0002] In the prior art, the operating unit life of a wind power generation unit can be predicted and evaluated by calculating the total load. However, not only is the calculation of the total load costly and time-consuming, but a unit model of the wind power generation unit needs to be obtained in the process of using the calculation of the total load. Meanwhile, there is a problem that the unit model of the wind power generation unit is difficult to obtain as core data. In addition, the operating life of a wind power generation unit can be evaluated by an expert performing actual measurements on-site, but such an evaluation method using actual measurements requires at least six months of testing time, which is a long time. Therefore, there is an urgent need for a versatile, quick and convenient method for predicting the operating life of a wind power generation unit. Summary of the Invention [Problem to be solved by the invention]

[0003] In order to predict the operating life of a wind power unit, and at the same time reduce the prediction cost and shorten the prediction time, the present invention proposes a method for building an operating life prediction model and an operating life prediction method for a wind power unit. [Means for solving the problem]

[0004] In a first aspect, the present invention provides a method for producing a composition comprising the steps of: determining a first wind condition parameter for each wind power unit; Establishing a first wind power unit operating life prediction model according to the correlation between each first wind condition parameter and the operating life of the wind power unit; obtaining first wind condition parameter values ​​corresponding to each first wind condition parameter of a plurality of wind power units; inputting each first wind condition parameter value into a first wind power unit operating life prediction model to obtain a first operating life of each wind power unit; obtaining a second operating life of each wind power unit, each second operating life being obtained by a total load calculation; and if a difference between the first operating life and the second operating life of the same wind power unit is greater than a predetermined value, adjusting the operating life prediction model of the first wind power unit according to each of the first operating life and each of the second operating life to obtain an operating life prediction model of the second wind power unit.

[0005] In the related art, when the operating life of a wind power unit is predicted by calculating the total load, it is costly, difficult to obtain a unit model, or time-consuming to evaluate the operating life by manual measurement. In view of this, the method of the present invention first establishes a first wind power unit operating life prediction model based on the correlation between wind condition parameters and wind power units, then adjusts the first wind power unit operating life prediction model with the operating life predicted by calculating the total load, and finally obtains a second wind power unit operating life prediction model for predicting the operating life of the wind power unit. By using the model of the present invention, there is no need to use the total load calculation, no need to obtain a unit model, and the operating life of the wind power unit can be obtained by only obtaining the reference life and reference wind condition parameters of the unit, which reduces the prediction cost, shortens the prediction time, and reduces the complexity of the prediction process.

[0006] In one alternative embodiment, when a difference between the first operating life and the second operating life of the same wind power unit is greater than a predetermined value, the step of adjusting the operating life prediction model of the first wind power unit according to each of the first operating life and each of the second operating life to obtain the operating life prediction model of the second wind power unit includes: When the difference between the first operating life and the second operating life of the same wind power generation unit is greater than a predetermined value, the positive 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 power generation unit becomes equal to or less than the predetermined value, thereby obtaining an operating life prediction model for the second wind power generation unit.

[0007] In one alternative embodiment, the step of determining each first wind condition parameter comprises: acquiring a plurality of second wind condition parameters; calculating a correlation between each second wind parameter and the operational life of the wind power unit; Screening each of the second wind condition parameters according to each correlation degree to obtain each of the first wind condition parameters.

[0008] In one alternative embodiment, the first wind condition parameters include turbulence intensity, average wind speed and average air density in a given period, and in the second wind power unit operating life prediction model, the turbulence intensity, the average wind speed and the average air density are all positively correlated with the operating life.

[0009] In one alternative embodiment, the formula for the second wind power unit operating life prediction model is:

[0010] JPEG2025009681000002.jpg1546, L O is the predicted 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 mean air density, ρ O is the average air density, I D is the reference turbulence intensity, I O is the turbulence intensity.

[0011] In a second aspect, the present invention provides a method for producing a composition comprising the steps of: obtaining a first wind condition parameter value for each of the wind power generating units; The present invention further provides a method for predicting an operating life of a wind power unit, the method including: predicting an operating life of the wind power unit according to each first wind condition parameter value and a wind power unit operating life prediction model, the wind power unit operating life prediction model being obtained by the method for constructing an operating life prediction model of a wind power unit described in the first aspect or any one of the embodiments of the first aspect.

[0012] Considering that in the related art, when predicting the operating life of a wind power unit by calculating the total load, it is costly, difficult to obtain a unit model, or time-consuming to evaluate the operating life by manual actual measurement, the method of the present invention uses a wind power unit operating life prediction model to predict the operating life of a wind power unit, and does not need to use the total load calculation or obtain a unit model corresponding to the wind power unit, thereby reducing the prediction cost, shortening the prediction time, and reducing the complexity of the prediction process.

[0013] In a third aspect, the present invention provides a method for producing a composition comprising the steps of: a determination module configured to determine a first wind condition parameter for each of the wind power units; an establishment module configured to establish a first wind power unit operating life prediction model according to a correlation between each first wind condition parameter and the operating life of the wind power unit; a first acquisition module configured to acquire a first wind condition parameter value corresponding to each first wind condition parameter of the plurality of wind power units; a first prediction module configured to input each first wind condition parameter value into a first wind power unit operating life prediction model to obtain a first operating life of each wind power unit; a second obtaining module configured to obtain a second operating life of each wind power unit, each second operating life being obtained by calculating an overall load; and an adjustment module configured to adjust the first wind power unit operating life prediction model according to each of the first operating lives and each of the second operating lives when a difference between the first operating life and the second operating life of the same wind power unit is greater than a predetermined value, to obtain a second wind power unit operating life prediction model.

[0014] In view of the fact that in the related art, when the operating life of a wind power unit is predicted by calculating the total load, it is costly, difficult to obtain a unit model, or time-consuming to evaluate the operating life by manual actual measurement, the device of the present invention first establishes a first wind power unit operating life prediction model based on the correlation between wind condition parameters and wind power units, then adjusts the first wind power unit operating life prediction model with the operating life predicted by calculating the total load, and finally obtains a second wind power unit operating life prediction model for predicting the operating life of a wind power unit. By using the model of the present invention, there is no need to use the total load calculation, no need to obtain a unit model, and the operating life of the wind power unit can be obtained by only obtaining the reference life and reference wind condition parameters of the unit, which reduces the prediction cost, shortens the prediction time, and reduces the complexity of the prediction process.

[0015] In a fourth aspect, the present invention provides a method for producing a composition comprising the steps of: a third acquisition module configured to acquire a first wind condition parameter value of each of the wind power units; and a second prediction module configured to predict the operating life of the wind power unit according to each of the first wind condition parameter values ​​and the wind power unit operating life prediction model, the wind power unit operating life prediction model being obtained by the method for constructing a wind power unit operating life prediction model described in the first aspect or any one of the embodiments of the first aspect.

[0016] In view of the fact that in the related art, when predicting the operating life of a wind power generation unit by calculating the total load, it is costly, difficult to obtain a unit model, or time-consuming to evaluate the operating life by manual actual measurement, the device of the present invention predicts the operating life of a wind power generation unit using a wind power generation unit operating life prediction model, and does not need to use the total load calculation or obtain a unit model corresponding to the wind power generation unit, thereby reducing the prediction cost, shortening the prediction time, and reducing the complexity of the prediction process.

[0017] In a fifth aspect, the present invention further provides a computing 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 perform the steps of the method for constructing an operational life prediction model of a wind power unit as described in the first aspect or any one of the embodiments of the first aspect, or the steps of the method for predicting an operational life of a wind power unit as described in the second aspect.

[0018] In a sixth aspect, the present invention further provides a computer readable storage medium having stored thereon a computer program, the computer program being configured to, when executed by a processor, realise the steps of the method for constructing an operational life prediction model for a wind power unit as described in the first aspect or any one of the embodiments of the first aspect, and the steps of the method for predicting an operational life of a wind power unit as described in the second aspect.

[0019] In order to more clearly describe the specific embodiments of the present invention or the technical solutions of the prior art, the following will briefly describe the drawings that need to be used to describe the specific embodiments or the prior art. It is obvious that the drawings described below are some embodiments of the present invention, and those skilled in the art can further derive other drawings based on these drawings without creative labor. [Brief description of the drawings]

[0020] [Figure 1]1 is a flow chart of a method for constructing an operational life prediction model of a wind power unit according to an exemplary embodiment. [Diagram 2] 2 is a flow chart of a method for predicting the operational life of a wind power unit according to an exemplary embodiment. [Diagram 3] 1 is a structural schematic diagram of a wind power unit operating life prediction model building device according to an exemplary embodiment; FIG. [Figure 4] 1 is a structural schematic diagram of a wind power unit operating life prediction device according to an exemplary embodiment; [Diagram 5] FIG. 2 is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] The technical solutions of the present invention will be described below clearly and completely with reference to the drawings, and it is obvious that the described embodiments are only some of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments that a person skilled in the art can obtain without creative labor fall within the scope of protection of the present invention.

[0022] Furthermore, the technical features according to the various embodiments of the present invention described below can be combined with each other unless they are inconsistent.

[0023] In order to predict the operating life of a wind power unit, and at the same time reduce the prediction cost and shorten the prediction time, the present invention proposes a method for building an operating life prediction model and an operating life prediction method for a wind power unit.

[0024] 1 is a flowchart of a method for constructing an operating life prediction model of a wind power unit according to an exemplary embodiment. As shown in FIG. 1, the method for constructing an operating life prediction model of a wind power unit includes the following steps S101 to S106.

[0025] Step S101: Determine a first wind condition parameter of each wind power generation unit.

[0026] In one alternative embodiment, the first wind condition parameter may be determined according to a correlation between the wind condition parameter and the operational lifetime of the wind power unit.

[0027] In one alternative embodiment, the wind condition parameters include, but are not limited to, turbulence intensity, average wind speed over a given period of time, and average air density.

[0028] Step S102: Establish a first wind power unit operating life prediction model according to the correlation between each first wind condition parameter and the operating life of the wind power unit.

[0029] In one optional embodiment, the correlation between the first wind condition parameter and the operating life of the wind power unit is a positive correlation, and an operating life prediction model of the first wind power unit can be established based on the positive correlation between the first wind condition parameter and the operating life of the wind power unit.

[0030] Step S103: Obtain a first wind condition parameter value corresponding to each of the first wind condition parameters of the multiple wind power generation units.

[0031] In one alternative embodiment, the first wind condition parameter value is an actual parameter value at the site of the wind power unit. The first wind condition parameter value of each wind power unit may be obtained by an instrument such as a sensor.

[0032] Step S104: Input each first wind condition parameter value into a first wind power unit operating life prediction model to obtain a first operating life of each wind power unit.

[0033] Step S105: Obtain a second operating life of each wind power unit, and each second operating life is obtained by calculating the total load.

[0034] In one optional embodiment, the second operating life of each wind power unit is predicted by calculating the overall load. When calculating the overall load, the model of each wind power unit needs to be obtained first, and then the wind model, wind shear, air density and other wind condition parameter values ​​of the wind power unit are obtained, and then the second operating life is obtained.

[0035] Step S106: If the difference between the first operating life and the second operating life of the same wind power unit is greater than a predetermined value, adjust the operating life prediction model of the first wind power unit according to each of the first operating life and each of the second operating life, and obtain the operating life prediction model of the second wind power unit.

[0036] In one optional embodiment, the magnitude of the predetermined value can be set according to actual needs, and is not specifically limited herein.

[0037] In the related art, when the operating life of a wind power unit is predicted by calculating the total load, it is costly, difficult to obtain a unit model, or time-consuming to evaluate the operating life by manual measurement. In view of this, the model construction method according to the embodiment of the present invention first establishes a first wind power unit operating life prediction model based on the correlation between wind condition parameters and wind power units, then adjusts the first wind power unit operating life prediction model with the operating life predicted by calculating the total load, and finally obtains a second wind power unit operating life prediction model for predicting the operating life of the wind power unit. By using the model according to the present invention, there is no need to calculate the total load or obtain a unit model. The operating life of the wind power unit can be obtained by only obtaining the reference life and reference wind condition parameters of the unit, which reduces the prediction cost, shortens the prediction time, and reduces the complexity of the prediction process.

[0038] 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 power generation unit is greater than a predetermined value, the positive 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 power generation unit becomes equal to or less than the predetermined value, thereby obtaining an operating life prediction model for the second wind power generation unit.

[0039] In one example, in step S101, the first wind condition parameters are determined by the following method.

[0040] First, a plurality of second wind condition parameters are obtained.

[0041] In one alternative embodiment, the second wind parameter may be a wind parameter such as wind speed, wind shear, air density, etc.

[0042] Next, the degree of correlation between each second wind condition parameter and the operational life of the wind power generation unit is calculated.

[0043] In one alternative embodiment, the degree of correlation may be expressed as a correlation coefficient, which is not particularly limited here.

[0044] Finally, the second wind condition parameters are screened according to the correlation degrees to obtain the first wind condition parameters.

[0045] In one alternative embodiment, a wind condition parameter having a correlation degree greater than a predetermined value is selected as the first wind condition parameter.

[0046] In one example, in the above step S106, the first wind condition parameters include turbulence intensity, average wind speed and average air density in a specified period, and in the second wind power unit operating life prediction model, the turbulence intensity, average wind speed and average air density are all positively correlated with the operating life.

[0047] In one example, in the above step S106, the formula of the second wind power generating unit operating life prediction model is as follows:

[0048] JPEG2025009681000003.jpg1446, L O is the predicted operating life, and L D is the reference life, i.e., the design life of the unit, and V D is the reference mean wind speed, i.e., the unit's design mean wind speed, and V O is the actual average wind speed at the site, and ρ D is the reference mean air density, i.e., the design mean air density of the unit, and ρ O is the actual average air density at the site, and I D is the reference turbulence intensity, i.e., the design turbulence intensity of the unit, and I O is the actual turbulence intensity in the field.

[0049] 2 is a flow chart of a method for predicting the operational life of a wind power unit according to an exemplary embodiment. As shown in FIG. 2, the method includes the following steps S201 to S202.

[0050] Step S201: Obtain the first wind condition parameter values ​​of each wind power generation unit.

[0051] Step S202: Predict the operating life of the wind power unit according to each first wind condition parameter value and the wind power unit operating life prediction model, which is obtained by the method for constructing the wind power unit operating life prediction model according to the above embodiment.

[0052] In view of the fact that in the related art, when predicting the operating life of a wind power unit by calculating the total load, it is costly, difficult to obtain a unit model, or time-consuming to evaluate the operating life by manual actual measurement, the prediction method of the present invention uses a wind power unit operating life prediction model to predict the operating life of a wind power unit, and does not need to use the total load calculation and does not need to obtain a unit model corresponding to the wind power unit, thereby reducing the prediction cost, shortening the prediction time, and reducing the complexity of the prediction process.

[0053] Based on the same inventive idea, the present invention further provides an operating life prediction apparatus for a wind power unit, as shown in FIG. 3 , the apparatus includes a determination module 301, an establishment module 302, a first acquisition module 303, a first prediction module 304, a second acquisition module 305 and an adjustment module 306.

[0054] The determining module 301 is configured to determine each first wind condition parameter of the wind power generation unit, the details of which can be referred to the description of step S101 in the above embodiment, and will not be described again here.

[0055] The establishment module 302 is configured to establish a first wind power unit operating life prediction model according to the correlation between each first wind condition parameter and the operating life of the wind power unit, for details, refer to the description of step S102 in the above embodiment, and will not be described repeatedly here.

[0056] The first acquisition module 303 is configured to acquire a first wind condition parameter value corresponding to each first wind condition parameter of the multiple wind power generation units, for details, refer to the description of step S103 in the above embodiment, and will not be repeated here.

[0057] The first prediction module 304 is configured to input each first wind condition parameter value into a first wind power unit operating life prediction model to obtain a first operating life of each wind power unit, for details, refer to the description of step S104 in the above embodiment, and will not be described repeatedly here.

[0058] The second acquisition module 305 is configured to obtain a second operating life of each wind power unit, and each second operating life is obtained by calculating the total load, for details, please refer to the description of step S105 in the above embodiment, and will not be repeated here.

[0059] The adjustment module 306 is configured to adjust the operation life prediction model of the first wind power unit according to each of the first operation life and each of the second operation life of the same wind power unit when the difference between the first operation life and the second operation life of the same wind power unit is greater than a predetermined value, and obtain the operation life prediction model of the second wind power unit. For details, refer to the description of step S106 in the above embodiment, and will not be described again here.

[0060] In view of the fact that in the related art, when the operating life of a wind power unit is predicted by calculating the total load, it is costly, difficult to obtain a unit model, or time-consuming to evaluate the operating life by manual actual measurement, the device of the present invention first establishes a first wind power unit operating life prediction model based on the correlation between wind condition parameters and wind power units, then adjusts the first wind power unit operating life prediction model with the operating life predicted by calculating the total load, and finally obtains a second wind power unit operating life prediction model for predicting the operating life of a wind power unit. By using the model of the present invention, there is no need to use the total load calculation, no need to obtain a unit model, and the operating life of the wind power unit can be obtained by only obtaining the reference life and reference wind condition parameters of the unit, which reduces the prediction cost, shortens the prediction time, and reduces the complexity of the prediction process.

[0061] In one example, the adjustment module 306 includes an adjustment sub-module: The adjustment submodule is configured to adjust the positive correlation between each first wind condition parameter and the operating life 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 power unit is equal to or smaller than the predetermined value when the difference between the first operating life and the second operating life of each wind power unit is greater than a predetermined value, and obtain the operating life prediction model of the second wind power unit. For details, refer to the description in the above embodiment, and the description will not be repeated here.

[0062] In one example, the determination module 301 includes an acquisition sub-module, a calculation sub-module, and a screening sub-module.

[0063] The acquisition submodule is configured to acquire a plurality of second wind condition parameters, and for details, refer to the description in the above embodiment, and will not be described repeatedly here.

[0064] The calculation submodule is configured to calculate the correlation degree between each second wind condition parameter and the operating life of the wind power generation unit, and the details thereof can be referred to the description in the above embodiment, and will not be described again here.

[0065] The screening sub-module is configured to screen each of the second wind condition parameters according to each correlation degree to obtain each of the first wind condition parameters. For details, refer to the description in the above embodiment, and will not be described again here.

[0066] In one example, in the adjustment module 306, the first wind condition parameters include turbulence intensity, average wind speed and average air density in a certain period, and in the second wind power unit operating life prediction model, the turbulence intensity, average wind speed and average air density are all positively correlated with the operating life. For details, refer to the description in the above embodiment, and will not be described again here.

[0067] In one example, in the adjustment module 306, the equation for the second wind power unit operating life prediction model is:

[0068] JPEG2025009681000004.jpg1346, L O is the predicted 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 mean air density, ρ O is the average air density, I D is the reference turbulence intensity, I O is the turbulence intensity. For details, please refer to the description in the above embodiment, and will not be described again here.

[0069] Based on the same inventive idea, the present invention further provides an operating life prediction apparatus for a wind power unit, which includes a third acquisition module 401 and a second prediction module 402, as shown in FIG.

[0070] The third acquisition module 401 is configured to acquire the first wind condition parameter values ​​of each of the wind power generating units, and for details, refer to the description of step S201 in the above embodiment, and will not be described again here.

[0071] The second prediction module 402 is configured to predict the operating life of the wind power unit according to each first wind condition parameter value and the wind power unit operating life prediction model, which is obtained by the wind power unit operating life prediction model building method in the above embodiment. For details, refer to the description of step S202 in the above embodiment, and will not be repeated here.

[0072] In view of the fact that in the related art, when predicting the operating life of a wind power unit by calculating the total load, it is costly, difficult to obtain a unit model, or time-consuming to evaluate the operating life by manual actual measurement, the device of the present invention predicts the operating life of a wind power unit by using a wind power unit operating life prediction model, and does not need to use the total load calculation and does not need to obtain a unit model corresponding to the wind power unit, thereby reducing the prediction cost, shortening the prediction time, and reducing the complexity of the prediction process.

[0073] Fig. 5 is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment. As shown in Fig. 5, the device includes one or more processors 510 and a memory 520, where the memory 520 includes a persistent memory, a volatile memory and a hard disk, and Fig. 5 takes one processor 510 as an example. The device may further include an input device 530 and an output device 540.

[0074] The processor 510, memory 520, input device 530, and output device 540 may be connected by a bus or other form of connection, and FIG. 5 illustrates a connection by a bus as an example.

[0075] The processor 510 may be a Central Processing Unit (CPU). The processor 510 may also be other general purpose processors, chips such as Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the various chips mentioned above. The general purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like.

[0076] The memory 520 includes a permanent memory, a volatile memory and a hard disk as a non-transitory computer readable storage medium, and can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as the method for constructing an operating life prediction model of a wind power unit and the program instructions / modules corresponding to the method for predicting the operating life of a wind power unit in the embodiments of the present application. The processor 510 executes the non-transitory software programs, instructions and modules stored in the memory 520 to execute various functional applications and data processing of the server, that is, to realize any one of the method for constructing an operating life prediction model of a wind power unit and the method for predicting the operating life of a wind power unit.

[0077] The memory 520 may include a program storage area and a data storage area, where the program storage area may store an operating system, an application program required for at least one function, and the data storage area may store data used as needed. The memory 520 may also include high-speed random access memory, and may include non-transitory memory, such as at least one disk memory device, flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 520 may optionally include memory located remotely from the processor 510, and these remote memories may be connected to the data processing device via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0078] The input device 530 can receive input numeric or textual information and generate signal inputs related to user settings and function control. The output device 540 can include a display device such as a display.

[0079] The one or more modules are stored in memory 520 and, when executed by one or more processors 510, perform the method shown in FIG. 1 or FIG.

[0080] The above product executes the method according to the embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in the embodiment, please refer to the relevant description in the embodiment shown in FIG. 1 or FIG. 2.

[0081] An embodiment of the present invention further provides a non-transitory computer storage medium, the computer storage medium storing computer executable instructions, the computer executable instructions being capable of executing the construction and prediction method in any one of the above method embodiments. The storage medium may 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), etc., or the storage medium may include a combination of the above types of memory.

[0082] It should be noted that, in this specification, relational terms such as "first" and "second" are merely used to distinguish one entity or operation from another and do not necessarily require or imply any actual relationship or order between those entities or operations. Also, the terms "comprise", "comprises", or any other variation thereof are intended to cover a non-exclusive inclusion, whereby a process, method, article, or device that includes a set of elements not only includes those elements, but also includes other elements not expressly listed or includes elements inherent to the process, method, article, or device. Unless further limited, an element qualified by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, article, or device that includes the element.

[0083] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or realize the present invention. Various modifications of 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 invention. Therefore, the present invention is not limited to these embodiments shown herein, but should be accorded the widest scope consistent with the principles and novel features asserted herein.

Claims

1. A method for constructing an operational life prediction model for a wind power unit, comprising the steps of: determining a first wind condition parameter for each wind power unit; Establishing a first wind power unit operating life prediction model according to the correlation between each of the first wind condition parameters and the operating life of the wind power unit; obtaining first wind condition parameter values ​​corresponding to the first wind condition parameters of each of a plurality of wind power units; inputting each of the first wind condition parameter values ​​into the first wind power unit operating life prediction model to obtain a first operating life of each of the wind power units; obtaining a second operating life of each of the wind power units, each of the second operating life being obtained by a total load calculation; and when a difference between a first operating life and a second operating life of the same wind power unit is greater than a predetermined value, adjusting the operating life prediction model of the first wind power unit according to each of the first operating lives and each of the second operating lives to obtain an operating life prediction model of a second wind power unit.

2. When the difference between the first and second operating lives of the same wind power unit is greater than a predetermined value, the step of adjusting the operating life prediction model of the first wind power unit according to each of the first operating lives and each of the second operating lives to obtain the operating life prediction model of the second wind power unit includes:

2. The method according to claim 1, further comprising the step of: when a difference between a first operating life and a second operating life of the same wind power unit is greater than a predetermined value, adjusting the positive correlation between each of the first wind condition parameters and the operating life according to each of the first operating lives and each of the second operating lives until the difference between the first operating life and the second operating life of each of the wind power units is equal to or less than the predetermined value, thereby obtaining the operating life prediction model of the second wind power unit.

3. The step of determining each first wind condition parameter comprises: acquiring a plurality of second wind condition parameters; calculating a correlation between each of the second wind parameters and the operational life of the wind power unit; and screening each of the second wind condition parameters according to each of the correlation degrees to obtain each of the first wind condition parameters.

4. 3. The method according to claim 2, wherein the first wind condition parameters include turbulence intensity, average wind speed and average air density in a predetermined period, and in the second wind power unit operating life prediction model, the turbulence intensity, the average wind speed and the average air density are all positively correlated with the operating life.

5. 5. The method of claim 4, wherein the formula of the second wind power unit operating life prediction model is: In the ceremony, L O is the predicted 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 mean air density, ρ O is the average air density, I D is the reference turbulence intensity, I O is the turbulence intensity.

6. 1. A method for predicting the operational life of a wind power unit, comprising: obtaining a first wind condition parameter value for each wind power unit; and a step of predicting the operating life of the wind power generation unit according to each of the first wind condition parameter values ​​and a wind power generation unit operating life prediction model, the wind power generation unit operating life prediction model being obtained by a method for constructing an operating life prediction model of a wind power generation unit described in any one of claims 1 to 5.

7. A device for constructing an operational life prediction model for a wind power generating unit, comprising: a determination module configured to determine a first wind condition parameter for each wind power unit; an establishment module configured to establish a first wind power unit operating life prediction model according to a correlation between each of the first wind condition parameters and the operating life of the wind power unit; a first acquisition module configured to acquire a first wind condition parameter value corresponding to each of the first wind condition parameters of a plurality of wind power units; a first prediction module configured to input each of the first wind condition parameter values ​​into the first wind power unit operating life prediction model to obtain a first operating life of each of the wind power units; a second obtaining module configured to obtain a second operating life of each of the wind power units, each of the second operating life being obtained by calculating an overall load; and an adjustment module configured to adjust the first wind power unit operating life prediction model according to each of the first operating lives and each of the second operating lives when a difference between a first operating life and a second operating life of the same wind power unit is greater than a predetermined value, to obtain a second wind power unit operating life prediction model.

8. An apparatus for predicting the operational life of a wind power generating unit, comprising: a third acquisition module configured to acquire a first wind condition parameter value of each of the wind power units; and a second prediction module configured to predict the operating life of the wind power unit according to each of the first wind condition parameter values ​​and a wind power unit operating life prediction model, the wind power unit operating life prediction model being obtained by the method for constructing an operating life prediction model of a wind power unit according to any one of claims 1 to 5.

9. 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 perform steps of the method for constructing an operating life prediction model of a wind power unit described in any one of claims 1 to 5, or the method for predicting the operating life of a wind power unit described in claim 6.

10. A computer-readable storage medium having a computer program stored therein, the computer program being characterized in that, when executed by a processor, the computer program realizes steps of a method for constructing an operating life prediction model for a wind power unit as described in any one of claims 1 to 5, or a method for predicting an operating life of a wind power unit as described in claim 6.

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