A wind turbine blade reliability modeling method, device, equipment, medium and product

Abstract

The application discloses a wind turbine blade reliability modeling method, device, equipment, medium and product, relates to the field of wind turbine blade reliability analysis, and comprises the following steps: determining a wind turbine blade system hypothesis; selecting an impact damage function, an amplification function and a threshold function based on the wind turbine blade system hypothesis; constructing a wind turbine blade degradation failure model; constructing a wind turbine blade sudden failure model; constructing a double interdependent relationship competing failure model based on the impact damage function, the amplification function, the threshold function, the wind turbine blade degradation failure model and the wind turbine blade sudden failure model; and determining the reliability of the wind turbine blade based on the double interdependent relationship competing failure model. The application can improve the convenience and accuracy of the reliability analysis of the wind turbine blade.

Description

Technical Field

[0001] This application relates to the field of wind turbine blade reliability analysis, and in particular to a method, apparatus, equipment, medium and product for wind turbine blade reliability modeling. Background Technology

[0002] During use, product performance inevitably declines gradually, due to factors such as rubber aging, gear wear, and resistance drift. This decline in performance is defined as the process of increasing product degradation, which is continuous over time. A degradation threshold is typically defined; when the total degradation (also known as the degradation state) reaches or exceeds this threshold, the product is considered to have degraded and failed.

[0003] Wind farms are typically located in complex environments such as high altitudes, oceans, or deserts. Therefore, wind turbine blades face multiple failure modes, including continuous degradation damage such as stiffness degradation, fatigue damage, airflow erosion, and gravel abrasion; they may also be subjected to random impact loads from atmospheric particles (such as hail) or wildlife (such as birds and bats). These impacts not only accelerate the degradation and failure process of wind turbine blades but can also directly damage them, significantly shortening their lifespan. Therefore, with increasing operating years, the probability of unexpected blade failures or even accidents increases significantly, seriously threatening the operational safety of wind turbines and the economic benefits of wind farms. However, current blade designs do not consider impact loads and instead introduce high safety factors, resulting in overly conservative designs. As wind turbine blades become stiffer and lighter, and health monitoring systems become more sophisticated, the design philosophy is shifting towards considering the impact of impact loads on reliability from a probabilistic perspective, making products more economical and practical.

[0004] Fiber-reinforced composite materials have been widely used in the manufacturing of large wind turbine blades. While simplifying the molding process and improving the blade's specific strength and stiffness, they also present the following challenges for reliability analysis: ① The failure mechanism of composite materials is complex, requiring careful consideration of the interrelationships of various influencing factors. For wind turbine blades facing a competition between degradation and extreme impact failure modes, non-fatal impacts can lead to a sudden increase in degradation levels. As degradation levels increase, the blade's resistance to impact weakens, and constant-intensity non-fatal impacts cause even greater sudden increases in degradation for severely degraded blades. ② The degradation performance characteristics of composite wind turbine blades include stiffness, fatigue damage, wear, and erosion. The emphasis varies in different environments and studies, resulting in diverse degradation models. This indicates that degradation trajectory models are not applicable to all situations. Therefore, it is necessary to construct a failure model framework for wind turbine blade systems to select the optimal module for specific problems.

[0005] However, existing research has some shortcomings: ① Most studies do not fully consider the interdependence relationship; ② Degradation modeling is mostly degradation trajectory model, which is difficult to adapt to complex and diverse realities. Summary of the Invention

[0006] The purpose of this application is to provide a method, apparatus, equipment, medium and product for modeling the reliability of wind turbine blades, which can accurately analyze the reliability of large composite material wind turbine blades with complex structures and harsh working environments.

[0007] To achieve the above objectives, this application provides the following solution:

[0008] Firstly, this application provides a method for modeling the reliability of wind turbine blades, including:

[0009] Determine the assumptions for the wind turbine blade system;

[0010] Based on the assumptions of the wind turbine blade system, an impact damage function, an amplification function, and a threshold function are selected.

[0011] Constructing a degradation failure model for wind turbine blades;

[0012] Constructing a sudden failure model for wind turbine blades;

[0013] Based on the impact damage function, the amplification function, the threshold function, the wind turbine blade degradation failure model, and the wind turbine blade sudden failure model, a dual interdependent competitive failure model is constructed.

[0014] The reliability of wind turbine blades is determined based on the aforementioned dual interdependence competitive failure model.

[0015] Secondly, this application provides a wind turbine blade reliability modeling device, comprising:

[0016] The wind turbine blade system assumption determination module is used to determine the assumptions of the wind turbine blade system.

[0017] The function selection module is used to select an impact damage function, an amplification function, and a threshold function based on the assumptions of the wind turbine blade system.

[0018] The first model building module is used to build a model of wind turbine blade degradation and failure.

[0019] The second model building module is used to build a sudden failure model for wind turbine blades;

[0020] The third model construction module is used to construct a dual-dependency competitive failure model based on the impact damage function, the amplification function, the threshold function, the wind turbine blade degradation failure model, and the wind turbine blade sudden failure model.

[0021] The wind turbine blade reliability determination module is used to determine the reliability of wind turbine blades based on the dual interdependence competitive failure model.

[0022] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described wind turbine blade reliability modeling method.

[0023] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described wind turbine blade reliability modeling method.

[0024] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described wind turbine blade reliability modeling method.

[0025] According to the specific embodiments provided in this application, this application has the following technical effects:

[0026] This application provides a method, apparatus, equipment, medium, and product for modeling the reliability of wind turbine blades. Based on the impact damage function, amplification function, threshold function, wind turbine blade degradation failure model, and wind turbine blade sudden failure model, a dual-dependency competitive failure model is constructed. This model can flexibly combine suitable degradation models and impact intensity distributions, and by adjusting the impact damage function, amplification function, and threshold function, the reliability of the blades can be made closer to the actual situation, thereby improving the convenience and accuracy of reliability analysis of wind turbine blades. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 A flowchart illustrating a wind turbine blade reliability modeling method provided in an embodiment of this application;

[0029] Figure 2 This is a schematic diagram of the degradation and failure process; where (a) represents the continuous degradation process, (b) represents the overall degradation process, and (c) represents the impact damage accumulation process.

[0030] Figure 3 This is a schematic diagram of an extreme impact-induced sudden failure process;

[0031] Figure 4 This is a schematic diagram of the impact damage function;

[0032] Figure 5 This is a schematic diagram of the amplification function;

[0033] Figure 6 This is a schematic diagram of the threshold function;

[0034] Figure 7 A schematic diagram of reliability curves under three different impact damage functions;

[0035] Figure 8 A schematic diagram of reliability curves under three different amplification functions;

[0036] Figure 9 A schematic diagram of reliability curves under three different threshold functions;

[0037] Figure 10 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0038] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0039] To address the challenges in reliability analysis of composite wind turbine blades, which involve competition between degradation failure and impact-induced sudden failure, as well as the coupling of various failure mechanisms, this application proposes a reliability modeling method for wind turbine blades based on a dual interdependence competitive failure model. Figure 2 The degradation failure process is illustrated: the total degradation S is the cumulative damage caused by the continuous degradation X and the impact. The sum of the two values; the degradation threshold H is a random variable whose mean and variance do not change over time. Figure 2 In (a)-(c), ΔX1 and ΔX2 represent the average value of the degradation threshold H, and ΔX1 and ΔX2 are the continuous degradation increments in different time intervals.

[0040] Figure 3 This demonstrates an extreme impact-induced sudden failure process, where the impact event is extremely short, approximating as an instantaneous process. Furthermore, it requires that the arrival of random impacts follows a homogeneous Poisson process. This means that the occurrence of impact events is uniform and random throughout the blade's lifespan, i.e., the probability of an impact occurring at any given time point is the same. Mathematically, this can be expressed as the impact arrival rate λ being a constant that does not change with time.

[0041] like Figures 2-3 As shown, the characteristic of the dual interdependence competitive failure model is that as the total degradation S increases, the impact threshold D decreases, while the impact damage Y caused by an impact W of the same intensity increases. Overall, the model in this application not only comprehensively considers the coupling relationships between various mechanisms, but also allows for the selection of appropriate degradation models and impact intensity distributions based on different materials and operating conditions, thereby improving the accuracy and versatility of reliability analysis for composite material wind turbine blades.

[0042] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] In one exemplary embodiment, such as Figure 1 As shown, a method for modeling the reliability of wind turbine blades is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, and includes the following steps S1 to S6. Wherein:

[0044] S1: Determine the assumptions about the wind turbine blade system. The assumptions about the wind turbine blade system include: the first assumption, the second assumption, the third assumption, the fourth assumption, the fifth assumption, the sixth assumption, the seventh assumption, and the eighth assumption.

[0045] This application establishes the following assumptions about the wind turbine blade system, and uses mathematical language to precisely describe the model's effects and applicability:

[0046] First assumption: There are only two failure modes in the wind turbine blade system: sudden failure due to extreme impact and continuous performance degradation failure.

[0047] Second hypothesis: The number of arrivals of random shocks follows a homogeneous Poisson process with parameter λ > 0.

[0048] The third assumption is that the impact type is divided into fatal impact and non-fatal impact according to the impact intensity W (W≥0); when W is greater than or equal to the impact threshold D, it is a fatal impact, and the wind turbine blade system suffers sudden failure; otherwise, it is a non-fatal impact, and the wind turbine blade system suffers impact damage.

[0049] Fourth hypothesis: The degradation threshold H is defined in the interval (h L ,h R Random variable on ) (0≤h) L <h R Its distribution parameter is a constant that does not change with time. L and h RThe specific value depends on the distribution of the degradation threshold H. For example, when H follows a log-normal distribution, h... L =0, h R =+∞.

[0050] Fifth assumption: The total degradation S of the wind turbine blade system is the sum of the continuous degradation X and all impact damage. Composition (S,X,Y) i ≥0); therefore, S is a left continuous function of time t, and its jump discontinuity is located at the moment when the non-fatal impact occurs; when S is greater than or equal to the degradation threshold H, the wind turbine blade system undergoes degradation failure.

[0051] Sixth assumption: Impact damage Y and W are independent of each other, i.e., P{Y≤y,W≤w}=P{Y≤y}P{W≤w}; and Y is proportional to the impact damage function g(A) (A≤0), where A is a random variable that is independent and identically distributed with W, and g(0)=0 and g(A) is monotonically increasing.

[0052] Seventh assumption: The total degradation of the wind turbine blade system increases with the increase of impact damage. As the performance degradation of the wind turbine blade system intensifies, the wind turbine blade system becomes more sensitive to impact, i.e., Y = g(A)ψ(S). ψ(S) is called the amplification function, and it is stipulated that ψ(0) = 1 and ψ(S) is monotonically increasing.

[0053] Eighth assumption: Non-fatal impacts lead to a step increase in total degradation. As the overall degradation of the wind turbine blade system intensifies, the system's ability to resist sudden failure weakens, i.e., D = η(S). η(S) is called the threshold function, and η(0) = D0 is defined. Furthermore, η(S) is monotonically decreasing; where D0 is the initial impact threshold, representing the strongest impact that a new wind turbine blade system without degradation can withstand. and h SD represents the mean and standard deviation of the degradation threshold H, respectively.

[0054] The first and second assumptions define the scope of application of the model and are necessary prerequisites for correctly analyzing the reliability of wind turbine blades.

[0055] The seventh and eighth assumptions reflect two pairs of interdependent relationships: ① there is an interdependent relationship between impact damage and total degradation; ② there is an interdependent relationship between the degradation process and the impact process. These two assumptions provide a guarantee for a comprehensive consideration of the interrelationships between various factors.

[0056] S2: Based on the assumptions of the wind turbine blade system, select the impact damage function, amplification function and threshold function.

[0057] Impact damage function is k1 > 0 and k2 > 0. Here, k1 and k2 are adjustable parameters. g(A) is defined on [0, +∞), initialized to 0 and monotonically increasing, satisfying g(0) = 0 and g′(A) > 0; therefore, the inverse function g of g(A) is g... -1 (A) It has the same domain and properties. For example... Figure 4 As shown, when k2 = 1, g(A) and g -1 (A) are all linear functions; when 0 < k2 < 1, g(A) is a convex function. -1 (A) is a concave function; when k2 > 1, g(A) is a concave function, g -1 (A) is a convex function.

[0058] The amplification function is p1 > 0 and p2 > 0. Where, Let p1 and p2 be the mean of the degradation threshold H, and p1 and p2 be adjustable parameters. ψ(S) is defined on [0,+∞), initialized with 1 and monotonically increasing, i.e., satisfying ψ(0)=1 and ψ′(S)>0. Figure 5 As shown, when p2 = 1, ψ(S) is a linear function; when 0 < p2 < 1, ψ(S) is a convex function; and when p2 > 1, ψ(S) is a concave function.

[0059] The threshold function is 0 < q1 ≤ 1 and q2 > 0. Where h SD Let be the standard deviation of the degradation threshold H, and q1 and q2 be adjustable parameters. η(S) is defined on [0, h]. R On the ), it takes D0 as its initial value and decreases monotonically, satisfying η(0)=D0, η′(S)<0 and Where D0 is the initial impact threshold, h R It is the upper limit of the range of values ​​for the degradation threshold H. For example... Figure 6 As shown, when q2 = 1, η(S) is a linear function; when 0 < q2 < 1, η(S) is a concave function; and when q2 > 1, η(S) is a convex function.

[0060] S3: Construct a degradation failure model for wind turbine blades. Specifically, determine the probability density function of the degradation threshold, the probability density function of continuous degradation increment, and the distribution function of continuous degradation increment, and then construct a degradation failure model for wind turbine blades.

[0061] The probability density function f of the degradation threshold H is obtained based on experience or experimentation. H (h) The distribution function F of the continuous degradation increment ΔX (ΔX=X(t)-X(t0)>0,t>t0) X (Δx,t,t0) and probability density function f X(Δx,t,t0) is used to determine the degradation failure model of wind turbine blades. If the continuous degradation amount X(t) follows a Gamma process or an inverse Gaussian process, then ΔX follows a Gamma distribution or an inverse Gaussian distribution.

[0062] Taking the accelerated stiffness degradation test of a certain type of wind turbine blade as an example, the degradation threshold H follows a log-normal distribution, and its probability density function is as follows:

[0063]

[0064] Wherein, parameter μ H =3.1155, σ H =0.0163, lower limit of interval h L =0, upper limit h R It is +∞.

[0065] The continuous degradation increment ΔX follows a Gamma distribution, with its probability density function and distribution function as follows:

[0066]

[0067] Where Γ(·) represents the Gamma function, exp(·) represents the exponential function, t0 and t represent the initial and current times, respectively, and the parameter α = 4.8 years. -1 β = 4.62 mm / N.

[0068] S4: Construct a sudden failure model for wind turbine blades. Specifically, determine the distribution function and probability density function of the impact arrival rate, initial impact threshold, and impact intensity, and construct a sudden failure model for wind turbine blades.

[0069] Accurately determine the impact arrival rate λ, initial impact threshold D0, and impact intensity W distribution function F based on experience or historical detection data. W (w) and probability density function f W (w), thus determining the sudden failure model of the wind turbine blade. W follows a uniform distribution U(a,b) (0≤a<b), a truncated normal distribution N(μ,σ,0,∞), or a Weibull distribution Wbl(ω,υ), etc.

[0070] Based on data provided by a wind farm, the average impact rate of each wind turbine to birds is λ = 0.21 times / year. By reviewing references and summarizing previous experience, the initial impact threshold D0 = 2.6 kJ can be obtained, and the impact intensity W follows a truncated normal distribution with the following probability density function and distribution function:

[0071]

[0072] Among them, the parameters μ = 1.3 kJ and σ = 0.4 kJ.

[0073] S5: Based on the impact damage function, the amplification function, the threshold function, the wind turbine blade degradation failure model, and the wind turbine blade sudden failure model, a dual interdependent competitive failure model is constructed.

[0074] The expression for the competition failure model of the dual interdependence relationship is:

[0075]

[0076] Where R(t) is the reliability at time t, λ is the impact arrival rate, and h L h is the lower limit of the degradation threshold distribution range. R f is the upper limit of the range of degradation threshold distribution. H f is the probability density function of the degradation threshold. X and F X These are the probability density function and cumulative distribution function of the continuous degradation increment, respectively, f W and F W Let be the probability density function and cumulative distribution function of the impact intensity, respectively, and h be the value of the degradation threshold H, t1, t2, ..., t n Let T1, T2, ..., T be the arrival times of n impacts. n The possible values ​​of a1, a2, ..., a n-1 To compare with the strengths W1, W2, ..., W of the previous n-1 impacts n-1 Independent and identically distributed random variables A1, A2, ..., A n-1 The values ​​of Δx1, Δx2, ..., Δx n , The time intervals are (0, t1), (t1, t2), ..., (t... n-1 ,t n ),(t n The continuous degradation increments ΔX1, ΔX2, ..., ΔX on (t) n , The values ​​of s1, s2, ..., s n From the initial time to t1, t2, ..., t n The total degradation amounts between S1, S2, ..., S n The value of .

[0077] s1,s2,…,s n The specific expression is as follows:

[0078]

[0079] S6: Determine the reliability of wind turbine blades based on the aforementioned dual interdependence competitive failure model.

[0080] The general solution to the time-varying reliability curve of the wind turbine blade under study can be obtained through the above process; the specific shape and trend of the curve are determined by the specific impact damage function, amplification function and threshold function and their adjustable parameters.

[0081] The following examples will demonstrate in detail the impact of different models and some key parameters on curve characteristics.

[0082] Example 1:

[0083] Taking the impact damage function g(A), amplification function ψ(S), and threshold function η(S) recommended in this application as examples, the adjustable parameters k2, p2, and q2 significantly influence the shaping of the function's shape, directly determining the concavity and convexity of the function curve. In contrast, while k1, p1, and q1 also participate in shaping the function, they only affect the slope of the function, and their effects are relatively minor. Therefore, to highlight the role of the main influencing factors and effectively reduce the complexity and number of experiments, k1 is fixed at 1.9, p1 at 2.3, and q1 at 0.85.

[0084] When k², p², and q² are all 1, g(A), ψ(S), and η(S) are all linear functions. Because linear functions have good mathematical properties such as simplicity, stability, and intuitive and controllable trends of change, these parameter values ​​are used as a baseline group for subsequent comparison of model performance with other parameter configurations.

[0085] The influence of the impact damage function g(A) on the reliability curve characteristics was studied using the controlled variable method. First, k2 was set as the independent variable, while p2 and q2 were kept constant as control variables. Then, two control groups were set up: in control group 1, k2 = 0.15, where g(A) exhibited a convex function; in control group 2, k2 = 1.7, where g(A) exhibited a concave function. The reliability curves based on the three different impact damage functions are shown below. Figure 7 As shown.

[0086] A comparison reveals significant differences among the three reliability curves over the period from year 2 to year 20; specifically, control group 1 has a higher reliability than the baseline group, while control group 2 has a lower reliability than the baseline group. Furthermore, from... Figure 7 The study found that the median lifespan of the wind turbine blades under the three conditions (baseline, control group 1, and control group 2) were 10.41 years, 11.50 years, and 9.27 years, respectively. These findings indicate that, under the same total degradation and impact intensity, the convex impact damage function causes less impact damage, thus improving the lifespan and reliability of the wind turbine blades; conversely, the concave impact damage function causes greater impact damage, thus reducing the lifespan and reliability of the wind turbine blades.

[0087] Example 2:

[0088] The influence of the amplification function ψ(S) on the reliability curve characteristics was studied using the controlled variable method. First, p2 was set as the independent variable, while k2 and q2 were kept constant as control variables. Then, two control groups were set up: in control group 3, p2 = 0.25, ψ(S) exhibited a convex function; in control group 4, p2 = 4, ψ(S) exhibited a concave function. The reliability curves based on the three different amplification functions are shown below. Figure 8 As shown.

[0089] A comparison reveals significant differences among the three reliability curves over the period from year 2 to year 20; control group 3 exhibits lower reliability than the baseline group, while control group 4 demonstrates higher reliability than the baseline group. From... Figure 8 The study also revealed that the median lifespan of the studied wind turbine blades under parameter configurations 3 and 4 was 9.18 years and 11.53 years, respectively. These findings indicate that the convex amplification function enhances the impact sensitivity of the dual-interdependence competitive failure model, thereby accelerating the failure process and thus reducing the lifespan and reliability of the wind turbine blades; while the concave amplification function weakens the impact sensitivity of the dual-interdependence competitive failure model, thereby mitigating the failure process and thus improving the lifespan and reliability of the wind turbine blades.

[0090] Example 3:

[0091] The influence of the threshold function η(S) on the reliability curve characteristics was studied using the controlled variable method. First, q2 was set as the independent variable, while k2 and p2 were kept constant as control variables. Then, two control groups were set up: in control group 5, q2 = 0.33, η(S) exhibited a concave function; in control group 6, q2 = 8, η(S) exhibited a convex function. The reliability curves based on the three different threshold functions are shown below. Figure 9 As shown.

[0092] The comparison reveals that the reliability of control group 5 is lower than that of the baseline group, while the reliability of control group 6 is higher than that of the baseline group, and the difference between control group 5 and the baseline group is more significant. Furthermore, from... Figure 9 The results show that the median lifespan of the wind turbine blades in control groups 5 and 6 were 6.81 years and 11.29 years, respectively. These findings indicate that the concave threshold function significantly weakens the shock resistance of the dual-interdependence competitive failure model, thus reducing the lifespan and reliability of the wind turbine blades; conversely, the convex threshold function enhances the shock resistance of the dual-interdependence competitive failure model, thus improving the lifespan and reliability of the wind turbine blades.

[0093] Based on the same inventive concept, this application also provides an apparatus for implementing the wind turbine blade reliability modeling method described above. The solution provided by this wind turbine blade reliability modeling apparatus is similar to the implementation scheme described in the above method. Therefore, the specific limitations in one or more embodiments of the wind turbine blade reliability modeling apparatus provided below can be found in the limitations of the wind turbine blade reliability modeling method described above, and will not be repeated here.

[0094] In one exemplary embodiment, a wind turbine blade reliability modeling apparatus is provided, comprising:

[0095] The wind turbine blade system assumption determination module is used to determine the assumptions of the wind turbine blade system.

[0096] The function selection module is used to select the impact damage function, amplification function, and threshold function based on the assumptions of the wind turbine blade system.

[0097] The first model building module is used to build a model of wind turbine blade degradation and failure.

[0098] The second model building module is used to build a sudden failure model for wind turbine blades.

[0099] The third model construction module is used to construct a dual-dependency competitive failure model based on the impact damage function, the amplification function, the threshold function, the wind turbine blade degradation failure model, and the wind turbine blade sudden failure model.

[0100] The wind turbine blade reliability determination module is used to determine the reliability of wind turbine blades based on the dual interdependence competitive failure model.

[0101] In one exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device may be a server or a terminal, and its internal structure diagram may be as follows: Figure 10As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a wind turbine blade system bus, and the communication interface is connected to the wind turbine blade system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating wind turbine blade system, computer programs, and a database. The internal memory provides the environment for the operation of the operating wind turbine blade system and computer programs stored in the non-volatile storage media. The database stores data to be processed. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a wind turbine blade reliability modeling method.

[0102] Those skilled in the art will understand that Figure 10 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0103] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0104] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0105] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0106] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0107] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0108] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A wind turbine blade reliability modelling method, characterised in that, The method comprises the following steps: determining wind turbine blade system assumptions; selecting an impact damage function, an amplification function and a threshold function based on the wind turbine blade system assumptions; constructing a wind turbine blade degradation failure model; constructing a wind turbine blade sudden failure model; constructing a double mutual dependence relationship competition failure model based on the impact damage function, the amplification function, the threshold function, the wind turbine blade degradation failure model and the wind turbine blade sudden failure model; determining the reliability of the wind turbine blade based on the double mutual dependence relationship competition failure model; the expression of the impact damage function g(A) is: wherein A is a random variable independent of the impact intensity W, and k1 and k2 are adjustable parameters; the expression of the amplification function ψ(S) is: Wherein, S is the total degradation of the wind turbine blade system, is the mean value of the degradation threshold H, and p1 and p2 are adjustable parameters; the expression of the threshold function η(S) is: where D0 is an initial impact threshold, h SD is the standard deviation of the degradation threshold H, and q1 and q2 are adjustable parameters; the expression of the double mutual dependence relationship competition failure model is: where R(t) is the reliability at time t, λ is the shock arrival rate, h L is the lower limit of the degradation threshold distribution range, h R is the upper limit of the degradation threshold distribution range, f H is the probability density function of the degradation threshold, f X and F X are the probability density function and the cumulative distribution function of the continuous degradation increment, f W and F W are the probability density function and the cumulative distribution function of the shock intensity, h is the value of the degradation threshold H, t1, t2, …, t n are the values of the n shock arrival times T1, T2, …, T n , a1, a2, …, a n-1 are the values of the intensities W1, W2, …, W n-1 independent and identically distributed random variables A1, A2, …, A n-1 , are the values of the continuous degradation increments in the time intervals (0, t1), (t1, t2), …, (t n-1 , t n ), (t n , t), s1, s2, …, s n are the values of the total degradation amounts S1, S2, …, S n from the initial time to t1, t2, …, t n .

2. The wind turbine blade reliability modeling method of claim 1, wherein, the wind turbine blade system assumptions comprise a first assumption, a second assumption, a third assumption, a fourth assumption, a fifth assumption, a sixth assumption, a seventh assumption and an eighth assumption; the first assumption is that the wind turbine blade system only has two failure modes, an extreme impact type sudden failure mode and a continuous performance degradation failure mode; the second assumption is that the number of random impact arrivals obeys a homogeneous Poisson process with a parameter λ>0; the third assumption is that the impact type is divided into fatal impact and non-fatal impact; when the impact intensity W is greater than or equal to the impact threshold D, it is a fatal impact, and the wind turbine blade system has a sudden failure; otherwise, it is a non-fatal impact, and the wind turbine blade system is damaged by impact; the fourth assumption is that the degradation threshold H is a random variable, and the distribution parameter is a constant that does not change with time; the fifth assumption is that the total degradation amount S of the wind turbine blade system is composed of the continuous degradation amount X and the sum of all impact damages; when S is greater than or equal to the degradation threshold H, the wind turbine blade system has a degradation failure; the sixth assumption is that the impact damage Y is independent of W, and Y is proportional to the impact damage function; the seventh assumption is that the total degradation amount of the wind turbine blade system increases with the increase of the impact damage, and the wind turbine blade system becomes more sensitive to impact as the performance degradation of the wind turbine blade system intensifies; the eighth assumption is that a fatal impact will cause a stepwise increase in the total degradation amount, and the ability of the wind turbine blade system to resist sudden failure will weaken as the overall degradation of the wind turbine blade system intensifies.

3. The wind turbine blade reliability modeling method of claim 1, wherein, The method for constructing the wind turbine blade degradation failure model specifically comprises: determining the probability density function of the degradation threshold, the probability density function of the continuous degradation increment and the continuous degradation increment distribution function, and then constructing the degradation failure model of the wind turbine blade.

4. The wind turbine blade reliability modeling method of claim 1, wherein, The method for constructing the wind turbine blade sudden failure model specifically comprises: determining the impact arrival rate, the initial impact threshold, the distribution function of the impact intensity and the probability density function of the impact intensity, and constructing the wind turbine blade sudden failure model.

5. A wind turbine blade reliability modelling apparatus, characterised in that, The device is applied to the wind turbine blade reliability modeling method of any one of claims 1-4, and the device comprises: a wind turbine blade system assumption determination module for determining wind turbine blade system assumptions; a function selection module for selecting an impact damage function, an amplification function and a threshold function based on the wind turbine blade system assumptions; The first model construction module is configured to construct a wind turbine blade degradation failure model. The second model construction module is configured to construct a wind turbine blade sudden failure model. The third model construction module is configured to construct a double-interdependence relationship competing failure model based on the impact damage function, the amplification function, the threshold function, the wind turbine blade degradation failure model and the wind turbine blade sudden failure model. The wind turbine blade reliability determination module is configured to determine the reliability of the wind turbine blade based on the double-interdependence relationship competing failure model.

6. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the wind turbine blade reliability modeling method of any one of claims 1-4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the wind turbine blade reliability modeling method of any one of claims 1-4.

8. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the wind turbine blade reliability modeling method of any one of claims 1-4.

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