Wind turbine blade reliability modeling method, device, equipment, medium and product

Abstract

The invention discloses a wind turbine blade reliability modeling method, device and equipment, a medium and a product, and relates to the field of wind turbine blade reliability analysis, and the method 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 assumption; constructing a wind turbine blade degradation failure model; constructing a wind turbine blade sudden 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, constructing a double-mutual-dependence-relation competition failure model; and determining the reliability of the wind turbine blade based on the dual-interdependence competitive failure model. The reliability analysis convenience and accuracy of the wind turbine blade can be improved.

Description

Technical Field

[0001] This application relates to the field of reliability analysis of wind turbine blades, and particularly to a reliability modeling method, device, equipment, medium and product for wind turbine blades. Background Art

[0002] During the usage process, the product performance inevitably gradually degrades, such as the aging of rubber, the wear of gears, the drift of resistors, etc. This phenomenon of performance degradation is defined as a process of increasing product degradation amount, which has time continuity. Usually, a degradation threshold is artificially specified, and when the total degradation amount (also known as the degradation state) reaches or exceeds this threshold, it is determined that the product degrades and fails.

[0003] Wind farms are usually located in complex environments such as high altitudes, oceans or deserts. Therefore, wind turbine blades face various failure modes, including not only continuous degradation damages such as stiffness degradation, fatigue damage, airflow erosion, and grit wear; but also may be subjected to impacts from atmospheric particles (such as hail) or wild animals (such as birds and bats), causing the blades to bear random impact loads. These impacts not only accelerate the degradation and failure process of wind turbine blades, but also may directly damage the blades, significantly shortening the blade life. Therefore, as the operation years increase, the probability of unexpected failures or even accidents of the blades greatly increases, seriously threatening the operation safety of wind turbines and the economic benefits of wind farms. However, the current blade design does not consider the impact load, but introduces a relatively high safety factor, resulting in an overly conservative design. With the development of wind turbine blades towards being harder and lighter, and the increasingly perfect health monitoring system, the design concept has changed to considering the impact of impact load on reliability from the perspective of probability theory, making the product more economical and practical.

[0004] Fiber-reinforced composite materials have been widely used in the production of large wind turbine blades, which simplifies the molding process and improves the specific strength and specific stiffness of the blades, but brings the following problems to reliability analysis: ① The failure mechanism of composite materials is complex, and it is necessary to fully consider the dependency relationship of various influencing factors. For wind turbine blades facing the competition of two failure modes of degradation and extreme impact, non-fatal impacts will cause a sudden increase in the degradation state. As the degradation state increases, the ability of the blade to resist impacts weakens, and non-fatal impacts with constant intensity cause a greater degradation increment to blades with severe degradation. ② The degradation performance characteristic quantities of composite material wind turbine blades include stiffness, fatigue damage, wear, erosion, etc., and their focuses are different in different environments and studies, resulting in a variety of degradation models. This indicates that the degradation trajectory model is 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, there are some deficiencies in the existing research results: ① Most of the research does not comprehensively consider the dependence relationship; ② Most of the degradation modeling is the degradation trajectory model, which is difficult to adapt to complex and diverse actual situations. Summary of the Invention

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

[0007] To achieve the above purpose, this application provides the following solutions:

[0008] In the first aspect, this application provides a reliability modeling method for wind turbine blades, including:

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

[0010] Select the impact damage function, amplification function and threshold function based on the assumptions of the wind turbine blade system;

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

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

[0013] Based on the impact damage function, the amplification function, the threshold function, the degradation failure model of the wind turbine blade and the sudden failure model of the wind turbine blade, construct a double mutual dependence competing failure model;

[0014] Determine the reliability of the wind turbine blade based on the double mutual dependence competing failure model.

[0015] In the second aspect, this application provides a reliability modeling device for wind turbine blades, including:

[0016] A wind turbine blade system assumption determination module, used to determine the assumptions of the wind turbine blade system;

[0017] A function selection module, used to select the impact damage function, amplification function and threshold function based on the assumptions of the wind turbine blade system;

[0018] A first model construction module, used to construct a degradation failure model for wind turbine blades;

[0019] A second model construction module, used to construct a sudden failure model for wind turbine blades;

[0020] A third model construction module, used to construct a double mutual dependence competing failure model based on the impact damage function, the amplification function, the threshold function, the degradation failure model of the wind turbine blade and the sudden failure model of the wind turbine blade;

[0021] A wind turbine blade reliability determination module, configured to determine the reliability of a wind turbine blade based on the dual-interdependent relationship competing failure model.

[0022] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the above-mentioned wind turbine blade reliability modeling method.

[0023] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above-mentioned wind turbine blade reliability modeling method is implemented.

[0024] In a fifth aspect, the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, the above-mentioned wind turbine blade reliability modeling method is implemented.

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

[0026] The present application provides a wind turbine blade reliability modeling method, device, equipment, medium, and product. 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-interdependent relationship competing failure model is constructed. This model can flexibly combine a suitable degradation model and impact strength distribution, and by adjusting the impact damage function, amplification function, and threshold function, the reliability of the blade is made closer to the actual situation, thereby improving the convenience and accuracy of the reliability analysis of wind turbine blades. Description of the Drawings

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0028] Figure 1 It is a flowchart of the wind turbine blade reliability modeling method provided by an embodiment of the present application;

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

[0030] Figure 3 It is a schematic diagram of the extreme impact type sudden failure process;

[0031] Figure 4 Schematic diagram of impact damage function

[0032] Figure 5 Schematic diagram of amplification function

[0033] Figure 6 Schematic diagram of threshold function

[0034] Figure 7 Schematic diagram of reliability curves under three different impact damage functions

[0035] Figure 8 Schematic diagram of reliability curves under three different amplification functions

[0036] Figure 9 Schematic diagram of reliability curves under three different threshold functions

[0037] Figure 10 Schematic diagram of the structure of a computer device provided by an embodiment of the present application Detailed implementation manners

[0038] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present application.

[0039] Aiming at the mutual competition between the degradation failure and impact-induced sudden failure of composite wind turbine blades, and the reliability analysis problem caused by the coupling of various failure mechanisms, the present application proposes a reliability modeling method for wind turbine blades based on a double-interdependent relationship competing failure model. Figure 2 Shows the degradation failure process: the total degradation amount S is the sum of the continuous degradation amount X and the stepwise cumulative damage caused by impacts ; the degradation threshold H is a random variable, and its mean and variance do not change with time. Figure 2 In (a)-(c) of represents the average value of the degradation threshold H, and ΔX1, ΔX2 are the continuous degradation increments in different time intervals.

[0040] Figure 3 Shows the extreme impact-induced sudden failure process. The duration of the impact event is extremely short and can be approximated as an instantaneous process. In addition, it is required that the arrival of random impacts follows a homogeneous Poisson process. This means that during the life cycle of the blade, the occurrence of impact events is uniform and random, that is, the probability of an impact occurring at any 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] As Figure 2 - Figure 3 shown, the characteristics of the double interdependent relationship competition failure model are that as the total degradation amount S increases, the shock threshold D decreases, while the shock damage Y caused by the same intensity shock W increases. Generally speaking, the model of the present application not only comprehensively considers the coupling relationship between various mechanisms, but also can select appropriate degradation models and shock intensity distributions according to different materials and working conditions, thereby improving the accuracy and generality of the reliability analysis of composite wind turbine blades.

[0042] To make the above objects, features, and advantages of the present application more obvious and understandable, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0043] In an exemplary embodiment, as Figure 1 shown, a method for reliability modeling of wind turbine blades is provided. This method is executed by a computer device, and specifically can be executed alone by a computer device such as a terminal or a server, or can be jointly executed by a terminal and a server. In the embodiments of the present application, taking this method applied to a server as an example for explanation, it includes the following steps S1 to S6. Among them:

[0044] S1: Determine the assumptions of the wind turbine blade system. The assumptions of 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] The present application establishes the following assumptions for the wind turbine blade system to accurately describe the effects and application scopes of the model in mathematical language:

[0046] The first assumption: There are only two failure modes in the wind turbine blade system, extreme shock type sudden failure and continuous performance degradation failure.

[0047] The second assumption: The arrival times of random shocks follow a homogeneous Poisson process with parameter λ > 0.

[0048] The third assumption: According to the shock intensity W (W ≥ 0), the shock types are divided into fatal shocks and non-fatal shocks; when W is greater than or equal to the shock threshold D, it is a fatal shock, and the wind turbine blade system has a sudden failure; otherwise, it is a non-fatal shock, and the wind turbine blade system is damaged by the shock.

[0049] The fourth assumption: The degradation threshold H is a random variable defined on the interval (h L , h R ) (0 ≤ h L < h R ), and its distribution parameters are constants that do not change with time. h L and h RThe specific value of depends on the distribution that the degradation threshold H follows. For example, when H follows a lognormal distribution, there are h L = 0, h R = +∞.

[0050] Fifth assumption: The total degradation amount S of the wind turbine blade system consists of the continuous degradation amount X and the sum of all impact damages constitute (S, X, Y i ≥ 0); Therefore, S is a left-continuous function of time t, and its jump discontinuity points are located at the moments when non-fatal impacts occur; when S is greater than or equal to the degradation threshold H, the wind turbine blade system undergoes degradation failure.

[0051] Sixth assumption: The impact damage Y and W are independent of each other, that is, 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 independently and identically distributed with W, and it is stipulated that g(0) = 0 and g(A) is monotonically increasing.

[0052] Seventh assumption: The total degradation amount 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 impacts, that is, Y = g(A)ψ(S), and ψ(S) is called the amplification function. It is stipulated that ψ(0) = 1 and ψ(S) is monotonically increasing.

[0053] Eighth assumption: Non-fatal impacts will cause a stepwise increase in the total degradation amount. As the overall degradation of the wind turbine blade system intensifies, the ability of the wind turbine blade system to resist sudden failure weakens, that is, D = η(S), and η(S) is called the threshold function. It is stipulated that η(0) = D0, and η(S) is monotonically decreasing; where D0 is the initial impact threshold, which represents the strongest impact that a new wind turbine blade system without degradation can resist, and h SD represent the mean and standard deviation of the degradation threshold H respectively.

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

[0055] The seventh assumption and the eighth assumption respectively reflect two pairs of interdependent relationships: ① There is an interdependent relationship between impact damage and total degradation amount; ② There is an interdependent relationship between the degradation process and the impact process; these two assumptions provide a guarantee for comprehensively considering the mutual relationships among various factors.

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

[0057] The impact damage function is k1 > 0 and k2 > 0. Wherein, k1 and k2 are adjustable parameters. g(A) is defined on [0, +∞), with an initial value of 0 and monotonically increasing, that is, g(0) = 0 and g′(A) > 0; thus, the inverse function g -1 (A) has the same domain and properties as it. As Figure 4 shown, when k2 = 1, both g(A) and g -1 (A) are linear functions; when 0 < k2 < 1, g(A) is a convex function and g -1 (A) is a concave function; when k2 > 1, g(A) is a concave function and g -1 (A) is a convex function.

[0058] The amplification function is p1 > 0 and p2 > 0. Wherein, is the mean value of the degradation threshold H, and p1 and p2 are adjustable parameters. ψ(S) is defined on [0, +∞), with an initial value of 1 and monotonically increasing, that is, ψ(0) = 1 and ψ′(S) > 0. As Figure 5 shown, when p2 = 1, ψ(S) is a linear function; when 0 < p2 < 1, ψ(S) is a convex function; when p2 > 1, ψ(S) is a concave function.

[0059] The threshold function is 0 < q1 ≤ 1 and q2 > 0. Wherein, h SD is the standard deviation of the degradation threshold H, and q1 and q2 are adjustable parameters. η(S) is defined on [0, h R )), with an initial value of D0 and monotonically decreasing, satisfying η(0) = D0, η′(S) < 0, and where D0 is the initial impact threshold and h R is the upper limit of the value range of the degradation threshold H. As Figure 6 shown, when q2 = 1, η(S) is a linear function; when 0 < q2 < 1, η(S) is a concave function; 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 the continuous degradation increment, and the continuous degradation increment distribution function, and then construct a degradation failure model for wind turbine blades.

[0061] According to experience or experiments, obtain the probability density function f H (h) of the degradation threshold H, the distribution function F X (Δx, t, t0) of the continuous degradation increment ΔX (ΔX = X(t) - X(t0) > 0, t > t0), and the probability density function f X(Δx, t, t0), thus determining the degradation failure model of the wind turbine blade. 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 stiffness accelerated degradation test of a certain type of wind turbine blade as an example, the degradation threshold H follows a lognormal distribution, and its probability density function is as follows:

[0063]

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

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

[0066]

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

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

[0069] According to experience or historical detection data, accurately find out the shock arrival rate λ, the initial shock threshold D0, the distribution function F W (w) and the probability density function f W (w) of the shock intensity 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 the data provided by a certain wind farm, the statistical average shock arrival rate of each wind turbine with birds is λ = 0.21 times / year; by consulting the references and summarizing the experience of predecessors, the initial shock threshold D0 = 2.6 kJ can be obtained, and the shock intensity W follows a truncated normal distribution, and its probability density function and distribution function are respectively as follows:

[0071]

[0072] Among them, the parameters are μ = 1.3 kJ, 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, construct a dual-interdependent competing failure model.

[0074] The expression of the dual-interdependent competing failure model is:

[0075]

[0076] where \(R(t)\) is the reliability at time \(t\), \(\lambda\) is the impact 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 respectively the probability density function and the cumulative distribution function of the continuous degradation increment, \(f\) W and \(F\) W are respectively the probability density function and the cumulative distribution function of the impact intensity, \(h\) is the value of the degradation threshold \(H\), \(t_1,t_2,\cdots,t\) n are the values of the \(n\) impact arrival times \(T_1,T_2,\cdots,T\) n of, \(a_1,a_2,\cdots,a\) n-1 are the values of the random variables \(A_1,A_2,\cdots,A\) n-1 that are independent and identically distributed with the intensities \(W_1,W_2,\cdots,W\) n-1 of the previous \(n - 1\) impacts, \(\Delta x_1,\Delta x_2,\cdots,\Delta x\) n , are the values of the continuous degradation increments \(\Delta X_1,\Delta X_2,\cdots,\Delta X\) n-1 in the time intervals \((0,t_1),(t_1,t_2),\cdots,(t\) n ),\((t\) n ,t)\), \(\Delta x_1,\Delta x_2,\cdots,\Delta x\) n , are the values of \(s_1,s_2,\cdots,s\) n which are the total degradation amounts \(S_1,S_2,\cdots,S\) n from the initial time to \(t_1,t_2,\cdots,t\) n .

[0077] The specific expressions of \(s_1,s_2,\cdots,s\) n are as follows:

[0078]

[0079] S6: Determine the reliability of the wind turbine blade based on the dual-interdependent competing failure model.

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

[0081] The following embodiments will detail the influence of different models and some key parameters on the 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. Among these functions, the adjustable parameters k2, p2 and q2 have a significant impact on shaping the function form, and they directly determine the concavity and convexity of the function curve. In contrast, although k1, p1 and q1 are also involved in shaping the function, they only affect the slope change of the function, and their role is relatively minor. Therefore, in order to highlight the role of the main influencing factors and effectively reduce the complexity and number of tests, k1 is fixed at 1.9, p1 is fixed at 2.3, and q1 is fixed 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 simple stability and intuitive and controllable change trends, these parameter values are used as the benchmark group for subsequent comparison with the model effects under other parameter configurations.

[0085] The influence of the impact damage function g(A) on the reliability curve characteristics is studied by the method of controlling variables. First, k2 is set as the independent variable, while p2 and q2 are kept unchanged as control variables; then, two control groups are set: in control group 1, k2 = 0.15, and at this time g(A) shows a convex function; in control group 2, k2 = 1.7, and at this time g(A) shows a concave function. The reliability curves based on three different impact damage functions are as Figure 7 shown.

[0086] Through comparison, it can be found that there are significant differences among the three reliability curves within the time range from the 2nd to the 20th year; specifically, the reliability of control group 1 is greater than that of the benchmark group, while the reliability of control group 2 is less than that of the benchmark group. In addition, it can be seen from Figure 7 that the median lifetimes of the studied wind turbine blades in the three cases of the benchmark group, control group 1 and control group 2 are 10.41 years, 11.50 years and 9.27 years respectively. The above conclusions show that on the premise of the same total degradation amount and impact intensity, the convex impact damage function causes less impact damage, so it improves the lifetime and reliability of the wind turbine blade; while the concave impact damage function causes more impact damage, so it reduces the lifetime and reliability of the wind turbine blade.

[0087] Example 2:

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

[0089] It can be found by comparison that within the time range from the 2nd to the 20th year, the differences among the three reliability curves are relatively large. Among them, the reliability of control group 3 is less than that of the reference group, while the reliability of control group 4 is greater than that of the reference group. From Figure 8 it can also be found that the median lifetimes of the wind turbine blades under the two parameter configurations of control group 3 and control group 4 are 9.18 years and 11.53 years respectively. The above conclusions show that the convex amplification function enhances the impact sensitivity of the double mutual dependence competition failure model, thus accelerating the failure process, and therefore reducing the lifetime and reliability of the wind turbine blades; while the concave amplification function weakens the impact sensitivity of the double mutual dependence competition failure model, thus slowing down the failure process, and therefore increasing the lifetime and reliability of the wind turbine blades.

[0090] Example 3:

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

[0092] It can be found by comparison that the reliability of control group 5 is less than that of the reference group, the reliability of control group 6 is greater than that of the reference group, and the difference between control group 5 and the reference group is more significant. In addition, from Figure 9 it can be known that the median lifetimes of the wind turbine blades in the two cases of control group 5 and control group 6 are 6.81 years and 11.29 years respectively. The above conclusions show that the concave threshold function greatly weakens the impact resistance of the double mutual dependence competition failure model, and therefore reduces the lifetime and reliability of the wind turbine blades; the convex threshold function enhances the impact resistance of the double mutual dependence competition failure model, and therefore increases the lifetime and reliability of the wind turbine blades.

[0093] Based on the same inventive concept, the embodiments of the present application further provide an apparatus for implementing the above-mentioned wind turbine blade reliability modeling method. The solution provided by the wind turbine blade reliability modeling apparatus for solving the problem is similar to the solution 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 refer to the limitations on the wind turbine blade reliability modeling method in the above text, and will not be repeated here.

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

[0095] A wind turbine blade system assumption determination module, configured to determine the wind turbine blade system assumption.

[0096] A function selection module, configured to select an impact damage function, an amplification function, and a threshold function based on the wind turbine blade system assumption.

[0097] A first model construction module, configured to construct a wind turbine blade degradation failure model.

[0098] A second model construction module, configured to construct a wind turbine blade sudden failure model.

[0099] A third model construction module, configured to construct a double mutual dependence 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.

[0100] A wind turbine blade reliability determination module, configured to determine the reliability of the wind turbine blade based on the double mutual dependence competing failure model.

[0101] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented. The computer device can be a server or a terminal, and its internal structure diagram can be as Figure 10As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a wind turbine blade system bus, and the communication interface is connected to the wind turbine blade system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating wind turbine blade system, a computer program, and a database. The internal memory provides an environment for the operation of the wind turbine blade system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data to be processed. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a method for reliability modeling of wind turbine blades.

[0102] Those skilled in the art can understand that Figure 10 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0103] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0104] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[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 for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0106] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, a database, or other media used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0107] The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0108] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, 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, it should be considered as the scope described in this specification.

[0109] In this article, specific examples are used to elaborate on the principles and implementation manners of the present application. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A reliability modeling method for wind turbine blades, characterized in that, Including: Determine the assumptions of the wind turbine blade system; Select the impact damage function, amplification function, and threshold function based on the assumptions of the wind turbine blade system; Construct a degradation failure model of the wind turbine blade; Construct a sudden failure model of the wind turbine blade; Based on the impact damage function, the amplification function, the threshold function, the degradation failure model of the wind turbine blade, and the sudden failure model of the wind turbine blade, construct a dual-interdependent competing failure model; Determine the reliability of the wind turbine blade based on the dual-interdependent competing failure model.

2. The reliability modeling method of a wind turbine blade according to claim 1, characterized in that The assumptions of 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; The first assumption is that there are only two failure modes in the wind turbine blade system, the extreme impact type sudden failure mode and the continuous performance degradation failure mode; The second assumption is that the arrival times of random impacts follow a homogeneous Poisson process with parameter λ > 0; The third assumption is that the impact types are divided into fatal impacts and non-fatal impacts; 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 undergoes a sudden failure; otherwise, it is a non-fatal impact, and the wind turbine blade system suffers impact damage; The fourth assumption is that the degradation threshold H is a random variable, and its distribution parameters are constants that do 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 undergoes 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 impact damage, and as the performance degradation of the wind turbine blade system intensifies, the wind turbine blade system becomes more sensitive to impacts; The eighth assumption is that a fatal impact will cause a stepwise increase in the total degradation amount, and as the overall degradation of the wind turbine blade system intensifies, the ability of the wind turbine blade system to resist sudden failure weakens.

3. The reliability modeling method for wind turbine blades according to claim 1, wherein The expression of the impact damage function g(A) is: where A is a random variable independently and identically distributed with the impact intensity W, and k1 and k2 are adjustable parameters; The expression of the amplification function ψ(S) is: where 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 the initial shock threshold, h SD is the standard deviation of the degradation threshold H, and q1 and q2 are adjustable parameters.

4. The reliability modeling method for wind turbine blades according to claim 1, wherein Constructing a degradation failure model of the wind turbine blade specifically includes: Determine 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 construct the degradation failure model of the wind turbine blade.

5. The reliability modeling method of a wind turbine blade according to claim 1, wherein, Constructing a sudden failure model of the wind turbine blade specifically includes: Determine 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 construct the sudden failure model of the wind turbine blade.

6. The reliability modeling method for a wind turbine blade according to claim 3, wherein The expression of the dual-interdependent competing failure model is: Among them, \(R(t)\) is the reliability at time \(t\), \(\lambda\) 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 respectively the probability density function and cumulative distribution function of the continuous degradation increment, \(f\) W and \(F\) W are respectively the probability density function and cumulative distribution function of the shock intensity, \(h\) is the value of the degradation threshold \(H\), \(t_1,t_2,\cdots,t\) n are the values of the arrival times \(T_1,T_2,\cdots,T\) n of \(n\) shocks, \(a_1,a_2,\cdots,a\) n-1 are the values of the random variables \(A_1,A_2,\cdots,A\) n-1 that are independently and identically distributed with the intensities \(W_1,W_2,\cdots,W\) n-1 of the previous \(n - 1\) shocks, are the values of the continuous degradation increments n-1 \(\Delta Z\) in the time intervals \((0,t_1),(t_1,t_2),\cdots,(t\) n \(_{n - 1},t\) n \(_n),(t\) \(_{n},t\), \(s_1,s_2,\cdots,s\) n are the values of the total degradation amounts \(S_1,S_2,\cdots,S\) n from the initial time to \(t_1,t_2,\cdots,t\) n \(_n\).

7. A reliability modeling device for a wind turbine blade, characterized in that, Including: A wind turbine blade system assumption determination module for determining the assumptions of the wind turbine blade system; A function selection module for selecting the impact damage function, amplification function, and threshold function based on the assumptions of the wind turbine blade system; A first model construction module for constructing a degradation failure model of the wind turbine blade; The second model construction module is used to construct a sudden failure model of a wind turbine blade; The third model construction module is used to construct a double mutual - dependence competing failure model based on the impact damage function, the amplification function, the threshold function, the degradation failure model of the wind turbine blade, and the sudden failure model of the wind turbine blade; The reliability determination module of the wind turbine blade is used to determine the reliability of the wind turbine blade based on the double mutual - dependence competing failure model.

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

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the wind turbine blade reliability modeling method according to any one of claims 1 - 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the wind turbine blade reliability modeling method according to any one of claims 1 - 6.

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