Reliability Simulation Evaluation Method, System, Electronic Device and Medium for Wind Turbine Blade

Abstract

The present invention discloses a reliability simulation evaluation method, system, electronic device and medium for wind turbine blades, relating to the technical field of reliability simulation of wind turbine blades. The method includes: loading the environmental loads of a sample wind turbine blade onto the finite element model of the wind turbine blade to be evaluated, and performing simulation on the finite element model to obtain weak links; performing FMECA analysis on the sample wind turbine blade to obtain an analysis result; obtaining the failure time of each fault point according to the weak links and the analysis result; performing distribution fitting on the failure times of each fault point to obtain the failure data distribution of each fault point; sampling the failure data distributions of all fault points; taking the shortest life among all fault points in the sampling result as the failure time; determining the failure type according to the failure time; determining the failure model according to the failure type; and performing reliability evaluation according to the failure times and failure models of each fault point. The present invention can improve the accuracy of reliability evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of reliability simulation of wind turbine blades, and particularly to a method, a system, an electronic device and a medium for reliability simulation evaluation of wind turbine blades. Background Art

[0002] At present, the relatively common reliability analysis methods internationally can be classified according to their principles as follows:

[0003] (1) Similarity analysis method: The basic theory of the similarity analysis method stems from the similarity theory. Based on the reliability data of similar products or in similar environments, the product or environmental conditions are compared and corrected to obtain the reliability analysis results. (2) Statistical analysis method: The basis of the statistical analysis method is the basic theory of statistics, and a large number of samples are required. (3) Failure physics method: The failure physics method is an analysis method that directly or indirectly analyzes its load time history, conducts failure physics modeling and determines its reliability. The reliability analysis method based on failure physics is based on reliability technology, introduces physical and chemical methods, and studies the failure mechanisms of the parts and materials of electromechanical products to reduce or eliminate the occurrence of failures, thereby improving the reliability of electromechanical products.

[0004] The performance degradation of products is inevitable, but due to the different working environments and random input variables of products, the performance degradation rate and sudden failure probability of products during service are different. The wind turbine blade often bears the interaction of material self-performance degradation, temperature change and alternating stress during operation, which is a typical multi-failure mode competition failure process. The analytical method based on performance degradation data is the main means to carry out the reliability analysis and evaluation of wind turbine blades. However, in the actual application process, the wind turbine blade is affected by many factors such as region, environment, technology, quality and management. Using the similarity analysis method or the statistical analysis method has certain limitations, resulting in inaccurate reliability results. Summary of the Invention

[0005] The purpose of the present invention is to provide a method, a system, an electronic device and a medium for reliability simulation evaluation of wind turbine blades, which can improve the accuracy of reliability evaluation.

[0006] To achieve the above purpose, the present invention provides the following solutions:

[0007] A method for reliability simulation evaluation of wind turbine blades, comprising:

[0008] Establishing a reliability database for a sample wind turbine blade; the reliability database includes: design information, test data, failure data, condition detection data and maintenance data;

[0009] Drawing an environmental load profile of the sample wind turbine blade based on the reliability database;

[0010] Construct a finite element model based on the airfoil data of the wind turbine blade to be evaluated;

[0011] Load the environmental loads in the environmental load profile diagram onto the finite element model, and perform thermal stress simulation, vibration stress simulation, and multi-stress coupling simulation on the finite element model respectively to obtain the weak links on the wind turbine blade to be evaluated; the weak links include the position information of the failure points, the heat concentration area, the vibration form, and the stress and strain distribution;

[0012] Perform FMECA analysis on the sample wind turbine blade based on the reliability database to obtain the hazard matrix analysis diagram and the FMECA table of the failure points on the sample wind turbine blade;

[0013] Obtain the failure time of each failure point on the wind turbine blade to be evaluated according to the weak links, the hazard matrix analysis diagram, and the FMECA table on the wind turbine blade to be evaluated;

[0014] Perform distribution fitting on the failure time of each failure point on the wind turbine blade to be evaluated to obtain the failure time distribution of each failure point on the wind turbine blade to be evaluated;

[0015] Use the Monte Carlo method to sample the failure time of all failure points on the wind turbine blade to be evaluated to obtain the sampling result;

[0016] Take the shortest life time among all the failure points in the sampling result as the failure time of the wind turbine blade to be evaluated;

[0017] Obtain the failure data of the wind turbine blade to be evaluated according to the failure time of the wind turbine blade to be evaluated;

[0018] Determine the failure type of the wind turbine blade to be evaluated according to the failure data of the wind turbine blade to be evaluated; the failure types include independent competing failure, dependent competing failure, degradation failure, and sudden failure;

[0019] Determine the failure model for reliability assessment according to the failure type of the wind turbine blade to be evaluated; the failure models include independent competing failure model, dependent competing failure model, degradation failure model, and sudden failure model;

[0020] Perform reliability assessment on the wind turbine blade to be evaluated according to the failure time of each failure point on the wind turbine blade to be evaluated and the failure model for reliability assessment.

[0021] Optionally, the multi-stress coupling simulation is a two-way fluid-structure interaction simulation.

[0022] Optionally, determining the failure type of the wind turbine blade to be evaluated based on the failure data of the wind turbine blade to be evaluated specifically includes:

[0023] Judging whether the failure type of the wind turbine blade to be evaluated is a competing failure according to the failure data of the wind turbine blade to be evaluated, and obtaining a first judgment result;

[0024] If the first judgment result is yes, calculate the correlation result between degradation failure and sudden failure;

[0025] If the correlation result shows correlation, determine that the failure type of the wind turbine blade to be evaluated is a related competing failure;

[0026] If the correlation result shows no correlation, determine that the failure type of the wind turbine blade to be evaluated is an independent competing failure;

[0027] If the first judgment result is no, judge whether the failure type of the wind turbine blade to be evaluated is a degradation failure, and obtain a second judgment result;

[0028] If the second judgment result is yes, determine that the failure type of the wind turbine blade to be evaluated is a degradation failure;

[0029] If the second judgment result is no, determine that the failure type of the wind turbine blade to be evaluated is a sudden failure.

[0030] A reliability simulation evaluation system for wind turbine blades, comprising:

[0031] A reliability database establishment module for establishing a reliability database of sample wind turbine blades; the reliability database includes: design information, test data, fault data, condition detection data, and maintenance data;

[0032] An environmental load profile drawing module for drawing an environmental load profile of the sample wind turbine blade based on the reliability database;

[0033] A finite element model construction module for constructing a finite element model based on the airfoil data of the wind turbine blade to be evaluated;

[0034] A simulation module for loading the environmental load in the environmental load profile onto the finite element model, and performing thermal stress simulation, vibration stress simulation, and multi-stress coupling simulation on the finite element model respectively to obtain weak links on the wind turbine blade to be evaluated; the weak links include fault point location information, heat concentration areas, vibration forms, and stress and strain distributions;

[0035] FMECA analysis module, which is used to perform FMECA analysis on the sample wind turbine blade based on the reliability database to obtain the hazard matrix analysis diagram and FMECA table of the fault points on the sample wind turbine blade;

[0036] Reliability assessment failure data sample determination module, which is used to obtain the failure time of each fault point on the wind turbine blade to be evaluated according to the weak links on the wind turbine blade to be evaluated, the hazard matrix analysis diagram and the FMECA table;

[0037] Failure data distribution determination module, which is used to perform distribution fitting on the failure time of each fault point on the wind turbine blade to be evaluated to obtain the failure time distribution of each fault point on the wind turbine blade to be evaluated;

[0038] Extraction module, which is used to sample the failure time of all fault points on the wind turbine blade to be evaluated by using the Monte Carlo method to obtain the sampling result;

[0039] Failure time determination module, which is used to take the shortest life time among all fault points in the sampling result as the failure time of the wind turbine blade to be evaluated;

[0040] Failure data determination module, which is used to obtain the failure data of the wind turbine blade to be evaluated according to the failure time of the wind turbine blade to be evaluated;

[0041] Failure type determination module, which is used to determine the failure type of the wind turbine blade to be evaluated according to the failure data of the wind turbine blade to be evaluated; the failure types include independent competing failure, dependent competing failure, degradation failure and sudden failure;

[0042] Failure model determination module, which is used to determine the failure model for reliability assessment according to the failure type of the wind turbine blade to be evaluated; the failure models include independent competing failure model, dependent competing failure model, degradation failure model and sudden failure model;

[0043] Reliability assessment module, which is used to perform reliability assessment on the wind turbine blade to be evaluated according to the failure time of each fault point on the wind turbine blade to be evaluated and the failure model for reliability assessment.

[0044] Optionally, the multi-stress coupling simulation is a two-way fluid-structure interaction simulation.

[0045] Optionally, the failure type determination module specifically includes:

[0046] The first judgment unit, which is used to judge whether the failure type of the wind turbine blade to be evaluated is a competing failure according to the failure data of the wind turbine blade to be evaluated to obtain the first judgment result;

[0047] A correlation calculation unit, configured to calculate a correlation result between degradation failure and sudden failure if the first judgment result is yes;

[0048] A related competing failure determination unit, configured to determine that the failure type of the wind turbine blade to be evaluated is a related competing failure if the correlation result is correlated;

[0049] An independent competing failure determination unit, configured to determine that the failure type of the wind turbine blade to be evaluated is an independent competing failure if the correlation result is not correlated;

[0050] A degradation failure judgment unit, configured to judge whether the failure type of the wind turbine blade to be evaluated is a degradation failure if the first judgment result is no, and obtain a second judgment result;

[0051] A degradation failure determination unit, configured to determine that the failure type of the wind turbine blade to be evaluated is a degradation failure if the second judgment result is yes;

[0052] A sudden failure determination unit, configured to determine that the failure type of the wind turbine blade to be evaluated is a sudden failure if the second judgment result is no.

[0053] An electronic device, comprising:

[0054] A memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned wind turbine blade reliability simulation evaluation method.

[0055] A computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned wind turbine blade reliability simulation evaluation method is implemented.

[0056] According to the specific embodiments provided by the present invention, the following technical effects are disclosed: The present invention loads the environmental loads of the sample wind turbine blade onto the finite element model of the wind turbine blade to be evaluated, performs simulation on the finite element model to obtain the weak links of the wind turbine blade to be evaluated; conducts FMECA analysis on the sample wind turbine blade to obtain the hazard matrix analysis diagram and FMECA table of the fault points on the sample wind turbine blade; obtains the failure time of each fault point on the wind turbine blade to be evaluated according to the weak links, hazard matrix analysis diagram and FMECA table, performs distribution fitting on the failure time of each fault point on the wind turbine blade to be evaluated to obtain the failure time distribution of each fault point on the wind turbine blade to be evaluated; samples the failure data distributions of all fault points; takes the shortest life time among all fault points in the sampling result as the failure time; obtains the failure data according to the failure time; determines the failure type according to the failure data; determines the failure model for reliability assessment according to the failure type, and conducts reliability assessment according to the failure time and failure model of each fault point on the wind turbine blade to be evaluated. The reliability of the wind turbine blade is evaluated based on failure physics, which can improve the accuracy of reliability assessment. Description of the Drawings

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention 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 invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0058] Figure 1 General method flowchart of the wind turbine blade reliability simulation and assessment method provided by the embodiment of the present invention;

[0059] Figure 2 Task profile provided by the embodiment of the present invention;

[0060] Figure 3 Environmental load profile provided by the embodiment of the present invention;

[0061] Figure 4 Specific flowchart of the two-way fluid-structure interaction simulation provided by the embodiment of the present invention;

[0062] Figure 5 Flowchart of the flow field analysis and solution provided by the embodiment of the present invention;

[0063] Figure 6 Hazard matrix analysis diagram provided by the embodiment of the present invention;

[0064] Figure 7 FMECA analysis flowchart provided by the embodiment of the present invention;

[0065] Figure 8 This is a flowchart of the step of "using Monte Carlo for data preprocessing, and obtaining the life and reliability parameters of wind turbine blades and conducting reliability simulation evaluation according to the correlation of multiple failure modes and the principle of competing failures" provided by the embodiments of the present invention;

[0066] Figure 9 This is a flowchart for determining the reliability of competing failures provided by the embodiments of the present invention;

[0067] Figure 10 This is a comparison chart of four reliability curves provided by the embodiments of the present invention;

[0068] Figure 11 This is a schematic diagram of the process of the wind turbine blade reliability simulation evaluation method provided by the embodiments of the present invention. Detailed implementation manners

[0069] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0070] To make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation manners.

[0071] According to research data, among the faults that occur during the operation of wind turbines, mechanical faults are significantly more than electronic wind turbine blade faults, with characteristics such as complex reliability problems, multiple failure modes, scattered failure data, and complex influencing factors. In response to the above problems, the present invention proposes a wind turbine blade reliability simulation evaluation method. Based on failure physics, starting from the research on the failure mechanism and failure physics of wind turbine blades, analyzing factors such as material properties, load characteristics, loading methods, and working environments, and using tests and simulation to conduct reliability evaluation, as Figure 1 and Figure 11 shown, the general steps of the wind turbine blade reliability simulation evaluation method are as follows: establishing a reliability database for sample wind turbine blades; establishing a three-dimensional model and a finite element model of the wind turbine blade to be evaluated, conducting thermal, vibration, and multi-stress coupling analysis on the model to find the weak links of the blade; conducting FMECA analysis on the sample wind turbine blades according to the reliability database to obtain an analysis table and a hazard matrix; using Monte Carlo for data preprocessing, and obtaining the life and reliability parameters of the wind turbine blade to be evaluated according to the correlation of multiple failure modes and the principle of competing failures, and conducting reliability simulation evaluation. The wind turbine blade reliability simulation evaluation method specifically includes:

[0072] Establish a reliability database for the sample wind turbine blade; the reliability database includes: design information, test data, failure data, condition monitoring data, and maintenance data; the design information includes: structure, material, environmental conditions, load, usage mode, etc.; the environmental conditions include: natural environmental factors such as temperature, vibration, and shock.

[0073] Draw the environmental load profile of the sample wind turbine blade based on the reliability database.

[0074] Construct a finite element model based on the airfoil data of the wind turbine blade to be evaluated; the airfoil data specifically includes: leading edge, trailing edge, chord line, chord length, maximum camber, maximum camber position, maximum thickness, maximum thickness position, leading edge radius, trailing edge angle, mean camber line, upper surface, and lower surface.

[0075] Load the environmental loads in the environmental load profile onto the finite element model, and perform thermal stress simulation, vibration stress simulation, and multi-stress coupling simulation on the finite element model respectively to obtain the weak links on the wind turbine blade to be evaluated; the weak links include the location information of the failure points, heat concentration areas, vibration forms, and stress-strain distributions.

[0076] Perform FMECA analysis on the sample wind turbine blade based on the reliability database to obtain the hazard matrix analysis diagram and FMECA table of the failure points on the sample wind turbine blade.

[0077] Obtain the failure time of each failure point on the wind turbine blade to be evaluated according to the weak links, the hazard matrix analysis diagram, and the FMECA table on the wind turbine blade to be evaluated.

[0078] Perform distribution fitting on the failure time of each failure point on the wind turbine blade to be evaluated to obtain the failure time distribution of each failure point on the wind turbine blade to be evaluated.

[0079] Use the Monte Carlo method to sample the failure time of all failure points on the wind turbine blade to be evaluated to obtain the sampling result.

[0080] Take the shortest life time among all the failure points in the sampling result as the failure time of the wind turbine blade to be evaluated.

[0081] Obtain the failure data of the wind turbine blade to be evaluated according to the failure time of the wind turbine blade to be evaluated.

[0082] Determine the failure type of the wind turbine blade to be evaluated according to the failure data of the wind turbine blade to be evaluated; the failure types include independent competing failure, dependent competing failure, degradation failure, and sudden failure.

[0083] Determine the failure model for reliability assessment according to the failure type of the wind turbine blade to be evaluated; the failure model includes an independent competing failure model, a dependent competing failure model, a degradation failure model, and a sudden failure model.

[0084] Conduct reliability assessment on the wind turbine blade to be evaluated according to the failure time of each fault point on the wind turbine blade to be evaluated and the failure model of the reliability assessment.

[0085] In practical applications, the multi-stress coupling simulation is a two-way fluid-structure interaction simulation.

[0086] In practical applications, determining the failure type of the wind turbine blade to be evaluated according to the failure data of the wind turbine blade to be evaluated specifically includes:

[0087] Judge whether the failure type of the wind turbine blade to be evaluated is a competing failure according to the failure data of the wind turbine blade to be evaluated, and obtain a first judgment result.

[0088] If the first judgment result is yes, calculate the correlation result between degradation failure and sudden failure.

[0089] If the correlation result shows correlation, determine that the failure type of the wind turbine blade to be evaluated is a dependent competing failure.

[0090] If the correlation result shows no correlation, determine that the failure type of the wind turbine blade to be evaluated is an independent competing failure.

[0091] If the first judgment result is no, judge whether the failure type of the wind turbine blade to be evaluated is a degradation failure, and obtain a second judgment result.

[0092] If the second judgment result is yes, determine that the failure type of the wind turbine blade to be evaluated is a degradation failure.

[0093] If the second judgment result is no, determine that the failure type of the wind turbine blade to be evaluated is a sudden failure.

[0094] In practical applications, establish a reliability database for the sample wind turbine blade, and draw the environmental load profile of the sample wind turbine blade based on the reliability database, specifically including:

[0095] Obtain an information matrix related to the faults of the sample wind turbine blade (fault mechanism type, fault mechanism location, fault mechanism occurrence time) based on mathematical statistics methods, and establish a reliability database for the sample wind turbine blade.

[0096] Clarify the time sequence of events, environments, and states of the faults of the sample wind turbine blade and condition data such as temperature, vibration, and shock, and draw a mission profile asFigure 2 As shown, the environmental load profile is as Figure 3 shown, Figure 3 (a) is the temperature load - time graph, Figure 3 (b) is the working load - time graph. Among them, the information included in the mission profile is the rated wind speed, start - up wind speed, safety wind speed, and working wind speed during the operation of the entire wind turbine generator. It only illustrates the basic state of the wind turbine generator operation. The mission profile is a chronological description of the events, environments, and states experienced by the sample wind turbine blade during the period of completing the specified tasks. The environmental load profile is drawn based on test data and design information, including temperature, load, and their durations.

[0097] In practical applications, the environmental loads in the environmental load profile are loaded onto the finite element model, and thermal stress simulation, vibration stress simulation, and multi - stress coupling simulation are respectively carried out on the finite element model to obtain the weak links on the wind turbine blade to be evaluated. Specifically, it includes:

[0098] The working environment where the wind turbine generator is located is very harsh. It often encounters operating conditions such as sand and dust, ice and snow, freezing rain, strong gusts, and the interaction of wind, rain, and waves, and is subjected to temperature changes and alternating stresses. It will inevitably be subjected to various loads such as aerodynamic force, centrifugal force, and elastic force. The interaction between air and blade structure affects the aerodynamic characteristics of the blade, and in severe cases, it affects the normal operation of the wind turbine.

[0099] First, according to the airfoil data of the wind turbine blade to be evaluated, a CAD simulation model is established. Then, the CAD simulation model is imported into the software ANSYS Workbench to establish a simulation digital platform to obtain a finite element model, and the loads and environmental loads (the environmental load profile includes temperature and working load) are loaded into the model, and thermal stress simulation, vibration stress simulation, and multi - stress coupling simulation are respectively carried out.

[0100] The thermal stress simulation is specifically as follows: Calculate and study the thermal stress distributions of different blade materials under the loads of standard temperature (22°C) and two extreme temperatures (45°C, - 60°C) respectively. Considering the influence of the environmental loads of the wind turbine blade to be evaluated, use the finite element analysis method to simulate the thermal stress distributions of blades with different materials in the steady - state temperature field of the blade.

[0101] The vibration stress simulation is specifically as follows: Conduct a modal analysis on the wind turbine blade to be evaluated, and study the natural frequencies and modal vibration modes of the blade under the action of no prestress. There are mainly three modal vibration modes of the blade: flap, pitch, and torsion. The larger the modal vibration mode, the smaller the participation coefficient of the vibration mode. On the contrary, the smaller the modal vibration mode, the larger the participation coefficient of the vibration mode. The participation coefficient of the vibration mode can be looked up in the results to determine the main modal vibration mode.

[0102] The multi-stress coupling simulation is specifically as follows: The two-way fluid-structure interaction simulation method is adopted to study the deformation and stress-strain distribution of the blade under the coupled action of the oncoming aerodynamic load and the rotational centrifugal force load. As Figure 4 shown, the two-way fluid-structure interaction specifically includes separately establishing the fluid domain and the solid domain, setting and coupling the fluid domain and the solid domain for solution, then performing numerical calculations, and finally analyzing the fitting results based on the calculation results. As Figure 5 shown, the process of flow field analysis and solution mainly includes: mesh generation, setting the solver, selecting the calculation model, setting the boundary conditions, and computer iteration. The structural analysis settings mainly include: structural mesh generation, basic settings of the blade model, setting of loads and constraints, and setting of the fluid-structure interaction surface.

[0103] Because during the operation of the wind turbine blade, it is subjected to the oncoming aerodynamic load and the centrifugal force load. These loads cause the structure of the wind turbine blade to deform. At the same time, these blade deformations and movements will in turn change the movement and pressure of the wind. The mutual coupling effect between the air and the wind turbine structure affects the aerodynamic characteristics of the wind turbine blade. Therefore, the two-way fluid-structure interaction calculation method is adopted to analyze the aerodynamic performance and deformation of the wind turbine blade under the action of the oncoming flow load and the centrifugal force load. The multi-stress coupling analysis obtains the deformation nephogram and the stress-strain nephogram under the coupled action of the oncoming aerodynamic load and the rotational centrifugal force load. It can also obtain the curve graph of the displacement and stress of some nodes of the blade changing with time. It can not only shorten the design cycle, save the design cost, but also effectively ensure that the calculation results are consistent with the essence of the motion phenomenon.

[0104] By analyzing the simulation results, the weak links of the blade are found. Specifically: By analyzing the stress-strain nephogram, the location with the maximum stress is the weak position of the component and the most prone to failure; by analyzing the thermal stress nephogram, the stress concentration area is the thermal concentration area of the component; by analyzing the modal vibration mode graph, the displacement form and vibration form of the blade are found.

[0105] In practical applications, based on the reliability database, FMECA analysis is performed on the sample wind turbine blade to obtain the hazard matrix analysis graph and the FMECA table of the fault points on the sample wind turbine blade, which specifically includes:

[0106] As Figure 7As shown, the FMECA analysis of the sample wind turbine blade is carried out according to the reliability database. According to the severity level classification standard and the failure occurrence frequency definition standard, the qualitative analysis results of the hazard analysis of the sample wind turbine blade are obtained. According to the qualitative analysis results, an FMECA table and a hazard matrix analysis chart are established, and the fault information and its fault impact information of the sample wind turbine blade are directly reflected in the FMECA analysis results (FMECA table and hazard matrix analysis chart). As shown in Table 1, the content of the FMECA analysis table includes: failure mode, failure cause, failure impact, severity category, probability level, preventive measures, etc.; the content of the hazard matrix analysis chart includes: the occurrence probability of the failure mode and the hazard level. The abscissa of the hazard matrix analysis chart usually represents the severity category, and the ordinate usually represents the hazard degree of the sample wind turbine blade or the probability level of the fault description occurrence, such as Figure 6 as shown Figure 6 from point a to point d in [the figure] corresponds to a gradually increasing fault hazard, where the event at point d has the greatest impact.

[0107] Table 1 FMECA Analysis Table of Wind Turbine Blade

[0108]

[0109] In practical applications, the weak links obtained through finite element simulation analysis and the weak links obtained by conducting FMECA analysis on the sample wind turbine blade according to the reliability database are mutually verified to obtain the failure time of each fault point on the wind turbine blade to be evaluated, increasing the credibility of the analysis.

[0110] The complexity of wind turbine blades in terms of structure and load effects makes it cumbersome to apply simple reliability block diagrams and fault tree analysis. FMECA analysis can deeply and systematically analyze system faults, comprehensively understand the operating mechanism of the system, identify the key reliability weak links through analysis, so as to take preventive measures and conduct key monitoring and tracking, and at the same time summarize the fault modes with the same impact.

[0111] In practical applications, as Figure 8 shown, first, using mathematical statistics methods, the failure time of each fault point on the wind turbine blade to be evaluated obtained from the hazard matrix analysis chart and the FMECA table established based on the weak links on the wind turbine blade to be evaluated and the analysis of the reliability database is subjected to distribution fitting to obtain the single-point fault data distribution. Among them, single-point fault data refers to a set of failure data for a single fault point, and each fault point has a set of failure data. Through distribution fitting, single-point fault data is obtained. The fault data is the abscissa - the observed time to first failure; the ordinate - the blade number, and the distribution fitting is carried out using MATLAB software.

[0112] Secondly, the Monte Carlo method is used to sample the single-point distribution, with 1000 samplings each time (this step is to ensure the accuracy and authenticity of the data). In each sampling, the shortest life among the fault points is selected as the failure time of the wind turbine blade to be evaluated, so as to obtain the failure data during the service life of the wind turbine blade to be evaluated.

[0113] Finally, for the failure data obtained during the service life of the wind turbine blade to be evaluated, it is judged whether its failure belongs to competing failure, as Figure 9 shown. If it belongs to competing failure, qualitative analysis is carried out on the failure data to clarify the correlation and degree of correlation between each failure mode (degradation failure and sudden failure), determine whether it is independent competing failure or related competing failure, and then estimate the model parameters on the basis of the degradation failure model and the sudden failure model to obtain the model parameters of the independent competing failure model or the related competing failure model. If it does not belong to competing failure, a degradation failure or sudden failure model is established (determined according to whether the failure mode is degradation or sudden). Finally, the reliability of the wind turbine blade to be evaluated is calculated using the failure model, an evaluation is made, and a reasonable explanation is given for the results.

[0114] For the wind turbine blade to be evaluated, considering that the variation law of the degradation amount and the data characteristics are different during the failure process, it is necessary to study the degradation failure process, the sudden failure process, and the competing failure process of the two under independent and related conditions simultaneously.

[0115] (1) Establish a degradation failure reliability function (degradation failure model) established by the Wiener process:

[0116]

[0117] In the formula, R d (t) represents the degradation failure reliability, D represents the failure threshold, t represents the failure time, φ(·) represents the standard normal function, and exp(·) represents the exponential function. σ represents the drift parameter, and μ represents the diffusion coefficient.

[0118] Estimation of degradation failure process parameters:

[0119] According to the probability density function of the Wiener process, establish the likelihood function L(ΔX, μ, σ 2 ) expression:

[0120]

[0121] In the formula, Δt ij = t ij - t i(j-1) is the difference between adjacent times. Assuming that M samples have performance degradation failures, at time t ij(i = 1, 2, …, M; j = 1, 2, …, M i ) is denoted as X(t ij ), M i represents the total time of the i-th sample. A sample includes performance degradation amounts collected at multiple times, and t ij represents the sampling time of the performance degradation amount at the j-th collection time of the i-th sample. ΔX(t ij ) = X(t ij ) - X(t i(j-1) ) is the degradation increment of the performance degradation failure sample in the adjacent time period t ij , t i(j-1) . M represents the number of performance degradation failure samples, and f x [ΔX(t ij )] represents the expression of the probability density function of the performance degradation increment ΔX(t ij ) with respect to the time t ij ).

[0122] Take the logarithm of both sides of the likelihood function expression of the performance degradation data, and then set the partial derivatives of μ and σ 2 to 0. According to the maximum likelihood method, the parameter estimation expression of the diffusion coefficient and the parameter estimation of the square of the diffusion coefficient σ are as follows: 2 The parameter estimation expression is:

[0123]

[0124]

[0125] Among them, represents the collection time of the performance degradation amount at the M i -th time in the i-th sample.

[0126] (2) Establish a sudden failure reliability function (sudden failure model) established with the Weibull distribution

[0127]

[0128] In the formula, R r (t) represents the sudden failure reliability, η is the scale parameter; m is the shape parameter.

[0129] Parameter estimation of the sudden failure process:

[0130] The distribution function of the two-parameter Weibull distribution is

[0131]

[0132] The distribution function of the two-parameter Weibull distribution can be expressed in the form of a linear equation as follows:

[0133]

[0134] Suppose n samples participate in the test and all fail. If t 1 , t 2 , …, t n are the observed failure times and F n (t i ) is its empirical distribution function, then in order to make these data conform to the linear equation form of the two-parameter Weibull distribution, we can let:

[0135]

[0136] Among them, the regression line equation to be fitted is:

[0137] y = a + bx

[0138] To sum up, the estimated value of the parameter model failure can be obtained as:

[0139]

[0140] In the formula, represents the estimated value of the shape parameter η of the sudden failure model; represents the estimated value of the shape parameter m of the sudden failure model.

[0141] (3) Establish the reliability function of the series model established under independent conditions (independent competing failure model)

[0142] Among them, R i (t) represents the independent competing failure reliability.

[0143] (4) Establish the competing failure reliability function with degradation variables established under related conditions (related competing failure model)

[0144]

[0145] In the formula, R r (x, t) represents the conditional probability of sudden failure with respect to the degradation quantity, R c (t) represents the related competing failure reliability, T represents the life, T r represents the degradation failure life, T d represents the sudden failure life, P(T r > t, T d > t) represents the probability that the failure time is less than the degradation failure life or less than the sudden failure life. g d(x, t) represents the probability density function of the performance degradation stochastic process, which is obtained by using the Fokker-Planck equation, and its specific form is:

[0146]

[0147] Among them, x represents the performance degradation amount, and D represents the failure threshold.

[0148] The present invention also provides a more specific embodiment to introduce the above method in detail:

[0149] According to the wind turbine blade fatigue crack growth test, the mapping relationship between the crack increment and the blade failure mode is obtained, and the degradation failure reliability curve R d (t), the sudden failure reliability curve R r (t), the series model reliability curve R i (t) established under independent conditions, and the competing failure reliability curve R c (t) with degradation variables established under related conditions are established. Among them, the estimated value of the degradation failure reliability model parameter is μ = 0.285, and σ = 0.181; the estimated value of the shape parameter of the sudden failure model The estimated value of the scale parameter The estimated value of the shape parameter of the competing failure reliability model under the correlation condition The estimated value of the scale parameter Substitute the above parameters into the failure model, and the degradation failure reliability curve R d (t), the sudden failure reliability curve R r (t), the competing failure reliability curve R i (t) of the two under independent conditions, and the competing failure reliability curve R c (t) of the two under related conditions can be obtained respectively. The four reliability curves are compared as Figure 10 shown.

[0150] It can be found through comparison that the competing failure reliability model considering the multi-failure mode related conditions changes more gently. However, when the number of cycles is before 3×10 8 , R c (t) is slightly higher than R i (t), and when the number of cycles is after 3×10 8 , R c (t) is less than R i (t). This is mainly because the competition between degradation failure and sudden failure of the wind turbine blade in the later stage of fatigue crack growth leads to a gradual decrease in reliability. It can be found that the reliability model considering multi-failure mode competing failure has higher accuracy and is closer to the actual engineering application.

[0151] For the above method, an embodiment of the present invention further provides a reliability simulation and evaluation system for a wind turbine blade, including:

[0152] A reliability database establishment module for establishing a reliability database of a sample wind turbine blade; the reliability database includes: design information, test data, failure data, condition detection data, and maintenance data.

[0153] An environmental load profile drawing module for drawing an environmental load profile of the sample wind turbine blade based on the reliability database.

[0154] A finite element model construction module for constructing a finite element model based on the airfoil data of the wind turbine blade to be evaluated.

[0155] A simulation module for loading the environmental loads in the environmental load profile onto the finite element model, and performing thermal stress simulation, vibration stress simulation, and multi-stress coupling simulation on the finite element model respectively to obtain the weak links on the wind turbine blade to be evaluated; the weak links include the position information of the failure points, the heat concentration areas, the vibration forms, and the stress and strain distributions.

[0156] An FMECA analysis module for performing FMECA analysis on the sample wind turbine blade based on the reliability database to obtain a hazard matrix analysis diagram and an FMECA table of the failure points on the sample wind turbine blade.

[0157] A reliability evaluation failure data sample determination module for obtaining the failure time of each failure point on the wind turbine blade to be evaluated according to the weak links, the hazard matrix analysis diagram, and the FMECA table on the wind turbine blade to be evaluated;

[0158] A failure data distribution determination module for performing distribution fitting on the failure time of each failure point on the wind turbine blade to be evaluated to obtain the failure time distribution of each failure point on the wind turbine blade to be evaluated.

[0159] An extraction module for sampling the failure time of all failure points on the wind turbine blade to be evaluated by using the Monte Carlo method to obtain a sampling result.

[0160] A failure time determination module for taking the shortest life time among all failure points in the sampling result as the failure time of the wind turbine blade to be evaluated.

[0161] A failure data determination module for obtaining the failure data of the wind turbine blade to be evaluated according to the failure time of the wind turbine blade to be evaluated.

[0162] A failure type determination module, configured to determine the failure type of the to-be-evaluated wind turbine blade according to the failure data of the to-be-evaluated wind turbine blade; the failure types include independent competing failure, related competing failure, degradation failure, and sudden failure.

[0163] A failure model determination module, configured to determine a failure model for reliability assessment according to the failure type of the to-be-evaluated wind turbine blade; the failure models include an independent competing failure model, a related competing failure model, a degradation failure model, and a sudden failure model.

[0164] A reliability assessment module, configured to perform reliability assessment on the to-be-evaluated wind turbine blade according to the failure time of each fault point on the to-be-evaluated wind turbine blade and the failure model for the reliability assessment.

[0165] In practical applications, the multi-stress coupling simulation is a two-way fluid-structure interaction simulation.

[0166] In practical applications, the failure type determination module specifically includes:

[0167] A first judgment unit, configured to judge whether the failure type of the to-be-evaluated wind turbine blade is a competing failure according to the failure data of the to-be-evaluated wind turbine blade, and obtain a first judgment result.

[0168] A correlation calculation unit, configured to calculate a correlation result between degradation failure and sudden failure if the first judgment result is yes.

[0169] A related competing failure determination unit, configured to determine that the failure type of the to-be-evaluated wind turbine blade is a related competing failure if the correlation result is correlated.

[0170] An independent competing failure determination unit, configured to determine that the failure type of the to-be-evaluated wind turbine blade is an independent competing failure if the correlation result is not correlated.

[0171] A degradation failure judgment unit, configured to judge whether the failure type of the to-be-evaluated wind turbine blade is a degradation failure if the first judgment result is no, and obtain a second judgment result.

[0172] A degradation failure determination unit, configured to determine that the failure type of the to-be-evaluated wind turbine blade is a degradation failure if the second judgment result is yes.

[0173] A sudden failure determination unit, configured to determine that the failure type of the to-be-evaluated wind turbine blade is a sudden failure if the second judgment result is no.

[0174] An embodiment of the present invention further provides an electronic device, including:

[0175] A memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the wind turbine blade reliability simulation evaluation method described in the above embodiments.

[0176] An embodiment of the present invention also provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the wind turbine blade reliability simulation evaluation method described in the above embodiments.

[0177] The present invention aims to solve the problem of reliability simulation evaluation in the process of research, development, production, management and maintenance of wind turbine blades. It is more efficient than the traditional manual-based reliability analysis method because the reliability analysis technology based on physics of failure analyzes the unreliable factors of wind turbine blades from the perspective of physical and chemical properties, conducts a series of defect evaluations, simulation tests and model establishment, analyzes the failure mechanism generated by wind turbine blades under complex stress, and then reduces the probability of failure through fault simulation. The predicted results obtained according to this method can show the failure mechanism, degradation law and fault cause of wind turbine blades, so as to find its weak links to ensure the realization of the reliability requirements of wind turbine blades. Therefore, the present invention provides guidance for the reliability design of wind turbine blades and the low-cost long-term operation and maintenance of wind farms based on physics of failure, and uses the method based on physics of failure to analyze the reliability of products, realizing the discovery of weak links of products, elimination of potential faults, reduction of test volume in the stages of research, development, maintenance and management, improving the inherent reliability of products. The results obtained by this method can show the failure mechanism, degradation law and fault cause of products, so as to find its weak links to ensure the realization of the reliability requirements of products.

[0178] In this specification, each embodiment is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0179] Specific examples are used in this article to elaborate on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A reliability simulation and evaluation method for wind turbine blades, characterized in that, it includes: Establish a reliability database for the sample wind turbine blades; The reliability database includes: design information, test data, failure data, condition detection data, and maintenance data; Draw the environmental load profile of the sample wind turbine blades based on the reliability database; Construct a finite element model based on the airfoil data of the wind turbine blades to be evaluated; Load the environmental loads in the environmental load profile onto the finite element model, and perform thermal stress simulation, vibration stress simulation, and multi-stress coupling simulation on the finite element model respectively to obtain the weak links on the wind turbine blades to be evaluated; The weak links include the location information of the failure points, the heat concentration area, the vibration form, and the stress-strain distribution; Conduct FMECA analysis on the sample wind turbine blades based on the reliability database to obtain the hazard matrix analysis diagram and FMECA table of the failure points on the sample wind turbine blades; Obtain the failure time of each failure point on the wind turbine blades to be evaluated according to the weak links, the hazard matrix analysis diagram, and the FMECA table on the wind turbine blades to be evaluated; Perform distribution fitting on the failure time of each failure point on the wind turbine blades to be evaluated to obtain the failure time distribution of each failure point on the wind turbine blades to be evaluated; Use the Monte Carlo method to sample the failure time of all failure points on the wind turbine blades to be evaluated to obtain the sampling result; Take the shortest life time among all failure points in the sampling result as the failure time of the wind turbine blades to be evaluated; Obtain the failure data of the wind turbine blades to be evaluated according to the failure time of the wind turbine blades to be evaluated; Determine the failure type of the wind turbine blades to be evaluated according to the failure data of the wind turbine blades to be evaluated; The failure types include independent competing failure, dependent competing failure, degradation failure, and sudden failure; Determine the failure model for reliability evaluation according to the failure type of the wind turbine blades to be evaluated; The failure models include independent competing failure model, dependent competing failure model, degradation failure model, and sudden failure model; Conduct reliability evaluation on the wind turbine blades to be evaluated according to the failure time of each failure point on the wind turbine blades to be evaluated and the failure model for reliability evaluation.

2. The reliability simulation and evaluation method for wind turbine blades according to claim 1, characterized in that, The multi-stress coupling simulation is a two-way fluid-structure interaction simulation.

3. The reliability simulation and evaluation method for wind turbine blades according to claim 1, characterized in that, The step of determining the failure type of the wind turbine blades to be evaluated according to the failure data of the wind turbine blades to be evaluated specifically includes: Judge whether the failure type of the wind turbine blades to be evaluated is a competing failure according to the failure data of the wind turbine blades to be evaluated, and obtain the first judgment result; If the first judgment result is yes, calculate the correlation result between degradation failure and sudden failure; If the correlation result shows a correlation, determine that the failure type of the wind turbine blades to be evaluated is a dependent competing failure; If the correlation result shows no correlation, determine that the failure type of the wind turbine blade to be evaluated is independent competing failure; If the first judgment result is negative, then determine whether the failure type of the wind turbine blade to be evaluated is degradation failure to obtain a second judgment result; If the second judgment result is positive, determine that the failure type of the wind turbine blade to be evaluated is degradation failure; If the second judgment result is negative, determine that the failure type of the wind turbine blade to be evaluated is sudden failure.

4. A reliability simulation evaluation system for wind turbine blades Characterized in that it includes: a reliability database establishment module for establishing a reliability database of sample wind turbine blades; The reliability database includes: design information, test data, fault data, condition detection data, and maintenance data; an environmental load profile drawing module for drawing an environmental load profile of the sample wind turbine blade based on the reliability database; a finite element model construction module for constructing a finite element model based on the airfoil data of the wind turbine blade to be evaluated; a simulation module for loading the environmental loads in the environmental load profile onto the finite element model, and performing thermal stress simulation, vibration stress simulation, and multi-stress coupling simulation on the finite element model respectively to obtain the weak links on the wind turbine blade to be evaluated; the weak links include fault point position information, heat concentration areas, vibration forms, and stress and strain distributions; an FMECA analysis module for performing FMECA analysis on the sample wind turbine blade based on the reliability database to obtain a hazard matrix analysis diagram and an FMECA table of the fault points on the sample wind turbine blade; a reliability evaluation failure data sample determination module for obtaining the failure time of each fault point on the wind turbine blade to be evaluated according to the weak links, the hazard matrix analysis diagram, and the FMECA table on the wind turbine blade to be evaluated; a failure data distribution determination module for performing distribution fitting on the failure time of each fault point on the wind turbine blade to be evaluated to obtain the failure time distribution of each fault point on the wind turbine blade to be evaluated; a sampling module for sampling the failure time of all fault points on the wind turbine blade to be evaluated by using the Monte Carlo method to obtain a sampling result; a failure time determination module for taking the shortest life time among all fault points in the sampling result as the failure time of the wind turbine blade to be evaluated; a failure data determination module for obtaining the failure data of the wind turbine blade to be evaluated according to the failure time of the wind turbine blade to be evaluated; a failure type determination module for determining the failure type of the wind turbine blade to be evaluated according to the failure data of the wind turbine blade to be evaluated; the failure types include independent competing failure, related competing failure, degradation failure, and sudden failure; a failure model determination module for determining a failure model for reliability evaluation according to the failure type of the wind turbine blade to be evaluated; the failure models include independent competing failure model, related competing failure model, degradation failure model, and sudden failure model; A reliability evaluation module for performing reliability evaluation on the wind turbine blade to be evaluated according to the failure time of each failure point on the wind turbine blade to be evaluated and the failure model of the reliability evaluation.

5. The wind turbine blade reliability simulation evaluation system according to claim 4, wherein, the multi-stress coupling simulation is a two-way fluid-structure interaction simulation.

6. The wind turbine blade reliability simulation evaluation system according to claim 4, wherein, the failure type determination module specifically includes: A first judgment unit for judging whether the failure type of the wind turbine blade to be evaluated is a competing failure according to the failure data of the wind turbine blade to be evaluated, and obtaining a first judgment result; A correlation calculation unit for calculating a correlation result between degradation failure and sudden failure if the first judgment result is yes; A related competing failure determination unit for determining that the failure type of the wind turbine blade to be evaluated is a related competing failure if the correlation result is correlated; An independent competing failure determination unit for determining that the failure type of the wind turbine blade to be evaluated is an independent competing failure if the correlation result is not correlated; A degradation failure judgment unit for judging whether the failure type of the wind turbine blade to be evaluated is a degradation failure if the first judgment result is no, and obtaining a second judgment result; A degradation failure determination unit for determining that the failure type of the wind turbine blade to be evaluated is a degradation failure if the second judgment result is yes; A sudden failure determination unit for determining that the failure type of the wind turbine blade to be evaluated is a sudden failure if the second judgment result is no.

7. An electronic device, wherein, comprising: A memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the wind turbine blade reliability simulation evaluation method according to any one of claims 1 to 3.

8. A computer-readable storage medium, wherein, it stores a computer program, and when the computer program is executed by a processor, it implements the wind turbine blade reliability simulation evaluation method according to any one of claims 1 to 3.

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