Wind power equipment operation reliability analysis method and system based on time-varying condition

Abstract

The invention provides a wind power equipment operation reliability analysis method and system based on a time-varying condition, and relates to the technical field of complex equipment safety management and maintenance decision analysis. Obtaining a maximum likelihood estimator and a distribution function of each distribution type in the wind power equipment system, and performing fitting processing on the distribution function of each distribution type according to an improved D-S evidence theory to obtain a fault data probability density function; according to the rough set theory, the scoring process of each fault mode in the FMEA method is optimized, and the criticality of the fault mode is calculated; and according to the reliability data of the wind power equipment system, constructing a dynamic reliability network model, and obtaining a reliability index function and reliability sensitivity of each risk node in the dynamic reliability network model. According to the invention, the accuracy of fault prediction and the objectivity of risk assessment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of safety management and maintenance decision analysis of complex equipment, and in particular to a method and system for analyzing the operating reliability of wind power equipment under time-varying conditions. Background Art

[0002] Industry is an important part of my country's equipment manufacturing industry and has an important impact on the national economy and national defense security. As a strategic industry, industry has promoted the development of heavy industry, light industry, electronic information and other industries, and promoted international trade. The power system is a key supporting equipment. 98% of ships use wind power equipment, and its reliability directly affects the normal operation and mission capability. With the advancement of technology, the power mode has experienced a development from steam engines to wind power equipment, gas turbines, nuclear power and all-electric propulsion. Wind power equipment is widely used in civilian use such as merchant ships and fishing boats due to its economy and controllability.

[0003] The Chinese patent with announcement number CN111310789B discloses a wind power interval prediction method based on kernel density estimation and its implementation system. First, a deterministic point prediction of wind power is performed based on a support vector machine modeling method based on continuous time period clustering, and a comparison chart of wind power prediction value and actual value is obtained; then, a probability density function is established for the prediction error in each power partition by using kernel density estimation and optimal window width selection, and a comparative analysis is performed with the probability density curve and error frequency histogram obtained by the recursive method and the sliding window width method; finally, the wind power prediction interval that meets a certain confidence probability is calculated by combining the deterministic point prediction results, and the interval coverage rate and the average interval width are used as evaluation indicators to compare and analyze with the recursive method and the sliding window width method. However, the method provided in the above application only studies the system as a whole, and does not use a variety of distribution types for comparison and optimization, which can easily lead to relatively low fitting accuracy. Therefore, it is very necessary to provide a method and system for analyzing the operating reliability of wind power equipment under time-varying conditions to improve the accuracy of fault prediction and the objectivity of risk assessment. Summary of the invention

[0004] In view of this, the present invention proposes a wind power equipment operation reliability analysis method and system based on time-varying conditions. Through the combination of maximum likelihood estimation and improved DS evidence theory, the recognition accuracy of fault data distribution characteristics is improved, and at the same time, the rough set theory is combined to optimize the FMEA scoring process to achieve objective fault mode hazard evaluation, thereby improving the accuracy of fault prediction and the objectivity of risk assessment.

[0005] The present invention provides a method for analyzing the operating reliability of wind power equipment under time-varying conditions, the method comprising:

[0006] Based on the maximum likelihood estimation method and the fault data of wind power equipment system, the maximum likelihood estimator and distribution function of each distribution type in the wind power equipment system are obtained, and the distribution function of each distribution type is fitted according to the improved DS evidence theory to obtain the probability density function of the fault data;

[0007] According to the rough set theory, the scoring process of each failure mode in the FMEA method is optimized, and the failure mode criticality is calculated, wherein the failure mode criticality includes the scoring rough number corresponding to the failure mode and the failure mode risk level ranking;

[0008] According to the reliability data of the wind power equipment system, a dynamic reliability network model is constructed to obtain the reliability index function and reliability sensitivity of each risk node in the dynamic reliability network model, wherein the dynamic reliability network model includes a plurality of risk nodes.

[0009] On the basis of the above technical solution, preferably, the method of obtaining the maximum likelihood estimator and distribution function of each distribution type in the wind power equipment system based on the maximum likelihood estimation method and the wind power equipment system fault data specifically includes:

[0010] Determine the number of groups according to parameter estimation and empirical formula, and calculate unknown parameters in the probability density function based on the maximum likelihood estimation method and the wind power equipment system fault data;

[0011] Based on the maximum likelihood estimation method and each distribution type in the wind power equipment system fault data, the maximum likelihood estimator of each distribution type is calculated.

[0012] On the basis of the above technical solution, preferably, the distribution function of each distribution type is fitted according to the improved DS evidence theory to obtain the probability density function of the fault data, which specifically includes:

[0013] Calculate the slope between two adjacent points of the probability density function corresponding to the fault data, the median of two adjacent points of the probability density function corresponding to the fault data, and the median difference of the probability density function corresponding to the fault data respectively;

[0014] The slope and the median difference are respectively subjected to maximum image processing, and the grey correlation degree between the probability density functions corresponding to the fault data is calculated to obtain the probability density function of the fault data.

[0015] More preferably, the process of optimizing the scoring of each failure mode in the FMEA method according to the rough set theory and calculating the criticality of the failure mode specifically includes:

[0016] Obtaining a scoring matrix for each failure mode and a weight vector corresponding to each risk factor, and converting the risk factor scores corresponding to all scoring matrices into a rough scoring number based on the scoring conversion function;

[0017] Under different risk factors, the distance from the rough number of each fault mode score to the ideal solution is calculated to obtain the group utility maximization ranking, individual regret minimization ranking and compromise ranking of each fault mode;

[0018] The failure modes are ranked according to the trade-off ranking to obtain a failure mode risk level sequence.

[0019] More preferably, the building of a dynamic reliability network model based on the reliability data of the wind power equipment system specifically includes:

[0020] Collecting reliability data of wind power equipment system and using various types of data in the reliability data as risk nodes, wherein the reliability data includes system components, failure modes, failure causes and reliability driver data;

[0021] According to the score rough number corresponding to each risk node, the ideal solution and negative ideal solution of each risk node under different risk factors are calculated to obtain the distance and compromise ranking from the score rough number corresponding to each risk node to the ideal solution;

[0022] Taking the compromise ranking as the node hazard degree, respectively obtaining the reliability hazard degrees corresponding to the wind power equipment system components, failure modes and failure causes, and taking each of the reliability hazard degrees as the node weight of the corresponding risk node;

[0023] Based on the fault data in the fault mode, the time function of the failure rate under different propagation relationships is calculated, and the time function is used as the edge weight of the corresponding risk node to construct a dynamic reliability network model.

[0024] More preferably, the method further includes evaluating the reliability level of the wind power equipment system in the current state based on the first system reliability level influence function and the second system reliability level influence function, wherein the expressions of the first system reliability level influence function and the second system reliability level influence function are:

[0025] C max =I max (B f E)W f→f W f→F (B F E)W F→F W F→P (B P E)W P→P W P→C

[0026] C t =I t (B f E)Wf→f W f→F (B F E)W F→F W F→P (B P E)W P→P W P→C

[0027] Among them, C max Represents the first system reliability level influence function, I max Represents the reliability index influence maximum value matrix, B f represents the weight matrix of the failure cause node, E represents the expert rating matrix, W f→f represents the influence weight matrix between failure causes, W f→F represents the influence weight matrix between the failure cause and the typical failure mode of the system, B F Represents the node weight matrix of the typical failure mode of the system, W F→F Represents the impact weight matrix between typical failure modes of the system, W F→P represents the influence weight matrix between the typical failure modes of the system and the system components, B P represents the system component node weight matrix, W P→P represents the influence weight matrix between system components, W P→C represents the influence weight matrix between system components and central nodes, C t represents the second system reliability level influence function, I t Represents the monitoring indicator impact matrix.

[0028] More preferably, the expression of the reliability index function is:

[0029]

[0030] Among them, C represents the influence of the central node, k represents the number of groups, P represents the influence of the system component node, i represents the i-th risk node, j represents the j-th risk node, F represents the influence of the system typical failure mode node, f represents the influence of the failure cause node, B f Represents the weight matrix of the failure cause node, B F represents the node weight matrix of the typical failure mode of the system, B P represents the node weight matrix of the system components, Represents the system component node P j To the system component node P i The influence weight of Represents the system component node P i The influence weight to the central node, Indicates failure mode F i To failure mode Fj The influence weight of Indicates failure mode F i To system components P k The influence weight of Indicates the failure cause node f j and the failure cause node f i The influence weight matrix, Indicates the failure cause node f j Node F with typical system failure mode k The influence weight matrix, dist() represents the distance function, Represents the monitoring indicator impact matrix, Interval Ii Indicates the number of intervals.

[0031] In a second aspect of the present application, a wind power equipment operation reliability analysis system based on time-varying conditions is provided, wherein the wind power equipment operation reliability analysis system comprises a function fitting module, a risk sorting module and a reliability analysis module, wherein:

[0032] The function fitting module is used to obtain the maximum likelihood estimator and distribution function of each distribution type in the wind power equipment system based on the maximum likelihood estimation method and the wind power equipment system fault data, and to fit the distribution function of each distribution type according to the improved DS evidence theory to obtain the probability density function of the fault data;

[0033] The risk ranking module is used to optimize the scoring process of each failure mode in the FMEA method according to the rough set theory, and calculate the failure mode criticality, wherein the failure mode criticality includes the scoring rough number corresponding to the failure mode and the failure mode risk level ranking;

[0034] The reliability analysis module is used to construct a dynamic reliability network model according to the reliability data of the wind power equipment system, and obtain the reliability index function and reliability sensitivity of each risk node in the dynamic reliability network model, wherein the dynamic reliability network model includes multiple risk nodes.

[0035] In a third aspect of the present application, an electronic device is provided, comprising a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory.

[0036] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. The computer program is executed by a processor to implement the steps of a method for analyzing the operating reliability of wind power equipment under time-varying conditions.

[0037] The wind power equipment operation reliability analysis method and system based on time-varying conditions provided by the present invention have the following beneficial effects compared with the prior art:

[0038] (1) By combining maximum likelihood estimation with improved DS evidence theory, the recognition accuracy of fault data distribution characteristics is improved. The obtained probability density function can more accurately reflect the fault characteristics of wind power equipment systems. At the same time, the FMEA scoring process is optimized by combining rough set theory to achieve objective fault mode hazard assessment, and the dynamic reliability network model is used to analyze the reliability index and sensitivity of each risk node, thus constructing a complete technical chain from data processing, risk assessment to reliability analysis, which not only improves the accuracy of fault prediction and the objectivity of risk assessment, but also enhances the reliability analysis capability.

[0039] (2) By constructing a scoring matrix and introducing a weight vector, the risk factor score is converted into a rough score number. At the same time, the ideal solution distance calculation is combined to achieve the dual optimization of maximizing group utility and minimizing individual regret. Finally, an objective failure mode risk level sequence is obtained, which effectively improves the scientificity and reliability of the FMEA scoring process, overcomes the defect of strong subjectivity in the traditional scoring method, and provides a more accurate and reliable quantitative basis for the risk assessment of wind power equipment failure modes.

[0040] (3) By collecting comprehensive reliability data including system components, failure modes, failure causes and reliability drivers, the ideal solution and negative ideal solution are calculated by combining the scoring rough number to obtain an objective compromise ranking, and the reliability hazard degree is converted into node weights. At the same time, the time function of the failure rate is considered as the edge weight. Finally, a dynamic reliability network model is constructed, which realizes the upgrade from static evaluation to dynamic analysis. It not only improves the accuracy and comprehensiveness of risk assessment, but also reflects the changing characteristics of system reliability over time, providing a more timely and targeted scientific basis for equipment maintenance decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 A schematic flow chart of a method for analyzing the operating reliability of wind power equipment under time-varying conditions provided by the present invention;

[0043] Figure 2 A schematic diagram of the reliability modeling process provided by the present invention;

[0044] Figure 3 A structural example diagram of the reliability network model provided by the present invention;

[0045] Figure 4 A schematic diagram of the structure of the wind power equipment operation reliability analysis system provided by the present invention;

[0046] Figure 5 This is a schematic structural diagram of an electronic device provided by the present invention.

[0047] Explanation of the reference numerals: 1. Wind power equipment operation reliability analysis system; 11. Function fitting module; 12. Risk sorting module; 13. Reliability analysis module; 2. Electronic device; 21. Processor; 22. Communication bus; 23. User interface; 24. Network interface; 25. Memory. DETAILED DESCRIPTION

[0048] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The present invention discloses a method for analyzing the reliability of wind power equipment operation under time-varying conditions, referring to Figure 1 The steps of the method include steps S1 to S3.

[0050] Step S1, based on the maximum likelihood estimation method and the wind power equipment system fault data, obtain the maximum likelihood estimator and distribution function of each distribution type in the wind power equipment system, and fit the distribution function of each distribution type according to the improved DS evidence theory to obtain the fault data probability density function.

[0051] This step also includes steps S11 to S14.

[0052] Step S11, determining the number of groups according to parameter estimation and empirical formulas, and calculating unknown parameters in the probability density function based on the maximum likelihood estimation method and wind power equipment system fault data.

[0053] In this step, based on parameter estimation, the empirical formula is used to determine the number of groups, optimize the probability density function fitting of the fault data, and reduce the fitting error. The calculation formula is:

[0054]

[0055] Among them, n F Indicates the sample size.

[0056] Furthermore, based on the maximum likelihood estimation method, the unknown parameters in the probability density function are calculated through the wind power equipment system fault data.

[0057] If (x 1 ,x 2 ,…,x n ) is a set of samples from the population, and its sampling probability is Construct the likelihood function:

[0058]

[0059] Among them, (x 1 ,x 2 ,…,x n ) represents a set of samples, represents a set of sampling probabilities, θ=[θ 1 ,θ 2 ,…,θ n ] represents the estimated parameters of the population.

[0060] Step S12: Calculate the maximum likelihood estimator of each distribution type based on the maximum likelihood estimation method and each distribution type in the wind power equipment system fault data.

[0061] The calculation formulas of the maximum likelihood estimators for each distribution type are shown in Table 1.

[0062] Table 1 Maximum likelihood estimators for each distribution type

[0063]

[0064] Furthermore, when using the rectangular kernel function for estimation under small sample conditions, in order to obtain a smooth probability density function curve, a smooth Gaussian kernel function is used for estimation, and the calculation formula is:

[0065]

[0066] Among them, N represents the number of samples, h represents the bandwidth size, and x n represents the nth sample.

[0067] Based on kernel density estimation, the appropriate bandwidth is calculated and selected to ensure the smoothness and accuracy of the probability density function of fault data, thereby optimizing the estimation results.

[0068] Since the bandwidth is a free parameter, its suitability will directly affect the accuracy of the probability density function estimation results of the fault data. Therefore, the selection of the bandwidth h should minimize the error of the probability density function estimation results. Generally, the mean integrated square error (MISE) is used to measure the error of the estimation results. The calculation formula is:

[0069]

[0070] To minimize MISE(h), we just need to find the minimum point of MISE(h). The bandwidth h that minimizes MISE(h) can be obtained by using the above formula: AMISE , the calculation formula is:

[0071]

[0072] R(g)=∫g(x) 2 dx;m 2 (K) = ∫x 2 K(x)dx

[0073] Once the kernel function is determined, R, m, and f″ in the above formula can all be determined, and the bandwidth calculation formula is:

[0074]

[0075] in, represents the sample standard deviation, and N represents the sample size.

[0076] Based on the kernel density estimation principle, the deviation of the kernel density estimation principle is calculated. The calculation formula is:

[0077]

[0078]

[0079] in, Assuming that the kernel function is bounded by [-1,1], when |u|>1, k(u)=0, so when x>h, a 0 (x)=1,a 1 (x) = 0, and when 0 < x < h, a 0 (x)≠1,a 1 (x)≠0, the deviation always exists. Use xk(x) as the kernel function to estimate f(x) and construct a new kernel function so that when 0<x<h, the deviation of the kernel density estimation is 0.

[0080] Step S13, respectively calculating the slope between two adjacent points of the probability density function corresponding to the fault data, the median of two adjacent points of the probability density function corresponding to the fault data, and the median difference of the probability density function corresponding to the fault data.

[0081] In this step, the linear correlation between the system fault data and its probability density function is determined based on the linear correlation coefficient between the two. The calculation formula is:

[0082]

[0083] Among them, x irepresents the value of the i-th fault data, Represents x i The mean value of y i Indicates that it is located at x i The function value at Represents y i The mean of .

[0084] Calculate the slope between two adjacent points of the fault data and its probability density function:

[0085]

[0086] Where n represents the number of samples, f(x j ) represents the sample x j The statistical function value at i (x j ) represents the probability density function at x j The function value at K i It represents the slope of the fault data between the jth sample point and the j+1th sample point, K ij Represents the slope of the probability density function between the jth sample point and the j+1th sample point.

[0087] Calculate the median of two adjacent points of the fault data and its probability density function:

[0088]

[0089] Calculate the slope difference and median difference between the fault data and its probability density function:

[0090]

[0091] Perform maximum-image processing on the slope and median difference:

[0092]

[0093] Step S14, performing maximum image processing on the slope and the median difference respectively, and calculating the grey correlation degree between the probability density functions corresponding to the fault data to obtain the probability density function of the fault data.

[0094] The grey correlation degree is calculated based on the slope of the fault data and its probability density function between two adjacent points, so as to determine the goodness of fit of the curve. The calculation formula is:

[0095]

[0096] After standardizing the grey relational degree, we get:

[0097]

[0098] Based on the improved DS evidence theory method, the goodness-of-fit indicators of the same distribution function are integrated, and then the five target distribution functions are optimized. The calculation formula is:

[0099]

[0100] Among them, m CCM (θ i ), m GRM (θ i ), m MDM (θ i ), m CSM (θ i ) represent the evidence of the ith distribution function in the correlation coefficient method, grey relational method, minimum distance method and correlation entropy method, respectively, and m is the number of distribution functions.

[0101] Step S2, optimizing the scoring process of each failure mode in the FMEA method according to the rough set theory, and calculating the failure mode criticality, wherein the failure mode criticality includes the scoring roughness number corresponding to the failure mode and the failure mode risk level ranking.

[0102] The improvement of FMEA dynamic scoring method focuses on fault severity (S), occurrence rate (O) and difficulty of detection (D). Among them, S and D are still evaluated by traditional methods because the scoring basis is stable. Since the occurrence rate changes the most during system operation, the failure rate of each fault mode is estimated by distribution function to obtain its probability density function. The calculation formula is:

[0103]

[0104] This step also includes steps S21 to S23.

[0105] Step S21, obtaining the scoring matrix of each fault mode and the weight vector corresponding to each risk factor, and converting the risk factor scores corresponding to all the scoring matrices into rough scoring numbers based on the scoring conversion function.

[0106] The rough set theory is used to improve the scoring process of each fault mode. The imprecision and fuzziness of information are described by lower approximation and upper approximation. Assuming that the kth expert scores each fault mode, the calculation formula is:

[0107]

[0108] Among them, EM k is the scoring matrix of the kth expert, m is the number of failure modes, is the score of the kth expert on the jth risk factor of the ith failure mode, EV k is the weight matrix of the kth expert, is the weight of the kth expert on the jth risk factor.

[0109] After obtaining the scoring matrix of each failure mode and the weight vector of each risk factor, each score is converted into a rough number. The calculation formula is:

[0110]

[0111] in, represents the lower bound of the rough number of risk factor scores, represents the upper boundary of the rough number of risk factor scores, represents the lower boundary of the rough number of risk factor weights, Represents the upper boundary of the rough number of risk factor weights.

[0112] The rough number of scores of each expert on the j-th risk factor of each failure mode is calculated as follows:

[0113]

[0114] According to the rough number operation rules, the scoring rough numbers of each fault mode and the weighted rough numbers of each risk factor are fused, and the calculation formula is:

[0115]

[0116] in, represents the rough score after the fusion of each fault mode, It represents the rough weight number after the fusion of each risk factor, and K represents the number of experts.

[0117] Step S22, calculating the distance from the rough number of each fault mode score to the ideal solution under different risk factors, so as to obtain the group utility maximization ranking, individual regret minimization ranking and compromise ranking of each fault mode.

[0118] Furthermore, according to the distance from the rough number of each fault mode score to the ideal solution under different risk factors, the group utility maximization ranking S, individual regret minimization ranking R and compromise ranking Q of each fault mode are calculated. The calculation formula is:

[0119]

[0120] Among them, S - =minS i , S + =maxS i , R - =minR i , R + =maxR i , v = 0.5, v is the maximum group utility weight.

[0121] Step S23, sorting the failure modes according to the compromise sorting to obtain a failure mode risk level sequence.

[0122] The fault modes are sorted according to the compromise ranking Q. The smaller the Q value, the higher the risk level of the fault mode. The calculation formula is:

[0123]

[0124] Among them, Q(fm (2) ) is the Q value corresponding to the second ranked fault mode, and m is the number of fault modes.

[0125] By constructing a scoring matrix and introducing a weight vector, the risk factor score is converted into a rough score number. At the same time, combined with the ideal solution distance calculation, the dual optimization of maximizing group utility and minimizing individual regret is achieved, and finally an objective failure mode risk level sequence is obtained, which effectively improves the scientificity and reliability of the FMEA scoring process, overcomes the strong subjective defects of the traditional scoring method, and provides a more accurate and reliable quantitative basis for the risk assessment of wind power equipment failure modes.

[0126] Step S3, construct a dynamic reliability network model based on the reliability data of the wind power equipment system, and obtain the reliability index function and reliability sensitivity of each risk node in the dynamic reliability network model, wherein the dynamic reliability network model includes multiple risk nodes. The dynamic reliability network model has 5 layers, which are reliability drivers, failure causes, system components, failure modes, and central nodes from the outside to the inside.

[0127] By collecting comprehensive reliability data including system components, failure modes, failure causes and reliability drivers, combining the ideal solution and negative ideal solution calculated by scoring rough numbers to obtain an objective compromise ranking, and converting the reliability hazard degree into node weights, and considering the time function of the failure rate as the edge weight, a dynamic reliability network model was finally constructed, which achieved the upgrade from static evaluation to dynamic analysis. It not only improved the accuracy and comprehensiveness of risk assessment, but also reflected the changing characteristics of system reliability over time, providing a more timely and targeted scientific basis for equipment maintenance decisions.

[0128] This step also includes steps S31 to S34.

[0129] Step S31, collecting reliability data of the wind power equipment system, and taking various types of data in the reliability data as risk nodes, wherein the reliability data includes system components, failure modes, failure causes and reliability driver data.

[0130] In this step, typical components, failure modes, failure causes, reliability drivers and corresponding failure data of wind power equipment are collected; and the wind power equipment N is sorted out. p System components, N F Typical failure modes and N f Failure cause data, collect and organize the failure data of failure modes caused by failure causes fi→Fj , Fault data F of the system components caused by the failure mode Fi→Pj , Fault data of system power shutdown caused by system component failure F Pi→C Among them, f i represents the i-th failure cause, F j represents the jth failure mode, P j represents the jth system component.

[0131] Step S32, according to the score rough number corresponding to each risk node, calculate the ideal solution and negative ideal solution of each risk node under different risk factors, so as to obtain the distance and compromise ranking from the score rough number corresponding to each risk node to the ideal solution.

[0132] Step S33, using the compromise ranking as the node hazard degree, respectively obtaining the reliability hazard degrees corresponding to the wind power equipment system components, failure modes and failure causes, and using each reliability hazard degree as the node weight of the corresponding risk node.

[0133] Furthermore, based on the reliability data, the improved FMEA method is used to perform failure mode and effect analysis. First, the scoring weights of different risk factors of each node are obtained, and data and modeling errors are introduced to form the rough scoring numbers of different risk factors of each node. Then, according to the rough scoring numbers of each node, the ideal solution and negative ideal solution of each node under different risk factors are calculated, and the distance from the rough scoring number to the ideal solution is calculated. Based on this, the nodes at each layer are calculated to obtain the group utility maximization ranking S, the individual loss minimization ranking R and the compromise ranking Q. The compromise ranking Q value is used as the node hazard degree to obtain the hazard degree of wind power equipment system components, failure modes and failure causes to the reliability of wind power equipment, and it is used as the node weight.

[0134] Step S34, based on the fault data in the fault mode, calculate the time function of the failure rate under different propagation relationships, and use the time function as the edge weight of the corresponding risk node to construct a dynamic reliability network model.

[0135] In this step, through historical fault data, parameter estimation, non-parametric estimation and other methods are used to fit a variety of distribution functions to the fault data, and different methods are used to perform goodness analysis on the fitted distribution functions. Then, the DS evidence theory method is used to optimize the distribution function to obtain the best distribution type of the fault data, and the distribution function is determined and the error is calculated under time-varying uncertainty conditions. Finally, the time function of the edge weights of the nodes between layers is determined, and the experience and knowledge of domain experts are used to score the influence relationship of the nodes within the layer to obtain the edge weights of the nodes within the layer.

[0136] like Figure 2 and Figure 3 As shown, the reliability network model can be defined as a triple, which can be expressed as:

[0137] RM=<V,R,W>

[0138] Where: V = {V C ,V P ,V F ,V f ,V I}, represents the set of all nodes in the reliability network model. V C is the central node of the model, V represents the comprehensive reliability index of wind power equipment, V P 、V F 、V f 、V I They represent the system components, typical failure modes, failure causes and reliability driver nodes in the reliability model respectively.

[0139] R={R I→f ,R f→f ,R f→F ,R F→F ,R F→P ,R P→P ,R P→C} is the set of association relationship matrices of nodes within and between layers in the reliability network model. I→f is the relationship matrix between reliability driver nodes and failure cause nodes, R f→F is the relationship matrix between the failure cause nodes and the system typical failure mode nodes, R F→P is the relationship matrix between the system typical failure mode nodes and the system component nodes, R P→C is the relationship matrix between the system component nodes and the central node; R f→f is the relationship matrix between failure cause nodes, R F→F is the relationship matrix between nodes of the typical failure mode of the system, R P→P It is the relationship matrix between the nodes of system components.

[0140] is the set of all weight matrices in the reliability network model. f is the weight matrix of the failure cause node, B F is the node weight matrix of the typical failure mode of the system, B P is the system component node weight matrix; is the influence weight matrix between failure causes and typical failure modes of the system, is the influence weight matrix between the typical failure modes of the system and the system components, is the influence weight matrix between system components and central nodes; is the influence weight matrix between failure causes, is the impact weight matrix between typical failure modes of the system, is the influence weight matrix between system components. Finally, a wind power equipment dynamic reliability model with nodes of different sizes and edges of different weights is formed.

[0141] Based on the dynamic reliability network model, the coupling relationship and fault correlation of wind power equipment system components are analyzed to provide reliability assessment and maintenance guidance, including:

[0142] Collect the monitoring indicators that affect system reliability under all failure causes and their normal threshold ranges, that is, reliability indicators, and use the S-type membership function to calculate their influence on the failure causes. The calculation formula is:

[0143]

[0144] Among them, q 1 Indicates the lower limit of the monitoring indicator threshold, q 2 Indicates the upper threshold of the monitoring indicator.

[0145] In order to obtain the reliability impact of the system under the worst conditions, all the reliability indexes’ impact inputs on the failure causes are set to the maximum value 1, and the reliability index impact maximum matrix I is obtained. max , and calculate the maximum value C of the system reliability level affected in this case max , where the weight of each node and edge is a function of time, but the time axis of each function is different. The time variable in each function is the time since the last failure of each node and edge.

[0146] Based on the first system reliability level influence function and the second system reliability level influence function, the reliability level of the wind power equipment system in the current state is evaluated, wherein the expressions of the first system reliability level influence function and the second system reliability level influence function are:

[0147] C max =I max (Bf E)W f→f W f→F (B F E)W F→F W F→P (B P E)W P→P W P→C

[0148] Calculate the real value of the impact of system reliability: In actual situations, most monitoring indicators are in normal state, and only a few monitoring indicators will deviate from their normal threshold range. According to the real-time monitoring value of each monitoring indicator and its normal threshold range, evaluate the impact of each monitoring indicator on the failure cause and form the monitoring indicator impact matrix I t . And calculate the degree of impact C of the system reliability level under the current state t The weights of each node and edge are the same as above, and the time variable in each function is the time since the last failure of each node and edge.

[0149] C t =I t (B f E)W f→f W f→F (B F E)W F→F W F→P (B P E)W P→P W P→C

[0150] Among them, C max Represents the first system reliability level influence function, I max Represents the reliability index influence maximum value matrix, B f represents the weight matrix of the failure cause node, E represents the expert rating matrix, W f→f represents the influence weight matrix between failure causes, W f→F represents the influence weight matrix between the failure cause and the typical failure mode of the system, B F Represents the node weight matrix of the typical failure mode of the system, W F→F Represents the impact weight matrix between typical failure modes of the system, W F→P represents the influence weight matrix between the typical failure modes of the system and the system components, B P represents the system component node weight matrix, W P→P represents the influence weight matrix between system components, W P→C represents the influence weight matrix between system components and central nodes, C t represents the second system reliability level influence function, I t Represents the monitoring indicator impact matrix.

[0151] In actual situations, most monitoring indicators are in normal state, and only a few monitoring indicators deviate from their normal threshold range. According to the real-time monitoring value of each monitoring indicator and its normal threshold range, the impact of each monitoring indicator on the failure cause is evaluated to form the monitoring indicator impact matrix I t . And calculate the degree of impact C of the system reliability level under the current state t The weights of each node and edge are the same as above, and the time variable in each function is the time since the last failure of each node and edge.

[0152] Based on sensitivity analysis, the importance of wind power equipment system nodes to reliability fluctuations is studied, and the influencing factors are traced to evaluate and optimize system performance, thereby achieving a comprehensive analysis of wind power equipment operation reliability under time-varying conditions.

[0153] The expression of the reliability index function is:

[0154]

[0155] Among them, C represents the influence of the central node, k represents the number of groups, P represents the influence of the system component node, i represents the i-th risk node, j represents the j-th risk node, F represents the influence of the system typical failure mode node, f represents the influence of the failure cause node, B f Represents the weight matrix of the failure cause node, B F represents the node weight matrix of the typical failure mode of the system, B P represents the node weight matrix of the system components, Represents the system component node P j To the system component node P i The influence weight of Represents the system component node P i The influence weight to the central node, Indicates failure mode F i To failure mode F j The influence weight of Indicates failure mode F i To system components P k The influence weight of Indicates the failure cause node f j and the failure cause node f i The influence weight matrix, Indicates the failure cause node f j Node F with typical system failure mode k The influence weight matrix, dist() represents the distance function, represents the monitoring indicator impact matrix, Indicates the number of intervals.

[0156] The reliability sensitivity of each node is calculated using the following formula:

[0157]

[0158] Conduct node reliability sensitivity error analysis, and the calculation formula is:

[0159]

[0160] Among them, C represents the influence of the central node, k represents the number of groups, P represents the influence of the system component nodes, F represents the influence of the system typical failure mode nodes, f represents the influence of the failure cause nodes, σ x is the error of each weight.

[0161] Dynamically identify the key risk points of the system. Since the sensitivity range of each node in the system is an interval, in order to analyze the relative importance of each node, it is necessary to sort the sensitivity intervals of each node in the system. The relative advantage ranking method of interval number is used to sort the sensitivity of each node. The specific calculation process of this method is as follows:

[0162] 1) Compare the numbers of each interval pairwise and construct a relative advantage matrix;

[0163]

[0164] Among them, A - represents the left limit of the interval number A, A + represents the right limit of the interval number A, B - represents the left limit of the interval number B, B + Represents the right limit of the interval number B.

[0165] 2) Calculate the sorting vector of each interval number;

[0166]

[0167] Among them, P represents the relative advantage matrix of interval numbers, and n represents the number of interval numbers.

[0168] 3) Use the sorting vector of each interval number to sort the interval numbers.

[0169] By combining maximum likelihood estimation with improved DS evidence theory, the recognition accuracy of fault data distribution characteristics is improved, and the obtained probability density function can more accurately reflect the fault characteristics of wind power equipment systems. At the same time, the FMEA scoring process is optimized by combining rough set theory to achieve objective fault mode hazard assessment, and the reliability index and sensitivity of each risk node are analyzed using a dynamic reliability network model, thus constructing a complete technical chain from data processing, risk assessment to reliability analysis, which not only improves the accuracy of fault prediction and the objectivity of risk assessment, but also enhances the reliability analysis capability.

[0170] Based on the above method, the embodiment of the present application discloses a wind power equipment operation reliability analysis system based on time-varying conditions, referring to Figure 4 The wind power equipment operation reliability analysis system 1 includes a function fitting module 11, a risk sorting module 12 and a reliability analysis module 13, wherein:

[0171] The function fitting module 11 is used to obtain the maximum likelihood estimator and distribution function of each distribution type in the wind power equipment system based on the maximum likelihood estimation method and the wind power equipment system fault data, and to fit the distribution function of each distribution type according to the improved DS evidence theory to obtain the probability density function of the fault data;

[0172] The risk ranking module 12 is used to optimize the scoring process of each failure mode in the FMEA method according to the rough set theory, and calculate the failure mode criticality, wherein the failure mode criticality includes the scoring rough number corresponding to the failure mode and the failure mode risk level ranking;

[0173] The reliability analysis module 13 is used to construct a dynamic reliability network model according to the reliability data of the wind power equipment system, and obtain the reliability index function and reliability sensitivity of each risk node in the dynamic reliability network model, wherein the dynamic reliability network model includes multiple risk nodes.

[0174] In one example, the function fitting module 11 is used to determine the number of groups based on parameter estimation and empirical formulas, calculate unknown parameters in the probability density function based on the maximum likelihood estimation method and wind power equipment system fault data; and calculate the maximum likelihood estimator of each distribution type based on the maximum likelihood estimation method and each distribution type in the wind power equipment system fault data.

[0175] In one example, the function fitting module 11 is used to respectively calculate the slope between two adjacent points of the probability density function corresponding to the fault data, the median of two adjacent points of the probability density function corresponding to the fault data, and the median difference of the probability density function corresponding to the fault data; perform maximum image processing on the slope and the median difference, and calculate the gray correlation between the probability density functions corresponding to the fault data to obtain the probability density function of the fault data.

[0176] In one example, the risk ranking module 12 is used to obtain the scoring matrix of each fault mode and the weight vector corresponding to each risk factor, and convert the risk factor scores corresponding to all scoring matrices into rough scoring numbers based on the scoring conversion function; calculate the distance from the rough scoring number of each fault mode to the ideal solution under different risk factors to obtain the group utility maximization ranking, individual waste minimization ranking and compromise ranking of each fault mode; sort the fault modes according to the compromise ranking to obtain a fault mode risk level sequence.

[0177] In one example, the reliability analysis module 13 is used to collect reliability data of a wind power equipment system, and use various types of data in the reliability data as risk nodes, wherein the reliability data includes system components, failure modes, failure causes, and reliability driver data; according to the score rough number corresponding to each risk node, the ideal solution and negative ideal solution of each risk node under different risk factors are calculated to obtain the distance and compromise ranking from the score rough number corresponding to each risk node to the ideal solution; using the compromise ranking as the node hazard degree, the reliability hazard degree corresponding to the wind power equipment system components, failure modes, and failure causes is obtained respectively, and each reliability hazard degree is used as the node weight of the corresponding risk node; based on the fault data in the fault mode, the time function of the failure rate under different propagation relationships is calculated, and the time function is used as the edge weight of the corresponding risk node to construct a dynamic reliability network model.

[0178] In one example, the method further includes evaluating the reliability level of the wind power equipment system in the current state based on the first system reliability level influence function and the second system reliability level influence function, wherein the expressions of the first system reliability level influence function and the second system reliability level influence function are:

[0179] C max =I max (B f E)W f→f W f→F (B F E)W F→F W F→P (B P E)W P→P W P→C

[0180] C t =I t (B f E)W f→f W f→F (B F E)W F→F W F→P (B P E)W P→P W P→C

[0181] Among them, C max Represents the first system reliability level influence function, I max Represents the reliability index influence maximum value matrix, B f represents the weight matrix of the failure cause node, E represents the expert rating matrix, W f→f represents the influence weight matrix between failure causes, W f→F represents the influence weight matrix between the failure cause and the typical failure mode of the system, B F Represents the node weight matrix of the typical failure mode of the system, W F→F Represents the impact weight matrix between typical failure modes of the system, W F→P represents the influence weight matrix between the typical failure modes of the system and the system components, B P represents the system component node weight matrix, W P→P represents the influence weight matrix between system components, W P→C represents the influence weight matrix between system components and central nodes, C t represents the second system reliability level influence function, I t Represents the monitoring indicator impact matrix.

[0182] In one example, the expression of the reliability index function is:

[0183]

[0184] Among them, C represents the influence of the central node, k represents the number of groups, P represents the influence of the system component node, i represents the i-th risk node, j represents the j-th risk node, F represents the influence of the system typical failure mode node, f represents the influence of the failure cause node, B f Represents the weight matrix of the failure cause node, B F represents the node weight matrix of the typical failure mode of the system, B P represents the node weight matrix of the system components, Represents the system component node P j To the system component node P i The influence weight of Represents the system component node P i The influence weight to the central node, Indicates failure mode F i To failure mode F j The influence weight of Indicates failure mode F i To system components P k The influence weight of Indicates the failure cause node f j and the failure cause node fi The influence weight matrix, Indicates the failure cause node f j Node F with typical system failure mode k The influence weight matrix, dist() represents the distance function, represents the monitoring indicator impact matrix, Indicates the number of intervals.

[0185] See also Figure 5 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5 As shown, the electronic device 2 may include: at least one processor 21 , at least one network interface 24 , a user interface 23 , a memory 25 , and at least one communication bus 22 .

[0186] The communication bus 22 is used to realize the connection and communication between these components.

[0187] The user interface 23 may include a display screen (Display) and a camera (Camera), and the optional user interface 23 may also include a standard wired interface and a wireless interface.

[0188] The network interface 24 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0189] Among them, the processor 21 may include one or more processing cores. The processor 21 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 25, and calling data stored in the memory 25. Optionally, the processor 21 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 21 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), a graphics processor (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 21, and it can be implemented separately through a chip.

[0190] Among them, the memory 25 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 25 includes a non-transitory computer-readable storage medium. The memory 25 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 25 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 25 may also be optionally at least one storage device located away from the aforementioned processor 21. As Figure 5 As shown, the memory 25 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a wind power equipment operation reliability analysis method based on time-varying conditions.

[0191] exist Figure 5 In the electronic device 2 shown, the user interface 23 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 21 can be used to call the wind power equipment operation reliability analysis method based on time-varying conditions stored in the memory 25. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

[0192] A computer-readable storage medium stores instructions, which, when executed by one or more processors, enable the computer to execute one or more methods in the above-mentioned embodiments.

[0193] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0194] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0195] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0196] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0197] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0198] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0199] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any variation, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the technical field that are not recorded in the present disclosure.

[0200] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for analyzing the operational reliability of wind power equipment under time-varying conditions, characterized in that: The method comprises: Based on the maximum likelihood estimation method and the fault data of wind power equipment system, the maximum likelihood estimator and distribution function of each distribution type in the wind power equipment system are obtained, and the distribution function of each distribution type is fitted according to the improved DS evidence theory to obtain the probability density function of the fault data; According to the rough set theory, the scoring process of each failure mode in the FMEA method is optimized, and the failure mode criticality is calculated, wherein the failure mode criticality includes the scoring rough number corresponding to the failure mode and the failure mode risk level ranking; According to the reliability data of the wind power equipment system, a dynamic reliability network model is constructed to obtain the reliability index function and reliability sensitivity of each risk node in the dynamic reliability network model, wherein the dynamic reliability network model includes a plurality of risk nodes.

2. The method according to claim 1, characterized in that The method of obtaining the maximum likelihood estimator and distribution function of each distribution type in the wind power equipment system based on the maximum likelihood estimation method and the wind power equipment system fault data specifically includes: Determine the number of groups according to parameter estimation and empirical formula, and calculate unknown parameters in the probability density function based on the maximum likelihood estimation method and the wind power equipment system fault data; Based on the maximum likelihood estimation method and each distribution type in the wind power equipment system fault data, the maximum likelihood estimator of each distribution type is calculated.

3. The method according to claim 1, characterized in that The distribution functions of each distribution type are fitted according to the improved DS evidence theory to obtain the probability density function of the fault data, specifically including: Calculate the slope between two adjacent points of the probability density function corresponding to the fault data, the median of two adjacent points of the probability density function corresponding to the fault data, and the median difference of the probability density function corresponding to the fault data respectively; The slope and the median difference are respectively subjected to maximum image processing, and the grey correlation degree between the probability density functions corresponding to the fault data is calculated to obtain the probability density function of the fault data.

4. The method according to claim 3, characterized in that The above-mentioned optimization of the scoring process of each failure mode in the FMEA method according to the rough set theory and calculation of the failure mode criticality specifically includes: Obtaining a scoring matrix for each failure mode and a weight vector corresponding to each risk factor, and converting the risk factor scores corresponding to all scoring matrices into a rough scoring number based on the scoring conversion function; Under different risk factors, the distance from the rough number of each fault mode score to the ideal solution is calculated to obtain the group utility maximization ranking, individual regret minimization ranking and compromise ranking of each fault mode; The failure modes are ranked according to the trade-off ranking to obtain a failure mode risk level sequence.

5. The method according to claim 4, characterized in that The step of constructing a dynamic reliability network model based on the reliability data of the wind power equipment system specifically includes: Collecting reliability data of wind power equipment system and using various types of data in the reliability data as risk nodes, wherein the reliability data includes system components, failure modes, failure causes and reliability driver data; According to the score rough number corresponding to each risk node, the ideal solution and negative ideal solution of each risk node under different risk factors are calculated to obtain the distance and compromise ranking from the score rough number corresponding to each risk node to the ideal solution; Taking the compromise ranking as the node hazard degree, respectively obtaining the reliability hazard degrees corresponding to the wind power equipment system components, failure modes and failure causes, and taking each of the reliability hazard degrees as the node weight of the corresponding risk node; Based on the fault data in the fault mode, the time function of the failure rate under different propagation relationships is calculated, and the time function is used as the edge weight of the corresponding risk node to construct a dynamic reliability network model.

6. The method according to claim 1, characterized in that It also includes evaluating the reliability level of the wind power equipment system in the current state based on the first system reliability level influence function and the second system reliability level influence function, wherein the expressions of the first system reliability level influence function and the second system reliability level influence function are: C max =I max (B f E)W f→f W f→F (B F E)W F→F W F→P (B P E)W P→P W P→C C t =I t (B f E)W f→f W f→F (B F E)W F→F W F→P (B P E)W P→P W P→C Among them, C max Represents the first system reliability level influence function, I max Represents the reliability index influence maximum value matrix, B f represents the weight matrix of the failure cause node, E represents the expert rating matrix, W f→f represents the influence weight matrix between failure causes, W f→F represents the influence weight matrix between the failure cause and the typical failure mode of the system, B F Represents the node weight matrix of the typical failure mode of the system, W F→F Represents the impact weight matrix between typical failure modes of the system, W F→P represents the influence weight matrix between the typical failure modes of the system and the system components, B P represents the system component node weight matrix, W P→P represents the influence weight matrix between system components, W P→C represents the influence weight matrix between system components and central nodes, C t represents the second system reliability level influence function, I t Represents the monitoring indicator impact matrix.

7. The method according to claim 1, characterized in that The expression of the reliability index function is: Among them, C represents the influence of the central node, k represents the number of groups, P represents the influence of the system component node, i represents the i-th risk node, j represents the j-th risk node, F represents the influence of the system typical failure mode node, f represents the influence of the failure cause node, B f Represents the weight matrix of the failure cause node, B F represents the node weight matrix of the typical failure mode of the system, B P represents the node weight matrix of the system components, Represents the system component node P j To the system component node P i The influence weight of Represents the system component node P i The influence weight to the central node, Indicates failure mode F i To failure mode F j The influence weight of Indicates failure mode F i To system components P k The influence weight of Indicates the failure cause node f j and the failure cause node f i The influence weight matrix, Indicates the failure cause node f j Node F with typical system failure mode k The influence weight matrix, dist() represents the distance function, represents the monitoring indicator impact matrix, Indicates the number of intervals.

8. A wind power equipment operation reliability analysis system based on time-varying conditions, characterized in that: The wind power equipment operation reliability analysis system (1) comprises a function fitting module (11), a risk ranking module (12) and a reliability analysis module (13), wherein: The function fitting module (11) is used to obtain the maximum likelihood estimator and distribution function of each distribution type in the wind power equipment system based on the maximum likelihood estimation method and the wind power equipment system fault data, and to perform fitting processing on the distribution function of each distribution type according to the improved DS evidence theory to obtain the probability density function of the fault data; The risk ranking module (12) is used to optimize the scoring process of each failure mode in the FMEA method according to the rough set theory, and calculate the failure mode criticality, wherein the failure mode criticality includes the scoring rough number corresponding to the failure mode and the failure mode risk level ranking; The reliability analysis module (13) is used to construct a dynamic reliability network model based on the reliability data of the wind power equipment system, and obtain the reliability index function and reliability sensitivity of each risk node in the dynamic reliability network model, wherein the dynamic reliability network model includes a plurality of risk nodes.

9. An electronic device, characterized in that: The electronic device (2) comprises a processor (21), a memory (25), a user interface (23) and a network interface (24), wherein the memory (25) is used to store instructions, the user interface (23) and the network interface (24) are used to communicate with other devices, and the processor (21) is used to execute the instructions stored in the memory (25) so that the electronic device (2) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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