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CN118965819B - Wind turbine reliability assessment method, device and medium - Google Patents

Wind turbine reliability assessment method, device and medium Download PDF

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CN118965819B
CN118965819B CN202411427873.0A CN202411427873A CN118965819B CN 118965819 B CN118965819 B CN 118965819B CN 202411427873 A CN202411427873 A CN 202411427873A CN 118965819 B CN118965819 B CN 118965819B
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CN118965819A (en
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王海朋
刘子轩
何玉灵
唐贵基
李瑞杰
牛宇航
李凯文
孙凯
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North China Electric Power University
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Abstract

The invention belongs to the technical field of electric power, and discloses a reliability assessment method, a device and a medium of a wind turbine, wherein the method comprises the steps of establishing a degradation-impact dependent competition failure model of each component of the wind turbine taking self-healing into consideration according to a nonlinear wiener process and a Markov principle, establishing a multi-failure model of each component of the wind turbine based on a multi-dimensional Copula function, selecting the Copula function, establishing a correlation model of failure among different components of the wind turbine, and carrying out parameter estimation on degradation and the Copula function based on a maximum likelihood method and a Monte Carlo method. The reliability curve of each component and system of the wind turbine can be obtained, and further the reliability of the wind turbine can be evaluated.

Description

Wind turbine generator reliability evaluation method, device and medium
Technical Field
The invention relates to the technical field of electric power, in particular to a method, a device and a medium for evaluating reliability of a wind turbine.
Background
Wind power generation is one of the most promising power generation modes in the field of new energy, and has complex structure composition and high coupling degree. Meanwhile, the working environment of the wind turbine generator is complex and changeable in service, and is often subjected to various stress load effects, so that the failure modes of the wind turbine generator are characterized by complexity, concealment and the like, and the failure modes are often the competing results of various failure modes. At present, competition failure aiming at multiple failure modes is often in extreme weather, and the problem that reliability assessment of a wind turbine generator is inaccurate and the like is caused because characteristics of recovery after impact of components of the wind turbine generator on the polar ends are not considered.
Disclosure of Invention
The invention provides a reliability evaluation method, a device and a medium for a wind turbine generator. In addition, in order to solve the problem of relevance of failure among the components of the wind turbine, the relevance among different components is obtained by establishing a multidimensional Archimedes Copula function model, and the reliability curves of the wind turbine system and the components are obtained by carrying out parameter estimation on the degradation quantity and Copula function model by using the maximum likelihood number and the Monte Carlo method.
The invention adopts the following technical scheme:
according to a first aspect of the present invention, there is provided a method for evaluating reliability of a wind turbine, the method comprising:
According to a nonlinear wiener process and a Markov principle, establishing a degradation-impact dependent competition failure model of each component of the wind turbine generator set considering self-healing;
Based on the multidimensional Copula function, establishing a multi-fault failure model of each component of the wind turbine generator;
Selecting a Copula function and establishing a failure correlation model among different components of the wind turbine generator;
And carrying out parameter estimation on the degradation amount and the Copula function based on a maximum likelihood method and a Monte Carlo method.
As an optimal technical scheme, the self-healing degradation-impact dependent competition failure model of each component of the wind turbine comprises a natural degradation model of the wind turbine, a random impact process model and a self-healing model of each component of the wind turbine, and the self-healing degradation-impact dependent competition failure model of each component of the wind turbine is established according to a nonlinear wiener process and a Markov principle, and the self-healing degradation-impact dependent competition failure model comprises the following steps:
establishing a natural degradation model of the wind turbine according to the nonlinear wiener process;
Establishing a random impact process model based on a Markov principle;
And establishing a self-healing model of each component of the wind turbine according to the natural degradation model and the random impact process model of the wind turbine.
As an optimal technical scheme, the method for establishing the natural degradation model of the wind turbine generator according to the nonlinear wiener process comprises the following steps:
the degradation model of the nonlinear wiener process is established as a natural degradation model of the wind turbine, and is expressed as follows:
;
Wherein: Representative of Degradation amount of time degradation process; as a drift coefficient, representing the degradation rate; As a non-decreasing time scale function, representing a nonlinear characteristic of the degradation process, Represents the amount of dimensional change per unit time,As a function of the drift,As a parameter of the time scale of the time,Is the diffusion coefficient; is a standard Brownian motion;
converting a closed expression of a probability density function of a natural degradation model of the wind turbine generator into a first-pass distribution probability solving problem that the standard Brownian motion exceeds a time-varying threshold value, and obtaining an approximate analytical formula of natural degradation failure is as follows:
;
Wherein: representing a natural degradation failure function, A time-varying threshold function is represented,Representing a given critical degradation failure threshold value,An exponential function based on e;
The cumulative distribution function approximation calculation formula of the distribution at the time of first pass is as follows:
;
Wherein: Representation of A cumulative distribution function of the distribution at the first pass threshold,The probability function is represented as a function of the probability,Indicating the moment at which the threshold value is reached,Is a composite function, representing a natural degenerate failure function,Representing a cumulative distribution function.
As a preferred technical solution, establishing a random impact process model based on a markov principle includes:
For the first Performance parameters, assuming Markov chains satisfying the 0-1 state for impact generation and natural degradation, are usedA representation state, expressed as:
;
describing the state transition process of the performance parameters by using the state transition matrix of the Markov chain to show the probability of mutual transition between different states, and if the current state is the occurrence of the impact, keeping the probability of the impact at the next moment as The probability of returning to natural degradation isAnd (2) andIf the current state is natural degradation, the probability of remaining natural degradation at the next moment isThe probability of impact occurrence isAnd (2) and;
Based on natural degradation and impact effect, will beDegradation of performance parametersExpressed as:
;
Wherein: To represent the degradation amount of the natural degradation process of the p-th performance parameter at time t, Is the additional degradation amount caused by impact at the t moment, the impact degradation amount,Subject to independent and identical expectationsVariance isIs a normal distribution of (c).
As an optimal technical scheme, the method for building the self-healing model of each component of the wind turbine according to the natural degradation model and the random impact process model of the wind turbine comprises the following steps:
When the system is subjected to the first Self-healing occurs after secondary impact, and the actual damage amount after self-healing of the system at the moment t is determined by the following formula:
;
Wherein: Is the first Duration of the secondary impact; is a self-healing influence function; Is the first The amount of degradation of the secondary impact;
determining the accumulated damage amount of the system at the moment t by the following formula :
;
Wherein: Is the first Duration of the secondary impact; Is the first The amount of degradation of the secondary impact is,Is the number of times of arrival of the impact.
As an optimal technical scheme, based on a multidimensional Copula function, a multi-fault failure model of each component of the wind turbine generator is established, and the method comprises the following steps:
determining a Copula function based on the joint distribution function and the edge distribution function of the multidimensional random variable The following relation is satisfied:
;
Wherein: In order to combine the functions of the distribution, As a function of the distribution of the edges,The degradation amount of the nth component of the wind turbine generator is the Copula function if the edge distribution function is continuousIs a deterministic function, if the edge distribution function is a unitary distribution, F (, ⋯,) is a joint distribution function with an edge distribution F 1(⋅) , F2(⋅) ,⋯, Fn (;
If the first wind turbine generator system H fault modes exist in each component, multiple coupling relations exist among the fault modes, k fault modes with multiple coupling relations exist, h-k fault modes with independent relations exist, and the corresponding joint distribution function is determined to be expressed as:
;
Wherein: For the life of the corresponding component, For a joint distribution function with a multiple coupling relationship,Component life for failure modes with multiple coupling relationships is less than nominal life,Component rated life for failure mode with multiple coupling relationships;
Establishing a k-dimensional Copula function Expressed as
;
Wherein: For all component rated life joint distribution functions with k multiple coupling relationship failure modes, For the ith component life distribution function with k multiple coupling relationship failure modes,Is a cumulative distribution function;
at the initial time, the component has no fault state, then The reliability of the individual components is:
;
Wherein, Is the firstThe degree of reliability of the individual components,As a function of the probability of occurrence,Is the firstComponent lifetime with k multiple coupling relationship failure modes,Is the firstThe life of the individual components;
representing the fault mode with the multi-element coupling relation as a Copula form to obtain the first The reliability of the individual components is:
;
Wherein: Is the first The corresponding first componentThe cumulative distribution function value of each failure mode,Is the firstCorresponding to the componentsAndThe combined distribution function value of the two fault modes; for reliability of failure modes without coupling relationship, In the form of Copula, a joint distribution function of failure modes with k multi-element coupling relations,Mathematical operators, expressed as the product of the reliability of failure modes without coupling relationships.
As a preferable technical scheme, the selection of the Copula function and the establishment of the correlation model of failure among different components of the wind turbine generator set comprise the following steps:
Establishing a first Copula function, a second Copula function and a third Copula function;
analyzing the multi-fault failure model of each component of the wind turbine by using the first Copula function, the second Copula function and the third Copula function to establish a correlation model of failure among different components of the wind turbine;
the distribution function expression and the probability density expression of the first Copula function are respectively:
;
Wherein: , is a different fault; Is a first parameter; is an Archimedes Copula distribution function; As a function of the probability density of the sample, A distribution function expression that is a first Copula function;
The distribution function expression and the probability density expression of the second Copula function are respectively:
;
Wherein: As a second parameter, the first parameter is, ,;A distribution function expression that is a second Copula function,A probability density expression that is a second Copula function;
the distribution function expression and the probability density expression of the third Copula function are respectively:
;
Wherein: as a third parameter, the first and second parameters, ;For the third Copula function distribution function expression,A probability density expression for a third Copula function;
The correlation model of failure among different components of the wind turbine generator comprises one of a series model, a parallel model and a voting model and a combination thereof;
in the series model, any one component failure can lead to an overall system failure;
in the parallel model, in the case where all components constituting the system are failed, the system is failed;
In the voting model, the system works normally under the condition that at least r components in n components forming the system have no faults, wherein r is more than or equal to 1 and less than or equal to n.
As a preferred technical solution, parameter estimation is performed on degradation and Copula functions based on a maximum likelihood method and a monte carlo method, including:
Constructing a maximum likelihood degradation model, wherein the maximum likelihood degradation model is used for carrying out parameter estimation on degradation quantity, and the maximum likelihood degradation model is expressed as:
;
Wherein: as a function of the maximum likelihood degradation, Is the firstThe dimensional parameters of the degradation of the individual components,Is the degradation process of the ith component, n is the number of components,In order to measure the number of times,Is the firstIn the degradation process ofFirst of all failureThe amount of degradation of the secondary measurement is,Is the firstThe total number of measurements of the individual faults,Is a distribution function of a standard normal variable,The time taken for the W-th measurement for the h-th fault,The variance of the time taken for the W-th measurement for the h-th fault of the i-th component,The expectation of the time taken for the W-th measurement of the h-th fault of the i-th component;
Parameter estimation is carried out on the Copula function based on the Monte Carlo method and the semi-parameter estimation method:
Solving parameter values by using a semi-parameter estimation method, and performing nuclear density distribution function Instead of an edge distribution function:
;
Wherein: Is the first A nuclear density distribution function of the individual components; is a semi-parameter estimate.
According to a second aspect of the present invention, there is provided a wind turbine reliability assessment apparatus, the apparatus comprising:
The first model building unit is configured to build a degradation-impact dependent competition failure model of each component of the wind turbine according to a nonlinear wiener process and a Markov principle, wherein the self-healing of the degradation-impact dependent competition failure model is considered;
The second model building unit is configured to build a multi-fault failure model of each component of the wind turbine generator based on the multi-dimensional Copula function;
the third model building unit is configured to select a Copula function and build a correlation model of failure among different components of the wind turbine generator;
And a parameter estimation unit configured to perform parameter estimation on the degradation amount and the Copula function based on the maximum likelihood method and the monte carlo method.
According to a third aspect of the present invention there is provided a readable storage medium storing one or more programs executable by one or more processors to implement the method as described above.
The invention has at least the following beneficial effects:
The method can better simulate the degradation process caused by multiple factors such as wind turbine generator environment and the like by adopting the nonlinear wiener method, and has the advantage of solving the random problem by using the Markov principle. In addition, each component of the wind turbine generator can adopt a self-protection mechanism when impacted, damage caused by the impact is self-recovered, and the reliability of the wind turbine generator can be accurately estimated by building a self-healing model. Secondly, the Archimedes Copula function family has good applicability in solving nonlinear relations, has simple structure and can accurately describe the correlation between fault modes and components. Finally, the Monte Carlo method and the semi-parameter estimation method have data simulation and iteration processes, so that the speed of estimating the unknown parameters can be further improved.
Drawings
FIG. 1 shows a flowchart of a wind turbine reliability assessment method according to an embodiment of the invention;
fig. 2 shows a block diagram of a wind turbine reliability evaluation device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the drawings and detailed description to enable those skilled in the art to better understand the technical scheme of the present invention. Embodiments of the present invention will be described in further detail below with reference to the drawings and specific examples, but not by way of limitation. The order in which the steps are described herein by way of example should not be construed as limiting if there is no necessity for a relationship between each other, and it should be understood by those skilled in the art that the steps may be sequentially modified without disrupting the logic of each other so that the overall process is not realized.
The embodiment of the invention provides a reliability evaluation method of a wind turbine, as shown in fig. 1, which is a flow chart of the method, and comprises the following steps:
and step1, establishing a degradation-impact dependent competition failure model of each component of the wind turbine according to the nonlinear wiener process and the Markov principle, wherein the self-healing of the degradation-impact dependent competition failure model is considered.
In this embodiment, the degradation-impact dependent competition failure model of each component of the wind turbine considering self-healing includes a natural degradation model of the wind turbine, a random impact process model and a self-healing model of each component of the wind turbine.
In some embodiments, step 1 is specifically implemented by:
and 11, building a natural degradation model of the wind turbine according to the nonlinear wiener process.
Currently, most competition failure studies use a linear regression model X (t) =a+bt to describe the degradation process of the system. But linear regression models do not characterize the fluctuations and nonlinearities that exist during the degradation of the system. The present embodiment models the degradation process using a nonlinear Wiener (Wiener) process to more accurately describe the degradation behavior of the system.
The degradation model N 0 based on the nonlinear Wiener (Wiener) process is:
;
Wherein: Representative of Degradation amount of time degradation process; as a drift coefficient, representing the degradation rate; As a non-decreasing time scale function, representing a nonlinear characteristic of the degradation process, Represents the amount of dimensional change per unit time,As a function of the drift,As a parameter of the time scale of the time,Is the diffusion coefficient; is a standard brownian motion.
When the degradation amountWhen the threshold k for the degradation failure exceeds a predetermined threshold for the first time, the failure is determined, and the time t at this time is referred to as the first-pass time, the life of the system when the degradation failure occurs can be regarded as the life of the system. For the degradation model N 0, converting the closed expression of the probability density function of the nonlinear degradation failure model into the first-pass distribution probability problem that the standard Brownian motion exceeds the time-varying threshold value, and obtaining the approximate analytical expression of the natural degradation failure as follows:
;
Wherein: representing a natural degradation failure function, A time-varying threshold function is represented,Representing a given critical degradation failure threshold value,An exponential function based on e is represented.
ThenThe cumulative distribution function approximation calculation formula of the distribution at the time of first pass is as follows:
;
Wherein: Representation of A cumulative distribution function of the distribution at the first pass threshold,The probability function is represented as a function of the probability,Indicating the moment at which the threshold value is reached,Is a composite function, representing a natural degenerate failure function,Representing a cumulative distribution function.
And 12, establishing a random impact process model based on a Markov principle.
Due to the influence of factors such as material properties, working characteristics and the like, the impact effect of the performance parameters has a recoverable phenomenon, namely, for a certain performance parameter, the value of the performance parameter is recovered to a natural degradation process after impact change occurs.
For the firstPerformance parameters, assuming Markov chains satisfying the 0-1 state for impact generation and natural degradation, are usedA representation state, expressed as:
;
The state transition matrix of the Markov chain is utilized to describe the state transition process of the performance parameters, and then the probability of the mutual transition between different states is displayed. If the current state is that the impact occurs, the probability that the impact still occurs at the next moment is kept as The probability of returning to natural degradation isAnd (2) andIf the current state is natural degradation, the probability of natural degradation is thatThe probability of impact occurrence isAnd (2) andExpressed as:
;
comprehensively consider natural degradation and impact effect, and finally, the first Degradation of performance parametersExpressed as:
;
Wherein: To represent the degradation amount of the natural degradation process of the p-th performance parameter at time t, Is the additional degradation amount caused by impact at the t moment, the impact degradation amount,Subject to independent and identical expectationsVariance isIs a normal distribution of (c).
And 13, building a self-healing model of each component of the wind turbine according to the natural degradation model and the random impact process model of the wind turbine.
When the system is subjected to the firstSelf-healing occurs after secondary impact, and the actual damage amount after self-healing of the system at the moment t is determined by the following formula:
;
Wherein: Is the first Duration of the secondary impact; is a self-healing influence function; Is the first The amount of degradation of the secondary impact;
determining the accumulated damage amount of the system at the moment t by the following formula :
;
Wherein: Is the first Duration of the secondary impact; Is the first The amount of degradation of the secondary impact is,Is the number of times of arrival of the impact.
Due to the non-monotonicity of the system degradation process with self-healing mechanism, it cannot be given directlyIs a function of the distribution of (a). For a dependent competition failure system with a self-healing mechanism, the system is not failed, and the size of each impact is ensured to be smaller than a failure threshold valueWhile the total degradation of the system is less than。
And 2, building a multi-fault failure model of each component of the wind turbine based on the multi-dimensional Copula function.
N-dimensional Copula function with simple structure and accurate description of the correlation between components and failure mode, and with the definition domain of 0,1, zero reference plane with N-dimensional increment, and arbitrary variableSatisfies the edge distribution。
By the multidimensional Sklar theorem, we assume that the joint distribution function of the multidimensional random variable isThe edge distribution function isThere is a Copula functionThe relation is satisfied:
;
Wherein: In order to combine the functions of the distribution, As a function of the distribution of the edges,For the nth component degradation of the wind turbine generator, if the edge distribution function is continuous, the Copula functionIs a deterministic function, if the edge distribution function is a unitary distribution, F (, ⋯,) is a joint distribution function with an edge distribution F 1(⋅) , F2(⋅) ,⋯, Fn ()
And (3) assuming h (h is more than or equal to 2) fault modes exist in an ith component in the wind turbine generator, and a multi-element coupling relation exists between the fault modes. For failure modeRepresenting that k failure modes with multiple coupling relations exist, h-k failures with independent relations exist, andAnd (3) representing. Set the firstFailure mode of individual components. The corresponding joint distribution function can be expressed as:
;
Wherein: For the actual life of the component of the ith failure mode having multiple coupling relationships, For a component lifetime joint distribution function with a multiple coupling relationship,Component life for failure modes with multiple coupling relationships is less than nominal life,Component rated life for failure mode with multiple coupling relationships;
Establishing a k-dimensional Copula function Expressed as
;
Wherein: For all component rated life joint distribution functions with k multiple coupling relationship failure modes, For the ith component life distribution function with k multiple coupling relationship failure modes,In the form of a complex function whose value is equal to;
At the initial time, the assembly has no fault state, the firstThe reliability of the individual components is:
;
Wherein, Is the firstThe degree of reliability of the individual components,As a function of the probability of occurrence,Is the firstComponent lifetime with k multiple coupling relationship failure modes,Is the firstThe life of the individual components;
According to the incoordination property and the multidimensional Sklar theorem, the fault mode with the multi-element coupling relation is expressed as a Copula form to obtain the first The reliability of the individual components is:
;
Wherein: Is the first The corresponding first componentThe cumulative distribution function value of each failure mode,Is the firstCorresponding to the componentsAndThe combined distribution function value of the two fault modes; for reliability of failure modes without coupling relationship, In the form of Copula, a joint distribution function of failure modes with k multi-element coupling relations,Mathematical operators, expressed as the product of the reliability of failure modes without coupling relationships.
And 3, selecting a Copula function and establishing a failure correlation model among different components of the wind turbine generator.
By using different Copula functions, different dependency structures can be described, thus more accurately characterizing the joint distribution of multidimensional random variables. Compared with an elliptic Copula function, the Archimedes Copula function has the advantage that the Archimedes Copula function is not limited by radial symmetry, and can accurately capture the dependency relationship of different upper tails and lower tails.
The archimedes Copula distribution function expression is:
;
Wherein: 、 And Respectively represent Gumbel Copula function, clayton Copula function and FrankCopula function, a, beta andRespectively, are the parameters corresponding to each other,、A kind of electronic deviceIs a weighting coefficient, and。
Deriving the above equation to obtain a probability density function of the Copula function:
;
Wherein: respectively is Is a derivative of the (c).
In this embodiment, three Copula functions, namely, a first Copula function, a second Copula function and a third Copula function, are set, and specifically described as follows:
(1) The first Copula function is a binary Gumbel Copula function, and the distribution function expression and the probability density expression are respectively:
;
Wherein: , is a different fault; Is a first parameter; is an Archimedes Copula distribution function; As a function of the probability density of the sample, Is a distribution function expression of the first Copula function.
(2) The second Copula function is a binary Clayton Copula function, and the distribution function expression and the probability density expression are respectively:
;
Wherein: As a second parameter, the first parameter is, ,;A distribution function expression that is a second Copula function,A probability density expression that is a second Copula function;
(3) The third Copula function is a binary Frank Copula function, and the distribution function expression and the probability density expression are respectively:
;
Wherein: as a third parameter, the first and second parameters, ;For the third Copula function distribution function expression,A probability density expression for a third Copula function;
and (3) analyzing the model constructed in the step (2) based on the Copula function to establish a correlation model. Typical reliability models between different components are mainly a series model, a parallel model and a voting model:
In the series model, if any component fails, the whole system will fail, and the system is assumed to be composed of i components, and the components are independent of each other, i.e. if one component fails, the reliability of other components will not be affected. The system reliability expression is that
;
Wherein: for system reliability, n is the number of components.
When all components forming the system fail, the parallel system fails, and if i independent components are connected in parallel, the system reliability expression is as follows
;
Wherein: for system reliability, n is the number of components.
In the voting model, at least r (r is more than or equal to 1 and less than or equal to n) components in n components forming the system have no faults, and the system can work normally. The system reliability expression is that
;
Wherein: for system reliability, n is the number of components.
And 4, carrying out parameter estimation on the degradation amount and the Copula function based on a maximum likelihood method and a Monte Carlo method.
In one embodiment, the step 4 is specifically implemented by the following steps:
step 41, estimating parameters (degradation amount) by using the maximum likelihood degradation model.
The maximum likelihood degradation model is expressed as:
;
Wherein: as a function of the maximum likelihood degradation, Is the firstThe dimensional parameters of the degradation of the individual components,Is the degradation process of the ith component, n is the number of components,In order to measure the number of times,Is the firstIn the degradation process ofFirst of all failureThe amount of degradation of the secondary measurement is,Is the firstThe total number of measurements of the individual faults,Is a distribution function of a standard normal variable,The time taken for the W-th measurement for the h-th fault,The variance of the time taken for the W-th measurement for the h-th fault of the i-th component,The expectation of the time taken for the W-th measurement of the h-th fault of the i-th component;
step 42, estimating Copula function parameters based on the Monte Carlo method and the semi-parameter estimation method.
Because the sample likelihood function involves multidimensional integral differentiation and the integral function itself is complex, the Markov Chain Monte Carlo (MCMC) method has a data simulation and iteration process, and therefore, the MCMC method is adopted to estimate unknown parameters. Solving parameter values by using a semi-parameter estimation method, and performing nuclear density distribution functionInstead of an edge distribution function:
;
Wherein: Is the first A nuclear density distribution function of the individual components; is a semi-parameter estimate.
In conclusion, the reliability curve of each component and system of the wind turbine can be obtained according to the above, and further the reliability of the wind turbine is evaluated.
The embodiment of the invention also provides a reliability evaluation device of the wind turbine, as shown in fig. 2, the device comprises:
A first model building unit 401 configured to build a self-healing considered degradation-impact dependent competition failure model of each component of the wind turbine according to a nonlinear wiener process and a markov principle;
a second model building unit 402, configured to build a multi-failure model of each component of the wind turbine based on a multi-dimensional Copula function;
the third model building unit 403 is configured to select a Copula function and build a correlation model of failure among different components of the wind turbine generator;
a parameter estimation unit 404 configured to perform parameter estimation on the degradation amount and the Copula function based on the maximum likelihood method and the monte carlo method.
It should be noted that the structures of the devices described in this embodiment and the methods described in the foregoing are the same technical concept, and achieve the same technical effects by the same principles, which are not repeated here.
Embodiments of the present invention also provide a readable storage medium storing one or more programs executable by one or more processors to implement the method of any of the embodiments above.
Furthermore, although exemplary embodiments have been described herein, the scope thereof includes any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of the various embodiments across), adaptations or alterations as pertains to the present application. The elements in the claims are to be construed broadly based on the language employed in the claims and are not limited to examples described in the present specification or during the practice of the application, which examples are to be construed as non-exclusive. It is intended, therefore, that the specification and examples be considered as exemplary only, with a true scope and spirit being indicated by the following claims and their full scope of equivalents.
The above description is intended to be illustrative and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. For example, other embodiments may be used by those of ordinary skill in the art upon reading the above description. In addition, in the above detailed description, various features may be grouped together to streamline the invention. This is not to be interpreted as an intention that the features of the claimed invention are essential to any of the claims. Rather, inventive subject matter may lie in less than all features of a particular inventive embodiment. Thus, the following claims are hereby incorporated into the detailed description as examples or embodiments, with each claim standing on its own as a separate embodiment, and it is contemplated that these embodiments may be combined with one another in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims (5)

1.一种风电机组可靠性评估方法，其特征在于，所述方法包括：1. A wind turbine reliability assessment method, characterized in that the method comprises: 根据非线性维纳过程和马尔可夫原理建立风电机组各组件考虑自愈的退化-冲击相依竞争失效模型；Based on the nonlinear Wiener process and Markov principle, a degradation-impact dependent competitive failure model of each component of the wind turbine considering self-healing is established; 基于多维Copula函数，建立风电机组各组件多故障失效模型；Based on the multi-dimensional Copula function, a multi-fault failure model of each component of the wind turbine is established; Copula函数的选择及建立风电机组不同组件之间失效的相关性模型；Selection of Copula function and establishment of correlation model of failure between different components of wind turbine; 基于最大似然数法和蒙特卡洛方法对退化量和Copula函数进行参数估计；The degradation amount and Copula function parameters are estimated based on the maximum likelihood method and Monte Carlo method; 所述风电机组各组件考虑自愈的退化-冲击相依竞争失效模型包括风电机组自然退化模型、随机冲击过程模型和风电机组各组件自愈模型，根据非线性维纳过程和马尔可夫原理建立风电机组各组件考虑自愈的退化-冲击相依竞争失效模型，包括：The degradation-impact dependent competitive failure model of each wind turbine component considering self-healing includes a natural degradation model of the wind turbine, a random impact process model and a self-healing model of each wind turbine component. The degradation-impact dependent competitive failure model of each wind turbine component considering self-healing is established according to the nonlinear Wiener process and the Markov principle, including: 根据非线性维纳过程建立风电机组自然退化模型；A natural degradation model of wind turbines is established based on the nonlinear Wiener process; 基于马尔可夫原理建立随机冲击过程模型；Establish a random shock process model based on the Markov principle; 根据风电机组自然退化模型和随机冲击过程模型建立风电机组各组件自愈模型；The self-healing model of each component of the wind turbine is established based on the natural degradation model of the wind turbine and the random impact process model; 根据非线性维纳过程建立风电机组自然退化模型，包括：The natural degradation model of wind turbines is established based on the nonlinear Wiener process, including: 建立非线性维纳过程的退化模型作为风电机组自然退化模型，表示为：The degradation model of nonlinear Wiener process is established as the natural degradation model of wind turbines, which is expressed as: ； ; 其中：代表时刻退化过程的退化量；为漂移系数，表示退化速率；为非减时间尺度函数，表示退化过程的非线性特征，表示单位时间内尺度变化量，为漂移函数，为时间尺度参数，为扩散系数；为标准布朗运动；in: represent The degradation amount of the moment degradation process; is the drift coefficient, indicating the degradation rate; is a non-decreasing time scale function, indicating the nonlinear characteristics of the degradation process. It represents the change of scale per unit time. is the drift function, is the time scale parameter, is the diffusion coefficient; is the standard Brownian motion; 将风电机组自然退化模型的概率密度函数的封闭表达式转化为标准布朗运动超过时变阈值的首穿分布求概率问题，得到自然退化失效的近似解析式为：The closed expression of the probability density function of the natural degradation model of the wind turbine is transformed into the probability problem of the first penetration distribution of the standard Brownian motion exceeding the time-varying threshold, and the approximate analytical expression of the natural degradation failure is obtained as follows: ； ; 其中：；表示自然退化失效函数，表示时变阈值函数，表示给定的临界退化失效阈值，表示以e为底数的指数函数；in: ; represents the natural degradation failure function, represents the time-varying threshold function, represents a given critical degradation failure threshold, represents the exponential function with e as base; 首穿时分布的累积分布函数近似计算公式为： The approximate calculation formula of the cumulative distribution function of the first penetration time distribution is: ； ; 其中：表示首穿阈值时分布的累积分布函数，表示概率函数，表示达到阈值的时刻，为复合函数，表示自然退化失效函数，表示累积分布函数；in: express The cumulative distribution function of the distribution when the threshold is first crossed, represents the probability function, represents the moment when the threshold is reached, is a composite function, representing the natural degradation failure function, represents the cumulative distribution function; 基于马尔可夫原理建立随机冲击过程模型，包括：A random shock process model is established based on the Markov principle, including: 对于第个性能参数，假设冲击发生和自然退化满足0-1状态的马尔可夫链，用表示状态，表示为：For performance parameters, assuming that the shock occurrence and natural degradation satisfy the 0-1 state Markov chain, Indicates the status, expressed as: ； ; 利用马尔可夫链的状态转移矩阵来描述性能参数的状态转移过程，以展现不同状态之间相互转换的概率，若当前状态为冲击发生，则下一时刻仍保持冲击发生的概率为，恢复为自然退化的概率为，且；若当前状态为自然退化，则下一刻仍保持自然退化的概率为，冲击发生的概率为，且；The state transition matrix of the Markov chain is used to describe the state transition process of the performance parameters to show the probability of mutual conversion between different states. If the current state is a shock, the probability of the shock occurring at the next moment is , the probability of returning to natural degradation is ,and ; If the current state is natural degradation, the probability of maintaining natural degradation in the next moment is The probability of a shock occurring is ,and ; 基于自然退化和冲击效应，将第个性能参数的退化量表示为：Based on natural degradation and impact effects, The degradation of performance parameters It is expressed as: ； ; 其中：为代表第p个性能参数在t时刻自然退化过程的退化量，为t时刻下由冲击造成的冲击退化量，服从独立且相同的期望为、方差为的正态分布；in: represents the degradation amount of the pth performance parameter in the natural degradation process at time t , is the impact degradation caused by the impact at time t , Subject to independent and identical expectations , the variance is Normal distribution of 根据风电机组自然退化模型和随机冲击过程模型建立风电机组各组件自愈模型，包括：Based on the natural degradation model of wind turbines and the random impact process model, the self-healing model of each component of the wind turbine is established, including: 当系统受到第次冲击后发生自愈，通过如下公式确定在时刻t系统自愈后的实际损伤量：When the system is subjected to After the impact, the system self-heals. The actual amount of damage after self-healing at time t is determined by the following formula: : ； ; 其中：为第次冲击持续时间；为自愈影响函数；为第次冲击的退化量；in: For the Secondary shock duration; is the self-healing influence function; For the The amount of degradation of the secondary impact; 通过如下公式确定时刻t系统的累积损伤量：The cumulative damage of the system at time t is determined by the following formula : ； ; 其中：为第次冲击持续时间；为第次冲击的退化量，为冲击的到达次数；in: For the Secondary shock duration; For the The degradation amount of each impact, is the number of arrivals of the shock; 基于最大似然数法和蒙特卡洛方法对退化量和Copula函数进行参数估计，包括：The degradation amount and Copula function parameters are estimated based on the maximum likelihood method and Monte Carlo method, including: 构建最大似然数退化模型，所述最大似然数退化模型用于对退化量进行参数估计；其中，所述最大似然数退化模型表示为：A maximum likelihood degradation model is constructed, and the maximum likelihood degradation model is used to perform parameter estimation on the degradation amount; wherein the maximum likelihood degradation model is expressed as: ； ; 其中：为最大似然数退化函数，为第个组件退化的尺度参数，为第i个组件的退化过程，n为组件个数，为测量次数，为第个退化过程中第个故障的第次测量的退化量，为第个故障的测量总次数，为标准正态变量的分布函数，为第h个故障的第W次测量所用的时间，为第i个组件的第h个故障的第W次测量所用时间的方差，为第i个组件的第h个故障的第W次测量所用时间的期望；in: is the maximum likelihood degradation function, For the The scale parameter of the component degradation, is the degradation process of the i-th component, n is the number of components, is the number of measurements, For the In the degradation process The first fault The degradation amount measured is For the The total number of measurements for each fault, is the distribution function of the standard normal variable, is the time taken for the Wth measurement of the hth fault, is the variance of the time taken for the Wth measurement of the hth fault of the ith component, is the expected time taken for the Wth measurement of the hth fault of the i - th component; 基于蒙特卡洛方法与半参数估计法对Copula函数进行参数估计：Parameter estimation of Copula function based on Monte Carlo method and semi-parametric estimation method: 利用半参数估计法求解参数值，将核密度分布函数代替边缘分布函数：The parameter value is solved by using the semi-parametric estimation method, and the kernel density distribution function is Replace the marginal distribution function: ； ; 其中：为第个组件的核密度分布函数；为半参数估计值。in: For the The kernel density distribution function of the components; is a semiparametric estimate. 2.根据权利要求1所述的方法，其特征在于，基于多维Copula函数，建立风电机组各组件多故障失效模型，包括：2. The method according to claim 1 is characterized in that, based on the multi-dimensional Copula function, a multi-fault failure model of each component of the wind turbine is established, comprising: 基于多维随机变量的联合分布函数和边缘分布函数，确定一个Copula函数满足如下关系式：Determine a Copula function based on the joint distribution function and marginal distribution function of multidimensional random variables Satisfies the following relationship: ； ; 其中：为联合分布函数，为边缘分布函数，为风电机组的第n个组件退化量；若边缘分布函数连续，则Copula函数是一个确定的函数；若边缘分布函数为一元分布，是具有边缘分布的联合分布函数；in: is the joint distribution function, is the marginal distribution function, is the degradation amount of the nth component of the wind turbine; if the marginal distribution function is continuous, then the Copula function is a definite function; if the marginal distribution function is a univariate distribution, Is a marginal distribution The joint distribution function of 若风电机组中的第个组件存在h种故障模式，且故障模式之间存在多元耦合关系，其中具有多元耦合关系的故障模式有k个，则具有独立关系的故障模式有h-k个，确定对应的联合分布函数表示为：If the wind turbine There are h kinds of failure modes for each component, and there are multivariate coupling relationships between the failure modes. There are k failure modes with multivariate coupling relationships, and there are h - k failure modes with independent relationships. The corresponding joint distribution function is expressed as: ； ; 其中：为第i个具有多元耦合关系的故障模式的组件实际寿命，为具有多元耦合关系的组件寿命联合分布函数，为具有多元耦合关系的故障模式的组件寿命小于额定寿命发生的概率，为具有多元耦合关系的故障模式的组件额定寿命；in: is the actual life of the component with the i-th failure mode with multivariate coupling relationship, is the joint distribution function of component life with multivariate coupling relationship, is the probability that the component life of the failure mode with multi-element coupling relationship is less than the rated life, Rating life of components with failure modes with multi-coupling relationships; 建立k维Copula函数，表示为Establish k-dimensional Copula function , expressed as ； ; 其中：为所有具有k种多元耦合关系故障模式的组件额定寿命联合分布函数，为第i个具有k种多元耦合关系故障模式的组件寿命分布函数，为累积分布函数；in: is the joint distribution function of the rated life of all components with k multivariate coupling failure modes, is the life distribution function of the i - th component with k multivariate coupling failure modes, is the cumulative distribution function; 在初始时刻，组件无故障状态，则第个组件的可靠度为：At the initial moment, the component is not in a faulty state, so The reliability of each component is: ； ; 其中，为第个组件的可靠度，为概率发生函数，为第个具有k种多元耦合关系故障模式的组件寿命，为第个组件的寿命；in, For the The reliability of each component, is the probability occurrence function, For the The component life with k multi-coupling failure modes, For the The life of each component; 将具有多元耦合关系的故障模式表示为Copula形式，得到第个组件的可靠度为：The failure mode with multivariate coupling relationship is expressed as Copula form, and the The reliability of each component is: ； ; 其中：为第个组件对应的第个故障模式的累积分布函数值，为第个组件对应的和两种故障模式的联合分布函数值；为不具有耦合关系的故障模式的可靠度，为Copula形式下，具有k种多元耦合关系的故障模式的联合分布函数，数学运算符号，表示为不具有耦合关系的故障模式的可靠度的乘积。in: For the The corresponding component The cumulative distribution function value of the failure mode, For the Components corresponding to and The joint distribution function value of the two failure modes; is the reliability of the failure mode without coupling relationship, is the joint distribution function of the failure modes with k multivariate coupling relationships in the Copula form, Mathematical operation symbol, expressed as the product of the reliabilities of failure modes that do not have a coupling relationship. 3.根据权利要求2所述的方法，其特征在于，Copula函数的选择及建立风电机组不同组件之间失效的相关性模型，包括：3. The method according to claim 2, characterized in that the selection of the Copula function and the establishment of the failure correlation model between different components of the wind turbine generator system include: 建立第一Copula函数、第二Copula函数和第三Copula函数；Establish the first Copula function, the second Copula function and the third Copula function; 利用第一Copula函数、第二Copula函数和第三Copula函数对风电机组各组件多故障失效模型进行分析以建立风电机组不同组件之间失效的相关性模型；The first Copula function, the second Copula function and the third Copula function are used to analyze the multi-fault failure model of each component of the wind turbine to establish a correlation model of failures between different components of the wind turbine; 所述第一Copula函数的分布函数表达式和概率密度表达式分别为：The distribution function expression and probability density expression of the first Copula function are: ； ; 其中：，为不同故障；，为第一参数；为阿基米德Copula分布函数；为概率密度函数，为第一Copula函数的分布函数表达式；in: , For different faults; , is the first parameter; is the Archimedean Copula distribution function; is the probability density function, is the distribution function expression of the first Copula function; 所述第二Copula函数的分布函数表达式和概率密度表达式分别为：The distribution function expression and probability density expression of the second Copula function are: ； ; 其中：为第二参数，，；为第二Copula函数的分布函数表达式，为第二Copula函数的概率密度表达式；in: is the second parameter, , ; is the distribution function expression of the second Copula function, is the probability density expression of the second Copula function; 所述第三Copula函数的分布函数表达式和概率密度表达式分别为：The distribution function expression and probability density expression of the third Copula function are respectively: ； ; 其中：为第三参数，；为第三Copula函数分布函数表达式，为第三Copula函数概率密度表达式；in: is the third parameter, ; is the distribution function expression of the third Copula function, is the probability density expression of the third Copula function; 所述风电机组不同组件之间失效的相关性模型包括串联模型、并联模型和表决模型中的一种及其组合；The failure correlation model between different components of the wind turbine generator set includes one of a series model, a parallel model and a voting model, and a combination thereof; 在所述串联模型中，任意一个组件故障都会导致整个系统故障；In the described series model, failure of any one component will lead to failure of the entire system; 在所述并联模型中，在组成系统的所有组件都发生故障的情况下，系统发生故障；In the parallel model described, the system fails when all components that make up the system fail; 在所述表决模型中，在组成系统的n个组件中至少有r个组件无故障的情况下，系统正常工作；其中1≤r≤n。In the voting model, the system works normally when at least r components among the n components constituting the system are fault-free; where 1≤ r ≤ n . 4.一种风电机组可靠性评估装置，用于实现如权利要求1所述的方法，其特征在于，所述装置包括：4. A wind turbine reliability assessment device, used to implement the method according to claim 1, characterized in that the device comprises: 第一模型构建单元，被配置为根据非线性维纳过程和马尔可夫原理建立风电机组各组件考虑自愈的退化-冲击相依竞争失效模型；The first model building unit is configured to establish a degradation-impact dependent competitive failure model of each component of the wind turbine considering self-healing according to a nonlinear Wiener process and a Markov principle; 第二模型构建单元，被配置为基于多维Copula函数，建立风电机组各组件多故障失效模型；The second model building unit is configured to build a multi-fault failure model of each component of the wind turbine generator based on a multi-dimensional Copula function; 第三模型构建单元，被配置为Copula函数的选择及建立风电机组不同组件之间失效的相关性模型；The third model building unit is configured to select the Copula function and establish a correlation model of failures between different components of the wind turbine; 参数估计单元，被配置为基于最大似然数法和蒙特卡洛方法对退化量和Copula函数进行参数估计。The parameter estimation unit is configured to perform parameter estimation on the degradation amount and the Copula function based on the maximum likelihood method and the Monte Carlo method. 5.一种存储有指令的非暂时性计算机可读存储介质，当所述指令由处理器执行时，执行根据权利要求1至3中任一项所述的方法。5 . A non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, executes the method according to claim 1 .
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