CN111881575B - Wind turbine generator reliability distribution method considering subsystem multi-state and fault correlation - Google Patents
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Abstract
The invention discloses a reliability distribution method of a wind turbine generator set considering multi-state and fault correlation of a subsystem, which is characterized in that in order to reasonably distribute reliability indexes distributed by the subsystem to each part, the reliability distribution initial weight of each part is calculated, a three-state transition model of the subsystem is established, and the reliability distribution initial weight of each part in the subsystem is corrected by comprehensively considering the propagation influence of faults on the basis of steady-state availability of the subsystem under the influence of each part; based on the reliability index, the subsystem is distributed to each part, and the reliability distribution index of each part is obtained. The improved subsystem reliability distribution method, namely the correction coefficient distribution method, overcomes the defects of the traditional fuzzy distribution method, comprehensively considers the multi-state and fault propagation characteristics of the subsystem and improves the objectivity and accuracy in the reliability distribution process.
Description
Technical Field
The invention belongs to the field of reliability distribution, relates to a configuration method of subsystem reliability of a wind turbine, and particularly relates to a wind turbine reliability distribution method considering subsystem multi-state and fault correlation.
Background
The reliability distribution is to distribute the system reliability index specified in the product development stage to each subsystem, equipment, unit and parts reasonably according to a certain distribution principle and method from top to bottom by gradual decomposition, so as to determine the quantitative reliability requirement of each component unit of the system. Reliability allocations include unconfined allocations and constrained allocations. The reliability allocation without constraint is aimed at that the designed product can meet the specified reliability index, and other constraint is not adopted except the reliability index. The conventional unconstrained reliability allocation methods include an equal allocation method, a scoring allocation method, a similar product method and the like, and are widely used in the early stages of reliability engineering development.
The equal distribution method considers that the reliability of each constituent unit of the system is the same, but the method is simple but not reasonable, because in actual products, the reliability level of each unit is generally not equal. The scoring distribution method is a distribution method for scoring several factors affecting reliability by experienced designers or specialists under the condition of very lack of reliability data, comprehensively analyzing scoring values to obtain reliability relative ratios among all the constituent units of the product, and distributing reliability indexes to each unit according to the relative ratios. The scoring distribution method generally considers factors such as the complexity of units, the technical development level, the working time of units, the environmental conditions and the like, and the method seriously depends on the experience and the self level of a scoring expert, so that the distribution result is relatively high in subjectivity. The similar product method is generally applied to the situation that the newly designed product and the old product are very similar, and according to the failure rate of each unit in the old product and the reliability requirement of the new product, the failure interest rate is allocated to each unit of the new product. The similar product approach considers that the original product basically reflects the level of reliability that can be achieved by the product within a certain period of time, and the individual units of the new product can not have any major breakthrough in technology, so that the new reliability index can be proportionally adjusted according to the original capability according to the actual level. The method is only suitable for the situation that the structures, materials, processes, use environments and the like of new and old products are similar, and the statistical data of the old products or the predicted data base of each existing component unit are distributed, so that the use conditions are severe.
In reliability distribution, due to the lack of reliability data in the early stage of design, experienced experts in the related field often score each reliability evaluation index, and the expert scoring method excessively depends on own experience of the expert, so that the evaluation result is too subjective. In order to solve the problem of strong subjectivity of expert scoring, a learner introduces a fuzzy theory into a reliability allocation method, and the fuzzy theory is widely applied to theoretical research and actual engineering. Chang et al consider that the allocation factor in the traditional reliability allocation is a single language variable, so that the reliability allocation combines a fuzzy language set and a minimum variance weight, and the allocation result is reduced from subjectivity; chen et al propose a numerical control machine tool rest reliability distribution method based on fuzzy comprehensive judgment, comprehensively considering various factors influencing the reliability of the numerical control machine tool rest. Ebrahimipour et al adopt a learning fuzzy inference system based on emotionalization, so that the accuracy and the generalization of the reliability evaluation of the system on the problem of redundant allocation are improved; srilamadas and the like represent the allocation factors as trapezoidal fuzzy numbers, and evaluate the trapezoidal fuzzy numbers by fuzzy linguistic variables in the reliability allocation process, and allocate the system reliability indexes to subsystems by using fuzzy scale factors; gianpaolo et al combine the analytic hierarchy process with the IFM process to assign different weights to different factors and different components according to the magnitude of the assignment process reliability.
In summary, in the early design stage, reliability statistics are lacking, and the influence of inaccuracy of subjective data and the like can be reduced by integrating the fuzzy mathematical method into reliability distribution. However, reliability allocation based on fuzzy theory still has the defects of lower precision and large influence by artifacts, and even when the reliability of a subsystem is greater than 1 during allocation, the reliability of the subsystem may be higher than 1. In addition, at present, most of the distribution results of reliability distribution research only stay at a subsystem layer, and the research of implementing the reliability distribution of parts by taking the reliability of the subsystem as an index is relatively less. Meanwhile, the current reliability allocation study does not consider the multi-state and fault propagation characteristics of the subsystem.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide a reliability distribution method of a wind turbine generator set, which considers the multi-state and fault correlation of a subsystem, and provides an improved reliability distribution method of the subsystem, namely a correction coefficient distribution method, on the basis of a fuzzy distribution method, wherein reliability indexes of the subsystem are distributed to all parts, so that the defects of the traditional fuzzy distribution method are overcome, the multi-state and fault propagation characteristics of the subsystem are comprehensively considered, and the objectivity and accuracy in the reliability distribution process are improved.
In order to achieve the above purpose, the invention adopts the technical proposal that,
a wind turbine generator reliability distribution method considering subsystem multi-state and fault correlation comprises the following steps:
1) Collecting historical operation fault data of each subsystem of the wind turbine generator, and calculating fault correlation among all parts in the subsystems by adopting a decision laboratory analysis method, namely a DEMATEL method;
2) Establishing a repairable three-state transition model of each subsystem according to the Markov state transition model;
3) And 2) constructing a state transition probability matrix of the subsystem according to the three-state transition model of the subsystem established in the step 2):
wherein: k (k) 11 ＝λ 1 +λ 3 ，k 22 ＝μ 1 +λ 2 ，k 33 ＝μ 2 +μ 3 ；λ 1 Failure rate for the subsystem from state 0 to state 1; lambda (lambda) 2 Failure rate for the subsystem from state 1 to state 2; lambda (lambda) 3 Failure rate for the subsystem from state 0 to state 2; mu (mu) 1 Maintenance rate for subsystem from state 1 to state 0; mu (mu) 2 Maintenance rate for subsystem from state 2 to state 1; mu (mu) 3 Maintenance rate for subsystem from state 2 to state 0;
4) According to the state transition probability matrix of the subsystem constructed in the step 3), comprehensively considering the fault correlation of each part in the subsystem, and calculating the steady-state availability of the subsystem under the influence of each part;
5) Analyzing factors influencing the reliability allocation of the subsystem, and calculating the reliability allocation initial weight of each part in the subsystem by adopting a superior-inferior solution distance method, namely a TOPSIS method;
6) Adopting the subsystem obtained in the step 4) to distribute initial weights to the reliability of the parts obtained in the steady-state availability correction step 5) affected by the parts, and obtaining reliability distribution correction weights of the parts:
wherein: w (w) pi Distributing correction weight for the reliability of the ith part of the subsystem;
7) And distributing correction weights according to the reliability of each part in the subsystem, and distributing the subsystem reliability to each part:
wherein:assigning a value to the reliability of the ith part of the subsystem; />The reliability of the parts is equal to that of the parts; r is R * Is a reliable subsystem; epsilon i Distributing a correction coefficient for the reliability of the ith part; beta is an adjusting factor which ensures that the reliability after distribution meets the subsystem reliability requirement, beta is E [0.5,1 ]]。
Said step 1) comprises the steps of:
1.1 According to the propagation influence relation among the fault modes, establishing a fault mode influence relation matrix:
wherein: p (P) F Affecting a relation matrix for a failure mode of the subsystem; f (f) ij Correlation of subsystem ith fault mode to jth fault mode; n is n F Is the number of subsystem failure modes;
1.2 According to the failure mode influencing relation matrix, defining a standardized matrix:
wherein:a standardized matrix for subsystem fault propagation effects;
1.3 Calculating a comprehensive influence relation matrix according to the standardized matrix:
wherein: i is an identity matrix; t is t ij The method is the original of the ith row and the jth column in the comprehensive influence relation matrix;
1.4 According to the comprehensive influence relation matrix, calculating the influence degree and the influenced degree among all parts in the subsystem:
wherein: r (p) t ,p k ) The influence degree of the nth part of the subsystem on the kth part is given; d (p) t ,p k ) The influence degree of the kth part on the kth part of the subsystem is shown as the kth part; p is p t Is the t-th part of the subsystem; p is p k Is the kth part of the subsystem; n is n t The number of fault modes in the t-th part; n is n k The number of fault modes for the kth part;
1.5 According to the influence degree and the influenced degree among the parts, calculating the fault correlation coefficient among the parts in the subsystem:
wherein:the fault correlation coefficient between the nth part and the kth part of the subsystem is obtained;
said step 2) comprises the steps of:
2.1 Three states in the repairable three-state transition model are defined as: the system comprises a normal state, a state to be maintained and a maintenance state, wherein the normal state is defined as that the subsystem has no potential safety hazard, the state to be maintained is defined as that the subsystem has potential safety hazard but still can normally operate, and the maintenance state is defined as that the system is stopped and maintenance is started;
2.2 In the repairable three-state transition model, respectively using a 0 state, a 1 state and a 2 state to represent the normal state, the to-be-maintained state and the maintenance state of the subsystem; the transition direction between the states is represented by an arrow, and the numerical value on the arrow represents the transition probability of the subsystem from the current state to the other state; by lambda i 、μ i I=1, 2,3 respectively represent a failure rate and a repair rate of the subsystem under various states, the failure rate is defined as a ratio of the number of failure failures of the subsystem to the total number of failure failures in a statistical period, and the repair rate is defined as a ratio of time spent for repairing the failure to the total time in the same statistical period.
Said step 4) comprises the steps of:
4.1 Calculating steady state availability of the subsystem under the influence of each failure mode:
wherein:for subsystem in failure mode f j Steady state availability under influence; f (f) j Is the j-th failure mode of the subsystem;
4.2 Steady state availability of the computing subsystem under the influence of the components:
wherein:for subsystem at part p i Steady state availability under influence; p is p i Is the ith part of the subsystem; n is n i Is the ith part p of the subsystem i The number of failure modes;
4.3 Generally, failure of one component may cause some other components to fail, such that some of the components exhibit some failure correlation, and therefore additional effects due to failure correlation should be considered in analyzing the effects of each component on steady state availability of the subsystem.
Said step 5) comprises the steps of:
5.1 Factors affecting subsystem reliability allocation mainly include importance of the component, failure hazard of the component, component complexity vector, current state of the art of the component, component working time and component working environment;
5.2 Scoring factors influencing the reliability allocation of the subsystem according to experience and historical data of scoring experts by adopting an expert scoring mode, and obtaining a reliability allocation comprehensive influence factor matrix:
wherein: gamma ray ij Assigning a scoring value of the influencing factor to the j-th reliability of the i-th component; n is the number of subsystem components;
5.3 Analyzing the reliability allocation comprehensive influence factor matrix in the step 5.2) by adopting a good-bad solution distance method to obtain the reliability allocation initial weight of each part in the subsystem.
Further, the step 4.3) includes the steps of:
4.3.1 When considering the first order fault propagation effects, calculating the steady state availability of the subsystem under the effect of the ith component:
wherein:to account for the first order fault propagation effects, the steady state availability of the subsystem under the influence of the ith component; />The fault correlation coefficients of the ith part and the kth part of the subsystem are obtained; />Steady state availability for the subsystem under the influence of the kth component;
4.3.2 When considering the second order fault propagation effects, the steady state availability of the computing subsystem under the influence of the ith component:
wherein:to account for the second order fault propagation effects, the steady state availability of the subsystem under the influence of the ith component; />The fault correlation coefficients of the kth part and the t part of the subsystem are obtained; />For sub-systems under the influence of the t-th componentSteady state availability'
Since higher order fault propagation after the second order has less impact on subsystem steady state availability, only the fault propagation impact of the previous second order is considered.
Further, the step 5.3) includes the steps of:
5.3.1 Standardized processing is carried out on the reliability distribution comprehensive influence factor matrix:
wherein:distributing a comprehensive influence factor matrix for the standardized reliability; />Assigning a value of an influence factor to the j-th reliability of the i-th component after standardization;
5.3.2 According to the standardized reliability distribution comprehensive influence factor matrix obtained in the step 5.3.1), calculating a weighted decision matrix:
wherein: v is a weighted decision matrix;assigning a weight of an influencing factor to the j-th reliability; v ij Is normalized and considered with respect to the weightThe j-th reliability of the i parts assigns the value of the influencing factor;
5.3.3 Calculating the optimal value and the worst value of each reliability allocation influence factor according to the weighted decision matrix obtained in the step 5.3.2):
wherein:assigning an optimal value of the influencing factor to the j-th reliability; />Assigning a worst value of the influence factors to the j-th reliability;
5.3.4 Calculating the distance from the reliability allocation influence factor set to the optimal value set and the worst value set of each part:
wherein:assigning a distance from the influence factor set to the optimal value set for the reliability of the ith part; />The distance from the influence factor set to the worst value set is distributed for the reliability of the ith part;
5.3.5 Calculating the proximity of the reliability allocation influence factor set and the optimal value set of each part:
wherein: c (C) i The reliability of the ith part distributes the proximity of the influence factor set and the optimal value set;
5.3.6 Reliability assignment initial weight for each component in the computing subsystem:
wherein:an initial weight is assigned to the reliability of the ith part.
Compared with the existing reliability distribution method, the reliability distribution method of the wind turbine generator set is provided, the reliability indexes distributed by the subsystem are reasonably distributed to all parts, the reliability distribution initial weight of all parts is calculated, a three-state transition model of the subsystem is built, steady-state availability of the subsystem under the influence of all parts is taken as a basis, and the reliability distribution initial weight of all parts of the subsystem is corrected by comprehensively considering the propagation influence of faults. Based on the reliability index, the subsystem is distributed to each part, and the reliability distribution index of each part is obtained. The improved subsystem reliability distribution method, namely the correction coefficient distribution method, overcomes the defects of the traditional fuzzy distribution method, comprehensively considers the multi-state and fault propagation characteristics of the subsystem and improves the objectivity and accuracy in the reliability distribution process.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a repairable three state transition model.
Detailed Description
The invention is further illustrated by the following description in conjunction with specific embodiments and the accompanying drawings.
As shown in FIG. 1, the invention relates to a method for distributing reliability of a wind turbine generator set by considering subsystem multi-state and fault correlation, which specifically comprises the following steps:
1) Taking a variable pitch system of a certain type of wind turbine as an analysis object, and collecting historical fault data of the type of wind turbine, wherein the historical fault data are shown in the following table: calculating fault correlation among all parts in the variable pitch system by adopting a decision laboratory analysis method;
2) According to the Markov state transition model, a repairable three-state transition model of the variable pitch system is established, and the established repairable three-state transition model is shown in fig. 2;
3) According to a repairable three-state transition model of the variable pitch system, a state transition probability matrix of the variable pitch system is constructed, and the transition probability among the states of the variable pitch system caused by each part can be represented by the failure frequency and the reciprocal of repair time of the variable pitch system under the influence of each part:
4) According to the state transition probability matrix of the variable pitch system constructed in the step 3), comprehensively considering the fault correlation of each part in the variable pitch system, and calculating the steady-state availability of the variable pitch system under the influence of each part;
5) Analyzing factors influencing the reliability allocation of the variable pitch system, and calculating the reliability allocation initial weight of each part in the variable pitch system by adopting a good-bad solution distance method;
6) Adopting the reliability allocation initial weight of each part obtained in the steady-state availability correction step 5) of the variable pitch system influenced by each part in the step 4) to obtain the reliability allocation correction weight of each part:
7) Distributing correction weights according to the reliability of each part in the pitch system, and distributing the pitch system reliability to each part, wherein the pitch system reliability distribution index is 0.9874:
/>
Claims (1)
1. the wind turbine generator reliability distribution method considering subsystem multi-state and fault correlation is characterized by comprising the following steps of:
1) Historical operation fault data of all subsystems of the wind turbine generator are collected, and a decision laboratory analysis method is adopted to calculate fault correlation among parts in all subsystems, and the method specifically comprises the following steps:
1.1 According to the propagation influence relation among the fault modes in the subsystem, establishing a fault mode influence relation matrix:
wherein: p (P) F Affecting a relation matrix for a failure mode of the subsystem; f (f) ij Correlation of subsystem ith fault mode to jth fault mode; n is n F Is the number of subsystem failure modes;
1.2 According to the failure mode influencing relation matrix, defining a standardized matrix:
wherein:a standardized matrix for subsystem fault propagation effects;
1.3 Calculating a comprehensive influence relation matrix according to the standardized matrix:
wherein: i is an identity matrix; t is t ij The method is the original of the ith row and the jth column in the comprehensive influence relation matrix;
1.4 According to the comprehensive influence relation matrix, calculating the influence degree and the influenced degree among all parts in the subsystem:
wherein: r (p) t ,p k ) The influence degree of the nth part of the subsystem on the kth part is given; d (p) t ,p k ) The influence degree of the kth part on the kth part of the subsystem is shown as the kth part; p is p t Is the t-th part of the subsystem; p is p k Is the kth part of the subsystem; n is n t The number of fault modes in the t-th part; n is n k The number of fault modes for the kth part;
1.5 According to the influence degree and the influenced degree among the parts, calculating the fault correlation coefficient among the parts in the subsystem:
wherein:the fault correlation coefficient between the nth part and the kth part of the subsystem is obtained;
2) According to the Markov state transition model, a repairable three-state transition model of each subsystem is established, and the method specifically comprises the following steps:
2.1 Three states in the repairable three-state transition model are defined as: the system comprises a normal state, a state to be maintained and a maintenance state, wherein the normal state is defined as that the subsystem has no potential safety hazard, the state to be maintained is defined as that the subsystem has potential safety hazard but still can normally operate, and the maintenance state is defined as that the system is stopped and maintenance is started;
2.2 In the repairable three-state transition model, respectively using a 0 state, a 1 state and a 2 state to represent the normal state, the to-be-maintained state and the maintenance state of the subsystem; the transition direction between the states is represented by an arrow, and the numerical value on the arrow represents the transition probability of the subsystem from the current state to the other state; by lambda i 、μ i I=1, 2,3 denote the failure rate and repair rate, respectively, of a subsystem transitioning between various states, the failure rate being defined as the subsystem within a statistical periodThe ratio of the number of faults to the total number of faults, the repair rate being defined as the ratio of the time taken to repair the fault to the total time in the same statistical period;
3) And 2) constructing a state transition probability matrix of the subsystem according to the three-state transition model of the subsystem established in the step 2):
wherein: k (k) 11 ＝λ 1 +λ 3 ，k 22 ＝μ 1 +λ 2 ，k 33 ＝μ 2 +μ 3 ；λ 1 Failure rate for the subsystem from state 0 to state 1; lambda (lambda) 2 Failure rate for the subsystem from state 1 to state 2; lambda (lambda) 3 Failure rate for the subsystem from state 0 to state 2; mu (mu) 1 Maintenance rate for subsystem from state 1 to state 0; mu (mu) 2 Maintenance rate for subsystem from state 2 to state 1; mu (mu) 3 Maintenance rate for subsystem from state 2 to state 0;
4) According to the subsystem state transition probability matrix constructed in the step 3), calculating the steady-state availability of the subsystem under the influence of each part, wherein the method specifically comprises the following steps:
4.1 Calculating steady state availability of the subsystem under the influence of each failure mode:
wherein:for subsystem in failure mode f j Steady state availability under influence; f (f) j Is the j-th failure mode of the subsystem;
4.2 Steady state availability of the computing subsystem under the influence of the components:
wherein:for subsystem at part p i Steady state availability under influence; p is p i Is the ith part of the subsystem; n is n i Is the ith part p of the subsystem i The number of failure modes;
4.3 If one part fails, some other parts also fail, so that part parts have certain failure correlation, therefore, when analyzing the influence of each part on the steady-state availability of the subsystem, the additional influence caused by the failure correlation should be considered, and the method specifically comprises the following steps:
4.3.1 When considering the first order fault propagation effects, calculating the steady state availability of the subsystem under the effect of the ith component:
wherein:to account for the first order fault propagation effects, the steady state availability of the subsystem under the influence of the ith component;the fault correlation coefficients of the ith part and the kth part of the subsystem are obtained; />Steady state availability for the subsystem under the influence of the kth component;
4.3.2 When considering the second order fault propagation effects, the steady state availability of the computing subsystem under the influence of the ith component:
wherein:to account for the second order fault propagation effects, the steady state availability of the subsystem under the influence of the ith component;the fault correlation coefficients of the kth part and the t part of the subsystem are obtained; />Steady state availability for the subsystem under the influence of the t-th component;
because the higher order fault propagation after the second order has less effect on the steady state availability of the subsystem, only the fault propagation effect of the previous second order is considered;
5) Analyzing factors influencing the reliability allocation of the subsystem, and calculating the reliability allocation initial weight of each part in the subsystem by adopting a superior-inferior solution distance method, namely a TOPSIS method, wherein the method specifically comprises the following steps of:
5.1 Factors affecting subsystem reliability allocation mainly include importance of the component, failure hazard of the component, component complexity vector, current state of the art of the component, component working time and component working environment;
5.2 Scoring factors influencing the reliability allocation of the subsystem according to experience and historical data of scoring experts by adopting an expert scoring mode, and obtaining a reliability allocation comprehensive influence factor matrix:
wherein: gamma ray ij Assigning an influencing factor to the j-th reliability of the i-th componentA scoring value; n is the number of subsystem components;
5.3 Analyzing the reliability allocation comprehensive influence factor matrix in the step 5.2) by adopting a good-bad solution distance method to obtain the reliability allocation initial weight of each part in the subsystem, and specifically comprising the following steps:
5.3.1 Standardized processing is carried out on the reliability distribution comprehensive influence factor matrix:
wherein:distributing a comprehensive influence factor matrix for the standardized reliability; />Assigning a value of an influence factor to the j-th reliability of the i-th component after standardization;
5.3.2 According to the standardized reliability distribution comprehensive influence factor matrix obtained in the step 5.3.1), calculating a weighted decision matrix:
wherein: v is a weighted decision matrix;assigning a weight of an influencing factor to the j-th reliability; v ij Assigning values of influencing factors to the j-th reliability of the i-th component after normalization and considering the weight;
5.3.3 Calculating the optimal value and the worst value of each reliability allocation influence factor according to the weighted decision matrix obtained in the step 5.3.2):
wherein:assigning an optimal value of the influencing factor to the j-th reliability; />Assigning a worst value of the influence factors to the j-th reliability;
5.3.4 Calculating the distance from the reliability allocation influence factor set to the optimal value set and the worst value set of each part:
wherein:assigning a distance from the influence factor set to the optimal value set for the reliability of the ith part; />The distance from the influence factor set to the worst value set is distributed for the reliability of the ith part;
5.3.5 Calculating the proximity of the reliability allocation influence factor set and the optimal value set of each part:
wherein: c (C) i The reliability of the ith part distributes the proximity of the influence factor set and the optimal value set;
5.3.6 Reliability assignment initial weight for each component in the computing subsystem:
wherein:assigning an initial weight to the reliability of the ith part;
6) The subsystem is adopted to correct the reliability distribution initial weight of each part in the steady state availability affected by each part, so as to obtain the reliability distribution correction weight of each part:
wherein:distributing correction weight for the reliability of the ith part of the subsystem;
7) And distributing correction weights according to the reliability of each part in the subsystem, and distributing the subsystem reliability to each part:
wherein:assigning a value to the reliability of the ith part of the subsystem; />The reliability of the parts is equal to that of the parts; r is R * Is the reliability of the subsystem; />Distributing a correction coefficient for the reliability of the ith part; n is the number of parts; beta is an adjusting factor which ensures that the reliability after distribution meets the subsystem reliability requirement, beta is E [0.5,1 ]]。
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