CN115705540A - Fuzzy evaluation method for reliability of wind driven generator - Google Patents
Fuzzy evaluation method for reliability of wind driven generator Download PDFInfo
- Publication number
- CN115705540A CN115705540A CN202110916091.3A CN202110916091A CN115705540A CN 115705540 A CN115705540 A CN 115705540A CN 202110916091 A CN202110916091 A CN 202110916091A CN 115705540 A CN115705540 A CN 115705540A
- Authority
- CN
- China
- Prior art keywords
- evaluation
- index
- reliability
- fuzzy
- driven generator
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 127
- 238000000034 method Methods 0.000 claims abstract description 38
- 239000011159 matrix material Substances 0.000 claims abstract description 27
- 230000015556 catabolic process Effects 0.000 claims abstract description 9
- 238000006731 degradation reaction Methods 0.000 claims abstract description 9
- 238000001514 detection method Methods 0.000 claims description 8
- 230000008439 repair process Effects 0.000 claims description 7
- 230000006866 deterioration Effects 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 239000000428 dust Substances 0.000 description 1
- 229910052731 fluorine Inorganic materials 0.000 description 1
- 125000001153 fluoro group Chemical group F* 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 238000013077 scoring method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Landscapes
- Control Of Eletrric Generators (AREA)
Abstract
The invention relates to the technical field of wind power industry, in particular to a fuzzy evaluation method for the reliability of a wind driven generator, which comprises the steps of determining the reliability evaluation index of the wind driven generator in a fault mode and establishing a reliability evaluation index system; establishing an evaluation grade according to the evaluation index; standardizing the evaluation index by using the degradation degree; establishing an evaluation membership matrix by using a ridge fuzzy membership function; determining the weight of the evaluation index by adopting an AHP-entropy method; calculating a reliability fuzzy evaluation matrix of the wind driven generator; and analyzing the judgment result. The method is based on the fuzzy set theory, calculates the hazard degree of each fault mode by quantifying failure probability, and obtains the hazard degree of each fault mode of the system, thereby being the complete machine reliability of the wind driven generator.
Description
Technical Field
The invention relates to the technical field of wind power industry, in particular to a fuzzy evaluation method for reliability of a wind driven generator.
Background
The wind driven generator works in severe environments such as sand, dust, low temperature, ice, snow, thunder, storm and the like for a long time, and related parts of the wind driven generator are easily damaged and break down due to the influence of load and wind speed on the wind driven generator. The distribution range of the wind power plant units is usually large, and the maintenance is very difficult. The reliability research of the wind driven generator mainly comprises the following steps: the reliability statistics of the unit and the components thereof, the reliability design and analysis of key components, the health state monitoring and management of the unit, the reliability analysis and evaluation of the whole unit and the like. At present, the reliability problems of equipment components or subsystems of the wind driven generator are mainly researched from the aspects of design, manufacture, operation, maintenance and the like of the wind driven generator, but further research is needed in the aspect of complete machine reliability evaluation.
In order to improve the self reliability and safety of the wind driven generator, the failure occurrence rate of the unit is reduced as much as possible, and reliability evaluation is performed on different failure modes of the unit. At present, many scholars at home and abroad combine the fuzzy theory and the FMECA analysis method to research the reliability of the system. However, there are the following problems:
1. because the imperfection and uncertainty of the evaluation information are often difficult to express by using accurate numerical values, the accuracy of the result is influenced by adopting the accurate numerical values;
2. evaluation index weight is not considered, and the method is not in accordance with reality;
3. the interaction relationship between failure modes is not considered.
Therefore, a technique for solving this problem is urgently required.
Disclosure of Invention
The invention aims to overcome the problems in the prior art, and provides a fuzzy comprehensive evaluation method for a wind driven generator by researching the existing reliability evaluation technology and fault evaluation technology.
The above purpose is realized by the following technical scheme:
a method for fuzzy evaluation of reliability of a wind driven generator comprises the following steps:
determining a reliability evaluation index of the wind driven generator in a fault mode, and establishing a reliability evaluation index system;
step (2) establishing an evaluation grade according to the evaluation index in the step (1);
step (3) standardizing the evaluation index by using the deterioration degree; establishing an evaluation membership matrix by using a ridge fuzzy membership function;
determining the weight of a judgment index by adopting an AHP-entropy method;
step (5), calculating a reliability fuzzy evaluation matrix of the wind driven generator;
and (6) analyzing the judgment result.
Further, the evaluation indexes in the step (1) comprise an average failure interval, an average repair time, a degree of damage, an ease of detection, an average bearing temperature of the gearbox and an average rotating speed of the generator, and the reliability evaluation index system is as follows:
U＝[MTBF MTTR C D u(T) u(n)]
in the formula, U is a reliability evaluation index system, MTBF is an average failure interval, MTTR is an average repair time, C is a degree of damage, D is a detection difficulty degree, U (T) is an average bearing temperature of a gearbox, and U (n) is an average rotating speed of a generator.
Further, the evaluation grades in the step (2) include 5 grades of low, normal, high and high.
Further, in the step (3), the evaluation index is normalized by using the degree of degradation, specifically, the evaluation index data is normalized to (0, 1) interval data by using a method of degree of degradation.
Further, in the step (3), a ridge-shaped fuzzy membership function is used to establish an evaluation membership matrix, specifically, the evaluation membership matrix can be obtained by determining a relationship between an evaluation index and an evaluation level by using the ridge-shaped fuzzy membership function, as follows:
in the formula, r ij As an evaluation index u i Membership to a rating v j The ith row in the R represents the membership degrees of the ith evaluation factor to different evaluation grade standards, and the sum of the membership degrees of each row is 1; column j indicates the degree of membership of each evaluation factor to the same evaluation level criteria.
Further, the step (4) specifically comprises:
calculating the weight omega of each index by using AHP method j ；
Calculating difference coefficient g of each index by using entropy method j ；
Then, the index weight A j The following formula:
in the formula, omega j Calculating the weight of the jth index obtained by the AHP method; g j Calculating the difference coefficient of the j index by an entropy method; a. The j Is the comprehensive weight of the j index of the AHP-entropy value method.
Further, the step (5) is specifically to obtain the index weight set through the steps (3) and (4), and obtain the evaluation result through a weighted average method, as follows:
in the formula, B is an evaluation result, W represents a weight matrix of evaluation indexes, and R represents a fuzzy relation matrix composed of the evaluation indexes.
Advantageous effects
According to the fuzzy evaluation method for the reliability of the wind driven generator, provided by the invention, based on a fuzzy set theory, the hazard degree of each fault mode is calculated by quantifying failure probability, and the hazard degree of each fault mode of the system is obtained, so that the reliability of the whole wind driven generator is improved.
Drawings
FIG. 1 is a flow chart of a method for fuzzy evaluation of reliability of a wind driven generator according to the present invention.
Detailed Description
The invention is explained in more detail below with reference to the figures and examples. The described embodiments are only some embodiments of the invention, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a method for fuzzy evaluation of reliability of a wind turbine includes the following steps:
determining a reliability evaluation index of the wind driven generator in a fault mode, and establishing a reliability evaluation index system;
step (2) establishing an evaluation grade according to the evaluation index in the step (1);
step (3) standardizing the evaluation index by using the deterioration degree; establishing an evaluation membership matrix by using a ridge fuzzy membership function;
determining the weight of the evaluation index by adopting an AHP-entropy method;
step (5), calculating a reliability fuzzy evaluation matrix of the wind driven generator;
and (6) analyzing the judgment result.
Specifically, the fuzzy comprehensive evaluation is to select an index having a relatively large correlation with an evaluated object on the basis of a fuzzy linear transformation and a maximum membership principle, quantize the index one by one, determine the weight of each index according to the influence degree of different indexes on the evaluated object, and further comprehensively and accurately judge the evaluated object.
The rationale can be expressed as:
B＝W·R
in the formula, B is an evaluation result, W represents a weight matrix of evaluation indexes, and R represents a fuzzy relation matrix composed of the evaluation indexes.
In this embodiment, the evaluation indexes in step (1) include an average failure interval, an average repair time, a degree of damage, an ease of detection, an average bearing temperature of the gearbox, and an average rotating speed of the generator, and the reliability evaluation index system is as follows:
U＝[MTBF MTTR C D u(T) u(n)]
in the formula, U is a reliability evaluation index system, MTBF is an average failure interval, MTTR is an average repair time, C is a degree of damage, D is a detection difficulty degree, U (T) is an average bearing temperature of a gearbox, and U (n) is an average rotating speed of a generator.
Specifically, the average bearing temperature u (T) of the gearbox and the average rotating speed u (n) of the generator can be obtained through historical monitoring data of an SCADA system; the difficulty degree of detection is recorded as D, the value of the D is directly determined by the detection grade by adopting an expert scoring method, and the value range is 0-1.
MTBF (mean time between failures) = (hours in a statistical period-time of no connection of SCADA system-hours of downtime due to failure)/total number of failures;
MTTR (mean time to repair) = number of downtime hours per total number of failures within a statistical period;
the failure mode hazard level (C) is calculated as follows:
failure mode criticality is used to evaluate the criticality of a product at a certain failure mode. The quantitative expression of the hazard degree of the fault mode in a certain severity level in the working time t is as follows:
C＝λ p αβt
in the formula, C represents the degree of damage caused by the fault mode j within the working time t; lambda [ alpha ] p The failure rate of the fan in the task stage is represented, and the unit is 1/hour; t represents working time in hours; α represents the failure mode percentage, representing the percentage of failures occurring in that failure mode; beta represents the conditional probability of the failure mode causing the fan to stop, and can be quantitatively estimated according to the following method:
actual loss of function 1; most likely 0.5 is lost; a possible loss of 0.1; 0.01 can be ignored; there is no effect 0.
In this example, the evaluation scale in step (2) includes 5 scales of low, normal, high, and high.
In particular, low corresponds to few faults, generally to occasional faults, high corresponds to occasional faults, and high corresponds to frequent faults, see the factors that affect the critical index and the rule table evaluating each index, as follows:
factors affecting critical indices and rule table for evaluating each index
In this embodiment, in the step (3), the evaluation index is normalized by the degradation degree, specifically, the evaluation index data is normalized to (0, 1) interval data by the degradation degree method.
Specifically, in order to comprehensively compare the indexes, the data is normalized and normalized to (0, 1) interval data.
The processing mode adopts the deterioration degree which represents the degree of deviation of the system operation from the normal operation state, and the value range is [0,1]. The calculation method of the degradation degree is different for different operation parameters.
The mathematical expression can be divided into a smaller and more optimal type and an intermediate type, as follows:
the smaller the more preferred the form, the following formula:
intermediate forms, of the formula:
in the formula, n (x) represents the result of the degree of deterioration of the evaluation index, that is, the normalized result thereof; x represents an evaluation index measured value; x is the number of max Expressing the maximum value of the evaluation index; x is a radical of a fluorine atom min Represents the minimum value of the evaluation index; x is the number of a Expressing the minimum allowable value of the evaluation index; x is the number of b Presentation judgmentThe index allows the maximum value.
And (5) carrying out degradation degree treatment on the bearing temperature of the gearbox and the rotating speed of the generator. Wherein, the temperature of the bearing of the gear box is selected to be smaller and more optimal; the rotation speed of the generator is of an intermediate type.
In this embodiment, the ridge-shaped fuzzy membership function is used to establish the evaluation membership matrix in step (3), and specifically, the evaluation membership matrix can be obtained by determining the relationship between the evaluation index and the evaluation level by using the ridge-shaped fuzzy membership function, as follows:
in the formula, r ij As an evaluation index u i Membership to a rating v j The ith row in the R represents the membership degree of the ith evaluation factor to different evaluation grade standards, and the sum of the membership degrees of each row is 1; column j indicates the degree of membership of each evaluation factor to the same evaluation level criteria.
The determination of the membership functions is the key point of the fuzzy comprehensive evaluation. Ridge-shaped fuzzy membership functions are selected in the study.
The distribution of the membership functions is shown in an evaluation grade and a membership function table as follows:
evaluation grade and membership function table
In the formula in the evaluation grade and membership function table, n ij Is x ij The normalized result of (2).
Through the formula, a membership matrix of the reliability evaluation index, namely I (x), can be obtained through calculation ij )＝[I ij (r 1 )I ij (r 2 )I ij (r 3 )I ij (r 4 )I ij (r 5 )]
In this embodiment, the step (4) specifically includes:
the weight is the magnitude of the effect of each factor on the evaluation result, and reflects the degree of influence of each factor on the evaluation result, and the numerical value thereof greatly affects the result of the overall evaluation. The weight is determined by a subjective and objective combination method, and the weight is calculated by an AHP-entropy method, so that the influence of subjective factors is reduced, the problem of deviation caused by weighting of the entropy method due to insufficient sample data is weakened, and the optimized weight is obtained. The process is as follows:
1. calculating the weight omega of each index by using AHP method j . Firstly, the evaluation indexes are compared by using a T.L.Saaty 1-5 scaling method to obtain a judgment matrix. And calculating the maximum characteristic root of the judgment matrix by using a sum-product method, and carrying out consistency check. When the ratio of the consistency index of the judgment matrix to the average random consistency index of the same order is less than 0.1; if not, the judgment matrix is adjusted until the consistency requirement is met. Normalizing the feature vector corresponding to the maximum feature root to obtain the weight of each index;
2. calculating difference coefficient g of each index by using entropy method j . First, Z normalization and normalization processing are performed on existing data to eliminate the influence of the dimension. Then, the specific gravity Y of the j index (j =1,2, \ 8230; (n)) of the i group (i =1,2, \ 8230; (m) data) is calculated ij Thereby calculating the information entropy e of the j index j ，
The difference coefficient g of the j index j ＝1-e j ；
3. Comprehensive weight A of j-th index j It can be calculated from the following formula:
in the formula, ω j Calculated for AHP methodThe weight of the j-th index; g j Calculating the difference coefficient of the j index by an entropy method; a. The j The comprehensive weight of the j index of the AHP-entropy value method.
Specifically, in the step (5), an index weight set is obtained through the steps (3) and (4), and an evaluation result is obtained through a weighted average method, which is as follows:
in the formula, B is an evaluation result, W represents a weight matrix of evaluation indexes, and R represents a fuzzy relation matrix composed of the evaluation indexes.
And finally, analyzing the evaluation result in the step (5) to obtain a corresponding conclusion.
The above description is for illustrative purposes only and is not intended to limit the present invention, and any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention will be apparent to those skilled in the art.
Claims (7)
1. A fuzzy evaluation method for reliability of a wind driven generator is characterized by comprising the following steps:
determining a reliability evaluation index of the wind driven generator in a fault mode, and establishing a reliability evaluation index system;
step (2) establishing an evaluation grade according to the evaluation index in the step (1);
step (3) standardizing the evaluation index by using the deterioration degree; establishing an evaluation membership matrix by using a ridge fuzzy membership function;
determining the weight of a judgment index by adopting an AHP entropy method;
step (5), calculating a reliability fuzzy evaluation matrix of the wind driven generator;
and (6) analyzing the judgment result.
2. The fuzzy evaluation method for the reliability of the wind driven generator according to claim 1, wherein the evaluation indexes in the step (1) comprise an average failure interval, an average repair time, a degree of damage, a detection difficulty degree, an average bearing temperature of a gearbox and an average rotating speed of the generator, and the reliability evaluation index system has the following formula:
U＝[MTBF MTTR C D u(T) u(n)]
in the formula, U is a reliability evaluation index system, MTBF is an average failure interval, MTTR is an average repair time, C is a degree of damage, D is a detection difficulty degree, U (T) is an average bearing temperature of a gearbox, and U (n) is an average rotating speed of a generator.
3. The fuzzy evaluation method for the reliability of the wind driven generator according to claim 1, wherein the evaluation grades in the step (2) comprise 5 grades of low, normal, high and high.
4. The fuzzy evaluation method for the reliability of the wind turbine generator according to claim 1, wherein in step (3), the evaluation index is normalized by using the degree of degradation, and in particular, the evaluation index data is normalized to (0, 1) interval data by using a method of degree of degradation.
5. The method according to claim 1, wherein in step (3), the ridge-shaped fuzzy membership function is used to establish the evaluation membership matrix, and specifically, the evaluation membership matrix is obtained by determining the relationship between the evaluation index and the evaluation level by using the ridge-shaped fuzzy membership function, as follows:
in the formula, r ij As an evaluation index u i Membership to a rating v j The ith row in the R represents the membership degree of the ith evaluation factor to different evaluation grade standards, and the sum of the membership degrees of each row is 1; column j indicates the evaluation factor pairsDifferent degrees of membership for the same rating scale.
6. The fuzzy evaluation method for the reliability of the wind driven generator according to claim 1, wherein the step (4) is specifically:
calculating the weight omega of each index by using an AHP method j ；
Calculating difference coefficient g of each index by using entropy method j ；
Then, the index weight A j The following formula:
in the formula, omega j Calculating the weight of the j index obtained by the AHP method; g j Calculating the difference coefficient of the j index by an entropy method; a. The j Is the comprehensive weight of the j index of the AHP-entropy value method.
7. The fuzzy evaluation method for the reliability of the wind turbine generator according to claim 6, wherein the step (5) is specifically to obtain the index weight set through the steps (3) and (4), and obtain the evaluation result through a weighted average method, as follows:
in the formula, B is an evaluation result, W represents a weight matrix of evaluation indexes, and R represents a fuzzy relation matrix composed of the evaluation indexes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110916091.3A CN115705540A (en) | 2021-08-10 | 2021-08-10 | Fuzzy evaluation method for reliability of wind driven generator |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110916091.3A CN115705540A (en) | 2021-08-10 | 2021-08-10 | Fuzzy evaluation method for reliability of wind driven generator |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115705540A true CN115705540A (en) | 2023-02-17 |
Family
ID=85179627
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110916091.3A Pending CN115705540A (en) | 2021-08-10 | 2021-08-10 | Fuzzy evaluation method for reliability of wind driven generator |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115705540A (en) |
-
2021
- 2021-08-10 CN CN202110916091.3A patent/CN115705540A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109359894B (en) | RPN-based risk evaluation method and device for electric power metering equipment | |
| CN110414154B (en) | Fan component temperature abnormity detection and alarm method with double measuring points | |
| US20240028937A1 (en) | Method for evaluating health status of petrochemical atmospheric oil storage tank using data from multiple sources | |
| EP2989705B1 (en) | Method and apparatus for defect pre-warning of power device | |
| US20100138267A1 (en) | System and method for wind turbine health management | |
| CN108508863A (en) | A kind of electromechanical equipment method for diagnosing faults based on gray model | |
| CN113614359A (en) | Method and system for predicting risk of observable damage in wind turbine gearbox assembly | |
| CN109636110A (en) | A kind of method and device obtaining protective relaying device operating status | |
| CN103942735A (en) | Method for evaluating relay protection states | |
| CN112417627A (en) | Power distribution network operation reliability analysis method based on four-dimensional index system | |
| CN116677570A (en) | Fault early warning method and system based on cabin temperature monitoring of offshore wind turbine | |
| CN116739399A (en) | High-voltage cable running state evaluation method | |
| CN118134700A (en) | Comprehensive intelligent maintenance decision method and system for bridge structure | |
| CN108416386A (en) | A kind of method and system judged extremely for Hydropower Unit bearing temperature | |
| CN118088666A (en) | Distributed intelligent lubrication monitoring and fault early warning system for wind power gearbox | |
| CN118365307A (en) | Power plant equipment state overhaul decision support method and system | |
| CN111192163B (en) | Generator reliability medium-short term prediction method based on wind turbine generator operating data | |
| JP3243065B2 (en) | Deterioration / damage prediction device for structural parts | |
| CN118188342A (en) | Fan-oriented fault early warning and life prediction method | |
| CN105930964A (en) | Power transmission line icing risk assessment method based on impact from space-time factors | |
| CN114169718A (en) | Method for improving reliability of wind turbine generator based on state evaluation of wind turbine generator | |
| CN117824588A (en) | Sampling optimization and early warning method for building safety monitoring inclination sensor | |
| KR100795967B1 (en) | Inspection and maintenance system of bridges using extreme distribution analysis | |
| CN117523793A (en) | Power plant equipment fault early warning method and computer equipment | |
| CN115705540A (en) | Fuzzy evaluation method for reliability of wind driven generator |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |