CN108827458A - A kind of blade of wind-driven generator intrinsic frequency online recognition method - Google Patents
A kind of blade of wind-driven generator intrinsic frequency online recognition method Download PDFInfo
- Publication number
- CN108827458A CN108827458A CN201810623339.5A CN201810623339A CN108827458A CN 108827458 A CN108827458 A CN 108827458A CN 201810623339 A CN201810623339 A CN 201810623339A CN 108827458 A CN108827458 A CN 108827458A
- Authority
- CN
- China
- Prior art keywords
- blade
- intrinsic frequency
- system order
- frequency
- signal
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
Abstract
The invention discloses a kind of blade of wind-driven generator intrinsic frequency online recognition methods, include the following steps：1) it acquires and stores the blade vibration signal that blade waves direction and edgewise direction；2) signal to be analyzed is chosen；3) it obtains through bandpass filtering and down-sampled time-domain signal；4) analytical calculation that Natural Frequency of Blade is carried out to time-domain signal, obtains the Natural Frequency of Blade under current system order；5) judge current system system order whether be more than setting maximum system order, when current system system order is less than the maximum system order of setting, then add 2 to current system order, then step 4) is gone to, when system order is more than or equal to the maximum system order of setting, then step 6) is gone to；6) all blade fixed frequencies being calculated under primal system order to maximum system order are automatically extracted, to obtain the true fixed frequency of blade, this method can identify the intrinsic frequency of blade of wind-driven generator in real time online.
Description
Technical field
The invention belongs to wind generating set structure intrinsic frequencies to identify field, and it is intrinsic to be related to a kind of blade of wind-driven generator
Frequency online recognition method.
Background technique
The in-service wind power generating set quantity in China increases year by year, and the O&M problem of unit is increasingly prominent.Wherein wind-power electricity generation
Machine blade is the critical component of unit energy conversion, while still by extreme dynamic load(loading) component the most serious, what it was subject to
Main Load has：Wind load (this include stablize wind speed generation fixation aerodynamic loading and fluctuating wind speed generate random load), again
Power load and inertial load.Under the comprehensive function of complex load, blade is easy to appear fatigue failure and cracks, blade initial stage
If the not repaired further deterioration of crackle, will generate immeasurable loss.And when in winter, blade of wind-driven generator can not
Avoid that there is ice conditions.If unit continuous service under blade ice condition will will lead to blade and tower overload, sternly
Ghost image rings the safe operation of unit, and significantly shortens unit service life.Therefore it realizes and the state of blade of wind-driven generator is supervised
It surveys for ensureing that unit safety operation is most important.Natural Frequency of Blade is the important indicator for reflecting blade construction health status,
It can determine whether the rigidity of structure and quality change using intrinsic frequency, that is, can determine whether blade structural damage occurs
And icing.In conclusion the online recognition of Natural Frequency of Blade is to realize the basis of blade state on-line monitoring.
Traditional experimental modal analysis method cannot achieve the real-time online detection of intrinsic frequency, therefore it is impossible to meet leaves
Piece monitors demand on-line.
Summary of the invention
It is an object of the invention to overcome the above-mentioned prior art, a kind of intrinsic frequency of blade of wind-driven generator is provided
Rate online recognition method, this method can identify the intrinsic frequency of blade of wind-driven generator in real time online.
In order to achieve the above objectives, blade of wind-driven generator intrinsic frequency online recognition method of the present invention includes following
Step：
1) the blade vibration signal that blade waves direction and edgewise direction is acquired and stored using double-shaft acceleration sensor；
2) the sum of the virtual value that blade waves the blade vibration signal of direction and edgewise direction is calculated, when what is be calculated has
When the sum of valid value is more than or equal to virtual value threshold value, then blade is selected to wave the blade vibration signal in direction as signal to be analyzed；
When the sum of virtual value being calculated be less than virtual value threshold value when, then select the blade vibration signal of blade edgewise direction as to
Analyze signal；
3) bandpass filtering is carried out to the signal to be analyzed that step 2) obtains, then the signal to be analyzed after bandpass filtering is carried out
It is down-sampled, it obtains through bandpass filtering and down-sampled time-domain signal；
4) leaf is carried out through bandpass filtering and down-sampled time-domain signal to what step 3) obtained using stochastic subspace
The analytical calculation of piece intrinsic frequency obtains the Natural Frequency of Blade under current system order；
5) judge current system system order whether be more than setting maximum system order, when current system system order be less than set
When fixed maximum system order, then add 2 to current system order, then go to step 4), when system order is more than or equal to setting
Maximum system order when, then go to step 6)；
6) all blade fixed frequencies being calculated under primal system order to maximum system order are mentioned automatically
It takes, to obtain the true fixed frequency of blade.
The frequency response range of double-shaft acceleration sensor in step 1) covers 1-10Hz, and double-shaft acceleration sensor
Sensitivity be more than or equal to 500mv/g, double-shaft acceleration sensor be installed on blade interior and close to blade tip position.
Bandpass filtering is carried out to the signal to be analyzed that step 2) obtains by bandpass filter in step 3), wherein band logical
The low cutoff frequency of filter is 1Hz, and the higher cutoff frequency of bandpass filter is 10Hz.
The concrete operations of step 6) are：
Ascending order row 1a) is carried out to all blade fixed frequencies being calculated under primal system order to maximum system order
Column, obtain intrinsic frequency sequence；
2a) to intrinsic frequency sequence carry out first-order difference operation, then choose in calculus of differences result it is all be greater than 0.1 value
Then corresponding position number divides intrinsic frequency sequence according to the position number of selection, obtain several intrinsic frequency clusters；
The intrinsic frequency mean value and intrinsic frequency sum for 3a) calculating each intrinsic frequency cluster, judge consolidating for each intrinsic frequency cluster
Have total number of frequencies whether be more than maximum system order a quarter, when the intrinsic frequency sum of each intrinsic frequency cluster is more than or equal to
When a quarter of maximum system order, then the intrinsic frequency mean value of the intrinsic frequency cluster is saved, when consolidating for each intrinsic frequency cluster
When a quarter for thering is total number of frequencies to be less than maximum system order, then the intrinsic frequency mean value of the intrinsic frequency cluster is deleted, will be protected
It deposits to obtain true fixed frequency of the intrinsic frequency mean value as blade of each intrinsic frequency cluster.
Step 2a) in intrinsic frequency sequence F=[f1,f2,f3,f4,L,fn] first-order difference operation is carried out, obtain first-order difference fortune
Result diff_F=[the f of calculation2-f1,f3-f2,f4-f3,L,fn-fn-1]。
The invention has the advantages that：
Blade of wind-driven generator intrinsic frequency online recognition method of the present invention is when specific operation, using with loom
Space arithmetic obtains current system rank to the analytical calculation for carrying out Natural Frequency of Blade through bandpass filtering and down-sampled time-domain signal
Natural Frequency of Blade under secondary, and then calculated Natural Frequency of Blade obtains the true intrinsic frequency of blade, realizes
The real-time online of Natural Frequency of Blade identifies there is extensive engineering application value, in addition, running mould relative to traditional frequency
State analysis method, the stochastic subspace of time domain have the characteristics that natural frequencies analysis is with high accuracy.It should be noted simultaneously that
The present invention therefrom automatically extracts blade by waving the vibration signal in direction and the vibration signal of edgewise direction on acquisition blade
The intrinsic frequency being excited, to realize Natural Frequency of Blade on-line real-time measuremen.
Detailed description of the invention
Fig. 1 is under operating states of the units, and blade waves the original vibration signal time-domain diagram of direction and edgewise direction；
Fig. 2 is under compressor emergency shutdown state, and blade waves the original vibration signal time-domain diagram of direction and edgewise direction；
Fig. 3 is under operating states of the units, blade wave the vibration signal of direction and edgewise direction after bandpass filtering when
Domain figure；
Fig. 4 is under compressor emergency shutdown state, blade wave the vibration signal of direction and edgewise direction after bandpass filtering when
Domain figure；
Fig. 5 is the flow chart of automatic identification Natural Frequency of Blade in the present invention；
Fig. 6 is the exemplary diagram of intrinsic frequency classification method.
Specific embodiment
The invention will be described in further detail with reference to the accompanying drawing：
Referring to figs. 1 to Fig. 6, wherein in Fig. 5, maximum system order is 40, and blade of wind-driven generator of the present invention is solid
There is frequency online recognition method to include the following steps：
1) the blade vibration signal that blade waves direction and edgewise direction is acquired and stored using double-shaft acceleration sensor；
2) the sum of the virtual value that blade waves the blade vibration signal of direction and edgewise direction is calculated, when what is be calculated has
When the sum of valid value is more than or equal to virtual value threshold value, then blade is selected to wave the blade vibration signal in direction as signal to be analyzed；
When the sum of virtual value being calculated be less than virtual value threshold value when, then select the blade vibration signal of blade edgewise direction as to
Analyze signal；The reason is that unit is in when blade waves the sum of virtual value of direction and edgewise direction more than given threshold
Operating status, blade wave that Natural Frequency of Blade contained by the vibration signal of direction is readily identified, contained by the vibration signal of edgewise direction
Natural Frequency of Blade is difficult to；Blade waves the sum of vibration signal virtual value of direction and edgewise direction and is no more than setting threshold
Value, unit be in shutdown status, and it is densely distributed that blade waves Natural Frequency of Blade contained by the vibration signal of direction, unfavorable to analysis,
Natural Frequency of Blade interval contained by edgewise direction vibration signal is sparse, is convenient for analysis and utilization.
3) bandpass filtering is carried out to the signal to be analyzed that step 2) obtains, then the signal to be analyzed after bandpass filtering is carried out
It is down-sampled, it obtains through bandpass filtering and down-sampled time-domain signal；
4) leaf is carried out through bandpass filtering and down-sampled time-domain signal to what step 3) obtained using stochastic subspace
The analytical calculation of piece intrinsic frequency obtains the Natural Frequency of Blade under current system order；
Wherein, the time-domain signal after bandpass filtering is analyzed using stochastic subspace, obtains current system rank
Intrinsic frequency calculated result under secondary, intrinsic frequency retain three decimals, are actuated to random wind loads suffered by blade of wind-driven generator,
Input stimulus can not be surveyed, therefore traditional experiment modal analysis method herein and is not suitable for, and is needed using operational modal analysis method
It is analyzed, and belongs to a kind of operational modal analysis method of time domain from Subspace algorithm at random, this is using stochastic subspace
The theoretical foundation of algorithm identification Natural Frequency of Blade.Stochastic subspace needs the line number to Hankel matrix, system order
It is set.Wherein, Hankel matrix line number is set as being more than required obtained intrinsic frequency quantity；System order is in step
It is rapid when 4) being calculated for the first time, it is set as 2, system includes conjugate solution, therefore systematic education need to be set as even number.
5) judge current system system order whether be more than setting maximum system order, when current system system order be less than set
When fixed maximum system order, then add 2 to current system order, then go to step 4), when system order is more than or equal to setting
Maximum system order when, then go to step 6)；
6) all blade fixed frequencies being calculated under primal system order to maximum system order are mentioned automatically
It takes, to obtain the true fixed frequency of blade；
The frequency response range of double-shaft acceleration sensor in step 1) covers 1-10Hz, and double-shaft acceleration sensor
Sensitivity be more than or equal to 500mv/g, double-shaft acceleration sensor be installed on blade interior and close to blade tip position, with guarantee
Acquisition blade vibration has higher signal-to-noise ratio, wherein it is blade inlet edge to the direction of rear that blade, which waves direction, and blade is shimmy
Direction is direction in blade face.
Bandpass filtering is carried out to the signal to be analyzed that step 2) obtains by bandpass filter in step 3), wherein band logical
The low cutoff frequency of filter is 1Hz, and the higher cutoff frequency of bandpass filter is 10Hz, to filter out blade rotation bring harmonic wave
Frequency filters out 10Hz or more high-frequency noise information, guarantees that signal has compared with high s/n ratio, down-sampled to help to reduce analysis data
Amount improves analysis efficiency, and the down-sampled rate of the signal after bandpass filtering need to meet nyquist sampling theorem, guarantees that signal does not lose
Very, it is conducive to subsequent analysis.
The concrete operations of step 6) are：
Ascending order row 1a) is carried out to all blade fixed frequencies being calculated under primal system order to maximum system order
Column, obtain intrinsic frequency sequence F=[f1,f2,f3,f4,L,fn]；
First-order difference operation 2a) is carried out to intrinsic frequency sequence, then chooses calculus of differences result diff_F=[f2-f1,f3-
f2,f4-f3,L,fn-fn-1] in it is all be greater than 0.1 the corresponding position number of value, then will be intrinsic according to the position number of selection
Frequency sequence is divided, and several intrinsic frequency clusters are obtained；
The intrinsic frequency mean value and intrinsic frequency sum for 3a) calculating each intrinsic frequency cluster, judge consolidating for each intrinsic frequency cluster
Have total number of frequencies whether be more than maximum system order a quarter, when the intrinsic frequency sum of each intrinsic frequency cluster is more than or equal to
When a quarter of maximum system order, then the intrinsic frequency mean value of the intrinsic frequency cluster is saved, when consolidating for each intrinsic frequency cluster
When a quarter for thering is total number of frequencies to be less than maximum system order, then the intrinsic frequency mean value of the intrinsic frequency cluster is deleted, will be protected
It deposits to obtain true fixed frequency of the intrinsic frequency mean value as blade of each intrinsic frequency cluster.
Claims (5)
1. a kind of blade of wind-driven generator intrinsic frequency online recognition method, which is characterized in that include the following steps：
1) the blade vibration signal that blade waves direction and edgewise direction is acquired and stored using double-shaft acceleration sensor；
2) the sum of the virtual value that blade waves the blade vibration signal of direction and edgewise direction is calculated, when the virtual value being calculated
The sum of be more than or equal to virtual value threshold value when, then select blade to wave the blade vibration signal in direction as signal to be analyzed；Work as meter
When the sum of obtained virtual value is less than virtual value threshold value, then select the blade vibration signal of blade edgewise direction as to be analyzed
Signal；
3) bandpass filtering is carried out to the signal to be analyzed that step 2) obtains, then drop is carried out to the signal to be analyzed after bandpass filtering and is adopted
Sample is obtained through bandpass filtering and down-sampled time-domain signal；
4) step 3) is obtained using stochastic subspace solid through bandpass filtering and down-sampled time-domain signal progress blade
There is the analytical calculation of frequency, obtains the Natural Frequency of Blade under current system order；
5) judge current system system order whether be more than setting maximum system order, when current system system order be less than setting
When maximum system order, then add 2 to current system order, then go to step 4), when system order is more than or equal to setting most
When big system order, then step 6) is gone to；
6) all blade fixed frequencies being calculated under primal system order to maximum system order are automatically extracted, with
Obtain the true fixed frequency of blade.
2. blade of wind-driven generator intrinsic frequency online recognition method according to claim 1, which is characterized in that step 1)
In double-shaft acceleration sensor frequency response range cover 1-10Hz, and the sensitivity of double-shaft acceleration sensor be greater than etc.
In 500mv/g, double-shaft acceleration sensor is installed on blade interior and the position close to blade tip.
3. blade of wind-driven generator intrinsic frequency online recognition method according to claim 1, which is characterized in that step 3)
In step 2) is obtained by bandpass filter signal to be analyzed carry out bandpass filtering, wherein the low cut-off of bandpass filter
Frequency is 1Hz, and the higher cutoff frequency of bandpass filter is 10Hz.
4. blade of wind-driven generator intrinsic frequency online recognition method according to claim 1, which is characterized in that step 6)
Concrete operations be：
Ascending order arrangement 1a) is carried out to all blade fixed frequencies being calculated under primal system order to maximum system order,
Obtain intrinsic frequency sequence；
First-order difference operation 2a) is carried out to intrinsic frequency sequence, then choose in calculus of differences result it is all be greater than 0.1 value it is corresponding
Position number, then intrinsic frequency sequence is divided according to the position number of selection, obtains several intrinsic frequency clusters；
The intrinsic frequency mean value and intrinsic frequency sum for 3a) calculating each intrinsic frequency cluster, judge the intrinsic frequency of each intrinsic frequency cluster
Rate sum whether be more than maximum system order a quarter, when each intrinsic frequency cluster intrinsic frequency sum be more than or equal to maximum
When a quarter of system order, then the intrinsic frequency mean value of the intrinsic frequency cluster is saved, when the intrinsic frequency of each intrinsic frequency cluster
When rate sum is less than a quarter of maximum system order, then the intrinsic frequency mean value of the intrinsic frequency cluster is deleted, will be saved
To true fixed frequency of the intrinsic frequency mean value as blade of each intrinsic frequency cluster.
5. blade of wind-driven generator intrinsic frequency online recognition method according to claim 4, which is characterized in that step
Intrinsic frequency sequence F=[f in 2a)1,f2,f3,f4,L,fn] first-order difference operation is carried out, obtain the result of first-order difference operation
Diff_F=[f2-f1,f3-f2,f4-f3,L,fn-fn-1]。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810623339.5A CN108827458A (en) | 2018-06-15 | 2018-06-15 | A kind of blade of wind-driven generator intrinsic frequency online recognition method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810623339.5A CN108827458A (en) | 2018-06-15 | 2018-06-15 | A kind of blade of wind-driven generator intrinsic frequency online recognition method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108827458A true CN108827458A (en) | 2018-11-16 |
Family
ID=64142170
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810623339.5A Pending CN108827458A (en) | 2018-06-15 | 2018-06-15 | A kind of blade of wind-driven generator intrinsic frequency online recognition method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108827458A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110346035A (en) * | 2019-06-28 | 2019-10-18 | 中铁大桥科学研究院有限公司 | Bridge real-time frequency test method and system |
| CN110486236A (en) * | 2019-08-08 | 2019-11-22 | 北京汉能华科技股份有限公司 | A kind of fault detection method and system of wind-driven generator |
| CN110553716A (en) * | 2019-10-12 | 2019-12-10 | 西安交通大学 | method for measuring vibration natural frequency of looped blade structure based on computer vision |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09179580A (en) * | 1995-12-27 | 1997-07-11 | Oki Electric Ind Co Ltd | Learning method for hidden markov model |
| CN106198020A (en) * | 2016-07-07 | 2016-12-07 | 华北电力大学（保定） | Wind turbines bearing failure diagnosis method based on subspace and fuzzy C-means clustering |
| CN107609291A (en) * | 2017-09-22 | 2018-01-19 | 哈尔滨工业大学 | A kind of false mode elimination method based on Density Clustering |
| CN107729592A (en) * | 2017-08-14 | 2018-02-23 | 西安理工大学 | Traced back the Time variable structure Modal Parameters Identification of track based on broad sense subspace |
| CN107782443A (en) * | 2017-10-25 | 2018-03-09 | 西安锐益达风电技术有限公司 | A kind of blade of wind-driven generator intrinsic frequency extraction method |
-
2018
- 2018-06-15 CN CN201810623339.5A patent/CN108827458A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09179580A (en) * | 1995-12-27 | 1997-07-11 | Oki Electric Ind Co Ltd | Learning method for hidden markov model |
| CN106198020A (en) * | 2016-07-07 | 2016-12-07 | 华北电力大学（保定） | Wind turbines bearing failure diagnosis method based on subspace and fuzzy C-means clustering |
| CN107729592A (en) * | 2017-08-14 | 2018-02-23 | 西安理工大学 | Traced back the Time variable structure Modal Parameters Identification of track based on broad sense subspace |
| CN107609291A (en) * | 2017-09-22 | 2018-01-19 | 哈尔滨工业大学 | A kind of false mode elimination method based on Density Clustering |
| CN107782443A (en) * | 2017-10-25 | 2018-03-09 | 西安锐益达风电技术有限公司 | A kind of blade of wind-driven generator intrinsic frequency extraction method |
Non-Patent Citations (1)
| Title |
|---|
| 张永祥 等: "基于随机子空间法的模态参数自动提取", 《机械工程学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110346035A (en) * | 2019-06-28 | 2019-10-18 | 中铁大桥科学研究院有限公司 | Bridge real-time frequency test method and system |
| CN110486236A (en) * | 2019-08-08 | 2019-11-22 | 北京汉能华科技股份有限公司 | A kind of fault detection method and system of wind-driven generator |
| CN110553716A (en) * | 2019-10-12 | 2019-12-10 | 西安交通大学 | method for measuring vibration natural frequency of looped blade structure based on computer vision |
| CN110553716B (en) * | 2019-10-12 | 2020-07-10 | 西安交通大学 | Method for measuring vibration natural frequency of looped blade structure based on computer vision |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101782475B (en) | Blade fault diagnosing method based on vibration of wind generating set | |
| CN102588210B (en) | Filtering method for preprocessing fitting data of power curve | |
| CN104677623B (en) | A kind of blade of wind-driven generator fault acoustics in place diagnostic method and monitoring system | |
| CN108827458A (en) | A kind of blade of wind-driven generator intrinsic frequency online recognition method | |
| CN104929864A (en) | Field programmable gate array (FPGA)-based embedded type operating state monitoring and fault diagnosis system for wind generating set | |
| CN110905732B (en) | Method and system for identifying unbalance of wind wheel of wind turbine generator and storage medium | |
| CN104075795A (en) | Method and system for monitoring vibration state of impeller of wind generating set | |
| CN105508149B (en) | Fault detection method and device for wind generating set | |
| CN107782443B (en) | Automatic extraction method for natural frequency of wind driven generator blade | |
| CN106762452B (en) | Fan master control system fault diagnosis and on-line monitoring method based on data-driven | |
| CN101949774A (en) | Method for monitoring remote real-time state of wind generating multi-set | |
| CN109000921A (en) | A kind of diagnostic method of wind generator set main shaft failure | |
| Joshuva et al. | Wind turbine blade fault diagnosis using vibration signals through decision tree algorithm | |
| CN104564542A (en) | Fault diagnosis system and fault diagnosis method based on massive data technology | |
| Ferguson et al. | Big data techniques for wind turbine condition monitoring | |
| CN110332080B (en) | Fan blade health real-time monitoring method based on resonance response | |
| CN116085212B (en) | Method and system for monitoring running state of new energy wind turbine generator in real time | |
| KR101468627B1 (en) | Wind Turbine Data Classification System | |
| Qiao et al. | Research on SCADA data preprocessing method of Wind Turbine | |
| Joshuva et al. | Logistic model tree classifier for condition monitoring of wind turbine blades | |
| CN111400959B (en) | Blade fault diagnosis method and device for wind generating set | |
| Su et al. | Lifetime evaluation and extension of wind turbines based on big data | |
| CN115711206B (en) | Wind driven generator blade icing state monitoring system based on clustering weight | |
| Joshuva et al. | Implementing rotation forest for wind turbine blade fault diagnosis | |
| Xiao et al. | Long-term wind power forecasting model by multi meteorological variables based on data compensation |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20181116 |