CN108508358A - A kind of online Wind turbines dual signal trouble-shooter and diagnostic method - Google Patents
A kind of online Wind turbines dual signal trouble-shooter and diagnostic method Download PDFInfo
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- CN108508358A CN108508358A CN201710116522.1A CN201710116522A CN108508358A CN 108508358 A CN108508358 A CN 108508358A CN 201710116522 A CN201710116522 A CN 201710116522A CN 108508358 A CN108508358 A CN 108508358A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
- G01R31/343—Testing dynamo-electric machines in operation
Abstract
The present invention provides a kind of online Wind turbines dual signal trouble-shooter and diagnostic method, and the vibration data of acquisition is uploaded to data processing A modules by vibrating data collection module for acquiring the vibration monitoring sensing data being arranged on wind turbine difference component；Data processing A modules calculate the average value of the frequency spectrum and characteristic value that include in the first weekly data, for establishing java standard library A module alarm mechanism threshold values；The vibration data of acquisition is uploaded to data processing B modules by oil product data acquisition module for acquiring the oil of supervising and control sensing data being arranged on wind turbine difference component；Data processing B modules calculate conductance change rate, change in dielectric constant rate and the rate of temperature change for including in the first weekly data, for establishing java standard library B module alarm mechanism threshold values；Alarm mechanism module is for formulating alarm threshold logic；Identification module of alarming judges fault alarm；While ensuring accuracy, the timeliness of fault diagnosis of wind turbines is improved.
Description
Technical field
The present invention relates to vibration of wind generating set on-line monitoring technique fields, and in particular to a kind of online Wind turbines
Dual signal trouble-shooter and diagnostic method.
Background technology
With the continuous improvement of wind-power electricity generation control technology, the single-machine capacity of Wind turbines is increasing, relevant third
Industry, that is, running of wind generating set maintenance, monitoring, fault diagnosis etc. has become industry new growth point.The building ring of Wind turbines
Border is severe, and wind speed has unstability, and under the action of cycling alternating load, the components such as transmission system of unit are easiest to damage, and
Wind turbines are in turn mounted at remote districts and, inconvenient maintenance very high away from ground, and the condition monitoring and fault diagnosis of Wind turbines exists
In this case have great importance.
The main function of Wind turbines oil liquid monitoring equipment is by monitoring oil product situation, extending equipment oil draining period or just
Really selection lubricant, to improve the service life of unit.After Wind turbines bearing breaks down, significant portion can be in fluid
It shows.But oil liquid monitoring equipment cannot distinguish between fault type, field maintenance person cannot be instructed to debug.
Current fan vibration on-line monitoring equipment is sampled by the vibrating sensor at Wind turbines measuring point
Data analyze the historical data of real time data and storage, to judge Wind turbines fault type.The prior art is finding that data are different
Chang Yihou, it usually needs shut down check, confirm the authenticity of sampled data, exclude vibrating sensor installation question and monitoring device
The information such as electric fault after, the operating condition of unit could be done and be judged.How while ensuring accuracy, wind-powered electricity generation is improved
The timeliness of unit fault diagnosis, instructs field maintenance person to exclude fan trouble in time, becomes current urgent problem to be solved.
Invention content
The object of the present invention is to provide one kind while ensuring accuracy, improves the timeliness of fault diagnosis of wind turbines
A kind of online Wind turbines dual signal trouble-shooter and diagnostic method.
In order to solve the problems existing in background technology, the present invention adopts the following technical solutions：A kind of online wind-powered electricity generation
Unit dual signal trouble-shooter and diagnostic method, trouble-shooter include：Vibrating data collection module, data processing A
Module, oil product data acquisition module, data processing B modules, java standard library A modules, java standard library B modules, alarm mechanism module, alarm
Identification module and fault alarm module；Wherein,
The vibrating data collection module connects data processing A modules, and data processing A modules connect java standard library A modules；
The oil product data acquisition module connects data processing B modules, and data processing B modules connect java standard library B modules；
The java standard library A modules, java standard library B modules are connected to alarm mechanism module；
Alarm mechanism module, data processing A modules and the data processing B modules is connected to alarm identification module；
The alarm identification module is connected with fault alarm module.
The method for diagnosing faults of the present invention comprises the following steps：
1, vibrating data collection module is used to acquire the vibration monitoring sensing data being arranged on wind turbine difference component, and
The vibration data of acquisition is uploaded into data processing A modules；Data processing A modules calculate the first weekly data in include frequency spectrum and
The average value of characteristic value, for establishing java standard library A module alarm mechanism threshold values；
2, oil product data acquisition module is used to acquire the oil of supervising and control sensing data being arranged on wind turbine difference component, and
The vibration data of acquisition is uploaded into data processing B modules；Data processing B modules calculate the conductance for including in the first weekly data and become
Rate, change in dielectric constant rate and rate of temperature change, for establishing java standard library B module alarm mechanism threshold values.
3, alarm mechanism module is for formulating alarm threshold logic, alarm identification module by comparing signal calculated value with
The relationship of threshold value, judgement fault alarm sends out fault alarm information if meeting alarming logic, and judges next group of number
According to；It does not meet such as, then judges next group of data.
As a further improvement on the present invention；The vibration monitoring sensing data and oil of supervising and control sensing data be
Synchronous acquisition, sample frequency, sampling duration and acquisition interval can be adjusted according to present situation, acquire 1 at regular intervals
Group.
As a further improvement on the present invention；The sample frequency, sampling duration and acquisition interval, default setting be,
Sample frequency 16384Hz samples duration 120s, and one group was acquired every 2 hours.
As a further improvement on the present invention；The oil of supervising and control sensing data, is identified using change rate.
As a further improvement on the present invention；The virtual value of the fan vibration signal is in VDI3834 critical fields.
After adopting the above technical scheme, the invention has the advantages that：
By two kinds of distinct devices to the malfunction monitoring at the same position of Wind turbines, the confidence level in single signal source is reduced
Risk improves the timeliness of fault diagnosis of wind turbines while ensuring accuracy.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
Obtain other attached drawings according to these attached drawings.
Fig. 1 is the structural schematic diagram of embodiment provided by the present invention；
Reference numeral：
101-vibrating data collection modules；102-data processing A modules；103-oil product data acquisition modules；104—
Data processing B modules；105-java standard library A modules；106-java standard library B modules；107-alarm mechanism modules；108-alarms are known
Other module；109-fault alarm modules.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, right below in conjunction with specific implementation mode
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are only to explain the present invention, and
It is not used in the restriction present invention.
Referring to Fig. 1, present embodiment uses following technical scheme：A kind of online Wind turbines dual signal failure
Diagnostic device and diagnostic method, trouble-shooter include：Vibrating data collection module 101, data processing A modules 102, oil product
Data acquisition module 103, data processing B modules 104, java standard library A modules 105, java standard library B modules 106, alarm mechanism module
107, alarm identification module 108 and fault alarm module 109；Wherein,
The vibrating data collection module 101 connects data processing A modules 102, the connection mark of data processing A modules 102
Quasi- library A modules 105；The oil product data acquisition module 103 connects data processing B modules 104, and data processing B modules 104 connect
Connect java standard library B modules 106；The java standard library A modules 105, java standard library B modules 106 are connected to alarm mechanism module 107；
Alarm mechanism module 107, data processing A modules 102 and the data processing B modules 104 is connected to alarm identification module
108；The alarm identification module 108 and fault alarm module 109 connect.
The method for diagnosing faults specifically comprises the following steps：
1, vibrating data collection module 101 is for acquiring the vibration monitoring sensor number being arranged on wind turbine difference component
According to, and the vibration data of acquisition is uploaded into data processing A modules 102；Data processing A modules 102 calculate in the first weekly data
Including frequency spectrum and characteristic value average value, for establishing 105 alarm mechanism threshold value of java standard library A modules；
2, oil product data acquisition module 103 is for acquiring the oil of supervising and control sensor number being arranged on wind turbine difference component
According to, and the vibration data of acquisition is uploaded into data processing B modules 104；Data processing B modules 104 calculate in the first weekly data
Including conductance change rate, change in dielectric constant rate and rate of temperature change, for establishing 106 alarm mechanism door of java standard library B modules
Limit value.
3, alarm mechanism module 107 is for formulating alarm door limit value, such as according to early warning setting standard formulation alarm door limit value
Under：
Group 1：Oil product data conductance changes the 120% of rate score as warning threshold value, and 150% is alarm door limit value.
Group 2：The 120% of oil product data dielectric constant numerical value change rate is warning threshold value, and 150% is alarm door limit value.
Group 3：The 120% of oil product data temperature numerical value change rate is warning threshold value, and 150% is alarm door limit value.
Group 4：The 120% of vibration data virtual value is warning threshold value, and 150% is alarm door limit value.
Group 5：The 120% of vibration data kurtosis value is warning threshold value, and 150% is alarm door limit value.
Group 6：Failure characteristic frequency is alarm door limit value in vibration data frequency spectrum.
Identification module 108 alarm by comparing the calculated value of signal and the relationship of threshold value, judges that fault alarm, alarm are set
It is as follows to set logic：
It is logical AND between group 1, group 2 and group 3；
Group 4, group 5 and group 6 between be logic or；
It is logical AND between oil product data and vibration data；
The logical AND of alarm and warning is set as alerting.
If meeting alarming logic, next group of data are judged, be concurrently out of order warning message, does not meet such as, then judges
Next group of data.
In time domain scale, the characteristic value of vibration signal waveforms x (t) can substantially react unit failure.Characteristic value itself
It is the limitation reference of the fault pre-alarming of wind power generating set on-line monitoring.
Characteristic value Computing Principle of the present invention：
Virtual value：
Kurtosis value：
In formula, T is the time cycle, and x (t) is vibration signal.
The vibration of rolling bearing, which is the dynamic excitation signal transmitted by the structure, working condition and bearing block of each section, to be had
It closes, major frequency components are the characteristic frequency of rolling bearing.
Fault characteristic frequency Computing Principle of the present invention：
Speed：fi=N/60；
Retainer frequency：
Speed：
Inner ring passes through frequency：
Outer shroud passes through frequency：
In formula, d is ball diameter, and a is contact angle, and z is ball quantity, and N is the rotating speed (r/min) of axis, and D is bearing section
Diameter.
The change in dielectric constant rate of fluid is to reflect a comprehensive parameters of lubricating oil pollution, influences fluid dielectric constant
The index of change rate has water content, acid value and metal worn particle.By the variation for monitoring fluid change in dielectric constant rate, so that it may learn
The degradation of lubricating oil, to achieve the purpose that monitor used-oil.Change in dielectric constant rate of the present invention calculates former
Reason：
Dielectric constant formula：
ε=ε0·εr=ε0·E0/ E=ε0·E0/(E0-E1-E2)
In formula：ε0For the change in dielectric constant rate (81854 × 10 of vacuum-12F/m)；εrOpposite dielectric for tested oil is normal
Number；E0The field strength generated between pole plate for supply voltage in vacuum；E1The reversed field strength generated for medium dipole；E2It is oily
The reversed field strength that impurity excites in magnetic field；E is formate field intensity (E=E0-E1-E2)。
Change in dielectric constant rate score calculation formula：
Δ ε=εy-εx/y-x
Conductance change rate numerical computational formulas：
Δ λ=λy-λx
Rate of temperature change numerical computational formulas：
Δ T=Ty-Tx
In formula：Δ ε is change in dielectric constant rate score；Δ λ is that conductance changes rate score；Δ T is temperature change rate score；
εyFor the dielectric constant calculated value at y moment；λyFor the conductivity measurements at y moment；TyFor the measured temperature at y moment；εxFor x when
The dielectric constant calculated value at quarter；λxFor the conductivity measurements at x moment；TxFor the measured temperature at x moment；.Y moment and x moment phases
A poor oil circulation period.
Fluid is aoxidized, and molecular polarity can change, i.e. E1It will increase.Lubricating oil is contaminated, degree of purity meeting
It changes, such as oil water inlet will produce H+、OH-Plasma, organic acid will produce H+、RCOO-Plasma, metallic particles meeting
E can all be made by generating free electron etc.2It significantly increases, to make formate field intensity E reduce, keeps the change in dielectric constant rate of oil notable
Ground increases.By fresh oil to same model in the comprehensive pollution journey for determining fluid with the comparison of oily change in dielectric constant rate
Degree, and according to the anomalous variation precognition equipment fault of test data equipment can be protected to take measures early.
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case of without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Profit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent requirements of the claims
Variation is included within the present invention.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped
Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should
It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art
The other embodiment being appreciated that.
Claims (5)
1. a kind of online Wind turbines dual signal trouble-shooter, which is characterized in that it includes：Vibrating data collection mould
Block, data processing A modules, oil product data acquisition module, data processing B modules, java standard library A modules, java standard library B modules, alarm
Mechanism module, alarm identification module and fault alarm module；Wherein,
The vibrating data collection module connects data processing A modules, and data processing A modules connect java standard library A modules；
The oil product data acquisition module connects data processing B modules, and data processing B modules connect java standard library B modules；
The java standard library A modules, java standard library B modules are connected to alarm mechanism module；
Alarm mechanism module, data processing A modules and the data processing B modules is connected to alarm identification module；
The alarm identification module is connected with fault alarm module.
2. a kind of online Wind turbines dual signal method for diagnosing faults, which is characterized in that it is comprised the following steps：
(1), vibrating data collection module is used to acquire the vibration monitoring sensing data being arranged on wind turbine difference component, and will
The vibration data of acquisition uploads to data processing A modules；Data processing A modules calculate the frequency spectrum for including in the first weekly data and spy
The average value of value indicative, for establishing java standard library A module alarm mechanism threshold values；
(2), oil product data acquisition module is used to acquire the oil of supervising and control sensing data being arranged on wind turbine difference component, and will
The vibration data of acquisition uploads to data processing B modules；Data processing B modules calculate the conductance variation for including in the first weekly data
Rate, change in dielectric constant rate and rate of temperature change, for establishing java standard library B module alarm mechanism threshold values.
(3), alarm mechanism module is for formulating alarm threshold logic, alarm identification module by comparing signal calculated value and door
The relationship of limit value, judgement fault alarm sends out fault alarm information if meeting alarming logic, and judges next group of data；
It does not meet such as, then judges next group of data.
3. a kind of online Wind turbines dual signal method for diagnosing faults according to claim 2, which is characterized in that described
Vibration monitoring sensing data and oil of supervising and control sensing data be synchronous acquisition, sample frequency, sampling duration and acquisition between
Every can be adjusted according to present situation, 1 group is acquired at regular intervals.
4. a kind of online Wind turbines dual signal method for diagnosing faults according to claim 3, which is characterized in that described
Sample frequency, sampling duration and acquisition interval, default setting be sample frequency 16384Hz, sampling duration 120s is small every 2
When acquire one group.
5. a kind of online Wind turbines dual signal method for diagnosing faults according to claim 2, which is characterized in that described
Oil of supervising and control sensing data, identified using change rate.
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Cited By (4)
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| CN109570137A (en) * | 2019-01-18 | 2019-04-05 | 西南交通大学 | A kind of ultrasound wave descaling device with self-diagnostic function |
| CN110173453A (en) * | 2019-04-04 | 2019-08-27 | 上海发电设备成套设计研究院有限责任公司 | A kind of online assessment method of power plant pressure fan state |
| CN110426583A (en) * | 2019-08-13 | 2019-11-08 | 南京东博智慧能源研究院有限公司 | A kind of data adaptive acquisition method of intelligent socket |
| CN111007827A (en) * | 2020-03-11 | 2020-04-14 | 天津美腾科技股份有限公司 | Alarm method of equipment, equipment and computer readable storage medium |
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| CB03 | Change of inventor or designer information |
Inventor after: Li Bin Inventor after: Shi Guichang Inventor after: Hu Yanan Inventor after: Li Lei Inventor before: Shi Guichang Inventor before: Li Bin Inventor before: Hu Yanan Inventor before: Li Lei |
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Application publication date: 20180907 |