CN106441547A - Transformer vibration monitoring method and apparatus - Google Patents
Transformer vibration monitoring method and apparatus Download PDFInfo
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- CN106441547A CN106441547A CN201610794009.3A CN201610794009A CN106441547A CN 106441547 A CN106441547 A CN 106441547A CN 201610794009 A CN201610794009 A CN 201610794009A CN 106441547 A CN106441547 A CN 106441547A
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- transformator
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- 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
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Abstract
The invention discloses a transformer vibration monitoring method and apparatus, belonging to the technical field of high voltage equipment on-line monitoring. The transformer vibration monitoring method comprises the steps: preprocessing a transformer vibration signal to be measured, and obtaining a time domain waveform; performing time-frequency conversion on the time domain waveform, and obtaining an instantaneous frequency characteristic waveform and a three dimensional time-frequency-amplitude spectrogram; according to the instantaneous frequency waveform and the time-frequency-amplitude spectrogram, extracting the characteristic quantity which is the amplitude characteristic in various frequency; and performing correlation calculation on the characteristic quantity of the transformer vibration data and the characteristic quantity of each fault type in a sample database one by one, and according to the magnitude of the correlation coefficient obtained through calculation, identifying the transformer fault type. The transformer vibration monitoring method and apparatus can realize direct detection and fault determination of a transformer through vibration signals.
Description
Technical field
The invention belongs to high pressure equipment on-line monitoring technique field is and in particular to a kind of transformator vibration monitoring method and dress
Put.
Background technology
Transformator once breaks down as equipment most important in power system and will directly affect the stable operation of electrical network.
Transformer core and winding are the critical pieces breaking down, when winding and iron core deform or loosen, magnetostriction and
Vibration between core lamination stack that leakage field causes and winding is the main cause causing transformer-cabinet vibration aggravation.Meanwhile,
Gradually form with China's alternating current-direct current mixing Transmission Mode, the problem that ac and dc systemses interfere also occurs therewith.
Vibration monitoring method sensitivity height is no electrically connected with power system, power transformer in being run by real-time monitoring
Vibration signal simultaneously carries out data processing, analyzing and diagnosing, obtains compression situation, displacement and the deformation of inside transformer iron core and winding
State etc., stops the generation of transformator latent fault, is Condition Assessment for Power Transformer and repair based on condition of component offer important evidence, right
Actively promote the construction of intelligent substation, improve high pressure equipment intelligence O＆M level, there is great actual application value.
Content of the invention
The present invention provides a kind of transformator vibration monitoring method and device, to realize by vibration signal, transformator being carried out
Directly detection and breakdown judge.
For solving above-mentioned technical problem, the present invention provides a kind of transformator vibration monitoring method, comprises the steps：
1) gather transformator vibration signal to be measured；
2) vibration signal to be measured for transformator is carried out pretreatment, obtain time domain waveform；
3) when time domain waveform being carried out-frequency conversion, obtain the temporal frequency characteristics waveform of transformator vibration signal to be measured with
And three-dimensional time-frequency-amplitude spectrum；
4) according to instantaneous frequency waveform and T/F-amplitude spectrum, extract characteristic quantity, characteristic quantity is various frequencies
Under amplitude characteristic；
5) characteristic quantity of the every kind of fault type in the characteristic quantity of transformator vibration data and Sample Storehouse is carried out phase one by one
Closing property calculates, and according to the size of calculated correlation coefficient, identifies transformer fault type.
Further, described characteristic quantity also includes peak-to-peak value and the virtual value of time domain waveform extraction.
Further, also include conditioned for the transformer vibration signal of collection, amplification, A/D and opto-electronic conversion, with FT3
Form exports the vibration time domain waveform of measured signal, and carries out noise reduction process, filter effect to time domain waveform using filtering algorithm
Using root-mean-square error：
To weigh, d is the smaller the better.
Further, described correlation calculations are Pearson correlation calculations method：
X is the corresponding characteristic vector of transformator data to be identified, and Y is the characteristic vector of a certain fault type in Sample Storehouse,
ρX,YFor correlation coefficient；ρX,YAbsolute value bigger, dependency is stronger.
Further, in the characteristic quantity according to T/F-amplitude spectrum extraction, special including the amplitude less than 1000Hz
Property and more than 1000Hz amplitude characteristic, the amplitude characteristic of 1000Hz frequencies above is used for the judgement to D.C. magnetic biasing fault.
The present invention also provides a kind of transformator vibration monitoring device, including such as lower module：
1) it is used for gathering the module of transformator vibration signal to be measured；
2) it is used for for vibration signal to be measured for transformator carrying out pretreatment, obtain the module of time domain waveform；
3) when being used for carrying out time domain waveform-frequency conversion, obtain the temporal frequency characteristics ripple of transformator vibration signal to be measured
Shape and the module of three-dimensional time-frequency-amplitude spectrum；
4) it is used for according to instantaneous frequency waveform and T/F-amplitude spectrum, extracts characteristic quantity, characteristic quantity is various
The module of the amplitude characteristic under frequency；
5) it is used for entering the characteristic quantity of the every kind of fault type in the characteristic quantity of transformator vibration data and Sample Storehouse one by one
Row correlation calculations, according to the size of calculated correlation coefficient, identify the module of transformer fault type.
Further, described characteristic quantity also includes peak-to-peak value and the virtual value of time domain waveform extraction.
Further, also include for will collection transformer vibration signal conditioned, amplify, A/D and opto-electronic conversion, with
The vibration time domain waveform of FT3 form output measured signal, and time domain waveform is carried out using filtering algorithm with the module of noise reduction process,
Filter effect adopts root-mean-square error：
To weigh, d is the smaller the better.
Further, described correlation calculations are Pearson correlation calculations method：
X is the corresponding characteristic vector of transformator data to be identified, and Y is the characteristic vector of a certain fault type in Sample Storehouse,
ρX,YFor correlation coefficient；ρX,YAbsolute value bigger, dependency is stronger.
Further, in the characteristic quantity according to T/F-amplitude spectrum extraction, special including the amplitude less than 1000Hz
Property and more than 1000Hz amplitude characteristic, the amplitude characteristic of 1000Hz frequencies above is used for the judgement to D.C. magnetic biasing fault.
The invention has the beneficial effects as follows：To transformator, vibration signal to be measured carries out pretreatment, obtains the time domain of vibration signal
Waveform, temporal frequency characteristics waveform and three-dimensional time-frequency-amplitude spectrum, extract characteristic quantity, including vibration letter under time domain waveform
Number peak-to-peak value and virtual value, and T/F-amplitude spectrum under transformator vibration data various frequencies under amplitude special
Property characteristic quantity, the characteristic quantity of the every kind of fault type in characteristic quantity and Sample Storehouse is carried out correlation calculations one by one, Neng Gouzhun
Really identify transformer fault type.Based on the method or device, realize by vibration signal, transformator directly being examined
Survey and breakdown judge, improve the recognition efficiency of transformer fault.
Brief description
Fig. 1 is transformator vibration monitoring method flow diagram.
Specific embodiment
Below in conjunction with the accompanying drawings, the present invention is described in further detail.
It is illustrated in figure 1 transformator vibration monitoring method flow diagram.
1) after transformator puts into operation, gather vibration signal under actual loading operating condition for the transformator.
2) transformer vibration signal will be gathered through signal condition, amplification, A/D and opto-electronic conversion, be exported with FT3 form original
And the vibration time domain waveform of measured signal.Noise reduction process is carried out to vibration data time domain waveform, filters random noise and spike arteries and veins
Punching interference.It is for instance possible to use the linear structure element that length is 7 carries out shape filtering (can certainly be filtered using other
Method), filter effect root-mean-square error：
To weigh, d is the smaller the better, the threshold value of d can be set as needed.If error is larger, structural element can be adjusted
Length is carried out preferably.
3) eigenvalue of time domain waveform is obtained according to the time domain beamformer after filtering noise reduction, including peak-to-peak value and virtual value,
As comparison feature input quantity.
4) time domain waveform treating survey vibration signal is normalized, when time-domain signal is carried out-frequency conversion, and obtain
The temporal frequency characteristics waveform of vibration signal to be measured and three-dimensional time-frequency-amplitude spectrum.
5) according to instantaneous frequency spectrogram and T/F-amplitude spectrum, extract the characteristic quantity of transformator, characteristic quantity is
Amplitude characteristic under various frequencies.
In the present embodiment, sample frequency is 10kHz, sample-duration 1s, and sampling time interval is 10s.Count each
The vibration data of the every 1s of passage, every passage is 10000 data, and data processing unit is to generation 4*10000 after Filtering Processing
Two-dimensional array, this array comprises channel number and sampling instant.The data of four passages is normalized, when will be identical
The sampled data of sequence is added to a passage, carries out equalization process, generates an one-dimension array having 10000 data.
Time-frequency convert is carried out to above-mentioned timing sequence vibration signal, obtain the temporal frequency characteristics of vibration signal and three-dimensional when
M- frequency-amplitude spectrum.For instantaneous frequency spectrogram and T/F-amplitude spectrum, extract transformator vibration data
23 characteristic quantities are as shown in table 1；
Table 1
6) characteristic quantity of the every kind of fault type in the characteristic quantity of transformator vibration data and Sample Storehouse is carried out one by one
Pearson correlation calculations：
X is the corresponding characteristic vector of transformator data to be identified, and Y is the characteristic vector of a certain fault type in Sample Storehouse,
ρX,YFor correlation coefficient；ρX,YAbsolute value bigger, dependency is stronger.According to the size of calculated correlation coefficient, identify
Transformer fault type.As other embodiment, except above-mentioned Pearson correlation calculations method, it would however also be possible to employ its
His formula of checking degree of correlation or algorithm.
In the present embodiment, characteristic quantity includes two kinds, and a kind of is the peak-to-peak value and virtual value that time domain waveform is extracted, in table 1
In be presented as the characteristic quantity of serial number 1 and 2；Another kind is the feature of the amplitude characteristic under the various frequencies that frequency-domain waveform extracts
Amount, is presented as the characteristic quantity of sequence number 3-23 in Table 1.As other embodiment, can only by frequency-domain waveform extract each
The calculating to carry out dependency for the characteristic quantity of the amplitude characteristic under kind frequency, and then identify fault type.
In the present embodiment, according to three-dimensional time-frequency-amplitude spectrum, extract the characteristic quantity of transformator, be various frequency
Under amplitude characteristic.Characteristic quantity includes the amplitude characteristic of amplitude characteristic less than 1000Hz and more than 1000Hz, wherein, 1000Hz
The amplitude characteristic of frequencies above can not judge to transformator mechanical breakdown, can be used for the judgement to D.C. magnetic biasing fault.
In the present embodiment, when transformator initially puts into operation, the reference oscillation signal of collection transformator, i.e. transformator
Vibration signal under zero load, nominal load operating mode；Then by lower for transformator reference signal time domain waveform calculated peak
Peak value and virtual value are stored in Sample Storehouse as the comparison basis of normal condition.In addition, various events should also be included in Sample Storehouse
The barrier corresponding sample of type is as the comparison basis of malfunction, such as core slackness, winding looseness fault difference corresponding three
Dimension T/F-amplitude situation, these fault samples can be obtained by related experiment or historical data, the ratio of normal condition
To benchmark except being obtained it is also possible to be obtained by historical data by above-mentioned initially putting into operation.
The present invention also provides a kind of transformator vibration monitoring device, including such as lower module：
1) it is used for gathering the module of transformator vibration signal to be measured；
2) it is used for for vibration signal to be measured for transformator carrying out pretreatment, obtain the module of time domain waveform；
3) when being used for carrying out time domain waveform-frequency conversion, obtain the temporal frequency characteristics ripple of transformator vibration signal to be measured
Shape and the module of three-dimensional time-frequency-amplitude spectrum；
4) it is used for according to instantaneous frequency waveform and T/F-amplitude spectrum, extracts characteristic quantity, characteristic quantity is various
The module of the amplitude characteristic under frequency；
5) it is used for entering the characteristic quantity of the every kind of fault type in the characteristic quantity of transformator vibration data and Sample Storehouse one by one
Row correlation calculations, according to the size of calculated correlation coefficient, identify the module of transformer fault type.
Above-mentioned transformator vibration monitoring device, is actually based on a kind of computer solution party of the inventive method flow process
Case, i.e. a kind of software architecture, above-mentioned various modules are each treatment progress corresponding with method flow or program.Due to upper
The introduction sufficiently clear stating method is complete, therefore no longer this device is described in detail.
Claims (10)
1. a kind of transformator vibration monitoring method is it is characterised in that comprise the steps：
1) gather transformator vibration signal to be measured；
2) vibration signal to be measured for transformator is carried out pretreatment, obtain time domain waveform；
3) when time domain waveform being carried out-frequency conversion, obtain the temporal frequency characteristics waveform and three of transformator vibration signal to be measured
Dimension T/F-amplitude spectrum；
4) according to instantaneous frequency waveform and T/F-amplitude spectrum, extract characteristic quantity, characteristic quantity is under various frequencies
Amplitude characteristic；
5) characteristic quantity of the every kind of fault type in the characteristic quantity of transformator vibration data and Sample Storehouse is carried out dependency one by one
Calculate, according to the size of calculated correlation coefficient, identify transformer fault type.
2. transformator vibration monitoring method according to claim 1 is it is characterised in that described characteristic quantity also includes time domain ripple
Peak-to-peak value and virtual value that shape is extracted.
3. transformator vibration monitoring method according to claim 1 and 2 is it is characterised in that also include the transformation of collection
Device vibration signal is conditioned, amplify, A/D and opto-electronic conversion, exports the vibration time domain waveform of measured signal with FT3 form, and right
Time domain waveform carries out noise reduction process using filtering algorithm, and filter effect adopts root-mean-square error：
To weigh, d is the smaller the better.
4. transformator vibration monitoring method according to claim 1 and 2 is it is characterised in that described correlation calculations are
Pearson correlation calculations method：
X is the corresponding characteristic vector of transformator data to be identified, and Y is the characteristic vector of a certain fault type in Sample Storehouse, ρX,YFor
Correlation coefficient；ρX,YAbsolute value bigger, dependency is stronger.
5. transformator vibration monitoring method according to claim 1 and 2 is it is characterised in that according to T/F-amplitude
In the characteristic quantity that spectrogram extracts, including the amplitude characteristic of the amplitude characteristic less than 1000Hz and more than 1000Hz, more than 1000Hz
The amplitude characteristic of frequency is used for the judgement to D.C. magnetic biasing fault.
6. a kind of transformator vibration monitoring device is it is characterised in that include as lower module：
1) it is used for gathering the module of transformator vibration signal to be measured；
2) it is used for for vibration signal to be measured for transformator carrying out pretreatment, obtain the module of time domain waveform；
3) be used for when time domain waveform is carried out-frequency changes, obtain the temporal frequency characteristics waveform of transformator vibration signal to be measured with
And the module of three-dimensional time-frequency-amplitude spectrum；
4) it is used for according to instantaneous frequency waveform and T/F-amplitude spectrum, extracts characteristic quantity, characteristic quantity is various frequencies
Under amplitude characteristic module；
5) it is used for for the characteristic quantity of the every kind of fault type in the characteristic quantity of transformator vibration data and Sample Storehouse carrying out phase one by one
Closing property calculates, and according to the size of calculated correlation coefficient, identifies the module of transformer fault type.
7. transformator vibration monitoring device according to claim 6 is it is characterised in that described characteristic quantity also includes time domain ripple
Peak-to-peak value and virtual value that shape is extracted.
8. the transformator vibration monitoring device according to claim 6 or 7 it is characterised in that also include for by collection
Transformer vibration signal is conditioned, amplify, A/D and opto-electronic conversion, exports the vibration time domain waveform of measured signal with FT3 form,
And time domain waveform is carried out using filtering algorithm with the module of noise reduction process, filter effect adopts root-mean-square error：
To weigh, d is the smaller the better.
9. the transformator vibration monitoring device according to claim 6 or 7 is it is characterised in that described correlation calculations are
Pearson correlation calculations method：
X is the corresponding characteristic vector of transformator data to be identified, and Y is the characteristic vector of a certain fault type in Sample Storehouse, ρX,YFor
Correlation coefficient；ρX,YAbsolute value bigger, dependency is stronger.
10. the transformator vibration monitoring device according to claim 6 or 7 is it is characterised in that according to T/F-amplitude
In the characteristic quantity that spectrogram extracts, including the amplitude characteristic of the amplitude characteristic less than 1000Hz and more than 1000Hz, more than 1000Hz
The amplitude characteristic of frequency is used for the judgement to D.C. magnetic biasing fault.
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Cited By (12)
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|---|---|---|---|---|
| CN106324406A (en) * | 2016-09-21 | 2017-01-11 | 许继集团有限公司 | Transformer direct-current magnetic bias fault diagnosis method and device |
| CN107037298A (en) * | 2017-06-21 | 2017-08-11 | 广州供电局有限公司 | transformer DC bias detection method, device, storage medium and computer equipment |
| CN108254627A (en) * | 2018-01-31 | 2018-07-06 | 杭州圣远医疗科技有限公司 | Electromagnetic field waveform signal acquires control methods and device |
| CN108844612A (en) * | 2018-08-27 | 2018-11-20 | 重庆大学 | A kind of identification method of transformer internal faults based on mathematical statistics probabilistic model |
| CN110320435A (en) * | 2019-07-11 | 2019-10-11 | 广东石油化工学院 | A kind of running state of transformer vibration sound detection signal reconfiguring method and system using data regularization |
| CN110634493A (en) * | 2019-09-09 | 2019-12-31 | 国网湖南省电力有限公司 | Transformer state identification method, system and medium based on voiceprint image characteristics |
| CN111521256A (en) * | 2020-04-13 | 2020-08-11 | 国家电网有限公司 | Main transformer surface vibration visualization detection method based on data mapping |
| CN111537836A (en) * | 2020-05-15 | 2020-08-14 | 国网山东省电力公司济宁供电公司 | Automatic power distribution network fault diagnosis method and system based on wave recording data |
| CN111767675A (en) * | 2020-06-24 | 2020-10-13 | 国家电网有限公司大数据中心 | Transformer vibration fault monitoring method and device, electronic equipment and storage medium |
| CN112945280A (en) * | 2021-02-07 | 2021-06-11 | 苏州森斯微电子技术有限公司 | Multivariable detection device based on capacitive microphone and disaster sensing method |
| CN113064398A (en) * | 2021-02-25 | 2021-07-02 | 北京强度环境研究所 | Air vane abnormal frequency-reduction jitter analysis method and system |
| CN114023540A (en) * | 2021-11-08 | 2022-02-08 | 国网河北省电力有限公司电力科学研究院 | Monitoring method, device, equipment and storage medium for transformer lifting seat and sleeve |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106324406A (en) * | 2016-09-21 | 2017-01-11 | 许继集团有限公司 | Transformer direct-current magnetic bias fault diagnosis method and device |
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| CN110320435A (en) * | 2019-07-11 | 2019-10-11 | 广东石油化工学院 | A kind of running state of transformer vibration sound detection signal reconfiguring method and system using data regularization |
| CN110634493A (en) * | 2019-09-09 | 2019-12-31 | 国网湖南省电力有限公司 | Transformer state identification method, system and medium based on voiceprint image characteristics |
| CN111521256A (en) * | 2020-04-13 | 2020-08-11 | 国家电网有限公司 | Main transformer surface vibration visualization detection method based on data mapping |
| CN111537836A (en) * | 2020-05-15 | 2020-08-14 | 国网山东省电力公司济宁供电公司 | Automatic power distribution network fault diagnosis method and system based on wave recording data |
| CN111767675A (en) * | 2020-06-24 | 2020-10-13 | 国家电网有限公司大数据中心 | Transformer vibration fault monitoring method and device, electronic equipment and storage medium |
| CN112945280A (en) * | 2021-02-07 | 2021-06-11 | 苏州森斯微电子技术有限公司 | Multivariable detection device based on capacitive microphone and disaster sensing method |
| CN113064398A (en) * | 2021-02-25 | 2021-07-02 | 北京强度环境研究所 | Air vane abnormal frequency-reduction jitter analysis method and system |
| CN114023540A (en) * | 2021-11-08 | 2022-02-08 | 国网河北省电力有限公司电力科学研究院 | Monitoring method, device, equipment and storage medium for transformer lifting seat and sleeve |
| CN114023540B (en) * | 2021-11-08 | 2023-11-17 | 国网河北省电力有限公司电力科学研究院 | Method, device, equipment and storage medium for monitoring transformer lifting seat and sleeve |
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