CN105866645A - Method and device for diagnosing power generator discharge faults according to noise characteristic frequency bands - Google Patents
Method and device for diagnosing power generator discharge faults according to noise characteristic frequency bands Download PDFInfo
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- CN105866645A CN105866645A CN201610368469.XA CN201610368469A CN105866645A CN 105866645 A CN105866645 A CN 105866645A CN 201610368469 A CN201610368469 A CN 201610368469A CN 105866645 A CN105866645 A CN 105866645A
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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
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1209—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing using acoustic measurements
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Abstract
The invention discloses a method and device for diagnosing power generator discharge faults according to noise characteristic frequency bands. A plurality of noise measurement points are distributed on the upper portion of a power generator stator, preset sound frequency bands (1000-6000 Hz) of noise signals of each noise measurement point are extracted to serve as the characteristic frequency bands of discharge faults of a power generator, and accordingly whether discharge faults exist close to the obtained measurement points are analyzed. The method and device have advantage that under the condition that operation of the power generator is not affected, the discharge faults of the power generator are quickly and accurately diagnosed. The scheme is simple and practical, and operation is convenient. Meanwhile, investment is little because an existing noise recorder or recording equipment can be used. Faults of a generator set can be comprehensively monitored.
Description
Technical field
The invention belongs to fault diagnosis technical field, be specifically related to one and utilize noise characteristic frequency
The method and device of section diagnosis generator discharge fault.
Background technology
The vibration signal of generating set truly reflects its running state information, and can not when monitoring
Affecting the properly functioning of equipment, analyzing by vibration signal being carried out amplitude, time domain, frequency domain etc., can be real
Time judge generating set whether operation exception and corresponding fault type.Vibration signal is few by environmental disturbances,
Therefore use that vibration monitoring diagnoses steam turbine generator, hydrogenerator, wind-driven generator fault exist
Obtain a wide range of applications both at home and abroad.But vibration monitoring there is also some application and limits, as vibration passes
Sensor mainly monitors low frequency signal, but a lot of fault cannot be found by low frequency signal；And at electro-mechanical part
Vibrating sensor etc. cannot be used in position or locking device.
Summary of the invention
In order to solve at present by vibration signal monitoring generating set time exist monitoring fault coverage limited,
Limited technical problem installed by vibrating sensor, and the present invention provides one to utilize the diagnosis of noise characteristic frequency range to send out
The method and device of motor discharge fault.
In order to realize above-mentioned technical purpose, the technical scheme is that,
A kind of method utilizing noise characteristic frequency range diagnosis generator discharge fault, comprises the following steps,
Step 1: arrange multiple noise testing point on generator；
Step 2: gather the noise signal of each noise testing point；
Step 3: extract the preset sound frequency range in the noise signal of each noise testing point as generator
The characteristic spectra of discharge fault；
Step 4: the energy summation to the characteristic spectra gathered in step 3；
Step 5: with the numbered abscissa of measuring point, with the energy of characteristic spectra with as ordinate, thus obtain
The characteristic spectra noise profile curve of different measuring points, and then obtain noise situation of change in distribution curve, as
Fruit occurs the abnormal measuring point raised, and this measuring point in one section of measuring point that continuous multiple rules are raised and lowered
The difference of value and this section of measuring point minimum of a value more than threshold value, then show to there is discharge fault near this measuring point.
A kind of described method utilizing noise characteristic frequency range diagnosis generator discharge fault, described step 1
In, at distance generator unit stator shrouding 0.5m, it is uniformly distributed measuring point, each measuring point interval 1m～2m.
A kind of described method utilizing noise characteristic frequency range diagnosis generator discharge fault, described step 2
In, the noise signal of each measuring point is gathered by the equipment of microphone or band sound-recording function, frequency range is
100～20kHz.
A kind of described method utilizing noise characteristic frequency range diagnosis generator discharge fault, described step 3
In, by hardware simulation wave filter or Speech processing algorithm, arranging high-pass filter is 1000Hz,
Low-pass filter frequency is 6000Hz, extracts 1000Hz to the 6000Hz conduct in generator noise signal
The characteristic spectra of the discharge fault of generator.
A kind of described method utilizing noise characteristic frequency range diagnosis generator discharge fault, described step 4
In, the energy summation of 1000Hz to 6000Hz characteristic spectra in measuring point noise signal each to generator；
A kind of described method utilizing noise characteristic frequency range diagnosis generator discharge fault, described step 5
In, with the numbered abscissa of measuring point, with the energy of 1000Hz to 6000Hz characteristic spectra with as ordinate,
Thus obtain the 1000-6000Hz noise profile curve of different measuring points, and then obtain noise in distribution curve
, if there is the abnormal survey raised in one section of measuring point that continuous multiple rules are raised and lowered in situation of change
Point, and the difference of the value of this measuring point and this section of measuring point minimum of a value is more than threshold value 1dB, then show at this measuring point
Near there is discharge fault.
A kind of device utilizing noise characteristic frequency range diagnosis generator discharge fault, including microphone, audio frequency
Transmission line, audio signal processor and display, described microphone is arranged on generator, described
Microphone be connected with audio signal processor by audio transmission line, audio signal processor general
To audio signal extract after characteristic spectra and show result by display.
Described a kind of device utilizing noise characteristic frequency range diagnosis generator discharge fault, described is transaudient
Device is uniformly distributed at distance generator unit stator shrouding 0.5m, each microphone interval 1m～2m.
Described a kind of device utilizing noise characteristic frequency range diagnosis generator discharge fault, described audio frequency
Signal processing apparatus includes wave filter, signal sampler and digital signal processor, and described wave filter leads to
Crossing audio transmission line to be connected with microphone, simulation is believed by the signal after filter filtering by signal sampler
Number being converted to data signal, the data signal that signal sampler inputs is entered by micro-process digital signal processor
Row shows result by display after processing.
Described a kind of device utilizing noise characteristic frequency range diagnosis generator discharge fault, described filtering
Device includes that high-pass filter that frequency is 1000Hz and frequency are the low pass filter of 6000Hz
The method have technical effect that, can be in the case of not affecting generator operation, fast and accurately
It is diagnosed to be generator discharge fault.And this programme is simple and practical, easy to operate.Can utilize existing simultaneously
Noise meter or sound pick-up outfit, small investment.And can comprehensively monitor the fault of generating set.
Accompanying drawing explanation
Fig. 1 is the flow chart of the present invention；
The structural representation that Fig. 2 is
Fig. 3 is point layout schematic diagram of the present invention；
Fig. 4 is the noise profile figure of the embodiment of the present invention；
Wherein, 1 be microphone, 2 for audio transmission line, 3 for wave filter, 4 for signal sampler, 5
For digital signal processor, 6 be display, 7 be stator, 8 for upper wind-tunnel.
Detailed description of the invention
Seeing Fig. 1, Fig. 2, Fig. 3, the present embodiment includes at microphone, audio transmission line, audio signal
Reason device and display, microphone is arranged on generator, and microphone is believed with audio frequency by audio transmission line
Number processing means connects, and audio signal processor passes through after the audio signal obtained is extracted characteristic spectra
Display display result.
Wherein microphone is uniformly distributed at distance generator unit stator shrouding 0.5m, and each microphone is spaced
1m～2m.
Audio signal processor includes wave filter, signal sampler and digital signal processor, wave filter
Being connected with microphone by audio transmission line, the signal after filter filtering will simulation by signal sampler
Signal is converted to data signal, the data signal that signal sampler is inputted by micro-process digital signal processor
Result is shown by display after processing.
Wave filter includes that high-pass filter that frequency is 1000Hz and frequency are the low pass filter of 6000Hz.
The present invention, when implementing, is first uniformly distributed measuring point along generator unit stator shrouding top 0.5m, each
Measuring point interval 1m～2m.Then the equipment by microphone or band sound-recording function gathers the noise of each measuring point
Signal, frequency range includes 100～20kHz.After obtaining noise signal, by hardware simulation wave filter or
Speech processing algorithm, arranging high-pass filter is 1000Hz, and low-pass filter frequency is 6000Hz,
Extract the feature frequency as the discharge fault of generator of 1000Hz to the 6000Hz in generator noise signal
Section.Next the energy of 1000Hz to 6000Hz characteristic spectra in measuring point noise signal each to generator
Summation.Finally with the numbered abscissa of measuring point, with the energy of 1000Hz to 6000Hz characteristic spectra be
Ordinate, thus obtain the 1000-6000Hz noise profile curve of different measuring points, and then obtain distribution song
Crest in line and trough situation of change, if the difference of crest value and valley value is more than 1dB, then show
Discharge fault is there is near measuring point corresponding at this crest.
Embodiment:
1, measuring point is laid.It is uniformly distributed along+Y-X-Y-+X direction at certain hydroelectric power plant 1# generator
16 measuring points, point layout is as it is shown in figure 1, measuring point is positioned at stator top 0.5m.
2, noise signal record.Each measuring point noise is remembered by the pulse analyzer using BK company of Denmark
Record 20 seconds.
3, noise signal is extracted and is calculated.The reflex the poster processing soft using BK company of Denmark extracts to be sent out
1000Hz to 6000Hz in noise of motor signal as the characteristic spectra of the discharge fault of generator, and
To the energy summation of 1000Hz to 6000Hz characteristic spectra in each measuring point noise signal；.
4, electric discharge position judges.With the numbered abscissa of measuring point, with 1000Hz to 6000Hz characteristic spectra
Energy and be ordinate, thus obtain the 1000-6000Hz noise profile curve of different measuring points, such as Fig. 3
Shown in, and then obtain noise situation of change in distribution curve, if be raised and lowered in continuous multiple rules
One section of measuring point in the abnormal measuring point raised occurs, and the difference of the value of this measuring point and this section of measuring point minimum of a value
More than threshold value 1dB, then show to there is discharge fault near this measuring point.From figure 3, it can be seen that from measuring point 1
Start to measuring point 5 as uniformly rising distribution, and measuring point 3 occurs in that abnormal rising, be i.e. several with surrounding
Measuring point is compared, and the value of measuring point 3 is substantially not belonging to normal condition, and measuring point 3 is compared with minimum point measuring point 1,
Difference is more than 1dB, therefore judges that measuring point 3 exists electric discharge.And the distribution of this section of measuring point of measuring point 6,7,8
For uniformly declining, situation belongs to normal.Thereafter measuring point 8-11 is also normal distribution.And measuring point 12-14
In, substantially there are unusual fluctuations in measuring point 13, and with the difference of measuring point 14 more than 1dB, therefore measuring point 13 is also
There is electric discharge.
5, result verification.Find to exist at measuring point 3 and measuring point 13 to put by local discharge test when overhaul
Electricity point and obvious spark tracking, demonstrate the correctness of the method.
Claims (10)
1. the method utilizing noise characteristic frequency range diagnosis generator discharge fault, it is characterised in that comprise the following steps,
Step 1: arrange multiple noise testing point on generator；
Step 2: gather the noise signal of each noise testing point；
Step 3: extract the characteristic spectra as the discharge fault of generator of the preset sound frequency range in the noise signal of each noise testing point；
Step 4: the energy summation to the characteristic spectra gathered in step 3；
Step 5: with the numbered abscissa of measuring point, with the energy of characteristic spectra with as ordinate, thus obtain the characteristic spectra noise profile curve of different measuring points, and then obtain noise situation of change in distribution curve, if the abnormal measuring point raised occurs in one section of measuring point that continuous multiple rules are raised and lowered, and the difference of the value of this measuring point and this section of measuring point minimum of a value is more than threshold value, then show to there is discharge fault near this measuring point.
A kind of method utilizing noise characteristic frequency range diagnosis generator discharge fault the most according to claim 1, it is characterised in that in described step 1, is uniformly distributed measuring point, each measuring point interval 1m～2m at distance generator unit stator shrouding 0.5m.
A kind of method utilizing noise characteristic frequency range diagnosis generator discharge fault the most according to claim 1, it is characterized in that, in described step 2, being gathered the noise signal of each measuring point by the equipment of microphone or band sound-recording function, frequency range is 100～20kHz.
A kind of method utilizing noise characteristic frequency range diagnosis generator discharge fault the most according to claim 1, it is characterized in that, in described step 3, by hardware simulation wave filter or Speech processing algorithm, arranging high-pass filter is 1000Hz, low-pass filter frequency is 6000Hz, extracts the characteristic spectra as the discharge fault of generator of 1000Hz to the 6000Hz in generator noise signal.
A kind of method utilizing noise characteristic frequency range diagnosis generator discharge fault the most according to claim 1, it is characterised in that in described step 4, the energy summation of 1000Hz to 6000Hz characteristic spectra in measuring point noise signal each to generator.
A kind of method utilizing noise characteristic frequency range diagnosis generator discharge fault the most according to claim 1, it is characterized in that, in described step 5, with the numbered abscissa of measuring point, with the energy of 1000Hz to 6000Hz characteristic spectra with as ordinate, thus obtain the 1000-6000Hz noise profile curve of different measuring points, and then obtain noise situation of change in distribution curve, if the abnormal measuring point raised occurs in one section of measuring point that continuous multiple rules are raised and lowered, and the difference of the value of this measuring point and this section of measuring point minimum of a value is more than threshold value 1dB, then show to there is discharge fault near this measuring point.
7. the device utilizing noise characteristic frequency range diagnosis generator discharge fault, it is characterized in that, including microphone, audio transmission line, audio signal processor and display, described microphone is arranged on generator, described microphone is connected with audio signal processor by audio transmission line, and audio signal processor shows result by display after the audio signal obtained is extracted characteristic spectra.
A kind of device utilizing noise characteristic frequency range diagnosis generator discharge fault the most according to claim 7, it is characterised in that described microphone is uniformly distributed at distance generator unit stator shrouding 0.5m, each microphone interval 1m～2m.
A kind of device utilizing noise characteristic frequency range diagnosis generator discharge fault the most according to claim 7, it is characterized in that, described audio signal processor includes wave filter, signal sampler and digital signal processor, described wave filter is connected with microphone by audio transmission line, signal after filter filtering converts analog signals into data signal by signal sampler, and micro-process digital signal processor shows result by display after the data signal that signal sampler inputs being processed.
A kind of device utilizing noise characteristic frequency range diagnosis generator discharge fault the most according to claim 7, it is characterised in that described wave filter includes that high-pass filter that frequency is 1000Hz and frequency are the low pass filter of 6000Hz.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112067961A (en) * | 2020-10-13 | 2020-12-11 | 哈尔滨工业大学（深圳） | Arc fault detection method, system and storage medium |
| CN112529059A (en) * | 2020-12-04 | 2021-03-19 | 湖南五凌电力科技有限公司 | Unit electromagnetic vibration diagnosis method, system, computer equipment and storage medium |
| CN113573980A (en) * | 2019-03-19 | 2021-10-29 | Wing航空有限责任公司 | Detecting impending motor failure using audio data |
| CN113790911A (en) * | 2021-08-18 | 2021-12-14 | 中国长江电力股份有限公司 | Abnormal sound detection method based on sound frequency spectrum statistical law |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3896376A (en) * | 1971-12-17 | 1975-07-22 | Micafil Ag | Circuit arrangement for testing insulation by partial discharge technique |
| JPH09127181A (en) * | 1995-10-30 | 1997-05-16 | Kawasaki Steel Corp | Detecting device for corona discharge of electric power equipment |
| CN101393049A (en) * | 2008-08-25 | 2009-03-25 | 北京天源科创风电技术有限责任公司 | Vibration monitoring and failure diagnosis method for wind generating set |
| CN102494894A (en) * | 2011-11-17 | 2012-06-13 | 高丙团 | Audio monitoring and fault diagnosis system for wind generating set and audio monitoring and fault diagnosis method for same |
| CN102680233A (en) * | 2011-03-17 | 2012-09-19 | 北汽福田汽车股份有限公司 | Motor failure diagnosis device and method |
| CN102706560A (en) * | 2012-05-25 | 2012-10-03 | 华锐风电科技(江苏)有限公司 | State monitoring method and device of wind turbine generator set |
| CN102778358A (en) * | 2012-06-04 | 2012-11-14 | 上海东锐风电技术有限公司 | Failure prediction model establishing method and system as well as fan monitoring pre-warning system and method |
| CN102914359A (en) * | 2012-10-10 | 2013-02-06 | 江苏银佳企业集团有限公司 | Wind driven generator vibrator monitoring device and monitoring method |
| CN103698677A (en) * | 2014-01-17 | 2014-04-02 | 福州大学 | Low-voltage arc fault test and analysis system |
| CN103744021A (en) * | 2013-12-23 | 2014-04-23 | 煤炭科学研究总院 | Apparatus and method for motor fault monitoring |
| CN205786975U (en) * | 2016-05-30 | 2016-12-07 | 国家电网公司 | A kind of device utilizing noise characteristic frequency range diagnosis generator discharge fault |
-
2016
- 2016-05-30 CN CN201610368469.XA patent/CN105866645B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3896376A (en) * | 1971-12-17 | 1975-07-22 | Micafil Ag | Circuit arrangement for testing insulation by partial discharge technique |
| JPH09127181A (en) * | 1995-10-30 | 1997-05-16 | Kawasaki Steel Corp | Detecting device for corona discharge of electric power equipment |
| CN101393049A (en) * | 2008-08-25 | 2009-03-25 | 北京天源科创风电技术有限责任公司 | Vibration monitoring and failure diagnosis method for wind generating set |
| CN102680233A (en) * | 2011-03-17 | 2012-09-19 | 北汽福田汽车股份有限公司 | Motor failure diagnosis device and method |
| CN102494894A (en) * | 2011-11-17 | 2012-06-13 | 高丙团 | Audio monitoring and fault diagnosis system for wind generating set and audio monitoring and fault diagnosis method for same |
| CN102706560A (en) * | 2012-05-25 | 2012-10-03 | 华锐风电科技(江苏)有限公司 | State monitoring method and device of wind turbine generator set |
| CN102778358A (en) * | 2012-06-04 | 2012-11-14 | 上海东锐风电技术有限公司 | Failure prediction model establishing method and system as well as fan monitoring pre-warning system and method |
| CN102914359A (en) * | 2012-10-10 | 2013-02-06 | 江苏银佳企业集团有限公司 | Wind driven generator vibrator monitoring device and monitoring method |
| CN103744021A (en) * | 2013-12-23 | 2014-04-23 | 煤炭科学研究总院 | Apparatus and method for motor fault monitoring |
| CN103698677A (en) * | 2014-01-17 | 2014-04-02 | 福州大学 | Low-voltage arc fault test and analysis system |
| CN205786975U (en) * | 2016-05-30 | 2016-12-07 | 国家电网公司 | A kind of device utilizing noise characteristic frequency range diagnosis generator discharge fault |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113573980A (en) * | 2019-03-19 | 2021-10-29 | Wing航空有限责任公司 | Detecting impending motor failure using audio data |
| CN112067961A (en) * | 2020-10-13 | 2020-12-11 | 哈尔滨工业大学（深圳） | Arc fault detection method, system and storage medium |
| CN112067961B (en) * | 2020-10-13 | 2023-08-15 | 哈尔滨工业大学（深圳） | Arc fault detection method, system and storage medium |
| CN112529059A (en) * | 2020-12-04 | 2021-03-19 | 湖南五凌电力科技有限公司 | Unit electromagnetic vibration diagnosis method, system, computer equipment and storage medium |
| CN113790911A (en) * | 2021-08-18 | 2021-12-14 | 中国长江电力股份有限公司 | Abnormal sound detection method based on sound frequency spectrum statistical law |
| CN113790911B (en) * | 2021-08-18 | 2023-05-16 | 中国长江电力股份有限公司 | Abnormal sound detection method based on sound spectrum statistics rule |
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Effective date of registration: 20170104 Address after: 100031 Xicheng District West Chang'an Avenue, No. 86, Beijing Applicant after: State Power Networks Co Applicant after: State Grid Hunan Electric Power Company Applicant after: Electric Power Research Institute, State Grid Hunan Electric Power Company Applicant after: Wu Ling Power Corporation bowl slope hydropower plant Address before: 100031 Xicheng District West Chang'an Avenue, No. 86, Beijing Applicant before: State Power Networks Co Applicant before: State Grid Hunan Electric Power Company Applicant before: Electric Power Research Institute, State Grid Hunan Electric Power Company |
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