CN106546882A - A kind of method of detection power transformer internal discharge failure - Google Patents
A kind of method of detection power transformer internal discharge failure Download PDFInfo
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- CN106546882A CN106546882A CN201610847073.3A CN201610847073A CN106546882A CN 106546882 A CN106546882 A CN 106546882A CN 201610847073 A CN201610847073 A CN 201610847073A CN 106546882 A CN106546882 A CN 106546882A
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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
- G01R31/1227—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 of components, parts or materials
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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
Abstract
The present invention relates to a kind of method of detection power transformer internal discharge failure, belongs to power transformer safety monitoring technology field.The method performs following steps：1) detecting system is set up, sets sample frequency and the sampling time of the detecting system；2) vibration signal on the transformator surface of transformator normal condition is gathered using the vibrating sensor of detecting system；3）Extract eigenvalue and preserve；4）Using step 1）Detecting system the transformator in work is monitored, obtain the vibration signal of transformator working condition；5）Extraction step 4）Vibration signal eigenvalue；6) using step 5）The eigenvalue of extraction is differentiated to the working condition of transformator.The present invention is using the vibration signal on transformator surface under the different running statuses of vibrating sensor detection and analyzes that, so as to differentiate to transformer state, platform building of the present invention is simple, and compared with superfrequency detection method, testing cost is relatively low, it is easy to Project Realization.
Description
Technical field
The present invention relates to a kind of method of detection power transformer internal discharge failure, belongs to power transformer safety monitoring
Technical field.
Background technology
Power transformer is one of key equipment of power system transmission ＆ distribution electric energy, and its security performance is largely affected
The safe operation reliability of whole electrical network.According to ASSOCIATE STATISTICS, three main class failures of power transformer (mechanical breakdown, conductor event
Barrier and insulation fault) in, insulation fault accounts for the largest percentage.And with the raising of voltage class of electric power system, shelf depreciation is
So become the major reason of inside transformer insulation degradation, thus be necessary to be monitored which, to finding in time and excluding
Power Transformer Faults hidden danger has directive significance.
At present, according to predetermined substance (pulse current, electromagnetic radiation and ultrasound wave etc.) produced during shelf depreciation,
Electric pulse detection method, hyperfrequency (UHF) detection method are generated correspondingly.Electric pulse detection method be at present using more method it
One, the quantity of electric charge of generation, 7354-2003 (IEC 60270 of GB/T are put using current sensor detection office：2000, IDT) and
DL/T 356-2010 has carried out clear stipulaties to its measurement of partial discharge and collimation technique, but this method capacity of resisting disturbance is weak, surveys
Amount frequency is low, and frequency band is narrow, it is impossible to be efficiently applied to on-line monitoring；UHF utilizes capacitance sensor or ultra-high frequency antenna detection office
Uhf electromagnetic wave produced by portion's electric discharge, detection frequency range and sensitivity height, can avoid conventional electrical interference, can supervise online
Survey, but it is higher to sensor mounting location requirement, as transformer-cabinet itself has shielding Electromagnetic Field, if sensor is straight
Installation case surface is connect, measurement signal is very weak.
The content of the invention
The technical problem to be solved in the present invention is, not enough for prior art, propose a kind of accuracy it is higher it is real-time
Method of the inside transformer with the presence or absence of discharge fault in line detection work.
The present invention in order to solve above-mentioned technical problem proposition technical scheme be：A kind of detection power transformer internal discharge
The method of failure, the method perform following steps：
1) detecting system is set up, sets sample frequency and the sampling time of the detecting system；
2) believed using the vibration on the transformator surface of the vibrating sensor collection transformator normal condition of the detecting system
Number；
3) extract eigenvalue A, the eigenvalue B and eigenvalue C in the vibration signal and preserve；
The eigenvalue A is vibration signal time domain stable state amplitude；
The eigenvalue B carries out Section 16 point singular spectrum entropy of WAVELET PACKET DECOMPOSITION for vibration signal；
The eigenvalue C carries out the meansigma methodss of the 25-32 node singular spectrum entropy of WAVELET PACKET DECOMPOSITION for vibration signal；
4) using step 1) detecting system the transformator in work is monitored, obtain shaking for transformator working condition
Dynamic signal；
5) eigenvalue A ', the eigenvalue B ' and eigenvalue C ' of extraction step vibration signal 4)；
The eigenvalue A ' be step 4) vibration signal time domain stable state amplitude；
The eigenvalue B ' is step 4) vibration signal carry out Section 16 point singular spectrum entropy of WAVELET PACKET DECOMPOSITION；
The eigenvalue C ' is step 4) vibration signal carry out the 25-32 node singular spectrum entropy of WAVELET PACKET DECOMPOSITION
Meansigma methodss；
6) using step 5) the eigenvalue A ', eigenvalue B ' and the eigenvalue C ' that extract sentenced to the working condition of transformator
Not；
Tentatively judged, when eigenvalue A ' is found less than the 30% of eigenvalue A, then the transformator has discharge fault；
After preliminary judgement has discharge fault again, determine whether, if eigenvalue B ' is less than eigenvalue B and eigenvalue C ' is big
In eigenvalue C, it is determined that the transformator has discharge fault；
There is no discharge fault in the transformator if eigenvalue B ' is less than eigenvalue C more than eigenvalue B or eigenvalue C ', but
It is still faulty.
The improvement of above-mentioned technical proposal is：The step 1) in sample frequency be 10kHz, the sampling time is no less than
0.3s, the frequency range of Section 16 point is 1562.5Hz-1718.75Hz, and the frequency range of the 25-32 nodes is
2500Hz-3750Hz。
The improvement of above-mentioned technical proposal is：The step 2) in vibrating sensor pass through bonding or to exist using magnet adsorption
Arbitrfary point position in the middle of the housing side of power transformer.
The improvement of above-mentioned technical proposal is that eigenvalue B, eigenvalue C, eigenvalue B ' and eigenvalue C ' are extracted in the method
Step is as follows：
A1:Step 2) or step 4) in vibration signal f (i) (i=1,2 ..., N) (N is sampling number), by small echo
Bag decomposes, n-th node reconstruction signal f of jth layer of vibration signal f (i) after being decomposedj,n(i), wherein j=5, n=32,
N is sampling number；
A2：To node coefficient sequence F being made up of the node reconstruction signal in step A1j,nAdding window, by the node coefficient sequence
Row Fj,nIt is divided into (N-M+1) individual window, and track matrix A is constructed with (N-M+1) individual windowj,n；
Node coefficient sequence is Fj,n={ fj,n(i), i=1,2 ..., N }, track matrix Aj,nFor,
A3：To track matrix Aj,nSingular value decomposition is carried out, singular value is obtainedM is singular value number, m=1,2 ...,
m0, m0=min (N-M+1, M), according to information entropy theory, defining its singular spectrum entropy isIn formula,Represent proportion of m-th singular value in whole singular value；
A4：The singular spectrum entropy of n node is combined, characteristic vector H=[H is obtainedj,1,Hj,2,...,Hj,n]。
The present invention using the beneficial effect of above-mentioned technical proposal is：The present invention is using the different operations of vibrating sensor detection
The vibration signal on transformator surface analyze under state, so as to differentiate to transformer state, as the present invention need not be with
Transformator carries out any electrical connection, therefore can realize the real-time monitoring of inside transformer discharge fault；Platform of the present invention is taken
Build simple, compared with superfrequency detection method, testing cost is relatively low；When vibration signal Jing Medium Propagations are to tank wall, it is easy to sensed
Device catches, and discharge characteristic frequency range is higher, avoids the vibration effect of transformer body and chiller, therefore the feature for being found
Amount can accurately judge transformer state, and feature is obvious, while being also easy to Project Realization.
Description of the drawings
The invention will be further described below in conjunction with the accompanying drawings：
Fig. 1 is a kind of method flow diagram of detection power transformer internal discharge failure of the embodiment of the present invention.
Fig. 2 is the time domain waveform of the power transformer normal condition of the embodiment of the present invention.
Fig. 3 is the time domain waveform of the power transformer discharge fault state of the embodiment of the present invention.
Fig. 4 is the power transformer right flank measuring point vibration signal characteristics vector spectrogram of the embodiment of the present invention.
Specific embodiment
Embodiment
A kind of method of detection power transformer internal discharge failure of the present embodiment, the method perform following steps：
1) detecting system is set up, sets sample frequency and the sampling time of the detecting system, specially 10kHz and 0.35s；
2) believed using the vibration on the transformator surface of the vibrating sensor collection transformator normal condition of the detecting system
Number；
3) extract eigenvalue A, the eigenvalue B and eigenvalue C in the vibration signal and preserve；
The eigenvalue A is vibration signal time domain stable state amplitude；
The eigenvalue B carries out Section 16 point singular spectrum entropy of WAVELET PACKET DECOMPOSITION for vibration signal；
The eigenvalue C carries out the meansigma methodss of the 25-32 node singular spectrum entropy of WAVELET PACKET DECOMPOSITION for vibration signal；
4) using step 1) detecting system the transformator in work is monitored, obtain shaking for transformator working condition
Dynamic signal；
5) eigenvalue A ', the eigenvalue B ' and eigenvalue C ' of extraction step vibration signal 4)；
The eigenvalue A ' be step 4) vibration signal time domain stable state amplitude；
The eigenvalue B ' is step 4) vibration signal carry out Section 16 point singular spectrum entropy of WAVELET PACKET DECOMPOSITION；
The eigenvalue C ' is step 4) vibration signal carry out the 25-32 node singular spectrum entropy of WAVELET PACKET DECOMPOSITION
Meansigma methodss；
6) using step 5) the eigenvalue A ', eigenvalue B ' and the eigenvalue C ' that extract sentenced to the working condition of transformator
Not；
Tentatively judged, when eigenvalue A ' is found less than the 30% of eigenvalue A, then the transformator has discharge fault；
After preliminary judgement has discharge fault again, determine whether, if eigenvalue B ' is less than eigenvalue B and eigenvalue C ' is big
In eigenvalue C, it is determined that the transformator has discharge fault；
There is no discharge fault in the transformator if eigenvalue B ' is less than eigenvalue C more than eigenvalue B or eigenvalue C ', but
It is still faulty.
The improvement of above-mentioned technical proposal is：The step 1) in sample frequency be 10kHz, the sampling time is no less than
0.3s, the frequency range of Section 16 point is 1562.5Hz-1718.75Hz, and the frequency range of the 25-32 nodes is
2500Hz-3750Hz。
The improvement of above-mentioned technical proposal is：The step 2) in vibrating sensor pass through bonding or to exist using magnet adsorption
Arbitrfary point position in the middle of the housing side of power transformer.
The improvement of above-mentioned technical proposal is that eigenvalue B, eigenvalue C, eigenvalue B ' and eigenvalue C ' are extracted in the method
Step is as follows：
A1:Step 2) or step 4) in vibration signal f (i) (i=1,2 ..., N) (N is sampling number), by small echo
Bag decomposes, n-th node reconstruction signal f of jth layer of vibration signal f (i) after being decomposedj,n(i), wherein j=5, n=32,
N is sampling number；
A2：To node coefficient sequence F being made up of the node reconstruction signal in step A1j,nAdding window, obtaining length of window is
The node coefficient f of Mj,n(1),fj,n(2),…,fj,n(M) window is moved to right into 1 step then, f is obtainedj,n(2),fj,n(3),…,fj,n
(M+1), the like, by node coefficient sequence Fj,nIt is divided into (N-M+1) individual window, and track is constructed with (N-M+1) individual window
Matrix Aj,n；
Node coefficient sequence is Fj,n={ fj,n(i), i=1,2 ..., N }, track matrix Aj,nFor,
A3：To track matrix Aj,nSingular value decomposition is carried out, singular value is obtainedM is singular value number m=1,2 ...,
m0, m0=min (N-M+1, M), according to information entropy theory, defining its singular spectrum entropy isIn formula,Represent proportion of m-th singular value in whole singular value；
A4：The singular spectrum entropy of n node is combined, characteristic vector H=[H is obtainedj,1,Hj,2,…,Hj,n]。
Actual 110kV power transformers discharge fault simulation test is carried out in certain transformator limited company, test becomes
Depressor model SFZ10-31500/110.
1 power transformer basic parameter of table
Experiment vibrating sensor model JF2020 used, sample frequency are 10kHz, and data analysiss length is 0.35s.
This experimental power supply is provided by one group of generating set, it is considered to which power supply capacity is limited, and this test voltage is 10.5kV volumes
Determine voltage, be carried in low-pressure side.Failure is arranged：Winding housing screw is loosened using spanner, about loosen and cause nail pressing
Till bowl can be moved left and right but can not leave nail pressing.8 nail pressings of A (a) phase windings side are all loosened using the method.
Fig. 2 and Fig. 3 is respectively zero load and normally runs and the time domain vibration signal under unloaded failure operation state, calculates respectively
The First Eigenvalue：Eigenvalue A and eigenvalue A ', eigenvalue A are 0.7-0.75, and eigenvalue A ' is 0.37-0.42.
According to the result detected by features described above value A and eigenvalue A ', it can be seen that the electric power run under malfunction becomes
The fisrt feature of depressor is significantly less than the value under normal condition.
Further analyze, by wavelet packet, phase space reconfiguration and singular value decomposition to vibration signal processing, using comentropy
Fundamental formular, calculates Second Eigenvalue (eigenvalue B and eigenvalue B ') and third feature value (eigenvalue C and eigenvalue C '),
Transformator normal condition and transformer fault state singular spectrum Characteristic Entropy are obtained, as shown in Figure 4.Eigenvalue B is 2.8861, feature
Value B ' is 2.7210, and eigenvalue C is 2.9328, and eigenvalue C ' is 3.6714.
From the above data, it can be seen that Section 16 point (1562.5- compared with normal condition, under malfunction
1718.75Hz) singular spectrum entropy reduces, the meansigma methodss increase of 25-32 nodes (2500-3750Hz) singular spectrum entropy.
The present invention is not limited to above-described embodiment.The technical scheme that all employing equivalents are formed, all falling within the present invention will
The protection domain asked.
Claims (4)
1. it is a kind of detection power transformer internal discharge failure method, it is characterised in that the method perform following steps：
1) detecting system is set up, sets sample frequency and the sampling time of the detecting system；
2) vibration signal on the transformator surface of transformator normal condition is gathered using the vibrating sensor of the detecting system；
3) extract eigenvalue A, the eigenvalue B and eigenvalue C in the vibration signal and preserve；
The eigenvalue A is vibration signal time domain stable state amplitude；
The eigenvalue B carries out Section 16 point singular spectrum entropy of WAVELET PACKET DECOMPOSITION for vibration signal；
The eigenvalue C carries out the meansigma methodss of the 25-32 node singular spectrum entropy of WAVELET PACKET DECOMPOSITION for vibration signal；
4) using step 1) detecting system the transformator in work is monitored, obtain transformator working condition vibration letter
Number；
5) eigenvalue A ', the eigenvalue B ' and eigenvalue C ' of extraction step vibration signal 4)；
The eigenvalue A ' be step 4) vibration signal time domain stable state amplitude；
The eigenvalue B ' is step 4) vibration signal carry out Section 16 point singular spectrum entropy of WAVELET PACKET DECOMPOSITION；
The eigenvalue C ' be step 4) vibration signal carry out WAVELET PACKET DECOMPOSITION 25-32 node singular spectrum entropy it is average
Value；
6) using step 5) the eigenvalue A ', eigenvalue B ' and the eigenvalue C ' that extract differentiated to the working condition of transformator；
Tentatively judged, when eigenvalue A ' is found less than the 30% of eigenvalue A, then the transformator has discharge fault；
After preliminary judgement has discharge fault, determine whether, if eigenvalue B ' is less than eigenvalue B and eigenvalue C ' is more than spy
Value indicative C, it is determined that the transformator has discharge fault；
There is no discharge fault in the transformator if eigenvalue B ' is less than eigenvalue C more than eigenvalue B or eigenvalue C ', but still have
Failure.
2. it is according to claim 1 detection power transformer internal discharge failure method, it is characterised in that：The step
1) sample frequency in is 10kHz, and the sampling time, the frequency range of Section 16 point was 1562.5Hz- no less than 0.3s
1718.75Hz, the frequency range of the 25-32 nodes is 2500Hz-3750Hz.
3. it is according to claim 1 detection power transformer internal discharge failure method, it is characterised in that：The step
2) in vibrating sensor pass through bonding or using magnet adsorption in the middle of the housing side of power transformer arbitrfary point position.
4. it is according to claim 1 detection power transformer internal discharge failure method, it is characterised in that extract feature
The step of value B, eigenvalue C, eigenvalue B ' and eigenvalue C ', is as follows：
A1:Step 2) or step 4) in vibration signal f (i) (i=1,2 ..., N) (N is sampling number), by wavelet packet point
Solution, n-th node reconstruction signal f of jth layer of vibration signal f (i) after being decomposedj,nI (), wherein j=5, n=32, N are
Sampling number；
A2：To node coefficient sequence F being made up of the node reconstruction signal in step A1j,nAdding window, by the node coefficient sequence
Fj,nIt is divided into (N-M+1) individual window, and track matrix A is constructed with (N-M+1) individual windowj,n；
Node coefficient sequence is Fj,n={ fj,n(i), i=1,2 ..., N }, track matrix Aj,nFor,
A3：To track matrix Aj,nSingular value decomposition is carried out, singular value is obtainedM is singular value number, m=1,2 ..., m0, m0
=min (N-M+1, M), according to information entropy theory, defining its singular spectrum entropy isIn formula,Represent proportion of m-th singular value in whole singular value；
A4：The singular spectrum entropy of n node is combined, characteristic vector H=[H is obtainedj,1,Hj,2,…,Hj,n]。
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Cited By (4)
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| CN107132033A (en) * | 2017-04-12 | 2017-09-05 | 国家电网公司 | A kind of mechanical method for diagnosing status of the winding based on transformer noise and system |
| CN111398735A (en) * | 2020-03-25 | 2020-07-10 | 云南电网有限责任公司大理供电局 | Transformer substation grounding grid fault detection method based on information entropy |
| CN112529096A (en) * | 2020-12-22 | 2021-03-19 | 哈尔滨工业大学 | PCA-based fault diagnosis method for multi-dimensional spacecraft telemetry data |
| CN117783794A (en) * | 2024-02-23 | 2024-03-29 | 国网山西省电力公司电力科学研究院 | Method and equipment for detecting internal fault discharge quantity of transformer |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107132033A (en) * | 2017-04-12 | 2017-09-05 | 国家电网公司 | A kind of mechanical method for diagnosing status of the winding based on transformer noise and system |
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| CN112529096A (en) * | 2020-12-22 | 2021-03-19 | 哈尔滨工业大学 | PCA-based fault diagnosis method for multi-dimensional spacecraft telemetry data |
| CN117783794A (en) * | 2024-02-23 | 2024-03-29 | 国网山西省电力公司电力科学研究院 | Method and equipment for detecting internal fault discharge quantity of transformer |
| CN117783794B (en) * | 2024-02-23 | 2024-04-19 | 国网山西省电力公司电力科学研究院 | Method and equipment for detecting internal fault discharge quantity of transformer |
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