CN109507503A - A kind of fault diagnostic method for transformer winding based on multi-channel noise - Google Patents
A kind of fault diagnostic method for transformer winding based on multi-channel noise Download PDFInfo
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- CN109507503A CN109507503A CN201811362011.9A CN201811362011A CN109507503A CN 109507503 A CN109507503 A CN 109507503A CN 201811362011 A CN201811362011 A CN 201811362011A CN 109507503 A CN109507503 A CN 109507503A
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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
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- G—PHYSICS
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Abstract
The invention discloses a kind of fault diagnostic method for transformer winding based on multi-channel noise, are related to transformer fault diagnosis field, and the present invention passes through the noise signal and record current signal for acquiring transformer to be measured first；Then load amendment is carried out to current signal, obtains the noise signal under normallized current；Secondly, carrying out 9 layers of decomposition and reconstruction to the noise signal under normallized current using wavelet function, and Characteristic Extraction is carried out, obtains the characteristic quantity of transformer noise signal to be measured；The negative Infinite Norm of related coefficient vector sum is calculated to vector element each in the matrix of the characteristic quantity of transformer noise signal to be measured progress correlation analysis with corresponding energy feature amount fingerprint comparison to the characteristic quantity of transformer noise signal to be measured；Finally, judging transformer to be measured with the presence or absence of potential faults according to the value of Infinite Norm.Test process does not influence inside transformer structure, and no influence is operated normally on it, and can accurately judge to survey transformer with the presence or absence of potential faults.
Description
Technical field
The present invention relates to transformer fault diagnosis field more particularly to a kind of transformer winding events based on multi-channel noise
Hinder diagnostic method.
Background technique
Transformer is one of most important equipment of electric system, close with national economic development whether safe operation
It links up.If transformer breaks down, it will lead to large-area power-cuts, not only affect the production of factory in this way, also shadow
The life of the common people is rung.Therefore, carry out the research of Diagnosis Method of Transformer Faults, find the accident potential of transformer in time, keep away
Exempt from burst accident, the reliability for improving transformer station high-voltage side bus has a very important significance.
For transformer winding fault predominantly under electromagnetic force or mechanical force, what the mechanical structure of winding occurred can not
Restore to change, common includes winding loosening, warpage, bulge and dislocation etc..Since inside transformer is mechanical, electrical structure is multiple
Miscellaneous, once winding mechanical structure changes, the characteristic parameter changed therewith is more, therefore is directed to the monitoring of different characteristic amount
A variety of transformer winding state monitoring schemes out are amplified.Include commonly more at present short circuit impedance method, frequency response method, sweeps
Frequency impedance method, Low Voltage Impulse Method and vibration, noise signal analysis method etc..Traditional transformer winding event based on vibration signal
Hindering diagnostic method must be sensor arrangement in vibration oil tank of transformer surface, for charging equipment, complexity in practical application
The adverse circumstances lower sensor such as component vibration surface and high temperature or grease arrangement is more difficult, and tested quantity limitation,
The vibration information for only reflecting local location fails the vibrational state information for obtaining transformer entirety.
Transformer noise be by vibration generate, such as shock, friction, alternate stress act under, because of the metal of equipment outside
The generations such as shell, coil, iron core, fan are vibrated and generate noise.Vibration and noise is in close relations, and the charging operation of transformer is inevitable
Generate vibration, and then radiated noise.Noise is an important indicator of weighing device operating status, when the operation of equipment component
When state changes, the noise signal of radiation also changes therewith.Therefore there is patent to propose that one kind based on transformer noise is non-and connect
The winding failure diagnostic method of touch, but the single-point noise signal that this method only passes through analysis two different times carries out winding
Machine performance diagnosis, accuracy is lower, interferes in complex environment vulnerable to ambient noise, be easy to cause erroneous judgement or fails to judge, can not
It realizes fault location, there is certain limitation in practical engineering applications.
Summary of the invention
The purpose of the present invention is to provide a kind of fault diagnostic method for transformer winding based on multi-channel noise, solve
The shortcomings that vibration analysis method that tradition is tested based on oil tank of transformer surface vibration and deficiency, and based on single-point noise signal
Fault diagnostic method for transformer winding there are information content few, poor anti jamming capability, the low disadvantage of accuracy.
To achieve the above object, the present invention provides a kind of transformer winding fault diagnosis side based on multi-channel noise
Method, comprising the following steps:
S1, by the noise signal of multichannel collecting transformer to be measured, while record current signal；
S2, load amendment is carried out to current signal, obtains the noise signal under normallized current；
S3, wavelet function feedback is carried out to the noise signal under normallized current using Daubechies wavelet function,
And Characteristic Extraction is carried out, obtain the characteristic quantity of transformer noise signal to be measured；
S4, to the characteristic quantity of transformer noise signal to be measured with corresponding energy feature amount fingerprint (i.e. under history health status
Energy feature amount) compare, carry out correlation analysis, obtain the negative Infinite Norm of related coefficient vector sum；
S5, judged transformer to be measured with the presence or absence of potential faults according to the value of negative Infinite Norm.
Further, the S3 specifically includes the following steps:
S31, wavelet decomposition is carried out to the noise signal under each normallized current using Daubechies wavelet function, decomposed
It is nine layers, obtains the frequency range of nine high frequencies and the coefficient of nine high frequencies；
S32, wavelet reconstruction is carried out to the signal of the frequency range of nine high frequencies: extracts nine layers of 9 frequency from low to high
Rate segment signal carries out signature analysis, is reconstructed to coefficient of wavelet decomposition, obtains corresponding reconstruction signal and resultant signal；
S33, the energy for calculating each reconstruction signal；
S34, Characteristic Extraction is carried out to the energy of each reconstruction signal, characteristic quantity is normalized, is obtained to be measured
The characteristic quantity of transformer noise signal.
Further, in the S33 each reconstruction signal energy balane formula are as follows:
In formula (2): xk(t) (k=1,2..., 9) indicates reconstruction signal Sk(t) amplitude of discrete point.
Further, the S34 specifically includes the following steps:
S3401, using the energy of each reconstruction signal as element construction feature vector, indicated with T:
T=[E1,E2,...,E9] (3)
S3402, it is obtained to after T normalized:
In formula (4),
E in formula (5)jThat is E in formula (2)k。
S3403, each channel noise signal characteristic moment matrix T ' is acquired by (4) formula respectivelyLL=(TA′,TB′,TC′)ΤTo get
To the characteristic quantity of transformer noise signal to be measured, TA'、TB'、TC', respectively indicate A, B, C three-phase windings in noise measuring contour line
The normalization characteristic amount of the noise signal of upper corresponding position.
Further, in the correlation analysis of the S4, the calculation method of related coefficient are as follows:
In formula (5), formula (6) and formula (7), K is related coefficient vector；covpqFor the covariance of sequence；Dp、DqIt is respectively each
From the variance of series；N is the length of matrix each element sequence of values, and i is sequence of values 1,2 ..., N arbitrary value；p(i),q(i)
Feature vector (the T of transformer noise signal respectively to be measuredA′,TB′,TC') with corresponding energy feature amount fingerprint (TAO′,TBO′,
TCO') element.
Further, the value of the negative Infinite Norm in the S5 is M, the condition of judgement are as follows: as 0.8≤M≤1, is become
Depressor is in normal condition；As 0.5≤M < 0.8, there are potential faults for transformer；As M < 0.5, there is serious event in transformer
Barrier.
Further, using the noise signal of microphone pick transformer to be measured, the microphone is fixed by the bracket
Region relatively spacious around transformer to be measured.
Compared with prior art, the invention has the following beneficial effects:
1, the fault diagnostic method for transformer winding provided by the present invention based on multi-channel noise, first by acquisition to
Survey the noise signal and record current signal of transformer；Then load amendment is carried out to current signal, obtained under normallized current
Noise signal；Secondly, using Daubechies wavelet function to 9 layers of decomposition of noise signal progress under normallized current and again
Structure, and Characteristic Extraction is carried out, obtain the characteristic quantity of transformer noise signal to be measured；To the feature of transformer noise signal to be measured
Amount with corresponding energy feature amount fingerprint comparison, to each vector element in the matrix of the characteristic quantity of transformer noise signal to be measured into
Related coefficient vector sum Infinite Norm is calculated in row correlation analysis；Finally, judging transformation to be measured according to the value of Infinite Norm
Device whether there is potential faults.Test process does not influence inside transformer structure, no influence is operated normally on it, and can be accurate
Judgement survey transformer whether there is potential faults.
2, the present invention is acquired the noise signal of transformer to be measured using microphone, and the microphone is fixed by the bracket
The region relatively spacious around transformer to be measured, contactless arrangement, Relative Vibration sensor is more simple and convenient, measurement
System is the same as, without electrical connection, avoiding human safety issues when test between electric system.Meanwhile, it is capable to each logical to reasonably selecting
Road microphone position obtains transformer body vibration noise signal, and opposite single channel noise is tested, and has stronger anti-dry
Immunity energy is handled by each channel data and is analyzed, realizes the Primary Location of winding failure.
Detailed description of the invention
It, below will be to attached drawing needed in embodiment description in order to illustrate more clearly of technical solution of the present invention
It is briefly described, it should be apparent that, the accompanying drawings in the following description is only one embodiment of the present of invention, general for this field
For logical technical staff, without creative efforts, it is also possible to obtain other drawings based on these drawings.
Fig. 1 is a kind of flow chart of the fault diagnostic method for transformer winding based on multi-channel noise of the present invention；
Fig. 2 is the fault oscillograph of the A phase winding of the embodiment of the present invention.
Specific embodiment
With reference to the attached drawing in the embodiment of the present invention, the technical solution in the present invention is clearly and completely described,
Obviously, described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based in the present invention
Embodiment, those of ordinary skill in the art's every other embodiment obtained without creative labor,
It shall fall within the protection scope of the present invention.
As shown in Figure 1, the fault diagnostic method for transformer winding provided by the present invention based on multi-channel noise includes:
S1, according to national standards chooses A, B, C three-phase windings corresponding position measuring point on transformer contour line to be measured
(transformer is three-phase transformer), microphone is fixed by the bracket region relatively spacious around transformer to be measured, passes through
The noise signal of each measuring point of microphone pick, while record current signal (supervise when can directly pass through transformer station high-voltage side bus by master control room
Record is surveyed to obtain), the corresponding noise signal of A phase winding corresponding position measuring point is SA, B phase winding corresponding position measuring point is corresponding to make an uproar
Acoustical signal is SB, the corresponding noise signal of C phase winding corresponding position measuring point is SC；Microphone was fixed by the bracket in transformer week
Opposite depletion region is enclosed, contactless arrangement, Relative Vibration sensor, more simple and convenient, measuring system is the same as between electric system
Without electrical connection, human safety issues when test are avoided.Meanwhile, it is capable to each channel microphone cloth set is reasonably selected
It sets, obtains transformer body vibration noise signal, opposite single channel noise is tested, and has stronger interference free performance.
S2, load amendment is carried out to current signal, obtains the noise signal under normallized current.
S3,9 layers of decomposition and reconstruction are carried out to the noise signal under normallized current using Daubechies wavelet function, and
Carry out Characteristic Extraction, the specific steps are as follows:
S31,9 layers of noise signal progress under each normallized current is decomposed using Daubechies wavelet function, first
Layer decomposes and divides a signal into low frequency part c1With high frequency section d1, then retain d1；Continue to c1It decomposes, obtains low frequency c2And high frequency
d2, retain d2；Continue to c2It decomposes, obtains low frequency c3With high frequency d3, retain d3；Continue to c3It decomposes, obtains low frequency c4And high frequency
d4, retain d4；Continue to c4It decomposes, obtains low frequency c5With high frequency d5, retain d5；Continue to c5It decomposes, obtains low frequency c6And high frequency
d6, retain d6；Continue to c6It decomposes, obtains low frequency c7With high frequency d7, retain d7；Continue to c7It decomposes, obtains low frequency c8And high frequency
d8, retain d8；Continue to c8It decomposes, obtains low frequency c9With high frequency d9, retain c8；D is taken after decomposition1~d9Carry out wavelet reconstruction, d1~
d9The frequency range (unit: Hz) that each frequency range includes be respectively (12.8k~25.6k), (6.4k~12.8k), (3.2k~
6.4k), (1.6k~3.2k), (800~1600), (400~800), (200~400), (100~200), (50~100)；Together
When obtain 9 high and low frequency coefficients of wavelet decomposition；
S32, wavelet reconstruction is carried out to the signal of the frequency ranges of nine high frequencies: extracts the 1st to 9 layer from low to high 9
A frequency band (d1~d9) signal progress signature analysis, 9 respective frequencies section (d that wavelet decomposition is obtained1~d9) corresponding system
Number is named as Xk(t) (k=1,2..., 9), is then reconstructed coefficient of wavelet decomposition, obtains reconstruction signal, uses Sk(t) it indicates
To Xk(t) reconstruction signal, then resultant signal S (t) is as shown in formula (1)；
S (t)=S1(t)+S2(t)+...+S9(t) (1)
S33, the energy for calculating each reconstruction signal, Sk(t) corresponding energy Ek(wherein k=1,2 ..., 9) indicate in this way
(2) shown in:
In formula (2): Xk(t) (k=1,2..., 9) indicates reconstruction signal Sk(t) amplitude of discrete point；
S34, Characteristic Extraction is carried out to the energy of each reconstruction signal, characteristic quantity is normalized, is obtained to be measured
The characteristic quantity of transformer noise signal；Specifically includes the following steps:
S3401, using the energy of each reconstruction signal as element construction feature vector, indicated with T:
T=[E1,E2,...,E9] (3)
S3402, it is obtained to after T normalized:
In formula (4),
E in formula (5)jThat is E in formula (2)k。
S3403, each channel noise signal characteristic moment matrix T ' can be acquired by (4) formula respectivelyALL=(TA′,TB′,TC′)Τ, i.e.,
Obtain the characteristic quantity of transformer noise signal to be measured, TA'、TB'、TC', respectively indicate A, B, C three-phase windings in noise measuring profile
The normalization characteristic amount of the noise signal of corresponding position on line.
S4, to the characteristic quantity of transformer noise signal to be measured and corresponding energy feature amount fingerprint (history transformer health shape
Noise characteristic amount under state) it compares, correlation analysis is carried out, related coefficient vector K=(K is obtainedA,KB,KC)Τ, wherein KA、KB、KC
It is respectively the related coefficient of tri- opposite position signal of A, B, C, and acquires its negative Infinite Norm M；
Specifically, correlation analysis is carried out to vector element each in the matrix of the characteristic quantity of transformer noise signal to be measured,
The calculation method of related coefficient are as follows:
In formula (5), formula (6) and formula (7), K is related coefficient vector；covpqFor the covariance of sequence；Dp、DqIt is respectively each
From the variance of series；N is the length of matrix each element sequence of values, and i is sequence of values 1,2 ..., N arbitrary value；p(i),q(i)
Feature vector (the T of transformer noise signal respectively to be measuredA′,TB′,TC') with corresponding energy feature amount fingerprint (TAO′,TBO′,
TCO') element；
According to the related coefficient vector of transformer noise signal characteristic quantity to be measured with corresponding energy feature amount fingerprint is obtained, count
Calculate its negative Infinite Norm M, since K is related coefficient vector, therefore M is the minimum value of element absolute value in K, it is contemplated that it is subsequent such as
Correlation analysis between multiple groups characteristic quantity, K may be matrix, therefore introduce negative Infinite Norm.
S5, judge that transformer to be measured is hidden with the presence or absence of failure according to the value of the negative Infinite Norm value M of related coefficient vector K
Suffer from, the condition of judgement is as follows:
As 0.8≤M≤1, transformer is in normal condition；
As 0.5≤M < 0.8, there are potential faults for transformer；
As M < 0.5, there are catastrophe failures for transformer；
According to judging result, failure if it exists then can be according to KA, KB, KCThree's size brings S5 into and carries out preliminary judgement energy standard
True judgement winding of problems is separate.
The operating method of fault diagnostic method for transformer winding the present invention is based on multi-channel noise is described in detail,
So that those skilled in the art know more about the present invention:
Case: multi-channel noise test is carried out to certain 220kV main transformer, obtains the noise of tri- opposite position of A, B, C
Signal, present load current are 143.54A (the feature parameter vectors fingerprint corresponding current is 135.23A, and voltage is approximately uniform), are examined
Consider the square directly proportional of basket vibration and electric current, is modified to currently noise signal is measured (i.e. multiplied by coefficient (135.23/
143.54)2), obtain the noise signal S under normallized currentA, SB, SC。
9 layers of decomposition and reconstruction are carried out to triple channel noise signal using Daubechies wavelet function, and extract characteristic quantity,
In conjunction with the feature parameter vectors fingerprint, related coefficient vector K=[0.65,0.92,0.87] is calculated, calculates it and bears infinite coefficient
M=0.65, therefore there may be potential faults.
Further judgement, M=0.65 and KA=0.65, KB=0.92KC=0.87, tentatively judge that A phase winding exists and asks
Topic.It is subsequent can be subsequent by inquiring the fault oscillograph (such as Fig. 2) of A phase winding it is found that A phase is recent once by short-circuit impact
The detection that has a power failure again shows that there may be the failures such as deformation for A phase winding.
Above disclosed is only a specific embodiment of the invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art in the technical scope disclosed by the present invention, can readily occur in variation or modification,
It is covered by the protection scope of the present invention.
Claims (7)
1. a kind of fault diagnostic method for transformer winding based on multi-channel noise, it is characterised in that: the following steps are included:
S1, by the noise signal of multichannel collecting transformer to be measured, while record current signal；
S2, load amendment is carried out to current signal, obtains the noise signal under normallized current；
S3, wavelet function feedback is carried out to the noise signal under normallized current using Daubechies wavelet function, gone forward side by side
Row Characteristic Extraction obtains the characteristic quantity of transformer noise signal to be measured；
S4, to the characteristic quantity of transformer noise signal to be measured and corresponding energy feature amount fingerprint comparison, carry out correlation analysis, obtain
To the negative Infinite Norm of related coefficient vector sum；
S5, judged transformer to be measured with the presence or absence of potential faults according to the value of negative Infinite Norm.
2. the fault diagnostic method for transformer winding according to claim 1 based on multi-channel noise, it is characterised in that: institute
State S3 specifically includes the following steps:
S31, wavelet decomposition is carried out to the noise signal under each normallized current using Daubechies wavelet function, is decomposed into nine
Layer, obtains the frequency range of nine high frequencies and the coefficient of nine high frequencies；
S32, wavelet reconstruction is carried out to the signal of the frequency range of nine high frequencies: extracts nine layers of 9 frequency band from low to high
Signal carries out signature analysis, is reconstructed to coefficient of wavelet decomposition, obtains corresponding reconstruction signal and resultant signal；
S33, the energy for calculating each reconstruction signal；
S34, Characteristic Extraction is carried out to the energy of each reconstruction signal, characteristic quantity is normalized, transformation to be measured is obtained
The characteristic quantity of device noise signal.
3. the fault diagnostic method for transformer winding according to claim 2 based on multi-channel noise, it is characterised in that: institute
State the energy balane formula of each reconstruction signal in S33 are as follows:
In formula (2): xk(t) (k=1,2..., 9) indicates reconstruction signal Sk(t) amplitude of discrete point.
4. the fault diagnostic method for transformer winding according to claim 3 based on multi-channel noise, it is characterised in that: institute
State S34 specifically includes the following steps:
S3401, using the energy of each reconstruction signal as element construction feature vector, indicated with T:
T=[E1,E2,...,E9] (3)
S3402, it is obtained to after T normalized:
In formula (4),
E in formula (5)jThat is E in formula (2)k。
S3403, each channel noise signal characteristic moment matrix T ' is acquired by (4) formula respectivelyALL=(TA′,TB′,TC′)ΤTo get to
Survey the characteristic quantity of transformer noise signal, TA'、TB'、TC' to respectively indicate A, B, C three-phase windings right on noise measuring contour line
Answer the normalization characteristic amount of the noise signal of position.
5. the fault diagnostic method for transformer winding according to claim 4 based on multi-channel noise, it is characterised in that: institute
It states in the correlation analysis of S4, the calculation method of related coefficient are as follows:
In formula (5), formula (6) and formula (7), K is related coefficient vector；covpqFor the covariance of sequence；Dp、DqRespectively respectively it is
The variance of column；N is the length of matrix each element sequence of values, and i is sequence of values 1,2 ..., N arbitrary value；P (i), q (i) are respectively
For the feature vector (T of transformer noise signal to be measuredA′,TB′,TC') with corresponding energy feature amount fingerprint (TAO′,TBO′,TCO′)
Element.
6. the fault diagnostic method for transformer winding according to claim 5 based on multi-channel noise, it is characterised in that: institute
The value for stating the negative Infinite Norm in S5 is M, the condition of judgement are as follows: as 0.8≤M≤1, transformer is in normal condition；When
When 0.5≤M < 0.8, there are potential faults for transformer；As M < 0.5, there are catastrophe failures for transformer.
7. the fault diagnostic method for transformer winding according to claim 1 based on multi-channel noise, it is characterised in that: adopt
With the noise signal of microphone pick transformer to be measured, the microphone is fixed by the bracket around transformer to be measured relatively empty
Spacious region.
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| CN113049086A (en) * | 2021-03-16 | 2021-06-29 | 国家电网有限公司 | Dynamic database forming method and device for acoustic diagnosis of transformer |
| CN113805105A (en) * | 2021-08-23 | 2021-12-17 | 浙江讯飞智能科技有限公司 | Three-phase transformer detection method and system |
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