CN103135035A  Transformer winding state diagnosis method  Google Patents
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 CN103135035A CN103135035A CN2011103799262A CN201110379926A CN103135035A CN 103135035 A CN103135035 A CN 103135035A CN 2011103799262 A CN2011103799262 A CN 2011103799262A CN 201110379926 A CN201110379926 A CN 201110379926A CN 103135035 A CN103135035 A CN 103135035A
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 238000003745 diagnosis Methods 0.000 title abstract 3
 238000001228 spectrum Methods 0.000 claims abstract description 24
 238000000354 decomposition reaction Methods 0.000 claims abstract description 4
 238000000034 methods Methods 0.000 claims description 10
 238000002405 diagnostic procedure Methods 0.000 claims description 9
 230000000875 corresponding Effects 0.000 claims description 6
 238000004458 analytical methods Methods 0.000 description 7
 230000035945 sensitivity Effects 0.000 description 4
 238000010276 construction Methods 0.000 description 3
 238000009413 insulation Methods 0.000 description 3
 230000000051 modifying Effects 0.000 description 3
 230000015556 catabolic process Effects 0.000 description 2
 230000001186 cumulative Effects 0.000 description 2
 230000004069 differentiation Effects 0.000 description 2
 238000009863 impact test Methods 0.000 description 2
 238000004519 manufacturing process Methods 0.000 description 2
 238000001845 vibrational spectrum Methods 0.000 description 2
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 239000012141 concentrates Substances 0.000 description 1
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Abstract
The invention discloses a transformer winding state diagnosis method. The transformer winding state diagnosis method comprises a first step of collecting vibration signals of a transformer box wall when a transformer is shortcircuited; a second step of performing prolongation pretreatment on left end points and right end points of the vibration signals; a third step of decomposing the vibration signals undergone prolongation into a plurality of product function components; a fourth step of enabling all the product function components obtained by decomposition to undergo Hilbert transform, and obtaining a Hilbert spectrum of the vibration signals; a fifth step of obtaining a Hilbert marginal spectrum and Hilbert energy according to the Hilbert spectrum; and a sixth step of distinguishing a state of a transformer winding according to the Hilbert marginal spectrum and variations of the Hilbert energy.
Description
Technical field
The present invention relates to a kind of signal monitoring method, relate in particular to a kind of diagnostic method of transformer winding state.
Background technology
Transformer is one of most important equipment in electric system, and the stability of its operation is great on the power system security impact.Along with the increase day by day of China's net capacity, capacity of short circuit also constantly increases thereupon, and the huge electromagnetic force that the dash current that cuttingout of voltage changer forms produces has consisted of serious threat to physical strength and the dynamic stability of Transformer Winding.At present the running environment of substation equipment and circuit allows of no optimist all the time, because of the distortion that external short circuit causes Transformer Winding to be hit to cause, is fault comparatively common in the transformer operational process, and operation has caused very large threat to security of system for it.
After transformer suffers sudden short circuit, at first loosening or slight deformation may occur in its winding, analyze deformation of transformer winding by a large amount of experimental studies and have cumulative effect, if can not in time find and repair for loosening or distortion, be accumulated to the antishort circuit capability that to a certain degree can make afterwards transformer in the loosening or distortion of transformer so and decline to a great extent and suffering also can cause under less dash current large accident and occur.
The distortion of winding can cause the decline of mechanical resistance shortcircuit current rush ability on the one hand, also can cause on the other hand coil inside minor insulation distance to change, make the local insulation thin spot that occurs, do the used time when running into superpotential, winding might occur between cake or turntoturn short circuit causes transformer insulated breakdown accident, perhaps cause shelf depreciation because local field strength increases, the insulation harm position can enlarge gradually, finally causes transformer generation dielectric breakdown accident and causes the further state of affairs and enlarge.
Therefore, in operational process after transformer has experienced the external short circuit accident or in the routine maintenance of operation after a period of time, whether how effectively to detect Transformer Winding exists loosening and distortion, thereby judge whether transformer needs maintenance to process and seem very important, be an important means that ensures the transformer safe operation, so the detection of deformation of transformer winding is one of present transformer conventional test project.
The detection method to transformer winding state of practical application at present mainly contains following three kinds:
1, short circuit impedance method
Transformer Short Circuit Impedance is the equiva lent impedance of transformer inside when loaded impedance is zero, shortcircuit impedance is the leakage reactance of Transformer Winding and the vector of resistance, because DC Resistance of Transformer is very little with respect to leakage reactance numerical value, so the reflection of the shortcircuit impedance of transformer is mainly the leakage reactance of Transformer Winding.By the theoretical analysis of transformer as can be known, transformer leakage reactance value is that the physical dimension by winding determines, in other words conj.or perhaps by the structures shape of winding, in case Transformer Winding deforms, therefore the leakage reactance of transformer is corresponding in theory also can change, and can reflect indirectly by the detection to Transformer Short Circuit Impedance whether Transformer Winding inside distortion has occured.
Generally, after operating transformer has been subject to the impact of shortcircuit current, or shortcircuit impedance value and the original record that records to be compared to judge whether winding distortion has occured when regularly routine inspection, if the shortcircuit impedance value changes greatly, for example be set as variation in GB and surpass 3%, can confirm that winding has remarkable distortion.
Stipulate according to related standards, transformer is in the shortcircuit impedance testing experiment, require to measure the shortcircuit impedance of each phase, and the data of shortcircuit impedance value measured after test with test are in the past compared, according to the degree of its variation, as judging whether one of qualified important evidence of tested Transformer Winding.
From practical situations, short circuit impedance method has been set up standard in longterm production practices, and criterion is comparatively clear and definite, has all clearly provided the criterion of winding deformation degree in international electrical engineering standard IEC600765 and GBl09585.But in a lot of situations, the sensitivity of this method is very low, and the recall rate of fault is lower, only can obtain clearer and more definite reflection when coil bulk deformation situation is comparatively serious.
2, Frequency Response Analysis method
The ultimate principle of Frequency Response Analysis method is that Transformer Winding is considered as a distributed parameter network, it consists of a passive linear twoport network by distribution parameters such as ground capacitance C, vertical electric capacity K, inductance L, and the characteristic of this network can be described with transfer function H (j ω) on frequency domain.
After the distortion of winding generation local mechanical, corresponding variation can occur in the distribution parameters such as its inner distributed inductance L, vertical electric capacity K and ground capacitance C, thereby is reflected on the transfer function H (j ω) of network.Whether the network electrical quantity that the situation of change of therefore analyzing the network transfer function curve of Transformer Winding just can be analyzed inside changes, thereby infer whether corresponding physical construction distortion has occured, this is foundation and the basis of Detecting Winding Deformation in Transformers with FRA Method.
The method of frequency response method test is at first with a stable sine sweep voltage signal V
_{i}Be applied to an end of tested Transformer Winding, then record simultaneously this port V
_{i}With the voltage V on other output port
_{o}Thereby, obtaining one group of Frequency Response curve of this tested winding, its expression formula is
H(jω)＝V
_{o}/V
_{i}
The measurement sensitivity of method of frequency response method is high than short circuit impedance method, but due to the complicacy of its frequency response waveform, and the differentiation of winding situation is needed more experience, and therefore the quantitative criteria that more difficult formation is clear and definite does not form discrimination standard so far.
abovementioned two kinds of methods are that to differentiate at present the Transformer Winding situation the most frequently used, two kinds of methods are all to adopt electric measuring method, the element electrical quantity that starting point all is based on correspondence in the obvious situation drag that is out of shape of Transformer Winding generation changes to measure differentiation, it is comparatively suitable that obvious deformation occurs to Transformer Winding in this, but to winding generation slight deformation, especially relatively loosening and state torsional deformation that Transformer Winding is existed can not provide clearer and more definite judgement, because the electrical quantity that is reflected in these situations in equivalentcircuit model does not almost change, the variation of its transport function is also just very little.Yet Transformer Winding is loosening or torsional deformation has a great impact its antishort circuit capability, and the situation of therefore studying winding need have the higher method of sensitivity to differentiate.
3, vibration analysis method
The ultimate principle of vibration analysis method is that Transformer Winding is regarded as a physical construction body, when winding construction or any variation of stressed generation, can change from its mechanical vibration performance and be reflected.Therefore, can detect by the duty of the vibration signal on the analysis tank wall to winding.Compare with aforementioned electric mensuration, the great advantage of vibration analysis method is to obtain by being adsorbed on vibration transducer on transformer box wall the vibration signal of transformer, judge the situation of change of winding state by the variation of analyzing its vibration characteristics, as long as the mechanical property of winding (become flexible as malformation, pretightning force etc.) change, can change from its mechanical vibration performance and be reflected, thereby greatly improve the sensitivity that detects.In addition, vibration detection and whole strong power system that vibration transducer is placed on tank wall directly are connected, and without any impact, therefore, can develop into a kind of more accurate, convenient, safe online monitoring method for the normal operation of whole electrical system.
Summary of the invention
The purpose of this invention is to provide a kind of transformer winding state diagnostic method, the method utilizes vibrational spectrum that the duty of Transformer Winding is differentiated.
In order to realize the foregoing invention purpose, the invention provides a kind of transformer winding state diagnostic method, it comprises the following steps:
When (1) gathering the transformer sudden short circuit, one section vibration signal of transformer box wall;
(2) left end point with described one section vibration signal carries out continuation from left to right according to following step, and the right endpoint of described one section vibration signal is carried out continuation from right to left according to following step:
Be x (1) 2a. establish the endpoint data of described one section vibration signal, described endpoint data is left end point data or right endpoint data, and the signal maximum point that occurs at first is eb
_{1}, the signal minimum point that occurs at first is es
_{1}, the whole signal maximum point eb in described one section vibration signal
_{i}Consist of a signal maximum value point set, the whole signal minimum point es in described one section vibration signal
_{i}Consist of a signal minimizer set, i=1,2,3 ... n is with x (1), eb
_{1}And es
_{1}The signal segment xs that consists of
_{1}(t) as the characteristic signal of described one section vibration signal, if signal maximum point eb
_{1}Prior to signal minimum point es
_{1}Occur, with eb
_{1}As unique point, if signal minimum point es
_{1}Be eb prior to the signal maximum point
_{1}Occur, with es
_{1}Be unique point, look for all and signal segment xs according to described characteristic signal and unique point in described one section vibration signal
_{1}(t) the signal segment xs that Waveform Matching and length are identical
_{i}(t), obtain signal segment xs
_{i}(t) '=xs
_{i}(t)+(eb
_{1}eb
_{2}), calculate signal segment xs
_{i}(t) ' with signal segment xs
_{1}(t) matching degree value e
_{i}=∑ (xs
_{1}(t)xs
_{i}(t) ')
^{2}, from all signal segment xs
_{i}(t) ' in pick out the signal segment xs of matching degree value minimum
_{i}(t) ", itself and signal segment xs
_{1}(t) matching degree value is e
_{min}
2b. with e
_{min}Compare with a threshold alpha of setting, if e
_{min}＜α judges this signal segment xs
_{i}(t) " be matching section; If e
_{min}〉=α judges xs
_{i}(t) " not matching section;
2c. get matching section xs in described one section vibration signal
_{i}(t) " signal maximum point eb
_{i}" two signal maximum point eb before
_{i1}, eb
_{i2}And two signal minimum point es
_{i1}, es
_{i2}, at abovementioned eb
_{i1}, eb
_{i2}, es
_{i1}, es
_{i2}Upper (the eb that adds respectively
_{1}eb
_{i}") obtains eb '
_{i1}, eb '
_{i2}, es '
_{i1}And es '
_{i2}, the end points place that then adds it to described signal maximum value point set and signal minimizer set according to its time sequencing corresponding in waveform is as continuation, and described end points place is high order end or low order end;
(3) according to following step, abovementioned one section vibration signal x (t) through continuation is decomposed into several multiplicative function components (Production Function is referred to as the PF component):
3a. to vibration signal x (t) differentiate, obtain time series y (t);
3b. the product that sequences y computing time (t) is adjacent 2
py
_{i}(t)＝y
_{i}(t)×y
_{i1}(t)
Wherein, i=2,3, L, n1, n are counting of vibration signal;
3c. according to product py
_{i}(t) and time series y (t) positive and negative, look for successively all Local modulus maxima eb (t) and all es of local minizing point (t) of vibration signal x (t):
Work as py
_{i}(t)＜0 o'clock: if py
_{i}(t)＜0 and y
_{i1}(t)＜0, x
_{i1}(t) be local minizing point; If py
_{i}(t)＜0 and y
_{i1}(t)＞0, x
_{i1}(t) be Local modulus maxima;
Work as py
_{i}(t)＞0 o'clock, x
_{i1}(t) be nonextreme point;
Work as py
_{i}(t)=0 o'clock, if y
_{i1}(t) 2 y are calculated in=0
_{i}(t) and y
_{i2}(t) product makes py
_{i}(t) '=y
_{i}(t) * y
_{i2}(t), if py
_{i}(t) '＜0 and y
_{i2}(t)＜0, x
_{i1}(t) be local minizing point; If py
_{i}(t) '＜0 and y
_{i2}(t)＞0, x
_{i1}(t) be Local modulus maxima; If y
_{i2}(t)=0, x
_{i1}(t) be nonextreme point;
3d. described all Local modulus maxima eb (t) and all es of local minizing point (t) are coupled together with cubic spline functions s (t) obtain respectively coenvelope line e
_{max}(t) and lower envelope line e
_{min}(t), described cubic spline functions s (t) is each the minizone [t at vibration signal x (t)
_{i}, t
_{i+1}] being no more than the polynomial expression of three times on (i=1,2, L, n1), its expression formula is
In formula, m
_{i}And m
_{i+1}For cubic spline functions s (t) at interval [t
_{i}, t
_{i+1}] the second derivative value at twoendpoint place; In this step, the algorithm of envelope is mathematical method commonly used in this area, so the inventor no longer is described in detail at this;
3e. according to the coenvelope line e that tries to achieve
_{max}(t) and lower envelope line e
_{min}(t) calculate the average m of upper and lower envelope
_{1i}(t)=(e
_{max}(t)+e
_{min}(t))/2, and envelope estimation function a
_{1i}(t)=(e
_{max}(t)e
_{min}(t))/2, i=1,2 ... n;
3f. adopt model s
_{11}(t)=[x (t)m
_{1i}(t)]/a
_{1i}(t) calculate s
_{11}(t);
3g. judgement s
_{11}(t) whether be a pure FM signal, if s
_{11}(t) not that a pure FM signal is with s
_{11}(t) repeat abovementioned steps 3a～3f n time altogether as original signal, until s
_{11}(t) be that (FM signal is the highfrequency signal that modulated signals has been modulated for pure FM signal, the high frequency waves that normally amplitude is constant, frequency changes with modulation signal, the concept of pure FM signal is known for the one of ordinary skilled in the art);
3h. with all envelope estimation function a that produce in iterative process
_{1i}(t) multiply each other, obtain the instantaneous frequency signal
3i. with instantaneous frequency signal and pure FM signal s
_{11}(t) multiply each other and obtain first multiplicative function
PF
_{1}＝a
_{1}(t)s
_{11}(t)
3j. with PF
_{1}Separate from vibration signal x (t), obtain residual signal y
_{1}(t);
3k. with residual signal y
_{1}(t) as original vibration signal repeating step 3a～3j, decomposite all multiplicative function components, until satisfy the iteration stop criterion, obtain whole multiplicative function components, described iteration stop criterion is: the residual signal y that obtains
_{k}(t) be a monotonic quantity or residual signal y
_{k}(t) energy is less than 3%～5% of original vibration signal x (t) energy;
(4) the whole multiplicative function components that decomposition obtained carry out Hilbert transform, obtain the hilbert spectrum H (ω, t) of vibration signal x (t); In this step, Hilbert transform is mathematical method commonly used in this area, so the inventor no longer is described in detail at this;
(5) abovementioned hilbert spectrum H (ω, t) is carried out integration to the time and obtain the Hilbert marginal spectrum, the square time is carried out integration and obtain the Hilbert energy abovementioned hilbert spectrum H (ω, t);
(6) according to Hilbert marginal spectrum and the Hilbert energy variation of vibration signal x (t), transformer winding state is differentiated: the high fdrequency component in Hilbert energy and Hilbert marginal spectrum is (greater than the frequency component of 100Hz, be generally the integral multiple of 50Hz) when increasing more than 5 times, Transformer Winding occurs loosening or distortion, need in time process this moment, avoids forming significant trouble.
In abovementioned transformer winding state diagnostic method, the span of the threshold alpha in described step 2b is 0.1～0.2.
Transformer winding state diagnostic method of the present invention utilizes vibrational spectrum that the duty of Transformer Winding is detected, thereby judge accurately and efficiently the duty of Transformer Winding, so that can in time pinpoint the problems, transformer is in time overhauled.
Description of drawings
Below in conjunction with the drawings and specific embodiments, transformer winding state diagnostic method of the present invention is described in further detail.
Fig. 1 is the transformer winding state that monitors in the present embodiment vibrational waveform when good.
Vibrational waveform when Fig. 2 is the Transformer Winding duty deterioration that monitors in the present embodiment.
Fig. 3 has shown the combination of in the present embodiment, vibration signal being decomposed for several PF components.
Fig. 4 has shown the marginal spectrogram of the Hilbert when in the present embodiment, transformer winding state is good.
Fig. 5 has shown the marginal spectrogram of Hilbert when in the present embodiment, transformer winding state worsens.
Embodiment
Carry out the shortcircuit impact test take certain 10kV of Utilities Electric Co. substation transformer as subjects.Test mesolow short circuit in winding, high pressure B phase winding loading power, carry out shortcircuit impact 25 times, have a power failure after each shortcircuit test and carry out the shortcircuit impedance detection, record simultaneously the vibration signal in each shortcircuit impact state procedure, Fig. 1 has shown the vibrational waveform when transformer winding state is good, and Fig. 2 has shown the vibrational waveform when the Transformer Winding duty worsens.Winding duty during according to the following step judgement transformer shortcircuit:
(1) gather the vibration signal of transformer box wall, select the vibration signal in the 0.8s time period;
(2) left end point with abovementioned vibration signal carries out continuation from left to right according to following step, and its right endpoint is also carried out continuation from right to left according to following step:
Be x (1) 2a. establish the endpoint data of this section vibration signal, the signal maximum point that occurs at first is eb
_{1}, the signal minimum point that occurs at first is es
_{1}, the whole signal maximum point eb in this section vibration signal
_{i}Consist of a signal maximum value point set, the whole signal minimum point es in this section vibration signal
_{i}Consist of a signal minimizer set, i=1,2,3 ... n is with x (1), eb
_{1}And es
_{1}The signal segment xs that consists of
_{1}(t) as the characteristic signal of this section vibration signal, if signal maximum point eb
_{1}Prior to signal minimum point es
_{1}Occur, with eb
_{1}As unique point, if the signal minimum point is es
_{1}Be eb prior to the signal maximum point
_{1}Occur, with es
_{1}Be unique point, look for all and signal segment xs according to abovementioned characteristic signal and unique point in this section vibration signal
_{1}(t) the signal segment xs that Waveform Matching and length are identical
_{i}(t), then obtain signal segment xs
_{i}(t) '=xs
_{i}(t)+(eb
_{1}eb
_{2}), calculate signal segment xs
_{i}(t) ' with signal segment xs
_{1}(t) matching degree value e
_{i}=∑ (xs
_{1}(t)xs
_{i}(t) ')
^{2}, from all signal segment xs
_{i}(t) ' in pick out the signal segment xs of matching degree value minimum
_{i}(t) ", itself and signal segment xs
_{1}(t) matching degree value is e
_{min}
2b. with e
_{min}Compare with the threshold alpha of setting=0.1, if e
_{min}＜α judges this signal segment xs
_{i}(t) " be matching section; If e
_{min}〉=α judges xs
_{i}(t) " not matching section;
2c. get matching section xs in this section vibration signal
_{i}(t) " signal maximum point eb
_{i}" two signal maximum point eb before
_{i1}, eb
_{i2}And two signal minimum point es
_{i1}, es
_{i2}, at abovementioned eb
_{i1}, eb
_{i2}, es
_{i1}, es
_{i2}Upper (the eb that adds respectively
_{1}eb
_{i}") obtains eb '
_{i1}, eb '
_{i2}, es '
_{i1}And es '
_{i2}, then add it end points place of described signal maximum value point set and signal minimizer set to as continuation according to its time sequencing corresponding in waveform;
(3) according to following step, abovementioned vibration signal x (t) through continuation is decomposed into several PF components;
3a. to vibration signal x (t) differentiate, obtain time series y (t);
3b. the product that sequences y computing time (t) is adjacent 2
py
_{i}(t)＝y
_{i}(t)×y
_{i1}(t)
Wherein, i=2,3, L, n1, n are counting of vibration signal;
3c. according to product py
_{i}(t) and time series y (t) positive and negative, look for successively all Local modulus maxima eb (t) and all es of local minizing point (t) of vibration signal x (t):
Work as py
_{i}(t)＜0 o'clock: if py
_{i}(t)＜0 and y
_{i1}(t)＜0, x
_{i1}(t) be local minizing point; If py
_{i}(t)＜0 and y
_{i1}(t)＞0, x
_{i1}(t) be Local modulus maxima;
Work as py
_{i}(t)＞0 o'clock, x
_{i1}(t) be nonextreme point;
Work as py
_{i}(t)=0 o'clock, if y
_{i1}(t) 2 y are calculated in=0
_{i}(t) and y
_{i2}(t) product makes py
_{i}(t) '=y
_{i}(t) * y
_{i2}(t), if py
_{i}(t) '＜0 and y
_{i2}(t)＜0, x
_{i1}(t) be local minizing point; If py
_{i}(t) '＜0 and y
_{i2}(t)＞0, x
_{i1}(t) be Local modulus maxima; If y
_{i2}(t)=0, x
_{i1}(t) be nonextreme point;
3d. all Local modulus maxima eb (t) and all es of local minizing point (t) are coupled together with cubic spline functions s (t) obtain respectively coenvelope line e
_{max}(t) and lower envelope line e
_{min}(t), this cubic spline functions s (t) is each the minizone [t at vibration signal x (t)
_{i}, t
_{i+1}] being no more than the polynomial expression of three times on (i=1,2, L, n1), its expression formula is
In formula, m
_{i}And m
_{i+1}For cubic spline functions s (t) at interval [t
_{i}, t
_{i+1}] the second derivative value at twoendpoint place; In this step, the algorithm of related envelope is the mathematical method that often has in this area, therefore the inventor no longer carries out the description of detailed computation process at this;
3e. according to the coenvelope line e that tries to achieve
_{max}(t) and lower envelope line e
_{min}(t) calculate the average m of upper and lower envelope
_{1i}(t)=(e
_{max}(t)+e
_{min}(t))/2, and envelope estimation function a
_{1i}(t)=(e
_{max}(t)e
_{min}(t))/2, i=1,2 ... n;
3f. adopt model s
_{11}(t)=[x (t)m
_{1i}(t)]/a
_{1i}(t) calculate s
_{11}(t);
3g. judgement s
_{11}(t) whether be a pure FM signal, if s
_{11}(t) not that a pure FM signal is with s
_{11}(t) repeat abovementioned steps 3a～3f n time altogether as original signal, until s
_{11}(t) be a pure FM signal;
3h. with all envelope estimation function a that produce in iterative process
_{1i}(t) multiply each other, obtain the instantaneous frequency signal
3i. with instantaneous frequency signal and pure FM signal s
_{11}(t) multiply each other and obtain first multiplicative function
PF
_{1}＝a
_{1}(t)s
_{11}(t)
3j. with PF
_{1}Separate from vibration signal x (t), obtain residual signal y
_{1}(t);
3k. with residual signal y
_{1}(t) as original vibration signal repeating step 3a～3j, decomposite all multiplicative function components, until satisfy the iteration stop criterion, obtain whole multiplicative function components, described iteration stop criterion is: the residual signal y that obtains
_{k}(t) be a monotonic quantity or residual signal y
_{k}(t) energy is less than 3%～5% of original vibration signal x (t) energy;
Through abovementioned steps, original vibration signal x (t) is broken down into 6 PF components, and Fig. 3 has shown that it is the result of 6 PF components that vibration signal is decomposed;
(4) the whole multiplicative function components that decomposition obtained carry out Hilbert transform, then amplitude are presented on the frequencytime plane, obtain the hilbert spectrum H (ω, t) of vibration signal x (t);
(5) abovementioned hilbert spectrum H (ω, t) is carried out integration to the time and obtain the Hilbert marginal spectrum, the square time is carried out integration and obtain the Hilbert energy abovementioned hilbert spectrum H (ω, t);
(6) then according to Hilbert marginal spectrum and the Hilbert energy variation of vibration signal x (t), winding state is differentiated: the high fdrequency component in Hilbert energy and Hilbert marginal spectrum is (greater than the frequency component of 100Hz, be generally the integral multiple of 50Hz) when increasing 5 times, Transformer Winding occurs loosening or distortion, need in time process this moment, avoids forming significant trouble.
Can find out from Fig. 4 and table 1, in the present embodiment, front 24 shortcircuit impacts test, the marginal spectrum peak value concentrates on the 100Hz place, in addition as can also be seen from Table 1, and along with the increase of shortcircuit impact number of times, the Hilbert energy presents steady increase, and this has embodied the cumulative effect of deformation of transformer winding.
Can find out from Fig. 5 and table 1, during the 25th shortcircuit impact test, the high fdrequency component on marginal spectrum centered by the 400Hz component obviously increases, in addition as can also be seen from Table 1, the Hilbert energy is increased to original 5 times, and all the indication transformer winding state is abnormal for this, need to overhaul it.
Table 1.
Be noted that above enumerate only for specific embodiments of the invention, obviously the invention is not restricted to above embodiment, many similar variations are arranged thereupon.If those skilled in the art all should belong to protection scope of the present invention from all distortion that content disclosed by the invention directly derives or associates.
Claims (2)
1. a transformer winding state diagnostic method, is characterized in that, comprises the following steps:
When (1) gathering the transformer sudden short circuit, one section vibration signal of transformer box wall;
(2) left end point with described one section vibration signal carries out continuation from left to right according to following step, and the right endpoint of described one section vibration signal is carried out continuation from right to left according to following step:
Be x (1) 2a. establish the endpoint data of described one section vibration signal, described endpoint data is left end point data or right endpoint data, and the signal maximum point that occurs at first is eb
_{1}, the signal minimum point that occurs at first is es
_{1}, the whole signal maximum point eb in described one section vibration signal
_{i}Consist of a signal maximum value point set, the whole signal minimum point es in described one section vibration signal
_{i}Consist of a signal minimizer set, i=1,2,3 ... n is with x (1), eb
_{1}And es
_{1}The signal segment xs that consists of
_{1}(t) as the characteristic signal of described one section vibration signal, if signal maximum point eb
_{1}Prior to signal minimum point es
_{1}Occur, with eb
_{1}As unique point, if the signal minimum point is es
_{1}Be eb prior to the signal maximum point
_{1}Occur, with es
_{1}Be unique point, look for all and signal segment xs according to described characteristic signal and unique point in described one section vibration signal
_{1}(t) the signal segment xs that Waveform Matching and length are identical
_{i}(t), obtain signal segment xs
_{i}(t) '=xs
_{i}(t)+(eb
_{1}eb
_{2}), calculate signal segment xs
_{i}(t) ' with signal segment xs
_{1}(t) matching degree value e
_{i}=∑ (xs
_{1}(t)xs
_{i}(t) ')
^{2}, from all signal segment xs
_{i}(t) ' in pick out the signal segment xs of matching degree value minimum
_{i}(t) ", itself and signal segment xs
_{1}(t) matching degree value is e
_{min}
2b. with e
_{min}Compare with a threshold alpha of setting, if e
_{min}＜α judges this signal segment xs
_{i}(t) " be matching section; If e
_{min}〉=α judges xs
_{i}(t) " not matching section;
2c. get matching section xs in described one section vibration signal
_{i}(t) " signal maximum point eb
_{i}" two signal maximum point eb before
_{i1}, eb
_{i2}And two signal minimum point es
_{i1}, es
_{i2}, at abovementioned eb
_{i1}, eb
_{i2}, es
_{i1}, es
_{i2}Upper (the eb that adds respectively
_{1}eb
_{i}") obtains eb '
_{i1}, eb '
_{i2}, es '
_{i1}And es '
_{i2}, the end points place that then adds it to described signal maximum value point set and signal minimizer set according to its time sequencing corresponding in waveform is as continuation, and described end points place is high order end or low order end;
(3) according to following step, abovementioned one section vibration signal x (t) through continuation is decomposed into several multiplicative function components:
3a. to vibration signal x (t) differentiate, obtain time series y (t);
3b. the product that sequences y computing time (t) is adjacent 2
py
_{i}(t)＝y
_{i}(t)×y
_{i1}(t)
Wherein, i=2,3, L, n1, n are counting of vibration signal;
3c. according to product py
_{i}(t) and time series y (t) positive and negative, look for successively all Local modulus maxima eb (t) and all es of local minizing point (t) of vibration signal x (t):
Work as py
_{i}(t)＜0 o'clock: if py
_{i}(t)＜0 and y
_{i1}(t)＜0, x
_{i1}(t) be local minizing point; If py
_{i}(t)＜0 and y
_{i1}(t)＞0, x
_{i1}(t) be Local modulus maxima;
Work as py
_{i}(t)＞0 o'clock, x
_{i1}(t) be nonextreme point;
Work as py
_{i}(t)=0 o'clock, if y
_{i1}(t) 2 y are calculated in=0
_{i}(t) and y
_{i2}(t) product makes py
_{i}(t) '=y
_{i}(t) * y
_{i2}(t), if py
_{i}(t) '＜0 and y
_{i2}(t)＜0, x
_{i1}(t) be local minizing point; If py
_{i}(t) '＜0 and y
_{i2}(t)＞0, x
_{i1}(t) be Local modulus maxima; If y
_{i2}(t)=0, x
_{i1}(t) be nonextreme point;
3d. described all Local modulus maxima eb (t) and all es of local minizing point (t) are coupled together with cubic spline functions s (t) obtain respectively coenvelope line e
_{max}(t) and lower envelope line e
_{min}(t), described cubic spline functions s (t) is each the minizone [t at vibration signal x (t)
_{i}, t
_{i+1}] being no more than the polynomial expression of three times on (i=1,2, L, n1), its expression formula is
In formula, m
_{i}And m
_{i+1}For cubic spline functions s (t) at interval [t
_{i}, t
_{i+1}] the second derivative value at twoendpoint place;
3e. according to the coenvelope line e that tries to achieve
_{max}(t) and lower envelope line e
_{min}(t) calculate the average m of upper and lower envelope
_{1i}(t)=(e
_{max}(t)+e
_{min}(t))/2, and envelope estimation function a
_{1i}(t)=(e
_{max}(t)e
_{min}(t))/2, i=1,2 ... n;
3f. adopt model s
_{11}(t)=[x (t)m
_{1i}(t)]/a
_{1i}(t) calculate s
_{11}(t);
3g. judgement s
_{11}(t) whether be a pure FM signal, if s
_{11}(t) not that a pure FM signal is with s
_{11}(t) repeat abovementioned steps 3a～3f n time altogether as original signal, until s
_{11}(t) be a pure FM signal;
3h. with all envelope estimation function a that produce in iterative process
_{1i}(t) multiply each other, obtain the instantaneous frequency signal
3i. with instantaneous frequency signal and pure FM signal s
_{11}(t) multiply each other and obtain first multiplicative function
PF
_{1}＝a
_{1}(t)s
_{11}(t)
3j. with PF
_{1}Separate from vibration signal x (t), obtain residual signal y
_{1}(t);
3k. with residual signal y
_{1}(t) as original vibration signal repeating step 3a～3j, decomposite all multiplicative function components, until satisfy the iteration stop criterion, obtain whole multiplicative function components, described iteration stop criterion is: the residual signal y that obtains
_{k}(t) be a monotonic quantity or residual signal y
_{k}(t) energy is less than 3%～5% of original vibration signal x (t) energy;
(4) the whole multiplicative function components that decomposition obtained carry out Hilbert transform, obtain the hilbert spectrum H (ω, t) of vibration signal x (t);
(5) abovementioned hilbert spectrum H (ω, t) is carried out integration to the time and obtain the Hilbert marginal spectrum, the square time is carried out integration and obtain the Hilbert energy abovementioned hilbert spectrum H (ω, t);
(6) according to Hilbert marginal spectrum and the Hilbert energy variation of vibration signal x (t), transformer winding state is differentiated: when the high fdrequency component in Hilbert energy and Hilbert marginal spectrum all increases more than 5 times, be judged to be Transformer Winding and occur loosening or distortion.
2. transformer winding state diagnostic method as claimed in claim 1, is characterized in that, the span of the threshold alpha in described step 2b is 0.1～0.2.
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Cited By (5)
Publication number  Priority date  Publication date  Assignee  Title 

CN105137277A (en) *  20150911  20151209  西安交通大学  Diagnosis method of winding machinery state in case of sudden short circuit of transformer 
CN105203914A (en) *  20150722  20151230  广东电网有限责任公司电力科学研究院  Method for diagnosing winding state of transformer under sudden short circuit 
CN106199703A (en) *  20160826  20161207  中国矿业大学  A kind of microseism focus is automatically positioned and Reliability Synthesis evaluation methodology 
CN106468751A (en) *  20160929  20170301  河海大学  A kind of transformer winding state recognition methodss of the neutral net that resonated based on fuzzy selfadaption 
CN107192917A (en) *  20170525  20170922  云南电网有限责任公司电力科学研究院  Antishort circuit capability experimental method and system of a kind of transformer under cumulative effect 
Citations (8)
Publication number  Priority date  Publication date  Assignee  Title 

JP2009115505A (en) *  20071102  20090528  Mitsubishi Electric Corp  Winding inspection device and inspection method 
CN101614775A (en) *  20090715  20091230  河北科技大学  Transformer State Assessment system and appraisal procedure thereof based on Multisource Information Fusion 
CN201497789U (en) *  20090925  20100602  沈阳变压器研究院股份有限公司  Device for measuring shortcircuit vibration displacement and deformation of transformer type products 
CN101738567A (en) *  20081125  20100616  上海市电力公司  System and method for detecting transformer winding state by using constantcurrent sweep frequency power source excitation 
CN101782426A (en) *  20100129  20100721  浙江大学  Detection method of looseness fault vibration of power transformer winding 
CN101833056A (en) *  20100326  20100915  中国电力科学研究院  Method for diagnosing deformation of transformer winding based on frequency response characteristics 
CN101937047A (en) *  20090630  20110105  上海市电力公司  Method of utilizing vibration waveform for detecting state of transformer winding 
CN102027660A (en) *  20080514  20110420  Abb研究有限公司  A method and a device for estimating the clamping force on a winding package of a transformer or a reactor 

2011
 20111125 CN CN2011103799262A patent/CN103135035A/en active Pending
Patent Citations (8)
Publication number  Priority date  Publication date  Assignee  Title 

JP2009115505A (en) *  20071102  20090528  Mitsubishi Electric Corp  Winding inspection device and inspection method 
CN102027660A (en) *  20080514  20110420  Abb研究有限公司  A method and a device for estimating the clamping force on a winding package of a transformer or a reactor 
CN101738567A (en) *  20081125  20100616  上海市电力公司  System and method for detecting transformer winding state by using constantcurrent sweep frequency power source excitation 
CN101937047A (en) *  20090630  20110105  上海市电力公司  Method of utilizing vibration waveform for detecting state of transformer winding 
CN101614775A (en) *  20090715  20091230  河北科技大学  Transformer State Assessment system and appraisal procedure thereof based on Multisource Information Fusion 
CN201497789U (en) *  20090925  20100602  沈阳变压器研究院股份有限公司  Device for measuring shortcircuit vibration displacement and deformation of transformer type products 
CN101782426A (en) *  20100129  20100721  浙江大学  Detection method of looseness fault vibration of power transformer winding 
CN101833056A (en) *  20100326  20100915  中国电力科学研究院  Method for diagnosing deformation of transformer winding based on frequency response characteristics 
NonPatent Citations (5)
Title 

唐卫民等: "大型变压器绕组状态振动分析法的试验研究", 《变压器》 * 
洪刚: "基于振动法的变压器绕组及铁心状态监测与故障诊断方法研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * 
王丰华等: "振动法在线监测突发短路时变压器绕组状态", 《控制工程》 * 
程军圣等: "局部均值分解法及其在滚动轴承故障诊断中的应用", 《中国机械工程》 * 
邵晨曦等: "一种自适应的EMD端点延拓方法", 《电子学报》 * 
Cited By (7)
Publication number  Priority date  Publication date  Assignee  Title 

CN105203914A (en) *  20150722  20151230  广东电网有限责任公司电力科学研究院  Method for diagnosing winding state of transformer under sudden short circuit 
CN105137277A (en) *  20150911  20151209  西安交通大学  Diagnosis method of winding machinery state in case of sudden short circuit of transformer 
CN105137277B (en) *  20150911  20181207  西安交通大学  The diagnostic method of winding machine performance when a kind of transformer sudden short circuit 
CN106199703A (en) *  20160826  20161207  中国矿业大学  A kind of microseism focus is automatically positioned and Reliability Synthesis evaluation methodology 
CN106468751A (en) *  20160929  20170301  河海大学  A kind of transformer winding state recognition methodss of the neutral net that resonated based on fuzzy selfadaption 
CN107192917A (en) *  20170525  20170922  云南电网有限责任公司电力科学研究院  Antishort circuit capability experimental method and system of a kind of transformer under cumulative effect 
CN107192917B (en) *  20170525  20191129  云南电网有限责任公司电力科学研究院  The antishort circuit capability experimental method and system of a kind of transformer under cumulative effect 
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