CN108594083A - A kind of scaling method of power cable fault electric discharge acoustic wave form starting point - Google Patents
A kind of scaling method of power cable fault electric discharge acoustic wave form starting point Download PDFInfo
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- CN108594083A CN108594083A CN201810737477.6A CN201810737477A CN108594083A CN 108594083 A CN108594083 A CN 108594083A CN 201810737477 A CN201810737477 A CN 201810737477A CN 108594083 A CN108594083 A CN 108594083A
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- starting point
- acoustic wave
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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
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
-
- 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
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/083—Locating faults in cables, transmission lines, or networks according to type of conductors in cables, e.g. underground
Abstract
A kind of scaling method of power cable fault electric discharge acoustic wave form starting point, belongs to power cable fault field of detecting.Include the following steps：Step 1, one section of voice signal is acquired using sound magnetic-synchro method and carries out analog-to-digital conversion；Step 2, voice signal is subjected to dyadic wavelet transform；Step 3, voice signal is subjected to soft-threshold processing, eliminates the noise in voice signal；Step 4, inverse wavelet transform is carried out, voice signal is returned into time domain from wavelet domain transform；Step 5, the first extreme point of data is found using 3 σ criterion；Step 6, voice signal starting point is demarcated.In the scaling method that this power cable fault discharges acoustic wave form starting point, using wavelet soft threshold de-noising technology and based on the waveform analysis techniques of signal characteristic, it being capable of automatic Calibration fault discharge acoustic wave form starting point, to the calculating sound magnetic signal time difference, distance or the position for helping tester's failure judgement point, greatly promote the working efficiency of power cable fault fixed point.
Description
Technical field
A kind of scaling method of power cable fault electric discharge acoustic wave form starting point, belongs to power cable fault field of detecting.
Background technology
Submarine transmission line of the power cable in Urban Underground power grid, the inside power supply circuit of industrial and mining enterprises and crossing river and sea
It is widely used in road.Cable once breaks down, and can be produced to enterprise and cause loss of outage, brought not to resident living
Just, thus cable break down after need to find failure as early as possible and repaired.
Cable fault is searched to generally require by three fault diagnosis, fault localization and position determination of fault steps.Fault diagnosis
It is the insulation resistance value that the connectivity of each phase of cable, failure phase are checked with the tools such as multimeter and equipment, it is therefore an objective to distinguish fault
Matter selects suitable test method for subsequent step；Fault localization is to measure cable between fault point and test point with instrument
Length, it is therefore an objective to substantially determine the region where cable fault, reduce the range of trouble shoot；Position determination of fault is detected with instrument
The intensity of fault-signal or arrival time, it is therefore an objective to move closer to and finally confirm location of fault.
At present in type cable failure positioning link, event is mainly searched by the method for detecting cable fault electric discharge sound both at home and abroad
Barrier point.There are two types of realization methods for this method：Sound detection harmony magnetic-synchro method.
The operation principle of sound detection is to receive Method of Cable Trouble Point electric discharge sound, sound letter using the sounding probe for being placed in ground
Number host is transmitted to by probe, the processing such as filtered, amplification, then by host will treated that voice signal is transmitted to intercepts ear
Machine intercepts and identified for tester, the distance of failure judgement point or position.
The strong and weak variation for the fault discharge sound that sound detection is mainly heard according to tester, the distance of failure judgement point or
Position.Since human ear is insensitive to sound intensity slight change, ambient noise interference and cable laying depth difference cause sound strong
The influence of the factors such as weak variation, the distance of sound detection accurate judgement fault point or position are very difficult.Cable at present
Position determination of fault seldom uses sound detection.
The operation principle of sound magnetic-synchro method is to synchronize to receive cable fault using the sound magnetic-synchro detection probe for being placed in ground
The voice signal and electromagnetic field signal that point electric discharge generates, sound and electromagnetic field signal are transmitted to host by popping one's head in, two kinds of host pair
Signal such as is filtered, amplifies at the processing, is then sent to voice signal and intercepts earphone, at the same by sound and field waveform include
On screen.Tester integrates the sound heard, the waveform seen and difference (the sound magnetic signal between sound magnetic signal arrival time
The time difference) identification fault discharge sound, the distance of failure judgement point or position.
Sound magnetic-synchro method can determine that the sound magnetic signal time difference, tester can be with accurate judgement failures according to the sound magnetic signal time difference
The distance of point or position.Type cable failure positioning majority uses sound magnetic-synchro method at present, but in the prior art, sound magnetic-synchro
There is also have following problem for method：Determine that the sound magnetic signal time difference needs to demarcate the starting point of cable fault electric discharge acoustic wave form, due to electricity
Cable fault discharge acoustic wave form is complicated, can only manually be demarcated by experienced tester at present.Artificial calibration waveform starting point training
Time is long, of high cost, and this technical ability is detached from site environment and is difficult to train, teach and inherit, and constrains detection of cable fault
The development of automatization level.
Invention content
The technical problem to be solved by the present invention is to：It overcomes the deficiencies of the prior art and provide and a kind of is disappeared using wavelet soft-threshold
Technology of making an uproar and waveform analysis techniques based on signal characteristic, can automatic Calibration fault discharge acoustic wave form starting point, to calculate
The sound magnetic signal time difference helps distance or the position of tester's failure judgement point, greatly promotes power cable fault fixed point work
The scaling method of the power cable fault electric discharge acoustic wave form starting point of efficiency.
The technical solution adopted by the present invention to solve the technical problems is：Power cable fault electric discharge acoustic wave form starting point
Scaling method, it is characterised in that：Include the following steps：
Step 1, the voice signal of a length of T and analog-to-digital conversion is carried out when acquiring one section using sound magnetic-synchro method, obtains x0
(i), i ∈ [1,2 ..., n]；
Step 2, Decomposition order m and wavelet basis function are set, to digitized voice signal x0(i) dyadic wavelet is carried out
Voice signal, is changed to wavelet field from time domain, obtains each layer coefficient of wavelet decomposition c by transformation0j(i), wherein i ∈ [1,2 ...,
N], j ∈ [1,2 ..., m]；
Step 3, wavelet field calculate each layer coefficient of wavelet decomposition the fault-free discharging sound period standard deviation sigmacj, then right
Each layer coefficient of wavelet decomposition carries out soft-threshold processing, the coefficient of wavelet decomposition c that obtains that treated1j(i), i ∈ [1,2 ..., n], disappears
Except the noise contained in voice signal；
Step 4, to soft-threshold processing data c1j(i) inverse wavelet transform is carried out, when voice signal is returned from wavelet domain transform
Domain obtains the hot-tempered sound signal data x that disappears1(i), i ∈ [1,2 ..., n]；
Step 5, standard deviation sigma of the time-domain signal in the fault-free discharging sound period after calculation processingx, then pressed using 3 σ criterion
It is incremented by direction according to sequence and finds first extreme point from signal；
Step 6, it since extreme point, finds signal according to sequence direction of successively decreasing and stops reducing or stop increased point,
Obtain fault discharge acoustic wave form starting point.
Preferably, in the step 5, the timing really of first extreme point is carried out, it is 3 σ that threshold value is arranged firstx, and
I=d+1 is enabled, following steps are then executed：
Step 5-1, ifIt enables the value of i add 1 and repeats this step, it is no to then follow the steps 5-2；
Step 5-2, ifAnd x1(i) ＜ x1(i+1) orAnd x1(i) ＞
x1(i+1), it enables the value of i add 1 and repeats this step, it is no to then follow the steps step 5-3；
Step 5-3 enables h=i, obtains x1(h) it is the first maximum, or obtains x1(h) it is the first minimum.
Wherein,For sound signal data x1(i) in the mean value of i ∈ [1,2 ..., d] period data；σxFor x1(i) in i ∈
The standard deviation of [1,2 ..., d] period data.
Preferably, constant current journey includes the following steps the starting point of fault discharge acoustic wave form described in step 6 really：
Step 6-1, if x1(i) ＞ x1(i-1) or x1(i) ＜ x1(i-1), it enables the value of i subtract 1 and repeats this step,
It is no to then follow the steps 6-2；
Step 6-2 enables k=i, obtains x1(k) it is fault discharge acoustic wave form starting point.
Preferably, the standard deviation sigma described in step 3cjCalculation formula be：
Wherein：c0j(i) it is each layer coefficient of wavelet decomposition,For each layer coefficient of wavelet decomposition c0j(i) i ∈ [1,2 ...,
D] period data mean value.
Preferably, the standard deviation sigma described in step 5xCalculation formula be：
Wherein：For sound signal data x1(i) in the mean value of i ∈ [1,2 ..., d] period data；x1(i) it is that sound is believed
Number x0(i) data after de-noising.
Preferably, in step 3, when carrying out the soft-threshold processing, using 3 σ criterion, threshold value is determined as 3 σcj。
Compared with prior art, advantageous effect possessed by the present invention is：
In the scaling method that this power cable fault discharges acoustic wave form starting point, using wavelet soft threshold de-noising technology and
Waveform analysis techniques based on signal characteristic, can automatic Calibration fault discharge acoustic wave form starting point, to calculating sound magnetic signal
The time difference helps distance or the position of tester's failure judgement point, greatly promotes the working efficiency of power cable fault fixed point.
Overcoming can only be by experienced survey when determining power cable fault position using sound magnetic-synchro method in the prior art
The drawbacks of examination person manually demarcates substantially reduces the training time, and cost substantially reduces, and accelerates detection of cable fault Automated water
Flat development.
Description of the drawings
Fig. 1 is the scaling method flow chart of power cable fault electric discharge acoustic wave form starting point.
Fig. 2 is calibration side's original sound signal oscillogram of power cable fault electric discharge acoustic wave form starting point.
Fig. 3 is oscillogram after calibration side's original sound signal de-noising of power cable fault electric discharge acoustic wave form starting point.
Fig. 4 is calibration side's original sound signal the first extreme point waveform of power cable fault electric discharge acoustic wave form starting point
Figure.
Fig. 5 is calibration side's original sound signal starting point oscillogram of power cable fault electric discharge acoustic wave form starting point.
Specific implementation mode
Fig. 1~5 are highly preferred embodiment of the present invention, and 1~5 the present invention will be further described below in conjunction with the accompanying drawings.
As shown in Figure 1, a kind of scaling method of power cable fault electric discharge acoustic wave form starting point, includes the following steps：
Step 1, one section of voice signal is acquired using sound magnetic-synchro method and carries out analog-to-digital conversion；
During power cable fault fixed test, at the breakdown moment of Method of Cable Trouble Point, cable completely will produce arteries and veins
Magnetic field signal is rushed, is acquired using signal triggering sound signal data, is recorded from failure and put by conventional sound magnetic-synchro method
The electric moment one section of sound signal data of a length of T and carries out analog-to-digital conversion when starting, and obtains x0(i), i ∈ [1,2 ..., n].
Step 2, voice signal is subjected to dyadic wavelet transform；
Decomposition order m and wavelet basis function are set, to digitized voice signal x0(i) dyadic wavelet transform is carried out,
Voice signal is changed to wavelet field from time domain, obtains each layer coefficient of wavelet decomposition c0j(i), wherein i ∈ [1,2 ..., n], j ∈
[1,2,…,m]。
Step 3, voice signal is subjected to soft-threshold processing, eliminates the noise in voice signal；
Standard deviation of each layer coefficient of wavelet decomposition in the fault-free discharging sound period is calculated in wavelet field, then utilizes 3 σ criterion
Soft-threshold processing is carried out to each layer coefficient of wavelet decomposition, eliminates the noise contained in voice signal.
To each layer coefficient of wavelet decomposition c0j(i), i ∈ [1,2 ..., d] periods (corresponding fault-free discharging sound period) number is calculated
According to mean valueThen the standard deviation sigma of the period each layer coefficient of wavelet decomposition is calculatedcj：
Setting threshold value is 3 σcj(3 σ criterion), to each layer coefficient of wavelet decomposition c0j(i) soft-threshold processing is carried out, is handled
Coefficient of wavelet decomposition c afterwards1j(i), i ∈ [1,2 ..., n].
Step 4, inverse wavelet transform is carried out, voice signal is returned into time domain from wavelet domain transform；
To soft-threshold processing data c1j(i) inverse wavelet transform is carried out, the hot-tempered sound signal data x that disappears is obtained1(i), i ∈ [1,
2,…,n]。
Step 5, the first extreme point of data is found using 3 σ criterion；
Then standard deviation of the time-domain signal in the fault-free discharging sound period after calculation processing is passed using 3 σ criterion according to sequence
Increase direction and finds first extreme point from signal.
To data x1(i), the mean value of i ∈ [1,2 ..., d] periods (corresponding fault-free discharging sound period) data is calculatedSo
The standard deviation sigma of the period data is calculated afterwardsx：
Setting threshold value is 3 σx(3 σ criterion), enables i=d+1, finds data x as steps described below1(i) the first extreme point：
Step 5-1, ifIt enables the value of i add 1 and repeats this step, it is no to then follow the steps 5-2.
Step 5-2, ifAnd x1(i) ＜ x1(i+1) (for waveform lofted features before maximum)
OrAnd x1(i) ＞ x1(i+1) (for before minimum waveform decline feature), enable the value of i add 1 and
This step is repeated, it is no to then follow the steps step 5-3.
Step 5-3 enables h=i, obtains x1(h) be the first maximum (for), or obtain x1(h)
For the first minimum (for)。
Step 6, voice signal starting point is demarcated；
Since extreme point, finds signal according to sequence direction of successively decreasing and stop reducing (corresponding maximum) or stop increasing
The point of (corresponding minimum), the point, that is, fault discharge acoustic wave form starting point.
I=h is enabled, as steps described below trouble-shooting electric discharge acoustic wave form starting point：
Step 6-1, if x1(i) ＞ x1(i-1) (for waveform lofted features before maximum) or x1(i) ＜ x1(i-
1) (feature is declined for waveform before minimum), the value of i is enabled to subtract 1 and repeats this step, it is no to then follow the steps 6-2；
Step 6-2 enables k=i, obtains x1(k) it is fault discharge acoustic wave form starting point.
Below by a specific embodiment, above-mentioned steps are described further：
Step 1, it uses sound magnetic-synchro method to acquire duration T as 100ms, obtains one section of sound that sampling number n is 800 and believe
Number, analog-to-digital conversion is carried out to signal and obtains data x0(i), i ∈ [1,2 ..., n], as shown in Figure 2.
Step 2, Decomposition order m=5, wavelet basis function db3 are set, to data x0(i) dyadic wavelet transform is done, is obtained each
Layer coefficient of wavelet decomposition c0j(i)。
Step 3, d=100 is enabled, to each layer coefficient of wavelet decomposition c0j(i), the equal of i ∈ [1,2 ..., d] period data is calculated
ValueThen the standard deviation sigma of the period each layer coefficient of wavelet decomposition is calculatedcj, setting threshold value is 3 σcj, to each layer wavelet decomposition system
Number c0j(i) soft-threshold processing is carried out, the coefficient of wavelet decomposition c that obtains that treated1j(i)。
Step 4, to soft-threshold processing data c1j(i) inverse wavelet transform is carried out, the hot-tempered voice signal that disappears as shown in Figure 3 is obtained
Data x1(i) waveform.
Step 5, the first extreme point of data, the waveform position a as corresponding to dotted line in Fig. 4 are found using 3 σ criterion.
Step 6, waveform starting point, the waveform position b as corresponding to dotted line in Fig. 5 are demarcated.
The above described is only a preferred embodiment of the present invention, being not that the invention has other forms of limitations, appoint
What those skilled in the art changed or be modified as possibly also with the technology contents of the disclosure above equivalent variations etc.
Imitate embodiment.But it is every without departing from technical solution of the present invention content, according to the technical essence of the invention to above example institute
Any simple modification, equivalent variations and the remodeling made, still fall within the protection domain of technical solution of the present invention.
Claims (6)
1. a kind of scaling method of power cable fault electric discharge acoustic wave form starting point, it is characterised in that：Include the following steps：
Step 1, the voice signal of a length of T and analog-to-digital conversion is carried out when acquiring one section using sound magnetic-synchro method, obtains x0(i), i ∈
[1,2,…,n]；
Step 2, Decomposition order m and wavelet basis function are set, to digitized voice signal x0(i) dyadic wavelet transform is carried out,
Voice signal is changed to wavelet field from time domain, obtains each layer coefficient of wavelet decomposition c0j(i), wherein i ∈ [1,2 ..., n], j ∈
[1,2,…,m]；
Step 3, wavelet field calculate each layer coefficient of wavelet decomposition the fault-free discharging sound period standard deviation sigmacj, then to each layer
Coefficient of wavelet decomposition carries out soft-threshold processing, the coefficient of wavelet decomposition c that obtains that treated1j(i), i ∈ [1,2 ..., n], elimination sound
The noise contained in sound signal；
Step 4, to soft-threshold processing data c1j(i) inverse wavelet transform is carried out, voice signal is returned into time domain from wavelet domain transform, is obtained
To the hot-tempered sound signal data x that disappears1(i), i ∈ [1,2 ..., n]；
Step 5, standard deviation sigma of the time-domain signal in the fault-free discharging sound period after calculation processingx, then utilize 3 σ criterion according to sequence
Row are incremented by direction and find first extreme point from signal；
Step 6, since extreme point, according to sequence successively decrease direction find signal stop reduce or stop increased point, obtain
Fault discharge acoustic wave form starting point.
2. the scaling method of power cable fault electric discharge acoustic wave form starting point according to claim 1, it is characterised in that：
In the step 5, the timing really of first extreme point is carried out, it is 3 σ that threshold value is arranged firstx, and i=d+1 is enabled, then execute
Following steps：
Step 5-1, ifIt enables the value of i add 1 and repeats this step, it is no to then follow the steps 5-2；
Step 5-2, ifAnd x1(i) ＜ x1(i+1) orAnd x1(i) ＞ x1(i+
1) it, enables the value of i add 1 and repeats this step, it is no to then follow the steps step 5-3；
Step 5-3 enables h=i, obtains x1(h) it is the first maximum, or obtains x1(h) it is the first minimum；
Wherein,For sound signal data x1(i) in the mean value of i ∈ [1,2 ..., d] period data；σxFor x1(i) i ∈ [1,
2 ..., d] period data standard deviation.
3. the scaling method of power cable fault electric discharge acoustic wave form starting point according to claim 1, it is characterised in that：Step
Really constant current journey includes the following steps fault discharge acoustic wave form starting point described in rapid 6：
Step 6-1, if x1(i) ＞ x1(i-1) or x1(i) ＜ x1(i-1), it enables the value of i subtract 1 and repeats this step, otherwise
Execute step 6-2：
Step 6-2 enables k=i, obtains x1(k) it is fault discharge acoustic wave form starting point.
4. the scaling method of power cable fault electric discharge acoustic wave form starting point according to claim 1, it is characterised in that：Step
Standard deviation sigma described in rapid 3cjCalculation formula be：
Wherein：c0j(i) it is each layer coefficient of wavelet decomposition,For each layer coefficient of wavelet decomposition c0j(i) in i ∈ [1,2 ..., the d] period
The mean value of data.
5. the scaling method of power cable fault electric discharge acoustic wave form starting point according to claim 1, it is characterised in that：Step
Standard deviation sigma described in rapid 5xCalculation formula be：
Wherein：For sound signal data x1(i) in the mean value of i ∈ [1,2 ..., d] period data；x1(i) it is voice signal x0
(i) data after de-noising.
6. the scaling method of power cable fault electric discharge acoustic wave form starting point according to claim 1, it is characterised in that：
In step 3, when carrying out the soft-threshold processing, using 3 σ criterion, threshold value is determined as 3 σcj。
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112147461A (en) * | 2020-09-10 | 2020-12-29 | 广东电网有限责任公司广州供电局 | Fault waveform starting point judgment method and device, computer equipment and medium |
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| CN103630808A (en) * | 2013-11-11 | 2014-03-12 | 华南理工大学 | Partial discharge signal denoising method based on lifting wavelet transform |
| CN103675617A (en) * | 2013-11-20 | 2014-03-26 | 西安交通大学 | Anti-interference method for high-frequency partial discharge signal detection |
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| KR20100022849A (en) * | 2008-08-20 | 2010-03-03 | 한국전기연구원 | Noise elimination method for dectecting partial discharge of generator stator winding using packet wavelet transform |
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