CN111781439A - Power cable partial discharge signal detection method and device - Google Patents
Power cable partial discharge signal detection method and device Download PDFInfo
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Abstract
The invention discloses a method and a device for detecting a partial discharge signal of a power cable, wherein the method comprises the following steps: performing wavelet transformation on the collected partial discharge signals of the power cable based on the selected wavelet basis and the decomposition layer number to obtain corresponding wavelet coefficients; obtaining a threshold value through calculation based on an algorithm selected by the threshold value, and performing threshold value processing on the wavelet coefficient based on the threshold value to obtain an estimated wavelet coefficient; reconstructing the estimated wavelet coefficient to obtain an estimated value of the partial discharge signal of the power cable; carrying out H-order cross overlapping differential transformation on the estimated value of the power cable partial discharge signal, and calculating to obtain the transformation quantity of the power cable partial discharge signal; and calculating to obtain the average energy value and the maximum value of the transformation quantity of the partial discharge signal of the power cable based on the selected data window. In the implementation of the invention, the identification accuracy of the local discharge signal in a complex noise environment is improved, and the detection efficiency is improved.
Description
Technical Field
The invention relates to the technical field of partial discharge, in particular to a method and a device for detecting a partial discharge signal of a power cable.
Background
Power cables are increasingly widely used due to the advantages of safe and reliable power supply, urban beautification and the like. The power cable has high safety operation reliability under the condition of not being influenced by external environment factors and artificial factors, but accessories such as an intermediate joint, a terminal and the like of the cable are easy to have problems in the cable laying and accessory installation and manufacturing processes. After the cable breaks down, the fault must be detected in time, otherwise serious economic loss and social influence can be caused. Therefore, the quick and effective detection of the insulation condition of the cable accessory has important significance for guaranteeing the safe and reliable operation of the cable.
Because partial discharge can well reflect the insulation state of the cable, the online monitoring method based on the partial discharge signal is widely applied and is the most effective method for judging the insulation state of the cable at present. However, when the partial discharge is monitored on line, the electrical equipment is in a normal working state, strong electromagnetic interference exists in the environment, and a partial discharge signal generated by a cable insulation defect is weak and is often submerged in the strong interference. Due to the existence of complex noise interferences such as white noise, periodic narrow-band signals and the like in the field environment, reliable detection and extraction of partial discharge signals are difficult to realize only based on a single time-frequency analysis method. Therefore, how to accurately identify and extract the partial discharge signal of the cable in various noise interferences needs to be solved.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and a device for detecting a partial discharge signal of a power cable, so that the identification accuracy of the partial discharge signal in a complex noise environment is improved.
In order to solve the above technical problem, an embodiment of the present invention provides a method for detecting a partial discharge signal of a power cable, where the method includes:
performing wavelet transformation on the collected partial discharge signals of the power cable based on the selected wavelet basis and the decomposition layer number to obtain corresponding wavelet coefficients;
obtaining a threshold value through calculation based on an algorithm selected by the threshold value, and performing threshold value processing on the wavelet coefficient based on the threshold value to obtain an estimated wavelet coefficient;
reconstructing the estimated wavelet coefficient to obtain an estimated value of the partial discharge signal of the power cable;
carrying out H-order cross overlapping differential transformation on the estimated value of the power cable partial discharge signal, and calculating to obtain the transformation quantity of the power cable partial discharge signal;
and calculating to obtain the average energy value and the maximum value of the transformation quantity of the partial discharge signal of the power cable based on the selected data window.
Optionally, the threshold value is obtained by calculating the algorithm selected based on the threshold value, and the specific calculation formula is as follows:
wherein, TvTo representThreshold value, σnRepresenting the standard deviation of the noise and N the length of the signal.
Optionally, the performing H-order cross-lap differential transformation on the estimated value of the power cable partial discharge signal, and obtaining a transformation quantity of the power cable partial discharge signal through calculation includes:
obtaining a transformation coefficient array through calculation based on the selected order H of the cross overlapping differential transformation;
and performing H-order cross overlapping differential transformation on the estimated value of the partial discharge signal of the power cable based on the transformation coefficient array, and calculating to obtain the transformation quantity of the partial discharge signal of the power cable.
Optionally, the obtaining a transform coefficient array by calculation based on the selected order H of the cross-lapped differential transform includes:
the transformation coefficient array of the H order is translated to the left by one bit to obtain a new array;
and calculating to obtain the transform coefficient corresponding to the H order based on the new array.
Optionally, based on the transform coefficient array, performing H-order cross-lapped differential transform on the estimated value of the power cable partial discharge signal, and obtaining a transform quantity of the power cable partial discharge signal through calculation, where a specific calculation formula is as follows:
wherein S isH.f(n) represents the amount of conversion of the partial discharge signal of the power cable, and (c)j)HAn array of transform coefficients is represented,
represents an estimate of the power cable partial discharge signal, H represents the order, and j represents the number of decomposition layers.
Optionally, the obtaining, by calculation, an average energy value and a maximum value of a transformation quantity of the partial discharge signal of the power cable based on the selected data window further includes: and if the maximum value is larger than the fixed value, detecting that the local discharge signal of the power cable is detected.
Optionally, the setting value is set by using an adaptive threshold, including:
if the discharge signal is not detected, the fixed value E6.setting＝Krel.1max(E6.normal) In which E6.normalIs the average energy value of the cable under normal conditions, Krel.1A reliability coefficient greater than 1;
if a discharge signal is detected, the value is fixed
Wherein EH(nmax) Is a maximum value, Krel.2A reliability factor greater than 1.
In addition, an embodiment of the present invention further provides a power cable partial discharge signal detection apparatus, where the apparatus includes:
a wavelet transformation module: the system is used for performing wavelet transformation on the collected partial discharge signals of the power cable based on the selected wavelet basis and the decomposition layer number to obtain corresponding wavelet coefficients;
a threshold processing module: the method comprises the steps that a threshold value is obtained through calculation based on an algorithm selected by the threshold value, and threshold value processing is carried out on the wavelet coefficient based on the threshold value to obtain an estimated wavelet coefficient;
a reconstruction module: the wavelet coefficient is used for reconstructing the estimated wavelet coefficient to obtain an estimated value of the partial discharge signal of the power cable;
a cross-lapped differential transform module: the device is used for carrying out H-order cross overlapping differential transformation on the estimated value of the power cable partial discharge signal and obtaining the transformation quantity of the power cable partial discharge signal through calculation;
a calculation module: and the average energy value and the maximum value of the transformation quantity of the partial discharge signal of the power cable are obtained through calculation based on the selected data window.
In the implementation of the invention, a threshold denoising method of wavelet transformation is utilized to suppress white noise interference, and on the basis, the transient characteristics of partial discharge signals are extracted by utilizing wavelet transformation and cross-lapped differential transformation, so that the identification accuracy of the partial discharge signals in a complex noise environment is improved; the cross-lapped differential transformation is suitable for extracting high-frequency transient signals, and detecting partial discharge signals by combining wavelet transformation and cross-lapped differential transformation, can effectively inhibit complex noise interference, and has higher detection sensitivity; the cross-over differential transformation algorithm is simple, and the efficiency of the detection method is greatly improved; in addition, the criterion adopts a self-adaptive threshold, so that the detection sensitivity is ensured, and the detection reliability is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a power cable partial discharge signal detection method in an embodiment of the present invention;
fig. 2 is a schematic structural component diagram of a power cable partial discharge signal detection device in an embodiment of the invention;
fig. 3 is a schematic flow chart of a power cable partial discharge signal detection method in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a power cable partial discharge signal detection method according to an embodiment of the present invention.
As shown in fig. 1, a method for detecting a partial discharge signal of a power cable includes:
s11: performing wavelet transformation on the collected partial discharge signals of the power cable based on the selected wavelet basis and the decomposition layer number to obtain corresponding wavelet coefficients;
in the specific implementation process of the invention, wavelet basis and decomposition layer number J are selected, and wavelet transformation is firstly carried out on the acquired signals f (n) to obtain corresponding wavelet coefficients Wj，k。
S12: obtaining a threshold value through calculation based on an algorithm selected by the threshold value, and performing threshold value processing on the wavelet coefficient based on the threshold value to obtain an estimated wavelet coefficient;
in the specific implementation process of the invention, a general threshold value selection algorithm is adopted to calculate the threshold value TvAnd for the wavelet coefficient W obtained by decompositionj，kSoft threshold value processing is carried out to obtain estimated wavelet coefficient
Specifically, the algorithm selected based on the threshold is calculated to obtain the threshold, and the specific calculation formula is as follows:
wherein, TvRepresents the threshold value, σnRepresenting the standard deviation of the noise and N the length of the signal, i.e. the number of sample points.
S13: reconstructing the estimated wavelet coefficient to obtain an estimated value of the partial discharge signal of the power cable;
in the implementation of the invention, the estimated wavelet coefficients are processed
Reconstructing to obtain the estimated value of the partial discharge signal of the power cable
S14: carrying out H-order cross overlapping differential transformation on the estimated value of the power cable partial discharge signal, and calculating to obtain the transformation quantity of the power cable partial discharge signal;
in a specific implementation process of the present invention, the performing H-order cross-lap differential transformation on the estimated value of the power cable partial discharge signal, and obtaining a transformation quantity of the power cable partial discharge signal through calculation includes: obtaining a transformation coefficient array through calculation based on the selected order H of the cross overlapping differential transformation; and performing H-order cross overlapping differential transformation on the estimated value of the partial discharge signal of the power cable based on the transformation coefficient array, and calculating to obtain the transformation quantity of the partial discharge signal of the power cable.
Wherein, the step of obtaining a transform coefficient array by calculation based on the selected order H of the cross-lapped differential transform comprises: the transformation coefficient array of the H order is translated to the left by one bit to obtain a new array; and calculating to obtain the transform coefficient corresponding to the H order based on the new array. Specifically, order (c)j)1＝[1 -1 0 … 0]1×(H+1)Represents an array (c)j)1The new array resulting after a bit shift to the left is then (c'j)1＝[0 1 -1 0 … 0]1×(H+1)J is 1, …, and H +1 represents the serial number of the element in the array; then the transform coefficients corresponding to each order can be calculated as: (c)j)h＝(cj)h-1-(c′j)h-1，h＝2，…，H。
In addition, in the step of performing H-order cross-lapped differential transform on the estimated value of the power cable partial discharge signal based on the transform coefficient array, and obtaining the transform quantity of the power cable partial discharge signal through calculation, a specific calculation formula is as follows:
wherein S isH.f(n) represents the amount of conversion of the partial discharge signal of the power cable, and (c)j)HAn array of transform coefficients is represented,
represents an estimate of the power cable partial discharge signal, H represents the order, and j represents the number of decomposition layers.
S15: and calculating to obtain the average energy value and the maximum value of the transformation quantity of the partial discharge signal of the power cable based on the selected data window.
In a specific implementation process of the present invention, the calculating, based on the selected data window, to obtain an average energy value and a maximum value of a transformation quantity of the partial discharge signal of the power cable further includes: and if the maximum value is larger than the fixed value, detecting that the local discharge signal of the power cable is detected.
Specifically, a certain time window T is taken to calculate SH.fThe average energy value of (n) is:
p is the number of sampling points corresponding to the time window T; and then calculate EHMaximum value E of (n)H(nmax) Wherein n ismaxThe sampling point corresponding to the maximum value is satisfied with EH(nmax)＞EH.settingThen detecting as a partial discharge signal, recording nmaxAnd a corresponding maximum value EH(nmax)。
It should be noted that, the fixed value is set by using an adaptive threshold, which includes:
if the discharge signal is not detected, i.e. q is 0, the fixed value E is set6.setting＝Krel.1max(E6.normal) In which E6.normalIs the average energy value of the cable under normal conditions, Krel.1To a coefficient of reliability greater than 1, i.e. Krel.1＝1.5-2.5；
If a discharge signal is detected, i.e. q > 0, the value is fixed
Wherein EH(nmax) Is a maximum value, Krel.2Reliability system of more than 1Number, i.e. Krel.2＝1.5-2.5。
In the implementation of the invention, a threshold denoising method of wavelet transformation is utilized to suppress white noise interference, and on the basis, the transient characteristics of partial discharge signals are extracted by utilizing wavelet transformation and cross-lapped differential transformation, so that the identification accuracy of the partial discharge signals in a complex noise environment is improved; the cross-lapped differential transformation is suitable for extracting high-frequency transient signals, and detecting partial discharge signals by combining wavelet transformation and cross-lapped differential transformation, can effectively inhibit complex noise interference, and has higher detection sensitivity; the cross-over differential transformation algorithm is simple, and the efficiency of the detection method is greatly improved; in addition, the criterion adopts a self-adaptive threshold, so that the detection sensitivity is ensured, and the detection reliability is improved.
Example two
Referring to fig. 2, fig. 2 is a schematic structural composition diagram of a power cable partial discharge signal detection device in an embodiment of the present invention.
As shown in fig. 2, a power cable partial discharge signal detecting apparatus, the apparatus comprising:
the wavelet transform module 11: the system is used for performing wavelet transformation on the collected partial discharge signals of the power cable based on the selected wavelet basis and the decomposition layer number to obtain corresponding wavelet coefficients;
the threshold processing module 12: the method comprises the steps that a threshold value is obtained through calculation based on an algorithm selected by the threshold value, and threshold value processing is carried out on the wavelet coefficient based on the threshold value to obtain an estimated wavelet coefficient;
a reconstruction module 13: the wavelet coefficient is used for reconstructing the estimated wavelet coefficient to obtain an estimated value of the partial discharge signal of the power cable;
cross-lapped differential transform module 14: the device is used for carrying out H-order cross overlapping differential transformation on the estimated value of the power cable partial discharge signal and obtaining the transformation quantity of the power cable partial discharge signal through calculation;
the calculation module 15: and the average energy value and the maximum value of the transformation quantity of the partial discharge signal of the power cable are obtained through calculation based on the selected data window.
Specifically, the working principle of the device related function module according to the embodiment of the present invention may refer to the description related to the first method embodiment, and is not described herein again.
In the implementation of the invention, a threshold denoising method of wavelet transformation is utilized to suppress white noise interference, and on the basis, the transient characteristics of partial discharge signals are extracted by utilizing wavelet transformation and cross-lapped differential transformation, so that the identification accuracy of the partial discharge signals in a complex noise environment is improved; the cross-lapped differential transformation is suitable for extracting high-frequency transient signals, and detecting partial discharge signals by combining wavelet transformation and cross-lapped differential transformation, can effectively inhibit complex noise interference, and has higher detection sensitivity; the cross-over differential transformation algorithm is simple, and the efficiency of the detection method is greatly improved; in addition, the criterion adopts a self-adaptive threshold, so that the detection sensitivity is ensured, and the detection reliability is improved.
EXAMPLE III
In the specific implementation, the sampling frequency is set to be 100MHz, a white noise signal and sinusoidal signals with different frequencies are superposed in a partial discharge signal to simulate the mixing of white noise and periodic narrow-band interference in the discharge signal, and a single-exponential and double-exponential damped oscillation model is adopted in the discharge signal without noise pollution; in addition, the signal-to-noise ratio snr of the white noise signal is 5; the mathematical expression for the periodic narrowband interfering signal is:
wherein, n is 6, fj100kHz, 200kHz, 500kHz, 1MHz, 3M Hz and 4.5M Hz respectively;
as shown in fig. 3, the specific implementation process is as follows:
the first step, selecting wavelet base db6 and decomposing layer number J as 4, then making wavelet transform on the collected signal f (n) to obtain corresponding wavelet coefficient Wj，k；
Secondly, calculating a threshold value T by adopting a general threshold value selection methodvThe method specifically comprises the following steps:
wherein σnIs the standard deviation of the noise, and N is the length of the signal, i.e. the number of sampling points;
thirdly, soft threshold value processing is carried out on the wavelet coefficient obtained by decomposition to obtain an estimated wavelet coefficient
Step four, for wavelet coefficient
Reconstructing to obtain the estimated value of the original signal
Fifthly, selecting the order H of the cross overlapping differential transformation to be 6, and calculating the transformation coefficient array (c)j)6The specific calculation method comprises the following steps:
a. order (c)j)1＝[1 -1 0 0 0 0 0]，(c′j)1Representative array (c)j)1The new array resulting after a bit shift to the left is then (c'j)1＝[0 1 -1 0 0 0 0]J 1, …, 7 represents the sequence deficiency of the elements in the array;
b. then the transform coefficients corresponding to H ═ 6 can be calculated as: (c)j)6＝[1 -6 15 -20 15 -6 1]；
Sixth, using the obtained coefficient (c)j)6To the signal
Performing 6-order cross overlap difference conversion to obtain conversion quantity S6.f(n), the specific calculation formula is as follows:
seventhly, selecting a time window T equal to 1us corresponding to 100 sampling points P, and setting the discharge frequency q equal to 0;
calculating S6.fThe average energy value of (n) is:
calculation of E6Maximum value E of (n)6(nmax) Wherein n ismaxThe sampling point corresponding to the maximum value is satisfied with E6(nmax)＞E6.settingThen, the partial discharge signal is detected, the number of discharges q is q +1, and n is recordedq.maxAnd a corresponding maximum value E6(nq.max)；
Wherein the fixed value adopts a self-adaptive threshold:
a. before the discharge signal is not detected (q is 0), take E6.settins＝Krel.1max(E6.normal)，E6.normalTaking the reliability coefficient K for the average energy value of the cable under the normal conditionrel.11.5-2.5, in this example Krel.1＝2；
b. If a discharge signal (q > 0) is detected, then fetch
Coefficient of reliability Krel.21.5-2.5, in this example Krel.2＝2。
In the implementation of the invention, a threshold denoising method of wavelet transformation is utilized to suppress white noise interference, and on the basis, the transient characteristics of partial discharge signals are extracted by utilizing wavelet transformation and cross-lapped differential transformation, so that the identification accuracy of the partial discharge signals in a complex noise environment is improved; the cross-lapped differential transformation is suitable for extracting high-frequency transient signals, and detecting partial discharge signals by combining wavelet transformation and cross-lapped differential transformation, can effectively inhibit complex noise interference, and has higher detection sensitivity; the cross-over differential transformation algorithm is simple, and the efficiency of the detection method is greatly improved; in addition, the criterion adopts a self-adaptive threshold, so that the detection sensitivity is ensured, and the detection reliability is improved.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: read-only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
In addition, the above detailed description is provided for the method and apparatus for detecting a partial discharge signal of a power cable according to the embodiments of the present invention, and a specific example should be used herein to explain the principle and the implementation of the present invention, and the description of the above embodiment is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (8)
1. A power cable partial discharge signal detection method, the method comprising:
performing wavelet transformation on the collected partial discharge signals of the power cable based on the selected wavelet basis and the decomposition layer number to obtain corresponding wavelet coefficients;
obtaining a threshold value through calculation based on an algorithm selected by the threshold value, and performing threshold value processing on the wavelet coefficient based on the threshold value to obtain an estimated wavelet coefficient;
reconstructing the estimated wavelet coefficient to obtain an estimated value of the partial discharge signal of the power cable;
carrying out H-order cross overlapping differential transformation on the estimated value of the power cable partial discharge signal, and calculating to obtain the transformation quantity of the power cable partial discharge signal;
and calculating to obtain the average energy value and the maximum value of the transformation quantity of the partial discharge signal of the power cable based on the selected data window.
2. The method for detecting a partial discharge signal of a power cable according to claim 1, wherein the algorithm selected based on the threshold is calculated to obtain the threshold, and the specific calculation formula is as follows:
wherein, TvRepresents the threshold value, σnRepresenting the standard deviation of the noise and N the length of the signal.
3. The method for detecting a partial discharge signal of a power cable according to claim 1, wherein said performing H-order cross-lapped differential transformation on the estimated value of the partial discharge signal of the power cable and calculating a transformation amount of the partial discharge signal of the power cable comprises:
obtaining a transformation coefficient array through calculation based on the selected order H of the cross overlapping differential transformation;
and performing H-order cross overlapping differential transformation on the estimated value of the partial discharge signal of the power cable based on the transformation coefficient array, and calculating to obtain the transformation quantity of the partial discharge signal of the power cable.
4. The method of claim 3, wherein the obtaining the transform coefficient array by calculation based on the selected order H of the cross-lapped differential transform comprises:
the transformation coefficient array of the H order is translated to the left by one bit to obtain a new array;
and calculating to obtain the transform coefficient corresponding to the H order based on the new array.
5. The method according to claim 3, wherein the conversion amount of the partial discharge signal of the power cable obtained by performing H-order cross-lapped differential conversion on the estimated value of the partial discharge signal of the power cable based on the conversion coefficient array is calculated by using a specific calculation formula as follows:
wherein S isH.f(n) represents the amount of conversion of the partial discharge signal of the power cable, and (c)j)HAn array of transform coefficients is represented,
represents an estimate of the power cable partial discharge signal, H represents the order, and j represents the number of decomposition layers.
6. The power cable partial discharge signal detection method of claim 1, wherein the calculating the mean energy value and the maximum value of the transformation amount of the power cable partial discharge signal based on the selected data window further comprises: and if the maximum value is larger than the fixed value, detecting that the local discharge signal of the power cable is detected.
7. The power cable partial discharge signal detection method of claim 6, wherein the constant value is set by an adaptive threshold, comprising:
if the discharge signal is not detected, the fixed value E6.setting＝Krel.1max(E6.normal) In which E6.normalIs the average energy value of the cable under normal conditions, Krel.1A reliability coefficient greater than 1;
if a discharge signal is detected, the value is fixed
Wherein EH(nmax) Is a maximum value, Krel.2A reliability factor greater than 1.
8. A power cable partial discharge signal detection apparatus, the apparatus comprising:
a wavelet transformation module: the system is used for performing wavelet transformation on the collected partial discharge signals of the power cable based on the selected wavelet basis and the decomposition layer number to obtain corresponding wavelet coefficients;
a threshold processing module: the method comprises the steps that a threshold value is obtained through calculation based on an algorithm selected by the threshold value, and threshold value processing is carried out on the wavelet coefficient based on the threshold value to obtain an estimated wavelet coefficient;
a reconstruction module: the wavelet coefficient is used for reconstructing the estimated wavelet coefficient to obtain an estimated value of the partial discharge signal of the power cable;
a cross-lapped differential transform module: the device is used for carrying out H-order cross overlapping differential transformation on the estimated value of the power cable partial discharge signal and obtaining the transformation quantity of the power cable partial discharge signal through calculation;
a calculation module: and the average energy value and the maximum value of the transformation quantity of the partial discharge signal of the power cable are obtained through calculation based on the selected data window.
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