CN110907770A - Partial discharge pulse feature extraction method and device, computer equipment and medium - Google Patents
Partial discharge pulse feature extraction method and device, computer equipment and medium Download PDFInfo
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Abstract
The application relates to a partial discharge pulse feature extraction method, a partial discharge pulse feature extraction device, computer equipment and a medium. The method comprises acquiring a partial discharge signal; decomposing the partial discharge signal based on a self-adaptive signal time-frequency processing method to obtain an eigenmode function component; randomly sequencing the eigenmode function components based on the statistical characteristics of the noise signals to obtain reconstructed signals; accumulating and averaging the reconstructed signals to obtain a noise-removed partial discharge signal; decomposing the de-noised partial discharge signal into a plurality of product functions based on a partial mean decomposition method; and extracting pulse characteristics according to the plurality of product functions. The method for extracting the partial discharge pulse characteristics can extract the pulse characteristics more completely, so that the partial discharge signals can be accurately analyzed.
Description
Technical Field
The present disclosure relates to the field of partial discharge monitoring technologies, and in particular, to a method and an apparatus for extracting partial discharge pulse characteristics, a computer device, and a medium.
Background
The partial discharge mainly refers to the discharge of transformers, power cables and other power equipment in a local range under the action of high voltage. Such discharges are limited to causing only isolated local shorts between conductors without forming conductive paths. Partial discharge is one of the main causes of insulation failure of high-voltage power equipment, and affects the insulation life of the equipment when partial discharge occurs in the equipment insulation. The detection frequency band is gradually developed towards high frequency and wide band, and when the device generates partial discharge, an ultrahigh frequency signal is generated, so that the ultrahigh frequency monitoring method is widely applied to device partial discharge monitoring.
The partial discharge monitoring is a common method for evaluating the insulation performance of the power equipment, and the extraction of a partial discharge pulse characteristic signal is taken as a key step of a partial discharge measurement signal preprocessing stage and is the basis for deeply analyzing a partial discharge signal. In the conventional technology, a wavelet threshold denoising method, an adaptive filtering method, a fourier transform digital filtering method, a singular value decomposition-reconstruction method, a steepness and sliding energy method, a microscopic characteristic parameter method and other methods are commonly used for extracting pulse characteristics of partial discharge signals.
However, for very high frequency signals, there is a problem that feature extraction is incomplete using these methods.
Disclosure of Invention
In view of the above, it is necessary to provide a partial discharge pulse feature extraction method, an apparatus, a computer device, and a medium for solving the above technical problems.
In order to achieve the above object, in one aspect, an embodiment of the present application provides a partial discharge pulse feature extraction method, where the method includes:
acquiring a partial discharge signal, wherein the partial discharge signal comprises a noise signal;
decomposing the partial discharge signal based on a self-adaptive signal time-frequency processing method to obtain eigenmode function components, wherein the eigenmode function components comprise N basic eigenmode function components and a trend term;
randomly sequencing the eigenmode function components based on the statistical characteristics of the noise signals to obtain reconstructed signals;
accumulating and averaging the reconstructed signals to obtain a noise-removed partial discharge signal;
decomposing the de-noised partial discharge signal into a plurality of product functions based on a partial mean decomposition method;
extracting pulse features from the plurality of product functions, wherein the pulse features include: instantaneous amplitude and instantaneous frequency.
In one embodiment, the acquiring the partial discharge signal includes:
collecting partial discharge pulses based on the phase resolved pulse sequence;
and carrying out detection processing on the partial discharge pulse to obtain the partial discharge signal.
In one embodiment, the detecting the partial discharge pulse to obtain the partial discharge signal includes:
inputting the partial discharge pulse into a detection chip to obtain a peak value holding partial discharge pulse;
and inputting the peak value holding partial discharge pulse into an analog-to-digital converter to obtain the partial discharge signal.
In one embodiment, the decomposing the denoised partial discharge signal into a plurality of product functions based on a local mean decomposition method includes:
calculating all local extreme points of the de-noised partial discharge signal and the time corresponding to the extreme points;
determining a local mean function and an initial envelope estimation function according to the extreme point and the time corresponding to the extreme point;
separating and removing the local mean function from the de-noised partial discharge signal to obtain a separated local mean function;
demodulating the separated local mean function to obtain a frequency modulation signal;
calculating an envelope estimation function of the frequency modulation signal to obtain a frequency modulation envelope estimation function, and adding the frequency modulation envelope estimation function into an envelope estimation function list;
if the frequency modulation envelope estimation function is not equal to a preset threshold value, updating the de-noised partial discharge signal by using the frequency modulation envelope estimation function, and returning to the step of executing to calculate the extreme points of all local areas of the de-noised partial discharge signal and the time corresponding to the extreme points;
if the frequency modulation envelope estimation function is equal to the preset threshold value, calculating the product of all the frequency modulation envelope estimation functions and the initial envelope estimation function in the envelope estimation function list to obtain an envelope signal;
determining the plurality of product functions based on the envelope signal and the frequency modulated signal.
In one embodiment, said determining said plurality of product functions based on said envelope signal and said frequency modulated signal comprises:
obtaining a product function according to the product of the envelope signal and the frequency modulation signal, and adding the product function into a product function list;
separating and removing the product function from the de-noised partial discharge signal before updating to obtain a separated product function;
if the separation product function is not a monotone function, updating the denoising partial discharge signal by using the separation product function, and returning to the step of executing to calculate the extreme points of all local areas of the denoising partial discharge signal and the time corresponding to the extreme points;
and if the separation product function is a monotone function, determining all product functions in the product function list as the plurality of product functions.
In one embodiment, the determining the local mean function and the initial envelope estimation function according to the extreme point and the time corresponding to the extreme point includes:
calculating an average value between adjacent extreme points, and performing linear continuation on the average value at the time corresponding to the adjacent extreme points to obtain a local average value line segment;
smoothing the local mean line segment to obtain a local mean function;
calculating a local envelope amplitude by using adjacent extreme points, and performing linear continuation on the local envelope amplitude at the time corresponding to the adjacent extreme points to obtain a local amplitude curve;
and smoothing the local amplitude curve to obtain the initial envelope estimation function.
In one embodiment, the extracting the pulse features according to the plurality of product functions includes:
determining an envelope signal and a frequency modulation signal according to the plurality of product functions;
determining the envelope signal as the instantaneous amplitude;
and obtaining the instantaneous frequency by derivation of the frequency modulation signal.
In one embodiment, the method further comprises:
acquiring an envelope transient function according to the plurality of product functions;
determining an envelope transient map according to the envelope transient function;
and comparing the envelope transient map with a preset pulse characteristic map to determine the characteristics of the partial discharge signal.
On the other hand, the embodiment of the present application further provides a partial discharge pulse feature extraction device, where the device includes:
the device comprises a partial discharge signal acquisition module, a partial discharge signal acquisition module and a partial discharge signal processing module, wherein the partial discharge signal acquisition module is used for acquiring a partial discharge signal, and the partial discharge signal comprises a noise signal;
the eigenmode function component determining module is used for decomposing the partial discharge signal based on a self-adaptive signal time-frequency processing method to obtain eigenmode function components, and the eigenmode function components comprise N basic eigenmode function components and a trend term;
a reconstructed signal determining module, configured to randomly sort the eigenmode function components based on the statistical characteristics of the noise signal to obtain a reconstructed signal;
the de-noise partial discharge signal determining module is used for accumulating and averaging the reconstructed signals to obtain de-noise partial discharge signals;
a product function determination module for decomposing the de-noised partial discharge signal into a plurality of product functions based on a local mean decomposition method;
a pulse feature extraction module, configured to extract pulse features according to the plurality of product functions, where the pulse features include: instantaneous amplitude and instantaneous frequency.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method as described above when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as set forth above.
According to the method, the device, the computer equipment and the medium for extracting the partial discharge pulse characteristics, the partial discharge signals are obtained; and processing the partial discharge signal based on a self-adaptive signal time-frequency processing method and the statistical characteristics of the noise signal to obtain a de-noised partial discharge signal. And decomposing the de-noised partial discharge signal into a plurality of product functions based on a partial mean decomposition method, and extracting pulse characteristics according to the product functions. According to the method, the interference of the noise signal to the useful signal can be reduced by denoising the partial discharge signal, so that the subsequent feature extraction is more complete, and the partial discharge signal can be accurately analyzed according to the extracted pulse feature. In addition, the method provided by the embodiment of the application is based on a self-adaptive signal time-frequency processing method, the partial discharge signal is decomposed to obtain an eigenmode function component, the eigenmode function component is randomly sequenced based on the statistical characteristic of the noise signal to obtain a reconstructed signal, the reconstructed signal is further accumulated and averaged to obtain a de-noised partial discharge signal, and therefore de-noising of the partial discharge signal is achieved. This method not only greatly reduces the noise signal, but also ensures the integrity of the useful signal for subsequent processing of the useful signal.
Drawings
Fig. 1 is a schematic flow chart illustrating steps of a partial discharge pulse feature extraction method according to an embodiment of the present application;
fig. 2 is a schematic flowchart illustrating steps of a partial discharge pulse feature extraction method according to an embodiment of the present application;
fig. 3 is a schematic flowchart illustrating steps of a partial discharge pulse feature extraction method according to an embodiment of the present application;
fig. 4 is a schematic flowchart illustrating steps of a partial discharge pulse feature extraction method according to an embodiment of the present application;
fig. 5 is a schematic flowchart illustrating steps of a partial discharge pulse feature extraction method according to an embodiment of the present application;
fig. 6 is a schematic flowchart illustrating steps of a partial discharge pulse feature extraction method according to an embodiment of the present application;
fig. 7 is a schematic flowchart illustrating steps of a partial discharge pulse feature extraction method according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a partial discharge pulse extraction apparatus according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The partial discharge pulse feature extraction method can be applied to a partial discharge monitoring system. The partial discharge monitoring system can comprise a control device, a collecting device and the like. The acquisition device can acquire partial discharge signals in real time; the control device may comprise a computer device which may be, but is not limited to, an industrial computer, a laptop, a smartphone, a tablet and a portable wearable device. The partial discharge pulse feature extraction method provided by the application can be realized through computer equipment, and the acquisition device can transmit acquired data to a memory of the computer equipment.
The following describes the technical solutions of the present application and how to solve the technical problems with the technical solutions of the present application in detail with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Referring to fig. 1, an embodiment of the present application provides a method for extracting a partial discharge pulse feature, including:
s100, acquiring a partial discharge signal, wherein the partial discharge signal comprises a noise signal.
The partial discharge signal includes a useful signal and a noise signal. The partial discharge signal may be an ultrahigh frequency partial discharge signal acquired by an ultrahigh frequency signal sensor. The ultrahigh frequency signal sensor transmits the acquired signal to the computer equipment, and the computer equipment receives the ultrahigh frequency partial discharge signal.
S200, decomposing the partial discharge signal based on a self-adaptive signal time-frequency processing method to obtain eigenmode function components, wherein the eigenmode function components comprise N basic eigenmode function components and a trend term.
The adaptive signal time-frequency processing method can decompose any nonlinear and non-stationary signal into a fixed number of fundamental eigenmode Function (IMF) components and a trend term, wherein each fundamental eigenmode Function component corresponds to a frequency band of the signal. The partial discharge signal is decomposed to obtain basic eigenmode function components and a trend term, wherein the basic eigenmode function components are arranged from high to low in a limited frequency band. Assuming that the partial discharge signal is x (t), the fundamental eigenmode function component is imf (t). If the trend term is recorded as the nth fundamental eigenmode function component, then the self-adaptation is performedDue to the completeness of the signal time-frequency processing method, the partial discharge signal can be expressed as:
wherein i represents the order. The IMF components of lower order are high frequency components of the partial discharge signal, and each IMF component contains a useful signal band and a noise signal band.
S300, randomly sequencing the eigenmode function components based on the statistical characteristics of the noise signals to obtain reconstructed signals.
According to the statistical characteristic of the noise, when the positions of the noise sampling points are randomly sequenced and the amplitude of the noise component at the corresponding position is kept unchanged, the total power of the noise is also kept unchanged. Randomly ordering the IMF components according to the statistical characteristics of the noise signal to obtain a new IMF component, and forming a reconstructed signal x by the new IMF component and the rest IMF components1(t)，x1(t) still satisfies the completeness of the adaptive signal time-frequency processing method, and x1The power of (t) is unchanged. Repeating the above operations, and randomly sequencing the IMF components for N times to obtain a reconstructed signal x2(t),x3(t)…xn(t)。
And S400, accumulating and averaging the reconstructed signals to obtain a noise-removed partial discharge signal.
Signals x (t), x1(t),x2(t)…xn(t) n +1 signals in total, and accumulating and averaging the n +1 signals to obtain the de-noise partial discharge signal xav(t), can be represented as
For the partial discharge signal x (t), the denoised partial discharge signal xav(t) the noise signal is theoretically attenuated to the original
While the power of said user signal remains substantially constant, when n is large to a certain extent, xav(t) said noise signal may be substantiallyIgnore it not to remember. This not only achieves the purpose of noise removal, but also enables the power of the noise signal to be largely attenuated, while the power of the useful signal remains substantially unchanged.
S500, decomposing the denoising partial discharge signal into a plurality of product functions based on a partial mean decomposition method.
The local mean decomposition method is a self-adaptive non-stationary signal processing method. The local mean decomposition method can adaptively decompose a complex non-stationary multi-component signal into a plurality of linear combinations of product functions, wherein each product function is obtained by multiplying an envelope signal and a pure frequency modulation signal, and the envelope signal is the instantaneous amplitude of the product function. The denoised partial discharge signal can be decomposed into a plurality of product functions by the local mean decomposition method.
S600, extracting pulse characteristics according to the plurality of product functions, wherein the pulse characteristics comprise: instantaneous amplitude and instantaneous frequency.
The de-noised partial discharge signal can be decomposed into a plurality of product functions, and the instantaneous amplitude and the instantaneous frequency are calculated and determined according to an envelope signal and a frequency modulation signal in the plurality of product functions. The algorithm for obtaining the instantaneous frequency is not limited in any way in the embodiment of the present application as long as the function thereof can be realized.
In the method for extracting the partial discharge pulse feature provided by this embodiment, a partial discharge signal is obtained; and processing the partial discharge signal based on a self-adaptive signal time-frequency processing method and the statistical characteristics of the noise signal to obtain a de-noised partial discharge signal. And decomposing the de-noised discharge signal into a plurality of product functions based on a local mean decomposition method, and extracting pulse characteristics according to the product functions. According to the method provided by the embodiment, the noise removal processing is performed on the partial discharge signal, so that the interference of the noise signal on the useful signal can be reduced, the subsequent pulse feature extraction is more complete, and the partial discharge signal can be accurately analyzed according to the extracted pulse feature. In addition, the method provided by the embodiment of the application is based on a self-adaptive signal time-frequency processing method, the partial discharge signal is decomposed to obtain an eigenmode function component, the eigenmode function component is randomly sequenced based on the statistical characteristic of the noise signal to obtain a reconstructed signal, the reconstructed signal is further accumulated and averaged to obtain a de-noised partial discharge signal, and therefore de-noising of the partial discharge signal is achieved. This method not only greatly reduces the noise signal, but also ensures the integrity of the useful signal for subsequent processing of the useful signal.
Referring to fig. 2, this embodiment relates to a possible implementation manner of the acquiring the partial discharge signal, and S100 includes:
s110, collecting partial discharge pulses based on the pulse sequence of phase resolution.
The Phase Resolved Pulse Sequence (PRPS) is used to record the relationship between the strength, Phase and number of power frequency cycles of the discharge signal over a certain period of time. According to the discharge signals collected by the collecting device, the PRPS can be used for collecting each partial discharge signal pulse with the phase mark according to the time sequence.
And S120, detecting the partial discharge pulse to obtain the partial discharge signal.
And carrying out detection processing on the partial discharge pulse to obtain the partial discharge signal. Therefore, the partial discharge pulse can keep the original characteristics, and the loss of the characteristics in subsequent calculation can be avoided, so that the final pulse characteristic extraction is more complete, and the partial discharge signal can be accurately analyzed according to the extracted pulse characteristics.
Referring to fig. 3, this embodiment relates to a possible implementation manner of performing detection processing on the partial discharge pulse to obtain the partial discharge signal, and S120 includes:
and S121, inputting the partial discharge pulse into a detection chip to obtain a peak holding partial discharge pulse.
And inputting the partial discharge pulse into a detection chip, and in a phase interval, if the detection chip detects the partial discharge pulse, carrying out peak value holding on the partial discharge pulse. In addition, the detection chip is reset when the next detection is carried out, so that the accuracy of each detection processing can be ensured. In a specific embodiment, each cycle of the partial discharge pulse may be equally divided into 72 phase intervals.
And S122, inputting the peak value holding partial discharge pulse into an analog-to-digital converter to obtain the partial discharge signal.
The peak-hold partial discharge pulse is input to the analog-to-digital converter, and the peak-hold partial discharge pulse can be converted from an analog signal to a digital signal to obtain the partial discharge signal for subsequent calculation.
Referring to fig. 4, this embodiment relates to a possible implementation manner of decomposing the denoised partial discharge signal into a plurality of product functions based on the local mean decomposition method, and S500 includes:
and S510, calculating all local extreme points of the de-noised partial discharge signal and the time corresponding to the extreme points.
S520, determining a local mean function and an initial envelope estimation function according to the extreme point and the time corresponding to the extreme point.
The de-noised partial discharge signal xavThe extreme point of (t) is represented by niThe extreme point niThe corresponding time is denoted tni。
Specifically, the step of determining the local mean function and the initial estimation function according to the extreme point and the time corresponding to the extreme point is shown in fig. 5.
And S521, calculating an average value between adjacent extreme points, and performing linear continuation on the average value at the moment corresponding to the adjacent extreme points to obtain a local average line segment.
S522, smoothing the local mean line segment to obtain a local mean function.
The average value between the adjacent extreme points is
For the average value miAt the time tn corresponding to the adjacent extremum pointiAnd tni+1Linear continuation is carried out on the local mean value line segment m11(t) of (d). For the local mean line segment m11(t) smoothing to obtain the local mean function m11’(t)。
S523, calculating the local envelope amplitude by using the adjacent extreme points, and linearly extending the local envelope amplitude at the time corresponding to the adjacent extreme points to obtain a local amplitude curve.
And S524, smoothing the local amplitude curve to obtain the initial envelope estimation function.
The local envelope amplitude value can be obtained according to the adjacent extreme points
For the local envelope amplitude at the time tn corresponding to the adjacent extreme pointiAnd tni+1Linear continuation is carried out on the local amplitude curve a11(t) of (d). For the local amplitude curve a11(t) smoothing to obtain the initial envelope estimation function a11’(t)。
S530, separating and removing the local mean function from the de-noised partial discharge signal to obtain a separated local mean function.
And S540, demodulating the separated local mean function to obtain a frequency modulation signal.
From the de-noised partial discharge signal xav(t) separating said local mean function m11' (t) obtaining said separation local mean function h11(t), the separation local mean function h11(t) can be expressed as: h is11(t)＝xav(t)-m11' (t). After the separation local mean function is demodulated, the separation local mean function can be obtainedObtaining the frequency-modulated signal s11(t) the frequency-modulated signal s11(t) can be expressed as:
s550, calculating an envelope estimation function of the frequency modulation signal to obtain a frequency modulation envelope estimation function, and adding the frequency modulation envelope estimation function into an envelope estimation function list.
Calculating the envelope estimation function of the frequency modulation signal by the same method to obtain a frequency modulation envelope estimation function a12' (t) and adding said fm envelope estimation function to said envelope estimation function list H, said envelope estimation function list including said fm envelope estimation function for said fm signal.
S551, judging whether the frequency modulation envelope estimation function is equal to a preset threshold value;
and S560, if the frequency modulation envelope estimation function is not equal to the preset threshold value, updating the de-noised partial discharge signal by using the frequency modulation envelope estimation function, and returning to execute the step S510.
S570, if the fm envelope estimation function is equal to the preset threshold, calculating a product of all the fm envelope estimation functions and the initial envelope estimation function in the envelope estimation function list to obtain an envelope signal.
The predetermined threshold is 1, i.e. if the fm envelope estimation function a12' (t) is not satisfied
The frequency modulation envelope estimation function a is used12' (t) is regarded as the de-noised partial discharge signal xav(t), return to step S510. If after iteration n times, the frequency modulation envelope estimation function a1n' (t) satisfies
I.e. the fm envelope estimation function a1n' (t) is characteristic of a pure frequency modulated signal, the envelope estimation function can be derivedList H ═ a12’(t),a13’(t),…,a1n' (t) }, the envelope signal
The specific iterative process is as follows:
wherein the content of the first and second substances,
the specific process of returning to continue executing step S510 is not described herein again.
S580, determining the plurality of product functions according to the envelope signal and the frequency modulation signal.
Specifically, the process of determining the product functions according to the envelope signal and the frequency modulation signal is shown in fig. 4, and S580 includes:
and S581, obtaining a product function according to the product of the envelope signal and the frequency modulation signal, and adding the product function into a product function list.
The envelope signal is a1(t) the frequency-modulated signal is s1n(t), then the product function may be expressed as PF1(t)＝a1(t)s1n(t) of (d). And adds the product function to the product function list K.
And S582, separating and removing the product function from the denoising partial discharge signal before updating to obtain a separation product function.
The de-noised partial discharge signal before updating is xav(t) separating said product function from said denoised partial discharge signal before updating to obtain said separated product function u (t).
S583, judging whether the separation product function is a monotone function.
S584, if the separation product function is not a monotonic function, the denoising partial discharge signal is updated by the separation product function, and the process returns to step S510.
S585, if the separate product functions are monotonic functions, determining all product functions in the list of product functions as the plurality of product functions.
If the separation product function u (t) is not a monotonic function, the separation product function u (t) is used as the de-noised partial discharge signal xav(t), return to step S510. If after n iterations, the separate product function un(t) is a monotonic function, the product function list K ═ PF can be obtained1(t),PF2(t),…,PFn(t) }. The product function un(t) the iterative process is
The specific process of returning to continue executing step S510 is not described herein again.
Referring to fig. 6, this embodiment relates to a possible implementation manner of extracting the pulse feature according to the product functions, and S600 includes:
s610, determining an envelope signal and a frequency modulation signal according to the plurality of product functions;
s620, determining the envelope signal as the instantaneous amplitude;
since each of the plurality of product functions is a combination of an envelope signal and a frequency modulated signal, the envelope signal and the frequency modulated signal can be determined by the plurality of product functions. The envelope signal may represent an instantaneous amplitude.
S630, deriving the frequency modulation signal to obtain the instantaneous frequency.
The frequency modulated signal may be represented as
Wherein the content of the first and second substances,
is the instantaneous phase. Deriving the frequency-modulated signal to obtain the instantaneous frequency
Referring to fig. 7, in one embodiment, the method further includes:
s700, acquiring an envelope transient function according to the plurality of product functions;
each of the plurality of product functions is formed by combining an envelope signal and a frequency modulation signal. The envelope value is a joint function with respect to time and frequency, and each envelope transient function a (w, t) is derived from each of said envelope signals, where w is frequency and t is time.
And S800, determining an envelope transient map according to the envelope transient function.
And S900, comparing the envelope instantaneous map with a preset pulse characteristic map to determine the characteristics of the partial discharge signal.
Said envelope transient function of said plurality of product functions is
The envelope transient map may be drawn from the envelope transient function. The pulse characteristic map of the partial discharge signal can be preset, and a user can analyze the characteristics of the partial discharge signal and accurately identify the partial discharge signal by comparing the envelope instantaneous map with the pulse characteristic map, so that the method is very simple and clear.
It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in the above-described flowcharts may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or the stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least a portion of the sub-steps or stages of other steps.
Referring to fig. 8, an embodiment of the present application provides a partial discharge pulse feature extraction apparatus 10, which includes a partial discharge signal obtaining module 100, an eigenmode function component determining module 200, a reconstructed signal determining module 300, a de-noising partial discharge signal determining module 400, a product function determining module 500, and a pulse feature extraction module 600. Wherein the content of the first and second substances,
the partial discharge signal acquiring module 100 is configured to acquire a partial discharge signal, where the partial discharge signal includes a noise signal;
the eigenmode function component determining module 200 is configured to decompose the partial discharge signal based on a self-adaptive signal time-frequency processing method to obtain eigenmode function components, where the eigenmode function components include N fundamental eigenmode function components and a trend term;
the reconstructed signal determining module 300 is configured to randomly sort the eigenmode function components based on the statistical characteristics of the noise signal to obtain a reconstructed signal;
the denoising partial discharge signal determining module 400 is configured to accumulate and average the reconstructed signals to obtain a denoising partial discharge signal;
the product function determining module 500 is configured to decompose the denoised partial discharge signal into a plurality of product functions based on a local mean decomposition method;
the pulse feature extraction module 600 is configured to extract pulse features according to the product functions, where the pulse features include: instantaneous amplitude and instantaneous frequency.
In one embodiment, the partial discharge signal acquisition module 100 is specifically configured to acquire a partial discharge pulse based on a phase-resolved pulse sequence; and carrying out detection processing on the partial discharge pulse to obtain the partial discharge signal.
In an embodiment, the partial discharge signal obtaining module 100 is specifically configured to input the partial discharge pulse to a detection chip to obtain a peak holding partial discharge pulse; and inputting the peak value holding partial discharge pulse into an analog-to-digital converter to obtain the partial discharge signal.
In an embodiment, the product function determining module 500 is specifically configured to calculate all local extremum points of the denoised partial discharge signal and time corresponding to the extremum points; determining a local mean function and an initial envelope estimation function according to the extreme point and the time corresponding to the extreme point; separating and removing the local mean function from the de-noised partial discharge signal to obtain a separated local mean function; demodulating the separated local mean function to obtain a frequency modulation signal; calculating an envelope estimation function of the frequency modulation signal to obtain a frequency modulation envelope estimation function, and adding the frequency modulation envelope estimation function into an envelope estimation function list; if the frequency modulation envelope estimation function is not equal to a preset threshold value, updating the de-noised partial discharge signal by using the frequency modulation envelope estimation function, and returning to the step of executing to calculate the extreme points of all local areas of the de-noised partial discharge signal and the time corresponding to the extreme points; if the frequency modulation envelope estimation function is equal to the preset threshold value, calculating the product of all the frequency modulation envelope estimation functions and the initial envelope estimation function in the envelope estimation function list to obtain an envelope signal; determining the plurality of product functions based on the envelope signal and the frequency modulated signal.
In an embodiment, the product function determining module 500 is specifically configured to obtain a product function according to a product of the envelope signal and the frequency modulation signal, and add the product function to a product function list; separating and removing the product function from the de-noised partial discharge signal before updating to obtain a separated product function; if the separation product function is not a monotone function, updating the denoising partial discharge signal by using the separation product function, and returning to the step of executing to calculate the extreme points of all local areas of the denoising partial discharge signal and the time corresponding to the extreme points; and if the separation product function is a monotone function, determining all product functions in the product function list as the plurality of product functions.
In an embodiment, the product function determining module 500 is specifically configured to calculate an average value between adjacent extreme points, and perform linear continuation on the average value at a time corresponding to the adjacent extreme points to obtain a local average line segment; smoothing the local mean line segment to obtain a local mean function; calculating a local envelope amplitude by using adjacent extreme points, and performing linear continuation on the local envelope amplitude at the time corresponding to the adjacent extreme points to obtain a local amplitude curve; and smoothing the local amplitude curve to obtain the initial envelope estimation function.
In an embodiment, the pulse feature extraction module 600 is specifically configured to determine an envelope signal and a frequency modulation signal according to the plurality of product functions; determining the envelope signal as the instantaneous amplitude; and obtaining the instantaneous frequency by derivation of the frequency modulation signal.
With continuing reference to fig. 8, the partial discharge pulse feature extraction apparatus 10 provided in an embodiment of the present application further includes an envelope transient map determination module 700. The envelope transient map determining module 700 is configured to obtain an envelope transient function according to the plurality of product functions; determining an envelope transient map according to the envelope transient function; and comparing the envelope transient map with a preset pulse characteristic map to determine the characteristics of the partial discharge signal.
For the specific definition of the partial discharge pulse feature extraction apparatus 10, reference may be made to the above definition of the partial discharge pulse feature extraction method, which is not described herein again. The respective modules in the partial discharge pulse feature extraction apparatus 10 described above may be wholly or partially implemented by software, hardware, or a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Referring to fig. 9, in one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing partial discharge pulses, partial discharge signals, etc. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a partial discharge pulse feature extraction method.
Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the following steps when executing the computer program:
acquiring a partial discharge signal, wherein the partial discharge signal comprises a noise signal;
decomposing the partial discharge signal based on a self-adaptive signal time-frequency processing method to obtain eigenmode function components, wherein the eigenmode function components comprise N basic eigenmode function components and a trend term;
randomly sequencing the eigenmode function components based on the noise statistical characteristics to obtain a reconstructed signal;
accumulating and averaging the reconstructed signals to obtain a noise-removed partial discharge signal;
decomposing the de-noised partial discharge signal into a plurality of product functions based on a partial mean decomposition method;
extracting pulse features from the plurality of product functions, wherein the pulse features include: instantaneous amplitude and instantaneous frequency.
In one embodiment, the processor when executing the computer program further performs the steps of: collecting partial discharge pulses based on the phase resolved pulse sequence; and carrying out detection processing on the partial discharge pulse to obtain the partial discharge signal.
In one embodiment, the processor when executing the computer program further performs the steps of: inputting the partial discharge pulse into a detection chip to obtain a peak value holding partial discharge pulse; and inputting the peak value holding partial discharge pulse into an analog-to-digital converter to obtain the partial discharge signal.
In one embodiment, the processor when executing the computer program further performs the steps of: calculating all local extreme points of the de-noised partial discharge signal and the time corresponding to the extreme points; determining a local mean function and an initial envelope estimation function according to the extreme point and the time corresponding to the extreme point; separating and removing the local mean function from the de-noised partial discharge signal to obtain a separated local mean function; demodulating the separated local mean function to obtain a frequency modulation signal; calculating an envelope estimation function of the frequency modulation signal to obtain a frequency modulation envelope estimation function, and adding the frequency modulation envelope estimation function into an envelope estimation function list; if the frequency modulation envelope estimation function is not equal to a preset threshold value, updating the de-noised partial discharge signal by using the frequency modulation envelope estimation function, and returning to the step of executing to calculate the extreme points of all local areas of the de-noised partial discharge signal and the time corresponding to the extreme points; if the frequency modulation envelope estimation function is equal to the preset threshold value, calculating the product of all the frequency modulation envelope estimation functions and the initial envelope estimation function in the envelope estimation function list to obtain an envelope signal; determining the plurality of product functions based on the envelope signal and the frequency modulated signal.
In one embodiment, the processor when executing the computer program further performs the steps of: obtaining a product function according to the product of the envelope signal and the frequency modulation signal, and adding the product function into a product function list; separating and removing the product function from the de-noised partial discharge signal before updating to obtain a separated product function; if the separation product function is not a monotone function, updating the denoising partial discharge signal by using the separation product function, and returning to the step of executing to calculate the extreme points of all local areas of the denoising partial discharge signal and the time corresponding to the extreme points; and if the separation product function is a monotone function, determining all product functions in the product function list as the plurality of product functions.
In one embodiment, the processor when executing the computer program further performs the steps of: calculating an average value between adjacent extreme points, and performing linear continuation on the average value at the time corresponding to the adjacent extreme points to obtain a local average value line segment; smoothing the local mean line segment to obtain a local mean function; calculating a local envelope amplitude by using adjacent extreme points, and performing linear continuation on the local envelope amplitude at the time corresponding to the adjacent extreme points to obtain a local amplitude curve; and smoothing the local amplitude curve to obtain the initial envelope estimation function.
In one embodiment, the processor when executing the computer program further performs the steps of: determining envelope signals and frequency modulation signals according to the plurality of product functions; determining the envelope signal as the instantaneous amplitude; and obtaining the instantaneous frequency by derivation of the frequency modulation signal.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring an envelope transient function according to the plurality of product functions; determining an envelope transient map according to the envelope transient function; and comparing the envelope transient map with a preset pulse characteristic map to determine the characteristics of the partial discharge signal.
The specific processes and advantages of the above method steps implemented by the computer device processor provided in the above embodiments are similar to those of the corresponding method embodiments, and are not described herein again.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a partial discharge signal, wherein the partial discharge signal comprises a noise signal;
decomposing the partial discharge signal based on a self-adaptive signal time-frequency processing method to obtain eigenmode function components, wherein the eigenmode function components comprise N basic eigenmode function components and a trend term;
randomly sequencing the eigenmode function components based on the noise statistical characteristics to obtain a reconstructed signal;
accumulating and averaging the reconstructed signals to obtain a noise-removed partial discharge signal;
decomposing the de-noised partial discharge signal into a plurality of product functions based on a partial mean decomposition method;
extracting pulse features from the plurality of product functions, wherein the pulse features include: instantaneous amplitude and instantaneous frequency.
In one embodiment, the computer program when executed by the processor further performs the steps of: collecting partial discharge pulses based on the phase resolved pulse sequence; and carrying out detection processing on the partial discharge pulse to obtain the partial discharge signal.
In one embodiment, the computer program when executed by the processor further performs the steps of: inputting the partial discharge pulse into a detection chip to obtain a peak value holding partial discharge pulse; and inputting the peak value holding partial discharge pulse into an analog-to-digital converter to obtain the partial discharge signal.
In one embodiment, the computer program when executed by the processor further performs the steps of: calculating all local extreme points of the de-noised partial discharge signal and the time corresponding to the extreme points; determining a local mean function and an initial envelope estimation function according to the extreme point and the time corresponding to the extreme point; separating and removing the local mean function from the de-noised partial discharge signal to obtain a separated local mean function; demodulating the separated local mean function to obtain a frequency modulation signal; calculating an envelope estimation function of the frequency modulation signal to obtain a frequency modulation envelope estimation function, and adding the frequency modulation envelope estimation function into an envelope estimation function list; if the frequency modulation envelope estimation function is not equal to a preset threshold value, updating the de-noised partial discharge signal by using the frequency modulation envelope estimation function, and returning to the step of executing to calculate the extreme points of all local areas of the de-noised partial discharge signal and the time corresponding to the extreme points; if the frequency modulation envelope estimation function is equal to the preset threshold value, calculating the product of all the frequency modulation envelope estimation functions and the initial envelope estimation function in the envelope estimation function list to obtain an envelope signal; determining the plurality of product functions based on the envelope signal and the frequency modulated signal.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining a product function according to the product of the envelope signal and the frequency modulation signal, and adding the product function into a product function list; separating and removing the product function from the de-noised partial discharge signal before updating to obtain a separated product function; if the separation product function is not a monotone function, updating the denoising partial discharge signal by using the separation product function, and returning to the step of executing to calculate the extreme points of all local areas of the denoising partial discharge signal and the time corresponding to the extreme points; and if the separation product function is a monotone function, determining all product functions in the product function list as the plurality of product functions.
In one embodiment, the computer program when executed by the processor further performs the steps of: calculating an average value between adjacent extreme points, and performing linear continuation on the average value at the time corresponding to the adjacent extreme points to obtain a local average value line segment; smoothing the local mean line segment to obtain a local mean function; calculating a local envelope amplitude by using adjacent extreme points, and performing linear continuation on the local envelope amplitude at the time corresponding to the adjacent extreme points to obtain a local amplitude curve; and smoothing the local amplitude curve to obtain the initial envelope estimation function.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining envelope signals and frequency modulation signals according to the plurality of product functions; determining the envelope signal as the instantaneous amplitude; and obtaining the instantaneous frequency by derivation of the frequency modulation signal.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring an envelope transient function according to the plurality of product functions; determining an envelope transient map according to the envelope transient function; and comparing the envelope transient map with a preset pulse characteristic map to determine the characteristics of the partial discharge signal.
The specific processes and advantageous effects of implementing the above method steps by the computer-readable storage medium provided by the above embodiments are similar to those of the corresponding method embodiments, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (11)
1. A partial discharge pulse feature extraction method, characterized by comprising:
acquiring a partial discharge signal, wherein the partial discharge signal comprises a noise signal;
decomposing the partial discharge signal based on a self-adaptive signal time-frequency processing method to obtain eigenmode function components, wherein the eigenmode function components comprise N basic eigenmode function components and a trend term;
randomly sequencing the eigenmode function components based on the statistical characteristics of the noise signals to obtain reconstructed signals;
accumulating and averaging the reconstructed signals to obtain a noise-removed partial discharge signal;
decomposing the de-noised partial discharge signal into a plurality of product functions based on a partial mean decomposition method;
extracting pulse features from the plurality of product functions, wherein the pulse features include: instantaneous amplitude and instantaneous frequency.
2. The method of claim 1, wherein the obtaining a partial discharge signal comprises:
collecting partial discharge pulses based on the phase resolved pulse sequence;
and carrying out detection processing on the partial discharge pulse to obtain the partial discharge signal.
3. The method according to claim 2, wherein the detecting the partial discharge pulse to obtain the partial discharge signal comprises:
inputting the partial discharge pulse into a detection chip to obtain a peak value holding partial discharge pulse;
and inputting the peak value holding partial discharge pulse into an analog-to-digital converter to obtain the partial discharge signal.
4. The method of claim 1, wherein decomposing the denoised partial discharge signal into a plurality of product functions based on a local mean decomposition method comprises:
calculating all local extreme points of the de-noised partial discharge signal and the time corresponding to the extreme points;
determining a local mean function and an initial envelope estimation function according to the extreme point and the time corresponding to the extreme point;
separating and removing the local mean function from the de-noised partial discharge signal to obtain a separated local mean function;
demodulating the separated local mean function to obtain a frequency modulation signal;
calculating an envelope estimation function of the frequency modulation signal to obtain a frequency modulation envelope estimation function, and adding the frequency modulation envelope estimation function into an envelope estimation function list;
if the frequency modulation envelope estimation function is not equal to a preset threshold value, updating the de-noised partial discharge signal by using the frequency modulation envelope estimation function, and returning to the step of executing to calculate the extreme points of all local areas of the de-noised partial discharge signal and the time corresponding to the extreme points;
if the frequency modulation envelope estimation function is equal to the preset threshold value, calculating the product of all the frequency modulation envelope estimation functions and the initial envelope estimation function in the envelope estimation function list to obtain an envelope signal;
determining the plurality of product functions based on the envelope signal and the frequency modulated signal.
5. The method of claim 4, wherein said determining said plurality of product functions from said envelope signal and said frequency modulated signal comprises:
obtaining a product function according to the product of the envelope signal and the frequency modulation signal, and adding the product function into a product function list;
separating and removing the product function from the de-noised partial discharge signal before updating to obtain a separated product function;
if the separation product function is not a monotone function, updating the denoising partial discharge signal by using the separation product function, and returning to the step of executing to calculate the extreme points of all local areas of the denoising partial discharge signal and the time corresponding to the extreme points;
and if the separation product function is a monotone function, determining all product functions in the product function list as the plurality of product functions.
6. The method of claim 4, wherein determining the local mean function and the initial envelope estimation function according to the extreme point and the time corresponding to the extreme point comprises:
calculating an average value between adjacent extreme points, and performing linear continuation on the average value at the time corresponding to the adjacent extreme points to obtain a local average value line segment;
smoothing the local mean line segment to obtain a local mean function;
calculating a local envelope amplitude by using adjacent extreme points, and performing linear continuation on the local envelope amplitude at the time corresponding to the adjacent extreme points to obtain a local amplitude curve;
and smoothing the local amplitude curve to obtain the initial envelope estimation function.
7. The method of claim 1, wherein said extracting pulse features according to said plurality of product functions comprises:
determining an envelope signal and a frequency modulation signal according to the plurality of product functions;
determining the envelope signal as the instantaneous amplitude;
and obtaining the instantaneous frequency by derivation of the frequency modulation signal.
8. The method of claim 1, further comprising:
acquiring an envelope transient function according to the plurality of product functions;
determining an envelope transient map according to the envelope transient function;
and comparing the envelope transient map with a preset pulse characteristic map to determine the characteristics of the partial discharge signal.
9. A partial discharge pulse feature extraction apparatus, characterized in that the apparatus comprises:
the device comprises a partial discharge signal acquisition module, a partial discharge signal acquisition module and a partial discharge signal processing module, wherein the partial discharge signal acquisition module is used for acquiring a partial discharge signal, and the partial discharge signal comprises a noise signal;
the eigenmode function component determining module is used for decomposing the partial discharge signal based on a self-adaptive signal time-frequency processing method to obtain eigenmode function components, and the eigenmode function components comprise N basic eigenmode function components and a trend term;
a reconstructed signal determining module, configured to randomly sort the eigenmode function components based on the statistical characteristics of the noise signal to obtain a reconstructed signal;
the de-noise partial discharge signal determining module is used for accumulating and averaging the reconstructed signals to obtain de-noise partial discharge signals;
a product function determination module for decomposing the de-noised partial discharge signal into a plurality of product functions based on a local mean decomposition method;
a pulse feature extraction module, configured to extract pulse features according to the plurality of product functions, where the pulse features include: instantaneous amplitude and instantaneous frequency.
10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 8.
11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 8.
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