CN116881703B - Cable multi-source signal identification method, device and storage medium - Google Patents
Cable multi-source signal identification method, device and storage medium Download PDFInfo
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Abstract
The application relates to a cable multi-source signal identification method, a device and a storage medium, which comprise the following steps: triggering type acquisition of time domain pulses in a multi-source high-frequency current time domain signal monitored by a cable; calculating the time-frequency energy center moment of the time-domain pulse; performing feature separation according to the time-frequency energy central moment, and dividing pulse groups; PRPS and PRPD map reduction is carried out based on the pulse group; and carrying out signal source identification based on the PRPS and PRPD patterns. Compared with the prior art, the method and the device can maximize the characteristic difference of the multi-source signals and realize the accurate classification of the multi-source signals in the cable.
Description
Technical Field
The application relates to the field of cable equipment monitoring and fault diagnosis in an electric power system, in particular to a cable multi-source signal identification method, device and storage medium based on time-frequency energy center moment characteristic separation.
Background
Cable installations play an important role in the power system, the operating state of which has a great influence on the power supply reliability of the power system. However, the cable equipment may be affected by factors such as process limitations, improper handling, and the like during design, manufacturing, installation, and operation and maintenance, and external reasons such as lightning overvoltage, pollution, weak insulation, and the like, often cause defects or hidden hazards to different degrees. These defects and hazards may cause equipment failures, manifested as external insulation to ground flashover, internal insulation to ground flashover, interphase insulation flashover, bushing flashover, etc. The occurrence of the fault of the cable equipment can bring serious consequences, the direct damage is that the cable equipment is damaged, and the indirect damage can cause the damage of the safety and the stability of the power grid due to the tripping of the cable equipment, so that the power failure accident occurs, and the loss is brought to the power consumption of users and the national economy.
Multi-source signal identification is an important issue in monitoring and fault diagnosis of cable devices. The conventional cable monitoring method is mainly based on analysis of time domain and frequency domain characteristics, but in high-frequency current monitoring of a cable, the problems of environmental interference and multi-source discharge signals are often encountered, so that the distinction of the environmental interference or the partial discharge and the identification of the multi-source discharge signals are difficult. Limitations of conventional approaches have resulted in challenges for accurate identification of multiple source signals in a cable.
Disclosure of Invention
The application aims to provide a cable multisource signal identification method, device and storage medium based on time-frequency energy center moment feature separation, which accurately extracts energy feature center moments of a time domain and a frequency domain by collecting cable monitoring data and applying a time-frequency energy center moment analysis technology; the signal groups of different sources are distinguished through a characteristic separation step, so that the accuracy and the reliability of identification are improved; in addition, the time domain pulses of different source signal groups are converted into PRPS and PRPD patterns so as to more effectively apply a machine learning algorithm or pattern recognition technology to carry out signal source recognition and provide a quick and accurate recognition result.
The aim of the application can be achieved by the following technical scheme:
a cable multi-source signal identification method based on time-frequency energy center moment feature separation comprises the following steps:
step 1) triggering type acquisition of time domain pulses in a multi-source high-frequency current time domain signal monitored by a cable;
step 2) calculating a time-frequency energy center moment of the time-domain pulse, wherein the time-frequency energy center moment comprises a time-domain capacity center distance and a frequency-domain energy center distance;
step 3) performing feature separation according to the time-frequency energy central moment, and dividing pulse groups;
step 4) performing PRPS and PRPD map reduction based on the pulse group;
and 5) carrying out signal source identification based on the PRPS and PRPD patterns.
In the step 1), the time domain pulse in the multi-source high-frequency current time domain signal monitored by the triggering type acquisition cable is specifically: detecting the amplitude value in the multi-source high-frequency current time domain signal in real time, and if the amplitude value is larger than a trigger threshold value, starting time domain pulse capturing and collecting time domain pulses.
The time domain pulse capturing specifically comprises the following steps: according to the set pulse duration T, from the trigger time T 0 The starting capture distance triggering time satisfies t-t 0 <A time domain signal of =t is used as a time domain pulse signal, and each trigger time T is recorded 0 。
The calculation formula for calculating the time-frequency energy center moment of the time domain pulse is as follows:
，
，
，
wherein,ts、tc、fcrespectively isThe time domain center of gravity, the time domain energy center moment and the frequency domain energy center moment of the time domain pulse signal,f(t) For the time-domain pulse signal to be calculated,Tfor the duration of the pulse,nis the second order central moment coefficient,fsfor the sampling frequency to be the same,F(f) Is thatf(t) Is a fourier transform of (a).
Said step 3) comprises the steps of:
step 3-1), drawing the time-frequency energy central moment of each time domain pulse on a two-dimensional plane by taking the time-domain energy central moment as an abscissa and the frequency-domain energy central moment as an ordinate;
step 3-2) setting a clustering grid window, and gridding the two-dimensional plane according to the clustering grid window;
step 3-3) connecting adjacent grids which have pulse energy central moment on the two-dimensional plane, and forming a group of pulse groups by pulse signals in all connected grid windows, thereby dividing the original pulse signals into a plurality of pulse groups.
The size and the number of the grid windows are adjusted according to the clustering performance, the cluster average distance of all clusters is determined by calculating the average distance of the time-frequency energy central moment in each cluster, if the cluster average distance of all clusters is larger than a first preset threshold value, the number of the grid windows is reduced, and if the cluster average distance of all clusters is smaller than a second preset threshold value, the number of the grid windows is increased, wherein the distance calculation formula between the time-frequency energy central moment 1 (x 1, y 1) and the time-frequency energy central moment 2 (x 2, y 2) is as follows:
in the step 4), each pulse group is converted into a PRPS map by the following conversion formula:
，
，
，
wherein,f(t) In the form of a pulsed time-domain signal,pulse.timefor the start time of the pulse time domain signal,T sample is a power frequency period of one time,Fis the power frequency; phase, period, amplitude are the phase, period and amplitude, respectively, of the corresponding PRPS profile.
The PRPD pattern is obtained by carrying out standardized processing and conversion on the PRPS pattern.
A cable multi-source signal identification device based on time-frequency energy center moment feature separation comprises a memory, a processor and a program stored in the memory, wherein the processor realizes the method when executing the program.
A storage medium having stored thereon a program which when executed performs a method as described above.
Compared with the prior art, the application has the following beneficial effects:
(1) Accurately classifying the multi-source signals: the traditional cable monitoring method is difficult to accurately distinguish the environment interference and the multi-source discharge signals, and the energy characteristic central moment of the time domain and the frequency domain can be accurately extracted by collecting the cable monitoring data and analyzing the energy central moment of the frequency domain, so that the characteristic difference of the multi-source signals can be maximized, and the accurate classification of the multi-source signals in the cable is realized.
(2) The accuracy and the reliability of identification are improved: according to the method, through a time-frequency energy central moment characteristic separation step, particularly through adjusting a second-order central moment coefficient, characteristic differences of multi-source signals in a time domain and a frequency domain can be maximized; through rectangular grid clustering, the accurate classification of multi-source signals in the cable can be effectively realized, pulse signal groups of different sources are effectively distinguished, and the accuracy and reliability of identification are improved.
(3) And (3) rapid fault diagnosis: the cable multi-source signal identification method provided by the application can be used for rapidly grouping and converting signals into PRPS and PRPD maps, analyzing the maps through a machine learning algorithm or a pattern identification technology, rapidly diagnosing the defect types in cable equipment, and providing a rapid and accurate identification result, thereby being beneficial to rapidly positioning a fault source and taking corresponding maintenance measures, and reducing the fault processing time and the maintenance cost.
(4) The reliability of the power system is improved: the application can prevent equipment faults from happening by timely finding and identifying multi-source signals in the cable, and avoid the occurrence of power grid safety and stability damage and power failure accidents, thereby ensuring electricity utilization safety and reducing the loss of electricity utilization of users and national economy.
Drawings
FIG. 1 is a flow chart of the method of the present application;
FIG. 2 is a schematic diagram of dividing pulse groups according to the time-frequency energy center moment of the present application.
Detailed Description
The application will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present application, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present application is not limited to the following examples.
The embodiment provides a cable multi-source signal identification method based on time-frequency energy center moment feature separation, which is shown in fig. 1 and comprises the following steps:
step 1) triggering type acquisition of time domain pulses in a multi-source high-frequency current time domain signal monitored by a cable.
Setting pulse duration T and trigger threshold th, and triggering pulse signals in the high-frequency current time domain signals: detecting the amplitude value in the multi-source high-frequency current time domain signal in real time, and if the amplitude value is larger than a trigger threshold th, starting time domain pulse capturing and collecting time domain pulses.
The time domain pulse capturing specifically comprises the following steps: according to the set pulse duration T, from the trigger time T 0 The starting capture distance triggering time satisfies t-t 0 <A time domain signal of =t is used as a time domain pulse signal, and each trigger time T is recorded 0 Sum time domain signalf(t)。
Step 2) calculating the time-frequency energy center moment of the time-domain pulse.
In this embodiment, the time-frequency energy center moment includes a time-domain energy center distance and a frequency-domain energy center distance, and the calculation method includes:
，
，
，
wherein,ts、tc、fcrespectively a time domain center of gravity, a time domain energy center moment and a frequency domain energy center moment of the time domain pulse signal,f(t) For the time-domain pulse signal to be calculated,Tfor the duration of the pulse,nis the second order central moment coefficient,fsfor the sampling frequency to be the same,F(f) Is thatf(t) Is a fourier transform of (a).
In this embodiment, the second order central moment coefficientnDefault to 2, can also be adjusted according to clustering performance. The cluster average distance of all clusters is determined by calculating the average distance of the time-frequency energy center moment in each cluster, wherein the distance calculation formula between the time-frequency energy center moment 1 (x 1, y 1) and the time-frequency energy center moment 2 (x 2, y 2) is as follows:
average distance of time-frequency energy center moment in clusters = distance of any two time-frequency energy centers and/or number of distances of any two centers, average distance of all clusters = average distance of all clusters and/or number of clusters.
If the cluster average distance of all clusters>4, second order central moment coefficientn=n0.5 if the cluster average distance of all clusters<2, second order central moment coefficientn=n+0.5。
And 3) carrying out feature separation according to the time-frequency energy central moment, and dividing pulse groups.
Specifically, step 3) includes the steps of:
and 3-1) drawing the time-frequency energy central moment of each time domain pulse on a two-dimensional plane by taking the time-domain energy central moment as an abscissa and the frequency-domain energy central moment as an ordinate.
Step 3-2) setting a clustering grid window (t_width, f_width), and gridding the two-dimensional plane according to the clustering grid window.
In this embodiment, the size and number of the grid windows are adjusted according to the clustering performance. And determining the cluster average distance of all clusters by calculating the average distance of the time-frequency energy central moments in each cluster according to the method for adjusting the second-order central moment coefficient, wherein the average distance of the time-frequency energy central moments in the clusters = the distance between any two time-frequency energy central distances and/or the number of the distances between any two central distances, and the cluster average distance of all clusters = the average distance of all clusters and/or the number of clusters.
If the cluster average distance of all clusters is >4, the number of grid windows is reduced, i.e., grid window = grid window-1; if the cluster average distance of all clusters is <2, the number of grid windows is increased, i.e., grid window=grid window+1.
Step 3-3) connecting adjacent grids which have pulse energy central moment on the two-dimensional plane, and forming a group of pulse groups by pulse signals in all connected grid windows, thereby dividing the original pulse signals into a plurality of pulse groups.
In one embodiment, as shown in FIG. 2, the division of groups of pulses is shown in one case.
And 4) carrying out PRPS and PRPD map reduction based on the pulse group.
For each pulse group, a PRPS profile is converted by the following conversion formula:
，
，
，
wherein,f(t) In the form of a pulsed time-domain signal,pulse.timefor the start time of the pulse time domain signal,T sample is a power frequency period of one time,Fis the power frequency; phase, period, amplitude are the phase, period and amplitude, respectively, of the corresponding PRPS profile.
Converting all pulses in the pulse group into a PRPS map by using the formula; and grouping pulses by a plurality of signal sources, and converting to obtain a plurality of PRPS maps.
The PRPD pattern is obtained by carrying out standardized treatment and conversion on the PRPS pattern.
And 5) carrying out signal source identification based on the PRPS and PRPD patterns.
And performing defect type identification through the converted characteristics of each PRPS map and each PRPD map by using a machine learning algorithm or a pattern identification technology, so as to accurately classify and identify the multi-source signals in the cable. Specifically, the machine learning algorithm or pattern recognition technique adopted belongs to the prior art, and is not described herein in detail in order to avoid ambiguity of the present application. In a specific implementation, reference may be made to the disclosure of patent CN 201910793780.2.
And outputting classification information of the multi-source signals according to the signal source identification result, and providing accurate judgment basis for monitoring and fault diagnosis of the cable equipment. The application has the characteristics of definite data processing flow and subsequent analysis by utilizing intermediate data, can effectively distinguish signal groups of different sources, and improves the accuracy and reliability of identification. Meanwhile, the PRPS and PRPD patterns are utilized for signal source identification, so that a quick and accurate identification result can be provided for the field of cable equipment monitoring and fault diagnosis in the power system, and the method has important practical significance and application value.
The above functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing describes in detail preferred embodiments of the present application. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the application by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by a person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (8)
1. The cable multi-source signal identification method based on time-frequency energy center moment feature separation is characterized by comprising the following steps of:
step 1) triggering type acquisition of time domain pulses in a multi-source high-frequency current time domain signal monitored by a cable;
step 2) calculating a time-frequency energy center moment of the time-domain pulse, wherein the time-frequency energy center moment comprises a time-domain capacity center distance and a frequency-domain energy center distance, and a calculation formula for calculating the time-frequency energy center moment of the time-domain pulse is as follows:
，
，
，
wherein,ts、tc、fcrespectively a time domain center of gravity, a time domain energy center moment and a frequency domain energy center moment of the time domain pulse signal,f(t) For the time-domain pulse signal to be calculated,Tfor the duration of the pulse,nis the second order central moment coefficient,fsfor the sampling frequency to be the same,F(f) Is thatf(t) Fourier transform of (a);
step 3) performing feature separation according to the time-frequency energy central moment, and dividing pulse groups;
step 4) performing PRPS and PRPD map reduction based on the pulse group;
step 5) carrying out signal source identification based on a PRPS and PRPD map;
said step 3) comprises the steps of:
step 3-1), drawing the time-frequency energy central moment of each time domain pulse on a two-dimensional plane by taking the time-domain energy central moment as an abscissa and the frequency-domain energy central moment as an ordinate;
step 3-2) setting a clustering grid window, and gridding the two-dimensional plane according to the clustering grid window;
step 3-3) connecting adjacent grids which have pulse energy central moment on the two-dimensional plane, and forming a group of pulse groups by pulse signals in all connected grid windows, thereby dividing the original pulse signals into a plurality of pulse groups.
2. The method for identifying cable multisource signals based on time-frequency energy center moment feature separation according to claim 1, wherein in the step 1), time domain pulses in multisource high-frequency current time domain signals monitored by a trigger acquisition cable are specifically: detecting the amplitude value in the multi-source high-frequency current time domain signal in real time, and if the amplitude value is larger than a trigger threshold value, starting time domain pulse capturing and collecting time domain pulses.
3. The method for identifying cable multisource signals based on time-frequency energy center moment feature separation according to claim 2, wherein the time domain pulse capturing specifically comprises: according to the set pulse duration T, from the trigger time T 0 The starting capture distance triggering time satisfies t-t 0 <A time domain signal of =t is used as a time domain pulse signal, and each trigger time T is recorded 0 。
4. The cable multisource signal recognition method based on time-frequency energy center moment feature separation according to claim 1, wherein the size and the number of the grid windows are adjusted according to clustering performance, the cluster average distance of all clusters is determined by calculating the average distance of time-frequency energy center moments in each cluster, if the cluster average distance of all clusters is larger than a first preset threshold value, the number of the grid windows is reduced, and if the cluster average distance of all clusters is smaller than a second preset threshold value, the number of the grid windows is increased, wherein a distance calculation formula between the time-frequency energy center moment 1 (x 1, y 1) and the time-frequency energy center moment 2 (x 2, y 2) is as follows:
。
5. the method for identifying multiple cable source signals based on time-frequency energy center moment feature separation according to claim 1, wherein in the step 4), each pulse group is converted into a PRPS pattern by the following conversion formula:
，
，
，
wherein,f(t) In the form of a pulsed time-domain signal,pulse.timefor the start time of the pulse time domain signal,T sample is a power frequency period of one time,Fis the power frequency; phase, period, amplitude are the phase, period and amplitude, respectively, of the corresponding PRPS profile.
6. The cable multisource signal identification method based on time-frequency energy center moment feature separation according to claim 1, wherein the PRPD pattern is obtained by performing standardized processing conversion on a PRPS pattern.
7. A cable multisource signal recognition device based on time-frequency energy center moment feature separation, comprising a memory, a processor, and a program stored in the memory, wherein the processor implements the method of any one of claims 1-6 when executing the program.
8. A storage medium having a program stored thereon, wherein the program, when executed, implements the method of any of claims 1-6.
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| US7978756B2 (en) * | 2008-04-07 | 2011-07-12 | Ikanos Communications, Inc. | Systems and methods for monitoring impulse noise |
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