CN117874603A - MOA resistive current extraction method based on CEEMD and fuzzy entropy - Google Patents
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Abstract
The invention discloses a MOA resistive current extraction method based on CEEMD and fuzzy entropy, which comprises the following steps: MOA signal collection; preprocessing data; CEEMD decomposition; calculating fuzzy entropy; feature classification and identification. The invention can effectively eliminate the influence of high-frequency noise, white noise, random pulse and other interference on MOA resistive current, further accurately extract fault current signals in MOA, accurately identify and classify the fault current signals, and further realize the state monitoring and fault diagnosis of MOA.
Description
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a MOA resistive current extraction method based on CEEMD and fuzzy entropy.
Background
Metal Oxide Arresters (MOAs) are used as important devices in electrical power systems, widely to protect against lightning overvoltage and operating overvoltage to ensure safe operation of the system. However, MOAs are affected by a variety of factors during online operation, including lightning overvoltage, operating overvoltage, temperature and humidity, chemical pollution, and pollution external environmental factors. Over time, MOAs may undergo aging degradation phenomena, commonly manifested by reduced voltage-dependent voltage, increased leakage current, and changes in the voltammetric characteristic. In severe cases, thermal runaway can also occur, severely affecting the performance of the MOA and the safety of the overall system. In order to prevent the influence of MOA aging on the system, online monitoring technology is researched and proposed to solve the problem of MOA aging monitoring. Through the on-line monitoring technology, the working state and performance index of the MOA can be accurately monitored in real time, problems can be found in time, and corresponding measures can be taken. Compared with the traditional preventive test, the on-line monitoring technology not only can improve the working efficiency, but also can reduce the damage to MOA and reduce the maintenance cost.
There are various methods for extracting the resistive current of the MOA, such as the resistive current harmonic compensation method in the research of the MOA harmonic resistive current compensation algorithm (high voltage electrical appliance (2013)), but the method can only extract the resistive current of the fundamental voltage, while the capacitive current compensation method proposed by the capacitive current compensation method of the on-line monitoring zinc oxide arrester (high voltage technology (2000)) extracts the resistive leakage current by decomposing the capacitive current, and the method considers the voltage harmonic but has obvious error as a result
Therefore, a new MOA resistive current extraction method is needed.
Disclosure of Invention
The present invention aims to solve the problems of the prior art, and therefore provides a resistive current processing method of a metal oxide arrester (metal oxide arrester, MOA) based on complete ensemble empirical mode decomposition (Complete Ensemble Empirical Mode Decomposition, CEEMD) and fuzzy entropy. The method combines the characteristics of high current signal decomposition precision and strong fuzzy entropy stability of CEEMD, can effectively eliminate the influence of high-frequency noise, white noise, random pulse and other interference on MOA resistive current, further accurately extracts fault current signals in MOA, and accurately identifies and classifies the fault current signals, thereby realizing state monitoring and fault diagnosis of the MOA.
The technical scheme for realizing the aim of the invention is to provide a thunderstorm prediction method, which comprises the following steps:
s1.MOA signal collection;
s2, preprocessing data;
s3, CEEMD decomposition;
s4, calculating fuzzy entropy;
s5, feature classification and identification.
Further, S1 specifically includes the following steps: the current signal of the MOA is collected with a sensor.
Further, S2 specifically includes the following steps: and (3) preprocessing the MOA current signals collected in the step (S1) to remove noise and interference current signals.
Further, S3 specifically includes the following steps: decomposing the MOA current signal subjected to S2 pretreatment by using CEEMD to obtain a plurality of inherent mode function components; the operation flow of CEEMD current signal decomposition is as follows:
s31, respectively initializing aggregation times M and white noise amplitude k to enable i=1;
s32, adding white noise n for the ith time i Performing EMD decomposition on the current signal after (t), adding a pair of white noise with equal amplitude and opposite sign into the original current signal x (t) to obtain P added with positive noise i (t) sequence, N with added negative noise i (t) a sequence;
current signal P after adding white noise for the ith time i (t) and N i (t) respectively performing EMD decomposition treatment to obtain q IMF component:
wherein,and->The jth IMF component obtained by the ith decomposition; q is the number of IMF components, +.>And->The remainder obtained by the ith EMD decomposition; j=1, 2, …, q;
s33, if i is less than M, enabling i to be equal to i+1, circularly running the step S32, and adding white noise with different amplitude values during each experiment;
s34, obtaining all corresponding IMF components obtained by M times of EMD decompositionAnd->Rest->Andis the overall average value of (2):
wherein c j (t) is the jth IMF component obtained from CEEMD decomposition; r (t) is the final residual component; thus, the raw current signal can be expressed as:
further, S4 specifically includes the following steps: performing fuzzy entropy calculation on each IMF component obtained by the S3 decomposition to obtain corresponding fuzzy entropy characteristics; the fuzzy entropy operation flow is as follows:
assume that the initial current signal sequence of length N is { u (i): i=1, 2, …, N }
S41, setting a mode dimension m, and constructing an m-dimensional vector by using an original current signal sequence to obtain:
wherein u is 0i The average value of m continuous u from the ith point of the current signal sequence;
s42, defining vectorsAnd other vectors +.>Distance between the two->The one of the two vectors corresponding to the element with the largest difference is recorded as:
s43, defining a fuzzy membership function mu (x, n, r) by adopting chaotic pseudorandom sequence complexity predictionAnd->Similarity of->：
Wherein the fuzzy membership functionN and r are the gradient and width of the similar tolerance boundary, respectively, as an exponential function;
s44, defining a function
S45, letting m=m+1, repeating steps S41 to S44 to obtain phi m+1 (N，m，n，r)；
S46, obtaining ideal fuzzy entropy:
considering that N generally takes a finite value in practical applications, it can be expressed as:
H(N，m，n，r)＝lnφ m -lnφ m (10)。
further, S5 specifically includes the following steps: extracting the characteristics of each IMF according to the fuzzy entropy value obtained by the S4 calculation; the current signals of the MOA are classified and identified according to the characteristics of each IMF.
According to another aspect of the present invention there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps in the MOA resistive current extraction method of the present invention based on CEEMD and fuzzy entropy.
According to yet another aspect of the present invention there is provided a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the MOA resistive current extraction method of the present invention based on CEEMD and fuzzy entropy when the program is executed.
Compared with the prior art, the invention has at least the following beneficial effects:
optimizing IMF selection: fuzzy entropy can be used to evaluate the complexity of each IMF, and by selecting IMFs with greater fuzzy entropy, the important information of the signal can be better preserved.
Reducing the effect of pseudo-modal functions: the fuzzy entropy can be used for evaluating the nonlinear characteristics of the IMF, and the influence of the pseudo-modal function on the decomposition result can be reduced by eliminating the IMF with smaller fuzzy entropy.
The accuracy of signal decomposition is improved: by combining CEEMD and fuzzy entropy, noise can be better suppressed, pseudo-periodic phenomenon can be reduced, and accuracy of signal decomposition can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following brief description of the drawings of the embodiments will make it apparent that the drawings in the following description relate only to some embodiments of the present invention and are not limiting of the present invention.
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of an exploded waveform of a fault signal of the MOA system of the present invention;
FIG. 3 is a flow chart of a fault signal decomposition process of the MOA system of the present invention;
fig. 4 is a current signal processing diagram of the present invention: (a) an original noisy signal, (b) a CEEMD processed signal, (c) a blurred quotient calculated signal.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs.
Specific embodiments of the present invention are described in detail below with reference to FIGS. 1-4 and examples.
Example 1: the invention provides a MOA resistive current extraction method based on CEEMD and fuzzy entropy, which comprises the following steps:
s1.moa signal collection.
A sensor is used to collect a current signal of a Metal Oxide Arrester (MOA).
S2, data preprocessing.
And (3) preprocessing the current signals of the Metal Oxide Arrester (MOA) collected in the step S1 to remove noise and interference current signals.
S3.CEEMD current signal decomposition.
And decomposing the MOA current signal after S2 pretreatment by using Complete Ensemble Empirical Mode Decomposition (CEEMD) to obtain a plurality of intrinsic mode function (Intrinsic Mode Function, IMF) components. The operation flow of CEEMD current signal decomposition is as follows:
and S31, respectively initializing the aggregation times M and the white noise amplitude k, and enabling i=1.
S32, adding white noise n for the ith time i And (3) performing EMD decomposition treatment on the current signal after (t). Adding a pair of white noise with equal amplitude and opposite sign to the original current signal x (t) to obtain P added with positive noise i (t) sequence, N with added negative noise i (t) sequence.
Current signal P after adding white noise for the ith time i (t) and N i (t) performing EMD decomposition treatment respectively to obtain q IMF components:
wherein,and->The jth IMF component obtained by the ith decomposition; q is the number of IMF components, ri1 (t) and ri2 (t) th EMDThe remainder obtained by decomposition; j=1, 2, …, q.
S33, if i is less than M, making i=i+1, circularly running the step S32, and adding white noise with different amplitude values during each experiment.
S34, obtaining all corresponding IMF components obtained by M times of EMD decompositionAnd->And the overall average of the remainders ri1 (t) and ri2 (t):
wherein c j (t) is the jth IMF component obtained from CEEMD decomposition; r (t) is the final residual component. Thus, the raw current signal can be expressed as:
the waveform diagram shown above is a CEEMD exploded view of the MOA system fault vibration signal, IMF0, resolved into 7 IMF components.
S4, calculating fuzzy entropy.
And (3) performing fuzzy entropy calculation on each IMF component obtained by the decomposition of the S3 to obtain corresponding fuzzy entropy characteristics. The fuzzy entropy is an index for describing the complexity of the current signal, and can reflect the nonlinear characteristics and randomness of the current signal, and the operation flow is as follows:
assume that the initial current signal sequence of length N is { u (i): i=1, 2, …, N }
S41, setting a mode dimension m, and constructing an m-dimensional vector by using an original current signal sequence to obtain:
wherein u is 0i Is the mean value of m consecutive u's of the current signal sequence from the i-th point.
S42, defining vectorsAnd other vectors +.>Distance between the two->The one of the two vectors corresponding to the element with the largest difference is recorded as:
s43, defining a fuzzy membership function mu (x, n, r) by adopting chaotic pseudorandom sequence complexity predictionAnd->Similarity of->
Wherein the fuzzy membership functionN and r are the gradient and width of the similar tolerance boundary, respectively, as an exponential function;
s44, defining a function
S45, m=m+1, repeating the steps 1) to 4) to obtain phi m+1 (N，m，n，r)。
S46, obtaining ideal fuzzy entropy (fuzzy En) as follows:
considering that N generally takes a finite value in practical applications, it can be expressed as:
H(N,m,n,r)＝lnφm-lnφm(10)
s5, feature classification and identification.
And (4) extracting the characteristics of each IMF according to the fuzzy entropy value obtained by the calculation in the step S4. The current signals of the MOA are classified and identified according to the characteristics of each IMF.
The invention has the advantage that CEEMD and fuzzy entropy are combined, so that the MOA current signal is efficiently processed. CEEMD is able to decompose complex nonlinear current signals into multiple IMFs, while fuzzy entropy is able to evaluate the complexity and nonlinear characteristics of each IMF, thereby selecting IMFs with high information content for processing. Through the decomposition and feature extraction of the current signals, fault current signals in the metal oxide arrester can be effectively extracted, and accurately identified and classified, so that the state monitoring and fault diagnosis of the metal oxide arrester are realized.
Although the Ensemble Empirical Mode Decomposition (EEMD) can effectively inhibit modal aliasing, the auxiliary white noise added in the decomposition process is finally needed to be counteracted by increasing the ensemble average times, so that the calculation time is long, and the reconstruction error is large. The auxiliary white noise of positive and negative pairs is added in CEEMD, and when the set is averaged, the decomposition efficiency can be effectively improved, and the problems of large EEMD reconstruction error and poor decomposition completeness are solved.
The combination of the fuzzy entropy and the CEEMD can further improve the effect of signal decomposition, and specific advantages include:
optimizing IMF selection: fuzzy entropy can be used to evaluate the complexity of each IMF, and by selecting IMFs with greater fuzzy entropy, the important information of the signal can be better preserved.
Reducing the effect of pseudo-modal functions: the fuzzy entropy can be used for evaluating the nonlinear characteristics of the IMF, and the influence of the pseudo-modal function on the decomposition result can be reduced by eliminating the IMF with smaller fuzzy entropy.
The accuracy of signal decomposition is improved: by combining CEEMD and fuzzy entropy, noise can be better suppressed, pseudo-periodic phenomenon can be reduced, and accuracy of signal decomposition can be improved.
Example 2:
the computer-readable storage medium of the present embodiment has stored thereon a computer program which, when executed by a processor, implements the steps in the MOA resistive current extraction method based on CEEMD and fuzzy entropy of embodiment 1.
The computer readable storage medium of the present embodiment may be an internal storage unit of the terminal, for example, a hard disk or a memory of the terminal; the computer readable storage medium of the present embodiment may also be an external storage device of the terminal, for example, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, etc. provided on the terminal; further, the computer-readable storage medium may also include both an internal storage unit of the terminal and an external storage device.
The computer-readable storage medium of the present embodiment is used to store a computer program and other programs and data required for a terminal, and the computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
Example 3:
the computer apparatus of this embodiment includes a memory, a processor, and a computer program stored on the memory and executable on the processor, which when executed implements the steps of the MOA resistive current extraction method of embodiment 1 based on CEEMD and fuzzy entropy.
In this embodiment, the processor may be a central processing unit, or may be other general purpose processors, digital signal processors, application specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like, where the general purpose processor may be a microprocessor or the processor may also be any conventional processor, or the like; the memory may include read only memory and random access memory, and provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory, e.g., the memory may also store information of the device type.
It will be appreciated by those skilled in the art that the embodiment(s) disclosure may be provided as a method, system, or computer program product. Thus, the present approach may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present aspects may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present aspects are described with reference to flowchart illustrations and/or schematic diagrams of methods, and computer program products according to embodiments of the present aspects, it being understood that each flowchart illustration and/or block diagram illustration, and combinations of flowchart illustrations and/or block diagrams, can be implemented by computer program instructions; these computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block or blocks.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-only memory (ROM), a random access memory (RandomAccessMemory, RAM), or the like.
The examples of the present invention are merely for describing the preferred embodiments of the present invention, and are not intended to limit the spirit and scope of the present invention, and those skilled in the art should make various changes and modifications to the technical solution of the present invention without departing from the spirit of the present invention.
Claims (8)
1. The MOA resistive current extraction method based on CEEMD and fuzzy entropy is characterized by comprising the following steps of:
s1.MOA signal collection;
s2, preprocessing data;
s3, CEEMD decomposition;
s4, calculating fuzzy entropy;
s5, feature classification and identification.
2. The method according to claim 1, wherein S1 comprises the steps of: the current signal of the MOA is collected with a sensor.
3. The method according to claim 1, wherein S2 comprises the steps of: and (3) preprocessing the MOA current signals collected in the step (S1) to remove noise and interference current signals.
4. The method according to claim 1, wherein S3 comprises the steps of: decomposing the MOA current signal subjected to S2 pretreatment by using CEEMD to obtain a plurality of inherent mode function components; the operation flow of CEEMD current signal decomposition is as follows:
s31, respectively initializing aggregation times M and white noise amplitude k to enable i=1;
s32, adding white noise n for the ith time i Performing EMD decomposition on the current signal after (t), adding a pair of white noise with equal amplitude and opposite sign into the original current signal x (t) to obtain p added with positive noise i (t) sequence, N with added negative noise i (t) a sequence;
current signal P after adding white noise for the ith time i (t) and N i (t) performing EMD decomposition treatment respectively to obtain q IMF components:
wherein,and->The jth IMF component obtained by the ith decomposition; q is the number of IMF components, r i 1 (t) and r i 2 (t) the margin obtained by the ith EMD decomposition; j=1, 2, …, q;
s33, if i is less than M, enabling i to be equal to i+1, circularly running the step S32, and adding white noise with different amplitude values during each experiment;
s34, obtaining all corresponding IMF components obtained by M times of EMD decompositionAnd->Remainder r i 1 (t) and r i 2 Overall average value of (t):
wherein c j (t) is the jth IMF component obtained from CEEMD decomposition; r (t) is the final residual component; thus, the raw current signal can be expressed as:
。
5. the method according to claim 1, wherein S4 comprises the steps of: performing fuzzy entropy calculation on each IMF component obtained by the S3 decomposition to obtain corresponding fuzzy entropy characteristics; the fuzzy entropy operation flow is as follows:
assume that the initial current signal sequence of length N is { u (i): i=1, 2, …, N }
S41, setting a mode dimension m, and constructing an m-dimensional vector by using an original current signal sequence to obtain:
wherein u is oi The average value of m continuous u from the ith point of the current signal sequence;
s42, defining vectorsAnd other vectors +.>Distance between the two->The one of the two vectors corresponding to the element with the largest difference is recorded as:
s43, defining a fuzzy membership function mu (x, n, r) by adopting chaotic pseudorandom sequence complexity predictionAnd->Similarity of->
Wherein the fuzzy membership functionN and r are the gradient and width of the similar tolerance boundary, respectively, as an exponential function;
s44, defining a function
S45, letting m=m+1, repeating steps S41 to S44 to obtain phi m+1 (N,m,n,r)；
S46, obtaining ideal fuzzy entropy:
considering that N generally takes a finite value in practical applications, it can be expressed as:
H(N,m,n,r)＝lnφ m -lnφ m (10)。
6. the method according to claim 1, wherein S5 comprises the steps of: extracting the characteristics of each IMF according to the fuzzy entropy value obtained by the S4 calculation; the current signals of the MOA are classified and identified according to the characteristics of each IMF.
7. A computer-readable storage medium having stored thereon a computer program, characterized by: the program when executed by a processor implements the steps in the MOA resistive current extraction method based on CEEMD and fuzzy entropy as claimed in any one of claims 1 to 6.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps in the CEEMD and fuzzy entropy based MOA resistive current extraction method as claimed in any one of claims 1 to 6 when the program is executed.
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