CN111473861A - Transformer state vibration and sound detection signal reconstruction method and system by using sparse errors - Google Patents

Transformer state vibration and sound detection signal reconstruction method and system by using sparse errors Download PDF

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CN111473861A
CN111473861A CN202010496545.1A CN202010496545A CN111473861A CN 111473861 A CN111473861 A CN 111473861A CN 202010496545 A CN202010496545 A CN 202010496545A CN 111473861 A CN111473861 A CN 111473861A
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Guangdong University of Petrochemical Technology
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Abstract

The embodiment of the invention discloses a method and a system for reconstructing a transformer state vibration and sound detection signal by using sparse errors, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102 of obtaining a matrix of optimal approximation eigenvaluesOPT(ii) a Step 103, an error approximation matrix A is obtained; step 104 of obtaining a reconstructed signal sequence Snew。

Description

Transformer state vibration and sound detection signal reconstruction method and system by using sparse errors
Technical Field
The invention relates to the field of electric power, in particular to a reconstruction method and a reconstruction system of a vibration sound signal of a transformer.
Background
With the high-speed development of the smart grid, the safe and stable operation of the power equipment is particularly important. At present, the detection of the operating state of the power equipment with ultrahigh voltage and above voltage grades, especially the detection of the abnormal state, is increasingly important and urgent. As an important component of an electric power system, a power transformer is one of the most important electrical devices in a substation, and its reliable operation is related to the safety of a power grid. Generally, the abnormal state of the transformer can be divided into core abnormality and winding abnormality. The core abnormality is mainly represented by core saturation, and the winding abnormality generally includes winding deformation, winding looseness and the like.
The basic principle of the transformer abnormal state detection is to extract each characteristic quantity in the operation of the transformer, analyze, identify and track the characteristic quantity so as to monitor the abnormal operation state of the transformer. The detection method can be divided into invasive detection and non-invasive detection according to the contact degree; the detection can be divided into live detection and power failure detection according to whether the shutdown detection is needed or not; the method can be classified into an electrical quantity method, a non-electrical quantity method, and the like according to the type of the detected quantity. In comparison, the non-invasive detection has strong transportability and is more convenient to install; the live detection does not affect the operation of the transformer; the non-electric quantity method is not electrically connected with the power system, so that the method is safer. The current common detection methods for the operation state of the transformer include a pulse current method and an ultrasonic detection method for detecting partial discharge, a frequency response method for detecting winding deformation, a vibration detection method for detecting mechanical and electrical faults, and the like. The detection methods mainly detect the insulation condition and the mechanical structure condition of the transformer, wherein the detection of the vibration signal (vibration sound) of the transformer is the most comprehensive, and the fault and the abnormal state of most transformers can be reflected.
In the running process of the transformer, the magnetostriction of the iron core silicon steel sheets and the vibration caused by the winding electrodynamic force can radiate vibration sound signals with different amplitudes and frequencies to the periphery. When the transformer normally operates, uniform low-frequency noise is emitted outwards; if the sound is not uniform, it is not normal. The transformer can make distinctive sounds in different running states, and the running state of the transformer can be mastered by detecting the sounds made by the transformer. It is worth noting that the detection of the sound emitted by the transformer in different operating states not only can detect a plurality of serious faults causing the change of the electrical quantity, but also can detect a plurality of abnormal states which do not endanger the insulation and do not cause the change of the electrical quantity, such as the loosening of internal and external parts of the transformer, and the like.
Disclosure of Invention
As mentioned above, the vibration and sound detection method utilizes the vibration signal emitted by the transformer, which is easily affected by the working environment, resulting in interruption of signal transmission and severe degradation of signal quality, so that the received partial vibration and sound signal cannot be used, and therefore how to effectively reconstruct the vibration and sound signal of the transformer is an important constraint factor for successful application of the method. The existing common method has insufficient attention to the problem, and no effective measure is taken to solve the problem.
The invention aims to provide a method and a system for reconstructing a vibration and sound detection signal in a transformer state by using a sparse error. The method has better signal reconstruction performance and simpler calculation.
In order to achieve the purpose, the invention provides the following scheme:
a transformer state vibration and sound detection signal reconstruction method using sparse errors comprises the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 of obtaining a matrix of optimal approximation eigenvaluesOPTThe method specifically comprises the following steps: judging the nth characteristic value gamma of the normalized average matrix BnWhether or not it is greater than or equal to the approach threshold0Obtaining a first judgment result; if the first judgment result shows the nth characteristic value gammanGreater than or equal to the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure BDA0002523110330000021
If the first judgment result shows the nth characteristic value gammanLess than the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure BDA0002523110330000022
Wherein the calculation formula of the normalized average matrix B is
Figure BDA0002523110330000023
m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; n is the length of the signal sequence S; the approximated threshold value0Has a value of
Figure BDA0002523110330000024
The SNR is the signal-to-noise ratio of the signal sequence S;
step 103, obtaining an error approximation matrix a, specifically: a is UOPTV; wherein U is a left eigenvector matrix of the normalized average matrix B; v is a right eigenvector matrix of the normalized average matrix B;
step 104 of obtaining a reconstructed signal sequence SnewThe method specifically comprises the following steps:
Figure BDA0002523110330000025
wherein x is an intermediate parameter vector;
Figure BDA0002523110330000026
representing S-Ax
Figure BDA0002523110330000027
And (5) molding.
A transformer state vibro-acoustic detection signal reconstruction system with sparse errors, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
module 202 finds the best approximation eigenvalue matrixOPTThe method specifically comprises the following steps: judging the nth characteristic value gamma of the normalized average matrix BnWhether or not it is greater than or equal to the approach threshold0Obtaining a first judgment result; if the first judgment result shows the nth characteristic value gammanGreater than or equal to the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure BDA0002523110330000028
If the first judgment result shows the nth characteristic value gammanLess than the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure BDA0002523110330000029
Wherein the calculation formula of the normalized average matrix B is
Figure BDA00025231103300000210
m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; n is the length of the signal sequence S; the approximated threshold value0Has a value of
Figure BDA00025231103300000211
SNR is the signalThe signal-to-noise ratio of the number sequence S;
the module 203 finds an error approximation matrix a, specifically: a is UOPTV; wherein U is a left eigenvector matrix of the normalized average matrix B; v is a right eigenvector matrix of the normalized average matrix B;
the module 204 finds the reconstructed signal sequence SnewThe method specifically comprises the following steps:
Figure BDA00025231103300000212
wherein x is an intermediate parameter vector;
Figure BDA00025231103300000213
representing S-Ax
Figure BDA00025231103300000214
And (5) molding.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
as mentioned above, the vibration and sound detection method utilizes the vibration signal emitted by the transformer, which is easily affected by the working environment, resulting in interruption of signal transmission and severe degradation of signal quality, so that the received partial vibration and sound signal cannot be used, and therefore how to effectively reconstruct the vibration and sound signal of the transformer is an important constraint factor for successful application of the method. The existing common method has insufficient attention to the problem, and no effective measure is taken to solve the problem.
The invention aims to provide a method and a system for reconstructing a vibration and sound detection signal in a transformer state by using a sparse error. The method has better signal reconstruction performance and simpler calculation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow chart of the system of the present invention;
FIG. 3 is a flow chart illustrating an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a schematic flow chart of a transformer state vibration and sound detection signal reconstruction method using sparse errors
Fig. 1 is a schematic flow chart of a transformer state vibro-acoustic detection signal reconstruction method using sparse errors according to the present invention. As shown in fig. 1, the method for reconstructing a transformer state vibro-acoustic detection signal by using a sparse error specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 of obtaining a matrix of optimal approximation eigenvaluesOPTThe method specifically comprises the following steps: judging the nth characteristic value gamma of the normalized average matrix BnWhether or not it is greater than or equal to the approach threshold0Obtaining a first judgment result; if the first judgment result shows the nth characteristic value gammanGreater than or equal to the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure BDA0002523110330000031
If the first judgment result shows the nth characteristic value gammanLess than the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure BDA0002523110330000032
Wherein the calculation formula of the normalized average matrix B is
Figure BDA0002523110330000033
m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; n is the length of the signal sequence S; the approximated threshold value0Has a value of
Figure BDA0002523110330000034
The SNR is the signal-to-noise ratio of the signal sequence S;
step 103, obtaining an error approximation matrix a, specifically: a is UOPTV; wherein U is a left eigenvector matrix of the normalized average matrix B; v is a right eigenvector matrix of the normalized average matrix B;
step 104 of obtaining a reconstructed signal sequence SnewThe method specifically comprises the following steps:
Figure BDA0002523110330000041
wherein x is an intermediate parameter vector;
Figure BDA0002523110330000042
representing S-Ax
Figure BDA0002523110330000043
And (5) molding.
FIG. 2 is a structural intention of a transformer state vibration and sound detection signal reconstruction system using sparse errors
Fig. 2 is a schematic structural diagram of a transformer state vibro-acoustic detection signal reconstruction system using sparse errors according to the present invention. As shown in fig. 2, the system for reconstructing a transformer state vibro-acoustic detection signal by using sparse errors comprises the following structures:
the module 201 acquires a signal sequence S acquired in time sequence;
module 202 finds the best approximation eigenvalue matrixOPTThe method specifically comprises the following steps: judging the nth characteristic value gamma of the normalized average matrix BnWhether or not it is greater than or equal to the approach threshold0Obtaining a first judgment result; if the first judgment result shows the nth characteristic value gammanGreater than or equal to the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure BDA0002523110330000044
If the first judgment result shows the nth characteristic value gammanLess than the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure BDA0002523110330000045
Wherein the calculation formula of the normalized average matrix B is
Figure BDA0002523110330000046
m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; n is the length of the signal sequence S; the approximated threshold value0Has a value of
Figure BDA0002523110330000047
The SNR is the signal-to-noise ratio of the signal sequence S;
the module 203 finds an error approximation matrix a, specifically: a is UOPTV; wherein U is a left eigenvector matrix of the normalized average matrix B; v is a right eigenvector matrix of the normalized average matrix B;
the module 204 finds the reconstructed signal sequence SnewThe method specifically comprises the following steps:
Figure BDA0002523110330000048
wherein x is an intermediate parameter vector;
Figure BDA0002523110330000049
representing S-Ax
Figure BDA00025231103300000410
And (5) molding.
The following provides an embodiment for further illustrating the invention
FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, the method specifically includes the following steps:
step 301, acquiring a signal sequence S acquired according to a time sequence;
step 302 of obtaining a matrix of best-fit eigenvaluesOPTThe method specifically comprises the following steps: judging the nth characteristic value gamma of the normalized average matrix BnWhether or not it is greater than or equal to the approach threshold0Obtaining a first judgment result; if the first judgment result shows the nth characteristic value gammanGreater than or equal to the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure BDA00025231103300000411
If the first judgment result shows the nth characteristic value gammanLess than the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure BDA00025231103300000412
Wherein the calculation formula of the normalized average matrix B is
Figure BDA00025231103300000413
m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; n is the length of the signal sequence S; the approximated threshold value0Has a value of
Figure BDA00025231103300000414
The SNR is the signal-to-noise ratio of the signal sequence S;
step 303 finds an error approximation matrix a, specifically: a is UOPTV; wherein U is a left eigenvector matrix of the normalized average matrix B; v is a right eigenvector matrix of the normalized average matrix B;
step 304 finds the reconstructed signal sequence SnewThe method specifically comprises the following steps:
Figure BDA0002523110330000051
wherein x is an intermediate parameter vector;
Figure BDA0002523110330000052
representing S-Ax
Figure BDA0002523110330000053
And (5) molding.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is simple because the system corresponds to the method disclosed by the embodiment, and the relevant part can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (2)

1. The method for reconstructing the transformer state vibration and sound detection signal by using the sparse error is characterized by comprising the following steps of:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 of obtaining a matrix of optimal approximation eigenvaluesOPTThe method specifically comprises the following steps: judging the nth characteristic value gamma of the normalized average matrix BnWhether or not greater thanOr is equal to the approximation threshold0Obtaining a first judgment result; if the first judgment result shows the nth characteristic value gammanGreater than or equal to the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure FDA0002523110320000011
If the first judgment result shows the nth characteristic value gammanLess than the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure FDA0002523110320000012
Wherein the calculation formula of the normalized average matrix B is
Figure FDA0002523110320000013
m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; n is the length of the signal sequence S; the approximated threshold value0Has a value of
Figure FDA0002523110320000014
The SNR is the signal-to-noise ratio of the signal sequence S;
step 103, obtaining an error approximation matrix a, specifically: a is UOPTV; wherein U is a left eigenvector matrix of the normalized average matrix B; v is a right eigenvector matrix of the normalized average matrix B;
step 104 of obtaining a reconstructed signal sequence SnewThe method specifically comprises the following steps:
Figure FDA0002523110320000015
Figure FDA0002523110320000016
wherein x is an intermediate parameter vector;
Figure FDA0002523110320000017
representing S-Ax
Figure FDA0002523110320000018
And (5) molding.
2. The system for reconstructing the transformer state vibration and sound detection signal by using the sparse error is characterized by comprising the following steps of:
the module 201 acquires a signal sequence S acquired in time sequence;
module 202 finds the best approximation eigenvalue matrixOPTThe method specifically comprises the following steps: judging the nth characteristic value gamma of the normalized average matrix BnWhether or not it is greater than or equal to the approach threshold0Obtaining a first judgment result; if the first judgment result shows the nth characteristic value gammanGreater than or equal to the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure FDA0002523110320000019
If the first judgment result shows the nth characteristic value gammanLess than the approximated threshold0Then the best approximation eigenvalue matrixOPTOf the nth diagonal element
Figure FDA00025231103200000110
Wherein the calculation formula of the normalized average matrix B is
Figure FDA00025231103200000111
m0Is the mean of the signal sequence S; sigma0Is the mean square error of the signal sequence S; n is the length of the signal sequence S; the approximated threshold value0Has a value of
Figure FDA00025231103200000112
The SNR is the signal-to-noise ratio of the signal sequence S;
module 203 finds the error approximation matrix A, havingThe body is as follows: a is UOPTV; wherein U is a left eigenvector matrix of the normalized average matrix B; v is a right eigenvector matrix of the normalized average matrix B;
the module 204 finds the reconstructed signal sequence SnewThe method specifically comprises the following steps:
Figure FDA0002523110320000021
Figure FDA0002523110320000022
wherein x is an intermediate parameter vector;
Figure FDA0002523110320000023
representing S-Ax
Figure FDA0002523110320000024
And (5) molding.
CN202010496545.1A 2020-06-04 2020-06-04 Transformer state vibration and sound detection signal reconstruction method and system by using sparse errors Withdrawn CN111473861A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112179479A (en) * 2020-11-06 2021-01-05 华北电力大学 Power signal reconstruction method and system by using shaping factor

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112179479A (en) * 2020-11-06 2021-01-05 华北电力大学 Power signal reconstruction method and system by using shaping factor

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