CN110657881B - Transformer vibration sound signal filtering method and system by utilizing sparse inversion - Google Patents

Transformer vibration sound signal filtering method and system by utilizing sparse inversion Download PDF

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CN110657881B
CN110657881B CN201910867657.0A CN201910867657A CN110657881B CN 110657881 B CN110657881 B CN 110657881B CN 201910867657 A CN201910867657 A CN 201910867657A CN 110657881 B CN110657881 B CN 110657881B
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翟明岳
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Guangdong University of Petrochemical Technology
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Abstract

The embodiment of the invention discloses a transformer vibration acoustic signal filtering method and a system by utilizing sparse inversion, wherein the method comprises the following steps: step 1, inputting an actually measured vibration sound signal sequence S; step 2, carrying out noise filtering processing on the signal sequence S according to a sparse inversion theory, wherein the signal sequence after noise filtering is SNEW(ii) a In particular to a method for preparing a high-performance nano-silver alloy,

Description

Transformer vibration sound signal filtering method and system by utilizing sparse inversion
Technical Field
The invention relates to the field of electric power, in particular to a method and a system for filtering 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.
Because the vibration sound detection method utilizes the vibration signal sent by the transformer, the vibration sound detection method is easily influenced by environmental noise, and therefore, how to effectively identify the vibration sound and the noise is the key for success of the method. The existing common method has insufficient attention to the problem, and no effective measure is taken to solve the problem.
Disclosure of Invention
The invention aims to provide a transformer vibration acoustic signal filtering method and a transformer vibration acoustic signal filtering system by utilizing sparse inversion. The method has better robustness and simpler calculation.
In order to achieve the purpose, the invention provides the following scheme:
a transformer vibro-acoustic signal filtering method utilizing sparse inversion comprises the following steps:
step 1, inputting an actually measured vibration sound signal sequence S;
step 2, according to sparse inversion theory, placeThe signal sequence S is subjected to noise filtering processing, and the signal sequence after noise filtering is SNEW(ii) a In particular to a method for preparing a high-performance nano-silver alloy,
Figure BDA0002201742630000021
wherein mu is a sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; d is a Wolk matrix; p is the intermediate parameter matrix.
A transformer vibro-acoustic signal filtering system using sparse inversion, comprising:
the acquisition module inputs an actually measured vibration sound signal sequence S;
the filtering module is used for carrying out noise filtering processing on the signal sequence S according to a sparse inversion theory, and the signal sequence after noise filtering is SNEW(ii) a In particular to a method for preparing a high-performance nano-silver alloy,
Figure BDA0002201742630000022
wherein mu is a sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; d is a Wolk matrix; p is the intermediate parameter matrix.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
although the transformer vibration and sound detection method is widely applied to monitoring the running state of the transformer and the technology is relatively mature, the vibration and sound detection method utilizes the vibration signal sent by the transformer and is easily influenced by the environmental noise, so that the method often cannot obtain satisfactory results when being applied in the actual working environment.
The invention aims to provide a transformer vibration acoustic signal filtering method and a transformer vibration acoustic signal filtering system by utilizing sparse inversion. The method has better robustness and simpler calculation.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the 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 diagram 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 vibro-acoustic signal filtering method using sparse inversion
Fig. 1 is a schematic flow chart of a transformer vibro-acoustic signal filtering method using sparse inversion according to the present invention. As shown in fig. 1, the transformer vibro-acoustic signal filtering method using sparse inversion specifically includes the following steps:
step 1, inputting an actually measured vibration sound signal sequence S;
step 2, carrying out noise filtering processing on the signal sequence S according to a sparse inversion theory, wherein the signal sequence after noise filtering is SNEW(ii) a In particular to a method for preparing a high-performance nano-silver alloy,
Figure BDA0002201742630000041
wherein mu is a sparsity factor; g is diluteA sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; d is a Wolk matrix; p is the intermediate parameter matrix.
Before the step 2, the method further comprises:
and 3, solving the sparsity factor mu, the sparse matrix G, the sparse reflection vector R, the sparse adjustment factor kappa and the Wolk matrix D.
The step 3 comprises the following steps:
step 301, obtaining a cyclic delay matrix DCThe method specifically comprises the following steps:
Figure BDA0002201742630000042
wherein:
sn: the nth element [ N ═ 1,2, …, N of the signal sequence S]
N: length of the signal sequence S
Step 302, obtaining the sparsity factor μ, specifically:
Figure BDA0002201742630000043
wherein:
Figure BDA0002201742630000044
matrix DCInverse matrix of
Step 303, obtaining the sparse matrix G, specifically:
G＝kron(DC,I)
wherein:
kron(DCand I): matrix DCKronecker multiplication with I
I: unit matrix
Step 304, solving the sparse reflection vector R, specifically:
Figure BDA0002201742630000051
wherein:
Figure BDA0002201742630000052
selection matrix
Step 305, obtaining the sparse adjustment factor κ, specifically:
Figure BDA0002201742630000053
wherein:
Figure BDA0002201742630000054
transforming vectors
Figure BDA0002201742630000055
The conversion vector
Figure BDA0002201742630000056
N is 1,2, …, N]
mS: mean value of the signal sequence S
σS: mean square error of the signal sequence S
Step 306, obtaining the wacker matrix D, specifically:
Figure BDA0002201742630000061
wherein
Figure BDA0002201742630000062
Figure BDA0002201742630000063
Δ＝max[|s1-s2|,|s2-s3|,…,|sN-s1|]-min[|s1-s2|,|s2-s3|,…,|sN-s1|]
FIG. 2 structural intention of transformer vibro-acoustic signal filtering system using sparse inversion
Fig. 2 is a schematic structural diagram of a transformer vibro-acoustic signal filtering system using sparse inversion according to the present invention. As shown in fig. 2, the transformer vibro-acoustic signal filtering system using sparse inversion includes the following structure:
the acquisition module 401 inputs an actually measured vibration and sound signal sequence S;
a filtering module 402, configured to perform noise filtering processing on the signal sequence S according to a sparse inversion theory, where the signal sequence after noise filtering is SNEW(ii) a In particular to a method for preparing a high-performance nano-silver alloy,
Figure BDA0002201742630000064
wherein mu is a sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; d is a Wolk matrix; p is the intermediate parameter matrix.
The system further comprises:
a calculation module 403 for obtaining the sparse inversion C and the optimal prediction vector mOPTShaping matrix B, system matrix L and aliasing matrix
Figure BDA0002201742630000065
The calculation module 403 includes the following units:
delay unit 4031 for obtaining cyclic delay matrix DCThe method specifically comprises the following steps:
Figure BDA0002201742630000071
wherein:
sn: the nth element [ N ═ 1,2, …, N of the signal sequence S]
N: length of the signal sequence S
The first calculation unit 4032 calculates the sparsity factor μ, specifically:
Figure BDA0002201742630000072
wherein:
Figure BDA0002201742630000073
matrix DCInverse matrix of
The second calculation unit 4033, which calculates the sparse matrix G, specifically is:
G＝kron(DC,I)
wherein:
kron(DCand I): matrix DCKronecker multiplication with I
I: unit matrix
The third calculation unit 4034, which calculates the sparse reflection vector R, specifically is:
Figure BDA0002201742630000074
wherein:
Figure BDA0002201742630000075
selection matrix
The fourth calculating unit 4035, which calculates the sparse adjustment factor κ specifically is:
Figure BDA0002201742630000081
wherein:
Figure BDA0002201742630000082
transforming vectors
Figure BDA0002201742630000083
The conversion vector
Figure BDA0002201742630000084
N is 1,2, …, N]
mS: mean value of the signal sequence S
σS: mean square error of the signal sequence S
The fifth calculation unit 4036, which calculates the wacker matrix D, specifically is:
Figure BDA0002201742630000085
wherein
Figure BDA0002201742630000086
Figure BDA0002201742630000087
Δ＝max[|s1-s2|,|s2-s3|,…,|sN-s1|]-min[|s1-s2|,|s2-s3|,…,|sN-s1|]
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:
1. inputting measured vibration sound signal sequence
S＝[s1,s2,…,sN-1,sN]
Wherein:
s: measured PLC signal data sequence with length N
siI is 1,2, …, N is measured PLC signal with serial number i
2. Determining a cyclic delay matrix
Figure BDA0002201742630000091
Wherein:
sn: the nth element [ N ═ 1,2, …, N of the signal sequence S]
N: length of the signal sequence S
3. Calculating a sparsity factor
Figure BDA0002201742630000092
Wherein:
Figure BDA0002201742630000093
matrix DCInverse matrix of
4. Obtaining sparse matrices
G＝kron(DC,I)
Wherein:
kron(DCand I): matrix DCKronecker multiplication with I
I: unit matrix
5. Finding sparse reflection vectors
Figure BDA0002201742630000094
Wherein:
Figure BDA0002201742630000095
selection matrix
6. Finding sparse adjustment factors
Figure BDA0002201742630000101
Wherein:
Figure BDA0002201742630000102
transforming vectors
Figure BDA0002201742630000103
The conversion vector
Figure BDA0002201742630000104
N is 1,2, …, N]
mS: mean value of the signal sequence S
σS: mean square error of the signal sequence S
7. Obtaining Wolk matrix
Figure BDA0002201742630000105
Wherein
Figure BDA0002201742630000106
Figure BDA0002201742630000107
Δ＝max[|s1-s2|,|s2-s3|,…,|sN-s1|]-min[|s1-s2|,|s2-s3|,…,|sN-s1|]
8. Filtering
And carrying out noise filtering processing on the signal sequence S according to a sparse inversion theory, wherein the signal sequence after noise filtering is SNEW(ii) a In particular to a method for preparing a high-performance nano-silver alloy,
Figure BDA0002201742630000108
wherein mu is a sparsity factor; g is a sparse matrix; r is a sparse reflection vector; kappa is a sparse adjustment factor; d is a Wolk matrix; p is the intermediate parameter matrix.
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 (1)

1. A transformer vibro-acoustic signal filtering method utilizing sparse inversion is characterized by comprising the following steps:
step 1, inputting an actually measured vibration sound signal sequence S;
step 2, obtaining a cyclic delay matrix DCThe method specifically comprises the following steps:
Figure FDA0002939689760000011
wherein:
sn: the nth element [ N ═ 1,2, …, N of the signal sequence S]；
N: the length of the signal sequence S;
step 3, obtaining a sparsity factor mu, specifically:
Figure FDA0002939689760000012
wherein:
Figure FDA0002939689760000013
matrix DCThe inverse matrix of (d);
step 4, solving a sparse matrix G, specifically:
G＝kron(DC,I)；
wherein:
kron(DCand I): matrix DCA kronecker product operation with I;
i: an identity matrix;
step 5, solving a sparse reflection vector R, specifically:
Figure FDA0002939689760000014
wherein:
Figure FDA0002939689760000015
selecting a matrix;
step 6, obtaining a sparse adjustment factor k, specifically:
Figure FDA0002939689760000021
wherein:
Figure FDA0002939689760000022
converting the vector;
Figure FDA0002939689760000023
the conversion vector
Figure FDA0002939689760000024
N is 1,2, …, N]；
mS: a mean value of the signal sequence S;
σS: the mean square error of the signal sequence S;
step 7, solving a Wolk matrix D, specifically:
Figure FDA0002939689760000025
wherein:
Figure FDA0002939689760000026
Figure FDA0002939689760000027
Δ＝max[|s1-s2|,|s2-s3|,…,|sN-s1|]-min[|s1-s2|,|s2-s3|,…,|sN-s1|]；
step 8, carrying out noise filtering processing on the signal sequence S according to a sparse inversion theory, wherein the signal sequence after noise filtering is SNEW(ii) a In particular to a method for preparing a high-performance nano-silver alloy,
Figure FDA0002939689760000028
where p is the intermediate parameter matrix.
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