CN111664934A - Transformer state vibration and sound detection signal filtering method and system using feature selection - Google Patents

Transformer state vibration and sound detection signal filtering method and system using feature selection Download PDF

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CN111664934A
CN111664934A CN202010637602.3A CN202010637602A CN111664934A CN 111664934 A CN111664934 A CN 111664934A CN 202010637602 A CN202010637602 A CN 202010637602A CN 111664934 A CN111664934 A CN 111664934A
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pca
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matrix
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翟明岳
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Guangdong University of Petrochemical Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
    • G01R31/62—Testing of transformers

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Abstract

The embodiment of the invention discloses a method and a system for filtering a transformer state vibration and sound detection signal by utilizing feature selection, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102, calculating a PCA delay constant; step 103, solving a PCA delay sequence; step 104, initializing iteration control parameters; 105, initializing a PCA filter matrix; step 106, updating in an iterative manner; step 107, ending the iteration; step 108 finds a signal sequence with noise filtered.

Description

Transformer state vibration and sound detection signal filtering method and system using feature selection
Technical Field
The invention relates to the field of electric power, in particular to a method and a system for filtering a vibration and sound detection 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.
Disclosure of Invention
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 invention aims to provide a transformer state vibration and sound detection signal filtering method and system based on feature selection. The method has better robustness and simpler calculation.
In order to achieve the purpose, the invention provides the following scheme:
a transformer state vibration and sound detection signal filtering method utilizing feature selection comprises the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, calculating a PCA delay constant, specifically: the PCA delay constant k is the number of non-zero eigenvalues of the average matrix B; wherein the formula of the average matrix B is B ═ S-m0]T[S-m0]；m0Is the mean of the signal sequence S;
step 103, obtaining a PCA delay sequence, specifically: the PCA delay sequence SpcaThe nth element of (1)
Figure BDA0002570087060000021
Is calculated by the formula
Figure BDA0002570087060000022
Wherein, N is the element serial number, and the value range is N ═ 1,2, ·, N; n is the length of the signal sequence S;
Figure BDA0002570087060000023
is the | n + κ |' of the signal sequence SNAn element; (| ventilation)NRepresenting a rounding operation modulo N;
step 104, initializing an iteration control parameter, wherein the initialized value of the iteration control parameter k is that k is 0;
step 105, initializing a PCA filter matrix, specifically: the initialization value of the PCA filter matrix is P0I, j, column elements thereof
Figure BDA0002570087060000024
Is calculated by the formula
Figure BDA0002570087060000025
Wherein the content of the first and second substances,i is a row sequence number, and the value range of i is 1,2, ·, N; j is a row serial number, and the value range of j is 1,2, ·, N; siIs the ith element of the signal sequence S; sjIs the jth element of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the average matrix B; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
step 106, performing iterative update, specifically: the iteration update value of the PCA filter matrix is Pk+1The calculation formula is
Figure BDA0002570087060000026
Wherein U is a left feature vector matrix of the average matrix B; a characteristic value matrix of the average matrix B;
ending the iteration of the step 107, specifically: calculating the iterative error by the formula | | | Pk+1-PkL; if the iteration error is greater than or equal to an iteration threshold
Figure BDA00025700870600000210
Adding 1 to the value of the iteration control parameter k; and returning to the step 106 and the step 107 until the iteration error is less than the iteration threshold
Figure BDA00025700870600000211
And applying the current value P of the PCA filter matrixk+1Assign to PCA optimal Filter matrix POPT(ii) a Wherein the iteration threshold value
Figure BDA00025700870600000212
Is taken as
Figure BDA00025700870600000213
Step 108, obtaining a signal sequence after noise filtering, specifically: the signal sequence S after noise filteringnewIs calculated by the formula Snew＝POPTS。
A transformer state vibro-acoustic detection signal filtering system with feature selection, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a PCA delay constant, specifically: the PCA delay constant k is the number of non-zero eigenvalues of the average matrix B; wherein the formula of the average matrix B is B ═ S-m0]T[S-m0]；m0Is the mean of the signal sequence S;
the module 203 finds a PCA delay sequence specifically as follows: the PCA delay sequence SpcaThe nth element of (1)
Figure BDA0002570087060000027
Is calculated by the formula
Figure BDA0002570087060000028
Wherein, N is the element serial number, and the value range is N ═ 1,2, ·, N; n is the length of the signal sequence S;
Figure BDA0002570087060000029
is the | n + κ |' of the signal sequence SNAn element; (| ventilation)NRepresenting a rounding operation modulo N;
module 204 initializes an iteration control parameter, where an initialized value of the iteration control parameter k is 0;
the module 205PCA filter matrix is initialized, specifically: the initialization value of the PCA filter matrix is P0I, j, column elements thereof
Figure BDA0002570087060000031
Is calculated by the formula
Figure BDA0002570087060000032
Wherein, i is a row serial number, and the value range thereof is i ═ 1,2, ·, N; j is a row serial number, and the value range of j is 1,2, ·, N; siIs the ith element of the signal sequence S; sjIs the jth element of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the average matrix B; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
module 206 iteratively updates, specifically: the iteration update value of the PCA filter matrix is Pk+1The calculation formula is
Figure BDA0002570087060000033
Wherein U is a left feature vector matrix of the average matrix B; a characteristic value matrix of the average matrix B;
the module 207 ends the iteration, specifically: calculating the iterative error by the formula | | | Pk+1-PkL; if the iteration error is greater than or equal to an iteration threshold
Figure BDA0002570087060000034
Adding 1 to the value of the iteration control parameter k; and returns to the block 206 and the block 207 until the iteration error is less than the iteration threshold
Figure BDA0002570087060000035
And applying the current value P of the PCA filter matrixk+1Assign to PCA optimal Filter matrix POPT(ii) a Wherein the iteration threshold value
Figure BDA0002570087060000036
Is taken as
Figure BDA0002570087060000037
The module 208 calculates a signal sequence after noise filtering, specifically: the signal sequence S after noise filteringnewIs calculated by the formula Snew＝POPTS。
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
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 invention aims to provide a transformer state vibration and sound detection signal filtering method and system based on feature selection. The method has better robustness 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 filtering method using feature selection
Fig. 1 is a schematic flow chart of a transformer state vibro-acoustic detection signal filtering method using feature selection according to the present invention. As shown in fig. 1, the method for filtering a transformer state ringing detection signal by using feature selection specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, calculating a PCA delay constant, specifically: the PCA delay constant k is the number of non-zero eigenvalues of the average matrix B; wherein the formula of the average matrix B is B ═ S-m0]T[S-m0]；m0Is the mean of the signal sequence S;
step 103, obtaining a PCA delay sequence, specifically: the PCA delay sequence SpcaThe nth element of (1)
Figure BDA0002570087060000041
Is calculated by the formula
Figure BDA0002570087060000042
Wherein, N is the element serial number, and the value range is N ═ 1,2, ·, N; n is the length of the signal sequence S;
Figure BDA0002570087060000043
is the | n + κ |' of the signal sequence SNAn element; (| ventilation)NRepresenting a rounding operation modulo N;
step 104, initializing an iteration control parameter, wherein the initialized value of the iteration control parameter k is that k is 0;
step 105, initializing a PCA filter matrix, specifically: the initialization value of the PCA filter matrix is P0I, j, column elements thereof
Figure BDA0002570087060000044
Is calculated by the formula
Figure BDA0002570087060000045
Wherein, i is a row serial number, and the value range thereof is i ═ 1,2, ·, N; j is a row serial number, and the value range of j is 1,2, ·, N; siIs the ith element of the signal sequence S; sjIs the jth element of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the average matrix B; lambda [ alpha ]maxIs the maximum bit of the average matrix BA characteristic value;
step 106, performing iterative update, specifically: the iteration update value of the PCA filter matrix is Pk+1The calculation formula is
Figure BDA0002570087060000046
Wherein U is a left feature vector matrix of the average matrix B; a characteristic value matrix of the average matrix B;
ending the iteration of the step 107, specifically: calculating the iterative error by the formula | | | Pk+1-PkL; if the iteration error is greater than or equal to an iteration threshold
Figure BDA0002570087060000047
Adding 1 to the value of the iteration control parameter k; and returning to the step 106 and the step 107 until the iteration error is less than the iteration threshold
Figure BDA0002570087060000048
And applying the current value P of the PCA filter matrixk+1Assign to PCA optimal Filter matrix POPT(ii) a Wherein the iteration threshold value
Figure BDA0002570087060000049
Is taken as
Figure BDA00025700870600000410
Step 108, obtaining a signal sequence after noise filtering, specifically: the signal sequence S after noise filteringnewIs calculated by the formula Snew＝POPTS。
FIG. 2 structural intent of a transformer state vibro-acoustic detection signal filtering system using feature selection
Fig. 2 is a schematic structural diagram of a transformer state vibro-acoustic detection signal filtering system using feature selection according to the present invention. As shown in fig. 2, the transformer state vibro-acoustic detection signal filtering system using feature selection comprises the following structures:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a PCA delay constant, specifically: the PCA delay constant k is the number of non-zero eigenvalues of the average matrix B; wherein the formula of the average matrix B is B ═ S-m0]T[S-m0]；m0Is the mean of the signal sequence S;
the module 203 finds a PCA delay sequence specifically as follows: the PCA delay sequence SpcaThe nth element of (1)
Figure BDA0002570087060000051
Is calculated by the formula
Figure BDA0002570087060000052
Wherein, N is the element serial number, and the value range is N ═ 1,2, ·, N; n is the length of the signal sequence S;
Figure BDA0002570087060000053
is the | n + κ |' of the signal sequence SNAn element; (| ventilation)NRepresenting a rounding operation modulo N;
module 204 initializes an iteration control parameter, where an initialized value of the iteration control parameter k is 0;
the module 205PCA filter matrix is initialized, specifically: the initialization value of the PCA filter matrix is P0I, j, column elements thereof
Figure BDA0002570087060000054
Is calculated by the formula
Figure BDA0002570087060000055
Wherein, i is a row serial number, and the value range thereof is i ═ 1,2, ·, N; j is a row serial number, and the value range of j is 1,2, ·, N; siIs the ith element of the signal sequence S; sjIs the jth element of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the average matrix B; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
module 206 iterates the update, in particular: the iteration update value of the PCA filter matrix is Pk+1The calculation formula is
Figure BDA0002570087060000056
Wherein U is a left feature vector matrix of the average matrix B; a characteristic value matrix of the average matrix B;
the module 207 ends the iteration, specifically: calculating the iterative error by the formula | | | Pk+1-PkL; if the iteration error is greater than or equal to an iteration threshold
Figure BDA0002570087060000057
Adding 1 to the value of the iteration control parameter k; and returns to the block 206 and the block 207 until the iteration error is less than the iteration threshold
Figure BDA0002570087060000058
And applying the current value P of the PCA filter matrixk+1Assign to PCA optimal Filter matrix POPT(ii) a Wherein the iteration threshold value
Figure BDA0002570087060000059
Is taken as
Figure BDA00025700870600000510
The module 208 calculates a signal sequence after noise filtering, specifically: the signal sequence S after noise filteringnewIs calculated by the formula Snew＝POPTS。
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, calculating a PCA delay constant, specifically: the PCA delay constant k is the number of non-zero eigenvalues of the average matrix B; wherein the calculation formula of the average matrix B is B ═ 2S-m0]T[S-m0]；m0Is the mean of the signal sequence S;
step 303 finds the PCA delay sequence, specifically: the PCA delay sequence SpcaThe nth element of (1)
Figure BDA00025700870600000511
Is calculated by the formula
Figure BDA00025700870600000512
Wherein, N is the element serial number, and the value range is N ═ 1,2, ·, N; n is the length of the signal sequence S;
Figure BDA00025700870600000513
is the | n + κ |' of the signal sequence SNAn element; (| ventilation)NRepresenting a rounding operation modulo N;
step 304, initializing an iteration control parameter, wherein the initialized value of the iteration control parameter k is that k is 0;
step 305PCA filter matrix initialization, specifically: the initialization value of the PCA filter matrix is P0I, j, column elements thereof
Figure BDA0002570087060000061
Is calculated by the formula
Figure BDA0002570087060000062
Wherein, i is a row serial number, and the value range thereof is i ═ 1,2, ·, N; j is a row serial number, and the value range of j is 1,2, ·, N; siIs the ith element of the signal sequence S; sjIs the jth element of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the average matrix B; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
step 306, iterative updating, specifically: the iteration update value of the PCA filter matrix is Pk+1The calculation formula is
Figure BDA0002570087060000063
WhereinU is a left eigenvector matrix of the average matrix B; a characteristic value matrix of the average matrix B;
step 307, ending the iteration, specifically: calculating the iterative error by the formula | | | Pk+1-PkL; if the iteration error is greater than or equal to an iteration threshold
Figure BDA0002570087060000064
Adding 1 to the value of the iteration control parameter k; and returning to the step 306 and the step 307 until the iteration error is less than the iteration threshold
Figure BDA0002570087060000065
And applying the current value P of the PCA filter matrixk+1Assign to PCA optimal Filter matrix POPT(ii) a Wherein the iteration threshold value
Figure BDA0002570087060000066
Is taken as
Figure BDA0002570087060000067
Step 308, obtaining the signal sequence after noise filtering, specifically: the signal sequence S after noise filteringnewIs calculated by the formula Snew＝POPTS。
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. A transformer state vibration and sound detection signal filtering method utilizing feature selection is characterized by comprising the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, calculating a PCA delay constant, specifically: the PCA delay constant k is the number of non-zero eigenvalues of the average matrix B; wherein the formula of the average matrix B is B ═ S-m0]T[S-m0]；m0Is the mean of the signal sequence S;
step 103, obtaining a PCA delay sequence, specifically: the PCA delay sequence SpcaThe nth element of (1)
Figure FDA0002570087050000011
Is calculated by the formula
Figure FDA0002570087050000012
Wherein N is an element serial number, and the value range of N is 1,2, … and N; n is the length of the signal sequence S;
Figure FDA0002570087050000013
is the | n + κ |' of the signal sequence SNAn element; (| ventilation)NRepresenting a rounding operation modulo N;
step 104, initializing an iteration control parameter, wherein the initialized value of the iteration control parameter k is that k is 0;
step 105, initializing a PCA filter matrix, specifically: the initialization value of the PCA filter matrix is P0I, j, column elements thereof
Figure FDA0002570087050000014
Is calculated by the formula
Figure FDA0002570087050000015
Wherein i is a row number and has a value range of i1,2, …, N; j is a column number, and the value range of j is 1,2, …, N; siIs the ith element of the signal sequence S; sjIs the jth element of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the average matrix B; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
step 106, performing iterative update, specifically: the iteration update value of the PCA filter matrix is Pk+1The calculation formula is
Figure FDA0002570087050000016
Figure FDA0002570087050000017
Wherein U is a left feature vector matrix of the average matrix B; a characteristic value matrix of the average matrix B;
ending the iteration of the step 107, specifically: calculating the iterative error by the formula | | | Pk+1-PkL; if the iteration error is greater than or equal to an iteration threshold
Figure FDA0002570087050000018
Adding 1 to the value of the iteration control parameter k; and returning to the step 106 and the step 107 until the iteration error is less than the iteration threshold
Figure FDA0002570087050000019
And applying the current value P of the PCA filter matrixk +1Assign to PCA optimal Filter matrix POPT(ii) a Wherein the iteration threshold value
Figure FDA00025700870500000110
Is taken as
Figure FDA00025700870500000111
Step 108, obtaining a signal sequence after noise filtering, specifically: the signal sequence S after noise filteringnewMeter (2)The formula is Snew＝POPTS。
2. A transformer state vibro-acoustic detection signal filtering system using feature selection, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a PCA delay constant, specifically: the PCA delay constant k is the number of non-zero eigenvalues of the average matrix B; wherein the formula of the average matrix B is B ═ S-m0]T[S-m0]；m0Is the mean of the signal sequence S;
the module 203 finds a PCA delay sequence specifically as follows: the PCA delay sequence SpcaThe nth element of (1)
Figure FDA0002570087050000021
Is calculated by the formula
Figure FDA0002570087050000022
Wherein N is an element serial number, and the value range of N is 1,2, … and N; n is the length of the signal sequence S;
Figure FDA0002570087050000023
is the | n + κ |' of the signal sequence SNAn element; (| ventilation)NRepresenting a rounding operation modulo N;
module 204 initializes an iteration control parameter, where an initialized value of the iteration control parameter k is 0;
the module 205PCA filter matrix is initialized, specifically: the initialization value of the PCA filter matrix is P0I, j, column elements thereof
Figure FDA0002570087050000024
Is calculated by the formula
Figure FDA0002570087050000025
Wherein, i is a row serial number, and the value range thereof is i ═ 1,2, …, N;j is a column number, and the value range of j is 1,2, …, N; siIs the ith element of the signal sequence S; sjIs the jth element of the signal sequence S; lambda [ alpha ]minIs the non-zero minimum eigenvalue of the average matrix B; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
module 206 iteratively updates, specifically: the iteration update value of the PCA filter matrix is Pk+1The calculation formula is
Figure FDA0002570087050000026
Figure FDA0002570087050000027
Wherein U is a left feature vector matrix of the average matrix B; a characteristic value matrix of the average matrix B;
the module 207 ends the iteration, specifically: calculating the iterative error by the formula | | | Pk+1-PkL; if the iteration error is greater than or equal to an iteration threshold
Figure FDA0002570087050000028
Adding 1 to the value of the iteration control parameter k; and returns to the block 206 and the block 207 until the iteration error is less than the iteration threshold
Figure FDA0002570087050000029
And applying the current value P of the PCA filter matrixk +1Assign to PCA optimal Filter matrix POPT(ii) a Wherein the iteration threshold value
Figure FDA00025700870500000210
Is taken as
Figure FDA00025700870500000211
The module 208 calculates a signal sequence after noise filtering, specifically: the signal sequence S after noise filteringnewIs calculated by the formula Snew＝POPTS。
CN202010637602.3A 2020-07-05 2020-07-05 Transformer state vibration and sound detection signal filtering method and system using feature selection Withdrawn CN111664934A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112254808A (en) * 2020-11-03 2021-01-22 华北电力大学 Method and system for detecting vibration and sound of running state of transformer by utilizing gradient change
CN114137444A (en) * 2021-11-29 2022-03-04 国网山东省电力公司日照供电公司 Transformer running state monitoring method and system based on acoustic signals

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112254808A (en) * 2020-11-03 2021-01-22 华北电力大学 Method and system for detecting vibration and sound of running state of transformer by utilizing gradient change
CN112254808B (en) * 2020-11-03 2021-12-31 华北电力大学 Method and system for detecting vibration and sound of running state of transformer by utilizing gradient change
CN114137444A (en) * 2021-11-29 2022-03-04 国网山东省电力公司日照供电公司 Transformer running state monitoring method and system based on acoustic signals
CN114137444B (en) * 2021-11-29 2024-04-02 国网山东省电力公司日照供电公司 Transformer running state monitoring method and system based on acoustic signals

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Application publication date: 20200915