CN110286289B - Filtering method for vibration and sound detection signal of transformer - Google Patents

Filtering method for vibration and sound detection signal of transformer Download PDF

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CN110286289B
CN110286289B CN201910584461.0A CN201910584461A CN110286289B CN 110286289 B CN110286289 B CN 110286289B CN 201910584461 A CN201910584461 A CN 201910584461A CN 110286289 B CN110286289 B CN 110286289B
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transformer
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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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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
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Abstract

The embodiment of the invention discloses a method and a system for detecting a signal by using a vibration sound of a running state of a transformer recovered by a low-rank matrix, 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 vibration sound signal sequence S to generate a data sequence S after noise filteringNEW(ii) a The method specifically comprises the following steps:
Figure DDA0002113446520000011
wherein the content of the first and second substances,
Figure DDA0002113446520000012
is an N-dimensional intermediate parameter vector;
Figure DDA0002113446520000013
frobenius norm of expression; lambda is a low rank matrix recovery factor; eta (X)i(ii) a α) represents a penalty function; xiIs the ith element in the N-dimensional intermediate parameter vector X; alpha represents a penalty factor.

Description

Filtering method for vibration and sound detection signal of transformer
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 in an operating state.
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 and sound detection signal filtering method, which utilizes the low-rank matrix characteristic of a transformer vibration and sound signal and realizes the filtering of background noise (including abnormal points) according to the low-rank matrix recovery principle. The method has the advantages of good robustness and simple calculation.
In order to achieve the purpose, the invention provides the following scheme:
a method for filtering a vibration and sound detection signal of a transformer running state by using low-rank matrix recovery comprises the following steps:
step 1, inputting an actually measured vibration sound signal sequence S;
step 2, carrying out noise filtering processing on the vibration sound signal sequence S to generate a data sequence S after noise filteringNEW(ii) a The method specifically comprises the following steps:
Figure GDA0003010834720000021
wherein the content of the first and second substances,
Figure GDA0003010834720000022
is an N-dimensional intermediate parameter vector;
Figure GDA0003010834720000023
frobenius norm of expression; lambda is a low rank matrix recovery factor; eta (X)i(ii) a α) represents a penalty function; xiIs the ith element in the N-dimensional intermediate parameter vector X; alpha represents a penalty factor.
A transformer operating state vibro-acoustic detection signal filtering system utilizing low rank matrix recovery, 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 vibration sound signal sequence S to generate a data sequence S after noise filteringNEW(ii) a The method specifically comprises the following steps:
Figure GDA0003010834720000024
wherein the content of the first and second substances,
Figure GDA0003010834720000025
is an N-dimensional intermediate parameter vector;
Figure GDA0003010834720000026
frobenius norm of expression; lambda is a low rank matrix recovery factor; eta (X)i(ii) a α) represents a penalty function; xiIs the ith element in the N-dimensional intermediate parameter vector X; alpha represents a penalty factor.
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 method and a system for filtering a vibration and sound detection signal of a transformer running state by using low-rank matrix recovery. The method has the advantages of good robustness and simple 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 operation state vibro-acoustic detection signal filtering method using low rank matrix recovery
Fig. 1 is a schematic flow chart of a method for filtering a vibro-acoustic detection signal in a transformer operating state by using low rank matrix recovery according to the present invention. As shown in fig. 1, the method for filtering the vibro-acoustic detection signal in the transformer operating state by using low rank matrix recovery 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 vibration sound signal sequence S to generate a data sequence S after noise filteringNEW(ii) a The method specifically comprises the following steps:
Figure GDA0003010834720000041
wherein the content of the first and second substances,
Figure GDA0003010834720000042
is an N-dimensional intermediate parameter vector;
Figure GDA0003010834720000043
frobenius norm of expression; lambda is a low rank matrix recovery factor; eta (X)i(ii) a α) represents a penalty function; xiIs the ith element in the N-dimensional intermediate parameter vector X; alpha represents a penalty factor.
Before the step 2, the method further comprises:
step (ii) of3, solving the low-rank matrix recovery factor lambda, the penalty factor alpha and the penalty function eta (X)i；α)。
The step 3 comprises the following steps:
step 301, determining a low rank matrix recovery factor λ, specifically:
Figure GDA0003010834720000044
wherein:
tr [ ]: traces representing matrices
*T: transpose of a representation matrix
Step 302, calculating the penalty factor α, specifically:
Figure GDA0003010834720000051
wherein:
μ: mean value of the vibro-acoustic signal sequence S
σ: mean square error of the vibro-acoustic signal sequence S
Step 303, calculating the penalty function eta (X)i(ii) a α), in particular:
Figure GDA0003010834720000052
wherein:
Xi: representing the ith element in the N-dimensional intermediate parameter vector X
X＝[X1,X2,…,XN]
Mu mean value of the vibro-acoustic signal sequence S
α: the penalty factor
FIG. 2 is a structural intention of a transformer operation state vibration and sound detection signal filtering system using low rank matrix recovery
Fig. 2 is a schematic structural diagram of a transformer operation state vibration and sound detection signal filtering system using low rank matrix recovery according to the present invention. As shown in fig. 2, the system for filtering the vibro-acoustic detection signal in the transformer operation state by using low rank matrix recovery comprises the following structures:
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 vibration sound signal sequence S to generate a data sequence S with noise filteredNEW(ii) a The method specifically comprises the following steps:
Figure GDA0003010834720000053
wherein the content of the first and second substances,
Figure GDA0003010834720000054
is an N-dimensional intermediate parameter vector;
Figure GDA0003010834720000055
frobenius norm of expression; lambda is a low rank matrix recovery factor; eta (X)i(ii) a α) represents a penalty function; xiIs the ith element in the N-dimensional intermediate parameter vector X; alpha represents a penalty factor.
The system further comprises:
a calculation module 403 for calculating the low rank matrix recovery factor λ, the penalty factor α and the penalty function η (X)i；α)。
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: real vibration and sound signal data sequence with length N
siI is 1,2, …, N is measured vibration sound signal with serial number i
2. Determining low rank matrix recovery factor
Figure GDA0003010834720000061
Wherein:
tr [ ]: traces representing matrices
*T: transpose of a representation matrix
3. Calculating a penalty factor
Figure GDA0003010834720000062
Wherein:
μ: mean value of the vibro-acoustic signal sequence S
σ: mean square error of the vibro-acoustic signal sequence S
4. Calculating a penalty function
Figure GDA0003010834720000063
Wherein:
Xi: representing the ith element in the N-dimensional intermediate parameter vector X
X＝[X1,X2,…,XN]
Mu mean value of the vibro-acoustic signal sequence S
α: the penalty factor
5. Filtering
Carrying out noise filtering processing on the vibration sound signal sequence S to generate a data sequence S after noise filteringNEW(ii) a The method specifically comprises the following steps:
Figure GDA0003010834720000071
wherein the content of the first and second substances,
Figure GDA0003010834720000072
is an N-dimensional intermediate parameter vector;
Figure GDA0003010834720000073
frobenius norm of expression; lambda is a low rank matrix recovery factor; eta (X)i(ii) a α) represents a penalty function; xiIs the ith element in the N-dimensional intermediate parameter vector X; alpha represents a penalty factor.
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 method for filtering a vibration and sound detection signal of a transformer is characterized by comprising the following steps:
step 1, inputting an actually measured vibration sound signal sequence S;
step 2, determining a low-rank matrix recovery factor lambda, specifically:
Figure FDA0002974971730000011
wherein:
tr [ ]: traces representing matrices;
*T: representing a transpose of a matrix;
step 3, solving a penalty factor alpha, specifically:
Figure FDA0002974971730000012
wherein:
μ: a mean value of the vibro-acoustic signal sequence S;
σ: the mean square error of the vibro-acoustic signal sequence S;
step 4, calculating a penalty function eta (X)i(ii) a α), in particular:
Figure FDA0002974971730000013
wherein:
Xi: the ith element in the N-dimensional intermediate parameter vector X;
X＝[X1,X2,…,XN]；
mu, the mean value of the vibration sound signal sequence S;
step 5, carrying out noise filtering processing on the vibration sound signal sequence S to generate a data sequence S after noise filteringNEW(ii) a The method specifically comprises the following steps:
Figure FDA0002974971730000014
wherein the content of the first and second substances,
Figure FDA0002974971730000015
is an N-dimensional intermediate parameter vector;
Figure FDA0002974971730000016
indicates the Frobenius norm.
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