CN112417994B - Vibration and sound detection signal filtering method and system using regularization factor - Google Patents
Vibration and sound detection signal filtering method and system using regularization factor Download PDFInfo
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- CN112417994B CN112417994B CN202011206213.1A CN202011206213A CN112417994B CN 112417994 B CN112417994 B CN 112417994B CN 202011206213 A CN202011206213 A CN 202011206213A CN 112417994 B CN112417994 B CN 112417994B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
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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
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- 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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- G—PHYSICS
- 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
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
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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
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Abstract
The embodiment of the invention discloses a method and a system for filtering a vibration and sound detection signal by utilizing a regularization factor, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102, generating a regular matrix; step 103, solving a regularization factor; step 104 finds the signal sequence after noise filtering.
Description
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
As mentioned above, the transformer vibration and noise detection method is widely applied to monitoring the operation state of the transformer, and the technology is relatively mature, but because the vibration and noise detection method utilizes the vibration signal emitted by the transformer, the vibration and noise detection method is easily affected by the environmental noise, and therefore, the method often fails to obtain satisfactory results when being applied in the actual working environment.
The invention aims to provide a vibration and sound detection signal filtering method and system utilizing a regularization factor. The method has better robustness and simpler calculation.
In order to achieve the purpose, the invention provides the following scheme:
a vibro-acoustic detection signal filtering method using a regularization factor, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, generating a regular matrix, specifically: the regular matrix is denoted as G, and the ith row and jth column element of the regular matrix are denoted as GijThe formula used is:
wherein:
t is the sampling interval of the signal sequence S,
f0being the center frequency of the signal sequence S,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
step 103, obtaining a regularization factor, specifically: the regularization factor is denoted as λ, and the formula used is:
wherein:
the SNR is the signal-to-noise ratio of the signal sequence S,
m0is the mean value of the signal sequence S,
sigma is the mean square error of the signal sequence S;
step 104, obtaining a signal sequence after noise filtering, specifically: the signal sequence after noise filtering is recorded as SnewThe method comprises the following steps: in all the intermediate vectors m, the formula is selected
The corresponding intermediate vector m when the maximum value is obtained is marked as SmaxThen assigned a value to Snew＝Smax。
A vibro-acoustic detection signal filtering system utilizing a regularization factor, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 generates a regular matrix, specifically: the regular matrix is denoted as G, and the ith row and jth column element of the regular matrix are denoted as GijThe formula used is:
wherein:
t is the sampling interval of the signal sequence S,
f0being the center frequency of the signal sequence S,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
the module 203 calculates a regularization factor, which specifically is: the regularization factor is denoted as λ, and the formula used is:
wherein:
the SNR is the signal-to-noise ratio of the signal sequence S,
m0is the mean value of the signal sequence S,
sigma is the mean square error of the signal sequence S;
the module 204 calculates a signal sequence after noise filtering, specifically: the signal sequence after noise filtering is recorded as SnewThe method comprises the following steps: in all the intermediate vectors m, the formula is selected
The corresponding intermediate vector m when the maximum value is obtained is marked as SmaxThen assigned a value to Snew＝Smax。
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
as mentioned above, the transformer vibration and noise detection method is widely applied to monitoring the operation state of the transformer, and the technology is relatively mature, but because the vibration and noise detection method utilizes the vibration signal emitted by the transformer, the vibration and noise detection method is easily affected by the environmental noise, and therefore, the method often fails to obtain satisfactory results when being applied in the actual working environment.
The invention aims to provide a vibration and sound detection signal filtering method and system utilizing a regularization factor. 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 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 method for filtering a vibro-acoustic detection signal using a regularization factor
Fig. 1 is a schematic flow chart of a vibro-acoustic detection signal filtering method using a regularization factor according to the present invention. As shown in fig. 1, the method for filtering a vibro-acoustic detection signal by using a regularization factor specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, generating a regular matrix, specifically: the regular matrix is denoted as G, and the ith row and jth column element of the regular matrix are denoted as GijThe formula used is:
wherein:
t is the sampling interval of the signal sequence S,
f0being the center frequency of the signal sequence S,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
step 103, obtaining a regularization factor, specifically: the regularization factor is denoted as λ, and the formula used is:
wherein:
the SNR is the signal-to-noise ratio of the signal sequence S,
m0is the mean value of the signal sequence S,
sigma is the mean square error of the signal sequence S;
step 104, obtaining a signal sequence after noise filtering, specifically: the signal sequence after noise filtering is recorded as SnewThe method comprises the following steps: in all the intermediate vectors m, the formula is selected
The corresponding intermediate vector m when the maximum value is obtained is marked as SmaxThen assigned a value to Snew＝Smax。
FIG. 2 structural intent of a vibro-acoustic detection signal filtering system using regularization factors
Fig. 2 is a schematic structural diagram of a vibro-acoustic detection signal filtering system using a regularization factor according to the present invention. As shown in fig. 2, the vibro-acoustic detection signal filtering system using regularization factor includes the following structure:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 generates a regular matrix, specifically: the regular matrix is denoted as G, and the ith row and jth column element of the regular matrix are denoted as GijThe formula used is:
wherein:
t is the sampling interval of the signal sequence S,
f0being the center frequency of the signal sequence S,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
the module 203 calculates a regularization factor, which specifically is: the regularization factor is denoted as λ, and the formula used is:
wherein:
the SNR is the signal-to-noise ratio of the signal sequence S,
m0is the mean value of the signal sequence S,
sigma is the mean square error of the signal sequence S;
the module 204 calculates a signal sequence after noise filtering, specifically: the signal sequence after noise filtering is recorded as SnewThe method comprises the following steps: in all the intermediate vectors m, the formula is selected
Obtaining and recording the corresponding intermediate vector m when the maximum value is obtainedIs SmaxThen assigned a value to Snew＝Smax。
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 generates a regular matrix, specifically: the regular matrix is denoted as G, and the ith row and jth column element of the regular matrix are denoted as GijThe formula used is:
wherein:
t is the sampling interval of the signal sequence S,
f0being the center frequency of the signal sequence S,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
step 303 finds a regularization factor, specifically: the regularization factor is denoted as λ, and the formula used is:
wherein:
the SNR is the signal-to-noise ratio of the signal sequence S,
m0is the mean value of the signal sequence S,
sigma is the mean square error of the signal sequence S;
step 304, obtaining a signal sequence after noise filtering, specifically: the signal sequence after noise filtering is recorded as SnewThe method comprises the following steps: in all the intermediate vectors m, the formula is selected
The corresponding intermediate vector m when the maximum value is obtained is marked as SmaxThen assigned a value to Snew＝Smax。
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 method for filtering a vibro-acoustic detection signal using a regularization factor, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, generating a regular matrix, specifically: the regular matrix is denoted as G, and the ith row and jth column element of the regular matrix are denoted as GijThe formula used is:
wherein:
t is the sampling interval of the signal sequence S,
f0being the center frequency of the signal sequence S,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
step 103, obtaining a regularization factor, specifically: the regularization factor is denoted as λ, and the formula used is:
wherein:
the SNR is the signal-to-noise ratio of the signal sequence S,
m0is the mean value of the signal sequence S,
sigma is the mean square error of the signal sequence S;
step 104, obtaining a signal sequence after noise filtering, specifically: the signal sequence after noise filtering is recorded as SnewThe method comprises the following steps: in all the intermediate vectors m, the formula is selected
The corresponding intermediate vector m when the maximum value is obtained is marked as SmaxThen assigned a value to Snew＝Smax。
2. A vibro-acoustic detection signal filtering system utilizing a regularization factor, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 generates a regular matrix, specifically: the regular matrix is denoted as G, and the ith row and jth column element of the regular matrix are denoted as GijThe formula used is:
wherein:
t is the sampling interval of the signal sequence S,
f0being the center frequency of the signal sequence S,
i is 1,2, N is a row number,
j is 1,2, N is a column number,
n is the length of the signal sequence S;
the module 203 calculates a regularization factor, which specifically is: the regularization factor is denoted as λ, and the formula used is:
wherein:
the SNR is the signal-to-noise ratio of the signal sequence S,
m0is the mean value of the signal sequence S,
sigma is the mean square error of the signal sequence S;
the module 204 calculates a signal sequence after noise filtering, specifically: the signal sequence after noise filtering is recorded as SnewThe method comprises the following steps: in all the intermediate vectors m, the formula is selected
The corresponding intermediate vector m when the maximum value is obtained is marked as SmaxThen assigned a value to Snew＝Smax。
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| CN105958470A (en) * | 2014-10-20 | 2016-09-21 | 国家电网公司 | Electric power system bilinear anti-error estimation method based on bilinear protruding optimization theory |
| CN110320435A (en) * | 2019-07-11 | 2019-10-11 | 广东石油化工学院 | A kind of running state of transformer vibration sound detection signal reconfiguring method and system using data regularization |
| CN110611522A (en) * | 2019-09-20 | 2019-12-24 | 广东石油化工学院 | PLC signal reconstruction method and system using multiple regular optimization theory |
| CN111141384A (en) * | 2020-02-18 | 2020-05-12 | 广东石油化工学院 | Transformer state vibration and sound detection signal reconstruction method and system by utilizing Frechet regularization |
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| US20180284758A1 (en) * | 2016-05-09 | 2018-10-04 | StrongForce IoT Portfolio 2016, LLC | Methods and systems for industrial internet of things data collection for equipment analysis in an upstream oil and gas environment |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105958470A (en) * | 2014-10-20 | 2016-09-21 | 国家电网公司 | Electric power system bilinear anti-error estimation method based on bilinear protruding optimization theory |
| CN110320435A (en) * | 2019-07-11 | 2019-10-11 | 广东石油化工学院 | A kind of running state of transformer vibration sound detection signal reconfiguring method and system using data regularization |
| CN110611522A (en) * | 2019-09-20 | 2019-12-24 | 广东石油化工学院 | PLC signal reconstruction method and system using multiple regular optimization theory |
| CN111141384A (en) * | 2020-02-18 | 2020-05-12 | 广东石油化工学院 | Transformer state vibration and sound detection signal reconstruction method and system by utilizing Frechet regularization |
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