CN110286289A - A kind of running state of transformer vibration sound detection signal filtering method and system using low-rank matrix recovery - Google Patents

A kind of running state of transformer vibration sound detection signal filtering method and system using low-rank matrix recovery Download PDF

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CN110286289A
CN110286289A CN201910584461.0A CN201910584461A CN110286289A CN 110286289 A CN110286289 A CN 110286289A CN 201910584461 A CN201910584461 A CN 201910584461A CN 110286289 A CN110286289 A CN 110286289A
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low
penalty
factor
rank matrix
acoustic signal
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CN110286289B (en
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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
    • 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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  • General Physics & Mathematics (AREA)
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  • Power Engineering (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Abstract

The embodiment of the present invention discloses a kind of running state of transformer vibration sound detection signal method and system using low-rank matrix recovery, which comprises step 1, inputs the acoustic signal sequence S of actual measurement；Step 2, the acoustic signal sequence S is carried out filtering out noise processed, generation filters out the data sequence S after noiseNEW；Specifically:Wherein,For N-dimensional intermediate parameters vector；Indicate the Frobenius norm of *；λ is that low-rank matrix restores the factor；η(Xi；α) indicate penalty；XiFor i-th of element in the N-dimensional intermediate parameters vector X；α indicates penalty factor.

Description

A kind of running state of transformer vibration sound detection signal filtering restored using low-rank matrix Method and system
Technical field
The present invention relates to power domain more particularly to a kind of running state of transformer vibration sound detection signal filtering method and System.
Background technique
With the high speed development of smart grid, power equipment safety stable operation, which seems, to be even more important.Currently, to super-pressure And the power equipment of above carries out condition monitoring, especially to the detection of abnormality seem it is further important and Urgently.Important component of the power transformer as electric system is one of most important electrical equipment in substation, can It is related to the safety of power grid by operation.In general, the abnormality of transformer can be divided into, iron core is abnormal and winding is abnormal.Iron core Exception is mainly shown as core sataration, and winding generally includes winding deformation extremely, winding loosens etc..
The basic principle of transformer exception state-detection is to extract each characteristic quantity of Transformer, and analysis, identification are simultaneously Tracking characteristics amount monitors the abnormal operating condition of transformer with this.Detection method according to exposure level can be divided into intrusive detection and Noninvasive testing；Live detection can be divided into and the detection that has a power failure according to whether detection need to be shut down；It can divide according to detection limit type For electrical quantity method and non-electric quantity method etc..In comparison, Noninvasive testing is portable strong, and installation is more convenient；Live detection Do not influence transformer station high-voltage side bus；Non-electric quantity method and electric system are safer without electrical connection.Current transformer operating status In common detection method, including detecting the pulse current method of shelf depreciation and the frequency of ultrasonic Detection Method, detection winding deformation Response method and detection machinery and the vibration detection method of electric fault etc..These detection methods predominantly detect transformer insulated situation And mechanical structure situation, wherein it is the most comprehensive with the detection of transformer vibration signal (vibration sound), for most of transformer fault And abnormality can be reacted.
In the process of running, vibration caused by the magnetostriction and winding electric power of iron core silicon-steel sheet can around for transformer Radiate the acoustic signal of different amplitudes and frequency.What transformer externally issued when operating normally is uniform low-frequency noise；If Uneven sound is issued, then belongs to abnormality.Transformer can issue different sound under different operating statuses, can lead to The detection made a sound to it is crossed, the operation conditions of transformer is grasped.It is worth noting that under transformer difference operating status The detection made a sound not only can detecte a variety of catastrophe failures for causing electrical quantity to change, and can also detect many and not endanger And abnormality for not causing electrical quantity to change of insulation, such as the loosening of transformer inside and outside components etc..
Since the vibration signal of transformer sending is utilized in vibration sound detection method, it is easy to it is influenced by ambient noise, Therefore vibration sound and noise how are efficiently identified, is the key that the method success.Existing frequently-used method, to this problem weight Depending on not enough, not taking effective measures also and solving the problems, such as this.
Summary of the invention
The object of the present invention is to provide a kind of running state of transformer vibration sound detection signals restored using low-rank matrix to filter Wave method and system, the method proposed are utilized the low-rank matrix characteristic of transformer acoustic signal, are restored according to low-rank matrix Principle realizes that ambient noise (including abnormal point) filters out.The method proposed has preferable robustness, calculates simple.
To achieve the above object, the present invention provides following schemes:
A kind of running state of transformer vibration sound detection signal filtering method restored using low-rank matrix, comprising:
Step 1, the acoustic signal sequence S of actual measurement is inputted；
Step 2, the acoustic signal sequence S is carried out filtering out noise processed, generation filters out the data sequence after noise SNEW；Specifically:Wherein,For N-dimensional intermediate parameters arrow Amount；Indicate the Frobenius norm of *；λ is that low-rank matrix restores the factor；η(Xi；α) indicate penalty；XiFor the N Tie up i-th of element in intermediate parameters vector X；α indicates penalty factor.
A kind of running state of transformer vibration sound detection signal filtering system restored using low-rank matrix, comprising:
Module is obtained, the acoustic signal sequence S of actual measurement is inputted；
Filter module carries out the acoustic signal sequence S to filter out noise processed, and generation filters out the data sequence after noise SNEW；Specifically:Wherein,For N-dimensional intermediate parameters arrow Amount；Indicate the Frobenius norm of *；λ is that low-rank matrix restores the factor；η(Xi；α) indicate penalty；XiFor the N-dimensional I-th of element in intermediate parameters vector X；α indicates penalty factor.
The specific embodiment provided according to the present invention, the invention discloses following technical effects:
Although transformer shakes, sound detection method has a wide range of applications in running state of transformer monitoring, and technology is opposite Maturation, but since the vibration signal of transformer sending is utilized in vibration sound detection method, it is easy to it is influenced by ambient noise, Institute usually cannot get satisfactory result when applying in actual working environment in this approach.
The object of the present invention is to provide a kind of running state of transformer vibration sound detection signals restored using low-rank matrix to filter Wave method and system, the method proposed are utilized the low-rank matrix characteristic of transformer acoustic signal, are restored according to low-rank matrix Principle realizes that ambient noise (including abnormal point) filters out.The method proposed has preferable robustness, calculates simple.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment Attached drawing to be used is needed to be briefly described.It is clear that drawings in the following description are only some embodiments of the invention, For those of ordinary skill in the art, without creative efforts, it can also be obtained according to these attached drawings Other attached drawings.
Fig. 1 is method flow schematic diagram of the invention；
Fig. 2 is system structure diagram of the invention；
Fig. 3 is the flow diagram of present invention specific implementation case.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description.Obviously, the described embodiment is only a part of the embodiment of the present invention, instead of all the embodiments.Based on this Embodiment in invention, every other reality obtained by those of ordinary skill in the art without making creative efforts Example is applied, shall fall within the protection scope of the present invention.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real Applying mode, the present invention is described in further detail.
A kind of process of the running state of transformer vibration sound detection signal filtering method restored using low-rank matrix of Fig. 1 is illustrated Figure
Fig. 1 is a kind of running state of transformer vibration sound detection signal filtering method restored using low-rank matrix of the present invention Flow diagram.As shown in Figure 1, a kind of running state of transformer vibration sound detection signal restored using low-rank matrix is filtered Wave method specifically includes the following steps:
Step 1, the acoustic signal sequence S of actual measurement is inputted；
Step 2, the acoustic signal sequence S is carried out filtering out noise processed, generation filters out the data sequence after noise SNEW；Specifically:Wherein,For N-dimensional intermediate parameters arrow Amount；Indicate the Frobenius norm of *；λ is that low-rank matrix restores the factor；η(Xi；α) indicate penalty；XiFor the N Tie up i-th of element in intermediate parameters vector X；α indicates penalty factor.
Before the step 2, the method also includes:
Step 3, it seeks the low-rank matrix and restores factor lambda, penalty factor α and penalty η (Xi；α).
The step 3 includes:
Step 301, determine that low-rank matrix restores factor lambda, specifically:
Wherein:
Tr [*]: the mark of representing matrix *
*T: the transposition of representing matrix *
Step 302, the penalty factor α is sought, specifically:
Wherein:
μ: the mean value of the acoustic signal sequence S
σ: the mean square deviation of the acoustic signal sequence S
Step 303, the penalty η (X is soughti；α), specifically:
Wherein:
Xi: indicate i-th of element in the N-dimensional intermediate parameters vector X
X=[X1,X2,…,XN]
μ: the mean value of the acoustic signal sequence S
α: the penalty factor
A kind of structure of the running state of transformer vibration sound detection signal filtering system restored using low-rank matrix of Fig. 2 is intended to
Fig. 2 is a kind of running state of transformer vibration sound detection signal filtering system restored using low-rank matrix of the present invention Structural schematic diagram.As shown in Fig. 2, a kind of running state of transformer vibration sound detection signal restored using low-rank matrix is filtered System includes with flowering structure:
Module 401 is obtained, the acoustic signal sequence S of actual measurement is inputted；
Filter module 402 carries out the acoustic signal sequence S to filter out noise processed, and generation filters out the data after noise Sequence SNEW；Specifically:Wherein,To join among N-dimensional Number vector；Indicate the Frobenius norm of *；λ is that low-rank matrix restores the factor；η(Xi；α) indicate penalty；XiFor institute State i-th of element in N-dimensional intermediate parameters vector X；α indicates penalty factor.
The system, further includes:
Computing module 403 seeks the low-rank matrix and restores factor lambda, penalty factor α and penalty η (Xi；α).
A specific implementation case is provided below, further illustrates the solution of the present invention
Fig. 3 is the flow diagram of present invention specific implementation case.As shown in figure 3, specifically includes the following steps:
1. inputting the acoustic signal sequence of actual measurement
S=[s1,s2,…,sN-1,sN]
Wherein:
S: actual measurement acoustic signal data sequence, length N
si, i=1,2 ..., N: serial number i actual measurement acoustic signal
2. determining that low-rank matrix restores the factor
Wherein:
Tr [*]: the mark of representing matrix *
*T: the transposition of representing matrix *
3. seeking penalty factor
Wherein:
μ: the mean value of the acoustic signal sequence S
σ: the mean square deviation of the acoustic signal sequence S
4. seeking penalty
Wherein:
Xi: indicate i-th of element in the N-dimensional intermediate parameters vector X
X=[X1,X2,…,XN]
μ: the mean value of the acoustic signal sequence S
α: the penalty factor
5. filtering
The acoustic signal sequence S is carried out to filter out noise processed, generation filters out the data sequence S after noiseNEW；Specifically Are as follows:Wherein,For N-dimensional intermediate parameters vector；Table Show the Frobenius norm of *；λ is that low-rank matrix restores the factor；η(Xi；α) indicate penalty；XiFor the N-dimensional intermediate parameters I-th of element in vector X；α indicates penalty factor.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other The difference of embodiment, the same or similar parts in each embodiment may refer to each other.For system disclosed in embodiment For, since it is corresponded to the methods disclosed in the examples, so description is relatively simple, related place is referring to method part illustration ?.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not It is interpreted as limitation of the present invention.

Claims (5)

  1. A kind of sound detection signal filtering method 1. running state of transformer restored using low-rank matrix is shaken, which is characterized in that packet It includes:
    Step 1, the acoustic signal sequence S of actual measurement is inputted；
    Step 2, the acoustic signal sequence S is carried out filtering out noise processed, generation filters out the data sequence S after noiseNEW；Tool Body are as follows:Wherein,For N-dimensional intermediate parameters vector； Indicate the Frobenius norm of *；λ is that low-rank matrix restores the factor；η(Xi；α) indicate penalty；XiFor ginseng among the N-dimensional I-th of element in number vector X；α indicates penalty factor.
  2. 2. the method according to claim 1, wherein before the step 2, the method also includes:
    Step 3, it seeks the low-rank matrix and restores factor lambda, penalty factor α and penalty η (Xi；α).
  3. 3. according to the method described in claim 2, it is characterized in that, the step 3 includes:
    Step 301, determine that low-rank matrix restores factor lambda, specifically:
    Wherein:
    Tr [*]: the mark of representing matrix *
    *T: the transposition of representing matrix *
    Step 302, the penalty factor α is sought, specifically:
    Wherein:
    μ: the mean value of the acoustic signal sequence S
    σ: the mean square deviation of the acoustic signal sequence S
    Step 303, the penalty η (X is soughti；α), specifically:
    Wherein:
    Xi: indicate i-th of element in the N-dimensional intermediate parameters vector X
    X=[X1,X2,…,XN]
    μ: the mean value of the acoustic signal sequence S
    α: the penalty factor.
  4. A kind of sound detection signal filtering system 4. running state of transformer restored using low-rank matrix is shaken, which is characterized in that packet It includes:
    Module is obtained, the acoustic signal sequence S of actual measurement is inputted；
    Filter module carries out the acoustic signal sequence S to filter out noise processed, and generation filters out the data sequence S after noiseNEW； Specifically:Wherein,For N-dimensional intermediate parameters vector；Indicate the Frobenius norm of *；λ is that low-rank matrix restores the factor；η(Xi；α) indicate penalty；XiFor in the N-dimensional Between i-th of element in parameter vector X；α indicates penalty factor.
  5. 5. system according to claim 4, which is characterized in that further include:
    Computing module seeks the low-rank matrix and restores factor lambda, penalty factor α and penalty η (Xi；α).
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