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 PDFInfo
- Publication number
- 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
- Authority
- CN
- China
- Prior art keywords
- low
- penalty
- factor
- rank matrix
- acoustic signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 239000011159 matrix material Substances 0.000 title claims abstract description 40
- 238000001514 detection method Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000001914 filtration Methods 0.000 title claims abstract description 16
- 238000011084 recovery Methods 0.000 title abstract 2
- 108010076504 Protein Sorting Signals Proteins 0.000 claims abstract description 25
- 238000005259 measurement Methods 0.000 claims abstract description 10
- 230000017105 transposition Effects 0.000 claims description 3
- 241000208340 Araliaceae Species 0.000 claims 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims 1
- 235000003140 Panax quinquefolius Nutrition 0.000 claims 1
- 235000008434 ginseng Nutrition 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 6
- 238000004804 winding Methods 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 5
- 230000005856 abnormality Effects 0.000 description 5
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 229910000976 Electrical steel Inorganic materials 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000017260 vegetative to reproductive phase transition of meristem Effects 0.000 description 1
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computing Systems (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- 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
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)
- 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. 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. 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 Sσ: the mean square deviation of the acoustic signal sequence SStep 303, the penalty η (X is soughti;α), specifically:Wherein:Xi: indicate i-th of element in the N-dimensional intermediate parameters vector XX=[X1,X2,…,XN]μ: the mean value of the acoustic signal sequence Sα: the penalty factor.
- 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. 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).
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910584461.0A CN110286289B (en) | 2019-06-30 | 2019-06-30 | Filtering method for vibration and sound detection signal of transformer |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910584461.0A CN110286289B (en) | 2019-06-30 | 2019-06-30 | Filtering method for vibration and sound detection signal of transformer |
Publications (2)
Publication Number | Publication Date |
---|---|
CN110286289A true CN110286289A (en) | 2019-09-27 |
CN110286289B CN110286289B (en) | 2021-06-11 |
Family
ID=68021476
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201910584461.0A Expired - Fee Related CN110286289B (en) | 2019-06-30 | 2019-06-30 | Filtering method for vibration and sound detection signal of transformer |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN110286289B (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112284520A (en) * | 2020-10-25 | 2021-01-29 | 广东石油化工学院 | Vibration and sound detection signal reconstruction method and system by using optimal rank approximation |
Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102217934A (en) * | 2011-04-08 | 2011-10-19 | 中国科学院深圳先进技术研究院 | Magnetic resonance imaging method and system |
CN103398769A (en) * | 2013-08-05 | 2013-11-20 | 国家电网公司 | Transformer on-line fault detecting method based on sampling integrated SVM (support vector machine) under wavelet GGD (general Gaussian distribution) feather and unbalanced K-mean value |
CN107132451A (en) * | 2017-05-31 | 2017-09-05 | 广州供电局有限公司 | The winding state detection method and system of transformer |
CN108151873A (en) * | 2017-12-26 | 2018-06-12 | 广东石油化工学院 | A kind of method for detaching centrifugal pump vibration signal and Heat Exchanger in Circulating Water System vibration signal |
CN108833024A (en) * | 2018-04-23 | 2018-11-16 | 温州市特种设备检测研究院 | A kind of channel wireless radio multi distribution field vehicle braking-distance figures transmission method |
CN109357751A (en) * | 2018-09-26 | 2019-02-19 | 东莞绿邦智能科技有限公司 | A kind of Winding in Power Transformer loosening defect detecting system |
CN208953639U (en) * | 2018-10-10 | 2019-06-07 | 贵州电网有限责任公司 | On-load tap changers of transformers Machinery State Monitoring System |
CN109871880A (en) * | 2019-01-23 | 2019-06-11 | 中山大学 | Feature extracting method based on low-rank sparse matrix decomposition, local geometry holding and classification information maximum statistical correlation |
CN109933865A (en) * | 2019-02-26 | 2019-06-25 | 河海大学 | A kind of OLTC method for diagnosing faults based on recurrence plot and recurrence quantification analysis |
-
2019
- 2019-06-30 CN CN201910584461.0A patent/CN110286289B/en not_active Expired - Fee Related
Patent Citations (9)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102217934A (en) * | 2011-04-08 | 2011-10-19 | 中国科学院深圳先进技术研究院 | Magnetic resonance imaging method and system |
CN103398769A (en) * | 2013-08-05 | 2013-11-20 | 国家电网公司 | Transformer on-line fault detecting method based on sampling integrated SVM (support vector machine) under wavelet GGD (general Gaussian distribution) feather and unbalanced K-mean value |
CN107132451A (en) * | 2017-05-31 | 2017-09-05 | 广州供电局有限公司 | The winding state detection method and system of transformer |
CN108151873A (en) * | 2017-12-26 | 2018-06-12 | 广东石油化工学院 | A kind of method for detaching centrifugal pump vibration signal and Heat Exchanger in Circulating Water System vibration signal |
CN108833024A (en) * | 2018-04-23 | 2018-11-16 | 温州市特种设备检测研究院 | A kind of channel wireless radio multi distribution field vehicle braking-distance figures transmission method |
CN109357751A (en) * | 2018-09-26 | 2019-02-19 | 东莞绿邦智能科技有限公司 | A kind of Winding in Power Transformer loosening defect detecting system |
CN208953639U (en) * | 2018-10-10 | 2019-06-07 | 贵州电网有限责任公司 | On-load tap changers of transformers Machinery State Monitoring System |
CN109871880A (en) * | 2019-01-23 | 2019-06-11 | 中山大学 | Feature extracting method based on low-rank sparse matrix decomposition, local geometry holding and classification information maximum statistical correlation |
CN109933865A (en) * | 2019-02-26 | 2019-06-25 | 河海大学 | A kind of OLTC method for diagnosing faults based on recurrence plot and recurrence quantification analysis |
Non-Patent Citations (5)
Title |
---|
GIJS VAN DER VEEN 等: "Global Identification of Wind Turbines Using a Hammerstein Identification Method", 《IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY》 * |
GREGORY ONGIE 等: "A Fast Algorithm for Convolutional Structured Low-Rank Matrix Recovery", 《IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY》 * |
何林知 等: "基于低秩矩阵恢复与协同表征的人脸识别算法", 《计算机应用》 * |
刘建 等: "干式电力变压器振动信号调理电路的设计", 《电子设计工程》 * |
李庆 等: "非凸罚正则化稀疏低秩矩阵的大型减速机圆锥滚子轴承微弱故障诊断", 《机械工程学报》 * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112284520A (en) * | 2020-10-25 | 2021-01-29 | 广东石油化工学院 | Vibration and sound detection signal reconstruction method and system by using optimal rank approximation |
CN112284520B (en) * | 2020-10-25 | 2022-06-24 | 广东石油化工学院 | Vibration and sound detection signal reconstruction method and system by using optimal rank approximation |
Also Published As
Publication number | Publication date |
---|---|
CN110286289B (en) | 2021-06-11 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN102721464A (en) | Method and system for detecting winding deformation faults of power transformer | |
CN110031089A (en) | A kind of filtering method and device of running state of transformer vibration sound detection signal | |
CN109932053A (en) | A kind of state monitoring apparatus and method for high-voltage shunt reactor | |
CN107037314A (en) | A kind of winding deformation of power transformer on-line fault diagnosis method | |
CN110161344A (en) | Transformer vibration sound detection signal filtering method and system based on Scale Decomposition | |
CN110286289A (en) | A kind of running state of transformer vibration sound detection signal filtering method and system using low-rank matrix recovery | |
CN110286287A (en) | A kind of running state of transformer vibration sound detection signal filtering method and system based on wavelet transformation | |
CN110514295A (en) | A kind of running state of transformer vibration sound detection signal filtering method and system using SVD decomposition | |
CN110703145B (en) | Transformer vibration sound signal reconstruction method and system by using multiple optimization theories | |
CN110545086A (en) | Transformer vibration sound signal filtering method and system by utilizing global optimization | |
CN110516645A (en) | A kind of transformer acoustic signal filtering method and system using mask operator | |
CN110320435A (en) | A kind of running state of transformer vibration sound detection signal reconfiguring method and system using data regularization | |
CN110286291A (en) | A kind of running state of transformer vibration sound detection method and system using principal component | |
CN110161363A (en) | Running state of transformer vibration sound detection method and system based on dominant frequency token state | |
CN110646691B (en) | Transformer vibration sound signal filtering method and system by utilizing stretching transformation | |
CN110514917A (en) | A kind of running state of transformer vibration sound detection signal filtering method and system using sparse matrix recovery | |
CN110320433A (en) | The signal filtering method and device of transformer exception state vibration sound detection | |
CN111141384A (en) | Transformer state vibration and sound detection signal reconstruction method and system by utilizing Frechet regularization | |
CN111751098A (en) | Vibration and sound detection signal reconstruction method and system by using Gaussian prediction model | |
Altaf et al. | Machine Health Monitoring and Fault Diagnosis Techniques Review in Industrial Power-Line Network | |
CN110161362A (en) | Running state of transformer vibration sound detection method and system based on projector space | |
CN110837013A (en) | Transformer state vibration and sound detection signal reconstruction method and system represented by sparse dictionary | |
CN111649819A (en) | Transformer state vibration and sound detection signal filtering method and system using iteration soft threshold | |
CN110261716A (en) | Running state of transformer vibration sound detection method and system based on dirac mixed mechanism | |
CN112307993B (en) | Method and system for filtering vibration and sound detection signals by using local similarity |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210611 |