CN112946531B - Transformer fault diagnosis device and diagnosis method - Google Patents
Transformer fault diagnosis device and diagnosis method Download PDFInfo
- Publication number
- CN112946531B CN112946531B CN202110149505.4A CN202110149505A CN112946531B CN 112946531 B CN112946531 B CN 112946531B CN 202110149505 A CN202110149505 A CN 202110149505A CN 112946531 B CN112946531 B CN 112946531B
- Authority
- CN
- China
- Prior art keywords
- voiceprint
- transformer
- data
- sequence
- noise
- 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.)
- Active
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 19
- 238000000034 method Methods 0.000 title claims description 17
- 238000001228 spectrum Methods 0.000 claims description 24
- 239000013598 vector Substances 0.000 claims description 19
- 238000000605 extraction Methods 0.000 claims description 10
- 230000007613 environmental effect Effects 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000013527 convolutional neural network Methods 0.000 claims description 4
- 230000001131 transforming effect Effects 0.000 claims description 2
- 238000004891 communication Methods 0.000 abstract description 6
- 238000012544 monitoring process Methods 0.000 abstract 2
- 238000003825 pressing Methods 0.000 description 8
- 238000007689 inspection Methods 0.000 description 6
- 238000012423 maintenance Methods 0.000 description 5
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical group [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 3
- 239000000919 ceramic Substances 0.000 description 3
- 239000002184 metal Substances 0.000 description 3
- 229910052751 metal Inorganic materials 0.000 description 3
- 238000001514 detection method Methods 0.000 description 2
- 230000026683 transduction Effects 0.000 description 2
- 238000010361 transduction Methods 0.000 description 2
- 238000004804 winding Methods 0.000 description 2
- 240000007651 Rubus glaucus Species 0.000 description 1
- 235000011034 Rubus glaucus Nutrition 0.000 description 1
- 235000009122 Rubus idaeus Nutrition 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000003475 lamination Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- 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
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
- Testing Electric Properties And Detecting Electric Faults (AREA)
Abstract
The invention discloses a transformer fault diagnosis device, which comprises: a controller; the wireless communication module is electrically connected with the controller; the connecting wires at the two ends of the U-shaped connecting rod are perpendicular to the two arms of the U-shaped connecting rod; the first piezoelectric transducer is fixedly connected to one end of the U-shaped connecting rod, the front face of the first piezoelectric transducer faces towards the other end of the U-shaped connecting rod, the front face of the first piezoelectric transducer is perpendicular to the connecting line of the two ends of the U-shaped connecting rod, and the first piezoelectric transducer is electrically connected with the controller; the screw hole is arranged at one end of the U-shaped connecting rod opposite to the first piezoelectric transducer, and the central axis of the screw hole is parallel to the connecting lines at the two ends of the U-shaped connecting rod; the screw rod is matched with the screw hole, and the screw rod is in threaded connection with the screw hole. The transformer fault monitoring system solves the problems that in the prior art, labor is wasted and diagnosis is not timely in transformer fault monitoring.
Description
Technical Field
The invention relates to the field of transformer overhaul, in particular to a transformer fault diagnosis device and a transformer fault diagnosis method.
Background
The transformer is one of the most commonly used devices in the power industry, and is related to the safety of a power system, so that operation and maintenance personnel need to detect the working state of the transformer at any time. The iron core and the winding are main components of the transformer, magnetostriction and magnetic leakage can lead to vibration of iron core lamination, when the winding is deformed or the iron core is loosened, the transformer can generate abnormal vibration, if failure is not found in time, the failure is deteriorated, the transformer is finally completely damaged, power failure of relevant areas of the transformer is caused, and great economic loss is caused. The prior art adopts the mode of manual inspection to the control of this kind of transformer trouble, but this kind of mode has the problem:
1) The labor is wasted, because the transformers are widely distributed, if each transformer needs operation and maintenance personnel to observe on site, the great labor is wasted;
2) The diagnosis is not timely, and because manual inspection is needed, the manual inspection cannot be performed by a person sending each transformer in real time, but is realized in a regular inspection mode, and therefore, the diagnosis result obtained in the inspection mode is not timely, and the transformer is damaged due to the fact that the problem is not found in advance.
Disclosure of Invention
In order to solve the above drawbacks and disadvantages of the prior art, an object of the present invention is to provide a transformer fault diagnosis apparatus and a diagnosis method.
The technical scheme of the invention is as follows: a transformer fault diagnosis apparatus comprising:
A controller;
The wireless communication module is electrically connected with the controller;
The connecting wires at the two ends of the U-shaped connecting rod are perpendicular to the two arms of the U-shaped connecting rod;
the first piezoelectric transducer is fixedly connected to one end of the U-shaped connecting rod, the front face of the first piezoelectric transducer faces towards the other end of the U-shaped connecting rod, the front face of the first piezoelectric transducer is perpendicular to the connecting line of the two ends of the U-shaped connecting rod, and the first piezoelectric transducer is electrically connected with the controller;
the screw hole is arranged at one end of the U-shaped connecting rod opposite to the first piezoelectric transducer, and the central axis of the screw hole is parallel to the connecting lines at the two ends of the U-shaped connecting rod;
the screw rod is matched with the screw hole, and the screw rod is in threaded connection with the screw hole.
Further, the method further comprises the following steps:
the pressing plate is fixedly connected to the end part of the screw rod, which is close to the first piezoelectric transducer, and is perpendicular to the screw rod;
the surface of the pressing plate, which is close to the first piezoelectric transducer, is provided with a rubber layer.
Further, the method further comprises the following steps:
And the screw cap is fixedly connected to one end of the screw rod far away from the first piezoelectric transducer.
Further, the method further comprises the following steps:
and the second piezoelectric transducer is electrically connected with the controller.
Further, the second piezoelectric transducer is connected to the U-shaped connecting rod through a buffer device, the buffer device includes:
The inner cavity of the sleeve is polygonal, the sleeve is made of metal, and the lower end of the sleeve is fixedly connected to the U-shaped connecting rod;
the pressure spring is fixedly connected to the bottom of the inner cavity of the sleeve;
the movable rod is a permanent magnet, the movable rod is matched with the inner cavity of the sleeve, the lower end of the movable rod is fixedly connected to the upper end of the pressure spring, and the movable rod is movably connected in the inner cavity of the sleeve.
A transformer fault diagnosis method, comprising the steps of:
step one, collecting transformer voiceprint data, and storing the transformer voiceprint data in a storage medium, wherein the transformer voiceprint data comprises good transformer voiceprint data and is marked as S1, namely bad transformer voiceprint data is marked as P1, and collecting background noise is marked as B1;
step two, extracting the voice print data S1, P1 and B1 of the transformer at intervals of 10 to 30 milliseconds to obtain voice print sequences S2 and P2 and environmental noise B2 which are taken as units of frames;
removing environmental noise B2 from the transformer voiceprint sequences S2 and P2 through a noise reduction algorithm, and only retaining transformer voiceprint data to obtain voiceprint sequences S3 and P3;
Extracting voiceprint features from the voiceprint sequences S3 and P3 through a feature extraction algorithm to obtain feature vectors S4 and P4;
fifthly, model training is carried out on the deep convolutional neural network by adopting feature vectors S4 and P4 with the number larger than 1 to obtain vector models S5 and P5;
Step six, storing the vector models S5 and P5 into a voiceprint database;
Step seven, collecting real-time transformer voiceprint data to be diagnosed and marking the data as T1, and collecting real-time transformer background noise to be diagnosed and marking the data as D1;
step eight, extracting T1 and D1 at intervals of 10 to 30 milliseconds to obtain voiceprint sequences T2 and D2 taking frames as units;
step nine, removing D2 from the voiceprint sequence T2 through a noise reduction algorithm, and only reserving the transformer voiceprint data to obtain a voiceprint sequence T3;
step ten, extracting voiceprint characteristics from the voiceprint sequence T3 through a characteristic extraction algorithm to obtain T4;
Step eleven, comparing the voiceprint characteristics T4 with voiceprint characteristics S5 and P5 stored in a database to obtain similarity R1 of S5 and T4 and similarity R2 of P5 and T4;
Step twelve, R1 is greater than R2, then the transformer is judged to be good, and if R1 is less than R2, then the transformer is judged to be about to be bad.
Further, the noise reduction algorithm in the third and ninth steps is as follows:
c) Performing Fourier transform on the background noise voiceprint sequence, and converting the background noise voiceprint sequence from a time domain signal to a frequency domain signal, so that the background noise spectrum characteristic is measured;
d) And performing reverse compensation operation on the transformer voiceprint sequence according to the frequency spectrum characteristics of noise to obtain the transformer voiceprint sequence after noise reduction.
Further, the feature extraction algorithm in the fourth and tenth steps is:
g) Obtaining a corresponding frequency spectrum of the voiceprint sequence through Fourier transform;
h) The above frequency spectrum is passed through a Mel filter bank to obtain Mel frequency spectrum;
i) Taking logarithm of Mel frequency spectrum, then DCT discrete cosine transforming, taking 2 nd to 13 th coefficients after DCT as voiceprint feature vector.
Further, in the eleventh step, the method for calculating the similarity R1 between S5 and T4 is as follows
The calculation method of the similarity R2 between P5 and T4 comprises the following steps:
Where (x 21,x22…,x2n) is the eigenvector constituting S5, (x 31,x32…,x3n) is the eigenvector constituting P5, and (x 11,x12…,x1n) is the eigenvector constituting T4.
The beneficial effects of the invention are as follows: in contrast to the prior art, the method has the advantages that,
1) According to the invention, the thin plate on the shell of the transformer or the radiator is placed between the first piezoelectric transducer and the end part of the screw rod, the screw rod is rotated in the screw hole by rotating the screw rod, so that the surface of the first piezoelectric transducer is fixed and clung to the surface of the transformer, the first piezoelectric transducer can detect the voiceprint of the transformer, the voiceprint of the transformer is sent to remote operation and maintenance personnel through a wireless communication module, the fault of the transformer is predicted by utilizing the characteristic of the voiceprint before the fault of the transformer and the characteristic of the voiceprint of the normal transformer, the fault of the transformer is predicted in advance, the operation and maintenance personnel is not required to go to the site for inspection, the labor is greatly saved, and meanwhile, the first piezoelectric transducer detects the voiceprint of the transformer in real time and sends the voiceprint of the transformer to the operation and maintenance personnel for judgment or through machine judgment, and the diagnosis is more timely;
2) According to the invention, the contact surface between the screw and the transformer is larger through the pressing plate, so that the surface of the transformer is prevented from being crushed by the screw;
3) According to the invention, the friction force between the pressing plate and the transformer is increased through the rubber layer, so that the pressing plate is prevented from sliding on the surface of the transformer;
4) According to the invention, the nut is easier to rotate because the diameter of the nut is larger than that of the screw rod;
5) According to the invention, the second piezoelectric transducer is used for detecting the environmental noise, and the controller is used for removing the environmental noise according to the detection result of the second piezoelectric transducer, so that the judgment on the faults of the transformer is more accurate;
6) When the vibration of the transformer is connected with the second piezoelectric transduction device through the buffer device, the movable rod of the permanent magnet is connected with the second piezoelectric transduction device, the movable sleeve and the movable rod are connected through the pressure spring because the movable rod and the sleeve are relatively movable, when the vibration is conducted to the sleeve, the movable rod and the sleeve can generate relative motion, and because the sleeve is metal, the connecting rod is a permanent magnet, the permanent magnet moves in the sleeve to form annular current on the sleeve, the annular current can form a magnetic field for preventing the movement of the connecting rod, and the larger the speed of the relative movement of the sleeve and the connecting rod is, the larger the resistance is, so that the motion of the movable rod is buffered, the vibration of the transformer is weakened, the phenomenon that the vibration of the transformer is transmitted to the second piezoelectric sensor, and the background noise distortion detected by the second piezoelectric sensor is avoided;
7) According to the invention, by detecting the voice print characteristics of the transformer and judging the similarity between the voice print characteristics of the current transformer and the voice print characteristics of the normal transformer and the voice print characteristics of the fault transformer, whether the transformer is about to break down or not is automatically judged, and in addition, by measuring the background noise, only the voice print data of the transformer is reserved, so that the fault detection of the transformer is more accurate.
Drawings
FIG. 1 is a perspective view of the present invention;
FIG. 2 is a front view of the present invention;
FIG. 3 is a cross-sectional view taken along line A-A of FIG. 2;
FIG. 4 is a block diagram of a circuit connection of the present invention;
Fig. 5 is a flow chart of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings and specific examples:
Referring to fig. 1 to 4, a transformer fault diagnosis apparatus includes: a controller 10; a wireless communication module 11, the wireless communication module 11 being electrically connected to the controller 10; the connecting line of the two ends of the U-shaped connecting rod 1 is perpendicular to the two arms of the U-shaped connecting rod 1; the first piezoelectric transducer 2 is fixedly connected to one end of the U-shaped connecting rod 1, the front face of the first piezoelectric transducer 2 faces the other end of the U-shaped connecting rod 1, the front face of the first piezoelectric transducer 2 is perpendicular to the connecting lines of the two ends of the U-shaped connecting rod 1, and the first piezoelectric transducer 2 is electrically connected with the controller 10; the screw hole 3 is formed in one end, opposite to the first piezoelectric transducer 2, of the U-shaped connecting rod 1, and the central axis of the screw hole 3 is parallel to the connecting lines of the two ends of the U-shaped connecting rod 1; the screw rod 4 is matched with the screw hole 3, and the screw rod 4 is in threaded connection with the screw hole 3. The controller 10 may be a control component of a peripheral circuit such as an Arduino, PLC, or raspberry group. The unnecessary communication module 11 may be a 4G module or a 5G module.
Further, an elastic pad 2-1 is arranged on the side edge of the front face of the first piezoelectric transducer 2, and the distance between the elastic pad 2-1 and the front face of the first piezoelectric transducer 2 is 1mm; further comprises: the pressing plate 5 is fixedly connected to the end part of the screw 4, which is close to the first piezoelectric transducer 2, and the pressing plate 5 is perpendicular to the screw 4; the surface of the pressing plate 5, which is close to the first piezoelectric transducer 2, is provided with a rubber layer 6.
Further, the method further comprises the following steps: and a screw cap 7, wherein the screw cap 7 is fixedly connected to one end of the screw 4 far away from the first piezoelectric transducer 2.
Further, the method further comprises the following steps: a second piezoelectric transducer 9, said second piezoelectric transducer 9 being electrically connected to a controller 10.
Further, the second piezoelectric transducer 9 is connected to the U-shaped connecting rod 1 by a buffer device, which includes: the inner cavity of the sleeve 8-1 is polygonal, the sleeve 8-1 is made of metal, and the lower end of the sleeve 8-1 is fixedly connected to the U-shaped connecting rod 1; the compression spring 8-2 is fixedly connected to the bottom of the inner cavity of the sleeve 8-1; the movable rod 8-3, the movable rod 8-3 is a permanent magnet, the movable rod 8-3 is matched with the inner cavity of the sleeve 8-1, the lower end of the movable rod 8-3 is fixedly connected to the upper end of the pressure spring 8-2, and the movable rod 8-3 is movably connected to the inner cavity of the sleeve 8-1.
Referring to fig. 5, a transformer fault diagnosis method of the present invention includes the steps of,
Step one, collecting transformer voiceprint data through a first piezoelectric transducer 2 and storing the data in a storage medium, wherein the transformer voiceprint data comprises good transformer voiceprint data and is marked as S1, namely bad transformer voiceprint data is marked as P1, and collecting background noise is marked as B1; the sensor used for voiceprint data acquired in the step is a piezoelectric ceramic sonic transducer, the piezoelectric ceramic sonic transducer is attached to a transformer to be tested when data are acquired, the transformer vibration is converted into an electric signal through the piezoelectric ceramic sonic transducer and is transmitted to a controller, the pickup method of B1 background noise is that a second piezoelectric transducer 5 is arranged at a position far away from the transformer, and the environment is almost identical because the second piezoelectric transducer 5 is far away from the transformer, so that the measured background noise is the background noise voiceprint without the influence of the transformer vibration;
step two, extracting the voice print data S1, P1 and B1 of the transformer at intervals of 10 to 30 milliseconds to obtain voice print sequences S2 and P2 and environmental noise B2 which are taken as units of frames;
Step three, removing the environmental noise B2 from the transformer voiceprint sequences S2 and P2 through a noise reduction algorithm, and only reserving transformer voiceprint data to obtain voiceprint sequences S3 and P3, wherein the noise reduction algorithm for reducing the noise of the S2 and the P2 in the step is as follows:
a) Performing Fourier transform on the background noise B2 voiceprint, and converting the background noise B2 voiceprint from a time domain signal to a frequency domain signal, so that the spectrum characteristics of the background noise are measured;
b) And performing inverse compensation operation on the S2 and the PS according to the frequency spectrum of the noise B2 to obtain S3 and P3.
Step four, extracting voiceprint features from the voiceprint sequences S3 and P3 through a feature extraction algorithm to obtain feature vectors S4 and P4, wherein the feature extraction algorithm comprises the following steps:
d) For S3 and P3, obtaining corresponding frequency spectrums through Fourier transformation;
e) The above frequency spectrum is passed through a Mel filter bank to obtain Mel frequency spectrum;
f) Taking the logarithm of the Mel frequency spectrum, then performing DCT discrete cosine transform, taking the 2 nd to 13 th coefficients after DCT as MFCC coefficients to obtain Mel frequency cepstrum coefficient MFCC, wherein the MFCC is the voiceprint feature vector of the S3 or P3.
Fifthly, model training is carried out on the deep convolutional neural network by adopting feature vectors S4 and P4 with the number larger than 1 to obtain vector models S5 and P5, and the deep convolutional neural network can be carried out by adopting a machine learning system Tensorflow developed by Google corporation;
Step six, storing the vector models S5 and P5 into a voiceprint database;
Step seven, collecting real-time transformer voiceprint data to be diagnosed and marking the data as T1, and collecting real-time transformer background noise to be diagnosed and marking the data as D1;
step eight, extracting T1 and D1 at intervals of 10 to 30 milliseconds to obtain voiceprint sequences T2 and D2 taking frames as units;
step nine, removing D2 from the voiceprint sequence T2 through a noise reduction algorithm, and only reserving transformer voiceprint data to obtain a voiceprint sequence T3, wherein the noise reduction algorithm for reducing the noise of the step T2 is as follows:
a) Performing Fourier transform on the background noise D2 voiceprint, and converting the background noise D2 voiceprint from a time domain signal to a frequency domain signal, so that the spectrum characteristics of the background noise are measured;
b) And (3) performing inverse compensation operation on the T2 according to the frequency spectrum of the noise D2 to obtain T3.
Step ten, extracting voiceprint characteristics from the voiceprint sequence T3 through a characteristic extraction algorithm to obtain T4, wherein the characteristic extraction algorithm of the step T3 is as follows:
d) For T3, obtaining a corresponding frequency spectrum through Fourier transform;
e) The above frequency spectrum is passed through a Mel filter bank to obtain Mel frequency spectrum;
f) The Mel frequency spectrum is logarithmized and then is realized through DCT discrete cosine transform, and the 2 nd to 13 th coefficients after DCT are taken as MFCC coefficients, and the MFCC is the voiceprint feature vector of T3.
Step eleven, comparing the voiceprint feature T4 with the voiceprint features S5 and P5 stored in the database to obtain a similarity R1 between S5 and T4, a similarity R2 between P5 and T4, and voiceprint features S5 (x 21,x22…,x2n)、P5(x31,x32…,x3n) and T4 (x 11,x12…,x1n) which are the quantities composed of a plurality of features, wherein the quantities can be marked by an n-dimensional vector, and the similarity of the judging vectors can be judged by the distance between the vectors in Euclidean geometry, and the calculation formula is as follows:
Step twelve, if R1 is larger than R2, judging that the transformer is good, and if R1 is smaller than R2, judging that the transformer is about to be bad; or another rule is adopted, an empirical threshold H is set, if R2> H, the transformer is judged to be about to be damaged, and if R2< H, the transformer is judged to be good.
The foregoing is a further detailed description of the invention in connection with the preferred embodiments, and it is not intended that the invention be limited to the specific embodiments described. It will be apparent to those skilled in the art that several simple deductions or substitutions may be made without departing from the spirit of the invention, and these should be considered to be within the scope of the invention.
Claims (3)
1. A method for diagnosing a fault in a transformer, comprising the steps of:
step one, collecting transformer voiceprint data, and storing the transformer voiceprint data in a storage medium, wherein the transformer voiceprint data comprises good transformer voiceprint data and is marked as S1, namely bad transformer voiceprint data is marked as P1, and collecting background noise is marked as B1;
step two, extracting the voice print data S1, P1 and B1 of the transformer at intervals of 10 to 30 milliseconds to obtain voice print sequences S2 and P2 and environmental noise B2 which are taken as units of frames;
removing environmental noise B2 from the transformer voiceprint sequences S2 and P2 through a noise reduction algorithm, and only retaining transformer voiceprint data to obtain voiceprint sequences S3 and P3;
Extracting voiceprint features from the voiceprint sequences S3 and P3 through a feature extraction algorithm to obtain feature vectors S4 and P4;
fifthly, model training is carried out on the deep convolutional neural network by adopting feature vectors S4 and P4 with the number larger than 1 to obtain vector models S5 and P5;
Step six, storing the vector models S5 and P5 into a voiceprint database;
Step seven, collecting real-time transformer voiceprint data to be diagnosed and marking the data as T1, and collecting real-time transformer background noise to be diagnosed and marking the data as D1;
step eight, extracting T1 and D1 at intervals of 10 to 30 milliseconds to obtain voiceprint sequences T2 and D2 taking frames as units;
step nine, removing D2 from the voiceprint sequence T2 through a noise reduction algorithm, and only reserving the transformer voiceprint data to obtain a voiceprint sequence T3;
step ten, extracting voiceprint characteristics from the voiceprint sequence T3 through a characteristic extraction algorithm to obtain T4;
Step eleven, comparing the voiceprint characteristics T4 with voiceprint characteristics S5 and P5 stored in a database to obtain similarity R1 of S5 and T4 and similarity R2 of P5 and T4;
Step twelve, if R1 is larger than R2, judging that the transformer is good, and if R1 is smaller than R2, judging that the transformer is about to be bad; the calculation method of the similarity R1 between S5 and T4 in the eleventh step is as follows
The calculation method of the similarity R2 between P5 and T4 comprises the following steps:
Where (x 21,x22...,x2n) is the eigenvector constituting S5, (x 31,x32...,x3n) is the eigenvector constituting P5, and (x 11,x12...,x1n) is the eigenvector constituting T4.
2. The transformer fault diagnosis method according to claim 1, wherein the noise reduction algorithm in step three and step nine is as follows:
a) Performing Fourier transform on the background noise voiceprint sequence, and converting the background noise voiceprint sequence from a time domain signal to a frequency domain signal, so that the background noise spectrum characteristic is measured;
b) And performing reverse compensation operation on the transformer voiceprint sequence according to the frequency spectrum characteristics of noise to obtain the transformer voiceprint sequence after noise reduction.
3. The transformer fault diagnosis method according to claim 1, wherein the feature extraction algorithm in the fourth and tenth steps is:
d) Obtaining a corresponding frequency spectrum of the voiceprint sequence through Fourier transform;
e) The above frequency spectrum is passed through a Mel filter bank to obtain Mel frequency spectrum;
f) Taking logarithm of Mel frequency spectrum, then DCT discrete cosine transforming, taking 2 nd to 13 th coefficients after DCT as voiceprint feature vector.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110149505.4A CN112946531B (en) | 2021-02-03 | 2021-02-03 | Transformer fault diagnosis device and diagnosis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110149505.4A CN112946531B (en) | 2021-02-03 | 2021-02-03 | Transformer fault diagnosis device and diagnosis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112946531A CN112946531A (en) | 2021-06-11 |
| CN112946531B true CN112946531B (en) | 2024-04-30 |
Family
ID=76242378
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110149505.4A Active CN112946531B (en) | 2021-02-03 | 2021-02-03 | Transformer fault diagnosis device and diagnosis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112946531B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115588439B (en) * | 2022-12-13 | 2023-04-07 | 杭州兆华电子股份有限公司 | Fault detection method and device of voiceprint acquisition device based on deep learning |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU69643U1 (en) * | 2007-04-18 | 2007-12-27 | Федеральное государственное учреждение "13 Государственный научно-исследовательский институт Министерства обороны Российский Федерации" | INSTALLATION MEASURING ULTRASONIC AND MECHANO-ACOUSTIC UNIT FOR IT |
| CN105158654A (en) * | 2015-08-24 | 2015-12-16 | 大连世有电力科技有限公司 | Intelligent robot for partial discharge diagnosis of transformer |
| CN209014131U (en) * | 2018-09-19 | 2019-06-21 | 安徽大学 | Acoustic velocity tester with variable angle of receiving transducer |
| CN110375846A (en) * | 2019-08-14 | 2019-10-25 | 杭州柯林电气股份有限公司 | Perceive the device of vocal print vibration inline diagnosis power transformer interior fault |
| CN210609695U (en) * | 2019-10-31 | 2020-05-22 | 精拓丽音科技（北京）有限公司 | Electronic device |
| CN112067956A (en) * | 2020-09-21 | 2020-12-11 | 国网河南省电力公司检修公司 | Ultrasonic probe fixing device |
| CN214952102U (en) * | 2021-02-03 | 2021-11-30 | 中国电建集团贵州电力设计研究院有限公司 | Transformer fault detector |
-
2021
- 2021-02-03 CN CN202110149505.4A patent/CN112946531B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU69643U1 (en) * | 2007-04-18 | 2007-12-27 | Федеральное государственное учреждение "13 Государственный научно-исследовательский институт Министерства обороны Российский Федерации" | INSTALLATION MEASURING ULTRASONIC AND MECHANO-ACOUSTIC UNIT FOR IT |
| CN105158654A (en) * | 2015-08-24 | 2015-12-16 | 大连世有电力科技有限公司 | Intelligent robot for partial discharge diagnosis of transformer |
| CN209014131U (en) * | 2018-09-19 | 2019-06-21 | 安徽大学 | Acoustic velocity tester with variable angle of receiving transducer |
| CN110375846A (en) * | 2019-08-14 | 2019-10-25 | 杭州柯林电气股份有限公司 | Perceive the device of vocal print vibration inline diagnosis power transformer interior fault |
| CN210609695U (en) * | 2019-10-31 | 2020-05-22 | 精拓丽音科技（北京）有限公司 | Electronic device |
| CN112067956A (en) * | 2020-09-21 | 2020-12-11 | 国网河南省电力公司检修公司 | Ultrasonic probe fixing device |
| CN214952102U (en) * | 2021-02-03 | 2021-11-30 | 中国电建集团贵州电力设计研究院有限公司 | Transformer fault detector |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112946531A (en) | 2021-06-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112946531B (en) | Transformer fault diagnosis device and diagnosis method | |
| CN110221137B (en) | Distribution transformer abnormal state detection method based on vibration-sound correlation | |
| CN110728257B (en) | Transformer winding fault monitoring method based on vibration gray level image | |
| CN105699839B (en) | A kind of transformer winding working state detecting method and system | |
| KR102309559B1 (en) | The method for detecting fault of motor | |
| CN103472350A (en) | Transformer diagnosis system and diagnosis method | |
| CN101254528B (en) | Bale discharging slag testing automatic control sytem | |
| CN214952102U (en) | Transformer fault detector | |
| CN107015140A (en) | A kind of analysis of vibration signal method for failure that load ratio bridging switch spring kinetic energy is not enough | |
| CN109470972B (en) | Transformer core looseness diagnosis method based on vibration image | |
| CN115641283A (en) | Transformer fault diagnosis method and system based on multi-sensor information fusion | |
| CN111521256A (en) | Main transformer surface vibration visualization detection method based on data mapping | |
| CN116643091A (en) | Online insulation monitoring method for converter transformer valve side sleeve based on multidimensional time sequence characteristics | |
| CN114485379A (en) | Transformer winding on-line monitoring method | |
| CN117763494A (en) | Bearing diagnosis method and system based on multi-mode and multi-scale fusion network | |
| CN112327218B (en) | Transformer online monitoring and fault diagnosis method | |
| CN116884432A (en) | VMD-JS divergence-based power transformer fault voiceprint diagnosis method | |
| CN117292713A (en) | Transformer voiceprint monitoring method and system based on end Bian Yun cooperation | |
| CN115828144A (en) | Signal sparse representation and fusion detection method, storage medium and electronic device | |
| CN208953639U (en) | On-load tap changers of transformers Machinery State Monitoring System | |
| CN115453158A (en) | Voiceprint monitoring device and adjusting method for adaptively adjusting monitoring position | |
| CN115754630A (en) | Non-contact partial discharge monitoring method and device based on acoustic array | |
| CN211478167U (en) | Fault diagnosis device and fault diagnosis system | |
| CN204495460U (en) | Transformer mechanical vibration wave spectrum on-line monitoring system | |
| Li et al. | GIS insulation defect diagnosis method based on improved MFCC and PCA-SVM model |
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 |