CN109374119A - Transformer vibration signal Characteristic Extraction method - Google Patents
Transformer vibration signal Characteristic Extraction method Download PDFInfo
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- CN109374119A CN109374119A CN201811150393.9A CN201811150393A CN109374119A CN 109374119 A CN109374119 A CN 109374119A CN 201811150393 A CN201811150393 A CN 201811150393A CN 109374119 A CN109374119 A CN 109374119A
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- vibration signal
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- kurtosis
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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
Abstract
The invention discloses a kind of transformer vibration signal Characteristic Extraction method, this method seeks filter parameter with quick spectrum kurtosis figure method, and design filter is filtered original vibration signal；Then a series of intrinsic mode functions are obtained with improvement Empirical mode decomposition decomposed signal, the related coefficient of each intrinsic mode function component and original signal is sought with correlation coefficient process, sensitive intrinsic mode function is extracted, and sensitive intrinsic mode function is reconstructed；Transformer vibration signal characteristic frequency, kurtosis, amplitude, mean value after finally extracting reconstruct constitute vibration signal characteristics vector；By judging transformer state with the comparison of normal characteristics vector.
Description
Technical field
The present invention relates to transformer fault detection fields, and in particular, to based on quickly spectrum kurtosis figure method, improves set warp
Test the transformer vibration signal Characteristic Extraction method of Mode Decomposition and correlation coefficient process.
Background technique
Diagnostic techniques research based on transformer vibration signal is broadly divided into simulation modeling and experimental study, signal processing two
General orientation.In terms of signal processing, Fourier transformation is the basis of signal frequency domain analysis, the distribution rule of reflection signal global frequencies
Rule has researcher to propose to diagnose based on transformer vibration signal fundamental frequency accounting and higher hamonic wave accounting etc. as transformer state
Characteristic quantity.With the further development of signal processing technology, scholar proposes the Hilbert xanthochromia based on empirical mode decomposition
It changes and the Time-frequency Analysis based on wavelet transformation.Time-frequency spectrum is mainly the mutation of monitoring signals on a timeline, transformer state
Change be mostly abnormal component long-term accumulated process, signal is still an irregular periodic signal in this process, and
The vibration signal frequency regularity of distribution does not change over time, and Time-frequency Spectrum Analysis still has certain limitation.In addition, existing transformation
Device vibration performance amount extractive technique seldom considers the de-noising of signal.
Summary of the invention
The present invention proposes the transformer for based on quick spectrum kurtosis figure method, improving set empirical mode decomposition and correlation coefficient process
Vibr ation signals extracting method.This method can effectively reduce low-frequency vibration signal interference, and can simultaneous reactions frequency domain with
Time domain peak feature.
For achieving the above object, this application provides the object of the present invention is to provide one kind based on quickly spectrum kurtosis figure
Method, the transformer vibration signal Characteristic Extraction method for improving set empirical mode decomposition and correlation coefficient process.
After this method handles signal, frequency, kurtosis, amplitude, the characteristics of mean of vibration signal, constitutive characteristic are extracted
Vector；The vibration signal characteristics vector of specified transformer normally, under malfunction is extracted, passes through feature vector and compares realization failure
Differentiate；It is characterized by: can remove a large amount of interference components by the signal after filtering, reconstruct, low-frequency vibration signal is effectively reduced
Interference；It, can simultaneous reactions frequency domain and time domain peak by the characteristic frequency of reconstruction signal, kurtosis, amplitude and mean value constitutive characteristic vector
Value tag；This method can eliminate the low frequency and High-frequency Interference of original vibration signal, improve the confidence level and utilization rate of signal；Together
When consider that transformer vibration signal time domain and frequency domain multi-characteristicquantity quantity identify transformer state, from multiple angles to signal into
Row judgement, improves accuracy.
Above-mentioned purpose is achieved through the following technical solutions:
A kind of vibrated based on the quick transformer for composing kurtosis figure method, improvement set empirical mode decomposition and correlation coefficient process is believed
Number Characteristic Extraction method, includes the following steps:
Step 1: transformer vibration signal denoising
Step 1.1: one section of transformer vibration signal of interception counts transformer vibration signal with quick kurtosis figure
It calculates, chooses f corresponding to maximum general kurtosis value SK value in result figurecAnd BwAs filtering parameter, designs filter and signal is carried out
Filtering removes interference component.
Step 1.2: empirical mode decomposition being improved to filtered signal, obtains a series of intrinsic mode function components.
The empirical mode decomposition of improvement set described in step 1.2 is a kind of using white noise as assisting handling data
Method.Its main process: white Gaussian noise is equably added on analysis signal, is made not using being uniformly distributed for white noise spectrum
Signal to be analyzed with time scale is distributed to automatically on reference scale appropriate.Then, according to the zero mean characteristic of white noise,
Eliminate the white noise being added by multiple averaging, then ensemble average value is final result.
Step 1.3: seek sensitive intrinsic mode function component with correlation coefficient process, to sensitive intrinsic mode function component into
Row reconstruct.
Step 2: transformer vibration signal Characteristic Extraction and fault diagnosis
Step 2.1: characteristic frequency, kurtosis, amplitude, the characteristics of mean amount of the vibration signal after reconstructing in step 1.3 are asked, and
Constitute the feature vector of vibration signal.Signal after reconstruct can eliminate the low frequency and High-frequency Interference of original vibration signal, improve letter
Number confidence level and utilization rate；Consider that transformer vibration signal time domain and frequency domain multi-characteristicquantity quantity know transformer state simultaneously
Not, signal is judged from multiple angles, improves accuracy.
Characteristic quantity described in step 2.1 is defined as follows:
(1) characteristic frequency: refer to that transformer core, basket vibration signal fundamental frequency are 100Hz, since nonlinear influencing factors are led
Cause the generation of the harmonics such as 200,300Hz.When transformer core and winding failure, basic frequency changes, therefore this hair
It is bright that the biggish frequency of amplitude in vibration signals spectrograph is defined as characteristic frequency.
(2) kurtosis: kurtosis is the mathematical statistics amount of reflected waveform spike degree, is dimensionless group, can describe point of signal
Cloth characteristic.Kurtosis indicates with letter K, is defined as:
Wherein, μ is the mean value of signal x, and σ is the standard deviation of signal x, and E (t) represents the desired value of variable t.When transformer shape
When state changes, the waveform spike degree of vibration signal can also generate corresponding change.
(3) amplitude: transformer vibration signal is periodic signal, and amplitude is also to reflect that the important parameter of operating status is (single
Position is g, acceleration of gravity), research shows that amplitude can change when expression of unusual operation of transformers.
(4) mean value: for transformer vibration signal, mean value can reflect the symmetrical degree of waveform, and (unit g, gravity add
Speed), the sinusoidal signal mean value of standard is 0, and vibration signal mean value thinks that the signal positive-negative half-cycle symmetry is good closer to 0
Good, the degree of distortion is smaller.
Step 2.2: by the feature of the vibration signal characteristics vector of same transformer fault state and normal vibration signal
Vector comparison, obtains the variation tendency of fault characteristic value.
Step 2.3: for the transformer of unknown operating status, can extract through the above steps vibration signal characteristics vector with
Comparing under normal condition, judges whether it exception occurs.
One or more technical solution provided by the present application, has at least the following technical effects or advantages:
1. the optimal filter parameter that the quick spectrum kurtosis figure method that the present invention uses can quickly acquire signal；The improvement of use passes through
The defect that mode decomposition overcomes Conventional wisdom mode decomposition modal overlap is tested, effectively decomposes vibration signal for different frequency
Intrinsic mode function component；Correlation coefficient process can correctly extract sensitive intrinsic mode function.By filtering and sensitive natural mode
State function component reconstruct after transformer vibration signal remain important component, filtered out most low frequency, High-frequency Interference at
Point, keep signal spectrum energy accumulating more preferable, frequency multiplication resolution ratio is higher.
2. working in the perfect existing transformer vibration signal analysis method of the present invention the denoising of signal, to transformation
The accurate extraction of device vibration signal characteristics is of great significance.
3. vibration signal frequency-kurtosis-amplitude-characteristics of mean vector that the method for the present invention is extracted contains transformer
The time-frequency characteristic of vibration is conducive to complete reflection signal characteristic.
Detailed description of the invention
Attached drawing described herein is used to provide to further understand the embodiment of the present invention, constitutes one of the application
Point, do not constitute the restriction to the embodiment of the present invention；
Fig. 1 is the flow chart of transformer vibration signal Characteristic Extraction method；
Fig. 2 a is the time domain waveform of testing transformer normal vibration signal；
Fig. 2 b is the spectrogram of testing transformer normal vibration signal；
Fig. 3 is testing transformer normal vibration signal by filtered time domain waveform；
It is a series of intrinsic to be that testing transformer normal vibration signal obtains after improving empirical mode decomposition by Fig. 4 a, 4b
Mode function component schematic diagram；
Fig. 5 is that testing transformer normal vibration signal passes through the time domain waveform after sensitive intrinsic mode function component reconstruct；
Fig. 6 is the spectrogram of testing transformer normal vibration signal reconstruction signal.
Specific embodiment
To better understand the objects, features and advantages of the present invention, with reference to the accompanying drawing and specific real
Applying mode, the present invention is further described in detail.It should be noted that in the case where not conflicting mutually, the application's
Feature in embodiment and embodiment can be combined with each other.
In the following description, numerous specific details are set forth in order to facilitate a full understanding of the present invention, still, the present invention may be used also
Implemented with being different from the other modes being described herein in range using other, therefore, protection scope of the present invention is not by under
The limitation of specific embodiment disclosed in face.
Case study on implementation:
Using PCB356A16 piezoelectric acceleration transducer and NI9234 data collecting instrument to a model S11-M-
10/10 transformer model machine carries out vibration signal test, sample frequency 25.6kHz.Extract testing transformer zero load vibration letter
Number characteristic quantity, secondary winding no current when unloaded, once winding current very little, at this time predominantly iron coring vibration.In front
The measuring point in portion can obtain the vibration information of high and low pressure side with shortest path, be best monitoring point, and Fig. 2 a-b is point vibration
Signal original waveform and spectrogram.
In Fig. 2 a, the distortion of iron coring vibration signal waveform is more serious, this is because interference signal superposition and iron core in environment
The non-linear harmonic for causing iron coring vibration signal to generate low frequency component and fundamental frequency of material.According to spectrogram it is found that former vibration
Dynamic signal is there are the low-frequency disturbance component of < 50Hz, and there are the harmonics of a small amount of fundamental frequency.
With 4000 data of measuring point vibration signal for a data segment, kurtosis figure method is quickly composed according to the data segment and is decomposed
As a result, determining filtering parameter: bandwidth Bw=6400Hz, centre frequency fc=9600Hz, designs filter accordingly.Fig. 3 is test
Transformer normal vibration signal passes through filtered time domain waveform.Empirical mode decomposition is improved to the waveform in Fig. 3, is schemed
4a-b is a series of intrinsic mode functions point that testing transformer normal vibration signal obtains after improving empirical mode decomposition
Measure schematic diagram.
The related coefficient of intrinsic mode function component and original signal in Fig. 4 a, 4b is sought with correlation coefficient process, as a result such as table 1
It is shown.As known from Table 1, related coefficient >'s 0.5 has: intrinsic mode function 5, intrinsic mode function 6, intrinsic mode function 7.Cause
This chooses these three components as sensitive component, and signal is reconstructed.Fig. 5 is that testing transformer normal vibration signal passes through sensitivity
Time domain waveform after the reconstruct of intrinsic mode function component is more smooth, clear with Fig. 3 comparison waveform.
The related coefficient of table 1 intrinsic mode function and original signal
Envelope spectrum is asked to the reconstruction signal in Fig. 5, Fig. 6 is the frequency spectrum of testing transformer normal vibration signal reconstruction signal
Figure, it is known that the characteristic frequency f=100Hz when no-load transformer operates normally is compared with Fig. 2 b and eliminated low-frequency disturbance, and energy is poly-
Collection property is higher.To the reconstruction signal of Fig. 5 ask kurtosis (being indicated with k, dimensionless), amplitude (indicated with m, unit: g), mean value (use q
It indicates, unit: g), calculated result is respectively as follows: k=2.64, m=0.0069g, q=4.9 × 10-5g.Comprehensive four parameters are constituted
The feature vector of vibration signal when testing transformer normal idle running:
Tkz=[100 2.64 0.0069 4.9 × 10-5]
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications may be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art
Mind and range.In this way, if these modifications and changes of the present invention belongs to the range of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to include these modifications and variations.
Claims (3)
1. transformer vibration signal Characteristic Extraction method, which is characterized in that the described method includes:
Step 1: one section of transformer vibration signal of interception is filtered using signal of the filter to interception；To filtered letter
Number empirical mode decomposition is improved, obtains a series of intrinsic mode function components；Sensitive natural mode of vibration is sought with correlation coefficient process
Sensitive intrinsic mode function component is reconstructed in function component；
Step 2: by the characteristic frequency of the vibration signal after reconstruct, kurtosis, amplitude, characteristics of mean amount, and constituting vibration signal
Feature vector；The feature vector of the vibration signal characteristics vector of same transformer fault state and normal vibration signal is carried out
Comparison, obtains the variation tendency of fault characteristic value；For the transformer of unknown operating status, by unknown operating status transformer pair
The vibration signal characteristics vector and the feature vector of normal vibration signal answered compare, and obtain the transformer of unknown operating status
The variation tendency of characteristic quantity；By the variation tendency of the variation tendency of the characteristic of transformer amount of unknown operating status and fault characteristic value
It is compared, judges whether the transformer of unknown operating status exception occurs.
2. transformer vibration signal Characteristic Extraction method according to claim 1, which is characterized in that one section of transformation of interception
Device vibration signal calculates transformer vibration signal with quick kurtosis figure, chooses maximum spectrum kurtosis value SK value in result
Corresponding fcAnd BwAs filtering parameter, designs filter and the signal of interception is filtered.
3. transformer vibration signal Characteristic Extraction method according to claim 1, which is characterized in that after described pair of filtering
Signal improve empirical mode decomposition, obtain a series of intrinsic mode function components, specifically include:
White Gaussian noise is equably added on analysis signal, makes different time scales using being uniformly distributed for white noise spectrum
Signal to be analyzed is distributed to appropriate with reference on scale automatically；According to the zero mean characteristic of white noise, make to add by multiple averaging
The white noise entered is eliminated, then the ensemble average value of former analysis signal and white noise is the final result of the analysis signal.
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Cited By (9)
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| CN110376455A (en) * | 2019-06-26 | 2019-10-25 | 深圳供电局有限公司 | Transformer working state detecting method, device, computer equipment and storage medium |
| CN111504450A (en) * | 2020-04-28 | 2020-08-07 | 郑州恩普特科技股份有限公司 | Equipment fault alarm method and device |
| CN112151249A (en) * | 2020-08-26 | 2020-12-29 | 国网安徽省电力有限公司检修分公司 | Active noise reduction method and system for transformer and storage medium |
| CN112231624A (en) * | 2020-09-16 | 2021-01-15 | 中电电气(江苏)变压器制造有限公司 | Real-time evaluation system for short-circuit resistance of multi-transformer winding based on Internet of things |
| CN112379221A (en) * | 2020-10-20 | 2021-02-19 | 华北电力大学 | Method and system for detecting vibration and sound by using transformer running state similar to L2 |
| CN112881839A (en) * | 2021-01-22 | 2021-06-01 | 上海电力大学 | Transformer diagnosis method based on mutual information of frequency concentration and vibration stability |
| CN113268924A (en) * | 2021-05-18 | 2021-08-17 | 国网福建省电力有限公司电力科学研究院 | Time-frequency characteristic-based fault identification method for on-load tap-changer of transformer |
| CN116296329A (en) * | 2023-03-14 | 2023-06-23 | 苏州纬讯光电科技有限公司 | Transformer core mechanical state diagnosis method, equipment and medium |
| CN116861219A (en) * | 2023-09-01 | 2023-10-10 | 华能新能源股份有限公司山西分公司 | Wind turbine generator pitch-variable fault diagnosis method |
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Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110376455A (en) * | 2019-06-26 | 2019-10-25 | 深圳供电局有限公司 | Transformer working state detecting method, device, computer equipment and storage medium |
| CN110376455B (en) * | 2019-06-26 | 2021-11-16 | 深圳供电局有限公司 | Transformer working state detection method and device, computer equipment and storage medium |
| CN111504450A (en) * | 2020-04-28 | 2020-08-07 | 郑州恩普特科技股份有限公司 | Equipment fault alarm method and device |
| CN112151249A (en) * | 2020-08-26 | 2020-12-29 | 国网安徽省电力有限公司检修分公司 | Active noise reduction method and system for transformer and storage medium |
| CN112151249B (en) * | 2020-08-26 | 2024-04-02 | 国网安徽省电力有限公司检修分公司 | Active noise reduction method and system for transformer and storage medium |
| CN112231624A (en) * | 2020-09-16 | 2021-01-15 | 中电电气(江苏)变压器制造有限公司 | Real-time evaluation system for short-circuit resistance of multi-transformer winding based on Internet of things |
| CN112231624B (en) * | 2020-09-16 | 2024-03-26 | 中电电气(江苏)变压器制造有限公司 | Real-time evaluation system for short-circuit resistance of multi-transformer winding based on Internet of things |
| CN112379221A (en) * | 2020-10-20 | 2021-02-19 | 华北电力大学 | Method and system for detecting vibration and sound by using transformer running state similar to L2 |
| CN112881839B (en) * | 2021-01-22 | 2022-10-25 | 上海电力大学 | Transformer diagnosis method based on mutual information of frequency concentration and vibration stability |
| CN112881839A (en) * | 2021-01-22 | 2021-06-01 | 上海电力大学 | Transformer diagnosis method based on mutual information of frequency concentration and vibration stability |
| CN113268924B (en) * | 2021-05-18 | 2023-01-31 | 国网福建省电力有限公司电力科学研究院 | Time-frequency characteristic-based fault identification method for on-load tap-changer of transformer |
| CN113268924A (en) * | 2021-05-18 | 2021-08-17 | 国网福建省电力有限公司电力科学研究院 | Time-frequency characteristic-based fault identification method for on-load tap-changer of transformer |
| CN116296329A (en) * | 2023-03-14 | 2023-06-23 | 苏州纬讯光电科技有限公司 | Transformer core mechanical state diagnosis method, equipment and medium |
| CN116296329B (en) * | 2023-03-14 | 2023-11-07 | 苏州纬讯光电科技有限公司 | Transformer core mechanical state diagnosis method, equipment and medium |
| CN116861219A (en) * | 2023-09-01 | 2023-10-10 | 华能新能源股份有限公司山西分公司 | Wind turbine generator pitch-variable fault diagnosis method |
| CN116861219B (en) * | 2023-09-01 | 2023-12-15 | 华能新能源股份有限公司山西分公司 | Wind turbine generator pitch-variable fault diagnosis method |
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