CN108693437B - Method and system for judging deformation of transformer winding - Google Patents
Method and system for judging deformation of transformer winding Download PDFInfo
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- CN108693437B CN108693437B CN201810239981.3A CN201810239981A CN108693437B CN 108693437 B CN108693437 B CN 108693437B CN 201810239981 A CN201810239981 A CN 201810239981A CN 108693437 B CN108693437 B CN 108693437B
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- 238000004458 analytical method Methods 0.000 claims description 6
- 238000012843 least square support vector machine Methods 0.000 claims description 6
- 238000012706 support-vector machine Methods 0.000 claims description 6
- 230000003595 spectral effect Effects 0.000 claims description 5
- 238000007781 pre-processing Methods 0.000 claims description 3
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- 238000000354 decomposition reaction Methods 0.000 description 2
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- G—PHYSICS
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- 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
- G01R31/72—Testing of electric windings
Abstract
The invention discloses a method and a system for judging transformer winding deformation, wherein the method comprises the following implementation steps: acquiring a vibration acceleration signal of a transformer to be detected; extracting independent characteristic parameters according to the vibration acceleration signals of the transformer to be detected; inputting the independent characteristic parameters into a trained machine learning model to obtain the current winding deformation state of the transformer to be detected, wherein the trained machine learning model comprises a mapping relation between the independent characteristic parameters and the winding deformation state; the system includes a computer device programmed to perform the above-described method. The method for detecting the deformation state of the transformer winding can effectively detect the deformation state of the transformer winding under the condition that the transformer is not stopped, and has the advantages of no contact with electrified equipment, realization of electrified detection, and convenience and high efficiency in testing.
Description
Technical Field
The invention relates to the field of transformer running state detection, in particular to a method and a system for judging transformer winding deformation.
Background
Transformers are an important component of electrical power systems and are responsible for the transmission of electrical energy between power networks of different voltage classes. The safe operation of the power supply device is of great significance for providing stable and reliable power supply for power consumers. The transformer winding is one of the more faulty components. By 2006, the accidents of 110kV and above grade power transformers in China caused by external short-circuit faults reach 50% of the total number of accidents. From the perspective of looking at the breakdown of a transformer after an accident, the majority of transformer failures caused by winding deformation are accounted for. Therefore, efficient detection of transformer winding deformation is required. At present, the commonly used transformer winding deformation detection method mainly comprises winding capacitance, winding frequency response and short-circuit impedance tests, the method needs to stop the transformer, and the problems of incapability of live-line detection, large potential safety hazard, long detection period, low detection efficiency and the like mainly exist.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: aiming at the problems in the prior art, the invention provides a method and a system for judging the deformation of a transformer winding, and the method can effectively detect the deformation state of the transformer winding under the condition that the transformer is not stopped, and has the advantages of no contact with electrified equipment, realization of electrified detection, and convenience and high efficiency in testing.
In order to solve the technical problems, the invention adopts the technical scheme that:
a method for judging deformation of a transformer winding comprises the following implementation steps:
1) acquiring a vibration acceleration signal of a transformer to be detected;
2) extracting independent characteristic parameters according to the vibration acceleration signals of the transformer to be detected;
3) and inputting the independent characteristic parameters into a trained machine learning model to obtain the current winding deformation state of the transformer to be detected, wherein the trained machine learning model comprises the mapping relation between the independent characteristic parameters and the winding deformation state.
Preferably, the detailed steps of step 2) include:
2.1) preprocessing a vibration acceleration signal of the transformer to be detected;
2.2) carrying out frequency spectrum analysis and wavelet packet analysis on the preprocessed vibration acceleration signals, and extracting winding deformation characteristic parameters from the preprocessed vibration acceleration signals in a related manner;
and 2.3) carrying out principal component analysis on the winding deformation characteristic parameters to obtain independent characteristic parameters.
Preferably, the detailed steps of step 2.2) include:
2.2.1) carrying out Fourier transformation on vibration acceleration signals obtained by detecting a plurality of measuring points of the transformer, intercepting vibration acceleration signal frequency spectrum within the range of 8kHz, carrying out arithmetic mean processing on the vibration acceleration frequency spectrum of the plurality of measuring points to form a comprehensive frequency spectrum, and calculating the dominant frequency specific gravity R of the comprehensive frequency spectrumm；
2.2.2) calculating the frequency spectrum complexity H of 50Hz and harmonic frequency thereof in the frequency spectrum range of the vibration acceleration signal 8 kHz;
2.2.3) respectively carrying out wavelet packet analysis on the vibration acceleration signals of a plurality of measuring points of the transformer, respectively calculating the energy of the wavelet packet and carrying out arithmetic average processing to obtain the comprehensive wavelet packet energy characteristic E;
2.2.4) specific gravity of dominant frequency RmFrequency spectrum complexity H and comprehensive wavelet packet energy characteristic EWhich together constitute the winding deformation characteristic parameter.
Preferably, the dominant frequency specific gravity R of the integrated spectrum in step 2.2.1)mThe formula (1) is shown in the formula;
in the formula (1), AmIs the amplitude of the main frequency component, A, of the vibration acceleration signaliThe amplitude of the ith harmonic frequency of 50Hz in the vibration acceleration signal is shown, and N is the number of the 50Hz harmonic frequencies of the signal in the range of 8 kHz.
Preferably, the calculation function expression of the spectral complexity H in step 2.2.2) is as shown in formula (1);
in the formula (2), RiThe vibration amplitude proportion of the ith harmonic frequency of 50 Hz.
Preferably, the calculation function expression of the energy characteristic E of the integrated wavelet packet in step 2.2.3) is shown as formula (3) and formula (4);
E＝(E1+E2+E3)/3 (4)
in the formula (3), EjThe energy of the wavelet packet at the jth measuring point, EjiAnd the energy of the ith wavelet packet sub-band of the jth measuring point is shown, and n is the number of wavelet packet decomposition layers.
Preferably, when the step 2.3) is performed on the winding deformation characteristic parameters, the dimension of the independent characteristic parameters output by the principal component analysis is 2 dimensions, and the principal component condition is that the contribution rate of the independent characteristic exceeds 85%, and finally the independent characteristic parameters corresponding to the winding deformation characteristic parameters are obtained.
Preferably, the machine learning model in step 3) is a least squares support vector machine classification model.
Preferably, the training step of the least squares support vector machine classification model comprises:
s1) respectively collecting vibration acceleration signals x when no winding deformation occurs for the sample transformer corresponding to the transformer to be detected1iAnd a vibration acceleration signal x when the winding deformation occurs2i；
S2) for the vibration acceleration signal x of the sample transformer when no winding deformation occurs1iAnd a vibration acceleration signal x when the winding deformation occurs2iExtracting independent characteristic parameters;
s3) classifying the independent characteristic parameters of the sample transformer according to whether the sample transformer is in a winding deformation state when the vibration acceleration signal is acquired, wherein the independent characteristic parameter category when the winding deformation does not occur is '1', and the characteristic parameter category of the independent characteristic parameter when the winding deformation occurs is '-1';
s4) forming a training set by the classified independent characteristic parameters and the characteristic parameter classes thereof, and training the training set by a least square support vector machine method to obtain a least square support vector machine classification model comprising the mapping relation between the independent characteristic parameters and the winding deformation state.
The present invention also provides a system for determining transformer winding deformation, comprising a computer device programmed to perform the steps of the method for determining transformer winding deformation of the present invention.
The method for judging the deformation of the transformer winding has the following advantages:
1. the invention can detect the winding deformation state of the transformer under the condition that the transformer does not stop running, and can realize live detection;
2. the invention has no electric contact with the charged equipment, and has higher safety;
3. the invention has shorter detection process and higher detection efficiency.
The system for judging the deformation of the transformer winding is a system corresponding to the method for judging the deformation of the transformer winding, and has the advantages of the method for judging the deformation of the transformer winding, and the details are not repeated.
Drawings
Fig. 1 is a schematic flow chart of the implementation of the method according to the embodiment of the present invention.
Fig. 2 is a vibration acceleration signal spectrum of the transformer when no winding deformation occurs in the embodiment of the present invention.
Fig. 3 is a frequency spectrum of a vibration acceleration signal of the transformer when a winding deformation occurs in the embodiment of the present invention.
Detailed Description
As shown in fig. 1, the implementation steps of the method for determining the deformation of the transformer winding according to the embodiment include:
1) acquiring a vibration acceleration signal of a transformer to be detected;
2) extracting independent characteristic parameters according to the vibration acceleration signals of the transformer to be detected;
3) and inputting the independent characteristic parameters into the trained machine learning model to obtain the current winding deformation state of the transformer to be detected, wherein the trained machine learning model comprises the mapping relation between the independent characteristic parameters and the winding deformation state.
The method for judging the deformation of the transformer winding can effectively detect the deformation state of the transformer winding under the condition that the transformer is not stopped, and has the advantages of no contact with electrified equipment, realization of electrified detection, and convenience and high efficiency in testing.
In this embodiment, the detailed steps of step 2) include:
2.1) preprocessing a vibration acceleration signal of the transformer to be detected (noise reduction processing);
2.2) carrying out frequency spectrum analysis and wavelet packet analysis on the preprocessed vibration acceleration signals, and extracting winding deformation characteristic parameters from the preprocessed vibration acceleration signals in a related manner;
and 2.3) carrying out principal component analysis on the winding deformation characteristic parameters to obtain independent characteristic parameters.
In this embodiment, the detailed steps of step 2.2) include:
2.2.1) carrying out Fourier transformation on vibration acceleration signals obtained by detecting a plurality of measuring points of the transformer, intercepting a vibration acceleration signal frequency spectrum within the range of 8kHz,and carrying out arithmetic mean processing on the vibration acceleration frequency spectrums of a plurality of measuring points to form a comprehensive frequency spectrum, and calculating the dominant frequency proportion R of the comprehensive frequency spectrumm(ii) a In the embodiment, the multiple measuring points of the transformer specifically refer to 3 measuring points, the measuring points are located at large-area flat positions at the height of 1/4 of the vertical surface of the oil tank on the high-voltage side of the transformer, the 3 measuring points respectively correspond to the positions of three-phase windings of the transformer, the positions of the measuring points are required to be the same each time, and the sampling frequency is not lower than 16 kHz; due to the fact that the vibration acceleration signals of the transformers at different positions have certain difference, the positions of the same measuring point are beneficial to guaranteeing that the testing result has comparability. Since the vibration acceleration signal is generally in the range of 8kHz when the transformer winding is deformed, the sampling frequency should not be lower than 16 kHz.
2.2.2) calculating the frequency spectrum complexity H of 50Hz and harmonic frequency thereof in the frequency spectrum range of the vibration acceleration signal 8 kHz;
2.2.3) respectively carrying out wavelet packet analysis on the vibration acceleration signals of a plurality of measuring points of the transformer, respectively calculating the energy of the wavelet packet and carrying out arithmetic average processing to obtain the comprehensive wavelet packet energy characteristic E;
2.2.4) specific gravity of dominant frequency RmThe frequency spectrum complexity H and the comprehensive wavelet packet energy characteristic E jointly form a winding deformation characteristic parameter.
In this embodiment, the dominant frequency specific gravity R of the synthesized spectrum in step 2.2.1)mThe formula (1) is shown in the formula;
in the formula (1), AmIs the amplitude of the main frequency component, A, of the vibration acceleration signaliThe amplitude of the ith harmonic frequency of 50Hz in the vibration acceleration signal is shown, and N is the number of the 50Hz harmonic frequencies of the signal in the range of 8 kHz.
In this embodiment, the expression of the calculation function of the spectral complexity H in step 2.2.2) is shown in formula (1);
in the formula (2), RiThe vibration amplitude proportion of the ith harmonic frequency of 50 Hz.
In this embodiment, the calculation function expression of the energy characteristic E of the integrated wavelet packet in step 2.2.3) is shown in formula (3) and formula (4);
E＝(E1+E2+E3)/3 (4)
in the formula (3), EjThe energy of the wavelet packet at the jth measuring point, EjiAnd the energy of the ith wavelet packet sub-band of the jth measuring point is shown, and n is the number of wavelet packet decomposition layers. The comprehensive wavelet packet energy characteristics reflect the distribution condition of the vibration acceleration signal energy of the transformer in different frequency bands.
In this embodiment, when the principal component analysis is performed on the winding deformation characteristic parameters in step 2.3), the dimension of the independent characteristic parameter output by the principal component analysis is 2 dimensions, and the principal component condition is that the contribution rate of the independent characteristic exceeds 85%, so as to finally obtain the independent characteristic parameters corresponding to the winding deformation characteristic parameters. Due to dominant frequency specific gravity RmThe spectral complexity H and the comprehensive wavelet packet energy characteristic E may be correlated with each other, so that in step 3) of this embodiment, a principal component analysis method is used to perform decorrelation processing on the complex wavelet packet energy characteristic E, so as to further reduce the characteristic quantity, and the final transformer winding deformation characteristic parameters are only two, namely "characteristic parameter 1" and "characteristic parameter 2". It should be noted that the dimensionality reduction by principal component analysis is a basic application of the principal component analysis method, and therefore, the specific steps for performing the principal component analysis are not described in detail herein.
In this embodiment, the machine learning model in step 3) is a least squares support vector machine classification model, and other machine learning models may be adopted as needed.
In this embodiment, the training step of the least squares support vector machine classification model includes:
s1) respectively collecting non-occurrence for sample transformers corresponding to the transformers to be detectedVibration acceleration signal x when winding is deformed1iAnd a vibration acceleration signal x when the winding deformation occurs2i；
S2) for the vibration acceleration signal x of the sample transformer when no winding deformation occurs1iAnd a vibration acceleration signal x when the winding deformation occurs2iExtracting independent characteristic parameters;
s3) classifying the independent characteristic parameters of the sample transformer according to whether the sample transformer is in a winding deformation state when the vibration acceleration signal is acquired, wherein the independent characteristic parameter category when the winding deformation does not occur is '1', and the characteristic parameter category of the independent characteristic parameter when the winding deformation occurs is '-1';
s4) forming a training set by the classified independent characteristic parameters and the characteristic parameter classes thereof, and training the training set by a least square support vector machine method to obtain a least square support vector machine classification model comprising the mapping relation between the independent characteristic parameters and the winding deformation state.
As shown in fig. 2, when no winding deformation occurs, the frequency spectrum of the vibration acceleration signal of the transformer is mainly concentrated in the range of 2kHz, the energy of the vibration acceleration signal of the transformer is mainly concentrated on even multiples of 50Hz such as 100Hz, 200Hz, 300Hz, 400Hz, 500Hz, and 600Hz, the primary frequency is 600Hz, the frequency components in the frequency spectrum are relatively few, and the complexity of the frequency spectrum is low. As shown in fig. 3, after the winding deformation occurs, the frequency spectrum distribution of the vibration acceleration signal changes significantly compared with the normal situation, the position of the main frequency changes, the main frequency is 400Hz, the proportion of the main frequency component is reduced, and the amplitude of the frequency component with the odd number times of 50Hz in the frequency spectrum and the frequency spectrum distribution range are increased. Through comparison, the main frequency specific gravity R before and after the deformation of the winding occursmThe frequency spectrum complexity H and the comprehensive wavelet packet energy characteristic E are obviously changed, and therefore the winding deformation problem of the transformer can be reflected by the formed related characteristic parameters.
The present embodiment also provides a system for determining transformer winding deformation, which includes a computer device programmed to execute the steps of the method for determining transformer winding deformation according to the present invention.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (5)
1. A method for judging the deformation of a transformer winding is characterized by comprising the following implementation steps:
1) acquiring a vibration acceleration signal of a transformer to be detected;
2) extracting independent characteristic parameters according to the vibration acceleration signals of the transformer to be detected;
3) inputting the independent characteristic parameters into a trained machine learning model to obtain the current winding deformation state of the transformer to be detected, wherein the trained machine learning model comprises a mapping relation between the independent characteristic parameters and the winding deformation state;
the detailed steps of the step 2) comprise:
2.1) preprocessing a vibration acceleration signal of the transformer to be detected;
2.2) carrying out frequency spectrum analysis and wavelet packet analysis on the preprocessed vibration acceleration signals, and extracting winding deformation characteristic parameters from the preprocessed vibration acceleration signals in a related manner;
2.3) carrying out principal component analysis on the winding deformation characteristic parameters to obtain independent characteristic parameters;
the detailed steps of step 2.2) include:
2.2.1) carrying out Fourier transformation on vibration acceleration signals obtained by detecting a plurality of measuring points of the transformer, intercepting vibration acceleration signal frequency spectrum within the range of 8kHz, carrying out arithmetic mean processing on the vibration acceleration frequency spectrum of the plurality of measuring points to form a comprehensive frequency spectrum, and calculating the main frequency specific gravity of the comprehensive frequency spectrumR m(ii) a Wherein, the dominant frequency proportion of the integrated frequency spectrumR mThe formula (1) is shown in the formula;
in the formula (1), the reaction mixture is,A mis the amplitude of the main frequency component of the vibration acceleration signal,A i is 50Hz in the vibration acceleration signaliThe amplitude of the sub-harmonic frequency is,Nthe number of 50Hz harmonic frequencies of signals in the range of 8 kHz;
2.2.2) calculating the spectral complexity of the vibration acceleration signal within the 8kHz spectral range, at 50Hz and its harmonic frequenciesH(ii) a Wherein the complexity of the frequency spectrumHThe formula (2) is shown in the formula;
in the formula (2), the reaction mixture is,R i is 50HziThe specific gravity of the vibration amplitude of the sub-harmonic frequency;
2.2.3) respectively carrying out wavelet packet analysis on the vibration acceleration signals of a plurality of measuring points of the transformer, respectively calculating the energy of the wavelet packet and carrying out arithmetic average processing to obtain the energy characteristic of the comprehensive wavelet packetE(ii) a Wherein the wavelet packet energy characteristics are combinedEThe formula (3) and the formula (4) are shown in the formula (4);
in the formula (3), the reaction mixture is,E j is as followsjThe energy of the wavelet packet at each measurement point,E ji is as followsjAt a measuring pointiThe sub-band energy of the wavelet packet,nthe number of layers is decomposed into wavelet packets, whereinjThe value range of (1), (2) and (3);
2.2.4) specific gravity of dominant frequencyR mSpectrum complexityHAnd synthesis ofWavelet packet energy characteristicsEThe three components together form the characteristic parameter of the deformation of the winding.
2. The method for judging the deformation of the transformer winding according to claim 1, wherein in the step 2.3), when principal component analysis is performed on the winding deformation characteristic parameters, the dimension of the independent characteristic parameters output by the principal component analysis is 2-dimensional, and the principal component condition is that the contribution rate of the independent characteristic exceeds 85%, and finally the independent characteristic parameters corresponding to the winding deformation characteristic parameters are obtained.
3. The method for judging the deformation of the transformer winding according to claim 1, wherein the machine learning model in the step 3) is a least squares support vector machine classification model.
4. The method of claim 3, wherein the training step of the least squares support vector machine classification model comprises:
s1) respectively collecting vibration acceleration signals when no winding deformation occurs for the sample transformers corresponding to the transformers to be detectedx 1iAnd a vibration acceleration signal when the winding is deformedx 2i；
S2) vibration acceleration signal when no winding deformation occurs to the sample transformerx 1iAnd a vibration acceleration signal when the winding is deformedx 2iExtracting independent characteristic parameters;
s3) classifying the independent characteristic parameters of the sample transformer according to whether the sample transformer is in a winding deformation state when the vibration acceleration signal is acquired, wherein the independent characteristic parameter category when the winding deformation does not occur is '1', and the characteristic parameter category of the independent characteristic parameter when the winding deformation occurs is '-1';
s4) forming a training set by the classified independent characteristic parameters and the characteristic parameter classes thereof, and training the training set by a least square support vector machine method to obtain a least square support vector machine classification model comprising the mapping relation between the independent characteristic parameters and the winding deformation state.
5. A system for determining deformation of a transformer winding, comprising computer means, characterized in that said computer means are programmed to perform the steps of the method for determining deformation of a transformer winding according to any one of claims 1 to 4.
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| CN110006527A (en) * | 2019-04-12 | 2019-07-12 | 国网湖南省电力有限公司 | High-voltage switch gear cabinet and its unreasonable design extraordinary noise diagnostic method, system and medium |
| CN110514924B (en) * | 2019-08-12 | 2021-04-27 | 武汉大学 | Power transformer winding fault positioning method based on deep convolutional neural network fusion visual identification |
| CN111537919B (en) * | 2020-05-14 | 2022-04-15 | 莫毓昌 | Transformer fault diagnosis method based on voiceprint characteristics |
| CN112665707B (en) * | 2020-12-15 | 2023-03-03 | 国网天津市电力公司电力科学研究院 | Cumulative effect after short circuit impact of transformer and diagnosis method |
| CN113701684B (en) * | 2021-08-05 | 2022-10-14 | 西安交通大学 | Transformer winding state detection method, device, equipment and storage medium |
| CN114485540A (en) * | 2022-01-20 | 2022-05-13 | 西安交通大学 | Method and system for rapidly acquiring deformation degree and position of transformer winding |
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