CN113189457A - Multi-scale feature extraction technology based on partial discharge original time domain waveform - Google Patents
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Abstract
The invention provides a multi-scale feature extraction technology based on a partial discharge original time domain waveform. The original time domain waveform of the partial discharge contains rich characteristic information, harmonic wavelet transformation is carried out on the original time domain waveform of the partial discharge to obtain sub-bands with different frequencies, and multi-scale characteristic quantities including multi-scale energy parameters and multi-scale sample entropy parameters on different sub-bands are extracted to be used as characteristic quantities to represent original signals of the partial discharge. These parameters reflect the original characteristics of the partial discharge signal from different angles, and different types of partial discharges can be distinguished accordingly.
Description
Technical Field
The invention relates to the technical field of partial discharge feature extraction, in particular to a multi-scale feature extraction technology based on a partial discharge original time domain waveform.
Background
Partial discharges are discharges occurring in the local range of the insulation of the electrical equipment under the action of a sufficiently strong electric field. The partial discharge amount of the power equipment is closely related to the insulation condition of the power equipment, and the change of the partial discharge amount indicates that the insulation has the defect that the safe operation of the cable is possibly endangered. The discharge process is usually accompanied by physical and chemical phenomena such as current pulse, electromagnetic radiation, sound production, heat generation and the like. The detection means of partial discharge is determined by using these phenomena. The current common method for detecting the partial discharge is a pulse current method, the apparent discharge amount, the discharge repetition rate, the discharge phase, the discharge energy and the like of a discharge signal are detected, the discharge data are processed and analyzed, and the severity and the insulation state of the partial discharge can be evaluated. In addition to the pulse current method, there are an ultrasonic detection method, a temperature detection method, a light detection method, and the like as a detection method of partial discharge.
The scholars do a lot of research work on the aspect of partial discharge characteristic characterization and extraction, after partial discharge original data are obtained, a statistical characteristic method is mostly adopted, such as constructing a partial discharge phase distribution pattern (PRPD), and extracting a statistical operator as characteristic quantity, but few researches start from a partial discharge original time domain waveform, and rich information contained in the time domain waveform is extracted to carry out classification and identification on partial discharge. Therefore, the method has important practical significance for guaranteeing safe and stable operation of the power equipment and improving the detection and diagnosis level of the power equipment by extracting the characteristic information contained in the original time domain waveform of the partial discharge, constructing the characteristic quantity and carrying out mode identification.
Disclosure of Invention
The invention aims to provide a multi-scale feature extraction method for partial discharge signals, which is characterized in that sub-bands with different frequencies are obtained by performing harmonic wavelet transformation on original partial discharge time domain waveforms, multi-scale feature quantities including multi-scale energy parameters and multi-scale sample entropy parameters on different sub-bands are extracted and used as feature quantities to represent original partial discharge signals, and a theoretical basis is laid for detection and defect diagnosis of power equipment.
In order to achieve the purpose, the invention adopts the following technical scheme, namely a multi-scale feature extraction technology based on partial discharge original time domain waveform, which comprises the following specific steps:
1) manufacturing a partial discharge defect model, building an experiment platform, and performing a partial discharge model experiment in a laboratory environment to obtain original partial discharge data;
2) designing a single partial discharge pulse signal extraction program according to the trigger amplitude and waveform characteristics of each type of partial discharge, and extracting a single partial discharge pulse;
3) selecting a proper denoising algorithm, and denoising the single partial discharge pulse signal;
4) decomposing the denoised partial discharge signal by using a wavelet transform method, and calculating characteristic parameters on different decomposition scales;
further, the experimental method is international standard IEC 60270-2000 for partial discharge measurement.
Further, the denoising method is wavelet threshold denoising, and the selected wavelet basis function is db2 wavelet.
The invention provides a multi-scale feature extraction technology based on a partial discharge original time domain waveform, which uses multi-scale feature quantities to represent partial discharge signals and has important significance for improving the detection and defect diagnosis level of electric equipment. The method has the specific beneficial effects that:
1. starting from the original partial discharge waveform, the method provides a multi-scale characterization method of the partial discharge signal, fully utilizes partial discharge characteristic information contained in the original time domain waveform, captures characteristic quantities describing the partial discharge signal from different angles, further realizes identification, and lays a theoretical foundation for detection and defect diagnosis of power equipment.
2. The method provides theoretical support for on-line monitoring of the partial discharge condition of the power equipment. With the continuous accumulation of the original time domain waveforms, the partial discharge detection precision can be continuously improved, and the method has important significance for improving the operation and maintenance level of the power equipment.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a partial discharge experimental circuit;
FIG. 2 is a denoised PD time domain waveform;
FIG. 3 is a distribution of multi-scale energy parameters;
FIG. 4 is a distribution of multi-scale sample entropy parameters;
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In a specific embodiment of the present invention, a partial discharge simulation experiment is performed in a laboratory environment according to the international IEC 60270-. Firstly, a single partial discharge pulse extraction program is designed according to the trigger amplitude and the waveform characteristics of each type of partial discharge, single partial discharge pulses are extracted from one power frequency period, and a wavelet threshold algorithm is adopted to carry out denoising processing on the single partial discharge pulses. Then, Harmonic Wavelet Packet Transform (HWPT) is performed on the denoised partial discharge signal to obtain a plurality of sub-bands, a multi-scale Energy parameter (RE) and a multi-scale sample entropy parameter (SampEn) of each sub-band are calculated, Relative Energy and sample entropy data of 100 groups of partial discharge pulse characteristic sub-bands are statistically analyzed, and a 95% confidence interval of the multi-scale characteristic parameters is obtained. The method comprises the following specific steps:
1. denoising by wavelet threshold:
the wavelet threshold denoising algorithm is simple in thought, different threshold functions can be selected according to the characteristics of signals, the flexibility is high, the denoising effect is good, and the wavelet threshold denoising algorithm is widely applied. The denoising effect can be evaluated by the signal-to-noise ratio. The signal-to-noise ratio refers to the ratio of the energy between the original signal and the noise, denoted as SNR.
Wherein f isiFor de-noised signals, yiN is the signal length. The larger the SNR value is, the better the denoising effect is. The SNR values of the PD pulses extracted from the power frequency period are all above 15, the wavelet denoising method has a good effect, and as can be seen from figure 2, the wavelet threshold denoising method can well reserve the PD signal characteristics and effectively filter white noise.
2. Feature extraction: the method comprises the steps of processing partial discharge signals of four typical defects by utilizing Harmonic Wavelet Packet Transform (HWPT), and extracting multi-scale characteristic parameters capable of effectively distinguishing different insulation defects from the difference of complexity and energy distribution of the partial discharge signals in a time-frequency domain, wherein the multi-scale characteristic parameters comprise a multi-scale energy parameter (RE) and a multi-scale sample entropy parameter (SampEn).
Harmonic wavelet transformation:
for a given signal f (t), its HWPT can be written as
In the formula, m and n are scale parameters, m is less than n, and the length of the harmonic wavelet frequency domain supporting interval is determined by the m and n.
Fourier transform is performed on the formula (2) to obtain a frequency domain expression of HWPT
In the formula (I), the compound is shown in the specification,
a Fourier transform (FFT) of f (t),
is composed of
Conjugation of (1).
Multi-scale characteristic parameter calculation:
for a given discrete signal x (i), i ═ 0, 1, 2.
(1) Carrying out j layers of harmonic wavelet packet decomposition on X (i) to obtain 2jSub-signal sequences of different frequency ranges, i.e. Xk(i)，k＝1，2，…，2j；i＝0，1，2，...，N-1。
(2) Calculating each subband signal Xk(i) To obtain a sequence of subband energies, i.e.
(3) Normalizing the subband signal energy sequence E (k) to obtain a subband relative energy value sequence, i.e.
(4) Calculating each subband signal Xk(i) The sample entropy value of (a) is obtained as a sub-band sample entropy sequence sampen (k), k is 1, 2, …, 2j。
The value of the sample entropy is related to the mode dimension m and the value of the similarity margin r, typically m 1 or m 2, r 0.1 to 0.25Std (Std is the standard deviation of the original data). Where m is 2 and r is 0.15 Std.
4-layer HWPT decomposition is carried out on 100 groups of single partial discharge pulses, 10 sub-bands with concentrated partial discharge energy are selected from 16 sub-bands, relative energy and sample entropy data of the partial discharge pulse characteristic sub-bands are statistically analyzed, and 95% confidence intervals of multi-scale energy characteristics and multi-scale sample entropy characteristics of 4 partial discharge signals are obtained, as shown in fig. 3 and fig. 4.
As can be seen from fig. 3, the energy of the free particle discharge and the pin plate discharge is mainly concentrated on the higher frequency sub-bands of E4, E5, E6, etc., and the energy of the suspension discharge and the creeping discharge is mainly concentrated on the lower frequency sub-bands of E1, E2, E3, etc.
As can be seen from fig. 4, the sample entropy sequence distributions of the characteristic sub-bands of different PDs are also different. In a sub-band (E1-E5) with concentrated partial discharge signal energy, the sample entropy value calculated by needle-plate discharge and surface discharge is obviously higher than that of free particle discharge and suspension discharge.
Therefore, the multi-scale relative energy parameters and the sample entropy parameters of different types of partial discharge have different distributions, and can be used as characteristic quantities to identify the types of the partial discharge.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (6)
1. The multi-scale feature extraction technology based on the partial discharge original time domain waveform is characterized by comprising the following specific steps:
1) manufacturing a typical partial discharge defect model, building an experiment platform, and performing a partial discharge simulation experiment in a laboratory environment to obtain original partial discharge data;
2) designing a single partial discharge pulse signal extraction program according to the trigger amplitude and waveform characteristics of each type of partial discharge, and extracting a single partial discharge pulse;
3) selecting a proper denoising algorithm, and denoising the single partial discharge pulse signal;
4) and decomposing the denoised partial discharge signal by using a wavelet transform method, and calculating characteristic parameters on different decomposition scales.
2. The multi-scale feature extraction technology based on the partial discharge original time domain waveform is characterized in that:
the wavelet transformation method is harmonic wavelet packet transformation, and the characteristic parameters are multi-scale energy parameters and multi-scale sample entropy parameters.
3. The feature extraction method according to claim 2, characterized in that: and 4), performing harmonic wavelet transformation on the denoised partial discharge signal to obtain sub-bands of different frequency bands, and calculating multi-scale energy parameters of the sub-bands.
4. The feature extraction method according to claim 2, characterized in that: and 4), performing harmonic wavelet transformation on the denoised partial discharge signal to obtain sub-bands of different frequency bands, and calculating multi-scale sample entropy parameters of the sub-bands.
5. The experimental method according to claim 1, characterized in that: the experiment adopted the international standard IEC 60270-2000 for partial discharge measurement.
6. The experimental method according to claim 1, characterized in that: the denoising method is wavelet threshold denoising, and the selected wavelet basis function is db2 wavelet.
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