CN113221615A - Partial discharge pulse extraction method based on noise reduction clustering - Google Patents
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Abstract
The invention relates to a partial discharge pulse extraction method based on noise reduction clustering, which is based on a wavelet algorithm and comprises the following steps: collecting an original power generation signal; determining wavelet decomposition coefficients of each level; extracting wavelet decomposition coefficients of each level; and clustering the extracted pulse segments of the wavelet decomposition coefficients of each level to obtain a subsequence of an aliasing pulse sequence, wherein the subsequence corresponds to independent signals of different discharge sources or different propagation paths. Based on the diversity and complexity of the current power equipment partial discharge detection environment interference, the method improves the signal-to-noise ratio and multi-discharge clustering calculation of interference signals in real time in the detection process, and practically improves the anti-interference performance of the partial discharge detection device.
Description
Technical Field
The invention relates to the technical field of high-frequency current detection of partial discharge, in particular to a pulse extraction method based on noise reduction clustering.
Background
Partial discharge is a main factor causing insulation degradation, is also an important representation of insulation degradation, can effectively reflect latent defects and faults inside equipment, and is an important index for evaluating the insulation state of the equipment. The partial discharge has better detection effect on the early detection of the sudden fault of the insulator, and has obvious advantage on the detection of the insulation defect or fault of the power equipment. Therefore, effective detection of the partial discharge signal is of great significance for guaranteeing safe and stable operation of the power equipment.
Chinese patent application publication No. CN106771905B discloses a pulse extraction method suitable for high-frequency current partial discharge detection, which processes signals based on wavelet decomposition, including;
step 1: firstly, performing wavelet decomposition on a digital signal sequence acquired by AD;
step 2: calculating the characteristic quantity of the signal after wavelet decomposition to obtain the peak value of the wavelet decomposition coefficient and the root mean square value corresponding to the peak value;
and step 3: determining the signal type and performing wavelet decomposition coefficient contraction according to the ratio of the peak value to the effective value of each level of wavelet decomposition coefficient;
and 4, step 4: reconstructing a wavelet coefficient containing a partial discharge pulse signal;
and 5: based on the reconstructed signal, pulse segments are extracted by automatic threshold calculation.
The method for performing wavelet decomposition on the digital signal sequence acquired by AD specifically comprises the following steps:
a) down-sampling an original sequence S (n) to obtain an odd sequence S (n/2-1) and an even sequence S (n/2), wherein S (n/2) and S (n/2-1) are respectively half of S (n);
b) performing multiplication and addition operation on the S (n/2-1) and a high-pass filter h (n/2-1) to obtain a sequence S1(n/2-1) and a high-frequency coefficient cD 1;
c) performing multiply-add operation on the S (n/2) and the low-pass filter g (n/2) to obtain a sequence S1(n/2) and a low-frequency coefficient cA 1;
d) s1(n/2) is extracted downwards to obtain an odd sequence S1(n/4-1) and an even series S1(n/4), wherein S1(n/4-1) and S1(n/4) are respectively half of S1(n/2) series;
e) performing multiplication and addition operation on the S (n/4-1) and a high-pass filter h (n/4-1) to obtain a sequence S2(n/4-1) and a high-frequency coefficient cD 2;
f) performing multiply-add operation on the S (n/4) and the low-pass filter g (n/4) to obtain a sequence S2(n/4) and a low-frequency coefficient cA 2;
g) and sequentially decomposing the low-frequency sequence to finally obtain wavelet coefficients respectively as follows: low-frequency coefficient cA6 and high-frequency coefficients cD 6-cD 1.
The technical scheme adopts a wavelet decomposition method, and the method can separate signals from noise, achieve the aim of improving the signal-to-noise ratio and realize real-time high-performance noise suppression under a high sampling rate.
However, the above technical solutions have poor interference resistance.
Disclosure of Invention
The application aims to provide a partial discharge pulse extraction method based on noise reduction clustering, which is used for solving the problem of poor anti-interference capability of the existing scheme.
In order to achieve the purpose, the invention provides a partial discharge pulse extraction method based on noise reduction clustering, which comprises the following steps:
collecting an original power generation signal;
determining wavelet decomposition coefficients of each level;
extracting wavelet decomposition coefficients of each level;
and clustering the extracted pulse segments of the wavelet decomposition coefficients of each level to obtain a subsequence of an aliasing pulse sequence, wherein the subsequence corresponds to independent signals of different discharge sources or different propagation paths.
Further, the method also comprises a step of diagnosing the clustered discharge statistical phase map.
Further, the method also comprises the step of calculating the signal-to-noise ratio of each level of wavelet decomposition coefficients, and if the signal-to-noise ratio is lower than a preset limit value, the level of wavelet decomposition coefficients are set to be zero.
Further, extracting pulse segments exceeding the corresponding set threshold value for the wavelet decomposition coefficient of each level; the set threshold is determined according to a calibration experiment.
Further, the set threshold is a multiple of the root mean square value of the wavelet decomposition coefficient of the corresponding level.
Further, the noise suppression ratio is used as an evaluation index to evaluate the interference suppression effect.
Further, the noise suppression ratio is expressed as: lambda [ alpha ]NRR＝10(lgσ1 2-lgσ2 2) (ii) a Wherein sigma1And σ2The standard deviation of the original signal and the denoised signal, respectively.
The invention has the beneficial effects that: compared with the prior art, the method has the advantages that the step of clustering the extracted pulse segments is added, so that the subsequence of the aliasing pulse sequence is obtained, and the subsequence corresponds to independent signals of different discharge sources or different propagation paths; the on-site anti-interference capability of the method can be improved. Aiming at key information such as a source, time-frequency characteristics and statistical characteristics of high-frequency electromagnetic interference of a transformer substation, in order to solve the complex problem that a threshold value and a multi-pulse signal cannot be set in a strong electromagnetic interference environment, the signal-to-noise ratio and multi-discharge clustering are realized in real time in the detection process, an efficient real-time wavelet decomposition-based multi-level intelligent noise reduction clustering integrated algorithm is provided, the anti-interference performance of a partial discharge detection device is practically improved, and the method is a key point for promoting popularization and application of a high-frequency current partial discharge detection technology and exerting good effectiveness.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a logic diagram of the present invention;
FIG. 3 is a graph of the 3-10 multi-PD source clustering separation effect;
FIG. 4 is a diagram illustrating verification of wavelet denoising effect of a ground actual measurement signal of a No. 1 main transformer C-phase iron core of a 500kV transformer substation according to the present invention;
FIG. 5 is a diagram illustrating verification of wavelet denoising effect of signals acquired by grounding of phase-A clamping pieces of a No. 1 main transformer of a 500kV transformer substation according to the present invention;
FIG. 6 is a diagram showing the structure of a Data _ prc module;
FIG. 7 is a diagram of a Frame _ sample _ tf block structure;
FIG. 8 is a block diagram of Fft _ cal;
FIG. 9 is a view showing the construction of an N _ cal module;
FIG. 10 is a view showing a structure of a T _ cal block;
FIG. 11 is a pulse waveform diagram;
FIG. 12 is a frequency domain plot of the signal of FIG. 11;
description of reference numerals: sn-original signal; a DFH-wavelet decomposition high pass filter; a DFL-wavelet decomposition low pass filter; CD-high frequency signal; CA — low frequency signal; n-number of snapshot.
Detailed Description
The method flow of the invention is shown in fig. 1 and fig. 2, and the specific steps are as follows:
(1) performing a down-sampling operation on an original signal S (i), i ═ 0, 1.., N, to obtain a half-length odd sequence sa (i) and a half-length even sequence sd (i), wherein sa (i) ═ S (2 i); sd (i) ═ S (2i +1), i ═ 0, 1,. ·, N/2;
(2) calculating the convolution of SA (i) and SD (i) with db2 wavelet decomposition low-pass filter DFL and high-pass filter DFH to obtain wavelet coefficients CA1(i) and CD1(i), wherein i is 0, 1.
(3) The wavelet coefficient CA1(i) is down-sampled to obtain its half-length odd sequence CA1A (i) and half-length even sequence CA1D (i), where:
CA1A(i)＝CA1(2i)，i＝0，1，...，N/4
CA1D(i)＝CA1(2i+1)，i＝0，1，...，N/4
(4) the sequences CA1A (i) and CA1D (i) are convolved with a wavelet decomposition low-pass filter DFL and a wavelet decomposition high-pass filter DFH, respectively, to obtain wavelet decomposition coefficients CA2(i) and CD2(i), i being 0, 1.
(5) The wavelet coefficient CA2(i) is down-sampled to obtain its half-length odd sequence CA2A (i) and half-length even sequence CA2D (i), where:
CA2A(i)＝CA2(2i)，i＝0，1，...，N/8
CA2D(i)＝CA2(2i+1)，i＝0，1，...，N/8
(6) the sequences CA2A (i) and CA2D (i) are convolved with the wavelet decomposition low-pass filter DFL and the wavelet decomposition high-pass filter DFH, respectively, to obtain wavelet decomposition coefficients CA3(i) and CD3(i), i being 0, 1.
(7) Wavelet coefficient CA3(i), down-sampled to obtain its half-length odd sequence CA3A (i) and half-length even sequence CA3D (i), where:
CA3A(i)＝CA3(2i)，i＝0，1，...，N/16
CA3D(i)＝CA3(2i+1)，i＝0，1，...，N/16；
(8) sequences CA3A (i) and CA3D (i) are convolved with a wavelet decomposition low-pass filter DFL and a wavelet decomposition high-pass coefficient DFH, respectively, to obtain wavelet decomposition coefficients CA4(i) and CD4(i), i being 0, 1.
(9) Wavelet coefficient CA4(i), down-sampled to obtain its half-length odd sequence CA4A (i) and half-length even sequence CA4D (i), where:
CA4A(i)＝CA4(2i)，i＝0，1，...，N/32
CA4D(i)＝CA3(2i+1)，i＝0，1，...，N/32
(10) the sequences CA4A (i) and CA4D (i) are convolved with the wavelet decomposition low-pass filter DFL and the wavelet decomposition high-pass coefficient DFH, respectively, resulting in wavelet decomposition coefficients CA5(i) and CD5(i), i being 0, 1.
Finally, wavelet decomposition coefficients CA5, CD5, CD4, CD3, CD2 and CD1 are obtained.
(11) And (3) calculating the signal-to-noise ratio:
calculating effective values (root mean square values) of wavelet coefficients of each level of CA5, CD5, CD4, CD3, CD2 and CD1, and respectively recording the effective values as Vrms (CD5), Vrms (CD4), Vrms (CD3), Vrms (CD2) and Vrms (CD 1);
extracting maximum values of wavelet coefficients of each level, namely Vmax (CD5), Vmax (CD4), Vmax (CD3), Vmax (CD2) and Vmax (CD 1);
calculating the signal-to-noise ratio of wavelet coefficients at each level
The ratio is used as the index for judging the signal-to-noise ratio, if the signal-to-noise ratio of a certain level of wavelet coefficient is less than 3, the level of wavelet coefficient is set to be 0, and the reconstruction is not participated in.
As shown in fig. 1 and fig. 2, in the wavelet decomposition multilevel noise reduction clustering algorithm for partial discharge of power equipment, the optimal principle of the signal-to-noise ratio of the wavelet decomposition coefficient of each level is optimized hierarchically, and the higher the signal-to-noise ratio is, the lower the background interference of the signal component of the level is, and the more significant the amplitude of the discharge pulse waveform signal is, and also, the hardware overhead and the algorithm efficiency are comprehensively considered.
(12) And (3) self-adaptive threshold setting, which is a set threshold value extracted by a pulse signal based on a certain multiple of the root mean square value of the wavelet decomposition level coefficient after a large number of tests are accumulated. The multiple is preset based on calibration experiments.
And extracting pulse segments exceeding the threshold value for the wavelet decomposition coefficients of the preferred level based on a preset adaptive threshold value so as to further reduce the expenditure of hardware storage resources and the consumption of computing resources.
Steps (1) - (12) are the same as the technical solution adopted in the reference cited in the background art.
(13) Based on the wavelet decomposition level, clustering is carried out on the extracted pulse segments to obtain a subsequence of an aliasing pulse sequence, wherein the subsequence corresponds to independent signals of different discharge sources or different propagation paths. The clustering effect is shown in fig. 3.
(14) And drawing a clustered and separated discharge statistical phase atlas PRPD according to a conventional partial discharge analysis means, and further executing a conventional diagnosis algorithm such as pattern recognition.
The verification method for the wavelet denoising effect of the field measured signal of the wavelet decomposition multilevel denoising clustering algorithm for the partial discharge of the power equipment as shown in fig. 4 and 5 is to denoise the field collected signal, and as can be seen from the figure, the adopted denoising method can effectively remove the periodic narrow-band interference and the white noise in the signal, the pulse signal submerged by the noise after the denoising treatment is exposed, and the high-frequency component of the signal is also displayed.
Because field test signals all contain noise interference of different degrees, parameters such as SNR, CC, MSE and ED cannot be obtained, and in order to evaluate the denoising effect of the adopted method on the field measured signals, a noise suppression ratio parameter is introduced as an evaluation index, and the specific expression is shown as the following formula:
λNRR＝10(lgσ1 2-lgσ2 2)
wherein sigma1And σ2Respectively the standard deviation of the original signal and the denoised signal; lambda [ alpha ]NRRReflects the degree of highlighting of the useful signal after interference suppression.
Calculated as λNRRThe values of the partial discharge signal and the partial discharge signal are respectively 18.37dB and 20.72dB, and as can be seen from the reconstructed signal waveform and the reconstructed signal spectrum in the fig. 3 and 4, the method can effectively remove the interference in the test signal and obtain a relatively real partial discharge signal waveform and signal spectrum diagram.
In the above embodiment, the limit value of the signal-to-noise ratio in step (11) is 3, but other values may be adopted as another embodiment. Step (12) sets an adaptive threshold for each level for extracting pulse segments that exceed the threshold in order to reduce resource consumption, as other embodiments may also extract in other ways (e.g., a fixed threshold, or all) to retain the required information.
The improvement of the embodiment focuses on the implementation of clustering, and specifically includes hardware design and software design.
The hardware is implemented by FPGA, and comprises a Data _ prc module, as shown in FIG. 6. The Data _ prc module is added with a tf calculation module (such as the leftmost box in fig. 6), the available register is set to open tf calculation enabling, and one path of Data is sent to the tf calculation module for calculation, and the input of the original processing module of the path is changed to 0. The output logic of Data _ prc is to see which frame _ sample module has Data to output which way of Data.
The Frame _ sample _ tf module is shown in fig. 7, that is, the tf calculation module calculates the difference that fft calculation is needed only for f values according to the tf calculation formula, so fft calculation is a single module. Although the calculation formulas after fft in tf calculation are consistent, the numerical bit widths required to be calculated are different, and in order to reduce resource utilization, the effective n values are respectively 2 individual blocks (n _ cal, s _ n _ cal), and the process of obtaining the effective n values and then obtaining the tf values is respectively another 2 blocks (t _ cal, s _ t _ cal). The calculation adopts a counter-pressure-free running water design, and the interval is ensured to be more than 2048 by the foremost start signal, so that the time of all calculation steps is sufficient. The delay of the required three results is guaranteed to be constant, subject to the maximum delay path (f).
The Fft _ cal module is shown in FIG. 8, and has the main function of fft calculation and also has the function of maximum value calculation. The ipcore of xilinx is used to calculate fft. When the input data is larger than the set threshold, fft calculation of 2048 data is started taking 56 data ahead. The two data of the real part and the imaginary part are obtained by calculation, and the square of the real part and the imaginary part and the square of the amplitude are calculated for subsequent calculation.
The N _ cal module, as shown in fig. 9, functions to calculate the effective N value and is composed of an accumulator, a multiplier, and a divider.
The T _ cal module, as shown in fig. 10, functions as the last step of calculating the T value or the f value, and includes an accumulator, a divider, and an evolution module.
The software algorithm comprises the following steps:
the upper computer configures the pulse extraction width, and the FPGA extracts the TF parameters according to the configured width. The following example is a case where the pulse width is 2048 dots.
T, F, the calculation input is a complete pulse waveform, which is described in this document by taking 1 pulse shown in fig. 11 as an example, the pulse waveform has 2048 points in total, the ordinate is V, the abscissa is n, the waveform sequence group is defined as V (n), and n takes values of 0-2048.
Calculating a parameter T:
(1) signal time domain normalization processing:
(2) and (3) solving the time gravity center of the signal:
(3) calculating equivalent duration
The equivalent time length T in the combination of the equations (1-1) to (1-2) is calculated as follows:
calculating a parameter F:
(1) first, FFT processing is performed on time domain signals to obtain frequency domain distribution as shown in fig. 12: the obtained frequency domain distribution sequence is marked as S(n)；
(2) And (3) normalizing the frequency domain signals:
(3) calculating the frequency domain gravity center of the frequency domain:
(4) calculating equivalent bandwidth
The equivalent bandwidth F is calculated by combining equations (1-4) to (1-5) as follows:
Claims (7)
1. a partial discharge pulse extraction method based on noise reduction clustering is characterized by comprising the following steps:
collecting an original power generation signal;
determining wavelet decomposition coefficients of each level;
extracting wavelet decomposition coefficients of each level;
and clustering the extracted pulse segments of the wavelet decomposition coefficients of each level to obtain a subsequence of an aliasing pulse sequence, wherein the subsequence corresponds to independent signals of different discharge sources or different propagation paths.
2. The method for extracting partial discharge pulses based on noise reduction clustering according to claim 1, further comprising the step of diagnosing the clustered discharge statistical phase map.
3. The method for extracting partial discharge pulses based on denoising clustering according to claim 1 or 2, further comprising calculating a signal-to-noise ratio of each level of wavelet decomposition coefficients, wherein if the signal-to-noise ratio is lower than a predetermined limit, the level of wavelet decomposition coefficients is set to zero.
4. The method according to claim 3, wherein pulse segments exceeding a corresponding set threshold are extracted for wavelet decomposition coefficients of each level; the set threshold is determined according to a calibration experiment.
5. The method according to claim 4, wherein the threshold is a multiple of the root mean square value of the wavelet decomposition coefficient of the corresponding level.
6. The method of claim 3, wherein the noise suppression ratio is used as an evaluation index to evaluate the interference suppression effect.
7. The method according to claim 3, wherein the noise suppression ratio is expressed as: lambda [ alpha ]NRR＝10(lgσ1 2-lgσ2 2) (ii) a Wherein sigma1And σ2The standard deviation of the original signal and the denoised signal, respectively.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114089135A (en) * | 2021-11-19 | 2022-02-25 | 南京征途信息技术有限公司 | Partial discharge sensor for variable distribution high-frequency current and method thereof |
| CN115389888A (en) * | 2022-10-28 | 2022-11-25 | 山东科华电力技术有限公司 | Partial discharge real-time monitoring system based on high-voltage cable |
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| CN110765834A (en) * | 2019-08-25 | 2020-02-07 | 青岛科技大学 | Parameter wavelet threshold signal denoising method based on improved artificial bee colony algorithm |
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| CN114089135A (en) * | 2021-11-19 | 2022-02-25 | 南京征途信息技术有限公司 | Partial discharge sensor for variable distribution high-frequency current and method thereof |
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