CN105652166B - A kind of Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring - Google Patents
A kind of Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring Download PDFInfo
- Publication number
- CN105652166B CN105652166B CN201610112468.9A CN201610112468A CN105652166B CN 105652166 B CN105652166 B CN 105652166B CN 201610112468 A CN201610112468 A CN 201610112468A CN 105652166 B CN105652166 B CN 105652166B
- Authority
- CN
- China
- Prior art keywords
- threshold
- mrow
- wavelet
- signal
- msub
- 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
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000012544 monitoring process Methods 0.000 title claims abstract description 15
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 5
- 230000008569 process Effects 0.000 claims abstract description 3
- 239000012467 final product Substances 0.000 claims 1
- 230000009467 reduction Effects 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000002592 echocardiography Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000005764 inhibitory process Effects 0.000 description 1
- 238000009413 insulation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011946 reduction process Methods 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000032258 transport 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
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Testing Relating To Insulation (AREA)
Abstract
A kind of Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring, the method are first according to similarity criterion and select a kind of mother wavelet, and the local discharge signal comprising noise is carried outnLayer wavelet decomposition；Then to signal noise varianceEstimated, with reference to coefficient of wavelet decomposition using corresponding threshold strategies estimation threshold value, the selection of each layer coefficients comprehensive uniform threshold, Birge Massart threshold values, penalty threshold values and the multi thresholds strategy obtained to decomposition weights, to coefficient acting threshold process；The coefficient after processing is finally recovered into original signal by wavelet reconstruction, that is, obtains the time domain plethysmographic signal after denoising.Weighted Threshold significantly improves the signal amplitude attenuation problem that Birge Massart threshold bands are come, while ensure that preferable signal local characteristic.The present invention is suitable for the denoising of local discharge signal inside the high voltage electric equipments such as high-tension switch cabinet, transformer, power cable, GIS device.
Description
Technical field
The present invention relates to a kind of Weighted Threshold for being used for high voltage electric equipment shelf depreciation (abbreviation partial discharge) on-line monitoring is small
Ripple noise-reduction method, belongs to high voltage electric equipment operation and maintenance service technique field.
Background technology
One of the main reason for causing high voltage electric equipment insulation ag(e)ing and destroying is shelf depreciation, is punctured in dielectric
The progressively development and enhancing of shelf depreciation are often shown as before.Therefore, partial discharge monitoring is to improving electric system
Safety and reliability has suitable practical value.The early stage local discharge signal of equipment internal flaw is fainter, and transports
Often there are powerful interference noise source around electrical equipment in row.Local discharge signal relative noise signal-to-noise ratio is low, some
Almost it is submerged in the scene interference of complexity.The various interference in scene can be divided into continuous week according to the difference of its temporal signatures
Phase property is disturbed, impulse type interference and white noise etc., and impulse type interference can be divided into stochastic pattern impulse disturbances and preiodic type pulse
Interference.
How interference key as partial discharge monitoring is effectively suppressed.For periodic narrowband interference, at present
The suppressing method of use mainly has：FFT threshold filters, adaptive-filtering, notch filter and mathematical morphology filter.Small echo becomes
It is more satisfactory for white noise and random disturbances inhibition to change method.Threshold filter and trapper method damage original shelf depreciation energy
Lose larger, when changing, filter parameter is difficult to determine interference band, and noise reduction is not satisfactory in scene；Adaptive filter
Although ripple can be according to signal automatically adjusting parameter, convergence rate is slower, and stability is poor, it is difficult to applied to on-line monitoring.
IIR notch filters for multi resonant wave component PERIODIC INTERFERENCE there are difficulty of parameter tuning, filtering time is long the problems such as.Due to
In orthogonal wavelet, the selection of orthogonal basis than conventional method closer to actual discharge signal in itself, so passing through wavelet transformation
Easier it can isolate noise.
The content of the invention
The object of the present invention is to make an uproar to effectively suppress the signal obtained in high voltage electric equipment partial discharge monitoring
Sound, the invention discloses a kind of Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring.
The technical scheme is that the present invention, which is first according to similarity criterion, selectes a kind of mother wavelet, to including noise
Local discharge signal carry out n-layer wavelet decomposition；Then to signal noise variances sigma2Estimated, adopted with reference to coefficient of wavelet decomposition
Estimate threshold value with corresponding threshold strategies, the comprehensive uniform threshold of each layer coefficients selection, the Birge-Massart thresholds obtained to decomposition
Value, penalty threshold values and the weighting of multi thresholds strategy, to coefficient acting threshold process；The coefficient after processing is finally passed through into small echo
Rebuild and recover original signal, that is, obtain the time domain plethysmographic signal after denoising.
Denoising method of the present invention comprises the following steps：
1) a local discharge signal f (n) is s (n) after noise pollution, its basic noise model can represent
For：
S (n)=f (n)+σ e (n) (1)
Wherein e (n) is noise, and σ is noise intensity, defines noise w (n)=σ e (n).In the simplest situations can be false
If e (n) is white Gaussian noise, σ=1.
To local discharge signal, the related coefficient γ that is determined with formula (2) assesses discharge pulse signal and small echo atom
Similitude.
In formula, xiFor partial discharge pulse's signal data sequence,For xiAverage；yiFor the data sequence of small echo atom,For yiAverage.The similitude of the bigger expression discharge signals of γ and selected small echo is higher.
Local discharge signal is precipitous with rising edge, and low amplitude value, the duration is short, and decay fast characteristic.With shelf depreciation
The characteristic of signal is compared, and there is higher similitude in compact schemes orthogonal wavelet Daubechies (db) wavelet functions race.In db small echos
In family of functions, db5 small echo supports length is 9, vanishing moment 5 so that the energy after conversion is more concentrated, and is provided simultaneously with preferable side
Boundary's characteristic.Decomposition order is also larger on noise reduction influence, and decomposing thinner low frequency part will preferably be suppressed, but be increased
Decomposition order needs more processing times, considers factors above, selects 5 layers of db5 wavelet decompositions to local discharge signal
Carry out noise reduction.
2) uniform threshold is estimated.Donoho and Johnstone proposes the concept of uniform threshold, and proves in all pairs
In angular estimation, factor 2logeN is optimal, and uniform threshold calculation formula is as follows：
In formula, σ2For the noise variance (σ is standard deviation) estimated from signal, N is wavelet details coefficient lengths at different levels, by
The formula calculates gradient thresholds at different levels.
It is zero that if M, which is P average, variance isIndependent Gaussian stochastic variable absolute value middle position, then can prove：
Ε{M}≈0.6745σ0 (4)
The M mathematic expectaions that Ε { M } is, by ignoring partial discharge pulse's signal influence of itself, using detail coefficients
Middle position M estimates the variance of noise W：
Bringing formula (5) into formula (3) can obtain being calculated as below the equivalence formula of uniform threshold：
3) Birge-Massart threshold estimations.Birge-Massart threshold strategies include two empirical coefficients M and α, meter
It is as follows to calculate rule：
A Decomposition order j specified is given, to j+1 and higher, all coefficients retain.To i-th layer (1≤i≤
J), the n of maximum absolute value is retainediA coefficient, niDetermined by following formula：
ni=M (j+2-i)α (7)
M and α is empirical coefficient in formula, and M=L (1), wavelet coefficient after namely the 1st layer decomposition of L (1) are taken under default condition
Length, under normal circumstances, M should meet L (1)≤M≤2L (1)；α takes α=3 in the case of noise reduction.
4) penalty threshold estimations.Penalty threshold values are derived from from Donoho-Johnstone methods, similar to formula
(6) uniform threshold provided, factor log2N is replaced, and computational methods see below formula：
The definition of M and N is identical with formula (6) in formula.
5) multi thresholds strategy weights, and obtains improved gradient threshold Tw(j)：
In formula, i identifies for threshold strategies, and j is wavelet systems several levels, and for lev to divide Decomposition order, N is total threshold strategies number,
Ti(j) the j-th stage threshold value determined for i-th kind of threshold strategies, wi(j) it is corresponding weight.To definite series j=j0For, power
Value coefficient wi(j0) meet following constraint formula：
The present invention is weighted using 5 grades of wavelet decompositions and 3 kinds of threshold strategies, therefore lev=5, N=3.
6) coefficient after processing is recovered into original signal by wavelet reconstruction, that is, obtains the time domain plethysmographic signal after denoising.
The invention has the advantages that uniform threshold de-noising signal is too smooth, some letters of signal in itself are lost
Breath, does not meet similarity criterion.Burr is more after penalty threshold deniosings, however it remains obvious noise signal.By table 1
Threshold values at different levels can be seen that uniform threshold is larger, eliminate part useful signal, add denoising risk.Penalty threshold values are firm
Well in contrast, threshold value is smaller, and noise reduction is undesirable.Birge-Massart threshold deniosing effects are preferable, but to signal amplitude
Have a certain impact.Utilize Tw(j) noise reduction is carried out to original signal, it is found that Weighted Threshold significantly improves Birge-Massart
The signal amplitude attenuation problem that threshold band is come, while ensure that preferable signal local characteristic.
The present invention is suitable for local inside the high voltage electric equipments such as high-tension switch cabinet, transformer, power cable, GIS device
The denoising of discharge signal.
Brief description of the drawings
Fig. 1 is the work flow diagram that the method for the present invention carries out high voltage electric equipment partial discharge monitoring signal denoising；
Fig. 2 is on-site signal coefficient of wavelet decomposition figures at different levels；
Fig. 3, which is on-site signal, carries out time domain beamformer after noise reduction process using improving threshold value.
Embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.Following embodiments will be helpful to this area
Technical staff further understand the present invention, but the invention is not limited in any way.It should be pointed out that to the general of this area
For logical technical staff, without departing from the inventive concept of the premise, various modifications and improvements can be made.These are belonged to
Protection scope of the present invention.
Embodiment：
The present embodiment scene local discharge signal comes from certain 500kV transformer online monitoring system.As shown in Figure 1, this reality
Apply example and line monitor signal denoising is carried out by Fig. 1 flows to local discharge signal.
As shown in Figure 2.Analyze field data to find, the present embodiment local discharge signal includes substantial amounts of interference signal, has
Even be submerged in interference.
Yn is the shelf depreciation electric discharge original signal of a power frequency period in Fig. 2, and other is coefficient of wavelet decomposition, wherein A5
For the 5th grade of approximation coefficient, Di (1≤i≤5) is i-stage detail coefficients.When D2 shows wavelet decomposition to the second layer, interference is
Substantially it is stripped.
The threshold value of 3 kinds of threshold value (uniform threshold, Birge-Massart threshold values, penalty threshold values) policy calculations more than
As shown in table 1, Fig. 3 shows the noise reduction of each threshold strategies, and wherein yn is original signal, and yd1~yd4 is de-noising signal.
Uniform threshold noise reduction is more satisfactory, but signal local feature is lost seriously；Penalty threshold values are generally less than normal, after noise reduction still
Disturbed in the presence of part；Birge-Massart threshold values are larger to the less impulse attenuation of amplitude, and part positive-negative polarity impulse attenuation is extremely
Unipolarity (preceding 5 pulses of yd3 in Fig. 3).
1 on-site signal of table is layered wavelet threshold
According to the characteristics of each threshold strategies, Weighted Threshold T is calculated by formula (10)w(j) it is shown in Table 1.Utilize Tw(j) to original
Beginning signal carries out noise reduction, and obtained de-noising signal is as shown in yd4 in Fig. 3.Compare preceding 5 pulses of yd3 and yd4 signals, can be with
It was found that Weighted Threshold significantly improves the signal amplitude attenuation problem that Birge-Massart threshold bands are come, while ensure that preferably
Signal local characteristic.
Claims (4)
- A kind of 1. Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring, it is characterised in that the method is first according to Similarity criterion selectes a kind of mother wavelet, and n-layer wavelet decomposition is carried out to the local discharge signal comprising noise；Then make an uproar to signal Sound variances sigma2Estimated, with reference to coefficient of wavelet decomposition using corresponding threshold strategies estimation threshold value；Each layer obtained to decomposition Coefficient selection uniform threshold, Birge-Massart threshold values and penalty threshold values, more thresholds are carried out based on above-mentioned three kinds of threshold values Value strategy weighting, to coefficient acting threshold process；The coefficient after processing is finally recovered into original signal by wavelet reconstruction, to obtain the final product Time domain plethysmographic signal after to denoising.
- 2. a kind of Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring according to claim 1, its feature exist In the n-layer wavelet decomposition selects 5 layers of db5 wavelet decompositions.
- 3. a kind of Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring according to claim 1, its feature exist In the multi thresholds strategy weighting, obtains improved gradient threshold Tw(j)：<mfenced open = "" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>T</mi> <mi>w</mi> </msub> <mrow> <mo>(</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>=</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>w</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>&CenterDot;</mo> <msub> <mi>T</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <mi>j</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mo>(</mo> <mn>1</mn> <mo>&le;</mo> <mi>j</mi> <mo>&le;</mo> <mi>l</mi> <mi>e</mi> <mi>v</mi> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> </mfenced>In formula, i is threshold strategies mark, and j is wavelet systems several levels, and lev is Decomposition order, and N is total threshold strategies number, Ti(j) it is The j-th stage threshold value that i-th kind of threshold strategies determines, wi(j) it is corresponding weight；To definite series j=j0For, weight coefficient wi(j0) meet following constraint formula：<mrow> <mtable> <mtr> <mtd> <mrow> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <msub> <mi>w</mi> <mi>i</mi> </msub> <mrow> <mo>(</mo> <msub> <mi>j</mi> <mn>0</mn> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mn>1</mn> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mo>(</mo> <mn>1</mn> <mo>&le;</mo> <msub> <mi>j</mi> <mn>0</mn> </msub> <mo>&le;</mo> <mi>l</mi> <mi>e</mi> <mi>v</mi> <mo>)</mo> </mrow> </mtd> </mtr> </mtable> <mo>.</mo> </mrow>
- 4. a kind of Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring according to claim 1, its feature exist In the multi thresholds strategy weighting, using 5 grades of wavelet decompositions and 3 kinds of threshold strategies weightings, therefore, lev=5, N=3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610112468.9A CN105652166B (en) | 2016-03-01 | 2016-03-01 | A kind of Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610112468.9A CN105652166B (en) | 2016-03-01 | 2016-03-01 | A kind of Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105652166A CN105652166A (en) | 2016-06-08 |
| CN105652166B true CN105652166B (en) | 2018-04-17 |
Family
ID=56492707
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610112468.9A Active CN105652166B (en) | 2016-03-01 | 2016-03-01 | A kind of Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105652166B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110471015A (en) * | 2019-09-05 | 2019-11-19 | 国网北京市电力公司 | Determination method and device, storage medium and the processor of sensor detection threshold |
| CN111323227A (en) * | 2020-01-03 | 2020-06-23 | 南昌航空大学 | Method for extracting fault features of aeroengine rotor |
| CN111239565B (en) * | 2020-02-26 | 2022-05-24 | 国网陕西省电力公司电力科学研究院 | Oil-filled casing partial discharge pulse signal processing method and system based on layered denoising model |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102135447A (en) * | 2010-01-08 | 2011-07-27 | 佳能株式会社 | Method and apparatus to measure electromagnetic waves |
| CN102247143A (en) * | 2011-06-03 | 2011-11-23 | 吉林大学珠海学院 | Integratable fast algorithm for denoising electrocardiosignal and identifying QRS waves |
| CN102393423A (en) * | 2011-09-28 | 2012-03-28 | 南京信息工程大学 | Lamb wave denoising method based on adaptive threshold value orthogonal wavelet transform |
| CN103961092A (en) * | 2014-05-09 | 2014-08-06 | 杭州电子科技大学 | Electroencephalogram signal denoising method based on self-adaption threshold processing |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7260272B2 (en) * | 2003-07-10 | 2007-08-21 | Samsung Electronics Co.. Ltd. | Method and apparatus for noise reduction using discrete wavelet transform |
| EA011104B1 (en) * | 2005-07-28 | 2008-12-30 | Эксонмобил Апстрим Рисерч Компани | Method for wavelet denoising of controlled source electromagnetic survey data |
-
2016
- 2016-03-01 CN CN201610112468.9A patent/CN105652166B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102135447A (en) * | 2010-01-08 | 2011-07-27 | 佳能株式会社 | Method and apparatus to measure electromagnetic waves |
| CN102247143A (en) * | 2011-06-03 | 2011-11-23 | 吉林大学珠海学院 | Integratable fast algorithm for denoising electrocardiosignal and identifying QRS waves |
| CN102393423A (en) * | 2011-09-28 | 2012-03-28 | 南京信息工程大学 | Lamb wave denoising method based on adaptive threshold value orthogonal wavelet transform |
| CN103961092A (en) * | 2014-05-09 | 2014-08-06 | 杭州电子科技大学 | Electroencephalogram signal denoising method based on self-adaption threshold processing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105652166A (en) | 2016-06-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103630808B (en) | A kind of partial discharge signal denoising method based on lifting wavelet transform | |
| CN105913393B (en) | A kind of adaptive wavelet threshold image de-noising method and device | |
| CN103576060B (en) | Based on the partial discharge signal denoising method of wavelet adaptive threshold | |
| CN107728018A (en) | A kind of noise-reduction method of power cable scene local discharge signal | |
| CN105652166B (en) | A kind of Weighted Threshold wavelet de-noising method for partial discharge on-line monitoring | |
| CN106353649A (en) | Method for denoising partial discharge signals on basis of lifting wavelet transformation | |
| CN110175508A (en) | A kind of Eigenvalue Extraction Method applied to ultrasonic partial discharge detection | |
| CN102930149A (en) | Sensor network sensing information denoising processing method based on principal component analysis (PCA) and empirical mode decomposition (EMD) | |
| CN104020402A (en) | Method for reducing noise of transformer substation partial discharging pulse signals collected through pulse triggering | |
| CN108880604B (en) | Multi-component frequency modulation interference suppression method and device in spread spectrum communication system | |
| Yusoff et al. | Denoising technique for partial discharge signal: A comparison performance between artificial neural network, fast fourier transform and discrete wavelet transform | |
| CN110081967A (en) | Mechanical oscillation signal Threshold Noise Reduction Methods based on spectrogram wavelet transformation | |
| CN112946442A (en) | Switch cabinet partial discharge detection method, terminal equipment and storage medium | |
| CN106023103B (en) | A kind of adaptive orthogonal wavelet image de-noising method based on the modeling of accurate local variance priori | |
| Yang et al. | Partial discharge signal extraction method based on EDSSV and low rank RBF neural network | |
| Sheybani et al. | Removing spikes while preserving data and noise using wavelet filter banks | |
| CN103915102B (en) | Method for noise abatement of LFM underwater sound multi-path signals | |
| Du et al. | Research on key parameters of speech denoising algorithm based on wavelet packet transform | |
| Madhu et al. | A novel algorithm for denoising of simulated partial discharge signals using adaptive wavelet thresholding methods | |
| CN115293219A (en) | Wavelet and kurtosis fused pulse signal denoising method | |
| CN112731063B (en) | Travelling wave-based multi-dimensional wavelet packet fault positioning method | |
| CN109409281A (en) | A kind of noise-reduction method based on improved wavelet threshold function | |
| Yang et al. | The de-noising method of threshold function based on wavelet | |
| Gu et al. | Weak fault feature extraction of rolling element bearings based on ensemble tunable Q-factor wavelet transform and non-dominated negentropy | |
| Dhandapani et al. | Partial discharge signal denoising based on VMD and group-sparse TV denoising |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20180508 Address after: 330096 No. 88, min Qiang Road, private science and Technology Park, Nanchang, Jiangxi Co-patentee after: State Grid Corporation of China Patentee after: Power Science Research Institute of Jiangxi Electric Power Co., Ltd. Address before: 330096 No. 88, min Qiang Road, private science and Technology Park, Nanchang, Jiangxi Co-patentee before: State Grid Corporation of China Patentee before: State's net Jiangxi Province Electric power Science Academe |