CN101046497A - Method of detecting local discharge signal in electric power equipment - Google Patents
Method of detecting local discharge signal in electric power equipment Download PDFInfo
- Publication number
- CN101046497A CN101046497A CN 200610025183 CN200610025183A CN101046497A CN 101046497 A CN101046497 A CN 101046497A CN 200610025183 CN200610025183 CN 200610025183 CN 200610025183 A CN200610025183 A CN 200610025183A CN 101046497 A CN101046497 A CN 101046497A
- Authority
- CN
- China
- Prior art keywords
- signal
- mode function
- local discharge
- intrinsic mode
- power equipment
- 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.)
- Granted
Links
Images
Landscapes
- Testing Relating To Insulation (AREA)
Abstract
The method of detecting local discharge signal in electric power equipment includes the empirical mode dissociation of acquired original signal, the adaptive filtering treatment on the obtained inherent mode functions of different orders, and final signal reconstruction with the treated new inherent mode functions to obtain the useful local discharge signal with inhibited narrow band interference signal. The method has high adaptive filtering effect and easy parameter setting, and may be used widely in the denoising treatment of local discharge signal in large electric power equipment and similar equipment.
Description
Technical field
The present invention relates to extraction to useful signal, more specifically refer to a kind of detection method to local discharge signal in electric power equipment, this method is based on the narrow-band interference rejection method of empirical modal decomposition with auto adapted filtering, be mainly used in the partial discharge monitoring system of power equipment, in all kinds of military or civilian monitoring systems, all also have wide practical use.
Technical background
Detecting shelf depreciation is the important means of the state of insulation of monitoring large scale electrical power unit, is paid close attention to by numerous insiders in recent years.When these large scale electrical power units moved, owing to exist a large amount of scenes to disturb, shelf depreciation often was submerged among the noise, in order to obtain effective local discharge signal, must be suppressed noise.In numerous at the scene interference noise source, the influence of the periodic narrow undesired signal that carrier communication, high frequency relay protection etc. causes is particularly serious, should be as the object that at first suppresses.
For narrow-band interference signal, multiple inhibition method is arranged at present, as FFT filtering, auto adapted filtering, wavelet transformation etc.Wherein, FFT filtering and wavelet transformation all are to reach the purpose that suppresses narrow band signal by the sub-band filter in the frequency domain, such way needs the priori of selective interference frequency, promptly need to know in advance the frequency of undesired signal, but how to obtain the priori of interfering frequency, also do not have at present feasible method, thereby be difficult to these methods are directly used in the processing of actual signal.
Than other method, auto adapted filtering can obtain effect preferably owing to need not to know in advance the frequency of selective interference when suppressing selective interference.But well-known, also there is some other problem in adaptive filter method self, and with regard to lowest mean square (LMS) algorithm just commonly used, its speed of convergence is slower, increases speed of convergence if adjust step-length, may cause the steady-state error increase again even disperses.Selective interference for single-frequency, analyze by Monte-Carlo (please representing) with Chinese, we can obtain optimum step-length, but the multi-frequency for the frequency distribution broad disturbs (communication carrier signal 40kHz～500kHz, broadcast singal from hundreds of kHz to several MHz), the problem that promptly has the coexistence of multi-frequency narrow-band interference signal selects suitable parameters just to seem very difficult.This is because system is difficult to approach finite impulse response (FIR) digital filter of a fixed coefficient in the process that data are constantly imported, so this filter filtering effect is often unstable, when converging factor is too small, it is undesirable to suppress noise effects, and when converging factor was excessive, useful signal was then filtered easily, also make the wave filter instability easily, serious deviation has appearred in the situation that can occur dispersing, result, therefore is difficult to select a suitable parameters.
Up to the present, how from numerous selective interference noise signals, the selective interference noise is carried out effectively inhibition, extract the local discharge signal that will detect useful large scale electrical power unit, also do not have a kind of practicable method.
Summary of the invention
To achieve these goals, the present invention adopts following technical scheme,
This detection method to local discharge signal in electric power equipment may further comprise the steps:
A at first, samples to the original signal that includes local discharge signal and noise signal around the power equipment;
B carries out empirical modal with the sampled signal that is obtained and decomposes, and is decomposed into the new intrinsic mode function of different frequency range;
C utilizes adaptive filter method to carry out auto adapted filtering to the new intrinsic mode function in each rank, with other narrow-band interference signal in each rank intrinsic mode function of filtering;
D, row signal stack reconstruct in the time of again will be through the intrinsic mode function of the new single-frequency that obtained behind the step c;
E, the local discharge signal in electric power equipment after the selective interference that is inhibited at last detects and finishes.
Described step b further may further comprise the steps:
B1 at first, extracts the envelope up and down of signal to be decomposed;
B2 again, calculates the average line of envelope up and down;
Next b3, calculates the intrinsic mode function of variant frequency range;
B4 then, calculates residual error;
B5, last, residual error is carried out iteration decompose, decompose termination condition until satisfying.
Described step c is meant the mode function of the single-frequency in each rank intrinsic mode function of different frequency range and residual error function is carried out auto adapted filtering respectively.
When carrying out filtering, introduce noise reference signal earlier, after with this signal delay, with horizontal sef-adapting filter noise reference signal is carried out filtering again, obtain the intrinsic mode function of new single-frequency.
Described steps d, when being superposeed reconstruct, new intrinsic mode function signal undertaken by following formula:
In the formula,
Be intrinsic mode function,
In technique scheme of the present invention, this method mainly is earlier the acquired original signal to be carried out empirical modal to decompose, obtain each the rank intrinsic mode function and the final residual error of signal, again each the rank intrinsic mode function that obtains being carried out auto adapted filtering respectively handles, at last, new intrinsic mode function after handling is reconstructed signal, has obtained being suppressed the useful local discharge signal behind the narrow-band interference signal.This method can be in conjunction with the frequency division characteristic and the adaptive filter method of empirical modal decomposition, the self-adaptation frequency division characteristic of utilizing empirical modal to decompose, multifrequency selective interference is decomposed different intrinsic mode functions, make multifrequency selective interference be converted into a plurality of unifrequent selective interference, the combining adaptive filtering method carries out filtering to narrow-band interference signal again.Do like this, can not only obtain, and can also solve under the multi-frequency selective interference situation problem that parameter in the common adaptive filter method is provided with difficulty than the better effect of common auto adapted filtering.This method can be widely used for the denoising of the local discharge signal of large scale electrical power unit or similar devices and handle.
Description of drawings
Fig. 1 is a detection method schematic flow sheet of the present invention.
Fig. 2 is in the method for the invention, one of empirical modal decomposition process synoptic diagram.
Fig. 3 is in the method for the invention, two of empirical modal decomposition process synoptic diagram.
Fig. 4 is an adaptive filtration theory block diagram of realizing detection method of the present invention.
Fig. 5 is the original local discharge signal waveform synoptic diagram that collection in worksite obtains.
Fig. 6 is the waveform synoptic diagram after decomposing through empirical modal.
Fig. 7 is through the waveform synoptic diagram behind the auto adapted filtering.
Fig. 8 is the local discharge signal waveform synoptic diagram that obtains after handling through method of the present invention.
Specific implementation method
In order to understand technical scheme of the present invention better, embodiments of the present invention are further described below in conjunction with accompanying drawing.
We once described in background technology, auto adapted filtering is owing to need not to know in advance the frequency of selective interference, when suppressing selective interference, can obtain effect preferably, but, this filtering method is selected very difficulty of suitable parameters, effect is often unstable, even the situation that can occur dispersing, and therefore can not adopt this method separately.So we have expected a kind of new signal analysis method that is proposed by American National aviation and space travel office (NASA) in recent years, be that empirical modal decomposes (EMD) method, this method can become signal decomposition the intrinsic mode function of different frequency range adaptively based on the local characteristics of signal.The multi-frequency narrow-band interference signal that contains in the shelf depreciation decomposes (EMD) afterwards through empirical modal, can decompose in the different intrinsic mode functions, on this basis intrinsic mode function is carried out filtering, the problem of multi-frequency Suppression of narrow band interference can be converted into the problem of a plurality of unifrequency Suppression of narrow band interference, thereby can solve the problem of sef-adapting filter parameter selection difficulty under the multi-frequency selective interference.
Please consult Fig. 1, shown in Figure 2 earlier,
Detection method to local discharge signal in electric power equipment of the present invention may further comprise the steps:
A at first, samples to the original signal that includes local discharge signal and noise signal around the power equipment.
B, the sampled signal that is obtained is carried out empirical modal decomposes, be decomposed into the new intrinsic mode function of different frequency range, as can be seen from Figure 2, after above-mentioned local discharge signal and noise signal are decomposed through empirical modal, be decomposed into several new intrinsic mode functions of different frequency section, the signal that contains the multi-frequency selective interference is after decomposing, the selective interference of different frequency can decompose in the different intrinsic mode functions, thereby multifrequency selective interference is converted into a plurality of unifrequent selective interference.
(see figure 2) when concrete the decomposition at first, is extracted the envelope up and down of signal to be decomposed, the average line of envelope and the intrinsic mode function of variant frequency range about calculating again, and count out the calculation residual error.At last, residual error is carried out iteration decompose, decompose termination condition until satisfying.
C utilizes adaptive filter method to carry out auto adapted filtering to the new intrinsic mode function in each rank, with other narrow-band interference signal in each rank intrinsic mode function of filtering.
D, the intrinsic mode function with the new single-frequency that obtained carries out signal stack reconstruct again.
E, the local discharge signal in electric power equipment after the selective interference that is inhibited at last detects and finishes.
Come method of the present invention is illustrated in more detail by an embodiment more below,
See also shown in Figure 3,
Phase one, for a given time series X (t), the process prescription that its empirical modal decomposes is as follows:
1) determines all extreme points of time series X (t);
2) maximum point and minimum point sequence are carried out interpolation with 3 spliness respectively, obtain coenvelope line u (t) and the lower envelope line v (t) of original sampled signal X (t);
3) the average line m (t) of the upper and lower envelope of calculating
m(t)＝(u(t)+v(t))/2 (1)
4) calculate the intrinsic mode function d (t) of variant frequency range by following formula
d(t)＝X(t)-m(t) (2)
If satisfy two conditions of intrinsic mode function, d (t) is the intrinsic mode function of X (t); Otherwise, regard d (t) as new time series, returned for the 1st step.Repeat said process, satisfy the condition of intrinsic mode function up to d (t).Two conditions of intrinsic mode function are: the one, and the difference of extreme point number and zero crossing number is not more than 1, two, and the average of d (t) levels off to 0.
The d (t) that is obtained by said process is the 1st rank intrinsic mode function, and note is made c
1(t), calculate:
r
1(t)＝X(t)-c
1(t) (3)
r
1(t) be corresponding to the 1st rank mode function c
1(t) residual error.
With r
1(t) regard one group of new time series as, repeat the 2nd rank intrinsic mode function c that above-mentioned 1～4 step experience decomposable process just can obtain former time series X (t)
2(t) and corresponding residual error r
2(t),
r
2(t)＝r
1(t)-c
2(t) (4)
Residual error is repeated by above-mentioned decomposable process, and signal finally can obtain single order intrinsic mode function c through once decomposing
1(t) and the first rank residual error r
1(t), to the first rank residual error r
1(t) decompose, can obtain the second rank intrinsic mode function c at last
2(t) and the second rank residual error r
2(t), so repeatedly, all be decomposed out up to all intrinsic mode functions.
Just can obtain all intrinsic mode function c of X (t)
j(t), j=1,2 ....
Work as r
jWhen (t) satisfying one of following two conditions, whole decomposable process finishes: the one, and r
j(t) less than predetermined error.The 2nd, residual error r
j(t) become a monotonic quantity, can not therefrom extract intrinsic mode function this moment again.
After above-mentioned empirical modal decomposition, obtain 13 rank intrinsic mode functions, but can obviously find out from Fig. 5, in preceding 5 intrinsic mode functions, also contain the narrow band noise undesired signal.
Subordinate phase, the intrinsic mode function c that decomposition is obtained
j(t) and residual error r
J(t) carry out auto adapted filtering respectively and handle, obtain new intrinsic mode function
And residual error
Concrete adaptive filtration theory and process are as follows:
In adaptive filter algorithm, lowest mean square (Least Mean Square-LMS) adaptive filter algorithm is an algorithm commonly used, adopting the wave filter of LMS algorithm is a horizontal sef-adapting filter structure, as shown in Figure 4, in Fig. 4, input signal x is put signal after the stack for the selective interference drawn game, and r is the reference signal of selective interference.In the processing of reality, r obtains through certain time-delay by x.The output e of wave filter is office's discharge signal that we wish to obtain.
In the sef-adapting filter that adopts the LMS algorithm, weight coefficient matrix is
W(n+1)＝W(n)+μe(n)V(n) (5)
W in the formula (n) is the weight coefficient of wave filter, and V (n) is the input signal of wave filter, and e (n) is the output of wave filter, and μ is a converging factor.In sef-adapting filter, the selection of converging factor μ is very crucial, and its value effect is to the accuracy of speed of convergence, stability and convergence solution.Usually
Wherein N is the length of signal, and P is the mean energy density of signal.
For intrinsic mode function c as shown in Figure 6
j(t) and residual error r
j(t), after handling through auto adapted filtering respectively, can obtain new intrinsic mode function as shown in Figure 7
And residual error
Comparison diagram 6 and Fig. 7, we can be clear that, originally intrinsic mode function c
j(t) narrow band noise that contains in is disturbed and has all been obtained effective inhibition, front 5 rank intrinsic mode functions are through behind the auto adapted filterings, and the noise composition is done substantially all by filtering in the arrowband that contains.
Phase III is to intrinsic mode function after treatment
And residual error
Carry out the reconstruct of local discharge signal:
With respect to the empirical modal decomposable process of X (t), it is much simple that reconstruct seems, carry out as long as adopt by the mode of following formula stack,
Wherein
Be and will detect the apparatus local discharge signal that obtains at last, (overall magnitude that narrow band noise is disturbed among this figure is up to 60mV for the local discharge signal that acquired original just illustrated in Figure 5 arrives, the major part local discharge signal has been flooded by the narrow band noise undesired signal) after disturbing and through the useful signal after superposeing through the removal narrow band noise, Fig. 8 and Fig. 5 are contrasted us can visually see, all noise interferences have obtained effective inhibition, and useful local discharge signal is then screened to come out.
Claims (5)
1, a kind of detection method to local discharge signal in electric power equipment,
It is characterized in that,
This detection method may further comprise the steps:
A at first, samples to the original signal that includes local discharge signal and noise signal around the power equipment;
B carries out empirical modal with the sampled signal that is obtained and decomposes, and is decomposed into the new intrinsic mode function of different frequency range;
C utilizes adaptive filter method to carry out auto adapted filtering to the new intrinsic mode function in each rank, with other narrow-band interference signal in each rank intrinsic mode function of filtering;
D, row signal stack reconstruct in the time of again will be through the intrinsic mode function of the new single-frequency that obtained behind the step c;
E, the local discharge signal in electric power equipment after the selective interference that is inhibited at last detects and finishes.
2, the detection method to local discharge signal in electric power equipment as claimed in claim 1,
It is characterized in that,
Described step b further may further comprise the steps:
B1 at first, extracts the envelope up and down of signal to be decomposed;
B2 again, calculates the average line of envelope up and down;
Next b3, calculates the intrinsic mode function of variant frequency range;
B4 then, calculates residual error;
B5, last, residual error is carried out iteration decompose, decompose termination condition until satisfying.
3, the detection method to local discharge signal in electric power equipment as claimed in claim 1,
It is characterized in that,
Described step c is meant the mode function of the single-frequency in each rank intrinsic mode function of different frequency range and residual error function is carried out auto adapted filtering respectively.
4, the detection method to local discharge signal in electric power equipment as claimed in claim 3,
It is characterized in that,
When carrying out filtering, introduce noise reference signal earlier, after with this signal delay, with horizontal sef-adapting filter noise reference signal is carried out filtering again, then with gathered contain local discharge signal and noise signal is carried out addition (right?), obtain the intrinsic mode function of new single-frequency.
5, the detection method to local discharge signal in electric power equipment as claimed in claim 1,
It is characterized in that:
Described steps d is undertaken by following formula when new intrinsic mode function being carried out signal stack reconstruct,
In the formula,
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2006100251838A CN100495054C (en) | 2006-03-29 | 2006-03-29 | Method of detecting local discharge signal in electric power equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CNB2006100251838A CN100495054C (en) | 2006-03-29 | 2006-03-29 | Method of detecting local discharge signal in electric power equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101046497A true CN101046497A (en) | 2007-10-03 |
| CN100495054C CN100495054C (en) | 2009-06-03 |
Family
ID=38771261
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB2006100251838A Active CN100495054C (en) | 2006-03-29 | 2006-03-29 | Method of detecting local discharge signal in electric power equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN100495054C (en) |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101685435A (en) * | 2008-09-26 | 2010-03-31 | 财团法人工业技术研究院 | Multi-dimension empirical modal analysis method for analyzing image texture |
| CN101408586B (en) * | 2008-11-28 | 2011-05-04 | 北京四方继保自动化股份有限公司 | On-line low-frequency oscillation detection and node coherence grouping method based on experience modal decomposition |
| CN101788582B (en) * | 2010-02-03 | 2012-07-04 | 华北电力大学 | Interference suppression method and device for local discharge detection of equipment |
| CN102841297A (en) * | 2012-09-10 | 2012-12-26 | 许昌学院 | Self-adaptive partial-discharge monitoring method and device for sampling frequency |
| CN103048593A (en) * | 2012-12-12 | 2013-04-17 | 安徽省电力公司亳州供电公司 | Identification method of insulation defect type of gas-insulated switchgear |
| CN103675617A (en) * | 2013-11-20 | 2014-03-26 | 西安交通大学 | Anti-interference method for high-frequency partial discharge signal detection |
| CN103701491A (en) * | 2013-12-18 | 2014-04-02 | 北京创毅视讯科技有限公司 | Method and device of restraining narrow-band interference |
| CN105606955A (en) * | 2015-12-04 | 2016-05-25 | 云南电网公司楚雄供电局 | Numerical differentiation and empirical mode decomposition-based fault line distinguishing method |
| CN105717422A (en) * | 2015-12-04 | 2016-06-29 | 国家电网公司 | High-voltage power equipment partial discharge feature extraction method and apparatus |
| CN105988067A (en) * | 2015-03-03 | 2016-10-05 | 北京华电智成电气设备有限公司 | Interference suppression method and device used in partial discharge UHF detection |
| CN106019102A (en) * | 2016-06-27 | 2016-10-12 | 国网北京市电力公司 | Signal de-noising method and apparatus |
| CN106130506A (en) * | 2016-06-13 | 2016-11-16 | 国网上海市电力公司 | A kind of band pass filter means being put on-line monitoring by remote control frequency-selecting enforcement office |
| CN106199351A (en) * | 2016-06-27 | 2016-12-07 | 国网北京市电力公司 | The sorting technique of local discharge signal and device |
| CN106546886A (en) * | 2016-11-03 | 2017-03-29 | 云南电网有限责任公司普洱供电局 | A kind of cable wave of oscillation Partial discharge signal feature extracting method |
| CN108108659A (en) * | 2017-11-23 | 2018-06-01 | 国网辽宁省电力有限公司 | Isolated island detection critical characteristic extraction method based on empirical mode decomposition |
| CN109932624A (en) * | 2019-04-01 | 2019-06-25 | 珠海华网科技有限责任公司 | A kind of cable partial discharge periodical narrow-band interference denoising method based on Gaussian scale-space |
| CN111862569A (en) * | 2020-08-12 | 2020-10-30 | 国网河南省电力公司邓州市供电公司 | Intelligent partial discharge monitoring data transmission algorithm |
| CN113671323A (en) * | 2021-07-22 | 2021-11-19 | 宁波骅厦智能科技有限公司 | Partial discharge signal detection method based on normalized minimum mean square error double-filter processing wavelet decomposition |
| CN116318437A (en) * | 2023-03-16 | 2023-06-23 | 中国科学院空天信息创新研究院 | Cross-medium communication interference suppression method and system |
-
2006
- 2006-03-29 CN CNB2006100251838A patent/CN100495054C/en active Active
Cited By (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101685435B (en) * | 2008-09-26 | 2014-03-12 | 财团法人工业技术研究院 | Multi-dimension empirical modal analysis method for analyzing image texture |
| CN101685435A (en) * | 2008-09-26 | 2010-03-31 | 财团法人工业技术研究院 | Multi-dimension empirical modal analysis method for analyzing image texture |
| CN101408586B (en) * | 2008-11-28 | 2011-05-04 | 北京四方继保自动化股份有限公司 | On-line low-frequency oscillation detection and node coherence grouping method based on experience modal decomposition |
| CN101788582B (en) * | 2010-02-03 | 2012-07-04 | 华北电力大学 | Interference suppression method and device for local discharge detection of equipment |
| CN102841297A (en) * | 2012-09-10 | 2012-12-26 | 许昌学院 | Self-adaptive partial-discharge monitoring method and device for sampling frequency |
| CN102841297B (en) * | 2012-09-10 | 2015-01-28 | 许昌学院 | Self-adaptive partial-discharge monitoring method and device for sampling frequency |
| CN103048593A (en) * | 2012-12-12 | 2013-04-17 | 安徽省电力公司亳州供电公司 | Identification method of insulation defect type of gas-insulated switchgear |
| CN103048593B (en) * | 2012-12-12 | 2015-09-09 | 国家电网公司 | A kind of recognition methods of gas-insulated switchgear insulation defect kind |
| CN103675617A (en) * | 2013-11-20 | 2014-03-26 | 西安交通大学 | Anti-interference method for high-frequency partial discharge signal detection |
| CN103701491A (en) * | 2013-12-18 | 2014-04-02 | 北京创毅视讯科技有限公司 | Method and device of restraining narrow-band interference |
| CN105988067A (en) * | 2015-03-03 | 2016-10-05 | 北京华电智成电气设备有限公司 | Interference suppression method and device used in partial discharge UHF detection |
| CN105988067B (en) * | 2015-03-03 | 2018-11-20 | 北京华电智成电气设备有限公司 | Disturbance restraining method and device in a kind of detection of partial-discharge ultrahigh-frequency |
| CN105606955A (en) * | 2015-12-04 | 2016-05-25 | 云南电网公司楚雄供电局 | Numerical differentiation and empirical mode decomposition-based fault line distinguishing method |
| CN105717422A (en) * | 2015-12-04 | 2016-06-29 | 国家电网公司 | High-voltage power equipment partial discharge feature extraction method and apparatus |
| CN105606955B (en) * | 2015-12-04 | 2019-02-05 | 云南电网公司楚雄供电局 | A kind of faulty line method of discrimination based on numerical differentiation and empirical mode decomposition |
| CN105717422B (en) * | 2015-12-04 | 2018-08-10 | 国家电网公司 | A kind of high-tension electricity apparatus local discharge feature extracting method and device |
| CN106130506A (en) * | 2016-06-13 | 2016-11-16 | 国网上海市电力公司 | A kind of band pass filter means being put on-line monitoring by remote control frequency-selecting enforcement office |
| CN106130506B (en) * | 2016-06-13 | 2019-10-25 | 国网上海市电力公司 | A kind of band pass filter means for implementing partial discharge on-line monitoring by remote control frequency-selecting |
| CN106019102A (en) * | 2016-06-27 | 2016-10-12 | 国网北京市电力公司 | Signal de-noising method and apparatus |
| CN106199351A (en) * | 2016-06-27 | 2016-12-07 | 国网北京市电力公司 | The sorting technique of local discharge signal and device |
| CN106546886A (en) * | 2016-11-03 | 2017-03-29 | 云南电网有限责任公司普洱供电局 | A kind of cable wave of oscillation Partial discharge signal feature extracting method |
| CN106546886B (en) * | 2016-11-03 | 2018-06-15 | 云南电网有限责任公司普洱供电局 | A kind of cable oscillation wave Partial discharge signal feature extracting method |
| CN108108659A (en) * | 2017-11-23 | 2018-06-01 | 国网辽宁省电力有限公司 | Isolated island detection critical characteristic extraction method based on empirical mode decomposition |
| CN108108659B (en) * | 2017-11-23 | 2021-11-09 | 国网辽宁省电力有限公司 | Island detection key feature extraction method based on empirical mode decomposition |
| CN109932624A (en) * | 2019-04-01 | 2019-06-25 | 珠海华网科技有限责任公司 | A kind of cable partial discharge periodical narrow-band interference denoising method based on Gaussian scale-space |
| CN109932624B (en) * | 2019-04-01 | 2021-07-02 | 珠海华网科技有限责任公司 | Cable partial discharge period narrow-band interference denoising method based on Gaussian scale space |
| CN111862569A (en) * | 2020-08-12 | 2020-10-30 | 国网河南省电力公司邓州市供电公司 | Intelligent partial discharge monitoring data transmission algorithm |
| CN111862569B (en) * | 2020-08-12 | 2021-07-06 | 国网河南省电力公司邓州市供电公司 | Intelligent partial discharge monitoring data transmission algorithm |
| CN113671323A (en) * | 2021-07-22 | 2021-11-19 | 宁波骅厦智能科技有限公司 | Partial discharge signal detection method based on normalized minimum mean square error double-filter processing wavelet decomposition |
| CN113671323B (en) * | 2021-07-22 | 2024-05-10 | 宁波骅厦智能科技有限公司 | Partial discharge signal detection method based on normalized least mean square error double filter after wavelet decomposition |
| CN116318437A (en) * | 2023-03-16 | 2023-06-23 | 中国科学院空天信息创新研究院 | Cross-medium communication interference suppression method and system |
| CN116318437B (en) * | 2023-03-16 | 2023-12-01 | 中国科学院空天信息创新研究院 | Cross-medium communication interference suppression method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN100495054C (en) | 2009-06-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101046497A (en) | Method of detecting local discharge signal in electric power equipment | |
| CN103278711B (en) | The early detection of a kind of short trouble and series arc faults and discrimination method | |
| CN110598166B (en) | Wavelet denoising method for adaptively determining wavelet layering progression | |
| CN102928517A (en) | Method for denoising acoustic testing data of porcelain insulator vibration based on wavelet decomposition threshold denoising | |
| CN110659621A (en) | Combined noise reduction method based on variational modal decomposition and permutation entropy | |
| CN105913835B (en) | Adaptive filtering method based on mathematical morphology | |
| CN104133248B (en) | A kind of high fidelity sound wave interference drawing method | |
| CN101076008A (en) | Method and apparatus for processing clipped wave | |
| CN102386889B (en) | Baseline shift minimizing technology, device and median filter | |
| CN102176312A (en) | System and method for reducing burst noise through wavelet trapped wave | |
| CN113269082A (en) | Partial discharge threshold denoising method based on improved variational modal decomposition | |
| CN106356072A (en) | Electronic denoising method and system thereof | |
| CN104730580B (en) | Seismic data anomalous amplitude drawing method | |
| CN102355273A (en) | Digital channelizing method and circuit | |
| CN109257068B (en) | Adaptive filtering method for power line communication signals | |
| CN108880621B (en) | Adaptive filtering method for power line communication signals | |
| CN110261910A (en) | Seismic data surface wave minimizing technology based on adaptive sparse S-transformation | |
| CN110609088B (en) | Post insulator fault identification method and device | |
| CN110596458B (en) | DEMON spectrum harmonic line spectrum and fundamental frequency automatic estimation method | |
| CN107991531A (en) | Power grid harmonic wave detection method based on variation mode decomposition and removal noise | |
| CN107070748A (en) | A kind of processing system and method for the big data that communicates | |
| CN113268924B (en) | Time-frequency characteristic-based fault identification method for on-load tap-changer of transformer | |
| CN1805011A (en) | Adaptive filter method and apparatus for improving speech quality of mobile communication apparatus | |
| CN109039379B (en) | Power line communication signal filtering method | |
| CN108427032B (en) | Frequency spectrum decomposition method and frequency-time inversion method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |