CN113447769A - PD pulse group manual rapid extraction system based on two-dimensional characteristic parameter plane - Google Patents
PD pulse group manual rapid extraction system based on two-dimensional characteristic parameter plane Download PDFInfo
- Publication number
- CN113447769A CN113447769A CN202110597449.0A CN202110597449A CN113447769A CN 113447769 A CN113447769 A CN 113447769A CN 202110597449 A CN202110597449 A CN 202110597449A CN 113447769 A CN113447769 A CN 113447769A
- Authority
- CN
- China
- Prior art keywords
- pulse
- characteristic parameter
- time sequence
- sub
- dimensional characteristic
- 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.)
- Pending
Links
- 238000000605 extraction Methods 0.000 title claims abstract description 62
- 238000001514 detection method Methods 0.000 claims abstract description 24
- 238000004422 calculation algorithm Methods 0.000 claims description 22
- 238000000034 method Methods 0.000 claims description 13
- 238000005516 engineering process Methods 0.000 claims description 5
- 239000000203 mixture Substances 0.000 claims description 2
- 230000007547 defect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000000926 separation method Methods 0.000 description 3
- 238000007621 cluster analysis Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 238000000819 phase cycle Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
The invention relates to a PD pulse group manual rapid extraction system based on a two-dimensional characteristic parameter plane, which comprises a pulse waveform-time sequence module, a two-dimensional characteristic parameter extraction and display module, a peak value-time sequence display module of a sub-pulse group and a PD pulse group manual rapid extraction module, wherein the pulse waveform-time sequence module is used for detecting and acquiring a PD source and a noise source which are mixed by an ultra-wide band; the two-dimensional characteristic parameter extraction and display module is respectively connected with a pulse waveform-time sequence module for mixing a PD source and a noise source, a peak value-time sequence display module of a sub-pulse group and a PD pulse group manual rapid extraction module, which are obtained by ultra-wideband detection. Compared with the prior art, the invention has the advantages of simplicity, rapidness, stability, practicality and the like.
Description
Technical Field
The invention relates to a detection technology of partial discharge, in particular to a PD pulse group manual rapid extraction system based on a two-dimensional characteristic parameter plane.
Background
When a Partial Discharge (PD) of a transformer device is subjected to online monitoring, live detection or offline voltage withstand diagnosis test, when a traditional detection system based on a PD pulse peak value-time sequence has multiple PD sources (including two) or abnormal interference noise sources, if the frequency spectrums of signal sources are overlapped, the acquired data is a peak value-time/phase sequence which is randomly aliased, and a corresponding discharge spectrogram is also randomly aliased, so that a diagnosis system constructed by using a single defect model database cannot give accurate analysis and judgment results. For the above working conditions, the international university of italy Bologna starts in 2002, and the national university of western ampere starts in 2008, and a multi-PD source detection technology based on broadband detection is successively proposed (fig. 1). The traditional pulse peak value-time sequence detection is changed into pulse waveform-time sequence detection, namely, a single PD pulse waveform and an acquisition time point (phase) thereof are recorded; the obtained mixed original pulse groups are quickly classified by using a certain 'method', each sub-pulse group consisting of similar pulses is converted into a peak-time sequence, and then data processing is carried out according to a traditional PRPD discharge spectrogram. Thus, the system not only solves the aliasing problem of the peak-time sequence, but also can detect and identify multiple PD sources with interference.
The above fast classification of the acquired mixed original pulse burst using some "method" is the key to the implementation of this technique, which is divided into 2 parts: 1) the method is a pulse waveform characteristic parameter extraction method; 2) the PD pulse group rapid extraction method means that the detected pulse group is distributed and displayed in a two-dimensional plane or a three-dimensional space or even a high-dimensional space based on 1) extraction results, and pulse group separation is realized by means of intelligent cluster analysis and the like to form sub-pulse groups with respective characteristics, so that separation of multiple PD sources and noise sources is realized. Therefore, the 2) PD pulse group rapid extraction method is the basis of the subsequent anti-interference analysis, pattern recognition and other work implementation. At present, the common pulse group rapid extraction method mainly adopts intelligent clustering algorithms such as fuzzy C-means (FCM) and the like.
However, in the FCM and other intelligent clustering methods in practical engineering application, firstly, the algorithm is complex and is not easy to implement in different programming software; secondly, due to the fact that the sub-pulse groups of the two-dimensional characteristic parameter plane are too close to each other, due to the fact that the intelligent algorithm is unstable and the common problem that the intelligent algorithm possibly falls into a local optimal solution, the separation result of intelligent clustering extraction is not ideal (fig. 2). Therefore, engineering application personnel urgently need a PD pulse group rapid extraction method capable of being manually controlled, so as to make up for the defects of the existing intelligent clustering algorithm.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a PD pulse group manual rapid extraction system based on a two-dimensional characteristic parameter plane.
The purpose of the invention can be realized by the following technical scheme:
a PD pulse group manual rapid extraction system based on a two-dimensional characteristic parameter plane comprises a pulse waveform-time sequence module, a two-dimensional characteristic parameter extraction and display module, a peak value-time sequence display module of a sub-pulse group and a PD pulse group manual rapid extraction module, wherein the pulse waveform-time sequence module is used for detecting and obtaining a PD source and a noise source mixed by an ultra-wide band;
the two-dimensional characteristic parameter extraction and display module is respectively connected with a pulse waveform-time sequence module for mixing a PD source and a noise source, a peak value-time sequence display module of a sub-pulse group and a PD pulse group manual rapid extraction module, which are obtained by ultra-wideband detection.
Preferably, the ultra-wideband detection module performs ultra-wideband detection on the PD source of the power device to form a pulse waveform-time sequence, where the PD source and the noise source are mixed.
Preferably, the ultra-wideband detection-acquired pulse waveform-time sequence module for mixing the PD source and the noise source records a single time-domain waveform and a pulse waveform-time sequence corresponding to a trigger time based on a pulse waveform trigger technique.
Preferably, the pulse waveform-time sequence is a mixed pulse waveform-time sequence containing a noise source.
Preferably, the two-dimensional characteristic parameter extraction and display module converts the pulse waveform original characteristics into a group of characteristic parameters with obvious physical significance or statistical significance by using a characteristic parameter extraction algorithm, and displays the characteristic parameters on a two-dimensional characteristic parameter spectrogram to visually reflect the number and distribution of PD sources and/or noise sources in the current pulse waveform-time sequence.
Preferably, the characteristic parameter extraction algorithm adopts an equivalent time-frequency method based on a pulse time-domain waveform and a frequency-domain waveform.
Preferably, the peak-time sequence display module of the sub-pulse group is used for displaying the peak-time sequence of the sub-pulse group.
Preferably, the PD pulse burst manual fast extraction module realizes fast extraction of the sub-pulse bursts in corresponding software according to a designed algorithm flow.
Preferably, the specific extraction process of the PD pulse group manual fast extraction module is as follows:
step1, determining the number N of extracted sub-pulse groups according to the distribution condition of the two-dimensional characteristic parameters;
step2, drawing an elliptic curve TQ containing the distribution of the characteristic parameters of the sub-pulse groups on a software display control by utilizing a mouse circleiWherein i is 1,.
Step3. obtaining the current elliptic curve TQiAll the characteristic parameter values correspond to the serial numbers of the pulse waveforms in the original mixed pulse group;
step4, obtaining fast extraction, namely recombinator pulse group pulse according to sequence numberi；
Step5. pulse for each subclass pulse groupiExtracting and displaying two-dimensional characteristic parameters, and classifying the nodesVerifying fruits;
step6. last, the sub-pulse group pulse is carried outiThe peak-time series of (a) is displayed, and the phase distribution or time series distribution thereof is checked.
Preferably, the sub-pulse group pulseiSub-pulse groups "class" in a two-dimensional feature parameter plane for self-similarityi。
Compared with the prior art, the invention has the following advantages:
1. under the specific distribution condition of a two-dimensional characteristic parameter plane, the number of pulse groups is determined by fusing artificial auxiliary judgment, and the sub-pulse groups required by engineering application personnel are manually circled, so that the common defects of instability of an intelligent algorithm and possible falling into a local optimal solution are avoided.
2. The calculation method is simple and easy to implement, high in efficiency, practical and reliable, and can be used for rapid classification and extraction of pulse groups, and the defects of the existing intelligent clustering algorithm are overcome.
Drawings
FIG. 1 is a schematic diagram of a conventional multi-PD source detection technique based on broadband detection;
FIG. 2 is a schematic diagram of the classification and extraction results of sub-pulse groups occurring when the FCM intelligent clustering algorithm is applied at present;
FIG. 3 is a schematic structural view of the present invention;
FIG. 4 is a schematic diagram of an example waveform-time sequence of a PD source and noise source mixture obtained by ultra-wideband detection;
FIG. 5 is a flowchart of the algorithm for manual fast extraction of PD pulse groups in the method of the present invention.
Fig. 6(a) to 6(f) are schematic diagrams of application examples of the method of the present invention in software.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
The invention aims to provide a PD pulse group manual rapid extraction method based on a two-dimensional characteristic parameter plane from the practical application. Detecting a mixed pulse waveform-time sequence formed by pulse sources such as PD (pulse width modulation) by using an ultra-wide band, and determining the number N of sub-pulse groups by artificial auxiliary judgment according to the specific distribution condition of the sequence in a two-dimensional characteristic parameter plane (such as equivalent time-frequency distribution); and drawing an elliptic curve TQ containing a sub-pulse group on a software display control for displaying a two-dimensional characteristic parameter plane by using a mouse and other toolsi(i ═ 1.., N), an elliptic curve TQ is obtainediAll the characteristic parameter values in the pulse group are corresponding to the serial numbers of the pulse waveforms in the original mixed pulse group, and the sub-pulse group pulse which has self-similarity and is 'classified' in a two-dimensional characteristic parameter plane can be obtainediTherefore, the purpose of separating the PD source from the noise source is achieved. The technology has the advantages of simplicity, rapidness, stability, practicability, independence on robustness of intelligent algorithms such as fuzzy C-means clustering (FCM) and the like, simplicity in algorithm, convenience in implementation and the like, and is suitable for constructing the pulse group rapid classification technology for ultra-wideband PD detection.
As shown in fig. 3, a PD pulse burst manual fast extraction system 1 based on a two-dimensional characteristic parameter plane includes an ultra-wideband detection-acquired pulse waveform-time sequence module 10 in which a PD source and a noise source are mixed, a two-dimensional characteristic parameter extraction and display module 11, a peak-time sequence display module 12 of a sub-pulse burst, and a PD pulse burst manual fast extraction module 13;
the two-dimensional characteristic parameter extraction and display module 11 is respectively connected with a pulse waveform-time sequence module 10 for mixing a PD source and a noise source, a peak value-time sequence display module 12 for a sub-pulse group and a PD pulse group manual rapid extraction module 13, which are obtained by ultra-wideband detection.
The pulse waveform-time sequence module 10 for hybrid PD source and noise source obtained by ultra-wideband detection performs ultra-wideband detection on the PD source of the power device to form a pulse waveform-time sequence.
The pulse waveform-time sequence module 10 for detecting the mixed PD source and noise source obtained by the ultra-wideband records a single time domain waveform and a pulse waveform-time sequence corresponding to a trigger time based on a pulse waveform trigger technique. FIG. 4 is an example of a pulse waveform-time series acquired for 50MHz analog bandwidth, 250MS/s ultra wideband detection.
The pulse waveform-time sequence is a mixed pulse waveform-time sequence containing a noise source.
The two-dimensional characteristic parameter extraction and display module 11 converts the original characteristics of the pulse waveform into a group of characteristic parameters with obvious physical significance or statistical significance by using a characteristic parameter extraction algorithm, displays the characteristic parameters on a two-dimensional characteristic parameter spectrogram, and visually reflects the number and distribution conditions of PD sources and/or noise sources contained in the current pulse waveform-time sequence. The characteristic parameter extraction algorithm adopts an equivalent time-frequency method based on pulse time domain waveforms and frequency domain waveforms, as shown in fig. 2.
The peak-time sequence display module 12 of the sub-pulse train is used for displaying the peak-time sequence of the sub-pulse train, and means that the original pulse train and the pulse train cluster analysis, that is, the extracted and separated sub-pulse train with the "class-like" characteristic is usually displayed in a spectrogram form by the peak-time sequence for diagnostic analysis such as discrimination of subsequent signals.
The PD pulse burst manual fast extraction module 13 realizes fast extraction of the sub-pulse bursts in corresponding software according to a designed algorithm flow.
As shown in fig. 5, the specific extraction process of the PD pulse group manual fast extraction module 13 is as follows:
step1, determining the number N of extracted sub-pulse groups according to the distribution condition of the two-dimensional characteristic parameters;
step2, drawing an elliptic curve TQ containing the distribution of the characteristic parameters of the sub-pulse groups on a software display control by utilizing a mouse circleiWherein i is 1,. The algorithm involved is as follows:
step3. obtainingFront elliptic curve TQiAll the characteristic parameter values correspond to the serial numbers of the pulse waveforms in the original mixed pulse group;
step4, obtaining fast extraction, namely recombinator pulse group pulse according to sequence numberi；
Step5. pulse for each subclass pulse groupiExtracting and displaying two-dimensional characteristic parameters, and verifying classification results;
step6. last, the sub-pulse group pulse is carried outiThe peak-time series of (a) is displayed, and the phase distribution or time series distribution thereof is checked.
The sub-pulse group pulseiSub-pulse groups "class" in a two-dimensional feature parameter plane for self-similarityi。
An example of 250MS/s ultra-wideband detection acquisition with 50MHz analog bandwidth is shown for fig. 6. FIG. 6(a) shows that the original mixed pulse waveform-time sequence contains 3 pulse sources; fig. 6(b) illustrates that 3 elliptic curves are circled on a two-dimensional characteristic parameter plane by using a PD pulse burst manual fast extraction algorithm flow after determining that the number of pulse sources is 3, and the extraction results of fig. 6(c) to (f) verify the feasibility and accuracy of the method of the present invention, wherein 3 self-pulse bursts corresponding to the 3 elliptic curves of fig. 6(c) are displayed in an overlapping manner, i.e., the result of the pulse burst manual fast extraction; FIG. 6(d) phase is featureless, a noise source; the pulse train phase ordering profiles of fig. 6(e) and 6(f) are for two PD sources.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A PD pulse group manual rapid extraction system based on a two-dimensional characteristic parameter plane is characterized by comprising a pulse waveform-time sequence module (10) for detecting and obtaining a PD source and a noise source mixed by an ultra-wide band, a two-dimensional characteristic parameter extraction and display module (11), a peak value-time sequence display module (12) of a sub-pulse group and a PD pulse group manual rapid extraction module (13);
the two-dimensional characteristic parameter extraction and display module (11) is respectively connected with a pulse waveform-time sequence module (10) for mixing a PD source and a noise source obtained by ultra-wide band detection, a peak value-time sequence display module (12) of a sub-pulse group and a PD pulse group manual rapid extraction module (13).
2. The system for manually and rapidly extracting the PD pulse burst based on the two-dimensional characteristic parameter plane as recited in claim 1, wherein said ultra-wideband detection module (10) that obtains the mixture of the PD source and the noise source performs ultra-wideband detection on the PD source of the power device to form a pulse waveform-time sequence.
3. The system for manually and rapidly extracting the PD pulse group based on the two-dimensional characteristic parameter plane as recited in claim 1, characterized in that the ultra-wideband detection acquired PD source and noise source mixed pulse waveform-time sequence module (10) records a single time domain waveform and a pulse waveform-time sequence corresponding to a trigger moment based on a pulse waveform trigger technology.
4. The system according to claim 2 or 3, wherein the pulse waveform-time sequence is a hybrid pulse waveform-time sequence including a noise source.
5. The system for manually and rapidly extracting the PD pulse burst based on the two-dimensional characteristic parameter plane as claimed in claim 1, wherein said two-dimensional characteristic parameter extracting and displaying module (11) utilizes a characteristic parameter extracting algorithm to convert the original characteristics of the pulse waveform into a set of characteristic parameters with obvious physical or statistical significance, and displays them on a two-dimensional characteristic parameter spectrogram, which visually reflects the number and distribution of PD sources and/or noise sources contained in the current pulse waveform-time sequence.
6. The system of claim 5, wherein the feature parameter extraction algorithm uses an equivalent time-frequency method based on pulse time-domain waveform and frequency-domain waveform.
7. The system for manual rapid extraction of PD pulse bursts based on two-dimensional characteristic parameter plane as claimed in claim 1, characterized in that said peak-time sequence display module (12) of sub-pulse bursts is used for peak-time sequence display of sub-pulse bursts.
8. The system for manually and rapidly extracting the PD pulse burst based on the two-dimensional characteristic parameter plane as claimed in claim 1, wherein said PD pulse burst manual rapid extraction module (13) implements rapid extraction of the sub-pulse burst in corresponding software according to a designed algorithm flow.
9. The system for manual rapid extraction of PD bursts based on two-dimensional characteristic parameter plane according to claim 1 or 8, characterized in that the specific extraction process of the PD burst manual rapid extraction module (13) is as follows:
step1, determining the number N of extracted sub-pulse groups according to the distribution condition of the two-dimensional characteristic parameters;
step2, drawing an elliptic curve TQ containing the distribution of the characteristic parameters of the sub-pulse groups on a software display control by utilizing a mouse circleiWherein i is 1,.
Step3. obtaining the current elliptic curve TQiAll the characteristic parameter values correspond to the serial numbers of the pulse waveforms in the original mixed pulse group;
step4, obtaining fast extraction, namely recombinator pulse group pulse according to sequence numberi；
Step5. pulse for each subclass pulse groupiExtracting and displaying two-dimensional characteristic parameters, and verifying classification results;
step6. the most importantThen performing sub-pulse group pulseiThe peak-time series of (a) is displayed, and the phase distribution or time series distribution thereof is checked.
10. The system as claimed in claim 9, wherein the sub-pulse group pulse is a pulse group of a PD, and the sub-pulse group pulse is a pulse group of a PDiSub-pulse groups "class" in a two-dimensional feature parameter plane for self-similarityi。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110597449.0A CN113447769A (en) | 2021-05-31 | 2021-05-31 | PD pulse group manual rapid extraction system based on two-dimensional characteristic parameter plane |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110597449.0A CN113447769A (en) | 2021-05-31 | 2021-05-31 | PD pulse group manual rapid extraction system based on two-dimensional characteristic parameter plane |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113447769A true CN113447769A (en) | 2021-09-28 |
Family
ID=77810424
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110597449.0A Pending CN113447769A (en) | 2021-05-31 | 2021-05-31 | PD pulse group manual rapid extraction system based on two-dimensional characteristic parameter plane |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113447769A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160116520A1 (en) * | 2013-05-10 | 2016-04-28 | Prysmian S.P.A. | Partial discharge signal processing method and apparatus employing neural network |
| CN107942210A (en) * | 2017-11-14 | 2018-04-20 | 国网上海市电力公司 | The classification of transformer pulse electric current Partial Discharge and denoising method and system |
| CN111103517A (en) * | 2020-01-20 | 2020-05-05 | 云南电网有限责任公司玉溪供电局 | Vacuum degree partial discharge pulse group classification and identification method |
| CN112364704A (en) * | 2020-10-16 | 2021-02-12 | 康威通信技术股份有限公司 | Clustering method and system based on clock synchronization partial discharge |
-
2021
- 2021-05-31 CN CN202110597449.0A patent/CN113447769A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160116520A1 (en) * | 2013-05-10 | 2016-04-28 | Prysmian S.P.A. | Partial discharge signal processing method and apparatus employing neural network |
| CN107942210A (en) * | 2017-11-14 | 2018-04-20 | 国网上海市电力公司 | The classification of transformer pulse electric current Partial Discharge and denoising method and system |
| CN111103517A (en) * | 2020-01-20 | 2020-05-05 | 云南电网有限责任公司玉溪供电局 | Vacuum degree partial discharge pulse group classification and identification method |
| CN112364704A (en) * | 2020-10-16 | 2021-02-12 | 康威通信技术股份有限公司 | Clustering method and system based on clock synchronization partial discharge |
Non-Patent Citations (4)
| Title |
|---|
| 司文荣等: "基于宽带检测的局放脉冲波形快速特征提取技术", 《电工电能新技术》 * |
| 司文荣等: "局部放电宽带检测系统分类性能的改善方法", 《西南交通大学学报》 * |
| 司文荣等: "局部放电脉冲群的实用快速分类技术及应用", 《西安交通大学学报》 * |
| 王彩雄等: "一种多源局部放电信号分离方法", 《中国电机工程学报》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104198898B (en) | Local discharge development process diagnosis method based on pulse-train analysis | |
| CN108693448B (en) | Partial discharge mode recognition system applied to power equipment | |
| CN102597790B (en) | Device and method for detecting and processing signals relating to partial electrical discharges | |
| CN103197218B (en) | A kind of high-voltage cable insulation defect partial discharge electrification detection diagnostic method | |
| CN110108992B (en) | Cable partial discharge fault identification method and system based on improved random forest algorithm | |
| CN111413588B (en) | Power distribution network single-phase earth fault line selection method | |
| CN104849633A (en) | Switchgear partial discharge mode recognition method | |
| CN107167716A (en) | A kind of shelf depreciation default kind identification method and device | |
| WO2016065959A1 (en) | Diagnostic method for ferromagnetic resonance in 10 kv neutral ungrounded system | |
| CN103698669A (en) | Local discharge pulse separating method for high-voltage power equipment | |
| CN107942210A (en) | The classification of transformer pulse electric current Partial Discharge and denoising method and system | |
| CN106483842B (en) | A kind of high-precision intelligent imitates nuclear signal generating system and its working method | |
| CN104545887A (en) | Method and device for identifying artifact electrocardiograph waveforms | |
| CN105759157A (en) | Alternating current-direct current signal detection system and measuring method for alternating current signal effective value | |
| Huda et al. | Power quality signals detection using S-transform | |
| CN105044566A (en) | GIS partial discharge fault detection method based on characteristic ultrahigh frequency signal | |
| CN110632465A (en) | HHT (Hilbert-Huang transform) normalized iteration-based high-voltage direct-current transmission line fault distance measurement method | |
| CN113805018A (en) | Intelligent identification method for partial discharge fault type of 10kV cable of power distribution network | |
| CN113447769A (en) | PD pulse group manual rapid extraction system based on two-dimensional characteristic parameter plane | |
| CN104125050A (en) | Ultrahigh-frequency RFID (radio frequency identification) reader protocol conformance testing method | |
| CN116842425A (en) | Method for detecting fault arc in low-voltage alternating current system | |
| CN112684311B (en) | Characteristic quantity extraction method for identifying oil paper insulation partial discharge type of transformer | |
| CN115310497A (en) | Abnormity identification method for bearing of numerical control machine tool | |
| CN109917245B (en) | Ultrasonic detection partial discharge signal mode identification method considering phase difference | |
| CN111398810A (en) | Slip ring electric spark detection and diagnosis system and detection and diagnosis method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210928 |