CN108445364A - Power plant's partial discharge of switchgear fault diagnosis method and system based on vibration signal - Google Patents
Power plant's partial discharge of switchgear fault diagnosis method and system based on vibration signal Download PDFInfo
- Publication number
- CN108445364A CN108445364A CN201810357886.3A CN201810357886A CN108445364A CN 108445364 A CN108445364 A CN 108445364A CN 201810357886 A CN201810357886 A CN 201810357886A CN 108445364 A CN108445364 A CN 108445364A
- Authority
- CN
- China
- Prior art keywords
- switch cabinet
- partial discharge
- power plant
- vibration
- phase space
- 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
- 238000000034 method Methods 0.000 title claims abstract description 60
- 238000003745 diagnosis Methods 0.000 title claims abstract description 20
- 238000012880 independent component analysis Methods 0.000 claims abstract description 23
- 239000011159 matrix material Substances 0.000 claims abstract description 20
- 238000010183 spectrum analysis Methods 0.000 claims abstract description 7
- 238000004458 analytical method Methods 0.000 claims abstract description 6
- 239000013598 vector Substances 0.000 claims description 18
- 238000005070 sampling Methods 0.000 claims description 7
- 238000000926 separation method Methods 0.000 claims description 7
- 230000003750 conditioning effect Effects 0.000 claims description 6
- 238000000354 decomposition reaction Methods 0.000 claims description 6
- 238000001228 spectrum Methods 0.000 claims description 6
- 101100127891 Caenorhabditis elegans let-4 gene Proteins 0.000 claims description 3
- 238000007476 Maximum Likelihood Methods 0.000 claims description 3
- 230000001143 conditioned effect Effects 0.000 claims description 3
- 230000008030 elimination Effects 0.000 claims description 3
- 238000003379 elimination reaction Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000007619 statistical method Methods 0.000 claims description 3
- 230000001934 delay Effects 0.000 claims description 2
- 238000000605 extraction Methods 0.000 abstract 1
- 230000007257 malfunction Effects 0.000 abstract 1
- 238000004064 recycling Methods 0.000 abstract 1
- 238000012795 verification Methods 0.000 abstract 1
- 238000001514 detection method Methods 0.000 description 4
- 238000009413 insulation Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000005672 electromagnetic field Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001052 transient effect 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
- G01R31/1209—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 using acoustic measurements
-
- 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
- G01R31/1227—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 of components, parts or materials
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- Testing Electric Properties And Detecting Electric Faults (AREA)
Abstract
Power plant's partial discharge of switchgear fault diagnosis method and system based on vibration signal, are first depending on phase space reconfiguration, and vibration signal higher-dimension phase space matrix is constructed in the case where not destroying signal dynamic characteristic itself；Recycling independent component analysis method carries out SNR estimation and compensation, extraction estimation source information component to vibration signal, and estimation source information component is reconstructed, and constructs the hot-tempered vibration signal that disappears；Finally offset it is hot-tempered after vibration signal carry out wavelet-packet energy spectrum analysis, judge partial discharge of switchgear malfunction according to analysis result.The present invention to collected power plant's partial discharge of switchgear vibration signal disappear hot-tempered first, solve the problems, such as that power station environment interference is stronger, again by disappear it is hot-tempered after vibration signal carry out wavelet-packet energy spectrum analysis, calculate each sub-band energy accounting, the generation of switchgear partial discharges fault and its fault severity level are judged according to each sub-band energy accounting, improve the reliability and precision of fault verification.
Description
Technical Field
The invention relates to a method and a system for diagnosing partial discharge faults of a power plant switch cabinet based on vibration signals, and belongs to the technical field of electrical equipment maintenance.
Background
The power plant switch cabinet is used as an important control device in a power plant, and whether the reliable operation of the power plant switch cabinet is directly related to the safety of the power plant. Long-term operating and maintenance experience has shown that insulation accidents inside a switchgear cabinet are one of the main causes of operating faults thereof. Partial discharge in the switch cabinet is an important factor for causing insulation aging and insulation fault in the switch cabinet. Therefore, the method has important significance for reliable operation of the whole power plant by accurately extracting the partial discharge fault signal of the switch cabinet and diagnosing the partial discharge fault. Due to the complex operating environment of the switch cabinet of the power plant, when the switch cabinet generates partial discharge, the signal is very weak and is often annihilated in strong interference noise, so that the accuracy of the detection result of the partial discharge of the switch cabinet is influenced. Therefore, eliminating the partial discharge signal noise of the switch cabinet is the key to improve the accuracy of the detection result.
According to the introduction of power plant workers, interference sources can be divided into two types according to a partial discharge detection method: firstly, vibration interference: the vibration interference mainly comes from two parts, namely vibration transmitted by the far ends (motors) of output sources of all switch cabinets along with power cables and background vibration of the large environment where the switch cabinets are located; secondly, electromagnetic interference: the electromagnetic interference is divided into the measurement interference of pulse electromagnetic waves to a vibration sensor, the interference of a space electromagnetic field to Transient Earth Voltage (TEV) measurement and the internal interference of a switch cabinet, and is subdivided into the electromagnetic wave interference of coil equipment and the interference of a fault source. Therefore, a vibration signal is selected as a method for detecting a partial discharge fault of a power plant switchgear. On one hand, the vibration signal is often annihilated in strong vibration interference noise, and the detection result of the partial discharge of the switch cabinet is influenced; on the other hand, in order to analyze the vibration interference characteristics, the vibration interference signal needs to be reserved, so an Independent Component Analysis (ICA) method is selected to extract the switch cabinet partial discharge vibration signal and reserve the vibration interference signal.
The independent component analysis method can realize effective separation of signal components with statistical independence in the mixed signal. However, the conventional ICA method requires the number of observation channels to be equal to or greater than the number of vibration source signals in application. In practical engineering, it is difficult for the observed signal to satisfy this assumption of the ICA method, which limits the application range of the ICA method. And the phase space reconstruction can construct a group of coordinate components representing the dynamic characteristics of the original system through a single system output time sequence, so that the defects of the traditional ICA method are overcome. Therefore, an ICA noise reduction method based on phase space reconstruction is provided by combining the respective advantages of the ICA method and the phase space reconstruction, and the method is applied to noise reduction of the partial discharge vibration signal of the power plant switch cabinet. And finally, performing wavelet packet energy spectrum analysis on the acquired switch cabinet vibration signal with the noise eliminated, diagnosing whether the switch cabinet has a partial discharge fault, and diagnosing the partial discharge fault of the switch cabinet by a vibration method (vibration signal).
Disclosure of Invention
The invention provides a method and a system for diagnosing partial discharge faults of a power plant switch cabinet based on vibration signals, in particular to the method and the system for diagnosing the partial discharge faults of the power plant switch cabinet in a strong interference environment.
In order to achieve the purpose, the invention adopts the following technical scheme:
a power plant switch cabinet partial discharge fault diagnosis method based on vibration signals is characterized by comprising the following steps:
step one, collecting a vibration signal of a switch cabinet, and preprocessing the signal;
calculating the time delay and the optimal embedding dimension of the vibration signal of the switch cabinet, and constructing a high-dimensional phase space matrix by reconstructing the acquired one-dimensional vibration signal through a phase space according to the calculated time delay and the optimal embedding dimension;
step three, taking the acquired high-dimensional phase space matrix as the input of an independent component analysis method, carrying out signal-noise separation on the high-dimensional phase space, outputting a series of independent components, and classifying the series of independent components to obtain an estimation source information component and a noise information component;
fourthly, discarding noise information components in the independent components, selecting estimation source information components in the independent components, reconstructing the vibration signals of the switch cabinet, and eliminating noise in the vibration signals of the switch cabinet;
and step five, performing wavelet packet energy spectrum analysis on the switch cabinet vibration signal with the noise eliminated, which is obtained in the step four, and diagnosing whether the switch cabinet has a partial discharge fault.
In order to optimize the technical scheme, the specific measures adopted further comprise:
in the first step, the frequency of sampling the vibration signal of the switch cabinet is 16kHz, and the sampling time length is 1 s.
In the second step, a mutual information method is selected to determine the time delay, and under the condition that the time delay is determined, the time delay is used as a prior condition, and an cao method is selected to determine the optimal embedding dimension.
In the second step, constructing the high-dimensional phase space matrix specifically comprises:
given a time series xnN1, 2.., N, the phase space matrix of embedding dimensions and time delays is defined by row vectors:
X＝[xi，xi+τ，…，xi+(m-1)τ]
wherein: i 1, 2., L ═ N- (m-1) τ, X is the reconstructed phase space vector, τ is the time delay, m is the embedding dimension, N is the original time series point number, L is the reconstructed phase space vector number, and X is the phase space matrix.
In the third step, the independent component analysis method adopts a maximum likelihood ICA blind separation algorithm.
In the fourth step, the elimination of the noise in the vibration signal of the switch cabinet specifically comprises:
firstly, constructing an estimation source information component space by the estimation source information components in the independent components, then multiplying each estimation source information component in the estimation source information component space by the corresponding column of the mixing matrix, and constructing a sub-phase space of the high-dimensional phase space:
Su＝auyu
wherein S isuA sub-phase space, y, of a high-dimensional phase spaceuTo estimate the source information component space, auIs the corresponding column space of the mixing matrix;
thus, the vibration signal after noise reductionComprises the following steps:
wherein,is the jth vector of the sub-phase space, and l is the number of sub-phase space vectors.
In the fifth step, the analysis by using the wavelet packet energy spectrum algorithm specifically comprises the following steps:
1) selecting a sym5 wavelet basis to carry out 5-layer wavelet packet decomposition on the vibration signal of the switch cabinet to obtain 32 sub-bands;
2) performing energy statistical analysis on the vibration signals under different frequency bands after wavelet packet 5-layer decomposition by adopting a wavelet packet energy spectrum algorithm, calculating the energy of each sub-frequency band of the vibration signals of the switch cabinet and the total energy of the total vibration signals, and forming 32 sub-frequency band energies into a 32-dimensional vector;
3) and performing energy ratio analysis on the energy of each sub-band, calculating the percentage of the energy of each sub-band in the total energy, and analyzing the local discharge fault of the switch cabinet according to the energy ratio of each sub-band.
In addition, a system based on the power plant switch cabinet partial discharge fault diagnosis method is further provided, and the system is characterized by comprising the following steps: the system comprises a switch cabinet for the power plant, a vibration sensor, a signal conditioning circuit, a data acquisition instrument, a fault diagnosis center and a PC (personal computer); the vibration sensor is arranged on the outer surfaces of a cable chamber and a bus chamber of the switch cabinet for the power plant and used for collecting vibration signals, the vibration signals are input into the data acquisition instrument after being conditioned by the signal conditioning circuit, the data acquisition instrument is used for collecting vibration signals on the outer surfaces of the cable chamber and the bus chamber of the switch cabinet for the power plant, and the fault diagnosis center analyzes and processes the collected vibration signals, so that the diagnosis of the partial discharge fault of the switch cabinet by a vibration method is realized, and the degree of the partial discharge fault is judged; and the PC is used for displaying the judgment result of the fault diagnosis center.
The model of the vibration sensor is CA-YD-103.
The model of the data acquisition instrument is NI PCIe-6320.
The invention has the beneficial effects that: the invention collects vibration signals of the cable chamber and the bus chamber outer surface of the power plant switch cabinet to judge the occurrence of the partial discharge fault of the switch cabinet and the fault severity thereof, firstly combines the respective advantages of an ICA method and phase space reconstruction, provides a noise reduction method and applies the noise reduction method to the partial discharge vibration signal of the power plant switch cabinet, and then analyzes the vibration signal of the power plant switch cabinet with noise eliminated by utilizing a wavelet packet energy spectrum algorithm to diagnose the fault state of the switch cabinet. The method comprises the steps of firstly eliminating the collected local discharge vibration signals of the power plant switch cabinet to solve the problem of strong environmental interference of the power plant, then carrying out wavelet packet energy spectrum analysis on the eliminated local discharge vibration signals of the power plant switch cabinet, calculating energy ratio of each sub-band, and judging the occurrence of local discharge faults of the switch cabinet and the fault severity of the local discharge faults according to the energy ratio of each sub-band. According to the method, the collected vibration signals of the power plant switch cabinet are eliminated, the partial discharge fault state of the switch cabinet is analyzed, and the reliability and accuracy of fault judgment can be improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is an ICA schematic block diagram of the present invention.
Fig. 3 is a block diagram of the system architecture of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
The method for diagnosing the partial discharge fault of the power plant switch cabinet based on the vibration signal as shown in FIG. 1 comprises the following steps:
firstly, collecting a vibration signal of a switch cabinet and preprocessing the signal, wherein the frequency of sampling the vibration signal of the switch cabinet is 16kHz, and the sampling time length is 1 s.
Calculating the time delay tau and the optimal embedding dimension m of the vibration signal of the switch cabinet, and reconstructing the acquired one-dimensional vibration signal through a phase space to construct a high-dimensional phase space matrix X according to the calculated time delay tau and the optimal embedding dimension m, wherein the method specifically comprises the following steps:
given a time series xnN is 1, 2, …, N, the phase space matrix X of embedding dimension m and time delay τ is defined by the row vectors:
X＝[xi，xi+τ，…，xi+(m-1)τ](1)
wherein: 1, 2, …, L ═ N- (m-1) τ; x: a reconstructed phase space vector; τ: reconstructing the sampling interval, i.e. the time delay; m: embedding dimensions; n: original time series point number; l: and the number of reconstructed phase space vectors.
And the determination of the time delay tau selects a mutual information method, and in the case of the determination of the time delay tau, the determination of the optimal embedding dimension is determined by selecting cao method as a priori condition.
Thirdly, the obtained high-dimensional phase space matrix X is used as the input of an Independent Component Analysis (ICA) method, wherein the ICA method adopts a maximum likelihood ICA (Infmax) blind separation algorithm, as shown in fig. 2, the signal-noise separation is carried out on the high-dimensional phase space, a series of independent components are output, and a series of components obtained after the processing of the independent component analysis method are classified to obtain an estimated source information component (y)1，y2，...，yn) And a noise information component (y)n+1，yn+2，...，ym)。
And fourthly, discarding the noise information component in the independent component, selecting the estimation source information component in the independent component, reconstructing the vibration signal of the switch cabinet, and eliminating the noise in the vibration signal of the switch cabinet.
In order to realize the elimination of noise, firstly, an estimation source information component space is constructed by the estimation source information components in the independent components, then, each estimation source information component in the estimation source information component space is multiplied by a corresponding column of a mixing matrix, and a sub-phase space of a high-dimensional phase space is constructed:
Su＝auyu(2)
in the formula: suA sub-phase space which is a high-dimensional phase space; y isuTo estimate a source information component space; a isuThe corresponding column space of the mixing matrix.
Thus, the vibration signal after noise reductionComprises the following steps:
in the formula:the jth vector of the subphase space; l is the number of the sub-phase space vectors.
Fifthly, performing wavelet packet energy spectrum analysis on the switch cabinet vibration signal with the noise eliminated, which is obtained in the fourth step, firstly selecting a sym5 wavelet basis to perform 5-layer wavelet packet decomposition on the switch cabinet vibration signal to obtain 32 sub-bands; then, carrying out energy statistical analysis on the vibration signals under different frequency bands after the wavelet packet 5-layer decomposition by adopting a wavelet packet energy spectrum algorithm, calculating the energy of each sub-frequency band of the vibration signals of the switch cabinet and the total energy of the total vibration signals, and forming 32 sub-frequency band energies into a 32-dimensional vector; and finally, performing energy ratio analysis on the energy of each sub-frequency band, calculating the percentage of the energy of each sub-frequency band to the total energy, and analyzing the local discharge fault of the switch cabinet according to the energy ratio of each sub-frequency band so as to diagnose the local discharge fault of the switch cabinet by a vibration method (vibration signals).
The system based on the power plant switch cabinet partial discharge fault diagnosis method shown in fig. 3 comprises a power plant switch cabinet, vibration sensors, a signal conditioning circuit, a data acquisition instrument, a fault diagnosis center and a PC, wherein the vibration sensors are installed on the outer surfaces of a cable chamber and a bus chamber of the switch cabinet and are used for acquiring vibration signals, the model of the selected vibration sensor is CA-YD-103, the vibration signals are conditioned by the signal conditioning circuit and then input into the data acquisition instrument, the data acquisition instrument is used for acquiring the vibration signals on the outer surfaces of the cable chamber and the bus chamber of the switch cabinet, the model of the selected data acquisition instrument is PCIe-6320, the fault diagnosis NI center analyzes and processes the acquired vibration signals, the local discharge fault diagnosis of the switch cabinet by a vibration method (vibration signals) is realized, and the local discharge fault degree is judged by combining energy occupation ratios of sub-frequency bands, and the PC is used for displaying the judgment result of the fault diagnosis center.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (10)
1. A power plant switch cabinet partial discharge fault diagnosis method based on vibration signals is characterized by comprising the following steps:
step one, collecting a vibration signal of a switch cabinet, and preprocessing the signal;
calculating the time delay and the optimal embedding dimension of the vibration signal of the switch cabinet, and constructing a high-dimensional phase space matrix by reconstructing the acquired one-dimensional vibration signal through a phase space according to the calculated time delay and the optimal embedding dimension;
step three, taking the acquired high-dimensional phase space matrix as the input of an independent component analysis method, carrying out signal-noise separation on the high-dimensional phase space, outputting a series of independent components, and classifying the series of independent components to obtain an estimation source information component and a noise information component;
fourthly, discarding noise information components in the independent components, selecting estimation source information components in the independent components, reconstructing the vibration signals of the switch cabinet, and eliminating noise in the vibration signals of the switch cabinet;
and step five, performing wavelet packet energy spectrum analysis on the switch cabinet vibration signal with the noise eliminated, which is obtained in the step four, and diagnosing whether the switch cabinet has a partial discharge fault.
2. The power plant switchgear partial discharge fault diagnosis method based on vibration signals as claimed in claim 1, characterized in that: in the first step, the frequency of sampling the vibration signal of the switch cabinet is 16kHz, and the sampling time length is 1 s.
3. The power plant switchgear partial discharge fault diagnosis method based on vibration signals as claimed in claim 1, characterized in that: in the second step, a mutual information method is selected to determine the time delay, and under the condition that the time delay is determined, the time delay is used as a prior condition, and an cao method is selected to determine the optimal embedding dimension.
4. The power plant switchgear partial discharge fault diagnosis method based on vibration signals as claimed in claim 3, characterized in that: in the second step, constructing the high-dimensional phase space matrix specifically comprises:
given a time series xnN1, 2.., N, the phase space matrix of embedding dimensions and time delays is defined by row vectors:
x＝[xi，xi+τ，…，xi+(m-1)τ]
wherein: i 1, 2., L ═ N- (m-1) τ, X is the reconstructed phase space vector, τ is the time delay, m is the embedding dimension, N is the original time series point number, L is the reconstructed phase space vector number, and X is the phase space matrix.
5. The power plant switchgear partial discharge fault diagnosis method based on vibration signals as claimed in claim 1, characterized in that: in the third step, the independent component analysis method adopts a maximum likelihood ICA blind separation algorithm.
6. The power plant switchgear partial discharge fault diagnosis method based on vibration signals as claimed in claim 1, characterized in that: in the fourth step, the elimination of the noise in the vibration signal of the switch cabinet specifically comprises:
firstly, constructing an estimation source information component space by the estimation source information components in the independent components, then multiplying each estimation source information component in the estimation source information component space by the corresponding column of the mixing matrix, and constructing a sub-phase space of the high-dimensional phase space:
Su＝auyu
wherein S isuA sub-phase space, y, of a high-dimensional phase spaceuTo estimate the source information component space, auIs the corresponding column space of the mixing matrix;
thus, the vibration signal after noise reductionComprises the following steps:
wherein,is the jth vector of the sub-phase space, and l is the number of sub-phase space vectors.
7. The power plant switchgear partial discharge fault diagnosis method based on vibration signals as claimed in claim 1, characterized in that: in the fifth step, the analysis by using the wavelet packet energy spectrum algorithm specifically comprises the following steps:
1) selecting a sym5 wavelet basis to carry out 5-layer wavelet packet decomposition on the vibration signal of the switch cabinet to obtain 32 sub-bands;
2) performing energy statistical analysis on the vibration signals under different frequency bands after wavelet packet 5-layer decomposition by adopting a wavelet packet energy spectrum algorithm, calculating the energy of each sub-frequency band of the vibration signals of the switch cabinet and the total energy of the total vibration signals, and forming 32 sub-frequency band energies into a 32-dimensional vector;
3) and performing energy ratio analysis on the energy of each sub-band, calculating the percentage of the energy of each sub-band in the total energy, and analyzing the local discharge fault of the switch cabinet according to the energy ratio of each sub-band.
8. A system based on the power plant switch cabinet partial discharge fault diagnosis method of any one of claims 1 to 7, characterized by comprising: the system comprises a switch cabinet for the power plant, a vibration sensor, a signal conditioning circuit, a data acquisition instrument, a fault diagnosis center and a PC (personal computer); the vibration sensor is arranged on the outer surfaces of a cable chamber and a bus chamber of the switch cabinet for the power plant and used for collecting vibration signals, the vibration signals are input into the data acquisition instrument after being conditioned by the signal conditioning circuit, the data acquisition instrument is used for collecting vibration signals on the outer surfaces of the cable chamber and the bus chamber of the switch cabinet for the power plant, and the fault diagnosis center analyzes and processes the collected vibration signals, so that the diagnosis of the partial discharge fault of the switch cabinet by a vibration method is realized, and the degree of the partial discharge fault is judged; and the PC is used for displaying the judgment result of the fault diagnosis center.
9. The system of the power plant switchgear partial discharge fault diagnosis method of claim 8, characterized in that: the model of the vibration sensor is CA-YD-103.
10. The system of the power plant switchgear partial discharge fault diagnosis method of claim 8, characterized in that: the model of the data acquisition instrument is NI PCIe-6320.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810357886.3A CN108445364A (en) | 2018-04-19 | 2018-04-19 | Power plant's partial discharge of switchgear fault diagnosis method and system based on vibration signal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810357886.3A CN108445364A (en) | 2018-04-19 | 2018-04-19 | Power plant's partial discharge of switchgear fault diagnosis method and system based on vibration signal |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108445364A true CN108445364A (en) | 2018-08-24 |
Family
ID=63200387
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810357886.3A Pending CN108445364A (en) | 2018-04-19 | 2018-04-19 | Power plant's partial discharge of switchgear fault diagnosis method and system based on vibration signal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108445364A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102735968A (en) * | 2012-06-13 | 2012-10-17 | 江苏省电力公司南京供电公司 | GIS (Geographic Information System) fault diagnosis system and method based on vibration signal spectrum analysis |
| CN105628419A (en) * | 2015-12-18 | 2016-06-01 | 国网安徽省电力公司 | System and method of diagnosing GIS (Gas Insulated Switchgear) mechanical defects based on independent component analysis denoising |
| CN105699869A (en) * | 2016-04-07 | 2016-06-22 | 国网江苏省电力公司南京供电公司 | Vibration signal based GIS equipment partial discharge detection method |
| CN105973621A (en) * | 2016-05-02 | 2016-09-28 | 国家电网公司 | Abnormal vibration analysis-based GIS (gas insulated switchgear) mechanical fault diagnosis method and system |
| CN106644423A (en) * | 2016-09-29 | 2017-05-10 | 国网江苏省电力公司检修分公司 | GIS partial discharge type identification system and GIS partial discharge type identification method based on vibration signal |
-
2018
- 2018-04-19 CN CN201810357886.3A patent/CN108445364A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102735968A (en) * | 2012-06-13 | 2012-10-17 | 江苏省电力公司南京供电公司 | GIS (Geographic Information System) fault diagnosis system and method based on vibration signal spectrum analysis |
| CN105628419A (en) * | 2015-12-18 | 2016-06-01 | 国网安徽省电力公司 | System and method of diagnosing GIS (Gas Insulated Switchgear) mechanical defects based on independent component analysis denoising |
| CN105699869A (en) * | 2016-04-07 | 2016-06-22 | 国网江苏省电力公司南京供电公司 | Vibration signal based GIS equipment partial discharge detection method |
| CN105973621A (en) * | 2016-05-02 | 2016-09-28 | 国家电网公司 | Abnormal vibration analysis-based GIS (gas insulated switchgear) mechanical fault diagnosis method and system |
| CN106644423A (en) * | 2016-09-29 | 2017-05-10 | 国网江苏省电力公司检修分公司 | GIS partial discharge type identification system and GIS partial discharge type identification method based on vibration signal |
Non-Patent Citations (5)
| Title |
|---|
| 刘清坤等: "基于相空间重构和独立分量分析的超声信号噪声消除", 《上海交通大学学报》 * |
| 张兢等: "基于小波包频带能量检测技术的故障诊断", 《微计算机信息》 * |
| 黄大伟等: "相空间重构单通道ICA算法及其在变形监测数据去噪中的应用", 《测绘工程》 * |
| 黄浩等: "基于PCA和LMD分解的滚动轴承故障特征提取方法", 《仪表技术与传感器》 * |
| 黄艳林等: "基于相空间重构与独立分量分析的局部独立投影降噪算法", 《振动与冲击》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Judd et al. | Partial discharge monitoring of power transformers using UHF sensors. Part I: sensors and signal interpretation | |
| Song et al. | Second generation wavelet transform for data denoising in PD measurement | |
| CN107894564A (en) | A kind of analog-circuit fault diagnosis method based on intersection wavelet character | |
| CN101201386B (en) | Method for locating parameter type fault of analogue integrated circuit | |
| CN104793124B (en) | On-off circuit method for diagnosing faults based on wavelet transformation and ICA feature extractions | |
| CN110146268A (en) | A kind of OLTC method for diagnosing faults based on mean value decomposition algorithm | |
| CN103018629A (en) | Method for analyzing power system fault recording data based on Marla algorithm | |
| CN104502732A (en) | Radiation source screening and positioning method based on STFT time frequency analysis | |
| CN201666935U (en) | Winding deformation tester using analyzing method of frequency response method | |
| CN109932053B (en) | State monitoring device and method for high-voltage shunt reactor | |
| Zhongrong et al. | Application of complex wavelet transform to suppress white noise in GIS UHF PD signals | |
| CN110824389A (en) | IFRA-based synchronous generator winding short-circuit fault detection method | |
| CN110991481A (en) | High-voltage shunt reactor internal loosening fault diagnosis method based on cross wavelet transformation | |
| Ward | Digital techniques for partial discharge measurements | |
| CN110703076A (en) | GIS fault diagnosis method based on vibration signal frequency domain energy ratio | |
| Chen et al. | Analysis of the partial discharge of ultrasonic signals in large motor based on Hilbert-Huang transform | |
| CN111160317A (en) | Weak signal blind extraction method | |
| Tang et al. | Blind source separation of mixed PD signals produced by multiple insulation defects in GIS | |
| Shahid et al. | Novel health diagnostics schemes analyzing corona discharge of operational aerial bundled cables in coastal regions | |
| CN117289013A (en) | Data processing method and system for pulse current test | |
| Yan et al. | Feature extraction by enhanced time–frequency analysis method based on Vold-Kalman filter | |
| DE102006005595B4 (en) | Apparatus and method for measuring spurious emissions in real time | |
| CN113671037B (en) | Post insulator vibration acoustic signal processing method | |
| CN109270404A (en) | A kind of voltage traveling wave accurate detecting method and device | |
| CN106772193B (en) | Measuring method using current transformer frequency characteristic measuring device |
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: 20180824 |