CN113917294B - Intelligent self-adaptive arc detection method based on wavelet decomposition and application device thereof - Google Patents
Intelligent self-adaptive arc detection method based on wavelet decomposition and application device thereof Download PDFInfo
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
- G06F2218/06—Denoising by applying a scale-space analysis, e.g. using wavelet analysis
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- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
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Abstract
An intelligent self-adaptive arc detection method based on wavelet decomposition and an application device thereof, comprising the steps of firstly, signal acquisition; step two, denoising the signals; step three, signal normalization; step four, wavelet decomposition reconstruction; step five, collecting and comparing target data; the method and the device utilize wavelet decomposition to process colleagues on limited-length waveforms to obtain frequency spectrums and time spectrums, have specific time response compared with simple Fourier transformation or short-time Fourier transformation, are convenient for instantaneous waveform detection of electric arcs, have simple and practical detection data, reduce the requirement on hardware, and are suitable for popularization and application in power industries such as power transmission, household power supply and the like.
Description
Technical Field
The invention relates to the field of electric energy quality detection, in particular to an intelligent self-adaptive arc detection method based on wavelet decomposition and an application device thereof.
Background
With the rapid progress of the rapid development technology of national economy, the electrification level of the whole society is higher and higher, various household appliances are driven into various families, and the electrification popularity of rural areas with big cities to remote cities is higher and higher. At present, under the age that the life experience of people is more happy, the people face huge risks and challenges, and the loss caused by life and economy in houses because the proportion of fire disasters which occur electrically is high is difficult to measure. The causes of electrical fires are mainly: (1) and (3) overcurrent. Fire disaster caused by heating of the wire due to overcurrent; (2) and (3) arc. The occurrence of arcing and thus fire may occur due to poor contact or poor operating conditions. The main causes of fires for arcs are: because the cable is folded or the artificial factors lead to the damage of the insulating protection layer, the service time is prolonged, the insulation is deteriorated, the service condition is improper, the electrical appliance is damaged, the environment is bad, the damage of animals bites, and the like, the damage of the insulating layer is easy to generate electric arcs, and the fire disaster is easy to be caused. The protection devices such as the micro-short circuit device MCB or the fuse on the market can protect the overcurrent or the leakage current, but there is a problem that if the arc occurs at this time but the current is smaller than the rated current, the protection devices cannot protect the circuit, and the arc generated by the broken cable which can cause the fire disaster cannot be effectively protected in the conventional circuit breakers.
In the prior art, there is also an arc detection device, for example, chinese patent document CN107064752a describes a discrimination algorithm for detecting an aviation fault arc, and firstly, fault arc current signals under different loads on an experimental platform are collected; then, judging whether the fault arc current signal is direct-current fault arc current or alternating-current fault arc current, and respectively extracting characteristic quantities with time domains and frequency domains; aiming at wavelet energy of the direct current fault arc, information entropy and current change rate and the wavelet energy of the alternating current fault arc, the information entropy and a fourth eigenvector function value of empirical mode decomposition are respectively used as training samples to train a support vector machine prediction model; finally, the fault and normal state of the arc are distinguished by using two support vector machine prediction models, and a plurality of characteristic quantities are selected, so that the accident of fault characteristics is reduced, and the distinguishing accuracy is improved; however, because multiple transformations such as wavelet energy, information entropy and current change rate are needed to be adopted for fault arc, strong hardware support is needed to support the application in practical application, the popularization of the technology is unfavorable,
disclosure of Invention
The invention aims to solve the technical problem of providing an intelligent self-adaptive arc detection method based on wavelet decomposition and an application device thereof, which adopt a wavelet transformation algorithm with depth optimization to accurately identify current signals of fault arcs from load currents, sort the signals, and compare and analyze the signals with historical data in a database so as to eliminate interference of arcs generated by normal operation of electric equipment, thereby accurately judging the generation of the fault arcs.
In order to solve the technical problems, the invention adopts the following technical scheme:
an intelligent self-adaptive arc detection method based on wavelet decomposition, the detection steps include:
step one, signal acquisition, namely establishing a power supply and different load connection circuits under standard working conditions, performing signal waveform simulation under various abnormal conditions, arranging a sampling circuit on the circuits, acquiring and amplifying power supply signals, and intercepting the waveform of a periodic signal of the power supply according to the power supply frequency to obtain a group of data D1[ N ];
denoising the obtained data D1[ N ] through a Mallat algorithm, performing multi-layer decomposition on the original signal, and reconstructing the decomposed data to obtain a signal D2[ N ];
step three, signal normalization, the signal D2[ N ] under different loads or abnormal conditions after reconstruction is normalized to obtain D3[ N ];
step four, data processing, detecting the obtained D3N signal, judging the times of zero crossing points of voltage or current under different loads and abnormal conditions, and selecting the maximum value M of zero crossing point data under various conditions through multiple detection and judgment;
and fifthly, arc fault judgment, namely setting zero crossing data of the target sampling data after analysis in the first to fourth steps as N, setting an error threshold coefficient K, if N is larger than K and M, conforming to the arc fault characteristics, repeatedly selecting signals of a plurality of periods to carry out judgment comparison, and judging that an arc is generated when accumulated data conforming to fault arc judgment characteristic values is larger than a set value in a preset measurement interval.
In the second step, the Mallat algorithm is used to decompose the data D1[ N ] into high and low frequency coefficients of the fourth layer, then some noise is removed from each layer of coefficients through a threshold method, a value delta is preset in each layer, the delta value is gradually decreased layer by layer, and the processed high and low frequency coefficients are substituted into the formula:
the signal D2[ N ] is obtained.
The signal normalization in the step three satisfies:
the device comprises a processor MCU, a sampling amplifying circuit, a display module, an alarm module, a wireless communication module and an interaction module, wherein a signal sensor is arranged in the sampling amplifying circuit and is connected with a target detection circuit, the output end of the sampling amplifying circuit is electrically connected with the input end of the processor MCU, and the output end of the processor MCU is connected with the alarm module.
The signal sensor adopts a current transformer.
The intelligent self-adaptive arc detection method and the application device thereof based on wavelet decomposition provided by the invention have the advantages that through carrying out denoising and normalization after amplifying the sampling signal of a target circuit, intercepting a detection value of one period in the power frequency, carrying out wavelet decomposition and reconstruction, judging whether an arc fault occurs according to the number of zero data of the processed signal.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a schematic diagram of a sampling circuit according to the present invention;
FIG. 2 is a schematic diagram of the mallat algorithm denoising in step two;
FIG. 3 is a schematic diagram showing the decomposition of the wavelet algorithm on MATLAB in step two;
FIG. 4 is a schematic flow chart of the method of the present invention;
fig. 5 is a block diagram of an application device of the present invention.
Detailed Description
As shown in fig. 4, an intelligent adaptive arc detection method based on wavelet decomposition, the detection steps include:
step one, signal acquisition, namely establishing a power supply and different load connection circuits under standard working conditions, performing signal waveform simulation under various abnormal conditions, arranging a sampling circuit on the circuits, acquiring and amplifying power supply signals, and intercepting the waveform of a periodic signal of the power supply according to the power supply frequency to obtain a group of data D1[ N ];
denoising the obtained data D1[ N ] through a Mallat algorithm, performing multi-layer decomposition on the original signal, and reconstructing the decomposed data to obtain a signal D2[ N ];
step three, signal normalization, the signal D2[ N ] under different loads or abnormal conditions after reconstruction is normalized to obtain D3[ N ];
step four, data processing, detecting the obtained D3N signal, judging the times of zero crossing points of voltage or current under different loads and abnormal conditions, and selecting the maximum value M of zero crossing point data under various conditions through multiple detection and judgment;
and fifthly, arc fault judgment, namely setting zero crossing data of the target sampling data after analysis in the first to fourth steps as N, setting an error threshold coefficient K, if N is larger than K and M, conforming to the arc fault characteristics, repeatedly selecting signals of a plurality of periods to carry out judgment comparison, and judging that an arc is generated when accumulated data conforming to fault arc judgment characteristic values is larger than a set value in a preset measurement interval.
In the second step, the Mallat algorithm is used to decompose the data D1[ N ] into high and low frequency coefficients of the fourth layer, then some noise is removed from each layer of coefficients through a threshold method, a value delta is preset in each layer, the delta value is gradually decreased layer by layer, and the processed high and low frequency coefficients are substituted into the formula:
the signal D2[ N ] is obtained.
As shown in fig. 2, after denoising, normal fluctuations in the waveform transformation process are eliminated within a single period range, and the range fluctuations of the waveform are prevented from affecting subsequent calculation results.
In the third step, since the waveforms of the normal and abnormal signals collected under different loads are very different, the collected signals have different characteristics under the conditions of different loads with different impedance properties and different load currents, and therefore, the signal data after noise removal and noise removal is normalized:
in the third step, because the normal and abnormal signal waveforms collected under different loads are quite different, the collected signals have different characteristics under the conditions of different loads with different impedance properties and different load currents, and therefore the electric arc detection under various conditions can be fully covered after the signal data subjected to noise elimination and impurity removal are subjected to normalization treatment.
The detection principle of the electric arc in the fourth step is as follows: by analyzing the arc generation mechanism, the alternating voltage in the circuit is insufficient to generate arc at the zero crossing point, so that a certain no-current area exists near the zero point, namely, the current is zero. Through repeated tests on load currents under normal conditions and under fault arc conditions, it is found that at most m zero data exist in the D3[ i ] array under normal conditions, and the zero data in the D3[ i ] array are far greater than the value under the fault arc conditions. An appropriate threshold value n (n > m) is set according to the method, whether the number of zero data in the D3 i array exceeds the threshold value is compared, signals larger than the set threshold value are counted and accumulated, so far, single measurement-processing-calculation is finished, in order to prevent misjudgment, the device adopts multiple measurement calculation, when accumulated data conforming to fault arc judgment characteristic values in a preset measurement interval are larger than a set value, the circuit is judged to generate fault arcs at the moment, and meanwhile fault arc data are stored to form a history record.
As shown in fig. 3, in a period, in the wavelet transformation process, compared with a normal signal, after the signal containing an arc is subjected to wavelet transformation, from a layer D1 to a layer D4, D1 decomposes the arc signal in the signal, and the time information in the period is continuously adjusted by passing through the maximum value of the threshold value in the layer D2 to the layer D4, so that the result is more and more accurate, and an accurate arc waveform can be obtained when the signal is subjected to the step D4, and the waveform zero crossing point is obviously different from the normal waveform for a plurality of times when each arc is generated.
As shown in fig. 1 and 5, the application device using the intelligent self-adaptive arc detection method based on wavelet decomposition comprises a processor MCU, a sampling amplifying circuit, a display module, an alarm module, a wireless communication module and an interaction module, wherein a signal sensor is arranged in the sampling amplifying circuit and is connected with a target detection circuit, the output end of the sampling amplifying circuit is electrically connected with the input end of the processor MCU, and the output end of the processor MCU is connected with the alarm module.
The signal sensor adopts a current transformer.
The device can set the relevant parameters of the fault arc judging conditions through the interaction module to realize man-machine interaction, thereby realizing a fault arc monitoring mode customized by a user and meeting the requirements of different users.
The signal sensor of the device adopts a high magnetic induction Hall element, can detect various irregular current signals such as alternating current, direct current, pulse and the like, is used for capturing weak signals caused by fault arcs, adopts a low-drift low-noise high-speed operational amplifier to amplify and isolate the captured weak signals of the fault arcs, enters an MCU (micro control unit) to carry out wavelet transformation after eliminating zero drift, extracts characteristic values of the fault arcs for calculating fault arc intensity, eliminates the interference of arcs generated by electric equipment during normal operation, and outputs control signals of real fault arc production through comparison and analysis with historical data of a database so as to avoid false alarm and omission;
when the MCU is calculated and analyzed, if the actual fault arc is judged to be generated, corresponding data and control signals are output. The data that MCU output is used for refreshing display module, and the relevant data of fault arc is displayed on display module, and the control signal of MCU output is used for starting alarm module, and alarm module drive local alarm lamp and bee calling organ, suggestion user overhauls the circuit, and alarm module can set up alarm lamp and bee calling organ's mode of operation through the interactive module of this device according to user's requirement to satisfy user's demand.
The MCU starts the wireless communication module at the same time, transmits the result of fault arc detection analysis to the user through the network, is convenient for the user to check and grasp relevant fault information in time, and can correct and adjust the device parameters through remote control according to the requirements of the user;
the interaction module used by the device realizes man-machine interaction operation through the entity keys, and completes setting of fault arc detection functions and condition parameters of the device so as to meet the requirement of customized service of users;
the power supply module used in the device adopts a rechargeable battery, and provides a high-reliability power supply for the core circuit and each functional module through the voltage stabilizing isolation circuit, so as to prevent signals of each module from interfering with each other through the power supply, and a power supply loop of the power supply module outputs through photoelectric isolation multiplexing, thereby ensuring that each module can be independently supplied with power without influencing the normal work of the module;
the arc detection device circuit structure is as follows:
the signal sensor is connected to the input end of the sampling circuit, the schematic diagram is shown in fig. 1, the output end of the sampling circuit is connected to the port of the MCU with ADC function, the display module and the wireless communication module are connected with the MCU through serial ports, the alarm module, the interaction module and the fault positioning module complete the transmission work of model instructions through the I/O port of the MCU.
Claims (5)
1. The intelligent self-adaptive arc detection method based on wavelet decomposition is characterized by comprising the following detection steps of:
step one, signal acquisition, namely establishing a power supply and different load connection circuits under standard working conditions, performing signal waveform simulation under various abnormal conditions, arranging a sampling circuit on the circuits, acquiring and amplifying power supply signals, and intercepting the waveform of a periodic signal of the power supply according to the power supply frequency to obtain a group of data D1[ N ];
denoising the obtained data D1[ N ] through a Mallat algorithm, performing multi-layer decomposition on the original signal, and reconstructing the decomposed data to obtain a signal D2[ N ];
step three, signal normalization, the signal D2[ N ] under different loads or abnormal conditions after reconstruction is normalized to obtain D3[ N ];
step four, data processing, detecting the obtained D3N signal, judging the times of zero crossing points of voltage or current under different loads and abnormal conditions, and selecting the maximum value M of zero crossing point data under various conditions through multiple detection and judgment;
and fifthly, arc fault judgment, namely setting zero crossing data of the target sampling data after analysis in the first to fourth steps as N, setting an error threshold coefficient K, if N is larger than K and M, conforming to the arc fault characteristics, repeatedly selecting signals of a plurality of periods to carry out judgment comparison, and judging that an arc is generated when accumulated data conforming to fault arc judgment characteristic values is larger than a set value in a preset measurement interval.
2. The intelligent adaptive arc detection method based on wavelet decomposition according to claim 1, wherein in the second step, the data D1[ N ] is decomposed into high-low frequency coefficients of a fourth layer by using a Mallat algorithm, then some noise is removed from each coefficient by a thresholding method, a value delta is preset in each layer, the delta value is gradually decreased layer by layer, and the processed high-low frequency coefficients are substituted into a formula:
the signal D2[ N ] is obtained.
3. The intelligent adaptive arc detection method based on wavelet decomposition according to claim 1, wherein the signal normalization in the third step satisfies:
4. the application device using the intelligent self-adaptive arc detection method based on wavelet decomposition according to any one of claims 1-3 is characterized by comprising a processor MCU, a sampling amplifying circuit, a display module, an alarm module, a wireless communication module and an interaction module, wherein a signal sensor is arranged in the sampling amplifying circuit and is connected with a target detection circuit, the output end of the sampling amplifying circuit is electrically connected with the input end of the processor MCU, and the output end of the processor MCU is connected with the alarm module.
5. The apparatus for applying a wavelet decomposition based intelligent adaptive arc detection method according to claim 4, wherein said signal sensor is a current transformer.
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| PCT/CN2021/126153 WO2023045008A1 (en) | 2021-09-25 | 2021-10-25 | Wavelet decomposition-based intelligent self-adaptive arc detection method and device using same |
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| CN116109017B (en) * | 2023-04-12 | 2023-06-09 | 国网信息通信产业集团有限公司 | Short-circuit current zero point fast and accurate prediction method and system |
| CN117930077A (en) * | 2023-12-25 | 2024-04-26 | 迪力普电子(常州)有限公司 | Wire harness on-off signal detection method based on wavelet analysis |
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| 矿用电连接器串联型故障电弧诊断方法研究;刘艳丽;郭凤仪;朱连勇;游江龙;吴仁基;张西瑞;刘丽智;王培龙;;电子测量与仪器学报(08);全文 * |
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