CN112583000A - Fault arc identification tripping device - Google Patents
Fault arc identification tripping device Download PDFInfo
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- CN112583000A CN112583000A CN202011353573.4A CN202011353573A CN112583000A CN 112583000 A CN112583000 A CN 112583000A CN 202011353573 A CN202011353573 A CN 202011353573A CN 112583000 A CN112583000 A CN 112583000A
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- 238000004146 energy storage Methods 0.000 claims description 11
- 238000000034 method Methods 0.000 claims description 10
- 238000000354 decomposition reaction Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 4
- 238000010891 electric arc Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
- H02H9/00—Emergency protective circuit arrangements for limiting excess current or voltage without disconnection
- H02H9/08—Limitation or suppression of earth fault currents, e.g. Petersen coil
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
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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
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Abstract
The invention discloses a fault arc identification tripping device, which is used for controlling an air switch to be closed, and comprises: the current acquisition unit is used for acquiring a main loop current signal; the fault identification unit is used for judging whether a series fault arc exists in the loop according to the main loop current signal and outputting a tripping request; the circuit breaking unit is used for executing the air switch breaking action and sending alarm information when receiving a tripping request; one end of the fault identification unit is connected with the current acquisition module, and the other end of the fault identification unit is connected with the circuit disconnection unit. The invention aims to solve the problem that the prior art cannot accurately identify the series fault arc in the complex power line and can complete the action of disconnecting the circuit in time.
Description
Technical Field
The invention relates to the technical field of fault arc identification, in particular to a fault arc identification tripping device.
Background
The arc, a phenomenon of intense light discharge with volatilization of the electrodes, has a central temperature of up to 5000K and continues to rise with an increase in the arc current. However, with the exception of metals and some rare substances, there are very few combustible solids with ignition temperatures exceeding 1000K. Therefore, when a fault arc occurs in a line, there is a high possibility that surrounding combustibles are ignited to cause a fire or even an explosion, and in China, the fault arc, particularly a series fault arc, has become a main cause of an electrical fire.
When a series arc occurs, the total current drops, similar to adding a time-varying resistor to the circuit. However, various fuses or low-voltage circuit breakers installed in civil buildings generally operate only when the line current meets an overload condition, and cannot identify the series fault arc which reduces the circuit current, so that the fault circuit cannot be cut off in time, and potential safety hazards are easily generated. Therefore, how to accurately identify the fault arc with reduced current in the multi-loop series circuit and timely complete the operation of breaking the circuit is a problem to be solved urgently at present.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a fault arc identification tripping device, which aims to solve the problem that the prior art cannot accurately identify series fault arcs in a complex power line and timely finish the operation of breaking the circuit.
In order to achieve the above object, the present invention provides a fault arc identification trip device for controlling an air switch to be closed, the fault arc identification trip device including:
the current acquisition unit is used for acquiring a main loop current signal;
the fault identification unit is used for judging whether series fault arcs exist in each stage of loop according to the main loop current signal and outputting a tripping request;
the circuit breaking unit is used for executing the air switch breaking action and sending alarm information when receiving a tripping request;
one end of the fault identification unit is connected with the current acquisition module, and the other end of the fault identification unit is connected with the circuit disconnection unit.
Optionally, in an embodiment of the present invention, the fault arc identification trip device further includes a housing;
a limiting groove is formed in the shell;
the circuit breaking unit executes the breaking action of the air switch through a telescopic structure;
the telescopic structure is arranged in the limit groove.
Optionally, in an embodiment of the present invention, the telescopic structure includes;
a connector connected to the air switch;
the contraction piece is arranged in the limiting groove, one end of the contraction piece is connected with the connecting piece, and the other end of the contraction piece is connected with one end of the limiting groove far away from the connecting piece.
Optionally, in an embodiment of the present invention, the connecting member is a hook, and the hook penetrates through the air switch structure;
the contraction part is an energy storage spring;
and a clamping groove matched with the hook is arranged in the limiting groove.
Optionally, in an embodiment of the present invention, the fault identifying unit performs wavelet transform on the acquired main loop current signal, obtains a wavelet coefficient of each frequency band, performs key feature extraction on the wavelet coefficient by using a time domain method, and constructs a feature library according to the key feature.
Optionally, in an embodiment of the present invention, the fault identifying unit selects one of the key features from the feature library, and sets a corresponding threshold according to the selected key feature;
and if the key characteristic value of the current signal to be detected exceeds a corresponding threshold value, judging that the fault electric arc exists in the loop, and outputting the tripping request.
Optionally, in an embodiment of the present invention, the fault identification unit selects at least two key features from the feature library to form a feature vector, builds a BP neural network, trains the BP neural network by using the feature vector as sample data, and inputs the feature vector of the current to be detected into the trained BP neural network to identify the fault arc after the construction of the BP neural network is completed;
and if the fault arc exists in the loop, outputting a tripping request.
Optionally, in an embodiment of the invention, the key features include kurtosis coefficients,
wherein, KiIs the crest factor of the i-th band high-frequency coefficient, Fi，jIs the jth high frequency coefficient of the ith frequency band,
is the mean value of the high frequency coefficients of the ith frequency band, NiThe number of high-frequency coefficients of the ith frequency band.
Optionally, in an embodiment of the present invention, the key feature further includes a peak-like factor,
wherein, CiPeak-like factor max (| F) of the ith band high-frequency coefficienti，j|) is the maximum value of the ith band high-frequency coefficient.
Optionally, in an embodiment of the present invention, the feature library is constructed by using the features:
G＝{K1,K2......,KI,C1,C2......,CI} (9)
wherein G is the feature library, and I is the number of decomposition layers.
This application is gathered the current signal of major loop through current acquisition unit, through carrying out fault current discernment to fault current, carries out air switch disconnection action rapidly when discerning fault circuit through circuit disconnection unit, can effectively avoid the potential safety hazard that trouble electric arc produced.
Drawings
Fig. 1 is a first structural schematic diagram of a fault arc identification tripping device of the invention;
fig. 2 is a schematic structural diagram of a fault arc identification tripping device according to the invention.
The reference numbers illustrate:
| reference numerals | Name (R) | Reference numerals | Name (R) |
| 10 | |
11 | Limiting |
| 20 | Connecting |
30 | Circuit to be tested |
| 40 | |
51 | Hook for hanging |
| 52 | |
100 | |
| 200 | Fault arc identification tripping device |
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the present invention provides a fault arc identification trip device for controlling an air switch to be closed, the fault arc identification trip device including:
the current acquisition unit is used for acquiring a main loop current signal;
the fault identification unit is used for judging whether series fault arcs exist in each stage of loop according to the main loop current signal and outputting a tripping request;
the circuit breaking unit is used for executing the air switch breaking action and sending alarm information when receiving a tripping request;
one end of the fault identification unit is connected with the current acquisition module, and the other end of the fault identification unit is connected with the circuit disconnection unit.
In this embodiment, the current collecting module is a high-precision current detecting coil, i.e., a current transformer 40. The high-precision current detection coil of the current acquisition module is arranged at the wire inlet of the main loop or each stage of sub-circuit (a live wire passes through the high-precision current detection coil), and plays a role in detecting a plurality of lower-stage loops at the same time; the current collection module transmits the collected main circuit current signal to the fault identification module through the connection line 20. The fault identification module extracts key features of the current collected by the current collection module, divides the extracted features into a normal state and a fault state based on an intelligent detection algorithm, and sends a circuit breaking instruction to the circuit breaking unit if a fault occurs in the circuit. And the circuit breaking unit executes the circuit breaking action and sends alarm information in time according to the instruction of the fault identification module.
This application is current low voltage circuit breaker's optional component, easily with current low voltage circuit breaker integration, and only need set up in the trunk circuit, can discern the series connection fault arc that arbitrary lower branch road takes place. The invention can more effectively prevent the electric fire caused by the fault electric arc and has higher practical value. The fault arc detection technology can well make up the defects of the traditional low-voltage circuit breaker, and can effectively avoid the defects that the existing fault arc detection device is large in size and has higher false alarm rate and misjudgment rate in the identification of fault arcs in a complex power utilization environment. Can better guarantee the safety of the lives and properties of the masses.
Optionally, in an embodiment of the present invention, the fault arc identification trip device further includes a housing;
a limiting groove is formed in the shell;
the circuit breaking unit executes the breaking action of the air switch through a telescopic structure;
the telescopic structure is arranged in the limit groove.
Optionally, in an embodiment of the present invention, the telescopic structure includes;
a connector connected to the air switch;
the contraction piece is arranged in the limiting groove, one end of the contraction piece is connected with the connecting piece, and the other end of the contraction piece is connected with one end of the limiting groove far away from the connecting piece.
In this embodiment, the connector is easily integrated with existing conventional current protection devices, such as air switches, as shown in fig. 2. The connecting member may be a hook and the contracting member may be a spring. When the air switch is in an off state, the energy storage spring is in a free stretching state, and when the air switch is closed, the energy storage spring is stretched until the latch on the hook is locked by the pull tube after the closing action is finished so as to ensure that the air switch is not pulled; when the circuit breaking unit receives a switching-off instruction sent by the fault identification module, the lock catch is opened, and the air switch is switched off by the hook due to the potential energy reset by the energy storage spring.
In addition, the structure for performing the opening action of the air switch may be a telescopic structure, and for the convenience of understanding, the specific structure of the telescopic structure is simply introduced, and the telescopic structure is not limited here, and in other embodiments, the function of closing/opening the air switch may also be performed by other structures.
Optionally, in an embodiment of the present invention, the connecting member is a hook, and the hook penetrates through the air switch structure;
the contraction part is an energy storage spring;
and a clamping groove matched with the hook is arranged in the limiting groove.
In the embodiment, when the air switch is used, the fault arc tripping device and the air switch are integrated together, then the circuit breaker functional component is inserted into an inner cavity of the air switch, when the air switch is in an off state, the energy storage spring is in a natural stretching state, and when the air switch is closed, the energy storage spring is stretched until a lock catch on the hook aligns with the clamping groove after the closing action is completed, and the energy storage spring is locked by the clamping groove to ensure that the air switch is not pulled to be switched off; when the breaker functional component receives a switching-off instruction sent by the fault arc detection component alarm unit, the lock catch is opened, and the air switch is switched off by the hook due to the elastic restoring force of the energy storage spring.
Optionally, in an embodiment of the present invention, the fault identifying unit performs wavelet transform on the acquired main loop current signal, obtains a wavelet coefficient of each frequency band, performs key feature extraction on the wavelet coefficient by using a time domain method, and constructs a feature library according to the key feature.
In the embodiment, based on an energy ratio method, an optimal wavelet basis function is selected; setting the number of decomposition layers, and performing wavelet transformation on the current signal according to the number of decomposition layers and the optimal wavelet basis function to obtain detail wavelet coefficients of each layer of the current signal; on the basis, the time domain method is utilized to extract the key characteristics of the detail wavelet coefficients of each layer, the time domain method and the time frequency method are organically combined, so that the peak state coefficient and the quasi-peak value factor of the wavelet detail coefficients of each layer of the current signal are obtained, and a current waveform characteristic library is constructed on the basis of the peak state coefficient and the quasi-peak value factor. Based on the feature library, any key feature can be directly selected, a corresponding threshold value is set, and when the key feature value of the current signal to be detected exceeds the corresponding threshold value, the fault arc is judged to appear; or the key features are combined into feature vectors to serve as sample data, the BP neural network is trained through the sample data, the fault current is identified through the BP neural network, the accuracy of fault current identification can be improved, and potential safety hazards caused by fault arcs can be effectively avoided.
Optionally, in an embodiment of the present invention, the fault identifying unit selects one of the key features from the feature library, and sets a corresponding threshold according to the selected key feature;
and if the key characteristic value of the current signal to be detected exceeds the corresponding threshold value of the key characteristic, judging that the fault arc exists in the loop, and outputting the tripping request.
Optionally, in an embodiment of the present invention, the fault identification unit selects at least two key features from the feature library to form a feature vector, builds a BP neural network, trains the BP neural network by using the feature vector as sample data, and inputs the feature vector of the current to be detected into the trained BP neural network to identify the fault arc after the construction of the BP neural network is completed;
and if the fault arc exists in the loop, outputting a tripping request.
In this embodiment, in order to ensure the reliability of fault arc identification, a plurality of key features (without limitation to types) are arbitrarily selected from the feature library, and even all the key features are combined to form a feature vector. In particular, feature vectors are extracted from typical experimental data and used as sample data to train the initialized BP neural network, and the BP neural network obtained by training is used to judge whether a series fault arc exists in the circuit to be tested, judge that the series fault arc exists in the circuit to be tested 30, and send out an instruction for turning off the air switch and simultaneously send out a fault arc early warning sound.
In this embodiment, the training of the BP neural network includes the following five steps:
s1: establishing a multi-load loop series fault arc experiment platform, developing fault arc experiments based on various application scenes, wherein the fault arc experiments comprise multi-load normal operation, single-load normal operation, series fault arc generation of a single-load loop, series fault arc generation of a branch of the multi-load loop, and current collection of a main trunk circuit;
s2: extracting the characteristics of the main circuit current signal to generate a characteristic vector;
s3: setting the input neuron number of the BP neural network;
s4: setting an initial weight of the BP neural network;
s5: and training the BP neural network by taking the feature vector as a sample, and modifying the weight to complete the construction of the BP neural network.
Optionally, in an embodiment of the invention, the key features include kurtosis coefficients,
wherein, KiIs the crest factor of the i-th band high-frequency coefficient, Fi，jIs the jth high frequency coefficient of the ith frequency band,
is the mean value of the high frequency coefficients of the ith frequency band, NiThe number of high-frequency coefficients of the ith frequency band.
In this embodiment, the crest factor of the wavelet coefficient creatively combines the wavelet transform and the concept in the time domain method, and constructs a fault characteristic which is more obvious and is not affected by the current of the rest load loops and the total current.
The key feature also includes a peak-like factor,
wherein, CiPeak-like factor max (| F) of the ith band high-frequency coefficienti，j|) is the maximum value of the ith band high-frequency coefficient.
In the embodiment, the peak-like factor of the wavelet coefficient is a fault feature which is more obvious and is not influenced by the rest load loop current and the total current according to the concept of the peak factor and creatively constructed by the applicant. At present, in the fault arc identification scheme disclosed in the industry, one or more of the time-frequency domain and energy domain characteristics are mostly adopted for judgment, and a scheme combining a time domain method and a time-frequency method is not adopted for the time. In the existing fault arc identification scheme, the high accuracy and the low false alarm rate of fault arc judgment are difficult to ensure under the conditions of various loads and complex circuit topological structure. In this embodiment, on the basis of wavelet transformation, the collected current signals are further based on the distribution rule of the high-frequency wavelet coefficients of the current signals, the peak state coefficients and the peak-like factors of the wavelet coefficients of each layer are respectively extracted, various characteristic quantities are extracted from the current signals, the defect that the fault identification characteristics of the previous fault arc detection are not obvious is overcome, and various detection factors are integrated, so that the fault arc detection can be more accurate.
Optionally, in an embodiment of the present invention, the feature library is:
G＝{K1,K2......,KI,C1,C2......,CI} (9)
wherein G is a feature library, I is the number of decomposition layers, KIIs the kurtosis coefficient of layer I, CIIs the peak-like factor of layer I.
In this embodiment, the calculated peak state coefficients and quasi-peak value factors of the high-frequency wavelet coefficients of each layer are combined into a feature library.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A fault arc identification trip unit for controlling the closing of an air switch, the fault arc identification trip unit comprising:
the current acquisition unit is used for acquiring a main loop current signal;
the fault identification unit is used for judging whether series fault arcs exist in each stage of loop according to the main loop current signal and outputting a tripping request;
the circuit breaking unit is used for executing the air switch breaking action and sending alarm information when receiving a tripping request;
one end of the fault identification unit is connected with the current acquisition module, and the other end of the fault identification unit is connected with the circuit disconnection unit.
2. The fault arc identification trip unit of claim 1,
the fault arc identification tripping device also comprises a shell;
a limiting groove is formed in the shell;
the circuit breaking unit executes the breaking action of the air switch through a telescopic structure;
the telescopic structure is arranged in the limit groove.
3. The fault arc identification trip unit of claim 2, wherein the telescoping structure comprises;
a connector connected to the air switch;
the contraction piece is arranged in the limiting groove, one end of the contraction piece is connected with the connecting piece, and the other end of the contraction piece is connected with one end of the limiting groove far away from the connecting piece.
4. The fault arc identification trip unit of claim 3,
the connecting piece is a hook which penetrates through the air switch structure;
the contraction part is an energy storage spring;
and a clamping groove matched with the hook is arranged in the limiting groove.
5. The fault arc identification trip device according to claim 1, wherein the fault identification unit performs wavelet transformation on the acquired main loop current signal, finds a wavelet coefficient of each frequency band, performs key feature extraction on the wavelet coefficient by using a time domain method, and constructs a feature library according to the key features.
6. The arc fault identification trip unit of claim 5, wherein the fault identification unit selects any one of the key features from the library of features and sets a corresponding threshold based on the selected key feature;
and if the key characteristic value of the current signal to be detected exceeds the corresponding threshold value, judging that the fault electric arc exists in the loop, and sending a tripping request.
7. The fault arc identification tripping device according to claim 5, wherein the fault identification unit selects at least two key features from the feature library to form a feature vector, constructs a BP neural network, trains the BP neural network by using the feature vector as sample data, and inputs the feature vector of the current to be detected into the trained BP neural network to identify the fault arc after completing the construction of the BP neural network;
and if the fault arc exists in the loop, outputting a tripping request.
8. The arc fault identification trip unit of claim 5, wherein the key characteristic comprises a kurtosis coefficient,
wherein, KiIs the crest factor of the i-th band high-frequency coefficient, Fi，jIs the jth high frequency coefficient of the ith frequency band,
is the mean value of the high frequency coefficients of the ith frequency band, NiThe number of high-frequency coefficients of the ith frequency band.
9. The arc fault identification trip unit of claim 5, wherein the key feature further comprises a crest factor-like value,
wherein, CiPeak-like factor max (| F) of the ith band high-frequency coefficienti，j|) is the maximum value of the ith band high-frequency coefficient.
10. The fault arc identification trip unit of claim 5, wherein the key features are utilized to populate a feature library:
G＝{K1，K2……，KI，C1，C2……，CI} (9)
wherein G is the feature library, and I is the number of decomposition layers.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113311227A (en) * | 2021-06-10 | 2021-08-27 | 中国科学技术大学先进技术研究院 | Current signal noise reduction method for fault arc diagnosis technology |
| CN113514720A (en) * | 2021-06-18 | 2021-10-19 | 浙江工业大学 | Arc fault identification method for low-voltage alternating-current series connection at edge side |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103543375A (en) * | 2013-08-26 | 2014-01-29 | 上海交通大学 | Method for detecting alternating-current fault arcs on basis of wavelet transformation and time-domain hybrid features |
| CN106655075A (en) * | 2016-11-18 | 2017-05-10 | 珠海格力电器股份有限公司 | Electric equipment arc fault protection device and electric equipment control system |
| CN106771898A (en) * | 2016-11-27 | 2017-05-31 | 福州大学 | Series fault arc detection device and its method based on Higher Order Cumulants identification |
| CN206907722U (en) * | 2017-02-21 | 2018-01-19 | 北京维森科技有限公司 | Air switch |
| CN108461361A (en) * | 2017-02-21 | 2018-08-28 | 北京维森科技有限公司 | Air switch and air switch control method |
| CN108646149A (en) * | 2018-04-28 | 2018-10-12 | 国网江苏省电力有限公司苏州供电分公司 | Fault electric arc recognition methods based on current characteristic extraction |
| CN110488161A (en) * | 2019-07-23 | 2019-11-22 | 南京航空航天大学 | A kind of detection of multi-load series arc faults and localization method |
| CN110824320A (en) * | 2019-12-16 | 2020-02-21 | 常熟开关制造有限公司(原常熟开关厂） | Direct current arc fault detection method and device |
| CN211374950U (en) * | 2020-01-03 | 2020-08-28 | 浙江正泰新能源开发有限公司 | Detection apparatus for collection flow box direct current series fault electric arc |
| CN111610416A (en) * | 2020-05-25 | 2020-09-01 | 南京航空航天大学 | Series arc fault intelligent circuit breaker |
| CN111707908A (en) * | 2020-07-29 | 2020-09-25 | 中国科学技术大学先进技术研究院 | Multi-load loop series fault arc detection method and device and storage medium |
-
2020
- 2020-11-25 CN CN202011353573.4A patent/CN112583000A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103543375A (en) * | 2013-08-26 | 2014-01-29 | 上海交通大学 | Method for detecting alternating-current fault arcs on basis of wavelet transformation and time-domain hybrid features |
| CN106655075A (en) * | 2016-11-18 | 2017-05-10 | 珠海格力电器股份有限公司 | Electric equipment arc fault protection device and electric equipment control system |
| CN106771898A (en) * | 2016-11-27 | 2017-05-31 | 福州大学 | Series fault arc detection device and its method based on Higher Order Cumulants identification |
| CN206907722U (en) * | 2017-02-21 | 2018-01-19 | 北京维森科技有限公司 | Air switch |
| CN108461361A (en) * | 2017-02-21 | 2018-08-28 | 北京维森科技有限公司 | Air switch and air switch control method |
| CN108646149A (en) * | 2018-04-28 | 2018-10-12 | 国网江苏省电力有限公司苏州供电分公司 | Fault electric arc recognition methods based on current characteristic extraction |
| CN110488161A (en) * | 2019-07-23 | 2019-11-22 | 南京航空航天大学 | A kind of detection of multi-load series arc faults and localization method |
| CN110824320A (en) * | 2019-12-16 | 2020-02-21 | 常熟开关制造有限公司(原常熟开关厂） | Direct current arc fault detection method and device |
| CN211374950U (en) * | 2020-01-03 | 2020-08-28 | 浙江正泰新能源开发有限公司 | Detection apparatus for collection flow box direct current series fault electric arc |
| CN111610416A (en) * | 2020-05-25 | 2020-09-01 | 南京航空航天大学 | Series arc fault intelligent circuit breaker |
| CN111707908A (en) * | 2020-07-29 | 2020-09-25 | 中国科学技术大学先进技术研究院 | Multi-load loop series fault arc detection method and device and storage medium |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113311227A (en) * | 2021-06-10 | 2021-08-27 | 中国科学技术大学先进技术研究院 | Current signal noise reduction method for fault arc diagnosis technology |
| CN113311227B (en) * | 2021-06-10 | 2022-06-24 | 中国科学技术大学先进技术研究院 | Current signal noise reduction method for fault arc diagnosis technology |
| CN113514720A (en) * | 2021-06-18 | 2021-10-19 | 浙江工业大学 | Arc fault identification method for low-voltage alternating-current series connection at edge side |
| CN113514720B (en) * | 2021-06-18 | 2024-04-19 | 浙江工业大学 | Arc fault identification method for edge side low-voltage alternating current series connection |
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Application publication date: 20210330 |