CN117473385A - Fault arc identification method, device, equipment and medium - Google Patents
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Abstract
The invention discloses a fault arc identification method, device, equipment and medium, belonging to the technical field of arc detection and aiming at solving the problems of low accuracy and large influence of load in the existing fault arc detection. The method comprises the following steps: acquiring current sampling data of a line to be tested; calculating the characteristic quantity of the current sampling data; the characteristic quantity comprises a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value; comparing each characteristic quantity ratio with a corresponding preset ratio threshold value, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio threshold value as detection times; the ratio of each characteristic quantity is the ratio of the characteristic value of the current average value, the current shoulder width and the wavelet high-frequency coefficient to the corresponding reference value; if the detection times exceed the arc identification threshold value, determining that the line to be detected generates fault arc. The fault arc identification method provided by the invention is used for improving the accuracy of fault arc detection and is not influenced by loads.
Description
Technical Field
The present invention relates to the field of arc detection technologies, and in particular, to a fault arc identification method, device, apparatus, and medium.
Background
Rural distribution lines are densely distributed, part of the rural distribution lines are exposed outside, and insulation aging, poor contact and the like are very easy to occur when the rural distribution lines are exposed to wind and rain. The circuit insulation is aged, the fault arc is easily caused by poor contact, the temperature of the arc center is up to 5000-15000 ℃, surrounding combustible objects are ignited, electric fire accidents occur, and the safe and reliable operation of the power distribution network is affected.
The existing method generally adopts a current amplitude as a judgment whether fault arcs are generated or not, and a power electronic load has nonlinear impedance characteristics under the background of a novel power system, so that the distortion of sine waves of an alternating current power grid is easy to cause, particularly, high-frequency electronic equipment is commonly used, the fault arcs and the normal current amplitude are difficult to distinguish, a single current amplitude threshold method is easy to cause false alarm and missing report, the fault arc recognition rate is low, the threshold values of characteristic quantities under different loads are different, and the false judgment and missing judgment are easy to cause by simply relying on the threshold value of a certain load as the threshold value of the whole fault arc detection, so that the fault arc recognition accuracy of the traditional fault arc detection method is low and the influence of the load is large.
Disclosure of Invention
The invention aims to provide a fault arc identification method, device, equipment and medium, which are used for solving the problems that the existing fault arc detection accuracy is low and the influence of load is large.
In order to achieve the above object, the present invention provides the following technical solutions:
in a first aspect, the present invention provides a fault arc identification method, including:
acquiring current sampling data of a line to be tested;
calculating the characteristic quantity of the current sampling data according to the current sampling data; the characteristic quantity comprises a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value;
comparing each characteristic quantity ratio with a corresponding preset ratio threshold value, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio threshold value as detection times; the ratio of each characteristic quantity is the ratio of the characteristic value of the current average value, the current shoulder width and the wavelet high-frequency coefficient to the corresponding reference value;
and if the detection times exceed the arc identification threshold value, determining that the line to be detected generates fault arc.
Compared with the prior art, the fault arc identification method provided by the invention comprises the steps of firstly acquiring current sampling data of a line to be tested; calculating the characteristic quantity of the current sampling data; the characteristic quantity comprises a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value; the three characteristic quantities have obvious differences when the circuit is normal and arc faults occur, so that the accuracy of fault arc detection can be improved; comparing each characteristic quantity ratio with a corresponding preset ratio threshold value, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio threshold value as detection times; the fault arc identification method is not influenced by the load of the line to be detected, simplifies the complexity of the fault arc identification method, adopts multi-feature information fusion such as a current average value, a current shoulder width, a wavelet high-frequency coefficient feature value and the like to carry out fault arc identification, and simultaneously adopts the feature value ratio as the basis for judging the fault arc, thereby solving the problem of fault arc misjudgment and misjudgment caused by the fact that the existing single current amplitude is adopted for fault identification and the threshold corresponding to the single load is adopted as the basis for judgment, accurately judging whether the line to be detected is subjected to arc discharge or not, effectively improving the accuracy of fault arc identification, and having better anti-interference performance.
In a second aspect, the present invention further provides a fault arc identification device, including:
the current sampling data acquisition module is used for acquiring current sampling data of the line to be tested;
the characteristic value calculation module is used for calculating the characteristic quantity of the current sampling data according to the current sampling data; the characteristic quantity comprises a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value;
the detection times determining module is used for comparing each characteristic quantity ratio with a corresponding preset ratio threshold value, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio threshold value as detection times; the ratio of each characteristic quantity is the ratio of the characteristic value of the current average value, the current shoulder width and the wavelet high-frequency coefficient to the corresponding reference value;
and the fault arc identification module is used for determining that the circuit to be tested generates fault arc if the detection times exceed an arc identification threshold value.
In a third aspect, the present invention also provides a fault arc identification apparatus, comprising:
the communication unit/communication interface is used for acquiring current sampling data of the line to be tested;
a processing unit/processor for calculating a feature quantity of the current sampling data according to the current sampling data; the characteristic quantity comprises a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value;
comparing each characteristic quantity ratio with a corresponding preset ratio threshold value, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio threshold value as detection times; the ratio of each characteristic quantity is the ratio of the characteristic value of the current average value, the current shoulder width and the wavelet high-frequency coefficient to the corresponding reference value;
and if the detection times exceed the arc identification threshold value, determining that the line to be detected generates fault arc.
In a fourth aspect, the present invention further provides a computer readable storage medium, where instructions are stored, and when the instructions are executed, the fault arc identification method is implemented.
Technical effects achieved by the apparatus class scheme provided in the second aspect, the device class scheme provided in the third aspect, and the computer-readable storage medium scheme provided in the fourth aspect are the same as those achieved by the method class scheme provided in the first aspect, and are not described herein.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a flow chart of a fault arc identification method provided by the invention;
FIG. 2 is a flow chart of a fault arc identification implementation provided by the present invention;
FIG. 3 is a schematic diagram of a fault arc identification apparatus according to the present invention;
fig. 4 is a schematic structural diagram of a fault arc identification device provided by the invention.
Detailed Description
In order to clearly describe the technical solution of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second", etc. are used to distinguish the same item or similar items having substantially the same function and effect. For example, the first threshold and the second threshold are merely for distinguishing between different thresholds, and are not limited in order. It will be appreciated by those of skill in the art that the words "first," "second," and the like do not limit the amount and order of execution, and that the words "first," "second," and the like do not necessarily differ.
In the present invention, the words "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the present invention, "at least one" means one or more, and "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a alone, a and B together, and B alone, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, a and b, a and c, b and c, or a, b and c, wherein a, b, c can be single or multiple.
In the prior art, current amplitude or wavelet transformation is generally adopted for current signals, fault arc identification is carried out based on wavelet transformation high-frequency coefficients, however, electronic charges have nonlinear impedance characteristics, so that sine wave distortion of an alternating current power grid is easy to cause, and fault arc and normal current are difficult to distinguish; the wavelet transformation needs to determine the wavelet function and the wavelet decomposition layer number in advance, the wavelet analysis is easy to generate a large amount of data redundancy, the time variability and the randomness of the high-frequency coefficient are strong, the arc diagnosis error is enlarged, the false alarm and the missing report of the fault arc are easy to be caused, and the setting of the threshold value is greatly influenced by the load.
In order to solve the above problems, the present invention provides a fault arc identification method, device, apparatus and medium, in which fault arc current is represented as current sudden change, current average value change, high frequency component, etc. after the current is zero-rest, the current is represented as current 'flat shoulder region' in time domain and frequency domain characteristics compared with normal current. The "flat shoulder region" and the current average variation can be extracted in the time domain by AD sampling. In the frequency domain, compared with the normal working current, the arc current shows the characteristic of broadband noise in the frequency domain, namely the spectrum amplitude of the arc current in the spectrum distribution rule is approximately inversely related to the frequency.
Therefore, the invention uses the width change of the current shoulder region, the current average value and the maximum change of the high-frequency coefficient after wavelet transformation as 3 basic characteristic quantities for fault arc identification, and identifies the fault arc after information fusion based on the 3 characteristic quantities, thereby effectively improving the accuracy of fault arc detection and being not influenced by load. The following description is made with reference to the accompanying drawings.
Fig. 1 is a flowchart of a fault arc identification method provided by the invention, the method comprises the following steps:
step 101: acquiring current sampling data of a line to be tested;
the current sampling data can be obtained through an AD sampling mode, and illustratively, the current change of the line is collected on the line to be tested through a current transformer penetrated by a zero line or a live line, a voltage signal is formed after passing through a sampling resistor, direct current bias is carried out on the voltage signal, and the current sampling data is obtained through AD sampling.
Step 102: calculating the characteristic quantity of the current sampling data according to the current sampling data;
the characteristic quantity includes a current average value, a current shoulder width, and a wavelet high-frequency coefficient characteristic value.
Step 103: comparing each characteristic quantity ratio with a corresponding preset ratio threshold value, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio threshold value as detection times;
the ratio of each characteristic quantity is the ratio of the characteristic value of the high-frequency coefficient of the current average value, the current shoulder width and the wavelet to the corresponding reference value. And taking the current average value, the current shoulder width and the wavelet high-frequency coefficient characteristic value in the normal state of the line as reference values.
Step 104: and if the detection times exceed the arc identification threshold value, determining that the line to be detected generates fault arc.
The arc identification threshold is a threshold set according to the number of sampling periods, and the arc identification threshold is smaller than the sampling period number, and for example, if the number of sampling periods is 10, the arc identification threshold may be a value of 9, 8, 7, or the like.
According to the method, the current average value, the current shoulder width and the wavelet high-frequency coefficient characteristic value have obvious differences when the circuit is in a normal state and an arc fault occurs, but the threshold value corresponding to each characteristic value under different loads is different, and misjudgment and missed judgment can be caused by simply relying on the threshold value corresponding to a certain load as the threshold value of the whole fault arc detection, so that the fault arc identification method takes the ratio of the characteristic values of the circuit in the arc fault state and the normal state as the judgment basis, compared with the method taking the characteristic value as the judgment basis, the method has the advantages that the influence of the load type is smaller, the applicability is wider, the fault arc is identified by adopting the three characteristic values of the current average value, the current shoulder width and the wavelet high-frequency coefficient characteristic value, the accuracy of fault arc identification can be greatly improved, meanwhile, the complexity of fault arc identification is simplified, and the average accuracy of the fault arc identification method is over 90% through testing, and the fault arc identification method has good anti-interference performance.
As an alternative, the current average is calculated as shown in the formula:
wherein I is a I (M) is the current sampling instantaneous value at the mth moment, and M is the number of current sampling points in each period.
As an alternative, calculating the current shoulder width from the current sample data includes:
setting the sampling value smaller than the zero threshold value in the current sampling data as 1, and setting the sampling value higher than the zero threshold value as 0 to obtain a string of binary sequences containing 0 and 1;
smoothing the binary sequence, namely median filtering, to obtain a target binary sequence;
and determining the width of the current shoulder region according to the target binary sequence. Illustratively, if the target binary sequence is "000111", "010111", the current shoulder region width is 3. Since the continuous 1 only appears once in the target binary sequence, the flat shoulder width of a single period can be calculated by calculating the number of continuous 1. The flat shoulder width is the flat shoulder duration.
As an alternative way, after the fault arc is generated, the mode maximum value of the high-frequency coefficient after the wavelet transformation of the current sampling data is obviously increased, especially the high-frequency coefficient, so that the change of the mode maximum value of the high-frequency coefficient can be used as the basis for the occurrence or non-occurrence of the fault arc; the calculating the wavelet high-frequency coefficient characteristic value of the current sampling data according to the current sampling data specifically comprises the following steps:
carrying out wavelet transformation processing on the current sampling data to obtain a high-frequency coefficient;
and carrying out coding treatment on the high-frequency coefficient to obtain a target coefficient sequence.
Specifically, a threshold is set according to the maximum value of the modulus in the high-frequency coefficient sequence, and the modulus lower than the threshold is classified as 0, so that a target coefficient sequence which is equal to the original series of high-frequency coefficients and contains a certain number of new maximum values can be obtained.
And carrying out quantity summation operation on the target coefficient sequence to obtain the characteristic value of the wavelet high-frequency coefficient.
As an alternative, the current average value, the current shoulder width, and the wavelet high frequency coefficient feature value include a current average value, a current shoulder width, and a wavelet high frequency coefficient feature value for N periods; n is greater than 1; the arc identification threshold is less than N; comparing the characteristic quantity ratios with corresponding preset ratio thresholds, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio thresholds as detection times comprises the following steps:
comparing the ratio of the characteristic quantity of each period with a corresponding preset ratio threshold value;
comparing the ratio of the current average value to the current average value reference value with a first preset ratio threshold; comparing the ratio of the current shoulder width to the current shoulder width reference value with a second preset ratio threshold; and comparing the ratio of the wavelet high-frequency coefficient characteristic value to the wavelet high-frequency coefficient characteristic value reference value with a third preset ratio threshold value.
When the three ratios are all larger than the corresponding preset ratio threshold values, the detection times are increased by one;
traversing N periods to obtain the detection times.
For example, each sampling period is set to be 20ms, the sampling period N is set to be 10, the arc identification threshold is set to be 7, fault arc identification is carried out on current sampling data in 10 sampling periods, and firstly, the current average value, the current shoulder width and the wavelet high-frequency coefficient characteristic value in the normal state of 10 sampling periods are stored as reference values; and (3) sampling current sampling data of 10 periods of the line to be tested, calculating three characteristic values of the current sampling data, comparing the ratio of each characteristic value to the reference value with a corresponding preset ratio threshold value, when the three ratio values in each period are larger than the preset ratio threshold value, not updating the reference value, adding 1 to the detection times, and when the detection times in the 10 periods are larger than 7, judging that the line to be tested generates fault arc.
As an optional manner, the obtaining the current sampling data of the line to be tested further includes:
filtering low-frequency harmonic components of the current sampling data by adopting a high-pass filter to obtain filtered current sampling data;
and calculating a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value of the current sampling data according to the filtered current sampling data.
In the frequency domain, when arc discharge occurs, the higher harmonic components contained in the current are obviously increased, and the high-pass filter is adopted to filter out the low-frequency harmonic components which do not meet the conditions, so that the low-frequency noise interference can be reduced, and meanwhile, the calculation amount of calculating the current average value, the current flat shoulder width and the wavelet high-frequency coefficient characteristic value is reduced.
As an alternative, the determining that the line under test generates the fault arc further includes:
outputting a tripping signal;
and alarming or turning off the power supply of the circuit to be tested according to the tripping signal.
The alarm can be arranged, the disconnection signal is sent to the alarm, the alarm gives an alarm, or the disconnection signal can be directly sent to a circuit breaker connected with a circuit, and the circuit breaker directly turns off a current switch after receiving the disconnection signal.
In specific implementation, referring to fig. 2, firstly, an AD sampling method is adopted to collect current sampling data, then signal processing is carried out on the current sampling data, low-frequency harmonic components which do not meet the conditions in the current sampling data are filtered, then arc characteristic quantity calculation is carried out, the width of a flat shoulder region is calculated through the current sampling data, the characteristic value of a wavelet high-frequency coefficient is calculated, whether an arc occurs on a line to be detected is judged according to the three characteristic quantities, if the arc occurs, the shedding buckle is executed, and if the arc does not occur, the next line to be detected is detected.
The embodiment of the invention can divide the functional modules according to the method example, for example, each functional module can be divided corresponding to each function, or two or more functions can be integrated in one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment of the present invention, the division of the modules is schematic, which is merely a logic function division, and other division manners may be implemented in actual implementation.
Fig. 3 shows a schematic structural diagram of a fault arc identification apparatus provided by the present invention in the case of dividing each functional module by corresponding each function. As shown in fig. 3, the apparatus includes:
the current sampling data acquisition module 301 is configured to acquire current sampling data of a line to be tested;
a feature value calculating module 302, configured to calculate a feature value of the current sampling data according to the current sampling data; the characteristic quantity comprises a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value;
the detection times determining module 303 is configured to compare each feature quantity ratio with a corresponding preset ratio threshold, and determine times when all feature quantity ratios are greater than the corresponding preset ratio threshold as detection times; the ratio of each characteristic quantity is the ratio of the characteristic value of the current average value, the current shoulder width and the wavelet high-frequency coefficient to the corresponding reference value;
and the fault arc identification module 304 is configured to determine that the line to be tested generates a fault arc if the detection frequency exceeds an arc identification threshold.
Optionally, the eigenvalue calculation module 302 may include a wavelet high frequency coefficient eigenvalue calculation unit, where the wavelet high frequency coefficient eigenvalue calculation unit may specifically be used to:
performing wavelet transformation on the current sampling data to obtain a high-frequency coefficient;
encoding the high-frequency coefficient to obtain a target coefficient sequence;
and carrying out quantity summation operation on the target coefficient sequence to obtain a wavelet high-frequency coefficient characteristic value.
Optionally, the calculation formula of the current average value is:
wherein I is a I (M) is the current sampling instantaneous value at the mth moment, and M is the number of current sampling points in each period.
Optionally, the feature value calculating module 302 may further include a current shoulder width calculating unit, which may specifically be configured to:
setting a sampling value smaller than a zero threshold value in the current sampling data as 1, and setting a sampling value higher than the zero threshold value as 0 to obtain a binary sequence;
smoothing the binary sequence to obtain a target binary sequence;
and determining the width of the current shoulder region according to the target binary sequence.
Optionally, the current average value, the current shoulder width and the wavelet high-frequency coefficient characteristic value comprise the current average value, the current shoulder width and the wavelet high-frequency coefficient characteristic value of N periods; n is greater than 1; the arc identification threshold is less than N; the detection number determining module 303 may include:
the comparison unit is used for comparing the characteristic quantity ratio value of each period with a corresponding preset ratio threshold value;
the detection frequency unit is used for adding one to the detection frequency when the three ratios are all larger than the corresponding preset ratio threshold;
and the traversing unit is used for traversing N periods to obtain the detection times.
Optionally, the device further includes a signal processing module, which may specifically be configured to filter out a low-frequency harmonic component of the current sampling data by using a high-pass filter, so as to obtain filtered current sampling data.
Optionally, the device further includes a trip signal processing module, which may specifically include:
the tripping signal output unit is used for outputting a tripping signal;
and the alarm or turn-off power supply unit is used for alarming or turning off the power supply of the circuit to be tested according to the tripping signal.
Fig. 4 shows a schematic structural diagram of a fault arc identification apparatus provided by the present invention in the case of using a corresponding integrated unit. As shown in fig. 4, the apparatus includes:
the communication unit/communication interface is used for acquiring current sampling data of the line to be tested;
a processing unit/processor for calculating a feature quantity of the current sampling data according to the current sampling data; the characteristic quantity comprises a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value;
comparing each characteristic quantity ratio with a corresponding preset ratio threshold value, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio threshold value as detection times; the ratio of each characteristic quantity is the ratio of the characteristic value of the current average value, the current shoulder width and the wavelet high-frequency coefficient to the corresponding reference value;
and if the detection times exceed the arc identification threshold value, determining that the line to be detected generates fault arc.
As shown in FIG. 4, the processor may be a general purpose central processing unit (central processing unit, CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present invention. The communication interface may be one or more. The communication interface may use any transceiver-like device for communicating with other devices or communication networks.
As shown in fig. 4, the terminal device may further include a communication line. The communication line may include a pathway to communicate information between the aforementioned components.
Optionally, as shown in fig. 4, the terminal device may further include a memory. The memory is used for storing computer-executable instructions for executing the scheme of the invention, and the processor is used for controlling the execution. The processor is configured to execute computer-executable instructions stored in the memory, thereby implementing the method provided by the embodiment of the invention.
As shown in fig. 4, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), a compact disc read-only memory (compact disc read-only memory) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, without limitation. The memory may be stand alone and be coupled to the processor via a communication line. The memory may also be integrated with the processor.
Alternatively, the computer-executable instructions in the embodiments of the present invention may be referred to as application program codes, which are not particularly limited in the embodiments of the present invention.
In a specific implementation, as one embodiment, as shown in FIG. 4, the processor may include one or more CPUs, such as CPU0 and CPU1 in FIG. 4.
In a specific implementation, as an embodiment, as shown in fig. 4, the terminal device may include a plurality of processors, such as the processors in fig. 4. Each of these processors may be a single-core processor or a multi-core processor.
In one aspect, a computer readable storage medium is provided, in which instructions are stored, which when executed implement the fault arc identification method described above.
The above description has been presented mainly in terms of interaction between the modules, and the solution provided by the embodiment of the present invention is described. It is to be understood that, in order to achieve the above-described functions, they comprise corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are performed in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, a terminal, a user equipment, or other programmable apparatus. The computer program or instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer program or instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that integrates one or more available media. The usable medium may be a magnetic medium, e.g., floppy disk, hard disk, tape; optical media, such as digital video discs (digital video disc, DVD); but also semiconductor media such as solid state disks (solid state drive, SSD).
Although the invention is described herein in connection with various embodiments, other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps, and the "a" or "an" does not exclude a plurality. A single processor or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.
Although the invention has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the invention. Accordingly, the specification and drawings are merely exemplary illustrations of the present invention as defined in the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the invention. It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. A fault arc identification method, comprising:
acquiring current sampling data of a line to be tested;
calculating the characteristic quantity of the current sampling data according to the current sampling data; the characteristic quantity comprises a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value;
comparing each characteristic quantity ratio with a corresponding preset ratio threshold value, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio threshold value as detection times; the ratio of each characteristic quantity is the ratio of the characteristic value of the current average value, the current shoulder width and the wavelet high-frequency coefficient to the corresponding reference value;
and if the detection times exceed the arc identification threshold value, determining that the line to be detected generates fault arc.
2. The fault arc identification method of claim 1 wherein calculating a wavelet high frequency coefficient feature value of current sample data from the current sample data comprises:
performing wavelet transformation on the current sampling data to obtain a high-frequency coefficient;
encoding the high-frequency coefficient to obtain a target coefficient sequence;
and carrying out quantity summation operation on the target coefficient sequence to obtain a wavelet high-frequency coefficient characteristic value.
3. The fault arc identification method according to claim 1, wherein the current average value, current shoulder width, and wavelet high frequency coefficient characteristic values include a current average value, current shoulder width, and wavelet high frequency coefficient characteristic values for N cycles; n is greater than 1; the arc identification threshold is less than N; comparing the characteristic quantity ratios with corresponding preset ratio thresholds, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio thresholds as detection times comprises the following steps:
comparing the ratio of the characteristic quantity of each period with a corresponding preset ratio threshold value;
when the three ratios are all larger than the corresponding preset ratio threshold values, the detection times are increased by one;
traversing N periods to obtain the detection times.
4. The fault arc identification method of claim 1 wherein the current average is calculated as:
wherein I is a I (M) is the current sampling instantaneous value at the mth moment, and M is the number of current sampling points in each period.
5. The fault arc identification method of claim 1 wherein calculating a current shoulder width from the current sample data comprises:
setting a sampling value smaller than a zero threshold value in the current sampling data as 1, and setting a sampling value higher than the zero threshold value as 0 to obtain a binary sequence;
smoothing the binary sequence to obtain a target binary sequence;
and determining the width of the current shoulder region according to the target binary sequence.
6. The fault arc identification method according to claim 1, wherein the step of obtaining current sampling data of the line to be tested further comprises:
filtering low-frequency harmonic components of the current sampling data by adopting a high-pass filter to obtain filtered current sampling data;
and calculating a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value of the current sampling data according to the filtered current sampling data.
7. The fault arc identification method of claim 1, wherein the determining that the line under test has generated a fault arc further comprises:
outputting a tripping signal;
and alarming or turning off the power supply of the circuit to be tested according to the tripping signal.
8. A fault arc identification device, comprising:
the current sampling data acquisition module is used for acquiring current sampling data of the line to be tested;
the characteristic value calculation module is used for calculating the characteristic quantity of the current sampling data according to the current sampling data; the characteristic quantity comprises a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value;
the detection times determining module is used for comparing each characteristic quantity ratio with a corresponding preset ratio threshold value, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio threshold value as detection times; the ratio of each characteristic quantity is the ratio of the characteristic value of the current average value, the current shoulder width and the wavelet high-frequency coefficient to the corresponding reference value;
and the fault arc identification module is used for determining that the circuit to be tested generates fault arc if the detection times exceed an arc identification threshold value.
9. A fault arc identification apparatus, comprising:
the communication unit/communication interface is used for acquiring current sampling data of the line to be tested;
a processing unit/processor for calculating a feature quantity of the current sampling data according to the current sampling data; the characteristic quantity comprises a current average value, a current shoulder width and a wavelet high-frequency coefficient characteristic value;
comparing each characteristic quantity ratio with a corresponding preset ratio threshold value, and determining the times that all the characteristic quantity ratios are larger than the corresponding preset ratio threshold value as detection times; the ratio of each characteristic quantity is the ratio of the characteristic value of the current average value, the current shoulder width and the wavelet high-frequency coefficient to the corresponding reference value;
and if the detection times exceed the arc identification threshold value, determining that the line to be detected generates fault arc.
10. A computer readable storage medium having instructions stored therein which, when executed, implement the fault arc identification method of any one of claims 1 to 7.
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