CN114527361A - Method and device for determining arc fault of alternating current loop - Google Patents
Method and device for determining arc fault of alternating current loop Download PDFInfo
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Abstract
The embodiment of the invention provides a method, a device, a storage medium and an electronic device for determining an arc fault of an alternating current loop, wherein the method comprises the following steps: acquiring a low-frequency current signal acquired by low-frequency signal acquisition equipment for a target alternating current loop and acquiring a high-frequency current signal acquired by high-frequency signal acquisition equipment for the target alternating current loop; analyzing the low-frequency current signal to obtain low-frequency characteristics, and analyzing the high-frequency current signal to obtain high-frequency characteristics; and determining whether the target alternating current loop is in fault or not based on the low-frequency characteristic and the high-frequency characteristic. According to the method and the device, the problem that the arc fault of the alternating current circuit is determined inaccurately in the related technology is solved, and the effect of improving the accuracy rate of determining the arc fault in the alternating current circuit is achieved.
Description
Technical Field
The embodiment of the invention relates to the field of electric fire protection arc fault identification, in particular to a method, a device, a storage medium and an electronic device for determining an alternating current loop arc fault.
Background
Since the concept of intelligent power utilization is proposed, the industry gradually recognizes the concept of intelligent power utilization, and intelligent and safe power utilization is more and more emphasized. With the continuous enlargement of the capacity and scale of a low-voltage distribution system, various novel electric equipment is widely applied to daily life and the continuous increase of high-rise buildings, and serious electrical fire hazard is brought. According to data issued by the fire rescue bureau of the emergency management department, the electric fire caused by the arc fault far exceeds the fire caused by private pull wires, ground faults, overload of electric equipment and the like. Because the temperature generated by the electric arc is extremely high instantaneously, if the circuit cannot be cut off in time, the life safety is seriously harmed. Although low voltage power distribution systems equipped with miniature circuit breakers, modular electrical fire detectors, residual current protectors and other related protective equipment are important for the proper operation of low voltage lines and for the reduction of electrical fires, these protective equipment are not effective in protecting against arc faults. The generation of arc faults can increase the resistance of a loop, which is generally lower than the tripping current of a miniature circuit breaker, and the residual current can not be generated, so that the protective equipment can not act. In addition, arc faults are affected by many factors in the line, and the fault characteristics are submerged in the load current and affected by the type of load, which greatly increases the difficulty of identification. It is easy to generate false action and missing report.
Therefore, the problem that the arc fault of the alternating current loop is determined inaccurately exists in the related art.
In view of the above problems in the related art, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a method, a device, a storage medium and an electronic device for determining an alternating current circuit arc fault, which are used for at least solving the problem of inaccurate determination of the alternating current circuit arc fault in the related technology.
According to an embodiment of the invention, there is provided a method of determining an ac loop arc fault, including: acquiring a low-frequency current signal acquired by a low-frequency signal acquisition device for a target alternating current loop, and acquiring a high-frequency current signal acquired by a high-frequency signal acquisition device for the target alternating current loop; analyzing the low-frequency current signal to obtain low-frequency characteristics, and analyzing the high-frequency current signal to obtain high-frequency characteristics; determining whether the target AC loop is faulty based on the low frequency characteristic and the high frequency characteristic.
According to another embodiment of the present invention, there is provided an apparatus for determining an ac loop arc fault, including: the acquisition module is used for acquiring a low-frequency current signal acquired by the low-frequency signal acquisition equipment for a target alternating-current loop and acquiring a high-frequency current signal acquired by the high-frequency signal acquisition equipment for the target alternating-current loop; the analysis module is used for analyzing the low-frequency current signal to obtain low-frequency characteristics and analyzing the high-frequency current signal to obtain high-frequency characteristics; a determination module for determining whether the target AC loop is faulty based on the low frequency signature and the high frequency signature.
According to yet another embodiment of the invention, there is also provided a computer-readable storage medium having a computer program stored therein, wherein the computer program, when executed by a processor, implements the steps of the method as set forth in any of the above.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the invention, the low-frequency current signal acquired by the low-frequency signal acquisition equipment for the target alternating current loop is acquired, the high-frequency current signal acquired by the high-frequency signal acquisition equipment for the target alternating current loop is acquired, the low-frequency current signal is analyzed to obtain the low-frequency characteristic, the high-frequency current signal is analyzed to obtain the high-frequency characteristic, and whether the target alternating current loop has a fault or not is determined according to the low-frequency characteristic and the high-frequency characteristic. The low-frequency characteristic and the high-frequency characteristic are integrated when whether the target alternating current loop has the fault or not is determined, so that the problem that the determination of the arc fault of the alternating current loop is inaccurate in the related technology can be solved, and the effect of improving the accuracy of determining the arc fault in the alternating current loop is achieved.
Drawings
FIG. 1 is a block diagram of a hardware configuration of a mobile terminal of a method of determining an AC loop arc fault according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of determining an AC circuit arc fault according to an embodiment of the present invention;
FIG. 3 is a flow chart for determining whether a fault has occurred in a target AC circuit in accordance with an exemplary embodiment of the present invention;
FIG. 4 is a schematic flow chart of determining a target feature vector according to an exemplary embodiment of the present invention;
FIG. 5 is a flow chart of a method of determining an AC loop arc fault in accordance with a specific embodiment of the present invention;
fig. 6 is a block diagram of an apparatus for determining an ac loop arc fault according to an embodiment of the present invention.
Detailed Description
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking the example of the method running on the mobile terminal, fig. 1 is a hardware block diagram of the mobile terminal of the method for determining the arc fault of the ac loop according to the embodiment of the present invention. As shown in fig. 1, the mobile terminal may include one or more processors 102 (only one is shown in fig. 1) (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data, wherein the mobile terminal may further include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used for storing computer programs, for example, software programs and modules of application software, such as a computer program corresponding to the method for determining an arc fault in an ac loop according to the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by executing the computer programs stored in the memory 104, so as to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In the present embodiment, a method for determining an ac loop arc fault is provided, and fig. 2 is a flowchart of a method for determining an ac loop arc fault according to an embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, acquiring a low-frequency current signal acquired by a low-frequency signal acquisition device for acquiring a target alternating current loop, and acquiring a high-frequency current signal acquired by a high-frequency signal acquisition device for acquiring the target alternating current loop;
step S204, analyzing the low-frequency current signal to obtain low-frequency characteristics, and analyzing the high-frequency current signal to obtain high-frequency characteristics;
and step S206, determining whether the target alternating current loop has a fault or not based on the low-frequency characteristic and the high-frequency characteristic.
In the above embodiment, the low-frequency signal acquisition device may be a low-frequency current transformer, and the high-frequency signal acquisition device may be a high-frequency current transformer. The target ac circuit may be a low voltage ac circuit. The low-frequency signal acquisition equipment and the high-frequency signal acquisition equipment can be arranged in a trunk and each branch of the target alternating current loop and respectively acquire low-frequency current signals and high-frequency current signals in the trunk and each branch. Wherein the low frequency current signal may be a low frequency arc current signal and the high frequency current signal may be a high frequency arc current signal.
In the above embodiment, the high-frequency current transformer and the low-frequency current transformer may be respectively used to collect the arc current signal in the low-voltage ac loop, and the obtained arc current signal data may be preprocessed. The low-voltage alternating current main loop and each branch can be subjected to real-time current signal acquisition by using a low-frequency current transformer and a high-frequency current transformer respectively, wherein the high-frequency current transformer can acquire a high-frequency current signal of 12MHz at most. When an arc is generated in the line, the high-frequency current is between 500Khz and 15 Mh. The defects of a single signal are made up by simultaneous sampling of low frequency and high frequency.
In the above embodiment, after the low-frequency current signal and the high-frequency current signal are obtained, the low-frequency current signal and the high-frequency current signal may be preprocessed. Firstly, the sampled signal is amplified, filtered and denoised, so that doped interference signal data and abnormal signal data caused in AD sampling or oversampling are removed, and final arc misinformation caused by the existence of interference and abnormal signals is avoided. And performing signal conversion and time-frequency analysis on the preprocessed high-frequency and low-frequency arc current signal data, and extracting various corresponding high-frequency characteristics and low-frequency characteristics. And determining whether a fault occurs in the target alternating current loop according to the high-frequency characteristic and the low-frequency characteristic.
Optionally, the main body of the above steps may be a background processor, or other devices with similar processing capabilities, and may also be a machine integrated with at least a signal acquisition device and a data processing device, where the signal acquisition device may include a current transformer and other devices, and the data processing device may include a computer, a mobile phone and other terminals, but is not limited thereto.
According to the invention, the low-frequency current signal acquired by the low-frequency signal acquisition equipment for the target alternating current loop is acquired, the high-frequency current signal acquired by the high-frequency signal acquisition equipment for the target alternating current loop is acquired, the low-frequency current signal is analyzed to obtain the low-frequency characteristic, the high-frequency current signal is analyzed to obtain the high-frequency characteristic, and whether the target alternating current loop has a fault or not is determined according to the low-frequency characteristic and the high-frequency characteristic. The low-frequency characteristic and the high-frequency characteristic are integrated when whether the target alternating current loop has the fault or not is determined, so that the problem that the determination of the arc fault of the alternating current loop is inaccurate in the related technology can be solved, and the effect of improving the accuracy of determining the arc fault in the alternating current loop is achieved.
In one exemplary embodiment, determining whether the target ac loop is faulty based on the low frequency signature and the high frequency signature includes: forming the low-frequency features and the high-frequency features into target feature vectors; determining a difference coefficient between the target feature vector and a predetermined reference feature vector; and determining whether the target alternating current loop is in fault or not based on the difference coefficient. In this embodiment, the low-frequency features and the high-frequency features may be combined into a target feature vector, a difference coefficient between the target feature vector and a predetermined reference feature vector is determined, and whether the target ac loop is faulty or not is determined according to the difference coefficient. The reference feature vector may be a feature vector composed of a normal low-frequency feature and a normal high-frequency feature determined under a normal state of the ac loop. The target feature vector may be a feature vector for arc identification facing edge calculation, which is composed by combining high frequency features and low frequency features. The extracted high-frequency distribution features and the extracted low-frequency distribution features form a feature vector facing the arc identification of the edge calculation, and the contingency and the limitation of a single feature can be made up.
In one exemplary embodiment, determining the difference coefficient between the target feature vector and the predetermined reference feature vector comprises: determining a product of each target sub-vector included in the target feature vector and a reference sub-vector corresponding to the target sub-vector included in the reference feature vector to obtain a plurality of first products; determining the sum of squares of each target sub-vector included in the target feature vector to obtain a first sum value; determining the sum of squares of each reference sub-vector included in the reference feature vector to obtain a second sum value; determining the difference coefficient based on the first product, the first sum value, and the second sum value. In this embodiment, when determining the difference coefficient, a product of each target sub-vector included in the target feature vector and a reference sub-vector corresponding to the target sub-vector in the reference feature vector may be determined to obtain a plurality of first products, a sum of squares of the target sub-vectors in the target feature vector may be determined to obtain a first sum value, a sum of squares of the reference sub-vectors in the reference feature vector may be determined to obtain a second sum value, and the difference coefficient may be determined according to the first sum value, the second sum value, and the first product.
In the above embodiments, the target feature vector may be expressed as
Wherein, If，SW,
For low frequency features, R may be a high frequency feature. The reference feature vector may be expressed as
The first product may be represented as x (j) x y (j), where x (j) represents a jth vector included in the target feature vector, and y (j) represents a jth vector included in the reference feature vector. The first sum may be expressed as
The second sum value may be expressed as
Where m represents the number of vectors included in the target feature vector. The target characteristic vector and the vectors included in the reference characteristic vector are in one-to-one correspondence, so that the number of the vectors included in the target characteristic vector is the same as the number of the vectors included in the reference characteristic vector.
In one exemplary embodiment, determining the difference coefficient based on the first product, the first sum value, and the second sum value comprises: determining an absolute value of a sum of a plurality of said first products to obtain a third sum; determining a first arithmetic square root of a product of the first sum and the second sum; determining a ratio of the third sum to the first arithmetic square root as the difference coefficient. In the present embodiment, a target feature vector is calculated
Reference feature vector
The difference coefficient can quantify the relative importance of each feature, so that each feature participates in identification, the inherent identical points and differences of each feature are searched, the mode of setting a fixed threshold value for each feature is replaced, the arc fault state and the normal operation state are distinguished, and the real-time alternating current arc fault diagnosis in different electrical environments is realized. Wherein the difference coefficient can be expressed as
In one exemplary embodiment, determining whether the target ac loop is faulty based on the difference coefficient includes: under the condition that the difference coefficient is smaller than a preset threshold value, determining that an arc occurs in the target alternating current loop in a current target period, and determining the current target period as an initial fault period; counting a first number of target periods in which arcs appear in the target alternating current loop after the current target period; determining a target period when the target number is reached as a final fault period when the first number reaches the target number; determining a time interval between the initial fault period and the final fault half period; and determining that the target alternating current loop is in fault when the time interval is less than a preset time. In this embodiment, after the difference coefficient is determined, whether an arc is generated in the line can be determined according to the difference coefficient and the number of arcs. In the case where the difference coefficient is smaller than the predetermined threshold value, it may be determined that an arc has occurred in the target ac circuit in the current target period, and the current target period may be determined as the initial fault period. And then counting a first number of target periods of the target alternating current loop after the current target period, determining the target periods when the first number reaches the target number to determine a final fault period, determining whether a time interval between the initial fault period and the final fault period is less than a preset time length, and determining that the target alternating current loop has a fault when the time interval is less than the preset time length. And if the time length is greater than or equal to the preset time length, clearing the first counted number, and restarting the fault judgment of the new target period.
In the above embodiment, the obtained difference coefficient ψ ranges from 0 to 1, and the closer ψ is to 0, it indicates that the lower the similarity of the eigenvectors of X, Y, the arc fault occurs. The closer ψ is to 1, the higher the eigenvector similarity of X, Y is, and the signal is operating normally. When the occurrence of the arc fault is detected, the number of the arcs at one time is recorded. When the arc fault in the second period is detected, the number of the arcs continues to be accumulated until the number of the arcs is 14 (corresponding to the target number, the value is only an exemplary illustration, and may also be a value larger than 14, such as 15, 18, etc., which is not limited by the invention). And determines whether the time of the first and 14 th failure cycles is less than 1s (corresponding to the above-described predetermined time period). If the time is more than 1s, continuing the acquisition and calculation of the next cycle. If the time is less than 1s, the existence of the arc fault in the alternating current line is confirmed. The flow chart for determining whether a fault occurs in the target ac loop can be seen in fig. 3.
In one exemplary embodiment, the target period comprises a half period. In this embodiment, the target cycle may be a half cycle, i.e., whether a fault occurs in the target loop may be determined once every half cycle.
In an exemplary embodiment, analyzing the low frequency current signal to obtain low frequency features comprises: determining time domain characteristics and frequency domain characteristics of the low-frequency current signal; determining the time domain features and the frequency domain features as the low frequency features. In this embodiment, after the low-frequency current signal is collected, time-frequency analysis may be performed on the low-frequency current signal to extract low-frequency time-domain and frequency-domain features. The time domain features and the frequency domain features are determined to be low frequency features.
In one exemplary embodiment, determining the time domain characteristic of the low frequency current signal comprises: determining a form factor of the low frequency current signal; determining the ratio of the number of shoulders of the low-frequency current signal in a half period to the number of sampling points to obtain the percentage of the shoulder width; determining the form factor and the shoulder width percentage as the time domain feature. In this embodiment, the form factor, the percentage of the shoulder width of the low frequency current signal may be determined as the time domain feature. The form factor is a ratio of an effective value and an absolute mean value of a current waveform in a time window, and is a dimensionless index without considering the magnitude of the load current. Taking a half period as a unit, the number of sampling points N of each half period, and each sampling current ikIs represented byfIs a wave form factor and can be expressed as
In the above embodiment, the percentage of the shoulder width may be a ratio of the number of shoulders to the number of sampling points in the target period, and the number of shoulders may be a number near the current zero point. Wherein the target period may be a half period.
In one exemplary embodiment, determining the frequency domain characteristic of the low frequency current signal comprises: determining a K-th harmonic factor of the low frequency current signal, whereinK is an integer within a predetermined interval; determining the K-th harmonic factor as the frequency domain feature. In this embodiment, when determining the frequency domain characteristics, fast fourier FFT transformation may be performed on the preprocessed data to obtain a power frequency fundamental amplitude and each harmonic amplitude. In order to eliminate the influence of different current magnitudes, a harmonic factor frequency domain characteristic is provided. The harmonic factor is defined as the ratio of the amplitude of each harmonic to the amplitude of the fundamental at power frequency. The harmonic factor is calculated as follows:
wherein, IKRepresenting the amplitude of the K harmonic, I1Is the amplitude of power frequency fundamental wave, HKRepresenting the harmonic factor of the K-th order. The predetermined interval is [2,10 ]]。
It should be noted that the predetermined interval is only an exemplary interval, and the present invention does not limit this interval. The predetermined interval may also be [2,11], [2,15], etc. Only K harmonics in a predetermined region are calculated, so that the amount of calculation can be reduced, and the efficiency of determining whether a fault occurs can be improved.
In one exemplary embodiment, determining the ratio of the number of flat shoulders to the number of sampling points of the low-frequency current signal in the target period comprises: determining the current corresponding to each sampling point in a target period to obtain a plurality of currents; determining a second number of currents included in the plurality of currents that are within a predetermined current interval; and determining the ratio of the second number to the number of the sampling points as the ratio of the number of the flat shoulders of the low-frequency current signal in the target period to the number of the sampling points. In this embodiment, when determining the shoulder width percentage, a current corresponding to each sampling point in the target period may be determined to obtain a plurality of currents, a second number of currents in a predetermined current interval in the plurality of currents is determined, and a ratio of the second number to the number of sampling points is determined as the shoulder width percentage of the low-frequency current signal in the target period.
In the above embodiment, when the target period is a half period, the number of sampling points N per half period is N, and i is used for each sampling currentkIs represented by ilRepresents the upper and lower threshold values near zero, SW is the width of the flat shoulderWhen the ratio is greater than
Wherein (-i)l，il) I.e. a predetermined current interval, ilMay be a preset current value, and the present invention is not limited thereto.
In an exemplary embodiment, analyzing the high frequency current signal to obtain a high frequency signature comprises: determining a high-frequency pulse signal included in the high-frequency current signal; determining the high frequency characteristic based on the high frequency pulse signal. In this embodiment, when determining the high-frequency characteristics, the high-frequency current signal may be analyzed to extract the corresponding high-frequency characteristics. Such as determining a high-frequency pulse signal included in the high-frequency current signal, and determining a high-frequency characteristic based on the high-frequency pulse signal.
In one exemplary embodiment, determining the high frequency characteristic based on the high frequency pulse signal includes: counting a third number of the high-frequency pulses included in the high-frequency current signal by taking a preset time period as a collection unit; determining the square of each high-frequency pulse amplitude to obtain a plurality of squares; determining a second arithmetic square root of a ratio of a fifth sum of a plurality of said squares to said third quantity; determining the second arithmetic square root as the high frequency feature. In this embodiment, the number of the high-frequency pulses, that is, the third number, may be counted by taking 10ms (corresponding to the predetermined time period, which is only an exemplary illustration, and may also be 8ms, 15ms, and the like, which is not limited in this respect) as an acquisition unit, and the root mean square value of the high-frequency pulses is obtained. The calculation formula is as follows:
wherein N represents the number of high frequency pulses, riRepresenting the amplitude of the pulse and R is the root mean square value of the high frequency pulse. After the high-frequency features and the low-frequency features are determined, the low-frequency features and the high-frequency features can be combined into a target feature vector. The schematic flow chart of determining the target feature vector can be seen in fig. 4.
The following description of the method for determining an arc fault in an ac circuit is provided in conjunction with the following embodiments:
fig. 5 is a flowchart of a method for determining an ac loop arc fault, according to an embodiment of the invention, as shown in fig. 5, the flowchart includes:
the method comprises the following steps: current signal acquisition and pretreatment: and respectively using a high-frequency current transformer and a low-frequency current transformer to acquire arc current signals in a low-voltage alternating current loop, and preprocessing the acquired arc current signal data.
Step two: signal transformation and feature extraction: and performing signal conversion and time-frequency analysis on the preprocessed high-frequency and low-frequency arc current signal data, and extracting various corresponding high-frequency characteristics and low-frequency characteristics.
Step three: and (4) combining multiple characteristics: and combining the high-frequency features and the low-frequency features to form a feature vector for arc identification facing to edge calculation.
Step four: and (3) real-time fault diagnosis: and judging whether the electric arc is generated in the line or not by comparing the correlation coefficients of the reference characteristic vector and the target signal characteristic vector when the line is normal and the number of the electric arcs.
In the embodiment, the high-frequency current transformer and the low-frequency current transformer are used for collecting the arc current signals in the low-voltage alternating-current loop, and the high-frequency signals and the low-frequency signals of the arc current are simultaneously positioned, so that the integrity of the input signals is ensured as much as possible. And pre-processes the acquired signal data. Compared with the traditional method for acquiring the arc current signal with a single frequency band, the scheme can realize the high-frequency, low-frequency and multi-frequency band sampling of the arc current signal, ensure the integrity of the signal as much as possible, remove the doped interference and abnormal signals through pretreatment, and further reduce the false alarm rate. By carrying out signal conversion and time-frequency analysis on the arc current signal data, various corresponding high-frequency features and low-frequency features are extracted to form a feature vector for arc identification facing edge calculation. The relative importance of each feature is quantified by calculating the correlation coefficient of the feature vector, so that each feature participates in identification, the inherent identical points and differences of each feature are searched, the mode of setting a fixed threshold value for each feature is replaced, the arc fault state and the normal operation state are distinguished, and the real-time alternating current arc fault diagnosis in different electrical environments is realized. A plurality of features are monitored in real time based on edge calculations. Compared with the traditional mode that a large amount of data are transmitted to the server side to be processed by a complex algorithm, the complexity of a hardware structure can be effectively reduced, the data transmission time is shortened, meanwhile, the high-efficiency and quick response to electric arcs in the circuit is realized, the circuit is cut off in time, and the risk of electrical fire is further reduced.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
In this embodiment, a device for determining an arc fault of an ac loop is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, which have already been described and are not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 6 is a block diagram of an apparatus for determining an arc fault in an ac circuit according to an embodiment of the present invention, as shown in fig. 6, the apparatus including:
the acquisition module 62 is configured to acquire a low-frequency current signal acquired by the low-frequency signal acquisition device through a target alternating-current loop, and acquire a high-frequency current signal acquired by the high-frequency signal acquisition device through the target alternating-current loop;
an analysis module 64, configured to analyze the low-frequency current signal to obtain a low-frequency feature, and analyze the high-frequency current signal to obtain a high-frequency feature;
a determination module 66 configured to determine whether the target ac loop is faulty based on the low frequency characteristic and the high frequency characteristic.
In an exemplary embodiment, the determination module 66 may determine whether the target ac loop is faulty based on the low frequency signature and the high frequency signature by: forming the low-frequency features and the high-frequency features into target feature vectors; determining a difference coefficient between the target feature vector and a predetermined reference feature vector; and determining whether the target alternating current loop is in fault or not based on the difference coefficient.
In an exemplary embodiment, the determining module 66 may determine the difference coefficient between the target feature vector and the predetermined reference feature vector by: determining a product of each target sub-vector included in the target feature vector and a reference sub-vector corresponding to the target sub-vector included in the reference feature vector to obtain a plurality of first products; determining the sum of squares of each target sub-vector included in the target feature vector to obtain a first sum value; determining the sum of squares of each reference sub-vector included in the reference feature vector to obtain a second sum value; determining the difference coefficient based on the first product, the first sum value, and the second sum value.
In an exemplary embodiment, determining module 66 may determine the difference coefficient based on the first product, the first sum, and the second sum by: determining an absolute value of a sum of a plurality of said first products to obtain a third sum; determining a first arithmetic square root of a product of the first sum and the second sum; determining a ratio of the third sum to the first arithmetic square root as the difference coefficient.
In an exemplary embodiment, the determination module 66 may determine whether the target ac loop is faulty based on the difference coefficient by: under the condition that the difference coefficient is smaller than a preset threshold value, determining that an arc occurs in the target alternating current loop in a current target period, and determining the current target period as an initial fault period; counting a first number of target periods in which arcs appear in the target alternating current loop after the current target period; determining a target period when the target number is reached as a final fault period when the first number reaches the target number; determining a time interval between the initial fault period and the final fault period; and determining that the target alternating current loop is in fault when the time interval is less than a preset time.
In one exemplary embodiment, the target period comprises a half period.
In an exemplary embodiment, the analyzing module 64 may analyze the low frequency current signal to obtain the low frequency characteristics by: determining time domain characteristics and frequency domain characteristics of the low-frequency current signal; determining the time domain features and the frequency domain features as the low frequency features.
In an exemplary embodiment, the analysis module 64 may determine the time domain characteristics of the low frequency current signal by: determining a form factor of the low frequency current signal; determining the ratio of the number of flat shoulders of the low-frequency current signal in a half period to the number of sampling points to obtain the percentage of the width of the flat shoulders; determining the form factor and the shoulder width percentage as the time domain feature.
In an exemplary embodiment, the analysis module 64 may determine the frequency domain characteristics of the low frequency current signal by: determining a K-th harmonic factor of the low-frequency current signal, wherein K is an integer within a predetermined interval; determining the K-th harmonic factor as the frequency domain feature.
In an exemplary embodiment, the analysis module 64 may determine the ratio of the number of flat shoulders to the number of sampling points of the low frequency current signal within a half period by: determining the current corresponding to each sampling point in a target period to obtain a plurality of currents; determining a second number of currents included in the plurality of currents that are within a predetermined current interval; and determining the ratio of the second number to the number of the sampling points as the ratio of the number of the flat shoulders of the low-frequency current signal in the target period to the number of the sampling points.
In an exemplary embodiment, the analyzing module 64 may analyze the high frequency current signal to obtain the high frequency characteristic by: determining a high-frequency pulse signal included in the high-frequency current signal; determining the high frequency characteristic based on the high frequency pulse signal.
In an exemplary embodiment, the analysis module 64 may determine the high frequency characteristic based on the high frequency pulse signal by: counting a third number of the high-frequency pulses included in the high-frequency current signal by taking a preset time period as a collection unit; determining the square of each high-frequency pulse amplitude to obtain a plurality of squares; determining a second arithmetic square root of a ratio of a fifth sum of a plurality of said squares to said third quantity; determining the second arithmetic square root as the high frequency feature.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method as set forth in any of the above.
In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Embodiments of the present invention further provide an electronic device, comprising a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details of this embodiment are not repeated herein.
It will be apparent to those skilled in the art that the various modules or steps of the invention described above may be implemented using a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and they may be implemented using program code executable by the computing devices, such that they may be stored in a memory device and executed by the computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into various integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (15)
1. A method of determining an ac loop arc fault, comprising:
acquiring a low-frequency current signal acquired by a low-frequency signal acquisition device for a target alternating current loop, and acquiring a high-frequency current signal acquired by a high-frequency signal acquisition device for the target alternating current loop;
analyzing the low-frequency current signal to obtain low-frequency characteristics, and analyzing the high-frequency current signal to obtain high-frequency characteristics;
determining whether the target AC loop is faulty based on the low frequency characteristic and the high frequency characteristic.
2. The method of claim 1, wherein determining whether the target ac loop is faulty based on the low frequency signature and the high frequency signature comprises:
forming the low-frequency features and the high-frequency features into target feature vectors;
determining a difference coefficient between the target feature vector and a predetermined reference feature vector;
and determining whether the target alternating current loop is in fault or not based on the difference coefficient.
3. The method of claim 2, wherein determining a difference coefficient between the target feature vector and a predetermined reference feature vector comprises:
determining a product of each target sub-vector included in the target feature vector and a reference sub-vector corresponding to the target sub-vector included in the reference feature vector to obtain a plurality of first products;
determining the sum of squares of each target sub-vector included in the target feature vector to obtain a first sum value;
determining the sum of squares of each reference sub-vector included in the reference feature vector to obtain a second sum value;
determining the difference coefficient based on the first product, the first sum value, and the second sum value.
4. The method of claim 3, wherein determining the difference coefficient based on the first product, the first sum, and the second sum comprises:
determining an absolute value of a sum of a plurality of said first products to obtain a third sum;
determining a first arithmetic square root of a product of the first sum and the second sum;
determining a ratio of the third sum to the first arithmetic square root as the difference coefficient.
5. The method of claim 2, wherein determining whether the target ac loop is faulty based on the difference coefficient comprises:
under the condition that the difference coefficient is smaller than a preset threshold value, determining that an arc occurs in the target alternating current loop in a current target period, and determining the current target period as an initial fault period;
counting a first number of target periods in which arcs appear in the target alternating current loop after the current target period;
determining a target period when the target number is reached as a final fault period when the first number reaches the target number;
determining a time interval between the initial fault period and the final fault period;
and determining that the target alternating current loop is in fault when the time interval is less than a preset time.
6. The method of claim 5, wherein the target period comprises a half-cycle.
7. The method of claim 1, wherein analyzing the low frequency current signal to obtain low frequency features comprises:
determining time domain characteristics and frequency domain characteristics of the low-frequency current signal;
determining the time domain features and the frequency domain features as the low frequency features.
8. The method of claim 7, wherein determining the time domain characteristic of the low frequency current signal comprises:
determining a form factor of the low frequency current signal;
determining the ratio of the number of flat shoulders of the low-frequency current signal in a half period to the number of sampling points to obtain the percentage of the width of the flat shoulders;
determining the form factor and the shoulder width percentage as the time domain feature.
9. The method of claim 7, wherein determining the frequency domain characteristic of the low frequency current signal comprises:
determining a K-th harmonic factor of the low-frequency current signal, wherein K is an integer within a predetermined interval;
determining the K-th harmonic factor as the frequency domain feature.
10. The method of claim 8, wherein determining a ratio of a number of flat shoulders to a number of sample points of the low frequency current signal within a half cycle comprises:
determining the current corresponding to each sampling point in a target period to obtain a plurality of currents;
determining a second number of currents included in the plurality of currents that are within a predetermined current interval;
and determining the ratio of the second number to the number of the sampling points as the ratio of the number of the flat shoulders of the low-frequency current signal in the target period to the number of the sampling points.
11. The method of claim 1, wherein analyzing the high frequency current signal to obtain high frequency characteristics comprises:
determining a high-frequency pulse signal included in the high-frequency current signal;
determining the high frequency characteristic based on the high frequency pulse signal.
12. The method of claim 11, wherein determining the high frequency signature based on the high frequency pulse signal comprises:
counting a third number of the high-frequency pulses included in the high-frequency current signal by taking a preset time period as a collection unit;
determining the square of each high-frequency pulse amplitude to obtain a plurality of squares;
determining a second arithmetic square root of a ratio of a fifth sum of a plurality of said squares to said third quantity;
determining the second arithmetic square root as the high frequency feature.
13. An apparatus for determining an ac circuit arc fault, comprising:
the acquisition module is used for acquiring a low-frequency current signal acquired by the low-frequency signal acquisition equipment for a target alternating-current loop and acquiring a high-frequency current signal acquired by the high-frequency signal acquisition equipment for the target alternating-current loop;
the analysis module is used for analyzing the low-frequency current signal to obtain low-frequency characteristics and analyzing the high-frequency current signal to obtain high-frequency characteristics;
a determination module for determining whether the target AC loop is faulty based on the low frequency signature and the high frequency signature.
14. A computer-readable storage medium, in which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method of one of claims 1 to 12.
15. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 12.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114878971A (en) * | 2022-05-31 | 2022-08-09 | 国网北京市电力公司 | Method, device, equipment and medium for positioning fault point of power distribution network |
| CN117434406A (en) * | 2023-12-20 | 2024-01-23 | 天津航空机电有限公司 | Arc fault detection method based on complementary set empirical mode decomposition |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114878971A (en) * | 2022-05-31 | 2022-08-09 | 国网北京市电力公司 | Method, device, equipment and medium for positioning fault point of power distribution network |
| CN114878971B (en) * | 2022-05-31 | 2024-01-30 | 国网北京市电力公司 | Power distribution network fault point positioning method, device, equipment and medium |
| CN117434406A (en) * | 2023-12-20 | 2024-01-23 | 天津航空机电有限公司 | Arc fault detection method based on complementary set empirical mode decomposition |
| CN117434406B (en) * | 2023-12-20 | 2024-04-09 | 天津航空机电有限公司 | Arc fault detection method based on complementary set empirical mode decomposition |
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