Method, device, arc detection equipment and medium for identifying arc signals

Abstract

The present application discloses a method, device, arc detection equipment and medium for identifying arc signals, which relates to the field of fault arc detection. It includes: obtaining the current frequency characteristics of a preset number of half-wave signals and the mean of the current frequency characteristics; obtaining the data characteristics of the current half-wave signal; obtaining the difference between the data characteristics and the mean; when the difference is greater than a first threshold, determining that the current half-wave signal is an arc signal under the current frequency characteristics. After identifying the current half-wave signal under all frequency characteristics, the number of frequency characteristics corresponding to determining that the current half-wave signal is an arc signal is obtained; if the number is greater than a second threshold, the current half-wave signal is determined to be an arc signal. In this method, arc signals are identified based on the characteristics of high-frequency signals; the mean value used will change with the power usage environment; multiple judgment results are combined to identify arc signals, which greatly avoids the interference of dynamic changes in the environment and the diversity of load types on arc detection, and can fully and effectively identify arc signals.

Description

Technical Field

[0001] The present application relates to the field of arc fault detection, and in particular to a method, device, arc detection equipment and medium for identifying arc signals. Background Art

[0002] Electrical fires account for a high proportion of fire accidents today, with arc faults being a major cause. When an arc fault occurs, it can easily cause a fire. In real-world electrical environments, electrical loads vary greatly, making it difficult to distinguish arc signals from normal signals, whether from a time domain or low-frequency perspective.

[0003] Traditional arc detection equipment, limited by the state of electronic technology, is unable to perform high-frequency sampling of circuit current signals. It can only distinguish arcs from normal conditions based on low-frequency current waveforms, making it particularly susceptible to interference from load types. Additionally, some methods convert the sampled current signals to the low-frequency domain, using feature-based training models or setting fixed thresholds directly based on the features. However, as the environment changes, feature values can change dynamically. Furthermore, in real-world scenarios, normal data is susceptible to significant interference, and there are many different load types. This can lead to numerous false positives. If an arc fault is not detected in the detected circuit, it could potentially cause a fire.

[0004] It can be seen that how to more accurately identify arc signals is a technical problem that people in this field urgently need to solve. Summary of the Invention

[0005] The purpose of this application is to provide a method, device, arc detection equipment and medium for identifying arc signals, which are used to more accurately identify arc signals.

[0006] To solve the above technical problems, the present application provides a method for identifying arc signals, comprising:

[0007] A half-wave signal of a target frequency band in an AC circuit is collected by an analog-to-digital converter; wherein a lower limit value of the target frequency band is greater than or equal to a frequency threshold value, and the frequency threshold value is determined according to the frequency of the arc signal;

[0008] Obtaining current frequency characteristics of a preset number of the half-wave signals and obtaining an average value of the current frequency characteristics of all the preset number of the half-wave signals; wherein there are multiple frequency characteristics;

[0009] Acquire data features of a current half-wave signal; wherein the current half-wave signal is the half-wave signal after the preset number;

[0010] Obtaining a difference between the data feature and the mean;

[0011] When the difference is greater than a first threshold, determining that the current half-wave signal is the arc signal under the current frequency characteristic; returning to the step of obtaining the current frequency characteristics of a preset number of half-wave signals until determining that the current half-wave signal is the arc signal or the non-arc signal under all the frequency characteristics is completed;

[0012] Obtaining the number of the frequency characteristics corresponding to determining that the current half-wave signal is the arc signal;

[0013] If the number of the frequency characteristics corresponding to when it is determined that the current half-wave signal is the arc signal is greater than a second threshold, the current half-wave signal is determined to be the arc signal.

[0014] Preferably, the obtaining of a preset number of current frequency characteristics of the half-wave signals includes:

[0015] The current frequency characteristics of the preset number of half-wave signals are obtained starting from when a load in the AC circuit is powered on.

[0016] Preferably, collecting the half-wave signal of the target frequency band in the AC circuit by using an analog-to-digital converter includes:

[0017] collecting the signal in the AC circuit by the analog-to-digital converter;

[0018] The signal is filtered through an LC filter circuit to obtain the half-wave signal of the target frequency band.

[0019] Preferably, the LC circuit includes an inductor, a first resistor, a first capacitor, a second resistor, a second capacitor, and a third resistor;

[0020] The first end of the first resistor, the first end of the inductor, and the first end of the first capacitor are connected to the live wire; the second end of the first resistor, the second end of the inductor, and the first end of the second capacitor are connected; the second end of the first capacitor is connected to the first end of the second resistor; the second end of the second capacitor is connected to the first end of the third resistor; the first capacitor and the second resistor are connected in series to form a first circuit, the second capacitor and the third resistor are connected in series to form a second circuit, and the first circuit and the second circuit are placed symmetrically above and below with respect to the parallel circuit formed by the inductor and the first resistor.

[0021] Preferably, obtaining the frequency characteristic of the half-wave signal includes:

[0022] Filtering the half-wave signal through a bandpass filter and obtaining the filtered half-wave signal;

[0023] Obtaining the frequency characteristics of the filtered half-wave signal by fast Fourier transform;

[0024] The frequency characteristic is filtered by a median filter to obtain the frequency characteristic of the half-wave signal.

[0025] Preferably, after determining that the current half-wave signal is the arc signal, the method further includes:

[0026] Outputting prompt information for indicating that the current half-wave signal is the arc signal.

[0027] In order to solve the above technical problems, the present application also provides a device for identifying arc signals, comprising:

[0028] an acquisition module, configured to acquire a half-wave signal of a target frequency band in an AC circuit through an analog-to-digital converter; wherein a lower limit value of the target frequency band is greater than or equal to a frequency threshold value, and the frequency threshold value is determined according to the frequency of the arc signal;

[0029] A first acquisition module is configured to acquire current frequency characteristics of a preset number of half-wave signals and to acquire an average value of the current frequency characteristics of all the preset number of half-wave signals; wherein there are multiple frequency characteristics;

[0030] A second acquisition module is used to obtain data features of the current half-wave signal; wherein the current half-wave signal is the half-wave signal after the preset number;

[0031] A third acquisition module is used to obtain the difference between the data feature and the mean;

[0032] A first determining module is configured to determine, when the difference is greater than a first threshold, that the current half-wave signal is the arc signal under the current frequency characteristic; and return to the step of obtaining the current frequency characteristics of a preset number of half-wave signals until determining whether the current half-wave signal is the arc signal or the non-arc signal under all the frequency characteristics;

[0033] A fourth acquisition module is used to acquire the number of the frequency characteristics corresponding to determining that the current half-wave signal is the arc signal;

[0034] The second determination module is configured to determine that the current half-wave signal is the arc signal if the number of the frequency characteristics corresponding to the current half-wave signal being the arc signal is greater than a second threshold.

[0035] In order to solve the above technical problems, the present application also provides an arc detection device, comprising:

[0036] memory for storing computer programs;

[0037] The processor is configured to implement the steps of the above-mentioned method for identifying arc signals when executing the computer program.

[0038] In order to solve the above technical problems, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method for identifying arc signals are implemented.

[0039] The method for identifying arc signals provided in the present application includes: collecting a half-wave signal of a target frequency band in an AC circuit through an analog-to-digital converter; wherein the lower limit value of the target frequency band is greater than or equal to a frequency threshold, and the frequency threshold is determined according to the frequency of the arc signal; obtaining the current frequency characteristics of a preset number of half-wave signals and obtaining the average of the current frequency characteristics of all preset number of half-wave signals; obtaining the data characteristics of the current half-wave signal; wherein the current half-wave signal is the half-wave signal after the preset number; obtaining the difference between the data characteristics and the average; when the difference is greater than a first threshold, determining that the current half-wave signal is an arc signal under the current frequency characteristics; returning to the step of obtaining the current frequency characteristics of the preset number of half-wave signals until the current half-wave signal is determined to be an arc signal or a non-arc signal under all frequency characteristics; obtaining the number of frequency characteristics corresponding to the determination that the current half-wave signal is an arc signal; if the number of frequency characteristics corresponding to the determination that the current half-wave signal is an arc signal is greater than a second threshold, determining that the current half-wave signal is an arc signal. In this method, the lower limit value of the target frequency band is greater than or equal to the frequency threshold, and the frequency threshold is determined according to the frequency of the arc signal. Since the arc signal is a high-frequency signal, the half-wave signal of the collected target frequency band belongs to the high-frequency signal, that is, the arc signal and the non-arc signal are distinguished according to the characteristics of the high-frequency signal in the AC circuit. Identifying the arc signal according to the characteristics of the high-frequency signal can reduce the influence of low-frequency interference in the environment on the discrimination, thereby greatly reducing the misjudgment rate. Secondly, since the mean is determined based on the half-wave signal in the collected AC circuit, when the AC circuit changes, the mean will also change dynamically with the change of the power environment, that is, taking the interference of the power environment into account, making the strategy of distinguishing between arc and normal more reasonable, and being able to more accurately identify the arc signal. In addition, in this method, the recognition results of the current half-wave signal under multiple frequency characteristics are comprehensively analyzed, and the recognition result of the current half-wave signal is finally determined, which greatly avoids the interference of dynamic changes in the environment and the diversity of load types on arc detection, and can fully and effectively distinguish arc signals from normal signals.

[0040] In addition, the present application also provides an apparatus for identifying arc signals, an arc detection device, and a computer-readable storage medium, which have the same or corresponding technical features as the above-mentioned method for identifying arc signals and have the same effects as above. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0042] Figure 1 A flowchart of a method for identifying arc signals provided in an embodiment of the present application;

[0043] Figure 2 A structural diagram of an arc discrimination device based on a dynamic threshold provided in an embodiment of the present application;

[0044] Figure 3 A structural diagram of a device for identifying arc signals provided in one embodiment of the present application;

[0045] Figure 4 This is a structural diagram of an arc detection device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0046] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0047] The core of this application is to provide a method, device, arc detection equipment and medium for identifying arc signals, which are used to more accurately identify arc signals.

[0048] The continuous advancement of society and the improvement of living standards are closely linked to the development of electricity. The occurrence of arc faults can seriously impact the reliable operation of power systems. Electrical fires caused by arc faults seriously endanger public safety and cause enormous economic losses to the country. Arc faults can be categorized as series arc faults and parallel arc faults based on the arc's location within the line. When a series arc fault occurs, the current amplitude in the distribution line is relatively small, making it difficult for traditional fuses, residual current devices, and overload protectors to effectively detect it. Therefore, research on detection methods for series arc faults is of great significance. Limited by the state of electronic technology, traditional arc detection equipment cannot acquire circuit current signals at a high sampling rate. Instead, it can only distinguish arcs from normal conditions based on low-frequency current waveforms, which are particularly susceptible to interference from load types. One approach involves converting the sampled current signals into the low-frequency domain and training models based on these low-frequency features to distinguish arcs from normal conditions. However, in some real-world scenarios, normal data can be significantly affected by interference, leading to frequent misjudgments. There is also a method to directly set fixed thresholds for arc and normal for the sampled low-frequency features, but as the environment changes, the baseline will change, making it difficult to effectively distinguish arc from normal.

[0049] In response to the problem that arc discrimination is easily misjudged in a real power usage environment, this application proposes an arc discrimination method based on a dynamic threshold. A dedicated arc sampling circuit is used, and the current signal in the sampling circuit is sampled by a high-speed analog-to-digital converter (ADC) module, focusing on observing the characteristic information of the high-frequency band. Considering that the load is basically in a normal state when powered on in a real dynamic environment, the average of the high-frequency characteristics of a certain number of half-wave signals at power-on is obtained as the baseline. The subsequent data characteristics are subtracted from the baseline for noise removal calculation, and a reasonable threshold is set for comparison with the noise removal calculation result to realize dynamic threshold discrimination of the arc signal. Further, multiple high-frequency features are selected to construct a dynamic threshold joint discrimination module, which fully utilizes the discrimination results of multiple dynamic thresholds to avoid interference with arc detection caused by dynamic changes in the environment and diversity of load types. It can fully and effectively identify arcs and normal conditions, which is conducive to the promotion and practical use of arc detection equipment.

[0050] In order to enable those skilled in the art to better understand the present application, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods. Figure 1 A flowchart of a method for identifying arc signals provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0051] S10: Collecting a half-wave signal of a target frequency band in the AC circuit through an analog-to-digital converter.

[0052] Traditional arc detection equipment, limited by electronic technology, is unable to perform high-frequency sampling of circuit current signals. This embodiment uses a dedicated circuit sampling circuit to continuously perform high-speed sampling on the live wire, using a high-speed ADC to acquire the half-wave signal of the target frequency band in the AC circuit. Because the lower limit of the target frequency band is greater than or equal to the frequency threshold, which is determined by the frequency of the arc signal, which is generally a high-frequency signal, the half-wave signal of the target frequency band acquired in this embodiment is also a high-frequency signal.

[0053] S11: Obtain current frequency characteristics of a preset number of half-wave signals and obtain an average value of the current frequency characteristics of all preset number of half-wave signals.

[0054] In order to improve the accuracy of identifying arc signals, multiple frequency features of half-wave signals are extracted, that is, multiple frequency features are obtained. Under each frequency feature, the frequency features of a preset number of half-wave signals are obtained, and the mean of the frequency features of all preset number of half-wave signals is obtained. There is no limit on the value of the preset number. After collecting a preset number of half-wave signals, the frequency features of the obtained preset number of half-wave signals can be extracted. Since the collected signals are high-frequency signals, the frequency features here specifically refer to high-frequency features. There is no limit on the method of extracting the frequency features of the half-wave signals, such as the fast Fourier transform (FFT) and the like. After extracting the high-frequency features of the preset number of half-wave signals, the mean of the high-frequency features of these half-wave signals is obtained. The mean is used as the baseline, and the mean will change dynamically with the change of the power consumption environment, that is, it is a dynamic baseline.

[0055] S12: Acquire data features of the current half-wave signal; wherein the current half-wave signal is a half-wave signal after a preset number of times.

[0056] S13: Obtain the difference between the data feature and the mean;

[0057] S14: When the difference is greater than the first threshold, determine that the current half-wave signal is an arc signal under the current frequency characteristics.

[0058] After obtaining the mean of the frequency characteristics of a preset number of half-wave signals, the data characteristics of the half-wave signals after the preset number are subtracted from the mean to perform noise cancellation calculation. For example, starting from the time the load is powered on, the high-frequency characteristics of 10 half-wave signals are obtained, and the average of the high-frequency characteristics of these 10 half-wave signals is taken. Then, the data characteristics of the half-wave signals after the 10th can be subtracted from the mean to perform noise cancellation calculation.

[0059] There are no restrictions on the first threshold, but the chosen threshold must be reasonable. In practice, based on a large amount of collected raw data, a series of processing is performed to obtain FFT features. Based on the high-frequency characteristics of arc and normal data, an optimal threshold for distinguishing between the two is established as the first threshold. The difference is compared with the first threshold. If the difference is greater than the first threshold, the current half-wave signal is determined to be an arc signal based on the threshold judgment. If the difference is less than or equal to the first threshold, the current half-wave signal is determined to be a non-arc signal based on the threshold judgment, that is, a normal signal.

[0060] S15: Determine whether the current half-wave signal is an arc signal or a non-arc signal under all frequency characteristics; if not, return to step S11; if so, proceed to step S16;

[0061] S16: Obtain the number of frequency features corresponding to determining that the current half-wave signal is an arc signal;

[0062] S17: If the number of frequency features corresponding to when the current half-wave signal is determined to be an arc signal is greater than a second threshold, the current half-wave signal is determined to be an arc signal.

[0063] For example, N high-frequency features are selected, including high-frequency features 1 to high-frequency features N. Here, the preset number is 10, and the current half-wave signal is the 11th half-wave signal, high-frequency feature 1, as an example to illustrate the process of identifying an arc signal for the 11th half-wave signal. The average of the high-frequency feature 1 of the 10 half-wave signals is obtained, and the data feature of the 11th half-wave signal is obtained. The average is subtracted from the data feature of the 11th half-wave signal to perform noise reduction calculation, and the difference is compared with the first threshold. If the difference is greater than the first threshold, it is determined that the 11th half-wave signal is an arc signal under high-frequency feature 1. The method of identifying the 11th half-wave signal as an arc signal or a non-arc signal under high-frequency features 2 to high-frequency features N is similar to the method of identifying the 11th half-wave signal as an arc signal or a non-arc signal under high-frequency feature 1 described above, and will not be repeated here.

[0064] After obtaining the discrimination results of the current half-wave signal under N high-frequency features, it is possible to jointly determine whether the current half-wave signal is an arc signal or a non-arc signal based on these discrimination results. Obtain the number of frequency features corresponding to when determining that the current half-wave signal is an arc signal. If the number is greater than the second threshold, the current half-wave signal is determined to be an arc signal. There is no limit on the second threshold, but the set second threshold must also be reasonable. As preferred, if there are more than N / 2+1 recognition results that are arc signals, the current half-wave signal is judged to be an arc signal. For example, assuming that the number N of selected high-frequency features is 7, if 5 of the recognition results of the 11th half-wave signal under 7 high-frequency features are arc signals, the 11th half-wave signal is determined to be an arc signal.

[0065] The method for identifying arc signals provided in this embodiment includes: collecting a half-wave signal of a target frequency band in an AC circuit through an analog-to-digital converter; wherein the lower limit value of the target frequency band is greater than or equal to a frequency threshold, and the frequency threshold is determined according to the frequency of the arc signal; obtaining the current frequency characteristics of a preset number of half-wave signals and obtaining the average of the current frequency characteristics of all preset number of half-wave signals; obtaining the data characteristics of the current half-wave signal; wherein the current half-wave signal is the half-wave signal after the preset number; obtaining the difference between the data characteristics and the average; when the difference is greater than a first threshold, determining that the current half-wave signal is an arc signal under the current frequency characteristics; returning to the step of obtaining the current frequency characteristics of the preset number of half-wave signals until the current half-wave signal is determined to be an arc signal or a non-arc signal under all frequency characteristics; obtaining the number of frequency characteristics corresponding to the determination that the current half-wave signal is an arc signal; if the number of frequency characteristics corresponding to the determination that the current half-wave signal is an arc signal is greater than a second threshold, determining that the current half-wave signal is an arc signal. In this method, the lower limit value of the target frequency band is greater than or equal to the frequency threshold, and the frequency threshold is determined according to the frequency of the arc signal. Since the arc signal is a high-frequency signal, the half-wave signal of the collected target frequency band belongs to the high-frequency signal, that is, the arc signal and the non-arc signal are distinguished according to the characteristics of the high-frequency signal in the AC circuit. Identifying the arc signal according to the characteristics of the high-frequency signal can reduce the influence of low-frequency interference in the environment on the discrimination, and greatly reduce the misjudgment rate; secondly, since the mean is determined based on the half-wave signal in the collected AC circuit, when the AC circuit changes, the mean will also change dynamically with the change of the power environment, that is, the interference of the power environment is taken into account, making the strategy of distinguishing between arc and normal more reasonable, and being able to more accurately identify the arc signal; in this method, the recognition results of the current half-wave signal under multiple frequency characteristics are comprehensively analyzed, and finally the recognition result of the current half-wave signal is determined, which greatly avoids the interference of dynamic changes in the environment and the diversity of load types on arc detection, and can fully and effectively distinguish arc signals from normal signals.

[0066] In the above embodiment, the data feature of the current half-wave signal is subtracted from the mean, where the mean is obtained by averaging the high-frequency features of a preset number of half-wave signals. To make the obtained mean more referenceable, in practice, a preferred embodiment is that obtaining the current frequency features of the preset number of half-wave signals includes:

[0067] Since the load in the AC circuit is powered on, current frequency characteristics of a preset number of half-wave signals are obtained.

[0068] Considering that the load in a real dynamic environment is generally in a normal state upon power-up, this embodiment acquires the current frequency characteristics of a preset number of half-wave signals starting from the time the load in the AC circuit is powered on. For example, the current frequency characteristics of ten half-wave signals are acquired starting from the time the load in the AC circuit is powered on.

[0069] This embodiment provides a method for obtaining the current frequency characteristics of a preset number of half-wave signals starting from the power-on of the load in the AC circuit. Since the load is basically in a normal state when powered on in a real dynamic environment, the average value obtained by this method is more referenceable.

[0070] When collecting half-wave signals, in order to minimize interference from low-frequency signals, a preferred embodiment is that collecting the half-wave signal of the target frequency band in the AC circuit by an analog-to-digital converter includes:

[0071] Collect the signal in the AC circuit through the analog-to-digital converter;

[0072] The signal is filtered through an LC filter circuit to obtain a half-wave signal in the target frequency band.

[0073] Figure 2 This is a structural diagram of an arc discrimination device based on a dynamic threshold provided in an embodiment of the present application. Figure 2 As shown, the LC filter circuit 1 includes an inductor L, a first resistor R1, a first capacitor C1, a second resistor R2, a second capacitor C2, and a third resistor R3;

[0074] The first end of the first resistor R1, the first end of the inductor L, and the first end of the first capacitor C1 are connected to the live wire; the second end of the first resistor R1, the second end of the inductor L, and the first end of the second capacitor C2 are connected; the second end of the first capacitor C1 is connected to the first end of the second resistor R2; the second end of the second capacitor C2 is connected to the first end of the third resistor R3; the first capacitor C1 and the second resistor R2 are connected in series to form a first circuit, and the second capacitor C2 and the third resistor R3 are connected in series to form a second circuit, and the first circuit and the second circuit are placed symmetrically above and below with respect to the parallel circuit formed by the inductor L and the first resistor R1.

[0075] In the method for obtaining a half-wave signal in a target frequency band provided in this embodiment, the signal sampled from the live wire is converted into a digital signal using a high-speed ADC with a sampling rate of up to 400 MHz, which can fully guarantee the information of the original signal; the inductor and the first resistor are connected in parallel and connected to the live wire, the first capacitor and the second resistor are connected in series, and the second capacitor and the third resistor are connected in series, and the upper and lower structures are symmetrical, which can fully sample the high-frequency signal and effectively block the low-frequency signal.

[0076] In practice, in order to reduce noise interference in high-frequency characteristics, a preferred embodiment is that obtaining the frequency characteristics of the half-wave signal includes:

[0077] filtering the half-wave signal through a bandpass filter and obtaining a filtered half-wave signal;

[0078] The frequency characteristics of the filtered half-wave signal are obtained by fast Fourier transform;

[0079] The frequency characteristics are filtered by a median filter to obtain the frequency characteristics of the half-wave signal.

[0080] In the method provided in this embodiment, through band-pass filtering with a frequency band of 2 MHz to 50 MHz, FFT transformation and median filtering, it is possible to output the FFT features of a specific high-frequency band for denoising.

[0081] After determining that the current half-wave signal is an arc signal, in order to enable the user to promptly understand the identification result of the current half-wave signal, a preferred embodiment is that the method for identifying the arc signal further includes:

[0082] The output is used to indicate that the current half-wave signal is an arc signal.

[0083] There is no limitation on the specific method of outputting the prompt information and the specific content of the prompt information, which are determined according to the actual situation. Through the prompt information, the user can intuitively understand the recognition result of the current half wave.

[0084] In the above embodiments, a method for identifying arc signals is described in detail. This application also provides corresponding embodiments of an apparatus for identifying arc signals and an arc detection device. It should be noted that this application describes the embodiments of the apparatus from two perspectives: one based on functional modules and the other based on hardware.

[0085] Figure 3 This is a structural diagram of an apparatus for identifying arc signals provided in one embodiment of the present application. This embodiment, based on the perspective of functional modules, includes:

[0086] An acquisition module 10 is configured to acquire a half-wave signal of a target frequency band in an AC circuit through an analog-to-digital converter; wherein a lower limit value of the target frequency band is greater than or equal to a frequency threshold value, and the frequency threshold value is determined based on the frequency of the arc signal;

[0087] A first acquisition module 11 is configured to acquire current frequency characteristics of a preset number of half-wave signals and to acquire an average of the current frequency characteristics of all preset number of half-wave signals; wherein the frequency characteristics are multiple;

[0088] The second acquisition module 12 is used to obtain data characteristics of the current half-wave signal; wherein the current half-wave signal is the half-wave signal after a preset number of half-wave signals;

[0089] The third acquisition module 13 is used to obtain the difference between the data feature and the mean;

[0090] The first determination module 14 is configured to determine, when the difference is greater than a first threshold value, whether the current half-wave signal is an arc signal under the current frequency characteristic; return to the first acquisition module 11, and continue to enter the fourth acquisition module 15 until the current half-wave signal is determined to be an arc signal or a non-arc signal under all frequency characteristics;

[0091] A fourth acquisition module 15 is used to obtain the number of frequency features corresponding to determining that the current half-wave signal is an arc signal;

[0092] The second determining module 16 is configured to determine that the current half-wave signal is an arc signal if the number of frequency features corresponding to the current half-wave signal being an arc signal is greater than a second threshold.

[0093] Since the embodiments of the apparatus part correspond to the embodiments of the method part, the embodiments of the apparatus part refer to the description of the embodiments of the method part, and are not repeated here. The apparatus for identifying arc signals provided in this embodiment has the same beneficial effects as the method for identifying arc signals mentioned above.

[0094] Figure 4 This is a structural diagram of an arc detection device provided in another embodiment of the present application. This embodiment is based on the hardware perspective, such as Figure 4 As shown, the arc detection equipment includes:

[0095] Memory 20, for storing computer programs;

[0096] The processor 21 is configured to implement the steps of the method for identifying arc signals as mentioned in the above embodiment when executing a computer program.

[0097] The arc detection device provided in this embodiment may include but is not limited to a smart phone, a tablet computer, a laptop computer, or a desktop computer.

[0098] Among them, the processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 can be implemented in at least one hardware form of a digital signal processor (DSP), a field programmable gate array (FPGA), and a programmable logic array (PLA). The processor 21 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a central processing unit (CPU); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 21 may be integrated with a graphics processing unit (GPU), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 21 may also include an artificial intelligence (AI) processor, which is used to process computing operations related to machine learning.

[0099] The memory 20 may include one or more computer-readable storage media, which may be non-transitory. The memory 20 may also include high-speed random access memory, and non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 20 is at least used to store the following computer program 201, wherein, after the computer program is loaded and executed by the processor 21, it can implement the relevant steps of the method for identifying arc signals disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 20 may also include an operating system 202 and data 203, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 202 may include Windows, Unix, Linux, etc. The data 203 may include but is not limited to the data involved in the above-mentioned method for identifying arc signals, etc.

[0100] In some embodiments, the arc detection device may further include a display screen 22 , an input / output interface 23 , a communication interface 24 , a power supply 25 , and a communication bus 26 .

[0101] Those skilled in the art will understand that Figure 4 The structure shown in the figure does not constitute a limitation of the arc detection device, and may include more or fewer components than shown in the figure.

[0102] The arc detection device provided in the embodiment of the present application includes a memory and a processor. When the processor executes the program stored in the memory, it can implement the following method: a method for identifying an arc signal, with the same effect as above.

[0103] The present application also provides an embodiment corresponding to a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps described in the above method embodiment.

[0104] It is understandable that if the method in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and executes all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0105] The computer-readable storage medium provided in this application includes the above-mentioned method for identifying arc signals, and the effect is the same as above.

[0106] In order to make the technical personnel in this field better understand the present application scheme, the following Figure 2 The present application is further described in detail with specific implementation methods. Figure 2The structural diagram of the arc discrimination device based on dynamic threshold described in the text includes a sampling circuit, a hardware digital signal processing unit 2, and a software algorithm processing unit 3. First, a dedicated arc sampling circuit is used to continuously perform high-speed sampling from the live wire. In the hardware digital signal processing unit 2, the digital signal converted by the high-speed ADC is band-pass filtered, and a specific high-frequency band can be selected and the noise in the band can be filtered out. Then, FFT transformation is used to obtain the high-frequency FFT characteristics of the signal. Finally, the pulse noise is filtered out through median filtering to output a relatively ideal high-frequency feature. In the software algorithm processing unit 3, the ideal high-frequency features output by the hardware part are first analyzed to screen out 1 to N high-frequency features for distinguishing arcs from normal ones, and then input into the N dynamic threshold discriminations of "high-frequency features--dynamic baseline--noise reduction calculation--threshold discrimination". The unit is based on the fact that the load in a real dynamic environment is basically in a normal state when it is powered on. In each dynamic threshold judgment unit, the average of the high-frequency characteristics of a certain number of half-wave signals at power-on is used as the dynamic baseline. The data characteristics of each subsequent half-wave are subtracted from the baseline for noise reduction calculation. A reasonable threshold is set for comparison with the noise reduction calculation result. When the subtraction difference is greater than the set threshold, the threshold is judged as an arc, otherwise it is normal, realizing dynamic threshold judgment based on a single high-frequency feature. Finally, the results of N dynamic threshold judgments are input into the dynamic threshold joint judgment module. If there are more than N / 2+1 dynamic thresholds that judge the signal as an arc, the signal of the half-wave period is judged as an arc, otherwise it is judged as normal.

[0107] In detail, for the software algorithm processing unit 3 of the dynamic threshold, its formulaic calculation process is described. The high-frequency feature is represented as HF, the number of high-frequency features is represented as N, the dynamic baseline is represented as DB, the noise elimination calculation is represented as NE, and the threshold judgment is represented as TD; take M cycles of normal data, and the high-frequency feature of a subsequent cycle data is recorded as HF*; use CU counting, the initial value of CU is 0, when the dynamic threshold judgment result is an arc, CU increases by 1, otherwise it remains unchanged. Taking HFn (high-frequency feature n) as an example, the calculation process of a single dynamic threshold judgment unit is explained. For HFn, there are:

[0108]

[0109] NEn=HFn*-DBn

[0110] For the judgment of N dynamic thresholds, if CU>N / 2+1, the judgment result of this cycle is finally an arc, otherwise it is normal.

[0111] In this application, a dedicated arc sampling circuit is built to facilitate the full sampling of high-frequency signals; a high-speed ADC is used for analog-to-digital conversion to fully ensure the original information of the signal; for digital signals, bandpass filtering, FFT transformation and median filtering are used to fully filter out noise in specific high-frequency bands and output relatively ideal high-frequency FFT features; based on the visualization analysis of arc and normal feature graphs, N high-frequency features that are easy to identify arcs are fully screened out; a dynamic threshold discrimination unit is designed, and the use of high-frequency features, dynamic baseline, noise reduction calculation, and threshold discrimination methods can more effectively avoid the interference of environmental changes and load type diversity on arc detection, which is more conducive to arc discrimination of various loads in real dynamic environments; a dynamic threshold joint discrimination module is designed to comprehensively analyze the discrimination results of multiple dynamic threshold discrimination units. When the power environment changes, the arc discrimination will not be affected, which greatly avoids misjudgment in real situations and is more conducive to the actual use and promotion of arc detection equipment.

[0112] The above is a detailed introduction to the method, device, arc detection equipment and medium for identifying arc signals provided by the present application. The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of this application.

[0113] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A method for identifying arc signals, characterized in that: include: A half-wave signal of a target frequency band in an AC circuit is collected by an analog-to-digital converter; wherein a lower limit value of the target frequency band is greater than or equal to a frequency threshold value, and the frequency threshold value is determined according to the frequency of the arc signal; Obtaining current frequency characteristics of a preset number of the half-wave signals and obtaining an average value of the current frequency characteristics of all the preset number of the half-wave signals; wherein there are multiple frequency characteristics; Acquire data features of a current half-wave signal; wherein the current half-wave signal is the half-wave signal after the preset number; Obtaining a difference between the data feature and the mean; When the difference is greater than a first threshold, determining that the current half-wave signal is the arc signal under the current frequency characteristic; returning to the step of obtaining the current frequency characteristics of a preset number of half-wave signals until determining that the current half-wave signal is the arc signal or the non-arc signal under all the frequency characteristics is completed; Obtaining the number of the frequency characteristics corresponding to determining that the current half-wave signal is the arc signal; If the number of the frequency characteristics corresponding to when it is determined that the current half-wave signal is the arc signal is greater than a second threshold, the current half-wave signal is determined to be the arc signal.

2. The method for identifying arc signals according to claim 1, characterized in that: The obtaining of a preset number of current frequency characteristics of the half-wave signals includes: The current frequency characteristics of the preset number of half-wave signals are obtained starting from when a load in the AC circuit is powered on.

3. The method for identifying arc signals according to claim 2, characterized in that: The collecting of the half-wave signal of the target frequency band in the AC circuit by the analog-to-digital converter comprises: collecting the signal in the AC circuit by the analog-to-digital converter; The signal is filtered through an LC filter circuit to obtain the half-wave signal of the target frequency band.

4. The method for identifying arc signals according to claim 3, characterized in that: The LC filter circuit includes an inductor, a first resistor, a first capacitor, a second resistor, a second capacitor, and a third resistor; The first end of the first resistor, the first end of the inductor, and the first end of the first capacitor are connected to the live wire; the second end of the first resistor, the second end of the inductor, and the first end of the second capacitor are connected; the second end of the first capacitor is connected to the first end of the second resistor; the second end of the second capacitor is connected to the first end of the third resistor; the first capacitor and the second resistor are connected in series to form a first circuit, the second capacitor and the third resistor are connected in series to form a second circuit, and the first circuit and the second circuit are placed symmetrically above and below with respect to the parallel circuit formed by the inductor and the first resistor.

5. The method for identifying arc signals according to any one of claims 1 to 4, characterized in that: Acquiring the frequency characteristic of the half-wave signal includes: Filtering the half-wave signal through a bandpass filter and obtaining the filtered half-wave signal; Obtaining the frequency characteristics of the filtered half-wave signal by fast Fourier transform; The frequency characteristic is filtered by a median filter to obtain the frequency characteristic of the half-wave signal.

6. The method for identifying arc signals according to claim 1, wherein: After determining that the current half-wave signal is the arc signal, the method further includes: Outputting prompt information for indicating that the current half-wave signal is the arc signal.

7. A device for identifying arc signals, characterized in that: include: an acquisition module, configured to acquire a half-wave signal of a target frequency band in an AC circuit through an analog-to-digital converter; wherein a lower limit value of the target frequency band is greater than or equal to a frequency threshold value, and the frequency threshold value is determined according to the frequency of the arc signal; A first acquisition module is configured to acquire current frequency characteristics of a preset number of half-wave signals and to acquire an average value of the current frequency characteristics of all the preset number of half-wave signals; wherein there are multiple frequency characteristics; A second acquisition module is used to obtain data features of the current half-wave signal; wherein the current half-wave signal is the half-wave signal after the preset number; A third acquisition module is used to obtain the difference between the data feature and the mean; A first determining module is configured to determine, when the difference is greater than a first threshold, that the current half-wave signal is the arc signal under the current frequency characteristic; and return to the step of obtaining the current frequency characteristics of a preset number of half-wave signals until determining whether the current half-wave signal is the arc signal or the non-arc signal under all the frequency characteristics; A fourth acquisition module is used to acquire the number of the frequency characteristics corresponding to determining that the current half-wave signal is the arc signal; The second determination module is configured to determine that the current half-wave signal is the arc signal if the number of the frequency characteristics corresponding to the current half-wave signal being the arc signal is greater than a second threshold.

8. An arc detection device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of the method for identifying an arc signal according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for identifying an arc signal according to any one of claims 1 to 6 are implemented.

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