Fault arc detection method, device and equipment and storage medium

Abstract

The invention relates to the technical field of electric power safety monitoring, in particular to a fault arc detection method, device and equipment and a storage medium, and the method comprises the steps: obtaining a current periodic waveform of an electric power system in a low-voltage alternating current scene, and collecting a first half-wave waveform of the current periodic waveform according to a time sequence, determining the next half-wave waveform which is continuous with the first half-wave waveform as a second half-wave waveform; acquiring a first current characteristic parameter of the power system in the first half-wave waveform, and acquiring a second current characteristic parameter of the power system in the second half-wave waveform; and determining an adjacent half-wave similar threshold according to the first current characteristic parameter and the second current characteristic parameter, and identifying the fault arc according to the adjacent half-wave similar threshold. The invention aims to improve the accuracy of identifying the fault arc.

Description

Technical Field

[0001] The present application relates to the technical field of power safety monitoring, and in particular to a fault arc detection method, device, equipment and storage medium. Background Art

[0002] Among the causes of electrical fires, line insulation ages due to long-term operation, environmental erosion and other factors, or is mechanically damaged by external forces, which in turn triggers fault arcs. This has become a key hidden danger in the field of electrical fire prevention and control.

[0003] Currently, traditional arc detection technology primarily focuses on analyzing current amplitude and time and frequency domain characteristics. This makes it difficult to accurately distinguish between controllable switching arcs (i.e., arcs that naturally extinguish when the AC current crosses zero) and irregular fault arcs (caused by line problems such as abnormal insulation and poor contact). Especially in low-voltage AC power, the weak signals of fault arcs are easily masked by harmonics and noise, resulting in delayed response of protection systems.

[0004] Therefore, how to improve the accuracy of identifying fault arcs is a technical problem that needs to be solved urgently. Summary of the Invention

[0005] The main purpose of this application is to provide a fault arc detection method, device, equipment and storage medium, aiming to improve the accuracy of identifying fault arcs.

[0006] To achieve the above objectives, the present application provides a method for detecting an arc fault, the method comprising: Acquire a current periodic waveform of the power system in a low-voltage alternating current scenario, collect a first half-wave waveform of the current periodic waveform in chronological order, and determine the next half-wave waveform continuous with the first half-wave waveform as the second half-wave waveform; Acquire a first current characteristic parameter of the power system in the first half-wave waveform, and acquire a second current characteristic parameter of the power system in the second half-wave waveform; An adjacent half-wave similarity threshold is determined according to the first current characteristic parameter and the second current characteristic parameter, and a fault arc is identified according to the adjacent half-wave similarity threshold.

[0007] In one embodiment, the step of obtaining the first current characteristic parameter of the power system in the first half-wave waveform includes: Determine a current time series array corresponding to the first half-wave waveform, where the current time series array includes N consecutive current sampling values, where N is a natural number greater than 1; Determining a current mutation amplitude of the power system during the first half-wave waveform according to a difference between a maximum current sampling value and an initial current sampling value in the current time series array; All the current sampling values are traversed according to the current mutation amplitude and the negative mutation amplitude to obtain a first current characteristic parameter, wherein the negative mutation amplitude is the opposite of the current mutation amplitude.

[0008] In one embodiment, the first current characteristic parameter includes a positive current characteristic, a negative current characteristic, and an arc current characteristic, and the step of traversing all the current sampling values according to the current mutation amplitude and the negative mutation amplitude to obtain the first current characteristic parameter includes: traversing the magnitudes of each current sampling value and the current mutation amplitude and the negative mutation amplitude in chronological order, accumulating the traversed current sampling values that are greater than the current mutation amplitude, and taking the obtained positive half-wave cumulative value as the forward current feature; Accumulate the current sampling values obtained through the traversal that are smaller than the negative mutation amplitude, and use the obtained negative half-wave cumulative value as the negative current feature; The current sampling values obtained through traversal that are greater than the negative mutation amplitude and less than the current mutation amplitude are accumulated, and the accumulated current mutation value obtained is used as the arc current feature.

[0009] In one embodiment, the step of obtaining the second current characteristic parameter of the power system in the second half-wave waveform includes: Use the second half-wave waveform as the next first half-wave waveform, return to the step of obtaining the first current characteristic parameters of the power system during the first half-wave waveform, and obtain the second current characteristic parameters of the power system during the second half-wave waveform.

[0010] In one embodiment, the first current characteristic parameter includes a positive current characteristic, a negative current characteristic, and an arc current characteristic, and the second current characteristic parameter includes a positive measurement characteristic, a negative measurement characteristic, and an arc measurement characteristic; wherein, The positive measurement characteristic is the positive half-wave cumulative value accumulated by the power system during the second half-wave waveform, the negative measurement characteristic is the negative half-wave cumulative value accumulated by the power system during the second half-wave waveform, and the arc measurement characteristic is the current mutation cumulative value accumulated by the power system during the second half-wave waveform; The step of determining the adjacent half-wave similarity threshold according to the first current characteristic parameter and the second current characteristic parameter comprises: Superimposing the arc current feature and the arc measurement feature to obtain an arc feature cumulative value, and determining a positive feature absolute difference between the positive current feature and the positive measurement feature, and a negative feature absolute difference between the negative current feature and the negative measurement feature; Determining first difference data between the arc feature cumulative value and the positive feature absolute difference value, and second difference data between the arc feature cumulative value and the negative feature absolute difference value; The adjacent half-wave similarity threshold is obtained by dividing the difference product data between the first difference data and the second difference data by the square of the arc current characteristic.

[0011] In one embodiment, the step of identifying the arc fault based on the adjacent half-wave similarity threshold comprises: Detecting whether the adjacent half-wave similarity threshold is greater than a preset half-wave similarity standard threshold; If the adjacent half-wave similarity threshold is less than or equal to the half-wave similarity standard threshold, assigning the second current characteristic parameter as the next first current characteristic parameter, and accumulating the number of operations for each assignment; Determine the next half-wave waveform continuous with the second half-wave waveform as the next second half-wave waveform, and return to the step of obtaining the second current characteristic parameter of the power system when the second half-wave waveform is present; Until it is detected that the adjacent half-wave similarity threshold is greater than the half-wave similarity standard threshold, determining whether the accumulated number of operations is within a preset arc detection threshold interval; If the accumulated number of operations is not in the arc detection threshold interval, after determining that the accumulated number of operations exceeds the arc detection threshold upper limit corresponding to the arc detection threshold interval, it is determined that a fault arc occurs in the power system.

[0012] In one embodiment, after the step of determining whether the accumulated number of operations is within a preset arc detection threshold range, the arc fault detection method includes: If the accumulated number of operations is within the arc detection threshold range, it is determined that a switching arc occurs in the power system.

[0013] In addition, to achieve the above-mentioned purpose, the present application also provides a fault arc detection device, which includes: a waveform acquisition module, configured to acquire a current periodic waveform of the power system in a low-voltage alternating current scenario, collect a first half-wave waveform of the current periodic waveform in chronological order, and determine a next half-wave waveform continuous with the first half-wave waveform as a second half-wave waveform; a characteristic acquisition module, configured to acquire a first current characteristic parameter of the power system in the first half-wave waveform, and to acquire a second current characteristic parameter of the power system in the second half-wave waveform; An arc identification module is used to determine an adjacent half-wave similarity threshold based on the first current characteristic parameter and the second current characteristic parameter, and to identify a fault arc based on the adjacent half-wave similarity threshold.

[0014] Each functional module of the arc fault detection device of the present application implements the steps of the arc fault detection method of the present application as described above during operation.

[0015] In addition, to achieve the above-mentioned purpose, the present application also provides a fault arc detection device, which includes a memory, a processor, and a fault arc detection program stored in the memory and runnable on the processor. When the fault arc detection program is executed by the processor, the steps of the above-mentioned fault arc detection method are implemented.

[0016] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium, which is a computer-readable storage medium. The computer-readable storage medium stores a fault arc detection program, and when the fault arc detection program is executed by the processor, the steps of the above-mentioned fault arc detection method are implemented.

[0017] The present application provides a fault arc detection method that optimizes the traditional arc detection technology in a targeted manner, significantly improving the accuracy of identifying fault arcs. Specifically, the current periodic waveform of the power system in a low-voltage alternating current scenario is obtained in real time, thereby utilizing the inherent periodic characteristics of low-voltage alternating current, taking half-wave as the minimum analysis unit, and extracting the continuous adjacent first half-wave waveform and second half-wave waveform from the current periodic waveform; subsequently, based on the first current characteristic parameter of the power system in the first half-wave waveform and the second current characteristic parameter in the second half-wave waveform, the adjacent half-wave similarity threshold can be accurately evaluated; finally, based on the evaluated adjacent half-wave similarity threshold, the high similarity of the adjacent half-wave waveforms when the normal switching arc is naturally extinguished at the current zero point can be accurately quantified, while at the same time, the waveform mutation characteristics of the fault arc caused by line insulation degradation, contact resistance abnormality, etc. are keenly captured, effectively avoiding the technical defects of the traditional arc detection technology that is susceptible to noise interference and difficult to effectively identify the fault arc, thereby significantly improving the accuracy of identifying the fault arc. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the first embodiment of the arc fault detection method of the present application; Figure 2 Schematic diagram of a normal current waveform involved in the embodiment of the present application; Figure 3 1 is a schematic diagram of a fault arc current waveform involved in an embodiment of the present application; Figure 4 Schematic diagram of the arc current waveform of the switch involved in the embodiment of the present application; Figure 5 It is a graphical diagram of normal current statistics involved in the embodiment of the present application; Figure 6 This is a graphical diagram of the statistical values of the arc fault current involved in the embodiment of the present application; Figure 7 It is a graphical diagram of the statistical values of the switch arc current involved in the embodiment of the present application; Figure 8 This is a schematic diagram of the arc fault detection process involved in the embodiment of the present application; Figure 9 1 is a schematic structural diagram of a fault arc detection device according to an embodiment of the present application; Figure 10 This is a schematic diagram of the structure of the arc fault detection device involved in the embodiment of the present application.

[0019] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0020] The present application provides a method for detecting arc faults. Figure 1 As shown, Figure 1 This is a flow chart of the first embodiment of the arc fault detection method of the present application.

[0021] Exemplary embodiments are described in detail herein, with examples illustrated in the accompanying drawings. When the following description refers to the drawings, identical numerals in different drawings represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this application.

[0022] Electrical fires account for a significant proportion of all fires, posing a serious threat to social security and stability. The primary source of electrical fires lies in residential wiring systems, including electrical wiring, socket wiring, internal wiring for household appliances, and power cords. Under complex, long-term, loaded operation, wire insulation can gradually age due to a combination of electrical and thermal stresses, as well as environmental factors, or break down due to mechanical damage. This significantly increases the probability of series and parallel arc faults between wires and short circuits to ground.

[0023] It is worth noting that there is a fundamental difference in the physical nature of the switching arc and the fault arc in the arc phenomenon. The former is a controllable arc channel formed when the electric field strength exceeds the dielectric breakdown threshold due to contact separation during normal opening and closing operations of the switching electrical appliance. Its arcing and arcing processes strictly follow the circuit parameter laws (such as the arc in the AC circuit naturally extinguishes when the current passes through zero), while the latter is caused by unexpected operating conditions such as damage to the line insulation layer and abnormal contact resistance. Its arc characteristics are relatively small current amplitude (usually lower than the short-circuit current threshold), severe current waveform distortion caused by grid harmonics / noise interference, and arc energy distribution with random pulse characteristics (including characteristic spectra such as high-frequency components and transient overvoltages). This instability makes it difficult for traditional detection devices based on current amplitude thresholds or overcurrent protection principles to effectively identify fault arcs, resulting in failure of the protection system and posing a fire hazard.

[0024] Therefore, in order to solve the technical problem that traditional arc detection devices are easily affected by grid harmonics / noise, resulting in poor accuracy in fault arc identification, the present application provides a fault arc detection method, device, equipment and storage medium.

[0025] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, mobile phone, database system, etc., or a device capable of implementing the above functions, such as an arc fault detection device. The following uses an arc fault detection device as an example to illustrate this embodiment and the following embodiments.

[0026] The arc fault detection method of the present application includes the following implementation steps S10 to S30.

[0027] Step S10: Obtain the current periodic waveform of the power system in a low-voltage AC scenario, collect the first half-wave waveform of the current periodic waveform in chronological order, and determine the next half-wave waveform continuous with the first half-wave waveform as the second half-wave waveform.

[0028] In this embodiment, the current periodic waveform of the power system in a low-voltage alternating current scenario is determined, and the first half-wave waveform in the current periodic waveform is accurately collected in chronological order, and the next half-wave waveform continuous with the first half-wave waveform is clearly defined as the second half-wave waveform, thereby realizing the use of the inherent periodic characteristics of low-voltage alternating current and capturing the dynamic change characteristics of the current in each half-wave with half-wave as the minimum analysis unit.

[0029] It should be noted that the current periodic waveform can cover Figure 2 The normal current waveform shown, Figure 3 The arc fault current waveform shown, and Figure 4 The switching arc current waveform is shown.

[0030] For example, traditional arc detection technology is susceptible to interference from grid harmonics and noise when faced with complex and variable current waveforms, making it difficult to accurately distinguish between fault arcs and switching arcs. However, this application leverages the inherent periodicity of low-voltage AC power to extract the consecutive first and second half-wave waveforms from the current periodic waveform. Using the half-wave as the minimum analysis unit, the dynamic variation characteristics of the current within each half-wave are captured, providing strong data support for subsequent fault arc identification.

[0031] Step S20: obtaining a first current characteristic parameter of the power system in the first half-wave waveform, and obtaining a second current characteristic parameter of the power system in the second half-wave waveform.

[0032] In this embodiment, the current signal of the power system during the first half-wave waveform is collected to obtain N consecutive current sampling values, and these N consecutive current sampling values are recorded as a current time series array. Next, because the current time series array is composed of N consecutive current sampling values arranged in chronological order, the current sampling value with the earliest sampling time in the current time series array can be used as the initial current sampling value, and the current sampling value with the largest sampling value in the current time series array can be used as the maximum current sampling value. Subsequently, based on the difference between the maximum current sampling value and the initial current sampling value in the current time series array, the current mutation amplitude of the power system during the first half-wave waveform can be accurately obtained. Subsequently, the opposite of the current mutation amplitude is determined to be the negative mutation amplitude, and all current sampling values are traversed based on the current mutation amplitude and the negative mutation amplitude, so that the dynamic change characteristics of the power system within the first half-wave waveform can be quickly and accurately captured to obtain the first current characteristic parameter. After determining the first current characteristic parameter of the power system, the second half-wave waveform is used as the next first half-wave waveform, and the step of obtaining the first current characteristic parameter of the power system at the first half-wave waveform is returned to obtain the second current characteristic parameter of the power system at the second half-wave waveform, thereby providing accurate and reliable first current characteristic parameters and second current characteristic parameters for subsequent evaluation of adjacent half-wave similarity thresholds.

[0033] Step S30: determining an adjacent half-wave similarity threshold according to the first current characteristic parameter and the second current characteristic parameter, and identifying a fault arc according to the adjacent half-wave similarity threshold.

[0034] In this embodiment, the first current characteristic parameter and the second current characteristic parameter are evaluated according to a preset similarity evaluation model, and the adjacent half-wave similarity threshold can be accurately evaluated; then, based on the adjacent half-wave similarity threshold obtained by evaluation, the high similarity of the adjacent half-wave waveforms when the normal switching arc is naturally extinguished at the current zero point can be accurately quantified, and at the same time, the waveform mutation characteristics of the fault arc caused by line insulation degradation, abnormal contact resistance, etc. are keenly captured, effectively avoiding the technical defects of traditional arc detection technology that is easily susceptible to noise interference and difficult to effectively identify fault arcs, thereby significantly improving the accuracy of identifying fault arcs.

[0035] In summary, the present application provides a fault arc detection method that optimizes the traditional arc detection technology in a targeted manner, significantly improving the accuracy of identifying fault arcs. Specifically, the current periodic waveform of the power system in a low-voltage alternating current scenario is obtained in real time, thereby utilizing the inherent periodic characteristics of low-voltage alternating current, taking half-wave as the minimum analysis unit, and extracting the continuous adjacent first half-wave waveform and second half-wave waveform from the current periodic waveform; subsequently, based on the first current characteristic parameter of the power system in the first half-wave waveform and the second current characteristic parameter in the second half-wave waveform, the adjacent half-wave similarity threshold can be accurately evaluated; finally, based on the evaluated adjacent half-wave similarity threshold, the high similarity of the adjacent half-wave waveforms of the normal switching arc when the current crosses zero and naturally extinguishes the arc can be accurately quantified, while sensitively capturing the waveform mutation characteristics of the fault arc caused by line insulation degradation, contact resistance abnormality, etc., effectively avoiding the technical defects of the traditional arc detection technology that is easily affected by noise interference and difficult to effectively identify the fault arc, thereby significantly improving the accuracy of identifying the fault arc.

[0036] Furthermore, based on the first embodiment of the arc fault detection method of the present application, a second embodiment of the arc fault detection method of the present application is proposed. In some feasible embodiments, the above step S20: obtaining the first current characteristic parameter of the power system during the first half-wave waveform, further includes the following implementation steps S201 to S203.

[0037] Step S201: determining a current time series array corresponding to the first half-wave waveform, wherein the current time series array includes N consecutive current sampling values, where N is a natural number greater than 1.

[0038] In this embodiment, the current signal of the power system in the first half-wave waveform is collected to obtain N consecutive current sampling values, and these N consecutive current sampling values are recorded as a current time series array.

[0039] Exemplarily, first, the values of various variables of the power system during the fault arc detection operation are initialized, the reference stable point sampled after initialization is recorded as SA, and the defined counting variable Cnt is set to zero; next, the current signal of the power system in the low-voltage AC scenario is sampled. Specifically, half-cycle data of the 50Hz AC current cycle is collected (for example, N consecutive current sampling values of the power system in the first half-wave waveform are collected) and recorded as a current sampling array Sn, where n is the data volume N of all current sampling values, and the data volume N can be 400 or customized according to user needs. This application does not impose any restrictions on this; then, the current sampling array Sn formed by sampling the current signal in the first half-wave waveform is data processed, that is, after assigning the preset current time series array An to the current sampling array Sn, the current sampling array Sn is set to zero and waits for the next sampling record. That is, the current time series array An can include the current value Sn, the current sampling value Sn-1, ..., and the current value sample S1 (that is, the reference stable point SA sampled after initialization).

[0040] Step S202: determining a current mutation amplitude of the power system during the first half-wave waveform according to a difference between a maximum current sampling value and an initial current sampling value in the current time series array.

[0041] In this embodiment, since the current time series array is N consecutive current sampling values arranged in chronological order, the current sampling value with the earliest sampling time in the current time series array can be used as the initial current sampling value, and the current sampling value with the largest sampling value in the current time series array can be used as the maximum current sampling value; then, according to the preset fault arc feature judgment algorithm, the difference between twice the maximum current sampling value and the initial current sampling value and the proportional data between the data amount N are rounded up, and the current mutation amplitude of the power system in the first half-wave waveform can be accurately obtained, thereby accurately quantifying the degree of abnormal fluctuation of the current signal.

[0042] It should be noted that the preset fault arc characteristic determination algorithm expression is: b=2(Sn_max-SA) / N, where Sn_max refers to the maximum current sampling value, SA refers to the initial current sampling value, and b refers to the current mutation amplitude.

[0043] In other words, the difference (Sn_max-SA) highlights the dynamic variation of the current signal relative to the stability reference. In particular, in abnormal conditions such as arc faults, the maximum current sampled value can significantly deviate from the current sampled value based on the reference stability point, allowing the difference (Sn_max-SA) to effectively reflect the severity of the anomaly. Subsequently, this difference (Sn_max-SA) is multiplied by 2 and divided by the data size N, thereby normalizing the difference (Sn_max-SA). This makes the calculation results for different data sizes comparable and avoids scale differences caused by different data lengths. Finally, a rounding-up operation ensures that the current mutation amplitude b is an integer, facilitating subsequent discretization or threshold comparison, and enhancing the robustness and practicality of the algorithm. The rounding-up operation can be understood as meaning that when 2(Sn_max-SA) / N is 3.7, the current mutation amplitude b is an integer of 4.

[0044] Step S203: traversing all the current sampling values according to the current mutation amplitude and the negative mutation amplitude to obtain a first current characteristic parameter, wherein the negative mutation amplitude is the opposite of the current mutation amplitude.

[0045] In this embodiment, the opposite of the current mutation amplitude is determined to be the negative mutation amplitude, and all current sampling values are traversed based on the current mutation amplitude and the negative mutation amplitude, so that the dynamic change characteristics of the power system within the first half-wave waveform can be quickly and accurately captured to obtain the first current characteristic parameter.

[0046] Furthermore, in some feasible embodiments, the first current characteristic parameter includes a positive current characteristic, a negative current characteristic and an arc current characteristic. The above step S203: traverses all the current sampling values according to the current mutation amplitude and the negative mutation amplitude to obtain the first current characteristic parameter, and also includes the following implementation steps S2031~S2033.

[0047] Step S2031: traverse each of the current sampling values in chronological order, compare them with the current mutation amplitude and the negative mutation amplitude, accumulate the current sampling values obtained that are greater than the current mutation amplitude, and use the obtained positive half-wave cumulative value as the forward current feature.

[0048] In this embodiment, each current sampling value is traversed in chronological order with the current mutation amplitude and the negative mutation amplitude, thereby achieving a one-to-one comparison between each current sampling value and the current mutation amplitude and the negative mutation amplitude; next, the current sampling values obtained that are greater than the current mutation amplitude are accumulated, and the obtained positive half-wave accumulated value is used as the positive current feature, thereby highlighting the positive current change within the half-wave period.

[0049] Step S2032: accumulating the current sampling values obtained through the traversal that are smaller than the negative mutation amplitude, and taking the obtained negative half-wave cumulative value as the negative current feature.

[0050] In this embodiment, the current sampling values obtained through traversal that are smaller than the negative mutation amplitude are accumulated, and the obtained negative half-wave accumulated value is used as the negative current feature, thereby highlighting the negative current change within the half-wave period.

[0051] Step S2033: accumulating the current sampling values obtained through traversal that are greater than the negative mutation amplitude and less than the current mutation amplitude, and using the obtained current mutation accumulated value as the arc current feature.

[0052] In this embodiment, the current sampling values obtained by traversal that are greater than the negative mutation amplitude and less than the current mutation amplitude are accumulated so that the arc current characteristics can be calculated, thereby highlighting the arc current changes within the half-wave period.

[0053] Furthermore, in some feasible embodiments, the above step S20: obtaining the second current characteristic parameter of the power system in the second half-wave waveform, further includes the following implementation step A10.

[0054] Step A10: Use the second half-wave waveform as the next first half-wave waveform, return to execute the step of obtaining the first current characteristic parameters of the power system during the first half-wave waveform, and obtain the second current characteristic parameters of the power system during the second half-wave waveform.

[0055] In this embodiment, the second half-wave waveform is used as the next first half-wave waveform, and the step of obtaining the first current characteristic parameter of the power system when it is in the first half-wave waveform is returned to obtain the second current characteristic parameter of the power system when it is in the second half-wave waveform. In other words, the second half-wave waveform is used as the next first half-wave waveform and the characteristic parameter acquisition step is cyclically executed, thereby realizing full-cycle and dynamic monitoring and analysis of the power system current signal, significantly improving the real-time performance and accuracy of fault detection and status assessment. Specifically, by utilizing the progressive iteration mechanism of the half-wave waveform, it is ensured that the characteristic parameters can be independently extracted within each half-wave cycle, thereby being able to track the continuous changes of the current in the time dimension in real time, and promptly discovering current distortion (such as sudden changes in amplitude, increased harmonic content, or waveform asymmetry) caused by load fluctuations, line aging, or fault arcs.

[0056] Furthermore, in other feasible embodiments, the first current characteristic parameter includes a positive current characteristic, a negative current characteristic and an arc current characteristic, and the second current characteristic parameter includes a positive measurement characteristic, a negative measurement characteristic and an arc measurement characteristic; wherein the positive measurement characteristic is the positive half-wave cumulative value accumulated by the power system during the second half-wave waveform, the negative measurement characteristic is the negative half-wave cumulative value accumulated by the power system during the second half-wave waveform, and the arc measurement characteristic is the current mutation cumulative value accumulated by the power system during the second half-wave waveform. The above-mentioned step S30: determining the adjacent half-wave similarity threshold based on the first current characteristic parameter and the second current characteristic parameter may also include the following implementation steps S301~S303.

[0057] Step S301: superimpose the arc current feature and the arc measurement feature to obtain an arc feature cumulative value, and determine the positive feature absolute difference between the positive current feature and the positive measurement feature, and the negative feature absolute difference between the negative current feature and the negative measurement feature.

[0058] In this embodiment, the arc current characteristics and the arc measurement characteristics can be summed according to the preset similarity evaluation model to obtain the arc characteristic cumulative value; next, the positive current characteristics and the positive measurement characteristics, and the negative current characteristics and the negative measurement characteristics are respectively subjected to difference processing according to the similarity evaluation model, and the absolute difference of the positive characteristics and the absolute difference of the negative characteristics can be accurately calculated.

[0059] Step S302: determining first difference data between the arc feature cumulative value and the positive feature absolute difference value, and second difference data between the arc feature cumulative value and the negative feature absolute difference value; Step S303: obtaining an adjacent half-wave similarity threshold based on the difference product data between the first difference data and the second difference data divided by the square of the arc current characteristic.

[0060] In this embodiment, after determining the first difference data between the cumulative value of the arc feature and the absolute difference value of the positive feature, and the second difference data between the cumulative value of the arc feature and the absolute difference value of the negative feature, the adjacent half-wave similarity threshold can be accurately calculated based on the difference product data between the first difference data and the second difference data divided by the square of the arc current feature.

[0061] It should be noted that the preset similarity evaluation model expression is:

[0062] in, represents the adjacent half-wave similarity threshold, Indicates the arc current characteristics, Indicates arc current characteristics and arc measurement characteristics, Represents the forward current characteristic, represents the forward measurement characteristic, Indicates negative current characteristics; Indicates a negative measurement characteristic.

[0063] Furthermore, in some feasible embodiments, the above step S30: identifying the fault arc according to the adjacent half-wave similarity threshold, may also include the following implementation steps B10 to B50.

[0064] Step B10: Detect whether the adjacent half-wave similarity threshold is greater than a preset half-wave similarity standard threshold.

[0065] In this embodiment, it is detected whether the adjacent half-wave similarity threshold is greater than the preset half-wave similarity standard threshold. The half-wave similarity standard threshold can be 80%, and can also be customized according to user needs. This application does not impose any restrictions here.

[0066] For example, when the adjacent half-wave similarity threshold is greater than the half-wave similarity standard threshold, it can be determined that the current signal within the half-wave cycle is normal current, such as Figure 5 The normal current statistics are graphically shown, where the dotted box represents the current statistics within a half-wave waveform, and the solid box represents the current statistics of the next half-wave waveform. When the adjacent half-wave similarity threshold is less than or equal to the half-wave similarity standard threshold, it is determined that the current signal within the half-wave cycle may be a fault arc current or a switching arc current; wherein, the fault arc current statistics are graphically shown. Figure 6 , graphical reference of switch arc current statistics Figure 7 .

[0067] Step B20: If the adjacent half-wave similarity threshold is less than or equal to the half-wave similarity standard threshold, the second current characteristic parameter is assigned as the next first current characteristic parameter, and the number of operations for each assignment is accumulated.

[0068] In this embodiment, if the adjacent half-wave similarity threshold is less than or equal to the half-wave similarity standard threshold, the adjacent half-wave waveforms are determined to be abnormally similar. Next, the second current characteristic parameter is assigned as the next first current characteristic parameter, enabling dynamic updating and transfer of current characteristic parameters. Furthermore, the number of assignment operations is accumulated to record the frequency of the abnormal waveforms, providing a quantitative basis for subsequent determination of arc fault occurrence.

[0069] Step B30: Determine the next half-wave waveform continuous with the second half-wave waveform as the next second half-wave waveform, and return to the step of obtaining the second current characteristic parameter of the power system when it is in the second half-wave waveform.

[0070] In this embodiment, the next half-wave waveform continuous with the second half-wave waveform is determined to be the next second half-wave waveform, and the step of obtaining the second current characteristic parameters of the power system at the second half-wave waveform is returned to execute, that is, the progressive iteration mechanism of the half-wave waveform is utilized to accurately extract the current characteristic parameters of the next half-wave waveform continuous with the second half-wave waveform, that is, by continuously obtaining new current characteristic parameters and comparing and analyzing them with the previous current parameters, abnormal mutations or continuous distortions of the current waveform can be discovered in time, ensuring the real-time and comprehensiveness of fault detection, and effectively avoiding missed faults due to detection intervals or omissions.

[0071] Step B40: until it is detected that the adjacent half-wave similarity threshold is greater than the half-wave similarity standard threshold, determine whether the accumulated number of operations is within the preset arc detection threshold range.

[0072] In this embodiment, if the adjacent half-wave similarity threshold is greater than the half-wave similarity standard threshold, the waveform similarity between the adjacent half-waves is determined to be normal, indicating that the abnormal current waveform may have been alleviated or ended. Next, a determination is made as to whether the accumulated number of operations is within the arc detection threshold range, which comprehensively considers the duration and intensity of the abnormal waveform.

[0073] The preset arc detection threshold interval may be [4, 6], where 4 represents the arc detection threshold lower limit value corresponding to the arc detection threshold interval, and 6 represents the arc detection threshold upper limit value corresponding to the arc detection threshold interval.

[0074] Step B50: If the accumulated number of operations is not within the arc detection threshold interval, after determining that the accumulated number of operations exceeds the arc detection threshold upper limit corresponding to the arc detection threshold interval, it is determined that a fault arc occurs in the power system.

[0075] In this embodiment, after determining that the cumulative number of operations is not in the arc detection threshold interval, it is detected whether the cumulative number of operations exceeds the arc detection threshold upper limit corresponding to the arc detection threshold interval. If the cumulative number of operations exceeds the arc detection threshold upper limit, it can be accurately determined that a fault arc has occurred in the power system.

[0076] Furthermore, in some feasible embodiments, after the above step B40: determining whether the accumulated number of operations is within a preset arc detection threshold range, the arc fault detection method may further include the following implementation step C10.

[0077] Step C10: If the accumulated number of operations is within the arc detection threshold range, it is determined that a switching arc occurs in the power system.

[0078] In this embodiment, since switching arcs are typically triggered by normal operations in the power system (such as circuit breaker opening and closing, and contactor operation), their current waveform anomalies may be transient, periodic, or strongly correlated with the timing of the operation, but they will not continuously undermine system stability. By presetting an arc detection threshold interval, current anomalies whose cumulative number of operations falls within this arc detection threshold interval are identified as switching arcs rather than fault arcs. This prevents arcs caused by normal operations from being misidentified as fault arcs, thereby reducing unnecessary protective actions (such as false tripping) and ensuring power supply continuity.

[0079] In a specific embodiment, referring to Figure 8 , Figure 8 FIG1 is a schematic diagram of a fault arc detection process according to an embodiment of the present application. The fault arc detection process includes at least steps A to J.

[0080] Step A: First, initialize the values of each variable, record the reference stable point sampled after initialization as SA, and set the defined counting variable Cnt to zero. Cnt can be understood as the number of assignment operations.

[0081] Step B: Sample and record the current signal, and collect half a cycle data of the 50 Hz AC current cycle as a one-dimensional array Sn (where n is the data amount).

[0082] Step C: Process the one-dimensional array Sn of sampled current signals to form a current time series array An = [Sn, Sn-1, ..., SA]. That is, when n = 1, SA is recorded as An; when n = n-1, Sn-1 is recorded as An-1; and when n = n, Sn is recorded as An. Next, the current sampling array Sn is reset to zero pending the next sampling and recording. For n = N = 400, the value of b is calculated as 2(Sn_max - SA) / N, rounded up to an integer.

[0083] Step D: Fault arc determination.

[0084] If An>b; when n=1, i1=A1; when n≥2, i m =i m -1+A n , where m is a natural number greater than 0 and less than n, that is, when n=1, the first collected current sampling value SA is directly used as the initial positive half-wave cumulative value i1, and the current sampling value An is added to the previous positive half-wave cumulative value i1 in an accumulation manner. m−1 Add together to get the new positive half-wave cumulative value i m ; If An<-b, when n=1, j1=A1, when n≥2, j s =j s-1 +An, where s is a natural number greater than 0 and less than n, that is, when n=1, the first collected current sampling value SA is directly used as the initial negative half-wave cumulative value j1, and the current sampling value An is added to the previous negative half-wave cumulative value j1 in an accumulation manner. s−1 Add together to get the new negative half-wave cumulative value j s ; If b>An>-b, when n=1, k1= A1, when n≥2, k y =k y-1+ An, where y is a natural number greater than 0 and less than n, that is, when n=1, the first collected current sampling value SA is directly used as the initial current mutation cumulative value k1, and the current sampling value An is added to the previous negative half-wave cumulative value k1 in an accumulation manner. y-1 Add together to get the new current mutation cumulative value k y After the above processing, the positive half-wave cumulative value i m Assign to At the same time m Set to zero, negative half-wave cumulative value j s Assign to At the same time s Set to zero, current mutation cumulative value k y Assign to At the same time k y Set to zero.

[0085] Step E: Sample and record the current signal again, and collect the next half-wave data that is continuous with the previous half-wave data, which is also recorded as a one-dimensional array Sn.

[0086] Step F: Process the one-dimensional array Sn of the sampled current signal. An = [Sn, Sn-1, ..., SA] forms the one-dimensional array An (n = 400 in a half-wave). Set the one-dimensional array Sn to zero for the next sampling record. The value of b is calculated as 2(Sn_max - SA) / N, rounded up to an integer.

[0087] If An>b; when n=1, i1=A1; when n≥2, i m =i m -1+A n ; If An<-b, when n=1, j1=A1, when n≥2, j s =j s-1 +An; If b>An>-b, when n=1, k1= A1, when n≥2, k y =ky-1+ An.

[0088] After the above processing, set it to zero and the positive half-wave cumulative value i collected this time m Assign to At the same time m Set to zero, negative half-wave cumulative value j s Assign to At the same time s Set to zero, current mutation cumulative value k y Assign to At the same time k y Zero The two calculations are formed by the assignment 、 、 and 、 、 The calculation formula for similar span is:

[0089] Step G: Compare the similarity thresholds of adjacent half-waves (i.e. Figure 8 Similarity span in Is it greater than 0.8? If it is not greater than 0.8, it means that there is a large difference between the two half waves. Then Cnt is increased by 1, the current x2, y2, and R2 are all assigned x1, y1, and R1, and the current x2, y2, and R2 are cleared to obtain the next half wave and then execute step D.

[0090] Step H: If the similarity span If Cnt is greater than 0.8, compare whether Cnt is greater than or equal to 4 and less than or equal to 6. If 4≤cnt≤6, it is determined that a switching arc occurs and the process ends; otherwise, execute step I.

[0091] Step I: Determine whether Cnt is greater than 6. If cnt>6, it is determined that a fault arc has occurred and the process ends; otherwise, the count Cnt is set to zero, and the current x2, y2, and R2 are all assigned x1, y1, and R1; the current x2, y2, and R2 are cleared to obtain the next half wave and then proceed to step D.

[0092] In summary, the present application provides a fault arc detection method, which is a method for distinguishing fault arcs from switching arcs based on the waveform front-back correlation and long-time correlation. It uses the characteristics of low-voltage alternating current to take half-wave as the basis, and by sorting out the basic waveform characteristics to compare the correlation and mutation of continuous half-waves, and the correlation and mutation of long-time half-waves, it can be judged whether a fault arc occurs and distinguish between fault arcs and switching arcs.

[0093] In addition, this application also provides a fault arc detection device, please refer to Figure 9 , Figure 9Schematic diagram of the structure of the arc fault detection device involved in the embodiment of the present application. The arc fault detection device provided in the present application includes: The waveform acquisition module H01 is used to obtain the current periodic waveform of the power system in the low-voltage alternating current scenario, collect the first half-wave waveform of the current periodic waveform in chronological order, and determine the next half-wave waveform continuous with the first half-wave waveform as the second half-wave waveform; a characteristic acquisition module H02 for acquiring a first current characteristic parameter of the power system in the first half-wave waveform, and acquiring a second current characteristic parameter of the power system in the second half-wave waveform; The arc identification module H03 is used to determine an adjacent half-wave similarity threshold according to the first current characteristic parameter and the second current characteristic parameter, and identify the fault arc according to the adjacent half-wave similarity threshold.

[0094] In addition, this application also provides a fault arc detection device. Figure 10 , Figure 10 Schematic diagram of the structure of the arc fault detection device involved in the embodiment of the present application. The device of the embodiment of the present application can be a device for locally running the arc fault detection method.

[0095] The present application provides a fault arc detection device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the fault arc detection device method in the above-mentioned embodiment 1.

[0096] Reference below Figure 10 , which shows a schematic diagram of the structure of an arc fault detection device suitable for implementing the embodiments of the present application. The arc fault detection device in the embodiments of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The arc fault detection device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0097] like Figure 10As shown, the arc fault detection device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the arc fault detection device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following devices may be connected to I / O interface 1006: input device 1007, such as a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output device 1008, such as a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003, such as a magnetic tape or hard disk; and communication device 1009. The communication device 1009 can allow the arc fault detection device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an arc fault detection device with various devices, it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively.

[0098] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0099] The arc fault detection device provided in this application, utilizing the arc fault detection device method described in the aforementioned embodiment, can address the technical issue of low reliability in arc fault detection devices. Compared to the prior art, the arc fault detection device provided in this application has the same beneficial effects as the arc fault detection device method described in the aforementioned embodiment. Other technical features of the arc fault detection device are the same as those disclosed in the aforementioned embodiment method and are not further elaborated here.

[0100] In addition, the present application provides a computer-readable storage medium having stored thereon a fault arc detection program, which implements the steps of the above-mentioned fault arc detection method when executed by a processor.

[0101] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or system 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 system. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or system comprising the element.

[0102] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0103] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a computer-readable storage medium (such as ROM / RAM, magnetic disk, optical disk) as mentioned above, and includes a number of instructions for enabling a device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0104] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for detecting arc faults, characterized in that: The arc fault detection method comprises: Acquire a current periodic waveform of the power system in a low-voltage alternating current scenario, collect a first half-wave waveform of the current periodic waveform in chronological order, and determine the next half-wave waveform continuous with the first half-wave waveform as the second half-wave waveform; Acquire a first current characteristic parameter of the power system in the first half-wave waveform, and acquire a second current characteristic parameter of the power system in the second half-wave waveform; An adjacent half-wave similarity threshold is determined according to the first current characteristic parameter and the second current characteristic parameter, and a fault arc is identified according to the adjacent half-wave similarity threshold.

2. The arc fault detection method according to claim 1, wherein: The step of obtaining the first current characteristic parameter of the power system in the first half-wave waveform includes: Determine a current time series array corresponding to the first half-wave waveform, where the current time series array includes N consecutive current sampling values, where N is a natural number greater than 1; Determining a current mutation amplitude of the power system during the first half-wave waveform according to a difference between a maximum current sampling value and an initial current sampling value in the current time series array; All the current sampling values are traversed according to the current mutation amplitude and the negative mutation amplitude to obtain a first current characteristic parameter, wherein the negative mutation amplitude is the opposite of the current mutation amplitude.

3. The arc fault detection method according to claim 2, wherein: The first current characteristic parameters include a positive current characteristic, a negative current characteristic, and an arc current characteristic. The step of traversing all the current sampling values according to the current mutation amplitude and the negative mutation amplitude to obtain the first current characteristic parameters includes: traversing the magnitudes of each current sampling value and the current mutation amplitude and the negative mutation amplitude in chronological order, accumulating the traversed current sampling values that are greater than the current mutation amplitude, and taking the obtained positive half-wave cumulative value as the forward current feature; Accumulate the current sampling values obtained through the traversal that are smaller than the negative mutation amplitude, and use the obtained negative half-wave cumulative value as the negative current feature; The current sampling values obtained through traversal that are greater than the negative mutation amplitude and less than the current mutation amplitude are accumulated, and the accumulated current mutation value obtained is used as the arc current feature.

4. The arc fault detection method according to claim 1, wherein: The step of obtaining the second current characteristic parameter of the power system in the second half-wave waveform includes: Use the second half-wave waveform as the next first half-wave waveform, return to the step of obtaining the first current characteristic parameters of the power system during the first half-wave waveform, and obtain the second current characteristic parameters of the power system during the second half-wave waveform.

5. The arc fault detection method according to claim 1, wherein: The first current characteristic parameters include positive current characteristics, negative current characteristics and arc current characteristics, and the second current characteristic parameters include positive measurement characteristics, negative measurement characteristics and arc measurement characteristics; wherein, The positive measurement characteristic is the positive half-wave cumulative value accumulated by the power system during the second half-wave waveform, the negative measurement characteristic is the negative half-wave cumulative value accumulated by the power system during the second half-wave waveform, and the arc measurement characteristic is the current mutation cumulative value accumulated by the power system during the second half-wave waveform; The step of determining the adjacent half-wave similarity threshold according to the first current characteristic parameter and the second current characteristic parameter comprises: Superimposing the arc current feature and the arc measurement feature to obtain an arc feature cumulative value, and determining a positive feature absolute difference between the positive current feature and the positive measurement feature, and a negative feature absolute difference between the negative current feature and the negative measurement feature; Determining first difference data between the arc feature cumulative value and the positive feature absolute difference value, and second difference data between the arc feature cumulative value and the negative feature absolute difference value; The adjacent half-wave similarity threshold is obtained by dividing the difference product data between the first difference data and the second difference data by the square of the arc current characteristic.

6. The arc fault detection method according to claim 5, characterized in that: The step of identifying the fault arc according to the adjacent half-wave similarity threshold comprises: Detecting whether the adjacent half-wave similarity threshold is greater than a preset half-wave similarity standard threshold; If the adjacent half-wave similarity threshold is less than or equal to the half-wave similarity standard threshold, assigning the second current characteristic parameter as the next first current characteristic parameter, and accumulating the number of operations for each assignment; Determine the next half-wave waveform continuous with the second half-wave waveform as the next second half-wave waveform, and return to the step of obtaining the second current characteristic parameter of the power system when the second half-wave waveform is present; Until it is detected that the adjacent half-wave similarity threshold is greater than the half-wave similarity standard threshold, determining whether the accumulated number of operations is within a preset arc detection threshold interval; If the accumulated number of operations is not in the arc detection threshold interval, after determining that the accumulated number of operations exceeds the arc detection threshold upper limit corresponding to the arc detection threshold interval, it is determined that a fault arc occurs in the power system.

7. The arc fault detection method according to claim 6, characterized in that: After the step of determining whether the accumulated number of operations is within a preset arc detection threshold range, the arc fault detection method includes: If the accumulated number of operations is within the arc detection threshold range, it is determined that a switching arc occurs in the power system.

8. A fault arc detection device, characterized in that: The arc fault detection device comprises: a waveform acquisition module, configured to acquire a current periodic waveform of the power system in a low-voltage alternating current scenario, collect a first half-wave waveform of the current periodic waveform in chronological order, and determine a next half-wave waveform continuous with the first half-wave waveform as a second half-wave waveform; a characteristic acquisition module, configured to acquire a first current characteristic parameter of the power system in the first half-wave waveform, and to acquire a second current characteristic parameter of the power system in the second half-wave waveform; An arc identification module is used to determine an adjacent half-wave similarity threshold based on the first current characteristic parameter and the second current characteristic parameter, and to identify a fault arc based on the adjacent half-wave similarity threshold.

9. A fault arc detection device, characterized in that: The arc fault detection device includes a memory, a processor, and an arc fault detection program stored in the memory and executable on the processor. When the processor executes the arc fault detection program, the steps of the arc fault detection method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores an arc fault detection program, which, when executed by a processor, implements the steps of the arc fault detection method according to any one of claims 1 to 7.

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