Fault arc detection method, apparatus, device, and storage medium

Abstract

The application relates to the technical field of power safety monitoring, in particular to a fault arc detection method, device and equipment and a storage medium, the method comprising the following steps: acquiring a current periodic waveform of a power system in a low-voltage alternating current scene, collecting a first half-wave waveform of the current periodic waveform in chronological order, and determining a next half-wave waveform 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; determining an adjacent half-wave similarity threshold value 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 value. The application 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 particularly relates to a fault arc detection method and device, equipment and a storage medium. BACKGROUND

[0002] Among the causes of electrical fires, line insulation may age due to long-term operation, environmental erosion and other factors, or may be mechanically damaged due to external forces, thereby causing a fault arc, which has become a key hidden danger in the field of electrical fire prevention and control.

[0003] Currently, traditional arc detection technology mainly focuses on the analysis of current amplitude and time domain and frequency domain characteristics, and it is difficult to accurately distinguish between controllable switch arcs (i.e., arcs that naturally extinguish when alternating current crosses zero) and irregular fault arcs (caused by line insulation abnormalities, poor contact and other line problems). Especially in low-voltage alternating current, the weak signal of the fault arc is easily covered by harmonics and noise, resulting in a lag in the response of the protection system.

[0004] Therefore, how to improve the accuracy of identifying fault arcs is a technical problem to be solved at present. SUMMARY

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

[0006] To achieve the above purpose, the present application provides a fault arc detection method, which comprises:

[0007] Obtaining a current periodic waveform of a power system in a low-voltage alternating current scenario, collecting a first half-wave waveform of the current periodic waveform in chronological order, and determining a next half-wave waveform continuous to the first half-wave waveform as a second half-wave waveform;

[0008] Obtaining a first current characteristic parameter of the power system at the first half-wave waveform, and obtaining a second current characteristic parameter of the power system at the second half-wave waveform;

[0009] Determining an adjacent half-wave similarity threshold value 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 value.

[0010] In an embodiment, the step of obtaining the first current characteristic parameter of the power system at the first half-wave waveform comprises:

[0011] Determining a current time sequence array corresponding to the first half-wave waveform, the current time sequence array comprising N consecutive current sample values, N being a natural number greater than 1;

[0012] determining a current mutation amplitude of the power system at the first half-wave waveform according to a difference between a maximum current sample value in the current time sequence array and an initial current sample initial value;

[0013] obtaining a first current characteristic parameter according to the current mutation amplitude and a negative mutation amplitude traversing all the current sample values, the negative mutation amplitude being an opposite number of the current mutation amplitude.

[0014] In an embodiment, the first current characteristic parameter includes a positive current characteristic, a negative current characteristic and an arc current characteristic, and the step of obtaining the first current characteristic parameter according to the current mutation amplitude and the negative mutation amplitude traversing all the current sample values includes:

[0015] traversing the size of each current sample value and the current mutation amplitude and the negative mutation amplitude in time sequence respectively, accumulating each current sample value greater than the current mutation amplitude obtained by traversal, and taking the obtained positive half-wave cumulative value as the positive current characteristic;

[0016] accumulating each current sample value less than the negative mutation amplitude obtained by traversal, and taking the obtained negative half-wave cumulative value as the negative current characteristic;

[0017] accumulating each current sample value greater than the negative mutation amplitude and less than the current mutation amplitude obtained by traversal, and taking the obtained current mutation cumulative value as the arc current characteristic.

[0018] In an embodiment, the step of obtaining the second current characteristic parameter of the power system at the second half-wave waveform includes:

[0019] taking the second half-wave waveform as the next first half-wave waveform, returning to execute the step of obtaining the first current characteristic parameter of the power system at the first half-wave waveform to obtain the second current characteristic parameter of the power system at the second half-wave waveform.

[0020] In an 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,

[0021] the positive measurement characteristic is a positive half-wave cumulative value accumulated by the power system at the second half-wave waveform, the negative measurement characteristic is a negative half-wave cumulative value accumulated by the power system at the second half-wave waveform, and the arc measurement characteristic is a current mutation cumulative value accumulated by the power system at the second half-wave waveform;

[0022] The step of determining the adjacent half-wave similarity threshold according to the first current characteristic parameter and the second current characteristic parameter comprises:

[0023] superimposing the arc current characteristic and the arc measurement characteristic to obtain an arc characteristic cumulative value, and determining a forward characteristic absolute difference between the forward current characteristic and the forward measurement characteristic, and a negative characteristic absolute difference between the negative current characteristic and the negative measurement characteristic;

[0024] determining first difference data between the arc characteristic cumulative value and the forward characteristic absolute difference, and second difference data between the arc characteristic cumulative value and the negative characteristic absolute difference;

[0025] dividing the difference product data between the first difference data and the second difference data by the square of the arc current characteristic to obtain the adjacent half-wave similarity threshold.

[0026] In an embodiment, the step of identifying the fault arc according to the adjacent half-wave similarity threshold comprises:

[0027] detecting whether the adjacent half-wave similarity threshold is greater than a preset half-wave similarity standard threshold;

[0028] 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 assignments each time;

[0029] determining a next half-wave waveform continuous with the second half-wave waveform as a next second half-wave waveform, and returning to the step of obtaining the second current characteristic parameter of the power system at the second half-wave waveform;

[0030] until the adjacent half-wave similarity threshold is detected to be greater than the half-wave similarity standard threshold, determining whether the accumulated number of operations is within a preset arc judgment threshold interval;

[0031] if the accumulated number of operations is not within the arc judgment threshold interval, determining that the power system has a fault arc after determining that the accumulated number of operations exceeds an upper limit of the arc judgment threshold corresponding to the arc judgment threshold interval.

[0032] In an embodiment, after the step of determining whether the accumulated number of operations is within the preset arc judgment threshold interval, the fault arc detection method comprises:

[0033] if the accumulated number of operations is within the arc judgment threshold interval, determining that the power system has a switch arc.

[0034] In addition, to achieve the above object, the application further provides a fault arc detection device, which comprises:

[0035] a waveform acquisition module, configured to acquire a current periodic waveform of a power system in a low-voltage alternating current scene, collect a first half-wave waveform of the current periodic waveform in chronological order, and determine a next half-wave waveform continuous to the first half-wave waveform as a second half-wave waveform;

[0036] a feature acquisition module, configured to acquire a first current feature parameter of the power system in the first half-wave waveform, and acquire a second current feature parameter of the power system in the second half-wave waveform;

[0037] an arc recognition module, configured to determine an adjacent half-wave similarity threshold according to the first current feature parameter and the second current feature parameter, and recognize a fault arc according to the adjacent half-wave similarity threshold.

[0038] The various functional modules of the fault arc detection device of the application realize the steps of the fault arc detection method of the application as described above when running.

[0039] In addition, to achieve the above object, the application further provides a fault arc detection device, which comprises a memory, a processor, and a fault arc detection program stored in the memory and executable on the processor, and the fault arc detection program realizes the steps of the fault arc detection method when executed by the processor.

[0040] In addition, to achieve the above object, the application further provides a storage medium, which is a computer readable storage medium, and the computer readable storage medium stores a fault arc detection program, and the fault arc detection program realizes the steps of the fault arc detection method when executed by a processor.

[0041] The application provides a fault arc detection method, which is optimized in view of traditional arc detection technology, and significantly improves the accuracy of identifying fault arcs. Specifically, the current periodic waveform of the power system in the low-voltage alternating current scene is obtained in real time, so as to realize the use of the inherent periodicity of low-voltage alternating current, take the half wave as the minimum analysis unit, and extract the first half wave waveform and the second half wave waveform which are continuous and adjacent in the current periodic waveform. Then, according to the first current characteristic parameter of the power system in the first half wave waveform and the second current characteristic parameter of the power system in the second half wave waveform, the adjacent half wave similarity threshold can be accurately evaluated. Finally, according to the adjacent half wave similarity threshold evaluated, the height similarity of the adjacent half wave waveform when the normal switch arc naturally extinguishes at the current zero point can be accurately quantified, and the waveform mutation characteristics caused by line insulation deterioration, abnormal contact resistance and the like of the fault arc can be sharply captured, so as to effectively avoid the technical defects that the traditional arc detection technology is easily disturbed by noise and is difficult to effectively identify the fault arc, and the accuracy of identifying the fault arc is significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 is a flowchart of the first embodiment of the fault arc detection method of the application;

[0043] Figure 2 is a schematic diagram of a normal current waveform related to the embodiment of the application;

[0044] Figure 3 is a schematic diagram of a fault arc current waveform related to the embodiment of the application;

[0045] Figure 4 is a schematic diagram of a switch arc current waveform related to the embodiment of the application;

[0046] Figure 5 is a graphical diagram of normal current statistical values related to the embodiment of the application;

[0047] Figure 6 is a graphical diagram of fault arc current statistical values related to the embodiment of the application;

[0048] Figure 7 is a graphical diagram of switch arc current statistical values related to the embodiment of the application;

[0049] Figure 8 is a schematic diagram of a fault arc detection process related to the embodiment of the application;

[0050] Figure 9 is a schematic diagram of the structure of a fault arc detection device related to the embodiment of the application;

[0051] Figure 10This is a schematic diagram of the structure of the arc fault detection device involved in the embodiment of the present application.

[0052] 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

[0053] The present application provides a method for detecting arc faults. Figure 1 As shown, Figure 1 It is a flow chart of the first embodiment of the arc fault detection method of the present application.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] It should be noted that the execution subject of the embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, a database system, etc., or a device capable of realizing the above functions, such as a fault arc detection device. The following will take the fault arc detection device as an example to describe the embodiment and the following embodiments.

[0059] The fault arc detection method of the present application comprises the following implementation steps S10 to S30.

[0060] Step S10: Obtain the current periodic waveform of the power system in the low-voltage alternating current scene, 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.

[0061] In the embodiment, the current periodic waveform of the power system in the low-voltage alternating current scene 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, so that the inherent periodic characteristics of low-voltage alternating current are utilized to capture the dynamic change characteristics of the current in each half-wave with half-wave as the minimum analysis unit.

[0062] It should be noted that the current periodic waveform can cover Figure 2 the normal current waveform shown in the figure, Figure 3 the fault arc current waveform shown in the figure, and Figure 4 the switching arc current waveform shown in the figure.

[0063] For example, the traditional arc detection technology is easily disturbed by power grid harmonics / noise when facing such complex and variable current waveforms, and it is difficult to accurately distinguish fault arcs and switching arcs. However, the present application utilizes the inherent periodic characteristics of low-voltage alternating current to extract the continuous adjacent first half-wave waveform and the second half-wave waveform in the current periodic waveform, and captures the dynamic change characteristics of the current in each half-wave with half-wave as the minimum analysis unit, thereby providing strong data support for subsequent fault arc identification.

[0064] Step S20: Obtain the first current characteristic parameter of the power system in the first half-wave waveform, and obtain the second current characteristic parameter of the power system in the second half-wave waveform.

[0065] In the embodiment, the current signal of the power system at the first half-wave waveform is collected to obtain N continuous current sampling values, and the N continuous current sampling values are recorded as a current time sequence array. Next, since the current time sequence array is a continuous N current sampling values arranged in time sequence, the earliest current sampling value in the current time sequence array is taken as an initial current sampling value, and the largest current sampling value in the current time sequence array is taken as a maximum current sampling value. Then, according to the difference between the maximum current sampling value and the initial current sampling value in the current time sequence array, the current mutation amplitude of the power system at the first half-wave waveform can be accurately obtained. Then, the negative of the current mutation amplitude is determined as a negative mutation amplitude, and all current sampling values are traversed according to the current mutation amplitude and the negative mutation amplitude, so that the dynamic change characteristics of the power system at the first half-wave waveform can be quickly and accurately captured to obtain a first current characteristic parameter. After the first current characteristic parameter of the power system is determined, the second half-wave waveform is taken 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 a second current characteristic parameter of the power system at the second half-wave waveform, so as to provide accurate and reliable first current characteristic parameters and second current characteristic parameters for subsequent evaluation of the adjacent half-wave similarity threshold.

[0066] 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.

[0067] In the embodiment, the first current characteristic parameter and the second current characteristic parameter are evaluated according to a preset similarity evaluation model, so that the adjacent half-wave similarity threshold can be accurately evaluated. Then, according to the evaluated adjacent half-wave similarity threshold, the high similarity of adjacent half-wave waveforms when the normal switch arc naturally extinguishes at the current zero point can be accurately quantified, and the waveform mutation characteristics caused by line insulation deterioration, abnormal contact resistance and the like can be sharply captured. The technical defects of the traditional arc detection technology, such as being easily disturbed by noise and being difficult to effectively identify the fault arc, are effectively avoided, and the accuracy of identifying the fault arc is significantly improved.

[0068] In summary, the application provides a fault arc detection method which optimizes the traditional arc detection technology and significantly improves the accuracy of identifying fault arcs. Specifically, the current periodic waveform of the power system in the low-voltage alternating current scenario is obtained in real time, so as to realize the use of the inherent periodic characteristics of low-voltage alternating current, take the half wave as the minimum analysis unit, and extract the first half wave waveform and the second half wave waveform which are continuous and adjacent in the current periodic waveform. Then, according to the first current characteristic parameter of the power system in the first half wave waveform and the second current characteristic parameter of the power system in the second half wave waveform, the adjacent half wave similarity threshold can be accurately evaluated. Finally, according to the adjacent half wave similarity threshold evaluated, the height similarity of the adjacent half wave waveform when the normal switch arc naturally extinguishes at the current zero point can be accurately quantified, and the waveform mutation characteristics caused by line insulation deterioration, abnormal contact resistance and the like can be sharply captured. The technical defects of the traditional arc detection technology that are vulnerable to noise interference and difficult to effectively identify fault arcs are effectively avoided, and the accuracy of identifying fault arcs is significantly improved.

[0069] Further, based on the first embodiment of the fault arc detection method of the application, the second embodiment of the fault arc detection method of the application is proposed. In some feasible embodiments, the step S20 of obtaining the first current characteristic parameter of the power system in the first half wave waveform further includes the following implementation steps S201-S203.

[0070] Step S201: determine the current time series array corresponding to the first half wave waveform, the current time series array including continuous N current sampling values, N being a natural number greater than 1.

[0071] In this embodiment, the current signal of the power system in the first half wave waveform is collected to obtain continuous N current sampling values, and the continuous N current sampling values are recorded as a current time series array.

[0072] For example, first, the initialization of the power system in the fault arc detection operation of the variable value, record the initialization of the sampling of the reference stable point as SA, the defined count variable Cnt is zero; next, the current signal of the power system in the low voltage alternating current scene is sampled, specifically, the half cycle data of the power frequency 50Hz alternating current cycle is collected (for example, the continuous N current sampling values of the power system in the first half wave waveform are collected) is recorded as the current sampling array Sn, wherein n is the data amount N of all current sampling values, which can be 400, or can be customized according to user demand, and the present application does not make any limitation here; subsequently, the current sampling array Sn formed by the current signal sampled in the first half wave waveform is processed, that is, after the preset current time sequence array An is assigned to the current sampling array Sn, the current sampling array Sn is zeroed for the next sampling record, that is, the current time sequence array An can include current values Sn, current sampling values Sn-1, …, and current value sampling S1 (that is, the reference stable point SA sampled after initialization).

[0073] Step S202: According to the difference between the maximum current sampling value in the current time sequence array and the initial current sampling initial value, the current mutation amplitude of the power system in the first half wave waveform is determined.

[0074] In this embodiment, since the current time sequence array is a continuous N current sampling value arranged in time sequence, the current sampling value with the earliest sampling time in the current time sequence array can be taken as the initial current sampling initial value, and the current sampling value with the maximum sampling value in the current time sequence array can be taken as the maximum current sampling value; subsequently, according to the preset fault arc feature judgment algorithm, the difference between twice the maximum current sampling value and the initial current sampling initial value is rounded up to the integer part, and the ratio data between the data amount N can accurately obtain the current mutation amplitude of the power system in the first half wave waveform, so as to accurately quantify the abnormal fluctuation degree of the current signal.

[0075] It should be noted that the preset fault arc feature judgment algorithm expression is: b=2(Sn_max-SA) / N, wherein Sn_max refers to the maximum current sampling value, SA refers to the initial current sampling initial value, and b refers to the current mutation amplitude.

[0076] That is, the dynamic change range of the current signal relative to the stable reference is highlighted by the difference (Sn_max-SA), and especially in abnormal situations such as fault arc, the maximum current sampling value deviates significantly from the current sampling value based on the reference stable point, so that the difference (Sn_max-SA) can effectively reflect the severity of the abnormality; then, the difference (Sn_max-SA) is multiplied by 2 and divided by the data quantity N, thereby realizing the normalization processing of the difference (Sn_max-SA), so that the calculation results under different data quantities have comparability, and the scale difference caused by different data lengths is avoided; finally, the upward rounding operation ensures that the current mutation amplitude b is an integer, which is convenient for subsequent discretization processing or threshold comparison, and enhances the robustness and practicality of the algorithm. The upward rounding operation can be understood as that when 2(Sn_max-SA) / N is 3.7, the current mutation amplitude b is an integer 4.

[0077] 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, the negative mutation amplitude being the opposite number of the current mutation amplitude.

[0078] In the embodiment, the opposite number of the current mutation amplitude is determined as the negative mutation amplitude, and all the current sampling values are traversed according to the current mutation amplitude and the negative mutation amplitude, so that the dynamic change characteristics of the power system in the first half-wave waveform can be quickly and accurately captured, and the first current characteristic parameter is obtained.

[0079] Further, in some feasible embodiments, the first current characteristic parameter includes a positive current characteristic, a negative current characteristic and an arc current characteristic, and the step S203 of traversing all the current sampling values according to the current mutation amplitude and the negative mutation amplitude to obtain a first current characteristic parameter further includes the following implementation steps S2031-S2033.

[0080] Step S2031: traversing each current sampling value and the current mutation amplitude and the negative mutation amplitude in time sequence respectively, accumulating each current sampling value greater than the current mutation amplitude obtained by traversal, and taking the obtained positive half-wave cumulative value as the positive current characteristic.

[0081] In the embodiment, each current sampling value is traversed and compared with the current mutation amplitude and the negative mutation amplitude in time sequence, and then each current sampling value greater than the current mutation amplitude obtained by traversal is accumulated, and the obtained positive half-wave cumulative value is taken as the positive current characteristic, so that the positive change of the current in the half-wave period can be highlighted.

[0082] Step S2032: Accumulate each of the current sampling values less than the negative mutation amplitude obtained by traversal, and take the obtained negative half-wave cumulative value as the negative current feature.

[0083] In this embodiment, each of the current sampling values greater than the negative mutation amplitude and less than the current mutation amplitude obtained by traversal is accumulated, and the obtained current mutation cumulative value is taken as the arc current feature, so that the arc current change in the half-wave period can be highlighted.

[0084] Step S2033: Accumulate each of the current sampling values greater than the negative mutation amplitude and less than the current mutation amplitude obtained by traversal, and take the obtained current mutation cumulative value as the arc current feature.

[0085] In this embodiment, each of the current sampling values greater than the negative mutation amplitude and less than the current mutation amplitude obtained by traversal is accumulated, so that the arc current feature can be calculated, and thus the arc current change in the half-wave period can be highlighted.

[0086] Further, in some possible embodiments, the step S20 of obtaining the second current feature parameter of the power system at the second half-wave waveform further includes the following implementation step A10.

[0087] Step A10: Taking the second half-wave waveform as the next first half-wave waveform, returning to execute the step of obtaining the first current feature parameter of the power system at the first half-wave waveform to obtain the second current feature parameter of the power system at the second half-wave waveform.

[0088] In this embodiment, the second half-wave waveform is taken as the next first half-wave waveform, and the step of obtaining the first current feature parameter of the power system at the first half-wave waveform is returned to execute to obtain the second current feature parameter of the power system at the second half-wave waveform. That is, the second half-wave waveform is taken as the next first half-wave waveform and the feature parameter obtaining step is executed cyclically, which realizes full-cycle and dynamic monitoring and analysis of the current signal of the power system, and significantly improves the real-time performance and accuracy of fault detection and state evaluation. Specifically, by using the progressive iteration mechanism of the half-wave waveform, the feature parameter can be extracted independently in each half-wave period, so that the continuous change of the current in the time dimension can be tracked in real time, and the current distortion (such as amplitude sudden change, harmonic content increase or waveform asymmetry) caused by load fluctuation, line aging or fault arc can be found in time.

[0089] Further, in some possible embodiments, the first current characteristic parameter comprises a forward current characteristic, a reverse current characteristic and an arc current characteristic, and the second current characteristic parameter comprises a forward measurement characteristic, a reverse measurement characteristic and an arc measurement characteristic; wherein the forward measurement characteristic is a forward half-wave cumulative value accumulated by the power system during the second half-wave waveform, the reverse measurement characteristic is a reverse half-wave cumulative value accumulated by the power system during the second half-wave waveform, and the arc measurement characteristic is a current mutation cumulative value accumulated by the power system during the second half-wave waveform. The step S30 of determining the adjacent half-wave similarity threshold according to the first current characteristic parameter and the second current characteristic parameter can further comprise the following steps S301-S303.

[0090] The step S301 comprises: superimposing the arc current characteristic and the arc measurement characteristic to obtain an arc characteristic cumulative value, and determining a forward characteristic absolute difference between the forward current characteristic and the forward measurement characteristic, and a reverse characteristic absolute difference between the reverse current characteristic and the reverse measurement characteristic.

[0091] In this embodiment, the arc current characteristic and the arc measurement characteristic can be summed according to a preset similarity evaluation model to obtain the arc characteristic cumulative value. Then, the forward current characteristic and the forward measurement characteristic, and the reverse current characteristic and the reverse measurement characteristic can be respectively processed by difference according to the similarity evaluation model, so that the forward characteristic absolute difference and the reverse characteristic absolute difference can be accurately calculated.

[0092] The step S302 comprises: determining first difference data between the arc characteristic cumulative value and the forward characteristic absolute difference, and second difference data between the arc characteristic cumulative value and the reverse characteristic absolute difference.

[0093] The step S303 comprises: dividing difference product data between the first difference data and the second difference data by the square of the arc current characteristic to obtain the adjacent half-wave similarity threshold.

[0094] In this embodiment, after the first difference data between the arc characteristic cumulative value and the forward characteristic absolute difference, and the second difference data between the arc characteristic cumulative value and the reverse characteristic absolute difference are determined, the adjacent half-wave similarity threshold can be accurately calculated by dividing the difference product data between the first difference data and the second difference data by the square of the arc current characteristic.

[0095] It should be noted that the preset similarity evaluation model expression is as follows:

[0096]

[0097] wherein, 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.

[0098] 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.

[0099] Step B10: Detect whether the adjacent half-wave similarity threshold is greater than a preset half-wave similarity standard threshold.

[0100] 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.

[0101] 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 .

[0102] 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.

[0103] 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.

[0104] Step B30: determining the next half-wave waveform continuous with the second half-wave waveform as a next second half-wave waveform, and returning to execute the step of acquiring the second current characteristic parameter of the power system at the second half-wave waveform.

[0105] In the embodiment, the next half-wave waveform continuous with the second half-wave waveform is determined as a next second half-wave waveform, and the step of acquiring the second current characteristic parameter of the power system at the second half-wave waveform is returned to execute, that is, by using the progressive iteration mechanism of the half-wave waveform, the current characteristic parameter of the next half-wave waveform continuous with the second half-wave waveform can be accurately extracted, that is, by continuously acquiring new current characteristic parameters and comparing and analyzing the previous current parameters, abnormal mutations or continuous distortions of the current waveform can be found in time, the real-time and comprehensiveness of fault detection are ensured, and fault omission caused by detection interval or omission is effectively avoided.

[0106] Step B40: until the adjacent half-wave similarity threshold is detected to be greater than the half-wave similarity standard threshold, determining whether the accumulated operation number is in a preset arc judgment threshold interval.

[0107] In the embodiment, until the adjacent half-wave similarity threshold is detected to be greater than the half-wave similarity standard threshold, it is determined that the similarity of the adjacent half-wave waveform is normal, that is, it means that the abnormal situation of the current waveform may be alleviated or ended. Next, it is determined whether the accumulated operation number is in the arc judgment threshold interval, which can comprehensively consider the time and intensity of the abnormal waveform.

[0108] The preset arc judgment threshold interval can be [4, 6], wherein 4 represents the arc judgment threshold lower limit value corresponding to the arc judgment threshold interval, and 6 represents the arc judgment threshold upper limit value corresponding to the arc judgment threshold interval.

[0109] Step B50: if the accumulated operation number is not in the arc judgment threshold interval, after it is determined that the accumulated operation number exceeds the arc judgment threshold upper limit corresponding to the arc judgment threshold interval, it is determined that the power system has a fault arc.

[0110] In the embodiment, after it is determined that the accumulated operation number is not in the arc judgment threshold interval, it is detected whether the accumulated operation number exceeds the arc judgment threshold upper limit corresponding to the arc judgment threshold interval, and if the accumulated operation number exceeds the arc judgment threshold upper limit, it can be accurately determined that the power system has a fault arc.

[0111] Further, in some feasible embodiments, after the step B40 of determining whether the accumulated operation number is in the preset arc judgment threshold interval, the fault arc detection method can further include the following implementation step C10.

[0112] Step C10: If the accumulated number of operations is in the arc judgment threshold interval, it is determined that a switch arc occurs in the power system.

[0113] In this embodiment, since the switch arc is usually caused by normal operation in the power system (such as circuit breaker opening and closing, contactor action), the current waveform anomaly has the characteristics of instantaneousness, periodicity or strong correlation with operation timing, but does not continuously destroy the stability of the system. By pre-setting the arc judgment threshold interval, the current anomaly accumulated in the arc judgment threshold interval is determined as a switch arc, not a fault arc, which avoids misjudging the arc caused by normal operation as a fault arc, thereby reducing unnecessary protection action (such as mis-trip) and ensuring power supply continuity.

[0114] In specific embodiments, with reference to Figure 8 , Figure 8 is a schematic diagram of a fault arc detection process involved in the embodiments of the present application. The fault arc detection process at least includes steps A to J.

[0115] Step A: First, initialize the values of each variable, record the baseline stable point after sampling as SA, and define the count variable Cnt as zero. The Cnt can be understood as the assigned operation number.

[0116] Step B: Sample and record the current signal, collect half-cycle data of 50Hz power frequency alternating current to form a one-dimensional array Sn (wherein n is the data amount).

[0117] Step C: Process the sampled current signal one-dimensional array Sn to form a current timing array An=[Sn, Sn-1, …, SA], that is, when n=1, record SA as An, when n=n-1, record Sn-1 as An-1, and when n=n, record Sn as An. Next, the current sampling array Sn is set to zero for next sampling record. Wherein n=N=400, the size of b is calculated as 2(Sn_max-SA) / N, and the integer value is taken upward.

[0118] Step D: Fault arc judgment.

[0119] If An>b; when n=1, i1=A1; when n≥2, i m =i m -1+A n , wherein 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 taken as the initial positive half-wave cumulative value i1, and the current sampling value An and the previous positive half-wave cumulative value i m−1 are added in an accumulative manner to obtain a new positive half-wave cumulative value i m .

[0120] If An<-b, j1=A1 when n=1, j =j-1+A when n≥2. s s-1 +An, where s is a natural number greater than 0 and less than n, i.e. when n=1, the first collected current sample value SA is directly taken as the initial negative half-wave cumulative value j1, and the current sample value An is added to the previous negative half-wave cumulative value j s−1 to obtain a new negative half-wave cumulative value j s .

[0121] If b> An>-b, k1=A1 when n=1, k =k-1+A when n≥2. y y-1+ An, where y is a natural number greater than 0 and less than n, i.e. when n=1, the first collected current sample value SA is directly taken as the initial current mutation cumulative value k1, and the current sample value An is added to the previous negative half-wave cumulative value k y-1 to obtain a new current mutation cumulative value k y ; after the above processing, An is set to zero, the positive half-wave cumulative value i m is assigned to , and i m is set to zero, the negative half-wave cumulative value j s is assigned to , and j s is set to zero, the current mutation cumulative value k y is assigned to , and k y is set to zero.

[0122] Step E: the current signal is sampled and recorded again, and the next half-wave data continuous to the previous half-wave data is collected, which is also recorded as a one-dimensional array Sn.

[0123] Step F: the sampled current signal one-dimensional array Sn is processed, An=[Sn, Sn-1, …, SA] forms a one-dimensional array An (n=400 in a half-wave), and the one-dimensional array Sn is set to zero for next sampling record. The size of b is calculated as 2(Sn_max-SA) / N, and the integer value is taken upward.

[0124] If An>b, i1=A1 when n=1, i =i-1+A when n≥2. m m -1+A n .

[0125] If An<-b, j1=A1 when n=1, j =j-1+A when n≥2. s s-1 +An.

[0126] ​​​​If b>An>-b, when n=1, k1= A1, when n≥2, k y =k y-1+ An.

[0127] 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

[0128] The two calculations are formed by the assignment 、 、 and 、 、 The calculation formula for similar span is:

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Further, the application also provides a fault arc detection device, please refer to Figure 9 , Figure 9 is a structural schematic diagram of the fault arc detection device involved in the embodiment of the application. The fault arc detection device provided by the application comprises:

[0134] The waveform acquisition module H01 is 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 to the first half-wave waveform as a second half-wave waveform.

[0135] The feature acquisition module H02 is configured to acquire a first current feature parameter of the power system in the first half-wave waveform, and acquire a second current feature parameter of the power system in the second half-wave waveform.

[0136] The arc identification module H03 is configured to determine an adjacent half-wave similarity threshold value according to the first current feature parameter and the second current feature parameter, and identify a fault arc according to the adjacent half-wave similarity threshold value.

[0137] Further, the application also provides a fault arc detection device. Please refer to Figure 10 , Figure 10 is a structural schematic diagram of the fault arc detection device involved in the embodiment of the application. The fault arc detection device provided by the application comprises:

[0138] The application provides a fault arc detection device, which comprises at least one processor and a memory in communication connection with 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 to enable the at least one processor to perform the fault arc detection device method in Embodiment I.

[0139] Reference will be made to Figure 10 , which shows a structural schematic diagram of a fault arc detection device suitable for implementing the embodiment of the application. The fault arc detection device in the embodiment of the application can include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistant), PADs (Portable Application Description), PMPs (Portable Media Player), vehicle-mounted terminals (for example, vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. Figure 4The 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.

[0140] like Figure 10 As 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.

[0141] 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.

[0142] The fault arc detection device provided in the application adopts the fault arc detection device method in the above embodiment, and can solve the technical problem of low reliability of the fault arc detection device. Compared with the prior art, the fault arc detection device provided in the application has the same beneficial effects as the fault arc detection device method provided in the above embodiment, and other technical features in the fault arc detection device are the same as the features disclosed in the previous embodiment method, which will not be described here.

[0143] In addition, the application provides a computer readable storage medium. The computer readable storage medium stores a fault arc detection program. When the processor executes the fault arc detection program, the steps of the above fault arc detection method are realized.

[0144] It should be noted that in this paper, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, method, article or system. Without more limitations, the element defined by the sentence "includes a" does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0145] The above application embodiment serial number is only for description, not representing the pros and cons of the embodiment.

[0146] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment method can be realized by means of software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the application 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 described above, and includes a plurality of instructions to make a device (which can be a mobile phone, computer, server, or network device, etc.) execute the method described in each embodiment of the application.

[0147] The above is only the preferred embodiment of the application, and does not limit the patent scope of the application. Any equivalent structure or equivalent process transformation, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the 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; 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; 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; 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 inverse of the current mutation amplitude, and the first current characteristic parameter includes 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 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.

2. 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.

3. 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.

4. The arc fault detection method according to claim 3, wherein: 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.

5. The arc fault detection method according to claim 4, 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.

6. 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; The waveform acquisition module is further configured to determine a current time series array corresponding to the first half-wave waveform, the current time series array comprising N consecutive current sampling values, where N is a natural number greater than 1; determine a current mutation amplitude of the power system during the first half-wave waveform based on a difference between a maximum current sampling value and an initial current sampling value in the current time series array; and traverse all the current sampling values ​​based on the current mutation amplitude and the negative mutation amplitude to obtain a first current characteristic parameter, wherein the negative mutation amplitude is the inverse of the current mutation amplitude, and the first current characteristic parameter comprises a positive current characteristic, a negative current characteristic, and an arc current characteristic. The waveform acquisition module is further configured to sequentially traverse the magnitudes of each current sampling value and the current mutation amplitude and the negative mutation amplitude in chronological order, accumulate the current sampling values ​​obtained that are greater than the current mutation amplitude, and use the obtained positive half-wave accumulated value as the positive current characteristic; accumulate the current sampling values ​​obtained that are less than the negative mutation amplitude, and use the obtained negative half-wave accumulated value as the negative current characteristic; accumulate the current sampling values ​​obtained that are greater than the negative mutation amplitude and less than the current mutation amplitude, and use the obtained current mutation accumulated value as the arc current characteristic; 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.

7. 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 5 are implemented.

8. 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 5.

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