Alternating current series arc detection method, system and device and readable storage medium

Abstract

An AC series arc detection method, system and device and a readable storage medium relate to the technical field of electrical engineering, and specifically comprise the following steps: generating a current anomaly event and a corresponding current anomaly time window based on a current signal at a current sampling moment and a current signal at a previous sampling moment; the current abnormal events comprise current short-time sudden change abnormity, high-frequency energy sudden increase abnormity and intermittent conduction interruption abnormity; generating a voltage abnormal event and a corresponding voltage abnormal time window according to all voltage signals in the current abnormal time window and the voltage signal at the current sampling moment, wherein the voltage abnormal event comprises zero-crossing conduction abnormity and voltage transient amplitude abnormity; determining a current and voltage dual-channel abnormal event based on the current abnormal time window and the voltage abnormal time window in a preset time alignment window; and determining an alternating-current series arc event according to the current-voltage dual-channel abnormal event to realize alternating-current series arc detection. According to the invention, the accuracy and realizability of alternating-current series arc detection can be ensured.

Description

Technical Field

[0001] This application relates to the field of electrical engineering technology, specifically to an AC series arc detection method, system, device, and readable storage medium. Background Technology

[0002] With the continuous development of power systems and the increasing electricity load, the demand for electrical safety is rising, especially the detection of series arcs in AC circuits. However, existing detection methods generally suffer from several problems: First, relying on a single signal of current or voltage is prone to false alarms when faced with changes in load conditions and interference; second, existing dual-channel solutions lack effective constraints and verification mechanisms for the timing correlation between current and voltage, making it easy for brief disturbances to be misjudged as arc events, ultimately leading to insufficient reliability of the judgment results; finally, some solutions rely on complex statistical models or machine learning algorithms, resulting in poor feasibility for engineering implementation, especially in resource-constrained embedded devices where real-time application is difficult.

[0003] Therefore, how to provide an AC series arc detection method to ensure the accuracy and feasibility of AC series arc detection is an urgent problem to be solved. Summary of the Invention

[0004] This application provides an AC series arc detection method, system, device, and readable storage medium, which can ensure the accuracy and feasibility of AC series arc detection.

[0005] In a first aspect, embodiments of this application provide an AC series arc detection method, the AC series arc detection method comprising: Current anomalies and their corresponding current anomaly time windows are generated based on the current signal at the current sampling time and the current signal at the previous sampling time. The current anomalies include short-term current surge anomalies, high-frequency energy surge anomalies, and intermittent conduction interruption anomalies. Voltage anomaly events and their corresponding voltage anomaly time windows are generated based on all voltage signals within the current anomaly time window and the voltage signal at the current sampling time. The voltage anomaly events include zero-crossing conduction anomalies and voltage transient amplitude anomalies. Within a preset time alignment window, current and voltage dual-channel abnormal events are determined based on current abnormality time windows and voltage abnormality time windows. AC series arc events are determined based on dual-channel current and voltage abnormal events to achieve AC series arc detection.

[0006] In conjunction with the first aspect, in one implementation, generating a current anomaly event and its corresponding current anomaly time window based on the current signal at the current sampling time and the current signal at the previous sampling time includes: Based on the current signal at the current sampling time, the current signal at the previous sampling time, and the preset short-term current change threshold, determine whether the current abnormal event is a short-term current change abnormality. The high-frequency energy ratio is determined based on the current signal within a preset sliding time window, and the current abnormality event is judged to be a high-frequency energy surge abnormality based on the high-frequency energy ratio and the preset high-frequency energy ratio threshold. Determine whether the current abnormal event is an intermittent conduction interruption abnormality based on the amplitude of the current signal at the current sampling time and the preset current interruption threshold. The first occurrence of any of the following anomalies is taken as the target current anomaly: short-term current surge anomaly, high-frequency energy surge anomaly, and intermittent conduction interruption anomaly. The sampling time corresponding to the first occurrence of the target current anomaly is taken as the start time of the current anomaly time window, and the sampling time corresponding to the disappearance of the target current anomaly is taken as the end time of the current anomaly time window.

[0007] In conjunction with the first aspect, in one implementation, determining whether a current anomaly event is a short-term current mutation based on the current signal at the current sampling time, the current signal at the previous sampling time, and a preset short-term current mutation threshold includes: The first-order difference value of the current at the current sampling time is determined based on the current signal at the current sampling time and the current signal at the previous sampling time. If the first-order difference value of the current is not less than the preset short-time change threshold of the current, the current abnormal event is determined to be a short-time change abnormality of the current. If the first-order difference value of the current is less than the preset short-time current change threshold, then the current abnormal event is determined to be not a short-time current change abnormality.

[0008] In conjunction with the first aspect, in one implementation, generating a voltage anomaly event and its corresponding voltage anomaly time window based on all voltage signals within the current anomaly time window and the voltage signal at the current sampling time includes: The zero-crossing turn-on delay time is determined based on the voltage signal at the current sampling time; Within a preset number of consecutive cycles, the system determines whether a voltage anomaly is a zero-crossing conduction anomaly based on the zero-crossing conduction delay duration and a preset delay threshold. Determine whether the voltage anomaly event is a transient voltage amplitude anomaly based on all voltage signals within the current anomaly time window, the voltage signal at the current sampling time, and the preset offset threshold. The first occurrence of either the zero-crossing conduction anomaly or the voltage transient amplitude anomaly is taken as the target voltage anomaly. The sampling time corresponding to the first occurrence of the target voltage anomaly is taken as the start time of the voltage anomaly time window, and the sampling time corresponding to the disappearance of the target voltage anomaly is taken as the end time of the voltage anomaly time window.

[0009] In conjunction with the first aspect, in one implementation, the step of determining whether a voltage anomaly event is a voltage transient amplitude anomaly based on all voltage signals within the current anomaly time window, the voltage signal at the current sampling time, and a preset offset threshold includes: The local average voltage value is determined based on all voltage signals within the current anomaly time window. The maximum transient offset is determined based on the voltage signal and the local average voltage value at the current sampling time; If the maximum transient offset is greater than the preset offset threshold, the voltage abnormality event is determined to be an abnormal voltage transient amplitude. If the maximum transient offset is not greater than the preset offset threshold, the voltage anomaly event is determined not to be a voltage transient amplitude anomaly.

[0010] In conjunction with the first aspect, in one implementation, determining the dual-channel abnormal events of current and voltage based on the current abnormality time window and the voltage abnormality time window includes: The time overlap ratio is determined based on the current anomaly time window and the voltage anomaly time window; If the time overlap ratio is not less than the preset ratio threshold, it is determined to be an abnormal event in both current and voltage channels. If the time overlap ratio is less than the preset ratio threshold, it is determined that it is not an abnormal event of the current and voltage dual channels.

[0011] In conjunction with the first aspect, in one implementation, determining the AC series arc event based on the dual-channel current and voltage abnormal events includes: If, within a preset number of consecutive cycles, the number of times a current-voltage dual-channel abnormal event occurs is greater than a preset number threshold or the duration of the current-voltage dual-channel abnormal event is greater than a preset duration threshold, then the current-voltage dual-channel abnormal event is determined to be an AC series arc event. If the number of times the current and voltage dual-channel abnormal events occur is greater than a preset threshold and the duration of the current and voltage dual-channel abnormal events is greater than a preset duration threshold, then the current and voltage dual-channel abnormal events are determined not to be AC ​​series arc events.

[0012] Secondly, embodiments of this application provide an AC series arc detection system, the AC series arc detection system comprising: The first processing module is used to generate current abnormal events and their corresponding current abnormal time windows based on the current signal at the current sampling time and the current signal at the previous sampling time. The current abnormal events include short-term current mutation abnormalities, high-frequency energy surge abnormalities, and intermittent conduction interruption abnormalities. The second processing module is used to generate voltage anomaly events and their corresponding voltage anomaly time windows based on all voltage signals within the current anomaly time window and the voltage signal at the current sampling time. The voltage anomaly events include zero-crossing conduction anomalies and voltage transient amplitude anomalies. The third processing module is used to determine the current and voltage dual-channel abnormal events within a preset time alignment window based on the current abnormal time window and the voltage abnormal time window. The fourth processing module is used to determine the AC series arc event based on the dual-channel abnormal events of current and voltage, so as to realize the AC series arc detection.

[0013] Thirdly, embodiments of this application provide an AC series arc detection device, the AC series arc detection device including a processor, a memory, and an AC series arc detection program stored in the memory and executable by the processor, wherein when the AC series arc detection program is executed by the processor, it implements the steps of the AC series arc detection method as described in any of the preceding claims.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing an AC series arc detection program, wherein when the AC series arc detection program is executed by a processor, it implements the steps of the AC series arc detection method as described in any of the preceding claims.

[0015] The beneficial effects of the technical solutions provided in this application include: Current anomaly events, including short-term current surges, high-frequency energy spikes, and intermittent conduction interruptions, and their corresponding time windows are generated based on the current signal at the current sampling time and the current signal at the previous sampling time. Voltage anomaly events, including zero-crossing conduction anomalies and transient voltage amplitude anomalies, and their corresponding time windows are generated based on all voltage signals within the current anomaly time windows and the voltage signal at the current sampling time. This is used for subsequent arc detection based on dual-channel current and voltage signals, avoiding false alarms when relying on only a single channel in the face of load changes and interference. Within a preset time alignment window, dual-channel current and voltage anomaly events are determined based on the current and voltage anomaly time windows. This approach enables the verification of anomalies in both current and voltage channels based on time correlation when determining abnormal events, thus solving the problem of lacking effective time correlation in existing dual-channel schemes. It identifies AC series arc events based on current and voltage dual-channel abnormal events and implements a verification mechanism to prevent transient interference from being misjudged as AC series arc events, improving the accuracy of real arc event determination. Furthermore, this application employs deterministic rules for determination, making the method easier to implement, especially suitable for resource-constrained embedded devices that meet real-time requirements. It avoids the use of complex statistical models or machine learning algorithms, thus enhancing its feasibility in practical applications. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of an embodiment of the AC series arc detection method of this application; Figure 2 This is a schematic diagram of the current anomaly time window and voltage anomaly time window in the embodiment of the AC series arc detection method of this application; Figure 3 This is a detailed flowchart illustrating an embodiment of the AC series arc detection method of this application; Figure 4 This is a functional module diagram of an embodiment of the AC series arc detection system of this application; Figure 5 This is a schematic diagram of the hardware structure of the AC series arc detection device involved in the embodiments of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0019] In a first aspect, embodiments of this application provide an AC series arc detection method.

[0020] In one embodiment, reference is made to Figure 1 , Figure 1 This is a schematic flowchart illustrating an embodiment of the AC series arc detection method of this application. Figure 1 As shown, the AC series arc detection method includes: Step S10: Generate current abnormal events and their corresponding current abnormal time windows based on the current signal at the current sampling time and the current signal at the previous sampling time. The current abnormal events include short-term current mutation abnormalities, high-frequency energy surge abnormalities, and intermittent conduction interruption abnormalities.

[0021] As an example, in this embodiment of the application, an air-core current sensor, preferably a Rogowski coil, is installed at the series current path upstream of the target load side in the monitored AC series circuit to collect the AC current signal in the circuit; at the same time, an isolation voltage sensor is set in the same circuit segment corresponding to the current sensor to isolate and sample the circuit voltage; wherein, the current sensor should be completely surrounded by a single conductor to avoid magnetic field cancellation caused by multiple conductors passing through the core; the voltage sensor meets electrical safety requirements through voltage division and isolation amplification.

[0022] It should be understood that the output signals of the current sensor and the voltage sensor can be input into the analog front-end processing circuit respectively to perform anti-aliasing filtering, amplitude conditioning and overvoltage protection processing on the signals; wherein, an integration circuit or equivalent compensation circuit is set for the Rogowski coil output signal to recover the time-domain current signal corresponding to the change of the measured current; the current signal and voltage signal after front-end processing are synchronously sampled under a unified time reference, the sampling frequency is preferably 20 kHz to 500 kHz, more preferably 50 kHz to 200 kHz, to cover the power frequency component and the high-frequency transient components generated by the series arc.

[0023] It should be noted that when the system starts up, resets, or is manually triggered for initialization, it enters the baseline establishment phase. The system continuously acquires the synchronously sampled current and voltage signals within a preset baseline time window. The length of the baseline time window is preferably 0.5 s to 10 s, and more preferably 1 s to 5 s. Within the baseline time window, the stability of the acquired current and voltage signals is assessed, and the stability assessment includes at least one or a combination of the following criteria: (a) Within the baseline time window, neither the current signal nor the voltage signal triggers the abnormal feature determination conditions defined in the subsequent steps; (b) The peak value variation of the current and voltage signals does not exceed 10% to 30% of their average value; (c) The ratio of the standard deviation of the current signal and the average value of the voltage signal does not exceed a preset ratio threshold. The specific value of the preset ratio threshold can be determined according to actual needs and is not limited here.

[0024] It should be understood that when the above stability judgment conditions are met, the sampled data within the baseline time window is considered normal operating data. Based on the normal operating data, the statistical characteristic parameters of the current signal and voltage signal (mean and standard deviation of the current signal and the mean and standard deviation of the voltage signal) are calculated respectively. Among these, the mean of the current signal... The standard deviation is used to characterize the current amplitude under normal operating conditions. The mean of the voltage signal is used to characterize the fluctuation characteristics of the current under normal operating conditions, and the standard deviation is used to characterize the voltage fluctuation characteristics under normal operating conditions.

[0025] It should be noted that the current signal at the current sampling moment refers to the current value collected in real time, while the current signal at the previous sampling moment refers to the current value collected previously. Current abnormal events include short-term current mutation anomalies, high-frequency energy surge anomalies, and intermittent conduction interruption anomalies. Among them, short-term current mutation anomalies refer to an abnormal event in which the current changes abruptly within a short period of time; high-frequency energy surge anomalies refer to an abnormal event in which the high-frequency energy in the current suddenly increases; and intermittent conduction interruption anomalies refer to an abnormal event in which the current is intermittently interrupted. These current abnormal events together define the current abnormality time window, which is the time period that identifies the occurrence of the current abnormality.

[0026] Specifically, after determining that the acquired current signal is normal operating data, the first-order difference value of the current is determined based on the current signal at the current sampling time and the current signal at the previous sampling time. The magnitude of the first-order difference value of the current is compared with the corresponding threshold to determine whether there is a short-term current change anomaly. The high-frequency energy ratio is calculated using the current signal at the current sampling time, and the magnitude of the high-frequency energy ratio is compared with the corresponding threshold to determine whether there is a high-frequency energy surge anomaly. The amplitude of the current signal at the current sampling time is monitored to determine whether there is an intermittent conduction interruption anomaly. These parameters together define the triggering conditions of the current anomaly event, ensuring the accurate generation of the current anomaly time window and providing a reliable input basis for subsequent determination of the time consistency of the current and voltage dual channels.

[0027] Step S20: Generate voltage anomaly events and their corresponding voltage anomaly time windows based on all voltage signals within the current anomaly time window and the voltage signal at the current sampling time. The voltage anomaly events include zero-crossing conduction anomalies and voltage transient amplitude anomalies.

[0028] In this embodiment of the application, as an example, all voltage signals within the current anomaly time window refer to the voltage data sequence collected within the current anomaly time window, and the voltage signal at the current sampling moment refers to the latest voltage value collected in real time. Voltage anomaly events include zero-crossing conduction anomalies and voltage transient amplitude anomalies. Specifically, a zero-crossing conduction anomaly event refers to an abnormal voltage conduction delay at the zero-crossing point, and a voltage transient amplitude anomaly event refers to an abnormal voltage transient amplitude. After determining that the collected voltage signal is normal operating data, the voltage zero-crossing conduction delay is determined based on all voltage signals within the current anomaly time window and the voltage signal at the current sampling moment. A zero-crossing conduction anomaly event is generated by detecting whether the voltage zero-crossing conduction delay exceeds a preset threshold, and a voltage transient amplitude anomaly event is generated by detecting whether the voltage transient amplitude exceeds a preset threshold. These voltage anomaly events together define the voltage anomaly time window, that is, the time period during which the voltage anomaly occurs.

[0029] Step S30: Within the preset time alignment window, determine the current and voltage dual-channel abnormal events based on the current abnormal time window and the voltage abnormal time window.

[0030] As an example, in the embodiments of this application, the function of the preset time alignment window is to determine whether the current abnormality time window and the voltage abnormality time window overlap or are related within the "allowable time range". That is, the time alignment window is used to limit the maximum allowable deviation range of the current abnormality event and the voltage abnormality event on the time axis. Its length can be set according to the sampling frequency and signal processing delay, and is not limited here. For example, the preset time alignment window can preferably be 0.5ms to 5ms, and more preferably 1ms to 3ms.

[0031] Specifically, within the preset time alignment window, based on the current anomaly time window (identifying the time period during which the current anomaly event occurs) and the voltage anomaly time window (identifying the time period during which the voltage anomaly event occurs), the time overlap ratio of the two time windows is calculated to quantify the temporal consistency between current anomalies and voltage anomalies. Then, based on the relationship between the time overlap ratio and the preset threshold, it is determined whether it is a dual-channel current and voltage anomaly event. This ensures that the review judgment is only entered when the current and voltage anomalies are highly overlapped in time, thereby effectively avoiding false alarms caused by single-channel interference and improving the reliability of arc fault detection.

[0032] It should be noted that during continuous system operation, provided that no dual-channel abnormal event of current and voltage is detected, the system updates according to the preset update strategy. The preset update strategy refers to updating the baseline parameters (i.e., the previously collected operating data) and the statistical parameters corresponding to the newly collected normal operating data by weighted averaging within the preset update cycle. When an abnormal current event or an abnormal voltage event is detected, the update of the baseline parameters is suspended.

[0033] Step S40: Determine the AC series arc event based on the current and voltage dual-channel abnormal events to realize AC series arc detection.

[0034] As an example, in this embodiment of the application, based on the dual-channel current and voltage abnormal event (referring to an event in which current and voltage abnormalities highly overlap in time), the occurrence frequency or cumulative duration of the event is counted within a preset number of consecutive cycles to determine whether the dual-channel current and voltage abnormal event is an AC series arc event. This ensures that the event is only considered a real arc when it recurs within multiple cycles or lasts for more than a set duration, thereby effectively distinguishing between real arcs and transient interferences (such as power grid disturbances or load start-up and shutdown), and significantly improving the reliability and accuracy of AC series arc detection.

[0035] This application generates current anomaly events, including short-term current surge anomalies, high-frequency energy surge anomalies, and intermittent conduction interruption anomalies, and their corresponding current anomaly time windows, based on the current signal at the current sampling time and the current signal at the previous sampling time. It also generates voltage anomaly events, including zero-crossing conduction anomalies and voltage transient amplitude anomalies, and their corresponding voltage anomaly time windows, based on all voltage signals within the current anomaly time windows and the voltage signal at the current sampling time. This allows for subsequent arc detection based on dual-channel current and voltage signals, avoiding false alarms caused by relying solely on a single channel when facing load changes and interference. Within a preset time alignment window, dual-channel current and voltage anomaly events are determined based on the current anomaly time windows and voltage anomaly time windows. This invention enables the verification of anomalies in both current and voltage dual-channel signals based on time correlation when determining abnormal events, thus solving the problem of the lack of effective timing correlation in existing dual-channel schemes. It identifies AC series arc events based on current and voltage dual-channel abnormal events and implements a verification mechanism to avoid misjudging transient interference as AC series arc events, thereby improving the accuracy of real arc event determination. Furthermore, this application employs deterministic rules for determination, making the method easier to implement, especially in resource-constrained embedded devices where real-time requirements can be guaranteed. It avoids the use of complex statistical models or machine learning algorithms, thus making it more feasible in practical applications.

[0036] Furthermore, in one embodiment, generating the current anomaly event and its corresponding current anomaly time window based on the current signal at the current sampling time and the current signal at the previous sampling time includes: Based on the current signal at the current sampling time, the current signal at the previous sampling time, and the preset short-term current change threshold, determine whether the current abnormal event is a short-term current change abnormality. The high-frequency energy ratio is determined based on the current signal within a preset sliding time window, and the current abnormality event is judged to be a high-frequency energy surge abnormality based on the high-frequency energy ratio and the preset high-frequency energy ratio threshold. Determine whether the current abnormal event is an intermittent conduction interruption abnormality based on the amplitude of the current signal at the current sampling time and the preset current interruption threshold. The first occurrence of any of the following anomalies is taken as the target current anomaly: short-term current surge anomaly, high-frequency energy surge anomaly, and intermittent conduction interruption anomaly. The sampling time corresponding to the first occurrence of the target current anomaly is taken as the start time of the current anomaly time window, and the sampling time corresponding to the disappearance of the target current anomaly is taken as the end time of the current anomaly time window.

[0037] As an example, in the embodiments of this application, the specific value of the preset short-time current change threshold can be determined according to actual needs and is not limited here. For example, the preset short-time current change threshold can preferably be 3 to 8 times the standard deviation of the current signal. The first-order difference value of the current can be calculated by the current signal at the current sampling time and the current signal at the previous sampling time, and then the current abnormal event can be judged as a short-time current change abnormality based on the relationship between the first-order difference value of the current and the preset short-time current change threshold.

[0038] It should be noted that the high-frequency components at each sampling moment can be obtained by bandpass filtering the current signal at each sampling moment within a preset sliding time window, and the high-frequency energy can be determined based on the high-frequency components at all sampling moments. The formula for calculating the high-frequency energy is as follows:

[0039] In the formula, n represents the nth sampling time within the preset sliding window, n = 0, 1, 2, ..., N-1 (N is the length of the sliding window, n=0 is the starting point of the preset sliding window, and n=N-1 is the ending point of the preset sliding window); This refers to the high-frequency component corresponding to the nth sampling time. The energy is high-frequency energy; then, the total energy is determined based on the current signal at all sampling moments within a preset sliding time window. The formula for calculating the total energy is:

[0040] In the formula, This is the current signal corresponding to the nth sampling time. The total energy is given; then, based on the preset minimum value, high-frequency energy, and total energy, the high-frequency energy ratio is determined. The formula for calculating the high-frequency energy ratio is:

[0041] In the formula, The preset minimum value is used to avoid extremely small positive numbers with a denominator of zero; This refers to the high-frequency energy ratio.

[0042] Specifically, the specific value of the preset high-frequency energy ratio threshold can be determined according to actual needs and is not limited here. For example, the preset high-frequency energy ratio threshold can preferably be selected from 0.03 to 0.3. If the high-frequency energy ratio is greater than the preset high-frequency energy ratio threshold, it indicates that the energy of the high-frequency components in the current signal is significantly enhanced, indicating the presence of a transient fault, discharge phenomenon, or other sudden event in the system. This sudden increase in high-frequency energy may be related to problems such as electric arc, short circuit, or equipment failure. In this case, the abnormal current event is judged as a sudden increase in high-frequency energy. If the high-frequency energy ratio is not greater than the preset high-frequency energy ratio threshold, it indicates that the high-frequency components in the current signal remain within the normal range and do not show a significant increase, indicating that the system is operating stably and no abnormalities have occurred. If a high-frequency phenomenon is detected, the current abnormality event is determined not to be a high-frequency energy surge abnormality. The specific value of the preset current interruption threshold can be determined according to actual needs and is not limited here. For example, the preset current interruption threshold can be preferably selected as 1 to 4 times the standard deviation of the current signal. If the amplitude of the current signal at the current sampling time is not greater than the preset current interruption threshold for a duration greater than the preset minimum interruption duration, the current abnormality event is determined to be an intermittent conduction interruption abnormality. If the amplitude of the current signal at the current sampling time is greater than the preset current interruption threshold or the amplitude of the current signal at the current sampling time is not greater than the preset current interruption threshold for a duration not greater than the preset minimum interruption duration, the current abnormality event is determined not to be an intermittent conduction interruption abnormality.

[0043] It should be understood that the target current anomaly refers to the first occurrence of an abnormal event among short-term current surge anomalies, high-frequency energy surge anomalies, and intermittent conduction interruption anomalies. The sampling time corresponding to its first occurrence is taken as the start time of the current anomaly time window, and the sampling time corresponding to its disappearance and duration not less than the preset recovery time is taken as the end time of the current anomaly time window. Together, they constitute the current anomaly time window, which is used to accurately identify the time period in which the current anomaly occurs. The specific value of the preset recovery time can be determined according to actual needs and is not limited here. For example, the preset recovery time can preferably be selected from 0.1ms to 5ms.

[0044] Further, in one embodiment, the step of determining whether a current abnormal event is a current short-term sudden change abnormality based on the current signal at the current sampling time, the current signal at the previous sampling time, and a preset current short-term sudden change threshold includes: The first-order difference value of the current at the current sampling time is determined based on the current signal at the current sampling time and the current signal at the previous sampling time. If the first-order difference value of the current is not less than the preset short-time change threshold of the current, the current abnormal event is determined to be a short-time change abnormality of the current. If the first-order difference value of the current is less than the preset short-time current change threshold, then the current abnormal event is determined to be not a short-time current change abnormality.

[0045] As an example, in this embodiment of the application, the current signal at the current sampling moment and the current signal at the previous sampling moment are substituted into the following calculation formula to obtain the first-order difference value of the current at the current sampling moment, the calculation formula being:

[0046] In the formula, This is the current signal at the current sampling time n; This is the current signal at the previous sampling time n-1; This is the first-order difference value of the current at the current sampling moment.

[0047] Specifically, if the first-order difference value of the current is not less than the preset short-time current change threshold, it indicates that there is a significant change in the current signal at the current sampling time compared with the current signal at the previous sampling time. This change indicates that the system may have experienced a transient fault or load change, and the current abnormal event is determined to be a short-time current change abnormality. If the first-order difference value of the current is less than the preset short-time current change threshold, it indicates that the change in the current signal at the current sampling time compared with the current signal at the previous sampling time is relatively small, indicating that the current signal remains relatively stable during this period, and the current abnormal event is determined not to be a short-time current change abnormality.

[0048] Further, in one embodiment, generating the voltage anomaly event and its corresponding voltage anomaly time window based on all voltage signals within the current anomaly time window and the voltage signal at the current sampling time includes: The zero-crossing turn-on delay time is determined based on the voltage signal at the current sampling time; Within a preset number of consecutive cycles, the system determines whether a voltage anomaly is a zero-crossing conduction anomaly based on the zero-crossing conduction delay duration and a preset delay threshold. Determine whether the voltage anomaly event is a transient voltage amplitude anomaly based on all voltage signals within the current anomaly time window, the voltage signal at the current sampling time, and the preset offset threshold. The first occurrence of either the zero-crossing conduction anomaly or the voltage transient amplitude anomaly is taken as the target voltage anomaly. The sampling time corresponding to the first occurrence of the target voltage anomaly is taken as the start time of the voltage anomaly time window, and the sampling time corresponding to the disappearance of the target voltage anomaly is taken as the end time of the voltage anomaly time window.

[0049] As an example, in the embodiments of this application, the zero-crossing time of the voltage (i.e., the time when the voltage changes from positive to negative or from negative to positive) is determined by detecting the sign change of the voltage signal at the current sampling time. Within the period corresponding to the voltage signal at the current sampling time, the target time when the current signal first exceeds the preset conduction threshold is obtained. The zero-crossing conduction delay time is determined based on the voltage zero-crossing time and the target time. The formula for calculating the zero-crossing conduction delay time is as follows:

[0050] In the formula, For the target time; This is the moment when the voltage crosses zero; This is the zero-crossing conduction delay time. It should be noted that the specific value of the preset conduction threshold can be determined according to actual needs and is not limited here.

[0051] Specifically, the exact value of the preset delay threshold can be determined according to actual needs and is not limited here. For example, a preset delay threshold of 50 is preferred. ~1ms; Within a preset number of consecutive periods, if the zero-crossing conduction delay is longer than the preset delay threshold, it indicates that the delay between the zero-crossing point of the voltage signal and the conduction state exceeds the normal range, indicating that there may be a fault or abnormal connection in the circuit, causing a lag in the conduction response. In this case, the voltage abnormal event is determined to be a zero-crossing conduction abnormality. If the zero-crossing conduction delay is not greater than the preset delay threshold, it indicates that the zero-crossing conduction process of the voltage signal is completed within the normal time range, indicating that the conduction state of the circuit responds to the voltage change in a timely manner. In this case, the voltage abnormal event is determined not to be a zero-crossing conduction abnormality.

[0052] It should be noted that the specific value of the preset offset threshold can be determined according to actual needs and is not limited here. For example, the preset offset threshold can be preferably a multiple of the standard deviation of the voltage signal. The maximum transient offset can be determined based on all voltage signals within the current anomaly time window and the voltage signal at the current sampling time. Then, the relationship between the maximum transient offset and the preset offset threshold is used to determine whether the voltage anomaly event is a voltage transient amplitude anomaly. The target voltage anomaly refers to the first anomaly event among zero-crossing conduction anomaly and voltage transient amplitude anomaly. The sampling time corresponding to its first occurrence is taken as the start time of the voltage anomaly time window, and the sampling time corresponding to its disappearance and duration not less than the preset recovery time is taken as the end time of the voltage anomaly time window. Together, they constitute the voltage anomaly time window, which is used to accurately identify the time period in which the voltage anomaly occurs.

[0053] Further, in one embodiment, the step of determining whether a voltage anomaly event is a voltage transient amplitude anomaly based on all voltage signals within the current anomaly time window, the voltage signal at the current sampling time, and a preset offset threshold includes: The local average voltage value is determined based on all voltage signals within the current anomaly time window. The maximum transient offset is determined based on the voltage signal and the local average voltage value at the current sampling time; If the maximum transient offset is greater than the preset offset threshold, the voltage abnormality event is determined to be an abnormal voltage transient amplitude. If the maximum transient offset is not greater than the preset offset threshold, the voltage anomaly event is determined not to be a voltage transient amplitude anomaly.

[0054] As an example, in an embodiment of this application, the local average voltage of all voltage signals within the current anomaly time window is calculated. Then, substitute the voltage signal and the local average voltage value at the current sampling time into the following calculation formula to obtain the maximum transient offset. The calculation formula is as follows:

[0055] In the formula, This represents the voltage signal at the current sampling moment; This is the local average voltage value. The maximum transient offset is the maximum transient offset. If the maximum transient offset is greater than the preset offset threshold, it indicates that the voltage signal has undergone a significant amplitude change in a short period of time, which indicates that the system may be affected by sudden interference or fault, such as lightning strikes or power fluctuations. In this case, the voltage abnormal event is determined to be a voltage transient amplitude abnormality. If the maximum transient offset is not greater than the preset offset threshold, it indicates that the amplitude change of the voltage signal remains within the normal range and does not show abnormal fluctuation characteristics. This indicates that the system is operating stably and has not been affected by sudden interference. In this case, the voltage abnormal event is determined not to be a voltage transient amplitude abnormality.

[0056] Furthermore, in one embodiment, determining the dual-channel abnormal events of current and voltage based on the current abnormality time window and the voltage abnormality time window includes: The time overlap ratio is determined based on the current anomaly time window and the voltage anomaly time window; If the time overlap ratio is not less than the preset ratio threshold, it is determined to be an abnormal event in both current and voltage channels. If the time overlap ratio is less than the preset ratio threshold, it is determined that it is not an abnormal event of the current and voltage dual channels.

[0057] As an example, in this embodiment of the application, the time overlap ratio is obtained by substituting the current anomaly time window and the voltage anomaly time window into the following calculation formula:

[0058] In the formula, This is the start time of the current anomaly time window; This refers to the start and end times of the current anomaly time window. This represents the start time of the voltage anomaly time window; This is the end time of the voltage anomaly time window; This refers to the time overlap ratio; refer to Figure 2 As shown, the horizontal axis represents time t under a unified time base, in milliseconds; the vertical axis represents abnormal current and abnormal voltage states, without units. Indicates the time window for abnormal current conditions. Indicates the time window for abnormal voltage conditions, [ , [] indicates the time overlap interval.

[0059] Specifically, if the time overlap ratio is not less than the preset ratio threshold, it indicates that the current abnormal event and the voltage abnormal event have a high degree of overlap, indicating that the abnormal changes in the current and voltage signals may be caused by the same fault or phenomenon and have a certain temporal correlation, then it can be determined as a current and voltage dual-channel abnormal event; if the time overlap ratio is less than the preset ratio threshold, it indicates that the time window overlap between the current abnormal event and the voltage abnormal event is low, indicating that the abnormal states of these two signals may occur independently and lack correlation, then it is determined not to be a current and voltage dual-channel abnormal event.

[0060] Further, in one embodiment, determining the AC series arc event based on the dual-channel current and voltage abnormal events includes: If, within a preset number of consecutive cycles, the number of times a current-voltage dual-channel abnormal event occurs is greater than a preset number threshold or the duration of the current-voltage dual-channel abnormal event is greater than a preset duration threshold, then the current-voltage dual-channel abnormal event is determined to be an AC series arc event. If the number of times the current and voltage dual-channel abnormal events occur is greater than a preset threshold and the duration of the current and voltage dual-channel abnormal events is greater than a preset duration threshold, then the current and voltage dual-channel abnormal events are determined not to be AC ​​series arc events.

[0061] As an example, in the embodiments of this application, the specific value of the preset number of continuous cycles (i.e., a number of consecutive AC cycles) can be determined according to actual needs and is not limited here. For example, the preset number of continuous cycles can preferably be 3 to 20 power frequency cycles, and more preferably 5 to 10 power frequency cycles; within this cycle range, the number of occurrences of dual-channel abnormal events is counted. The specific value of the preset number threshold can be determined according to actual needs and is not limited here. For example, the preset number threshold can preferably be 2 to 5 times. At the same time, the duration of the current and voltage dual-channel abnormal event within this period range (i.e., the cumulative duration) can also be calculated. The specific value of the preset duration threshold can be determined according to actual needs and is not limited here. For example, the preset duration threshold can preferably be selected from 1ms to 50ms.

[0062] Specifically, within a preset number of consecutive periods, the number of times a dual-channel current and voltage abnormal event is detected is... The number of occurrences exceeds the preset threshold or the duration of dual-channel current and voltage abnormal events. If the duration exceeds the preset threshold, it indicates that abnormal current and voltage conditions occur frequently and persistently in the power system, indicating the presence of a continuous arcing phenomenon. This may be due to equipment aging, insulation damage, or poor connection. In this case, the dual-channel current and voltage abnormal event is determined to be an AC series arcing event. Once it is finally confirmed as an AC series arcing event, the system executes at least one preset response operation, including audible and visual alarms, remote alarms, circuit breaker protection, and event reporting. At the same time, the system stores the current and voltage synchronization waveforms, event timestamps, abnormal characteristic parameters, and determination process logs corresponding to the AC series arcing event. It should be noted that if no abnormal events were detected in the dual current and voltage channels... Duration during which no abnormal current and voltage events are detected, exceeding the preset threshold number of occurrences. If the duration exceeds the preset threshold, it indicates that there are few abnormal current and voltage states during the monitoring period and the duration is short. These abnormal phenomena do not meet the characteristics of an arc event and may be caused by external interference, transient fluctuations or other non-continuous factors. Therefore, the current and voltage dual-channel abnormal event is determined to be not an AC series arc event, but a transient interference event, and the event is recorded. It should be understood that, with reference Figure 3As shown, this application synchronously acquires current and voltage signals in the monitored AC circuit, wherein the current and voltage signals are sampled under a unified time reference; then, based on the current signal, abnormal current characteristics are detected in the current channel, and an abnormal current event and a corresponding abnormal current time window are generated when the abnormal current characteristics are detected; simultaneously, based on the voltage signal, abnormal voltage characteristics are detected in the voltage channel, and an abnormal voltage event and a corresponding abnormal voltage time window are generated when the abnormal voltage characteristics are detected; subsequently, the abnormal current time window and the abnormal voltage time window are time-aligned and consistent. When the abnormal current event and the abnormal voltage event meet the time overlap condition within a preset time alignment window, they are determined to be a dual-channel abnormal event; finally, the dual-channel abnormal event is reviewed and judged within a continuous number of AC cycles. Only when the dual-channel abnormal event meets the preset repeatability condition or persistence condition within the continuous AC cycle is it confirmed that an AC series arc has occurred; if the review and judgment is not passed, the dual-channel abnormal event is not confirmed as an AC series arc.

[0063] Secondly, embodiments of this application also provide an AC series arc detection system.

[0064] In one embodiment, reference is made to Figure 4 , Figure 4 This is a functional module diagram of an embodiment of the AC series arc detection system of this application. Figure 4 As shown, the AC series arc detection system includes: The first processing module is used to generate current abnormal events and their corresponding current abnormal time windows based on the current signal at the current sampling time and the current signal at the previous sampling time. The current abnormal events include short-term current mutation abnormalities, high-frequency energy surge abnormalities, and intermittent conduction interruption abnormalities. The second processing module is used to generate voltage anomaly events and their corresponding voltage anomaly time windows based on all voltage signals within the current anomaly time window and the voltage signal at the current sampling time. The voltage anomaly events include zero-crossing conduction anomalies and voltage transient amplitude anomalies. The third processing module is used to determine the current and voltage dual-channel abnormal events within a preset time alignment window based on the current abnormal time window and the voltage abnormal time window. The fourth processing module is used to determine the AC series arc event based on the dual-channel abnormal events of current and voltage, so as to realize the AC series arc detection.

[0065] Furthermore, in one embodiment, the first processing module is specifically used for: Based on the current signal at the current sampling time, the current signal at the previous sampling time, and the preset short-term current change threshold, determine whether the current abnormal event is a short-term current change abnormality. The high-frequency energy ratio is determined based on the current signal within a preset sliding time window, and the current abnormality event is judged to be a high-frequency energy surge abnormality based on the high-frequency energy ratio and the preset high-frequency energy ratio threshold. Determine whether the current abnormal event is an intermittent conduction interruption abnormality based on the amplitude of the current signal at the current sampling time and the preset current interruption threshold. The first occurrence of any of the following anomalies is taken as the target current anomaly: short-term current surge anomaly, high-frequency energy surge anomaly, and intermittent conduction interruption anomaly. The sampling time corresponding to the first occurrence of the target current anomaly is taken as the start time of the current anomaly time window, and the sampling time corresponding to the disappearance of the target current anomaly is taken as the end time of the current anomaly time window.

[0066] Furthermore, in one embodiment, the first processing module is specifically used for: The first-order difference value of the current at the current sampling time is determined based on the current signal at the current sampling time and the current signal at the previous sampling time. If the first-order difference value of the current is not less than the preset short-time change threshold of the current, the current abnormal event is determined to be a short-time change abnormality of the current. If the first-order difference value of the current is less than the preset short-time current change threshold, then the current abnormal event is determined to be not a short-time current change abnormality.

[0067] Furthermore, in one embodiment, the second processing module is specifically used for: The zero-crossing turn-on delay time is determined based on the voltage signal at the current sampling time; Within a preset number of consecutive cycles, the system determines whether a voltage anomaly is a zero-crossing conduction anomaly based on the zero-crossing conduction delay duration and a preset delay threshold. Determine whether the voltage anomaly event is a transient voltage amplitude anomaly based on all voltage signals within the current anomaly time window, the voltage signal at the current sampling time, and the preset offset threshold. The first occurrence of either the zero-crossing conduction anomaly or the voltage transient amplitude anomaly is taken as the target voltage anomaly. The sampling time corresponding to the first occurrence of the target voltage anomaly is taken as the start time of the voltage anomaly time window, and the sampling time corresponding to the disappearance of the target voltage anomaly is taken as the end time of the voltage anomaly time window.

[0068] Furthermore, in one embodiment, the second processing module is specifically used for: The local average voltage value is determined based on all voltage signals within the current anomaly time window. The maximum transient offset is determined based on the voltage signal and the local average voltage value at the current sampling time; If the maximum transient offset is greater than the preset offset threshold, the voltage abnormality event is determined to be an abnormal voltage transient amplitude. If the maximum transient offset is not greater than the preset offset threshold, the voltage anomaly event is determined not to be a voltage transient amplitude anomaly.

[0069] Furthermore, in one embodiment, the third processing module is specifically used for: The time overlap ratio is determined based on the current anomaly time window and the voltage anomaly time window; If the time overlap ratio is not less than the preset ratio threshold, it is determined to be an abnormal event in both current and voltage channels. If the time overlap ratio is less than the preset ratio threshold, it is determined that it is not an abnormal event of the current and voltage dual channels.

[0070] Furthermore, in one embodiment, the fourth processing module is specifically used for: If, within a preset number of consecutive cycles, the number of times a current-voltage dual-channel abnormal event occurs is greater than a preset number threshold or the duration of the current-voltage dual-channel abnormal event is greater than a preset duration threshold, then the current-voltage dual-channel abnormal event is determined to be an AC series arc event. If the number of times the current and voltage dual-channel abnormal events occur is greater than a preset threshold and the duration of the current and voltage dual-channel abnormal events is greater than a preset duration threshold, then the current and voltage dual-channel abnormal events are determined not to be AC ​​series arc events.

[0071] This application generates current anomaly events, including short-term current surge anomalies, high-frequency energy surge anomalies, and intermittent conduction interruption anomalies, and their corresponding current anomaly time windows, based on the current signal at the current sampling time and the current signal at the previous sampling time. It also generates voltage anomaly events, including zero-crossing conduction anomalies and voltage transient amplitude anomalies, and their corresponding voltage anomaly time windows, based on all voltage signals within the current anomaly time windows and the voltage signal at the current sampling time. This allows for subsequent arc detection based on dual-channel current and voltage signals, avoiding false alarms caused by relying solely on a single channel when facing load changes and interference. Within a preset time alignment window, dual-channel current and voltage anomaly events are determined based on the current anomaly time windows and voltage anomaly time windows. This invention enables the verification of anomalies in both current and voltage dual-channel signals based on time correlation when determining abnormal events, thus solving the problem of the lack of effective timing correlation in existing dual-channel schemes. It identifies AC series arc events based on current and voltage dual-channel abnormal events and implements a verification mechanism to avoid misjudging transient interference as AC series arc events, thereby improving the accuracy of real arc event determination. Furthermore, this application employs deterministic rules for determination, making the method easier to implement, especially in resource-constrained embedded devices where real-time requirements can be guaranteed. It avoids the use of complex statistical models or machine learning algorithms, thus making it more feasible in practical applications.

[0072] The functions of each module in the AC series arc detection system described above correspond to the steps in the AC series arc detection method embodiment described above, and their functions and implementation processes will not be described in detail here.

[0073] Thirdly, embodiments of this application provide an AC series arc detection device, which can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0074] Reference Figure 5 , Figure 5 This is a schematic diagram of the hardware structure of the AC series arc detection device involved in the embodiments of this application. In the embodiments of this application, the AC series arc detection device may include a processor, a memory, a communication interface, and a communication bus.

[0075] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0076] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the AC series arc detection equipment, as well as interfaces used for interconnecting the AC series arc detection equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0077] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0078] The processor can be a general-purpose processor, which can call the AC series arc detection program stored in the memory and execute the AC series arc detection method provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the AC series arc detection program is called can be referred to in the various embodiments of the AC series arc detection method of this application, and will not be repeated here.

[0079] Those skilled in the art will understand that Figure 5 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0080] Fourthly, embodiments of this application also provide a readable storage medium.

[0081] The present application has an AC series arc detection program stored on a readable storage medium, wherein when the AC series arc detection program is executed by a processor, it implements the steps of the AC series arc detection method described above.

[0082] The method implemented when the AC series arc detection procedure is executed can be referred to in various embodiments of the AC series arc detection method of this application, and will not be repeated here.

[0083] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0084] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0085] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0086] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0087] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0088] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they 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 this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0089] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. An alternating current series arc detection method, characterized by, The alternating current series arc detection method comprises: generating a current abnormal event and a corresponding current abnormal time window based on a current signal at a current sampling moment and a current signal at a previous sampling moment, the current abnormal event comprising a current short-time mutation abnormality, a high-frequency energy sudden increase abnormality, and an intermittent conduction interruption abnormality; generating a voltage abnormal event and a corresponding voltage abnormal time window based on all voltage signals in the current abnormal time window and a voltage signal at the current sampling moment, the voltage abnormal event comprising a zero-crossing conduction abnormality and a voltage transient amplitude abnormality; determining a current-voltage dual-channel abnormal event based on the current abnormal time window and the voltage abnormal time window within a preset time alignment window; determining an alternating current series arc event according to the current-voltage dual-channel abnormal event, so as to realize alternating current series arc detection.

2. The AC series arc detection method of claim 1, wherein, The generating of the current abnormal event and the corresponding current abnormal time window based on the current signal at the current sampling moment and the current signal at the previous sampling moment comprises: determining whether the current abnormal event is the current short-time mutation abnormality based on the current signal at the current sampling moment, the current signal at the previous sampling moment, and a preset current short-time mutation threshold value; determining whether the current abnormal event is the high-frequency energy sudden increase abnormality based on a high-frequency energy ratio determined based on the current signal within a preset sliding time window and a preset high-frequency energy ratio threshold value; determining whether the current abnormal event is the intermittent conduction interruption abnormality according to an amplitude of the current signal at the current sampling moment and a preset current interruption threshold value; taking the first abnormality among the current short-time mutation abnormality, the high-frequency energy sudden increase abnormality, and the intermittent conduction interruption abnormality as a target current abnormality; taking a sampling moment corresponding to the first occurrence of the target current abnormality as a starting moment of the current abnormal time window, and taking a sampling moment corresponding to disappearance of the target current abnormality as an ending moment of the current abnormal time window.

3. The AC series arc detection method of claim 2, wherein, The determining of whether the current abnormal event is the current short-time mutation abnormality based on the current signal at the current sampling moment, the current signal at the previous sampling moment, and the preset current short-time mutation threshold value comprises: determining a first-order differential value of the current at the current sampling moment based on the current signal at the current sampling moment and the current signal at the previous sampling moment; if the first-order differential value of the current is not less than the preset current short-time mutation threshold value, determining that the current abnormal event is the current short-time mutation abnormality; if the first-order differential value of the current is less than the preset current short-time mutation threshold value, determining that the current abnormal event is not the current short-time mutation abnormality.

4. The AC series arc detection method of claim 1, wherein, The generating of the voltage abnormal event and the corresponding voltage abnormal time window based on all voltage signals in the current abnormal time window and the voltage signal at the current sampling moment comprises: determining a zero-crossing conduction delay duration based on the voltage signal at the current sampling moment; determining whether the voltage abnormal event is the zero-crossing conduction abnormality based on the zero-crossing conduction delay duration and a preset delay threshold value within a preset number of continuous periods; determining whether the voltage abnormal event is the voltage transient amplitude abnormality according to all voltage signals in the current abnormal time window, the voltage signal at the current sampling moment, and a preset offset threshold value; taking the first abnormality among the zero-crossing conduction abnormality and the voltage transient amplitude abnormality as a target voltage abnormality. The sampling moment corresponding to the first occurrence of the target voltage abnormality is taken as the starting moment of the voltage abnormality time window, and the sampling moment corresponding to the disappearance of the target voltage abnormality is taken as the ending moment of the voltage abnormality time window.

5. The AC series arc detection method of claim 4, wherein, The method comprises the following steps of: determining a voltage local average value according to all voltage signals in the current abnormality time window; determining a maximum transient offset according to the voltage signal at the current sampling moment and the voltage local average value; if the maximum transient offset is greater than the preset offset threshold, determining that the voltage abnormality event is a voltage transient amplitude abnormality; if the maximum transient offset is not greater than the preset offset threshold, determining that the voltage abnormality event is not a voltage transient amplitude abnormality.

6. The AC series arc detection method of claim 1, wherein, The method comprises the following steps of: determining a time coincidence ratio based on the current abnormality time window and the voltage abnormality time window; if the time coincidence ratio is not less than a preset ratio threshold, determining that it is a current-voltage dual-channel abnormality event; if the time coincidence ratio is less than the preset ratio threshold, determining that it is not a current-voltage dual-channel abnormality event.

7. The AC series arc detection method of claim 1, wherein, The method comprises the following steps of: if the number of times of detecting the current-voltage dual-channel abnormality event in a preset number of continuous periods is greater than a preset number threshold or the duration of the current-voltage dual-channel abnormality event is greater than a preset duration threshold, determining that the current-voltage dual-channel abnormality event is an AC series arc event; if the number of times of detecting the current-voltage dual-channel abnormality event is not greater than the preset number threshold and the duration of the current-voltage dual-channel abnormality event is not greater than the preset duration threshold, determining that the current-voltage dual-channel abnormality event is not an AC series arc event.

8. An alternating current series arc detection system characterized by, The AC series arc detection system comprises: a first processing module configured to generate a current abnormality event and a corresponding current abnormality time window based on a current signal at a current sampling moment and a current signal at a previous sampling moment, the current abnormality event comprising a current short-time mutation abnormality, a high-frequency energy sudden increase abnormality, and an intermittent conduction interruption abnormality; a second processing module configured to generate a voltage abnormality event and a corresponding voltage abnormality time window based on all voltage signals in the current abnormality time window and a voltage signal at the current sampling moment, the voltage abnormality event comprising a zero-crossing conduction abnormality and a voltage transient amplitude abnormality; a third processing module configured to determine a current-voltage dual-channel abnormality event based on the current abnormality time window and the voltage abnormality time window within a preset time alignment window; a fourth processing module configured to determine an AC series arc event based on the current-voltage dual-channel abnormality event, so as to realize AC series arc detection.

9. An alternating current series arc detection device, characterized by, The AC series arc detection device comprises a processor, a memory, and an AC series arc detection program stored in the memory and executable by the processor, wherein when the AC series arc detection program is executed by the processor, the steps of the AC series arc detection method according to any one of claims 1 to 7 are implemented. The AC series arc detection device comprises a processor, a memory, and an AC series arc detection program stored in the memory and executable by the processor, wherein when the AC series arc detection program is executed by the processor, the steps of the AC series arc 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 alternating current series arc detection program. When the alternating current series arc detection program is executed by the processor, the steps of the alternating current series arc detection method according to any one of claims 1 to 7 are implemented.

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