Intelligent electric meter series arc fault detection method, system, device, medium and equipment

Abstract

The invention relates to the technical field of ammeter fault detection, and provides an intelligent ammeter series arc fault detection method, system and device, a medium and equipment, and the method comprises the steps: firstly obtaining an ammeter data set of an intelligent ammeter, which comprises a pre-ammeter voltage, a post-ammeter voltage and a total loop current in a continuous power frequency period, and then carrying out the division through a sliding time window, obtaining a plurality of ammeter data windows; for each window, calculating a voltage difference sequence and a dynamic loss impedance sequence; when the absolute value of the total loop current is smaller than a preset threshold value, determining the current zero-break interval of each power frequency period and the current zero-break time corresponding to the window; determining an arc voltage estimation value according to a voltage difference between non-zero-break intervals in the zero-break interval; and finally, integrating the current zero break time, the arc voltage estimated value and the dynamic loss impedance of each window to judge whether a series arc fault exists, and if so, triggering a protection action. According to the embodiment of the invention, high-reliability identification of the series arc fault is realized.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of electric meter fault detection, in particular to a smart meter series arc fault detection method, system, device, medium and equipment. BACKGROUND

[0002] In a low-voltage power distribution system, the smart meter series arc fault is a serious electrical safety hazard caused by line virtual connection, insulation aging, connector carbonization and other reasons. The high temperature generated by the fault arc not only can burn the internal components of the meter, but also can ignite the surrounding combustible materials and cause electrical fires, directly threatening the life and property safety of users.

[0003] There are obvious limitations in related detection technologies, mainly divided into two categories: one relies on an external high-frequency current or optical sensor to capture the high-frequency signal of the arc for detection. Although this method has high sensitivity, the hardware cost is significantly increased, and it is difficult to popularize in cost-sensitive smart meters. The other is based on the existing sampling data of the meter, using current waveform analysis or simple threshold comparison. Although the cost is controlled, the anti-interference ability is poor, especially in distinguishing real arcs from the current characteristics of dimmers, incandescent lamps and other loads, the misjudgment rate is high, and unnecessary power outages are easily caused. SUMMARY

[0004] The embodiments of the present disclosure at least provide a smart meter series arc fault detection method, system, device, medium and equipment, which realizes high reliability identification of series arc fault by comprehensively utilizing the existing pre-meter voltage, post-meter voltage and total loop current sampling data of the smart meter.

[0005] The embodiments of the present disclosure provide a smart meter series arc fault detection method, comprising: obtaining an electric meter data set of a smart meter; wherein the electric meter data set comprises pre-meter voltage, post-meter voltage and total loop current in a plurality of power frequency periods; dividing the electric meter data set into a plurality of electric meter data windows using a sliding time window; wherein each electric meter data window corresponds to at least one power frequency period; for each electric meter data window, performing the following operations: based on the pre-meter voltage and the post-meter voltage in the window, calculating a voltage difference sequence, and based on the voltage difference sequence and the total loop current sequence in the window, calculating a dynamic loss impedance sequence; when the absolute value of the total loop current is less than a preset current threshold, determining a current zero interval of each power frequency period; and based on the duration of each current zero interval in the electric meter data window, determining a current zero time corresponding to the electric meter data window; determine, in each of the plurality of current zero interval identified in the meter data window, an arc voltage estimation value corresponding to the meter data window based on the voltage difference of the non-zero interval in each current zero interval; determine, based on the current zero interval duration, the arc voltage estimation value and the dynamic loss impedance sequence in each meter data window, whether the smart meter has a series arc fault;

[0006] The embodiment of the present disclosure provides a smart meter series arc fault detection system, comprising: a multi-channel synchronous sampling front end configured to obtain a meter data set of a smart meter; wherein the meter data set comprises a front meter voltage, a back meter voltage and a total loop current in a plurality of consecutive power frequency periods; a metering and control unit connected to the multi-channel synchronous sampling front end and configured to: divide the meter data set into a plurality of meter data windows by using a sliding time window; wherein each meter data window corresponds to at least one power frequency period; for each meter data window, the following operations are performed: calculate a voltage difference sequence based on the front meter voltage and the back meter voltage in the window; calculate a dynamic loss impedance sequence according to the voltage difference sequence and a total loop current sequence in the window; when the absolute value of the total loop current is less than a preset current threshold, determine a current zero interval of each power frequency period and record the duration of the current zero interval; determine, in each of the plurality of current zero interval identified in the meter data window, an arc voltage estimation value corresponding to the meter data window based on the voltage difference of the non-zero interval in each current zero interval; determine, based on the current zero interval duration, the arc voltage estimation value and the dynamic loss impedance sequence in each meter data window, whether the smart meter has a series arc fault; a protection execution module connected to the metering and control unit and configured to trigger a protection action when it is determined that there is a series arc fault.

[0007] The embodiment of the present disclosure provides a smart meter series arc fault detection device, comprising: a data acquisition module configured to obtain a meter data set of a smart meter; wherein the meter data set comprises a front meter voltage, a back meter voltage and a total loop current in a plurality of consecutive power frequency periods; a data division module configured to divide the meter data set into a plurality of meter data windows by using a sliding time window; wherein each meter data window corresponds to at least one power frequency period; a data calculation module configured to, for each meter data window, perform the following operations: calculate a voltage difference sequence based on the pre-meter voltage and the post-meter voltage within the window, and calculate a dynamic loss impedance sequence according to the voltage difference sequence and a total loop current sequence within the window; determine a current zero-hold interval for each power frequency cycle when the absolute value of the total loop current is less than a preset current threshold, and determine a current zero-hold time corresponding to the meter data window based on the duration of each current zero-hold interval in the meter data window; determine an arc voltage estimate value corresponding to the meter data window based on the voltage difference of a non-zero-hold interval in each current zero-hold interval identified in the meter data window; a fault judgment module configured to determine whether the smart meter has a series arc fault based on the current zero-hold time, the arc voltage estimate value, and the dynamic loss impedance within each meter data window, and trigger a protection action if the smart meter has a series arc fault.

[0008] The embodiment of the present disclosure provides a computer device, comprising a processor, a memory, and a bus, the memory stores machine readable instructions executable by the processor, when the computer device is running, the processor and the memory communicate through the bus, and the machine readable instructions are executed by the processor to perform the smart meter series arc fault detection method in any possible embodiment described above.

[0009] The embodiment of the present disclosure provides a computer readable storage medium, which stores a computer program, and the computer program is run by a processor to implement the smart meter series arc fault detection method in any possible embodiment described above.

[0010] The smart meter series arc fault detection method, system, device, medium, and equipment provided in the embodiment of the present disclosure can comprehensively and meticulously process and analyze the meter data of different time periods by acquiring a meter data set containing pre-meter voltage, post-meter voltage, and total loop current in a plurality of continuous power frequency cycles, and dividing the meter data set by using a sliding time window, and effectively capture the characteristic performance of arc faults in different time scales. Meanwhile, by calculating a voltage difference sequence, a dynamic loss impedance sequence, determining a current zero-hold interval, a current zero-hold time, and an arc voltage estimate value, and comprehensively determining whether the smart meter has a series arc fault based on these key parameters, the accuracy and reliability of arc fault detection are improved, potential series arc faults can be more accurately identified, protection actions can be triggered in time, electrical fires and other safety accidents caused by arc faults can be effectively avoided, the safety of electricity use is ensured, and equipment damage and economic losses caused by misjudgment or missed judgment are reduced.

[0011] In order to make the above objectives, characteristics and advantages of the present disclosure more obvious and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are described in detail as follows. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings required to be cited in the embodiments will be briefly introduced as follows. The drawings herein are incorporated into the description and constitute a part of the description. The drawings show the embodiments consistent with the present disclosure and are used to explain the technical solutions of the present disclosure together with the description. It should be understood that the following drawings only show some embodiments of the present disclosure, and therefore should not be considered as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0013] Figure 1 A flow chart of a smart meter series arc fault detection method provided by the embodiments of the present disclosure is shown; Figure 2 A flow chart of an arc voltage estimation value calculation method provided by the embodiments of the present disclosure is shown; Figure 3 A flow chart of a protection action triggering method provided by the embodiments of the present disclosure is shown; Figure 4 A structural schematic diagram of a smart meter series arc fault detection system provided by the embodiments of the present disclosure is shown; Figure 5 A structural schematic diagram of a smart meter series arc fault detection device provided by the embodiments of the present disclosure is shown; Figure 6 A structural schematic diagram of a computer device provided by the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0014] In order to make the objectives, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions in the embodiments of the present disclosure will be described clearly and completely in the following with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only some of the embodiments of the present disclosure, but not all the embodiments. The components of the embodiments of the present disclosure described and shown in the drawings herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present disclosure provided in the drawings is not intended to limit the scope of the claimed present disclosure, but only represents selected embodiments of the present disclosure. Based on the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present disclosure.

[0015] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0016] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0017] Series arcing faults in smart meters are serious electrical safety hazards caused by factors such as loose connections, aging insulation, carbonized connectors, oxidation of aluminum wires, or leakage due to moisture. The high temperature generated by the arc can easily ignite surrounding combustibles, potentially damaging the meter or even causing a fire.

[0018] Research has revealed two main problems with related detection technologies: The first relies on external high-frequency current sensors (HFCTs) or ultraviolet / optical sensors, detecting high-frequency electromagnetic radiation or light signals generated by electric arcs. While these methods offer high sensitivity, they significantly increase system cost and complexity, making them difficult to implement in cost-sensitive smart meters. The second type is based on algorithms using existing smart meter sampling data, such as current waveform singularity analysis or simple threshold comparison methods. While these methods are low-cost, they are highly susceptible to interference from load characteristics, especially when identifying high-resistivity loads (such as dimmers and old-fashioned incandescent bulbs) or capacitive / inductive loads with similar current zero-crossing characteristics. This can easily lead to misjudgments, causing unplanned power outages and impacting user experience.

[0019] The classic Mayr and Cassie arc physics model reveals the core characteristics of low-voltage AC arcs: the arc resistance changes drastically before and after the current crosses zero, causing its conductivity to drop sharply in the zero-crossing region (i.e., the "zero rest" interval); the arc voltage is relatively stable under certain conditions (typically around 20-50V). However, how to transform these theoretical model characteristics into engineered algorithms that can run stably on resource-constrained smart meter microprocessors (MCUs) is a pressing technical challenge that needs to be solved.

[0020] Based on the above research, this disclosure provides a method, system, device, medium, and equipment for detecting series arc faults in smart meters. By acquiring a data set of meter data including the voltage before and after the meter and the total circuit current over multiple consecutive power frequency cycles, and dividing it using a sliding time window, it can comprehensively and meticulously process and analyze meter data from different time periods, effectively capturing the characteristic manifestations of arc faults at different time scales. Simultaneously, by calculating the voltage difference sequence and dynamic loss impedance sequence, it determines the current zero-rest interval, current zero-rest time, and arc voltage estimate, and comprehensively uses these key parameters to determine whether a series arc fault exists in the smart meter. This improves the accuracy and reliability of arc fault detection, enabling more accurate identification of potential series arc faults, timely triggering of protection actions, and effectively preventing electrical fires and other safety accidents caused by arc faults, ensuring electrical safety, and reducing equipment damage and economic losses due to misjudgments or omissions.

[0021] To facilitate understanding of this embodiment, the executing entity of the smart meter series arc fault detection method provided in this disclosure will first be described in detail. The executing entity of the smart meter series arc fault detection method provided in this disclosure is a computer device. This computer device can be a terminal device or a server. The terminal device can also be a mobile device, user terminal, terminal, handheld device, computing device, vehicle-mounted device, wearable device, etc. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data, and artificial intelligence platforms. Optionally, this method can also be applied to an implementation environment composed of computer devices and servers.

[0022] The method for detecting series arc faults in smart meters provided in this application will be described in detail below with reference to the accompanying drawings. See also: Figure 1 The diagram shows a flowchart of a method for detecting series arc faults in a smart meter according to an embodiment of this disclosure. The method includes the following steps S101 to S104: S101, Obtain the smart meter's data set.

[0023] As we can understand, smart meters are terminal devices used for electricity metering and power consumption management. They can continuously monitor and record grid parameters and interact with the power system, providing key data for power metering, monitoring, and control. They can collect various parameters during power operation in real time and accurately. Here, the meter dataset refers to the time-series data of the voltage before and after the meter, as well as the total loop current, synchronously collected over multiple consecutive power frequency cycles. The voltage before the meter refers to the voltage at the measurement point directly connected to the grid at the smart meter's input terminal, reflecting the power supply potential on the grid side. The voltage after the meter refers to the voltage at the connection point between the smart meter's output terminal and the user's load; its value is affected by line impedance and load status. The total loop current is the vector sum of the load currents flowing through the smart meter's metering channel. The power frequency cycle is the periodic change unit of the AC power in the grid. For example, if the frequency of the AC power in a certain area is 50 Hz, the corresponding power frequency cycle is 20 milliseconds, meaning that the AC power completes a complete change from positive to negative and back to positive every 20 milliseconds.

[0024] In some possible embodiments, data acquisition using a smart meter is typically accomplished by a multi-channel synchronous sampling analog-to-digital converter integrated within the meter. This converter can simultaneously sample multiple analog signals and convert them into digital signals for subsequent processing and analysis. It can simultaneously capture the three signals (i.e., the voltage before the meter, the voltage after the meter, and the total loop current) at a rate no less than a preset sampling frequency (e.g., 3.2 kHz). Here, the preset sampling frequency is set to ensure accurate acquisition of signal changes. A sampling frequency of 3.2 kHz means 3200 samples per second, which ensures the ability to distinguish subtle current characteristics while maintaining time alignment between data from different channels.

[0025] Furthermore, after obtaining the original synchronous sampling data, the acquired signals can be digitally filtered to obtain the final meter dataset for analysis. Specifically, digital filtering refers to processing discrete sampled signals using numerical algorithms to retain specific frequency components and suppress irrelevant frequency components. Here, the process of digitally filtering the acquired signals can include using a moving average filter or a low-pass filter: a moving average filter effectively smooths random noise by calculating the arithmetic mean of sampling points within a fixed-length window; a low-pass filter allows low-frequency signal components to pass while attenuating high-frequency components, and its cutoff frequency is typically set in a range much higher than the power frequency but lower than half the sampling frequency, such as 500 Hz to 1 kHz. The main purpose of digital filtering is to suppress power frequency harmonic interference and high-frequency noise introduced during sampling, thereby improving the accuracy and reliability of subsequent feature extraction and fault diagnosis.

[0026] S102, the electricity meter dataset is divided into multiple electricity meter data windows by using a sliding time window.

[0027] Specifically, a sliding time window is a data processing technique that uses a fixed-length window to slide sequentially along the time axis, covering a portion of the dataset each time, thus enabling segmented data analysis. The sliding step size of the window can be equal to the window length for non-overlapping partitioning, or smaller than the window length for partially overlapping coverage; the latter helps capture transient features crossing window boundaries. The length of each meter data window can typically be set to cover one or two power frequency cycles, such as 20 milliseconds or 40 milliseconds. This ensures that a single window contains complete or major stage information of key dynamic processes such as current zero crossing, arc establishment, and arc extinction, while also meeting the algorithm's real-time analysis requirements. It helps to independently calculate and analyze the electrical characteristics within each window while maintaining data continuity, providing a temporally local data foundation for subsequent fault feature extraction.

[0028] In some possible implementations, the length of the sliding time window can be adaptively adjusted rather than being fixed. For example, the window length can be dynamically adjusted based on the rate of change of the total loop current or the fluctuation of the dynamic loss impedance. When a rapid change in current or impedance is detected, a shorter window length can be automatically switched to improve the ability to capture transient processes and time resolution; during periods of relatively stable current and impedance, a longer window length can be used to obtain more stable statistical characteristics and reduce the frequency of calculations. The window length can also be associated with the load type; for known loads prone to interference (such as motor loads), specific window strategies can be used to optimize discrimination. The specific implementation strategy of the sliding time window can be configured according to the actual application scenario and detection performance requirements, and is not specifically limited here.

[0029] S103, for each meter data window, perform the following operations: calculate the voltage difference sequence based on the voltage before and after the meter within the window, and calculate the dynamic loss impedance sequence based on the voltage difference sequence and the total loop current sequence within the window; when the absolute value of the total loop current is less than a preset current threshold, determine the current zero-rest interval for each power frequency cycle; and determine the current zero-rest time corresponding to the meter data window based on the duration of each current zero-rest interval in the meter data window; within the multiple current zero-rest intervals identified in the meter data window, determine the arc voltage estimate corresponding to the meter data window based on the voltage difference of the non-zero-rest intervals in each current zero-rest interval.

[0030] Understandably, for each meter data window, a voltage difference sequence can be calculated based on the voltage sequence before and after the meter within the window. This voltage difference sequence is a set of difference data obtained by subtracting the instantaneous value of the voltage after the meter from the instantaneous value of the voltage before the meter at each sampling moment. The voltage difference mainly represents the equivalent impedance voltage drop of the line segment from the meter's inlet to its outlet. Its magnitude reflects the overall impedance characteristics and power loss of that line segment. In an ideal circuit, this voltage drop is very small; however, it changes significantly when there are loose connections, insulation aging, or arcing faults in the line.

[0031] Next, based on Ohm's law, the dynamic loss impedance sequence can be calculated using the voltage difference sequence and the total loop current sequence within the window. The dynamic loss impedance is a time-varying equivalent resistance parameter, representing the dynamic characteristics of the line impedance as current changes. Specifically, for each synchronous sampling point in the voltage difference sequence and the total loop current sequence, the ratio of the current voltage difference value to the current total loop current value is calculated to obtain the dynamic loss impedance value corresponding to that sampling point in the dynamic loss impedance sequence. In the current zero-crossing region, when the absolute value of the current is less than a preset minimum value, such as 0.001 amperes, to avoid calculation overflow or numerical instability caused by the denominator approaching zero, the algorithm sets the current total loop current value to this preset minimum value to prevent calculation overflow and ensure the robustness of the calculation.

[0032] Specifically, when identifying the zero-current interval, the filtered total loop current sequence can be monitored. When its absolute value is less than a preset current threshold, the time period can be determined as the zero-current interval. The zero-current interval refers to the period before and after the AC current crosses zero in each power frequency cycle, during which the current value stagnates or changes extremely slowly near zero due to the characteristics of the electric arc. Here, the preset current threshold can be set to 1% to 2% of the rated current of the smart meter. For example, for a meter with a rated current of 16 amps, the threshold can be set to 0.16 to 0.32 amps.

[0033] In some other embodiments, the filtered current signal can first undergo zero-crossing detection to determine the theoretical zero-crossing moment when the current waveform transitions from the positive half-cycle to the negative half-cycle or vice versa. Then, in the monitored total loop current sequence, centered on the detected theoretical zero-crossing moment, a search is conducted to determine if there exists a segment within a specific time range before and after it where the absolute value of the total loop current is consistently less than a preset current threshold, thereby achieving precise location of the zero-crossing interval. In this way, by combining the phase information of the zero-crossing point with the current amplitude judgment, the accuracy of zero-crossing interval identification can be improved, especially when the load current waveform is distorted.

[0034] Here, after determining the zero-crossing intervals, the duration of each zero-crossing interval can be recorded, i.e., the length of time the absolute value of the current remains below the threshold. For a meter data window, the corresponding current zero-crossing time can be the maximum value, average value, or other statistical measure of the duration of all zero-crossing intervals identified within that window, used to characterize the intensity of the arc zero-crossing characteristic within that window.

[0035] Furthermore, within each identified zero-current interval, the estimated arc voltage for that meter data window can be determined based on the voltage difference data of the corresponding non-zero-current interval. The estimated arc voltage reflects the equivalent voltage drop of the arc during the stable combustion phase. The non-zero-current interval is the time interval in each power frequency cycle excluding the zero-current interval, i.e., the phase where the current is normally conducting and the arc may maintain stable combustion.

[0036] For example, refer to Figure 2 As shown, when calculating the estimated arc voltage corresponding to the meter data window, the following steps S201~S203 may be included: S201, for each zero current interval, extract the voltage difference sequence of the non-zero current interval corresponding to the zero current interval.

[0037] Here, the voltage difference sequence refers to the discrete data set consisting of the differences between the voltage before and after the meter at all sampling points within the non-zero time interval. For example, for a non-zero time interval lasting 15 milliseconds, if the sampling frequency is 3.2 kHz, approximately 48 consecutive voltage difference sampling values ​​can be extracted.

[0038] S202, for each non-zero rest interval, calculate the median of the voltage difference sequence of the non-zero rest interval, and use it as the initial arc voltage estimate of the non-zero rest interval.

[0039] Understandably, the median is a statistical measure that refers to the value in the middle of a set of data arranged in ascending order. The purpose of using the median instead of the arithmetic mean in this disclosure is that the median is less sensitive to outliers or transient spikes in the data, thus exhibiting greater robustness. For example, a voltage difference sequence in a non-zero interval, after being sorted, is [28.1, 28.3, 28.5, 29.0, 35.0] volts. Its median is 28.5 volts, effectively ignoring the 35.0 volt outlier that might be caused by sampling interference.

[0040] S203, based on the initial arc voltage estimate values ​​of each non-zero interval within the meter data window, determine the arc voltage estimate value corresponding to the meter data window.

[0041] Specifically, after obtaining the initial arc voltage estimates for each non-zero interval, a final arc voltage estimate for the meter data window can be determined using a specific data aggregation method. Here, the aggregation method can be either calculating the arithmetic mean of these initial estimates or taking their median again. For example, if two non-zero intervals are identified within a meter data window covering two power frequency cycles, with initial arc voltage estimates of 30 volts and 32 volts respectively, then the average arc voltage estimate for the window is 31 volts. The two-stage statistical processing structure proposed in this disclosure (first the median within the interval, then aggregation within the window) can progressively filter out random interference and extract characteristic quantities that stably reflect the arc voltage level.

[0042] S104. Based on the current zero-rest time, the estimated arc voltage, and the dynamic loss impedance in the data windows of each meter, determine whether the smart meter has a series arc fault; if so, trigger the protection action.

[0043] Here, after obtaining the current zero-rest time, arc voltage estimate and dynamic loss impedance in the data window of each meter, it is possible to determine whether there is a series arc fault in the smart meter based on the pre-constructed multi-dimensional comprehensive criterion. This criterion aims to improve the accuracy and reliability of detection and reduce the risk of misjudgment by integrating multiple characteristic parameters that reflect different physical characteristics of the arc.

[0044] Specifically, for each meter data window, the following three fault determination conditions can be sequentially judged based on a pre-constructed multi-dimensional comprehensive criterion to determine whether they are simultaneously met. The first condition is that the current zero-crossing time is within a preset duration range, which can be set to 0.2 milliseconds to 1.2 milliseconds. Normal resistive load currents cross zero rapidly with a very short duration, while arc faults significantly prolong the current's residence time at the zero-crossing point. For example, a zero-crossing time of 0.9 milliseconds meets this criterion. The second condition is that the arc voltage estimate is within a preset voltage range. The arc voltage estimate is a characteristic quantity reflecting the equivalent voltage drop during arc combustion, calculated based on the voltage difference sequence in the non-zero-crossing interval. The preset voltage range can be set to 15 volts to 50 volts based on the typical physical characteristics of low-voltage AC arcs. If the calculated arc voltage estimate is 30 volts, this criterion is met; however, a voltage drop of several volts caused by a loose resistive load line does not meet this criterion. The third condition is that the volatility of the dynamic loss impedance sequence is not lower than a preset volatility threshold. Volatility is an indicator that quantifies the degree of impedance change. The calculation formula can be expressed as the difference between the maximum and minimum values ​​of the dynamic loss impedance sequence within a meter data window, divided by the average value of the sequence. Here, the preset volatility threshold can be set to 50%. Arcing faults cause impedance to change drastically and non-linearly with the instantaneous value of current, while the impedance of a normal load is usually relatively stable. For example, a window with a volatility of 85% meets this criterion.

[0045] Understandably, various transient interferences may exist in actual circuits, such as electrical noise generated by load switching or electromagnetic interference from adjacent equipment. These interferences may briefly simulate some arc characteristics at certain moments, but usually do not persist. To help distinguish stable fault states from transient random interferences, a continuous counting threshold mechanism can be set, requiring fault characteristics to appear continuously within a certain number of continuous analysis windows before a fault is finally confirmed. For example, setting the continuous counting threshold to 3 requires that the three fault judgment conditions mentioned above—current zero-rest time, arc voltage estimate, and dynamic loss impedance fluctuation rate—must be simultaneously met for three consecutive meter data windows before a persistent series arc fault is finally determined to exist in the circuit. Once a fault is determined, protection actions can be triggered immediately, such as issuing a disconnect command to a fast solid-state switch to cut off the circuit within milliseconds, thereby achieving physical isolation of the circuit and preventing the fault from further developing and causing dangers such as fires. Here, the fast solid-state switch is a contactless switch made of semiconductor devices (such as thyristors or MOSFETs), and its response time is typically within 1 millisecond.

[0046] For example, refer to Figure 3 As shown, when a protection action is triggered, the following steps S301~S303 may be included: S301, when a series arc fault is detected, controls the fast solid-state switch to disconnect the circuit.

[0047] Here, a fast solid-state switch is an electronic switch built on fully controlled power semiconductor devices (such as metal-oxide-semiconductor field-effect transistors or insulated-gate bipolar transistors). It is characterized by the absence of mechanical contacts and extremely fast operating speed, and is designed to quickly establish electrical isolation, cut off the fault current path, and thus force the arc to extinguish.

[0048] S302, after disconnecting the circuit, waits for a preset delay time and then automatically performs the closing operation.

[0049] Specifically, after successfully disconnecting the circuit, a preset waiting state can be entered. This preset delay time is a configurable parameter, typically set within the range of 1 to 2 seconds. The purpose is to provide sufficient time for any potential transient fault sources to dissipate, such as the vaporization of metal particles caused by a momentary impact or the restoration of normal air ionization. After the delay ends, a closing command will be automatically issued to control the fast solid-state switch to re-energize and attempt to restore power to the load.

[0050] S303: After the closing operation, monitor the dynamic loss impedance sequence. If the dynamic loss impedance sequence recovers to the normal line impedance level and continues for a preset time, it is determined that the fault has been cleared; otherwise, disconnect the circuit again and report the fault information to the operation terminal.

[0051] Understandably, after the closing operation is completed and the line is re-energized, continuous monitoring of the dynamic loss impedance sequence will immediately commence. The dynamic loss impedance sequence is a parameter reflecting the real-time changes in the line's equivalent impedance. The normal line impedance level serves as a reference value, the range of which depends on the conductor material, cross-section, length, and normal contact resistance of the connectors within the specific line. During monitoring, it will continuously assess whether the monitored dynamic loss impedance sequence has recovered and stabilized within this normal range, and whether this stabilization time exceeds a preset stabilization time threshold, such as 1 to 3 power frequency cycles. If this condition is met, the previous fault is determined to be a transient fault and has been automatically cleared, allowing the fault handling state to be exited and normal power supply restored. If the condition is not met, i.e., the dynamic loss impedance sequence still exhibits high fluctuations or continues to deviate from the normal range, the fault will be determined to be a permanent fault, such as the presence of non-recoverable physical damage or carbonization pathways in the line. At this point, the fast solid-state switch can be immediately controlled to disconnect the circuit again, and simultaneously, detailed fault information (including fault type, occurrence time, key electrical parameters, etc.) can be reported to the remote operation terminal or data collection system via the built-in communication module, requesting maintenance personnel to perform repairs. Here, the communication module can be a power line carrier communication module or a low-power wireless communication module.

[0052] The smart meter series arc fault detection method, system, device, medium, and equipment provided in this disclosure acquire a meter dataset containing the voltage before and after the meter and the total circuit current over multiple consecutive power frequency cycles. By dividing the dataset using a sliding time window, it can comprehensively and meticulously process and analyze meter data from different time periods, effectively capturing the characteristic manifestations of arc faults at different time scales. Simultaneously, by calculating the voltage difference sequence and dynamic loss impedance sequence, it determines the current zero-rest interval, current zero-rest time, and arc voltage estimate. By comprehensively considering these key parameters, it determines whether a series arc fault exists in the smart meter, improving the accuracy and reliability of arc fault detection. This enables more accurate identification of potential series arc faults, timely triggering of protection actions, and effectively preventing electrical fires and other safety accidents caused by arc faults, ensuring electrical safety and reducing equipment damage and economic losses due to misjudgments or omissions.

[0053] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0054] Based on the same inventive concept, this disclosure also provides a smart meter series arc fault detection system corresponding to the smart meter series arc fault detection method. Since the principle of the system in this disclosure for solving the problem is similar to the smart meter series arc fault detection method described above, the implementation of the system can refer to the implementation of the method, and the repeated parts will not be described again.

[0055] Reference Figure 4 The diagram shown is a schematic of a series arc fault detection system for smart meters provided in an embodiment of this disclosure. The system includes: A multi-channel synchronous sampling front-end is used to acquire the meter data set of the smart meter; wherein, the meter data set includes the voltage before the meter, the voltage after the meter, and the total circuit current over multiple consecutive power frequency cycles; The metering and control unit, connected to the multi-channel synchronous sampling front end, is configured as follows: The electricity meter dataset is divided into multiple electricity meter data windows using a sliding time window method; each electricity meter data window corresponds to at least one power frequency cycle. For each meter data window, perform the following operations: Calculate the voltage difference sequence based on the voltage before and after the meter within the window; Calculate the dynamic loss impedance sequence based on the voltage difference sequence and the total loop current sequence within the window; When the absolute value of the total circuit current is less than a preset current threshold, the current zero-rest interval for each power frequency cycle is determined, and the duration of the current zero-rest interval is recorded. Within the multiple zero-current intervals identified in the meter data window, the estimated arc voltage corresponding to the meter data window is determined based on the voltage difference between the non-zero intervals in each zero-current interval. Based on the duration of the current zero-rest interval within the data window of each meter, the estimated value of the arc voltage, and the dynamic loss impedance sequence, it is determined whether the smart meter has a series arc fault. The protection execution module, connected to the metering and control unit, is used to trigger protection action when a series arc fault is detected.

[0056] In some possible embodiments, the metering and control unit is specifically used for: For each sampling point in the voltage difference sequence and the total loop current sequence, calculate the ratio of the current voltage difference value to the current total loop current value to obtain the dynamic loss impedance value corresponding to the sampling point in the dynamic loss impedance sequence; Specifically, when the absolute value of the current total loop current is less than a preset minimum value, the current total loop current is set to the preset minimum value to avoid calculation overflow.

[0057] In some possible embodiments, the metering and control unit is specifically used for: For each zero-current interval, extract the voltage difference sequence of the corresponding non-zero-current interval; wherein, the non-zero-current interval is the time interval in each power frequency cycle other than the zero-current interval; For each non-zero rest interval, the median of the voltage difference sequence of the non-zero rest interval is calculated as the initial arc voltage estimate of the non-zero rest interval. Based on the initial arc voltage estimate values ​​of each non-zero interval within the meter data window, the arc voltage estimate value corresponding to the meter data window is determined.

[0058] In some possible embodiments, the metering and control unit is specifically used for: For each meter data window, determine whether the following fault determination conditions are met simultaneously: The zero-rest time of the current is within a preset duration range; The estimated arc voltage is within a preset voltage range; The volatility of the dynamic loss impedance sequence is not lower than a preset volatility threshold. If the fault determination conditions are met in a consecutive preset number of meter data windows, then a series arc fault is determined to exist.

[0059] In some possible embodiments, the protection execution module is specifically used for: When a series arc fault is detected, the fast solid-state switch is controlled to disconnect the circuit. After disconnecting the circuit, wait for a preset delay time and then automatically perform the closing operation; After the closing operation, the dynamic loss impedance sequence is monitored. If the dynamic loss impedance sequence recovers to the normal line impedance level and remains so for a preset time, the fault is determined to be cleared; otherwise, the circuit is disconnected again and the fault information is reported to the operation terminal.

[0060] In some possible embodiments, the multi-channel synchronous sampling front-end is specifically used for: The front voltage, back voltage, and total circuit current of the smart meter are simultaneously collected at a rate not lower than the preset sampling frequency for multiple consecutive power frequency cycles. The collected signals are digitally filtered to obtain the meter dataset.

[0061] Based on the same inventive concept, this disclosure also provides a smart meter series arc fault detection device corresponding to the smart meter series arc fault detection method. Since the principle of the device in this disclosure for solving the problem is similar to the smart meter series arc fault detection method described above, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0062] Reference Figure 5 The diagram shown is a schematic of a series arc fault detection device 500 for a smart meter provided in an embodiment of this disclosure. The device includes: The data acquisition module 501 is used to acquire the meter data set of the smart meter; wherein, the meter data set includes the voltage before the meter, the voltage after the meter, and the total circuit current within multiple consecutive power frequency cycles; The data partitioning module 502 is used to partition the electricity meter dataset into multiple electricity meter data windows using a sliding time window; wherein each electricity meter data window corresponds to at least one power frequency cycle; Data calculation module 503 is used to perform the following operations for each meter data window: Based on the voltage before and after the meter within the window, calculate the voltage difference sequence, and calculate the dynamic loss impedance sequence based on the voltage difference sequence and the total loop current sequence within the window. When the absolute value of the total circuit current is less than a preset current threshold, the current zero-rest interval for each power frequency cycle is determined; and based on the duration of each current zero-rest interval in the meter data window, the current zero-rest time corresponding to the meter data window is determined. Within the multiple zero-current intervals identified in the meter data window, the estimated arc voltage corresponding to the meter data window is determined based on the voltage difference between the non-zero intervals in each zero-current interval. The fault judgment module 504 is used to determine whether the smart meter has a series arc fault based on the current zero rest time, the arc voltage estimate and the dynamic loss impedance in the data window of each meter; if so, it triggers the protection action.

[0063] In some possible embodiments, the data calculation module 503 is specifically used for: For each sampling point in the voltage difference sequence and the total loop current sequence, calculate the ratio of the current voltage difference value to the current total loop current value to obtain the dynamic loss impedance value corresponding to the sampling point in the dynamic loss impedance sequence; Specifically, when the absolute value of the current total loop current is less than a preset minimum value, the current total loop current is set to the preset minimum value to avoid calculation overflow.

[0064] In some possible embodiments, the data calculation module 503 is specifically used for: For each zero-current interval, extract the voltage difference sequence of the corresponding non-zero-current interval; wherein, the non-zero-current interval is the time interval in each power frequency cycle other than the zero-current interval; For each non-zero rest interval, the median of the voltage difference sequence of the non-zero rest interval is calculated as the initial arc voltage estimate of the non-zero rest interval. Based on the initial arc voltage estimate values ​​of each non-zero interval within the meter data window, the arc voltage estimate value corresponding to the meter data window is determined.

[0065] In some possible embodiments, the fault determination module 504 is specifically used for: For each meter data window, determine whether the following fault determination conditions are met simultaneously: The zero-rest time of the current is within a preset duration range; The estimated arc voltage is within a preset voltage range; The volatility of the dynamic loss impedance sequence is not lower than a preset volatility threshold. If the fault determination conditions are met in a consecutive preset number of meter data windows, then a series arc fault is determined to exist.

[0066] In some possible embodiments, the fault determination module 504 is specifically used for: When a series arc fault is detected, the fast solid-state switch is controlled to disconnect the circuit. After disconnecting the circuit, wait for a preset delay time and then automatically perform the closing operation; After the closing operation, the dynamic loss impedance sequence is monitored. If the dynamic loss impedance sequence recovers to the normal line impedance level and remains so for a preset time, the fault is determined to be cleared; otherwise, the circuit is disconnected again and the fault information is reported to the operation terminal.

[0067] In some possible embodiments, the data acquisition module 501 is specifically used for: The front voltage, back voltage, and total circuit current of the smart meter are simultaneously collected at a rate not lower than the preset sampling frequency for multiple consecutive power frequency cycles. The collected signals are digitally filtered to obtain the meter dataset.

[0068] Based on the same technical concept, this disclosure also provides a computer device. (See also...) Figure 6 The diagram shows the structure of a computer device 600 provided in this embodiment of the present disclosure, including a processor 601, a memory 602, and a bus 603. The memory 602 stores execution instructions and includes a main memory 6021 and an external memory 6022. The main memory 6021, also called internal memory, is used to temporarily store computational data in the processor 601 and data exchanged with external memory 6022 such as a hard disk. The processor 601 exchanges data with the external memory 6022 through the main memory 6021.

[0069] In this embodiment, the memory 602 is specifically used to store application code that executes the solution of this application, and its execution is controlled by the processor 601. That is, when the computer device 600 is running, the processor 601 communicates with the memory 602 through the bus 603, so that the processor 601 executes the application code stored in the memory 602, and then executes the method described in any of the foregoing embodiments.

[0070] The memory 602 may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0071] Processor 601 may be an integrated circuit chip with signal processing capabilities. The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor.

[0072] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the computer device 600. In other embodiments of this application, the computer device 600 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0073] This disclosure also provides a computer-readable storage medium storing a computer program. When executed by a processor, the computer program performs the steps of the smart meter series arc fault detection method described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0074] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the smart meter series arc fault detection method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0075] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0076] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In the several embodiments provided in this disclosure, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection may be through some communication interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0077] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0078] In addition, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0079] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0080] Finally, it should be noted that the above-described embodiments are merely specific implementations of this disclosure, used to illustrate the technical solutions of this disclosure, and not to limit it. The protection scope of this disclosure is not limited thereto. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this disclosure. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be covered within the protection scope of this disclosure. Therefore, the protection scope of this disclosure should be determined by the protection scope of the claims.

Claims

1. A method for detecting series arc faults in smart meters, characterized in that, include: Obtain the meter data set of the smart meter; wherein, the meter data set includes the voltage before the meter, the voltage after the meter, and the total circuit current over multiple consecutive power frequency cycles; The electricity meter dataset is divided into multiple electricity meter data windows using a sliding time window method; each electricity meter data window corresponds to at least one power frequency cycle. For each meter data window, perform the following operations: Based on the voltage before and after the meter within the window, calculate the voltage difference sequence, and calculate the dynamic loss impedance sequence based on the voltage difference sequence and the total loop current sequence within the window. When the absolute value of the total circuit current is less than a preset current threshold, the current zero-rest interval for each power frequency cycle is determined; and based on the duration of each current zero-rest interval in the meter data window, the current zero-rest time corresponding to the meter data window is determined. Within the multiple zero-current intervals identified in the meter data window, the estimated arc voltage corresponding to the meter data window is determined based on the voltage difference between the non-zero intervals in each zero-current interval. Based on the current zero-rest time, the estimated arc voltage, and the dynamic loss impedance in each meter's data window, it is determined whether the smart meter has a series arc fault; if so, a protection action is triggered.

2. The method according to claim 1, characterized in that, The step of calculating the dynamic loss impedance sequence based on the voltage difference sequence and the total loop current sequence within the window includes: For each sampling point in the voltage difference sequence and the total loop current sequence, calculate the ratio of the current voltage difference value to the current total loop current value to obtain the dynamic loss impedance value corresponding to the sampling point in the dynamic loss impedance sequence; Specifically, when the absolute value of the current total loop current is less than a preset minimum value, the current total loop current is set to the preset minimum value to avoid calculation overflow.

3. The method according to claim 1, characterized in that, Determining the arc voltage estimate corresponding to the electricity meter data window includes: For each zero-current interval, extract the voltage difference sequence of the corresponding non-zero-current interval; wherein, the non-zero-current interval is the time interval in each power frequency cycle other than the zero-current interval; For each non-zero rest interval, the median of the voltage difference sequence of the non-zero rest interval is calculated as the initial arc voltage estimate of the non-zero rest interval. Based on the initial arc voltage estimate values ​​of each non-zero interval within the meter data window, the arc voltage estimate value corresponding to the meter data window is determined.

4. The method according to claim 3, characterized in that, The determination of whether the smart meter has a series arc fault includes: For each meter data window, determine whether the following fault determination conditions are met simultaneously: The zero-rest time of the current is within a preset duration range; The estimated arc voltage is within a preset voltage range; The volatility of the dynamic loss impedance sequence is not lower than a preset volatility threshold. If the fault determination conditions are met in a consecutive preset number of meter data windows, then a series arc fault is determined to exist.

5. The method according to claim 4, characterized in that, The triggering of the protection action includes: When a series arc fault is detected, the fast solid-state switch is controlled to disconnect the circuit. After disconnecting the circuit, wait for a preset delay time and then automatically perform the closing operation; After the closing operation, the dynamic loss impedance sequence is monitored. If the dynamic loss impedance sequence recovers to the normal line impedance level and remains so for a preset time, the fault is determined to be cleared; otherwise, the circuit is disconnected again and the fault information is reported to the operation terminal.

6. The method according to claim 1, characterized in that, The process of obtaining the smart meter's data set includes: The front voltage, back voltage, and total circuit current of the smart meter are simultaneously collected at a rate not lower than the preset sampling frequency for multiple consecutive power frequency cycles. The collected signals are digitally filtered to obtain the meter dataset.

7. A series arc fault detection system for smart meters, characterized in that, include: A multi-channel synchronous sampling front-end is used to acquire the meter data set of the smart meter; wherein, the meter data set includes the voltage before the meter, the voltage after the meter, and the total circuit current over multiple consecutive power frequency cycles; The metering and control unit, connected to the multi-channel synchronous sampling front end, is configured as follows: The electricity meter dataset is divided into multiple electricity meter data windows using a sliding time window method; each electricity meter data window corresponds to at least one power frequency cycle. For each meter data window, perform the following operations: Calculate the voltage difference sequence based on the voltage before and after the meter within the window; Calculate the dynamic loss impedance sequence based on the voltage difference sequence and the total loop current sequence within the window; When the absolute value of the total circuit current is less than a preset current threshold, the current zero-rest interval for each power frequency cycle is determined, and the duration of the current zero-rest interval is recorded. Within the multiple zero-current intervals identified in the meter data window, the estimated arc voltage corresponding to the meter data window is determined based on the voltage difference between the non-zero intervals in each zero-current interval. Based on the duration of the current zero-rest interval within the data window of each meter, the estimated value of the arc voltage, and the dynamic loss impedance sequence, it is determined whether the smart meter has a series arc fault. The protection execution module, connected to the metering and control unit, is used to trigger protection action when a series arc fault is detected.

8. A device for detecting series arc faults in smart meters, characterized in that, include: The data acquisition module is used to acquire the meter dataset of the smart meter; wherein, the meter dataset includes the voltage before the meter, the voltage after the meter, and the total circuit current over multiple consecutive power frequency cycles; The data partitioning module is used to partition the electricity meter dataset into multiple electricity meter data windows using a sliding time window; wherein each electricity meter data window corresponds to at least one power frequency cycle; The data calculation module performs the following operations for each meter data window: Based on the voltage before and after the meter within the window, calculate the voltage difference sequence, and calculate the dynamic loss impedance sequence based on the voltage difference sequence and the total loop current sequence within the window. When the absolute value of the total circuit current is less than a preset current threshold, the current zero-rest interval for each power frequency cycle is determined; and based on the duration of each current zero-rest interval in the meter data window, the current zero-rest time corresponding to the meter data window is determined. Within the multiple zero-current intervals identified in the meter data window, the estimated arc voltage corresponding to the meter data window is determined based on the voltage difference between the non-zero intervals in each zero-current interval. The fault diagnosis module is used to determine whether the smart meter has a series arc fault based on the current zero-down time, the arc voltage estimate and the dynamic loss impedance in the data window of each meter; if so, it triggers the protection action.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 6.

10. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

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