Fault recording method, device and circuit breaker

Abstract

This invention discloses a fault recording method. The method includes the following steps: simultaneously recording steady-state and transient waveforms of the line; performing data fusion: first, aligning the steady-state metering data and transient waveform data in time; then generating a corresponding fusion status identifier based on the comparison between the effective current value calculated from the transient waveform data and the effective current value in the steady-state metering data at the same time; finally, combining the fusion status identifier, steady-state metering data, and transient waveform data into fused recording data; and determining in real-time whether the fault recording triggering conditions are met based on the current mutation rate, voltage drop slope, power factor mutation amplitude, and temperature. If so, the fused recording data, including the pre-fault triggering segment, the continuous fault segment, and the post-fault segment, is collected and output as fault recording data. This invention also discloses a fault recording device and a circuit breaker. Compared with the prior art, this invention can effectively improve the accuracy of fault detection and diagnosis.

Description

Technical Field

[0001] This invention relates to a fault recording method. Background Technology

[0002] A circuit breaker is an electrical device used to control and protect circuits. Its main function is to automatically cut off the current when abnormal conditions such as overload, short circuit, or undervoltage occur in the circuit, preventing equipment damage, line burnout, or even fire accidents. It can not only automatically respond to faults but also be manually operated to connect or disconnect the circuit, making it an indispensable safety device in modern power systems. Once a circuit breaker trips, it is necessary to analyze and locate the fault point before resetting the circuit breaker for normal operation. Recording the fault waveform is particularly important at this time. By recording changes in voltage and current waveforms, the correctness of the protection and circuit breaker operation can be verified, the cause of the fault can be accurately analyzed, the fault type can be accurately determined, and the fault point can be located by combining line parameters, shortening repair time, avoiding misjudgments caused by relying solely on experience, discovering hidden defects, preventing recurring accidents, and supporting system operation optimization and technical improvements.

[0003] Existing fault recording schemes typically detect faults based on real-time monitoring of transient current and transient voltage instantaneous values. Upon fault detection, they save and / or upload transient waveform data from the pre-fault triggering phase, the continuous phase during the fault, and the post-fault phase as fault recording data. However, fault recording data obtained using traditional schemes only includes these two dimensions: transient current and transient voltage instantaneous values. Using this data for fault detection and diagnostic analysis can easily lead to missed faults and the failure to detect certain fault trends. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a fault recording method that uses transient waveform data and steady-state measurement data for fault detection and fault recording, which can effectively improve the accuracy of fault detection and diagnosis.

[0005] The present invention specifically adopts the following technical solutions to solve the above-mentioned technical problems: A fault recording method includes the following steps: Simultaneously, steady-state and transient waveforms of the line are recorded to obtain steady-state metering data including effective voltage values, effective current values, active power, reactive power and power factor, as well as transient waveform data including transient instantaneous current values ​​and transient instantaneous voltage values. The steady-state measurement data and transient waveform data are fused using the following method to generate fused waveform data: First, the steady-state measurement data and transient waveform data are time-aligned. Then, the effective current value calculated from the transient waveform data is compared with the effective current value in the steady-state measurement data at the same time, and corresponding fusion status identifiers are generated. If both are within the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, and the error between them is within a preset error range, a first fusion status identifier is generated. If both are within the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, but the error between them exceeds the preset error range, a second fusion status identifier is generated. If either of them exceeds the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, a third fusion status identifier is generated. The first to third fusion status identifiers are all different. Finally, the fusion status identifier, steady-state measurement data, and transient waveform data at that time are combined into the fusion waveform data at that time. Based on steady-state metering data and transient waveform data, it is determined in real time whether the fault recording trigger condition is met. If so, the fused recording data including the pre-fault triggering segment, the continuous segment during the fault, and the post-fault segment is collected and output as fault recording data. The specific fault recording trigger condition is that at least one of the following four parameters exceeds the limit: current mutation rate, voltage drop slope, power factor mutation amplitude, and temperature.

[0006] Preferably, the steady-state measurement data further includes temperature measurement data.

[0007] Preferably, the fault recording triggering condition is specifically: at least one of the following parameters exceeds the limit: effective voltage value, effective current value, active power, reactive power, power factor, instantaneous transient current value, instantaneous transient voltage value, current change rate, voltage drop slope, power factor change amplitude, and temperature.

[0008] Preferably, the duration of the pre-triggering segment before the fault is triggered is set according to the degree of exceeding the limit of the parameter; the greater the degree of exceeding the limit, the longer the duration of the pre-triggering segment before the fault is triggered.

[0009] Preferably, the preset error range is ±5%.

[0010] Furthermore, the fault recording method also includes: Using the fault waveform data as model input, a locally configured lightweight AI fault diagnosis model is used for fault diagnosis.

[0011] Based on the same inventive concept, the following technical solutions can also be obtained: A fault recording device, comprising: The steady-state metering sampling unit is used to perform steady-state waveform recording of the line to obtain steady-state metering data including the effective value of voltage, the effective value of current, active power, reactive power and power factor; The transient sampling unit is used to perform transient waveform recording of the line to obtain transient waveform data containing transient current instantaneous values ​​and transient voltage instantaneous values; The data fusion unit is used to fuse steady-state measurement data and transient waveform data according to the following method to generate fused waveform recording data: First, the steady-state measurement data and transient waveform data are time-aligned. Then, the effective current value calculated from the transient waveform data is compared with the effective current value in the steady-state measurement data at the same time, and corresponding fusion status identifiers are generated. If both are within the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, and the error between them is within a preset error range, a first fusion status identifier is generated. If both are within the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, but the error between them exceeds the preset error range, a second fusion status identifier is generated. If either of them exceeds the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, a third fusion status identifier is generated. The first to third fusion status identifiers are all different. Finally, the fusion status identifier, steady-state measurement data, and transient waveform data at that time are combined into the fusion waveform data at that time. The dynamic triggering unit is used to determine whether the fault recording triggering conditions are met based on steady-state measurement data and transient waveform data. If so, it collects fused recording data including the pre-fault triggering segment, the continuous segment during the fault, and the post-fault segment as fault recording data for output.

[0012] Preferably, the steady-state measurement data further includes temperature measurement data.

[0013] Preferably, the fault recording triggering condition is that at least one of the following parameters exceeds the limit: effective voltage value, effective current value, active power, reactive power, power factor, instantaneous transient current value, instantaneous transient voltage value, current mutation rate, voltage drop slope, power factor mutation amplitude, and temperature.

[0014] Preferably, the duration of the pre-triggering segment before the fault is triggered is set according to the degree of exceeding the limit of the parameter; the greater the degree of exceeding the limit, the longer the duration of the pre-triggering segment before the fault is triggered.

[0015] Preferably, the preset error range is ±5%.

[0016] Furthermore, the fault recording device also includes: An edge computing unit, located locally, is used to perform fault diagnosis using the fault waveform data as model input and a lightweight AI fault diagnosis model.

[0017] A circuit breaker includes a fault recording device as described in any of the above technical solutions.

[0018] Compared with the prior art, the technical solution of the present invention has the following beneficial effects: This invention employs independent sampling along both steady-state and transient paths, while using data fusion to ensure the completeness and accuracy of the recorded waveform data. It also triggers fault recording based on a combination of multi-dimensional electrical parameters to avoid missing some transient faults with a single triggering method, and provides more information for fault diagnosis and analysis, thereby improving the accuracy of fault diagnosis. This invention further dynamically adjusts the pre-trigger recording length, adapting the pre-recording duration to the severity of the fault, ensuring that key features before and after the fault can be fully captured, and providing a reliable basis for fault diagnosis. Attached Figure Description

[0019] Figure 1 This is a structural block diagram of a preferred embodiment of the fault recording device of the present invention; Figure 2 This is a flowchart illustrating the process of homology verification. Figure 3 This is a schematic diagram of the fault recording trigger logic. Detailed Implementation

[0020] To address the shortcomings of existing technologies, the present invention employs independent sampling along two paths: steady-state measurement and transient measurement. Simultaneously, it uses data fusion to ensure the completeness and accuracy of the recorded waveform data. Furthermore, it triggers fault recording based on a combination of multi-dimensional electrical parameters to avoid missing some transient faults using a single triggering method. This provides more information for fault diagnosis and analysis, thereby improving the accuracy of fault diagnosis.

[0021] The present invention specifically adopts the following technical solutions to solve the above-mentioned technical problems: A fault recording method includes the following steps: Simultaneously, steady-state and transient waveforms of the line are recorded to obtain steady-state measurement data including effective voltage values, effective current values, active power, reactive power, power factor, and temperature, as well as transient waveform data including instantaneous values ​​of transient current and transient voltage. The steady-state measurement data and transient waveform data are fused using the following method to generate fused waveform data: First, the steady-state measurement data and transient waveform data are time-aligned. Then, the effective current value calculated from the transient waveform data is compared with the effective current value in the steady-state measurement data at the same time, and corresponding fusion status identifiers are generated. If both are within the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, and the error between them is within a preset error range, a first fusion status identifier is generated. If both are within the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, but the error between them exceeds the preset error range, a second fusion status identifier is generated. If either of them exceeds the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, a third fusion status identifier is generated. The first to third fusion status identifiers are all different. Finally, the fusion status identifier, steady-state measurement data, and transient waveform data at that time are combined into the fusion waveform data at that time. Based on steady-state metering data and transient waveform data, it is determined in real time whether the fault recording trigger condition is met. If so, the fused recording data, including the pre-fault triggering segment, the continuous segment during the fault, and the post-fault segment, is collected and output as fault recording data. The specific fault recording trigger condition is that at least one of the following parameters exceeds the limit: effective voltage value, effective current value, active power, reactive power, power factor, transient instantaneous current value, transient instantaneous voltage value, current mutation rate, voltage drop slope, power factor mutation amplitude, and temperature.

[0022] The severity of changes in electrical parameters is often related to the severity of the fault. This invention further dynamically adjusts the pre-trigger recording length according to the severity of the changes in electrical parameters. The more severe the change, the longer the pre-trigger recording length, ensuring that key waveforms before the fault are not lost and recording waveforms throughout the entire process before, during, and after the fault. Specifically, the duration of the pre-trigger segment before the fault is set according to the degree of exceeding the limits of the parameters. The greater the degree of exceeding the limits, the longer the duration of the pre-trigger segment before the fault.

[0023] Preferably, the preset error range is ±5%.

[0024] Existing fault waveform data is typically uploaded to an upstream server for further fault diagnosis and analysis. Thanks to the development of AI technology, numerous lightweight AI models have emerged. To enable timely and rapid fault diagnosis, this invention further utilizes locally deployed lightweight AI models for direct fault diagnosis and analysis. Specifically, the fault waveform recording method also includes: Using the fault waveform data as model input, a locally configured lightweight AI fault diagnosis model is used for fault diagnosis.

[0025] To facilitate public understanding, the technical solution of the present invention will be described in detail below through a specific embodiment and in conjunction with the accompanying drawings: The fault recording device in this embodiment, such as Figure 1 As shown, it includes: a steady-state metering sampling unit, a transient sampling unit, a dynamic triggering unit, a data fusion unit, and an edge computing unit.

[0026] The steady-state metering sampling unit is composed of a high-precision current transformer and a voltage sampling circuit, along with a dedicated metering chip. It is used to record the steady-state waveform of the line, that is, to collect the steady-state electrical quantities of the line and complete the metering calculation, thereby obtaining steady-state metering data (which can also be metering channel or steady-state channel data) including the effective value of voltage, the effective value of current, active power, reactive power, power factor and temperature. In this embodiment, the sampling frequency of the dedicated metering chip is adjustable in the range of 1Hz-10Hz.

[0027] The transient sampling unit consists of a protective current transformer and a high-speed analog-to-digital converter. It is used to record transient waveforms of the line, that is, to collect transient electrical quantities of the line without interfering with the steady-state metering sampling path, and obtain transient waveform data (also known as waveform recording channel or transient channel data) containing the instantaneous values ​​of transient current and transient voltage.

[0028] The two input terminals of the data fusion unit are connected to the output terminals of the steady-state metering sampling unit and the transient sampling unit, respectively. They are used to fuse steady-state metering data and transient waveform data to generate fused waveform data according to the following method: First, the steady-state metering data and transient waveform data are time-aligned. Then, the effective current value calculated from the transient waveform data is compared with the effective current value in the steady-state metering data at the same time, and a corresponding fusion status identifier is generated. If both are within the overlapping area of ​​the effective measurement ranges of the steady-state and transient waveforms and the error between them is within a preset error range, a first fusion status identifier is generated. If both are within the overlapping area of ​​the effective measurement ranges of the steady-state and transient waveforms but the error between them exceeds the preset error range, a second fusion status identifier is generated. If either one exceeds the overlapping area of ​​the effective measurement ranges of the steady-state and transient waveforms, a third fusion status identifier is generated. The first to third fusion status identifiers are all different. Finally, the fusion status identifier, steady-state metering data, and transient waveform data at that time are combined to form the fused waveform data for that time.

[0029] The dynamic triggering unit has two input terminals connected to the output terminals of the steady-state metering sampling unit and the transient sampling unit, respectively. It is used to determine whether the fault recording triggering conditions are met based on the steady-state metering data and transient waveform data. If so, it collects fused recording data including the pre-fault triggering segment, the continuous segment during the fault, and the post-fault segment as fault recording data for output. The specific fault recording triggering conditions are: at least one of the following parameters exceeding the limit: effective voltage value, effective current value, active power, reactive power, power factor, transient instantaneous current value, transient instantaneous voltage value, current mutation rate, voltage drop slope, power factor mutation amplitude, and temperature.

[0030] An edge computing unit, located locally, is used to perform fault diagnosis using the fault waveform data as model input and a lightweight AI fault diagnosis model.

[0031] The data fusion unit, dynamic triggering unit, and edge computing unit in this device are the core of the technical solution of this invention, and will be described in further detail below: Data time alignment in data fusion refers to aligning multiple time series datasets according to the same timestamp for unified analysis and processing. It is a mature existing technology and will not be elaborated here.

[0032] For time-aligned steady-state metering data and transient waveform data, firstly, the corresponding effective current value is calculated based on the instantaneous values ​​of transient current and transient voltage in the transient waveform data, denoted as I2; the effective current value in the steady-state metering data is denoted as I1; then... Figure 2 As shown, determining whether the two are within the overlapping region of the effective measurement ranges of steady-state and transient waveforms essentially involves checking whether the current primary current is simultaneously within the effective linear operating range of both the steady-state and transient channels. Assuming In is the rated current of the circuit breaker, the effective measurement range of the steady-state channel is typically 0.01In to 1.2In, with the lower limit limited by the signal-to-noise ratio and the upper limit limited by core saturation; the effective measurement range of the transient channel is typically 0.5In to 15In, with the lower limit limited by sensitivity and the upper limit limited by the maximum protection current designed for the circuit breaker. Therefore, the overlapping region of the effective measurement ranges of steady-state and transient waveforms can be set to 0.5In to 1.2In. If either I1 or I2 exceeds 0.5 In to 1.2 In, a third fusion status identifier is generated (1 in this embodiment). If both I1 and I2 are within the range of 0.5 In to 1.2 In, a homology check is further performed to determine whether the error between them exceeds a preset error range (±5% in this embodiment). If |I1-I2| ≤ 0.05 × I1, the homology check is successful, and a first fusion status identifier is generated (0 in this embodiment). If |I1-I2| > 0.05 × I1, the homology check fails, and a second fusion status identifier is generated (0xFF in this embodiment). A homology check alarm can also be generated simultaneously. Finally, the generated fusion status identifier is combined with steady-state metering data and transient waveform data to form fusion waveform data for that moment, resulting in fusion waveform data containing at least 9 dimensions (fusion status identifier, effective voltage value, effective current value, active power, reactive power, power factor, temperature, transient current instantaneous value, and transient voltage instantaneous value), which is then transmitted to the edge computing unit.

[0033] The dynamic trigger unit of the present invention is used to monitor the changes in line electrical parameters. Taking the parameter combination of the current mutation rate of the transient channel, the voltage drop slope, the mutation amplitude of the power factor of the steady-state channel, and the abnormal line temperature as an example, it triggers the fault recording when the triggering conditions are met and sends a fault recording trigger signal to the edge computing unit. The fault recording trigger logic is as follows Figure 3 shown. The triggering conditions include: current mutation rate di / dt, voltage drop slope dv / dt, mutation amplitude of power factor, and temperature anomaly. In this embodiment, the severity of the fault is also classified according to the severity of the above electrical parameter changes. For example: When the current mutation rate di / dt ≥ 10A / mS, it is defined as a conventional power grid disturbance or motor startup; when di / dt ≥ 20 - 30A / mS, it is defined as a typical short-circuit fault or load impact; when di / dt ≥ 30A / mS, it is defined as an extreme fault such as lightning strike or power device breakdown; When the voltage drop slope dv / dt ≥ 5V / mS, it is defined as a conventional power grid disturbance or motor startup; when dv / dt ≥ 10V / mS, it is defined as a typical short-circuit fault or load impact; when dv / dt ≥ 20% V / mS, it is defined as an extreme fault such as lightning strike or power device breakdown; When the mutation amplitude of the power factor △PF ≥ 0.10 / 20mS, it is defined as photovoltaic or wind power fluctuation; when △PF ≥ 0.15 / 20mS, it is defined as industrial load switching; when △PF ≥ 0.20 / 20mS, it is defined as motor startup impact.

[0034] When the line temperature anomaly t ≥ 90°C, it is defined as the initial stage of mild overload; when t ≥ 110°C, it is defined as severe insulation deterioration; when t ≥ 130°C, it is defined as a fatal short-circuit fire risk.

[0035] In this embodiment, the dynamic trigger unit is divided into level I, II, and III triggers according to the severity of the electrical parameter changes. Different levels of trigger signals correspond to different lengths of the pre-trigger segment of the fault recording before the fault. Level I trigger corresponds to the pre-trigger recording length t1 of the fault recording before the fault, level II trigger corresponds to the pre-trigger recording length t2 of the fault recording before the fault, and level III trigger corresponds to the pre-trigger recording length t3 of the fault recording before the fault, where t1 < t2 < t3. t1, t2, and t3 are adjustable within 100ms - 500ms. The waveform data of the fault recording after the fault includes the pre-trigger segment before the fault, the middle segment of the fault, and the post-fault segment. The waveform of 200ms is recorded after the fault.

[0036] The edge computing unit integrates a microcontroller unit and a lightweight neural network processing unit. It receives fused waveform data output from the data fusion unit and, based on the fault waveform trigger signal output from the dynamic trigger unit, collects fault waveform data from the fused waveform data and inputs it into the lightweight neural network processing unit for fault diagnosis. When no fault waveform trigger signal is received, the edge computing unit only receives the fused waveform data from the data fusion unit and does not perform fault diagnosis. Once a fault waveform trigger signal is received, it immediately collects data of corresponding lengths before, during, and after the fault moment from the fused waveform data according to the trigger level of the fault waveform trigger signal, and inputs it into the lightweight neural network processing unit for fault diagnosis. The lightweight neural network processing unit includes a pre-trained lightweight AI fault diagnosis model. The lightweight neural network processing unit defines a label mask matrix based on the data fusion status identifier. For example, if the trigger conditions are "abnormal temperature, current mutation rate, voltage drop slope, and power factor mutation amplitude":

[0037] Table 1 The mask corresponding to identifier 0 is [1,0,0,0], which means that only the temperature anomaly trigger condition is valid. The mask corresponding to identifier 1 is [1,1,1,0], which indicates that the triggering conditions of temperature anomaly, current mutation rate, and voltage drop slope are valid. The mask corresponding to 0xFF is [1,1,0,1], indicating that the trigger conditions for abnormal temperature, current mutation rate, and power factor mutation amplitude are valid. Provide a clear "contextual" cue to the pre-trained lightweight AI fault diagnosis model, telling it which operating condition it is currently dealing with. This helps the model distinguish feature distributions under different identifiers. Through feature extraction and pattern matching, it quickly classifies and judges the fault type, location, and severity, outputting three types of results: fault type (including short circuit fault, overload fault, overvoltage / undervoltage fault, phase loss / three-phase imbalance fault, etc.); fault location (distinguishing between faults on the downstream load side of the circuit breaker or abnormalities on the upstream power supply side of the circuit breaker); and fault severity level (mild: alarm, no tripping; moderate: delayed tripping; severe: immediate tripping), facilitating hierarchical handling by operation and maintenance. Also, add corresponding fault tags to each fault waveform data file: fault type, fault time, fault location, and severity tags.

Claims

1. A fault recording method, characterized in that, Includes the following steps: Simultaneously, steady-state and transient waveforms of the line are recorded to obtain steady-state measurement data including effective voltage values, effective current values, active power, reactive power and power factor, as well as transient waveform data including transient instantaneous current values ​​and transient instantaneous voltage values. The steady-state measurement data and transient waveform data are fused using the following method to generate fused waveform data: First, the steady-state measurement data and transient waveform data are time-aligned. Then, the effective current value calculated from the transient waveform data is compared with the effective current value in the steady-state measurement data at the same time, and corresponding fusion status identifiers are generated. If both are within the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, and the error between them is within a preset error range, a first fusion status identifier is generated. If both are within the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, but the error between them exceeds the preset error range, a second fusion status identifier is generated. If either of them exceeds the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, a third fusion status identifier is generated. The first to third fusion status identifiers are all different. Finally, the fusion status identifier, steady-state measurement data, and transient waveform data at that time are combined into the fusion waveform data at that time. Based on steady-state measurement data and transient waveform data, it is determined in real time whether the fault recording trigger condition is met. If so, the fused recording data, including the pre-fault triggering segment, the continuous segment during the fault, and the post-fault segment, is collected and output as the fault recording data.

2. The fault recording method as described in claim 1, characterized in that, The steady-state measurement data also includes temperature measurement data.

3. The fault recording method as described in claim 2, characterized in that, The specific fault recording triggering conditions are: at least one of the following parameters exceeding the limit: effective voltage value, effective current value, active power, reactive power, power factor, instantaneous transient current value, instantaneous transient voltage value, current mutation rate, voltage drop slope, power factor mutation amplitude, and temperature.

4. The fault recording method as described in claim 3, characterized in that, The duration of the pre-triggering segment before the fault is determined based on the degree to which the parameter exceeds the limit; the greater the degree of exceeding the limit, the longer the duration of the pre-triggering segment before the fault is.

5. The fault recording method as described in claim 1, characterized in that, The preset error range is ±5%.

6. The fault recording method according to any one of claims 1 to 5, characterized in that, Also includes: Using the fault waveform data as model input, a locally configured lightweight AI fault diagnosis model is used for fault diagnosis.

7. A fault recording device, characterized in that, include: The steady-state metering sampling unit is used to perform steady-state waveform recording of the line to obtain steady-state metering data including the effective value of voltage, the effective value of current, active power, reactive power and power factor; The transient sampling unit is used to perform transient waveform recording of the line to obtain transient waveform data containing transient current instantaneous values ​​and transient voltage instantaneous values; The data fusion unit is used to fuse steady-state measurement data and transient waveform data according to the following method to generate fused waveform recording data: First, the steady-state measurement data and transient waveform data are time-aligned. Then, the effective current value calculated from the transient waveform data is compared with the effective current value in the steady-state measurement data at the same time, and corresponding fusion status identifiers are generated. If both are within the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, and the error between them is within a preset error range, a first fusion status identifier is generated. If both are within the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, but the error between them exceeds the preset error range, a second fusion status identifier is generated. If either of them exceeds the overlapping area of ​​the effective measurement range of the steady-state waveform and the transient waveform, a third fusion status identifier is generated. The first to third fusion status identifiers are all different. Finally, the fusion status identifier, steady-state measurement data, and transient waveform data at that time are combined into the fusion waveform data at that time. The dynamic triggering unit is used to determine whether the fault recording triggering conditions are met based on steady-state measurement data and transient waveform data. If so, it collects fused recording data including the pre-fault triggering segment, the continuous segment during the fault, and the post-fault segment as fault recording data for output.

8. The fault recording device as described in claim 7, characterized in that, The steady-state measurement data also includes temperature measurement data.

9. The fault recording device as described in claim 8, characterized in that, The specific fault recording triggering conditions are: at least one of the following parameters exceeding the limit: effective voltage value, effective current value, active power, reactive power, power factor, instantaneous transient current value, instantaneous transient voltage value, current mutation rate, voltage drop slope, power factor mutation amplitude, and temperature.

10. The fault recording device as described in claim 9, characterized in that, The duration of the pre-triggering segment before the fault is determined based on the degree to which the parameter exceeds the limit; the greater the degree of exceeding the limit, the longer the duration of the pre-triggering segment before the fault is.

11. The fault recording device as described in claim 7, characterized in that, The preset error range is ±5%.

12. The fault recording device according to any one of claims 7 to 11, characterized in that, Also includes: An edge computing unit, located locally, is used to perform fault diagnosis using the fault waveform data as model input and a lightweight AI fault diagnosis model.

13. A circuit breaker, characterized in that, Includes the fault recording device as described in any one of claims 7 to 12.

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