A high sensitivity arc fault detection method and solid state power controller

Abstract

The application discloses a high-sensitivity arc fault detection method and a solid-state power controller, and relates to the technical field of power electronics. In the method, the solid-state power controller first extracts a current mutation characteristic component through time-frequency analysis, and then calculates a signal-to-noise ratio of the current mutation characteristic component and an original current signal. When the signal-to-noise ratio is lower than a preset threshold, it is judged that an arc fault characteristic in the signal is covered by noise. At this time, the solid-state power controller performs weighted enhancement processing on voltage and current signals by using an enhanced weight coefficient, the weight coefficient is calculated based on a ratio of a preset reference amplitude to an amplitude of the current mutation characteristic component, and the weak fault signal is adaptively amplified. Through the intelligent enhancement processing, the arc fault characteristic originally submerged by noise is exposed, so that the solid-state power controller can detect the weak arc fault signal in a high-noise environment, and the sensitivity of arc fault detection is improved.

Description

Technical Field

[0001] This application relates to the field of power electronics technology, and in particular to a highly sensitive arc fault detection method and a solid-state power controller. Background Technology

[0002] Solid-state power controllers (SSPCs), as a new generation of power control equipment, are widely used in high-reliability fields such as aerospace, new energy vehicles, and smart grids, replacing traditional mechanical relays and fuses. In these applications, arcing faults are a significant threat to system safety, including both series and parallel arcing faults. Series arcing faults are typically caused by poor wire contact or loose connectors, leading to increased contact resistance and voltage drops. Parallel arcing faults are often caused by insulation aging or short circuits due to foreign objects, manifesting as an abnormally high current. Regardless of the type, arcing faults can cause serious accidents such as insulation burnout and fires; therefore, arcing fault detection is crucial for ensuring the safe operation of systems.

[0003] The SSPC arc fault detection technology in related technologies is mainly based on the characteristic analysis method of current signals. A typical implementation involves acquiring circuit current signals using a current sensor, extracting characteristic parameters such as peak value, frequency, and fluctuation amplitude of the current waveform using digital signal processing technology, and then setting a detection threshold to determine whether an arc fault has occurred. Specifically, when the detected current peak value exceeds a preset threshold (usually 10%-15% of the rated current) or the current fluctuation frequency is within a specific range, an arc fault is determined to have occurred, and protection action is triggered.

[0004] However, when an arc fault is in its initial stage, the current fluctuation is often very small (less than 5% of the rated current). Threshold detection methods in related technologies tend to ignore these weak fault signals as normal current fluctuations, resulting in insufficient sensitivity of related technologies in detecting arc faults. Summary of the Invention

[0005] This application provides a highly sensitive arc fault detection method and a solid-state power controller to improve the sensitivity of arc fault detection.

[0006] Firstly, a highly sensitive arc fault detection method is provided, applied to a solid-state power controller. The method includes: the solid-state power controller performing time-frequency analysis on the current signal of a circuit to obtain a current abrupt change characteristic component; the solid-state power controller calculating the signal-to-noise ratio (SNR) of the current abrupt change characteristic component to the current signal; when the SNR is less than a preset SNR threshold, the solid-state power controller performs weighted enhancement processing on the voltage and current signals of the circuit based on an enhancement weighting coefficient to obtain an enhanced voltage signal and an enhanced current signal, where the enhancement weighting coefficient is the ratio of a preset reference amplitude to the amplitude of the current abrupt change characteristic component; the solid-state power controller calculates the change amplitudes of the enhanced voltage signal and the enhanced current signal respectively within a preset time window, where the change amplitudes are the rising change amplitude and / or the falling change amplitude; when the rising change amplitude of the enhanced voltage signal is greater than a preset series arc voltage detection threshold and the falling change amplitude of the enhanced current signal is greater than a preset series arc current detection threshold, the solid-state power controller determines the circuit to be a series arc fault; when the falling change amplitude of the enhanced voltage signal is greater than a preset parallel arc voltage detection threshold and the rising change amplitude of the enhanced current signal is greater than a preset parallel arc current detection threshold, the solid-state power controller determines the circuit to be a parallel arc fault.

[0007] By employing the above technical solution, the solid-state power controller first extracts the current surge characteristic component through time-frequency analysis. Then, it calculates the signal-to-noise ratio (SNR) of this current surge characteristic component with the original current signal. When the SNR is lower than a preset threshold, it is determined that the arc fault characteristics in the signal are masked by noise. At this point, the solid-state power controller uses an enhancement weighting coefficient to perform weighted enhancement processing on the voltage and current signals. This weighting coefficient is calculated based on the ratio of a preset reference amplitude to the amplitude of the current surge characteristic component, adaptively amplifying the weak fault signal. Through this intelligent enhancement processing, the arc fault characteristics that were originally submerged by noise are revealed, enabling the solid-state power controller to detect weak arc fault signals in high-noise environments, thereby improving the sensitivity of arc fault detection.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, before the solid-state power controller determines that the circuit is a series arc fault when the amplitude of the rise of the enhanced voltage signal is greater than a preset series arc voltage detection threshold and the amplitude of the fall of the enhanced current signal is greater than a preset series arc current detection threshold, the method further includes: the solid-state power controller looking up a threshold correction coefficient in a preset environmental compensation table based on the circuit's temperature parameters, humidity parameters, and air pressure parameters; the preset environmental compensation table records the threshold correction coefficients corresponding to each circuit type under different temperature, humidity, and air pressure conditions; the solid-state power controller multiplies the threshold correction coefficients by the initial arc detection threshold to obtain a preset environmental correction arc detection threshold, the preset environmental correction arc detection threshold including a preset series arc voltage detection threshold, a preset series arc current detection threshold, a preset parallel arc voltage detection threshold, and a preset parallel arc current detection threshold.

[0009] By adopting the above technical solution, before judging arc faults, the solid-state power controller first looks up the corresponding threshold correction coefficient in a preset environmental compensation table based on the current ambient temperature, humidity, and air pressure parameters. By multiplying the correction coefficient by the initial detection threshold, the solid-state power controller dynamically adjusts the voltage and current detection thresholds of series and parallel arcs to adapt them to the current environmental conditions. This environmental adaptive adjustment mechanism improves detection accuracy and reduces the false alarm rate caused by environmental changes.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the step of the solid-state power controller performing weighted enhancement processing on the voltage signal and current signal of the circuit based on enhancement weighting coefficients to obtain enhanced voltage signals and enhanced current signals specifically includes: the solid-state power controller determining the background noise of the current signal based on the voltage signal and current signal of the circuit; the solid-state power controller calculating a noise suppression factor based on the background noise, wherein the noise suppression factor is inversely proportional to the background noise level; the solid-state power controller multiplying the enhancement weighting coefficients by the noise suppression factor to obtain noise compensation enhancement weighting coefficients; and the solid-state power controller performing weighted enhancement processing on the voltage signal and current signal based on the noise compensation enhancement weighting coefficients to obtain enhanced voltage signals and enhanced current signals.

[0011] By adopting the above technical solution, the solid-state power controller first determines the background noise level based on the current voltage and current signals. Then, it calculates a noise suppression factor that is inversely proportional to the background noise level; the higher the noise level, the smaller the suppression factor, which is used to adjust the enhancement intensity. Multiplying the enhancement weighting coefficient by the noise suppression factor yields the noise compensation enhancement weighting coefficient. This dual adjustment mechanism can suppress noise amplification while amplifying the useful signal. Finally, weighted enhancement processing is performed based on this compensation coefficient, improving the signal-to-noise ratio of the enhanced signal, thereby improving the accuracy and reliability of subsequent arc fault detection.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the step of the solid-state power controller performing weighted enhancement processing on voltage and current signals based on noise compensation enhancement weighting coefficients to obtain enhanced voltage and current signals specifically includes: the solid-state power controller dividing the voltage and current signals into different sub-time periods, the length of which is determined based on the change frequency of the current abrupt change characteristic component; the solid-state power controller calculating the standard deviation and mean of the signal within the sub-time period; the solid-state power controller using the ratio of the standard deviation to the mean as the segmented enhancement weighting coefficient for the sub-time period; the solid-state power controller performing weighted enhancement processing on the voltage and current signals within each sub-time period based on the segmented enhancement weighting coefficients; and the solid-state power controller splicing the weighted enhancement signals from each sub-time period to obtain enhanced voltage and current signals.

[0013] By adopting the above technical solution, the solid-state power controller dynamically determines the length of the sub-time period based on the frequency of change of the current transient characteristic component. Within each sub-time period, the standard deviation and mean of the signal are calculated. The ratio of the standard deviation to the mean reflects the degree of signal change within that time period. Then, the standard deviation and the mean are used as the segment enhancement weight coefficients for that segment. Thus, a larger segment enhancement weight coefficient is used for time periods with drastic changes, while a smaller segment enhancement weight coefficient is used for stable time periods. This differentiated processing can highlight time periods with obvious fault characteristics.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, after the solid-state power controller performs weighted enhancement processing on the voltage signal and current signal of the circuit based on the enhancement weight coefficient to obtain the enhanced voltage signal and enhanced current signal, the method further includes: the solid-state power controller extracts waveform feature parameters of the enhanced voltage signal and enhanced current signal; the solid-state power controller matches the waveform feature parameters in a preset interference feature library, and when an interference feature is matched, the signal segment corresponding to the interference feature is removed.

[0015] By employing the above technical solution, the solid-state power controller extracts multi-dimensional waveform feature parameters of the enhanced voltage and current signals. These parameters describe key characteristics such as signal amplitude, frequency, and phase. The extracted feature parameters are then matched against various known interference modes in a pre-set interference feature library, which includes typical feature modes of non-arc interference such as switching operations, load surges, and electromagnetic interference. When a matching interference feature is detected, the corresponding signal segment is removed, thereby reducing the interference of these non-fault signals on arc detection and thus reducing the false alarm rate.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after the solid-state power controller determines that the circuit is a series arc fault or a parallel arc fault, the method further includes: the solid-state power controller acquiring historical data of voltage and current signals within a preset backtracking time window before the fault confirmation time; the solid-state power controller sending the historical data to the operation and maintenance management center; after receiving the historical data, the operation and maintenance management center performing time-frequency domain joint analysis on the historical data to obtain the characteristic parameter change trajectory during the arc fault evolution process; the operation and maintenance management center constructing an arc fault evolution model based on the characteristic parameter change trajectory and the historical fault database; the operation and maintenance management center inferring the initial triggering time and triggering cause of the arc fault based on the arc fault evolution model; and the operation and maintenance management center generating a fault analysis report based on the initial triggering time and triggering cause.

[0017] By adopting the above technical solution, the solid-state power controller immediately acquires historical data within a preset time window prior to the fault upon confirmation of an arc fault. The operation and maintenance management center receives this historical data and uses time-frequency domain joint analysis technology to extract the fault evolution trajectory. This analysis method can reveal the development process of an arc fault from its inception to confirmation. The arc fault evolution model constructed based on the characteristic parameter change trajectory and historical fault database can describe the development laws and characteristic patterns of different types of arc faults. Through model-based back-analysis, the initial triggering time and cause of the fault are determined, and a fault analysis report is generated, providing a foundation for preventative maintenance and system optimization.

[0018] In conjunction with some embodiments of the first aspect, in some embodiments, after the step of the operation and maintenance management center generating a fault analysis report based on the initial triggering time and triggering cause, the method further includes: the operation and maintenance management center statistically analyzing the signal-to-noise ratio (SNR) value when an arc fault is successfully detected in the trajectory of changes in characteristic parameters; the operation and maintenance management center using the maximum value of the SNR value multiplied by a preset safety factor as an optimized SNR threshold; the operation and maintenance management center using the minimum value of the current mutation characteristic component amplitude in the initial stage of the arc fault in the arc fault evolution model as an optimized reference amplitude; the operation and maintenance management center calculating the time interval from the initial triggering time to the fault confirmation time; the operation and maintenance management center using the time interval as an optimized time window; the solid-state power controller replacing the preset SNR threshold, preset reference amplitude, and preset time window with the optimized SNR threshold, optimized reference amplitude, and optimized time window respectively; and the solid-state power controller performing subsequent arc fault detection based on the replaced preset SNR threshold, preset reference amplitude, and preset time window.

[0019] By adopting the above technical solutions, the operation and maintenance management center statistically analyzes the signal-to-noise ratio (SNR) values ​​when successfully detecting arc faults and uses the product of the maximum value and a safety factor as the optimized SNR threshold. Simultaneously, it extracts the amplitude of the minimum current surge characteristic component in the initial stage of the fault from the fault evolution model as the optimized baseline amplitude, enabling the system to detect even weaker fault signals. By calculating the time interval between fault triggering and confirmation, the time window parameters are optimized to achieve earlier fault warnings. These optimized parameters are fed back to the solid-state power controller, replacing the original thresholds and forming a closed-loop adaptive optimization mechanism, thereby continuously improving detection sensitivity and accuracy.

[0020] In a second aspect, embodiments of this application provide a solid-state power controller, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the solid-state power controller to perform the method described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on a solid-state power controller, cause the solid-state power controller to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on a solid-state power controller, cause the solid-state power controller to perform the method described in the first aspect and any possible implementation thereof.

[0023] Understandably, the solid-state power controller provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the methods provided in the embodiments of this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods, and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. The solid-state power controller first extracts the current surge characteristic component through time-frequency analysis, then calculates the signal-to-noise ratio (SNR) of this current surge characteristic component with the original current signal. When the SNR is lower than a preset threshold, it is determined that the arc fault characteristics in the signal are masked by noise. At this point, the solid-state power controller uses an enhancement weighting coefficient to perform weighted enhancement processing on the voltage and current signals. This weighting coefficient is calculated based on the ratio of a preset reference amplitude to the amplitude of the current surge characteristic component, adaptively amplifying the weak fault signal. Through this intelligent enhancement processing, the arc fault characteristics that were originally submerged by noise are revealed, enabling the solid-state power controller to detect weak arc fault signals in high-noise environments, thereby improving the sensitivity of arc fault detection.

[0026] 2. The solid-state power controller first determines the background noise level based on the current voltage and current signals, then calculates a noise suppression factor inversely proportional to the background noise level; the higher the noise level, the smaller the suppression factor, used to adjust the enhancement intensity. Multiplying the enhancement weighting coefficient by the noise suppression factor yields the noise compensation enhancement weighting coefficient. This dual adjustment mechanism can suppress noise amplification while amplifying the useful signal. Finally, weighted enhancement processing is performed based on this compensation coefficient, improving the signal-to-noise ratio of the enhanced signal, thereby improving the accuracy and reliability of subsequent arc fault detection.

[0027] 3. The Operations and Maintenance Management Center statistically analyzes the signal-to-noise ratio (SNR) values ​​when successfully detecting arc faults and uses the product of the maximum value and a safety factor as the optimized SNR threshold. Simultaneously, it extracts the amplitude of the minimum current surge characteristic component in the initial stage of the fault from the fault evolution model as the optimized baseline amplitude, enabling the system to detect even weaker fault signals. By calculating the time interval between fault triggering and confirmation, the time window parameters are optimized to achieve earlier fault warnings. These optimized parameters are fed back to the solid-state power controller, replacing the original thresholds and forming a closed-loop adaptive optimization mechanism, thereby continuously improving detection sensitivity and accuracy. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a highly sensitive arc fault detection method in an embodiment of this application.

[0029] Figure 2 This is another flowchart illustrating a highly sensitive arc fault detection method in the embodiments of this application.

[0030] Figure 3 This is a schematic diagram of the physical device structure of a solid-state power controller in the embodiments of this application. Detailed Implementation

[0031] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0032] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0033] This application provides a highly sensitive arc fault detection method and a solid-state power controller to improve the sensitivity of arc fault detection.

[0034] The following describes a highly sensitive arc fault detection method according to an embodiment of this application:

[0035] Please see Figure 1 This is a flowchart illustrating a highly sensitive arc fault detection method in an embodiment of this application.

[0036] S101, the solid-state power controller performs time-frequency analysis on the current signal of the circuit to obtain the current change characteristic components.

[0037] Solid-state power controllers are power control devices based on semiconductor devices, capable of real-time monitoring and control of circuits. Current signals refer to the waveform data of the current flowing through a circuit as it changes over time. Time-frequency analysis is a mathematical method that analyzes signals simultaneously in the time and frequency domains, revealing the frequency component changes of the signal at different times. Current abrupt change characteristic components are used to represent sudden changes in current signals, which are often closely related to the occurrence of arcing faults.

[0038] Specifically, this step is performed during the normal operation of the solid-state power controller (SSD) while monitoring the circuit status. The SSD first acquires the current signal from the circuit, which includes the circuit's base load current and potential fault characteristics. Subsequently, the SSD processes the acquired current signal using wavelet transform, short-time Fourier transform, or other time-frequency analysis algorithms. By analyzing the frequency distribution characteristics of the signal at various points in time, it identifies and extracts components with abrupt changes. These abrupt change components are mainly high-frequency transient components in the current waveform; their amplitude changes, duration, and frequency characteristics reflect the instability of the current path during arc discharge.

[0039] In some embodiments, the extraction of current abrupt change feature components can be achieved in several ways: Optionally, a continuous wavelet transform method can be used. First, a suitable mother wavelet function (such as the Morlet wavelet) is set. Then, the current signal is decomposed into multiple scales. Next, the wavelet coefficients at each scale are extracted. Finally, the abrupt change feature components are obtained through thresholding and reconstruction. Optionally, an empirical mode decomposition method can also be used. First, the envelope of the current signal is calculated and the mean is extracted. Then, multiple intrinsic mode functions are obtained through a screening process. Finally, the mode function containing high-frequency abrupt change information is selected as the abrupt change feature component.

[0040] S102, Solid-state power controller calculates the signal-to-noise ratio of the current transient characteristic component to the current signal.

[0041] Specifically, this step is executed immediately after the successful extraction of the current abrupt change characteristic component. The solid-state power controller first obtains the power of the current abrupt change characteristic component by squaring the characteristic component signal and integrating it within a certain time window. Then, it calculates the noise power in the current signal by subtracting the current abrupt change characteristic component from the original current signal to obtain the noise component and calculate its power. Next, the power of the current abrupt change characteristic component is divided by the noise power to obtain the signal-to-noise ratio (SNR). The SNR directly reflects the strength of the arc characteristic signal relative to the background noise. A high SNR indicates that the arc characteristic is obvious and easy to detect, while a low SNR indicates that the arc characteristic is weak and requires signal enhancement processing.

[0042] In some embodiments, the signal-to-noise ratio (SNR) can be calculated in several ways: Optionally, a power spectral density method can be used. First, a fast Fourier transform is performed on the current abrupt change characteristic component to obtain its frequency domain representation. Then, its power spectral density is calculated and integrated within the frequency band of interest to obtain the signal power. Next, the residual signal after subtracting the characteristic component from the original current signal is processed in the same way to obtain the noise power. Finally, the ratio of the two is calculated to obtain the SNR. Optionally, a time-domain statistical method can also be used. First, the root mean square (RMS) value of the current abrupt change characteristic component is calculated as a signal strength index. Then, the RMS value of the noise component is calculated as a noise strength index. Finally, the square of the signal strength is divided by the square of the noise strength to obtain the SNR.

[0043] S103. When the signal-to-noise ratio is less than the preset signal-to-noise ratio threshold, the solid-state power controller performs weighted enhancement processing on the voltage and current signals of the circuit based on the enhancement weighting coefficient to obtain enhanced voltage and current signals.

[0044] The preset signal-to-noise ratio (SNR) threshold refers to a pre-set critical value for SNR. Signal enhancement processing is triggered when the actual SNR falls below this threshold. The enhancement weighting coefficient is a multiplier factor used to amplify weak signals; its value is the ratio of the preset reference amplitude to the amplitude of the current transient characteristic component. The enhanced voltage signal represents the voltage waveform data after weighted enhancement processing. The enhanced current signal represents the current waveform data after weighted enhancement processing.

[0045] Specifically, this step is executed when the solid-state power controller calculates the signal-to-noise ratio (SNR) and determines that it is less than a preset SNR threshold. It primarily addresses the problem of weak arc signals being masked by noise and difficult to detect. When the SNR of the current abrupt change characteristic component is low, the solid-state power controller first calculates an enhancement weighting coefficient. This coefficient is obtained by dividing a preset reference amplitude by the amplitude of the current abrupt change characteristic component. The purpose is to amplify the weak arc characteristic signal to a sufficient level for detection. The preset reference amplitude can be determined in advance based on historical data statistics of the monitored circuit, simulation analysis, or according to safety specifications (e.g., set as a minimum fault amplitude that can be reliably detected by existing technology). Then, the solid-state power controller multiplies this enhancement weighting coefficient by the voltage and current signals respectively, achieving proportional amplification of the original signals. This weighted enhancement process increases the amplitude of the weak arc signal, making it more prominent relative to noise.

[0046] In some embodiments, signal weighting enhancement processing can be implemented in several ways: Optionally, a linear enhancement method can be used, firstly calculating enhancement weight coefficients based on a preset reference amplitude and the amplitude of the current abrupt change characteristic component, then multiplying these coefficients by the value of each sampling point of the voltage and current signals, and then limiting the amplitude of the enhanced signal to prevent signal distortion caused by over-amplification. Optionally, an adaptive enhancement method can also be used, firstly analyzing the dynamic range and noise level of the voltage and current signals, then dynamically adjusting the enhancement weight coefficients according to the signal characteristics, and then applying different enhancement factors to signal components in different frequency bands.

[0047] S104 The solid-state power controller calculates the change amplitude of the enhanced voltage signal and the enhanced current signal respectively within a preset time window.

[0048] The preset time window refers to the length of the pre-defined time interval used to calculate signal changes. The amplitude of change indicates the degree of amplitude change of the signal within the specified time window, including two types: rising amplitude and falling amplitude. Rising amplitude refers to the amount of change in the signal from its minimum to its maximum value. Falling amplitude refers to the amount of change in the signal from its maximum to its minimum value.

[0049] Specifically, this step is performed after the solid-state power controller completes signal enhancement processing. The solid-state power controller first sets an appropriate time window, the length of which is typically determined based on the typical duration of an arc discharge, generally ranging from tens of microseconds to milliseconds. Then, within this time window, the solid-state power controller scans the enhanced voltage and current signals to identify their rising and falling amplitudes. These amplitude values ​​will serve as the basis for subsequent arc fault type determination, as series and parallel arcs cause different voltage and current variation patterns.

[0050] In some embodiments, the signal variation amplitude can be calculated in several ways: Optionally, a sliding window method can be used, where a fixed-length time window is first set and slid across the enhanced signal, then the maximum, minimum, and variation amplitude of the signal are calculated at each window position, and finally the value with the largest variation amplitude across all windows is selected as the final result. Optionally, a peak detection method can also be used, where the first and second derivatives of the enhanced voltage and current signals are first calculated, then the peak and valley positions of the signal are identified by derivative zero-point detection, and finally the amplitude difference between adjacent peaks and valleys is calculated as the variation amplitude.

[0051] S105. When the amplitude of the rise of the enhanced voltage signal is greater than the preset series arc voltage detection threshold and the amplitude of the fall of the enhanced current signal is greater than the preset series arc current detection threshold, the solid-state power controller determines that the circuit is a series arc fault.

[0052] The preset series arc voltage detection threshold refers to a pre-set critical value for voltage change used to determine series arc faults. The preset series arc current detection threshold refers to a pre-set critical value for current change used to determine series arc faults. A series arc fault indicates an arc discharge phenomenon occurring in a series connection part of a circuit, usually caused by poor contact, insulation aging, etc.

[0053] Specifically, this step is executed after the solid-state power controller completes the calculation of the amplitude change of the enhanced signal, and is used to identify the type of series arc fault. Series arc faults have typical electrical characteristics, such as a voltage surge greater than 15% and a current drop greater than 10%. When a series arc occurs in a circuit, the presence of arc resistance increases the total impedance of the loop, causing the voltage to rise at the arc location while the current decreases accordingly. The solid-state power controller ensures the accuracy of detection through a dual-condition judgment: first, it checks whether the amplitude of the rise in the enhanced voltage signal exceeds a preset series arc voltage detection threshold; then, it checks whether the amplitude of the fall in the enhanced current signal exceeds a preset series arc current detection threshold. Only when both conditions are met simultaneously will the solid-state power controller determine that a series arc fault has occurred in the circuit.

[0054] In some embodiments, an environmentally adaptive threshold adjustment method can be used to improve the detection accuracy of series arc faults. Specifically, the solid-state power controller first looks up the corresponding threshold correction coefficient in a preset environmental compensation table based on the temperature, humidity and air pressure parameters of the current circuit. Then, it multiplies the correction coefficient with the initial series arc detection threshold to obtain a dynamic detection threshold adapted to the current environment. Finally, it uses the adjusted threshold to determine the series arc fault.

[0055] S106. When the decrease in the enhanced voltage signal exceeds the preset parallel arc voltage detection threshold and the increase in the enhanced current signal exceeds the preset parallel arc current detection threshold, the solid-state power controller determines that the circuit is in the case of a parallel arc fault.

[0056] The preset parallel arc voltage detection threshold refers to the critical value of voltage change that the system pre-sets for judging parallel arc faults. The preset parallel arc current detection threshold refers to the critical value of current change that the system pre-sets for judging parallel arc faults. Parallel arc faults are used to indicate the arc discharge phenomenon that occurs between parallel branches in a circuit, and are usually caused by insulation damage, wire insulation breakage, etc.

[0057] Specifically, this step is executed after the solid-state power controller completes the calculation of the amplitude of the enhanced signal change, and is used to identify the type of parallel arc fault. Parallel arc faults have electrical characteristics opposite to those of series arc faults, such as a voltage drop greater than 10% and a current surge greater than 15%. When a parallel arc occurs in a circuit, the arc forms an additional conductive path, equivalent to connecting a low-impedance arc resistor in parallel with the original circuit. This causes a decrease in the total circuit impedance, resulting in a decrease in voltage and an increase in current. The solid-state power controller employs a dual-judgment mechanism similar to that used for series arc detection: first, it checks whether the amplitude of the drop in the enhanced voltage signal exceeds a preset parallel arc voltage detection threshold; then, it checks whether the amplitude of the rise in the enhanced current signal exceeds a preset parallel arc current detection threshold. Only when both conditions are met simultaneously will the solid-state power controller determine that a parallel arc fault has occurred in the circuit. This cross-validation method ensures the reliability and accuracy of parallel arc fault detection.

[0058] In some embodiments, an environmental adaptive threshold adjustment method can also be used to improve the detection accuracy of parallel arc faults, as explained in step S106, and will not be repeated here.

[0059] In the above embodiments, the solid-state power controller first extracts the current surge characteristic component through time-frequency analysis, and then calculates the signal-to-noise ratio (SNR) of the current surge characteristic component and the original current signal. When the SNR is lower than a preset threshold, it is determined that the arc fault characteristics in the signal are masked by noise. At this time, the solid-state power controller uses an enhancement weighting coefficient to perform weighted enhancement processing on the voltage and current signals. This weighting coefficient is calculated based on the ratio of a preset reference amplitude to the amplitude of the current surge characteristic component, adaptively amplifying the weak fault signal. Through this intelligent enhancement processing, the arc fault characteristics that were originally submerged by noise are revealed, enabling the solid-state power controller to detect weak arc fault signals in high-noise environments, thereby improving the sensitivity of arc fault detection.

[0060] However, the above embodiments still have certain limitations in complex application environments. On the one hand, high background noise may cause noise and useful signals to be amplified synchronously, which may affect the detection effect. On the other hand, transient signals generated by normal electrical activities such as switching operations and load changes may be misjudged as arc faults, resulting in false alarms. In addition, fluctuations in detection sensitivity may occur under different temperature and humidity conditions. In order to solve the above technical problems and further improve the accuracy and reliability of arc fault detection, the present invention proposes the following improved technical solutions.

[0061] Please see Figure 2 This is another flowchart illustrating a highly sensitive arc fault detection method in this application.

[0062] S201, the solid-state power controller performs time-frequency analysis on the current signal of the circuit to obtain the current change characteristic components.

[0063] S202, Solid-state power controller calculates the signal-to-noise ratio of the current transient characteristic component to the current signal.

[0064] Step S201 is similar to step S101, and step S202 is similar to step S102, so they will not be described again here.

[0065] S203, the solid-state power controller determines the background noise of the current signal based on the voltage and current signals of the circuit.

[0066] Specifically, this step is performed after the solid-state power controller calculates the signal-to-noise ratio (SNR) of the current transient characteristic component and the current signal. It primarily addresses the issue of secondary noise contamination of weak signals during enhancement processing. When the solid-state power controller performs weighted enhancement processing on weak arc signals, it not only amplifies the useful arc characteristic signal but also amplifies the noise component by the same proportion. This secondary noise amplification may actually degrade the enhanced signal quality. Therefore, the solid-state power controller needs to assess the background noise level in the current signal to provide a basis for subsequent noise compensation processing. The solid-state power controller first sets a short time window to segment the voltage and current signals, then calculates the variance and root mean square (RMS) value of the signal within each window. Next, it selects several windows with the smallest variance as noise benchmarks and calculates their average noise level as the background noise.

[0067] S204, the solid-state power controller calculates the noise suppression factor based on the background noise, and the noise suppression factor is inversely proportional to the background noise level.

[0068] The noise suppression factor is a numerical coefficient used to reduce the noise amplification effect, aiming to reduce the impact of noise during signal enhancement. The inverse relationship means that the noise suppression factor decreases as the noise floor level increases and increases as the noise floor level decreases. This inverse relationship reduces the enhancement amplitude in high-noise environments to avoid excessive noise amplification, while maintaining a higher enhancement amplitude in low-noise environments to extract arc characteristics.

[0069] Specifically, this step is performed after the solid-state power controller determines the noise floor of the current signal. Since direct weighted enhancement amplifies both the useful signal and noise simultaneously, when the noise floor level is high, this simultaneous amplification may cause noise to dominate in the enhanced signal, thus reducing the identifiability of the arc characteristics. Therefore, the solid-state power controller needs to dynamically adjust the enhancement strategy based on the actual noise level. The solid-state power controller first analyzes the specific value of the noise floor, and then calculates the corresponding noise suppression factor based on a preset inverse proportional function relationship. This function is typically Noise Suppression Factor = K / (Noise Floor Level + C), where K is the gain constant and C is the offset constant used to avoid the denominator being zero and to adjust the function characteristics. For example, when K = 1 and C = 0.1, if the noise floor level is 0.1, the suppression factor is 5; if the noise floor level is 1.0, the suppression factor is 0.91. This design allows for stronger signal enhancement in low-noise environments and limits the enhancement amplitude in high-noise environments to prevent excessive noise amplification.

[0070] S205, the solid-state power controller multiplies the enhancement weighting coefficient by the noise suppression factor to obtain the noise compensation enhancement weighting coefficient.

[0071] Specifically, this step is performed after the solid-state power controller calculates the noise suppression factor. Directly using the original enhancement weighting coefficients for signal enhancement may result in the simultaneous amplification of both noise and useful signals, potentially even causing the noise component to exceed the arc signal component in high-noise environments. By multiplying the enhancement weighting coefficients by the noise suppression factor, the solid-state power controller intelligently adjusts the enhancement strategy: when the noise level is low, the noise suppression factor is close to or greater than 1, and the multiplied noise compensation enhancement weighting coefficients maintain a high value, strongly enhancing weak arc signals. When the noise level is high, the noise suppression factor is less than 1, and the multiplied coefficients are appropriately reduced, decreasing the degree of noise amplification.

[0072] S206 The solid-state power controller performs weighted enhancement processing on the voltage and current signals based on the noise compensation enhancement weighting coefficient to obtain enhanced voltage and current signals.

[0073] Specifically, this step is performed after the solid-state power controller calculates the noise compensation enhancement weighting coefficients. Arc signals exhibit different intensity and frequency characteristics at different times, and using fixed enhancement weighting coefficients may lead to over-enhancement in some periods and under-enhancement in others. Therefore, the solid-state power controller employs a segmented adaptive enhancement strategy, determining the temporal granularity of signal division by analyzing the time-domain characteristics of the current abrupt change characteristic components. Specifically, the solid-state power controller detects the time interval between adjacent abrupt change events in the current abrupt change characteristic components, using half of the minimum time interval as the base length of the sub-time period. Then, the solid-state power controller divides the voltage and current signals into multiple consecutive sub-time periods according to this base length. For each sub-time period, the solid-state power controller calculates the standard deviation and mean of the signal within that segment, using the ratio of these two values ​​as the segmented enhancement weighting coefficient reflecting the degree of signal variation in that segment. Finally, the solid-state power controller combines the segmented enhancement weighting coefficients with the noise compensation enhancement weighting coefficients to perform differentiated weighted enhancement processing on the voltage and current signals within each sub-time period, and then splices the processed signal segments sequentially to form complete enhanced voltage and current signals.

[0074] S207, the solid-state power controller extracts waveform characteristic parameters of the enhanced voltage signal and enhanced current signal.

[0075] Among them, waveform characteristic parameters are used to represent numerical indicators that describe key characteristics of signal waveforms such as shape, frequency, and amplitude, including statistical parameters such as peak value, RMS value, waveform factor, kurtosis, and skewness.

[0076] Specifically, this step is performed after the solid-state power controller completes signal weighting and enhancement processing. Because various non-arc-type interference signals exist in the circuit environment, such as switching operations, load surges, and electromagnetic interference, these interference signals may be misidentified as arc signals after enhancement, leading to false alarms. Therefore, the solid-state power controller needs to identify and eliminate these interference components before judging arc faults. The solid-state power controller performs waveform analysis on the enhanced voltage and current signals to extract multi-dimensional feature parameters. These parameters include time-domain features such as the signal's maximum, minimum, mean, standard deviation, peak factor, and waveform factor; frequency-domain features such as main frequency components, spectral energy distribution, and harmonic distortion; and time-frequency-domain features such as wavelet coefficients and instantaneous frequency changes.

[0077] S208, the solid-state power controller matches waveform characteristic parameters in a preset interference characteristic library, and when an interference characteristic is matched, the signal segment corresponding to the interference characteristic is removed.

[0078] The preset interference feature library is used to represent a pre-established database containing various known interference signal feature patterns. This library records typical feature parameters of non-arc interference such as switching operations, load changes, electromagnetic interference, and contactor operation.

[0079] Specifically, this step is performed after the solid-state power controller extracts waveform feature parameters. The solid-state power controller compares the extracted waveform feature parameters with various interference modes in a preset interference feature library one by one, and uses a similarity calculation method to determine whether the waveform feature parameters match a certain known interference type. During the similarity calculation process, the solid-state power controller first normalizes the current waveform feature parameter vector to ensure that feature parameters of different dimensions are comparable. Then, it calculates the Euclidean distance or cosine similarity between this vector and the feature vector of each interference mode in the interference feature library. The smaller the distance or the higher the similarity, the higher the degree of matching. When the similarity exceeds a preset matching threshold, the solid-state power controller confirms that the current signal segment is an interference signal, records the matched interference type and the corresponding time interval information, and marks or directly removes the enhanced voltage signal and enhanced current signal within this time period. Alternatively, a fuzzy matching method can be used. First, a fuzzy membership function based on Gaussian or trigonometric functions is established for each type of interference. These functions define the degree to which the feature parameter belongs to the interference type. Then, the current feature parameter is substituted into the membership function of each interference type to calculate its membership value for each interference type. Next, the most likely interference type is determined by combining the membership values ​​through fuzzy inference rules, and a confidence threshold is set for elimination decision. When the maximum membership value exceeds the confidence threshold, the signal segment elimination operation is performed.

[0080] S209 The solid-state power controller calculates the variation amplitude of the enhanced voltage signal and the enhanced current signal respectively within a preset time window.

[0081] Step S209 is similar to step S104, and will not be described again here.

[0082] S210. When the amplitude of the rise of the enhanced voltage signal is greater than the preset series arc voltage detection threshold and the amplitude of the fall of the enhanced current signal is greater than the preset series arc current detection threshold, the solid-state power controller determines that the circuit is a series arc fault.

[0083] S211. When the decrease in the enhanced voltage signal exceeds the preset parallel arc voltage detection threshold and the increase in the enhanced current signal exceeds the preset parallel arc current detection threshold, the solid-state power controller determines that the circuit is in the case of a parallel arc fault.

[0084] Step S210 is similar to step S105, and step S211 is similar to step S106, so they will not be described again here.

[0085] In some embodiments, after steps S210 and S211, the solid-state power controller acquires historical data of voltage and current signals within a preset retrospective time window before the fault confirmation time. This retrospective time window is typically set to 30 seconds to 5 minutes before fault confirmation, to capture the complete evolution process of the arc fault from its inception to confirmation. The solid-state power controller packages this historical data along with metadata such as fault type identifier, environmental parameter information, and detection threshold settings and sends it to the operation and maintenance management center. After receiving the historical data, the operation and maintenance management center first preprocesses the data, including noise filtering, data alignment, and missing value compensation. Then, it uses time-frequency domain joint analysis techniques such as wavelet transform and Hilbert-Huang transform to extract key characteristic parameters in the arc fault evolution process, such as the instantaneous amplitude, frequency components, phase relationship, and harmonic content of voltage and current, and constructs the characteristic parameter change trajectory. Based on these characteristic parameter change trajectories and the historical fault database maintained by the operation and maintenance management center, an arc fault evolution model is constructed using a combination of deep learning and time series analysis. Specifically, the Operations and Maintenance Management Center first uses a Long Short-Term Memory (LSTM) network to establish a time-series evolution model of arc faults. This network includes an input layer, multiple LSTM hidden layers, and an output layer. The input layer receives multi-dimensional feature parameter time-series data, including voltage amplitude change rate, current mutation frequency, harmonic distortion factor, and power factor change. The LSTM hidden layers learn the long-term dependencies and short-term fluctuation characteristics in the arc fault evolution process through a gating mechanism, capturing the nonlinear evolution law of the arc gradually intensifying from a weak discharge. Simultaneously, the Operations and Maintenance Management Center constructs a fault state transition model based on a Hidden Markov Model (HMM), dividing the arc fault evolution process into multiple discrete states such as normal state, pre-fault state, fault initiation state, fault development state, and fault confirmation state. The transition probability matrix between each state and the observation probability distribution corresponding to each state are obtained through training with historical data. Furthermore, a fault intensity prediction model based on support vector regression was established, which uses the feature parameters at the current moment to predict the severity of the fault at future moments. A fault cause inference model was also constructed by combining this model with a Bayesian network. This model can infer the causes of the fault, such as insulation aging, loose contact, and environmental factors, based on the characteristic change patterns during the fault evolution process. Through the collaborative work of these multi-level models, the arc fault evolution model can describe the complete time series process of an arc fault, starting from the initial microscopic discharge phenomenon, undergoing intermediate evolution stages such as increased contact resistance, local temperature rise, decreased insulation performance, and intensified discharge intensity, ultimately developing into a macroscopic fault signal that can be identified by the detection system.

[0086] Through reverse engineering analysis of this evolutionary model, the operations and maintenance management center can determine the initial triggering time of an arc fault and analyze the triggering cause of the fault by combining factors such as environmental parameters, load changes, and equipment aging. This ultimately generates a comprehensive fault analysis report including a fault timeline, cause analysis, impact assessment, and prevention recommendations. Simultaneously, the operations and maintenance management center optimizes the detection system parameters based on the fault analysis results. First, it statistically analyzes the signal-to-noise ratio (SNR) values ​​of all successfully detected arc fault moments in the characteristic parameter change trajectory, identifies the maximum value, and multiplies it by a preset safety factor to obtain the optimized SNR threshold. Then, it extracts the amplitude of the current mutation characteristic component in the initial stage of the arc fault from the arc fault evolution model and uses its minimum value as the optimization benchmark amplitude. The operations and maintenance management center also calculates the precise time interval from the initial triggering time to the fault confirmation time, using this time interval as the optimization time window. Finally, the operation and maintenance management center sends these optimized parameters to the solid-state power controller. The solid-state power controller replaces the original preset signal-to-noise ratio threshold, preset reference amplitude, and preset time window with the optimized signal-to-noise ratio threshold, optimized reference amplitude, and optimized time window, respectively, forming an adaptive parameter adjustment mechanism to continuously improve the sensitivity of arc fault detection.

[0087] The above embodiments improve the overall performance of arc fault detection by introducing an adaptive noise compensation mechanism, a segmented enhancement processing strategy, intelligent interference signal identification, dynamic environmental parameter compensation, and a parameter optimization mechanism based on historical data. Specifically, the application of noise compensation enhancement weighting coefficients solves the problem of synchronous noise amplification during signal enhancement, thus enabling the extraction of weak arc fault features even in complex noise environments. The segmented enhancement processing strategy refines the time-varying characteristics of arc faults, highlighting fault features in different time periods. Waveform feature matching and interference removal mechanisms reduce the false alarm rate and improve the reliability of detection results. Environmental parameter compensation improves detection stability, while parameter self-optimization based on the fault evolution model continuously improves detection performance.

[0088] The above describes a highly sensitive arc fault detection method in the embodiments of this application. The following describes an exemplary solid-state power controller 300 provided in the embodiments of this application.

[0089] Figure 3This is a schematic diagram of an exemplary hardware structure of a solid-state power controller 300 provided in an embodiment of this application. In some embodiments, the solid-state power controller 300 is a computer device, which includes a processor, a memory, and a network interface connected via a solid-state power controller bus. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the solid-state power controller and the computer programs stored in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or solid-state power controllers via a network connection. In some embodiments, the network interface can be a wired network interface; in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, it implements a highly sensitive arc fault detection method according to an embodiment of this application.

[0090] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0091] In some embodiments of this application, a computer-readable storage medium is also provided, including instructions that, when executed on the solid-state power controller 300, cause the solid-state power controller 300 to perform a highly sensitive arc fault detection method according to an embodiment of this application.

[0092] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0093] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0094] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0095] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A highly sensitive method for detecting electric arc faults, characterized in that, Applied to solid-state power controllers, the method includes: The solid-state power controller performs time-frequency analysis on the current signal of the circuit to obtain the current change characteristic components. The solid-state power controller calculates the signal-to-noise ratio of the current transient characteristic component to the current signal; When the signal-to-noise ratio is less than a preset signal-to-noise ratio threshold, the solid-state power controller performs weighted enhancement processing on the voltage signal and the current signal of the circuit based on the enhancement weighting coefficient to obtain enhanced voltage signal and enhanced current signal. The enhancement weighting coefficient is the ratio of the preset reference amplitude to the amplitude of the current change characteristic component. The step of the solid-state power controller performing weighted enhancement processing on the voltage signal and the current signal of the circuit based on enhancement weighting coefficients to obtain enhanced voltage signals and enhanced current signals specifically includes: the solid-state power controller determining the background noise of the current signal based on the voltage signal and the current signal of the circuit; the solid-state power controller calculating a noise suppression factor based on the background noise, wherein the noise suppression factor is inversely proportional to the background noise level; the solid-state power controller multiplying the enhancement weighting coefficients by the noise suppression factor to obtain noise compensation enhancement weighting coefficients; and the solid-state power controller performing weighted enhancement processing on the voltage signal and the current signal based on the noise compensation enhancement weighting coefficients to obtain enhanced voltage signals and enhanced current signals. The solid-state power controller calculates the change amplitudes of the enhanced voltage signal and the enhanced current signal respectively within a preset time window, wherein the change amplitudes are the rising change amplitude and / or the falling change amplitude; When the rise of the enhanced voltage signal is greater than the preset series arc voltage detection threshold and the fall of the enhanced current signal is greater than the preset series arc current detection threshold, the solid-state power controller determines that the circuit is experiencing a series arc fault. When the decrease in the enhanced voltage signal exceeds a preset parallel arc voltage detection threshold and the increase in the enhanced current signal exceeds a preset parallel arc current detection threshold, the solid-state power controller determines that the circuit is experiencing a parallel arc fault.

2. The method according to claim 1, characterized in that, Before the step of the solid-state power controller determining that the circuit has a series arc fault when the rise amplitude of the enhanced voltage signal is greater than a preset series arc voltage detection threshold and the fall amplitude of the enhanced current signal is greater than a preset series arc current detection threshold, the method further includes: The solid-state power controller searches for a threshold correction coefficient in a preset environmental compensation table based on the circuit's temperature, humidity, and air pressure parameters. The preset environmental compensation table records the threshold correction coefficients corresponding to each circuit type under different temperature, humidity, and air pressure conditions. The solid-state power controller multiplies the threshold correction coefficient by the initial arc detection threshold to obtain a preset environment-corrected arc detection threshold. The preset environment-corrected arc detection threshold includes a preset series arc voltage detection threshold, a preset series arc current detection threshold, a preset parallel arc voltage detection threshold, and a preset parallel arc current detection threshold.

3. The method according to claim 1, characterized in that, The step of the solid-state power controller performing weighted enhancement processing on the voltage signal and the current signal based on the noise compensation enhancement weighting coefficient to obtain enhanced voltage signal and enhanced current signal specifically includes: The solid-state power controller divides the voltage signal and the current signal into different sub-time periods, and the length of the sub-time period is determined according to the change frequency of the current abrupt characteristic component. The solid-state power controller calculates the standard deviation and mean of the signal within the sub-time period; The solid-state power controller uses the ratio of the standard deviation to the mean as the segmented enhancement weight coefficient for the sub-time period; The solid-state power controller performs weighted enhancement processing on the voltage and current signals in each of the sub-time periods based on the segmented enhancement weighting coefficients. The solid-state power controller splices the weighted and enhanced signals from each of the sub-time periods to obtain enhanced voltage and enhanced current signals.

4. The method according to claim 1, characterized in that, After the step of the solid-state power controller performing weighted enhancement processing on the voltage signal and the current signal of the circuit based on enhancement weighting coefficients to obtain enhanced voltage signals and enhanced current signals, the method further includes: The solid-state power controller extracts waveform characteristic parameters of the enhanced voltage signal and the enhanced current signal; The solid-state power controller matches the waveform feature parameters in a preset interference feature library, and when an interference feature is matched, it removes the signal segment corresponding to the interference feature.

5. The method according to claim 1, characterized in that, After the solid-state power controller determines that the circuit is experiencing a series arc fault or a parallel arc fault, the method further includes: The solid-state power controller acquires historical data of voltage and current signals within a preset backtracking time window prior to the fault confirmation time. The solid-state power controller sends the historical data to the operation and maintenance management center; After receiving the historical data, the operation and maintenance management center performs time-frequency domain joint analysis on the historical data to obtain the characteristic parameter change trajectory in the arc fault evolution process. The operation and maintenance management center constructs an arc fault evolution model based on the characteristic parameter change trajectory and historical fault database; The operation and maintenance management center uses the arc fault evolution model to infer the initial triggering time and triggering cause of the arc fault; The operation and maintenance management center generates a fault analysis report based on the initial trigger time and the trigger reason.

6. The method according to claim 5, characterized in that, After the step of the operation and maintenance management center generating a fault analysis report based on the initial trigger time and the trigger cause, the method further includes: The operation and maintenance management center statistically analyzes the signal-to-noise ratio values ​​when arc faults are successfully detected in the trajectory of the characteristic parameter changes. The operation and maintenance management center uses the product of the maximum value of the signal-to-noise ratio and the preset safety coefficient as the optimized signal-to-noise ratio threshold. The operation and maintenance management center uses the minimum value of the current mutation characteristic component amplitude in the initial stage of the arc fault in the arc fault evolution model as the optimization benchmark amplitude. The operation and maintenance management center calculates the time interval from the initial trigger time to the fault confirmation time; The operation and maintenance management center uses the time interval as an optimization time window; The solid-state power controller replaces the preset signal-to-noise ratio threshold, the preset reference amplitude, and the preset time window with the optimized signal-to-noise ratio threshold, the optimized reference amplitude, and the optimized time window, respectively. The solid-state power controller performs subsequent arc fault detection based on the replaced preset signal-to-noise ratio threshold, the preset reference amplitude, and the preset time window.

7. A solid-state power controller, characterized in that, The solid-state power controller includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the solid-state power controller to perform the method as described in any one of claims 1-6.

8. A computer program product containing instructions, characterized in that, When the computer program product is run on the solid-state power controller, it causes the solid-state power controller to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the solid-state power controller, the solid-state power controller performs the method as described in any one of claims 1-6.

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