Fault arc detection method and system based on frequency band analysis and parameter condition judgment

Abstract

The present application relates to the technical field of electrical safety detection, and in particular to a fault arc detection method and system based on frequency band analysis and parameter condition judgment, which comprises: collecting current signals according to a set collection period, and performing current data preprocessing on the collected current data of each period; after the preprocessing, performing feature extraction on the current data, performing frequency band analysis and condition judgment according to the extracted features, and judging whether the fault arc existence condition is met; when the fault arc existence condition is met, accumulating and reducing the fault period through a fault period accumulation mechanism and a fault period reduction mechanism; dynamically adjusting the fault judgment period threshold according to the current RMS value, and judging whether there is a fault arc according to the dynamically adjusted fault judgment period threshold. The present application realizes efficient and accurate fault arc detection by combining frequency band current amplitude variation analysis and condition judgment.

Description

Technical Field

[0001] This invention relates to the field of electrical safety testing technology, specifically to a fault arc detection method and system based on frequency band analysis and parameter condition judgment. Background Technology

[0002] Arc fault detection technology is a safety technique for preventing electrical fires. Arc faults are typically caused by short circuits, aging wiring, poor wiring connections, or electrical load failures. Arc faults are characterized by their long duration and concentrated energy, making them highly susceptible to causing fires. Conventional circuit protection devices cannot detect this type of fault. Therefore, specialized detection methods are needed for this type of circuit fault.

[0003] Currently, fault arc detection technology mainly relies on the extraction and analysis of electrical signal features. Common technical solutions include detection based on low-frequency current waveform distortion, detection based on high-frequency current features, and detection based on time-frequency analysis. In low-frequency current waveform distortion detection, low-frequency methods only focus on the power frequency and its low-order harmonics, making it difficult to capture the rapidly changing high-frequency characteristic signals during arc discharge, resulting in insufficient sensitivity and accuracy. In high-frequency current feature detection, the limitation of single-frequency band detection is that the current harmonic characteristics of different electrical appliances vary greatly, and power grid noise and electromagnetic interference from other equipment may affect specific frequency bands, leading to misjudgments. In time-frequency analysis-based detection, time-frequency analysis methods require the analysis of signals in a high-dimensional space, resulting in high computational complexity, and the influence of noise and power grid interference may cause instability in time-frequency features. Summary of the Invention

[0004] This invention addresses the shortcomings of existing fault arc detection methods in terms of false identification, real-time performance, adaptability, and anti-interference capabilities. It provides a fault arc detection method and system based on frequency band analysis and parameter condition judgment to achieve efficient and accurate fault arc detection.

[0005] This invention is achieved through the following technical solution:

[0006] A fault arc detection method based on frequency band analysis and parameter condition judgment is provided. The method includes the following steps:

[0007] Step S10: Acquire the current signal of the circuit in the device to be tested according to the set acquisition cycle, and perform current data preprocessing on the current data of each cycle.

[0008] Step S20: Convert the preprocessed current data into a spectrum through Fourier transform, extract the key features of the current data in the frequency domain, segment the current frequency band, perform frequency band analysis and condition judgment based on the extracted features, and determine whether the conditions for the existence of a fault arc are met.

[0009] Step S30: When the condition for the existence of a fault arc is met, the fault cycle is accumulated and reduced through the fault cycle accumulation mechanism and the fault cycle reduction mechanism;

[0010] Step S40: Dynamically adjust the fault determination cycle threshold according to the current RMS value, and determine whether there is a fault arc based on the dynamically adjusted fault determination cycle threshold.

[0011] Preferably, the current data preprocessing step S10 for each acquired cycle includes noise suppression and filtering to ensure good current data quality during subsequent feature extraction. After noise suppression and filtering, the effective value (RMS) of the current data for the current cycle is calculated. This value reflects the energy level of the current in the current cycle and is used to determine whether the current in the current cycle is within the normal range. The load status of the current cycle is determined based on the calculated RMS value. When the calculated RMS value is lower than 1.5A, it is determined that the current cycle is in a low load or no-load state and lacks sufficient signal characteristics. It is then determined whether to skip further detection of the current cycle and directly enter the next cycle to avoid misjudgment or invalid analysis due to insufficient signal amplitude.

[0012] Preferably, in step S20, the preprocessed current data is converted into a spectrum using Fourier transform to extract key features of the current data in the frequency domain, including:

[0013] Fundamental frequency amplitude characteristics: This is usually the amplitude corresponding to the power frequency, which is typically 50Hz. It serves as a reference for normal operation and is used for comparative analysis of amplitude changes during the detection process.

[0014] High-frequency component characteristics: Fault arcs usually show significant changes in the high-frequency band. The cumulative amplitude change is obtained by calculating the sum of the amplitudes of all frequency points in each frequency band as the high-frequency component characteristics, which is an important basis for fault arc detection. The frequency calculation range is from the 40th point, corresponding to 2000Hz, to the 512th point, corresponding to 25600Hz, and is based on a step size of 50 points (2500Hz).

[0015] Low-frequency harmonic characteristics: The fundamental component is removed, and the sum of the amplitudes of the remaining low-frequency harmonics (within the 10th order) is calculated to obtain the low-frequency harmonic characteristics in order to assess the degree of harmonic distortion.

[0016] Total amplitude characteristics: The total amplitude of the entire spectrum is calculated to obtain the total amplitude characteristics, which reflect the overall energy distribution of the current data.

[0017] Preferably, step S20, which involves segmenting the current frequency band, performing frequency band analysis and condition judgment based on the extracted features, and determining whether the conditions for the existence of a fault arc are met, includes:

[0018] Current frequency band segmentation: From the 40th FFT point to the 512th FFT point, corresponding to 2000Hz to 25600Hz, it is divided into a certain number of frequency bands with a fixed step size, that is, every 50 FFT points, which is equivalent to every 2500Hz. The sum of the amplitude is accumulated in each frequency band to obtain a multi-band amplitude accumulation array for the current period.

[0019] Frequency band analysis: The cumulative amplitude of each preset frequency band in the current period is compared with the cumulative amplitude of the same frequency band recorded in the normal period. The rate of change of each frequency band is calculated. The rate of change reflects the abnormal increase or decrease of the amplitude of a specific frequency band in the current period. When the amplitude of a frequency band is higher than the preset threshold than the normal state, it is determined that the frequency band is abnormal. The number of frequency bands exceeding the threshold is counted. When the number of frequency bands that have changed is greater than 2, the frequency band abnormality condition is met in this period.

[0020] Conditional Judgment: Judgment conditions are set, including changes in fundamental frequency amplitude, total amplitude, effective current value, and harmonic distortion. Fundamental frequency amplitude change refers to a rate of change of less than 1.1 between the current cycle and the normal cycle, to exclude fundamental frequency fluctuations caused by normal electrical operation. First, the degree of harmonic distortion is monitored. Total amplitude change refers to the total amplitude change being maintained within a certain range. Effective current value change refers to the change in the RMS value of the current cycle compared to the RMS value under normal conditions being controlled within a certain range. Harmonic distortion refers to calculating the difference between the total amplitude of the 10th harmonic and the previous cycle. When the harmonic distortion rate exceeds 6%, an abnormal value is detected. The constant-cycle judgment further enhances the reliability of the judgment. It integrates multi-band and multi-condition judgment. Only when the cumulative amplitude change of multiple frequency bands (at least 3 frequency bands) exceeds the set threshold, and other conditions (fundamental frequency, total amplitude, RMS, etc.) meet the fault characteristics, will it be considered that there is a fault arc in the current cycle. That is, when other set judgment conditions are met at the same time, the judgment frequency band analysis meets the abnormal conditions, that is, it meets the fault arc conditions, and there may be a fault arc phenomenon in this cycle. The fault flag count is increased. In the next judgment, when the fault flag count is greater than 0, the harmonic distortion rate is no longer judged, and other conditions are judged directly.

[0021] Preferably, step S30, when the condition for the existence of a fault arc is met, includes the steps of accumulating and reducing the fault period through a fault period accumulation mechanism and a fault period reduction mechanism, which include:

[0022] The cumulative mechanism of the fault cycle: When the current cycle meets the fault arc condition, the alarm is not triggered immediately, but the fault cycle counter is incremented. This cumulative mechanism ensures that the fault is finally confirmed only when the detection result shows abnormality for several consecutive cycles (accumulating to the dynamically adjusted threshold number of cycles). This helps to eliminate false judgments caused by short-term fluctuations or occasional noise in a single cycle. For example, under normal conditions, if the current RMS is less than 1.5A, the circuit is considered to be in an unloaded or low-power state. At this time, the normal cycle flag count is increased, and when no fault is detected for several consecutive cycles, the fault flag count is decreased, eventually returning to the initial state.

[0023] Fault cycle decay mechanism: When no abnormality is detected within a continuous cycle, the fault cycle decay mechanism is activated to gradually reduce the fault cycle counter. The decay mechanism ensures that the fault flag can drop in a timely manner after the fault state disappears or returns to normal, avoiding the false alarm state for a long time. At the same time, when the fault cycle counter returns to zero, the normal cycle characteristic data is updated, including the cumulative amplitude of each frequency band, the fundamental frequency, the total amplitude, and the RMS value, in order to adapt to the slow changes in the environment or load.

[0024] Preferably, the step S40, which involves dynamically adjusting the fault determination period threshold based on the current RMS value and determining whether a fault arc exists based on the dynamically adjusted fault determination period threshold, includes:

[0025] Dynamically Adjusted Threshold: The fault arc detection cycle threshold is not fixed, but dynamically adjusted according to the current RMS value. When the current RMS value is greater than 6.0A, the detection current is large, and the arc phenomenon is more dangerous. Therefore, the fault arc detection cycle threshold is reduced to achieve faster response. When the current RMS value is less than or equal to 6.0A, the detection current is small, and the fault arc detection cycle threshold is restored to the default state, that is, 8 cycles.

[0026] Alarm judgment: When the value of the fault cycle counter is greater than the dynamically adjusted fault arc judgment cycle threshold, the existence of a fault arc is determined and an alarm is triggered immediately.

[0027] Update features: When the current cycle detection result does not meet the alarm conditions and the fault cycle counter is zero, the feature data of the current cycle, including the cumulative amplitude of each frequency band, the fundamental frequency, the total amplitude and the RMS value, are updated to the new normal cycle reference data.

[0028] Furthermore, to achieve the above objectives, the present invention also proposes a fault arc detection system based on frequency band analysis and parameter condition judgment, wherein the fault arc detection system based on frequency band analysis and parameter condition judgment includes:

[0029] Current data sampling and preprocessing module: used to acquire current signals according to the set acquisition period, and to preprocess the current data of each acquired period;

[0030] Feature extraction and fault judgment module: It is used to convert the preprocessed current data into a spectrum through Fourier transform, extract the key features of the current data in the frequency domain, segment the current frequency band, perform frequency band analysis and condition judgment based on the extracted features, and determine whether the conditions for the existence of fault arc are met.

[0031] Fault cycle accumulation and reduction module: When the condition for the existence of a fault arc is met, the fault cycle is accumulated and reduced through the fault cycle accumulation mechanism and the fault cycle reduction mechanism.

[0032] Dynamic threshold adjustment and alarm judgment module: used to dynamically adjust the fault judgment cycle threshold according to the current RMS value, and to determine whether there is a fault arc based on the dynamically adjusted fault judgment cycle threshold.

[0033] Furthermore, to achieve the above objectives, the present invention also proposes a fault arc detection device based on frequency band analysis and parameter condition judgment. The device includes: a memory, a processor, and programs such as a fault arc detection algorithm based on frequency band analysis and parameter condition judgment stored in the memory and executable on the processor. The fault arc detection algorithm and other programs based on frequency band analysis and parameter condition judgment are the steps for implementing the fault arc detection method based on frequency band analysis and parameter condition judgment as described above.

[0034] In addition, to achieve the above objectives, the present invention also provides a computer program product, which includes programs such as a fault arc detection algorithm based on frequency band analysis and parameter condition judgment. When the fault arc detection algorithm based on frequency band analysis and parameter condition judgment is executed by a processor, it implements the fault arc detection method based on frequency band analysis and parameter condition judgment as described above.

[0035] The advantages and effects of this invention are:

[0036] This invention offers significant advantages over existing technologies in terms of adaptability, false identification rate, detection accuracy, and real-time performance. Through multi-band detection, dynamic threshold adjustment, and a high sampling rate design, this invention can accurately and quickly detect fault arcs in complex electrical environments, reducing false positives, improving the reliability of fault identification, and possessing greater versatility and adaptability, thus meeting the arc detection needs of different electrical appliances and operating environments. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart of the fault arc detection method based on frequency band analysis and parameter condition judgment of the present invention.

[0039] Figure 2 This is a schematic diagram of the fault arc detection system based on frequency band analysis and parameter condition judgment according to the present invention.

[0040] Figure 3 This is a schematic block diagram of the electronic device for fault arc detection based on frequency band analysis and parameter condition judgment according to the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] like Figure 1 As shown, in one embodiment of the present invention, the fault arc detection method based on frequency band analysis and parameter condition judgment includes the following steps:

[0043] Step S10: Acquire the current signal of the circuit in the device under test according to the set acquisition period. Perform current data preprocessing on the current data of each acquired period. For example, use a high sampling rate, such as 51.2kHz, to sample the current signal to ensure that the acquired data can cover the frequency range from 0Hz to a maximum of 25.6kHz. The number of data points in each sampling period is set to 1024. This data length (must be a power of 2) facilitates the subsequent Fast Fourier Transform (FFT) processing and also improves the frequency resolution.

[0044] Specifically, step S10, which involves preprocessing the current data for each cycle, includes noise suppression and filtering to ensure good current data quality during subsequent feature extraction. After noise suppression and filtering, the effective value (RMS) of the current data for the current cycle is calculated. This value reflects the energy level of the current in the current cycle and is used to determine whether the current in the current cycle is within the normal range. The load status of the current cycle is determined based on the calculated RMS value. When the calculated RMS value is lower than 1.5A, it is determined that the current cycle is in a low-load or no-load state and lacks sufficient signal characteristics. It is then determined whether to skip further detection of the current cycle and directly enter the next cycle to avoid misjudgment or invalid analysis due to insufficient signal amplitude.

[0045] Step S20: Convert the preprocessed current data into a spectrum using Fourier transform, extract the key features of the current data in the frequency domain, segment the current frequency band, perform frequency band analysis and condition judgment based on the extracted features, and determine whether the conditions for the existence of a fault arc are met.

[0046] Specifically, in step S20, the preprocessed current data is converted into a spectrum using Fourier transform to extract key features of the current data in the frequency domain, including:

[0047] Fundamental frequency amplitude characteristics: This is usually the amplitude corresponding to the power frequency, which is typically 50Hz. It serves as a reference for normal operation and is used for comparative analysis of amplitude changes during the detection process.

[0048] High-frequency component characteristics: Fault arcs usually show significant changes in the high-frequency band. The cumulative amplitude change is obtained by calculating the sum of the amplitudes of all frequency points in each frequency band as the high-frequency component characteristics, which is an important basis for fault arc detection. The frequency calculation range is from the 40th point, corresponding to 2000Hz, to the 512th point, corresponding to 25600Hz, and is based on a step size of 50 points (2500Hz).

[0049] Low-frequency harmonic characteristics: The fundamental component is removed, and the sum of the amplitudes of the remaining low-frequency harmonics (within the 10th order) is calculated to obtain the low-frequency harmonic characteristics in order to assess the degree of harmonic distortion.

[0050] Total amplitude characteristics: The total amplitude of the entire spectrum is calculated to obtain the total amplitude characteristics, which reflect the overall energy distribution of the current data.

[0051] Specifically, step S20 involves segmenting the current frequency band, performing frequency band analysis and condition judgment based on the extracted features, and determining whether the conditions for the existence of a fault arc are met.

[0052] Current frequency band segmentation: From the 40th FFT point to the 512th FFT point, corresponding to 2000Hz to 25600Hz, it is divided into a certain number of frequency bands with a fixed step size, that is, every 50 FFT points, which is equivalent to every 2500Hz. The sum of the amplitude is accumulated in each frequency band to obtain a multi-band amplitude accumulation array for the current period.

[0053] Frequency band analysis: The cumulative amplitude results of each preset frequency band in the current period, such as 2000Hz–4500Hz, 4500Hz–7000Hz, 7000Hz–9500Hz, etc., are compared with the cumulative amplitude of the same frequency band recorded in the normal period. The rate of change of each frequency band is calculated. The rate of change reflects the abnormal increase or decrease of the amplitude of a specific frequency band in the current period. When the amplitude of a frequency band is higher than the preset threshold than the normal state, for example, more than 1.6 times, it is determined that the frequency band is abnormal. The number of frequency bands exceeding the threshold is counted. When the number of frequency bands that have changed is greater than 2, the frequency band abnormality condition is met in this period.

[0054] Conditional Judgment: Judgment conditions are set, including fundamental frequency amplitude change, total amplitude change, current RMS value change, and harmonic variation. Fundamental frequency amplitude change refers to the rate of change of the fundamental frequency amplitude between the current cycle and the normal cycle being less than 1.1, to exclude fundamental frequency fluctuations caused by normal electrical operation. First, the degree of harmonic distortion is monitored. Total amplitude change refers to the total amplitude change being maintained within a certain range, for example, a rate of change of not less than 0.95, to ensure the overall energy distribution is basically stable. Current RMS value change refers to the change between the current cycle's RMS value and the RMS value under normal conditions being controlled within a certain range, for example, a rate of change less than 1.6, to avoid misjudgment due to sudden load changes. Harmonic variation refers to calculating the difference between the total amplitude of the harmonics within the last 10th order and the previous cycle. When the harmonic distortion rate exceeds 6... When the value reaches %, the system enters the abnormal cycle judgment stage to further enhance the reliability of the judgment. It integrates multi-band and multi-condition judgment. Only when the cumulative amplitude change of multiple frequency bands (at least 3 frequency bands) exceeds the set threshold, and other conditions (fundamental frequency, total amplitude, RMS, etc.) meet the fault characteristics, will the current cycle be considered to have a fault arc. That is, when other set judgment conditions are met at the same time, such as the fundamental amplitude change is less than 10%, the current RMS value change is less than 60%, and the total amplitude change is greater than or equal to 95%, the abnormal conditions are met in the frequency band analysis, that is, the fault arc conditions are met, and the fault arc phenomenon may exist in this cycle. The fault flag count is increased. In the next judgment, when the fault flag count is greater than 0, the harmonic distortion rate is no longer judged, and other conditions are judged directly.

[0055] Step S30: When the condition for the existence of a fault arc is met, the fault cycle is accumulated and reduced through the fault cycle accumulation mechanism and the fault cycle reduction mechanism.

[0056] Specifically, step S30, when the condition for the existence of a fault arc is met, includes the steps of accumulating and reducing the fault period through the fault period accumulation mechanism and the fault period reduction mechanism, which include:

[0057] The cumulative mechanism of the fault cycle: When the current cycle meets the fault arc condition, the alarm is not triggered immediately, but the fault cycle counter is incremented. This cumulative mechanism ensures that the fault is finally confirmed only when the detection result shows abnormality for several consecutive cycles (accumulating to the dynamically adjusted threshold number of cycles). This helps to eliminate false judgments caused by short-term fluctuations or occasional noise in a single cycle. For example, under normal conditions, if the current RMS is less than 1.5A, the circuit is considered to be in an unloaded or low-power state. At this time, the normal cycle flag count is increased, and when no fault is detected for several consecutive cycles, the fault flag count is decreased, eventually returning to the initial state.

[0058] Fault cycle decay mechanism: When no abnormality is detected within a continuous cycle, the fault cycle decay mechanism is activated to gradually reduce the fault cycle counter. The decay mechanism ensures that the fault flag can drop in a timely manner after the fault state disappears or returns to normal, avoiding prolonged false alarms. At the same time, when the fault cycle counter reaches zero, the normal cycle characteristic data is updated, including the cumulative amplitude, fundamental frequency, total amplitude, and RMS value of each frequency band, in order to adapt to slow changes in the environment or load. For example, if the fault condition is not met continuously within 3 cycles, the fault cycle count will gradually decrease. When the fault cycle counter reaches the set threshold (default 8 cycles, which can be dynamically adjusted), a fault arc will be confirmed and an alarm signal will be triggered.

[0059] Step S40: Dynamically adjust the fault determination cycle threshold according to the current RMS value, and determine whether there is a fault arc based on the dynamically adjusted fault determination cycle threshold.

[0060] Specifically, step S40, which involves dynamically adjusting the fault determination period threshold based on the current RMS value and determining whether a fault arc exists based on the dynamically adjusted fault determination period threshold, includes the following steps:

[0061] Dynamically Adjusted Threshold: The fault arc detection cycle threshold is not fixed, but dynamically adjusted according to the current RMS value. When the current RMS value is greater than 6.0A, the detection current is large, and the arc phenomenon is more dangerous. Therefore, the fault arc detection cycle threshold is reduced to achieve a faster response. When the current RMS value is less than or equal to 6.0A, the detection current is small, and the fault arc detection cycle threshold is restored to the default state, which is 8 cycles. For example, when the current RMS exceeds 6.0A, for every 2.0A increase in the current RMS exceeding 6.0A, the cycle threshold is reduced by 1. The part less than 2.0A is calculated as 2.0A, but it cannot be less than 4 cycles, i.e., 0.08s, to avoid misjudgment.

[0062] Alarm judgment: When the value of the fault cycle counter is greater than the dynamically adjusted fault arc judgment cycle threshold, the existence of the fault arc is determined and an alarm is triggered immediately, for example, by calling the alarm pulse signal;

[0063] Update features: When the current cycle detection result does not meet the alarm conditions and the fault cycle counter is zero, the feature data of the current cycle, including the cumulative amplitude of each frequency band, fundamental frequency, total amplitude and RMS value, are updated to new normal cycle reference data. In addition, regardless of the fault counter value, the low frequency harmonic amplitude of the current cycle is saved and extracted for comparison in the next cycle. This update mechanism enables the system to adapt to the slow characteristic adjustment of electrical appliances caused by load changes, environmental changes and other factors during long-term operation, thereby improving the accuracy and adaptability of subsequent detection.

[0064] In addition, such as Figure 2 As shown, in one embodiment of the present invention, a fault arc detection system based on frequency band analysis and parameter condition judgment is proposed. The fault arc detection system based on frequency band analysis and parameter condition judgment includes:

[0065] Current data sampling and preprocessing module: used to acquire current signals according to the set acquisition period, and to preprocess the current data of each acquired period;

[0066] Feature extraction and fault judgment module: It is used to convert the preprocessed current data into a spectrum through Fourier transform, extract the key features of the current data in the frequency domain, segment the current frequency band, perform frequency band analysis and condition judgment based on the extracted features, and determine whether the conditions for the existence of fault arc are met.

[0067] Fault cycle accumulation and reduction module: When the condition for the existence of a fault arc is met, the fault cycle is accumulated and reduced through the fault cycle accumulation mechanism and the fault cycle reduction mechanism.

[0068] Dynamic threshold adjustment and alarm judgment module: used to dynamically adjust the fault judgment cycle threshold according to the current RMS value, and to determine whether there is a fault arc based on the dynamically adjusted fault judgment cycle threshold.

[0069] The fault arc detection system based on frequency band analysis and parameter condition judgment provided in this application, employing the fault arc detection method based on frequency band analysis and parameter condition judgment in the above embodiments, can solve the technical problems of poor real-time performance, adaptability, and anti-interference ability of existing fault arc detection methods. Compared with the prior art, the beneficial effects of the fault arc detection system based on frequency band analysis and parameter condition judgment provided in this application are the same as those of the fault arc detection method based on frequency band analysis and parameter condition judgment provided in the above embodiments, and other technical features of the fault arc detection system based on frequency band analysis and parameter condition judgment are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0070] This application provides a fault arc detection device based on frequency band analysis and parameter condition judgment, which can be applied to intelligent measuring switches. The fault arc detection device based on frequency band analysis and parameter condition judgment includes: at least one processor; and a memory communicatively connected to at least one processor; wherein the memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor to enable at least one processor to execute the fault arc detection method based on frequency band analysis and parameter condition judgment in the above embodiment 1.

[0071] like Figure 3 As shown in the illustration, in one embodiment of the present invention, a structural schematic diagram of a fault arc detection device suitable for implementing the frequency band analysis and parameter condition judgment embodiments of this application is presented. The fault arc detection device based on frequency band analysis and parameter condition judgment in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), etc., as well as fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The fault arc detection device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0072] Figure 3The fault arc detection device based on frequency band analysis and parameter condition judgment shown may include a processor 1001 (e.g., a central processing unit, graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1002 or a program loaded from storage device 1003 into machine-readable storage medium (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the fault arc detection device based on frequency band analysis and parameter condition judgment. The processor 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication unit 1009. Communication unit 1009 allows the fault arc detection device based on frequency band analysis and parameter condition judgment to exchange data with other devices wirelessly or via wired communication. Although the figure shows fault arc detection devices based on frequency band analysis and parameter condition judgment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0073] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication system, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processor 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0074] The fault arc detection device based on frequency band analysis and parameter condition judgment provided in this application, employing the fault arc detection method based on frequency band analysis and parameter condition judgment in the above embodiments, can solve the technical problems of poor real-time performance, adaptability, and anti-interference capability of existing fault arc detection methods. Compared with the prior art, the beneficial effects of the fault arc detection device based on frequency band analysis and parameter condition judgment provided in this application are the same as those of the fault arc detection method based on frequency band analysis and parameter condition judgment provided in the above embodiments, and other technical features in this fault arc detection device based on frequency band analysis and parameter condition judgment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0075] The various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0076] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the fault arc detection method based on frequency band analysis and parameter condition judgment as described above.

[0077] The computer program product provided in this application can solve the technical problems of poor real-time performance, adaptability, and anti-interference ability of existing fault arc detection methods. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the fault arc detection method based on frequency band analysis and parameter condition judgment provided in the above embodiments, and will not be repeated here.

[0078] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A fault arc detection method based on frequency band analysis and parameter condition judgment, characterized in that, The method includes: Step S10: Acquire the current signal of the circuit in the device to be tested according to the set acquisition cycle, and perform current data preprocessing on the current data of each cycle. Step S20: Convert the preprocessed current data into a spectrum through Fourier transform, extract the features of the current data in the frequency domain, segment the current frequency band, perform frequency band analysis and condition judgment based on the extracted features, and determine whether the conditions for the existence of a fault arc are met. Step S30: When the condition for the existence of a fault arc is met, the fault cycle is accumulated and reduced through the fault cycle accumulation mechanism and the fault cycle reduction mechanism; Step S40: Dynamically adjust the fault determination cycle threshold according to the current RMS value, and determine whether there is a fault arc based on the dynamically adjusted fault determination cycle threshold. The step S20, which involves segmenting the current frequency band, performing frequency band analysis and condition judgment based on the extracted features, and determining whether the conditions for the existence of a fault arc are met, includes the following steps: Current frequency band segmentation: From the 40th FFT point to the 512th FFT point, corresponding to 2000Hz to 25600Hz, it is divided into a certain number of frequency bands with a fixed step size, that is, every 50 FFT points, which is equivalent to every 2500Hz. The sum of the amplitude is accumulated in each frequency band to obtain a multi-band amplitude accumulation array for the current period. Frequency band analysis: The cumulative amplitude of each preset frequency band in the current period is compared with the cumulative amplitude of the same frequency band recorded in the normal period. The rate of change of each frequency band is calculated. The rate of change reflects the abnormal increase or decrease of the amplitude of a specific frequency band in the current period. When the amplitude of a frequency band is higher than the preset threshold than the normal state, it is determined that the frequency band is abnormal. The number of frequency bands exceeding the threshold is counted. When the number of frequency bands that have changed is greater than 2, the frequency band abnormality condition is met in this period. Conditional Judgment: Set judgment conditions, including fundamental frequency amplitude change, total amplitude change, current effective value change, and harmonic change. Fundamental frequency amplitude change refers to the requirement that the rate of change of the fundamental frequency amplitude between the current cycle and the normal cycle is less than 1.

1. Total amplitude change refers to the total amplitude change being maintained within a certain range. Current effective value change refers to the RMS value of the current cycle being controlled within a certain range compared with the RMS value under normal conditions. Harmonic change refers to the difference between the total amplitude of the harmonics within the last 10th order and the previous cycle. When the harmonic distortion rate exceeds 6%, it enters the abnormal cycle judgment. When other set judgment conditions are met at the same time, the abnormal conditions are met in the frequency band analysis, that is, the fault arc condition is met. The fault arc phenomenon exists in this cycle, and the fault flag count is increased. In the next judgment, when the fault flag count is greater than 0, the harmonic distortion rate is no longer judged, and other conditions are judged directly. In step S30, when the condition for the existence of a fault arc is met, the steps of accumulating and reducing the fault period through the fault period accumulation mechanism and the fault period reduction mechanism include: Fault cycle accumulation mechanism: When the current cycle is detected to meet the fault arc condition, the alarm is not triggered immediately, but the fault cycle counter is incremented; Fault cycle reduction mechanism: When no abnormality is detected within a continuous cycle, the fault cycle reduction mechanism is activated to reduce the fault cycle counter. At the same time, when the fault cycle counter reaches zero, the normal cycle characteristic data is updated. The step S40, which involves dynamically adjusting the fault determination period threshold based on the current RMS value and determining whether a fault arc exists based on the dynamically adjusted fault determination period threshold, includes: Dynamically adjust threshold: The fault arc determination cycle threshold is dynamically adjusted based on the current RMS value. When the current RMS value is greater than 6.0A, the fault arc determination cycle threshold is reduced. When the current RMS value is less than or equal to 6.0A, the fault arc determination cycle threshold is restored to the default state. Alarm judgment: When the value of the fault cycle counter is greater than the dynamically adjusted fault arc judgment cycle threshold, the existence of a fault arc is determined and an alarm is triggered immediately. Update features: When the current cycle detection result does not meet the alarm conditions and the fault cycle counter is zero, the feature data of the current cycle, including the cumulative amplitude of each frequency band, the fundamental frequency, the total amplitude and the RMS value, are updated to the new normal cycle reference data.

2. The fault arc detection method based on frequency band analysis and parameter condition judgment according to claim 1, characterized in that, The step S10, which involves preprocessing the current data for each cycle, includes noise suppression and filtering. After noise suppression and filtering are completed, the effective value (RMS) of the current data for the current cycle is calculated. Based on the calculated RMS value, the load status of the current cycle is determined, and it is determined whether to skip further detection of the current cycle and proceed directly to the next cycle.

3. The fault arc detection method based on frequency band analysis and parameter condition judgment according to claim 1, characterized in that, In step S20, the preprocessed current data is converted into a spectrum using Fourier transform to extract the features of the current data in the frequency domain, including: Fundamental frequency amplitude characteristics: The amplitude corresponding to the power frequency, serving as a reference standard for normal operation; High-frequency component characteristics: The fault arc changes in the high-frequency band. The cumulative amplitude change is obtained by calculating the sum of the amplitudes of all frequency points in each frequency band as the high-frequency component characteristics. Low-frequency harmonic characteristics: The low-frequency harmonic characteristics are obtained by removing the fundamental frequency component and calculating the sum of the amplitudes of the remaining low-frequency harmonics. Total amplitude characteristics: Calculate the sum of the amplitudes of the entire spectrum to obtain the total amplitude characteristics.

4. A fault arc detection system based on frequency band analysis and parameter condition judgment, characterized in that, The method for detecting fault arcs based on frequency band analysis and parameter condition judgment as described in claim 1 includes: Current data sampling and preprocessing module: used to acquire current signals according to the set acquisition period, and to preprocess the current data of each acquired period; Feature extraction and fault judgment module: It is used to convert the preprocessed current data into a spectrum through Fourier transform, extract the features of the current data in the frequency domain, segment the current frequency band, perform frequency band analysis and condition judgment based on the extracted features, and determine whether the conditions for the existence of fault arc are met. Fault cycle accumulation and reduction module: When the condition for the existence of a fault arc is met, the fault cycle is accumulated and reduced through the fault cycle accumulation mechanism and the fault cycle reduction mechanism. Dynamic threshold adjustment and alarm judgment module: used to dynamically adjust the fault judgment cycle threshold according to the current RMS value, and to determine whether there is a fault arc based on the dynamically adjusted fault judgment cycle threshold; The feature extraction and fault judgment module performs segmentation of the current frequency band, frequency band analysis and condition judgment based on the extracted features, and the steps to determine whether the conditions for the existence of a fault arc are met include: Current frequency band segmentation: From the 40th FFT point to the 512th FFT point, corresponding to 2000Hz to 25600Hz, it is divided into a certain number of frequency bands with a fixed step size, that is, every 50 FFT points, which is equivalent to every 2500Hz. The sum of the amplitude is accumulated in each frequency band to obtain a multi-band amplitude accumulation array for the current period. Frequency band analysis: The cumulative amplitude of each preset frequency band in the current period is compared with the cumulative amplitude of the same frequency band recorded in the normal period. The rate of change of each frequency band is calculated. The rate of change reflects the abnormal increase or decrease of the amplitude of a specific frequency band in the current period. When the amplitude of a frequency band is higher than the preset threshold than the normal state, it is determined that the frequency band is abnormal. The number of frequency bands exceeding the threshold is counted. When the number of frequency bands that have changed is greater than 2, the frequency band abnormality condition is met in this period. Conditional Judgment: Set judgment conditions, including fundamental frequency amplitude change, total amplitude change, current effective value change, and harmonic change. Fundamental frequency amplitude change refers to the requirement that the rate of change of the fundamental frequency amplitude between the current cycle and the normal cycle is less than 1.

1. Total amplitude change refers to the total amplitude change being maintained within a certain range. Current effective value change refers to the RMS value of the current cycle being controlled within a certain range compared with the RMS value under normal conditions. Harmonic change refers to the difference between the total amplitude of the harmonics within the last 10th order and the previous cycle. When the harmonic distortion rate exceeds 6%, it enters the abnormal cycle judgment. When other set judgment conditions are met at the same time, the abnormal conditions are met in the frequency band analysis, that is, the fault arc condition is met. The fault arc phenomenon exists in this cycle, and the fault flag count is increased. In the next judgment, when the fault flag count is greater than 0, the harmonic distortion rate is no longer judged, and other conditions are judged directly. The fault cycle accumulation and reduction module, when the condition for the existence of a fault arc is met, includes the following steps for accumulating and reducing the fault cycle through the fault cycle accumulation mechanism and the fault cycle reduction mechanism: Fault cycle accumulation mechanism: When the current cycle is detected to meet the fault arc condition, the alarm is not triggered immediately, but the fault cycle counter is incremented; Fault cycle reduction mechanism: When no abnormality is detected within a continuous cycle, the fault cycle reduction mechanism is activated to reduce the fault cycle counter. At the same time, when the fault cycle counter reaches zero, the normal cycle characteristic data is updated. The dynamic adjustment threshold and alarm judgment module dynamically adjusts the fault judgment cycle threshold based on the current RMS value, and the step of judging whether a fault arc exists based on the dynamically adjusted fault judgment cycle threshold includes: Dynamically adjust threshold: The fault arc determination cycle threshold is dynamically adjusted based on the current RMS value. When the current RMS value is greater than 6.0A, the fault arc determination cycle threshold is reduced. When the current RMS value is less than or equal to 6.0A, the fault arc determination cycle threshold is restored to the default state. Alarm judgment: When the value of the fault cycle counter is greater than the dynamically adjusted fault arc judgment cycle threshold, the existence of a fault arc is determined and an alarm is triggered immediately. Update features: When the current cycle detection result does not meet the alarm conditions and the fault cycle counter is zero, the feature data of the current cycle, including the cumulative amplitude of each frequency band, the fundamental frequency, the total amplitude and the RMS value, are updated to the new normal cycle reference data.

5. A fault arc detection device based on frequency band analysis and parameter condition judgment, characterized in that, include: The system includes a memory, a processor, and a fault arc detection program based on frequency band analysis and parameter condition judgment stored in the memory and executable on the processor. When the fault arc detection program based on frequency band analysis and parameter condition judgment is executed by the processor, it implements the fault arc detection method based on frequency band analysis and parameter condition judgment as described in any one of claims 1 to 3.

6. A computer program product, characterized in that, The system includes a fault arc detection program based on frequency band analysis and parameter condition judgment. When the fault arc detection program based on frequency band analysis and parameter condition judgment is executed by the processor, it implements the fault arc detection method based on frequency band analysis and parameter condition judgment as described in any one of claims 1 to 3.

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