Fault detection method and fault detection device for power distribution network

Abstract

The invention discloses a fault detection method and a fault detection device for a power distribution network. The method comprises the following steps: acquiring an arc electromagnetic radiation signal corresponding to a power distribution network; controlling a signal amplifier to enhance the signal intensity in the target frequency band range in the arc electromagnetic radiation signal to obtain an enhanced radiation signal; controlling a target filter to filter the enhanced radiation signal, and reserving a frequency band signal in a predetermined frequency band range to obtain a filtered radiation signal; controlling an envelope detection module to convert the filtered radiation signal into a low-frequency envelope signal; controlling a multi-stage comparison module to determine an arc fault grade corresponding to the power distribution network according to the low-frequency envelope signal; and the control grading judgment algorithm module counts the number of signal pulses in a preset time period, and determines an arc fault type corresponding to the power distribution network according to the arc fault grade and the number of signal pulses. According to the invention, the technical problem that the fault is difficult to accurately detect in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution networks, and more specifically, to a fault detection method and a fault detection device for power distribution networks. Background Technology

[0002] The power distribution system serves as a bridge connecting the power grid and end users. Its operational stability and power supply reliability directly impact the efficiency and safety of residents' lives, industrial production, and social operations. Short-circuit faults occur frequently in the distribution network, with the majority being arc faults. Once an arc fault occurs, it is often accompanied by localized high temperatures, transient electromagnetic radiation, and acoustic impacts, easily leading to large-scale power outages and equipment damage, seriously threatening the stable operation of the power system and the safety of personnel and equipment.

[0003] Currently, research on arc fault detection in distribution networks mainly focuses on the extraction and analysis of electrical characteristic parameters, including zero-sequence current transient detection, voltage transient analysis, harmonic component extraction, wavelet transform, fuzzy logic recognition, and artificial neural network criteria. These methods determine fault occurrence by measuring changes in current and voltage waveforms at the fault point, but they suffer from the following major shortcomings in practical applications: Detection based on zero-sequence current or voltage transients relies on direct contact with the faulty circuit, resulting in often small signal amplitudes that are easily overwhelmed by power frequency interference, load fluctuations, and grid reconfiguration signals, leading to high false alarm rates and a significant risk of missed alarms; traditional methods often only provide a binary judgment of whether a fault "exists," failing to distinguish between transient, intermittent, and continuous arcs, and also failing to reflect the fault intensity level, making it difficult to provide maintenance personnel with accurate handling solutions; wavelet transform, neural networks, etc. While algorithms can extract more features, their high computational complexity and large processing latency make them unsuitable for achieving millisecond-level rapid alarms in unattended towers or substations. Existing detection devices mostly employ multi-channel analog sensing and complex signal processing board designs, resulting in bulky sizes and power consumption of several watts, making them unsuitable for long-term outdoor deployment and even less suitable for power supply by solar energy or small-capacity batteries. Although arc discharge generates broadband electromagnetic radiation, this potential feature has been applied in areas such as partial discharge and lightning location, but a mature non-contact, broadband real-time detection solution has not yet been developed for power distribution network fault monitoring.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a fault detection method and device for power distribution networks, which at least solves the technical problem of difficulty in accurately detecting faults in related technologies.

[0006] According to one aspect of the present invention, a fault detection method for a power distribution network is provided, comprising: acquiring an arc electromagnetic radiation signal corresponding to the power distribution network, wherein the arc electromagnetic radiation signal is received by an antenna unit in a fault detection device, the arc electromagnetic radiation signal is a radiation signal released when an arc fault occurs in the power distribution network, the antenna unit uses a target antenna as a receiver and receives the corresponding radiation signal in a non-contact manner, the target antenna is an antenna with a predetermined fractal structure, the predetermined fractal structure is determined based on the frequency band coverage range of the arc fault detection of the power distribution network, and the self-similarity and space-filling property corresponding to the fractal structure, the frequency of the arc electromagnetic radiation signal is higher than a predetermined frequency threshold, the fault detection device further comprises a signal conditioning module, an envelope detection module, a multi-level comparison module, and a hierarchical judgment algorithm module, the signal conditioning module comprising a signal amplifier and a target filter; and controlling the signal amplifier to amplify the arc electromagnetic radiation signal. The signal intensity within a target frequency band of the electromagnetic radiation signal is used to obtain an enhanced radiation signal, wherein the signal effectiveness within the target frequency band is greater than a predetermined effective threshold. The target filter is controlled to filter the enhanced radiation signal, retaining the frequency band signal within the predetermined frequency band to obtain a filtered radiation signal, wherein the signal characterization within the predetermined frequency band is greater than a predetermined characterization threshold. The envelope detection module is controlled to convert the filtered radiation signal into a low-frequency envelope signal, wherein the frequency corresponding to the low-frequency envelope signal is lower than the frequency corresponding to the filtered radiation signal. The multi-level comparison module is controlled to determine the arc fault level corresponding to the distribution network based on the low-frequency envelope signal. The hierarchical judgment algorithm module is controlled to count the number of signal pulses within a predetermined time period, and determines the arc fault type corresponding to the distribution network based on the arc fault level and the number of signal pulses, wherein the number of signal pulses is obtained based on the low-frequency envelope signal.

[0007] Optionally, controlling the multi-level comparison module to determine the arc fault level corresponding to the distribution network based on the low-frequency envelope signal includes: when the multi-level comparison module includes multiple comparators, determining the connection status of the input voltage and reference voltage corresponding to each of the multiple comparators based on the voltage value of the low-frequency envelope signal and the threshold voltage corresponding to each of the multiple comparators, wherein the voltage of the low-frequency envelope signal is connected to the input terminal of each of the multiple comparators, and the corresponding threshold voltage is connected to the reference terminal of the corresponding comparator; comparing the voltage value corresponding to the input terminal of each comparator with the threshold voltage of the corresponding reference terminal in real time according to the comparator logic rules and connection status of each of the multiple comparators, triggering the corresponding level output operation to obtain the level signal corresponding to each of the multiple comparators; performing level signal combination processing based on the level signals corresponding to each of the multiple comparators to obtain a combined level signal; and obtaining the arc fault level corresponding to the distribution network based on the preset mapping relationship between the combined level signal and the arc fault level.

[0008] Optionally, the hierarchical judgment algorithm module is controlled to count the number of signal pulses within a predetermined time period, and to determine the arc fault type corresponding to the distribution network based on the arc fault level and the number of signal pulses. This includes: retrieving the pulse fault mapping relationship corresponding to the distribution network, wherein the pulse fault mapping relationship represents the mapping relationship between the number of pulses and the fault type; acquiring the number of signal pulses within the predetermined time period by sliding according to preset sliding window parameters, wherein the sliding window parameters include a sliding time window and a sliding step size; and determining the arc fault type corresponding to the distribution network based on the number of signal pulses and the pulse fault mapping relationship, wherein the arc fault type includes transient arc fault, intermittent strong arc fault, and continuous discharge fault.

[0009] Optionally, controlling the envelope detection module to convert the filtered radiation signal into a low-frequency envelope signal includes: when the envelope detection module is composed of diodes, capacitors, and resistors, retrieving a time constant function corresponding to the envelope detection module, wherein the time constant function includes the product of a capacitance term and a resistance term; determining the predicted duration of the arc fault; adjusting the resistance value of the resistance term in the time constant function according to the predicted duration to obtain a target time constant value; and converting the filtered radiation signal into a low-frequency envelope signal according to the target time constant value.

[0010] According to one aspect of the present invention, a fault detection device is provided, comprising: an antenna unit, a signal conditioning module, an envelope detection module, a multi-level comparison module, and a hierarchical judgment algorithm module. The signal conditioning module includes a signal amplifier and a target filter. The antenna unit is connected to the signal amplifier, the signal amplifier is connected to the target filter, the target filter is connected to the envelope detection module, the envelope detection module is connected to both the multi-level comparison module and the hierarchical judgment algorithm module, and the multi-level comparison module is connected to the hierarchical judgment module. The antenna unit is used to acquire an arc electromagnetic radiation signal corresponding to a power distribution network. The arc electromagnetic radiation signal is the radiation signal released when an arc fault occurs in the power distribution network. The antenna unit uses a target antenna as a receiver and receives the corresponding radiation signal in a non-contact manner. The target antenna is an antenna with a predetermined fractal structure. The predetermined fractal structure is based on the frequency band coverage range of the power distribution network arc fault detection and the self-alignment of the fractal structure. Similarity and space-filling properties are determined, and the frequency of the arc electromagnetic radiation signal is higher than a predetermined frequency threshold. The signal amplifier is used to enhance the signal strength within the target frequency band of the arc electromagnetic radiation signal to obtain an enhanced radiation signal, wherein the signal effectiveness within the target frequency band is greater than a predetermined effectiveness threshold. The target filter is used to filter the enhanced radiation signal and retain the frequency band signal within the predetermined frequency band to obtain a filtered radiation signal, wherein the signal characterization within the predetermined frequency band is greater than a predetermined characterization threshold. The envelope detection module is used to convert the filtered radiation signal into a low-frequency envelope signal, wherein the frequency corresponding to the low-frequency envelope signal is lower than the frequency corresponding to the filtered radiation signal. The multi-level comparison module is used to determine the arc fault level corresponding to the distribution network based on the low-frequency envelope signal. The graded judgment algorithm module is used to count the number of signal pulses within a predetermined time period and determine the arc fault type corresponding to the distribution network based on the arc fault level and the number of signal pulses.

[0011] Optionally, the antenna element includes a conductor, a dielectric substrate, and a ground plane, wherein the dielectric constant of the dielectric substrate material of the antenna element is within a predetermined constant range, and the balance index corresponding to the conductor width is greater than a predetermined balance threshold. The balance index represents the balance between radiation efficiency, size influence, and extended bandwidth.

[0012] Optionally, the antenna design size of the antenna element is smaller than a predetermined size threshold, and the matching index corresponding to the feed point position of the antenna element is greater than a predetermined matching threshold, wherein the feed point position is the position where the antenna is connected to the feed line, and the matching index is an index representing the degree of matching between the antenna input impedance and the line impedance.

[0013] Optionally, the device further includes a power supply module, which is connected to the signal conditioning module, the envelope detection module, the multi-level comparison module, and the hierarchical judgment algorithm module.

[0014] According to one aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement a fault detection method for a power distribution network as described in any of the above embodiments.

[0015] According to one aspect of the present invention, a computer-readable storage medium is provided, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform a fault detection method for a power distribution network as described above.

[0016] In this embodiment of the invention, an arc electromagnetic radiation signal corresponding to the power distribution network is acquired. This arc electromagnetic radiation signal is received by an antenna unit in the fault detection device. The arc electromagnetic radiation signal is the radiation signal released when an arc fault occurs in the power distribution network. The antenna unit uses a target antenna as the receiver and receives the corresponding radiation signal in a non-contact manner. The target antenna is an antenna with a predetermined fractal structure. The predetermined fractal structure is determined based on the frequency band coverage of the arc fault detection in the power distribution network, as well as the self-similarity and space-filling property of the fractal structure. The frequency of the arc electromagnetic radiation signal is higher than a predetermined frequency threshold. The fault detection device also includes a signal conditioning module, an envelope detection module, a multi-level comparison module, and a hierarchical judgment algorithm module. The signal conditioning module includes a signal amplifier and a target filter; the control signal amplifier enhances the arc electromagnetic radiation signal. The signal strength within the target frequency band is used to obtain an enhanced radiated signal, wherein the signal effectiveness within the target frequency band is greater than a predetermined effective threshold. The target filter is controlled to filter the enhanced radiated signal, retaining the frequency band signal within the predetermined frequency band to obtain a filtered radiated signal, wherein the signal characterization within the predetermined frequency band is greater than a predetermined characterization threshold. The envelope detection module is controlled to convert the filtered radiated signal into a low-frequency envelope signal, wherein the frequency corresponding to the low-frequency envelope signal is lower than the frequency corresponding to the filtered radiated signal. The multi-level comparison module is controlled to determine the arc fault level corresponding to the distribution network based on the low-frequency envelope signal. The graded judgment algorithm module is controlled to count the number of signal pulses within a predetermined time period and determine the arc fault type corresponding to the distribution network based on the arc fault level and the number of signal pulses, wherein the number of signal pulses is obtained from the low-frequency envelope signal. This invention employs a comprehensive approach combining non-contact broadband signal sensing and multi-dimensional intelligent judgment. It utilizes an optimized fractal antenna to receive broadband arc electromagnetic radiation signals with high sensitivity. The signals are then amplified and filtered by a signal amplifier and target filter to extract effective features. Envelope detection converts the signals into low-frequency envelope signals, which are then used to quantify the arc intensity level using a multi-level comparison module. Simultaneously, a hierarchical judgment algorithm module counts pulse frequencies within a sliding time window. Finally, a joint judgment is made based on the combined intensity level and pulse frequency information. This achieves high-sensitivity detection and interference-resistant extraction of arc faults in power distribution networks, enabling rapid and reliable identification and classification of arc faults. This solves the technical problem of accurately detecting faults in related technologies. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of a fault detection method for a power distribution network according to an embodiment of the present invention;

[0019] Figure 2 This is a block diagram of the overall structure of the device used in an optional embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of a fractal antenna structure used in an optional embodiment of the present invention;

[0021] Figure 4 Example waveform diagrams of the inventive antenna detected by an optional embodiment of the present invention;

[0022] Figure 5 The output waveforms of each module of the detection circuit used in an optional embodiment of the present invention are shown. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. 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 should fall within the scope of protection of the present invention.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] Example 1

[0026] According to an embodiment of the present invention, an embodiment of a fault detection method for a power distribution network is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0027] Figure 1 This is a flowchart of a fault detection method for a distribution network according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0028] Step S102: Obtain the arc electromagnetic radiation signal corresponding to the power distribution network. The arc electromagnetic radiation signal is received by the antenna unit in the fault detection device. The arc electromagnetic radiation signal is the radiation signal released when an arc fault occurs in the power distribution network. The antenna unit uses a target antenna as the receiver and receives the corresponding radiation signal in a non-contact manner. The target antenna is an antenna with a predetermined fractal structure. The predetermined fractal structure is determined based on the frequency band coverage of the arc fault detection in the power distribution network and the self-similarity and space filling property of the fractal structure. The frequency of the arc electromagnetic radiation signal is higher than a predetermined frequency threshold. The fault detection device also includes a signal conditioning module, an envelope detection module, a multi-level comparison module, and a hierarchical judgment algorithm module. The signal conditioning module includes a signal amplifier and a target filter.

[0029] In step S102 of this application, the broadband electromagnetic radiation signal released by the power distribution network when an arc fault occurs is received in a non-contact manner through the antenna unit in the fault detection device. The frequency of the signal is higher than a preset threshold. The detection device is composed of an antenna unit, a signal conditioning module, an envelope detection module, a multi-level comparison module, and a hierarchical judgment algorithm module. The signal conditioning module further includes a signal amplifier and a target filter, thereby clearly defining the signal source, reception method, and device composition of the detection object.

[0030] Among them, the electric arc electromagnetic radiation signal is a high-frequency electromagnetic wave radiation generated during the discharge process when an electric arc fault occurs in the power distribution network. The frequency band is usually in the range of several MHz to hundreds of MHz, and it has transient and wide-band characteristics, which can be used for non-contact fault detection.

[0031] This involves a target antenna, which is a fractal structure antenna. By leveraging the self-similarity and high space filling rate of the fractal structure, it achieves wideband reception capability within a limited size and adapts to the frequency band characteristics of electric arc radiation signals.

[0032] This involves a predetermined frequency threshold, which is a pre-set lower limit of frequency used to define the effective frequency band of the arc electromagnetic radiation signal, ensuring that the received signal is mainly the high-frequency component generated by the arc discharge, and avoiding low-frequency interference.

[0033] This involves a signal amplifier, which is a low-noise amplifier used to amplify the weak arc radiation signal received by the antenna and increase the signal amplitude for subsequent processing.

[0034] This involves a target filter, which is a bandpass or bandstop filter used to filter out power frequency and harmonic interference in the signal and retain the effective frequency band signal that characterizes the arc.

[0035] This step clarifies that the input signal for fault detection is the broadband electromagnetic radiation emitted by the electric arc, the receiving method is non-contact, and the receiving antenna is a fractal structure antenna with broadband receiving capability. It also clearly defines the overall architecture of the detection device and the functions of each module, providing a clear signal source and hardware foundation for subsequent processing steps such as signal amplification, filtering, envelope extraction, intensity grading, and type identification. This ensures the systematic nature and feasibility of the detection process and lays the technical prerequisite for achieving high sensitivity, strong anti-interference, and non-contact detection.

[0036] Step S104: Control the signal amplifier to enhance the signal strength in the target frequency band of the arc electromagnetic radiation signal to obtain an enhanced radiation signal, wherein the signal effectiveness in the target frequency band is greater than a predetermined effective threshold.

[0037] In step S104 of this application, the signal amplifier in the control signal conditioning module amplifies the arc electromagnetic radiation signal located in the target frequency band, which is received by the antenna unit and preliminarily screened, thereby increasing its signal amplitude and obtaining an enhanced radiation signal. The target frequency band is preset based on the typical characteristics of the arc radiation signal. Within this frequency band, the arc fault characteristic information contained in the signal is significant and its effectiveness is higher than the preset threshold, which is beneficial for subsequent accurate analysis.

[0038] This involves the target frequency band range, which is one or more frequency intervals pre-defined based on the spectral characteristics of the arc electromagnetic radiation signal. Within this interval, the energy of the arc signal is concentrated and its characteristics are obvious, and it is relatively less affected by background interference such as power frequency.

[0039] This involves signal effectiveness, which measures the proportion or significance of information components in the received signal within the target frequency band that truly characterize the features of an electric arc fault. High effectiveness means a high signal-to-noise ratio and that the arc features are easily identifiable.

[0040] This involves a predetermined effective threshold, which is a pre-set minimum standard or threshold value used to determine whether the signal effectiveness meets the requirements for subsequent processing. When the signal effectiveness exceeds this threshold, the signal within the target frequency band is considered suitable for subsequent enhancement and processing.

[0041] This step utilizes a signal amplifier to specifically enhance the effective arc radiation signal within the target frequency band, thereby improving the signal-to-noise ratio and detectability of weak fault signals. It overcomes the inherent problems of small signal amplitude and susceptibility to environmental noise in non-contact detection. This operation provides a sufficiently high-amplitude and reliable input signal for subsequent filtering, envelope extraction, and fault determination, and is a key preliminary step to ensure the high sensitivity and reliability of the entire detection method.

[0042] Step S106: Control the target filter to filter the enhanced radiation signal and retain the frequency band signal within the predetermined frequency band range to obtain the filtered radiation signal, wherein the signal characterization within the predetermined frequency band range is greater than the predetermined characterization threshold.

[0043] In step S106 of this application, the target filter in the control signal conditioning module performs frequency screening on the amplified enhanced radiation signal, filters out frequency components outside the predetermined frequency band range, and retains only the effective signal components within the predetermined frequency band range, thereby obtaining the filtered radiation signal. The predetermined frequency band is optimized and selected, and the arc fault characteristic information contained in the signal is most typical and prominent within this frequency band, and its characterization is higher than the preset characterization threshold.

[0044] This involves a predetermined frequency band range, which is one or more passband frequency ranges pre-optimized based on the typical spectral distribution and interference noise characteristics of the electric arc electromagnetic radiation signal, with the aim of maximizing the preservation of the electric arc characteristic signal while minimizing background interference.

[0045] This involves signal characterization, which measures the typicality, distinguishability, and informational value of signals retained within a predetermined frequency band for differentiating and determining arc fault types. High characterization means that the signal in that frequency band can more clearly and stably reflect the pattern differences of different arc faults.

[0046] This involves a predetermined characterization threshold, which is a pre-set minimum standard used to determine whether a signal retained within a selected frequency band has sufficient ability to distinguish different fault types. Only when the characterization of a signal exceeds this threshold is the frequency band considered suitable for subsequent fault level and type determination.

[0047] This step utilizes a target filter to perform precise frequency domain filtering on the amplified signal, which can suppress inherent strong power frequency and harmonic interference in the power distribution network environment, as well as random noise in other frequency bands, thereby improving the signal-to-noise ratio and purity of the signal. At the same time, by focusing on the predetermined frequency band with the strongest characterization, the signal characteristics on which subsequent processing depends become more prominent and stable, laying a solid foundation for envelope detection to extract effective amplitude information and for multi-level comparison and hierarchical judgment algorithms to accurately identify fault levels and types, thus enhancing the anti-interference capability and judgment accuracy of the entire detection method.

[0048] Step S108: Control the envelope detection module to convert the filtered radiation signal into a low-frequency envelope signal, wherein the frequency of the low-frequency envelope signal is lower than the frequency of the filtered radiation signal.

[0049] In step S108 provided in this application, the envelope detection module is controlled to perform envelope extraction on the filtered radiation signal after filtering, and the high-frequency modulated pulse signal is converted into a low-frequency envelope voltage signal whose amplitude varies with time. This conversion process demodulates the effective information of the signal from the high-frequency carrier, forming a low-frequency waveform with a frequency much lower than the original filtered radiation signal frequency, so as to facilitate subsequent low-speed sampling and digital logic processing.

[0050] This involves filtering the radiation signal, which is the arc electromagnetic radiation signal obtained in step S106 that has been amplified and filtered, retaining the high-frequency pulse characteristics within the predetermined frequency band, but is still a high-frequency signal.

[0051] This involves low-frequency envelope signals, which are the output signals of the envelope detection module. They are the low-frequency representation of the amplitude information of the filtered radiation signal, and are usually slowly varying voltage signals with frequencies at or below the kHz level. Their waveform profile reflects the amplitude variation pattern and occurrence density of the original high-frequency pulse signal.

[0052] This step converts the high-frequency pulse signal, which characterizes the intensity and frequency of the electric arc, into a low-frequency analog voltage signal that is easily sampled, identified, and statistically analyzed by a microcontroller. This conversion is crucial, as it significantly reduces the requirements for sampling rate and processing speed in subsequent digital processing units, avoiding the enormous hardware overhead and power consumption associated with directly processing high-frequency signals of hundreds of MHz. Simultaneously, the extracted envelope signal reflects the amplitude and density of the arc pulse, providing the most direct and easily processed input data for the next steps of intensity grading through voltage comparison and type determination through time window counting. This is a key preprocessing step for achieving intelligent grading and determination.

[0053] Step S110: Control the multi-level comparison module to determine the arc fault level corresponding to the distribution network based on the low-frequency envelope signal;

[0054] In step S110 of this application, a low-frequency envelope signal from the envelope detection module is received by controlling the multi-level comparison module, and the analog voltage value of the signal is compared with multiple preset threshold voltages in real time. Based on the comparison result, a corresponding digital level combination is output to determine a fault level that reflects the intensity of the arc signal.

[0055] This includes the arc fault level, which is a severity level of the fault based on the voltage amplitude of the low-frequency envelope signal. It is a discrete preliminary classification result based on signal strength, providing a basis for determining the specific fault type by combining time characteristics.

[0056] This step utilizes a multi-stage voltage comparator to achieve rapid, real-time digitization and preliminary classification of the analog envelope signal. This process transforms continuous amplitude information into discrete intensity level codes, reducing the burden on the subsequent microprocessor for complex algorithm processing. Furthermore, it introduces quantitative judgment of fault intensity into the detection process for the first time. This overcomes the limitation of traditional methods that can only perform binary judgments of presence or absence, providing crucial intensity criteria for subsequent steps that combine pulse frequency characteristics to intelligently distinguish between different types of arc faults, such as transient, intermittent, and continuous ones. It is one of the core links in achieving accurate fault classification and type identification.

[0057] Step S112: The control classification judgment algorithm module counts the number of signal pulses within a predetermined time period, and determines the arc fault type corresponding to the distribution network based on the arc fault level and the number of signal pulses. The number of signal pulses is obtained based on the low-frequency envelope signal.

[0058] In step S112 of this application, the hierarchical judgment algorithm module is controlled to count the digital pulses generated based on the low-frequency envelope signal within a preset time period to obtain the total number of signal pulses within the time window; then, the arc fault level determined in step S110 and the number of signal pulses are combined and analyzed according to the preset judgment rules to finally determine the specific type of arc fault that occurred in the distribution network.

[0059] This involves a predetermined time period, which is a continuous or sliding time window length set by the hierarchical judgment algorithm. It can be used to count the number of effective arc event pulses that occur within this time period to characterize the persistence or intermittent nature of the fault.

[0060] This involves the number of signal pulses, which is the total number of valid digital pulses output by the multi-level comparison module that exceed its minimum set threshold within a predetermined time period. This value directly reflects the occurrence density of arc discharge events in the time dimension.

[0061] This includes the types of arc faults, which are categories of fault nature determined by a combination of the intensity and time characteristics of the arc. These can include transient arc faults, intermittent strong arc faults, and continuous severe discharge faults.

[0062] This step integrates the intensity level of the electric arc with the frequency of pulse occurrence within the time window, enabling a fundamental leap from simply determining the presence or absence of arc faults in the distribution network to identifying their specific type. This intelligent judgment rule can effectively distinguish between different types of faults, such as instantaneous discharge, intermittent repetitive discharge, and continuous severe discharge, providing accurate fault nature information to support differentiated handling decisions and improve the accuracy and efficiency of fault response and operation and maintenance management.

[0063] Through the above steps S102-S112, the arc electromagnetic radiation signal corresponding to the distribution network is obtained. This arc electromagnetic radiation signal is received by the antenna unit in the fault detection device. The arc electromagnetic radiation signal is the radiation signal released when an arc fault occurs in the distribution network. The antenna unit uses a target antenna as the receiver and receives the corresponding radiation signal in a non-contact manner. The target antenna is an antenna with a predetermined fractal structure. The predetermined fractal structure is determined based on the frequency band coverage of the arc fault detection in the distribution network, as well as the self-similarity and space-filling property of the fractal structure. The frequency of the arc electromagnetic radiation signal is higher than a predetermined frequency threshold. The fault detection device also includes a signal conditioning module, an envelope detection module, a multi-level comparison module, and a hierarchical judgment algorithm module. The signal conditioning module includes a signal amplifier and a target filter; the control signal amplifier enhances the arc electromagnetic radiation. The signal strength within the target frequency band of the signal is used to obtain an enhanced radiated signal, wherein the signal effectiveness within the target frequency band is greater than a predetermined effective threshold. The target filter is controlled to filter the enhanced radiated signal, retaining the frequency band signal within the predetermined frequency band to obtain a filtered radiated signal, wherein the signal characterization within the predetermined frequency band is greater than a predetermined characterization threshold. The envelope detection module is controlled to convert the filtered radiated signal into a low-frequency envelope signal, wherein the frequency corresponding to the low-frequency envelope signal is lower than the frequency corresponding to the filtered radiated signal. The multi-level comparison module is controlled to determine the arc fault level corresponding to the distribution network based on the low-frequency envelope signal. The graded judgment algorithm module is controlled to count the number of signal pulses within a predetermined time period and determine the arc fault type corresponding to the distribution network based on the arc fault level and the number of signal pulses, wherein the number of signal pulses is obtained from the low-frequency envelope signal. This invention employs a comprehensive approach combining non-contact broadband signal sensing and multi-dimensional intelligent judgment. It utilizes an optimized fractal antenna to receive broadband arc electromagnetic radiation signals with high sensitivity. The signals are then amplified and filtered by a signal amplifier and target filter to extract effective features. Envelope detection converts the signals into low-frequency envelope signals, which are then used to quantify the arc intensity level using a multi-level comparison module. Simultaneously, a hierarchical judgment algorithm module counts pulse frequencies within a sliding time window. Finally, a joint judgment is made based on the combined intensity level and pulse frequency information. This achieves high-sensitivity detection and interference-resistant extraction of arc faults in power distribution networks, enabling rapid and reliable identification and classification of arc faults. This solves the technical problem of accurately detecting faults in related technologies.

[0064] As an optional embodiment, the control multi-level comparison module determines the arc fault level corresponding to the distribution network based on the low-frequency envelope signal, including: when the multi-level comparison module includes multiple comparators, determining the connection status of the input voltage and reference voltage corresponding to each of the multiple comparators based on the voltage value of the low-frequency envelope signal and the threshold voltage corresponding to each of the multiple comparators, wherein the voltage of the low-frequency envelope signal is connected to the input terminal of each of the multiple comparators, and the corresponding threshold voltage is connected to the reference terminal of each of the corresponding comparators; comparing the voltage value corresponding to the input terminal of each comparator with the threshold voltage of the corresponding reference terminal in real time according to the comparator logic rules and connection status of each of the multiple comparators, triggering the corresponding level output operation to obtain the level signal corresponding to each of the multiple comparators; performing level signal combination processing based on the level signals corresponding to each of the multiple comparators to obtain a combined level signal; and obtaining the arc fault level corresponding to the distribution network based on the preset mapping relationship between the combined level signal and the arc fault level.

[0065] This embodiment illustrates the process of accurately determining the arc fault level by using multiple parallel comparators and combining analog voltage comparison with digital logic encoding.

[0066] This involves multiple comparators, which refers to at least two voltage comparators connected in parallel in a multi-stage comparison module. Each comparator operates independently and is set with different reference thresholds to compare the same input signal with different thresholds.

[0067] This involves a threshold voltage, which is a fixed or adjustable voltage value that is preset and applied to the reference terminal of each comparator. It serves as a benchmark for judging the strength of the input signal, and different thresholds correspond to different arc intensity thresholds.

[0068] This involves the docking state, which describes the electrical connection and comparison relationship established between the voltage of the low-frequency envelope signal (connected to the input terminal) and each threshold voltage (connected to the reference terminal) within each comparator. It is a prerequisite for comparison operations.

[0069] This involves comparator logic rules, which are inherent to each voltage comparator: when the input voltage is higher than the reference voltage, the output is high; when the input voltage is lower than the reference voltage, the output is low. For comparators with hysteresis, the logic rules may also include a distinction between rising and falling trigger thresholds.

[0070] This involves level signals, which are digital signals output by each comparator based on its logic rules and real-time comparison results. These signals contain only two states: high and low, and represent whether the input voltage exceeds the threshold set by that comparator.

[0071] This involves combined level signals, which are digital combinations formed by arranging or encoding level signals output by multiple comparators in a predetermined order. Each combination uniquely corresponds to the amplitude range of an input voltage.

[0072] This involves a preset mapping relationship, which is a lookup table or decoding rule pre-stored in the microcontroller or hardware logic that corresponds to the combined level signal and the arc fault level.

[0073] In this step, firstly, the low-frequency envelope signal is simultaneously connected to the input terminals of multiple parallel comparators, and different threshold voltages are connected to the reference terminals of each comparator to establish a voltage comparison loop. Next, each comparator, according to its own logic rules, compares the envelope voltage value at its input terminal with the threshold voltage at its reference terminal in real time, and triggers a corresponding level output operation based on the comparison result to generate its own level signal. Then, the level signals output by all these comparators are collected and combined to form a combined level signal representing the current envelope voltage amplitude. Finally, by querying a preset mapping relationship, the combined level signal is decoded into the corresponding arc fault level.

[0074] This method allows multiple comparators to operate simultaneously, quantizing the analog voltage into a multi-bit digital code in a single pass. This processing speed is faster than the traditional analog-to-digital converter (ADC) sampling followed by software judgment, shortening response time and meeting the real-time requirements of fault detection. Utilizing the hard-decision characteristic of the comparators effectively suppresses false triggering caused by signal jitter and noise, ensuring stable and reliable fault level determination. The output combined level signal is a structured digital quantity, eliminating the need for complex amplitude calculations by the microcontroller. Simple table lookups or logical judgments are sufficient to obtain the fault level, reducing algorithm complexity and microcontroller resource consumption, which is beneficial for device miniaturization and low-power design.

[0075] As an optional embodiment, the control classification judgment algorithm module counts the number of signal pulses within a predetermined time period and determines the arc fault type corresponding to the distribution network based on the arc fault level and the number of signal pulses. This includes: retrieving the pulse fault mapping relationship corresponding to the distribution network, wherein the pulse fault mapping relationship represents the mapping relationship between the number of pulses and the fault type; acquiring the number of signal pulses within a predetermined time period based on preset sliding window parameters, wherein the sliding window parameters include a sliding time window and a sliding step size; and determining the arc fault type corresponding to the distribution network based on the number of signal pulses and the pulse fault mapping relationship, wherein the arc fault type includes transient arc fault, intermittent strong arc fault, and continuous discharge fault.

[0076] This embodiment illustrates the process of intelligently identifying specific arc fault types by combining the pulse generation frequency with the determined intensity level and according to preset mapping rules.

[0077] This involves a pulse fault mapping relationship, which is a predefined judgment logic or data table stored in the microcontroller. This relationship specifies the matching rules between the range of pulse counts within a specific time window and the corresponding arc fault type under different arc fault levels.

[0078] This involves sliding window parameters, which are a set of parameters used to control the statistical method of time series data. They mainly include the sliding time window and the sliding step size. The sliding window mechanism enables the statistics to be performed continuously, covering all time periods.

[0079] This involves sliding acquisition, a data statistics method in which a statistical window moves forward continuously along the time axis with a fixed step size, and the number of signal pulses occurring within each window position is counted to obtain a pulse density sequence that changes continuously over time.

[0080] In this step, firstly, a preset pulse fault mapping relationship is retrieved from memory, which integrates the pulse number threshold and fault level conditions. Next, the microcontroller continuously counts the digital pulses output by the multi-level comparison module using a sliding acquisition method based on the set sliding time window and sliding step size to obtain the number of signal pulses within the current time window. Finally, the obtained number of signal pulses and the determined arc fault level are substituted into the pulse fault mapping relationship for matching, thereby outputting the determined arc fault type.

[0081] This method correlates and fuses fault levels with signal pulse counts, making a comprehensive judgment based on the mapping relationship. This results in more scientific and accurate type identification, effectively distinguishing faults of different natures and improving the precision of operation and maintenance decisions. The judgment process is based on a preset mapping relationship and sliding window counting, with clear logic and low computational load. It can run efficiently on resource-constrained embedded microcontrollers, meeting the device's requirements for real-time performance and low power consumption, which is conducive to engineering implementation and long-term stable operation.

[0082] As an optional embodiment, controlling the envelope detection module to convert the filtered radiation signal into a low-frequency envelope signal includes: when the envelope detection module is composed of diodes, capacitors, and resistors, retrieving the time constant function corresponding to the envelope detection module, wherein the time constant function includes the product of a capacitance term and a resistance term; determining the predicted duration of the arc fault; adjusting the resistance value of the resistance term in the time constant function according to the predicted duration to obtain a target time constant value; and converting the filtered radiation signal into a low-frequency envelope signal according to the target time constant value.

[0083] This embodiment illustrates the process of optimizing the envelope extraction effect of the envelope detection circuit for arc signals with different characteristics by dynamically adjusting the key parameters of the envelope detection circuit.

[0084] This involves a time constant function, which describes the dynamic characteristics of the resistor-capacitor integration stage in the envelope detector circuit. This constant determines the speed and smoothness of the envelope voltage build-up.

[0085] This involves a capacitance term, which is the capacitance parameter in the time constant function. It is a fixed energy storage element in the envelope detector circuit, and its value is usually selected and remains unchanged. Together with the resistance, it determines the circuit response.

[0086] This involves a resistance term, which is the resistance parameter in the time constant function. It is an adjustable element in the envelope detector circuit, and the time constant of the circuit can be adjusted by changing its resistance value.

[0087] This involves the prediction of duration, which refers to the range of possible durations of a single arc pulse or arc cluster estimated based on historical data, knowledge of arc type, or on-site conditions, and is used to guide the setting of the time constant.

[0088] This involves the target time constant value, which is the optimal time constant calculated or matched based on the predicted duration, enabling the envelope detector output waveform to best represent the original pulse envelope.

[0089] In this step, firstly, the basic structure of the envelope detection module is determined to be a typical circuit containing diodes, capacitors, and resistors, and its time constant function is defined. Next, based on the application scenario or experience, the predicted duration characteristic of the arc fault signal to be detected is determined. Then, based on the predicted duration, the required target time constant value is derived by calculation or table lookup, and the resistance value of the variable resistor in the circuit is adjusted to make the time constant of the actual circuit equal to the target value. Finally, the envelope detection circuit with this target time constant value is used to process the filtered radiation signal to complete the conversion to a low-frequency envelope signal.

[0090] By dynamically adjusting the time constant based on the predicted arc signal duration, the envelope output waveform can better follow short burst pulses or smoothly cover long pulse clusters, avoiding envelope distortion caused by a fixed time constant. This allows for more accurate preservation of the original signal's amplitude and temporal characteristics. The same detection device can be better adapted to different types and scenarios of arc faults in the distribution network through parameter adjustments, improving the robustness and signal fidelity of the envelope detection process and laying a more reliable signal foundation for subsequent accurate classification and type identification. Introducing a parameter adjustment mechanism based on signal characteristics enhances the intelligence and adaptability of the entire detection device, making it better suited to the complex and ever-changing needs of engineering sites.

[0091] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0092] In related technologies, research on arc fault detection in distribution networks mainly focuses on the extraction and analysis of electrical characteristic parameters, including zero-sequence current transient detection, voltage transient analysis, harmonic component extraction, wavelet transform, fuzzy logic recognition, and artificial neural network criteria. These methods determine the occurrence of faults by measuring the changes in current and voltage waveforms at the fault point, but they have the following main shortcomings in practical applications: (1) Low sensitivity and poor anti-interference: Detection based on zero-sequence current or voltage transients depends on direct contact with the fault circuit. The signal amplitude is often small and easily submerged by power frequency interference, load fluctuations, and grid reconfiguration signals, resulting in a high false alarm rate and a high risk of missed alarms. (2) Insufficient classification capability: Traditional methods can only provide a binary judgment of whether a fault "exists" and cannot distinguish between transient, intermittent, and continuous arcs, nor can they reflect the fault intensity level, making it difficult to provide accurate handling solutions for maintenance personnel. (3) Limited response speed and real-time performance: Although wavelet transform, neural network, and other algorithms can extract more features, they have high computational complexity and large computational delay, which is not conducive to achieving millisecond-level rapid alarms in unattended towers or substation environments. (4) Large size and high power consumption: Existing detection devices mostly adopt multi-channel analog sensing and complex signal processing board design, which are bulky and consume several watts of power, making them unsuitable for long-term outdoor deployment and even less suitable for power supply by solar energy or small-capacity batteries. (5) Lack of non-contact detection solutions: Although arc discharge will generate wide-bandwidth (a few MHz to hundreds of MHz) electromagnetic radiation, this potential feature has been applied in the fields of partial discharge and lightning location, but a mature non-contact, wide-bandwidth real-time detection solution has not yet been formed in power distribution network fault monitoring.

[0093] In view of this, an optional embodiment of the present invention provides a non-contact online arc fault monitoring device based on a wideband fractal antenna and an adjustable multi-level detection circuit. This device can receive arc electromagnetic radiation signals with high sensitivity within the range of 0.1–500 MHz, determine the fault type in real time, and supports near-field parameter configuration, Mesh networking remote alarm, and low-power management. Figure 1 This is a flowchart of a fault detection method for a power distribution network according to an embodiment of the present invention; Figure 2 This is a block diagram of the overall structure of the device used in an optional embodiment of the present invention; Figure 3 This is a schematic diagram of a fractal antenna structure used in an optional embodiment of the present invention; Figure 4 Example waveform diagrams of the inventive antenna detected by an optional embodiment of the present invention; Figure 5 The following are functional output waveforms of each module of the detection circuit used in an optional embodiment of the present invention. Figure 2-5 As shown, it will be introduced below.

[0094] (1) Antenna unit: HFSS simulation optimized fifth-order Hilbert microstrip patch fractal antenna, size 100 mm×100 mm×1.1 mm, operating frequency band 0.1–500 MHz, feed impedance 50 Ω; (2) Signal conditioning module: integrated low noise amplifier (NF<1.2 dB, gain≈20 dB) and bandpass / notch filter (center 200 MHz, bandwidth 100 MHz±10MHz), effectively suppressing power frequency and high-order harmonics; (3) Envelope detection and multi-level comparison: ADL5511 envelope detector chip and TLV3202 Schmitt trigger are used, and low threshold V_L, medium threshold V_M, and high threshold V_H (recommended 1.0 V, 2.0 V, 2.9 V) are set, corresponding to "early warning", "medium" and "severe" fault outputs respectively; (4) Grading judgment algorithm: STM32 MCU in sliding window (default 1 (5) Parameter configuration and display storage: Three-color LED and buzzer on-site indication, SPI NOR Flash or EEPROM record fault timestamp, level and number of pulses; (6) Low power management: MPPT solar charging controller and lithium battery management unit, system static power consumption ≤150 mW, supports long life operation in environment of -40~85 ℃; (7) Protection and maintenance: Aluminum alloy shell + silicone seal up to IP67, equipped with DIN rail and clamp bracket; plug-in modular design, easy to quickly replace and upgrade.

[0095] Alternatively, a specific implementation method is provided:

[0096] Step 1: Antenna receiving module;

[0097] This invention employs a Hilbert fifth-order fractal microstrip patch antenna, whose structure features self-similarity, maximized path length, and good compactness, effectively capturing broadband electromagnetic radiation signals generated by arc discharge. The antenna was simulated and modeled using HFSS, exhibiting multiple resonant points within the 20 MHz–500 MHz range, and achieving a high VSWR. Less than -10dB. The antenna is made of FR4 material and measures 100 mm × 100 mm × 1.1 mm, making it suitable for installation on power distribution towers.

[0098] The specific principle of the Hilbert fractal antenna is as follows: The Hilbert fractal curve is a simple fractal structure, as shown in the figure. Each order is formed by replicating the curve of the previous order four times, then rotating two of them and connecting them with short lines. As the fractal order n increases, the fractal dimension D of the Hilbert curve also increases, expressed as:

[0099]

[0100] That is, as the order of the Hilbert fractal gradually increases, the fractal dimension of the curve will approach 2 infinitely, and the curve will approximately cover the entire two-dimensional plane. Under the same size, the Hilbert fractal curve has a higher space fill rate, which enables the antenna to obtain a larger equivalent electrical length, reduce the resonant frequency of the antenna, and achieve miniaturization.

[0101] Factors affecting the performance of Hilbert fractal antennas: The performance of an antenna directly affects the accuracy and feasibility of detecting electromagnetic radiation from electric arcs, and the structure of fractal antennas makes theoretical modeling complex. Therefore, this paper directly adopts a parametric simulation method, using ANSYS HFSS software and its built-in parameter scanning function to simulate and analyze four key variables: fractal order, conductor width, dielectric substrate thickness, and feed point location. By comparing indicators such as return loss and voltage standing wave ratio (VSWR), the optimal parameters of the antenna are selected and optimized. The final design goal is set as a VSWR of less than 3 and a return loss of less than -6dB at low frequencies, i.e., a transmission efficiency greater than 75%. A simulation model of a Hilbert fractal antenna is built based on a microstrip antenna. The antenna is divided into three parts: the uppermost conductor, the lower dielectric substrate, and the lower ground plane, as shown below. Figure 3As shown: a) Structural dimensions: The antenna design dimensions are 100 mm × 100 mm × 1.1 mm, meeting the installation requirements of outdoor poles and towers. b) Dielectric substrate thickness and material selection: The dielectric substrate is made of epoxy resin FR4 with a relative permittivity of 4.4. The dielectric thickness is optimized to adjust the resonant point. c) Feed point location: The location where the antenna connects to the feed line is the feed point. The choice of feed point location affects the matching degree between the antenna input impedance and the line impedance. Different feed point locations have a significant impact on the antenna's return loss and voltage standing wave ratio. d) Conductor width: The conductor width affects the antenna's loss, impedance matching, and radiation efficiency. Increasing the conductor width reduces conductor resistance, thereby reducing loss and improving radiation efficiency, but it also changes the input impedance and effective permittivity of the radiating patch. Wider conductors can expand bandwidth but occupy more physical space, limiting miniaturization design. Narrower conductors, while beneficial for compact layout, result in increased resistance and shortened electrical length, requiring an increase in antenna size to maintain the previous resonant frequency. Therefore, a balance needs to be found between efficiency, size, and bandwidth in the design. Installation and connection: Fix the antenna to the top of the tower or the switch cabinet housing using clamps or DIN rail brackets; transmit the RF signal to the front-end circuit via SMA coaxial cable.

[0102] Step 2: High-pass filter circuit design;

[0103] During transmission, arc radiation signals may be superimposed with a large amount of low-frequency interference, including the 50 Hz power frequency signal from the grid, low-frequency harmonics, sensor background noise, and vibration interference. To filter out these low-frequency components while preserving the high-frequency pulse characteristics of the arc light, a Sallen-Key type second-order active high-pass filter is introduced at the front end, with a cutoff frequency designed to be 1 kHz. This filter uses a high-speed operational amplifier RS8751XF, with an operating voltage range of 2.5 V–5.5 V, a bandwidth of 250 MHz, and a slew rate of 180 V / µs, making it suitable for subsequent processing circuits and high-frequency signal environments. Its structure not only provides stable amplification but also ensures that its load does not affect the filtering characteristics.

[0104] Step 3: Envelope detection module;

[0105] To simplify high-frequency signal processing, this device employs an envelope detection circuit to convert high-frequency pulses into time-varying low-frequency envelope voltages, facilitating sampling and analysis by the subsequent MCU. The core of this module consists of a 1SS400 high-speed diode, capacitors, and resistors, utilizing the RC integration principle for smoothing. The time constant τ = RC determines the response speed and smoothness of the envelope voltage, while the resistors are adjustable to accommodate pulse waveforms of arcs with varying durations. The ultra-high-speed 1SS400 diode is selected, featuring extremely low forward voltage drop and reverse recovery time, making it suitable for unidirectional conduction and envelope extraction in high-frequency scenarios.

[0106] Step 4: Multi-stage hysteresis comparator module;

[0107] To further quantize the analog envelope signal, this invention introduces a multi-threshold hysteresis comparator module to achieve multi-level arc intensity determination. This module uses a TLV3202 rail-to-rail dual comparator, which features excellent hysteresis and low power consumption, is compatible with 3.3V power supply, and can be directly recognized by the MCU. 1) The comparator input is connected to the envelope voltage signal, and the reference is connected to a set threshold voltage (e.g., 1.0V, 2.0V, 2.9V); 2) If the input voltage is higher than VTH, a rising edge triggers a high-level output; 3) If it is lower than VTL, a falling edge outputs a low level; 4) If the voltage is between these two values, the original state is maintained, effectively suppressing jitter and false triggering caused by high-frequency noise. By setting multiple hysteresis comparators to operate in parallel, 2-bit or 3-bit digital output encoding can be achieved, and preliminary "grading quantization" of the arc intensity can be performed, providing a basic criterion for subsequent logic in the MCU.

[0108] Step 5: MCU sliding window counting and LED grading determination;

[0109] This invention uses the STM32F103C8T6 as the control core, with a main frequency of 72 MHz, and has excellent interrupt handling, analog-to-digital conversion, digital filtering, and GPIO control capabilities. The specific logic is as follows: 1) Sliding window counting mechanism: The MCU sets a 1-second sliding time window and periodically collects the output pulses of the hysteresis comparator; the total number of pulses N_t within this time window is counted; the arc type is determined according to the preset threshold (N_th1=50, N_th2=200) and intensity level (whether V_H is triggered). 2) Grading Judgment Rules: If N_t = 0 → no arc is identified, and the three-color LED is off; if 0 < N_t < N_th1 → instantaneous arc is identified → yellow light is constantly on; if N_th1 ≤ N_t < N_th2 and a high threshold is triggered → intermittent strong arc is identified → red light is constantly on; if N_t ≥ N_th2 → continuous heavy discharge is identified → red light flashes rapidly (PWM); 3) Output Interface: The grading status is output by controlling the LED indicator through GPIO; the buzzer is controlled to emit intermittent or continuous sounds; all judgment results, waveform characteristics, and timestamps are stored in SPI Flash or EEPROM for preservation.

[0110] Step 6: Power supply and structural design;

[0111] Power supply module: Utilizes a solar panel and MPPT controller to ensure stable power input; the battery management chip supports 3.7V lithium battery charge and discharge protection; the overall operating power consumption is ≤150 mW, and the sleep power consumption is as low as 10 µA, supporting an intermittent wake-up detection mechanism. Housing structure: The device adopts an aluminum alloy structure with silicone sealing, achieving an IP67 protection rating; it supports both DIN rail and clamp mounting methods; the modular design facilitates quick maintenance and replacement of components such as the antenna, MCU, and power supply.

[0112] The above optional implementation methods can achieve at least the following beneficial effects:

[0113] (1) High sensitivity and strong anti-interference capability: The Hilbert fifth-order fractal patch antenna is applied to the reception of arc radiation signals. The feed point, linewidth and substrate parameters are optimized through HFSS simulation to achieve a wide bandwidth reception capability of 0.1–500 MHz, with high sensitivity and good directivity. The front-end circuit integrates a low-noise amplifier and a multi-stage bandpass filter, which can effectively suppress typical interference of the power distribution network (such as power frequency and harmonics) and ensure the effective detection of subsequent signals;

[0114] (2) Intelligent grading and on-site visualization of arc intensity: The above optional implementation device uses envelope detection and two comparators to classify arc signals of different amplitudes. The MCU uses a sliding time window to statistically analyze pulse frequency and combines amplitude levels to intelligently distinguish between transient, intermittent, and continuous arcs. The grading results are displayed intuitively by three-color LEDs (yellow light: transient, red light: intermittent / continuous, off light: no fault), and supports buzzer alarm and historical data storage, significantly improving operation and maintenance efficiency and emergency response capabilities;

[0115] (3) Miniaturization, low cost and low power consumption synergistic optimization design: The device adopts a highly integrated modular design, with the overall size less than 200 mm × 150 mm × 50 mm. It uses FR4 substrate and standard PCB process to reduce manufacturing costs. It supports MPPT solar power supply and lithium battery management. The system sleep power consumption is less than 150 mW, which can adapt to extreme outdoor environments such as high temperature / low temperature and meet the engineering requirements of long-term unattended operation.

[0116] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0117] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0118] Example 2

[0119] According to an embodiment of the present invention, a fault detection device is also provided, comprising: an antenna unit, a signal conditioning module, an envelope detection module, a multi-level comparison module, and a hierarchical judgment algorithm module. The signal conditioning module includes a signal amplifier and a target filter. The antenna unit is connected to the signal amplifier, the signal amplifier is connected to the target filter, the target filter is connected to the envelope detection module, the envelope detection module is connected to both the multi-level comparison module and the hierarchical judgment algorithm module, and the multi-level comparison module is connected to the hierarchical judgment module. The antenna unit is used to acquire the arc electromagnetic radiation signal corresponding to the distribution network. The arc electromagnetic radiation signal is the radiation signal released when an arc fault occurs in the distribution network. The antenna unit uses a target antenna as a receiver and receives the corresponding radiation signal in a non-contact manner. The target antenna is an antenna with a predetermined fractal structure. The predetermined fractal structure is based on the frequency band coverage range of the distribution network arc fault detection and the self-phase of the fractal structure. Similarity and space-filling properties are determined, and the frequency of the arc electromagnetic radiation signal is higher than a predetermined frequency threshold. A signal amplifier is used to enhance the signal strength within the target frequency band of the arc electromagnetic radiation signal to obtain an enhanced radiation signal, wherein the signal effectiveness within the target frequency band is greater than a predetermined effective threshold. A target filter is used to filter the enhanced radiation signal and retain the frequency band signal within the predetermined frequency band to obtain a filtered radiation signal, wherein the signal characterization within the predetermined frequency band is greater than a predetermined characterization threshold. An envelope detection module is used to convert the filtered radiation signal into a low-frequency envelope signal, wherein the frequency corresponding to the low-frequency envelope signal is lower than the frequency corresponding to the filtered radiation signal. A multi-level comparison module is used to determine the arc fault level corresponding to the distribution network based on the low-frequency envelope signal. A graded judgment algorithm module is used to count the number of signal pulses within a predetermined time period and determine the arc fault type corresponding to the distribution network based on the arc fault level and the number of signal pulses.

[0120] This involves an antenna unit, which is a component used for non-contact reception of broadband electromagnetic radiation signals released by arc faults in power distribution networks. Its core is an antenna with a predetermined fractal structure. This structure is designed based on the self-similarity and space-filling properties of fractals, aiming to achieve high-sensitivity, small-size signal reception within a specific frequency band.

[0121] This includes a signal conditioning module, which is a functional module that preprocesses the raw arc electromagnetic radiation signal received by the antenna unit. It integrates a signal amplifier and a target filter to sequentially amplify and filter the signal in order to extract effective and highly representative signal components for subsequent analysis.

[0122] This includes an envelope detection module, which is a circuit module that converts a conditioned high-frequency pulse signal into a low-frequency envelope signal. A typical envelope detection circuit composed of high-speed diodes, capacitors, and resistors can be used. By adjusting the time constant, it can be adapted to arc waveforms of different durations, which facilitates subsequent digital quantization processing.

[0123] This involves a multi-level comparison module, which is a circuit module that quantizes the analog low-frequency envelope signal into a digital level signal and initially classifies the fault level. It can use multiple voltage comparators with hysteresis characteristics, set different threshold voltages, and output combined level signals to correspond to different arc intensity levels.

[0124] This includes a graded judgment algorithm module, which is a processing unit that performs logical analysis and fault type determination based on digital signals, and can typically be implemented by a microcontroller. Its core function is to count the number of signal pulses from multiple comparison modules within a predetermined time window, and, by combining arc fault level information, determine the fault type according to preset rules.

[0125] In this embodiment, firstly, the antenna unit captures the broadband arc electromagnetic radiation signal generated by the power distribution network fault point in a non-contact manner. This signal is then sent to the signal conditioning module: firstly, the signal in the target effective frequency band is amplified with low noise by a signal amplifier; then, out-of-band interference is filtered out by a target filter to obtain a highly representative filtered radiation signal. Next, the envelope detection module converts this high-frequency filtered signal into a low-frequency envelope signal. This envelope signal is sent to a multi-level comparison module, which compares it with multiple preset threshold voltages, outputting a combined level signal representing different arc intensity levels; secondly, its waveform or the pulse signal output by the comparison module is acquired by the hierarchical judgment algorithm module. The hierarchical judgment algorithm module counts the number of pulses within a preset sliding time window and, combined with the arc intensity level information obtained from the multi-level comparison module, ultimately determines the specific arc fault type through a built-in algorithm.

[0126] This embodiment constructs a complete hardware system from non-contact signal sensing to intelligent fault classification. This device achieves non-contact, high-sensitivity detection. Utilizing a specially designed wideband fractal antenna, it effectively captures high-frequency electromagnetic radiation signals generated by electric arcs without contacting power grid conductors. This avoids the drawbacks of traditional contact detection, such as susceptibility to power frequency interference and complex installation, while improving detection sensitivity and applicability. It enables effective signal extraction and reliable conversion. Through integrated low-noise amplifiers and bandpass filters, signal conditioning extracts effective arc characteristic signals from complex electromagnetic environments. Envelope detection then converts these signals into easily processed low-frequency signals, laying the foundation for subsequent accurate quantization and judgment. A multi-level comparison module quantizes the analog envelope signal into digital signals representing different intensity levels, while a hierarchical judgment algorithm module statistically analyzes the pulse frequency. Combining this information provides a comprehensive judgment, offering far more refined diagnostic information than traditional binary judgment, enabling joint intelligent judgment of fault intensity and frequency. The functional modules are interconnected to form a pipelined processing structure. This modular design not only facilitates independent optimization of each part but also benefits device maintenance, upgrades, and deployment and application in a wider range of monitoring scenarios.

[0127] As an optional embodiment, the antenna element includes a conductor, a dielectric substrate, and a ground plane. The dielectric constant of the dielectric substrate material of the antenna element is within a predetermined constant range, and the balance index corresponding to the conductor width is greater than a predetermined balance threshold. The balance index represents the balance between radiation efficiency, size influence, and extended bandwidth.

[0128] This involves wires, which are conductive metal parts that constitute the antenna radiator or receiver. They can refer to metal patches or traces with predetermined fractal structures formed on a dielectric substrate. Their shape, length, and width directly affect the antenna's resonant frequency, impedance matching, and radiation characteristics.

[0129] This involves a dielectric substrate, which is an insulating material layer that supports the conductors and is located between the conductors and the ground plane. Its material and thickness affect the effective dielectric constant of the antenna, and thus affect the electrical dimensions and performance of the antenna.

[0130] This involves a ground plane, which is a continuous conductive plane located on the other side of the dielectric substrate. Together with the wires, it forms the basic structure of the microstrip antenna, provides a signal reference ground, and affects the antenna's radiation pattern and back radiation.

[0131] This involves the dielectric constant, a physical quantity that characterizes the degree of polarization of a dielectric material in an electric field and affects the propagation speed of electromagnetic waves in the medium. The dielectric constant of the antenna substrate is a key parameter that determines the relationship between the physical size and electrical size of the antenna. A higher dielectric constant helps to reduce the physical size of the antenna, but may affect bandwidth and efficiency.

[0132] This involves a predetermined constant range, which is the allowable range of dielectric constant values ​​for the substrate material pre-defined according to the antenna design objectives. Selecting materials within this range can ensure that the antenna has acceptable performance while meeting the requirements of a compact structure.

[0133] This involves the wire width, which is the lateral dimension of the metal wire that makes up the fractal antenna. The wire width affects the antenna's conductor loss, input impedance matching, and effective electrical length, and is a key adjustable parameter in antenna design.

[0134] This involves the balance index, which is an indicator or parameter used to quantitatively evaluate the degree of comprehensive balance between antenna radiation efficiency, physical size influence, and operating bandwidth expansion capability. If the index is greater than a predetermined balance threshold, it means that the design has achieved a better compromise among multiple mutually restrictive performance indicators.

[0135] This involves a predetermined balance threshold, which is a minimum balance index value set in advance according to the engineering application requirements. The balance index is required to be greater than this threshold to ensure that the antenna design does not excessively sacrifice radiation efficiency and necessary bandwidth while pursuing miniaturization, thus meeting the basic requirements of power distribution network arc fault detection for antenna performance.

[0136] In this optional embodiment, the antenna element is limited to a stacked structure consisting of wires, a dielectric substrate, and a ground plane. During the design process, a dielectric substrate material with a dielectric constant within a predetermined range must be selected to achieve a basic balance between size and performance. Simultaneously, the width of the wires needs to be optimized to ensure that the balance index calculated or evaluated is higher than a preset threshold. This essentially sets clear collaborative optimization goals and constraints for the key structural parameters of the antenna.

[0137] This optional embodiment clarifies the specific structural path for achieving high-performance miniaturization of the antenna. By defining the classic three-layer structure of the microstrip antenna, a reliable physical carrier is provided for complex wiring such as fractals, ensuring the feasibility and stability of the design. Introducing the balance index as a comprehensive parameter and setting a threshold concretizes and measures the design concept of finding a balance between efficiency, size, and bandwidth. This guides designers not to pursue a single indicator, but to achieve an acceptable balance among multiple key performance aspects that meets the detection requirements, thereby improving the scientific nature and goal orientation of the antenna design. The constraints on the dielectric constant range of the substrate and the balance of the conductor width jointly ensure that the final antenna can achieve performance that covers the target frequency band and has sufficient receiving sensitivity within a predetermined compact size, thus providing a reliable first-level signal sensing guarantee for non-contact arc fault detection.

[0138] As an optional embodiment, the antenna design size of the antenna element is smaller than a predetermined size threshold, and the matching index corresponding to the feed point position of the antenna element is greater than a predetermined matching threshold. Here, the feed point position is the position where the antenna is connected to the feed line, and the matching index is an index that represents the degree of matching between the antenna input impedance and the line impedance.

[0139] This involves antenna design dimensions, which are the maximum external dimensions of the antenna element in three-dimensional space, typically described by length, width, and thickness.

[0140] This involves a predetermined size threshold, which is the upper limit of the maximum allowable physical size of the antenna pre-set according to the target installation space. The requirement that the antenna design size be smaller than this threshold is to ensure that the antenna can be accommodated in the specified installation location, thereby achieving miniaturization and convenient deployment of the device.

[0141] This involves the location of the feed point, which is the specific physical connection point on the antenna's RF port used to connect to an external feed line for input or output signals. The selection of this point directly affects the value of the antenna's input impedance and is a key factor in determining whether the antenna and subsequent circuits can achieve impedance matching.

[0142] This involves the matching index, which is an indicator used to quantitatively characterize the degree of matching between the antenna input impedance and the characteristic impedance of the transmission line. Commonly used matching indices can include voltage standing wave ratio (VSWR) and return loss.

[0143] This involves a predetermined matching threshold, which is a minimum performance standard set for the matching index based on the system's requirements for signal transmission efficiency and reliability. The requirement that the matching index after the feed point location optimization be greater than this threshold is to ensure that the signal received from the antenna can be fed into the subsequent processing circuit with sufficiently low loss, thus guaranteeing detection sensitivity.

[0144] In this alternative embodiment, two specific design requirements for the antenna element are proposed. First, the overall physical size of the antenna must be controlled below a certain preset maximum value to meet the constraints of miniaturization and compactness in actual engineering installations. Second, when designing the antenna structure, the location of the feed point must be selected and optimized so that the matching degree between the antenna input impedance corresponding to the finally determined location and the standard feed line impedance can reach a preset excellent level.

[0145] This optional embodiment achieves miniaturization and installability of the antenna device. By setting an upper limit on size, it ensures that the antenna can be adapted to outdoor or industrial environments with limited space, such as power distribution towers and switch cabinets, providing a physical basis for the widespread deployment of the entire fault detection device. By setting a performance threshold for the matching index and requiring targeted optimization of the feed point location, it minimizes signal reflection and energy loss caused by impedance mismatch, allowing weak arc electromagnetic radiation signals to be transmitted to the subsequent amplification and processing circuits as lossless as possible, thereby directly improving the receiving sensitivity and signal-to-noise ratio of the entire detection system.

[0146] As an optional embodiment, the device further includes a power supply module, which is connected to the signal conditioning module, the envelope detection module, the multi-level comparison module, and the hierarchical judgment algorithm module.

[0147] The device is a complete equipment entity used to realize non-contact arc fault detection in power distribution networks, which includes at least an antenna unit, a signal conditioning module, an envelope detection module, a multi-level comparison module, and a hierarchical judgment algorithm module.

[0148] This includes a power supply module, which is a power circuit and management system used to convert, manage, and stably distribute external or internally stored electrical energy to various electronic functional modules within the device.

[0149] In this optional embodiment, a power supply module is added, which is connected to the power interface of the signal conditioning module, envelope detection module, multi-level comparison module and hierarchical judgment algorithm module through physical connection; the power supply module is responsible for obtaining electrical energy from external energy or releasing electrical energy from internal energy storage unit, and after necessary voltage conversion, voltage regulation and management, providing the required operating voltage and current to the modules that need electrical energy.

[0150] This optional embodiment enables the device to achieve energy self-sufficiency and independent operation. By integrating a dedicated power supply module, the entire fault detection device does not need to rely on an external fixed power source. It can operate autonomously for a long time in environments without mains power, such as outdoor poles, using renewable energy sources such as solar energy or built-in batteries. This greatly expands the deployment flexibility and application range of the device. The power supply module is connected to each core circuit module to provide them with clean and stable power, avoiding signal processing errors, logic misjudgments, or device damage caused by power noise and voltage fluctuations. This ensures the electrical reliability of the entire chain from signal acquisition to fault determination.

[0151] Example 3

[0152] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute instructions to implement the power distribution network fault detection method of any of the above embodiments.

[0153] Example 4

[0154] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform any of the above-described fault detection methods for power distribution networks.

[0155] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0156] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0157] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0158] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0159] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0160] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0161] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A fault detection method for a power distribution network, characterized in that, include: The method involves acquiring the electromagnetic radiation signal of an electric arc corresponding to a power distribution network. The electric arc electromagnetic radiation signal is received by an antenna unit in a fault detection device. The signal is the radiation emitted when an electric arc fault occurs in the power distribution network. The antenna unit uses a target antenna as a receiver and receives the corresponding radiation signal in a non-contact manner. The target antenna is an antenna with a predetermined fractal structure. The predetermined fractal structure is determined based on the frequency band coverage of the power distribution network arc fault detection, as well as the self-similarity and space-filling property of the fractal structure. The frequency of the electric arc electromagnetic radiation signal is higher than a predetermined frequency threshold. The fault detection device also includes a signal conditioning module, an envelope detection module, a multi-level comparison module, and a hierarchical judgment algorithm module. The signal conditioning module includes a signal amplifier and a target filter. The signal amplifier is controlled to enhance the signal strength in the target frequency band of the electric arc electromagnetic radiation signal to obtain an enhanced radiation signal, wherein the signal effectiveness in the target frequency band is greater than a predetermined effective threshold. The target filter is controlled to filter the enhanced radiation signal and retain the frequency band signal within a predetermined frequency band range to obtain the filtered radiation signal, wherein the signal characterization within the predetermined frequency band range is greater than a predetermined characterization threshold. The envelope detection module is controlled to convert the filtered radiation signal into a low-frequency envelope signal, wherein the frequency of the low-frequency envelope signal is lower than the frequency of the filtered radiation signal. The multi-level comparison module is controlled to determine the arc fault level corresponding to the power distribution network based on the low-frequency envelope signal; The control algorithm module counts the number of signal pulses within a predetermined time period and determines the type of arc fault corresponding to the power distribution network based on the arc fault level and the number of signal pulses, wherein the number of signal pulses is obtained based on the low-frequency envelope signal.

2. The method according to claim 1, characterized in that, The control module determines the arc fault level corresponding to the distribution network based on the low-frequency envelope signal, including: When the multi-level comparison module includes multiple comparators, the connection state between the input voltage and the reference voltage corresponding to each of the multiple comparators is determined based on the voltage value of the low-frequency envelope signal and the threshold voltage corresponding to each of the multiple comparators. The voltage of the low-frequency envelope signal is connected to the input terminal of each of the multiple comparators, and the corresponding threshold voltage is connected to the reference terminal of the corresponding comparator. Based on the comparator logic rules and docking status of the multiple comparators, the voltage value at the input terminal of the comparator is compared with the threshold voltage at the corresponding reference terminal in real time, triggering the corresponding level output operation to obtain the level signal corresponding to the multiple comparators. Based on the level signals corresponding to the plurality of comparators, level signal combination processing is performed to obtain a combined level signal; Based on the preset mapping relationship between the combined level signal and the arc fault level, the arc fault level corresponding to the distribution network is obtained.

3. The method according to claim 1, characterized in that, The control module for the graded judgment algorithm counts the number of signal pulses within a predetermined time period, and determines the arc fault type corresponding to the distribution network based on the arc fault level and the number of signal pulses, including: Retrieve the pulse fault mapping relationship corresponding to the distribution network, wherein the pulse fault mapping relationship represents the mapping relationship between the number of pulses and the fault type; Based on preset sliding window parameters, the number of signal pulses within the predetermined time period is obtained by sliding acquisition, wherein the sliding window parameters include a sliding time window and a sliding step size; Based on the number of signal pulses and the pulse fault mapping relationship, the arc fault type corresponding to the distribution network is determined, wherein the arc fault type includes transient arc fault, intermittent strong arc fault, and continuous discharge fault.

4. The method according to any one of claims 1 to 3, characterized in that, Controlling the envelope detection module to convert the filtered radiation signal into a low-frequency envelope signal includes: When the envelope detection module is composed of diodes, capacitors and resistors, the time constant function corresponding to the envelope detection module is retrieved, wherein the time constant function includes the product of the capacitance term and the resistance term; Determine the predicted duration of the arc fault; Based on the predicted duration, the resistance value of the resistance term in the time constant function is adjusted to obtain the target time constant value; Based on the target time constant value, the filtered radiation signal is converted into a low-frequency envelope signal.

5. A fault detection device, characterized in that, include: The system includes an antenna unit, a signal conditioning module, an envelope detection module, a multi-level comparison module, and a hierarchical judgment algorithm module. The signal conditioning module comprises a signal amplifier and a target filter. The antenna unit is connected to the signal amplifier, the signal amplifier is connected to the target filter, the target filter is connected to the envelope detection module, the envelope detection module is connected to both the multi-level comparison module and the hierarchical judgment algorithm module, and the multi-level comparison module is connected to the hierarchical judgment module. The antenna unit is used to acquire the electromagnetic radiation signal of the electric arc corresponding to the power distribution network. The electromagnetic radiation signal of the electric arc is the radiation signal released when an electric arc fault occurs in the power distribution network. The antenna unit uses a target antenna as a receiver and receives the corresponding radiation signal in a non-contact manner. The target antenna is an antenna with a predetermined fractal structure. The predetermined fractal structure is determined based on the frequency band coverage of the electric arc fault detection of the power distribution network and the self-similarity and space-filling property of the fractal structure. The frequency of the electromagnetic radiation signal of the electric arc is higher than a predetermined frequency threshold. The signal amplifier is used to enhance the signal strength in the target frequency band of the electric arc electromagnetic radiation signal to obtain an enhanced radiation signal, wherein the signal effectiveness in the target frequency band is greater than a predetermined effective threshold. The target filter is used to filter the enhanced radiation signal, retain the frequency band signal within a predetermined frequency band range, and obtain the filtered radiation signal, wherein the signal characterization within the predetermined frequency band range is greater than a predetermined characterization threshold. The envelope detection module is used to convert the filtered radiation signal into a low-frequency envelope signal, wherein the frequency of the low-frequency envelope signal is lower than the frequency of the filtered radiation signal. The multi-level comparison module is used to determine the arc fault level corresponding to the distribution network based on the low-frequency envelope signal; The graded judgment algorithm module is used to count the number of signal pulses within a predetermined time period, and determine the arc fault type corresponding to the distribution network based on the arc fault level and the number of signal pulses.

6. The apparatus according to claim 5, characterized in that, The antenna element includes a conductor, a dielectric substrate, and a ground plane. The dielectric constant of the dielectric substrate material of the antenna element is within a predetermined constant range, and the balance index corresponding to the conductor width is greater than a predetermined balance threshold. The balance index represents the balance between radiation efficiency, size influence, and extended bandwidth.

7. The apparatus according to claim 5, characterized in that, The antenna design size of the antenna element is smaller than a predetermined size threshold, and the matching index corresponding to the feed point position of the antenna element is greater than a predetermined matching threshold. The feed point position is the position where the antenna is connected to the feed line, and the matching index is an index that represents the degree of matching between the antenna input impedance and the line impedance.

8. The apparatus according to any one of claims 5 to 7, characterized in that, The device also includes a power supply module, which is connected to the signal conditioning module, the envelope detection module, the multi-level comparison module, and the hierarchical judgment algorithm module.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the fault detection method for the power distribution network as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the fault detection method for the power distribution network as described in any one of claims 1 to 4.

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