Cable fault detection method and device based on intelligent cable fault testing device

Abstract

The invention provides a cable fault detection method based on an intelligent cable fault test device and a test device, and the method comprises the steps: synchronously connecting a fault cable and an adjacent healthy cable through a cable fault tester, and enabling the fault cable to be connected with the adjacent healthy cable when a high-voltage pulse is applied; and synchronously capturing an active reflection signal of the fault cable and a passive coupling induction signal of the healthy cable. Through cross-cable signal feature comparison, a real fault signal is accurately identified, a roughly measured fault distance is calculated according to the real fault signal, and fuzziness of traditional waveform interpretation is overcome. According to the spectrum characteristics of the real fault signal, the optimal filtering parameter is adaptively configured for the acoustic-magnetic synchronous positioning instrument, and the signal-to-noise ratio of the accurate fixed point is improved. And accurate point positioning is carried out near the rough measurement distance, and secondary verification is carried out at the corresponding position of the healthy cable, so that closed-loop positioning of the fault is completed in a mode of confirming that no sound magnetic signal exists. According to the invention, accurate extraction and positioning guidance of fault features are realized, and the accuracy and efficiency of cable fault detection are greatly improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of cable fault detection, and in particular relates to a cable fault detection method and device based on an intelligent cable fault testing device. Background Art

[0002] As the lifeline of modern energy supply, the safe and stable operation of urban underground cable networks is crucial. Over long-term operation, cables inevitably fail due to insulation aging, damage from external forces, and joint defects. Once a failure occurs, quickly and accurately locating the fault point and performing emergency repairs are crucial for restoring power and minimizing economic losses. Currently, the high-voltage flashover method is the most widely used cable fault location technology. Its basic process involves first using a cable fault tester to roughly measure the fault distance, followed by a precise acoustic or magnetic field location using an acoustic-magnetic synchronous pinpointer within the roughly measured distance.

[0003] In practice, the reflected waveforms generated by a faulty cable under a high-voltage pulse are often very complex. These waveforms include not only the reflected signal from the fault point but also reflections from multiple impedance mismatches, such as cable joints, branches, and terminals, as well as various environmental electromagnetic interference. Operators rely on personal experience to discern the true fault reflection from a sea of similar waveforms, making misjudgment highly likely and resulting in significant deviations from the rough measurement range. Furthermore, the acoustic-magnetic signal generated by the fault point discharge is typically very weak and easily drowned out by ambient noise, such as traffic and construction. Manual adjustments to the pinpointer's filtering parameters have varying results. Often, the discharge sound is lost due to a low signal-to-noise ratio, leading to repeated searches within the rough measurement range without success. Summary of the Invention

[0004] The present invention provides a cable fault detection method and device based on an intelligent cable fault testing device to solve the above technical problems.

[0005] In a first aspect, the present invention provides a cable fault detection method based on an intelligent cable fault testing device, which is applied to the cable fault testing device. The cable fault testing device includes a cable fault tester and an acoustic-magnetic synchronous positioning instrument. The method includes the following steps:

[0006] Connect the high-voltage flash unit and the first signal acquisition channel of the cable fault tester to the target fault cable, and connect the second signal acquisition channel to an adjacent healthy cable laid in parallel with the target fault cable;

[0007] A high-voltage pulse is applied to the target fault cable through a high-voltage flash unit, and the active reflection signal of the target fault cable and the passive coupling induction signal of the adjacent healthy cable are synchronously captured using the first signal acquisition channel and the second signal acquisition channel;

[0008] Based on the active reflection signal and using time domain reflectometry, the rough fault distance of the fault point in the target fault cable is calculated;

[0009] Identify the real fault signal in the active reflection signal and the passive coupling induction signal by comparing the cross-cable signal characteristics;

[0010] According to the spectrum characteristics of the real fault signal, the optimal acoustic filter parameters of the acoustic channel are configured for the acoustic-magnetic synchronous positioning instrument;

[0011] The configured acoustic-magnetic synchronous locator is used to accurately locate the target fault cable near the rough fault distance. A secondary verification is performed at the corresponding position of the adjacent healthy cable to confirm the absence of acoustic-magnetic signals, completing the closed-loop positioning of the fault point.

[0012] Optionally, the step of synchronously capturing the active reflection signal of the target faulty cable and the passive coupling induction signal of the adjacent healthy cable by using the first signal acquisition channel and the second signal acquisition channel comprises the following steps:

[0013] Setting a synchronous acquisition trigger threshold and a pre-trigger data buffer in the first signal acquisition channel and the second signal acquisition channel;

[0014] monitoring the signal amplitude of the signal in the first signal acquisition channel in real time, and starting high-frequency synchronous data acquisition of the first signal acquisition channel and the second signal acquisition channel when the signal amplitude exceeds a synchronous acquisition trigger threshold;

[0015] Based on a preset acquisition time window, the original waveform data of the first signal acquisition channel and the second signal acquisition channel after the high voltage pulse occurs are recorded respectively;

[0016] The collected raw waveform data is digitally filtered and normalized to generate the active reflection signal of the target fault cable and the passive coupling induction signal of the adjacent healthy cable.

[0017] Optionally, the identifying a true fault signal in the active reflection signal and the passive coupling induction signal by comparing cross-cable signal characteristics comprises the following steps:

[0018] The energy attenuation gradient analysis module of the cable fault tester is called to calculate and compare the energy attenuation gradients of the active reflection signal and the passive coupling induction signal to obtain the energy source determination result;

[0019] The initial phase offset verification module of the cable fault tester is called to calculate and verify the initial phase offset of the active reflection signal and the passive coupling induction signal to obtain the coupling path confirmation result;

[0020] Performing a logical AND operation on the energy source determination result and the coupling path confirmation result to form a signal recognition result;

[0021] According to the signal recognition result, the active reflection signal is marked as the real fault signal, and the spectrum characteristics of the real fault signal are extracted. At the same time, the passive coupling induction signal is marked as the interference signal, and the interference signal is suppressed.

[0022] Optionally, the step of calling the energy attenuation gradient analysis module of the cable fault tester to calculate and compare the energy attenuation gradients of the active reflection signal and the passive coupling induction signal to obtain the energy source determination result includes the following steps:

[0023] Perform fast Fourier transform on the active reflection signal and the passive coupling induction signal respectively to obtain the amplitude-frequency characteristic curves of the two signals;

[0024] Calculating broadband total energy of the active reflection signal and the passive coupling induction signal, and generating a first energy ratio;

[0025] The amplitude-frequency characteristic curves of the two signals are divided into a plurality of preset non-overlapping narrowband frequency bands;

[0026] Calculating the signal energy in each narrowband frequency band respectively, and generating a second energy ratio corresponding to each frequency band;

[0027] Analyze the trend of the second energy ratio changing with frequency to quantify the energy attenuation gradient;

[0028] The first energy ratio and the energy attenuation gradient are compared with a preset coupling model threshold, and an energy source determination result is output.

[0029] Optionally, the calling of the initial phase offset verification module of the cable fault tester to calculate and verify the initial phase offsets of the active reflection signal and the passive coupling induction signal to obtain the coupling path confirmation result includes the following steps:

[0030] Upsampling the active reflection signal and the passive coupled induction signal by an interpolation algorithm;

[0031] Calculating the first-order derivatives of the active reflection signal and the passive coupling induction signal after upsampling, and determining the starting time point of the signal waveform by the first non-zero point of the first-order derivative;

[0032] Calculate the time difference between the corresponding starting time points of the active reflection signal and the passive coupling induction signal to obtain the absolute time value of the initial phase offset;

[0033] Convert the absolute time value into a phase angle according to the main frequency of the real fault signal;

[0034] The phase angle is compared with the theoretical phase offset range set according to the cable spacing and dielectric parameters, and the coupling path is output to confirm the result.

[0035] Optionally, configuring optimal acoustic filtering parameters of an acoustic channel for the acoustic-magnetic synchronous pinpointer according to the spectrum characteristics of the real fault signal comprises the following steps:

[0036] A high-voltage flashover unit is used to trigger multiple high-voltage flashovers in the area determined by the rough fault distance, and an acoustic-magnetic synchronous point-finding instrument is used to synchronously collect magnetic field signals and continuous on-site acoustic signals.

[0037] Taking the triggering moment of each magnetic field signal as the benchmark, multiple synchronous acoustic samples are intercepted from the continuous on-site acoustic signal. Each synchronous acoustic sample contains the fault discharge sound and background noise.

[0038] A plurality of asynchronous acoustic samples not containing fault discharge sound are intercepted from the continuous on-site acoustic signal as background noise samples;

[0039] Calculate the average spectrum of the synchronous acoustic samples and the average spectrum of the background noise samples respectively;

[0040] By comparing the average spectrum, the characteristic frequency band where the signal-to-noise ratio of the fault discharge sound is higher than the background noise is identified, and the optimal acoustic filtering parameters of the acoustic channel of the acoustic-magnetic synchronous pinpointer are dynamically generated based on the characteristic frequency band.

[0041] Optionally, the step of identifying a characteristic frequency band in which the signal-to-noise ratio of the fault discharge sound is higher than that of the background noise by comparing the average frequency spectrum, and dynamically generating the optimal acoustic filtering parameters of the acoustic channel of the acoustic-magnetic synchronous pointing instrument based on the characteristic frequency band includes the following steps:

[0042] Subtract the average spectrum of the synchronized acoustic samples from the average spectrum of the background noise samples to obtain the estimated spectrum of the pure fault sound;

[0043] Calculate the signal-to-noise ratio between the estimated spectrum of the pure fault sound and the average spectrum of the background noise samples, and generate a signal-to-noise ratio spectrum graph covering the entire frequency band;

[0044] Searching the signal-to-noise ratio spectrum for all frequency bands whose signal-to-noise ratio values exceed a preset judgment threshold as characteristic frequency bands;

[0045] Analyze the frequency position, bandwidth and continuity of all characteristic frequency bands, and select the optimal filter type based on the analysis results. The filter types include high-pass filter, low-pass filter and band-pass filter;

[0046] The determined filter type and the corresponding cutoff frequency are packaged to form the optimal acoustic filter parameters of the acoustic channel of the acoustic-magnetic synchronous pointing instrument.

[0047] Optionally, the configured acoustic-magnetic synchronous locator is used to accurately locate the target fault cable near the rough fault distance, and a secondary verification is performed at the corresponding position of the adjacent healthy cable to confirm the absence of the acoustic-magnetic signal. The closed-loop location of the fault point includes the following steps:

[0048] Utilize the configured acoustic-magnetic synchronous locator to receive the synchronized first acoustic signal and the first magnetic field signal near the rough fault distance of the target fault cable, and calculate the signal time difference between the first acoustic signal and the first magnetic field signal in real time;

[0049] The acoustic-magnetic synchronous positioning instrument is moved based on the signal time difference until the signal time difference reaches the minimum, and the physical location of the acoustic-magnetic synchronous positioning instrument is determined as the preliminary fault point of the target fault cable;

[0050] After completing secondary verification of the adjacent healthy cables corresponding to the initial fault point using the acoustic-magnetic synchronous positioning instrument, the initial fault point is locked as the final fault point, completing the closed-loop positioning.

[0051] Optionally, after completing secondary verification of the adjacent healthy cables corresponding to the position of the preliminary fault point by the acoustic-magnetic synchronous positioning instrument, the preliminary fault point is locked as the final fault point, and the closed-loop positioning is completed, including the following steps:

[0052] Move the probe of the acoustic-magnetic synchronous pointing instrument horizontally to the top of the adjacent healthy cable;

[0053] Using an acoustic-magnetic synchronous pointing device to continuously monitor a second acoustic signal and a second magnetic field signal originating from an adjacent healthy cable;

[0054] Determining whether there is a valid second acoustic signal at the synchronization moment when the second magnetic field signal is triggered;

[0055] If no valid second acoustic signal is detected within the preset verification time, the secondary verification is determined to be successful;

[0056] If the secondary verification passes, the preliminary fault point will be locked as the final fault point through the acoustic-magnetic synchronous positioning instrument, and the geographical coordinates of the final fault point will be determined.

[0057] In a second aspect, the present invention also provides an intelligent cable fault testing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the cable fault detection method based on the intelligent cable fault testing device as described in the first aspect is implemented.

[0058] The beneficial effects of the present invention are:

[0059] The present invention solves the bottleneck problems of complex reflected signals, numerous interferences, and difficulty in identifying true fault waveforms in traditional cable fault testing. By introducing a healthy cable laid in parallel with the faulty cable as a reference system, the synchronous capture and feature comparison of cross-cable signals are achieved, and the unique and true fault signal can be accurately separated from the complex reflected waves, which greatly improves the reliability of the rough fault detection stage and eliminates subsequent errors caused by waveform misjudgment. The present invention deeply integrates electrical measurement with acoustic precision positioning, and uses the spectral characteristics of the confirmed true fault signal to automatically configure the optimal filtering parameters for the acoustic-magnetic synchronous positioning instrument, so that the positioning instrument has intelligent and targeted listening capabilities, realizing the transition from traditional blind listening and guessing based on experience to targeted and precise listening, and significantly improving the signal-to-noise ratio and success rate of precise positioning under strong environmental noise. The final secondary verification step constructs a closed-loop logic for positioning, completely eliminating the possibility of misjudgment and comprehensively improving the overall accuracy and efficiency of fault positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of a cable fault detection method based on an intelligent cable fault testing device in one embodiment of the present application.

[0061] Figure 2 This is a schematic diagram of a panel of a cable fault tester in one embodiment of the present application.

[0062] Figure 3 This is a connection diagram of flash current sampling in one embodiment of the present application.

[0063] Figure 4 This is a schematic diagram of the changing pattern of the flashover test waveform in one embodiment of the present application.

[0064] Description of reference numerals:

[0065] 1. Power adapter charging socket; 2. Instrument indicator light; 3. Output socket; 4. Output amplitude; 5. Switch; 6. Display screen; 7. Reset button; 8. USB port; 9. Touch mouse. DETAILED DESCRIPTION

[0066] The following will be combined with the accompanying drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the embodiments described are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0067] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0068] Figure 1 1 is a flow chart of a cable fault detection method based on an intelligent cable fault testing device in one embodiment. Figure 1 The cable fault detection method based on the intelligent cable fault testing device is applied to the cable fault testing device, which includes a cable fault tester (such as Figure 2 It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 1 At least part of the steps in the above process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps. Figure 1 As shown, the cable fault detection method based on the intelligent cable fault testing device disclosed in the present invention specifically includes the following steps:

[0069] S101. Connect the high-voltage flash unit and the first signal acquisition channel of the cable fault tester to the target fault cable, and simultaneously connect the second signal acquisition channel to an adjacent healthy cable laid in parallel with the target fault cable.

[0070] First, the high-voltage flash unit integrated in the cable fault tester is safely connected to the conductor core wire of the target fault cable to be detected through a dedicated high-voltage output cable. Figure 3As shown, T1 is a 3kVA / 0.22kV voltage regulator, T2 is a 3kVA / 50kV AC / DC high-voltage transformer, D is a high-voltage rectifier silicon stack, C is a high-voltage pulse capacitor, V is a voltmeter, and B is a current sampler. The tester's first signal acquisition channel, a highly sensitive signal sensor, is also connected in parallel to this connection point to capture the subsequent return signal from the target cable itself. The key is to connect the tester's second signal acquisition channel in the same manner to a healthy cable that is run adjacent to and parallel to the target faulty cable and is confirmed to be intact. This dual-channel parallel arrangement is the basis for subsequent signal comparison analysis and interference suppression. The adjacent healthy cable is chosen because when a high-voltage pulse propagates through the target cable and a fault discharge occurs, the transient electromagnetic field generated by it will spatially couple to induce a signal with a related but weaker intensity in the adjacent healthy cable. Once all physical connections are completed, the entire test system forms a differential detection structure that not only monitors the direct response of the faulty cable but also simultaneously records its electromagnetic impact on the surrounding environment.

[0071] S102. Apply a high voltage pulse to the target faulty cable through the high voltage flash unit, and use the first signal acquisition channel and the second signal acquisition channel to synchronously capture the active reflection signal of the target faulty cable and the passive coupling induction signal of the adjacent healthy cable.

[0072] After completing the physical connection, the cable fault tester is operated to activate its internal high-voltage flash unit, applying one or more instantaneous high-voltage pulses to the target fault cable. This pulse acts as an energy probe, propagating at high speed along the cable line. When it encounters a weak point or damage in the cable insulation (i.e., the fault point), it will break through the dielectric and produce instantaneous discharge. This discharge process will generate two types of key signals: First, inside the target fault cable, a part of the pulse energy will form a reflected wave and return to the tester along the original path. This is the active reflection signal (such as Figure 4 As shown); secondly, at the moment of fault discharge, the drastically changing current will generate a strong electromagnetic field, which will radiate outward and induce a weak current pulse in the adjacent healthy cable. This is the passive coupling induction signal. In order to accurately capture these two signals that occur almost simultaneously but have different characteristics, the two signal acquisition channels are set to high-frequency synchronous acquisition mode. When the amplitude of the active reflection signal detected by the first channel exceeds the preset trigger threshold, the data recording function of the two channels will be immediately started at the same time to ensure that the two signal waveforms are strictly aligned in time. The effect of this synchronous acquisition mechanism is to obtain two sets of raw waveform data, which have a precise correspondence on the time axis, and fully record the dynamic response of the target cable and the adjacent cable under the action of the high-voltage pulse, providing a high-quality raw data foundation for the subsequent identification of the real fault signal through cross-cable signal comparison.

[0073] S103. Based on the active reflection signal, a rough fault distance of the fault point in the target fault cable is calculated using time domain reflectometry.

[0074] Among them, after obtaining two synchronous signals, the active reflection signal from the target fault cable is first used to preliminarily locate the fault point. This step adopts the classic traveling wave method, also known as time domain reflectometry (TDR). Its basic principle is the same as echolocation, and the distance is calculated by measuring the time difference between the sent detection signal and the reflected signal returned from the fault point. On the waveform diagram of the active reflection signal, two significant waveform features can be clearly observed: one is the high-voltage pulse wave head initially emitted, and the other is the reflected pulse wave head generated by the fault point after a delay. Through a high-precision timing algorithm, the interval length of the two wave heads on the time axis can be accurately measured. Since the propagation speed of electromagnetic waves in a specific type of cable is a basically known physical constant, the distance from the fault point to the test end can be converted using a simple physical formula. The specific implementation method of this calculation is as follows: Where L represents the calculated rough fault distance, v represents the propagation velocity of electromagnetic waves in the cable medium under test, and Δt represents the time difference between the transmitted pulse and the first significant reflected pulse recorded by the instrument. The division by 2 in the formula reflects the round-trip distance the signal travels through the cable. This step quickly provides a rough estimate of the fault location, known as the rough fault distance.

[0075] S104. Identify the real fault signal in the active reflection signal and the passive coupling induction signal by comparing the cross-cable signal characteristics.

[0076] After obtaining a rough distance measurement, a critical signal identification step is required to ensure that subsequent analysis and precise location are based on real fault information rather than misleading interference. This step is accomplished by performing a deep feature comparison between the active reflection signal of the target cable and the passive coupling induction signal of an adjacent healthy cable. Since one signal is a direct product of the fault and the other is an indirect product of its electromagnetic coupling, there must be essential differences in their energy distribution and phase relationship. The analysis process utilizes a built-in intelligent algorithm module to first distinguish from the energy perspective and calculate the energy attenuation gradient of the two signals. The source signal (active reflection signal) generally has higher energy, and its energy distribution characteristics in different frequency bands are significantly different from those of the coupled signal. Next, a phase verification is performed. Since electromagnetic coupling requires time, the wave head of the passive coupling induction signal will inevitably have a slight delay compared to the wave head of the active reflection signal. This delay time can be calculated using a high-precision algorithm and converted into an initial phase offset. The conversion method is: . Here, φ is the calculated initial phase offset angle, f is the main frequency component of the signal, and Δτ is the absolute time difference between the starting points of the two signal waveforms. This calculated phase offset must conform to the theoretical coupling model determined by the physical spacing of the two cables and the insulation medium parameters. Only when the judgment results of both energy and phase dimensions match (logical AND operation) can the system finally confirm that the active reflection signal is a true fault signal. The effect of this step is to greatly enhance the accuracy of fault diagnosis, effectively eliminate the complex electromagnetic interference on site through cross-validation, and label the main signal as "real", providing reliable data input for subsequent spectrum analysis and acoustic filter configuration.

[0077] S105. Configure optimal acoustic filtering parameters of the acoustic channel for the acoustic-magnetic synchronous pinpointer based on the spectrum characteristics of the actual fault signal.

[0078] Once a true fault signal is successfully identified, the spectral information it contains is used to optimize the performance of the subsequent acoustic-magnetic synchronous locator. High-voltage discharge at a fault point generates not only an electrical signal but also a weak acoustic signal, which is the physical basis for accurate locating. However, ambient noise on-site (such as traffic, human voices, and wind) is often far louder than the fault discharge sound, making it nearly impossible to distinguish directly through monitoring. To address this issue, an optimal acoustic filter must be configured for the acoustic channel of the acoustic-magnetic synchronous locator, enabling it to "focus" on the fault discharge sound while ignoring noise at other frequencies. This configuration is based on the previously identified spectral characteristics of the true fault signal, as the spectral characteristics of electrical signals are strongly correlated with those of acoustic signals. By performing a Fast Fourier Transform (FFT) on the true fault signal, a spectrum of its energy distribution across the entire frequency domain is obtained. The frequency bands with concentrated energy on the spectrum correspond to the frequency ranges most likely to contain the fault discharge sound. Based on these spectral characteristics, an optimal set of filter parameters can be dynamically generated, such as the center frequency and bandwidth of a bandpass filter or the cutoff frequency of a high-pass or low-pass filter. This method of presetting acoustic detection parameters based on electrical signal characteristics is a form of intelligent, adaptive adjustment. The result is that the acoustic sensor of the acoustic-magnetic synchronous positioning instrument is pre-informed of the frequency characteristics of the sound it is looking for. This allows it to extremely effectively extract the weak but critical fault discharge acoustic signal from noisy background noise, improving the signal-to-noise ratio by tens or even hundreds of times, creating the prerequisite for high-precision on-site positioning.

[0079] S106. Use the configured acoustic-magnetic synchronous locator to accurately locate the target fault cable near the rough fault distance, and perform secondary verification at the corresponding position of the adjacent healthy cable to confirm the absence of the acoustic-magnetic signal, thus completing the closed-loop positioning of the fault point.

[0080] After completing all preliminary preparations and parameter configuration, the final phase of precise on-site location determination and closed-loop verification begins. Carrying an acoustic-magnetic synchronous locator with optimized acoustic filter parameters, the operator proceeds to the area near the previously estimated fault distance. Directly above the cable path, another high-voltage flash is triggered. The locator then receives two signals simultaneously: a nearly instantaneous magnetic field signal from the fault discharge, and an acoustic signal that arrives slightly delayed after propagating through a medium (such as soil). The locator's core function is to accurately measure the time difference between these two signals. Because the magnetic field signal propagates at a speed close to the speed of light, its arrival time can be considered zero delay. Therefore, this time difference is roughly equivalent to the time it takes for the acoustic wave to travel from the underground fault point to the surface locator. According to the speed of sound, when the locator is directly above the fault point, the acoustic wave travels the shortest distance and the shortest time. Therefore, the operator moves the locator in small increments along the cable path, continuously observing the acoustic-magnetic time difference displayed on the screen. When this time difference reaches its minimum, the locator's physical location is preliminarily determined as the fault point. To ensure complete safety, a secondary verification, known as a "closed-loop" operation, is required. The operator moves the pinpointer to a position directly above the adjacent healthy cable and repeats the flash test. Because the fault is only in the target cable, no discharge sound should be generated at the corresponding location on the healthy cable. If the pinpointer detects no valid acoustic signal synchronized with the magnetic field signal above the healthy cable within the preset verification time, the secondary verification is considered successful.

[0081] In one embodiment, synchronously capturing the active reflection signal of the target faulty cable and the passive coupling induction signal of the adjacent healthy cable using the first signal acquisition channel and the second signal acquisition channel includes the following steps:

[0082] Setting a synchronous acquisition trigger threshold and a pre-trigger data buffer in the first signal acquisition channel and the second signal acquisition channel;

[0083] monitoring the signal amplitude of the signal in the first signal acquisition channel in real time, and starting high-frequency synchronous data acquisition of the first signal acquisition channel and the second signal acquisition channel when the signal amplitude exceeds a synchronous acquisition trigger threshold;

[0084] Based on a preset acquisition time window, the original waveform data of the first signal acquisition channel and the second signal acquisition channel after the high voltage pulse occurs are recorded respectively;

[0085] The collected raw waveform data is digitally filtered and normalized to generate the active reflection signal of the target fault cable and the passive coupling induction signal of the adjacent healthy cable.

[0086] In this embodiment, the core of this step is to establish an intelligent waiting and response mechanism in the two signal acquisition channels. The specific operation is to set a clear synchronous acquisition trigger threshold for the system, which is a specific voltage value, and its role is to serve as the threshold for judging whether the signal is a valid event. Only when the strength of the signal exceeds this threshold is it considered a potential fault signal worthy of recording. At the same time, in order to ensure that the complete appearance of the signal is captured, especially the initial rising part of the signal from nothing to something before crossing the trigger threshold, the system will open up a memory area called the pre-trigger data buffer. This buffer is like a short-term memory library for loop recording. It has been continuously and temporarily storing the latest data in an overwritten manner before the formal trigger acquisition. This design ensures that when a real trigger event occurs, the waveform data stored in the buffer before the trigger moment is retained, thereby avoiding the risk of losing key waveform leading edge information due to acquisition startup delays.

[0087] After completing the presets, the system enters the real-time signal monitoring and synchronous triggering execution phase. During this phase, the high-speed analog-to-digital converter (ADC) within the first signal acquisition channel (connected to the target fault cable) continuously samples the input signal at an extremely high frequency and compares its voltage amplitude with the pre-set synchronous acquisition trigger threshold in real time. This process is a continuous judgment loop, with the system constantly monitoring the dynamic changes in signal amplitude. If, at a certain moment, the monitored signal amplitude suddenly jumps and exceeds the set trigger threshold, for example, if the following conditions are met: ,in is the real-time input signal amplitude, is the preset trigger threshold voltage. The instant this condition is met, the system immediately generates an internal trigger command. The key to this command lies in its synchronization: it simultaneously activates the first and second signal acquisition channels (connected to the adjacent healthy cable) into high-speed data acquisition mode with unwavering accuracy. This strict synchronization mechanism is crucial, ensuring that the two subsequently recorded waveforms are perfectly aligned in time.

[0088] Once synchronous acquisition is triggered, the system begins to formally record the raw waveform data within the preset time window. This acquisition time window is not infinitely long, but a precisely calculated finite duration, which is set based on the physical length of the cable under test and the inherent speed of electromagnetic waves propagating within it. The duration of the window must be long enough to ensure that the entire process from the issuance of the high-voltage pulse to the signal reaching the farthest fault point and then to the reflected signal returning to the tester can be fully recorded. The minimum duration required can be estimated as follows: .in, represents the length of the acquisition time window, is the maximum possible length of the cable under test, and is the propagation speed of electromagnetic waves in the cable medium. After a trigger command is issued, the system solidifies the data temporarily stored in the pre-trigger buffer and continues to acquire and store post-trigger data until the entire time window is filled. This process occurs simultaneously in the first and second signal acquisition channels, ultimately generating a sequence of discrete data points representing the voltage variation over time in their respective memories.

[0089] After acquiring the raw waveform data, it cannot be directly used for analysis because it is mixed with various noises and has varying amplitudes. It requires final purification and normalization. This step involves two key steps: digital filtering and normalization. First, digital filtering is performed on each of the two sets of collected raw waveform data. A bandpass filter is typically used to remove interference components unrelated to the fault signal characteristics, such as low-frequency power frequency interference introduced by the power supply system and high-frequency white noise generated by electronic components and the external electromagnetic environment. The filtering process significantly improves the signal-to-noise ratio, making subtle fault characteristics hidden in the noise (such as the inflection point of the reflected wave) clearly discernible. Next, the two filtered signal waveforms are normalized. Because the active reflected signal has high energy and the passive coupled signal is very weak, their absolute amplitudes differ significantly, making direct comparison meaningless. Normalization aims to eliminate this amplitude difference and scale the two waveforms to a common relative scale, such as the [-1, 1] interval. A common implementation is maximum normalization: .in, is each data point of the filtered signal, is the maximum absolute value of all data points of the signal, The result of this step is the generation of two clean, clear signal curves with uniform amplitude scales: the active reflection signal of the target faulty cable and the passive coupling induction signal of the adjacent healthy cable, which are ultimately used for analysis. These provide high-quality, comparable, and ideal data for subsequent fault identification and feature extraction.

[0090] In one embodiment, identifying a true fault signal in an active reflection signal and a passive coupling induction signal by comparing cross-cable signal characteristics includes the following steps:

[0091] The energy attenuation gradient analysis module of the cable fault tester is called to calculate and compare the energy attenuation gradients of the active reflection signal and the passive coupling induction signal to obtain the energy source determination result;

[0092] The initial phase offset verification module of the cable fault tester is called to calculate and verify the initial phase offset of the active reflection signal and the passive coupling induction signal to obtain the coupling path confirmation result;

[0093] Performing a logical AND operation on the energy source determination result and the coupling path confirmation result to form a signal recognition result;

[0094] According to the signal recognition result, the active reflection signal is marked as the real fault signal, and the spectrum characteristics of the real fault signal are extracted. At the same time, the passive coupling induction signal is marked as the interference signal, and the interference signal is suppressed.

[0095] In this embodiment, the total energy of the active reflected signal as the energy source must be much higher than the passive coupling signal generated by indirect induction of the space electromagnetic field. More importantly, the coupling process itself is not equivalent to simple signal attenuation, and the attenuation of high-frequency components is much more severe than the attenuation of low-frequency components. Therefore, the spectral morphology of the passive coupling signal will be distorted, and the energy proportion of its high-frequency part will drop sharply. During the analysis, the two signals are first fast Fourier transformed to obtain their respective amplitude-frequency characteristic curves, and then the broadband total energy is calculated to form a total energy ratio. Next, the entire spectrum is divided into multiple continuous narrowband frequency bands, and the energy ratio in each frequency band is calculated. By observing the trend of these energy ratios changing with increasing frequency, the energy attenuation gradient can be quantified. A key judgment indicator can be set as: .in is the energy gradient factor, and are the energies of active and passive signals in the high frequency band, and and is their energy in the low frequency band. If it is significantly greater than 1, it indicates that the coupling attenuation model is met.

[0096] After completing the analysis of the energy dimension, it is necessary to cross-validate from the time dimension and call the initial phase offset verification module to confirm the causal relationship between the signals. The physical principle is very clear: the passive coupling induction signal as the result must be generated later than the active reflection signal as the cause, and there is an extremely short but measurable transmission delay between the two. In order to accurately capture this tiny delay, the original discrete signal data needs to be upsampled through an interpolation algorithm, which is equivalent to adding more details to the time axis and improving the time resolution. Then, the first-order derivatives of the two signal waveforms after upsampling are calculated. The moment when the derivative first appears to be non-zero can be accurately defined as the physical starting point of the signal waveform. By calculating the time difference between the two starting points, an absolute time value of the initial phase offset can be obtained. In order to make it have universal comparative significance, this absolute time value needs to be converted into a frequency-related phase angle. The conversion method is: Here, is the calculated initial phase offset angle, is the center frequency of the signal, and is the time difference between the starting points of the two signal waveforms. Finally, the calculated phase angle is compared with a theoretical phase offset range that is preset based on physical parameters such as cable spacing and insulation medium.

[0097] After obtaining independent determination results for both energy and phase, a rigorous logical AND operation is performed on these two results to form a final, high-confidence identification conclusion. This integrated decision-making mechanism is based on the fact that single-dimensional analysis can introduce the risk of misjudgment. For example, a strong interfering pulse with a very similar energy signature can cause confusion in the energy analysis phase. Similarly, random noise that coincidentally follows the true signal can slip through the phase verification process. However, the probability of an external interference perfectly mimicking the coupled signal signature in two completely independent physical characteristics, energy attenuation gradient and initial phase offset, is extremely low. The logical AND operation requires that both the energy source determination and the coupling path confirmation results must be "true" for the final signal identification to be considered "true." This process can be described by a simple logical expression: the identification result is "passed" only when both the energy determination and the phase determination pass. This step significantly improves the robustness and reliability of signal identification, acting as a double-check mechanism that combines evidence chains from two different physical dimensions.

[0098] After the logical operation yields a clear signal identification result, the final signal labeling and feature utilization phase begins. This crucial step connects the preceding and subsequent steps, transforming the analysis conclusions into a direct basis for subsequent operations. If the signal identification result is "True," the active reflection signal captured by the first signal acquisition channel, originally connected to the target faulty cable, is officially marked as a true fault signal at the data level. This designation confers the highest priority and reliability. Next, the key attributes of this true fault signal are extracted and stored, particularly its spectral characteristics—the distribution of its energy across different frequencies. This spectral data serves as the core basis for configuring the filter parameters of the acoustic-magnetic synchronous point finder in the next stage. Simultaneously, the passive coupling induction signal captured by the second signal acquisition channel, originally connected to an adjacent healthy cable, is marked as an interference signal. The most straightforward approach is to suppress or eliminate it from all subsequent analysis and display to prevent visual or algorithmic interference.

[0099] In one embodiment, calling the energy attenuation gradient analysis module of the cable fault tester to calculate and compare the energy attenuation gradients of the active reflection signal and the passive coupling induction signal to obtain the energy source determination result includes the following steps:

[0100] Perform fast Fourier transform on the active reflection signal and the passive coupling induction signal respectively to obtain the amplitude-frequency characteristic curves of the two signals;

[0101] Calculating broadband total energy of the active reflection signal and the passive coupling induction signal, and generating a first energy ratio;

[0102] The amplitude-frequency characteristic curves of the two signals are divided into a plurality of preset non-overlapping narrowband frequency bands;

[0103] Calculating the signal energy in each narrowband frequency band respectively, and generating a second energy ratio corresponding to each frequency band;

[0104] Analyze the trend of the second energy ratio changing with frequency to quantify the energy attenuation gradient;

[0105] The first energy ratio and the energy attenuation gradient are compared with a preset coupling model threshold, and an energy source determination result is output.

[0106] In this embodiment, the active reflection signal and the passive coupling induction signal are converted from the time domain to the frequency domain. This involves transforming the signal representation from voltage changes over time to energy distribution at different frequencies. This conversion is achieved using the Fast Fourier Transform (FFT). This algorithm efficiently decomposes any complex waveform into its constituent sinusoidal components. Applying the FFT algorithm to the active reflection signal and the passive coupling induction signal, two new sets of data are generated. Each set describes the energy level or amplitude intensity at each frequency point across the entire spectrum, from low to high frequencies. Plotting this data on a graph with frequency on the horizontal axis and amplitude on the vertical axis yields two intuitive amplitude-frequency characteristic curves. After obtaining the spectral fingerprints of the two signals, the first comparison is to assess their overall energy intensity. This is a macroscopic, holistic comparison designed to quickly determine which signal is the source of the energy. The physical principle is that the energy generated by the fault discharge is primarily concentrated in the target faulty cable, with only a small amount leaking into adjacent healthy cables through electromagnetic coupling. Therefore, the total energy of the active reflection signal is necessarily much greater than that of the passive coupling induction signal.

[0107] The method for calculating total energy is to integrate the amplitude-frequency characteristic curve. In discrete digital signal processing, this is equivalent to summing the squared energy of the signal at all frequency points. After calculating the broadband total energy of the two signals, for easy comparison, they are divided to generate a ratio, namely the first energy ratio. It is calculated as follows: .in, is the first energy ratio, represents the broadband total energy of the active reflected signal, Represents the broadband total energy of the passive coupling induction signal. The implementation of this step results in a simple and direct quantitative indicator. If If the value of is much larger than 1, for example, exceeds a preset empirical threshold of 10, it preliminarily confirms the speculation that the active reflection signal is the energy source.

[0108] Simply comparing total energy is insufficient, as it fails to exclude equally strong external interference. For a more refined and discerning analysis, the two complete amplitude-frequency characteristic curves need to be segmented. This step is based on the fact that electromagnetic coupling naturally attenuates high-frequency signals, a characteristic that is crucial for distinguishing true coupling from random interference. Specifically, the frequency axes (horizontal axes) of the two curves are divided from low to high into multiple pre-defined, non-overlapping, continuous frequency bands. For example, the entire spectrum from 0 to 1 MHz can be divided into 10 narrowband frequency bands of 100 kHz: 0-100 kHz, 100-200 kHz, and so on. The divisions can be based on the energy distribution characteristics of typical cable fault signals, with finer divisions being used in areas with concentrated signal energy and coarser divisions in areas with sparse energy. After dividing the spectrum into multiple narrowband frequency bands, the signal energy can be calculated and compared in detail for each frequency band. The goal of this step is to quantify the energy difference between the two signals within each frequency interval. The specific operation is to calculate the total energy of the active reflection signal and the passive coupling induction signal falling into the first narrowband frequency band (for example, 0-100kHz) respectively. Then, divide these two energy values to obtain the energy ratio corresponding to the frequency band. Then, repeat the same calculation process for the second, third, and last narrowband frequency bands. The original total energy ratio is expanded into a series of energy ratios corresponding to each frequency band, that is, the second energy ratio sequence. For the energy ratio of the kth frequency band, it is calculated as follows: .in, represents the second energy ratio of the k-th narrowband frequency band, To actively reflect the signal energy in this frequency band, is the energy of the passive coupling induction signal in this frequency band.

[0109] After obtaining the sequence data that describes the change of energy ratio with frequency, the last step is to analyze the overall trend of this sequence and quantify it into a specific value, namely the energy attenuation gradient. The core principle is that if it is a real coupling relationship, since the high-frequency component attenuates more violently, the energy of the passive coupling signal will drop rapidly with the increase of frequency, causing the energy ratio of the two signals (the second energy ratio) to increase significantly with the increase of frequency. This steadily increasing trend is the embodiment of the energy attenuation gradient. The most common method to analyze this trend is to use linear regression analysis, using a series of second energy ratios as the vertical coordinate and the center frequency of each frequency band as the horizontal coordinate, to fit a straight line in the coordinate system that best represents the trend of these data points. The slope of this straight line accurately quantifies the energy attenuation gradient. This gradient value can be expressed as: Here, is the final calculated energy attenuation gradient, Represents the center frequency of the kth narrowband frequency band.

[0110] After calculating the first energy ratio, representing the overall energy difference, and the energy attenuation gradient, representing the trend of the energy difference, a final comprehensive assessment can be made, outputting the determination of the energy source. This assessment is based on two pre-set coupling model thresholds: a total energy threshold for the first energy ratio, and a gradient threshold for the energy attenuation gradient. The judgment logic is extremely strict, requiring two conditions to be met simultaneously: the calculated first energy ratio must be greater than the total energy threshold, and the calculated energy attenuation gradient must also be greater than the gradient threshold. Only when both conditions are met does the system ultimately determine that the active reflection signal is the energy source. This dual verification mechanism ensures extremely high reliability, requiring not only that the source signal has greater overall energy, but also that its energy relationship with the coupled signal strictly adheres to the physical model of more severe attenuation at high frequencies.

[0111] In one embodiment, comparing the first energy ratio and the energy attenuation gradient with a preset coupling model threshold and outputting the energy source determination result specifically includes the following steps:

[0112] The amplitude-frequency characteristic curve of the passive coupling induction signal is used as a benchmark to construct a dynamic coupling attenuation fingerprint reflecting the current test environment.

[0113] Based on the dynamic coupling attenuation fingerprint, the amplitude-frequency characteristic curve of the active reflection signal is scaled and morphologically fitted to generate an amplitude-frequency characteristic curve of a theoretical coupling prediction signal;

[0114] Calculate the mismatch between the actual amplitude-frequency characteristic curve of the active reflection signal and the amplitude-frequency characteristic curve of the theoretical coupling prediction signal;

[0115] If the mismatch exceeds a preset mismatch threshold, it is determined that the energy attenuation gradient of the active reflection signal does not conform to the coupled propagation characteristics;

[0116] The first energy ratio and the energy attenuation gradient are combined to determine whether they do not conform to the coupled propagation characteristics, and the energy source determination result is finally output.

[0117] In this embodiment, in order to fundamentally reveal the inherent physical connection between the active reflection signal and the passive coupling induction signal, it is necessary to first establish an accurate and dynamic attenuation model for the current test environment. Compared with other embodiments, this embodiment abandons the traditional method of using a fixed theoretical model, and instead uses the amplitude-frequency characteristic curve of the passive coupling induction signal itself as a feature to describe the coupling attenuation characteristics under the current environment. The basic principle is that the passive coupling induction signal itself is formed after the fault source signal passes through the medium (such as air, soil) between the target cable and the adjacent cable. Therefore, its spectrum form naturally and completely records all the attenuation information of different frequency signals of this specific path. In specific implementation, the amplitude-frequency characteristic curve is obtained by performing a fast Fourier transform on the passive coupling induction signal. is directly defined as the dynamic coupling attenuation fingerprint.

[0118] After constructing a dynamic coupling attenuation fingerprint that reflects the real environment, the next step is to perform theoretical deduction based on this fingerprint, that is, to generate a theoretical coupling prediction signal. Specifically, using the dynamic coupling attenuation fingerprint as a template, the amplitude-frequency characteristic curve of the active reflection signal is adjusted so that the attenuation ratio of its high-frequency portion and the overall shape of the spectrum tend to conform to the fingerprint characteristics. This process can be abstracted as the application of a transfer function to generate the amplitude-frequency characteristic curve of the theoretical prediction signal: .in, is the actual amplitude-frequency characteristic curve of the active reflection signal, and Based on the dynamic coupling attenuation fingerprint The equivalent transfer function established to represent the coupling channel characteristics.

[0119] After generating the theoretical standard image, it is necessary to accurately compare it with the actual measured situation and quantify the degree of difference between them. This degree of difference is called mismatch. The principle is that if the active reflection signal is indeed the source of the fault, then the predicted signal spectrum obtained through theoretical deduction should be highly similar to the actual measured passive coupling induction signal spectrum. On the contrary, if the active reflection signal has no direct physical coupling relationship with the passive signal, then the prediction based on it will inevitably be different from the actual situation. The purpose of calculating the mismatch is to express this degree of "similarity" or "dissimilarity" with a specific numerical value. A commonly used calculation method is to calculate the normalized mean square error between the two amplitude-frequency characteristic curves. The specific calculation formula is:

[0120]

[0121] in, is the mismatch degree, is the actual measured passive coupling signal amplitude-frequency curve, is the amplitude-frequency curve of the theoretically predicted signal generated in the previous step. This formula calculates the sum of the squares of the differences between the two curves across the entire frequency domain and normalizes this sum by the total energy of the actual signal. This step effectively condenses the morphological differences between the two complex curves into a single, intuitive quantitative metric. The closer the mismatch is to 0, the closer the theoretical and practical results match.

[0122] After calculating the mismatch value representing the difference between theory and reality, a clear logical judgment must be made based on this value. This step transforms the quantitative calculation results into a qualitative conclusion. This is achieved by comparing the calculated mismatch with a pre-set mismatch threshold. This mismatch threshold is a critical value derived from extensive experimental data and empirical analysis. It represents the upper limit of the normal deviation between theory and reality caused by random factors such as noise that the system can tolerate. The judgment logic is very straightforward: if the calculated mismatch value exceeds this pre-set mismatch threshold, a judgment can be made that there is a significant discrepancy between the theoretical model and the actual situation, and this discrepancy exceeds the normal error range. The conclusion is that the initial assumption is not valid, that is, the energy attenuation gradient of the active reflection signal does not conform to the expected coupled propagation characteristics. Finally, to ensure the final conclusion of the energy source is foolproof, it is necessary to comprehensively consider evidence from both the macroscopic and microscopic levels. This step integrates the first energy ratio calculated earlier (representing the macroscopic energy difference) with the judgment of whether the energy attenuation gradient just obtained conforms to the coupling characteristics (representing the consistency of the microscopic spectral morphology). The final determination of the energy source must pass both of these tests. The logic is as follows: First, the total energy of the active reflection signal must be significantly greater than that of the passive coupling induction signal. Specifically, the first energy ratio must exceed a high threshold, ensuring that it is the source in terms of energy magnitude. Second, the spectral shape of the active reflection signal must accurately predict the spectral shape of the passive coupling signal. Specifically, the mismatch must be below a preset threshold, resulting in a determination that it "meets the coupled propagation characteristics." Only when both of these conditions are met will the system ultimately output a determination that the energy source is the active reflection signal. If any of these conditions are not met, the relationship will be determined to be uncoupled.

[0123] In one embodiment, calling an initial phase offset verification module of a cable fault tester to calculate and verify the initial phase offsets of the active reflection signal and the passive coupling induction signal to obtain a coupling path confirmation result includes the following steps:

[0124] Upsampling the active reflection signal and the passive coupled induction signal by an interpolation algorithm;

[0125] Calculating the first-order derivatives of the active reflection signal and the passive coupling induction signal after upsampling, and determining the starting time point of the signal waveform by the first non-zero point of the first-order derivative;

[0126] Calculate the time difference between the corresponding starting time points of the active reflection signal and the passive coupling induction signal to obtain the absolute time value of the initial phase offset;

[0127] Convert the absolute time value into a phase angle according to the main frequency of the real fault signal;

[0128] The phase angle is compared with the theoretical phase offset range set according to the cable spacing and dielectric parameters, and the coupling path is output to confirm the result.

[0129] In this embodiment,

[0130] In one embodiment, configuring optimal acoustic filtering parameters of an acoustic channel for an acoustic-magnetic synchronous pinpointing instrument based on the spectrum characteristics of a real fault signal includes the following steps:

[0131] A high-voltage flashover unit is used to trigger multiple high-voltage flashovers in the area determined by the rough fault distance, and an acoustic-magnetic synchronous point-finding instrument is used to synchronously collect magnetic field signals and continuous on-site acoustic signals.

[0132] Taking the triggering moment of each magnetic field signal as the benchmark, multiple synchronous acoustic samples are intercepted from the continuous on-site acoustic signal. Each synchronous acoustic sample contains the fault discharge sound and background noise.

[0133] A plurality of asynchronous acoustic samples not containing fault discharge sound are intercepted from the continuous on-site acoustic signal as background noise samples;

[0134] Calculate the average spectrum of the synchronous acoustic samples and the average spectrum of the background noise samples respectively;

[0135] By comparing the average spectrum, the characteristic frequency band where the signal-to-noise ratio of the fault discharge sound is higher than the background noise is identified, and the optimal acoustic filtering parameters of the acoustic channel of the acoustic-magnetic synchronous pinpointer are dynamically generated based on the characteristic frequency band.

[0136] In this embodiment, in order to accurately measure the microsecond or even nanosecond time difference between two signals, the temporal resolution of the original digital signal must first be improved. The originally acquired signal consists of a series of discrete points equally spaced in time. The starting point of its true waveform is likely to fall between two sampling points, and a direct search will result in significant errors. Active reflection signals and passive coupled induction signals are upsampled using an interpolation algorithm. New data points can be calculated and inserted between existing data points based on mathematical rules. Common interpolation methods include linear interpolation, polynomial interpolation, or the more precise sinc interpolation. After obtaining a high-resolution signal waveform, an objective and precise method is required to define when the waveform begins. When a signal is silent, its value does not change, and its rate of change is zero. The moment the signal starts, its value begins to change dramatically, and the rate of change also jumps from zero.

[0137] Using this principle, the moment when the change starts is captured by calculating the first-order derivative of the signal. The first-order derivative directly reflects the instantaneous rate of change of the signal. For discrete signal data points after upsampling, its first-order derivative can be approximated by calculating the difference between adjacent data points. The time point at which the first-order derivative curve appears a non-zero value for the first time can be accurately defined as the starting time point of the signal waveform. To avoid noise interference, it is usually necessary to look for the time point when the derivative value first continuously exceeds a very small threshold ε. , that is, satisfy After determining the precise starting times of the active reflection signal and the passive coupling induction signal, calculating the timing relationship between them becomes straightforward. The goal of this step is to quantify the delay of the passive coupling signal relative to the active reflection signal. Because electromagnetic fields require time to propagate, the coupling induction process from the faulty cable to the adjacent healthy cable inevitably produces a slight time lag, and the passive signal's starting point is inevitably later than the active signal.

[0138] By performing a simple subtraction operation on the starting time points of the two signals, the specific value of the delay time can be obtained. This value is called the absolute time value of the initial phase offset. It is calculated as follows: .in, is the calculated absolute time difference, is the starting time point of the passive coupling induction signal, and It is the starting time point of the active reflection signal. After obtaining the absolute time difference in seconds, it needs to be converted into a more universal physical quantity, namely the phase angle. A fixed time difference, for signals of different frequencies, represents a completely different degree of phase deviation. Therefore, the absolute time value must be associated with the period of the signal itself in order to objectively evaluate its degree of delay. This step first requires determining the main frequency of the real fault signal through spectrum analysis, that is, the frequency point where its energy is most concentrated. Then, based on the main frequency, calculate the time required for a complete cycle of the signal. Finally, compare the absolute time difference with this cycle time and convert it into a 360-degree angle system. The conversion formula is: In this formula, is the final converted phase angle, is the absolute time difference calculated in the previous step, and is the main frequency of the real fault signal.

[0139] Finally, the measured and calculated phase angle is compared with a theoretical range based on physical laws to make a final determination on the authenticity of the coupling path. This theoretical phase offset range is not a guesstimate; rather, it is a reasonable and acceptable phase delay range calculated based on electromagnetic field theory, the physical spacing between the two cables in the field, the dielectric constant of the insulating material between them, and other parameters. This range defines the physical boundary of possibility. The comparison process determines whether the measured value falls within this theoretical range. If the measured and calculated phase angle falls within the theoretical range, the measurement result is considered consistent with physical reality, and the coupling path is confirmed. Conversely, if the measured value falls outside the theoretical range, it indicates that the timing relationship between the two signals does not conform to the physical laws of coupling.

[0140] In one embodiment, by comparing the average spectrum, identifying a characteristic frequency band in which the signal-to-noise ratio of the fault discharge sound is higher than that of the background noise, and dynamically generating the optimal acoustic filtering parameters of the acoustic channel of the acoustic-magnetic synchronous pinpointer based on the characteristic frequency band includes the following steps:

[0141] Subtract the average spectrum of the synchronized acoustic samples from the average spectrum of the background noise samples to obtain the estimated spectrum of the pure fault sound;

[0142] Calculate the signal-to-noise ratio between the estimated spectrum of the pure fault sound and the average spectrum of the background noise samples, and generate a signal-to-noise ratio spectrum graph covering the entire frequency band;

[0143] Searching the signal-to-noise ratio spectrum for all frequency bands whose signal-to-noise ratio values exceed a preset judgment threshold as characteristic frequency bands;

[0144] Analyze the frequency position, bandwidth and continuity of all characteristic frequency bands, and select the optimal filter type based on the analysis results. The filter types include high-pass filter, low-pass filter and band-pass filter;

[0145] The determined filter type and the corresponding cutoff frequency are packaged to form the optimal acoustic filter parameters of the acoustic channel of the acoustic-magnetic synchronous pointing instrument.

[0146] In this implementation, to extract the true nature of the fault discharge sound from the noisy on-site sounds, a spectrum subtraction operation is first performed. The basic principle is that the synchronous acoustic sample contains both the fault discharge sound and background noise, while the background noise sample contains only stable background noise. By subtracting the average spectrum of the background noise at each frequency point from the average spectrum containing the fault sound, the constant noise component can theoretically be eliminated, resulting in a relatively pure spectrum representing only the fault discharge sound. This process can be expressed as: Here, is the estimated power spectrum of the pure fault sound, is the average power spectrum of the synchronized acoustic samples, and is the average power spectrum of the background noise samples.

[0147] After obtaining the estimated spectrum of the pure fault sound, it is necessary to further quantify the significance of this fault sound signal relative to the background noise at each frequency point. This requires calculating the signal-to-noise ratio, which is the ratio of signal strength to noise strength. By calculating the ratio between the estimated spectrum of the pure fault sound and the average spectrum of the background noise point by point, a new signal-to-noise ratio spectrum graph covering the entire frequency band can be generated. The vertical axis of this graph is no longer the absolute energy level, but the relative value of how many times stronger the signal is than the noise. The calculation method is: .in, is the signal-to-noise ratio at frequency point f. Next, we need to conduct a comprehensive search from low frequency to high frequency on the signal-to-noise ratio spectrum, and mark all frequency intervals where the signal-to-noise ratio values are continuously higher than this judgment threshold as characteristic frequency bands. For example, if all frequency points in a frequency band meet the condition ,in is the preset signal-to-noise ratio judgment threshold, then this frequency band is confirmed as a valid characteristic frequency band.

[0148] After all characteristic frequency bands are identified, the most effective filter type needs to be selected based on their distribution across the spectrum. This is a rule-based decision-making process, aiming to achieve the best denoising and fidelity using the simplest tools. The analysis focuses on the position, width, and continuity of all characteristic frequency bands. If all characteristic frequency bands are concentrated above a certain high frequency, a high-pass filter is most appropriate. Conversely, if they are all concentrated below a certain low frequency, a low-pass filter should be used. If the characteristic frequency bands are concentrated in the middle of the spectrum or have a dominant core frequency band with the highest energy concentration, a band-pass filter is the best choice. Once the optimal filter type is determined, the final step is to combine this decision with specific frequency parameters and encapsulate it into a set of executable instructions. This process translates the analysis results into actual device configuration. Based on the analysis results from the previous step and the specific boundaries of the characteristic frequency bands, the filter cutoff frequency is determined. If a high-pass filter is selected, its cutoff frequency is generally set to the lowest frequency among all characteristic frequency bands. If a low-pass filter is selected, its cutoff frequency is set to the highest frequency. If a bandpass filter is selected, a lower cutoff frequency and an upper cutoff frequency need to be set. Ultimately, the determined filter type and its corresponding cutoff frequency values are packaged together to form a complete set of optimized acoustic filter parameters.

[0149] In one embodiment, a configured acoustic-magnetic synchronous locator is used to precisely locate the target fault cable near the rough fault distance, and a secondary verification is performed at the corresponding position of an adjacent healthy cable to confirm the absence of an acoustic-magnetic signal. The closed-loop location of the fault point includes the following steps:

[0150] Utilize the configured acoustic-magnetic synchronous locator to receive the synchronized first acoustic signal and the first magnetic field signal near the rough fault distance of the target fault cable, and calculate the signal time difference between the first acoustic signal and the first magnetic field signal in real time;

[0151] The acoustic-magnetic synchronous positioning instrument is moved based on the signal time difference until the signal time difference reaches the minimum, and the physical location of the acoustic-magnetic synchronous positioning instrument is determined as the preliminary fault point of the target fault cable;

[0152] After completing secondary verification of the adjacent healthy cables corresponding to the initial fault point using the acoustic-magnetic synchronous positioning instrument, the initial fault point is locked as the final fault point, completing the closed-loop positioning.

[0153] In this embodiment, in the rough fault area, the configured acoustic-magnetic synchronous positioning instrument is used for on-site precise positioning. When a high-voltage flash triggers a fault discharge, a magnetic field signal propagating at the speed of light and an acoustic signal propagating at the speed of sound are simultaneously generated. The magnetic field probe of the positioning instrument captures the first magnetic field signal almost instantaneously, and uses this as the zero time reference to start the internal high-precision timer. At the same time, the acoustic channel filtered by the optimal parameters begins to monitor the first acoustic signal. When the acoustic probe captures a sound wave with matching characteristics, the timing stops. The device calculates and displays the time difference between the two in real time. ,in is the signal time difference, and The arrival times of the acoustic and magnetic field signals, respectively. Since the acoustic-magnetic synchronous locator moves along the cable path at the surface, while the underground fault point is fixed, the acoustic wave propagation distance is shortest when the locator is directly above the fault point, and therefore the measured time difference of the acoustic-magnetic signals is also minimal. Based on this principle, the locator can be controlled to move back and forth along the cable path near the rough fault distance. Through repeated comparisons, the physical location where the time difference reading consistently reaches the minimum is found. This point is identified as the preliminary fault point and marked.

[0154] To ensure absolute accuracy in positioning and eliminate any possibility of misjudgment, a secondary verification is required. The probe of the acoustic-magnetic synchronous locator is moved horizontally from the initial fault point to directly above the adjacent healthy cable laid in parallel. The tester again applies a high-voltage flash to the target faulty cable. Since the fault exists only in the target cable, the healthy adjacent cable should not produce any discharge sound at this location. If, within the preset verification time, the locator fails to detect any valid acoustic signal on the adjacent healthy cable at the synchronized moment of the magnetic field signal triggering, the verification is successful. This step confirms the uniqueness of the signal source through a rigorous comparative test, completing the diagnostic loop. Once verification is passed, the initial fault point is locked as the final fault point, and the positioning work is completed.

[0155] In one embodiment, after completing secondary verification of the adjacent healthy cables corresponding to the initial fault point using an acoustic-magnetic synchronous positioning instrument, the initial fault point is locked as the final fault point, and the closed-loop positioning is completed, including the following steps:

[0156] Move the probe of the acoustic-magnetic synchronous pointing instrument horizontally to the top of the adjacent healthy cable;

[0157] Using an acoustic-magnetic synchronous pointing device to continuously monitor a second acoustic signal and a second magnetic field signal originating from an adjacent healthy cable;

[0158] Determining whether there is a valid second acoustic signal at the synchronization moment when the second magnetic field signal is triggered;

[0159] If no valid second acoustic signal is detected within the preset verification time, the secondary verification is determined to be successful;

[0160] If the secondary verification passes, the preliminary fault point will be locked as the final fault point through the acoustic-magnetic synchronous positioning instrument, and the geographical coordinates of the final fault point will be determined.

[0161] In this embodiment, this step first requires precisely translating the probe of the acoustic-magnetic locator from the initial fault point until it is aligned directly above the adjacent healthy cable running parallel to the target fault cable. This translation is intended to change the target while maintaining all other test conditions (such as the high-voltage flash setting and ambient noise) unchanged. After the probe is moved directly above the adjacent healthy cable, the acoustic-magnetic locator resumes continuous monitoring. The tester still applies a high-voltage pulse to the original target fault cable. This means that the magnetic field signal (the second magnetic field signal), which serves as the time reference, will still be captured by the locator's magnetic field probe due to the spatial propagation of the electromagnetic field. However, the acoustic probe is now monitoring the soil beneath the adjacent healthy cable. The locator continues to operate, synchronously collecting the magnetic field signal and any possible acoustic signals (the second acoustic signal). This process is identical to locating on the target cable, aiming to search for signals with the same criteria.

[0162] After acquiring monitoring data from locations adjacent to healthy cables, the core judgment process begins. This process utilizes magnetic field signals as precise synchronization trigger signals. Whenever the pinpointer captures a second magnetic field signal generated by a high-voltage flash on the target cable, its internal processor immediately checks the acoustic channel for a valid second acoustic signal within a very short time window. A valid acoustic signal is one whose energy and spectral characteristics conform to the pre-defined fault discharge acoustic model. The logic behind this judgment is that if the fault source is indeed on the target cable, there should be no acoustic waves generated by the discharge at the corresponding location adjacent to the healthy cable. The judgment condition can be expressed as follows: when the second magnetic field signal triggers, if the measured second acoustic signal energy is less than a minimum background noise energy threshold, the signal is considered to be non-valid. To increase the reliability of the verification conclusion and avoid the inaccuracies of a single measurement, repeated confirmation is performed within a pre-set verification time. This means that the high-voltage flash and synchronous monitoring judgment process is repeated several times, for example, 5 to 10 flash tests within a minute. If, during the entire verification period, each triggering of the second magnetic field signal is not accompanied by a valid second acoustic signal, the secondary verification is considered passed. Once the secondary verification is deemed passed, the location of the preliminary fault point has been finally confirmed, eliminating all reasonable doubts. At this point, the preliminary fault point is officially identified as the final fault point using an acoustic-magnetic synchronous locator. Modern locators typically integrate a Global Positioning System (GPS) module. Upon identifying the fault point, the device automatically triggers the GPS module to accurately obtain and record the current geographic coordinates. These coordinates are stored along with other test data to form a complete fault report.

[0163] The present invention also discloses an intelligent cable fault testing device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the cable fault detection method based on the intelligent cable fault testing device described in any one of the above embodiments is implemented.

[0164] Among them, the processor can adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0165] Among them, the memory can be an internal storage unit of a computer device, such as a hard disk or memory of a computer device, or an external storage device of a computer device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD) or flash memory card (FC) equipped on the computer device. In addition, the memory can also be a combination of an internal storage unit and an external storage device of a computer device. The memory is used to store computer programs and other programs and data required by the computer device. The memory can also be used to temporarily store data that has been output or is to be output. This application does not impose any restrictions on this.

[0166] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of protection of the present application is limited to these examples. In line with the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0167] The one or more embodiments of this application are intended to encompass all such substitutions, modifications, and variations that fall within the broad scope of this application. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this application should be included in the scope of protection of this application.

Claims

1. A cable fault detection method based on an intelligent cable fault testing device, characterized in that: Applied to a cable fault testing device, the cable fault testing device includes a cable fault tester and an acoustic-magnetic synchronous positioning instrument, and the method includes the following steps: Connect the high-voltage flash unit and the first signal acquisition channel of the cable fault tester to the target fault cable, and connect the second signal acquisition channel to an adjacent healthy cable laid in parallel with the target fault cable; A high-voltage pulse is applied to the target fault cable through a high-voltage flash unit, and the active reflection signal of the target fault cable and the passive coupling induction signal of the adjacent healthy cable are synchronously captured using the first signal acquisition channel and the second signal acquisition channel; Based on the active reflection signal and using time domain reflectometry, the rough fault distance of the fault point in the target fault cable is calculated; Identify the real fault signal in the active reflection signal and the passive coupling induction signal by comparing the cross-cable signal characteristics; According to the spectrum characteristics of the real fault signal, the optimal acoustic filter parameters of the acoustic channel are configured for the acoustic-magnetic synchronous positioning instrument; The configured acoustic-magnetic synchronous locator is used to accurately locate the target fault cable near the rough fault distance. A secondary verification is performed at the corresponding position of the adjacent healthy cable to confirm the absence of acoustic-magnetic signals, completing the closed-loop positioning of the fault point.

2. The cable fault detection method based on the intelligent cable fault testing device according to claim 1, characterized in that: The method of synchronously capturing the active reflection signal of the target faulty cable and the passive coupling induction signal of the adjacent healthy cable using the first signal acquisition channel and the second signal acquisition channel comprises the following steps: Setting a synchronous acquisition trigger threshold and a pre-trigger data buffer in the first signal acquisition channel and the second signal acquisition channel; monitoring the signal amplitude of the signal in the first signal acquisition channel in real time, and starting high-frequency synchronous data acquisition of the first signal acquisition channel and the second signal acquisition channel when the signal amplitude exceeds a synchronous acquisition trigger threshold; Based on a preset acquisition time window, the original waveform data of the first signal acquisition channel and the second signal acquisition channel after the high voltage pulse occurs are recorded respectively; The collected raw waveform data is digitally filtered and normalized to generate the active reflection signal of the target fault cable and the passive coupling induction signal of the adjacent healthy cable.

3. The cable fault detection method based on the intelligent cable fault testing device according to claim 1, characterized in that: The method of identifying the real fault signal in the active reflection signal and the passive coupling induction signal by comparing the cross-cable signal characteristics comprises the following steps: The energy attenuation gradient analysis module of the cable fault tester is called to calculate and compare the energy attenuation gradients of the active reflection signal and the passive coupling induction signal to obtain the energy source determination result; The initial phase offset verification module of the cable fault tester is called to calculate and verify the initial phase offset of the active reflection signal and the passive coupling induction signal to obtain the coupling path confirmation result; Performing a logical AND operation on the energy source determination result and the coupling path confirmation result to form a signal recognition result; According to the signal recognition result, the active reflection signal is marked as the real fault signal, and the spectrum characteristics of the real fault signal are extracted. At the same time, the passive coupling induction signal is marked as the interference signal, and the interference signal is suppressed.

4. The cable fault detection method based on the intelligent cable fault testing device according to claim 3, characterized in that: The energy attenuation gradient analysis module of the cable fault tester is called to calculate and compare the energy attenuation gradients of the active reflection signal and the passive coupling induction signal to obtain the energy source determination result, which includes the following steps: Perform fast Fourier transform on the active reflection signal and the passive coupling induction signal respectively to obtain the amplitude-frequency characteristic curves of the two signals; Calculating broadband total energy of the active reflection signal and the passive coupling induction signal, and generating a first energy ratio; The amplitude-frequency characteristic curves of the two signals are divided into a plurality of preset non-overlapping narrowband frequency bands; Calculating the signal energy in each narrowband frequency band respectively, and generating a second energy ratio corresponding to each frequency band; Analyze the trend of the second energy ratio changing with frequency to quantify the energy attenuation gradient; The first energy ratio and the energy attenuation gradient are compared with a preset coupling model threshold, and an energy source determination result is output.

5. The cable fault detection method based on the intelligent cable fault testing device according to claim 3, characterized in that: The calling of the initial phase offset verification module of the cable fault tester to calculate and verify the initial phase offset of the active reflection signal and the passive coupling induction signal to obtain the coupling path confirmation result includes the following steps: Upsampling the active reflection signal and the passive coupled induction signal by an interpolation algorithm; Calculating the first-order derivatives of the active reflection signal and the passive coupling induction signal after upsampling, and determining the starting time point of the signal waveform by the first non-zero point of the first-order derivative; Calculate the time difference between the corresponding starting time points of the active reflection signal and the passive coupling induction signal to obtain the absolute time value of the initial phase offset; Convert the absolute time value into a phase angle according to the main frequency of the real fault signal; The phase angle is compared with the theoretical phase offset range set according to the cable spacing and dielectric parameters, and the coupling path is output to confirm the result.

6. The cable fault detection method based on the intelligent cable fault testing device according to claim 1, characterized in that: The method of configuring the optimal acoustic filtering parameters of the acoustic channel for the acoustic-magnetic synchronous pinpointer according to the spectrum characteristics of the real fault signal comprises the following steps: A high-voltage flashover unit is used to trigger multiple high-voltage flashovers in the area determined by the rough fault distance, and an acoustic-magnetic synchronous point-finding instrument is used to synchronously collect magnetic field signals and continuous on-site acoustic signals. Taking the triggering moment of each magnetic field signal as the benchmark, multiple synchronous acoustic samples are intercepted from the continuous on-site acoustic signal. Each synchronous acoustic sample contains the fault discharge sound and background noise. A plurality of asynchronous acoustic samples not containing fault discharge sound are intercepted from the continuous on-site acoustic signal as background noise samples; Calculate the average spectrum of the synchronous acoustic samples and the average spectrum of the background noise samples respectively; By comparing the average spectrum, the characteristic frequency band where the signal-to-noise ratio of the fault discharge sound is higher than the background noise is identified, and the optimal acoustic filtering parameters of the acoustic channel of the acoustic-magnetic synchronous pinpointer are dynamically generated based on the characteristic frequency band.

7. The cable fault detection method based on the intelligent cable fault testing device according to claim 6, characterized in that: The method of identifying a characteristic frequency band in which the signal-to-noise ratio of the fault discharge sound is higher than that of the background noise by comparing the average spectrum, and dynamically generating the optimal acoustic filtering parameters of the acoustic channel of the acoustic-magnetic synchronous pointing instrument based on the characteristic frequency band includes the following steps: Subtract the average spectrum of the synchronized acoustic samples from the average spectrum of the background noise samples to obtain the estimated spectrum of the pure fault sound; Calculate the signal-to-noise ratio between the estimated spectrum of the pure fault sound and the average spectrum of the background noise samples, and generate a signal-to-noise ratio spectrum graph covering the entire frequency band; Searching the signal-to-noise ratio spectrum for all frequency bands whose signal-to-noise ratio values exceed a preset judgment threshold as characteristic frequency bands; Analyze the frequency position, bandwidth and continuity of all characteristic frequency bands, and select the optimal filter type based on the analysis results. The filter types include high-pass filter, low-pass filter and band-pass filter; The determined filter type and the corresponding cutoff frequency are packaged to form the optimal acoustic filter parameters of the acoustic channel of the acoustic-magnetic synchronous pointing instrument.

8. The cable fault detection method based on the intelligent cable fault testing device according to claim 1, characterized in that: The method uses the configured acoustic-magnetic synchronous locator to accurately locate the target fault cable near the rough fault distance, and performs secondary verification at the corresponding position of the adjacent healthy cable to confirm the absence of the acoustic-magnetic signal, completing the closed-loop location of the fault point, including the following steps: Utilize the configured acoustic-magnetic synchronous locator to receive the synchronized first acoustic signal and the first magnetic field signal near the rough fault distance of the target fault cable, and calculate the signal time difference between the first acoustic signal and the first magnetic field signal in real time; The acoustic-magnetic synchronous positioning instrument is moved based on the signal time difference until the signal time difference reaches the minimum, and the physical location of the acoustic-magnetic synchronous positioning instrument is determined as the preliminary fault point of the target fault cable; After completing secondary verification of the adjacent healthy cables corresponding to the initial fault point using the acoustic-magnetic synchronous positioning instrument, the initial fault point is locked as the final fault point, completing the closed-loop positioning.

9. The cable fault detection method based on the intelligent cable fault testing device according to claim 8, characterized in that: After completing secondary verification of the adjacent healthy cables corresponding to the initial fault point using the acoustic-magnetic synchronous positioning instrument, the initial fault point is locked as the final fault point, and the closed-loop positioning is completed, including the following steps: Move the probe of the acoustic-magnetic synchronous pointing instrument horizontally to the top of the adjacent healthy cable; Using an acoustic-magnetic synchronous pointing device to continuously monitor a second acoustic signal and a second magnetic field signal originating from an adjacent healthy cable; Determining whether there is a valid second acoustic signal at the synchronization moment when the second magnetic field signal is triggered; If no valid second acoustic signal is detected within the preset verification time, the secondary verification is determined to be successful; If the secondary verification passes, the preliminary fault point will be locked as the final fault point through the acoustic-magnetic synchronous positioning instrument, and the geographical coordinates of the final fault point will be determined.

10. An intelligent cable fault testing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the cable fault detection method based on the intelligent cable fault testing device according to any one of claims 1 to 9 is implemented.

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