A distribution network cable defect detection method, device, equipment and storage medium

Abstract

This invention discloses a method, apparatus, device, and storage medium for detecting defects in distribution network cables. Its features include: generating a high-frequency, short-time excitation signal using an avalanche transistor circuit and injecting the high-frequency, short-time excitation signal into a cable simulation model of the target distribution network cable; acquiring scattered signal parameters of the cable simulation model based on at least one preset signal acquisition port; generating a time-frequency domain optimal excitation waveform corresponding to each scattered signal parameter; and sequentially injecting each time-frequency domain optimal excitation waveform into the cable simulation model to determine the distribution network cable defect corresponding to the cable simulation model. This invention can effectively eliminate interference caused by cable topology, achieve high-sensitivity detection of weak defects, and can be safely implemented without power outages, effectively identifying real defects in the distribution network power supply.

Description

Technical Field

[0001] This invention relates to the field of power distribution cable technology, and in particular to a method, apparatus, equipment and storage medium for detecting defects in power distribution cables. Background Technology

[0002] With the increasing cable coverage of urban power distribution networks, cable lines have become a key component of power supply systems. However, cables are mostly laid underground or in utility tunnels, operating in complex environments. They are prone to defects due to insulation aging, mechanical damage, and joint deterioration, ultimately leading to faults and power outages. Quickly and accurately locating cable defects is crucial for ensuring power supply reliability and shortening repair time.

[0003] Traditional cable defect detection methods mainly rely on time-domain reflectometry and frequency-domain reflectometry. Time-domain reflectometry injects a step or narrow pulse signal into the cable and analyzes the time and amplitude of the reflected wave to determine the fault location. Frequency-domain reflectometry injects a swept-frequency signal and analyzes the reflection coefficient in the frequency domain to determine the fault location. However, these methods have significant limitations when applied to complex distribution network cables: distribution network cables often contain branches, joints, and other structures, causing multiple reflections at various impedance discontinuities, resulting in aliasing of reflected waves and difficulty in identifying wavefronts; signal attenuation and dispersion during propagation cause severe loss of high-frequency components, leading to insufficient sensitivity for detecting early, weak defects; and these traditional methods typically require power outages and disconnection for offline testing, severely impacting the continuity of urban power supply.

[0004] Electromagnetic time-reversal (ETR) technology has attracted attention in the fields of nondestructive testing and fault location due to its excellent spatial focusing capability and time compression characteristics. This technology utilizes the time-reversal invariance of the wave equation to revert the received scattered signal in time and re-emit it, allowing the energy to be adaptively focused at the original scattering source. Previous research has attempted to apply this technology to partial discharge location in cables, achieving some success, but its application to online defect detection in distribution network cables still faces significant challenges. First, the complex topology of the cable network itself generates strong background scattering, interfering with the focusing of the actual defect signal. Second, how to safely, efficiently, and non-invasively couple the required high-frequency excitation signal to the energized cable is a key engineering problem. Therefore, there is an urgent need for an online detection system integrating advanced hardware and intelligent algorithms that can achieve high-precision and more adaptable defect detection even when cables are energized. Summary of the Invention

[0005] This invention provides a method, apparatus, equipment, and storage medium for detecting defects in distribution network cables, thereby solving the technical problem of rapid and efficient detection of defects in distribution network cables without interrupting power supply in the prior art.

[0006] According to one aspect of the present invention, a method for detecting defects in distribution network cables is provided, comprising: A high-frequency short-time excitation signal is generated by an avalanche transistor circuit, and the high-frequency short-time excitation signal is injected into the cable simulation model of the target distribution network cable. The scattered signal parameters of the cable simulation model are acquired based on at least one preset signal acquisition port; For each of the scattered signal parameters, generate the optimal time-frequency domain excitation waveform corresponding to the scattered signal parameter; Each of the optimal time-frequency domain excitation waveforms is sequentially injected into the cable simulation model to determine the distribution network cable defects corresponding to the cable simulation model.

[0007] According to another aspect of the present invention, a distribution network cable defect detection device is provided, comprising: The detection signal injection module is used to generate a high-frequency short-time excitation signal through an avalanche transistor circuit and inject the high-frequency short-time excitation signal into the cable simulation model of the target distribution network cable; The signal acquisition module is used to acquire the scattered signal parameters of the cable simulation model based on at least one preset signal acquisition port; The time inversion module is used to generate the optimal time-frequency domain excitation waveform corresponding to each of the scattered signal parameters. The inversion analysis module is used to sequentially inject each of the optimal time-frequency domain excitation waveforms into the cable simulation model to determine the distribution network cable defects corresponding to the cable simulation model.

[0008] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the distribution network cable defect detection method according to any embodiment of the present invention.

[0009] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the distribution network cable defect detection method according to any embodiment of the present invention.

[0010] The technical solution of this invention generates a high-frequency short-time excitation signal through an avalanche transistor circuit and injects the high-frequency short-time excitation signal into the cable simulation model of the target distribution network cable. The nanosecond-level pulse of the high-frequency short-time excitation signal provides initial high resolution, effectively improving defect location accuracy and reducing interference from distribution network power to the signal. Scattered signal parameters of the cable simulation model are acquired based on at least one preset signal acquisition port. For each scattered signal parameter, a time-frequency domain optimal excitation waveform corresponding to the scattered signal parameter is generated. Time inversion can effectively extract weak fault features, demonstrating excellent detection capabilities for weak defects such as high-resistance faults and early partial discharge. Each time-frequency domain optimal excitation waveform is sequentially injected into the cable simulation model to determine the corresponding distribution network cable defect. This method is applicable to cable networks with arbitrary topologies and can still work reliably in complex distribution networks. It solves the technical problem of rapid and efficient detection of distribution network cable defects without power interruption, effectively eliminating interference from cable topology, achieving high-sensitivity detection of weak defects, and can be safely implemented without power interruption, effectively identifying real defects in the distribution network power.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart of a method for detecting defects in distribution network cables is provided in this embodiment of the invention; Figure 2 This is a schematic diagram of the circuit principle of an avalanche transistor Marx circuit disclosed in an embodiment of the present invention; Figure 3 This is a circuit diagram of an online coupling device disclosed in an embodiment of the present invention; Figure 4 A flowchart of another method for detecting defects in distribution network cables provided in an embodiment of the present invention; Figure 5 A flowchart of another method for detecting defects in distribution network cables provided in an embodiment of the present invention; Figure 6 A circuit topology diagram of a cable simulation model provided in an embodiment of the present invention; Figure 7 A fault location diagram of a distribution network cable defect provided in an embodiment of the present invention; Figure 8 Another fault location diagram of a distribution network cable defect provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of the structure of a power distribution cable defect detection device provided in an embodiment of the present invention; Figure 10 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Figure 1 This invention provides a flowchart of a method for detecting defects in distribution network cables. This embodiment is applicable to situations where cable defects in real-world distribution network cables are simulated and analyzed online in real time using a proportionally modeled cable simulation model. This method can be executed by a distribution network cable defect detection device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. A high-frequency short-time excitation signal is generated by an avalanche transistor circuit, and the high-frequency short-time excitation signal is injected into the cable simulation model of the target distribution network cable.

[0017] The avalanche transistor circuit can be a Marx avalanche transistor circuit, which is used to generate a high-frequency, short-time excitation signal; for example, Figure 2 This is a schematic diagram of the Marx avalanche transistor circuit disclosed in an embodiment of the present invention, as shown below. Figure 2 As shown, before the trigger signal arrives, all avalanche transistors are in the off state. Power supply Vcc charges capacitor C1, causing capacitors C1 and C2 to have a voltage of +Vcc. When the trigger signal arrives, the high amplitude of the leading edge of the trigger signal superimposed on Vcc, reaching the transistor's breakdown voltage. The avalanche transistor in the first unit is the first to undergo avalanche breakdown. At this time, capacitor C1 begins to discharge, providing the avalanche current. The potential at the left end of C1 is pulled down to zero. Due to the continuity of capacitor voltage, the voltage at the right end of capacitor C1 becomes -Vcc. Simultaneously, since the voltage at the left end of C2 is +Vcc, the voltage across the avalanche transistor in the second unit becomes 2Vcc, much greater than Vcc. CBO (Collector-base reverse breakdown voltage), the avalanche transistor in the second unit undergoes avalanche breakdown. This process continues, with subsequent cascaded avalanche transistors undergoing avalanche breakdown sequentially. Ultimately, all capacitors are connected in series to discharge to the load, generating a pulse signal with an extremely fast leading edge and extremely high amplitude—a high-frequency, short-time excitation signal. The parallel structure of the avalanche transistors reduces parasitic inductance and improves peak current capability. Power combining is achieved through a Wilkinson power divider, ultimately yielding a highly stable pulse signal with a rise time ≤180ps, a peak voltage ≥4kV, and a repetition frequency up to 10kHz.

[0018] The high-frequency short-time excitation signal is a nanosecond-level rising edge, high-voltage pulse signal. This high-frequency short-time excitation signal can reduce attenuation over long distances and improve defect detection sensitivity.

[0019] The target distribution network cable can be any distribution network cable to be inspected for defects. The cable simulation model can be a mathematical model built based on the geometric structure and material properties of the target distribution network cable. It should be noted that in constructing the cable simulation model of the target distribution network cable, the dimensions and shape of the cable's layered structure are accurately replicated from the actual target distribution network cable, and the material, electrical, thermal, and mechanical properties of each part are restored. Furthermore, the geometric deformation and overall aging of each area of ​​the target distribution network cable are accurately simulated, and the cable simulation model is realistically analyzed through experiments and tests to achieve precise simulation of the target distribution network cable.

[0020] Optionally, based on the avalanche transistor circuit, a high-frequency short-time excitation signal is generated. By acquiring the signal of the actual avalanche transistor circuit, and through sampling, quantization and encoding, analog-to-digital conversion is completed to obtain the digital form of the high-frequency short-time excitation signal, which can be used for the cable simulation model of the target distribution network cable.

[0021] Specifically, a high-frequency short-time excitation signal is generated by an avalanche transistor circuit and injected into the cable simulation model of the target distribution network cable.

[0022] Optionally, when injecting a high-frequency short-time excitation signal into the target distribution network cable, an online coupling device can be used to safely and non-invasively inject the high-frequency short-time excitation signal into the cable simulation model. The online coupling device employs a non-invasive inductive coupling signal injector, which consists of a high-frequency magnetic core coil, a magnetic shielding shell, and a matching integrating resistor. The high-frequency magnetic core coil is wound around the cable shielding layer. When the excitation pulse current flows through the coil, a current of the same frequency is induced in the cable conductor, thereby achieving signal coupling. The magnetic shielding shell is used to suppress external electromagnetic interference and concentrate the magnetic field, improving coupling efficiency and signal-to-noise ratio. The matching integrating resistor is used to adjust the frequency response characteristics of the coupling circuit, ensuring efficient and flat signal injection within a wide frequency band from 10MHz to 2GHz. This device is a reciprocal two-port network, which can be used for both signal coupling injection and signal induction acquisition, achieving bidirectional non-invasive operation. For example, Figure 3 The circuit diagram of an online coupling device disclosed in an embodiment of the present invention is shown below. The basic circuit principle of the non-invasive inductively coupled signal injector is as follows: The basic structure of the non-invasive inductively coupled signal injector consists of a coil, a magnetic core shielding box, and an integrating resistor. M is the mutual inductance coefficient of the coil, Ls is the equivalent inductance of the coil, Cs is the equivalent stray capacitance of the coil, Rs is the equivalent resistance of the coil, u(t) is the mutual inductance electromotive force of the coil, Rc is the integrating resistor, i(t) is the mutual inductance current of the coil, and u0(t) is the voltage across the integrating resistor. In this equivalent circuit, the basic equations of the circuit are as follows: Due to the following relationship: And since Cs is generally very small, the voltage derivation relationship can be obtained as follows: In the equivalent circuit diagram, port 1 is the signal input terminal. After coupling through a coil, the signal is output at port 2, thus realizing the acquisition of the signal from port 1. Since this circuit only contains resistors, capacitors, and inductors, and no active components, the equivalent circuit of this sensor is a reciprocal two-port network. Therefore, in actual detection, the signal can be injected from output port 2 and then coupled back to input port 1 through a coil, i.e., reverse signal injection, thereby realizing online monitoring of defective cables.

[0023] S120. Acquire the scattering signal parameters of the cable simulation model based on at least one preset signal acquisition port.

[0024] The signal acquisition ports can be pre-set ports at various cable locations in the cable simulation model. It should be noted that multiple signal acquisition ports are set in the cable simulation model, each connected to a synchronous data acquisition unit. The synchronous data acquisition unit acquires signal data from the signal acquisition ports to obtain the electromagnetic signal data transmitted by the cable in the cable simulation model.

[0025] The scattered signal parameters can be the time-domain data of the signal parameters corresponding to the scattered signals generated during the transmission of the high-frequency short-time excitation signal in the cable simulation model by the signal acquisition port after the high-frequency short-time excitation signal is injected into the cable simulation model. It should be noted that during the transmission of the high-frequency short-time excitation signal in the cable simulation model, scattered signals generated due to the structure and defects of the cable simulation model are acquired by the synchronous data acquisition unit, and the scattered signal parameters are recorded.

[0026] Optionally, since the signal acquisition ports are set at different locations in the cable simulation model, the scattered signal parameters acquired by different signal acquisition ports are different, and the number of scattered signal parameters acquired is the same as the number of signal acquisition ports.

[0027] Specifically, after a high-frequency short-time excitation signal is injected into the cable simulation model of the target distribution network cable, a synchronous data acquisition unit that is pre-set at the signal acquisition port of the cable simulation model is activated, and the scattered signal parameters corresponding to each signal acquisition port are acquired through the synchronous data acquisition unit.

[0028] S130. For each of the scattered signal parameters, generate the optimal time-frequency domain excitation waveform corresponding to the scattered signal parameter.

[0029] Optionally, the optimal time-frequency domain excitation waveform for each signal acquisition port is the electromagnetic wave signal obtained by time inversion of the scattered signal parameters. It should be noted that by reversing the scattered signal parameters on the time axis and performing conjugation processing, the optimal time-frequency domain excitation waveform obtained by time inversion of the scattered signal parameters is obtained. This optimal time-frequency domain excitation waveform can be the optimal electromagnetic wave signal obtained after time inversion, which can effectively analyze the defects of the target distribution network cable.

[0030] Optionally, since the scattered signal parameters of different signal acquisition ports are different, a separate time inversion is performed on each scattered signal parameter to obtain the optimal excitation waveform in the time-frequency domain corresponding to that scattered signal parameter.

[0031] Specifically, for each scattering signal parameter, the optimal excitation waveform in the time-frequency domain corresponding to the scattering signal parameter is generated.

[0032] S140. Inject each of the optimal time-frequency domain excitation waveforms into the cable simulation model in sequence to determine the distribution network cable defects corresponding to the cable simulation model.

[0033] Among them, distribution network cable defects can refer to cable defects existing in the cable simulation model after simulating the target distribution network cable. It should be noted that distribution network cable defects can include partial discharge defects caused by manufacturing processes or aging, conductor, insulation layer and outer shielding layer defects, joint insulation defects caused by installation processes, cable extrusion and deformation caused by external forces, and cable intermediate joints with abnormal structures.

[0034] Optionally, time reversal for defect detection of the target distribution network cable involves completely reversing the process of acquiring each scattered signal parameter. Therefore, by completely reversing the process of acquiring each scattered signal parameter, signal energy can be focused, thereby detecting defects in the distribution network cable.

[0035] Optionally, when injecting each time-frequency domain optimal excitation waveform into the cable simulation model, the reverse playback time order is determined based on the time order of the scattered signal parameters acquired by each signal acquisition port, and the time-frequency domain optimal excitation waveform is injected into each signal acquisition port in strict accordance with the reverse playback time order.

[0036] Specifically, after injecting each optimal excitation waveform in the time-frequency domain into the cable simulation model in sequence, the energy focusing phenomenon of each optimal excitation waveform in the time-frequency domain in the cable simulation model is analyzed in order to identify the various distribution network cable defects existing in the cable simulation model.

[0037] Optionally, if the cable simulation model does not exhibit energy focusing, then the target distribution network cable can be considered free of distribution network cable defects.

[0038] The technical solution of this invention generates a high-frequency short-time excitation signal through an avalanche transistor circuit and injects the high-frequency short-time excitation signal into the cable simulation model of the target distribution network cable. The nanosecond-level pulse of the high-frequency short-time excitation signal provides initial high resolution, effectively improving defect location accuracy and reducing interference from distribution network power to the signal. Scattered signal parameters of the cable simulation model are acquired based on at least one preset signal acquisition port. For each scattered signal parameter, a time-frequency domain optimal excitation waveform corresponding to the scattered signal parameter is generated. Time inversion can effectively extract weak fault features, demonstrating excellent detection capabilities for weak defects such as high-resistance faults and early partial discharge. Each time-frequency domain optimal excitation waveform is sequentially injected into the cable simulation model to determine the corresponding distribution network cable defect. This method is applicable to cable networks with arbitrary topologies and can still work reliably in complex distribution networks. It solves the technical problem of rapid and efficient detection of distribution network cable defects without power interruption, effectively eliminating interference from cable topology, achieving high-sensitivity detection of weak defects, and can be safely implemented without power interruption, effectively identifying real defects in the distribution network power.

[0039] Figure 4 This is a flowchart of another method for detecting defects in distribution network cables provided by an embodiment of the present invention. The relationship between this embodiment and the above embodiments is that, after acquiring the scattered signal parameters, time inversion is performed based on the scattered signal parameters to obtain the optimal excitation waveform in the time-frequency domain. For example... Figure 4 As shown, the method includes: S410. A high-frequency short-time excitation signal is generated by an avalanche transistor circuit, and the high-frequency short-time excitation signal is injected into the cable simulation model of the target distribution network cable.

[0040] S420. Acquire the scattering signal parameters of the cable simulation model based on at least one preset signal acquisition port.

[0041] S430. Construct an abnormal frequency domain focusing operator matrix based on all the scattered signal parameters; perform differential calculation based on the pre-acquired normal frequency domain focusing operator matrix and abnormal frequency domain focusing operator matrix to determine the frequency domain focusing differential operator matrix.

[0042] Optionally, the anomalous frequency domain focusing operator matrix can be a multi-port scattering matrix constructed from the scattered signal parameters. It should be noted that the anomalous frequency domain focusing operator matrix represents the scattering information of electromagnetic waves in the cable simulation model from each signal acquisition port, and the matrix elements in the anomalous frequency domain focusing operator matrix consist of the incident coefficient and reflection coefficient of each signal acquisition port.

[0043] Optionally, the normal frequency domain focusing operator matrix can be obtained by pre-simulating the scattering of a defect-free cable simulation model, collecting scattered signals, and calculating the multi-port scattering matrix. It should be noted that by setting the cable simulation model to a completely defect-free state, a sweep frequency or pulse excitation signal covering 10MHz to 2GHz is injected into the cable, and the incident and reflected waves at each port are simultaneously measured using a vector network analyzer, directly calculating the normal frequency domain focusing operator matrix. The normal frequency domain focusing operator matrix contains the scattering matrix of the entire line topology and scattering information.

[0044] Optionally, when acquiring the normal frequency domain focusing operator matrix, the cable simulation model is also set to a suspected fault state, and a sweep frequency or pulse excitation signal covering 10MHz to 2GHz is injected into the cable. The incident and reflected waves at each port are measured synchronously using a vector network analyzer to obtain the scattering matrix under the suspected fault state. Data synchronization and consistency are achieved by using the normal frequency domain focusing operator matrix and the scattering matrix under the suspected fault state.

[0045] Optionally, the frequency domain focusing differential operator matrix can be a scattering matrix obtained by differentiating the normal frequency domain focusing operator matrix and the abnormal frequency domain focusing operator matrix. By eliminating the influence of the cable model topology and end reflections through differentiation, the frequency domain focusing differential operator matrix represents the scattering influence of defects on electromagnetic wave signals in the cable simulation model. This eliminates the background scattering influence generated by fixed topology structures such as cable branches and joints. Furthermore, through time inversion, electromagnetic waves can be effectively concentrated in the defect-sensitive frequency band, improving the detection accuracy of distribution network cable defects.

[0046] Specifically, an abnormal frequency domain focusing operator matrix is ​​constructed based on all scattered signal parameters; a difference calculation is performed on the pre-acquired normal frequency domain focusing operator matrix and abnormal frequency domain focusing operator matrix to determine the frequency domain focusing difference operator matrix. For example, through L... f (ω) represents the abnormal frequency domain focusing operator matrix of the cable simulation model, through L n (ω) represents the normal frequency domain focusing operator matrix of the cable simulation model. The process of performing difference calculation between the normal frequency domain focusing operator matrix and the abnormal frequency domain focusing operator matrix is ​​as follows: Where H(ω) represents the frequency domain focusing difference operator matrix.

[0047] S440. Perform eigenvalue decomposition on the frequency domain focusing differential operator matrix to determine the abnormal signal vector; for each of the scattered signal parameters, construct the optimal excitation waveform in the time-frequency domain based on the abnormal signal vector and the scattered signal parameter.

[0048] Optionally, the anomalous signal vector can be obtained by performing eigenvalue decomposition on the differential-time inversion operator constructed from the frequency-domain focusing difference operator matrix, and selecting the eigenvector with the largest eigenvalue. It should be noted that the anomalous signal vector represents the most sensitive feature signal in the frequency-domain focusing difference operator matrix.

[0049] Optionally, after obtaining the frequency domain focusing difference operator matrix, a differential time inversion operator is constructed based on the frequency domain focusing difference operator matrix. The differential time inversion operator is then subjected to eigenvalue decomposition, and the eigenvector with the largest eigenvalue magnitude is selected as the anomalous signal vector.

[0050] Specifically, the frequency domain focusing differential operator matrix is ​​eigenvalued to determine the anomalous signal vector; for each scattering signal parameter, the optimal excitation waveform in the time-frequency domain is constructed based on the anomalous signal vector and the scattering signal parameter.

[0051] Optionally, in another optional embodiment of the present invention, the step of constructing the optimal time-frequency domain excitation waveform based on the abnormal signal vector and the scattering signal parameters includes: Empirical mode decomposition is performed on the scattered signal parameters to obtain a set of intrinsic mode functions (IMFs); anomaly screening is performed on the IMF set to determine the set of anomalous IMFs corresponding to the fault characteristic frequency band; and the optimal excitation waveform in the time-frequency domain is reconstructed based on the set of anomalous IMFs and the anomalous signal vector.

[0052] Optionally, the set of intrinsic mode functions (EMFs) can be a set of EMFs obtained by performing Empirical Mode Decomposition (EMD) on the current scattered signal parameters. It should be noted that for each scattered signal parameter, EMFs are performed to obtain multiple EMFs and residual components. Each EMF is narrowband, suitable for analyzing non-stationary and nonlinear defect scattered signals, and can more accurately extract and preserve the time-frequency characteristics of the defect, thereby reconstructing a more concentrated and targeted excitation waveform.

[0053] Optionally, the abnormal intrinsic mode function set can be a set of intrinsic mode functions related to the fault characteristic frequency band. It should be noted that by identifying the fault characteristic frequency band for each intrinsic mode function in the intrinsic mode function set, the main intrinsic mode functions related to the fault characteristic frequency band are selected and used as the abnormal intrinsic mode function set.

[0054] Specifically, for a given scattering signal parameter, empirical mode decomposition is performed to obtain a set of intrinsic mode functions (EMFs) and residual components. Each EMF is compared with the fault characteristic frequency band to identify and filter out the main EMFs related to the fault characteristic frequency band, thus constructing a set of anomalous EMFs for the scattering signal parameter. In the cable simulation model, anomalous signal vectors and anomalous EMFs associated with distribution network cable defects are used. Based on the scattering signal parameter, time inversion is performed using the set of anomalous EMFs and anomalous signal vectors to construct the reconstructed optimal excitation waveform in the time-frequency domain corresponding to the scattering signal parameter.

[0055] Optionally, in another optional embodiment of the present invention, the reconstructing of the optimal time-frequency domain excitation waveform based on the set of anomalous intrinsic mode functions and the anomalous signal vector includes: Perform an inverse Fourier transform on the abnormal signal vector to determine the time-domain characteristic signal; reconstruct the optimal time-frequency domain excitation waveform based on the abnormal intrinsic mode function set and the time-domain characteristic signal.

[0056] The time-domain feature signal can be the time-domain representation of the anomalous signal vector. It should be noted that by performing an inverse Fourier transform on the anomalous signal vector, it is converted into a time-domain form, yielding the time-domain feature signal. For example, the anomalous signal vector can be represented as a time-domain representation of the anomalous signal vector. Represent the abnormal signal vector Perform inverse Fourier transform to obtain the time-domain characteristic signal. .

[0057] Optionally, after obtaining the time-domain feature signal, the reconstruction process based on the time-domain feature signal and the set of anomalous intrinsic mode functions (IMFs) is as follows: The IMFs in the set of anomalous IMFs are summed, and then the time-domain feature signal and the summation result are convolved in the time domain. The residual components are then superimposed to obtain the optimal excitation waveform in the time-frequency domain. For example, the IMF set is obtained through {IMF... k The residual components are represented by r(t), and the intrinsic modes in the set of anomalous intrinsic modes are represented by the index set of the intrinsic mode function set by φ. The reconstruction calculation process is as follows: The symbol * represents the convolution operation. This represents the summation of the IMF components in the selected set of anomalous intrinsic mode functions.

[0058] Specifically, an inverse Fourier transform is performed on the abnormal signal vector to determine the time-domain characteristic signal; the optimal excitation waveform in the time-frequency domain is reconstructed based on the set of abnormal intrinsic mode functions and the time-domain characteristic signal.

[0059] S450. Inject each of the optimal time-frequency domain excitation waveforms into the cable simulation model in sequence to determine the distribution network cable defects corresponding to the cable simulation model.

[0060] This invention utilizes the difference in scattering parameters between fault and normal states to fundamentally eliminate the background scattering effects caused by fixed topologies such as cable branches and joints. It is applicable to cable networks with arbitrary topologies and can still reliably analyze defects in complex branch networks. Nanosecond-level pulses provide initial high resolution, and adaptive waveform reconstruction concentrates the excitation energy in the defect-sensitive frequency band, giving it excellent detection capabilities for weak defects such as high-resistance faults and early partial discharges.

[0061] Figure 5 This is a flowchart of another method for detecting defects in distribution network cables provided by an embodiment of the present invention. The relationship between this embodiment and the above embodiments is as follows: [Further details omitted]. Figure 5 As shown, the method includes: S510. A high-frequency short-time excitation signal is generated by an avalanche transistor circuit, and the high-frequency short-time excitation signal is injected into the cable simulation model of the target distribution network cable.

[0062] S520. Acquire the scattering signal parameters of the cable simulation model based on at least one preset signal acquisition port.

[0063] S530. Construct an abnormal frequency domain focusing operator matrix based on all the scattered signal parameters; perform differential calculation based on the pre-acquired normal frequency domain focusing operator matrix and abnormal frequency domain focusing operator matrix to determine the frequency domain focusing differential operator matrix.

[0064] S540. Perform eigenvalue decomposition on the frequency domain focusing differential operator matrix to determine the abnormal signal vector; for each of the scattered signal parameters, construct the optimal time-frequency domain excitation waveform based on the abnormal signal vector and the scattered signal parameter.

[0065] S550: The optimal excitation waveform in the time and frequency domain is sequentially injected into the cable simulation model, and the time domain response signals of multiple preset observation points are collected simultaneously.

[0066] Optionally, the preset observation points can be pre-set observation points used to measure the time-domain response signal of the cable simulation model. It should be noted that the preset observation points are typically set between different signal acquisition ports. The time-domain response in the cable simulation model is acquired by collecting data from these preset observation points after injecting the optimal time-frequency excitation waveform at each signal acquisition port. For example, Figure 6 This is a circuit topology diagram of a cable simulation model provided in an embodiment of the present invention. For example... Figure 6As shown, ports 1, 2, 3, 4, 5, 6, and 7 are different signal acquisition ports, and there are other ports between each signal acquisition port. The distance between each port is set. A, B, C, D, E, F, G, and I are different preset observation points. Preset observation points A, D, G, and I are set between ports 1 and 7; preset observation points B and C are set between ports 2 and 3; preset observation points E and F are set between ports 4 and 5; and preset observation points H and I are set between ports 6 and 7. The optimal time-frequency domain excitation waveforms corresponding to different signal acquisition ports are sequentially injected into the cable simulation model. The preset observation points A, B, C, D, E, F, G, and I synchronously acquire the time-domain response signals.

[0067] Optionally, the time-domain response signal can be the time-domain response of the electromagnetic wave signal in the cable simulation model after the optimal excitation waveform in the time and frequency domain is injected into the cable simulation model.

[0068] Specifically, the optimal excitation waveform in the time and frequency domain is sequentially injected into the cable simulation model, and the time-domain response signals of multiple preset observation points are collected simultaneously.

[0069] S560. Identify the distribution network cable defects corresponding to the cable simulation model based on all the time-domain response signals.

[0070] Specifically, after obtaining the time-domain response signals of each preset observation point, the location of defects in the cable simulation model is analyzed based on all the time-domain response signals, and the distribution network cable defects in the cable simulation model are determined.

[0071] Optionally, in another optional embodiment of the present invention, identifying the distribution cable defects corresponding to the cable simulation model based on all of the time-domain response signals includes: The process of identifying distribution network cable defects corresponding to the cable simulation model based on all the time-domain response signals includes: Wavelet packet decomposition is performed on each time-domain response signal to determine the corresponding frequency band sub-signal; energy calculation is performed on each frequency band sub-signal to determine the corresponding sub-band energy; weighted summation is performed on all the sub-band energies to determine the total focused energy; the time-frequency focused energy spectrum of the time-domain response signal is determined based on each total focused energy; the time-frequency focused energy spectrum of each preset observation point is identified to determine the defect of the distribution network cable.

[0072] In this context, a frequency band sub-signal can be a time-domain response signal divided into multiple non-overlapping or partially overlapping frequency bands in the frequency domain, with each band corresponding to a segment of the time-domain signal. It's important to note that each frequency band sub-signal of the time-domain response signal only contains information about the time-domain response signal within a specific frequency range. The sub-band energy is the sum of the energy of the frequency band sub-signals in the time domain. The sub-band energy represents the degree of characteristic contribution of the frequency band sub-signals to the time-domain response signal.

[0073] Optionally, wavelet packet decomposition can be a signal analysis method that recursively decomposes the low-frequency and high-frequency components of the time-domain response signal. By providing richer and more uniform time-frequency resolution based on the time-domain response signal through wavelet packet decomposition, the frequency bands related to typical defect features can be effectively identified.

[0074] Optionally, the total focused energy can be the weighted sum of the sub-band energies of all frequency band sub-signals, used to highlight defect characteristics. It should be noted that in this invention, during the weighted summation of the sub-band energies of the frequency band sub-signals, the weight coefficients corresponding to each frequency band sub-signal are pre-set based on prior knowledge of fault characteristics, assigning higher weights to frequency bands related to typical defect characteristics, effectively enhancing defect response and suppressing noise. The total focused energy representing the distribution network cable defect is obtained by weighted summation of the sub-band energies of all frequency band sub-signals based on the weight coefficients corresponding to each frequency band sub-signal. That is, the higher the total focused energy, the more likely a distribution network cable defect exists.

[0075] The time-frequency focused energy spectrum can be considered as an energy distribution spectrum on a two-dimensional time-frequency plane. This spectrum effectively locates the energy distribution of time-domain signals and reflects the degree of energy focusing in frequency bands related to defect characteristics. It should be noted that each preset observation point is analyzed individually to obtain its time-frequency focused energy spectrum, thereby analyzing the location of defects in the distribution network cables within the cable simulation model.

[0076] Specifically, wavelet packet decomposition is performed on each time-domain response signal to determine the corresponding frequency band sub-signals; energy calculation is performed on each frequency band sub-signal to determine the sub-band energy; weighted summation of all sub-band energies is performed to determine the total focused energy; the time-frequency focused energy spectrum of the time-domain response signal is determined based on each total focused energy; the time-frequency focused energy spectrum of each preset observation point is identified to determine the defects in the distribution network cable. For example, the time-domain response signal of the preset acquisition point x is set as h(x,t), and L-level wavelet packet decomposition is performed on the time-domain response signal to obtain 2... L Individual frequency band sub-signal h (x,j) (t), where j=1,2,3,4,...,2 L The energy of each frequency band sub-signal within the time window T is calculated as follows: The total focused energy at the preset acquisition point x is obtained by weighted summation of the energies of all sub-bands, forming the time-frequency focused energy spectrum E(x): in, The weighting coefficients for the j-th frequency band sub-signal are determined based on prior knowledge of fault characteristics.

[0077] Optionally, in another optional embodiment of the present invention, identifying the time-frequency focusing energy spectrum of each of the preset observation points to determine the distribution network cable defect includes: Identify the time-frequency focused energy spectrum of each of the preset observation points, and determine at least one energy peak point of the time-frequency focused energy spectrum; identify the preset observation point corresponding to each energy peak point as a defect in the distribution network cable.

[0078] The energy peak point can be the point with the highest energy in the time-frequency focusing energy spectrum. It should be noted that the energy peak point can be understood as the excitation energy being concentrated in the defect-sensitive frequency band, which causes this frequency band to appear as an energy focusing peak in the time-frequency focusing energy spectrum.

[0079] Specifically, the time-frequency focused energy spectrum of each preset observation point is identified, and at least one energy peak point of the time-frequency focused energy spectrum is determined; the preset observation point corresponding to each energy peak point is identified as a defect in the distribution network cable. For example, Figure 7 A fault location diagram of a distribution network cable defect provided in an embodiment of the present invention; Figure 8 This is another fault location diagram for a distribution network cable defect provided in an embodiment of the present invention. Figure 6 Based on the cable simulation model's line topology diagram, time-frequency focused energy spectra were collected at preset observation points A, B, C, D, E, F, G, and I, and then analyzed to obtain... Figure 7 As shown, an energy peak was found at 1.8km along the ADGH line, indicating a defect in the distribution network cable; Figure 8 As shown, an energy peak was found at 1.2km along the ADF line, which was identified as a defect in the distribution network cable.

[0080] In this embodiment of the invention, waveform reconstruction concentrates the excitation energy in the defect-sensitive frequency band, and wavelet packet time-frequency energy analysis can effectively extract weak fault features. The system has excellent detection capabilities for high-resistivity faults and weak defects in early partial discharge. Waveform reconstruction and wavelet packet time-frequency focusing energy analysis enable the energy to achieve three-dimensional focusing in space, time and frequency at the defect point, and the positioning accuracy can reach within the meter level.

[0081] Figure 9 This is a schematic diagram of a distribution network cable defect detection device provided in an embodiment of the present invention. Figure 9As shown, the device includes: a detection signal injection module 910, a signal acquisition module 920, a time inversion module 930, and an inversion analysis module 940; wherein, The detection signal injection module 910 is used to generate a high-frequency short-time excitation signal through an avalanche transistor circuit and inject the high-frequency short-time excitation signal into the cable simulation model of the target distribution network cable. The signal acquisition module 920 is used to acquire the scattered signal parameters of the cable simulation model based on at least one preset signal acquisition port; The time inversion module 930 is used to generate the optimal time-frequency domain excitation waveform corresponding to each of the scattered signal parameters. The inversion analysis module 940 is used to sequentially inject each of the optimal time-frequency domain excitation waveforms into the cable simulation model to determine the distribution network cable defects corresponding to the cable simulation model.

[0082] The technical solution of this invention generates a high-frequency short-time excitation signal through an avalanche transistor circuit and injects the high-frequency short-time excitation signal into the cable simulation model of the target distribution network cable. The nanosecond-level pulse of the high-frequency short-time excitation signal provides initial high resolution, effectively improving defect location accuracy and reducing interference from distribution network power to the signal. Scattered signal parameters of the cable simulation model are acquired based on at least one preset signal acquisition port. For each scattered signal parameter, a time-frequency domain optimal excitation waveform corresponding to the scattered signal parameter is generated. Time inversion can effectively extract weak fault features, demonstrating excellent detection capabilities for weak defects such as high-resistance faults and early partial discharge. Each time-frequency domain optimal excitation waveform is sequentially injected into the cable simulation model to determine the corresponding distribution network cable defect. This method is applicable to cable networks with arbitrary topologies and can still work reliably in complex distribution networks. It solves the technical problem of rapid and efficient detection of distribution network cable defects without power interruption, effectively eliminating interference from cable topology, achieving high-sensitivity detection of weak defects, and can be safely implemented without power interruption, effectively identifying real defects in the distribution network power.

[0083] Optionally, the time reversal module 930 is specifically used for: An anomalous frequency domain focusing operator matrix is ​​constructed based on all the scattered signal parameters; The frequency domain focusing differential operator matrix is ​​determined by performing differential calculations based on the pre-acquired normal frequency domain focusing operator matrix and abnormal frequency domain focusing operator matrix; The frequency domain focusing differential operator matrix is ​​subjected to eigenvalue decomposition to determine the abnormal signal vector; For each of the scattered signal parameters, an optimal excitation waveform in the time-frequency domain is constructed based on the abnormal signal vector and the scattered signal parameters.

[0084] Optionally, the time reversal module 930 is further used for: Empirical mode decomposition is performed on the scattered signal parameters to obtain a set of intrinsic mode functions; Anomaly screening is performed on the intrinsic mode function set to determine the abnormal intrinsic mode function set corresponding to the fault characteristic frequency band; The optimal excitation waveform in the time-frequency domain is reconstructed based on the set of anomalous intrinsic mode functions and the anomalous signal vector.

[0085] Optionally, the time reversal module 930 is further used for: Perform an inverse Fourier transform on the abnormal signal vector to determine the time-domain characteristic signal; The optimal excitation waveform in the time-frequency domain is reconstructed based on the set of anomalous intrinsic mode functions and the time-domain characteristic signal.

[0086] Optionally, the inversion analysis module 940 is specifically used for: The optimal excitation waveform in the time-frequency domain is sequentially injected into the cable simulation model, and the time-domain response signals of multiple preset observation points are simultaneously acquired. Based on all the time-domain response signals, the defects of the distribution network cable corresponding to the cable simulation model are identified.

[0087] Optionally, the inversion analysis module 940 is further used for: Wavelet packet decomposition is performed on each time-domain response signal to determine the corresponding frequency band sub-signal; energy calculation is performed on each frequency band sub-signal to determine the sub-band energy corresponding to the frequency band sub-signal; The total focusing energy is determined by weighted summation of all the sub-band energies. The time-frequency focusing energy spectrum of the time-domain response signal is determined based on the total focusing energy of each of the above. Identify the time-frequency focusing energy spectrum of each of the preset observation points to determine the defects in the distribution network cable.

[0088] Optionally, the inversion analysis module 940 is further used for: Identify the time-frequency focused energy spectrum of each of the preset observation points, and determine at least one energy peak point of the time-frequency focused energy spectrum; Each energy peak point is identified as a preset observation point as a defect in the distribution network cable.

[0089] The distribution network cable defect detection device provided in the embodiments of the present invention can execute the distribution network cable defect detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0090] Figure 10A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their patterns are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0091] like Figure 10 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0092] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer grids such as the Internet and / or various telecommunications grids.

[0093] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as distribution network cable defect detection methods.

[0094] In some embodiments, the distribution network cable defect detection method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the distribution network cable defect detection method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the distribution network cable defect detection method by any other suitable means (e.g., by means of firmware).

[0095] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0096] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the patterns / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0097] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0098] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0099] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or grid browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication grid). Examples of communication grids include local area networks (LANs), wide area networks (WANs), blockchain grids, and the Internet.

[0100] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS servers, such as high management difficulty and weak business scalability.

[0101] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0102] This embodiment provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, the program implements the steps of the distribution network cable defect detection method provided in any embodiment of the present invention. The method includes: A high-frequency short-time excitation signal is generated by an avalanche transistor circuit, and the high-frequency short-time excitation signal is injected into the cable simulation model of the target distribution network cable. The scattered signal parameters of the cable simulation model are acquired based on at least one preset signal acquisition port; For each of the scattered signal parameters, generate the optimal time-frequency domain excitation waveform corresponding to the scattered signal parameter; Each of the optimal time-frequency domain excitation waveforms is sequentially injected into the cable simulation model to determine the distribution network cable defects corresponding to the cable simulation model.

[0103] The computer storage medium of this invention can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0104] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0105] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0106] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of mesh, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a grid of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0108] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0109] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for detecting defects in distribution network cables, characterized in that, include: A high-frequency short-time excitation signal is generated by an avalanche transistor circuit, and the high-frequency short-time excitation signal is injected into the cable simulation model of the target distribution network cable. The scattered signal parameters of the cable simulation model are acquired based on at least one preset signal acquisition port; For each of the scattered signal parameters, generate the optimal time-frequency domain excitation waveform corresponding to the scattered signal parameter; Each of the optimal time-frequency domain excitation waveforms is sequentially injected into the cable simulation model to determine the distribution network cable defects corresponding to the cable simulation model.

2. The method according to claim 1, characterized in that, The step of generating the optimal time-frequency domain excitation waveform corresponding to each of the scattered signal parameters includes: An anomalous frequency domain focusing operator matrix is ​​constructed based on all the scattered signal parameters; The frequency domain focusing differential operator matrix is ​​determined by performing differential calculations based on the pre-acquired normal frequency domain focusing operator matrix and abnormal frequency domain focusing operator matrix; The frequency domain focusing differential operator matrix is ​​subjected to eigenvalue decomposition to determine the abnormal signal vector; For each of the scattered signal parameters, an optimal excitation waveform in the time-frequency domain is constructed based on the abnormal signal vector and the scattered signal parameters.

3. The method according to claim 2, characterized in that, The construction of the optimal time-frequency domain excitation waveform based on the abnormal signal vector and the scattered signal parameters includes: Empirical mode decomposition is performed on the scattered signal parameters to obtain a set of intrinsic mode functions; Anomaly screening is performed on the intrinsic mode function set to determine the abnormal intrinsic mode function set corresponding to the fault characteristic frequency band; The optimal excitation waveform in the time-frequency domain is reconstructed based on the set of anomalous intrinsic mode functions and the anomalous signal vector.

4. The method according to claim 3, characterized in that, The reconstruction of the optimal time-frequency domain excitation waveform based on the set of anomalous intrinsic mode functions and the anomalous signal vector includes: Perform an inverse Fourier transform on the abnormal signal vector to determine the time-domain characteristic signal; The optimal excitation waveform in the time-frequency domain is reconstructed based on the set of anomalous intrinsic mode functions and the time-domain characteristic signal.

5. The method according to claim 1, characterized in that, The step of sequentially injecting each of the optimal time-frequency domain excitation waveforms into the cable simulation model to determine the distribution network cable defects corresponding to the cable simulation model includes: The optimal excitation waveform in the time-frequency domain is sequentially injected into the cable simulation model, and the time-domain response signals of multiple preset observation points are simultaneously acquired. Based on all the time-domain response signals, the defects of the distribution network cable corresponding to the cable simulation model are identified.

6. The method according to claim 5, characterized in that, The process of identifying distribution network cable defects corresponding to the cable simulation model based on all the time-domain response signals includes: Wavelet packet decomposition is performed on each time-domain response signal to determine the corresponding frequency band sub-signal; energy calculation is performed on each frequency band sub-signal to determine the sub-band energy corresponding to the frequency band sub-signal; The total focusing energy is determined by weighted summation of all the sub-band energies. The time-frequency focusing energy spectrum of the time-domain response signal is determined based on the total focusing energy of each of the above. Identify the time-frequency focusing energy spectrum of each of the preset observation points to determine the defects in the distribution network cable.

7. The method according to claim 6, characterized in that, The step of identifying the time-frequency focusing energy spectrum of each of the preset observation points and determining the defects in the distribution network cable includes: Identify the time-frequency focused energy spectrum of each of the preset observation points, and determine at least one energy peak point of the time-frequency focused energy spectrum; Each energy peak point is identified as a preset observation point as a defect in the distribution network cable.

8. A defect detection device for distribution network cables, characterized in that, include: The detection signal injection module is used to generate a high-frequency short-time excitation signal through an avalanche transistor circuit and inject the high-frequency short-time excitation signal into the cable simulation model of the target distribution network cable; The signal acquisition module is used to acquire the scattered signal parameters of the cable simulation model based on at least one preset signal acquisition port; The time inversion module is used to generate the optimal time-frequency domain excitation waveform corresponding to each of the scattered signal parameters. The inversion analysis module is used to sequentially inject each of the optimal time-frequency domain excitation waveforms into the cable simulation model to determine the distribution network cable defects corresponding to the cable simulation model.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the distribution network cable defect detection method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the distribution network cable defect detection method according to any one of claims 1-7.

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