Cable defect positioning method based on broadband impedance-clustering algorithm and related device

Abstract

The invention provides a cable defect positioning method based on a broadband impedance-clustering algorithm and a related device, and belongs to the technical field of cable defect localization, and the method comprises the steps: calculating the broadband impedance spectrum of a defective cable according to the distribution parameters of the defective cable, carrying out the discrete Fourier transform of the broadband impedance spectrum, extracting the characteristic information of the broadband impedance spectrum, and carrying out the clustering of the broadband impedance. According to the method, a cable defect initial positioning spectrogram is obtained, a clustering result of initial positioning spectrum data points is obtained from data in the cable defect initial positioning spectrogram through a DBSCAN algorithm, a final diagnosis spectrogram is drawn, defect peak data points and non-defect section data points in the final diagnosis spectrogram are distinguished, and therefore extraction and positioning of defect points are completed. According to the method, centralized weak defects such as single-point and multi-point high-resistance faults of the cable can be effectively positioned, the identification degree of the weak defects of the cable can be improved, and misjudgment caused by human subjective factors and objective factors on a test site is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cable defect location, and in particular to a cable defect location method based on a broadband impedance-clustering algorithm and a related device. Background Art

[0002] With the acceleration of urbanization, power cables are gradually replacing overhead lines to become the backbone of urban power grids due to their advantages such as small footprint and high power supply reliability. However, in actual operation, cables may suffer from insulation defects due to moisture, aging, or external mechanical stress, leading to equipment damage, power outages and other safety accidents. Therefore, it is of great significance to quickly and accurately detect and locate cable defects.

[0003] At present, the most commonly used methods for detecting cable status are partial discharge detection method, time domain reflection detection method and frequency domain reflection detection method. The most common partial discharge method is the oscillation wave detection method, which has a good detection effect, but the detection objects are mainly cable accessories and cable installation defects, and it is difficult to detect latent defects in the cable body. The time domain reflection method injects a pulse signal at the head end of the cable, and then directly identifies the reflected waveform in the time domain. This method can intuitively obtain the propagation distance and polarity of the reflected wave, but the incident signal has few high-frequency components, and its attenuation and dispersion phenomena in the propagation process are serious, the positioning accuracy is low, and it is difficult to identify weak defects; the frequency domain reflection method uses a swept frequency signal, which is mainly divided into broadband impedance spectroscopy (Broadband Impedance Spectrum). However, in the process of locating defects by the BIS method, when Fourier transforming the impedance spectrum, interference peaks will be generated on the final positioning spectrum due to spectrum leakage and other reasons, thus affecting the positioning of the actual defect peak. At the same time, the signal attenuation of the long cable is quite serious during the actual test, and the influence of on-site noise will further increase the difficulty of subjective judgment of defect peaks. Summary of the invention

[0004] The purpose of the present invention is to provide a cable defect location method and related devices based on a broadband impedance-clustering algorithm to overcome the problems existing in the prior art. The present invention proposes a BIS-DBSCAN cable defect joint location method, which extracts the impedance discontinuity points of the actual cable under complex conditions, can automatically cluster the location spectrum data points, and extract the position and peak value of the defect in the location spectrum according to different types of data points after classification. The detection effect of this method on weak defects is verified through simulation and experiments. It can not only effectively locate concentrated weak defects such as single-point and multi-point high-resistance faults of the cable, but also improve the recognition of weak defects in the cable, reducing misjudgment caused by human subjective factors and objective factors on the test site.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A cable defect location method based on broadband impedance-clustering algorithm comprises the following steps:

[0007] Step 1: Calculate the broadband impedance spectrum of the defective cable according to the distribution parameters of the defective cable, perform discrete Fourier transform on the broadband impedance spectrum to extract the characteristic information of the broadband impedance spectrum, and obtain a preliminary positioning spectrum of the cable defect;

[0008] Step 2: Use the DBSCAN algorithm to obtain the clustering results of the preliminary positioning spectrum data points from the data in the cable defect preliminary positioning spectrum, draw the final diagnosis spectrum, and distinguish the defect peak data points from the non-defect segment data points in the final diagnosis spectrum, so as to complete the extraction and positioning of the defect points;

[0009] Furthermore, the step 1 specifically includes:

[0010] According to the distribution parameters of the defective cable, the broadband impedance spectrum of the defective cable is calculated by transmission line theory. The specific calculation formula is:

[0011]

[0012] Where Z0 is the broadband characteristic impedance; Γ L is the reflection coefficient at the end of the cable; P is the cable propagation constant factor, P = e -2γl ;γ is the propagation coefficient of the cable; l is the length of the cable;

[0013] The broadband impedance spectrum is subjected to discrete Fourier transform to extract the broadband impedance spectrum characteristic information, and the preliminary cable defect positioning spectrum Lc is obtained. The specific formula is:

[0014] Lc=abs(DFT(Z Im ));

[0015] Where DFT(·) is discrete Fourier transform; Z Im =Im(Z l ), indicating taking Z l The imaginary part of is processed; abs(·) is the modulus value;

[0016] Furthermore, the calculation formula of the broadband characteristic impedance Z0 is:

[0017]

[0018] In the formula, R0 is the distributed resistance per unit length of the cable; G0 is the distributed conductance per unit length of the cable; j is the imaginary unit; ω is the angular frequency; L0 is the distributed inductance per unit length of the cable; C0 is the distributed capacitance per unit length of the cable; ωL0>>R0, ωC0>>G0;

[0019] Furthermore, the clustering results of the preliminary positioning spectrum data points obtained by the DBSCAN algorithm in the step 2 specifically include: inputting the parameter neighborhood radius eps and the minimum number of points minPts in the neighborhood radius into the DBSCAN algorithm, the DBSCAN algorithm calculates the number of data points in the eps-neighborhood, obtains the positioning spectrum data points, and divides the positioning spectrum data points into core points, boundary points and noise points, selects the core points, connects all the points in the eps-neighborhood of each core point to form a cluster, and then classifies the boundary points into the cluster of core points connected to the boundary points, and then marks the points that are not classified into any cluster as noise points, and obtains the clustering results of the positioning spectrum data points;

[0020] Furthermore, the positioning spectrum data points are divided into core points N eps (p) Specifically: if the eps-neighborhood of sample p contains at least minPts number of points, then sample p is a core point, that is:

[0021] N eps (p)≥minPts;

[0022] Where eps is the neighborhood radius; minPts is the minimum number of points within the neighborhood radius;

[0023] Furthermore, the classification of the positioning spectrum data points into boundary points is specifically as follows: if sample b is not a core point, but is within the eps-neighborhood of a core point, then sample b is a boundary point, that is:

[0024]

[0025] Furthermore, the classification of the positioning spectrum data points into noise points is specifically as follows: if the sample n is neither a core point nor a boundary point, then the sample n is a noise point, that is:

[0026]

[0027] A cable defect location system based on broadband impedance-clustering algorithm, comprising:

[0028] The preliminary positioning spectrum module is used to calculate the broadband impedance spectrum of the defective cable according to the distribution parameters of the defective cable, perform discrete Fourier transform on the broadband impedance spectrum to extract the characteristic information of the broadband impedance spectrum, and obtain the preliminary positioning spectrum of the cable defect;

[0029] The final diagnostic spectrum module is used to obtain the clustering results of the preliminary positioning spectrum data points by using the DBSCAN algorithm to obtain the data in the cable defect preliminary positioning spectrum and draw the final diagnostic spectrum;

[0030] The defect point positioning module is used to distinguish the defect peak data points and non-defect segment data points in the final diagnosis spectrum, thereby completing the extraction and positioning of the defect points.

[0031] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0032] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0033] The above technical solution has the following advantages or beneficial effects:

[0034] The present invention provides a cable defect location method and related devices based on a broadband impedance-clustering algorithm. Firstly, according to the transmission line theory, the broadband impedance spectrum of the defective cable is derived from the cable distribution parameter equivalent model, and the impedance spectrum characteristic information is extracted by fast Fourier transform to obtain the cable defect location spectrum. Through broadband impedance spectrum analysis and feature extraction, the subtle changes of cable defects in a broadband range can be captured, thereby achieving precise positioning. Compared with traditional methods, the method has significantly improved the positioning accuracy. Secondly, the positioning spectrum data points are substituted into the DBSCAN algorithm, and the clustering results of the positioning spectrum data points by DBSCAN are used to automatically distinguish the defect peak data points from the non-defect segment data points, thereby extracting the position of the actual defect point. The method of the present invention not only has accurate positioning, but also can effectively solve the problem of misjudgment in cable defect identification. In addition, the method of the present invention can also realize automatic and intelligent operation, reducing the difficulty and cost of operation.

[0035] Furthermore, the calculation of broadband impedance spectrum through transmission line theory can more accurately reflect the impedance characteristics of the cable at different frequencies, thereby improving the accuracy of defect location; the introduction of discrete Fourier transform makes it possible to extract more characteristic information about cable defects from the frequency domain perspective, further enhancing the positioning effect.

[0036] Furthermore, by refining the calculation formula of the broadband characteristic impedance Z0 and considering the inductance and capacitance effects at high frequencies, the impedance characteristics of the cable within a broadband range can be more accurately reflected.

[0037] Furthermore, inputting the parameters eps (neighborhood radius) and minPts (minimum number of points within the neighborhood radius) into the DBSCAN algorithm is the key to the DBSCAN algorithm. For each data point in the preliminary positioning spectrum, calculating the number of data points within its eps-neighborhood helps to determine whether each point is a core point, boundary point or noise point. It can effectively handle noise and outliers in the data, and help reduce the possibility of misjudgment and misclassification.

[0038] The present invention also provides a cable defect location system based on a broadband impedance-clustering algorithm. The preliminary location spectrum module uses broadband impedance spectrum and discrete Fourier transform technology to extract broadband impedance spectrum characteristic information of cable defects and generate a preliminary location spectrum, which effectively highlights the defect characteristics and provides a reliable data basis for subsequent clustering analysis and defect point location. The final diagnosis spectrum module performs cluster analysis on the data points in the preliminary location spectrum through the DBSCAN algorithm, further distinguishes the defect peak data points and non-defect segment data points. The adaptability and anti-noise ability of the DBSCAN algorithm ensure the accuracy and stability of the clustering results, thereby improving the accuracy of defect location. The system realizes the rapid location of cable defects in an automated and intelligent manner, effectively solves the problem of misjudgment in cable defect identification, and enables the system to complete a large amount of data processing and analysis work in a short time, thereby improving the diagnosis efficiency. At the same time, the defect point location module can directly extract and locate defect points without manual intervention or additional detection steps, shortening the diagnosis time and reducing the diagnosis cost.

[0039] The present invention also provides a computer device, which can efficiently implement the steps of the cable defect location method based on the broadband impedance-clustering algorithm of the present invention by executing a specific computer program through a processor. When the computer device performs data processing tasks, it can accurately perform numerical calculations and logical judgments, avoiding errors caused by human factors. At the same time, since the computer program has a high degree of stability and reliability, the accuracy and consistency of the data processing results can be ensured.

[0040] The present invention also provides a computer-readable storage medium. By programming the steps of the cable defect location method based on the broadband impedance-clustering algorithm of the present invention into a computer program and storing them on a computer-readable storage medium, users can easily load these programs onto any compatible computer device and execute them without rewriting or converting the code, thereby greatly improving the convenience and flexibility of program execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of a cable defect location method based on a broadband impedance-clustering algorithm of the present invention;

[0042] Figure 2It is a schematic diagram of the cable defect location system based on the broadband impedance-clustering algorithm of the present invention;

[0043] Figure 3 This is a diagram showing the BIS-DBSCAN joint positioning effect in Example 2 of the present invention;

[0044] Figure 4 This is a diagram showing the diagnostic results of cable sample No. 1 in Example 3 of the present invention;

[0045] Figure 5 This is a diagram showing the diagnostic results of cable sample No. 2 in Example 3 of the present invention;

[0046] Figure 6 This is a diagram showing the diagnostic results of cable sample No. 3 in Example 3 of the present invention;

[0047] Figure 7 This is a diagram showing the diagnostic results of cable sample No. 4 in Example 3 of the present invention;

[0048] Figure 8 A schematic diagram of a platform for implementing the present invention;

[0049] Fig. 9 It is a schematic diagram of the equivalent model of cable distribution parameters of the present invention;

[0050] Fig.10 It is a schematic diagram of the structure of the computer device of the present invention. DETAILED DESCRIPTION

[0051] The present invention is further described in detail below in conjunction with specific embodiments, which are intended to explain the present invention rather than to limit it.

[0052] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0053] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0054] Embodiment 1:

[0055] like Figure 1 As shown, the present invention provides a cable defect location method based on a broadband impedance-clustering algorithm, comprising the following steps:

[0056] Step 1: Calculate the broadband impedance spectrum of the defective cable according to the distribution parameters of the defective cable, perform discrete Fourier transform on the broadband impedance spectrum to extract the characteristic information of the broadband impedance spectrum, and obtain a preliminary positioning spectrum of the cable defect;

[0057] Preferably, step one specifically includes:

[0058] According to the distribution parameters of the defective cable, the broadband impedance spectrum of the defective cable is calculated by transmission line theory. The specific calculation formula is:

[0059]

[0060] Where Z0 is the broadband characteristic impedance; Γ L is the reflection coefficient at the end of the cable; P is the cable propagation constant factor, P = e -2γl ;γ is the propagation coefficient of the cable; l is the length of the cable;

[0061] The calculation formula of broadband characteristic impedance Z0 is:

[0062]

[0063] In the formula, R0 is the distributed resistance per unit length of the cable; G0 is the distributed conductance per unit length of the cable; j is the imaginary unit; ω is the angular frequency; L0 is the distributed inductance per unit length of the cable; C0 is the distributed capacitance per unit length of the cable; ωL0>>R0, ωC0>>G0;

[0064] The broadband impedance spectrum is subjected to discrete Fourier transform to extract the broadband impedance spectrum characteristic information, and the preliminary cable defect positioning spectrum Lc is obtained. The specific formula is:

[0065] Lc=abs(DFT(Z Im ));

[0066] Where DFT(·) is discrete Fourier transform; Z Im =Im(Z l ), indicating taking Z l The imaginary part of is processed; abs(·) is the modulus value;

[0067] Step 2: Use the DBSCAN algorithm to obtain the clustering results of the preliminary positioning spectrum data points from the data in the cable defect preliminary positioning spectrum, draw the final diagnosis spectrum, and distinguish the defect peak data points from the non-defect segment data points in the final diagnosis spectrum, so as to complete the extraction and positioning of the defect points;

[0068] Preferably, obtaining the clustering results of the preliminary positioning spectrum data points by the DBSCAN algorithm specifically includes: inputting the parameter neighborhood radius eps and the minimum number of points minPts in the neighborhood radius into the DBSCAN algorithm, the DBSCAN algorithm calculates the number of data points in the eps-neighborhood, obtains the positioning spectrum data points, and divides the positioning spectrum data points into core points, boundary points and noise points, selects the core points, connects all points in the eps-neighborhood of each core point to form a cluster, and then classifies the boundary points into the cluster of core points connected to the boundary points, and then marks the points not classified into any cluster as noise points, and obtains the clustering results of the positioning spectrum data points;

[0069] Specifically, the positioning spectrum data points are divided into core points N eps (p) Specifically: if the eps-neighborhood of sample p contains at least minPts number of points, then sample p is a core point, that is:

[0070] N eps (p)≥minPts;

[0071] Where eps is the neighborhood radius; minPts is the minimum number of points within the neighborhood radius;

[0072] The positioning spectrum data points are divided into boundary points as follows: if sample b is not a core point, but is in the eps-neighborhood of a core point, then sample b is a boundary point, that is:

[0073]

[0074] The positioning spectrum data points are divided into noise points as follows: if sample n is neither a core point nor a boundary point, then sample n is a noise point, that is:

[0075]

[0076] The present invention also provides a cable defect location system based on a broadband impedance-clustering algorithm, comprising:

[0077] The preliminary positioning spectrum module is used to calculate the broadband impedance spectrum of the defective cable according to the distribution parameters of the defective cable, perform discrete Fourier transform on the broadband impedance spectrum to extract the characteristic information of the broadband impedance spectrum, and obtain the preliminary positioning spectrum of the cable defect;

[0078] The final diagnostic spectrum module is used to obtain the clustering results of the preliminary positioning spectrum data points by using the DBSCAN algorithm to obtain the data in the cable defect preliminary positioning spectrum and draw the final diagnostic spectrum;

[0079] The defect point positioning module is used to distinguish the defect peak data points and non-defect segment data points in the final diagnosis spectrum, thereby completing the extraction and positioning of the defect points.

[0080] Embodiment 2:

[0081] like Figure 1 As shown, the present invention provides a cable defect location method based on a broadband impedance-clustering algorithm, comprising the following steps:

[0082] Step 1: Principle of cable broadband impedance spectrum positioning: Calculate the broadband impedance spectrum of the defective cable according to the distribution parameters of the defective cable, perform discrete Fourier transform on the broadband impedance spectrum to extract the broadband impedance spectrum feature information, and obtain the preliminary cable defect positioning spectrum;

[0083] According to transmission line theory, when the cable is very long or the signal frequency is high, the cable's signal propagation characteristics need to be described by a distributed parameter model; in this case, the cable can be regarded as a cascade of an infinite number of infinitely small distributed parameter element circuits, such as Fig. 9 As shown, the distributed parameters of the defective cable include the distributed resistance R0 per unit length of the transmission line, the distributed inductance L0 per unit length of the transmission line, the distributed conductance G0 per unit length of the transmission line, and the distributed capacitance C0 per unit length of the transmission line;

[0084] Preferably, R0, L0, G0, and C0 are respectively expressed as:

[0085]

[0086]

[0087]

[0088]

[0089] In formulas (1) to (4), ω is the angular frequency, ω = 2πf; r c is the cable core radius; r s is the inner radius of the metal shielding layer; ρc is the cable core; s is the resistivity of the metal shielding layer; μ0 is the vacuum magnetic permeability; ε is the dielectric constant of the dielectric; σ is the conductivity;

[0090] According to the transmission line theory, the broadband impedance spectrum (BIS) of the cable length l is calculated as:

[0091]

[0092] In formula (5), Z0 is the broadband characteristic impedance; Γ L is the reflection coefficient at the end of the cable; P is the cable propagation constant factor, P = e -2γl ; γ is the propagation coefficient of the cable; l is the length of the cable; they are respectively expressed as:

[0093]

[0094]

[0095]

[0096] In formulas (6) to (8), R0 is the distributed resistance per unit length of the cable; G0 is the distributed conductance per unit length of the cable; j is the imaginary unit; ω is the angular frequency; L0 is the distributed inductance per unit length of the cable; C0 is the distributed capacitance per unit length of the cable; ωL0>>R0, ωC0>>G0; Z L is the load impedance at the end of the cable; α is the attenuation coefficient, which characterizes the attenuation characteristics of the signal; β is the phase coefficient, which satisfies β=2πf / v, characterizing the phase lag characteristics of the signal; v is the propagation speed of the electromagnetic wave in the cable;

[0097] When the cable end is open circuit, i.e., ΓL = 1, expand equation (5) using Euler's formula and only take its imaginary part for analysis, and we get:

[0098]

[0099] In formula (9), Im(Z l ) is the impedance spectrum of the first end Z l Part of the imaginary part, without considering the attenuation factor e -2αl From formula (6), we can know that the frequency component f l =2l / v, which is equivalent to the round-trip propagation time of the signal from the cable head end to the point l away from the head end, so it can be processed to obtain the defect position of the cable; this paper uses Discrete Fourier Transform (DFT) to extract the characteristic information of broadband impedance spectrum and obtain the preliminary positioning spectrum of cable defects Lc:

[0100] Lc=abs(DFT(Z Im ))(10)

[0101] In formula (10), DFT(·) is discrete Fourier transform; Z Im =Im(Z l ), indicating taking Z l The imaginary part of is processed; abs(·) is the modulus value;

[0102] Ideally, the above method has a good effect on the identification of centralized defects such as low-resistance grounding faults. However, in the above detection of cable defects using BIS, the defect is mainly reflected in the peak value in the positioning spectrum. Most of the current defect identification methods rely on manual identification. In actual tests, due to the spectrum leakage problem caused by Fourier transform and the influence of noise on the test site, "pseudo-peaks" will appear in the positioning spectrum. Considering that the tester's judgment on the defect location is relatively subjective, misjudgment is easy to occur, so it is necessary to introduce a new algorithm for improvement;

[0103] Step 2, DBSCAN cable defect identification: The data in the cable defect preliminary positioning spectrum is clustered using the DBSCAN algorithm to obtain the preliminary positioning spectrum data points, and the final diagnostic spectrum is drawn to distinguish the defect peak data points from the non-defect segment data points in the final diagnostic spectrum, thereby completing the extraction and positioning of the defect points;

[0104] Preferably, Figure 2 As shown in the figure, DBSCAN is a representative density-based clustering algorithm. Different from partitioning and hierarchical clustering methods, it defines a cluster as the largest set of density-connected points, can divide areas with sufficiently high density into clusters, and can find clusters of arbitrary shapes in noisy spatial databases.

[0105] Specifically, in the DBSCAN algorithm, density is defined by the number of points in the neighborhood of a given point within a specified radius. The clustering results of the preliminary positioning spectrum data points obtained by the DBSCAN algorithm specifically include: inputting the parameter neighborhood radius eps and the minimum number of points minPts in the neighborhood radius into the DBSCAN algorithm, the DBSCAN algorithm calculates the number of data points in the eps-neighborhood, obtains the positioning spectrum data points, and divides the positioning spectrum data points into core points, boundary points and noise points;

[0106] Define eps appropriately. If the value chosen is too small, a large part of the data will not be clustered. If the value chosen is too large, clusters will merge and many data points will be in the same cluster. Generally speaking, the smaller the eps value, the better. Use minPts to determine whether a point is a core point. Assuming that minPts is set to 4, if there are at least 4 points close to the point, the point can be considered a core point. If there are less than 4 points near a point, the point is considered a non-core point. Generally speaking, minPts ≥ the dimension of the dataset + 1. For noisy datasets, larger values ​​are usually better. The minimum value of minPts must be 3, but the larger the dataset, the larger the minPts value must be.

[0107] Divide the positioning spectrum data points into core points N eps (p) Specifically: if the eps-neighborhood of sample p contains at least minPts number of points, then sample p is a core point, that is:

[0108] N eps (p)≥minPts(11)

[0109] Where eps is the neighborhood radius; minPts is the minimum number of points within the neighborhood radius;

[0110] The positioning spectrum data points are divided into boundary points as follows: if sample b is not a core point, but is in the eps-neighborhood of a core point, then sample b is a boundary point, that is:

[0111]

[0112] The positioning spectrum data points are divided into noise points as follows: if sample n is neither a core point nor a boundary point, then sample n is a noise point, that is:

[0113]

[0114] Then, core points are selected, and all points in the eps-neighborhood of each core point are connected to form a cluster. The boundary points are then classified into the cluster of core points connected to the boundary points. Points that are not classified into any cluster are marked as noise points, and the clustering results of the positioning spectrum data points are obtained.

[0115] Since the BIS method uses the DFT algorithm, the preliminary positioning spectrum data points are evenly distributed on the horizontal axis scale, but the differences on the vertical axis scale are obvious - the distribution difference of the defective segment data points on the vertical axis scale is much greater than that of the non-defective segment. It is necessary to introduce an algorithm that can automatically determine the distribution difference of data points, so as to separate the defective segment and the non-defective segment. Since the difference of data points on the vertical axis is actually manifested as the difference of data point density on the vertical axis scale, the DBSCAN algorithm is considered to be used to solve it.

[0116] For a 1000m simulated cable, a high-resistance fault is set at 700m. The simulation results are as follows: Figure 3 As shown, it can be seen that the defect peak of the high-resistance fault is not obvious, but the DBSCAN algorithm can automatically cluster according to the density of the original data points, separate the defect segment data cluster and the non-defect segment data cluster, and successfully detect the high-resistance fault data cluster distributed at about 700m. The enlarged diagnostic spectrum can be verified (data points of different colors represent different types of data clusters), which shows that this method can be applied to cable defect identification.

[0117] Embodiment 3:

[0118] The signal transmission characteristics of the coaxial cable are similar to those of the power cable. To verify the effectiveness of the method of the present invention, a SYV50-3-4 radio frequency cable with a total length of 1000 m was tested in the laboratory.

[0119] During the experiment, the sheath and insulation layer of the RF cable were stripped, and the transition resistor was connected between the core and the shielding layer to simulate a high-resistance fault. The sweep frequency range was set to 300kHz-50MHz, and the number of scan points was 50001. The coaxial cable sample was tested using the BIS-DBSCAN joint positioning method provided by the present invention. The results are as follows: Figure 4 , Figure 5 and Figure 6 As shown in Figure 1, cables 1, 2, and 3 are cables with single-point defects at 300m, 500m, and 700m, respectively, indicating that this method can identify single-point defects at different locations on the same cable; Figure 7 As shown, cable No. 4 is a cable with multiple point defects at 300m and 700m, indicating that this method can identify multiple point defects occurring on the same cable.

[0120] In summary, the cable defect location method based on broadband impedance-clustering algorithm provided by the present invention can effectively locate concentrated weak defects such as single-point and multi-point high-resistance faults in cables, and can improve the recognition of weak defects in cables to reduce the possibility of misjudgment caused by human judgment.

[0121] Embodiment 4:

[0122] As shown in FIG8 , the present invention discloses an implementation platform of a cable defect location method based on a broadband impedance-clustering algorithm, and the working process of the platform is as follows:

[0123] The computer assigns signal parameters and inputs them into the signal generator;

[0124] The actual signal generated by the signal generator is used as the incident signal and enters the cable under test through the directional coupler;

[0125] The reflected signal generated by the cable under test enters the oscilloscope through the directional coupler and is collected;

[0126] The oscilloscope transmits the collected incident and reflected signals to the computer;

[0127] The computer processes the incident signal and the reflected signal, calculates the impedance spectrum of the first end according to the formula, and uses the BIS method to obtain the preliminary positioning spectrum of the cable defect;

[0128] The computer processes the positioning spectrum data using the DBSCAN algorithm and draws the final diagnostic spectrum;

[0129] Local defects can be located by detecting the mutation position of the diagnostic function value in the final diagnostic spectrum and the category of the data points after clustering processing.

[0130] Embodiment 5:

[0131] like Fig.10 As shown, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium; the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the cable defect location method based on the broadband impedance-clustering algorithm.

[0132] Embodiment 6:

[0133] The present invention also provides a computer-readable storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and extended storage media supported by the computer device; the computer-readable storage medium provides a storage space, which stores the operating system of the terminal; and one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory; the processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the cable defect location method based on the broadband impedance-clustering algorithm in the embodiment.

[0134] Those skilled in the art should understand that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects; moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0135] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as the combination of the processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0136] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0137] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cable defect location method based on broadband impedance-clustering algorithm, characterized in that: The following steps are involved: S1, calculate the broadband impedance spectrum of the defective cable according to the distribution parameters of the defective cable, perform discrete Fourier transform on the broadband impedance spectrum to extract the characteristic information of the broadband impedance spectrum, and obtain the preliminary positioning spectrum of the cable defect; S2, the data in the cable defect preliminary positioning spectrum is obtained through the DBSCAN algorithm to obtain the clustering results of the preliminary positioning spectrum data points, and the final diagnostic spectrum is drawn to distinguish the defect peak data points and the non-defect segment data points in the final diagnostic spectrum, so as to complete the extraction and positioning of the defect points.

2. A cable defect location method based on broadband impedance-clustering algorithm according to claim 1, characterized in that: The S1 specifically includes: According to the distribution parameters of the defective cable, the broadband impedance spectrum of the defective cable is calculated by transmission line theory. The specific calculation formula is: Where Z0 is the broadband characteristic impedance; Γ L is the reflection coefficient at the end of the cable; P is the cable propagation constant factor, P = e -2γl ;γ is the propagation coefficient of the cable; l is the length of the cable; The broadband impedance spectrum is subjected to discrete Fourier transform to extract the broadband impedance spectrum characteristic information, and the preliminary cable defect positioning spectrum Lc is obtained. The specific formula is: Lc=abs(DFT(Z Im )); Where DFT(·) is discrete Fourier transform; Z Im =Im(Z l ), indicating taking Z l The imaginary part of is processed; abs(·) is the modulus value.

3. A cable defect location method based on broadband impedance-clustering algorithm according to claim 2, characterized in that: The calculation formula of the broadband characteristic impedance Z0 is: In the formula, R0 is the distributed resistance per unit length of the cable; G0 is the distributed conductance per unit length of the cable; j is the imaginary unit; ω is the angular frequency; L0 is the distributed inductance per unit length of the cable; C0 is the distributed capacitance per unit length of the cable; ωL0>>R0, ωC0>>G0.

4. A cable defect location method based on broadband impedance-clustering algorithm according to claim 1, characterized in that: The clustering results of the preliminary positioning spectrum data points obtained by the DBSCAN algorithm in S2 specifically include: inputting the parameter neighborhood radius eps and the minimum number of points minPts within the neighborhood radius into the DBSCAN algorithm, the DBSCAN algorithm calculates the number of data points within the eps-neighborhood, obtains the positioning spectrum data points, and divides the positioning spectrum data points into core points, boundary points and noise points, selects the core points, connects all the points within the eps-neighborhood of each core point to form a cluster, and then classifies the boundary points into the cluster of core points connected to the boundary points, and then marks the points that are not classified into any cluster as noise points, to obtain the clustering results of the positioning spectrum data points.

5. A cable defect location method based on broadband impedance-clustering algorithm according to claim 4, characterized in that: The positioning spectrum data points are divided into core points N eps (p) Specifically: if the eps-neighborhood of sample p contains at least minPts number of points, then sample p is a core point, that is: N eps (p)≥minPts; Where eps is the neighborhood radius; minPts is the minimum number of points within the neighborhood radius.

6. A cable defect location method based on broadband impedance-clustering algorithm according to claim 4, characterized in that: The method of dividing the positioning spectrum data points into boundary points is as follows: if sample b is not a core point but is within the eps-neighborhood of a core point, then sample b is a boundary point, that is:

7. A cable defect location method based on broadband impedance-clustering algorithm according to claim 4, characterized in that: The method of dividing the positioning spectrum data points into noise points is as follows: if sample n is neither a core point nor a boundary point, then sample n is a noise point, that is:

8. A cable defect location system based on broadband impedance-clustering algorithm, characterized in that: include: The preliminary positioning spectrum module is used to calculate the broadband impedance spectrum of the defective cable according to the distribution parameters of the defective cable, perform discrete Fourier transform on the broadband impedance spectrum to extract the characteristic information of the broadband impedance spectrum, and obtain the preliminary positioning spectrum of the cable defect; The final diagnostic spectrum module is used to obtain the clustering results of the preliminary positioning spectrum data points by using the DBSCAN algorithm to obtain the data in the cable defect preliminary positioning spectrum and draw the final diagnostic spectrum; The defect point positioning module is used to distinguish the defect peak data points and non-defect segment data points in the final diagnosis spectrum, thereby completing the extraction and positioning of the defect points.

9. A computer 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 steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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