Cable defect detection method and apparatus

Abstract

The application discloses a cable defect detection method and device, and relates to the field of power equipment fault detection. The method comprises the following steps: inputting an electric signal of N different frequencies into a target cable in a preset frequency range; testing the input impedance of the target cable under the electric signal of N different frequencies, so as to obtain the broadband impedance of the target cable in the frequency range; generating the impedance amplitude and the impedance phase of the target cable at each preset frequency point according to the broadband impedance, and constructing an impedance matrix; inputting the impedance matrix into a fault classification model, identifying and outputting the category of the target cable through the fault classification model; if the target cable is faulty, extracting the resonance frequency with the maximum amplitude in the broadband impedance amplitude spectrum of the target cable, and determining the fault position of the target cable through a fault positioning model according to the resonance frequency with the maximum amplitude and the typical wave speed of the target cable. The application realizes automatic, rapid and accurate identification of the fault of the target cable and positioning of the fault position.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power equipment fault detection, and particularly relates to a cable defect detection method and device. BACKGROUND

[0002] Power cables often have partial defects due to long-term use or external environmental influences. These defects can cause the performance of power cables to decline, and in severe cases, can cause power system failures. Therefore, it is particularly important to accurately and quickly detect and locate partial defects in power cables.

[0003] In existing fault detection methods, the direct current method determines short circuits or overloads by detecting whether the current exceeds a threshold value, but cannot locate the fault position; the direct current test (such as insulation resistance test) method can only determine whether there is a leakage in a certain section of line, and also cannot locate the fault position. SUMMARY

[0004] The purpose of the present application is to provide a cable defect detection method and device to solve the problem that existing fault detection methods cannot locate the fault position.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] In a first aspect, the present application provides a cable partial defect detection method, comprising:

[0007] generating N different frequency electrical signals in a preset frequency range and inputting the target cable, testing the input impedance of the target cable under N different frequency electrical signals respectively, and obtaining the broadband impedance of the target cable in the frequency range;

[0008] According to the broadband impedance of the target cable, the impedance amplitude and impedance phase of the target cable at each preset frequency point are generated, and an impedance matrix X ∈ R N×2 is constructed.

[0009] The impedance matrix X is input into the constructed fault classification model, and the category of the target cable is identified and output by the fault classification model; wherein the category includes fault or normal;

[0010] If the target cable is faulty, the resonance frequency with the maximum amplitude in the broadband impedance amplitude spectrum of the target cable is extracted, and the fault position of the target cable is determined by the constructed fault positioning model according to the resonance frequency with the maximum amplitude and the typical wave speed of the target cable.

[0011] Optionally, the fault classification model is constructed according to the following steps:

[0012] Construct a training data set:

[0013] According to the constructed cable distribution parameter equivalent model, the transmission line equation is established;

[0014] Determine an electrical parameter calculation formula at any frequency based on the transmission line equation, and calculate the electrical parameters per unit length of each sample cable at any frequency using the electrical parameter calculation formula;

[0015] Correcting at least one electrical parameter per unit length of each sample cable at any frequency according to a preset correction rule to obtain the electrical parameter per unit length of each sample cable at any frequency of each fault mode;

[0016] Within a preset frequency range, N frequency points are set for each sample cable according to the preset frequency range and frequency scanning step, and the input impedance of the head end of each sample cable under each fault mode and each frequency point in the normal mode is calculated according to the electrical parameters per unit length of each sample cable under any frequency in the normal mode;

[0017] Based on the input impedance of the head end of each sample cable at each frequency point in each fault mode and normal mode, the impedance amplitude and impedance phase of the head end of each sample cable at each frequency point in each fault mode and normal mode are generated, and broadband impedance spectrum data of each sample cable at N frequency points in each fault mode and normal mode are obtained to form a training data set;

[0018] According to the training data set, a CNN-based classification model is trained to obtain the fault classification model.

[0019] Optionally, the correction rule includes a first correction rule, a second correction rule and a third correction rule, wherein:

[0020] The first amendment rule includes:

[0021] R fault = R k R , k R ∈[0.001,0.1];

[0022] The second revised rule includes:

[0023] C fault = C k C , k C ∈[0.2,0.5];

[0024] The third revised rule includes:

[0025] G aging = G k σ , k σ ∈[10,1000];

[0026] Among them, R fault is the corrected resistance per unit length, C fault is the corrected capacitance per unit length, G aging is the corrected conductance per unit length, R represents the resistance per unit length, G It represents the insulation conductivity per unit length, C Represents capacitance per unit length, k R is the resistance correction factor, k C is the capacitance correction factor, k σ is the conductance correction factor.

[0027] Optionally, the failure mode includes a short circuit failure, a partial discharge failure, and an insulation aging failure;

[0028] The step of correcting at least one electrical parameter per unit length of each sample cable at any frequency according to a preset correction rule to obtain the electrical parameter per unit length of each sample cable at any frequency of each fault mode specifically includes:

[0029] Correcting the resistance per unit length of each of the sample cables using the first correction rule while maintaining the inductance, conductance, and capacitance per unit length of each of the sample cables unchanged, thereby obtaining electrical parameters per unit length of each of the sample cables at any frequency of a short-circuit fault;

[0030] Correcting the capacitance per unit length of each of the sample cables using the second correction rule while keeping the inductance, conductance, and resistance per unit length of each of the sample cables unchanged, thereby obtaining electrical parameters per unit length of each of the sample cables at any frequency of a partial discharge fault;

[0031] The conductance per unit length of each of the sample cables is corrected by the third correction rule, while the inductance, capacitance, and resistance per unit length of each of the sample cables are kept unchanged, thereby obtaining the electrical parameters per unit length of each of the sample cables at any frequency of insulation aging failure.

[0032] Optionally, the step of constructing a training data set further includes:

[0033] Before correcting at least one electrical parameter per unit length of each sample cable at any frequency according to a preset correction rule, each sample cable is divided into M segments from the head end to the terminal end of each sample cable;

[0034] Correcting the resistance per unit length of each of the sample cables by using the first correction rule includes:

[0035] Starting from different segments of the M segments of each sample cable, the same k R And the resistance per unit length of the subsequent segments is corrected by the first correction rule to simulate short circuit faults at different locations; and / or, starting from the same segment of the M segments of each sample cable, different k R and correcting the resistance per unit length of the subsequent segments using the first correction rule to simulate short-circuit faults of different short-circuit degrees;

[0036] Maintaining the capacitance, inductance, and conductance per unit length of each section of each sample cable and the resistance per unit length of other sections unchanged;

[0037] The second correction rule is used to correct the capacitance per unit length of each sample cable, including:

[0038] The capacitance per unit length of each sample cable is measured using different k C And the correction is performed by the second correction rule to simulate partial discharge faults with different air gap sizes;

[0039] The conductance per unit length of each sample cable is corrected by the third correction rule, including:

[0040] The conductance per unit length of each sample cable is measured using different k σ The second correction rule is used to perform correction to simulate insulation aging faults with different insulation aging degrees.

[0041] Optionally, calculating the input impedance of the head end of each sample cable at each frequency point in each fault mode and normal mode according to the electrical parameters per unit length of each sample cable at any frequency in each fault mode and normal mode specifically includes:

[0042] According to the following formula, starting from the Mth segment of each sample cable at each frequency point in each fault mode or normal mode, the input impedance of the first segment of each sample cable at each frequency point in each fault mode and normal mode is calculated recursively from back to front. :

[0043] ;

[0044] ;

[0045] ;

[0046] in, The first frequency of each sample cable under any fault mode or normal mode i The input impedance of the segment, The first frequency of each sample cable under any fault mode or normal mode i The characteristic impedance of the segment, The first frequency of each sample cable under any fault mode or normal mode i +1 segment input impedance, The first frequency of each sample cable under any fault mode or normal mode i The propagation constant of the segment, is the length of each section of the sample cable, 、 、 、 Corresponding to the first frequency point of each sample cable in any fault mode or normal mode i The resistance, inductance, conductance and capacitance of the segment, , is the imaginary unit, represents the angular frequency, ω = 2 πf , f Indicates the frequency of any frequency point.

[0047] Optionally, the fault classification model includes a CNN network and a fault classifier, wherein:

[0048] The fault classifier is used to identify the category of the target cable according to the input overall feature vector, the category includes fault or normal, and the fault includes short circuit fault, partial discharge fault, and insulation aging fault;

[0049] The CNN network includes:

[0050] The first convolutional layer is used to extract local features of the impedance matrix X to obtain local frequency domain features;

[0051] A maximum pooling layer is used to reduce the dimension of the local frequency domain features to obtain the local frequency domain features after dimensionality reduction;

[0052] The second convolutional layer is used to extract deep features of the local frequency domain features after dimensionality reduction to obtain deep frequency domain features;

[0053] The global average pooling layer is used to perform a global average pooling operation on the deep frequency domain features and output the overall feature vector.

[0054] Optionally, the fault classifier includes:

[0055] a first fault classifier configured to identify a category of the target cable as a short-circuit fault or normal according to the overall feature vector;

[0056] a second fault classifier configured to identify a category of the target cable as a partial discharge fault or normal according to the overall feature vector;

[0057] a third fault classifier configured to identify a category of the target cable as an insulation aging fault or normal according to the overall feature vector.

[0058] Optionally, the fault locating model comprises:

[0059] ;

[0060] wherein, is a fault distance, is a typical wave speed preset according to a cable type, is a resonance frequency with a maximum amplitude in a broadband impedance amplitude spectrum of the target cable.

[0061] In a second aspect, the present application provides a cable partial defect detection device, comprising:

[0062] an impedance test module configured to generate electrical signals of N different frequencies into the target cable in a preset frequency range, test input impedance of the target cable under electrical signals of N different frequencies respectively, and obtain a broadband impedance of the target cable in the frequency range;

[0063] an impedance matrix construction module configured to generate impedance amplitude and impedance phase of the target cable at each preset frequency point according to the broadband impedance of the target cable, and construct an impedance matrix X e R N×2 ;

[0064] a fault identification module configured to input the impedance matrix X into a constructed fault classification model, identify and output a category of the target cable through the fault classification model, wherein the category comprises a fault or normal;

[0065] a fault locating module configured to extract a resonance frequency with a maximum amplitude in a broadband impedance amplitude spectrum of the target cable if the target cable is faulty, and determine a fault position of the target cable through a constructed fault locating model according to the resonance frequency with the maximum amplitude and a typical wave speed of the target cable.

[0066] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0067] The application provides a cable partial defect detection method and device, the broadband impedance of a target cable is obtained through testing, the impedance amplitude and impedance phase of the target cable at each frequency point are generated according to the broadband impedance of the target cable, and then an impedance matrix is constructed to comprehensively reflect the electrical characteristics of different fault types; the impedance matrix X is input into the constructed fault classification model, the fault classification model is combined with the impedance matrix X to identify and output the fault category of the target cable, and the fault of the target cable is automatically identified; if the target cable is faulty, the resonance frequency with the maximum amplitude in the broadband impedance amplitude spectrum of the target cable is extracted, and the fault location of the target cable is determined through the constructed fault positioning model according to the resonance frequency with the maximum amplitude and the typical wave speed of the target cable, the fault position of the target cable is positioned, and the problem that the existing fault detection method cannot position the fault position is solved. In addition, the fault of the target cable is automatically identified through the fault classification model, compared with the prior art, the fault type and fault position of the target cable do not need to be determined by artificial dependence on expert experience, and the cable partial defect detection efficiency is effectively improved; meanwhile, the impedance matrix X contains multi-dimensional frequency impedance information (impedance amplitude + impedance phase), and can comprehensively reflect the electrical characteristics of different fault types, therefore, the fault category of the target cable can be identified through the fault classification model combined with the impedance matrix X, and the accuracy of identifying the fault category of the target cable is improved; in conclusion, the application realizes automatic, rapid and accurate identification of the fault of the target cable and positioning of the fault position. BRIEF DESCRIPTION OF DRAWINGS

[0068] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description only constitute some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0069] Figure 1 A flowchart of a cable partial defect detection method provided by an embodiment of the application;

[0070] Figure 2 A schematic diagram of a cable distributed parameter equivalent model provided by an embodiment of the application;

[0071] Figure 3 A structural schematic diagram of a fault classification model provided by another embodiment of the application;

[0072] Figure 4 A functional module schematic diagram of a cable partial defect detection device provided by an embodiment of the application;

[0073] Figure 5 A structural schematic diagram of a computer device provided by an embodiment of the application. DETAILED DESCRIPTION

[0074] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0075] The above purposes, features and advantages of the present application will be more obvious and easy to understand. The present application will be described in further detail below with reference to the drawings and specific embodiments.

[0076] In one exemplary embodiment, as shown in Figure 1 , a cable partial defect detection method is provided, which is executed by a computer device and includes the following steps 101 to 104. Wherein:

[0077] Step 101, generating N different frequency electrical signals to input a target cable in a preset frequency range, testing the input impedance of the target cable under the N different frequency electrical signals respectively, and obtaining the broadband impedance of the target cable in the frequency range.

[0078] In the embodiments of the present application, the target cable refers to the cable to be detected. The network analyzer is used to perform frequency sweep test on the target cable in the set frequency range, and the complex input impedance of the target cable at each frequency point is measured. The broadband impedance of the target cable in the frequency range includes the input impedance of the target cable under N different frequency electrical signals respectively.

[0079] The number N of frequency points is determined by the preset frequency range and the step size. If the lower limit value of the preset frequency range is f min , the upper limit value of the frequency range is f max , and the preset step size is Δf, then f min is the frequency value of the first frequency point, N is the result of (f max -f min ) / Δf, and the frequency of the second frequency point is f min +Δf, and so on. The frequency value of the next frequency point is obtained by moving Δf based on the frequency value of the previous frequency point, until the frequency value f min +(N-1)Δf of the next frequency point is not less than f max . At this time, f max is taken as the frequency value of the last frequency point. For example, if f min =0Hz, f max =10MHz, and Δf=1kHz, then N=1000. N determines the dimension and precision of the impedance matrix.

[0080] Step 102, according to the broadband impedance of the target cable, the impedance amplitude and the impedance phase of the target cable at each frequency point are generated, and an impedance matrix X is constructed.

[0081] In the embodiments of the present application, X ∈ R N×2 Each row of the impedance matrix corresponds to the impedance amplitude and the impedance phase of the corresponding frequency point. The impedance matrix X contains multi-dimensional frequency impedance information (impedance amplitude + impedance phase), which can comprehensively reflect the electrical characteristics of different fault types.

[0082] Step 103, input the impedance matrix X into the constructed fault classification model, and identify and output the fault category of the target cable through the fault classification model.

[0083] In the embodiments of the present application, the impedance matrix X is used in combination with the fault classification model to identify different faults, which effectively improves the identification ability and generalization ability of the fault classification model for different faults.

[0084] Step 104, if the target cable is faulty, the resonance frequency with the maximum amplitude in the broadband impedance amplitude spectrum of the target cable is extracted, and according to the extracted resonance frequency with the maximum amplitude and the typical wave speed of the target cable, the fault location of the target cable is determined through the constructed fault positioning model.

[0085] In the embodiments of the present application, the impedance amplitudes of the target cable at each frequency point are arranged according to the frequency, that is, the broadband impedance amplitude spectrum is formed. The typical wave speed of the target cable is determined according to the type of the target cable. Specifically, the wave speed of the target cable is determined by the dielectric constant of the insulating material, and the typical wave speed value of the target cable can be obtained by looking up the table or experience.

[0086] By implementing the above steps 101 to 104, the broadband impedance of the target cable is obtained through testing, and the impedance amplitude and impedance phase of the target cable at each frequency point are generated based on the broadband impedance of the target cable, thereby constructing an impedance matrix to comprehensively reflect the electrical characteristics of different fault types. By inputting the impedance matrix X into the constructed fault classification model, the fault classification model is combined with the impedance matrix X to identify and output the fault type of the target cable, thereby automatically identifying the fault of the target cable. If the target cable is faulty, the resonant frequency with the maximum amplitude in the broadband impedance amplitude spectrum of the target cable is extracted, and the fault location model is constructed based on the resonant frequency with the maximum amplitude and the typical wave velocity of the target cable, thereby determining the fault location of the target cable. This achieves fault location of the target cable, solving the problem that existing fault detection methods cannot locate the fault location. In addition, the fault classification model is used to automatically identify the faults of the target cable. Compared with the existing technology, there is no need to manually rely on expert experience to determine the fault type and fault location of the target cable, which effectively improves the efficiency of local cable defect detection. At the same time, because the impedance matrix X contains multi-dimensional frequency impedance information (impedance amplitude + impedance phase), it can fully reflect the electrical characteristics of different fault types. Therefore, the fault classification model combined with the impedance matrix X can improve the accuracy of identifying the fault type of the target cable. In summary, the present application realizes the automatic, rapid and accurate identification of faults in the target cable and the location of the fault.

[0087] In another exemplary embodiment of the present application, the above-mentioned fault classification model is constructed according to the following steps 201 to 202. In which:

[0088] Step 201: construct a training data set.

[0089] In the embodiment of the present application, a training data set is constructed according to the following steps 21 to 225. In which:

[0090] Step 2021: Establish a transmission line equation based on the constructed cable distribution parameter equivalent model.

[0091] In the embodiment of the present application, the cable distribution parameter equivalent model constructed is as follows Figure 2 As shown, it is an equivalent circuit unit based on transmission line theory. Figure 2 In, Δ l Indicates unit length (micro cable length), I ( x ) indicates that the x The current at V ( x ) indicates that the x Voltage at position x is the starting point of the unit length cable, Δ I Represents the current change per unit length, ΔV It represents the voltage change per unit length. R Indicates the resistance per unit length (Ω / m), L Indicates the inductance per unit length (H / m), G Indicates the insulation conductivity per unit length (S / m), C Indicates capacitance per unit length (F / m).

[0092] according to Figure 2 , the transmission line equations constructed include:

[0093] ;

[0094] ;

[0095] in, represents the imaginary unit, represents the angular frequency, ω = 2 πf , f Indicates frequency.

[0096] Step 2022: Determine an electrical parameter calculation formula at any frequency based on the transmission line equation, and calculate the electrical parameters per unit length of each sample cable at any frequency based on the electrical parameter calculation formula.

[0097] In the embodiments of the present application, the aforementioned electrical parameters include resistance, inductance, capacitance, and conductance. The sample cables are fault-free cables. Therefore, the calculated electrical parameters per unit length of each sample cable at any frequency are the electrical parameters per unit length of the sample cable at any frequency in normal mode.

[0098] Step 2023: According to a preset correction rule, at least one electrical parameter per unit length of each sample cable at any frequency is corrected to obtain the electrical parameter per unit length of each sample cable at any frequency of each fault mode.

[0099] In step 2024, within a preset frequency range, N frequency points are set for the sample cables according to the preset frequency range and the frequency scanning step Δf. Based on the electrical parameters per unit length of each sample cable at any frequency point in each fault mode and the normal mode, the input impedance of the head end of each sample cable at each frequency point in each fault mode and the normal mode is calculated.

[0100] At step 2025, according to the input impedance of each sample cable at the head end at each frequency point in each fault mode and the normal mode, the impedance amplitude and the impedance phase of each sample cable at the head end at each frequency point in each fault mode and the normal mode are generated, the broadband impedance spectrum data of each sample cable at N frequency points in each fault mode and the normal mode is obtained, and a training data set is formed.

[0101] In the embodiments of the present application, the broadband impedance spectrum data of each sample cable at N frequency points in any fault mode or the normal mode has a size of N x 2, N rows correspond to N frequency points, and each row of each broadband impedance spectrum data is the impedance amplitude and the impedance phase of the sample cable at the corresponding frequency point in any fault mode or the normal mode.

[0102] The data amount of the training data set is not specifically limited, and can be set by actual needs. For example, 500 pieces of short-circuit fault data of different positions, 500 pieces of partial discharge fault data of different air gap sizes, 500 pieces of aging fault data, and 500 pieces of normal mode data are set by using multiple sample cables.

[0103] At step 202, a classification model based on CNN is trained according to the training data set constructed, and a fault classification model is obtained.

[0104] In the embodiments of the present application, the classification model based on CNN includes a CNN network and a classifier. The CNN network is used for feature extraction of the impedance matrix of the input sample cable in a certain fault mode or the normal mode, and the classifier is used for predicting the category of the input according to the features extracted by the CNN network. In the training process, the training data set is divided into a training set and a test set, each broadband impedance spectrum data in the training set is input into the classification model based on CNN, the classification model based on CNN predicts the category label corresponding to each broadband impedance spectrum data, and according to the predicted category label and the real category label, the hyperparameters are continuously adjusted to make the prediction performance of the classification model based on CNN meet the requirements. Finally, the classification model based on CNN trained is tested by the test set, and when the test result does not meet the requirements, the hyperparameters of the classification model based on CNN are continuously adjusted until the prediction performance of the classification model based on CNN meets the requirements, that is, the fault classification model is obtained.

[0105] In another exemplary embodiment of the present application, according to the transmission line equation, the electrical parameter calculation formula obtained includes:

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] in, is the vacuum permeability, represents the angular frequency, ω = 2 πf , f Indicates frequency, is the core radius of the sample cable, is the inner radius of the shielding layer of the sample cable, is the core resistivity of the sample cable, is the shield resistivity of the sample cable, is the dielectric constant of the shielding layer of the sample cable, is the core conductivity of the sample cable.

[0111] In another exemplary embodiment of the present application, in the above step 2023, the correction rule includes a first correction rule, a second correction rule, and a third correction rule, wherein:

[0112] The First Amendment rules include:

[0113] R fault = R k R , k R ∈[0.001,0.1];

[0114] The second amendment rules include:

[0115] C fault = C k C , k C ∈[0.2,0.5];

[0116] The third amendment rules include:

[0117] G aging = G k σ , k σ ∈[10,1000];

[0118] Among them, R fault is the corrected resistance per unit length, C fault is the corrected capacitance per unit length, G aging is the corrected conductance per unit length, k R is the resistance correction coefficient, k C is the capacitance correction factor, k σ is the conductance correction factor.

[0119] In another example embodiment of the present application, the fault modes in step 2023 include short-circuit fault, partial discharge fault and insulation aging fault.

[0120] Accordingly, step 2023 includes:

[0121] By the first correction rule, the resistance per unit length of each sample cable is corrected, and the inductance, conductance and capacitance per unit length of each sample cable are kept unchanged, to obtain the electrical parameters per unit length of each sample cable at any frequency under short-circuit fault;

[0122] By the second correction rule, the capacitance per unit length of each sample cable is corrected, and the inductance, conductance and resistance per unit length of each sample cable are kept unchanged, to obtain the electrical parameters per unit length of each sample cable at any frequency under partial discharge fault;

[0123] By the third correction rule, the conductance per unit length of each sample cable is corrected, and the inductance, capacitance and resistance per unit length of each sample cable are kept unchanged, to obtain the electrical parameters per unit length of each sample cable at any frequency under insulation aging fault.

[0124] In the embodiment of the present application, based on the design of fault physical mechanism and characteristic separation, only one dominant electrical parameter is corrected for each fault mode.

[0125] In another example embodiment of the present application, step 201 further includes:

[0126] Before step 2023, each sample cable is divided into M segments from the head end to the terminal end of each sample cable.

[0127] The short-circuit fault includes short-circuit faults of different positions and / or short-circuit degrees, the partial discharge fault includes partial discharge faults of different air gap sizes, and the insulation aging fault includes insulation aging faults of different insulation aging degrees.

[0128] At this time, by the first correction rule, the resistance per unit length of each sample cable is corrected, and the inductance, conductance and capacitance per unit length of each sample cable are kept unchanged, including:

[0129] Starting from different segments of the M segments of each sample cable, the same k R and the resistance per unit length of the subsequent segment is corrected by the first correction rule to simulate short-circuit faults of different positions; and / or, starting from the same segment of the M segments of each sample cable, different k R and the resistance per unit length of the subsequent segment is corrected by the first correction rule to simulate short-circuit faults of different short-circuit degrees;

[0130] The capacitance, inductance, and conductance per unit length of each cable segment and the resistance per unit length of the other segments were kept constant for each sample cable.

[0131] The capacitance per unit length of each sample cable is corrected by the second correction rule, while the inductance, conductance, and resistance per unit length of each sample cable are kept unchanged, including:

[0132] The capacitance per unit length of each sample cable is measured using different k C (k C The larger the value, the smaller the partial discharge fault) and the second correction rule is used to simulate partial discharge faults with different air gap sizes.

[0133] The conductance per unit length of each sample cable is corrected using the third correction rule, including:

[0134] The conductance per unit length of each sample cable is measured using different k σ (The greater the insulation aging, the greater the k σ The larger the value, the better) and corrected by the second correction rule to simulate insulation aging faults with different insulation aging degrees.

[0135] In another exemplary embodiment of the present application, in the above step 2024, the input impedance of the head end of each sample cable at each frequency point in each fault mode and normal mode is calculated based on the electrical parameters per unit length of each sample cable at any frequency in each fault mode and normal mode, including:

[0136] According to the following formula, starting from the Mth segment of each sample cable at each frequency point in each fault mode or normal mode, recursively calculate the input impedance of the first segment of each sample cable at each frequency point in each fault mode and normal mode from back to front: :

[0137] ;

[0138] ;

[0139] ;

[0140] in, The first frequency of each sample cable under any fault mode or normal mode i The input impedance of the segment, The first frequency of each sample cable under any fault mode or normal mode i The characteristic impedance of the segment, The first frequency of each sample cable under any fault mode or normal mode i +1 segment input impedance, The first frequency of each sample cable under any fault mode or normal mode i The propagation constant of the segment, is the length of each section of the sample cable, = l / N, l is the total length of the sample cable; 、 、 、 The corresponding value is the first frequency point of each sample cable under any fault mode or normal mode. i The resistance, inductance, conductance and capacitance of the segment, .

[0141] In the embodiment of the present application, the terminal impedance Z of each sample cable is initialized. L =∞ (indicates that the far end of the sample cable is open).

[0142] In another exemplary embodiment of the present application, it can be seen from the above that the fault classification model includes a CNN network and a fault classifier, wherein the fault classifier is used to identify the category of the target cable based on the input overall feature vector, the category includes fault or normal, and the fault includes short circuit fault, partial discharge fault, and insulation aging fault.

[0143] like Figure 3 As shown, the CNN network includes:

[0144] The first convolutional layer is used to extract local features of the impedance matrix X and obtain local frequency domain features;

[0145] The maximum pooling layer is used to reduce the dimension of the local frequency domain features and obtain the local frequency domain features after dimensionality reduction;

[0146] The second convolutional layer is used to extract the deep features of the local frequency domain features after dimensionality reduction to obtain deep frequency domain features;

[0147] The global average pooling layer is used to perform global average pooling operations on deep frequency domain features and output the overall feature vector.

[0148] In another exemplary embodiment of the present application, the first convolutional layer has 64 filters with a kernel length of 5, a step size of 1, and an activation function of ReLU. The window size of the maximum pooling layer is 2, and the step size is 2. The second convolutional layer uses 128 filters with a kernel length of 3, a step size of 1, and an activation function of ReLU to capture deep frequency domain features (finer granularity, variation features across multiple frequency points, such as local frequency perturbations, weak resonances, etc., which help identify complex fault modes). The global average pooling layer outputs a 128-dimensional feature vector h, using a fully connected approach with a total of 128 neurons, and the activation function uses ReLU for subsequent fault classification.

[0149] In another example embodiment of the present application, the fault classifier comprises:

[0150] a first fault classifier configured to identify the category of the target cable as short-circuit fault or normal according to the overall feature vector output by the global average pooling layer;

[0151] a second fault classifier configured to identify the category of the target cable as partial discharge fault or normal according to the overall feature vector output by the global average pooling layer;

[0152] a third fault classifier configured to identify the category of the target cable as insulation aging fault or normal according to the overall feature vector output by the global average pooling layer.

[0153] In the example embodiment of the present application, the overall feature vector output by the global average pooling layer is simultaneously input to the first fault classifier, the second fault classifier and the third fault classifier, and the short-circuit fault, the partial discharge fault and the insulation aging fault are identified by the first fault classifier, the second fault classifier and the third fault classifier. If the cable has a short-circuit fault, the categories output by the second fault classifier and the third fault classifier are both normal, and only the category output by the first fault classifier is fault. If the cable has a partial discharge fault, the categories output by the first fault classifier and the third fault classifier are both normal, and only the category output by the second fault classifier is fault. If the cable has an insulation aging fault, the categories output by the first fault classifier and the second fault classifier are both normal, and only the category output by the third fault classifier is fault.

[0154] In another example embodiment of the present application, the first fault classifier, the second fault classifier and the third fault classifier adopt SVM (Support Vector Machine) classifier.

[0155] In the example embodiment of the present application, the kernel function of the SVM classifier adopts Gaussian kernel function. Compared with other classifiers, the SVM classifier effectively improves the nonlinear processing capability and the calculation speed.

[0156] The CNN can automatically learn effective features from the original impedance spectrum without manually designing feature extraction rules (such as selecting frequency points, calculating gradients, etc.), avoiding the construction of complex feature engineering. The SVM classifies based on the high-dimensional features extracted by the CNN network, without relying on artificially defined discriminant boundaries, thus reducing the dependence on prior knowledge and artificial experience. Therefore, by combining the CNN network with the SVM classifier, the construction of feature engineering is reduced, and the dependence on prior knowledge is reduced.

[0157] In another example embodiment of the present application, the fault classification model outputs the confidence of the fault category of the target cable.

[0158] In the embodiments of the present application, the confidence is usually calculated based on the decision distance output by the SVM. The greater the distance, the more confident the fault classification model is that the prediction is correct, i.e., the closer to the center of a certain class, the higher the confidence. The decision distance output by the SVM is normalized to a probability type confidence of 0~1 through a sigmoid function, which is used for multi-class comparison and reliability evaluation.

[0159] In another exemplary embodiment of the present application, the fault locating model in step 104 comprises:

[0160] ;

[0161] wherein, is the fault distance, which refers to the straight-line distance from the start end (the electric signal injection end) of the target cable to the fault point, is the typical wave speed preset according to the cable type, is the resonance frequency with the maximum amplitude in the broadband impedance amplitude spectrum of the target cable.

[0162] In the embodiments of the present application, since the resonance order n=1 is the strongest and clearest fundamental frequency mode, in order to improve the positioning reliability and simplify the calculation, the fault position is calculated according to the fundamental frequency mode (resonance order n=1).

[0163] Based on the same inventive concept, the embodiments of the present application also provide a cable partial defect detection device for implementing the above-mentioned cable partial defect detection method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, and therefore the specific limitations in one or more cable partial defect detection device embodiments provided below can refer to the limitations of the cable partial defect detection method in the foregoing, which will not be described here again.

[0164] In one exemplary embodiment, as shown in Figure 4 a cable partial defect detection device 30 is provided, comprising:

[0165] The impedance test module 301 is configured to generate N different frequency electric signals to input into the target cable in a preset frequency range, test the input impedance of the target cable under N different frequency electric signals respectively, and obtain the broadband impedance of the target cable in the frequency range.

[0166] The impedance matrix construction module 302 is configured to generate the impedance amplitude and impedance phase of the target cable at each preset frequency point according to the broadband impedance of the target cable, and construct the impedance matrix X∈R N×2 ;

[0167] The fault identification module 303 is configured to input the impedance matrix X into a constructed fault classification model, identify and output a category of the target cable through the fault classification model; and the category includes a fault or normal.

[0168] The fault positioning module 304 is configured to extract a resonance frequency with a maximum amplitude in a broadband impedance amplitude spectrum of the target cable if the target cable is faulty, and determine a fault position of the target cable through a constructed fault positioning model according to the resonance frequency with the maximum amplitude and a typical wave speed of the target cable.

[0169] In the embodiment, the target cable refers to a cable to be detected. The network analyzer is used to perform a sweep frequency test on the target cable in a set frequency range, and the complex input impedance of the target cable at each frequency point is measured.

[0170] The number N of frequency points is determined by the preset frequency range and the step size. If the lower limit value of the preset frequency range is f min , the upper limit value of the frequency range is f max , and the preset step size is Δf, then f min is the frequency value of the first frequency point, N is the result of (f max -f min ) / Δf, and the frequency of the second frequency point is f min +Δf. Similarly, the frequency value of the next frequency point is obtained by moving Δf based on the frequency value of the previous frequency point, until the frequency value f min +(N-1)Δf of the next frequency point is not less than f max . At this time, f max is taken as the frequency value of the last frequency point. For example, if f min =0Hz, f max =10MHz, and Δf=1kHz, then N=1000. N determines the dimension and precision of the impedance matrix.

[0171] Each row of the impedance matrix corresponds to the impedance amplitude and impedance phase of the frequency point. The impedance matrix X contains multi-dimensional frequency impedance information (impedance amplitude + impedance phase), which can comprehensively reflect the electrical characteristics of different fault types. Using the impedance matrix X in combination with the fault classification model can effectively improve the identification ability and generalization ability of the fault classification model for different faults.

[0172] The impedance amplitudes of the target cable at each frequency point are arranged according to the frequency, and a broadband impedance amplitude spectrum is formed. The typical wave speed of the target cable is determined according to the type of the target cable. Specifically, the wave speed of the target cable is determined by the dielectric constant of the insulating material, and the typical wave speed value of the target cable can be obtained by looking up a table or experience.

[0173] The broadband impedance of the target cable is obtained through testing, and the impedance amplitude and impedance phase of the target cable at each frequency point are generated according to the broadband impedance of the target cable, and then an impedance matrix is constructed to comprehensively reflect the electrical characteristics of different fault types; the impedance matrix X is input into the constructed fault classification model, and the fault classification model is combined with the impedance matrix X to identify and output the fault category of the target cable, thereby realizing automatic identification of the fault of the target cable; if the target cable is faulty, the resonance frequency with the maximum amplitude in the broadband impedance amplitude spectrum of the target cable is extracted, and the fault location model is constructed according to the resonance frequency with the maximum amplitude and the typical wave speed of the target cable, so as to determine the fault location of the target cable, thereby realizing the fault location positioning of the target cable and solving the problem that the existing fault detection method cannot locate the fault position. In addition, the fault classification model automatically identifies the fault of the target cable, which does not need to rely on manual determination of the fault type and fault position of the target cable according to expert experience, thereby effectively improving the cable partial defect detection efficiency; at the same time, since the impedance matrix X contains multi-dimensional frequency impedance information (impedance amplitude + impedance phase), it can comprehensively reflect the electrical characteristics of different fault types, so that the fault classification model combined with the impedance matrix X can improve the accuracy of identifying the fault category of the target cable; in summary, the present application realizes automatic, rapid and accurate identification of the fault category of the target cable and fault location positioning.

[0174] In another exemplary embodiment of the present application, the fault identification module 303 described above is further used for:

[0175] The training data set is constructed:

[0176] According to the constructed cable distributed parameter equivalent model, a transmission line equation is established;

[0177] According to the transmission line equation described above, an electrical parameter calculation formula at any frequency is determined, and the electrical parameter per unit length of each sample cable at any frequency is calculated according to the electrical parameter calculation formula;

[0178] According to the preset correction rule, at least one electrical parameter per unit length of each sample cable at any frequency is corrected to obtain the electrical parameter per unit length of each sample cable at any frequency in each fault mode;

[0179] In the preset frequency range, N frequency points are set for the sample cable according to the preset frequency range and the frequency scanning step Δf, and the input impedance of the first end of each sample cable at each frequency point in each fault mode and the normal mode is calculated according to the electrical parameter per unit length of each sample cable at each frequency point in each fault mode and the normal mode;

[0180] Based on the input impedance of the head end of each sample cable at each frequency point in each fault mode and normal mode, the impedance amplitude and impedance phase of the head end of each sample cable at each frequency point in each fault mode and normal mode are generated, and broadband impedance spectrum data of each sample cable at N frequency points in each fault mode and normal mode are obtained to form a training data set;

[0181] According to the constructed training data set, a CNN-based classification model is trained to obtain a fault classification model.

[0182] In the embodiment of the present application, the cable distribution parameter equivalent model constructed is as follows Figure 2 As shown, it is an equivalent circuit unit based on transmission line theory. Figure 2 In, Δ l Indicates unit length (micro cable length), I ( x ) indicates that the x The current at V ( x ) indicates that the x Voltage at position x is the starting point of the unit length cable, Δ I Represents the current change per unit length, Δ V It represents the voltage change per unit length. R Indicates the resistance per unit length (Ω / m), L Indicates the inductance per unit length (H / m), G Indicates the insulation conductivity per unit length (S / m), C Indicates capacitance per unit length (F / m).

[0183] according to Figure 2 , the transmission line equations constructed include:

[0184] ;

[0185] ;

[0186] in, represents the imaginary unit, represents the angular frequency, ω = 2 πf , f Indicates frequency.

[0187] The sample cables are cables without faults. Therefore, the calculated electrical parameters per unit length of each sample cable at any frequency are the electrical parameters per unit length of the sample cable at any frequency in the normal mode.

[0188] The size of each sample cable in the wideband impedance spectrum data in any fault mode or normal mode is N x 2, N rows correspond to N frequency points, and each row of each wideband impedance spectrum data is the impedance amplitude and impedance phase of the head end of the sample cable at the corresponding frequency point in any fault mode or normal mode.

[0189] The CNN-based classification model includes a CNN network and a classifier. The CNN network is used to extract features of the input impedance matrix of the sample cable in a certain fault mode or normal mode, and the classifier is used to predict the input category according to the features extracted by the CNN network. In the training process, the training data set is divided into a training set and a test set, each wideband impedance spectrum data in the training set is input into the CNN-based classification model, the CNN-based classification model predicts the corresponding category label of each wideband impedance spectrum data, and according to the predicted category label and the real category label, the hyperparameters are continuously adjusted to make the prediction performance of the CNN-based classification model meet the requirements. Finally, the CNN-based classification model obtained by training is tested through the test set, and when the test result does not meet the requirements, the hyperparameters of the CNN-based classification model are continuously adjusted until the prediction performance of the CNN-based classification model obtained by testing meets the requirements, that is, the fault classification model is obtained.

[0190] For the related introduction of the electrical parameters, the electrical parameter calculation formula, the correction rule, the fault mode and the fault classification model, please refer to the description of the above method embodiments, which will not be repeated here.

[0191] In another exemplary embodiment of the present application, the fault modes include short-circuit fault, partial discharge fault and insulation aging fault.

[0192] Correspondingly, the fault identification module 303 described above is also used for:

[0193] By the first correction rule, the resistance per unit length of each sample cable is corrected, and the inductance, conductance and capacitance per unit length of each sample cable are kept unchanged, to obtain the electrical parameters per unit length of each sample cable at any frequency under the short-circuit fault mode;

[0194] By the second correction rule, the capacitance per unit length of each sample cable is corrected, and the inductance, conductance and resistance per unit length of each sample cable are kept unchanged, to obtain the electrical parameters per unit length of each sample cable at any frequency under the partial discharge fault mode;

[0195] By the third correction rule, the conductance per unit length of each sample cable is corrected, and the inductance, capacitance and resistance per unit length of each sample cable are kept unchanged, to obtain the electrical parameters per unit length of each sample cable at any frequency under the insulation aging fault mode.

[0196] In another exemplary embodiment of the present application, the fault identification module 303 is further configured to:

[0197] Before correcting at least one electrical parameter per unit length of each sample cable at any frequency according to a preset correction rule, each sample cable is divided into M segments from the head end to the terminal end.

[0198] In another exemplary embodiment of the present application, the fault identification module 303 is further configured to:

[0199] Starting from different segments of the M segments of each sample cable, the same k R And the resistance per unit length of the subsequent segments is corrected by the first correction rule to simulate short circuit faults at different locations; and / or, starting from the same segment of the M segments of each sample cable, different k R The resistance per unit length of the subsequent segment is corrected by the first correction rule to simulate short-circuit faults of different short-circuit degrees;

[0200] Keep the capacitance, inductance and conductance per unit length of each section of each sample cable and the resistance per unit length of other sections constant;

[0201] The capacitance per unit length of each sample cable is measured using different k C (k C The larger the value, the smaller the partial discharge fault) and the second correction rule is used to simulate partial discharge faults with different air gap sizes;

[0202] The conductance per unit length of each sample cable is measured using different k σ (The greater the insulation aging, the greater the k σ The larger the value, the better) and corrected by the second correction rule to simulate insulation aging faults with different insulation aging degrees.

[0203] In another exemplary embodiment of the present application, the fault identification module 303 is further configured to:

[0204] According to the following formula, starting from the Mth segment of each sample cable at each frequency point in each fault mode or normal mode, recursively calculate the input impedance of the first segment of each sample cable at each frequency point in each fault mode and normal mode from back to front: :

[0205] ;

[0206] ;

[0207] ;

[0208] in, is the input impedance of the i-th segment of each sample cable at any frequency point of any fault mode or normal mode, i is the characteristic impedance of the i-th segment of each sample cable at any frequency point of any fault mode or normal mode, is the input impedance of the i+1-th segment of each sample cable at any frequency point of any fault mode or normal mode, i is the characteristic impedance of the i-th segment of each sample cable at any frequency point of any fault mode or normal mode, is the input impedance of the i+1-th segment of each sample cable at any frequency point of any fault mode or normal mode, i is the propagation constant of the i-th segment of each sample cable at any frequency point of any fault mode or normal mode, is the length of each segment of the sample cable, i l l is the total length of the sample cable; , , , correspond to the resistance, inductance, conductance, and capacitance of the i-th segment of each sample cable at any frequency point of any fault mode or normal mode, i .

[0209] In the embodiments of the present application, the terminal impedance Z of the sample cable is calculated according to the above formula. L =∞ (which indicates that the sample cable is open at the far end).

[0210] In another exemplary embodiment of the present application, the fault location module 304 is further configured to:

[0211] determine the fault location of the target cable according to the following fault location model:

[0212] ;

[0213] wherein, is the fault distance, which refers to the straight-line distance from the start end (the end where the electrical signal is injected) of the target cable to the fault point, is the typical wave speed preset according to the type of the cable, is the resonant frequency with the largest amplitude in the broadband impedance amplitude spectrum of the target cable.

[0214] In the embodiments of the present application, since the resonant order n=1 is the strongest and clearest fundamental frequency mode, the fault location is calculated according to the fundamental frequency mode (resonant order n=1) to improve the positioning reliability and simplify the calculation.

[0215] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and an internal structure diagram of the computer device can be as shown in​​​​​ Figure 5 The computer device shown in the figure includes a processor, a memory, an Input / Output (I / O) interface, and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store cable partial defect detection data. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through network connection. The computer program is executed by the processor to implement a cable partial defect detection method.

[0216] Those skilled in the art can understand that, Figure 5 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0217] In an exemplary embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in each of the method embodiments described above.

[0218] In an exemplary embodiment, a computer readable storage medium is provided, storing a computer program, which is executed by a processor to implement the steps in each of the method embodiments described above.

[0219] In an exemplary embodiment, a computer program product is provided, including a computer program, which is executed by a processor to implement the steps in each of the method embodiments described above.

[0220] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use, and processing of related data need to comply with relevant regulations.

[0221] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, databases or other media used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0222] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0223] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combinations of the technical features do not exist, they should be considered as the scope of the present application.

[0224] The principles and implementation modes of the present application are described by applying specific examples in this paper, and the above-mentioned embodiments are only used to help understand the method and its core idea of the present application. Meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for detecting local defects in a cable, characterized in that: The cable local defect detection method comprises: Generating N electrical signals of different frequencies within a preset frequency range and inputting them into a target cable, testing the input impedance of the target cable under the electrical signals of the N different frequencies, and obtaining the broadband impedance of the target cable within the frequency range; According to the broadband impedance of the target cable, the impedance amplitude and impedance phase of the target cable at each preset frequency point are generated, and the impedance matrix X∈R is constructed. N×2 ; Inputting the impedance matrix X into the constructed fault classification model, identifying and outputting the category of the target cable through the fault classification model; wherein the category includes fault or normal; If the target cable is faulty, extract the resonance frequency with the largest amplitude in the broadband impedance amplitude spectrum of the target cable, and determine the fault location of the target cable using a constructed fault location model based on the resonance frequency with the largest amplitude and a typical wave velocity of the target cable; The fault classification model is constructed according to the following steps: Construct a training dataset: According to the constructed cable distribution parameter equivalent model, the transmission line equation is established; Determine an electrical parameter calculation formula at any frequency based on the transmission line equation, and calculate the electrical parameters per unit length of each sample cable at any frequency using the electrical parameter calculation formula; Correcting at least one electrical parameter per unit length of each sample cable at any frequency according to a preset correction rule to obtain the electrical parameter per unit length of each sample cable at any frequency of each fault mode; Within a preset frequency range, N frequency points are set for each sample cable according to the preset frequency range and frequency scanning step, and the input impedance of the head end of each sample cable under each fault mode and each frequency point in the normal mode is calculated according to the electrical parameters per unit length of each sample cable under any frequency in the normal mode; Based on the input impedance of the head end of each sample cable at each frequency point in each fault mode and normal mode, the impedance amplitude and impedance phase of the head end of each sample cable at each frequency point in each fault mode and normal mode are generated, and broadband impedance spectrum data of each sample cable at N frequency points in each fault mode and normal mode are obtained to form a training data set; Training a CNN-based classification model according to the training data set to obtain the fault classification model; The fault location model includes: ； in, is the fault distance, is the typical wave speed preset according to the cable type, is the resonant frequency with the maximum amplitude in the broadband impedance amplitude spectrum of the target cable.

2. The cable local defect detection method according to claim 1, characterized in that: The correction rules include a first correction rule, a second correction rule and a third correction rule, wherein: The first amendment rule includes: R fault = R k R ,k R ∈[0.001,0.1]; The second revised rule includes: C fault = C k C ,k C ∈[0.2,0.5]; The third revised rule includes: G aging = G k σ ,k σ ∈[10,1000]; Among them, R fault is the corrected resistance per unit length, C fault is the corrected capacitance per unit length, G aging is the corrected conductance per unit length, R represents the resistance per unit length, G It represents the insulation conductivity per unit length, C Represents capacitance per unit length, k R is the resistance correction factor, k C is the capacitance correction factor, k σ is the conductance correction factor.

3. The cable local defect detection method according to claim 2, characterized in that: The failure modes include short circuit failure, partial discharge failure and insulation aging failure; The step of correcting at least one electrical parameter per unit length of each sample cable at any frequency according to a preset correction rule to obtain the electrical parameter per unit length of each sample cable at any frequency of each fault mode specifically includes: Correcting the resistance per unit length of each of the sample cables using the first correction rule while maintaining the inductance, conductance, and capacitance per unit length of each of the sample cables unchanged, thereby obtaining electrical parameters per unit length of each of the sample cables at any frequency of a short-circuit fault; Correcting the capacitance per unit length of each of the sample cables using the second correction rule while keeping the inductance, conductance, and resistance per unit length of each of the sample cables unchanged, thereby obtaining electrical parameters per unit length of each of the sample cables at any frequency of a partial discharge fault; The conductance per unit length of each of the sample cables is corrected by the third correction rule, while the inductance, capacitance, and resistance per unit length of each of the sample cables are kept unchanged, thereby obtaining the electrical parameters per unit length of each of the sample cables at any frequency of insulation aging failure.

4. The cable local defect detection method according to claim 3, characterized in that: The step of constructing a training data set further includes: Before correcting at least one electrical parameter per unit length of each sample cable at any frequency according to a preset correction rule, each sample cable is divided into M segments from the head end to the terminal end of each sample cable; Correcting the resistance per unit length of each of the sample cables by using the first correction rule includes: Starting from different segments of the M segments of each sample cable, the same k R And the resistance per unit length of the subsequent segments is corrected by the first correction rule to simulate short circuit faults at different locations; and / or, starting from the same segment of the M segments of each sample cable, different k R and correcting the resistance per unit length of the subsequent segments using the first correction rule to simulate short-circuit faults of different short-circuit degrees; Maintaining the capacitance, inductance, and conductance per unit length of each section of each sample cable and the resistance per unit length of other sections unchanged; The second correction rule is used to correct the capacitance per unit length of each sample cable, including: The capacitance per unit length of each sample cable is measured using different k C And the correction is performed by the second correction rule to simulate partial discharge faults with different air gap sizes; The conductance per unit length of each sample cable is corrected by the third correction rule, including: The conductance per unit length of each sample cable is measured using different k σ The second correction rule is used to perform correction to simulate insulation aging faults with different insulation aging degrees.

5. The cable local defect detection method according to claim 1, characterized in that: Calculating the input impedance of the head end of each sample cable at each frequency point in each fault mode and normal mode according to the electrical parameters per unit length of each sample cable at any frequency in each fault mode and normal mode specifically includes: According to the following formula, starting from the Mth segment of each sample cable at each frequency point in each fault mode or normal mode, the input impedance of the first segment of each sample cable at each frequency point in each fault mode and normal mode is calculated recursively from back to front. : ； ； ； in, The first frequency of each sample cable under any fault mode or normal mode i The input impedance of the segment, The first frequency of each sample cable under any fault mode or normal mode i The characteristic impedance of the segment, The first frequency of each sample cable under any fault mode or normal mode i +1 segment input impedance, The first frequency of each sample cable under any fault mode or normal mode i The propagation constant of the segment, is the length of each section of the sample cable, 、 、 、 Corresponding to the first frequency point of each sample cable in any fault mode or normal mode i The resistance, inductance, conductance and capacitance of the segment, , is the imaginary unit, represents the angular frequency, ω = 2 πf , f Indicates the frequency of any frequency point.

6. The cable local defect detection method according to claim 1, characterized in that: The fault classification model includes a CNN network and a fault classifier, where: The fault classifier is used to identify the category of the target cable according to the input overall feature vector, the category includes fault or normal, and the fault includes short circuit fault, partial discharge fault, and insulation aging fault; The CNN network includes: The first convolutional layer is used to extract local features of the impedance matrix X to obtain local frequency domain features; A maximum pooling layer is used to reduce the dimension of the local frequency domain features to obtain the local frequency domain features after dimensionality reduction; The second convolutional layer is used to extract deep features of the local frequency domain features after dimensionality reduction to obtain deep frequency domain features; The global average pooling layer is used to perform a global average pooling operation on the deep frequency domain features and output the overall feature vector.

7. The cable local defect detection method according to claim 6, characterized in that: The fault classifier comprises: A first fault classifier is used to identify the category of the target cable as a short circuit fault or normal according to the overall feature vector; A second fault classifier is used to identify the category of the target cable as a partial discharge fault or a normal condition based on the overall feature vector; The third fault classifier is used to identify the category of the target cable as insulation aging fault or normal according to the overall feature vector.

8. A cable local defect detection device, characterized in that: The cable local defect detection device comprises: An impedance testing module is configured to generate N electrical signals of different frequencies within a preset frequency range and input them into a target cable, thereby testing the input impedance of the target cable under the electrical signals of N different frequencies and obtaining the broadband impedance of the target cable within the frequency range; The impedance matrix construction module is used to generate the impedance amplitude and impedance phase of the target cable at each preset frequency point according to the broadband impedance of the target cable, and construct the impedance matrix X∈R N×2 ; A fault identification module is configured to input the impedance matrix X into the constructed fault classification model, identify and output the category of the target cable through the fault classification model; wherein the category includes fault or normal; The fault classification model is constructed according to the following steps: Construct a training dataset: According to the constructed cable distribution parameter equivalent model, the transmission line equation is established; Determine an electrical parameter calculation formula at any frequency based on the transmission line equation, and calculate the electrical parameters per unit length of each sample cable at any frequency using the electrical parameter calculation formula; Correcting at least one electrical parameter per unit length of each sample cable at any frequency according to a preset correction rule to obtain the electrical parameter per unit length of each sample cable at any frequency of each fault mode; Within a preset frequency range, N frequency points are set for each sample cable according to the preset frequency range and frequency scanning step, and the input impedance of the head end of each sample cable under each fault mode and each frequency point in the normal mode is calculated according to the electrical parameters per unit length of each sample cable under any frequency in the normal mode; Based on the input impedance of the head end of each sample cable at each frequency point in each fault mode and normal mode, the impedance amplitude and impedance phase of the head end of each sample cable at each frequency point in each fault mode and normal mode are generated, and broadband impedance spectrum data of each sample cable at N frequency points in each fault mode and normal mode are obtained to form a training data set; Training a CNN-based classification model according to the training data set to obtain the fault classification model; a fault location module, configured to extract the resonance frequency with the maximum amplitude in the broadband impedance amplitude spectrum of the target cable if the target cable is faulty, and determine the fault location of the target cable using a constructed fault location model based on the resonance frequency with the maximum amplitude and the typical wave velocity of the target cable; The fault location model includes: ； in, is the fault distance, is the typical wave speed preset according to the cable type, is the resonant frequency with the maximum amplitude in the broadband impedance amplitude spectrum of the target cable.

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