Grounding cable high-frequency signal detection method, system, equipment and medium

Abstract

The invention relates to a ground cable high-frequency signal detection method, system, equipment and medium, adopts a compressed sensing (CS) technology, remarkably reduces the sampling data volume through sparse representation and compressed sampling of a cable reflection signal, and adopts a sparse signal reconstruction algorithm to realize efficient reconstruction of the cable reflection signal, thereby improving the detection accuracy of the high-frequency signal of a ground cable and improving the detection accuracy of the high-frequency signal of the ground cable. The problems that in the prior art, the signal processing speed is low, the anti-interference capacity is poor, and the detection precision is not high are solved. The system is composed of a signal acquisition module, a compression sampling module, a signal reconstruction module and a fault detection and positioning module, can accurately judge the type and position of a cable fault in a complex environment, and is especially suitable for online monitoring of a long-distance cable. The device and the medium detect the high-frequency signal of the grounding cable based on the above detection method and system, compared with an existing time domain reflectometry (TDR), the detection sensitivity, the anti-interference capability and the processing speed are higher, cable fault diagnosis and attenuation compensation can be completed under the condition of no reference data, and the method and the system have good application prospects. A rapid and reliable solution is provided for cable state monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of cable status detection, and in particular to a grounding cable high-frequency signal detection method, system, equipment and medium. Background Art

[0002] With the development of the power system, cables, as an important component of the power transmission network, are widely used in urban power supply, industrial and mining enterprises, and transportation infrastructure. Among them, the grounding cable is an important component to ensure the safe operation of the equipment, and its health is directly related to the stability and reliability of the power system. However, during the long-term operation of the cable, it may be affected by moisture, aging, mechanical damage, and external environmental stress, which may cause hidden dangers such as insulation aging and partial discharge, and eventually cause cable failure. Therefore, it is of great significance to carry out status monitoring and fault detection of grounding cables.

[0003] Traditional cable detection methods, such as time domain reflectometry (TDR) and frequency domain reflectometry (FDR), mainly detect the fault location in the cable based on the propagation characteristics of low-frequency signals. However, due to the complex layout structure of the grounding cable and external interference, low-frequency signals are prone to attenuation and overlapping of reflected waveforms during long-distance transmission, resulting in a decrease in detection accuracy and sensitivity. In addition, as the cable length increases, the resolution ability of low-frequency signals is also limited, making it difficult to effectively identify multi-point faults or complex insulation defects. In contrast, high-frequency signals have shorter wavelengths and higher resolution, and can effectively avoid signal attenuation and multiple reflection problems in longer cable transmission paths. Therefore, the use of high-frequency signals for grounding cable detection has become a potential technical means.

[0004] At present, the high-frequency signal detection method requires uniform and dense sampling of the entire frequency range. In long-distance cables or complex environments, the time and resource requirements for sampling and processing increase significantly, increasing the cost of the detection system and reducing the detection efficiency. The method of directly sampling high-frequency signals in cables follows the Nyquist sampling theorem, requiring the sampling frequency to be at least twice the highest frequency of the signal. The sampling time required in long-distance cables is longer, resulting in a huge amount of sampled data, which in turn requires more storage space to store sampled data and stronger processing capabilities of the processor. Rao Xianjie, Xu Zhonglin, Ding Yuqin, Hu Xiaoyu, Zhou Kai. Cable defect location method based on short-time fractional Fourier transform and time-frequency domain reflection method [J / OL]. Transactions of the Chinese Society of Electrotechnical Engineering. A cable defect location method based on short-time fractional Fourier transform (STFRFT) and traditional time-frequency domain reflection method (TFDR) is proposed to solve the problems of low time-frequency resolution and serious cross-term interference in traditional time-frequency domain reflection method (TFDR). These methods also have the problem of too much sampling data and different application objects for high-frequency signal methods. Summary of the invention

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a method, system, device and medium for detecting high-frequency signals of grounding cables, which adopts compressed sensing (CS) technology to achieve efficient detection and fault diagnosis of cable signals through sparse representation and compressed sampling of cable reflection signals; this method can greatly reduce the amount of data collection while improving the sensitivity and accuracy of detection.

[0006] In order to achieve the above object, the specific technical solution adopted by the present invention is as follows:

[0007] A method for detecting a high-frequency signal of a grounding cable comprises the following steps:

[0008] Step 1: inject a detection signal into the cable to generate a partial reflection signal at a discontinuous point in the cable, and capture the reflected signal through a cable reflection signal acquisition module;

[0009] Step 2: Perform sparse transformation on the reflected signal captured by the cable reflection signal acquisition module to extract key features in the signal, including time difference, amplitude change, attenuation coefficient and frequency domain response of the reflected signal, which can reflect the cable status or fault location, remove redundant information in the signal, and provide the sparse part containing signal features, i.e., sparse signal, to the compression sampling module;

[0010] Step 3: construct a Gaussian measurement matrix, perform random projection on the sparse signal provided in step 2, realize compressed sampling of the sparse signal, and output the compressed sparse signal;

[0011] Step 4: The sparse signal compressed in step 3 is reconstructed by a sparse signal reconstruction algorithm to obtain a reconstructed original signal containing cable fault and location information.

[0012] Step 5: Reconstruct the original signal output from step 4 Perform spectrum analysis to obtain the characteristics of the reflected signal;

[0013] Step 6: According to the characteristics of the reflected signal obtained in step 5, including reflection amplitude, phase change and pulse width, identify the fault signal, and judge the cable fault type and the cable switch state and switch operation.

[0014] The specific method of step 2 is:

[0015] The reflected signal captured by the cable reflection signal acquisition module in step 1 is recorded as a sparse signal For sparse signals Sparse representation is performed using the discrete wavelet transform DWT, θ = DWT(x), where θ is the sparse coefficient in the wavelet basis; the matrix expression of the discrete wavelet transform DWT is the wavelet basis matrix, which is composed of wavelet basis functions; the wavelet basis functions include the mother wavelet function ψ j,k (t) = 2 j / 2 ψ(2 j t - k) and the scaling function where j represents the scale and k represents the translation; for the cable reflection signal x of length N, discrete wavelet transform is performed, and the wavelet basis matrix is:

[0016] which is an N×N matrix, ψ0(t k ) represents the value of the mother wavelet function at different scales j and sampling points t k under the condition, represents the value of the scaling function at different scales j and sampling points t k under the condition;

[0017] Then, a threshold ε is set, and the coefficients less than ε are set to zero to obtain the sparsified coefficient The sparse signal x is represented as x = Aθ, where A is the wavelet basis matrix representing the cable reflection signal and θ is the sparse coefficient in the wavelet basis.

[0018] 3. A method for detecting high-frequency signals of a grounded cable according to claim 1, wherein the specific method of step 3 is as follows:

[0019] Construct a random M×N-dimensional Gaussian measurement matrix Ф, and the wavelet basis matrix is where M << N, and each element φ in the random Gaussian measurement matrix Ф ij is a random variable that follows a Gaussian distribution with a mean of 0 and a variance of 1: Through the random Gaussian measurement matrix Ф, the sparse signal x in step 2 is randomly projected to obtain the compressed signal y = Фx, reducing the number of sampling points.

[0020] The specific method of step 4 is as follows:

[0021] According to the compressed signal y obtained in step 3 and the wavelet basis matrix Set the compressed signal y as the original signal that has been compressed and sampled by the Gaussian measurement matrix Ф, initialize the residual signal r0 as the compressed signal y, r0 = y, and set the residual as the original observed signal; select the index of the wavelet basis vector Select the basis vector most relevant to the current residual r from the wavelet basis matrix A, as follows: k as follows:

[0022] 1) Select the basis vector with the highest correlation: find the maximized That is The index i k , where A i is the i-th column of the wavelet basis matrix A, representing the i-th wavelet basis vector;

[0023] 2) The index of the selected wavelet basis vector Add to index set S k Middle: S k+1 =S k ∪{i k};

[0024] 3) Use the least squares method on the current index set S k+1 The optimal sparse coefficient of the reconstructed signal is calculated on

[0025]

[0026] in, is the submatrix composed of the selected basis vectors;

[0027] 4) Update residual

[0028] 5) Set the residual threshold ε1 and the upper limit of the number of iterations to K;

[0029] 6) When the residual r k+1 When it is less than the preset threshold ε1, or the number of iterations reaches K, the algorithm stops;

[0030] 7) Output the reconstructed original signal containing cable fault and location information

[0031] The specific method of step 5 is:

[0032] The reconstructed original signal is obtained through step 4 Calculate the reflection coefficient at different injection signal frequencies:

[0033] 1) Reconstruct the original signal Convert to frequency domain V(f) through Laplace transform;

[0034] 2) The converted frequency domain V(f) contains the injection signal V(f)zhuru and the reflection signal V(f)fans, that is, V(f)=V(f)fans+V(f)zhuru; then calculate the reflection coefficient at different injection signal frequencies Among them, V fans (f) is the reflected signal amplitude, V zhuru (f) is the injected signal amplitude;

[0035] By injecting different signal frequencies, the characteristics of the reflected signal, that is, the spectrum formed by the reflection coefficient changing with frequency, are obtained.

[0036] The specific method of step 6 is:

[0037] According to step 5, the change of reflection coefficient is measured at different signal frequencies to establish a frequency response curve; the reflection power coefficient R=|Γ is obtained from the reflection coefficient 2 , the fault type and switch status are judged by the reflection coefficient and reflected power coefficient.

[0038] The fault type and switch status are judged by the reflection coefficient and the reflection power coefficient, as shown in the following table:

[0039]

[0040] A grounding cable high-frequency signal detection system, comprising:

[0041] The cable reflection signal acquisition module is used to capture the partial reflection signal generated by the detection signal injected into the cable at the discontinuous point in the cable in step 1;

[0042] The acquisition signal sparse representation module is used in step 2 to perform sparse transformation on the reflection signal captured in step 1, extract key features in the signal, remove redundant information in the signal, and obtain a sparse part containing signal features;

[0043] A compression sampling module is used in step 3 to construct a Gaussian measurement matrix, perform random projection on the sparse signal obtained in step 2, realize compression sampling of the sparse signal, and output the compressed sparse signal;

[0044] A signal reconstruction module, used in step 4, to process the sparse signal compressed in step 3 through a sparse signal reconstruction algorithm, and output a reconstructed original signal containing cable fault and location information;

[0045] The fault detection and location module is used in step 5 to perform spectrum analysis on the reconstructed original signal outputted in step 4 to obtain the characteristics of the reflected signal;

[0046] The fault type and switch state judgment module is used in step 6 to identify the fault signal according to the characteristics of the reflected signal obtained in step 5, and judge the cable fault type and the cable switch state and switch operation.

[0047] A grounding cable high-frequency signal detection device comprises: a memory and a processor, wherein the memory stores a computer program so that the processor executes the grounding cable high-frequency signal detection method.

[0048] A storage medium for receiving user input programs, wherein when the stored computer program is executed by a processor, it can realize efficient detection and fault diagnosis of cable signals based on the grounding cable high-frequency signal detection method.

[0049] Compared with the prior art, the present invention has the following beneficial effects:

[0050] 1. The present invention adopts the compression sampling technology to perform sparse transformation on the captured reflection signal, extract the key features in the signal, remove the redundant information in the signal, effectively reduce the amount of sampled data, reduce the pressure of storage and transmission, and maintain high-precision detection of fault location and type. This feature makes the application of this technology in large-scale monitoring systems more efficient.

[0051] 2. By introducing compressed sensing technology (CS) and signal sparse representation methods, cable reflection signals can be collected and reconstructed more efficiently, thereby improving the sensitivity and accuracy of cable fault detection, especially in long-distance cables and complex environments, and can effectively deal with signal attenuation and noise interference.

[0052] 3. The present invention adopts an optimized reconstruction algorithm in the signal processing process to obtain a reconstructed original signal containing cable fault and location information, thereby reducing the calculation complexity.

[0053] 4. During the detection process, the present invention does not need to rely on the reference data of the complete cable, and can directly perform fault detection and attenuation compensation on the cable on site. Compared with the traditional FDR method, it solves the limitation of requiring intact cable reference test data and improves practicality and flexibility.

[0054] In summary, the detection method of the present invention adopts Compressed Sensing (CS) technology, and realizes efficient detection and fault diagnosis of cable signals through sparse representation and compressed sampling of cable reflection signals; it has obvious advantages in detection accuracy, processing speed and anti-interference ability, and effectively solves the shortcomings of the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention, the drawings required for the embodiments will be briefly introduced below. The drawings are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0056] Figure 1 The present invention provides a principle block diagram of a grounding cable high-frequency signal detection method. DETAILED DESCRIPTION

[0057] The present invention will be further described below in conjunction with the embodiments, but it does not constitute any limitation to the present invention. Any limited number of modifications made by anyone within the scope of the claims of the present invention are still within the scope of the claims of the present invention.

[0058] like Figure 1 As shown, the relationship between the voltage phasor on the grounding cable and the current phasor flowing through the grounding cable and the resistance and reactance of the grounding cable.

[0059] A method for detecting a high-frequency signal of a grounding cable comprises the following steps:

[0060] Step 1: The detection signal injected into the cable generates a partial reflection signal at a discontinuous point in the cable, and the reflected signal is captured by a cable reflection signal acquisition module;

[0061] Step 2: Perform sparse transformation on the reflected signal captured by the cable reflection signal acquisition module to extract key features in the signal, including the time difference, amplitude change, attenuation coefficient and frequency domain response of the reflected signal, which can reflect the cable status or fault location, remove redundant information in the signal, and provide the sparse part containing the signal features, i.e., the sparse signal, to the compression sampling module; the specific steps are as follows:

[0062] The reflected signal captured by the cable reflection signal acquisition module in step 1 is recorded as a sparse signal Applying discrete wavelet transform DWT to sparse signal for sparse representation θ=DWT(x), where θ is the sparse coefficient under the wavelet basis; the matrix of discrete wavelet transform DWT is expressed as wavelet basis matrix, which is composed of wavelet basis functions; wavelet basis functions include mother wavelet function ψ j,k (t) = 2 j / 2 ψ(2 j tk) and scaling function Where j represents the scale and k represents the translation. For the cable reflection signal x with a length of N, a discrete wavelet transform is performed, and the wavelet basis matrix is:

[0063] is an N×N matrix, ψ0(t k ) represents the mother wavelet function at different scales j and sampling points t k The value below Represents the scale function at different scales j and sampling points t k The value below;

[0064] Then, set the threshold ε, set the coefficients less than ε to zero, and get the sparse coefficients The sparse signal x is expressed as x=Aθ, where A is a wavelet basis matrix representing the cable reflection signal and θ is a sparse coefficient under the wavelet basis.

[0065] Step 3: The compression sampling module randomly projects the sparse signal provided in step 2 through a preset measurement matrix to achieve compression sampling of the sparse signal. The compressed sparse signal enters the signal reconstruction module; the details are as follows:

[0066] Construct a random Gaussian measurement matrix Ф of M×N dimensions, and the wavelet basis matrix is where M << N, and each element φ in the random Gaussian measurement matrix Ф ij is a random variable that follows a Gaussian distribution with a mean of 0 and a variance of 1: Through the random Gaussian measurement matrix Ф, the sparse signal x in step 2 is randomly projected to obtain the compressed signal y = Фx, reducing the number of sampling points.

[0067] Step 4: The signal reconstruction module uses a sparse signal reconstruction algorithm, such as orthogonal matching pursuit (OMP), etc., on the sparse signal compressed in step 3 to obtain the reconstructed original signal containing cable fault and location information Transmit the obtained reconstructed original signal to the fault detection and location module; specifically as follows:

[0068] Based on the compressed signal y obtained in step 3 and the wavelet basis matrix Set the compressed signal y as the original signal that has been compressed and sampled by the Gaussian measurement matrix Ф. Initialize the residual signal r0 as the compressed signal y, r0 = y, and set the residual as the original observed signal; select the index of the wavelet basis vector Select the basis vector in the wavelet basis matrix A that is most relevant to the current residual r k as follows:

[0069] 1) Select the basis vector with the highest correlation: Find the index i that maximizes That is of k , where A i is the i-th column of the wavelet basis matrix A, representing the i-th wavelet basis vector;

[0070] 2) Add the index of the selected wavelet basis vector to the index set S k : S k+1 = S k ∪{i k};

[0071] 3) Use the least squares method to calculate the optimal sparse coefficient of the reconstructed signal on the current index set S k+1 , that is

[0072]

[0073] where is the submatrix composed of the selected basis vectors;

[0074] 4) Update the residual

[0075] 5) Set the residual threshold ε1, and the upper limit of the iteration times is K;

[0076] 6) When the residual r k+1 When it is less than the preset threshold ε1, or the number of iterations reaches K, the algorithm stops;

[0077] 7) Output the reconstructed original signal containing cable fault and location information

[0078] Step 5: The fault detection and location module performs spectrum analysis on the reconstructed original signal output from step 4 to obtain the characteristics of the reflected signal and identify possible fault signals; the details are as follows:

[0079] The reconstructed original signal is obtained through step 4 Calculate the reflection coefficient at different injection signal frequencies:

[0080] 1) Reconstruct the original signal Convert to frequency domain V(f) through Laplace transform;

[0081] 2) The converted frequency domain V(f) contains the injection signal V(f)zhuru and the reflection signal V(f)fans, that is, V(f)=V(f)fans+V(f)zhuru; then calculate the reflection coefficient at different injection signal frequencies Among them, V fans (f) is the reflected signal amplitude, V zhuru (f) is the injected signal amplitude;

[0082] By injecting different signal frequencies, the characteristics of the reflected signal, that is, the spectrum formed by the reflection coefficient changing with frequency, are obtained.

[0083] Step 6: The fault type and switch status judgment module judges the cable fault type, cable switch status and switch operation according to the characteristics of the reflected signal obtained in step 5, including reflection amplitude, phase change and pulse width; the details are as follows:

[0084] According to step 5, the change of reflection coefficient is measured at different signal frequencies to establish a frequency response curve; the reflection power coefficient R=|Γ is obtained from the reflection coefficient 2 , the fault type and switch status are judged by the reflection coefficient and reflected power coefficient.

[0085] The fault type and switch status are judged by the reflection coefficient and the reflection power coefficient, as shown in the following table:

[0086]

[0087] A grounding cable high-frequency signal detection system, comprising:

[0088] The cable reflection signal acquisition module is used to capture the partial reflection signal generated by the detection signal injected into the cable at the discontinuous point in the cable in step 1;

[0089] The acquisition signal sparse representation module is used in step 2 to perform sparse transformation on the reflection signal captured in step 1, extract key features in the signal, remove redundant information in the signal, and obtain a sparse part containing signal features;

[0090] A compression sampling module is used in step 3 to construct a Gaussian measurement matrix, perform random projection on the sparse signal obtained in step 2, realize compression sampling of the sparse signal, and output the compressed sparse signal;

[0091] A signal reconstruction module, used in step 4, to process the sparse signal compressed in step 3 through a sparse signal reconstruction algorithm, and output a reconstructed original signal containing cable fault and location information;

[0092] The fault detection and location module is used in step 5 to perform spectrum analysis on the reconstructed original signal outputted in step 4 to obtain the characteristics of the reflected signal;

[0093] The fault type and switch state judgment module is used in step 6 to identify the fault signal according to the characteristics of the reflected signal obtained in step 5, and judge the cable fault type and the cable switch state and switch operation.

[0094] Table 1 Advantages of the invented detection method compared with the traditional method for cables of different lengths

[0095]

Claims

1. A grounding cable high-frequency signal detection method, characterized in that: The specific steps include: Step 1: inject a detection signal into the cable to generate a partial reflection signal at a discontinuous point in the cable, and capture the reflected signal through a cable reflection signal acquisition module; Step 2: Perform sparse transformation on the reflected signal captured by the cable reflection signal acquisition module to extract key features in the signal, including time difference, amplitude change, attenuation coefficient and frequency domain response of the reflected signal, which can reflect the cable status or fault location, remove redundant information in the signal, and provide the sparse part containing signal features, i.e., sparse signal, to the compression sampling module; Step 3: construct a Gaussian measurement matrix, perform random projection on the sparse signal provided in step 2, realize compressed sampling of the sparse signal, and output the compressed sparse signal; Step 4: The sparse signal compressed in step 3 is reconstructed by a sparse signal reconstruction algorithm to obtain a reconstructed original signal containing cable fault and location information. Step 5: Reconstruct the original signal output from step 4 Perform spectrum analysis to obtain the characteristics of the reflected signal; Step 6: According to the characteristics of the reflected signal obtained in step 5, including reflection amplitude, phase change and pulse width, identify the fault signal, and judge the cable fault type and the cable switch state and switch operation.

2. A grounding cable high-frequency signal detection method according to claim 1, characterized in that: The specific method of step 2 is: The reflected signal captured by the cable reflection signal acquisition module in step 1 is recorded as a sparse signal For sparse signals The discrete wavelet transform DWT is used for sparse representation θ=DWT(x), where θ is the sparse coefficient under the wavelet basis; the matrix of the discrete wavelet transform DWT is expressed as a wavelet basis matrix, which is composed of wavelet basis functions; the wavelet basis function includes the mother wavelet function ψ j,k (t) = 2 j / 2 ψ(2 j tk) and scaling function Where j represents the scale and k represents the translation. For the cable reflection signal x with a length of N, a discrete wavelet transform is performed, and the wavelet basis matrix is: is an N×N matrix, ψ0(t k ) represents the mother wavelet function at different scales j and sampling points t k The value below Represents the scale function at different scales j and sampling points t k The value below; Then, set the threshold ε, set the coefficients less than ε to zero, and get the sparse coefficients The sparse signal x is expressed as x=Aθ, where A is a wavelet basis matrix representing the cable reflection signal and θ is a sparse coefficient under the wavelet basis.

3. A grounding cable high-frequency signal detection method according to claim 1, characterized in that: The specific method of step 3 is: Construct a random Gaussian measurement matrix Ф of M×N dimensions, and the wavelet basis matrix is where M << N, and each element φ in the random Gaussian measurement matrix Ф ij is a random variable, following a Gaussian distribution with a mean of 0 and a variance of 1: Through the random Gaussian measurement matrix Ф, the sparse signal x in step 2 is randomly projected to obtain the compressed signal y = Фx, reducing the number of sampling points.

4. A grounding cable high-frequency signal detection method according to claim 1, characterized in that: The specific method of step 4 is: According to the compressed signal y and wavelet basis matrix obtained in step 3 Assume that the compressed signal y is the original signal compressed and sampled by the Gaussian measurement matrix Ф, initialize the residual signal r0 to the compressed signal y, r0 = y, and set the residual to the original observed signal; select the index of the wavelet basis vector Select the current residual r from the wavelet basis matrix A k The most relevant basis vectors are as follows: 1) Select the most relevant basis vector: find the maximum Right now The index i k , where A i is the i-th column of the wavelet basis matrix A, representing the i-th wavelet basis vector; 2) The index of the selected wavelet basis vector Add to index set S k Middle: S k+1 =S k ∪{i k }; 3) Use the least squares method on the current index set S k+1 The optimal sparse coefficient of the reconstructed signal is calculated on in, is the submatrix composed of the selected basis vectors; 4) Update residual 5) Set the residual threshold ε1 and the upper limit of the number of iterations to K; 6) When the residual r k+1 When it is less than the preset threshold ε1, or the number of iterations reaches K, the algorithm stops; 7) Output the reconstructed original signal containing cable fault and location information 5. A grounding cable high-frequency signal detection method according to claim 1, characterized in that: The specific method of step 5 is: The reconstructed original signal is obtained through step 4 Calculate the reflection coefficient at different injection signal frequencies: 1) Reconstruct the original signal Convert to frequency domain V(f) through Laplace transform; 2) The converted frequency domain V(f) contains the injection signal V(f)zhuru and the reflection signal V(f)fans, that is, V(f)=V(f)fans+V(f)zhuru; then calculate the reflection coefficient at different injection signal frequencies Among them, V fans (f) is the reflected signal amplitude, V zhuru (f) is the injected signal amplitude; By injecting different signal frequencies, the characteristics of the reflected signal, that is, the spectrum formed by the reflection coefficient changing with frequency, are obtained.

6. A grounding cable high-frequency signal detection method according to claim 1, characterized in that: The specific method of step 6 is: According to step 5, the change of reflection coefficient is measured at different signal frequencies to establish a frequency response curve; the reflection power coefficient R=|Γ is obtained from the reflection coefficient 2 , the fault type and switch status are judged by the reflection coefficient and reflected power coefficient.

7. A grounding cable high-frequency signal detection method according to claim 6, characterized in that: The fault type and switch status are judged by the reflection coefficient and the reflection power coefficient, as shown in the following table:

8. A grounding cable high frequency signal detection system, characterized in that: include: The cable reflection signal acquisition module is used to capture the partial reflection signal generated by the detection signal injected into the cable at the discontinuous point in the cable in step 1; The acquisition signal sparse representation module is used in step 2 to perform sparse transformation on the reflection signal captured in step 1, extract key features in the signal, remove redundant information in the signal, and obtain a sparse part containing signal features; A compression sampling module is used in step 3 to construct a Gaussian measurement matrix, perform random projection on the sparse signal obtained in step 2, realize compression sampling of the sparse signal, and output the compressed sparse signal; A signal reconstruction module, used in step 4, to process the sparse signal compressed in step 3 through a sparse signal reconstruction algorithm, and output a reconstructed original signal containing cable fault and location information; The fault detection and location module is used in step 5 to perform spectrum analysis on the reconstructed original signal outputted in step 4 to obtain the characteristics of the reflected signal; The fault type and switch state judgment module is used in step 6 to identify the fault signal according to the characteristics of the reflected signal obtained in step 5, and judge the cable fault type and the cable switch state and switch operation.

9. A grounding cable high frequency signal detection device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program so that the processor executes the grounding cable high-frequency signal detection method.

10. A program storage medium for receiving user input, characterized in that: When the stored computer program is executed by the processor, it can realize efficient detection and fault diagnosis of cable signals based on the grounding cable high-frequency signal detection method.

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