Cable soft fault positioning method, device, equipment and medium
Through the improved CEEMDAN and MUSIC algorithms, the accuracy and cost of cable soft fault positioning are solved, and efficient and accurate cable fault detection is achieved.
Patent Information
- Application Number
- CN202510620259.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-14
- Publication Date
- 2025-07-08
AI Technical Summary
When detecting soft cable failures, especially in complex environments, existing cable fault positioning technologies have problems such as high cost, complex equipment and inaccurate positioning. Traditional time-domain reflection methods and frequency-domain reflection methods are difficult to effectively deal with soft faults of long-domain cables.
The improved CEEMDAN method is used to decompose the real signal of the cable transmission function. Combined with the MUSIC algorithm, the frequency components related to soft faults are accurately extracted through orthogonal subspace analysis, and noise interference is reduced to achieve efficient positioning of soft faults in the cable.
It improves the accuracy of soft fault positioning of cables, reduces costs, simplifies operating procedures, and is suitable for long-distance cable fault detection in complex environments. It has the characteristics of high efficiency, accuracy and low cost.
Smart Images
Figure CN120275773A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of cable fault detection, and particularly relates to a method, device, equipment and medium for locating soft cable faults. Background Art
[0002] With the increase in the service life of power cables, the problem of insulation aging has become increasingly prominent. Under harsh environmental conditions, cables are prone to soft faults, such as partial discharge and corrosion of the copper shielding layer of the cable. Traditional cable fault location technologies often require the support of complex hardware equipment, which is costly and difficult to maintain, especially in on-site operations, it is difficult to achieve efficient detection. With the rapid development of smart grids and Industry 4.0, the requirements for cable detection methods are getting higher and higher. It is not only required to work stably in complex environments, but also to have the characteristics of high efficiency, accuracy and low cost.
[0003] The existing time domain reflectometry (TDR) is sensitive to the attenuation of high-frequency signals and is easily affected by multiple reflections when dealing with long-distance cables, making it difficult to accurately locate soft faults.
[0004] Although the frequency domain reflectometry (FDR) can obtain more frequency domain information, it is limited by the problems of spectral leakage and pseudo peaks, and the detection effect on soft faults is poor. Summary of the Invention
[0005] The purpose of the present invention is to provide a method, device, equipment and medium for locating soft cable faults to solve the problems existing in the detection of soft cable faults in the prior art.
[0006] To achieve the above purpose, the present invention adopts the following technical solutions: In the first aspect of the present invention, a method for locating soft cable faults is provided, including the following steps: Obtain the real part signal of the cable transfer function and rewrite the real part signal of the cable transfer function into a discrete expression; Based on the discrete expression, use the improved CEEMDAN method to decompose the real part signal to obtain the IMF components at each sampling point; reconstruct the real part signal from the IMF components, restore the reconstructed real part signal to a complex signal, and define a complex signal vector according to the restored complex signal; Convert the complex signal vector into an orthogonal subspace; wherein, the orthogonal subspace includes a signal subspace and a noise subspace; estimate the MUSIC-pseudo spectrum based on the noise subspace; Determine the location of the soft cable fault according to the MUSIC-pseudo spectrum.
[0007] Further, based on the discrete expression, using the improved CEEMDAN method to decompose the real part signal to obtain the IMF components at each sampling point includes: In the first iteration, the representation of the real part of the cable transfer function in the discrete domain is used as the original signal; white noise is added to the original signal to generate the original auxiliary signal at each sampling point ; for the original auxiliary signal at each sampling point perform EMD decomposition respectively to obtain the first-order residual signal at each sampling point , and calculate the mean value of the first-order residual signal according to the first-order residual signal at each sampling point ; obtain the first-order IMF at each sampling point through the difference between the original signal and the mean value of the first-order residual signal; In the k-th iteration, white noise is added to the mean value of the (k - 1)-th order residual signal to obtain the (k - 1)-th order auxiliary signal at each sampling point ; for the (k - 1)-th order auxiliary signal at each sampling point perform EMD decomposition respectively to obtain the k-th order residual signal at each sampling point , and calculate the mean value of the k-th order residual signal according to the k-th order residual signal at each sampling point ; obtain the k-th order IMF through the difference between the (k - 1)-th order residual signal and the mean value of the k-th order residual signal ; if the mean value of the k-th order residual signal is a monotonic signal or reaches the preset maximum decomposition order K, stop the decomposition, otherwise continue to iteratively calculate the next-order IMF until the stop condition is met.
[0008] Further, before the step of reconstructing the real part signal from the IMF components, it further includes: Screen the IMF components according to the kurtosis and energy entropy of the IMF components, and the screened IMF components are used for reconstructing the real part signal.
[0009] Further, converting the complex signal vector into an orthogonal subspace includes: Determine the covariance matrix of the complex signal vector, decompose the covariance matrix of the complex signal vector, and obtain the signal subspace and noise subspace corresponding to the complex signal vector.
[0010] In the second aspect of the present invention, a cable soft fault location device is provided, including: A signal acquisition module, configured to acquire the real part signal of the cable transfer function and rewrite the real part signal of the cable transfer function into a discrete expression; wherein, the real part signal of the cable transfer function includes the weak reflection signal generated by the cable soft fault and the interference signals of multiple reflections; A signal decomposition module, configured to decompose the real - part signal using an improved CEEMDAN method based on the discrete expression to obtain IMF components at each sampling point; reconstruct the real - part signal from the IMF components, restore the reconstructed real - part signal to a complex signal, and define a complex - signal vector according to the restored complex signal. A signal conversion module, configured to convert the complex - signal vector into an orthogonal subspace; wherein, the orthogonal subspace includes a signal subspace and a noise subspace; estimate the MUSIC - pseudo - spectrum based on the noise subspace. A positioning module, configured to determine the location of the cable soft fault according to the MUSIC - pseudo - spectrum.
[0011] Further, in the signal decomposition module, decomposing the real - part signal using an improved CEEMDAN method based on the discrete expression to obtain IMF components at each sampling point includes: In the first iteration, use the representation of the real part of the cable transfer function in the discrete domain as the original signal; add white - noise signals to the original signal to generate original auxiliary signals at each sampling point ; perform EMD decomposition on the original auxiliary signals at each sampling point respectively to obtain the first - order residual signals at each sampling point , calculate the mean value of the first - order residual signals according to the first - order residual signals at each sampling point ; obtain the first - order IMF at each sampling point through the difference between the original signal and the mean value of the first - order residual signals. In the k - th iteration, add white - noise signals to the mean value of the (k - 1) - th order residual signals to obtain the (k - 1) - th order auxiliary signals at each sampling point ; perform EMD decomposition on the (k - 1) - th order auxiliary signals at each sampling point respectively to obtain the k - th order residual signals at each sampling point , and calculate the mean value of the k - th order residual signals according to the k - th order residual signals at each sampling point ; obtain the k - th order IMF through the difference between the (k - 1) - th order residual signals and the mean value of the k - th order residual signals ; if the mean value of the k - th order residual signals is a monotonic signal or reaches the preset maximum decomposition order K, stop the decomposition, otherwise continue the iterative calculation of the next - order IMF until the stop condition is met.
[0012] Further, before reconstructing the real - part signal from the IMF components in the signal decomposition module, it further includes: Screen the IMF components according to the kurtosis and energy entropy of the IMF components, and the screened IMF components are used for reconstructing the real - part signal.
[0013] Further, in the signal conversion module, converting the complex signal vector into an orthogonal subspace includes: Determine the covariance matrix of the complex signal vector, decompose the covariance matrix of the complex signal vector, and obtain the signal subspace and noise subspace corresponding to the complex signal vector.
[0014] In a third aspect of the present invention, an electronic device is provided, including a processor and a memory. The processor is configured to execute a computer program stored in the memory to implement the cable soft fault location method as described above.
[0015] In a fourth aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the cable soft fault location method as described above is implemented.
[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: The cable soft fault location method provided by the present invention, by combining the improved CEEMDAN algorithm and the MUSIC algorithm, can accurately extract the frequency components related to soft faults from the real part signal of the complex cable transfer function, effectively reduce the interference of other noises, and improve the location accuracy of cable soft faults. The improved CEEMDAN algorithm and the MUSIC algorithm have powerful signal processing capabilities. Based on this, the method of the present invention is applicable to the fault detection of long-distance cables and can also work stably in complex environments. A cable soft fault location device, an electronic device, and a computer-readable storage medium provided by the present invention also solve the problems raised in the background art section.
[0017] This method mainly relies on signal processing and spectrum analysis technologies, without the support of complex hardware devices, reducing costs and simplifying the operation process. In on-site operations, cable fault detection can be carried out more efficiently.
[0018] Compared with traditional cable fault location technologies, the present invention does not require a large investment in hardware devices and has a lower maintenance cost. At the same time, due to the simple operation, a large amount of labor cost and time cost can be saved.
[0019] The method of the present invention is applicable to the soft fault location of power cables and can also be extended to the fault detection of other types of cables and transmission lines, enhancing the application value and market potential of the method.
[0020] In summary, the cable soft fault location method provided by the present invention has the advantages of high location accuracy, simple operation, strong adaptability, high cost-effectiveness, and wide application range, providing a new and effective means for cable fault detection and having important practical application value. Description of the Drawings
[0021] The accompanying drawings of the specification, which form a part of the present application, are used to provide a further understanding of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a schematic flow chart of a method for locating a soft cable fault according to an embodiment of the present invention; Figure 2 is a schematic diagram of the principle of a method for locating a soft cable fault according to an embodiment of the present invention; Figure 3 is a schematic diagram of an acceptance array model in an embodiment of the present invention; Figure 4 is a schematic diagram of a defective fault model in an embodiment of the present invention; Figure 5 is a simulation location diagram of a soft fault in an embodiment of the present invention; Figure 6 is a location diagram of a soft fault example in an embodiment of the present invention; Figure 7 is a structural block diagram of a device for locating a soft cable fault according to an embodiment of the present invention; Figure 8 is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Embodiments
[0022] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other.
[0023] The following detailed descriptions are all exemplary descriptions, aiming to provide further detailed descriptions of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as commonly understood by those of ordinary skill in the art to which the present application belongs. The terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention.
[0024] Embodiment 1 As Figure 1 and Figure 2 shown, a method for locating a soft cable fault includes the following steps: S1. Obtain the real part signal of the cable transfer function and rewrite the real part signal of the cable transfer function into a discrete expression.
[0025] Among them, the real part signal of the cable transfer function includes the weak reflection signal generated by the soft cable fault and the interference signals of multiple reflections.
[0026] Specifically, rewrite the real part signal of the cable transfer function into a discrete expression: ; whereX ( n ) is the representation of the real part of the cable transfer function in the discrete domain, n is the serial number of the sampling point, q is the order of the harmonic, is the -th reflection coefficient, is the i -th frequency corresponding to the reflection, = , is the wave velocity of the signal transmission in the cable, is the distance from the reflection point to the measurement point.
[0027] Specifically, the real part signal of the cable transfer function can be measured by an LCR radio frequency impedance tester. For example, a linear frequency sweep signal is injected into the cable, and the real part of the cable transfer function is measured by the LCR radio frequency impedance tester.
[0028] S2. Based on the discrete expression, use the improved CEEMDAN method to decompose the real part signal to obtain the IMF components of each sampling point; reconstruct the IMF components to obtain the real part signal, restore the reconstructed real part signal to a complex signal, and define a complex signal vector according to the restored complex signal.
[0029] Specifically, in the first iteration, the representation of the real part of the cable transfer function in the discrete domain is used as the original signal; white noise signal is added to the original signal to generate the original auxiliary signal of each sampling point; the EMD decomposition is performed on the original auxiliary signal of each sampling point respectively to obtain the first-order residual signal of each sampling point, and the first-order residual signal mean is calculated according to the first-order residual signal of each sampling point; the first-order IMF of each sampling point is obtained through the difference between the original signal and the first-order residual signal mean; In the k-th iteration, white noise signal is added to the (k - 1)-th order residual signal mean to obtain the (k - 1)-th order auxiliary signal of each sampling point; the EMD decomposition is performed on the (k - 1)-th order auxiliary signal of each sampling point respectively to obtain the k-th order residual signal of each, and the k-th order residual signal mean is calculated according to the k-th order residual signal of each; the k-th order IMF is obtained through the difference between the (k - 1)-th order residual signal and the k-th order residual signal mean ; if the k-th order residual signal mean If it is a monotonic signal or reaches the preset maximum decomposition order K, stop the decomposition; otherwise, continue the iterative calculation for the next order of IMF until the stop condition is met.
[0030] As a specific embodiment, step S2 includes: S21. In the first iteration, the real part of the cable transfer function represented in the discrete domain is used as the original signal; white noise signal is added to the original signal to generate the original auxiliary signal for each sampling point ;
[0031] where, is the noise intensity coefficient, used to control the amplitude of the noise; represents the white noise signal; is the normalization process for the white noise signal .
[0032] The original auxiliary signal for each sampling point is respectively subjected to EMD decomposition to obtain the first-order residual signal for each sampling point , and the first-order residual signal mean is calculated according to the first-order residual signal for each sampling point;
[0033] where, N is the total number of sampling points.
[0034] The first-order IMF for each sampling point is obtained through the difference between the original signal and the first-order residual signal mean:
[0035] S22. In the second iteration, white noise signal is added to the first-order residual signal mean to obtain the first-order auxiliary signal for each sampling point ; ; The first-order auxiliary signal for each sampling point is respectively subjected to EMD decomposition to obtain the second-order residual signal for each sampling point , and the second-order residual signal mean is calculated according to the second-order residual signal for each sampling point: ; The second-order IMF for each sampling point is obtained through the difference between the first-order residual signal and the second-order residual signal mean : ; S23. In the third iteration, white noise signal is added to the second-order residual signal mean to obtain the second-order auxiliary signal for each sampling point ; ; For the second-order auxiliary signals at each sampling point perform EMD decomposition respectively to obtain the third-order residual signals at each point , and calculate the mean value of the third-order residual signals based on the third-order residual signals at each point : ; Obtain the third-order IMF at each sampling point through the difference between the second-order residual signal and the mean value of the third-order residual signals : ; S24. In the k-th iteration, add white noise signals to the mean value of the (k - 1)-th order residual signals to obtain the (k - 1)-th order auxiliary signals at each sampling point ;
[0036] where is the noise intensity coefficient; Perform EMD decomposition on the (k - 1)-th order auxiliary signals at each sampling point respectively to obtain the k-th order residual signals at each point , and calculate the mean value of the k-th order residual signals based on the k-th order residual signals at each point :
[0037] Obtain the k-th order IMF through the difference between the (k - 1)-th order residual signal and the mean value of the k-th order residual signals :
[0038] If the mean value of the k-th order residual signals is a monotonic signal or reaches the preset maximum decomposition order K, stop the decomposition. Otherwise, continue the iterative calculation for the next order of IMF until the stop condition is met.
[0039] It should be noted that the white noise added at different sampling points is independently generated, and the white noise added in each iteration is also different.
[0040] S25. Screen the IMF by combining energy entropy and kurtosis.
[0041] Specifically, S25 includes: Calculate the kurtosis value of each IMF:
[0042] where: is the mean value of; is The standard deviation; is the fourth-order central moment.
[0043] Calculate the energy entropy of each IMF:
[0044]
[0045]
[0046] where: is the energy of the k-th IMF; is the energy proportion of the k-th IMF; is the energy entropy of the k-th IMF.
[0047] Remove the IMFs with head-end reflections, and preferentially select the IMFs with high kurtosis values and low energy entropy. As an example, the kurtosis of the cable fault signal is significantly higher than that of the noise (e.g., the kurtosis of the fault IMF > 5). Therefore, distinguish the noise and the cable fault signal according to the kurtosis; then screen the IMFs according to the preset entropy value threshold; for example, the entropy value threshold takes the average of the energy entropies of all IMFs, and select the IMF components with entropy values lower than 60% of the mean for the reconstruction in the subsequent steps.
[0048] S26. Reconstruct the screened IMF components to obtain the reconstructed real part signal, restore the reconstructed real part signal to a complex signal, and define a complex signal vector according to the restored complex signal.
[0049] The reconstructed real part signal can be expressed as follows: = (11) Restore the reconstructed real part signal to a complex signal: (12) Define a complex signal vector according to the restored complex signal: (13) In the formula, represents the complex signal restored according to the reconstructed real part signal; μ jj represents that each IMF represents a specific frequency band component in the original signal; jj represents the cut-off serial number of the IMF selected when reconstructing the soft fault signal, that is, starting from the 2nd IMF to the jj -th IMF for superposition to reconstruct the soft fault signal; j represents the serial number of the intrinsic mode function IMF; q is the order of the harmonic; Denote the amplitude of the i-th reconstructed harmonic; n represents the discrete-time variable used to discretize the continuous form of the cable transfer function, representing the sampling point number; e(n) represents additive white noise with a mean of zero and a variance of ; Denote the complex exponential form, is the angular frequency; S ( n ) denotes the complex signal vector defined according to the restored complex signal, which contains the sampled values of the complex signal at different times; M Denote having array elements.
[0050] S3. Convert the complex signal vector into an orthogonal subspace; wherein, the orthogonal subspace includes a signal subspace and a noise subspace; estimate the MUSIC-pseudo-spectrum based on the noise subspace.
[0051] Figure 3 is the receiving array model of formula (13). The MUSIC algorithm was initially used to solve the problem of estimating the arrival angle of radar received signals. The array is equivalent to having multiple antennas when receiving signals. Since the cable signal is similar to this, this algorithm is used to estimate different frequencies. The different frequencies are as follows: The reflected signal of a defective cable can be regarded as composed of signals of different frequencies, and it can be understood that each defect corresponds to a signal of a specific frequency. Figure 3 in is the distance between the m-th array element and the first array element. For the array observation model, the following assumptions are made: Assumption 1: For the injection signals of different frequencies , the fault reflected signals at the m-th frequency are linearly independent of each other.
[0052] Assumption 2: is additive white noise with a mean of zero and a variance of , which is independent of the injection signal.
[0053] Since the injection signals of different frequencies are injected into the cable at different times, Assumption 1 is satisfied. The noise in actual measurement is usually Gaussian white noise, so Assumption 2 is satisfied.
[0054] The basic principle of the MUSIC algorithm is to decompose the covariance matrix of the array data to obtain the signal subspace and the noise subspace corresponding to the signal components. The signal subspace and the noise subspace are orthogonal to each other. Then, estimate the spatial spectrum and perform a full-frequency domain search to find the harmonic frequencies corresponding to all peaks.
[0055] S3 specifically includes: determining the covariance matrix of the complex signal vector, decomposing the covariance matrix of the complex signal vector to obtain the signal subspace and the noise subspace corresponding to the complex signal vector, including the following steps: Definition s ( n ) 、C and e ( n ) respectively satisfy the following equations: (14) In the formula, C represents the transmission matrix, represents the steering vector corresponding to the frequency, and its elements are related to the frequency and the length of the complex signal vector. The steering vector is used to relate the frequency information to the signal subspace and the noise subspace in the calculation of the MUSIC pseudospectrum. By calculating the orthogonality relationship between the steering vector and the noise subspace, the MUSIC pseudospectrum values at different frequencies are obtained.
[0056] Then, combining equations (12), (13), and (14) gives the following equation: (15) Calculation S ( n ) covariance matrix R is: (16) Among them, P is s ( n ) covariance matrix, which is a diagonal matrix; E represents the expectation operation, H represents the conjugate transpose operation, CP represents the result of the transmission matrix C, the covariance matrix P of the complex signal vector, and the conjugate transpose of C, represents the variance, represents the identity matrix, which plays a role similar to "1" in real number operations in matrix operations. In the formula, the identity matrix is used to represent the contribution of the noise part to the covariance matrix to ensure the correct representation of the noise in the covariance matrix; (17) It can be seen from (17) that R is a positive definite matrix. The eigenvalue decomposition of R: ; among them, U is the eigenvector matrix of R; = diag( λ 11 , λ 22 …, λ qq … λ MM ) is the eigenvalue matrix of R, which is the eigenvalue matrix containing the first q large eigenvalues of E; e contains Eigenvalue matrix of the last M - q small eigenvalues of s.
[0057] From Assumption 2, define λ ii as: (18) where, λ ii represents the eigenvalues of the covariance matrix R. In the MUSIC algorithm, the eigenvalues of the covariance matrix are divided into two categories. The first q larger eigenvalues (corresponding to ii = 1, 2…, q ) are related to the signal components and contain the energy information of the signal; the subsequent M-q M - q smaller eigenvalues (corresponding to ii = q + 1, …, M) are mainly related to the noise and are used to construct the noise subspace; After performing eigenvalue decomposition on the covariance matrix, its eigenvalues and the corresponding eigenvectors will be obtained. Sort the calculated eigenvalues in descending order to obtain formula (18). The first q larger eigenvalues are the eigenvalues of the signal subspace; the subsequent M - q smaller eigenvalues are the eigenvalues of the noise subspace. These M - q eigenvalues correspond to M - q eigenvectors, and the noise subspace Ue is composed of these M - q eigenvectors.
[0058] Estimate the MUSIC - pseudospectrum based on the noise subspace : (19) where, U is the eigenvector matrix obtained by eigenvalue decomposition of the covariance matrix, is the matrix representation of the noise subspace; is the frequency corresponding steering vector.
[0059] S4. Determine the location of the cable soft fault according to the MUSIC - pseudospectrum.
[0060] Specifically, determining the location of the cable soft fault according to the MUSIC - pseudospectrum includes: performing a frequency - domain scan on the MUSIC - pseudospectrum to find all significant peaks, and the accurate frequency of each harmonic can be estimated. Determine the location of the cable soft fault according to the frequency corresponding to the significant peak and the cable soft fault location expression.
[0061] More specifically, determining the location of the cable soft fault according to the frequency corresponding to the significant peak and the cable soft fault location expression includes: the distance from the i th soft fault location to the measurement point is: x i = v / 2 (20) Among them, v is the propagation speed of the signal in the cable.
[0062] To verify the effectiveness of the proposed method, a simulation model was established as Figure 4 shown. A simulation program was written in MATLAB 2017a to simulate the process of moisture intrusion into the underground cable.
[0063] The model parameters were set as = 800 m; = 0.3 m; = 200 m; = 0.5 m; Terminal B was set to open circuit ( = ), and the sampling step = 20000 Hz.
[0064] Among them, is the total length of the cable; is the length of the connection between the signal source and the cable, which can be understood as the connecting wire; is the distance from the head end of the cable to the fault; is the total length of the fault defect.
[0065] End A is the test end, and a series of sine signals with frequencies ranging from 0.15 to 100 MHz are injected into End A by the frequency sweeping method. The result of improving the CEEMDAN decomposition of the cable transfer function is as Figure 5 (a) shown. It can be seen from Figure 5 (a) that after improving the CEEMDAN decomposition of the cable transfer function, the real part signal of the cable transfer function can be decomposed into multiple intrinsic mode functions (IMFs), and these IMFs respectively represent the components in different frequency bands of the original signal, which helps to extract the frequency feature information related to the soft fault from the complex cable signal and provides a basis for subsequent fault location. The positioning result was compared with the traditional FDR positioning result, as Figure 5 (b) shown. Figure 5 The blue dotted line in (b) indicates that FDR locates the soft fault at the position of 200.3 m. However, under the influence of the interference wave, false peaks appear at 400.5 m and 600.1 m, which may lead to misjudgment of the soft fault. There are two small peaks at 351 m and 552.4 m due to spectral leakage. Since Figure 5 there are no false peaks in the red positioning curve in (b), the results show that the method of the present invention can effectively suppress the influence of the interference wave.
[0066] To verify the test feasibility of the method in the actual soft fault location of underground cables, a test of an example was carried out.
[0067] The case is that a 10 kV outdoor underground distribution cable stops operating due to a soft fault. According to the information provided by power system staff, the total length of the cable is about 1600 meters; joints are set at 264 m, 660 m, 850 m, and 1200 m respectively, and the cable insulation resistance is 50 M . A sweep signal from 0.15 to 10 MHz is injected into the test terminal at a step of 12462.5 Hz to measure the transfer function of the cable.
[0068] The results of decomposing the cable transfer function by improved CEEMDAN are as Figure 6 shown in (a). After decomposing the cable transfer function by improved CEEMDAN, the real part signal of the cable transfer function can be decomposed into multiple intrinsic mode functions (IMFs), and these IMFs respectively represent the components in different frequency bands of the original signal, which helps to extract frequency feature information related to soft faults from complex cable signals and provides a basis for subsequent fault location. The original cable transfer function and the reconstructed cable transfer function are as Figure 6 shown in (b). The positioning result is compared with the traditional FDR positioning result, as Figure 6 shown in (c).
[0069] As Figure 6 shown in (b), compared with , it can better reflect the periodicity and attenuation of harmonics. In addition to the five main peaks, there are many false peaks in the positioning curve of FDR, as Figure 6 shown by the blue dotted line in (c). In the position curve of this method, there are only five peaks, corresponding to the four joints and the cable terminal respectively, as Figure 6 shown by the red curve in (c). Therefore, there are no other soft faults in the cable.
[0070] It can be seen that by simulating and comparing the positioning results of the method of the present invention with those of the traditional FDR method on site, the superiority of this method is verified.
[0071] Embodiment 2 As Figure 7 shown, based on the same inventive concept as the above embodiment, the present invention also provides a cable soft fault positioning device, including: A signal acquisition module, which is used to acquire the real part signal of the cable transfer function and rewrite the real part signal of the cable transfer function into a discrete expression; wherein, the real part signal of the cable transfer function includes weak reflection signals generated by cable soft faults and interference signals of multiple reflections; A signal decomposition module, configured to decompose the real part signal using the improved CEEMDAN method based on the discrete expression to obtain the IMF components at each sampling point; reconstruct the IMF components to obtain the real part signal, restore the reconstructed real part signal to a complex signal, and define a complex signal vector according to the restored complex signal; A signal conversion module, configured to convert the complex signal vector into an orthogonal subspace; wherein, the orthogonal subspace includes a signal subspace and a noise subspace; estimate the MUSIC pseudo-spectrum based on the noise subspace; A positioning module, configured to determine the position of the cable soft fault according to the MUSIC pseudo-spectrum.
[0072] Embodiment 3 As Figure 8 shown, the present invention further provides an electronic device 100 for implementing a cable soft fault positioning method; The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
[0073] The memory 101 can be used to store the computer program 103. The processor 102 realizes the steps of the cable soft fault positioning method in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.
[0074] The memory 101 mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the electronic device 100 (such as audio data, etc.). In addition, the memory 101 can include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash device, or other non-volatile solid state storage devices.
[0075] At least one processor 102 can be a Central Processing Unit (CPU), or can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 can be a microprocessor or the processor 102 can also be any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and connects various parts of the entire electronic device 100 through various interfaces and lines.
[0076] The memory 101 in the electronic device 100 stores multiple instructions to implement a cable soft fault location method. The processor 102 can execute the multiple instructions to implement: Obtain the real part signal of the cable transfer function, and rewrite the real part signal of the cable transfer function into a discrete expression; Based on the discrete expression, use the improved CEEMDAN method to decompose the real part signal to obtain the IMF components of each sampling point; reconstruct the IMF components to obtain the real part signal, restore the reconstructed real part signal to a complex signal, and define a complex signal vector according to the restored complex signal; Convert the complex signal vector into an orthogonal subspace; where the orthogonal subspace includes a signal subspace and a noise subspace; estimate the MUSIC-pseudo spectrum based on the noise subspace; Determine the location of the cable soft fault according to the MUSIC-pseudo spectrum.
[0077] Embodiment 4 If the modules / units integrated in the electronic device 100 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, and read-only memory (ROM, Read-Only Memory).
[0078] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, system, or computer program product. Therefore, the present invention can take the form of an all-hardware embodiment, an all-software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0079] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.
[0080] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.
[0081] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, causing a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one process or a plurality of processes and / or blocks Figure 1 one process or a plurality of processes and / or blocks Figure 1 steps for implementing the functions specified in one block or a plurality of blocks.
[0082] In the description of the present specification, the descriptions with reference to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In the present specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.
[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: still modifications or equivalent replacements can be made to the specific embodiments of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.
Claims
1. A method for locating soft cable faults, characterized in that, It includes the following steps: Obtain the real part signal of the cable transfer function, and rewrite the real part signal of the cable transfer function into a discrete expression; Based on the discrete expression, use the improved CEEMDAN method to decompose the real part signal to obtain the IMF components of each sampling point; Reconstruct the IMF components to obtain the real part signal, restore the reconstructed real part signal to a complex signal, and define a complex signal vector according to the restored complex signal; Convert the complex signal vector into an orthogonal subspace; wherein, the orthogonal subspace includes a signal subspace and a noise subspace; estimate the MUSIC-pseudo spectrum based on the noise subspace; Determine the location of the cable soft fault according to the MUSIC-pseudo spectrum.
2. The cable soft fault location method according to claim 1, characterized in that, Based on the discrete expression, use the improved CEEMDAN method to decompose the real part signal to obtain the IMF components of each sampling point, including: In the first iteration, the representation of the real part of the cable transfer function in the discrete domain is used as the original signal; white noise is added to the original signal to generate the original auxiliary signal at each sampling point ; the original auxiliary signal at each sampling point is respectively decomposed by EMD to obtain the first-order residual signal at each sampling point , and the mean value of the first-order residual signal is calculated according to the first-order residual signal at each sampling point ; the first-order IMF at each sampling point is obtained by the difference between the original signal and the mean value of the first-order residual signal; In the k-th iteration, white noise signals are added to the mean of the (k - 1)-th order residual signals to obtain the (k - 1)-th order auxiliary signals at each sampling point ; perform EMD decomposition on the (k - 1)-th order auxiliary signals at each sampling point respectively to obtain the k-th order residual signals , and calculate the mean of the k-th order residual signals according to the k-th order residual signals ; obtain the k-th order IMF through the difference between the (k - 1)-th order residual signals and the mean of the k-th order residual signals ; if the mean of the k-th order residual signals is a monotonic signal or reaches the preset maximum decomposition order K, stop the decomposition, otherwise continue the iterative calculation of the next order IMF until the stop condition is satisfied.
3. The cable soft fault location method according to claim 1, characterized in that, Before the step of reconstructing the IMF components to obtain the real part signal, it further includes: Screen the IMF components according to the kurtosis and energy entropy of the IMF components, and the screened IMF components are used for reconstructing the real part signal.
4. The cable soft fault location method according to claim 1, characterized in that, Converting the complex signal vector into an orthogonal subspace includes: Determine the covariance matrix of the complex signal vector, decompose the covariance matrix of the complex signal vector, and obtain the signal subspace and noise subspace corresponding to the complex signal vector.
5. A cable soft fault location device, characterized in that, It includes: A signal acquisition module, configured to obtain the real part signal of the cable transfer function, and rewrite the real part signal of the cable transfer function into a discrete expression; wherein, the real part signal of the cable transfer function includes a weak reflection signal generated by the cable soft fault and interference signals of multiple reflections; A signal decomposition module, configured to decompose the real part signal using the improved CEEMDAN method based on the discrete expression to obtain the IMF components of each sampling point; reconstruct the IMF components to obtain the real part signal, restore the reconstructed real part signal to a complex signal, and define a complex signal vector according to the restored complex signal; A signal conversion module, configured to convert the complex signal vector into an orthogonal subspace; wherein, the orthogonal subspace includes a signal subspace and a noise subspace; estimate the MUSIC-pseudo spectrum based on the noise subspace; A positioning module, configured to determine the location of the cable soft fault according to the MUSIC-pseudo spectrum.
6. The cable soft fault location device according to claim 5, characterized in that, In the signal decomposition module, based on the discrete expression, using the improved CEEMDAN method to decompose the real part signal to obtain the IMF components of each sampling point, including: In the first iteration, the representation of the real part of the cable transfer function in the discrete domain is used as the original signal; white noise is added to the original signal to generate the original auxiliary signal at each sampling point ; for the original auxiliary signal at each sampling point EMD decomposition is performed separately to obtain the first-order residual signal at each sampling point , and the mean value of the first-order residual signal is calculated based on the first-order residual signal at each sampling point ; the first-order IMF at each sampling point is obtained by the difference between the original signal and the mean value of the first-order residual signal; In the k-th iteration, white noise signals are added to the mean of the (k - 1)-th order residual signals to obtain the (k - 1)-th order auxiliary signals at each sampling point ; the (k - 1)-th order auxiliary signals at each sampling point are respectively subjected to EMD decomposition to obtain the k-th order residual signals , and the mean of the k-th order residual signals is calculated based on the k-th order residual signals ; the k-th order IMF is obtained through the difference between the (k - 1)-th order residual signals and the mean of the k-th order residual signals ; if the mean of the k-th order residual signals is a monotonic signal or reaches the preset maximum decomposition order K, the decomposition is stopped; otherwise, the next order IMF is continuously iteratively calculated until the stop condition is met.
7. The cable soft fault location device according to claim 5, characterized in that, In the signal decomposition module, before reconstructing the IMF components to obtain the real part signal, it further includes: Screen the IMF components according to the kurtosis and energy entropy of the IMF components, and the screened IMF components are used for reconstructing the real part signal.
8. The cable soft fault location device according to claim 5, characterized in that, In the signal conversion module, converting the complex signal vector into an orthogonal subspace includes: Determine the covariance matrix of the complex signal vector, decompose the covariance matrix of the complex signal vector, and obtain the signal subspace and noise subspace corresponding to the complex signal vector.
9. An electronic device, characterized in that, It includes a processor and a memory, and the processor is configured to execute a computer program stored in the memory to implement the cable soft fault location method according to any one of claims 1 to 4.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the cable soft fault location method described in any one of claims 1 to 4 is implemented.