Cable segmentation wave velocity acquisition method, device and system based on Prony algorithm, and medium

Abstract

The invention provides a Prony algorithm-based cable segment wave velocity acquisition method, device and system, and a medium, and the method comprises the steps: testing a to-be-tested cable, and obtaining a cable signal transfer function; on the basis of the cable signal transfer function, in combination with cable joint distribution, constructing a cable signal approximation function based on a Prony method; constructing a linear prediction model of the cable signal based on the cable signal approximation function; performing denoising processing on the linear prediction model based on a singular value decomposition method to obtain a denoised linear prediction model; solving the denoised linear prediction model to obtain an attenuation coefficient; and calculating the segmented wave velocity of the cable based on the attenuation coefficient. According to the method, the Prony estimation method is combined with the singular value decomposition noise reduction algorithm, high-precision extraction of attenuation constants and segmented wave velocity decoupling are achieved, and then the electrical distance positioning precision is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of electric power engineering, and particularly relates to a cable segmented wave speed acquisition method, device, system and medium based on a Prony algorithm. BACKGROUND

[0002] With the ground transformation of urban power grids, cross-linked polyethylene cables are widely applied to urban power transmission lines due to their excellent insulation and heat resistance. As an important carrier of urban power transmission lines, the stable operation of cross-linked polyethylene cables has become the core of ensuring the stability and reliability of urban power grids. With the increasing of cable operation years, most of the urban underground cables have served for nearly 20 years, and a large area has entered the aging stage, which is prone to induce cable faults and seriously endanger the safe operation of power grids. Therefore, accurately locating the early defects and fault positions of power cables is of great significance to ensure the stable operation and maintenance of power transmission lines.

[0003] Partial discharge detection is an important means for evaluating the insulation state of cables. The partial discharge signal is collected online by installing distributed sensors, and the wave propagation principle is used to judge the position of the fault point. The accuracy is restricted by the accuracy of the wave speed parameter. At the same time, the power cable is usually composed of multiple layers of media such as body, insulation layer and shielding layer, and is prone to segmented aging due to electro-thermal stress in long-term operation, resulting in non-uniform distribution of wave speed along the cable length. The traditional uniform wave speed model cannot accurately reflect the actual working condition, which seriously restricts the reliability of fault location.

[0004] In recent years, the time domain reflection method and the frequency domain reflection method as the core of the traveling wave detection have a significant effect on the operation and maintenance of cables, which can quickly find cable defects and fault positions in the offline state of the cable, and therefore are widely used in the operation and maintenance detection of urban distribution power grids. However, the injected signal of the time domain reflection method is usually composed of a large number of low-frequency components and a small number of high-frequency components. Affected by the dispersion effect, the propagation speed of the low-frequency signal is not the same, which easily affects the time delay of the reflected wave signal and causes a large positioning error. At the same time, the skin effect of the cable conductor and the frequency-dependent characteristics of the insulation dielectric constant cause the propagation constant to change nonlinearly with the frequency, and the frequency domain reflection method usually uses fixed frequency points or narrowband analysis, which is difficult to decouple the coupling relationship between the attenuation constant and the phase constant, resulting in difficulty in obtaining the wave speed and large electrical distance deviation. SUMMARY

[0005] The present application aims to solve the problems of the prior art, such as insensitivity to wave speed spatial distribution and difficulty in phase parameter decoupling. A cable segmented wave speed acquisition method, device, system and medium based on a Prony algorithm are provided. The Prony estimation method is combined with the singular value decomposition denoising algorithm to realize high-precision extraction of the attenuation constant and decoupling of the segmented wave speed, thereby improving the electrical distance positioning accuracy.

[0006] To achieve the above object, the present application adopts the following technical scheme.

[0007] The present application provides a cable segment wave speed acquisition method based on Prony algorithm, which comprises the following steps:

[0008] Testing the cable to be measured to obtain a cable signal transfer function;

[0009] Based on the cable signal transfer function, an approximate function of the cable signal based on Prony method is constructed in combination with the cable joint distribution;

[0010] Based on the approximate function of the cable signal, a linear prediction model of the cable signal is constructed;

[0011] The linear prediction model is denoised based on a singular value decomposition method to obtain a denoised linear prediction model;

[0012] The denoised linear prediction model is solved to obtain an attenuation coefficient;

[0013] The cable segment wave speed is calculated based on the attenuation coefficient.

[0014] Further, the approximate function of the cable signal is expressed as:

[0015] ;

[0016] Wherein, A i is the amplitude, a i is the damping coefficient, θ i is the initial phase angle, and ∆t is the sampling interval, , , , is 2x i / v, x i is the equivalent position at the i-th joint.

[0017] Further, the linear prediction model of the cable signal is specifically:

[0018] ;

[0019] Wherein, x is a historical signal matrix, a p is the estimated attenuation coefficient, is a target signal matrix, p is the model order, and N is the total number of sampling points.

[0020] Further, the denoising processing of the linear prediction model based on the singular value decomposition method comprises:

[0021] Let: ;

[0022] The singular value decomposition matrix is:

[0023] ;

[0024] Wherein, R and V are orthogonal matrices, respectively, P is the left singular matrix and the right singular matrix of P; The singular value matrix is: the singular values in the singular value matrix are arranged in descending order;

[0025] The mean value of all singular values is used as the threshold gate:

[0026] ;

[0027] There is an m, so that λ m ≥gate≥λ m+1 The diagonal matrix of the main signal component is:

[0028] ;

[0029] For smaller singular values λ m+1 …λ p , the singular values greater than m in the singular value decomposition matrix are set to 0 to suppress the influence on the cable transfer function, and the linear prediction model after singular value decomposition processing is:

[0030]

[0031] Wherein, x s is the original signal matrix corrected by singular value decomposition, a p为 is the estimated attenuation coefficient, is the target signal matrix, and the linear prediction model of the cable signal is corrected by combining the singular value decomposition matrix, and the denoising processing of the linear prediction model is completed.

[0032] Further, the denoised linear prediction model is solved to obtain the attenuation coefficient, including:

[0033] For the reflected wave at the i th joint, the attenuation constant is calculated according to the following formula:

[0034] (11);

[0035] Wherein, x i represents the position of the i th joint, a i is the estimated attenuation coefficient, and Δf is the scanning frequency step, and n is the index;

[0036] The cable segment wave velocity is calculated based on the attenuation coefficient, including:

[0037] ;

[0038] wherein, is the input signal frequency, is the dielectric constant of the insulating medium, is a constant.

[0039] A cable segment wave velocity acquisition device based on Prony algorithm, comprising:

[0040] A cable signal transfer function acquisition module, configured to test a to-be-tested cable and acquire a cable signal transfer function;

[0041] A cable signal transfer function construction module, configured to construct a cable signal approximation function based on the Prony method based on the cable signal transfer function and in combination with cable joint distribution;

[0042] A linear prediction model construction module, configured to construct a linear prediction model of the cable signal based on the cable signal approximation function;

[0043] A linear prediction model denoising module, configured to perform denoising processing on the linear prediction model based on a singular value decomposition method to obtain a denoised linear prediction model;

[0044] An attenuation coefficient acquisition module, configured to solve the denoised linear prediction model to obtain an attenuation coefficient;

[0045] A cable segment wave velocity acquisition module, configured to calculate a cable segment wave velocity based on the attenuation coefficient.

[0046] Further, the cable signal approximation function is expressed as:

[0047] ;

[0048] wherein, A i is an amplitude, a i is a damping coefficient, θ i is an initial phase angle, and ∆t is a sampling interval, , , , is 2x i / v, and x i is an equivalent position at the i-th joint.

[0049] Further, the linear prediction model of the cable signal is specifically:

[0050] ;

[0051] wherein, x is a historical signal matrix, a p is an estimated attenuation coefficient, is a target signal matrix, p is a model order, and N is a total number of sampling points.

[0052] Further, the linear prediction model denoising module denoises the linear prediction model based on a singular value decomposition method, including:

[0053] Let:

[0054] The singular value decomposition matrix is:

[0055]

[0056] R and V are orthogonal matrices, and and are left and right singular matrices of P, respectively; is a singular value matrix; singular values in the singular value matrix are arranged in descending order;

[0057] The mean value of all singular values is used as a threshold gate:

[0058]

[0059] There is an m, such that λ m ≥ gate ≥ λ m+1 The diagonal matrix of the main signal component is:

[0060]

[0061] For smaller singular values λ m+1 … λ p , singular values greater than m in the singular value decomposition matrix are set to 0 to suppress the influence on the cable transfer function. The linear prediction model after singular value decomposition processing is:

[0062]

[0063] where x s is the original signal matrix after singular value decomposition correction, a p为 is an estimated attenuation coefficient, is a target signal matrix, and the linear prediction model of the cable signal is corrected by combining the singular value decomposition matrix, to complete the denoising processing of the linear prediction model.

[0064] Further, the attenuation coefficient acquisition module solves the denoised linear prediction model to obtain the attenuation coefficient, including:

[0065] For the reflected wave at the i th joint, the attenuation constant is calculated according to the following formula:

[0066] (11) ​​​​

[0067] wherein, x i denotes the position of the i-th joint, a i is the estimated attenuation coefficient, delta f is the scanning frequency step, and n is the index;

[0068] The cable segment wave speed acquisition module calculates the cable segment wave speed based on the attenuation coefficient, comprising:

[0069] ;

[0070] wherein, is the input signal frequency, is the dielectric constant of the insulating medium, is a constant.

[0071] A cable segment wave speed acquisition system based on Prony algorithm, comprising: a computer readable storage medium and a processor;

[0072] The computer readable storage medium is used to store executable instructions;

[0073] The processor is used to read the executable instructions stored in the computer readable storage medium, and execute the cable segment wave speed acquisition method based on Prony algorithm.

[0074] A non-transitory computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the cable segment wave speed acquisition method based on Prony algorithm.

[0075] The cable segment wave speed acquisition method based on Prony algorithm provided by the present application has the following beneficial effects:

[0076] 1) The present application is based on Prony method, constructs a linear prediction model, and can realize the decoupling of the attenuation coefficient; at the same time, singular value decomposition is introduced to realize denoising, and the influence of noise on the linear prediction model is reduced;

[0077] 2) The present application combines Prony estimation and singular value decomposition denoising technology, realizes the technical bottleneck of phase-attenuation parameter coupling analysis, and realizes high-precision decoupling of cable segment wave speed;

[0078] 3) The present application can improve the cable electrical distance positioning accuracy based on the cable segment wave speed. BRIEF DESCRIPTION OF DRAWINGS

[0079] Figure 1 is the cable transfer function obtained in experimental example 1;

[0080] Figure 2 is the cable positioning spectrum obtained in experimental example 1;

[0081] Figure 3Envelope fitting result on cable transfer function after singular value decomposition processing in experimental example 1;

[0082] Figure 4 Cable positioning spectrum obtained in experimental example 2;

[0083] Figure 5 Cable transfer function obtained in experimental example 2.

[0084] Figure 6 Flow chart of a cable electrical distance positioning method based on Prony algorithm. DETAILED DESCRIPTION

[0085] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, 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 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 other embodiments obtained by those of ordinary skill in the art without creative labor fall within the protection scope of the present application.

[0086] Please refer to Figure 6 The present embodiment provides a cable electrical distance positioning method based on Prony algorithm, which comprises the following steps:

[0087] S1, testing the cable to be measured to obtain a cable signal transfer function.

[0088] In this step, a vector network analyzer is used to inject an electromagnetic wave with a known power and phase into the first end of the cable, a reflected wave is tested, and a first end reflection coefficient is obtained by calculating the ratio of the reflected wave signal to the incident wave signal as the cable signal transfer function. The real part module of the cable signal transfer function is taken as x(n) for subsequent analysis.

[0089] S2, constructing a cable signal approximation function based on the Prony method based on the cable signal transfer function and in combination with cable joint distribution.

[0090] Suppose that the cable has p points of impedance mismatch (here, the number of joints), then the transfer function will have at least p reflected harmonics, and the cable transfer function Γ(ω) is:

[0091] (1);

[0092] wherein A i is the amplitude of the reflected wave at the i th joint, x i is the equivalent position of the i th joint, α i (ω) is the cable attenuation coefficient of the reflected wave at the i th joint, and β i(ω) is the phase coefficient of the reflected wave at the i th joint, ω = 2πf, f is the frequency of the injected signal. Since the real part and the imaginary part of the above formula are equivalent, the real part is Euler expanded to obtain:

[0093] (2) ;

[0094] where k is the attenuation constant, Δf is the step of the scanning frequency, = 2x i / v is the equivalent frequency, that is, there is a reflected wave at the position x i of the cable, and the transfer function of the cable will have a harmonic component with a resonance frequency of , the size of which is related to the position x i of the impedance mismatch point. The modulus of the Euler expanded transfer function is taken and set to x(n)

[0095] (3) ;

[0096] where , .

[0097] The amplitude spectrum of the cable transfer function contains the amplitude information of multiple reflected waves, and in mathematics, it can be expressed as the linear superposition of p exponential decay signals. The Prony method based on the linear combination fitting of exponential terms has a natural advantage in extracting exponential decay signals. By solving the linear prediction matrix equation, the parameters such as amplitude, phase, and damping coefficient of the signal can be accurately obtained from the complex sampling data.

[0098] Therefore, the cable signal can be approximated as a linear combination of different modes, and the approximate function of the cable signal is expressed as:

[0099] (4) ;

[0100] where A i is the amplitude, a i is the damping coefficient, that is, the attenuation coefficient, θ i is the initial phase angle, Δt is the sampling interval, , , , is (2x i / v), x i is the equivalent position of the i th joint, and v is the speed of electromagnetic waves. The least squares of the error can be used as the optimization objective of the fitting function for parameter estimation, and the objective function ε is:

[0101] (5) ;

[0102] In order to fit the real function x(n) closely, it is necessary to make Minimum.

[0103] S3, constructing a linear prediction model of the cable signal based on the cable signal approximation function.

[0104] The linear prediction model of the cable signal according to the objective function is:

[0105] (6);

[0106] Wherein, a p That is, the estimated attenuation coefficient. By decoupling the attenuation coefficient, the wave velocity of the cable can be obtained. However, the cable often operates in complex working conditions, and due to noise interference, the signal-to-noise ratio of the cable transfer function is reduced, and in severe cases, the shape of the transfer function will be distorted, so further processing is required.

[0107] S4, denoising the linear prediction model based on the singular value decomposition method to obtain a denoised linear prediction model.

[0108] Because the traditional Prony method has a great influence on the data matrix of the noisy signal. Singular value decomposition is used to reduce the order of the data matrix to extract the main features of the signal to improve numerical stability and robustness.

[0109] Let: (7);

[0110] The singular value decomposition matrix is:

[0111] (8);

[0112] Wherein, R and V are orthogonal matrices, respectively, the left singular matrix and the right singular matrix of P; is a singular value matrix; the singular values in the singular value matrix are arranged in descending order. The singular value corresponds to the contribution of different components to the amplitude envelope after modal decomposition, that is, the higher the attenuation of a transfer function component, the larger the corresponding singular value. For the noise signal of the original matrix, the singular values corresponding to the first few signal components can be obtained, and the noise signal corresponds to the remaining singular values. In order to distinguish the boundary conditions of the useful signal and the noise signal, a threshold needs to be set. Here, the mean of all singular values is used as the threshold gate, that is,

[0113] (9);

[0114] Therefore, there is an m, such that λ m ≥gate≥λ m+1 The diagonal matrix of the main signal component is

[0115] (10);

[0116] for smaller singular values λ m+1 … λ p The noise component can be set to 0, i.e., the singular values greater than m in equation (8) are set to 0, to suppress the influence on the cable transfer function.

[0117] Therefore, in combination with equations (7) and (8), equation (6) is modified to complete the denoising processing of the linear prediction model.

[0118] The linear prediction model after singular value decomposition processing is:

[0119]

[0120] where x s is the original signal matrix after singular value decomposition correction, a p为 is the estimated attenuation coefficient, is the target signal matrix, and the linear prediction model of the cable signal is modified in combination with the singular value decomposition matrix to complete the denoising processing of the linear prediction model.

[0121] S5, solving the denoised linear prediction model to obtain the attenuation coefficient.

[0122] In this embodiment, the least square method is used to solve the denoised linear prediction model (6) to obtain the attenuation coefficient , and the specific solving process is as follows.

[0123] (1) Construct the objective function to minimize the sum of squared errors between the predicted value and the actual value. The minimized objective function is as follows

[0124]

[0125] where j is an index variable traversing p to N-1, J is the minimized objective function, a i is the predicted attenuation coefficient.

[0126] (2) Derive the objective function and set the derivative to zero,

[0127]

[0128] (3) Obtain the equation below to obtain the predicted attenuation coefficient a

[0129]

[0130] where,

[0131]

[0132] S6, calculating the cable segment wave velocity based on the attenuation coefficient.

[0133] For the reflected wave at the i-th joint, the attenuation constant is calculated according to the following formula:

[0134] (11) ;

[0135] Where, x i represents the position of the i-th joint,

[0136] Then, according to the attenuation constant, the segmented wave speed is calculated according to the following formula:

[0137] ;

[0138] Where, ω = 2πf, f is the frequency of the injected signal, is the dielectric constant of the insulating medium, taking the value of 2.2~2.3, is a constant, related to the specification and voltage level of the cable, here taking the value of 3.5×10 -4 .

[0139] In order to verify the effectiveness of the method, a simulation model is established in Matlab 2018b. In the simulation, the cable is a 1500 m, 10 kV cross-linked polyethylene cable, and the commonly used 500 m is a roll. Therefore, the intermediate joint is connected at 500 m and 1000 m, and the capacitance change multiple of the intermediate joint is set to 0.85 times the capacitance of the cable body. The other specifications of the cable are shown in Table 1.

[0140] Table 1 Cable simulation parameters

[0141]

[0142] The parameters of the test signal are: the frequency interval is 150 kHz~1 MHz, the sampling point number is 3001, and the sampling method is uniform sampling. First, the segmented wave speed is calculated by the method, and then the electrical positioning is carried out. The FDR positioning results and the cable transfer function are shown in Figure 1 、 2 .

[0143] Orange is the original cable transfer function, blue is the singular value decomposition of the cable transfer function. From the test results, the cable transfer function contains a large amount of noise, which will not only affect the overall shape of the transfer function spectrum, but also greatly affect the identification of the reflection peak. After singular value decomposition noise reduction, the smaller singular values are truncated and set to zero, thereby strengthening the main components of the original data, and the reconstructed matrix has a lower rank, eliminating the interference of high-frequency noise, making the amplitude distribution of the noise tend to be uniform, and the upper envelope spectrum of the cable transfer function will not exist larger distortion. Therefore, the envelope spectrum of the transfer function after singular value decomposition is fitted, as shown in Figure 3 .

[0144] According to the fitting results, the amplitude spectrum, attenuation constant and electromagnetic wave velocity of the cable are shown in Table 2.

[0145] Table 2 Calculation results

[0146]

[0147] According to the decoupling attenuation constant, the wave velocity distribution of each section of the 1500 meter cable line can be well represented. However, it is worth noting that the wave velocity value here is the average wave velocity of each section.

[0148] Experimental example 2

[0149] In order to further verify the accuracy of the results, a 10kV cross-linked polyethylene power cable is tested in a certain place. The total length of the cable is 682 meters, there is a middle joint at 328 meters, and it was put into operation in 2014, the insulation resistance test is MΩ level, and there is cable aging.

[0150] The test frequency is 150kHz~20MHz, the test point number is 3001, the sampling is equidistant, and the wave velocity is set to 172m / μs. First, the wave velocity of each section is calculated by the method of the application, and then the electrical positioning is carried out by the FDR positioning method. The FDR positioning result and the cable transfer function are shown in Figure 4 , 5 .

[0151] The orange curve is the original test data, the blue is the data after singular value decomposition noise reduction, and the purple is the data fitted by the Prony method. The actual cable in operation has a lot of noise signals due to the complex site environment, and a lot of stray peaks interference are generated on the positioning spectrum. After singular value decomposition noise reduction, noise interference can be well suppressed, and good data is provided for the Prony method. The test results show that the total length of the cable is consistent with the given cable information, but the joint position has a deviation of +12 meters. By decoupling the attenuation constant and the electromagnetic wave velocity of each section through the model, the segmented attenuation constant and the electromagnetic wave velocity are shown in Table 3.

[0152] Table 3 Cable test data

[0153]

[0154] From the decoupling result, it can be known that the cable electromagnetic wave velocity at 0-340 meters is obviously decreased, which is the cable segment that is more seriously aged.

[0155] The embodiment of the application further provides a cable segment wave velocity acquisition device based on the Prony algorithm, comprising:

[0156] The cable signal transfer function acquisition module is used for testing the cable to be measured and acquiring the cable signal transfer function.

[0157] The cable signal transfer function construction module is used for constructing the cable signal approximate function based on the Prony method based on the cable signal transfer function and in combination with the cable joint distribution.

[0158] The linear prediction model construction module is used for constructing the linear prediction model of the cable signal based on the cable signal approximate function.

[0159] The linear prediction model denoising module is used for denoising the linear prediction model based on the singular value decomposition method to obtain the denoised linear prediction model.

[0160] The attenuation coefficient acquisition module is used for solving the denoised linear prediction model to obtain the attenuation coefficient.

[0161] The cable segment wave velocity acquisition module is used for calculating the cable segment wave velocity based on the attenuation coefficient.

[0162] Another embodiment of the application provides a cable segment wave velocity acquisition system based on the Prony algorithm, comprising a computer readable storage medium and a processor.

[0163] The computer readable storage medium is used for storing executable instructions.

[0164] The processor is used for reading the executable instructions stored in the computer readable storage medium and executing the cable segment wave velocity acquisition method based on the Prony algorithm.

[0165] Another embodiment of the application provides a non-transient computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the cable segment wave velocity acquisition method based on the Prony algorithm.

[0166] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0167] The present application is described in reference to the flowchart and / or block diagrams of the method, apparatus (system) and computer program product according to embodiments of the application. It should be understood that each flow and / or block in the flowchart and / or block diagrams, and a combination of flows and / or blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, a special purpose computer, an embedded processor or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagrams block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart and / or block diagram block or blocks.

[0168] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagrams block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart and / or block diagram block or blocks.

[0169] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagrams block or blocks. Figure 1 one or more flows and / or blocks Figure 1 means for carrying out the function specified in the flowchart and / or block diagram block or blocks.

[0170] Finally, it should be noted that the above-mentioned embodiments are merely intended for describing the technical solutions of the present application, but not for limiting the same. Although the present application is described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement without departing from the spirit and scope of the present application should be included in the protection scope of the claims of the present application.

Claims

1. A method for obtaining wave velocity in cable segmentation based on Prony algorithm, characterized in that, The method comprises the following steps: Testing a cable to be tested to obtain a cable signal transfer function; Based on the cable signal transfer function, a cable signal approximation function based on the Prony method is constructed in combination with cable joint distribution; Based on the cable signal approximation function, a linear prediction model of the cable signal is constructed; The linear prediction model is denoised based on a singular value decomposition method to obtain a denoised linear prediction model; The denoised linear prediction model is solved to obtain an attenuation coefficient; The cable segment wave speed is calculated based on the attenuation coefficient.

2. The Prony algorithm based method for cable segment wave velocity acquisition according to claim 1, wherein, The cable signal approximation function is expressed as: ； where A i is the amplitude, a i is the damping coefficient, θ i is the initial phase angle, and At is the sampling interval, , , , is 2x i / v, x i is the equivalent position at the i-th joint.

3. The Prony algorithm based method for cable segment wave velocity acquisition according to claim 2, wherein, The linear prediction model of the cable signal is specifically: ； where x is the historical signal matrix, a p i.e. the estimated attenuation coefficient, is the target signal matrix, p is the model order, and N is the total number of samples.

4. The method of claim 3, wherein, The linear prediction model is denoised based on a singular value decomposition method, It comprises: Let ; The singular value decomposition matrix is: ； Wherein, R and V are orthogonal matrices, and and are left and right singular matrices of P, respectively; is a singular value matrix; singular values in the singular value matrix are arranged in descending order; The mean of all singular values is used as the threshold gate: ； There is an m such that λ m ≥ gate ≥ λ m+1 The diagonal matrix of the main signal component is: ； For smaller singular values λ m+1 … λ p The singular values greater than m in the singular value decomposition matrix are set to 0 to suppress the influence on the cable transfer function, and the linear prediction model after the singular value decomposition processing is: ； wherein x s is the original signal matrix modified by singular value decomposition, a p为 is the estimated attenuation coefficient, is the target signal matrix, which combines the singular value decomposition matrix to modify the linear prediction model of the cable signal, thereby completing the denoising processing of the linear prediction model.

5. The Prony algorithm based method for cable segment wave velocity acquisition according to claim 4, wherein, The denoised linear prediction model is solved to obtain an attenuation coefficient, which comprises: For the reflected wave at the i th joint, the attenuation constant is calculated according to the following formula: （11）； where x i represents the position of the i-th joint, a i is the estimated attenuation coefficient, Af is the scan frequency step size, and n is the index; The cable segment wave speed is calculated based on the attenuation coefficient, which comprises: ； wherein, is the frequency of the input signal, is the dielectric constant of the insulating medium, is a constant.

6. A device for obtaining wave velocity in cable segments based on Prony's algorithm, characterized by It comprises: A cable signal transfer function acquisition module is configured to test a cable to be tested to obtain a cable signal transfer function; A cable signal transfer function construction module is configured to construct a cable signal approximation function based on the Prony method based on the cable signal transfer function in combination with cable joint distribution; A linear prediction model construction module is configured to construct a linear prediction model of the cable signal based on the cable signal approximation function; A linear prediction model denoising module is configured to denoise the linear prediction model based on a singular value decomposition method to obtain a denoised linear prediction model; An attenuation coefficient acquisition module is configured to solve the denoised linear prediction model to obtain an attenuation coefficient; A cable segment wave speed acquisition module is configured to calculate the cable segment wave speed based on the attenuation coefficient.

7. The Prony algorithm based apparatus for acquiring wave velocities in cable segments according to claim 6, wherein, The cable signal approximation function is expressed as: ； where A i is the amplitude, a i is the damping coefficient, θ i is the initial phase angle, and ∆t is the sampling interval, , , , is 2x i / v, x i is the equivalent position at the i-th joint.

8. The Prony algorithm based cable segment wave velocity acquisition apparatus of claim 7, wherein, The linear prediction model of the cable signal is specifically: ； where x is the historical signal matrix, a p i.e. the estimated attenuation coefficient, is the target signal matrix, p is the model order, and N is the total number of samples.

9. The Prony algorithm based cable segment wave velocity acquisition apparatus of claim 8, wherein, The linear prediction model denoising module denoises the linear prediction model based on a singular value decomposition method, It comprises: Let ; The singular value decomposition matrix is: ； Wherein, R and V are orthogonal matrices, and and are left and right singular matrices of P, respectively; is a singular value matrix; singular values in the singular value matrix are arranged in descending order; The mean of all singular values is used as the threshold gate: ； There is an m such that λ m ≥ gate ≥ λ m+1 The diagonal matrix of the main signal component is: ； For smaller singular values λ m+1 … λ p The singular values greater than m in the singular value decomposition matrix are set to 0 to suppress the influence on the cable transfer function, and the linear prediction model after the singular value decomposition processing is: ； wherein x s is the original signal matrix modified by singular value decomposition, a p为 is the estimated attenuation coefficient, is the target signal matrix, which combines the singular value decomposition matrix to modify the linear prediction model of the cable signal, thereby completing the denoising processing of the linear prediction model.

10. The Prony algorithm based cable segment wave velocity acquisition apparatus of claim 9, wherein, The attenuation coefficient acquisition module solves the denoised linear prediction model to obtain an attenuation coefficient, which comprises: For the reflected wave at the i th joint, the attenuation constant is calculated according to the following formula: （11）； where x i represents the position of the i-th joint, a i is the estimated attenuation coefficient, Af is the scan frequency step size, and n is the index; The cable segment wave speed acquisition module calculates the cable segment wave speed based on the attenuation coefficient, which comprises: ； wherein, is the frequency of the input signal, is the dielectric constant of the insulating medium, is a constant.

11. A Prony algorithm based cable segment wave velocity acquisition system comprising: A computer readable storage medium and a processor; The computer readable storage medium is configured to store executable instructions; The processor is configured to read the executable instructions stored in the computer readable storage medium and execute the Prony algorithm-based cable segment wave speed acquisition method in any one of claims 1-5.

12. A non-transitory computer readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement the Prony algorithm-based cable segment wave speed acquisition method in any one of claims 1-5.

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