Defect positioning and diagnosis method and device based on cable reflection coefficient spectrum parameter estimation, computer storage medium and electronic equipment

Abstract

The invention belongs to the technical field of power cable insulation diagnosis and signal processing, and discloses a defect positioning and diagnosis method and device based on cable reflection coefficient spectrum parameter estimation, a computer medium and electronic equipment. Constructing a cable head end reflection coefficient spectrum real part model composed of N reflection points to obtain a cable head end reflection coefficient spectrum real part fitting value; constructing a target function according to the difference between the real value and the fitting value of the real part of the cable head end reflection coefficient spectrum; and obtaining the optimal solutions of the reflection coefficients and the positions of all the reflection points through minimizing the objective function by using a group optimization algorithm. According to the invention, accurate fitting of the position of the defect point and the reflection coefficient can be realized, so that positioning and quantitative diagnosis of the cable defect are realized.

Description

Technical Field

[0001] This invention belongs to the field of power engineering technology and relates to power cable insulation diagnosis and signal processing. Specifically, it relates to a defect location and diagnosis method, device, computer storage medium, and electronic equipment based on cable reflection coefficient spectrum parameter estimation. The method first constructs a cable reflection coefficient spectrum parameter model that considers the cable propagation characteristics. Then, taking the particle swarm optimization algorithm as an example, the measured reflection coefficient spectrum is fitted with the constructed cable reflection coefficient spectrum parameter model to obtain the location and reflection coefficient information of each defect point, thereby realizing accurate location and quantitative diagnosis of cable defects. Background Technology

[0002] The traditional frequency domain reflectionometry (FDR) defect location technology is a technique used to measure defects or faults in cables or conductors. By sending a sweep test signal in a specific frequency band, a strong reflected signal with the same frequency as the transmitted signal but at a different time will be generated at the conductor impedance mismatch. These signals are analyzed by Fourier transform, and the distance to the line fault point is calculated by measuring the frequency of the peak value of the reflected signal.

[0003] However, traditional FDR technology calculates the defect distance using only a fixed wave velocity value during location, without considering the impact of dispersion effects (i.e., the frequency variation characteristics of wave velocity) on the location results, leading to errors in defect location. Secondly, traditional FDR technology is affected by signal attenuation characteristics and preceding impedance mismatch points in defect diagnosis. It can only judge the degree of defect by the relative reflection intensity between reflection peaks, and cannot quantitatively assess the degree of defect. This makes it impossible to directly compare the severity of defect points that are far apart, resulting in misjudgment or missed judgment. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in the prior art by proposing a defect location and diagnosis method based on the estimation of cable reflection coefficient spectrum parameters, thereby enabling the location and quantitative diagnosis of cable defects.

[0005] To achieve the above objectives, the present invention provides a defect location and diagnosis method based on cable reflection coefficient spectrum parameter estimation, comprising the following steps:

[0006] Obtain the true value of the real part of the reflection coefficient spectrum at the cable head end;

[0007] Construct a parameter model of the real part of the reflection coefficient spectrum at the cable head end, consisting of N reflection points, to obtain the fitted value of the real part of the reflection coefficient spectrum at the cable head end.

[0008] An objective function is constructed based on the difference between the true and fitted values ​​of the real part of the reflection coefficient spectrum at the cable head end;

[0009] By using a swarm optimization algorithm, the optimal solution for the reflection coefficient and position of all reflection points is obtained by minimizing the objective function.

[0010] In one possible implementation, the real part parameter model of the reflection coefficient spectrum at the cable head end, consisting of N reflection points, is expressed as: ; Where, ρ n Let l be the reflection coefficient of the nth defect point. n Let α be the location of the nth defect point, and β be the attenuation constant and phase shift constant of the cable, respectively.

[0011] In one feasible approach, the objective function, constructed based on the difference between the true and fitted values ​​of the real part of the reflection coefficient spectrum at the cable head end, is expressed as: ; in, This represents the true value of the real part of the reflection coefficient spectrum at the cable's head end. denoted as the fitted value of the real part of the reflection coefficient spectrum at the cable head, and J is the number of sample points in the reflection coefficient spectrum.

[0012] In one possible implementation, the swarm optimization algorithm is a particle swarm optimization algorithm, an artificial bee colony algorithm, an ant colony algorithm, or a bat algorithm, etc.

[0013] Furthermore, this invention utilizes the particle swarm optimization algorithm to obtain the optimal solution for the reflection coefficients and positions of all reflection points by minimizing the objective function, including the following steps:

[0014] (1) Initialize the particle swarm;

[0015] (2) Calculate the fitness of each particle and determine the global optimal solution of the entire particle swarm;

[0016] (3) Update the position and velocity of each particle in the particle swarm;

[0017] (4) Determine whether the iteration stopping condition is met. If it is met, proceed to step (5); otherwise, return to step (2).

[0018] (5) Output the global optimal solution of the particle swarm.

[0019] In step (1) above, the position of each particle in the random initialization particle swarm is determined by the reflection coefficient and position of each reflection point, which can be represented as X=[ρ1, l1; ρ2, l2; …; ρ n , l n ;…; ρ N , l N ], where N is the number of reflection points on the cable; particle velocity represents the direction and rate of particle iterative search.

[0020] In step (2) above, the objective function value is used as the fitness.

[0021] In step (3) above, the velocity v update process for each particle is as follows: ; Where i represents the i-th particle, w represents the inertial weight, c1 and c2 are acceleration terms, and r1 and r2 are random perturbations uniformly distributed in [0,1].

[0022] The update process for the position X of each particle is as follows:

[0023] ; Where t and t+1 represent the t-th and t+1-th iterations, respectively.

[0024] Since the reflection coefficient spectrum contains equivalent frequencies in the frequency domain that reflect the location, polarity, and degree of cable defects, a cable reflection coefficient parameter model can be constructed to fit the collected reflection coefficient spectrum, ensuring that the fitted reflection coefficient spectrum coincides with the collected reflection coefficient spectrum curve. In this case, the reflection coefficient spectrum parameter model can be considered to accurately describe the cable under inspection from a mathematical perspective. Therefore, cable defects can be quantitatively diagnosed and precisely located directly based on the parameters in the cable parameter model, avoiding traditional Fourier transform-based harmonic analysis methods for the reflection coefficient spectrum, and naturally eliminating the spectral leakage problem caused by Fourier transform. In the process of solving the cable parameter model parameters, this invention employs a swarm optimization algorithm. Furthermore, based on the requirements of the swarm optimization algorithm and considering the application characteristics in cable defect and fault diagnosis and location, this invention redesigns the particle velocity update process and position update process of the particle swarm algorithm.

[0025] The present invention also provides a defect location and diagnosis device based on cable reflection coefficient spectrum parameter estimation, characterized in that it includes:

[0026] The acquisition module is used to obtain the true value of the real part of the reflection coefficient spectrum at the cable head end;

[0027] The model building module is used to construct a real part parameter model of the reflection coefficient spectrum of the cable head end consisting of N reflection points, so as to obtain the fitting value of the real part of the reflection coefficient spectrum of the cable head end.

[0028] The objective function construction module is used to construct an objective function based on the difference between the true value and the fitted value of the real part of the reflection coefficient spectrum at the cable head end;

[0029] The optimization module is used to obtain the optimal solution for the reflection coefficient and position of all reflection points by minimizing the objective function using a swarm optimization algorithm.

[0030] A computer storage medium storing a plurality of instructions adapted for loading by a processor and executing the steps of a defect location and diagnosis method based on cable reflection coefficient spectral parameter estimation as described in any of the preceding implementable embodiments.

[0031] An electronic device includes a processor and a computer storage medium; the processor executes the steps of a defect location and diagnosis method based on cable reflection coefficient spectral parameter estimation, as described in any of the preceding implementable methods, stored in the computer storage medium.

[0032] Compared with existing technologies, the defect location and diagnosis method based on cable reflection coefficient spectrum parameter estimation provided by this invention has the following advantages:

[0033] This invention first constructs a cable reflection coefficient model, and then uses a swarm optimization algorithm to fit the collected reflection coefficient spectrum, which can achieve accurate fitting of the location of the defect point and the reflection coefficient, thereby realizing the location and quantitative diagnosis of cable defects, overcoming the shortcomings of traditional FDR based on fast Fourier transform, such as spectral leakage and inability to diagnose. Attached Figure Description

[0034] Figure 1 This is a schematic diagram of the defect location and diagnosis method based on cable reflection coefficient spectrum parameter estimation provided in an embodiment of the present invention;

[0035] Figure 2 Let be the equivalent frequency of the real part of the cable reflection coefficient spectrum; where (a) corresponds to a defect-free cable and (b) corresponds to a defective cable.

[0036] Figure 3 This is a schematic diagram illustrating the process of obtaining the optimal solution for the reflection coefficients and locations of all reflection points based on the parameter estimation method.

[0037] Figure 4 The figure shows the real part of the reflection coefficient spectrum of the intact cable, fitted using a parameter estimation method; in the figure, x is the frequency (Hz).

[0038] Figure 5 The figure shows the real part of the reflection coefficient spectrum of the defective cable fitted based on the parameter estimation method; in the figure, x is the frequency (Hz). Detailed Implementation

[0039] The technical solutions of various embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1

[0041] Based on the above analysis, this embodiment provides a defect location and diagnosis method based on cable reflection coefficient spectrum parameter estimation, such as... Figure 1 As shown, it includes the following steps:

[0042] S1, obtain the true value of the real part of the reflection coefficient spectrum at the cable head end;

[0043] S2, construct a parameter model of the real part of the reflection coefficient spectrum of the cable head end consisting of N reflection points, and obtain the fitting value of the real part of the reflection coefficient spectrum of the cable head end;

[0044] S3, construct the objective function based on the difference between the real value and the fitted value of the real part of the reflection coefficient spectrum at the cable head end;

[0045] S4 uses a swarm optimization algorithm to obtain the optimal solution for the reflection coefficient and position of all reflection points by minimizing the objective function.

[0046] In step S1 above, the true value of the reflection coefficient spectrum at the cable head end can be obtained by a reflection coefficient spectrum tester, which is the ratio of the reflected voltage to the incident voltage, and the real part of the reflection coefficient spectrum at the cable head end is obtained by taking its real part.

[0047] In step S2 above, according to transmission line theory, the reflection coefficient Γ(z) at any position z of a healthy cable of length l can be expressed as: (1); In equation (1), U I U is the incident signal voltage. R Z represents the reflected signal voltage. L Z0 is the load impedance at the cable end, and Z0 is the characteristic impedance of the cable body. Let Z0 be the signal propagation coefficient in the cable. The characteristic impedance Z0 can be expressed as... (2); Under high frequency conditions, it makes , Therefore, under normal circumstances, the effects of R and G can be ignored, and Z0 and It can be represented as:

[0048] (3); (4); In equation (4), α and β are the attenuation constant and phase shift constant of the cable, respectively. ω is the angular frequency, f is the frequency of the injected signal, and v is the propagation speed of the injected signal.

[0049] In formula (1), when the cable end is open (Z) L When z = ∞, the reflection coefficient at the head end (z = 0) can be expressed as: (5);

[0050] Expanding equation (5) using Euler's formula yields:

[0051] (6);

[0052] The reflection coefficient spectrum contains multiple characteristic quantities that characterize the cable properties. This embodiment only analyzes and discusses the real part of the reflection coefficient. When a high-frequency sweep signal is injected, substituting equation (4) into equation (6) yields the expression formula for the real part of the first-end reflection coefficient spectrum: (7);

[0053] At high frequencies, v can be considered a constant. Therefore, in equation (7), with f as the independent variable, 2l / v can be considered as the equivalent frequency generated at the end of the cable, for example... Figure 2 (a) is the real part of the reflection coefficient spectrum where there is only a reflection point at the end of the cable, and its image envelope is a smooth, decaying oscillation.

[0054] When the cable is at point z, structural changes, such as intermediate joints or defects, can lead to… This will generate a component with an equivalent frequency of 2z / v. To better illustrate the positioning principle, Figure 2 (a) A defect is set near the beginning of the line, such as Figure 2 (b) shows a low-frequency equivalent frequency component caused by a defect at 1 m, which causes obvious waveform distortion.

[0055] As can be seen from equation (7), the real part of the reflection coefficient spectrum at the cable head contains the characteristic information of the defects on the cable, such as the location of the defect point and the reflection intensity. However, the traditional FDR positioning technology can only locate the location of the defect point and the relative reflection intensity, but cannot quantify the reflection intensity of the cable defect point.

[0056] When the cable type is known (i.e., the cable core, main insulation material, and structural parameters are known), the distributed parameters R0, L0, G0, and C0 when the cable is intact can be estimated (see Novel Method for Local Defect Location of Power Cables Based on Reflection Coefficient Spectrum; Xie Min, Zhou Kai, Zhao Shilin, He Min, Zhang Fuzhong; Power System Technology, 2017, No. 9, pp. 3083-3089). Furthermore, formula (4) can express the propagation coefficient formula as: (8);

[0057] Knowing the cable structure, model, and electrical parameters, the cable's attenuation coefficient and phase shift constant can be obtained. The reflection coefficient spectrum of a healthy cable, excluding all reflection points except the end reflection point, can be estimated using formula (7).

[0058] However, in reality, cables cannot be completely continuous. Therefore, if the cable under test has N impedance mismatch points, and since the length of the defect is usually considered a single point relative to the entire cable, it can be assumed that the non-impedance mismatched portion of the cable is uniform. Then, the real part of the reflection coefficient spectrum at the cable's beginning can be expressed as a linear superposition of the reflection information from each impedance mismatch point, i.e., as follows: (9).

[0059] In step S3 above, the reflection coefficient ρ at each defect point can be used as a reference. n and position l n Using the parameters ρ and l, the minimum error between the real part of the fitted reflection coefficient spectrum and the real part of the measured reflection coefficient spectrum is used as the objective function for iterative search. Finding the optimal solutions ρ and l allows for a quantitative estimation of the defect point's state. Least squares error is a classic method for measuring the difference between the fitted value and the true value; its mathematical expression is: (10); In the formula, This represents the true value of the real part of the reflection coefficient spectrum at the cable's head end. J is the fitted value of the real part of the reflection coefficient spectrum at the beginning of the cable (calculated by formula (9)), and J is the number of sample points of the reflection coefficient spectrum.

[0060] In step S4 above, the particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence. It simulates the social behavior of flocks of birds or schools of fish, finding the optimal solution through cooperation and information sharing among individuals (particles) in the swarm. It belongs to gradient-free optimization methods and is suitable for complex optimization problems in continuous or discrete spaces.

[0061] Step S4 above, as Figure 3 As shown, it includes the following steps:

[0062] S41, Initialize the particle swarm.

[0063] Based on the dimensions and constraints of the problem, the positions and velocities of each particle in the particle swarm are randomly initialized, and the size of the particle swarm is represented by M.

[0064] The particle position representation vector is composed of the reflection coefficients and positions of each reflection point, and can be represented here as X=[ρ1,l1; ρ2, l2; …; ρ n , l n ;…; ρ N , l N ], where N is the number of reflection points on the cable.

[0065] Particle velocity represents the direction and rate of particle iterative search; a smaller value can be selected.

[0066] S42, calculate the fitness of each particle and determine the global optimal solution for the entire particle swarm.

[0067] Based on the problem definition, calculate the fitness of each particle. The fitness function varies depending on the problem and can be a value of the objective function or other comprehensive evaluation index; the formula is provided here. The minimum objective function is: (11);

[0068] The fitness of each particle is compared with its historical best and the global historical best. If a particle's fitness is better than (i.e., less than) the historical best, the historical best is updated; if the minimum fitness of all current particles is better than the global historical best, the global historical best is updated. The individual best solution pbest is the best solution found by the particle itself, and the global best solution gbest is the best solution among all particles.

[0069] S43, update the position and velocity of each particle in the particle swarm.

[0070] The particle's velocity and position are updated by using its current position, velocity, and historical best solutions.

[0071] The velocity v of each particle is updated using the formula. This includes acceleration terms {c1, c2} and inertial weights w, used to maintain the original velocity direction and control its range; t represents the t-th iteration, such as v t Let represent the velocity matrix at step t, pbest represent the individual's historical best position, gbest represent the global best position, and r1 and r2 represent random perturbations uniformly distributed in [0,1] to avoid local optima.

[0072] (12); The update of the position X of each particle is related to its velocity, as shown in Equation (13):

[0073] (13).

[0074] S44. Determine whether the iteration stopping condition is met. If it is met, proceed to step S45; otherwise, return to step S42.

[0075] Repeat steps S42-S44 until a predetermined stopping condition is met. The stopping condition can be reaching the maximum number of iterations, the fitness value reaching a certain threshold, or meeting a given fitness range condition, etc. In this embodiment, the maximum number of iterations is used as the stopping condition.

[0076] S45 outputs the global optimal solution for the particle swarm.

[0077] The global optimal solution of the particle swarm is returned as the final result.

[0078] (14).

[0079] The following simulation analysis verifies the defect location and diagnosis method based on cable reflection coefficient spectrum parameter estimation provided by this invention.

[0080] (a) Simulation of intact cables

[0081] To verify the effectiveness of the method, the reflection coefficient spectrum of a 500m intact power cable was simulated, with a frequency sweep range of 150kHz to 30MHz. After fitting the data using the defect location and diagnosis method based on cable reflection coefficient spectrum parameter estimation, the fitted graph and optimal solution are shown below. Figure 4 As shown in the figure, the minimum error is 6.8726. It can be seen from the figure that the fitted curve and the true curve have a high degree of overlap. Optima =[0.9771,502.4121], the reflection coefficient error is less than 2.3%, and the positioning error is less than 0.5%. That is, while overcoming the positioning deviation problems caused by spectrum leakage and fence effect in the traditional FDR IFT / FT positioning method, this method can also realize the quantitative assessment of the defect reflection coefficient, thereby realizing the assessment of the severity of the defect.

[0082] (ii) Testing of Defective Cables

[0083] To verify the effectiveness of the method in diagnosing and locating defects in a defective cable, a defect point with a reflection coefficient of 0.4 was simulated at 200 locations on a 500m cable as described earlier. After fitting the cable defect diagnosis method based on particle swarm optimization (PSO) parameter fitting, the resulting fitting graph and optimal solution are shown below. Figure 5 As shown.

[0084] At this point, the minimum error is 9.2223. As can be seen from the graph, the fitted curve and the true curve have a high degree of overlap. Optima =[0.4139,202.4309,0.9834,504.5892], the reflection coefficient error is less than 3.5%, and the positioning error is less than 1.3%, which means that this method can achieve quantitative determination of the reflection coefficient of defects.

[0085] Example 2

[0086] This embodiment provides a defect location and diagnosis device based on cable reflection coefficient spectrum parameter estimation, which is used to implement the defect location and diagnosis method based on cable reflection coefficient spectrum parameter estimation in Embodiment 1.

[0087] The defect location and diagnosis device based on cable reflection coefficient spectrum parameter estimation includes:

[0088] The acquisition module is used to obtain the true value of the real part of the reflection coefficient spectrum at the cable head end;

[0089] The model building module is used to construct a real part parameter model of the reflection coefficient spectrum of the cable head end consisting of N reflection points, so as to obtain the fitting value of the real part of the reflection coefficient spectrum of the cable head end.

[0090] The objective function construction module is used to construct an objective function based on the difference between the true value and the fitted value of the real part of the reflection coefficient spectrum at the cable head end;

[0091] The optimization module is used to obtain the optimal solution for the reflection coefficient and position of all reflection points by minimizing the objective function using a swarm optimization algorithm.

[0092] The acquisition module, model building module, objective function building module, and optimization module described above are operated according to the steps given in Example 1 for defect location and diagnosis based on cable reflection coefficient spectrum parameter estimation.

[0093] Example 3

[0094] This embodiment provides a computer storage medium that stores multiple instructions, which are adapted to be loaded by a processor and executed by the defect location and diagnosis method steps based on cable reflection coefficient spectrum parameter estimation provided in Embodiment 1.

[0095] Example 4

[0096] This embodiment provides an electronic device, including a processor and a computer storage medium. The processor executes the defect location and diagnosis method steps based on cable reflection coefficient spectrum parameter estimation provided in Embodiment 1, which are stored in the computer storage medium.

[0097] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A defect location and diagnosis method based on cable reflection coefficient spectral parameter estimation, characterized in that, Includes the following steps: Obtain the true value of the real part of the reflection coefficient spectrum at the cable head end; Construct a parameter model of the real part of the reflection coefficient spectrum at the cable head end, consisting of N reflection points, to obtain the fitted value of the real part of the reflection coefficient spectrum at the cable head end. An objective function is constructed based on the difference between the true and fitted values ​​of the real part of the reflection coefficient spectrum at the cable head end; By using a swarm optimization algorithm, the optimal solution for the reflection coefficient and position of all reflection points is obtained by minimizing the objective function.

2. The defect location and diagnosis method based on cable reflection coefficient spectral parameter estimation according to claim 1, characterized in that, The real part parameter model of the reflection coefficient spectrum at the cable head end, which consists of N reflection points, is expressed as: ； Where, ρ n Let l be the reflection coefficient of the nth defect point. n Let α be the location of the nth defect point, and β be the attenuation constant and phase shift constant of the cable, respectively.

3. The defect location and diagnosis method based on cable reflection coefficient spectral parameter estimation according to claim 1, characterized in that, The objective function, constructed based on the difference between the true and fitted values ​​of the real part of the reflection coefficient spectrum at the cable's head end, is expressed as follows: ； in, This represents the true value of the real part of the reflection coefficient spectrum at the cable's head end. denoted as the fitted value of the real part of the reflection coefficient spectrum at the cable head, and J is the number of sample points in the reflection coefficient spectrum.

4. The defect location and diagnosis method based on cable reflection coefficient spectral parameter estimation according to any one of claims 1 to 3, characterized in that, The swarm optimization algorithm is either particle swarm optimization, artificial bee colony optimization, ant colony optimization, or bat optimization.

5. The defect location and diagnosis method based on cable reflection coefficient spectral parameter estimation according to claim 4, characterized in that, The swarm optimization algorithm is a particle swarm optimization algorithm. The optimal solution for minimizing the objective function to obtain the reflection coefficients and positions of all reflection points includes the following steps: (1) Initialize the particle swarm; (2) Calculate the fitness of each particle and determine the global optimal solution of the entire particle swarm; (3) Update the position and velocity of each particle in the particle swarm; (4) Determine whether the iteration stopping condition is met. If it is met, proceed to step (5); otherwise, return to step (2). (5) Output the global optimal solution of the particle swarm.

6. The defect location and diagnosis method based on cable reflection coefficient spectrum parameter estimation according to claim 5, characterized in that, In step (1), the position of each particle in the particle swarm is randomly initialized by the reflection coefficient and position of each reflection point, represented as X=[ρ1, l1; ρ2, l2; …; ρ n , l n ;…; ρ N , l N ], where N is the number of reflection points on the cable; Particle velocity represents the direction and rate of particle iterative search.

7. The defect location and diagnosis method based on cable reflection coefficient spectrum parameter estimation according to claim 5, characterized in that, In step (2), the objective function value is used as the fitness.

8. The defect location and diagnosis method based on cable reflection coefficient spectral parameter estimation according to claim 5, characterized in that, In step (3), the velocity v update process for each particle is as follows: ； Where i represents the i-th particle, w represents the inertial weight, c1 and c2 are acceleration terms, and r1 and r2 are random perturbations uniformly distributed in [0,1]. The update process for the position X of each particle is as follows: ； Where t and t+1 represent the t-th and t+1-th iterations, respectively.

9. A defect location and diagnosis device based on cable reflection coefficient spectrum parameter estimation, characterized in that, include: The acquisition module is used to obtain the true value of the real part of the reflection coefficient spectrum at the cable head end; The model building module is used to construct a real part parameter model of the reflection coefficient spectrum of the cable head end consisting of N reflection points, so as to obtain the fitting value of the real part of the reflection coefficient spectrum of the cable head end. The objective function construction module is used to construct an objective function based on the difference between the true value and the fitted value of the real part of the reflection coefficient spectrum at the cable head end; The optimization module is used to obtain the optimal solution for the reflection coefficient and position of all reflection points by minimizing the objective function using a swarm optimization algorithm.

10. A computer storage medium, characterized in that, The computer storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the steps of the defect location and diagnosis method based on cable reflection coefficient spectrum parameter estimation as described in any one of claims 1-8.

11. An electronic device, characterized in that, It includes a processor and the computer storage medium of claim 11; the processor executes the defect location and diagnosis method steps of the cable reflection coefficient spectrum parameter estimation method of any one of claims 1-8 stored in the computer storage medium.

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