Power cable fault intelligent positioning method and system

Abstract

The invention discloses a power cable fault intelligent positioning method and system, and relates to the technical field of fault positioning, and the method comprises the steps: inputting the obtained cable reflection signal data under the initial time domain reflectometer resolution into a preset reflection path evaluation model, and obtaining the reflection path aliasing coefficient of each reflection point; obtaining a preliminary screening effective reflection point set based on the reflection path aliasing coefficient of each reflection point and a preset reflection path aliasing threshold value; performing random gain on weak signals in the time domain reflectogram of the primarily screened effective reflection point set to obtain a plurality of effective reflection point sets under different time domain reflectometer resolutions; calculating the definition change coefficient of the effective reflection point set under a plurality of time-domain reflectometer resolutions in the primarily screened effective reflection point set so as to obtain the optimal time-domain reflectometer resolution; the problems that a traditional time domain reflectometer cannot accurately distinguish the aliasing degree of transmitted signals and selection of resolution cannot adapt to poor reflected signals under different cable fault conditions are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of fault location, and more specifically, to a method and system for intelligently locating a power cable fault. Background Art

[0002] With the rapid development of power systems and the continuous increase in power demand, power cables, as important power transmission channels, are of vital importance to the reliability of power systems in terms of operational stability and safety. However, power cables are susceptible to a variety of factors during long-term operation, such as mechanical damage, aging, temperature changes, moisture penetration, etc. These factors may cause different forms of cable failures, such as disconnection, insulation damage, short circuits, etc. Power cable failures not only affect the stability of power supply, but may also cause more serious power system accidents, and even lead to personal injury and property losses.

[0003] Traditional time domain reflectometry (TDR) technology often suffers from signal aliasing (i.e., superposition of multiple reflected signals) in long-distance cables and complex fault conditions. The aliasing phenomenon makes it difficult to distinguish reflected signals, resulting in reduced positioning accuracy. In addition, the resolution of traditional time domain reflectometry is fixed and cannot adapt to the differences in reflected signals under different cable fault conditions. In view of the above problems, the present invention proposes a solution. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for intelligently locating a power cable fault. Through a method and system for intelligently locating a power cable fault, the problem that a traditional time domain reflectometer cannot accurately distinguish the aliasing degree of the transmitted signal and the selection of resolution cannot adapt to the difference in reflected signals under different cable fault conditions is solved.

[0005] To achieve the above object, the present invention provides the following technical solutions: A method for intelligently locating a power cable fault comprises the following steps: obtaining cable reflection signal data at an initial time domain reflectometer resolution and performing preprocessing; inputting the cable reflection signal data at the initial time domain reflectometer resolution into a preset reflection path evaluation model to calculate the reflection path aliasing coefficient of each reflection point; sorting each reflection point based on the reflection path aliasing coefficient of each reflection point, and obtaining a preliminary screening effective reflection point set based on a preset reflection path aliasing threshold; performing random gain on weak signals in a time domain reflectogram of the preliminary screening effective reflection point set to obtain a plurality of effective reflection point sets at different time domain reflectometer resolutions; calculating the clarity variation coefficients of the effective reflection point sets at the time domain reflectometer resolutions in the preliminary screening effective reflection point set, and obtaining an optimal time domain reflectometer resolution based on the clarity variation coefficient.

[0006] In a preferred embodiment, each reflection point is sorted based on the reflection path aliasing coefficient of each reflection point, and a preliminary screening effective reflection point set is obtained based on a preset reflection path aliasing threshold, specifically: the reflection path aliasing coefficient of each reflection point is sorted in ascending order to obtain a sorted reflection point set; the average value and standard deviation of the reflection path aliasing coefficients of the reflection points in the sorted reflection point set are calculated; based on the average value and standard deviation, a reflection path aliasing threshold is set; and reflection points whose reflection path aliasing coefficients are less than the reflection path aliasing threshold are divided into the preliminary screening effective reflection point set.

[0007] In a preferred embodiment, the weak signals in the time domain reflectogram of the primary screening effective reflection point set are randomly gained to obtain several effective reflection point sets under different time domain reflectometer resolutions, specifically: the time domain reflectogram of the primary screening effective reflection point set is obtained based on the time domain reflectometer; the noise area of ​​the time domain reflectogram of the primary screening effective reflection point set is obtained; the average value and standard deviation of the signal amplitude and signal-to-noise ratio of each effective reflection point in the noise area of ​​the time domain reflectogram of the primary screening effective reflection point set are calculated; based on the average value and standard deviation of the signal amplitude and signal-to-noise ratio of each effective reflection point in the noise area, the weak signals in the time domain reflectogram of the primary screening effective reflection point set are identified; the weak signals in the time domain reflectogram of the primary screening effective reflection point set are randomly gained to obtain several effective reflection point sets under different time domain reflectometer resolutions.

[0008] In a preferred embodiment, the noise area of ​​the time domain reflectogram of the initial screening effective reflection point set is obtained by: performing peak and trough detection on the time domain reflectogram to identify the time interval of the reflection signal; selecting a time period of a preset time window size before and after the time interval of the reflection signal; and using the time period of a preset time window size before and after the time interval of the reflection signal as the noise area of ​​the time domain reflectogram of the initial screening effective reflection point set.

[0009] In a preferred embodiment, the weak signal in the time domain reflection diagram of the initial screening effective reflection point set is randomly gained to obtain several effective reflection point sets under different time domain reflectometer resolutions, specifically: the natural fluctuation of the reflection point signal gain is simulated to obtain the gain coefficient of each weak signal; the maximum gain coefficient and the minimum gain coefficient of the gain coefficient of each weak signal are used as the random number generation interval; the random number of the random number generation interval is obtained by using a uniform distribution method; the gain coefficient of each weak signal is randomly superimposed based on the random number to obtain several effective reflection point sets under different time domain reflectometer resolutions.

[0010] In a preferred embodiment, the clarity variation coefficients of the effective reflection point sets at several time domain reflectometer resolutions in the primary screening effective reflection point set are calculated, and the optimal time domain reflectometer resolution is obtained based on the clarity variation coefficients, specifically: the amplitude data of the reflection points, the time interval between the emission points and the signal-to-noise ratio data of the effective reflection point sets at several time domain reflectometer resolutions in the primary screening effective reflection point set are obtained; the reflection point clarity of the effective reflection point sets at several time domain reflectometer resolutions is obtained based on the reflection point clarity calculation formula using the amplitude data of the reflection points, the time interval between the emission points and the signal-to-noise ratio data; based on the reflection point clarity of the effective reflection point sets at the several time domain reflectometer resolutions, the clarity variation coefficients of the effective reflection point sets at the several time domain reflectometer resolutions are calculated; and the time domain reflectometer resolution corresponding to the effective reflection point set with the smallest clarity variation coefficient is taken as the optimal time domain reflectometer resolution.

[0011] The technical effects and advantages of the power cable fault intelligent positioning method and system of the present invention are as follows: 1. The present invention obtains the cable reflection signal at the initial resolution and performs preprocessing, laying the foundation for subsequent signal analysis. Then, the reflection path evaluation model is used to calculate the reflection path aliasing coefficient of each reflection point, and the reflection points are sorted according to the coefficient, and the effective reflection point set is screened out by setting a threshold. This step effectively removes interference signals and noise, providing a reliable data source for subsequent accurate fault location.

[0012] 2. The present invention enhances the clarity of weak signals in the time domain reflectogram by performing random gain on weak signals in the initial screening effective reflection point set, allowing more potential fault points to appear. By calculating the clarity variation coefficient of the reflection point set, the resolution of the time domain reflectometer can be scientifically evaluated and optimized, further improving the accuracy of positioning. In short, through multi-dimensional analysis and optimization, this method can accurately identify fault points and dynamically adjust system parameters in complex power cable fault detection scenarios, thereby improving the efficiency and reliability of fault location and having strong practicality and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 The present invention is a schematic diagram of the structure of a method for intelligently locating a power cable fault.

[0014] Figure 2 The present invention is a schematic diagram of the structure of a power cable fault intelligent positioning system. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Embodiment 1, Figure 1 The present invention provides a method for intelligently locating a power cable fault, comprising the following steps: S1, obtaining the cable reflection signal data at the initial time domain reflectometer resolution and performing preprocessing; In this example, the cable reflection signal data at the initial time domain reflectometer resolution is: Reflection point location data: usually expressed in the form of time (such as nanoseconds or microseconds), reflecting the time interval for the reflected signal to travel from the emission point to the reflection point and back. Through this time interval and the speed of signal propagation in the cable, the specific location of the reflection point in the cable can be calculated; Amplitude data at the reflection point: Indicates the strength or power of the reflected signal. Amplitude changes can provide information about cable loss, breakage, bad joints, and other faults. Generally, larger amplitude changes may mean more serious faults; Noise and background signals: In addition to the reflected signal, some background noise and possible other interfering signals will also be recorded. These data help evaluate the signal-to-noise ratio (SNR) of the reflected signal, that is, the ratio of the reflected signal strength to the noise strength, and thus determine the reliability of the reflected signal; Time domain reflectometer parameter settings: Record the time domain reflectometer parameters used during the test (such as test wavelength, pulse width, measurement range, etc.).

[0017] It should be noted that obtaining the cable reflection signal data at the initial time domain reflectometer resolution and preprocessing it is a crucial first step in the process of locating power cable faults. First, the cable reflection signal data is obtained through the time domain reflectometer (TDR). This data contains the information of the reflection point and can reflect the specific location and nature of the internal fault of the cable. The time domain reflectometer takes a pulse signal as input and measures the reflection characteristics in the cable based on the signal reflection time. The time delay of the reflection signal is proportional to the location of the cable fault.

[0018] Next, the acquired reflection signal data is preprocessed. This process usually includes operations such as noise removal, signal smoothing, and filtering to ensure that the effective information in the original data is retained while removing interference signals and background noise. Through processing methods such as data smoothing and filtering, the high-frequency noise in the signal can be reduced, making the signal clearer and facilitating subsequent analysis and positioning. In addition, the preprocessing process may also include operations such as signal standardization and removal of DC components to facilitate effective analysis by subsequent algorithms.

[0019] The advantages of this method are: first, data preprocessing helps to improve the quality and accuracy of the signal, remove useless noise and interference, and enhance the reliability of subsequent fault diagnosis; second, it ensures that the original reflection signal is effectively extracted and retained to avoid misjudgment due to noise interference; finally, through the optimized preprocessing process, it can provide clearer data support for further analysis of the time domain reflection map, thereby improving the accuracy and efficiency of fault location.

[0020] S2, inputting the cable reflection signal data at the initial time domain reflectometer resolution into a preset reflection path evaluation model to calculate the reflection path aliasing coefficient of each reflection point; In this example, the cable reflection signal data at the initial time domain reflectometer resolution is input into a preset reflection path evaluation model to calculate the reflection path aliasing coefficient of each reflection point. The specific formula is: in, is the aliasing coefficient of the reflection path for each reflection point, is the signal-to-noise ratio at the reflection point, is the maximum signal-to-noise ratio among all reflection points, is the reflection signal time of reflection point i, is the maximum value of the reflection signal time of all reflection points, is the reflection signal time of reflection point i, is a small constant, , and is the weight coefficient.

[0021] It should be noted that the cable reflection signal data is obtained through the time domain reflectometer (TDR) and input into the preset reflection path evaluation model to calculate the reflection path aliasing coefficient of each reflection point. The core of this step is the reflection path evaluation, which can effectively analyze the reflection points in the signal and quantify the degree of aliasing of these reflection points. The aliasing coefficient essentially reflects the interference or overlap between multiple reflection signals. When the signal propagates in the cable, reflections will occur when it encounters discontinuities (such as joints, damage, insulation faults, etc.). The reflection path aliasing coefficient can quantify the quality of the reflected signal, especially when there are multiple reflections or signal interference, and evaluate the clarity and accuracy of the reflected signal.

[0022] Specifically, the reflection path assessment model uses the time domain characteristics of the reflected signal, combined with the amplitude, arrival time and attenuation of the signal, to calculate the aliasing coefficient of each reflection point. The higher the coefficient, the more interference or overlap the signal at the reflection point is subject to, which may affect the accuracy of fault location. By analyzing the aliasing coefficient of the reflection path, possible fault points can be effectively screened out and unclear or false reflection signals can be eliminated, thereby improving the reliability of fault diagnosis.

[0023] The advantages of this method lie in its accuracy and adaptability. First, by quantifying the aliasing of the reflection path, it is possible to better distinguish effective fault signals from noise or interference signals and reduce misjudgments. Second, the reflection path evaluation model can dynamically adapt to the characteristics and fault types of different cables and has strong universality. Finally, this method provides a scientific basis for subsequent signal enhancement and resolution optimization, and can gradually improve the accuracy and efficiency of power cable fault location, especially in complex power systems, and can significantly improve the reliability of detection.

[0024] S3, sorting each reflection point based on the reflection path aliasing coefficient of each reflection point, and obtaining a preliminary screening effective reflection point set based on a preset reflection path aliasing threshold; In this example, each reflection point is sorted based on the reflection path aliasing coefficient of each reflection point, and a preliminary screening effective reflection point set is obtained based on a preset reflection path aliasing threshold, specifically: Sorting the reflection path aliasing coefficients of each reflection point in ascending order to obtain a sorted reflection point set; Calculate the average value and standard deviation of the reflection path aliasing coefficients of the reflection points in the sorted reflection point set; Based on the mean value and the standard deviation, setting a reflection path aliasing threshold; The reflection points whose reflection path aliasing coefficients are less than the reflection path aliasing threshold are divided into a primary screening effective reflection point set.

[0025] It should be noted that the reflection points are screened by the reflection path aliasing coefficient to obtain a preliminary screening effective reflection point set, thereby improving the accuracy of power cable fault location. The specific steps include: first, the reflection path aliasing coefficient of each reflection point is sorted in ascending order to identify which reflection points have a lower degree of aliasing. Generally, a lower aliasing coefficient means that the reflection point may be clearer and have less signal interference. Then, the mean and standard deviation of the reflection path aliasing coefficient in the sorted reflection point set are calculated. The mean value provides the central trend of the overall reflection path aliasing, while the standard deviation reflects the degree of discreteness of the data. Based on these two statistics, a suitable reflection path aliasing threshold is set. Usually, a certain range is set based on the mean value and the standard deviation, so that the threshold can contain most of the valid signals and exclude excessive noise and unclear reflection points. Finally, the reflection points with a reflection path aliasing coefficient lower than the threshold are divided into the preliminary screening effective reflection point set, so that those reflection points that may be related to cable faults and have clearer signals can be screened out, which is convenient for subsequent fault analysis.

[0026] The advantages of this method are its adaptability and accuracy. By sorting in ascending order and setting the aliasing threshold based on statistics, the screening criteria can be automatically adjusted according to the specific reflection signal data, avoiding the subjectivity of manually setting the threshold and improving the level of automation. In addition, using the mean and standard deviation of the aliasing coefficient of the reflection path to set the threshold not only effectively removes noise, but also ensures that the screened reflection points have a high degree of credibility, thereby improving the accuracy and reliability of subsequent fault location. This method is applicable to various cable types and different fault scenarios, and has strong universality and practicality.

[0027] S4, performing random gain on the weak signals in the time domain reflectogram of the primary screening effective reflection point set to obtain a plurality of effective reflection point sets at different time domain reflectometer resolutions; In this example, the weak signal in the time domain reflectogram of the primary screening effective reflection point set is randomly amplified to obtain several effective reflection point sets under different time domain reflectometer resolutions, specifically: Based on the time domain reflectometer, a time domain reflectogram of the initial screening effective reflection point set is obtained; Obtain the noise area of ​​the time domain reflection map of the initial screening effective reflection point set; Calculate the average value and standard deviation of the signal amplitude and signal-to-noise ratio of each effective reflection point in the noise area of ​​the time domain reflectogram of the primary screening effective reflection point set; Based on the average and standard deviation of the signal amplitude and signal-to-noise ratio of each effective reflection point in the noise area, the weak signal in the time domain reflection map of the initial screening effective reflection point set is identified; Random gain is performed on the weak signals in the time domain reflectogram of the primary screening effective reflection point set to obtain a number of effective reflection point sets under different time domain reflectometer resolutions.

[0028] It should be noted that the time domain reflectogram obtained based on the time domain reflectometer is a preliminary signal image for fault location, which shows the distribution of reflection points in the cable and their corresponding reflection signals. Next, the noise area in the time domain reflectogram is identified and extracted to distinguish between effective signals and background noise. By calculating the signal amplitude and the average and standard deviation of the signal-to-noise ratio (SNR) of each effective reflection point in the noise area, a quantitative basis can be provided for the identification of weak signals. This calculation process can help determine which reflection points have weak signals or are interfered by noise, further improving the accuracy of signal processing.

[0029] On this basis, by setting appropriate gain parameters, random gain processing is performed on the identified weak signals. This gain processing enhances the amplitude of weak signals to make them more significant, thereby improving the signal clarity in the time domain reflectogram and helping with subsequent analysis and positioning. Through this random gain processing, several effective reflection point sets at different time domain reflectometer resolutions can be obtained, providing data support for further resolution optimization.

[0030] The advantages of this method are: first, it can effectively identify and enhance weak signals in the time domain reflection diagram, especially in the case of large noise interference, which improves the signal identifiability; second, random gain processing can adapt to the signal characteristics of different reflection points, improve the system's adaptability, especially for the detection of complex fault types, it can dynamically adjust the signal gain to ensure that each reflection point can be accurately identified; finally, through multi-dimensional enhancement with resolution optimization, it can provide clearer and more reliable signals for subsequent cable fault location, effectively improving the accuracy and efficiency of fault diagnosis.

[0031] In this example, the noise area of ​​the time domain reflectogram of the initial screening effective reflection point set is obtained, specifically: Detect the peaks and troughs of the time domain reflection graph and identify the time interval of the reflection signal; Selecting a time period of a preset time window size before and after the time interval of the reflected signal; A time period of a preset time window size before and after the time interval of the reflection signal is used as a noise area of ​​a time domain reflection diagram for preliminary screening of a valid reflection point set.

[0032] It should be noted that the above process is intended to identify and extract noise areas from the time domain reflection graph, providing a more accurate reference for subsequent signal processing and fault location. First, by performing peak and trough detection on the time domain reflection graph, the time interval of the reflected signal can be accurately located. Peak and trough detection is a basic method in signal analysis, which aims to identify the maximum and minimum amplitudes in the signal. These peaks and troughs usually represent changes in the reflected signal in the cable, which can help determine the potential area of ​​the fault location in the cable. Therefore, peak and trough detection can not only reveal the reflected signal in the cable, but also provide a basis for the subsequent noise area division.

[0033] Next, based on the time interval of the reflected signal, the data within a specific time window before and after it is selected as the noise area. The size of this time window is preset and is usually adjusted according to the resolution and noise characteristics of the time domain reflectogram. By selecting the time period before and after the reflected signal as the noise area, the background noise and interference signals can be effectively removed from the reflected signal. Since a certain amount of background noise is generated in the cable, this noise is often distributed in the surrounding area of ​​the reflected signal. Therefore, by selecting a suitable time window and dividing the noise based on the interval before and after the reflected signal, the noise area can be accurately identified and extracted, laying the foundation for subsequent signal enhancement and analysis.

[0034] The advantages of this method are its efficiency and flexibility. By detecting the peaks and troughs, the time interval of the reflected signal can be quickly and accurately located, providing an accurate basis for the identification of the noise area. By setting the time window to select the noise area, it is possible to avoid misjudging the valid signal as noise and flexibly adjust the range of the noise area according to the actual situation of the cable signal. This method can effectively remove noise in complex power cable fault detection, improve the accuracy of subsequent signal gain and analysis, and thus improve the reliability and accuracy of fault diagnosis.

[0035] In this example, the weak signal in the time domain reflectogram of the primary screening effective reflection point set is randomly amplified to obtain several effective reflection point sets under different time domain reflectometer resolutions, specifically: Simulate the natural fluctuation of the signal gain at the reflection point to obtain the gain coefficient of each weak signal; Using the maximum gain coefficient and the minimum gain coefficient of the gain coefficient of each weak signal as a random number generation interval; Obtaining random numbers in the random number generation interval using a uniform distribution method; The gain coefficient of each weak signal is randomly superimposed based on the random number to obtain a plurality of effective reflection point sets under different time domain reflectometer resolutions.

[0036] It should be noted that the gain coefficient of each weak signal is first calculated by simulating the natural gain fluctuation of the reflection point signal. This gain coefficient fluctuation simulation can reflect the natural change of the signal based on the characteristics of the actual signal, such as signal amplitude, signal-to-noise ratio, etc. The fluctuation range of the gain coefficient is the maximum gain coefficient and the minimum gain coefficient of the signal, and these values ​​are the upper and lower limits of the random number generation interval.

[0037] Next, the uniform distribution method is used to generate random numbers within the interval. The uniform distribution method is a common random number generation method that can ensure that random values ​​with the same probability are generated within the set gain coefficient interval, thereby ensuring that the gain change of each weak signal is random and evenly distributed. In this way, the gain change of weak signals under different reflectometer resolutions can be simulated. Finally, based on the generated random numbers, the gain coefficients of each weak signal are randomly superimposed to obtain several effective reflection point sets under different time domain reflectometer resolutions. This process effectively enhances the weak signals in the time domain reflectogram and provides a variety of reflection point sets under different gain conditions for subsequent signal analysis and fault location.

[0038] The advantage of this method is that it enhances the flexibility and adaptability of signal processing. By simulating the natural fluctuations of weak signal gain and generating random numbers in combination with the uniform distribution method, the problem of over-enhancement or under-enhancement that may be caused by fixed gain settings can be avoided. Random gain superposition can not only improve the signal-to-noise ratio of weak signals, but also simulate the response of the reflectometer resolution to the signal under different conditions, and enhance the system's adaptability to different signal conditions. In addition, this method can generate reflection point sets under multiple gain conditions, thereby providing richer data for fault location and analysis, and improving the reliability and accuracy of fault diagnosis.

[0039] S5, calculating the clarity variation coefficients of the effective reflection point sets at several time domain reflectometer resolutions in the primary screening effective reflection point set, and obtaining the optimal time domain reflectometer resolution based on the clarity variation coefficients.

[0040] In this example, the clarity variation coefficients of the effective reflection point sets at several time domain reflectometer resolutions in the primary screening effective reflection point set are calculated, and the optimal time domain reflectometer resolution is obtained based on the clarity variation coefficients, specifically: Obtaining the amplitude data of the reflection points, the time interval between the emission points and the signal-to-noise ratio data of the effective reflection point set of several effective reflection point sets at the resolution of the time domain reflectometer in the initial screening effective reflection point set; The amplitude data of the reflection points, the time interval between the emission points and the signal-to-noise ratio data are used to obtain the reflection point clarity of the effective reflection point set under the resolution of several time domain reflectometers based on the reflection point clarity calculation formula; Calculating a clarity variation coefficient of the effective reflection point set at the resolution of the time domain reflectometer based on the clarity of the reflection point of the effective reflection point set at the resolution of the time domain reflectometer; The time domain reflectometer resolution corresponding to the effective reflection point set with the smallest clarity variation coefficient is taken as the optimal time domain reflectometer resolution.

[0041] In this example, the calculation formula for the reflection point clarity is as follows:

[0042] in, For each reflection point clarity, is the maximum signal value of the reflection point in the time domain reflection diagram, is the signal-to-noise ratio at the reflection point, is the minimum time interval between reflection points.

[0043] In this example, the clarity variation coefficient of the effective reflection point set at the time domain reflectometer resolution in the primary screening effective reflection point set is calculated, and the specific formula is:

[0044] in, is the clarity variation coefficient, is the clarity of the i-th reflection point at the k-th resolution, is the clarity of the i-th reflection point at the initial resolution.

[0045] It should be noted that the above content describes a method for determining the optimal time domain reflectometer resolution. The method first obtains the amplitude data, time interval between emission points, and signal-to-noise ratio data of the reflection points at several different time domain reflectometer resolutions in the initial screening effective reflection point set. These data are key parameters for evaluating the quality of reflection points.

[0046] Next, the reflection point clarity calculation formula is used to combine the above data to calculate the reflection point clarity of the effective reflection point set at each resolution. Clarity is an important indicator to measure the quality of the reflection point signal, which reflects the comprehensive effect of the signal resolution in time and amplitude and the signal-to-noise ratio.

[0047] Then, based on these clarity data, the clarity variation coefficients of the effective reflection point sets at several time domain reflectometer resolutions are further calculated. This coefficient reflects the stability or volatility of the clarity as the resolution changes.

[0048] Finally, the time domain reflectometer resolution corresponding to the effective reflection point set with the smallest clarity variation coefficient is determined as the optimal resolution, which means that at this resolution, the signal quality of the reflection point is the most stable, and the accuracy and reliability of the measurement results are also the highest.

[0049] The advantage of this method is that it comprehensively considers multiple key parameters of the reflection point and can objectively determine the optimal time domain reflectometer resolution by calculating the clarity and clarity variation coefficient, thereby improving the accuracy and efficiency of the measurement.

[0050] Embodiment 2, Figure 2 The present invention provides an intelligent power cable fault positioning system, including an acquisition module, a reflection path aliasing module, a reflection point screening module, a signal gain module, and a clarity evaluation module: A data acquisition module, used for acquiring cable reflection signal data at an initial time domain reflectometer resolution and performing preprocessing; A reflection path aliasing module, used for inputting the cable reflection signal data at the initial time domain reflectometer resolution into a preset reflection path evaluation model to calculate the reflection path aliasing coefficient of each reflection point; A reflection point screening module, used for sorting each reflection point based on the reflection path aliasing coefficient of each reflection point, and obtaining a preliminary screening effective reflection point set based on a preset reflection path aliasing threshold; A signal gain module, used for performing random gain on weak signals in the time domain reflectogram of the primary screening effective reflection point set, to obtain several effective reflection point sets under different time domain reflectometer resolutions; The clarity evaluation module is used to calculate the clarity variation coefficients of the effective reflection point sets under the time domain reflectometer resolutions in the primary screening effective reflection point set, and obtain the optimal time domain reflectometer resolution based on the clarity variation coefficients.

[0051] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0052] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0053] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0054] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0055] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0056] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for intelligently locating a power cable fault, characterized in that: The following steps are involved: Acquire the cable reflection signal data at the initial time domain reflectometer resolution and perform preprocessing; Inputting the cable reflection signal data at the initial time domain reflectometer resolution into a preset reflection path evaluation model to calculate the reflection path aliasing coefficient of each reflection point; Sorting each reflection point based on the reflection path aliasing coefficient of each reflection point, and obtaining a pre-screened effective reflection point set based on a preset reflection path aliasing threshold; Randomly gaining the weak signal in the time domain reflectogram of the primary screening effective reflection point set to obtain a number of effective reflection point sets at different time domain reflectometer resolutions; The clarity variation coefficients of the effective reflection point sets at several time domain reflectometer resolutions in the primary screening effective reflection point set are calculated, and the optimal time domain reflectometer resolution is obtained based on the clarity variation coefficients.

2. The method for intelligently locating a power cable fault according to claim 1, characterized in that: Each reflection point is sorted based on the reflection path aliasing coefficient of each reflection point, and a preliminary screening effective reflection point set is obtained based on a preset reflection path aliasing threshold, specifically: Sorting the reflection path aliasing coefficients of each reflection point in ascending order to obtain a sorted reflection point set; Calculate the average value and standard deviation of the reflection path aliasing coefficients of the reflection points in the sorted reflection point set; Based on the mean value and the standard deviation, setting a reflection path aliasing threshold; The reflection points whose reflection path aliasing coefficients are less than the reflection path aliasing threshold are divided into a primary screening effective reflection point set.

3. The method for intelligently locating power cable faults according to claim 2, characterized in that: The weak signal in the time domain reflectogram of the initial screening effective reflection point set is randomly amplified to obtain several effective reflection point sets under different time domain reflectometer resolutions, specifically: Based on the time domain reflectometer, a time domain reflectogram of the initial screening effective reflection point set is obtained; Obtain the noise area of ​​the time domain reflection map of the initial screening effective reflection point set; Calculate the average value and standard deviation of the signal amplitude and signal-to-noise ratio of each effective reflection point in the noise area of ​​the time domain reflectogram of the primary screening effective reflection point set; Based on the average and standard deviation of the signal amplitude and signal-to-noise ratio of each effective reflection point in the noise area, the weak signal in the time domain reflection map of the initial screening effective reflection point set is identified; Random gain is performed on the weak signals in the time domain reflectogram of the primary screening effective reflection point set to obtain a number of effective reflection point sets under different time domain reflectometer resolutions.

4. The method for intelligently locating a power cable fault according to claim 3, characterized in that: The noise area of ​​the time domain reflectogram of the initial screening effective reflection point set is obtained, specifically: Detect the peaks and troughs of the time domain reflection graph and identify the time interval of the reflection signal; Selecting a time period of a preset time window size before and after the time interval of the reflected signal; A time period of a preset time window size before and after the time interval of the reflection signal is used as a noise area of ​​a time domain reflection diagram for preliminary screening of a valid reflection point set.

5. The method for intelligently locating a power cable fault according to claim 4, characterized in that: The weak signal in the time domain reflectogram of the initial screening effective reflection point set is randomly amplified to obtain several effective reflection point sets under different time domain reflectometer resolutions, specifically: Simulate the natural fluctuation of the signal gain at the reflection point to obtain the gain coefficient of each weak signal; Using the maximum gain coefficient and the minimum gain coefficient of the gain coefficient of each weak signal as a random number generation interval; Obtaining random numbers in the random number generation interval using a uniform distribution method; The gain coefficient of each weak signal is randomly superimposed based on the random number to obtain a plurality of effective reflection point sets under different time domain reflectometer resolutions.

6. The method for intelligently locating a power cable fault according to claim 5, characterized in that: The clarity variation coefficients of the effective reflection point sets at the time domain reflectometer resolutions in the primary screening effective reflection point set are calculated, and the optimal time domain reflectometer resolution is obtained based on the clarity variation coefficients, specifically: Obtaining the amplitude data of the reflection points, the time interval between the emission points and the signal-to-noise ratio data of the effective reflection point set of several effective reflection point sets at the resolution of the time domain reflectometer in the initial screening effective reflection point set; The amplitude data of the reflection points, the time interval between the emission points and the signal-to-noise ratio data are used to obtain the reflection point clarity of the effective reflection point set under the resolution of several time domain reflectometers based on the reflection point clarity calculation formula; Calculating the clarity variation coefficients of the effective reflection point sets at the resolutions of the time domain reflectometers based on the clarity of the reflection points of the effective reflection point sets at the resolutions of the time domain reflectometers; The time domain reflectometer resolution corresponding to the effective reflection point set with the smallest clarity variation coefficient is taken as the optimal time domain reflectometer resolution.

7. The method for intelligently locating a power cable fault according to claim 6, characterized in that: The cable reflection signal data at the initial time domain reflectometer resolution is input into the preset reflection path evaluation model to calculate the reflection path aliasing coefficient of each reflection point. The specific formula is: in, is the aliasing coefficient of the reflection path for each reflection point, is the signal-to-noise ratio at the reflection point, is the maximum signal-to-noise ratio among all reflection points, is the reflection signal time of reflection point i, is the maximum value of the reflection signal time of all reflection points, is the reflection signal time of reflection point i, is a small constant, , and is the weight coefficient.

8. The method for intelligently locating a power cable fault according to claim 7, characterized in that: The reflection point clarity calculation formula is specifically: in, For each reflection point clarity, is the maximum signal value of the reflection point in the time domain reflection diagram, is the signal-to-noise ratio at the reflection point, is the minimum time interval between reflection points.

9. The method for intelligently locating a power cable fault according to claim 8, characterized in that: The clarity variation coefficient of the effective reflection point set at the time domain reflectometer resolution of the initial screening effective reflection point set is calculated. The specific formula is: in, is the clarity variation coefficient, is the clarity of the i-th reflection point at the k-th resolution, is the clarity of the i-th reflection point at the initial resolution.

10. A system using the power cable fault intelligent location method according to any one of claims 1 to 9, characterized in that: Including data acquisition module, reflection path aliasing module, reflection point screening module, signal gain module, clarity evaluation module: A data acquisition module, used for acquiring cable reflection signal data at an initial time domain reflectometer resolution and performing preprocessing; A reflection path aliasing module, used for inputting the cable reflection signal data at the initial time domain reflectometer resolution into a preset reflection path evaluation model to calculate the reflection path aliasing coefficient of each reflection point; A reflection point screening module, used for sorting each reflection point based on the reflection path aliasing coefficient of each reflection point, and obtaining a preliminary screening effective reflection point set based on a preset reflection path aliasing threshold; A signal gain module, used for performing random gain on weak signals in the time domain reflectogram of the primary screening effective reflection point set, to obtain several effective reflection point sets under different time domain reflectometer resolutions; The clarity evaluation module is used to calculate the clarity variation coefficients of the effective reflection point sets under the time domain reflectometer resolutions in the primary screening effective reflection point set, and obtain the optimal time domain reflectometer resolution based on the clarity variation coefficients.

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