CN119375773B - A multi-core cable fault testing system and testing method - Google Patents
A multi-core cable fault testing system and testing method Download PDFInfo
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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract
本申请涉及线缆故障测试技术领域，具体涉及一种多芯线缆故障测试系统和测试方法，该方法包括：基于目标芯线测试电流信号中所有相邻信号波峰所在周期之间的差异以及周期性参数，确定目标芯线测试电流信号的故障发生系数；基于不同电压条件下目标芯线与其他所有芯线之间的相关数据集合的相似性，以及目标芯线测试电流信号中任一极大值点附近的信号幅值分布，确定目标芯线测试电流信号的构成丰富度；将构成丰富度和故障发生系数进行正向融合，对预设的初始分解区间尺度进行调整，同时筛选部分分量信号进行重构，对重构后的信号进行故障检测。本申请旨在实现对测试电流信号的精准分析，提高故障诊断的准确性。
The present application relates to the field of cable fault testing technology, and specifically to a multi-core cable fault testing system and testing method, the method comprising: determining the fault occurrence coefficient of the target core test current signal based on the difference between the periods of all adjacent signal peaks in the target core test current signal and the periodic parameters; determining the composition richness of the target core test current signal based on the similarity of the relevant data sets between the target core and all other cores under different voltage conditions, and the signal amplitude distribution near any maximum point in the target core test current signal; forward fusion of the composition richness and the fault occurrence coefficient, adjustment of the preset initial decomposition interval scale, screening some component signals for reconstruction, and performing fault detection on the reconstructed signal. The present application aims to achieve accurate analysis of the test current signal and improve the accuracy of fault diagnosis.
Description
Technical Field
The application relates to the technical field of cable fault testing, in particular to a multi-core cable fault testing system and a multi-core cable fault testing method.
Background
With the progress of modern industry and informatization, cables have become a key component in the fields of communication, energy, traffic, automation and the like, and are widely used for transmitting power, signals and data. Due to long-term use, the cable may have faults such as breakage, abrasion, poor contact and the like, and the safety and stability of the system are affected.
Existing multi-core cable fault testing methods generally rely on real-time current data to analyze whether abnormal impedance or interference exists in each core wire and identify multi-core cable faults. However, since the multi-core cable has a complex structure, each cable includes a plurality of core wires, and signal crosstalk may exist between different core wires, which may cause deviation in the relationship between the test current of the multi-core cable and the actual fault of the cable, thereby affecting the accuracy of fault diagnosis.
Disclosure of Invention
In order to solve the technical problems, the application provides a multi-core cable fault test system and a test method, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present application provides a method for testing a fault of a multi-core cable, including the steps of:
Acquiring a test current signal of each core wire in the multi-core cable;
Based on the target core wire test current signal and the difference conditions between the target core wire test current signal and all other core wire test current signals, determining the self-adaptive decomposition interval scale of the target core wire test current signal when the signal decomposition is carried out by adopting robust local mean decomposition, wherein the self-adaptive decomposition interval scale comprises the following specific steps:
a1, determining a fault occurrence coefficient of a target core wire test current signal based on differences among periods of all adjacent signal peaks in the target core wire test current signal and periodic parameters;
a2, acquiring test current signals of all other core wires with the target core wire under the same voltage condition, and recording the test current signals as a related data set of the target core wire;
A3, determining a high-frequency noise coefficient of any maximum value point based on the probability that the signal amplitude of the maximum value point near any maximum value point in the target core wire test current signal respectively appears, the slope maximum value near any maximum value point, and the signal amplitude difference and the time interval difference between any maximum value point and the adjacent minimum value point on the right side of the any maximum value point;
A4, combining the average level of high-frequency noise coefficients of all maximum points and signal distortion factors to determine the composition richness of the target core wire test current signal;
A5, forward fusion is carried out on the richness and the fault occurrence coefficient, and the scale adjustment coefficient of the self-adaptive decomposition section of the target core wire test current signal is determined;
And after the signals are decomposed, partial component signals are screened for reconstruction, and fault detection is carried out on the reconstructed signals.
Preferably, the fault occurrence coefficient is determined by the result of inverse fusion of the periodic parameter and the differences between the periods of all adjacent signal peaks.
Preferably, the signal distortion factor is determined by an average level of similarity of the correlation data set between the target core and all other cores under all voltage conditions.
Preferably, the method for determining the high-frequency noise coefficient is as follows:
Combining the probability of the occurrence of the signal amplitude of the maximum value point near any maximum value point and the slope maximum value near any maximum value point to determine the noise coefficient of any maximum value point;
calculating the average level of the multiplication results of the signal amplitude differences and the time interval differences of all maximum points;
And forward fusing the average level of the multiplied result and the noise coefficient to obtain the high-frequency noise coefficient.
Preferably, the noise coefficient is determined by inverse fusion of the slope maximum value and the probability that the signal amplitudes of all the maximum value points respectively appear.
Preferably, the slope maximum is further determined as the maximum of the absolute values of the slopes of all signals in the vicinity of the arbitrary maximum point.
Preferably, the constituent richness is determined by a product result of an average level of high-frequency noise coefficients of all maximum points and a signal distortion factor.
Preferably, the adaptive decomposition interval scale of the target core wire test current signal is recorded as,In the formula (I), in the formula (II),The scale adjustment coefficient of the self-adaptive decomposition section representing the target core wire test current signal, and Q represents the preset initial decomposition section scale of the target core wire test current signal.
Preferably, the screening method of the partial component signals is that the signal amplitude mean value of all the component signals is calculated, and the partial component signals are screened according to the sequence from small to large, wherein the first preset number of the component signals are screened partial component signals.
In a second aspect, an embodiment of the present application further provides a multi-core cable fault testing system, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor executes the computer program to implement the steps of any one of the multi-core cable fault testing methods described above.
The application has at least the following beneficial effects:
The application aims to solve the problem that in the existing multi-core cable fault test method, the relation between test current and voltage data and cable faults is deviated due to the fact that the multi-core cable is complex in structure and signal crosstalk possibly exists among different core wires, and the accuracy of fault diagnosis is affected. The application provides a fault diagnosis method based on real-time test current signals, which comprises the steps of firstly, extracting a fault occurrence coefficient of each core wire by analyzing the difference between periods of adjacent signal peaks in the test current signals of each core wire and periodical parameters to measure periodical characteristic changes influenced after faults occur in the core wire test current signals, then, further analyzing distortion conditions of the test current signals in local areas of each core wire in a multi-core wire cable, considering noise types caused by faults, particularly high-frequency noise influences which are usually accompanied with strong electromagnetic interference, quantitatively analyzing the mutual influence degree of the core wires, judging the positions where the high-frequency noise occurs more accurately, and further facilitating cable fault positioning, and finally, extracting the self-adaptive decomposition strength of the core wire test current signals by combining the fault occurrence coefficient and the composition richness, thereby realizing accurate analysis of the test current signals and improving the accuracy of fault diagnosis.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a multi-core cable fault testing method provided by an embodiment of the application;
fig. 2 is a flowchart of a method for determining an adaptive decomposition interval scale according to an embodiment of the present application.
Detailed Description
In order to further describe the technical means and effects adopted by the application to achieve the preset aim, the following detailed description refers to the specific implementation, structure, characteristics and effects of a multi-core cable fault testing system and a testing method according to the application with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
The following specifically describes a specific scheme of a multi-core cable fault testing system and a testing method provided by the application with reference to the accompanying drawings.
The embodiment of the application provides a multi-core cable fault testing system and a testing method.
Specifically, the following method for testing faults of a multi-core cable is provided, referring to fig. 1, the method includes the following steps:
A first step of obtaining a test current signal for each core of the multi-core cable.
In the embodiment, an independent power supply is provided for each core wire in the multi-core cable, so that the voltage of each core wire can be independently regulated, a proper connector and a proper connector are selected, the output end of the independent power supply is connected with the initial end of the core wire to be tested, and stable direct current voltage is provided for the test core wire through the cable, so that a current signal is measured, and stable and reliable connection is ensured. The fixed voltage condition when testing the current signal selects the most common working voltage condition in specific working engineering for analysis, and the fixed voltage condition is specifically set by an implementer.
Then, installing current sensors in the cable of each core wire and on the surface of the multi-core cable respectively, so as to ensure that each sensor can accurately measure the current signal of the corresponding core wire;
In this embodiment, the sampling frequency of the sensor is set to 5kHz to achieve real-time acquisition. In order to ensure the quality of the acquired signals, the signals are subjected to preliminary denoising treatment through a band-pass filter after being acquired, so that the accuracy of the subsequent analysis results is improved.
So far, the test current signal of each core wire in the multi-core cable can be obtained through the method.
And a second step of determining an adaptive decomposition interval scale of the target core wire test current signal when the signal decomposition is performed by adopting robust local mean decomposition based on the difference condition between the target core wire test current signal and all other core wire test current signals.
In the normal multi-core cable transmission process, each core wire is in a normal working state, current stably flows according to a preset path, and a current signal presents a regular and stable fluctuation mode. However, when one or more core wires inside the cable fail, the transmission of current will be disturbed, resulting in signal fluctuations becoming unstable, representing an irregular or sudden abnormal fluctuation pattern.
According to the method, the self-adaptive decomposition interval scale of the target core wire test current signal when the signal decomposition is carried out by adopting the robust local mean decomposition is determined based on the difference condition between the target core wire test current signal and all other core wire test current signals, the self-adaptive decomposition interval scale is used for carrying out the signal decomposition on the test current signal of the target core wire by adopting the Robust Local Mean Decomposition (RLMD), the fault detection is carried out by extracting the characteristics, the problems that the relation between test current and voltage data and the cable faults is deviated due to the fact that the multi-core cable structure is complex and signal crosstalk possibly exists between different core wires are avoided, and the fault detection accuracy is improved. The robust local mean decomposition is known in the art and will not be described in detail.
In this embodiment, a flowchart of a method for determining the scale of the adaptive decomposition section is shown in fig. 2, and specifically includes:
A1, determining a fault occurrence coefficient of the target core wire test current signal based on differences among periods of all adjacent signal peaks in the target core wire test current signal and periodic parameters.
The present embodiment performs the subsequent analysis with the i-th core wire as the target core wire.
Firstly, a periodic parameter T of a target core wire test current signal is obtained, and the periodic parameter T of the target core wire test current signal is extracted by adopting an autocorrelation function, wherein the larger the value of T is, the stronger the periodicity of the test current signal is, namely the more regular the signal fluctuation mode is. The autocorrelation function is a known technique and will not be described in detail. Fourier transforms may also be employed to extract the periodicity parameters in other embodiments of the present application.
In addition, due to the fact that local resistance is increased caused by factors such as poor contact of the core wire, when a certain periodicity exists in a fluctuation mode of a test current signal, signal waveforms of the test current signal are distorted, and the situation still belongs to the situation that the core wire may have faults.
Accordingly, the present embodiment utilizes a signal peak detection technique to obtain all signal peaks in the test current signal, wherein the signal peak detection technique is a known technique and will not be described in detail.
Preferably, in this embodiment, the differences between the periods of all adjacent signal peaks and the periodic parameters are reversely fused, so as to obtain the fault occurrence coefficient of the target core wire test current signal. The period of the signal peak is the time from the signal peak to the next signal peak.
It will be appreciated that the reverse fusion is a fusion method such as subtraction and division between data, and the specific reverse fusion method is determined by an implementer according to actual situations, and the present application is not limited in particular.
As an implementation manner, in this embodiment, dynamic Time Warping (DTW) is used to perform DTW matching on signal amplitudes of any two adjacent signal peaks in the test current signal under the same time sequence, obtain distance sets between all matching points, and calculate variances of the distance setsThe larger the value is, the larger the waveform difference between the periods of two adjacent signal peaks is, namely the signal waveform is distorted. Wherein, the dynamic time warping is a known technique and will not be described in detail. Using periodic parameters T and variancesConstruction of specific core test current failure occurrence coefficientsThe formula of (2) is:
In the formula, A fault occurrence coefficient representing a target core wire test current signal,A periodic parameter representative of the target core test current signal,Representing the number of all signal peaks in the target core test current signal,Represents the firstThe signal wave peak isWave crest distortion coefficients between the individual signal wave crests.
In the multi-core cable fault test, the circuit faults (such as open circuit, short circuit and the like) of the core wires can cause abnormal current fluctuation to be shown as an irregular fluctuation mode, and the local resistance change caused by poor contact and the like can not change the periodicity of current, but can increase the resistance of a current path, thereby causing distortion of a current signal waveform.
When the core wire test current signal has a certain periodicity, the distortion degree between the signal waveforms still needs to be further analyzed. To measure the current failure occurrence coefficient of the core wire.
Because the internal structure of the multi-core cable is complex, the multi-core cable generally comprises a plurality of core wires which are arranged in a staggered way, serious signal crosstalk can occur between the core wires based on fault test of the test current signals, so that the overall current performance of the cable in the test process is influenced, and therefore, the fault occurrence coefficient acquired based on the test current signals can not accurately reflect the fault probability of the cable.
A2, acquiring test current signals of all other core wires with the target core wire under the same voltage condition, recording the test current signals as a related data set of the target core wire, and determining a signal distortion factor of the test current signals of the target core wire based on the similarity of the related data sets of the target core wire and all other core wires under different voltage conditions.
In the multi-core cable, even if the respective cores operate under the same voltage condition, electrical anomalies may occur in the failed core due to the distance between the cores and the influence of the signal frequencies, causing different signal frequencies, and electromagnetic interference and mutual influence between the internal cores are not completely uniform.
According to the method, different voltage conditions are independently and respectively distributed to the target core wires, Q different voltage conditions are set in the embodiment, the Q value is 10, the test current signals of the target core wires under the different voltage conditions are obtained, and in addition, the same voltage conditions are respectively distributed to all other core wires in the multi-core cable, and the corresponding test current signals are obtained. Wherein, Q different voltage conditions are specifically set by the practitioner.
In this embodiment, test current signals of all other core wires under the same voltage condition as the target core wire are obtained, and the test current signals of all other core wires under the corresponding voltage condition are formed into a relevant data set of the target core wire.
Further, under the same voltage condition, the similarity between the related data set of the target core wire and the related data set of any other core wire is obtained, and the larger the value is, the stronger electromagnetic interference exists between the target core wire combinations.
Preferably, in the present embodiment, the signal distortion factor is determined by an average level of similarity of the correlation data set between the target core wire and all other core wires under all voltage conditions.
As one embodiment, the formula for constructing the signal distortion factor R of a specific target core wire test current signal is:
Wherein R represents the signal distortion factor of the target core wire test current signal, Q represents the number of different voltage conditions, Representing the number of cores inside the multi-core cable other than the target core,Representing the relevant data set of the target core under the q-th voltage condition,Representing the relevant data set of the nth core wire under the q-th voltage condition,Representing a set of related dataAnd (3) withPearson correlation coefficient therebetween. The pearson correlation coefficient is a known technique and will not be described in detail.
It should be appreciated that electromagnetic interference and signal distortion are typically caused by electric and magnetic field coupling between different cores. In multi-core cables, voltage variations can cause current fluctuations that can propagate to adjacent cores, causing signal distortion. Therefore, by calculating the similarity between the correlation data sets of the target core wire and the other core wires, the degree of interaction therebetween can be quantitatively analyzed.
A3, determining the noise coefficient of any maximum value point based on the probability that the signal amplitude of the maximum value point near any maximum value point in the target core wire test current signal respectively appears and the slope maximum value near any maximum value point.
In actual operation, faults of different types can cause test current signals in a fault core wire to generate noise with different frequencies, mainly comprising low-frequency noise and high-frequency noise, for example, faults such as wire breakage, short circuit and the like can form sharp fluctuation of current to generate high-frequency noise, and faults such as core wire aging and the like can cause abnormal low-frequency current fluctuation, namely low-frequency noise.
It is noted that high frequency noise not only affects the core signal quality, but may also extend the electromagnetic interference range, causing greater interference to surrounding equipment and systems. Because of the strong radiation of the high-frequency noise signal, the transmission is far, the electromagnetic influence on the environment can be further aggravated, and the operation and measurement accuracy of the equipment are affected.
Accordingly, the test current signal of the target core wire under the condition of fixed voltage is taken as an analysis object. Noise signals due to core faults often exhibit a disturbed ripple pattern in the test current signal, i.e. the fault noise signal exhibits a small time-lapse glitch ripple pattern at the maxima of the local signal.
The method marks all maximum value points and minimum value points in the test current signal of the target core wire, takes any maximum value point in the test current signal as a starting point, constructs a window, and sets the window size as 10 signal sampling points in the embodiment. Wherein signal points within the window may be used to characterize signal points near the maximum point.
The method comprises the steps of obtaining the probability that the signal amplitude of a maximum value point in a window where any maximum value point is located appears in a test current signal of a target core wire, wherein the smaller the probability that the signal amplitude of the maximum value point appears is, which means that the sampling point of the target signal appears less in the whole signal, namely the signal is likely to have abnormal fluctuation, obtaining the slope maximum value of all signals in the window where any maximum value point is located, wherein the larger the value is, which means that the signal fluctuation of the target core wire at the maximum value point is more intense.
Further, the application determines the noise coefficient of any maximum point based on the probability that the signal amplitude of the maximum point near any maximum point in the target core wire test current signal respectively appears and the slope maximum near any maximum point, and is used for representing the high-frequency noise intensity at each maximum point, thereby being convenient for identifying the high-frequency noise.
Preferably, the noise coefficient is determined by inverse fusion of the slope maximum value and the probability that the signal amplitudes of all the maximum value points respectively appear.
In other embodiments of the present application, the noise figure may be further determined by combining the signal amplitudes of all the maximum points around any one maximum point.
As one embodiment, constructing the noise figure of the j-th maximum point in the test current signal of a specific target core wireThe formula of (2) is:
In the formula, A noise figure representing the j-th maximum point in the test current signal of the target core wire,The maximum value of the absolute values of the slopes of all signals in the window in which the jth maximum value point in the test current signal representing the target core wire is located,A set of all maximum points within a window in which a j-th maximum point in the test current signal representing the target core wire is located,Representative collectionThe first of (3)The point of the maximum value of the number,Represents the firstThe probability of the signal amplitude of each maximum point occurring in the test current signal of the target conductor.
It should be appreciated that the core fault region results in an unstable current signal that does not quickly recover from normal fluctuations after passing through the fault region, and is typically characterized by severe fluctuations and delay spikes. The probability of the maximum point of the signal increases, the more severe the fluctuation, the more obvious the delay burr, indicating that the area may be a fault area current signal.
Further, the noise component of the fault region current signal is analyzed, and the greater the electromagnetic influence range of the high-frequency noise is, the higher the constituent richness of the region signal is.
Therefore, the method calculates the signal amplitude difference between each maximum point and the adjacent minimum point on the right side of the test current signal of the target core wire, the larger the value is, the more severe the surrounding fluctuation of the signal is, and the existence of high-frequency components is usually reflected.
In addition, it is noted that if there is no minimum point to the right of the last maximum point, then no analysis is performed on the last maximum point.
Preferably, in this embodiment, the average level of the multiplication result of the signal amplitude differences and the time interval differences of all the maximum points is calculated, and the average level of the multiplication result and the noise coefficient are forward fused, so as to obtain the high-frequency noise coefficient.
It is understood that the forward fusion is a fusion method such as addition and multiplication between data, and the specific forward fusion method is determined by an implementer according to actual situations, and the present application is not limited in particular.
As one embodiment, constructing the high-frequency noise figure of the j-th maximum point in the test current signal of a specific target core wireThe formula of (2) is:
In the formula, A high frequency noise figure representing the j-th maximum point in the test current signal of the target core wire,A noise figure representing the j-th maximum point in the test current signal of the target core wire,Represents the firstThe signal amplitude difference between a maximum point and its right adjacent minimum point,Represents the firstThe time interval distance between each maximum value point and the adjacent minimum value point on the right side of the maximum value point.
It is understood that the position where the high-frequency noise occurs can be more accurately judged according to the high-frequency noise coefficient, so that the cable fault location is facilitated.
And A4, combining the average level of the high-frequency noise coefficients of all the maximum points and the signal distortion factor to determine the constituent richness of the target core wire test current signal.
When the core wire fault generates more high-frequency noise, the electromagnetic influence range thereof is enlarged. Meanwhile, due to the decrease of electromagnetic compatibility, the core wire is more susceptible to other electromagnetic interference in the surrounding environment, thereby resulting in an increase in complexity and richness of the signal.
Further, the composition richness of the target core wire test current signal is determined by combining the average level of the high-frequency noise coefficients of all the maximum points and the signal distortion factor.
Preferably, the constituent richness is determined as a result of a product of an average level of high-frequency noise coefficients of all maximum points and a signal distortion factor.
In other embodiments of the present application, the constituent richness may also be determined by the average level of the high-frequency noise coefficients of all the maximum points and other forward fusion methods of the signal distortion factors.
It should be understood that, when the average level of the high-frequency noise coefficients and the signal distortion factor of all the maximum points in the test current signal of the target core wire are larger, the more likely that the target core wire has high-frequency noise, the more likely that electromagnetic interference and signal distortion phenomena are present, the more likely that the core wire is in fault, that is, the greater the constituent richness of the test current signal of the core wire.
And A5, forward fusing the composition richness and the fault occurrence coefficient, determining a scale adjustment coefficient of the self-adaptive decomposition section of the target core wire test current signal, and adjusting the scale of the initial decomposition section by using the scale adjustment coefficient to determine the scale of the self-adaptive decomposition section.
And acquiring a signal decomposition denoising scale adjustment coefficient of the target core wire test current signal according to the composition richness and the fault occurrence coefficient of the target core wire test current signal.
Preferably, the composition richness and the fault occurrence coefficient are subjected to forward fusion, and the scale adjustment coefficient of the self-adaptive decomposition section of the target core wire test current signal is obtained.
As an embodiment, a formula for constructing a specific adaptive decomposition interval scale adjustment coefficient is as follows:
In the formula, The scale adjustment coefficient representing the adaptive decomposition interval of the target core wire test current signal,For the normalization function, F represents the constituent richness of the target core wire test current signal, and W represents the failure occurrence coefficient of the target core wire test current signal.
In the conventional RLMD signal decomposition algorithm, a local mean value in a sliding window of each data point in the test current signal is calculated, and an initial decomposition interval scale Q is obtained according to the change of the local mean value in the test current signal. In this embodiment, the size of the sliding window is set to 20 signal points, and the method for calculating the initial decomposition interval scale is not described in detail in the prior art.
Preferably, in this embodiment, the formula for constructing the specific adaptive decomposition interval scale is: In the formula (I), in the formula (II), An adaptive decomposition interval scale representing the target core test current signal,The scale adjustment coefficient of the self-adaptive decomposition section representing the target core wire test current signal, and Q represents the preset initial decomposition section scale of the target core wire test current signal.
And thirdly, screening partial component signals after the signal decomposition, reconstructing the partial component signals, and detecting faults of the reconstructed signals.
And obtaining an original test current signal of the target core wire through RLMD signal decomposition algorithm, and obtaining component signals of each frequency and amplitude characteristic. The components are arranged in a high to low order of importance, each component representing a component of the original signal at a particular frequency and amplitude.
By selecting a component signal with a smaller signal amplitude for reconstruction, a stable and reliable real current signal can be obtained.
In this embodiment, the partial component screening method includes calculating signal amplitude averages of all component signals and selecting the first 3 component signals as screened partial component signals according to the order from small to large, where the number of the screened component signals can be selected according to actual situations in other embodiments.
Finally, after the test current signal of each core wire in the multi-core cable is processed, the signal characteristics are analyzed by using a traditional characteristic extraction method (such as amplitude, frequency, phase and the like).
Meanwhile, a large amount of historical data is utilized for training, including test current signals in normal and fault states, and the data source can be actual operation data, simulation or experimental data. The test current signal is typically pre-processed by denoising, normalization, etc., prior to training.
The Convolutional Neural Network (CNN) model is adopted for training, and after training, the model can accurately identify the fault mode in the real-time test current signal and accurately position the fault position and property. The model training process of the convolutional neural network is a known technology and will not be described in detail.
Based on the same inventive concept as the method, the embodiment of the application further provides a multi-core cable fault test system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to realize the steps of the multi-core cable fault test method according to any one of the methods.
The embodiments of the present application are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments.
It should be noted that unless otherwise specified and limited, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such article or apparatus. Without further limitation, the statement "comprises an" or "comprising" does not exclude that an additional identical element is present in an article or device comprising the element. In addition, the term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof.
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| CN115015698A (en) * | 2022-03-03 | 2022-09-06 | 国网山西省电力公司阳泉供电公司 | A method for detecting faults of secondary cables in substations |
| CN116068336A (en) * | 2023-01-31 | 2023-05-05 | 国网河南省电力公司漯河供电公司 | Cable fault positioning method based on parameter improved multivariate variation modal decomposition algorithm and kurtosis |
| CN116520096A (en) * | 2023-07-04 | 2023-08-01 | 常州长创力智能科技有限公司 | Traveling wave fault positioning method and device based on LMD decomposition |
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