A method for detecting cable insulation fault

Abstract

The present invention relates to the field of electrical measurement and power system monitoring technology, and in particular to a method for detecting cable insulation faults. The content includes: measuring electric field signals and constructing an electric field distribution model inside the cable to obtain a signal mapping result of the cable insulation state; mapping the signal mapping result of the cable insulation state to the frequency domain and performing smoothing processing, performing multi-scale analysis on the smoothed signal, extracting the fault characteristics of the cable, and quantifying the insulation state of the cable, calculating the probability of insulation faults in the cable, and providing a basis for fault warning and maintenance. It solves the problems that most traditional insulation fault detection methods rely on a single electrical or spectral characteristic detection, resulting in incomplete detection information; poor stability, which easily leads to misjudgment or missed judgment; some methods require power outages or direct contact with the cable for measurement, affecting the continuous operation of the cable; it is difficult to accurately describe the complex electric field distribution and its dynamic changes inside the cable, and predict the development trend of the fault.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical measurement and power system monitoring, and in particular to a method for detecting cable insulation faults. Background Art

[0002] In modern power systems, industrial control, and communications networks, cables serve as the core transmission medium. Their safe and stable operation is directly related to system reliability and economic efficiency. As cables age, their insulation degrades due to long-term electrical stress, environmental factors, and mechanical damage. This can lead to cable breakdown, short circuits, or other failures, potentially damaging equipment, disrupting power supply, and even causing serious safety incidents. Therefore, how to efficiently and accurately assess cable insulation status and promptly predict potential failures has become a matter of great concern for power systems and related industries.

[0003] Throughout a cable's lifecycle, its insulation condition is affected by multiple factors, including electrical aging, ambient humidity, temperature fluctuations, electromagnetic interference, and mechanical stress. These factors can lead to increased dielectric loss, partial discharge, changes in dielectric constant, and electric field distortion within the cable, degrading the cable's insulation performance and, in turn, affecting its normal operation. Therefore, accurately analyzing the electric field distribution within the cable, extracting the cable's frequency response characteristics, constructing topological electric field signatures, and combining them with a multi-dimensional health index assessment method to conduct real-time monitoring of the cable's insulation condition and predict faults are of great theoretical and practical significance.

[0004] However, traditional insulation fault detection methods have the following technical problems: most of them rely on a single electrical or spectral characteristic test, which cannot fully reflect the comprehensive status of cable insulation at different frequencies, spatial positions and time dimensions, resulting in incomplete detection information; they have poor stability under complex working conditions, which can easily lead to misjudgment or missed judgment; some methods require power outages or direct contact with the cable for measurement, and lack online real-time monitoring capabilities, affecting the continuous operation of the cable; most of them are based on simple mathematical models, which make it difficult to accurately describe the complex electric field distribution and its dynamic changes inside the cable, and cannot accurately locate local insulation deterioration areas, nor can they predict future fault development trends. Summary of the Invention

[0005] The present invention provides a method for detecting cable insulation faults to solve the problems that traditional insulation fault detection methods mostly rely on single electrical or spectral characteristic detection, cannot fully reflect the comprehensive status of cable insulation in different frequencies, spatial positions and time dimensions, resulting in incomplete detection information; poor stability under complex working conditions, easily leading to misjudgment or missed judgment; some methods require power outages or direct contact with the cable for measurement, do not have online real-time monitoring capabilities, and affect the continuous operation of the cable; most methods are based on simple mathematical models, which make it difficult to accurately describe the complex electric field distribution and its dynamic changes inside the cable, and cannot accurately locate local insulation deterioration areas, nor predict future fault development trends.

[0006] A method for detecting cable insulation faults of the present invention specifically includes the following technical solutions:

[0007] A method for detecting cable insulation faults comprises the following steps:

[0008] S1. Measure the electric field signal, construct an electric field distribution model inside the cable, and obtain a signal mapping result of the cable insulation status; convert the signal mapping result of the cable insulation status from a time domain signal to a frequency domain signal, and perform smoothing to obtain a smoothed signal;

[0009] S2. Perform multi-scale analysis on the smoothed signal to extract the cable's fault characteristics and quantify the cable's insulation status to obtain a cable health index. Based on the cable health index, calculate the probability of insulation failure in the cable, providing a basis for fault warning and maintenance.

[0010] Preferably, the S1 specifically includes:

[0011] The electric field signals at different positions and times in the cable are subjected to data interpolation, Fourier transform and error correction to obtain the electric field distribution data. The electric field distribution model inside the cable is fitted using the least squares method to obtain the electric field distribution inside the cable.

[0012] Preferably, the S1 specifically includes:

[0013] Based on the influence of time, space and frequency on the electric field distribution, a nonlinear modulation factor is introduced to construct the kernel function of signal propagation. Combined with the electric field distribution inside the cable, the signal mapping result of the cable insulation status is obtained.

[0014] Preferably, the S1 specifically includes:

[0015] The signal mapping result of the cable insulation status is mapped to the frequency domain through Fourier transform to obtain the Fourier transform result; the Fourier transform result is subjected to Laplace transform to obtain a smoothed signal.

[0016] Preferably, the S2 specifically includes:

[0017] The smoothed signal is discretized into space, time and frequency dimensions to generate a third-order signal matrix. Based on the third-order signal matrix, a dynamic time window is introduced to construct a fourth-order tensor. The fourth-order tensor is decomposed to extract the fault characteristics of the cable.

[0018] Preferably, the S2 specifically includes:

[0019] Based on the fault characteristics of the cable, the normalization factor and the topological integral normalization factor are introduced to obtain the normalized electric field topological mapping.

[0020] Preferably, the S2 specifically includes:

[0021] Based on normalized electric field topological mapping, the insulation status of the cable is quantified by integrating signal characteristics, topological electric field parameters, and frequency response to obtain the cable health index. The integrated signal characteristics are the distribution of electric field signals at different positions and times along the cable and their attenuation characteristics over time. The topological electric field parameters refer to the topological changes of the electric field inside the cable.

[0022] Preferably, the S2 specifically includes:

[0023] The cable health index is integrated and exponential decay is introduced to calculate the probability of insulation failure in the cable. When the insulation condition of the cable area deteriorates, the cable health index of the corresponding area increases, and the probability of insulation failure in the cable increases, triggering an early warning.

[0024] The beneficial effects of the technical solution of the present invention are:

[0025] 1. The present invention not only performs time domain analysis on the collected electric field signals, but also maps the electric field signals to the frequency domain through Fourier transform to eliminate unnecessary time domain interference and ensure that the insulation response characteristics at different frequencies can be extracted. Furthermore, through multi-scale analysis technology, the signal is dynamically adjusted to ensure that the changing trend of the signal in different time windows can be accurately modeled, thereby comprehensively reflecting the evolution process of the cable insulation state.

[0026] 2. Since the cable runs in a complex environment and is easily affected by external electromagnetic interference and background noise, the present invention uses Laplace transform for smoothing, which effectively reduces the impact of high-frequency noise and makes the calculation results more stable.

[0027] 3. The present invention extracts the topological structure changes of the cable insulation status by constructing an electric field topology map; constructs a cable health index model by integrating signal characteristics, topological electric field parameters and frequency response data, so that the cable health status can be quantified, and the possible future failure of the cable can be predicted by the changing trend of the cable health index; the calculation method of the cable health index takes into account the nonlinear attenuation, spatial distribution and dynamic characteristics of the electric field signal, so that the evaluation results of the cable insulation status have more practical engineering application value.

[0028] 4. The insulation failure probability calculation method of the present invention is based on the integral calculation of the cable health index and adopts an exponential decay form to ensure the numerical stability of the calculation result, while accurately reflecting the health status of the cable throughout its entire length. Once the insulation condition of a certain area deteriorates seriously, the insulation failure probability value will increase rapidly, thereby issuing an early warning signal in time, allowing maintenance personnel to carry out maintenance in advance and avoid cable damage or power outages caused by sudden insulation failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a flow chart of a method for detecting cable insulation faults according to the present invention. DETAILED DESCRIPTION

[0030] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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 making creative efforts shall fall within the scope of protection of the present invention.

[0031] 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 invention belongs.

[0032] The following describes in detail a method for detecting cable insulation faults provided by the present invention with reference to the accompanying drawings.

[0033] Refer to the attached Figure 1 , which shows a flow chart of a method for detecting cable insulation faults provided by an embodiment of the present invention, the method comprising the following steps:

[0034] S1. Measure the electric field signal, construct an electric field distribution model inside the cable, and obtain a signal mapping result of the cable insulation status; convert the signal mapping result of the cable insulation status from a time domain signal to a frequency domain signal, and perform smoothing to obtain a smoothed signal;

[0035] To determine the initial state of the cable's insulation, it's necessary to measure the electrical variables within the cable to construct a complete signal mapping model. Specifically, a specific pulsed current signal is applied to the cable's input, while simultaneously acquiring electric field signals at multiple spatial locations. These electric field signals are the outward manifestation of the electrical variables (potential, current density, polarization charge, etc.) acting in the dielectric environment. The measurement process relies on an array of electric field sensors, arranged to cover different insulation regions of the cable, thereby capturing the electric field distribution along the cable's length.

[0036] Because the cable's insulation exhibits different dielectric properties at different frequencies, the collected electric field signal not only contains the insulation's instantaneous electric field response but also implicitly reflects its attenuation characteristics at different frequencies. To ensure the stability and accuracy of electric field signal acquisition, multiple measurements are taken at each sampling point and the average value is calculated to reduce the impact of external noise and random interference.

[0037] Because the cable's insulation layer may have locally degraded areas, causing the electric field to attenuate or distort during propagation, a mathematical model is constructed to accurately describe the electric field response within the cable's interior. The electric field distribution within the cable is not only affected by the applied electrical signal but also modulated by factors such as the inherent non-uniformity of the cable's insulation material, localized charge accumulation, and material aging. The spatial propagation of the electric field involves its transmission and reflection at different locations, while its temporal variation is primarily determined by the polarization and charge accumulation effects of the cable dielectric. Furthermore, the signal's frequency component also affects the propagation characteristics of the electric field, particularly at high frequencies, where insulation material losses increase. Therefore, the electric field strength at different positions and times in the cable is obtained through capacitive coupling measurement, high-frequency pulse injection, partial discharge electromagnetic wave detection, and external magnetic field measurement. After data interpolation, Fourier transform, and error correction, the electric field distribution data is obtained, and a continuous surface model, i.e., the electric field distribution model inside the cable, is fitted by the least squares method to describe the electric field distribution inside the cable. The influence of time, space, and frequency on the electric field distribution is comprehensively considered to obtain the signal mapping result of the cable insulation state. The data interpolation, Fourier transform, error correction, and least squares method are all technical means well known to those skilled in the art and will not be elaborated here. The specific formula for the signal mapping result of the cable insulation state is as follows:

[0038] ,

[0039] ,

[0040] in, The signal mapping result representing the cable insulation status is constructed based on the existing convolution integral theory; It is the kernel function of signal propagation, which is used to characterize the diffusion and transmission mode of the electric field inside the medium; is the position coordinate of the cable along its length; is a time variable; is the angular frequency, which represents the frequency characteristics of the electric field signal; is the total cable length; is the total time window of the measurement; It is the electric field distribution inside the cable, which is used to describe the electric field strength at different positions and times inside the cable; and are the integral variables of position and time respectively; The frequency response of the electric field signal over time is represented by the standard wave propagation theory, which is a technical means well known to those skilled in the art and will not be described in detail here. It is the attenuation factor of the medium, which is determined experimentally by the dielectric constant and conductivity of the insulating material, and its value range is ; Indicates the non-uniformity of the insulation layer, which is obtained by measuring the dielectric loss tangent value in different areas. The value range is ; is the dielectric charge storage factor, which is used to describe the charge storage effect of the dielectric and is determined by dielectric polarization test. Its value range is ; It is the time rate of change factor, which is used to describe the rate of change of the electric field signal over time. It is obtained by measuring the rate of change of the electric field signal over time. Its value range is Since dielectric inhomogeneity (such as local loss of the insulating layer) can cause nonlinear attenuation characteristics of electric field propagation, a nonlinear modulation factor is introduced. ,in, Used to represent the nonlinear effects of electric fields during propagation.

[0041] After obtaining the signal mapping results of the cable insulation status, further transformation is required to extract the characteristics of different frequency components. Since the insulation status of the cable affects the propagation characteristics of the electric field signal at different frequencies, directly analyzing the cable insulation condition in the time domain may be affected by other factors such as power supply noise and environmental interference. In order to more effectively analyze the frequency distribution of the electric field signal, it is necessary to use Fourier transform to map the signal mapping results of the cable insulation status to the frequency domain, realize the conversion of time domain signal to frequency domain signal, so that different frequency components can be clearly separated, and then analyze how the cable insulation status affects the response of the electric field signal at each frequency.

[0042] The time variable is converted into a frequency variable through Fourier transform, so that the various frequency components of the electric field signal can be extracted, thereby analyzing the impact of the cable insulation state on different frequency signals.

[0043] Since electric field signals are often interfered with by high-frequency noise during measurement, the high-frequency components of the Fourier transform results fluctuate, making it difficult to extract fault features. To improve the stability of the calculation and suppress the influence of noise on frequency domain analysis, the Fourier transform results are Laplace transformed to obtain the Laplace transformed signal, that is, the smoothed signal.

[0044] The Fourier transform and Laplace transform are technical means well known to those skilled in the art and will not be described in detail here.

[0045] S2. Perform multi-scale analysis on the smoothed signal to extract the cable's fault characteristics and quantify the cable's insulation status to obtain a cable health index. Based on the cable health index, calculate the probability of insulation failure in the cable, providing a basis for fault warning and maintenance.

[0046] Perform multi-scale analysis on the smoothed signal to further extract the fault characteristics of the cable; since the change in the insulation state of the cable not only affects the spatial distribution of the electric field signal, but also causes the nonlinear attenuation of the electric field signal in time, and the response to different frequency signals will also be different, it is necessary to construct a third-order signal matrix that can simultaneously contain space, time and frequency information; each dimension of the third-order signal matrix corresponds to the spatial sampling point, time sampling point and frequency channel along the length of the cable, so that the electric field signal characteristics at different positions, different times and different frequencies can be fully captured. Specifically, the spatial, time and frequency dimensions are discretized into a finite number of sampling points to generate a third-order signal matrix , the specific definition formula is as follows:

[0047] ,

[0048] in, Is a third-order signal matrix used to store the Laplace transformed signal , indicating the 𝑚th spatial sampling point , the 𝑛th time sampling point , the 𝑓th frequency channel Signal strength at represents the field of real numbers; The number of spatial sampling points indicates the number of sampling points along the length of the cable, which is set according to the actual requirements of the cable length and the required measurement accuracy. is the number of time sampling points, which is determined by the sampling rate and the duration of the entire sampling window; is the number of frequency channels, which is determined by the spectral range and frequency resolution of the signal. Subsequently, in order to introduce time dynamic characteristics, a dynamic time window dimension is added to the third-order signal matrix to define a fourth-order tensor:

[0049] ,

[0050] in, is a fourth-order tensor; It is the signal attenuation factor, which is used to simulate the attenuation rate of the signal over time. It is obtained through experiments, and the value range is determined by the dielectric properties of the cable insulation material and environmental factors. It is a dynamic time window index, representing the change of the electric field signal over the time window. The value range depends on the number of time windows. The fourth-order tensor is used to store signal data of different scales. CP decomposition or Tucker decomposition is used to decompose the fourth-order tensor to extract key features and obtain the fault characteristics of the cable. CP decomposition and Tucker decomposition are both well-known technical means for those skilled in the art and will not be described in detail here.

[0051] To further analyze the cable insulation status, based on the cable fault characteristics, the normalized electric field topology mapping is obtained by introducing the normalization factor and the topological integral normalization factor through the classical electric field integral theory to obtain the electric field distribution of the cable under different states. The formula of the normalized electric field topology mapping is as follows:

[0052] ,

[0053] in, It is a normalized electric field topology map used to characterize the topological changes in the cable insulation fault area; is the normalization factor, obtained through experimental fitting to ensure dimension matching, and its value range is ; is the topological integral normalization factor, used to adjust the topological integral The scale is obtained through experiments and the value range is ; The closed path representing the change of electric field is obtained through experiments; is a small increment on the integration path, obtained through electromagnetic field theory and numerical calculation methods.

[0054] In order to further analyze the health status of the cable, a health index model is constructed by integrating signal characteristics, topological electric field parameters and frequency response, quantifying the cable insulation status and calculating the cable health index. The comprehensive signal characteristics refer to the distribution of electric field signals along the cable at different positions and times and their attenuation characteristics over time. The topological electric field parameters refer to the topological changes of the electric field inside the cable. Specifically, each term in the formula represents the response of the electric field at different positions, times and frequencies, including the attenuation effect of the amplitude of the signal component, the effect of the frequency change and the influence of the electric field phase change. By calculating the cable health index, the insulation status of the cable is comprehensively evaluated, and the possible fault area of ​​the cable is inferred. The specific formula of the health index model is as follows:

[0055] ,

[0056] in, Is the cable health index, indicating the cable is in position ,time and frequency The health status of the following; Derived from the classic oscillation signal description and decay model, represents the comprehensive signal characteristics, represents the frequency response; It represents the electric field topological characteristics of the cable. Considering that there may be local defects and inhomogeneities in the cable insulation layer, it is necessary to combine the distribution of the electric field inside the cable and the topological characteristics of the local fault. is a logarithmic function used to smooth the topological effects of the electric field; It is from The amplitude of the signal component extracted from spatial sampling points and the The signal strength at each time sampling point; is the time attenuation coefficient, which is spatial sampling points and the The signal attenuation factor at each time sampling point is obtained through experiments and has a value range of ; It is in spatial sampling points and the The signal angular frequency at each time sampling point; is the phase offset, which is spatial sampling points and the The phase angle at each time sampling point is obtained through Fourier transform and its value range is ; Indicates the rate of change of the electric field signal; is a normalization factor used to ensure uniformity of dimensions, obtained through experiments, and has a value range of ; is the normalized voltage factor, which is a reference voltage set according to expert experience and matches the amplitude of the electric field signal component. Its value range is ; is the normalized length factor, which is the reference length set according to expert experience and has a value range of , which matches the spatial distribution of the electric field signal; Indicates the electric field strength.

[0057] The probability of insulation failure in a cable is calculated by integrating the cable health index, covering the health status of the entire cable length. The exponential decay form is used to ensure the stability of the value and reflect the contribution of different areas to the overall insulation failure risk. The formula for calculating the probability of insulation failure in a cable is as follows:

[0058] ,

[0059] in, Represents the probability of insulation failure of the cable, and the value range is between [0,1]. Indicates complete health, Indicates an extremely high risk of failure; is the normalized scale factor, obtained through experimental fitting to ensure that the exponential function parameter is dimensionless and has a value range of ; It represents the cumulative calculation of the cable health index along the entire length of the cable, ensuring that the health of the entire cable is comprehensively considered, so that the impact of local damage on the overall cable can be accurately assessed.

[0060] When the insulation condition of a certain area of ​​the cable deteriorates sharply, the cable health index value of the corresponding area will increase, causing the probability of insulation failure of the cable to rise rapidly, thereby triggering an early warning, ensuring that the cable insulation status can be accurately reflected in actual applications and providing a reliable preventive maintenance reference.

[0061] In summary, a method for detecting cable insulation faults is completed.

[0062] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0064] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for detecting cable insulation fault, characterized in that: The following steps are involved: S1. Measure the electric field signal, build an electric field distribution model inside the cable, and obtain the signal mapping result of the cable insulation status; Converting the signal mapping result of the cable insulation status from a time domain signal to a frequency domain signal and performing smoothing processing to obtain a smoothed signal; S2. Perform multi-scale analysis on the smoothed signal to extract the fault characteristics of the cable; Based on the fault characteristics of the cable, the normalization factor and the topological integral normalization factor are introduced to obtain the normalized electric field topological mapping; Based on the normalized electric field topology mapping, a health index model is constructed by integrating signal characteristics, topological electric field parameters and frequency response to quantify the insulation status of the cable and obtain the cable health index. The cable health index is integrated and exponential decay is introduced to calculate the probability of insulation failure in the cable, providing a basis for fault warning and maintenance.

2. A method for detecting cable insulation fault according to claim 1, characterized in that: Said S1 specifically includes: The electric field signals at different positions and times in the cable are subjected to data interpolation, Fourier transform and error correction to obtain the electric field distribution data. The electric field distribution model inside the cable is fitted using the least squares method to obtain the electric field distribution inside the cable.

3. A method for detecting cable insulation fault according to claim 2, characterized in that: Said S1 specifically includes: Based on the influence of time, space and frequency on the electric field distribution, a nonlinear modulation factor is introduced to construct the kernel function of signal propagation. Combined with the electric field distribution inside the cable, the signal mapping result of the cable insulation status is obtained.

4. A method for detecting cable insulation fault according to claim 3, characterized in that: Said S1 specifically includes: The signal mapping result of the cable insulation status is mapped to the frequency domain through Fourier transform to obtain the Fourier transform result; the Fourier transform result is subjected to Laplace transform to obtain a smoothed signal.

5. The method for detecting cable insulation fault according to claim 1, wherein: Said S2 specifically includes: The smoothed signal is discretized into space, time and frequency dimensions to generate a third-order signal matrix. Based on the third-order signal matrix, a dynamic time window is introduced to construct a fourth-order tensor. The fourth-order tensor is decomposed to extract the fault characteristics of the cable.

6. A method for detecting cable insulation fault according to claim 1, characterized in that: Said S2 specifically includes: The comprehensive signal characteristics are the distribution of electric field signals at different positions and times along the cable and their attenuation characteristics over time; the topological electric field parameters refer to the topological changes of the electric field inside the cable.

7. A method for detecting cable insulation fault according to claim 1, characterized in that: Said S2 specifically includes: When the insulation condition of the cable area deteriorates, the cable health index of the corresponding area increases, and the probability of insulation failure of the cable increases, triggering an early warning.

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