Cable accessory aging detection method based on multi-parameter fusion wavelet transform

Abstract

The invention discloses a cable accessory aging detection method based on multi-parameter fusion wavelet transform, and the method comprises the steps: building a cable accessory AC steady-state equivalent circuit model, and obtaining the current and voltage dynamic data of a target cable accessory sample at a test temperature; decomposing the signal by using wavelet transform, setting a dynamic threshold according to the wavelet coefficient amplitude difference under each scale, and separating noise from a target signal; dielectric property change data with a high signal-to-noise ratio are extracted through signal reconstruction, and the aging state of the cable accessory is analyzed in combination with a dielectric loss angle. According to the method, the problems that a traditional method is long in detection period and prone to interference are solved, the accuracy and real-time performance of cable accessory aging and degradation monitoring are remarkably improved, and electric power safety accidents can be effectively prevented.

Description

Technical Field

[0001] The present invention relates to a power cable state monitoring technology, and in particular to a cable accessory aging detection method based on multi-parameter fusion wavelet transform. Background Art

[0002] The aging and degradation of cable accessory insulation materials is a long-term critical issue facing power systems. It directly leads to a decline in electrical performance, weakened mechanical strength, and even serious accidents such as short circuits and fires. Currently, traditional detection methods such as partial discharge monitoring and infrared temperature measurement have the following limitations: (1) Complex operation: They rely on complex equipment and manual intervention, making automated monitoring difficult to achieve; (2) Long detection cycles: Offline detection cannot meet real-time requirements; (3) Susceptibility to environmental interference: Noise interference causes signal distortion, affecting the accuracy of aging assessment.

[0003] In the existing technology, although the detection method based on the change of dielectric properties can indirectly reflect the degree of insulation aging, it is seriously affected by noise interference and lacks efficient denoising methods. Summary of the Invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a cable accessories aging detection method based on multi-parameter fusion wavelet transform, which can solve the problems of long detection cycle and weak anti-noise interference ability of traditional technologies, and realize accurate judgment of the aging and degradation degree of cable accessories.

[0005] Technical solution: The present invention provides a cable accessory aging detection method based on multi-parameter fusion wavelet transform, comprising:

[0006] Establish an AC steady-state equivalent circuit model for the cable accessories, and obtain dynamic current and voltage data of the target cable accessory sample at the test temperature as a function of test time from the AC steady-state equivalent circuit model; derive the calculation formula for the dielectric loss angle from the sinusoidal steady-state voltage-current relationship matrix, and calculate the dielectric loss angle of the cable accessories using the calculation formula for the dielectric loss angle;

[0007] Perform wavelet transform decomposition on the current and voltage dynamic data to obtain multi-scale wavelet coefficients;

[0008] Based on the normal distribution characteristics of the noise signal and the multi-parameter fusion mechanism, a dynamic threshold is set in combination with the amplitude difference of the wavelet coefficients at each scale. The dynamic threshold is used to filter out the wavelet coefficients of the target signal from the multi-scale wavelet coefficients and filter out the noise in the target signal.

[0009] Perform signal reconstruction on the filtered wavelet coefficients and extract dielectric property change data from the reconstructed signal;

[0010] Combine the dielectric performance change data with the dielectric loss angle to evaluate the degree of aging and degradation of the insulation materials of cable accessories.

[0011] Furthermore, the process of establishing the AC steady-state equivalent circuit model of the cable accessories is as follows:

[0012] When the cable accessories are operating normally, a micro-segment dx is intercepted at a distance X from the head end. The AC steady-state equivalent circuit model of the cable accessories includes three groups of series-connected micro-segments' along-line resistance R0dx and micro-segments' along-line inductance L0dx, and the output end of the first group, the input end and output end of the second group, and the input end of the third group are all connected to a group of parallel-connected micro-segments' conductance to ground G0dx and capacitance to ground C0dx.

[0013] Furthermore, the db4 wavelet basis function is used to perform wavelet transform decomposition on the current and voltage dynamic data to obtain multi-scale wavelet coefficients.

[0014] Furthermore, based on the normal distribution characteristics of the noise signal and the multi-parameter fusion mechanism, the dynamic threshold is set in combination with the amplitude difference of the wavelet coefficients at each scale, including:

[0015] The standard deviation σ of the noise wavelet coefficients in the nth layer is calculated based on the statistical distribution characteristics of the noise wavelet coefficients. n , used to characterize the intensity fluctuation of noise at this scale;

[0016] Calculate the signal energy E of the nth layer s,n and the noise energy E of the nth layer n,n The ratio of is used to reflect the energy distribution difference between the target signal and the noise at a specific scale;

[0017] By formula Determine the adaptive dynamic threshold ζ of the nth layer n , where υ is the basic attenuation factor and α is the adaptive adjustment factor;

[0018] The maximum modulus of the wavelet coefficient of the noise decreases with the increase of scale, and the maximum modulus of the wavelet coefficient of the target signal increases with the increase of scale. The adaptive dynamic threshold ζ of the nth layer n Decay with scale.

[0019] Furthermore, the standard deviation σ of the noise wavelet coefficients of the nth layer is calculated based on the statistical distribution characteristics of the noise wavelet coefficients. n , used to characterize the intensity fluctuation of noise at this scale, including:

[0020] In the initial decomposition layer, assuming that noise is dominant, the initial decomposition layer wavelet coefficient D is extracted n,k , k is the coefficient index;

[0021] Calculate the standard deviation σ of the noise wavelet coefficients of the nth layer by statistical methods n :

[0022]

[0023] in, N is the total number of wavelet coefficients;

[0024] For subsequent scales n>1, the noise standard deviation decreases according to the empirical formula:

[0025] σ n =σ n-1 ·ν

[0026] Among them, ν is the basic attenuation factor, which reflects the attenuation characteristics of noise energy as the scale increases.

[0027] Furthermore, the n-th layer signal energy E s,n The expression is as follows:

[0028]

[0029] in, is the wavelet coefficient retained after denoising; N is the total number of wavelet coefficients;

[0030] The noise energy E of the nth layer n,n The expression is as follows:

[0031]

[0032] Among them, D n,k is the wavelet coefficient of the nth layer before denoising.

[0033] Furthermore, the adaptive adjustment factor α is used to balance scale attenuation and energy weight, and its value range is 0.1 to 0.3.

[0034] Furthermore, a dynamic threshold is used to filter out the wavelet coefficients of the target signal from the multi-scale wavelet coefficients and to filter out the noise in the target signal, including:

[0035] If the wavelet coefficient at the current scale is greater than the adaptive dynamic threshold ζ n Then it is retained as the target signal. If the wavelet coefficient at the current scale is less than the adaptive dynamic threshold ζ n It is determined to be noise and filtered out, where the adaptive dynamic threshold ζ n Dynamic adjustment through multi-parameter fusion mechanism.

[0036] Furthermore, the dielectric performance change data includes dielectric loss factor and capacitance change rate, which are used to quantitatively analyze the degree of deterioration of the insulation material of the cable accessories.

[0037] Furthermore, the calculation formula of the dielectric loss angle is derived from the sinusoidal steady-state voltage-current relationship matrix, and the dielectric loss angle of the cable accessories is calculated using the calculation formula of the dielectric loss angle, including:

[0038] Considering that the voltage and current waveforms of the cable accessories are sinusoidal steady-state waveforms, the voltage and current relationship matrix at both ends of the cable accessories line is established as follows:

[0039]

[0040] Where γ is the propagation coefficient of the line; Z0 is the wave impedance of the line; and e is the natural logarithm. The expressions of the propagation coefficient γ and the wave impedance Z0 of the line are as follows:

[0041]

[0042] The voltage-current relationship matrix at both ends of the cable accessory line is obtained:

[0043]

[0044] The dielectric loss angle of cable accessories satisfies the following formula:

[0045]

[0046] From the above formula, we can get:

[0047]

[0048] It can be seen from the above formula that the ratio of the current difference between the two ends of the cable accessory to the average voltage at the two ends will not change due to the load current flowing through the cable accessory; the complementary angle of the phase angle difference between the current difference between the two ends of the cable accessory and the average voltage at the two ends is regarded as the dielectric loss angle of the cable accessory.

[0049] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are as follows: (1) Multi-parameter fusion dynamic threshold technology: By introducing the noise standard deviation σ n Quantization noise intensity, combined with the signal-to-noise energy ratio E s,n / E n,nReflect the energy distribution difference between signal and noise at different scales, and use the adaptive factor α (value range 0.1~0.3) to dynamically adjust the threshold weight, solving the technical problems of traditional denoising methods that rely on manual experience to adjust the threshold and rely solely on scale attenuation characteristics, resulting in difficulty in balancing noise suppression and signal retention, weak anti-interference ability, and low detection accuracy. It achieves accurate separation of noise and target signals, improves the signal-to-noise ratio, reduces the calculation error of the dielectric loss angle, and significantly enhances the accuracy of aging assessment; (2) Adaptive adjustment mechanism: Based on the experimental calibration of the adaptive factor α (0.1~0.3), combined with the signal characteristics under different noise environments, the threshold attenuation rate and energy weight distribution are dynamically optimized: This solves the problem that traditional methods have poor adaptability in complex noise scenarios, require repeated manual parameter adjustment, and are cumbersome and inefficient. It can adapt to high-noise (such as industrial environment) and low-noise (such as laboratory) scenarios, improve the adaptive matching rate of denoising parameters, reduce manual intervention, and enhance the practicality and universality of the method. (3) The following technical means are used to simplify the operation process and realize automated detection: Automated data acquisition: Use high-precision sensors to collect current and voltage dynamic data in real time, and automatically calculate the dielectric loss angle through the equivalent circuit model to reduce manual intervention; Intelligent signal processing: Based on the multi-parameter fusion dynamic threshold algorithm, it automatically separates noise and target signals without relying on manual experience to adjust the denoising parameters; One-click aging assessment: Through the automatic extraction and formula analysis of dielectric performance data, it realizes real-time online assessment of aging status. This solves the operational complexity problems of traditional methods, mainly reflected in the dependence of data acquisition on manual operation, the need for experience adjustment of denoising parameters, and the need for manual calculation of dielectric parameters for aging assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the process of the present invention;

[0051] Figure 2 This is a schematic diagram of the AC steady-state equivalent circuit model of the cable accessories;

[0052] Figure 3 Schematic diagram of the target signal generation process for multi-parameter fusion thresholding. DETAILED DESCRIPTION

[0053] The technical solution of the present invention is described in detail below in conjunction with specific implementation methods and the accompanying drawings.

[0054] Aiming at the problem that the existing technology cannot accurately judge the aging and degradation degree of cable accessories, which leads to frequent power safety accidents, a cable accessories aging detection method based on multi-parameter fusion wavelet transform is proposed. Figure 1 As shown, the cable accessories aging detection method based on multi-parameter fusion wavelet transform of the present invention includes the following steps:

[0055] S1. Establish an AC steady-state equivalent circuit model for cable accessories, and obtain dynamic current and voltage data of the target cable accessory sample at the test temperature as a function of test time from the AC steady-state equivalent circuit model; derive the calculation formula for the dielectric loss angle from the sinusoidal steady-state voltage-current relationship matrix, and calculate the dielectric loss angle of the cable accessory using the calculation formula for the dielectric loss angle;

[0056] When the cable accessories are operating normally, a micro-element segment dx is cut at a distance X from the head end to construct the following Figure 2 The AC steady-state equivalent circuit model of a cable accessory is shown. This cable accessory AC steady-state equivalent circuit model includes three sets of serially connected micro-segment resistances R0dx and micro-segment inductances L0dx. The output of the first set, the input and output of the second set, and the input of the third set are all connected to a parallel set of micro-segment conductances G0dx and capacitances C0dx. In the model, U1 and I1 represent the voltage and current at the head end of the cable accessory, respectively, while U2 and I2 represent the voltage and current at the tail end. This cable accessory AC steady-state equivalent circuit model includes parameters such as the micro-segment resistance, inductance, conductance to ground, and capacitance to ground. These parameters can be obtained from experimental measurements, cable specifications, or historical data. For example, the micro-segment resistance (R0dx) can be calculated based on the resistivity, cross-sectional area, and length of the cable accessory; the capacitance to ground (C0dx) can be calculated based on the cable accessory's geometry and the dielectric constant of the insulation material.

[0057] Under the test temperature conditions, the current change data and voltage change data generated by the target cable accessory sample during the test time are collected.

[0058] In this embodiment, the dielectric loss angle calculation formula is derived from the sinusoidal steady-state voltage-current relationship matrix, and the dielectric loss angle of the cable accessory is calculated using the dielectric loss angle calculation formula. The details are as follows:

[0059] For high-voltage cable accessory lines, the voltage and current relationship per unit length is as follows:

[0060]

[0061] Considering that the voltage and current waveforms of the cable accessories are sinusoidal steady-state waveforms, the voltage and current relationship matrix at both ends of the cable accessories line is established as follows:

[0062]

[0063] Where γ is the propagation coefficient of the line; Z0 is the wave impedance of the line; and e is the natural logarithm. The expressions of the propagation coefficient γ and the wave impedance Z0 of the line are as follows:

[0064]

[0065] The voltage-current relationship matrix at both ends of the cable accessory line is obtained:

[0066]

[0067] The dielectric loss angle of cable accessories satisfies the following formula:

[0068]

[0069] From the above formula we can get:

[0070]

[0071] It can be seen from the above formula that the ratio of the current difference between the two ends of the cable accessory to the average voltage at the two ends will not change due to the load current flowing through the cable accessory; the complementary angle of the phase angle difference between the current difference between the two ends of the cable accessory and the average voltage at the two ends is used as the dielectric loss angle of the cable accessory, which can effectively judge the degree of insulation aging.

[0072] S2. Perform wavelet transform decomposition on the current and voltage dynamic data to obtain multi-scale wavelet coefficients.

[0073] The current and voltage dynamic data containing noise are decomposed by wavelet transform. After the current and voltage dynamic data are subjected to wavelet transform, the wavelet coefficients at different scales can be obtained, and the amplitudes of the target signal and the noise signal at most scales have obvious amplitude differences. The amplitude of the target signal is high at most scales, while the amplitude of the noise signal decays rapidly with the scale and approaches 0. Among them, the first step of decomposition is to select a suitable wavelet basis function so that the signal energy can be distributed on a few bases. This property of the wavelet mainly depends on the regularity of the signal function, the order of the vanishing moment of the wavelet function and the size of the support. Daubechies wavelet (dbN) is widely used in the field of signal processing because of its excellent regularity and sparse basis characteristics. In the present invention, after many experiments and analyses, the db4 wavelet basis function is selected to perform wavelet transform decomposition on the current and voltage dynamic data. The advantages of choosing the db4 wavelet basis function are as follows: (1) The db4 wavelet basis function can maintain a high degree of smoothness during signal reconstruction, reducing signal distortion; (2) The vanishing moment order of the db4 wavelet basis function is 4, which is moderate and can effectively extract the characteristic information of the signal while avoiding overfitting; (3) The support size of the db4 wavelet basis function is 8, which can reduce computational complexity while maintaining the local characteristics of the signal. The number of decomposition layers was experimentally verified and determined to be 5 layers. The db4 wavelet basis function was used to decompose the signal into 5 layers to ensure that the signal characteristics are fully extracted at different scales.

[0074] S3. Based on the normal distribution characteristics of the noise signal and the multi-parameter fusion mechanism, a dynamic threshold is set in combination with the amplitude difference of the wavelet coefficients at each scale; the dynamic threshold is used to filter out the wavelet coefficients of the target signal from the multi-scale wavelet coefficients and filter out the noise in the target signal.

[0075] When using wavelet transforms to remove noise interference, signals and noise exhibit distinct characteristics at different scales. For continuous signal functions, the wavelet transform coefficients increase as the scale increases; however, the wavelet transform coefficients of noise decrease as the scale increases. Based on this characteristic, in wavelet space, the signal and noise can be distinguished based on the different propagation characteristics of their wavelet coefficients as they vary with scale. Wavelet threshold denoising is an effective denoising method. It uses wavelets to perform threshold denoising on the signal and extract the fundamental signal by setting a threshold and removing only the details that exceed the set value.

[0076] like Figure 3 As shown, the specific implementation process of step S3 is as follows:

[0077] S3.1. Based on the normal distribution characteristics of the noise signal and the multi-parameter fusion mechanism, the dynamic threshold is set in combination with the amplitude difference of the wavelet coefficients at each scale. The specific steps are as follows:

[0078] S3.1.1. Calculate the standard deviation σ of the noise wavelet coefficients in the nth layer by the statistical distribution characteristics of the noise wavelet coefficients n , which is used to characterize the intensity fluctuation of noise at this scale; the specific steps are as follows:

[0079] S3.1.1.1. In the initial decomposition layer (such as the highest frequency layer n = 1), assuming that noise is dominant, extract the initial decomposition layer wavelet coefficient D n,k , k is the coefficient index.

[0080] S3.1.1.2. Calculate the standard deviation σ of the noise wavelet coefficients in the nth layer by statistical methods n :

[0081]

[0082] in, N is the total number of wavelet coefficients;

[0083] S3.1.1.3 For subsequent scales n>1, the noise standard deviation is reduced according to the empirical formula:

[0084] σ n =σ n-1 ·v

[0085] Among them, ν is the basic attenuation factor (ranging from 0.5 to 0.8), which reflects the attenuation characteristics of noise energy as the scale increases.

[0086] The smaller the noise standard deviation, the faster the noise energy decays, and the stricter the threshold setting needs to be to suppress the residual noise.

[0087] S3.1.2. Calculate the signal energy E of the nth layer s,n and the noise energy E of the nth layer n,n The ratio of is used to reflect the energy distribution difference between the target signal and the noise at a specific scale.

[0088] In this embodiment, the n-th layer signal energy E is calculated by the residual signal after preliminary threshold denoising (such as hard threshold method). s,n , the signal energy E of the nth layer s,n The expression is as follows:

[0089]

[0090] in, is the wavelet coefficient retained after denoising, and N is the total number of wavelet coefficients.

[0091] In this embodiment, the noise energy E of the nth layer is calculated by the wavelet coefficient of the denoised signal. n,n , the noise energy E of the nth layer n,n The expression is as follows:

[0092]

[0093] in, is the wavelet coefficient retained after denoising, D n,k is the wavelet coefficient of the nth layer before denoising, and N is the total number of wavelet coefficients.

[0094] The larger the energy ratio, the more dominant the signal is at that scale, and the threshold needs to be increased to retain the valid signal; otherwise, the threshold needs to be lowered to enhance the denoising ability.

[0095] S3.1.3, by formula Determine the adaptive dynamic threshold ζ of the nth layer n , where υ is the basic attenuation factor (ranging from 0.5 to 0.8), and α is the adaptive adjustment factor.

[0096] S3.1.4. The maximum modulus of the wavelet coefficient of the noise decreases with the increase of scale, and the maximum modulus of the wavelet coefficient of the target signal increases with the increase of scale. The adaptive dynamic threshold ζ of the nth layer n Decay with scale.

[0097] In this embodiment, the adaptive adjustment factor α is used to balance scale attenuation and energy weighting. Specifically, the adaptive adjustment factor α balances the weight coefficient of the contribution of the signal-to-noise energy ratio to the threshold. A larger α value has a more significant impact on the energy ratio, making it suitable for environments with weak signals and complex noise. Conversely, a smaller α value emphasizes scale attenuation.

[0098] In this embodiment, the adaptive adjustment factor α is determined as follows:

[0099] (1) Select typical cable accessory samples (such as new cables and severely aged cables) and apply different noise intensities to them.

[0100] (2) Through comparative experiments, the optimal α value is calibrated using signal-to-noise ratio (SNR) and dielectric loss angle error as evaluation indicators.

[0101] (3) Experimental results show that the denoising effect is best when the adaptive adjustment factor α is in the range of 0.1 to 0.3.

[0102] S3.2. Use a dynamic threshold to filter out the wavelet coefficients of the target signal from the multi-scale wavelet coefficients and filter out the noise in the target signal.

[0103] If the wavelet coefficient at the current scale is greater than the adaptive dynamic threshold ζ n Then it is retained as the target signal. If the wavelet coefficient at the current scale is less than the adaptive dynamic threshold ζ n It is determined to be noise and filtered out, where the adaptive dynamic threshold ζ n Dynamic adjustment through multi-parameter fusion mechanism.

[0104] As the scale increases, the threshold gradually decreases to ensure that noise signals are effectively filtered out while the target signal is retained. Setting different thresholds at different scales minimizes noise attenuation while better preserving the target signal. This results in a higher signal-to-noise ratio for the signal characteristics, increasing the accuracy of the measured signal.

[0105] In step S3, appropriate threshold values ​​are set according to the wavelet coefficients of the signal at each scale, and the threshold values ​​are used to separate and filter the target signal from the noise signal. The method for setting the dynamic threshold is: according to the normal distribution characteristics of the noise signal, the threshold values ​​are set separately at different scales. Specifically, the maximum modulus of the wavelet coefficients of the noise decays as the scale increases, while the maximum modulus of the wavelet coefficients of the target signal increases as the scale increases. By analyzing the normal distribution characteristics of the noise signal, the threshold value at each scale can be determined. The present invention utilizes a multi-parameter fusion mechanism and introduces the signal-noise energy ratio on the basis of the original denoising to reflect the energy ratio of the signal and noise at a specific scale. In the low-frequency layer (signal-dominated), the energy ratio increases significantly, and the threshold value is increased to retain more effective signals; in the high-frequency layer (noise-dominated), the energy ratio decreases, and the threshold value is lowered to enhance the denoising ability. At the same time, an adaptive adjustment factor is introduced to flexibly adjust the contribution of the energy ratio to the threshold value to adapt to different noise environments.

[0106] In step S3, by introducing the noise standard deviation, signal-to-noise energy ratio and adaptive factor, the dynamic threshold setting is more in line with the actual signal characteristics, which solves the limitation of the traditional method of single reliance on scale attenuation and significantly improves the denoising accuracy and signal fidelity.

[0107] S4. Reconstruct the signal of the filtered wavelet coefficients and extract the dielectric property change data from the reconstructed signal.

[0108] The new wavelet coefficient sequence group after analysis and processing is rearranged to finally obtain the complete target signal after noise removal.

[0109] The dielectric performance change data includes dielectric loss factor and capacitance change rate, which are used to quantitatively analyze the degree of deterioration of the insulation materials of cable accessories.

[0110] S5. Combine the dielectric performance change data with the dielectric loss angle to evaluate the degree of aging and degradation of the insulation materials of cable accessories.

[0111] The present invention constructs an AC steady-state equivalent circuit model of cable accessories, combines wavelet transform signal decomposition, adaptive dynamic threshold screening and dielectric performance analysis, to achieve real-time and accurate assessment of the aging status of the insulation material of cable accessories. It is particularly suitable for degradation detection and power safety early warning of XLPE cable accessories.

[0112] The wavelet transform-based dynamic threshold denoising technology proposed in this invention significantly improves signal fidelity and the accuracy of aging assessment through a multi-parameter fusion mechanism (noise standard deviation, signal-to-noise energy ratio, and adaptive adjustment factor) combined with dielectric loss angle analysis, providing an innovative solution for real-time status monitoring of cable accessories.

[0113] The present invention solves the problems of long detection cycle and susceptibility to interference in traditional methods, significantly improves the accuracy and real-time performance of cable accessory aging and degradation monitoring, and can effectively prevent power safety accidents.

[0114] The technical effect of the cable accessories aging detection method based on multi-parameter fusion wavelet transform of the present invention is verified through specific examples below.

[0115] This embodiment aims to demonstrate how to use the cable accessory insulation material aging degradation detection method based on wavelet transform to perform aging assessment on actual cable accessories and select representative cable accessory samples for detailed detection and analysis.

[0116] 1. Sample Selection

[0117] From the cable accessory samples included in the State Grid's report on the performance analysis of old power cables, a 110kV high-voltage cable accessory (25 years of operation) and a 35kV medium-voltage cable accessory (0 years of operation, representing a new cable accessory) were selected as the target samples for this test. These two cable accessories were chosen because their significant age difference helps clearly demonstrate the effectiveness and differences of the test method when applied to cable accessories of varying degrees of age.

[0118] 2. Establishing AC steady-state equivalent circuit model of cable accessories

[0119] When the cable accessory is operating normally, a micro-segment dx is cut at a distance X from the cable accessory's head end to construct an AC steady-state equivalent circuit model of the cable accessory. For a 110kV high-voltage cable accessory and a 35kV cable accessory, the key parameters in the model are determined: the resistance along the micro-segment (R0dx), inductance (L0dx), conductance to ground (G0dx), and capacitance to ground (C0dx). These parameters are determined based on factors such as the cable accessory's specific specifications, material properties, and actual operating environment. They are obtained by consulting relevant technical documents, referencing historical data of similar cables, or conducting field measurements. For example, for a 110kV high-voltage cable with 25 years of operation, after carefully reviewing its cable accessory design documentation, referring to the parameters of similar cable accessories under similar operating conditions, and conducting actual field measurements, it was determined that R0dx = 0.01Ω, L0dx = 0.1mH, and G0dx = 10 -6 S, C0dx = 0.1μF; for a 35kV medium voltage cable accessory, refer to the standard parameters of the new cable accessories of the same type, and make fine adjustments based on the actual situation to determine its R0dx = 0.008Ω, L0dx = 0.08mH, G0dx = 8×10 -7 S, C0dx = 0.08 μF.

[0120] The dielectric loss angle is derived using the sinusoidal steady-state voltage-current relationship matrix. Considering that the voltage and current waveforms of the cable accessories present sinusoidal steady-state waveforms, the voltage and current relationship matrix at both ends of the cable accessory line is constructed. Through relevant formulas, such as the operation involving parameters such as the propagation coefficient (γ) and wave impedance (Z0), the dielectric loss angle is gradually calculated. For a certain 110kV high-voltage cable accessory, its propagation coefficient γ = 0.01 + j0.05 and wave impedance Z0 = 50 + j20 are obtained through precise calculation. According to the standard calculation formula:

[0121]

[0122] Where ε' is the relative dielectric constant; ε" is the loss index; ω is the angular frequency; ε0 is the vacuum dielectric constant; ε r is the relative dielectric constant; g is the equivalent conductivity of the dielectric relaxation polarization loss; I p is the active component of the total current in the medium under the alternating electric field; I q is the reactive component loss of the total dielectric current under an alternating electric field. Calculations show a dielectric loss angle of 3°, with a tangent of approximately 0.05. Similarly, rigorous calculations for a 35kV medium-voltage cable accessory yield a dielectric loss angle of 1.5°, with a tangent of approximately 0.02.

[0123] 3. Obtaining Current and Voltage Dynamic Data

[0124] At a set test temperature of 25°C, an AC voltage with a frequency of 50Hz and an amplitude of 10kV was applied to two selected cable accessory samples to simulate the actual operating conditions of the cable accessories. High-precision current and voltage sensors were used to continuously collect dynamic current and voltage data over a 100-second period, at intervals of 0.01 seconds, as the test progressed. During the data collection process, the precise installation position of the sensors was strictly ensured, and effective shielding measures were taken to prevent external interference, thereby ensuring the high accuracy and reliability of the collected data. The collected data was preliminarily organized and recorded, and some of the data is shown below:

[0125]

[0126] 4. Wavelet transform decomposition signal

[0127] Based on the patent's description of wavelet basis function selection, and in combination with actual signal characteristics and processing experience, the db4 wavelet basis function was selected to perform wavelet transform decomposition on the acquired current and voltage dynamic data. After multiple trials and analyses, the optimal number of decomposition layers, five, was determined to fully extract the signal's characteristic information. Through the wavelet transform, the original signal is decomposed into distinct frequency subbands, resulting in significantly different characteristics between the target signal and the noise signal at different scales, laying a solid foundation for subsequent denoising and feature extraction.

[0128] 5. Setting Dynamic Threshold and Denoising

[0129] According to the normal distribution characteristics of the noise signal and the new dynamic threshold selection equation, the threshold is set at different scales. The maximum modulus of the wavelet transform coefficient of the noise signal at the first scale is calculated to be ζ1. For 110kV high-voltage cable, after calculating σ1 = 0.5, the signal energy E after the first layer of denoising is s,1 =120, noise energy E n,1 =60, then the energy ratio E s，1 / E n,1 =2,ν=0.6, the adaptive adjustment factor α was calibrated through multiple experiments, and finally α=0.2 was selected to balance the weight of signal retention and noise suppression, then ζ1=0.6×0.5×(1+0.2×2)=0.42; for the new 35kV cable, ζ1=0.3, refer to the relevant formula in the patent to calculate the initial threshold. As the scale increases, the threshold size is reasonably adjusted in strict accordance with the principle of attenuation of the maximum value of the wavelet coefficient modulus of the noise and enhancement of the maximum value of the wavelet coefficient modulus of the target signal. Taking the third layer as an example, after careful analysis and calculation, taking a certain 110kV high-voltage cable accessory as an example, σ3=0.2, E s,3 / E n,3 =5, then ζ3 = 0.6 × 0.2 × (1 + 0.2 × 5) = 0.24. Similarly, the threshold for new 35kV cables is set to 0.15. During the denoising process, through threshold comparison, when the wavelet coefficient at a certain scale is greater than the threshold, it is retained as the target signal; when the wavelet coefficient is less than the threshold, it is determined to be noise and filtered out. For example, among the wavelet coefficients of the third layer of a high-voltage cable accessory, the wavelet coefficient with a value of 0.3 (greater than the threshold of 0.24) is retained, while the wavelet coefficient with a value of 0.1 (less than the threshold of 0.24) is filtered out. The new 35kV cable is processed according to the same rules.

[0130] 6. Signal Reconstruction and Dielectric Property Data Extraction

[0131] Signal reconstruction is performed on the filtered wavelet coefficients, resulting in a pure signal after removing noise. Dielectric property change data, including key indicators such as dielectric loss factor and capacitance change rate, are accurately extracted from the reconstructed signal. After a series of rigorous calculations and analyses, the dielectric loss factor of the 110kV high-voltage cable accessories was determined to be 0.05, with a capacitance change rate of 5%. The dielectric loss factor of the new 35kV cable accessories was 0.02, with a capacitance change rate of 1%. These data are consistent with the precise calculations above and accurately reflect the differences in dielectric properties between the two cable accessories.

[0132] 7. Assess the degree of aging and degradation of cable insulation materials

[0133] Based on the analysis of the reconstructed signal and dielectric loss angle, the degree of aging and degradation of the insulation materials of cable accessories was assessed. Comparing the dielectric performance data and dielectric loss angle of 110kV high-voltage cable accessories with those of new 35kV cable accessories, it is clear that the dielectric loss factor (0.05) of a certain high-voltage cable accessory is significantly higher than that of the new cable accessory (0.02). The capacitance change rate (5%) is also much greater than that of the new cable accessory (1%). Furthermore, the dielectric loss angle of the high-voltage cable accessory (3°) is greater than that of the new cable (1.5°). Combined with the conclusions of the State Grid report on cable accessory aging, it can be seen that the insulation material of a certain high-voltage cable accessory has a high degree of aging, indicating that it may contain a large number of internal defects and aging products, which seriously affect the electrical performance of the cable accessory. In contrast, the insulation performance of the new 35kV cable accessory is relatively good, with minimal signs of aging. Comprehensively judging, the degree of aging and deterioration of the insulation material of a certain 110kV high-voltage cable accessory is quite serious, requiring enhanced monitoring or timely corresponding maintenance treatment; the new 35kV cable accessories can continue to operate normally, but still need to be inspected regularly as required to ensure their long-term stable operation.

Claims

1. A cable accessories aging detection method based on multi-parameter fusion wavelet transform, characterized in that: include: Establish an AC steady-state equivalent circuit model for the cable accessories, and obtain dynamic current and voltage data of the target cable accessory sample at the test temperature as a function of test time from the AC steady-state equivalent circuit model; derive the calculation formula for the dielectric loss angle from the sinusoidal steady-state voltage-current relationship matrix, and calculate the dielectric loss angle of the cable accessories using the calculation formula for the dielectric loss angle; Perform wavelet transform decomposition on the current and voltage dynamic data to obtain multi-scale wavelet coefficients; Based on the normal distribution characteristics of the noise signal and the multi-parameter fusion mechanism, the dynamic threshold is set in combination with the amplitude difference of the wavelet coefficients at each scale; The dynamic threshold is used to filter out the wavelet coefficients of the target signal from the multi-scale wavelet coefficients and filter out the noise in the target signal; Perform signal reconstruction on the filtered wavelet coefficients and extract dielectric property change data from the reconstructed signal; Combine the dielectric performance change data with the dielectric loss angle to evaluate the degree of aging and degradation of the insulation materials of cable accessories.

2. The cable accessories aging detection method based on multi-parameter fusion wavelet transform according to claim 1 is characterized in that: The process of establishing the AC steady-state equivalent circuit model of the cable accessories is as follows: When the cable accessories are operating normally, a micro-segment dx is intercepted at a distance X from the head end. The AC steady-state equivalent circuit model of the cable accessories includes three groups of series-connected micro-segments' along-line resistance R0dx and micro-segments' along-line inductance L0dx, and the output end of the first group, the input end and output end of the second group, and the input end of the third group are all connected to a group of parallel-connected micro-segments' conductance to ground G0dx and capacitance to ground C0dx.

3. The cable accessories aging detection method based on multi-parameter fusion wavelet transform according to claim 1 is characterized in that: The db4 wavelet basis function is used to perform wavelet transform decomposition on the current and voltage dynamic data to obtain multi-scale wavelet coefficients.

4. The cable accessories aging detection method based on multi-parameter fusion wavelet transform according to claim 1 is characterized in that: Based on the normal distribution characteristics of the noise signal and the multi-parameter fusion mechanism, the dynamic threshold is set in combination with the amplitude difference of the wavelet coefficients at each scale, including: The standard deviation σ of the noise wavelet coefficients in the nth layer is calculated based on the statistical distribution characteristics of the noise wavelet coefficients. n , used to characterize the intensity fluctuation of noise at this scale; Calculate the signal energy E of the nth layer s,n and the noise energy E of the nth layer n,n The ratio of is used to reflect the energy distribution difference between the target signal and the noise at a specific scale; By formula Determine the adaptive dynamic threshold ζ of the nth layer n , where υ is the basic attenuation factor and α is the adaptive adjustment factor; The maximum modulus of the wavelet coefficient of the noise decreases with the increase of scale, and the maximum modulus of the wavelet coefficient of the target signal increases with the increase of scale. The adaptive dynamic threshold ζ of the nth layer n Decay with scale.

5. The cable accessories aging detection method based on multi-parameter fusion wavelet transform according to claim 4 is characterized in that: The standard deviation σ of the noise wavelet coefficients in the nth layer is calculated based on the statistical distribution characteristics of the noise wavelet coefficients. n , used to characterize the intensity fluctuation of noise at this scale, including: In the initial decomposition layer, assuming that noise is dominant, the initial decomposition layer wavelet coefficient D is extracted n,k , k is the coefficient index; Calculate the standard deviation σ of the noise wavelet coefficients of the nth layer by statistical methods n : in, N is the total number of wavelet coefficients; For subsequent scales n>1, the noise standard deviation decreases according to the empirical formula: s n =s n-1 ·n Among them, ν is the basic attenuation factor, which reflects the attenuation characteristics of noise energy as the scale increases.

6. The cable accessories aging detection method based on multi-parameter fusion wavelet transform according to claim 4 is characterized in that: The n-th layer signal energy E s,n The expression is as follows: in, is the wavelet coefficient retained after denoising; N is the total number of wavelet coefficients; The noise energy E of the nth layer n,n The expression is as follows: Among them, D n,k is the wavelet coefficient of the nth layer before denoising.

7. The cable accessories aging detection method based on multi-parameter fusion wavelet transform according to claim 4 is characterized in that: The adaptive adjustment factor α is used to balance scale attenuation and energy weight, and its value range is 0.1 to 0.

3.

8. The cable accessories aging detection method based on multi-parameter fusion wavelet transform according to claim 1 is characterized in that: The dynamic threshold is used to filter out the wavelet coefficients of the target signal from the multi-scale wavelet coefficients and filter out the noise in the target signal, including: If the wavelet coefficient at the current scale is greater than the adaptive dynamic threshold ζ n Then it is retained as the target signal. If the wavelet coefficient at the current scale is less than the adaptive dynamic threshold ζ n It is determined to be noise and filtered out, where the adaptive dynamic threshold ζ n Dynamic adjustment through multi-parameter fusion mechanism.

9. The cable accessories aging detection method based on multi-parameter fusion wavelet transform according to claim 1 is characterized in that: The dielectric performance change data includes dielectric loss factor and capacitance change rate, which are used to quantitatively analyze the degree of deterioration of the insulation material of the cable accessories.

10. The cable accessories aging detection method based on multi-parameter fusion wavelet transform according to claim 1, characterized in that: The dielectric loss angle calculation formula is derived from the sinusoidal steady-state voltage-current relationship matrix. The dielectric loss angle of the cable accessories is calculated using the dielectric loss angle calculation formula, including: Considering that the voltage and current waveforms of the cable accessories are sinusoidal steady-state waveforms, the voltage and current relationship matrix at both ends of the cable accessories line is established as follows: Where γ is the propagation coefficient of the line; Z0 is the wave impedance of the line; and e is the natural logarithm. The expressions of the propagation coefficient γ and the wave impedance Z0 of the line are as follows: The voltage-current relationship matrix at both ends of the cable accessory line is obtained: The dielectric loss angle of cable accessories satisfies the following formula: From the above formula we can get: It can be seen from the above formula that the ratio of the current difference between the two ends of the cable accessory to the average voltage at the two ends will not change due to the load current flowing through the cable accessory; the complementary angle of the phase angle difference between the current difference between the two ends of the cable accessory and the average voltage at the two ends is regarded as the dielectric loss angle of the cable accessory.

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