Composite insulator aging evaluation method based on nuclear magnetic resonance transverse relaxation time spectrum

Abstract

The invention relates to the technical field of power equipment operation inspection, in particular to a nuclear magnetic resonance transverse relaxation time spectrum-based composite insulator aging evaluation method, which comprises the following steps of: measuring a composite insulator sample by using a nuclear magnetic resonance spectrometer to obtain a transverse relaxation time distribution T2 spectral line of the sample, and extracting key characteristic parameters; and establishing a mapping relation model of the characteristic parameters and the aging degree, calculating an aging index, and realizing evaluation of the aging degree of the composite insulator and judgment of aging factors. According to the method, starting from the change of the microstructure of the material, the aging degree is represented through the nuclear magnetic resonance signal, the signal abnormity at the initial stage of aging can be detected when the appearance of the insulator has no obvious change, and an earlier basis is provided for formulating a maintenance strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power equipment inspection. BACKGROUND

[0002] Composite insulators are widely used in power transmission systems due to their lightweight, high strength, and good anti-fouling performance. However, during long-term outdoor operation, the organic materials of composite insulators are affected by factors such as ultraviolet radiation, environmental pollution, and electric field stress, leading to aging and a decrease in electrical performance and mechanical strength, which affects the safe operation of power systems. Existing composite insulator aging detection methods mainly include visual inspection, infrared thermography, and leakage current monitoring. However, these methods are mostly based on macroscopic observation and are difficult to reveal the aging mechanism from the perspective of material microstructure changes. At the same time, the existing methods lack sensitivity in detecting early aging and are difficult to achieve accurate quantitative evaluation. Nuclear magnetic resonance (NMR) technology is an important analysis tool in the field of material science, which can non-destructively obtain material molecular structure and dynamics information. In particular, nuclear magnetic resonance transverse relaxation time (T2) spectrum can reflect the molecular chain segment motion characteristics and microphase structure of high polymer materials, providing a new way for the microcharacterization of composite insulator aging. However, there is currently no systematic composite insulator aging evaluation method based on nuclear magnetic resonance signals. SUMMARY

[0003] In order to overcome the problem that existing composite insulator aging detection methods are difficult to reveal the aging mechanism from the perspective of material microstructure changes, the present application provides a composite insulator aging evaluation method based on nuclear magnetic resonance transverse relaxation time spectrum.

[0004] The technical solution adopted by the present application to achieve the above-mentioned purpose is: a composite insulator aging evaluation method based on nuclear magnetic resonance transverse relaxation time spectrum, comprising the following steps:

[0005] S1, sampling from the composite insulator to be tested to obtain a sample to be tested;

[0006] S2, measuring the sample to be tested using a nuclear magnetic resonance instrument to obtain a free induction decay (FID) signal of the sample to be tested;

[0007] S3, performing inverse Laplace transform on the free induction decay (FID) signal of the sample to be tested to obtain a transverse relaxation time distribution (T2) spectrum line of the sample to be tested;

[0008] S4, performing feature extraction on the transverse relaxation time distribution (T2) spectrum line of the sample to be tested to obtain a T2 spectrum line feature parameter of the sample to be tested;

[0009] S5, based on a large number of known service life and environmental condition composite insulator sample test data, through multiple linear regression analysis, the mapping relationship model of T2 spectrum characteristic parameters of known samples and the aging degree of composite insulator is established, the aging index calculation formula and the correlation between the wave peak characteristics of T2 spectrum and the aging factors are obtained;

[0010] S6, the characteristic parameters of the sample to be tested obtained in step S4 are substituted into the aging index calculation formula, the aging index is calculated, and the aging degree is judged according to the aging index;

[0011] S7, the aging factors are judged through the wave peak characteristics of the transverse relaxation time distribution T2 spectrum of the sample to be tested, and the aging evaluation of the composite insulator is completed.

[0012] Preferably, in step S1, the sample to be tested is cut from the middle of the shed of the composite insulator to be tested or the end close to the high-voltage electrode.

[0013] Preferably, in step S2, a low-field nuclear magnetic resonance instrument is used, and a CPMG pulse sequence is adopted.

[0014] Preferably, in step S2, the low-field nuclear magnetic resonance instrument is set to have an echo interval of 0.1 ms, a number of echoes of 10,000, and a sampling number of 32.

[0015] Preferably, in step S3, the transverse relaxation time T2 is taken as the abscissa, and the free induction decay FID signal after inverse Laplace transform is taken as the ordinate, to establish the transverse relaxation time distribution T2 spectrum.

[0016] Preferably, in step S4, the T2 spectrum characteristic parameters include the main wave peak position and the corresponding relaxation time, the secondary peak position and the corresponding relaxation time, the main wave peak intensity, the secondary wave peak intensity, the main wave peak half-height width, the wave peak intensity ratio, the secondary wave peak half-height width, and the total spectrum area.

[0017] Preferably, in step S5, the large number of known service life and environmental condition composite insulator samples include aged samples and unaged samples, and the aging index calculation formula is: ;

[0018] wherein, is the aging index, is the main wave peak position, the wave peak intensity ratio, the main wave peak half-height width, and the total spectrum area of the unaged sample, is the change amount of the main wave peak position, the wave peak intensity ratio, the main wave peak half-height width, and the total spectrum area of the aged sample relative to the unaged sample, is the weight coefficient, the weight coefficient has a value range of [0, 1] and satisfies .

[0019] Preferably, in step S6, the aging index is determined according to the position of the main peak Determine the aging degree: ;

[0020] Preferably, in step S7, the aging factor is determined as follows: the position of the main peak Shifted more than 15% to the direction of low relaxation time, and the intensity of the secondary peak is weakened: mainly affected by ultraviolet radiation; the position of the secondary peak Shifted more than 10% to the direction of high relaxation time, and the intensity ratio of the main peak Increased: mainly affected by pollution; the half-height width of the main peak Increased by more than 20%, and the spectrum line is obviously broadened: mainly affected by electric field stress.

[0021] The beneficial effects of the present application are:

[0022] The composite insulator aging detection method of the present application starts from the microstructure changes of the material, and characterizes the aging degree through nuclear magnetic resonance signals. Compared with the traditional detection methods based on electrical and thermal characteristics, the potential degradation inside the composite insulator can be detected earlier. The present application takes local samples without affecting the overall performance and strength of the composite insulator, and can realize in-situ and online detection, which is suitable for health status evaluation of the running insulator. Compared with the traditional method of detecting by disassembling or replacing the entire insulator, the present application has lower destructiveness and avoids the limitations of traditional sampling detection. The present application can simultaneously obtain the distribution and change information of components inside the material such as silicone rubber, filler and moisture, and reveal the multi-factor coupling mechanism of chemical degradation, physical structure relaxation and water absorption during the aging process. The present application establishes a quantitative analysis model of the aging degree through signal parameters, and compared with the traditional qualitative or semi-quantitative appearance inspection and dielectric property test method, the evaluation result is more objective and accurate. The present application is sensitive to micro-nano level structural changes, and can detect signal abnormalities in the initial stage of aging when there is no obvious change in the appearance of the insulator, which provides an earlier basis for formulating maintenance strategies. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is a flowchart of an embodiment of the present application. DETAILED DESCRIPTION

[0024] The embodiment of the present application provides a composite insulator aging evaluation method based on nuclear magnetic resonance transverse relaxation time spectrum, comprising the following steps:

[0025] S1. Cut the sample to be tested from the middle or end of the shed of the composite insulator to be tested, near the high voltage electrode, to ensure that each sample is in the same structural part of the composite insulator to ensure lateral comparability. Use a laser cutter or diamond wire cutter to prepare a sample with a size of 3mm × 3mm × 10mm. After cutting, wipe the sample surface with an alcohol swab to remove dust and particles, number the sample, place it in a constant temperature desiccator, set it to 40℃ and dry for 12 hours to eliminate the influence of moisture. The preparation is complete and the sample to be tested is obtained.

[0026] Once prepared, the samples should be sealed in aluminum foil and placed in a small, sealed bag with a desiccant when not in use to prevent moisture absorption.

[0027] S2. Using a low-field nuclear magnetic resonance spectrometer, the CPMG pulse sequence was used to measure the sample under test. The low-field nuclear magnetic resonance spectrometer was set with an echo interval of 0.1ms, an echo number of 10000, and a sampling number of 32 times to obtain the free induction attenuation (FID) signal of the sample under test.

[0028] S3. Perform an inverse Laplace transform on the free induction decay (FID) signal of the sample to be tested: ;

[0029] in, It is a time-domain function. This is the complex frequency domain expression of the freely inductively attenuated FID signal after Laplace transform. To Perform the inverse Laplace transform. For the complex frequency domain independent variable, For time variables, For real numbers, This is a normalization constant to ensure the correctness of the integral. Integrating along a straight line parallel to the imaginary axis in the complex plane is called the Bromwich integration path.

[0030] Using the transverse relaxation time T2 as the abscissa and the free induction decay FID signal after inverse Laplace transform as the ordinate, the transverse relaxation time distribution T2 spectrum of the sample under test is obtained.

[0031] S4. The transverse relaxation time distribution T2 spectral line exhibits multiple peak characteristics. Feature extraction is performed on the transverse relaxation time distribution T2 spectral line of the sample to obtain the T2 spectral line characteristic parameters of the sample, including the position of the main peak. and their corresponding relaxation time and secondary peak position and their corresponding relaxation time and main peak intensity Secondary peak intensity , main peak half-width Crest intensity ratio , half-height width of secondary peak , and total area of spectrum

[0032] S5, composite insulator samples are sampled from different sites such as power transmission lines, substation equipment, and industrial equipment, covering various stages from new products to severe aging (0 years, 3 years, 5 years, 10 years, 15 years or more), and the sampling areas cover typical environments such as coastal areas (high salt mist, strong wind, high humidity, corrosive gas, typically power transmission lines by the sea), high humidity and high temperature areas (southern plains, lake areas, rainforests), arid and high temperature areas (northwest deserts, Gobi), and high altitude areas (slopes, mountainous areas, stronger ultraviolet rays, low air pressure). According to relevant national standards, combined with the actual application area distribution and environmental degradation zoning of equipment, it is ensured that the samples are representative. At the same time, under laboratory conditions, by setting parameters such as high temperature (70°C, 100°C, 120°C), high humidity (90% RH, 100% RH), salt mist (35 g / L NaCl), ultraviolet rays (0.89 W / m²), and environmental factors such as freeze-thaw cycles, pollution deposition, and acid corrosion, the aging process of composite insulators is simulated and accelerated. These experimental environmental parameters are regularly set according to industry standards, usually using grading, orthogonal design, or full-factor design methods, and the effects of single and multiple environmental factors and their interactions are systematically investigated. In this way, a large number of composite insulator sample test data with known service life and environmental conditions are obtained, including aged and unaged samples;

[0033] Based on a large number of composite insulator sample test data with known service life and environmental conditions, the test data includes aged and unaged samples. Through multiple linear regression analysis, a mapping relationship model of T2 spectral line characteristic parameters of known samples and the aging degree of composite insulators is established, and the aging index calculation formula and the correlation between the peak characteristics of T2 spectrum and the aging factors are obtained;

[0034] The aging index calculation formula is:

[0035] wherein, is the aging index, is the main peak position, peak intensity ratio, main peak half-height width, and total area of spectrum of the unaged sample, is the change amount of the main peak position, peak intensity ratio, main peak half-height width, and total area of spectrum of the aged sample relative to the unaged sample, is the weight coefficient, and the specific value depends on the correlation between each spectral line characteristic and the aging degree. In order to enhance the interpretability and stability of the model, the weight coefficient value range is [0, 1] and satisfies . ​​​

[0036] The optimal coefficient combination of the weight coefficient is: under the above constraint conditions, the aging index is minimized The weight value combination with the minimum prediction error between the sample actual aging grade and the sample actual aging grade is the optimal coefficient, which is usually close to 0, indicating that the feature has little effect on aging, and some are significantly large, indicating key features, such as In this embodiment, the weight coefficient is determined by using a multiple linear regression analysis method, and the steps are as follows:

[0037] (1) Collect composite insulator samples with known aging grades or service life to obtain T2 spectrum characteristic parameter values;

[0038] (2) Normalize each characteristic value to eliminate dimensional differences;

[0039] (3) Take the aging grade or the service life as the dependent variable , and take the normalized characteristic parameters as the independent variable , and establish a model: ;

[0040] (4) Use the least squares method to regress to obtain the estimated value of ;

[0041] (5) Evaluate the regression effect by cross-validation, and select the coefficient combination that minimizes the residual error;

[0042] (6) If the model stability is poor, regularized methods such as ridge regression and Lasso regression can be used for optimization.

[0043] S6, substitute the characteristic parameters of the sample to be tested obtained in step S4 into the aging index calculation formula to calculate the aging index: ;

[0044] According to the aging index, the aging degree is determined: ;

[0045] When the aging is extreme, it is recommended to replace the composite insulator.

[0046] S7, determine the aging factors by the peak characteristics of the transverse relaxation time distribution T2 spectrum of the sample to be tested:

[0047] The main peak position shifts more than 15% to the low relaxation time direction, that is, the abscissa value of moves more than 15% to the left side (low relaxation time direction) of the abscissa of the reference state (not disturbed), and the secondary peak intensity is weakened, indicating that the aging factor is mainly affected by ultraviolet radiation;

[0048] Sub-peak position Shifted more than 10% to the high relaxation time direction, i.e. The abscissa value of the sub-peak is shifted more than 10% to the right of the abscissa (high relaxation time direction) from the reference state (when not disturbed), and the peak intensity ratio Increases, indicating that the aging factor is mainly affected by pollution;

[0049] Main peak half-height width Increases more than 20%, and the spectrum line is obviously widened, indicating that the aging factor is mainly affected by electric field stress;

[0050] Complete the aging evaluation of the composite insulator.

[0051] The present application is described by way of examples, and those skilled in the art will appreciate that various modifications or equivalent replacements can be made to these features and examples without departing from the spirit and scope of the present application. In addition, under the guidance of the present application, modifications can be made to these features and examples to adapt to specific conditions and materials without departing from the spirit and scope of the present application. Therefore, the present application is not limited by the specific examples disclosed herein, and all examples falling within the scope of the claims of the present application are within the protection scope of the present application.

Claims

1. A composite insulator aging evaluation method based on nuclear magnetic resonance transverse relaxation time spectrum, characterized in that, The method comprises the following steps: S1, sampling from the composite insulator to be tested to obtain a sample to be tested; S2, measuring the sample to be tested using a nuclear magnetic resonance instrument to obtain a free induction decay (FID) signal of the sample to be tested; S3, performing inverse Laplace transformation on the free induction decay (FID) signal of the sample to be tested to obtain a transverse relaxation time distribution T2 spectrum line of the sample to be tested; S4, performing feature extraction on the transverse relaxation time distribution T2 spectrum line of the sample to be tested to obtain a T2 spectrum line feature parameter of the sample to be tested; S5, based on a large number of composite insulator sample test data of known service life and environmental conditions, a mapping relationship model of the T2 spectrum line feature parameter of the known sample and the aging degree of the composite insulator is established through multivariate linear regression analysis, an aging index calculation formula is obtained, and the correlation between the peak feature of the T2 spectrum line and the aging factor is obtained; S6, substituting the feature parameter of the sample to be tested obtained in step S4 into the aging index calculation formula to calculate the aging index, and judging the aging degree according to the aging index; S7, judging the aging factor through the peak feature of the transverse relaxation time distribution T2 spectrum line of the sample to be tested, and completing the aging evaluation of the composite insulator.

2. The composite insulator aging assessment method based on transverse relaxation time spectrum of nuclear magnetic resonance according to claim 1, characterized in that, In step S1, the sample to be tested is cut from the middle of the shed of the composite insulator to be tested or the end close to the high-voltage electrode.

3. The composite insulator aging assessment method based on transverse relaxation time spectrum of nuclear magnetic resonance according to claim 1, characterized in that, In step S2, a low-field nuclear magnetic resonance instrument is used to measure the sample to be tested by using a CPMG pulse sequence.

4. The composite insulator aging assessment method based on transverse relaxation time spectrum of nuclear magnetic resonance according to claim 3, characterized in that, In step S2, the low-field nuclear magnetic resonance instrument is set to have an echo interval of 0.1 ms, a number of echoes of 10,000, and a sampling number of 32.

5. The composite insulator aging assessment method based on transverse relaxation time spectrum of nuclear magnetic resonance according to claim 1, characterized in that, In step S3, the transverse relaxation time T2 is taken as the abscissa, and the free induction decay (FID) signal after inverse Laplace transformation is taken as the ordinate to establish the transverse relaxation time distribution T2 spectrum line.

6. The composite insulator aging assessment method based on transverse relaxation time spectrum of nuclear magnetic resonance according to claim 1, characterized in that, In step S4, the T2 spectrum line feature parameters include the main peak position and the corresponding relaxation time, the secondary peak position and the corresponding relaxation time, the main peak intensity, the secondary peak intensity, the main peak half-height width, the peak intensity ratio, the secondary peak half-height width, and the total area of the spectrum line.

7. The composite insulator aging assessment method based on transverse relaxation time spectrum of nuclear magnetic resonance according to claim 6, characterized in that, In step S5, the large number of composite insulator samples of known service life and environmental conditions include aged samples and unaged samples, and the aging index calculation formula is: ； wherein, is an aging index, is the main peak position, the peak intensity ratio, the main peak half-height width and the total area of the spectrum of the unaged sample, is the change amount of the main peak position, the peak intensity ratio, the main peak half-height width and the total area of the spectrum of the aged sample relative to the unaged sample, respectively, is a weight coefficient, the weight coefficient has a value range of [0, 1] and satisfies .

8. The composite insulator aging assessment method based on transverse relaxation time spectrum of nuclear magnetic resonance according to claim 7, characterized in that, In step S6, the aging index is used to determine the degree of aging Degree of aging 。 9. The composite insulator aging assessment method based on transverse relaxation time spectrum of nuclear magnetic resonance according to claim 7, characterized in that, In step S7, the judgment of aging factors is: main peak position Shifted more than 15% to low relaxation time direction, and the secondary peak intensity weakened: mainly affected by ultraviolet radiation; secondary peak position Shifted more than 10% to high relaxation time direction, and the peak intensity ratio Increased: mainly affected by pollution; main peak half-height width Increased more than 20%, and the spectrum line showed obvious broadening: mainly affected by electric field stress.

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