Method for assessing the ageing risk of crosslinked polyethylene cable insulation

Abstract

The application discloses a kind of crosslinking polyethylene cable insulation aging risk assessment method, it is related to cable insulation aging assessment field, comprising: setting electric field induction array carries out high-speed sampling to dynamic electric field signal, to the electric field signal data after pre-processing is completed with spatial correction;Fourier decomposition is carried out to high-frequency standing wave field signal, simulates the time-space variation of electric field distribution, establishes standing wave and direct current electric field superposition model, captures the time-space variation of hot spot area;The dynamic peak hot spot trajectory of electric field intensity in superposition strengthening area is analyzed, the migration speed and residence time of dynamic peak hot spot are quantified, to provide quantitative description for the dynamic evolution characteristics of risk area;The local aging degree of risk area is analyzed, and the trend of local aging induced chain reaction is evaluated in combination with the dynamic evolution characteristics of each risk area peak hot spot;Based on chain reaction trend evaluation result, the aging trend of cable insulation is risk degree calibration.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of cable insulation aging assessment, in particular to a cross-linked polyethylene cable insulation aging risk assessment method. BACKGROUND

[0002] Cross-linked polyethylene (XLPE) cable insulation aging is usually accompanied by complex dynamic processes of local electric field distortion, hotspot region migration and electrical aging chain reaction. These phenomena not only affect the reliability of the cable, but also may cause equipment failure and power system accidents, so the risk assessment of the aging trend of XLPE cable insulation is of great significance. In the scene of alternating current and direct current cooperative operation, the standing wave effect of alternating current and the superposition of direct current electric field will further aggravate the distortion of electric field intensity in local area, forming dynamic peak hotspots. The migration speed and residence time of these hotspots directly determine the dynamic evolution characteristics of the risk area, and the traditional insulation aging assessment method is based on partial discharge detection or static electric field distribution analysis, which is difficult to capture the space-time variation characteristics of electric field in cable operation.

[0003] Therefore, how to quantitatively analyze the migration speed and residence time of dynamic peak hotspots, combined with the local aging degree analysis of risk area, to realize the comprehensive assessment of the aging chain reaction trend is a problem to be solved. SUMMARY

[0004] To solve the above technical problems, a cross-linked polyethylene cable insulation aging risk assessment method is provided, which solves the problems raised in the background art.

[0005] To achieve the above purposes, the technical scheme adopted by the present application is:

[0006] A cross-linked polyethylene cable insulation aging risk assessment method, comprising:

[0007] Set up an electric field sensing array to sample the dynamic electric field signal at high speed, complete and spatially correct the preprocessed electric field signal data;

[0008] Fourier decomposition is performed on the high-frequency standing wave field signal to simulate the space-time variation of electric field distribution, establish a standing wave and direct current electric field superposition model, and capture the space-time variation of hotspot area;

[0009] Analyze the dynamic peak hotspot trajectory of the electric field intensity in the superposition reinforcement area, quantitatively analyze the migration speed and residence time of the dynamic peak hotspot, and provide quantitative description for the dynamic evolution characteristics of the risk area;

[0010] Analyze the local aging degree of the risk area, and evaluate the trend of local aging induced chain reaction combined with the dynamic evolution characteristics of the peak hotspot of each risk area;

[0011] Based on the chain reaction trend assessment results, the cable insulation aging trend is calibrated for risk.

[0012] Preferably, the setting of the electric field sensing array to perform high-speed sampling of the dynamic electric field signal and the completion and spatial correction of the pre-processed electric field signal data specifically include:

[0013] An array of electric field sensing devices is arranged at multiple points on the outer insulation surface and inside the cable. Each optical fiber electric field sensing device senses the local electric field intensity through the electro-optical effect.

[0014] The acquisition device is connected to the time synchronization module for time calibration to unify the time base of all electric field sensing devices;

[0015] The high-speed sampling frequency is set to perform high-speed sampling on the dynamic electric field signal. The collected initial signal is stored in the form of a time series. The signal includes the instantaneous value of the electric field intensity and the three-dimensional coordinate information of the spatial position;

[0016] Clear the high-frequency noise and low-frequency interference of the collected initial signal, dynamically gain the low-amplitude signal, adjust the low-amplitude signal, and capture the micro-electric field fluctuations of the collected initial signal;

[0017] An interpolation algorithm is used to complete the data in uncovered areas. At the same time, the electric field signal is spatially corrected according to the cable geometry and insulation layer characteristics to make the data distribution consistent with the actual physical structure of the cable.

[0018] Preferably, the performing Fourier decomposition on the high-frequency standing wave field signal, simulating the spatiotemporal variation of the electric field distribution, establishing a standing wave and DC electric field superposition model, and capturing the spatiotemporal variation of the hotspot area specifically includes:

[0019] Perform Fourier decomposition on the high-frequency standing wave field signal, breaking it down into the fundamental wave and a set number of high-frequency harmonic components. By extracting the amplitude and phase information of each harmonic, calculate its distribution characteristics in space, and simulate the transient change characteristics of the standing point of the standing wave;

[0020] Obtain the insulation material of cross-linked polyethylene cables and determine the dielectric constant and conductivity characteristics of the insulation material;

[0021] The actual distribution data of space charge is obtained through pulse voltage excitation, and the dynamic diffusion of charge in insulating materials is simulated by finite element method, with real-time annotation of the migration and accumulation process of simulated space charge.

[0022] Using time and space as variables, the dynamic diffusion distribution of the DC field is combined with the dynamic variation characteristics of the standing wave field to generate a global dynamic electric field distribution model within the insulation layer.

[0023] In the global dynamic electric field distribution model, the spatial information of the significant enhancement of the local electric field caused by the overlap of the charge retention area and the AC field standing wave point is recorded, and the standing wave point distribution area corresponding to the spatial information is marked as the superposition enhancement area.

[0024] Preferably, the analysis and superposition of the dynamic peak hotspot trajectory of the electric field intensity in the enhanced region, quantifying the migration speed and residence time of the dynamic peak hotspot, and providing a quantitative description of the dynamic evolution characteristics of the risk region specifically include:

[0025] The electric field model is divided into equally spaced grids. The electric field intensity in each grid is scanned to extract the local maximum value. The Gaussian noise filtering algorithm is used to remove the false peaks that may be introduced by the model error.

[0026] The local peak point at each acquisition time point is combined with the location of its spatial coordinate mark and associated with its intensity value to form a dynamic peak hotspot trajectory of the electric field intensity in the superimposed enhancement area;

[0027] Based on the global dynamic electric field distribution model, the global electric field mean is calculated for all grid point data at each time point;

[0028] Based on the dynamic peak hot spot trajectory and global electric field mean value extracted above, the dynamic enhancement amplification coefficient of the superimposed enhancement area at each time point is calculated, and the enhancement amplification coefficient is integrated in the time dimension to generate a time series curve of the enhancement amplification coefficient;

[0029] Set a risk threshold for the enhanced amplification factor, dynamically mark high-risk locations in the time series curve that exceed the risk threshold, and delineate risk areas based on spatial distribution trajectories;

[0030] The migration speed and residence time of dynamic peak hotspots in risk areas are recorded to provide a quantitative description of the dynamic evolution characteristics of peak hotspots in risk areas.

[0031] Preferably, analyzing the degree of local aging in the risk area and evaluating the tendency of the chain reaction caused by local aging in combination with the dynamic evolution characteristics of the peak hotspots in each risk area specifically includes:

[0032] Use an infrared thermal imager to measure the surface temperature distribution of the designated risk area, calculate the deviation between the average temperature of the risk area and the safe operating temperature of the insulation material, and calculate the thermal cumulative aging score;

[0033] Extract discharge intensity data of the designated risk area from the discharge detection device, including discharge amplitude and discharge frequency. Define the discharge activity energy per unit time based on the discharge amplitude and discharge frequency, and calculate the discharge aging degree score.

[0034] Perform weighted comprehensive calculation on the thermal accumulation aging degree score and the discharge aging degree score to obtain the comprehensive local aging degree of the risk area;

[0035] Based on the residence time of dynamic peak hotspots in the risk area, the cumulative impact of hotspots on local aging is evaluated. The time expansion range of the risk area is predicted based on the migration speed of the hotspots. The comprehensive aging degree of the expanded area is integrated over time and space, and the integration results are recorded and marked as the aging chain reaction trend assessment value.

[0036] Preferably, the risk assessment of the cable insulation aging trend based on the chain reaction trend assessment result specifically includes:

[0037] Obtain the aging chain reaction trend assessment values ​​of all risk areas and delineate the hazard gradient for the aging chain reaction trend assessment values;

[0038] Based on the hazard gradient delineation results, the risk area is calibrated for aging hazard level, and the cable insulation is repaired according to the calibration results.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] By setting up an electric field sensing array for high-speed sampling of dynamic electric field signals and performing Fourier decomposition of high-frequency standing wave field signals, (FDTD) simulation of the spatiotemporal variations in the electric field distribution can accurately capture the dynamic behavior characteristics of hotspot areas under the superposition of standing waves and DC electric fields. By quantifying the migration speed and residence time of dynamic peak hotspots, combined with analysis of the local aging level in risk areas, a comprehensive assessment of the aging chain reaction trends can be achieved. Based on these assessment results, the insulation aging trend of XLPE cables is calibrated for risk, which not only provides a scientific basis for cable operation and maintenance, but also effectively improves the operational safety and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of a method for assessing the aging risk of cross-linked polyethylene cable insulation according to the present invention.

[0042] Figure 2 A flow chart of the present invention for setting up an electric field sensing array to perform high-speed sampling of dynamic electric field signals, and performing completion and spatial correction on pre-processed electric field signal data;

[0043] Figure 3 The present invention performs Fourier decomposition on high-frequency standing wave field signals, simulates the spatiotemporal changes of electric field distribution, establishes a superposition model of standing waves and DC electric fields, and captures the spatiotemporal changes of hotspot areas;

[0044] Figure 4The present invention analyzes the dynamic peak hotspot trajectory of the electric field intensity in the superimposed enhancement area, quantifies the migration speed and residence time of the dynamic peak hotspot, and provides a quantitative description flow chart for the dynamic evolution characteristics of the risk area;

[0045] Figure 5 This is a flow chart for analyzing the degree of local aging in risk areas and evaluating the tendency of chain reactions caused by local aging in combination with the dynamic evolution characteristics of the peak hotspots in each risk area;

[0046] Figure 6 This is a flow chart for calibrating the risk level of cable insulation aging trends based on the chain reaction trend assessment results of the present invention. DETAILED DESCRIPTION

[0047] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0048] Reference Figure 1 As shown, a method for assessing the aging risk of cross-linked polyethylene cable insulation includes:

[0049] An electric field sensing array is set up to perform high-speed sampling of dynamic electric field signals, and to perform completion and spatial correction on the pre-processed electric field signal data;

[0050] Perform Fourier decomposition on high-frequency standing wave field signals to simulate the spatiotemporal changes in electric field distribution, establish a superposition model of standing waves and DC electric fields, and capture the spatiotemporal changes in hotspot areas;

[0051] Analyze the trajectory of the dynamic peak hotspots of the electric field intensity within the superimposed enhancement area, quantify the migration speed and residence time of the dynamic peak hotspots, and provide a quantitative description of the dynamic evolution characteristics of the risk area;

[0052] Analyze the degree of local aging in risk areas and assess the tendency of local aging to trigger chain reactions based on the dynamic evolution characteristics of peak hotspots in each risk area;

[0053] Based on the chain reaction trend assessment results, the cable insulation aging trend is calibrated for risk.

[0054] Reference Figure 2 As shown, a method for assessing the aging risk of cross-linked polyethylene cable insulation is provided. An electric field sensing array is set to perform high-speed sampling of dynamic electric field signals, and the pre-processed electric field signal data is supplemented and spatially corrected. Specifically, the method includes:

[0055] An array of electric field sensing devices is arranged at multiple points on the outer insulation surface and inside the cable. Each optical fiber electric field sensing device senses the local electric field intensity through the electro-optical effect. The array of electric field sensing devices must cover the key areas of the cable to ensure global acquisition of dynamic electric field signals and achieve high-precision detection of the complex electric field distribution of the cable.

[0056] The acquisition device is connected to the time synchronization module for time calibration, unifying the time base of all electric field sensing devices. A high-precision time synchronization module (such as GPS or PTP protocol) is used to connect to the optical fiber sensing device to record the timestamp of each sampling point, so that the time accuracy reaches ±1 microsecond. Each sensing device performs sampling based on a unified time base, effectively eliminating the impact of device time deviation on signal comparison and ensuring data time consistency.

[0057] The high-speed sampling frequency is set to perform high-speed sampling on the dynamic electric field signal. The collected initial signal is stored in the form of a time series. The signal includes the instantaneous value of the electric field intensity and the three-dimensional coordinate information of the spatial position. The sampling frequency is set to , the electric field time series signal is used express, To set the sampling frequency under the acquisition time potential, are the three-dimensional coordinate information of the spatial position of the electric field signal.

[0058] After the high-frequency noise and low-frequency interference of the signal are removed through optimization processing, the dynamic gain adjustment technology can capture the weak low-amplitude signal micro-electric field fluctuations and realize accurate detection of micro-electric field fluctuations.

[0059] An interpolation algorithm is used to complete the data in uncovered areas. At the same time, the electric field signal is spatially corrected based on the cable geometry and insulation layer characteristics, so that the data distribution is consistent with the actual physical structure of the cable (i.e., spatial position). This enables high-precision acquisition and processing of full-process data, providing high-quality data support for electric field distribution modeling, risk area identification, and aging trend analysis, significantly improving the accuracy and reliability of cable insulation monitoring.

[0060] Reference Figure 3 As shown, the high-frequency standing wave field signal is Fourier decomposed to simulate the spatiotemporal changes of the electric field distribution, and a superposition model of standing waves and DC electric fields is established to capture the spatiotemporal changes of hot spots. Specifically, the following are included:

[0061] Perform Fourier decomposition on the high-frequency standing wave field signal and decompose it into the fundamental wave and a set number of high-frequency harmonic components. By extracting the amplitude and phase information of each harmonic, calculate its distribution characteristics in space, and simulate the transient change characteristics of the standing wave stationary point, specifically:

[0062] Dynamic electric field signal Perform fast Fourier transform to obtain frequency domain representation: , f is the frequency of each harmonic in the corresponding electric field signal, Represents the electric field intensity component at frequency f.

[0063] The spatial distribution simulation synthesis formula of fundamental wave and harmonics is:

[0064]

[0065] Where, is the amplitude of the nth harmonic, is the frequency of the nth harmonic, is the phase of the nth harmonic, is the wave number ( , is the wavelength).

[0066] Obtain cross-linked polyethylene cable insulation material and determine the dielectric constant and conductivity characteristics of the insulation material.

[0067] The actual distribution data of space charge is obtained through pulse voltage excitation. The dynamic diffusion of charge in insulating materials is simulated by finite element method. The migration and accumulation process of space charge is simulated in real time. The dynamic diffusion of space charge satisfies the charge migration equation: ,in, is the space charge density, μ is the charge mobility, which indicates the mobility of charge under the action of electric field. is the electric field strength, is the diffusion coefficient, measured in square meters per second, which indicates the ability of charge to diffuse due to concentration gradient. Based on the dynamic diffusion of space charge, the electric field distribution corresponding to the space charge is marked with express.

[0068] Using time domain and space as variables, the dynamic diffusion distribution of the DC field is combined with the dynamic change characteristics of the standing wave field to generate a global dynamic electric field distribution model in the insulating layer. , + .

[0069] In the global dynamic electric field distribution model, the overlap of the charge retention region and the AC field standing wave stationary point leads to a significant enhancement of the local electric field. and The overlapping area and In areas where the electric field intensity exceeds the average value by 50% or more, the standing wave stagnation point distribution area corresponding to the spatial information is marked as a superposition enhancement area.

[0070] Reference Figure 4As shown in the figure, the dynamic peak hotspot trajectory of the electric field intensity in the superimposed enhancement area is analyzed, the migration speed and residence time of the dynamic peak hotspot are quantified, and a quantitative description of the dynamic evolution characteristics of the risk area is provided. Specifically, the following are included:

[0071] The electric field model is divided into equally spaced grids, the electric field intensity in each grid is scanned, and the local maximum is extracted. express, For the corresponding spatial coordinates, the Gaussian noise filtering algorithm is used to eliminate the false peaks that may be introduced by model errors. By scanning the local maximum values ​​in the grid, the possible electric field hotspots are determined, laying the foundation for subsequent dynamic analysis.

[0072] The local peak point at each acquisition time point is combined with the location of its spatial coordinate mark and associated with its intensity value to form the dynamic peak hotspot trajectory of the electric field intensity in the superimposed enhancement area, specifically:

[0073] For each collection time point , mark the spatial coordinates of the local peak point and strength , associate the peak information at each time point to form a dynamic trajectory data set , , is the total number of sampling time points.

[0074] Based on the global dynamic electric field distribution model, the global electric field mean is calculated for all grid point data at each time point. , , is the spatial coordinate In time The electric field strength at .

[0075] Based on the dynamic peak hotspot trajectory and global electric field mean extracted above, the dynamic enhancement amplification coefficient of the superimposed enhancement area at each time point is calculated, and the enhancement amplification coefficient is integrated in the time dimension to generate a time series curve of the enhancement amplification coefficient. The dynamic enhancement amplification coefficient is calculated as the ratio of the electric field intensity corresponding to the dynamic peak hotspot trajectory of the electric field intensity in the time series to the global electric field mean.

[0076] Based on the dielectric constant and conductivity characteristics of the insulating material, the risk threshold of the enhancement amplification factor is set in combination with the specific value of the enhancement amplification factor. The high-risk positions that exceed the risk threshold in the time series curve are dynamically marked in time, and the risk area is delineated in combination with the spatial distribution trajectory.

[0077] The migration speed and residence time of dynamic peak hotspots in the risk area are recorded to provide a quantitative description of the dynamic evolution characteristics of the peak hotspots in the risk area. The migration speed of the dynamic peak hotspot is calculated by dividing the spatial displacement of adjacent time points by the time interval. The Euclidean distance of the hotspot positions is calculated for any two adjacent time points. The migration speed value of each time interval is stored and used to analyze the hotspot migration law and define a specific range within the risk area. That is, when the electric field strength and position of the peak hotspot meet specific conditions at the same time, it is recorded as residence.

[0078] Reference Figure 5 As shown, the degree of local aging in risk areas is analyzed, and the trend of chain reactions caused by local aging is evaluated by combining the dynamic evolution characteristics of the peak hotspots in each risk area. Specifically, the following are included:

[0079] Use an infrared thermal imager to measure the surface temperature distribution of the designated risk area, calculate the deviation between the average temperature of the risk area and the safe operating temperature of the insulation material, and calculate the thermal cumulative aging score as follows:

[0080] Use a high-resolution infrared thermal imager (e.g. 640×480 pixels) to scan the surface temperature of the designated risk area, generate a two-dimensional temperature distribution map, and calculate the average temperature within the risk area. , In the risk area The temperature in °C, R is the spatial extent of the risk area, is the area of ​​the risk zone.

[0081] A thermal cumulative aging test is conducted on the insulation layer material to set the thermal aging weight factor. The thermal aging weight factor and the temperature deviation are multiplied by the time the temperature in the risk area exceeds the safe operating temperature to calculate the thermal cumulative aging degree score.

[0082] The discharge intensity data of the designated risk area is extracted from the discharge detection device, including the discharge amplitude and discharge frequency. The discharge activity energy per unit time is defined based on the discharge amplitude and discharge frequency, and the discharge aging degree score is calculated as follows:

[0083] Extracting the discharge amplitude from the discharge detection device and discharge frequency , then the corresponding definition of discharge activity energy per unit time is The calculation should be: , the discharge activity energy integration within the total discharge detection time, the discharge aging weight factor is set based on the dielectric constant and conductivity of the insulating material. A higher dielectric constant has a stronger resistance to discharge and should be given a lower weight. A higher conductivity is easy to form a conductive path, which aggravates aging and should be given a higher weight. The specific settings are measured by experiments and will not be repeated here.

[0084] The product of the discharge activity energy integration result within the total discharge detection time and the discharge aging weight factor is used as the discharge aging score.

[0085] The thermal accumulation aging degree score and the discharge aging degree score are normalized to unity and weighted comprehensive calculation is performed to obtain the comprehensive local aging degree of the risk area. express.

[0086] Evaluate the scope of the cumulative impact of hot spots on local aging based on the residence time of dynamic peak hot spots in the risk area , Assuming that the dynamic peak hotspot migrates at an average speed v, the time expansion range of the risk area is predicted based on the hotspot migration speed. , .

[0087] Perform time and space integration on the comprehensive aging degree of the extended area, record the integration results, and mark them as the aging chain reaction trend evaluation value. The calculation expression of the aging chain reaction trend evaluation value is: , is the aging chain reaction trend evaluation value at time t, For the comprehensive local aging degree of position w at time t, set the aging growth rate critical value Th, and calculate the aging chain reaction trend evaluation value at time t , screen out the aging chain reaction trend assessment value greater than or equal to the aging growth rate critical value Th (i.e. >=Th) in the dangerous area.

[0088] Reference Figure 6 As shown in the figure, based on the chain reaction trend assessment results, the risk level calibration of cable insulation aging trend specifically includes:

[0089] Obtain aging chain reaction trend assessments for all risk areas and categorize them into multiple gradients, such as low risk, medium risk, high risk, and extremely high risk. Each gradient represents a varying degree of aging intensity, reflecting the range of risk areas from mild aging to potential insulation failure. The hazard gradients are based on experimental data, engineering experience, and the actual dynamic characteristics of the risk areas, such as changes in temperature, discharge activity intensity, and dynamic peak hotspots. Thresholds can be adjusted based on cable material properties and usage scenarios.

[0090] Based on the hazard gradient delineation results, the aging hazard level of the risk area is calibrated. Cable insulation repairs are performed based on the calibration results, with priority given to high-risk and extremely high-risk areas, based on the hazard level. These areas are typically where dynamic hotspot migration is concentrated, where hotspots persist for long periods of time, or where there is a clear trend of expansion. After the repair is complete, the electric field strength, temperature distribution, and discharge activity in the repaired area are tested to verify that it has returned to a safe range.

[0091] Furthermore, the present invention also proposes a storage medium for a method for assessing the aging risk of insulation of a cross-linked polyethylene cable, on which a computer-readable program is stored. When the computer-readable program is called, the above-mentioned method for assessing the aging risk of insulation of a cross-linked polyethylene cable is executed.

[0092] It is understandable that the storage medium may be a magnetic medium, such as a floppy disk, a hard disk, or a magnetic tape; an optical medium, such as a DVD; or a semiconductor medium, such as a solid state disk (SSD).

[0093] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for assessing the aging risk of cross-linked polyethylene cable insulation, characterized in that: include: An electric field sensing array is set up to perform high-speed sampling of dynamic electric field signals, and to perform completion and spatial correction on the pre-processed electric field signal data; Perform Fourier decomposition on high-frequency standing wave field signals to simulate the spatiotemporal changes in electric field distribution, establish a superposition model of standing waves and DC electric fields, and capture the spatiotemporal changes in hotspot areas; Analyze the trajectory of the dynamic peak hotspots of the electric field intensity within the superimposed enhancement area, quantify the migration speed and residence time of the dynamic peak hotspots, and provide a quantitative description of the dynamic evolution characteristics of the risk area; Analyze the degree of local aging in risk areas and assess the tendency of local aging to trigger chain reactions based on the dynamic evolution characteristics of peak hotspots in each risk area; Based on the chain reaction trend assessment results, the cable insulation aging trend is calibrated for risk; The setting of the electric field sensing array to perform high-speed sampling of the dynamic electric field signal specifically includes: An array of electric field sensing devices is arranged at multiple points on the outer insulation surface and inside the cable. Each optical fiber electric field sensing device senses the local electric field intensity through the electro-optical effect. The acquisition device is connected to the time synchronization module for time calibration to unify the time base of all electric field sensing devices; The high-speed sampling frequency is set to perform high-speed sampling on the dynamic electric field signal. The collected initial signal is stored in the form of a time series. The signal includes the instantaneous value of the electric field intensity and the three-dimensional coordinate information of the spatial position; Clear the high-frequency noise and low-frequency interference of the collected initial signal, dynamically gain the low-amplitude signal, adjust the low-amplitude signal, and capture the micro-electric field fluctuations of the collected initial signal; The Fourier decomposition of the high-frequency standing wave field signal is performed to simulate the spatiotemporal variation of the electric field distribution, establish a standing wave and DC electric field superposition model, and capture the spatiotemporal variation of the hotspot area. Specifically, the following steps are performed: Perform Fourier decomposition on the high-frequency standing wave field signal, breaking it down into the fundamental wave and a set number of high-frequency harmonic components. By extracting the amplitude and phase information of each harmonic, calculate its distribution characteristics in space, and simulate the transient change characteristics of the standing point of the standing wave; Obtain the insulation material of cross-linked polyethylene cables and determine the dielectric constant and conductivity characteristics of the insulation material; The actual distribution data of space charge is obtained through pulse voltage excitation. The dynamic diffusion of charge in insulating materials is simulated by finite element method. The migration and accumulation process of simulated space charge is annotated in real time. The corresponding electric field distribution of space charge is annotated based on the dynamic diffusion of space charge. Using time and space as variables, the dynamic diffusion distribution of the DC field is combined with the dynamic variation characteristics of the standing wave field to generate a global dynamic electric field distribution model within the insulation layer. Record the spatial information of the significant enhancement of the local electric field caused by the overlap of the charge retention area and the AC field standing wave point in the global dynamic electric field distribution model, and mark the standing wave point distribution area corresponding to the spatial information as the superposition enhancement area; The analysis superimposes and strengthens the trajectory of the dynamic peak hotspots of the electric field intensity within the region, quantifies the migration speed and residence time of the dynamic peak hotspots, and provides a quantitative description of the dynamic evolution characteristics of the risk region. Specifically, the following are included: The electric field model is divided into equally spaced grids, the electric field intensity in each grid is scanned, the local maximum is extracted, and the Gaussian noise filtering algorithm is used to remove the false peaks introduced by the model error. The local peak point at each acquisition time point is combined with the location of its spatial coordinate mark and associated with its intensity value to form a dynamic peak hotspot trajectory of the electric field intensity in the superimposed enhancement area; Based on the global dynamic electric field distribution model, the global electric field mean is calculated for all grid point data at each time point; Based on the dynamic peak hotspot trajectory and global electric field mean extracted above, the dynamic enhancement amplification coefficient of the superimposed enhancement area at each time point is calculated. The enhancement amplification coefficient is integrated in the time dimension to generate a time series curve of the enhancement amplification coefficient. The dynamic enhancement amplification coefficient is calculated as the ratio of the electric field intensity corresponding to the dynamic peak hotspot trajectory of the electric field intensity in the time series to the global electric field mean. Set a risk threshold for the enhanced amplification factor, dynamically mark high-risk locations in the time series curve that exceed the risk threshold, and delineate risk areas based on spatial distribution trajectories; Record the migration speed and residence time of dynamic peak hotspots in risk areas, and provide a quantitative description of the dynamic evolution characteristics of peak hotspots in risk areas; The analysis of the local aging degree in the risk area and the assessment of the chain reaction tendency caused by local aging in combination with the dynamic evolution characteristics of the peak hotspots in each risk area specifically include: Use an infrared thermal imager to measure the surface temperature distribution of the designated risk area, calculate the deviation between the average temperature of the risk area and the safe operating temperature of the insulation material, and calculate the thermal cumulative aging score; Extract discharge intensity data of the designated risk area from the discharge detection device, including discharge amplitude and discharge frequency. Define the discharge activity energy per unit time based on the discharge amplitude and discharge frequency, and calculate the discharge aging degree score. Perform weighted comprehensive calculation on the thermal accumulation aging degree score and the discharge aging degree score to obtain the comprehensive local aging degree of the risk area; Based on the residence time of dynamic peak hotspots in the risk area, the cumulative impact of hotspots on local aging is evaluated. The temporal expansion range of the risk area is predicted based on the migration speed of the hotspots. The comprehensive aging degree of the expanded area is integrated over time and space, and the integration results are recorded and marked as the aging chain reaction trend assessment value. The cumulative effect of hot spots on local aging was evaluated as , is the dwell time, is the comprehensive local aging degree of the risk area; Temporal extension of the risk area , v is the average velocity of the dynamic peak hotspot; The calculation expression of the aging chain reaction trend evaluation value is: , is the aging chain reaction trend evaluation value at time t, is the comprehensive local aging degree of position w at time t.

2. The method for assessing the aging risk of cross-linked polyethylene cable insulation according to claim 1, wherein: The completion and spatial correction of the pre-processed electric field signal data specifically includes: An interpolation algorithm is used to complete the data in uncovered areas. At the same time, the electric field signal is spatially corrected according to the cable geometry and insulation layer characteristics to make the data distribution consistent with the actual physical structure of the cable.

3. The method for assessing the aging risk of cross-linked polyethylene cable insulation according to claim 2, wherein: The risk level calibration of the cable insulation aging trend based on the chain reaction trend assessment results specifically includes: Obtain the aging chain reaction trend assessment values ​​of all risk areas and delineate the hazard gradient for the aging chain reaction trend assessment values; Based on the hazard gradient delineation results, the risk area is calibrated for aging hazard level, and the cable insulation is repaired according to the calibration results.

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