Cable aging diagnosis monitoring method and system

Abstract

The invention relates to the technical field of cable aging diagnosis, in particular to a cable aging diagnosis monitoring method and system. The method comprises the following steps: acquiring cable operation log data and cable internal structure design data, extracting a cable structure distribution condition in combination with an operation log, and evaluating cable operation state data; electric power sudden demand characteristics are identified, and then a non-uniform thermal coupling unbalance state is determined; based on the thermal coupling unbalance and the operation state data, detecting the coupling failure condition of the cable multilayer structure, and further judging the structural integrity damage condition; deducing a load stability disintegration situation according to a structure damage and insulation strength reduction trend; further detecting the decline of the quality of transmitted electric energy, analyzing the increase degree of abnormal voltage fluctuation, and identifying the fatigue trend of the metal material of the cable; through the above cable aging diagnosis, the cable aging diagnosis data is output. According to the cable aging diagnosis method and device, cable aging diagnosis is diagnosed, so that cable aging diagnosis is more accurate and efficient.

Description

Technical Field

[0001] The present invention relates to the technical field of cable aging diagnosis, and in particular to a cable aging diagnosis and monitoring method and system. Background Art

[0002] As core power transmission and distribution components, power cables face increasingly severe aging issues over long periods of service, becoming a key factor impacting the safety and stability of power grid operations. Cables are susceptible to the combined effects of multiple factors, including temperature and humidity fluctuations, external mechanical stress, electromagnetic shock, and transient current surges. This can gradually degrade the insulation structure, leading to heat accumulation in conductor joints, cracks in the dielectric layer, and even breakdown. This can lead to decreased transmission performance and increased safety hazards across the entire transmission link. Traditional cable inspection methods, which rely primarily on periodic manual inspections or offline testing, struggle to respond promptly to sudden operational changes and identify localized anomalies within the multi-layer structure. This approach aims to identify the evolution of thermal coupling, the spread of structural instability, sudden electrical performance changes, and the reduction of system safety margins under sudden operating conditions, enabling high-precision, multi-dimensional visual diagnosis of cable aging trends. However, traditional cable aging diagnostics often suffer from inaccurate analysis of coupling failures within the cable's multi-layer structure and inaccurate diagnosis of cable aging. Summary of the Invention

[0003] Based on this, it is necessary to provide a cable aging diagnosis and monitoring method and system to solve at least one of the above technical problems.

[0004] To achieve the above objectives, a cable aging diagnosis and monitoring method includes the following steps:

[0005] Step S1: obtaining cable operation log data and cable internal structure design data; collecting cable structure distribution status based on the cable operation log data; querying cable operation environment parameters based on the cable structure distribution status and cable operation log data; and evaluating cable operation status data based on the cable operation environment parameters and cable internal structure design data.

[0006] Step S2: determining the cable power sudden demand data based on the cable operation log data; determining the cable non-uniform thermal coupling imbalance state based on the cable operation status data; detecting the cable multi-layer structure coupling failure condition based on the cable operation status data and the cable non-uniform thermal coupling imbalance state;

[0007] Step S3: detecting a cable structural integrity damage condition based on a cable multi-layer structure coupling failure condition; evaluating a cable electrical insulation strength degradation trend based on the cable structural integrity damage condition; and determining a cable load stability collapse condition based on the cable structural integrity damage condition and the cable electrical insulation strength degradation trend;

[0008] Step S4: Detect the degradation of the cable transmission power quality based on the collapse of the cable load stability; determine the degree of growth of the cable operating voltage abnormal fluctuation based on the degradation of the cable transmission power quality; detect the fatigue trend of the cable transmission metal material based on the degree of growth of the cable operating voltage abnormal fluctuation; perform cable aging diagnosis based on the fatigue trend of the cable transmission metal material to obtain cable aging diagnosis data.

[0009] The present invention introduces a joint analysis method of cable operation log data and structural design data to achieve a comprehensive characterization of cable operation conditions and structural distribution status, thereby enhancing the accuracy of operation status assessment and the depth of correlation with the structural environment; further constructs a thermal coupling imbalance identification path based on sudden power demand data, so that the response chain between non-uniform heat accumulation and cable multi-layer structure failure can be quantified, thereby improving the linkage detection capability of local hot spots and structural degradation; based on the coordinated judgment of structural integrity and insulation strength evolution trend, accurately identifies the trigger point of current carrier stability collapse, and improves the response sensitivity to early signs of faults; introduces continuous monitoring means of power quality and operating voltage fluctuations, enhances the continuity of tracking fatigue trends of metal transmission materials, and helps to convert local degradation processes into quantifiable diagnostic basis; through the logical deduction of fatigue trends and aging status, the diagnostic results have stronger structural traceability and thermal-electrical-mechanical comprehensive judgment capabilities, realizing the systematic closed-loop construction of cable aging status identification. Therefore, the present invention is an optimization treatment of traditional aging diagnosis monitoring, which solves the problems of inaccurate analysis of the coupling failure status of the cable multi-layer structure and inaccurate cable aging diagnosis in traditional aging diagnosis monitoring, and improves the accuracy of the coupling failure status of the cable multi-layer structure and the accuracy of cable aging diagnosis.

[0010] The present invention further provides a cable aging diagnosis and monitoring system for executing the cable aging diagnosis and monitoring method described above. The cable aging diagnosis and monitoring system comprises:

[0011] The cable operation status data evaluation module is used to obtain cable operation log data and cable internal structure design data; collect cable structure distribution status based on cable operation log data; query cable operation environment parameters based on cable structure distribution status and cable operation log data; and evaluate cable operation status data based on cable operation environment parameters and cable internal structure design data;

[0012] The cable multi-layer structure failure detection module is used to determine the cable power sudden demand data based on the cable operation log data; determine the cable non-uniform thermal coupling imbalance state based on the cable operation status data; and detect the cable multi-layer structure coupling failure condition based on the cable operation status data and the cable non-uniform thermal coupling imbalance state;

[0013] The cable load stability detection module is used to detect the damage to the cable structure integrity based on the coupling failure of the cable multi-layer structure; evaluate the decline trend of the cable electrical insulation structure strength based on the damage to the cable structure integrity; and determine the collapse of the cable load stability based on the damage to the cable structure integrity and the decline trend of the cable electrical insulation structure strength;

[0014] The cable aging diagnosis module is used to detect the degradation of the cable transmission power quality based on the collapse of the cable load stability; determine the degree of abnormal fluctuation growth of the cable operating voltage based on the degradation of the cable transmission power quality; detect the fatigue trend of the cable transmission metal material based on the degree of abnormal fluctuation growth of the cable operating voltage; perform cable aging diagnosis based on the fatigue trend of the cable transmission metal material to obtain cable aging diagnosis data.

[0015] The cable aging diagnosis and monitoring system of the present invention can implement the aging diagnosis and monitoring method of any cable of the present invention, and is used to combine the operation and signal transmission medium between various modules to complete the cable aging diagnosis and monitoring method. The internal modules of the system cooperate with each other to achieve accurate diagnosis and full-process monitoring of the cable aging degree through multi-dimensional operation data fusion and structural status chain analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic flow chart of the steps of a cable aging diagnosis and monitoring method;

[0017] Figure 2 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0020] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0021] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0022] To achieve this, please refer to Figures 1 to 2 A cable aging diagnosis and monitoring method includes the following steps:

[0023] Step S1: obtaining cable operation log data and cable internal structure design data; collecting cable structure distribution status based on the cable operation log data; querying cable operation environment parameters based on the cable structure distribution status and cable operation log data; and evaluating cable operation status data based on the cable operation environment parameters and cable internal structure design data.

[0024] In an embodiment of the present invention, cable operation log data is acquired, including but not limited to current load timing data, temperature sensor data collected during operation, voltage fluctuation data, and records of unexpected events during the operation cycle. Simultaneously, cable internal structural design data is extracted, including conductor material information (e.g., copper and aluminum grain size and conductivity), insulation material type and thickness distribution, metal shielding layer layout, filler material properties, and sheath layer structure hierarchy. After unifying these design data with the operation log data, a structure recognition algorithm is used to analyze the distribution of the cable's internal multi-layer structure. A structure distribution mapping table is established based on the correspondence between structure hierarchy and data. In this specific operation, thermal imaging data is compared with a cable manufacturing number database to obtain data on the distribution of structural layers, material layer sequence, and spatial structure density within a specific section of the cable. Based on this structural distribution, along with the geographic environment tags, laying method codes, burial depth information, ventilation records, and an external climate database recorded in the operation log, a parameter indexing operation is performed to derive a set of cable operating environment parameters for different time periods, including ambient air humidity, ground temperature variation curves, solar radiation intensity, and electromagnetic interference density. The cable operating environment parameters and structural design data are input into the condition assessment program. Using a distributed physical parameter mapping analysis method, the stress bearing capacity, thermal conductivity change value, and material temperature resistance coefficient decrease rate of each structural section under specific environmental conditions are calculated layer by layer, thereby forming a complete cable operating status data sequence, which serves as the basic data set for subsequent thermal coupling analysis and degradation trend identification.

[0025] Step S2: determining the cable power sudden demand data based on the cable operation log data; determining the cable non-uniform thermal coupling imbalance state based on the cable operation status data; detecting the cable multi-layer structure coupling failure condition based on the cable operation status data and the cable non-uniform thermal coupling imbalance state;

[0026] In this embodiment of the present invention, based on the cable operation log data obtained in step S1, the recorded power demand data is identified. The power demand data refers to significant changes in the cable's current, voltage, and load frequency during specific time periods, such as load surges, grid-connection shocks, and short-circuit transients. A typical burst time period is selected, and by comparing the incremental indicators of the normal operating period data with the abnormal period data, key parameters such as peak current amplitude, duration, current growth rate, and voltage disturbance amplitude are extracted to form a set of burst operation excitation factors. The burst demand data is matched with the operating status data constructed in S1, and the heating degree and temperature response speed of each cable layer are calculated according to the time series and structural layering. The non-uniform thermal field distribution within the cable is identified, and the thermal gradient variation coefficient at the interface between the insulation layer and the shielding layer is calculated using the heat flux interpolation analysis method to determine whether a thermal coupling imbalance state exists. If the temperature rise difference between different structural layers is greater than a specified threshold (e.g., 15°C / layer), it is marked as a non-uniform thermal coupling imbalance state. Furthermore, based on the temperature rise gradient map and the structural stress response curve, the heat diffusion path is verified using feedback data from multi-point thermal probes. The local material expansion rate differences, electric field distortion intensity, and interface insulation stress concentration coefficient at the multi-layer coupling points within the structure are evaluated to form the cable multi-layer structure coupling failure status data. The data content includes: the failure location (marked with structural layer and distance coordinates), failure type (such as interlayer delamination, void expansion, local carbonization), corresponding thermal stress peak value, and material softening degree index, providing direct input for subsequent integrity analysis.

[0027] Step S3: detecting a cable structural integrity damage condition based on a cable multi-layer structure coupling failure condition; evaluating a cable electrical insulation strength degradation trend based on the cable structural integrity damage condition; and determining a cable load stability collapse condition based on the cable structural integrity damage condition and the cable electrical insulation strength degradation trend;

[0028] In an embodiment of the present invention, the cable multilayer structure coupling failure condition data obtained in step S2 is imported into the structural integrity assessment module. The assessment module uses a finite element calculation method to simulate and calculate the overall structural stability based on the relative displacement amplitude between each layer of the structure, the structural void growth rate and the insulation layer tension distribution density map, and identifies the interface debonding length, the uneven wrapping area and the degree of deformation of the shielding layer, thereby generating cable structural integrity damage condition data. The data includes the coordinates of the damaged area, the size of the damaged range (in square millimeters), the damage level (divided into three levels: minor damage, medium damage, and severe damage), etc. Based on the damage condition, the insulation material strength reduction rate is calculated in combination with indicators such as the withstand voltage level and the insulation strength design redundancy ratio in the structural design parameters. By recording the insulation penetration voltage reduction value, leakage current growth rate, partial discharge onset voltage reduction rate, etc. in different time periods, an insulation strength degradation curve is drawn to quantify its downward trend. The structural integrity damage status and the insulation strength reduction trend are jointly input into the stability judgment unit. This unit analyzes whether the current-carrying stability of the cable under different load levels collapses based on the conductor current-carrying capacity reduction value, voltage maintenance capacity reduction ratio and thermal expansion resonance point offset, and marks the critical load range where collapse occurs and the corresponding thermal instability point position, forming a complete record of the cable load stability collapse.

[0029] Step S4: Detect the degradation of the cable transmission power quality based on the collapse of the cable load stability; determine the degree of growth of the cable operating voltage abnormal fluctuation based on the degradation of the cable transmission power quality; detect the fatigue trend of the cable transmission metal material based on the degree of growth of the cable operating voltage abnormal fluctuation; perform cable aging diagnosis based on the fatigue trend of the cable transmission metal material to obtain cable aging diagnosis data.

[0030] In this embodiment of the present invention, the cable load stability collapse data obtained in step S3 is used as input. Based on the power system energy transmission continuity constraint, the degradation of the cable's transmitted power quality under different operating conditions is detected. During the specific detection process, based on the energy flow continuity deviation curve, voltage drop distribution density diagram, and power waveform distortion data, the presence of frequent short-term voltage sags, increased harmonic content, and transient current imbalance is analyzed. The frequency, duration, and impact range of these phenomena are extracted to obtain a power quality degradation curve. Based on this degradation curve and combined with the voltage fluctuation data before and after the collapse point in S3, the degree of abnormal operating voltage fluctuation growth is calculated. The maximum increase in voltage deviation from the design operating value, the fluctuation rate (the number of voltage changes per unit time), and the diffusion rate along the cable length are statistically analyzed to construct a voltage fluctuation hotspot map. By comparing the hotspot location with metal conductor fatigue test data, the fatigue trend of the transmission metal material is analyzed, primarily detecting the grain boundary migration of the conductor layer, the increase in intergranular oxidation, and the microcrack formation rate caused by sudden changes in current density. Through high-frequency resistance component testing and micro-crack probe measurement, the residual value of fatigue life of metal materials and the cumulative index of structural micro-loss are obtained. The above fatigue trend data are input into the aging diagnosis module. Combined with the insulation strength degradation value, thermal coupling imbalance point distribution, and structural damage level diagram, the overall aging diagnosis data of the cable is derived. The diagnostic data should include aging level assessment (such as mild, moderate, severe), location marking of deteriorated areas, estimation of remaining operating life cycle (in hours) and a list of coordinates of key monitoring points as the output of the cable status assessment system.

[0031] Preferably, step S1 includes the following steps:

[0032] Step S11: obtaining cable operation log data and cable internal structure design data;

[0033] In an embodiment of the present invention, a data recording unit deployed in the substation control center reads cable operation log data through an interface. This log data is uploaded by a transmission protocol controller using the Modbus TCP protocol and includes data such as the cable's power-on time, single power-on duration, instantaneous current value, phase voltage, load power, duty cycle, energy metering data, and daily cumulative heat gain. All data is periodically stored in a master control database in CSV format. Simultaneously, the cable's internal structural design data is parsed from the structural BOM list and two-dimensional cross-sectional drawings provided by the cable manufacturer. This includes parameter information such as the type of insulation material for each layer, conductor cross-sectional area, sheath material structure hierarchy, shielding layer configuration, temperature resistance rating, and conductor winding method. After the above drawings are imported into the structure recognition platform in vector graphics format (DWG), a CAD parsing tool is used in conjunction with the material code to extract each layer of the structure and identify the structural hierarchy. This step outputs two data sets: one is the cable operation log data set A (including electrical and thermal parameters after timestamp alignment); the other is the cable structure design data set B (indicating material hierarchy, dimensional parameters, and numbering). This data set will serve as the data basis for subsequent structural state identification and environmental adaptation analysis.

[0034] Step S12: collecting cable structure distribution status based on cable operation log data;

[0035] In this embodiment of the present invention, during the cable structure distribution status collection, the cable operation log dataset A obtained in step S11 is time-series segmented. Operating cycle segments are divided using a fixed window length (6 hours) based on the daily current fluctuation mean, cumulative thermal power increment, and load curve shape. Subsequently, thermal imaging of the cable surface is captured using an embedded infrared imager (with a resolution of no less than 320×240 pixels). A thermal imaging fixture is installed every two meters on the cable surface, and periodic scanning is performed using a controllable rotating axis. All infrared image data is uploaded to a thermal image processing server, where pixel temperature extraction and linear interpolation are used to construct a thermal distribution map for each cable segment. Combining the heat changes, power-on time, and load current recorded in the operation log data for the corresponding time period, a mapping relationship between thermal power distribution curves and physical coordinates is constructed. Furthermore, heat concentration segments are identified based on the thermal image temperature rise differences. Based on the structural layer numbers in the CAD drawings, the corresponding coordinate regions are mapped to structural segment numbers, thereby generating cable structure distribution data C, which includes a table of associations between thermal anomaly regions and structural numbers, a length mapping index, and a hierarchical mapping relationship between heat concentration segments. This data set is used for subsequent running environment parameter matching.

[0036] Step S13: querying cable operation environment parameters according to the cable structure distribution status and cable operation log data;

[0037] In an embodiment of the present invention, the query of cable operation environment parameters is based on the data set C (structural distribution status), and each identified structure number and its corresponding coordinate position are matched in real time in combination with the environmental sensor data deployed in the station. The cable section corresponding to each structure number is associated with its physical coordinates. The physical coordinate system is uniformly established through the cable laying BIM model, and on this basis, data such as temperature, humidity, radiation intensity, contact material type, wind speed and ventilation conditions at the laying location are extracted. The environmental parameters are collected by the sensor array, the sensor layout density is one group per 10 meters, and the sampling frequency is 1Hz. After the time period information recorded in the operation log is aligned with the sampling timestamp, the temperature and humidity parameters, heat dissipation environment, thermal conductivity of the contact material, etc. at the corresponding position of each structure number are integrated to form the cable operation environment parameter set D. This parameter set records the environmental adaptability status of the cable in each structural segment, and provides environmental background variables for subsequent operation status evaluation.

[0038] Step S14: collecting cable internal hierarchical structure data according to the cable internal structure design data;

[0039] In an embodiment of the present invention, this step is carried out based on the cable structure design dataset B obtained by parsing in step S11. In the specific operation, a high-frequency ultrasonic lateral imager (center frequency not less than 25MHz) is used to scan and image the cable cross section to identify the internal layered structure. The imaging device collects a cross-sectional image every 0.5 meters along the longitudinal direction of the cable, and determines the thickness and boundary position of each layer of material by matching the image grayscale histogram with the material acoustic impedance value. The obtained image corresponds one-to-one with the structural layer number in the structural BOM data. In order to enhance the accuracy of interlayer identification, laser Raman spectroscopy is used for different insulating materials to extract the corresponding polymer characteristic peak position, and the dielectric constant curve is obtained by combining a high-frequency capacitive coupling probe to reconstruct all structural information into a hierarchical structure dataset E. This dataset clearly records the material level, thickness, boundary position and material physical property parameters (such as thermal conductivity, thermal expansion coefficient, and electrical breakdown strength) of each cable section. Data E will be directly used in the next step to evaluate the interaction between the structural state and the environment.

[0040] Step S15: Evaluate the cable operating status data based on the cable operating environment parameters and the cable internal hierarchical structure data.

[0041] In this embodiment of the present invention, the environmental parameter set D obtained in step S13 and the hierarchical structure dataset E obtained in step S14 are used as input to construct condition assessment data for the cable at different physical locations. During the assessment, a finite element thermodynamic simulation platform is used to predict the temperature rise of each structural segment. Simulation parameters include the conductor center temperature, insulation boundary temperature, sheath thermal hysteresis, and surface cooling rate. The simulation step size is 0.1 seconds, and the total time window covers nearly 24 hours. The simulation uses ambient temperature and humidity boundary conditions recorded by real sensors, and applies the current load from the log as the heat source input. Heat conduction equation parameters are set for each layer based on the material properties. After the simulation is complete, the temperature change curve and thermal expansion deformation of each layer are extracted. Combined with the insulation breakdown threshold and structural deformation differential provided in structure E, areas with abnormal operating conditions are identified. The evaluation results for various status indicators, such as structural deformation, heat accumulation anomalies, and breakdown risk, are output as a dataset F. Each data record contains operating parameters such as the coordinate segment number, structural layer number, maximum temperature, deformation rate, and insulation margin value. Data F serves as the foundation for subsequent cable multilayer coupling failure analysis.

[0042] Preferably, step S15 includes the following steps:

[0043] Step S151: setting the temperature measurement range of the temperature sensor to 0-100°C, the minimum temperature change to 0.05°C, and the temperature sampling frequency to 10Hz;

[0044] In an embodiment of the present invention, when setting the measurement parameters of the temperature sensor, a high-precision digital temperature sensor is selected. The sensor adopts a 16-bit analog-to-digital converter and has a factory calibration function. The temperature measurement range is set to 0°C to 100°C, covering the entire temperature range experienced by the operating environment of industrial cables, to avoid low-temperature freezing and high-temperature degradation without monitoring. The minimum temperature change resolution of the sensor is set to 0.05°C, which can still accurately capture the temperature change trend under slight thermal disturbances. The sensor is connected to the industrial embedded controller through the SPI interface, and the temperature sampling frequency is set to 10Hz inside the controller, that is, the temperature change data is collected 10 times per second. This frequency can record the continuity of the temperature curve in real time during dynamic operation.

[0045] Step S152: Setting the humidity sensor's humidity measurement range to 0% to 100% RH, the minimum humidity change to 0.1% RH, and the humidity sampling frequency to 5 Hz;

[0046] In the embodiment of the present invention, when setting the humidity monitoring parameters, a digital humidity sensor with temperature and humidity combined monitoring function (model HDC2080, manufacturer Texas Instruments) is used. The device supports I 2C communication, with ±2% RH humidity accuracy. In the embedded controller configuration interface, the humidity sensor's measurement range is set to 0% RH to 100% RH, covering operating environments from extremely dry to extremely humid. The minimum humidity resolution is set to 0.1% RH, meeting the need to monitor slowly fluctuating relative humidity. The sampling frequency is also set to 5Hz, recording five humidity sampling points per second. These data are timestamped and synchronized with the temperature sampling data to provide data support for subsequent coupled analysis of ambient temperature and humidity.

[0047] Step S153: using a humidity sensor and a temperature sensor to collect the temperature and humidity status of the cable operating environment parameters to obtain the cable operating environment temperature and humidity data;

[0048] In an embodiment of the present invention, after completing the parameter setting of the temperature and humidity sensor, the above-mentioned sensor is synchronously connected to a multi-channel acquisition terminal based on the Modbus RTU protocol through the edge acquisition node of the industrial Internet of Things. The system jointly collects and caches the temperature and humidity data through a unified timestamp, and constructs a synchronous temperature and humidity time series data based on 5Hz. Among them, the temperature acquisition nodes are arranged on the surface of the cable outer sheath, the joint position and the corner of the bridge, and the humidity sensor is arranged inside the distribution box, the vent of the cable trough and the bottom of the trench, etc., which are prone to condensation or moisture. Through this structural layout and synchronous acquisition, a temperature and humidity data set of the cable operating environment is generated. The data set is structured and stored according to the timestamp, location number, temperature value (℃), and humidity value (%RH). The data format is CSV or SQLite table format, which serves as the data input basis for the subsequent structural stability evaluation.

[0049] Step S154: Evaluate the cable outer layer protection structure strength parameters based on the cable internal layered structure data;

[0050] In an embodiment of the present invention, the internal layered structure data of the cable obtained in step S14 (including the conductor layer diameter, insulation layer thickness, shielding layer material properties and sheath layer type) is used to calculate the strength parameters of the cable outer protective structure through a structural strength analytical algorithm. This process uses the static structure module in ANSYS for simulation modeling, and sets the elastic modulus, Poisson's ratio, and thickness information of the sheath layer material (such as PVC, low-smoke halogen-free, cross-linked polyethylene, etc.). By applying a typical mechanical load (such as 5kN uniform lateral pressure, 1Hz vibration simulation), the maximum stress σ_max and allowable stress σ_allow of the outer sheath layer are solved, and the structural strength parameter S_P=σ_max / σ_allow is output. If S_P>1, it means that the structure is at the edge of the stress limit; if S_P<1, it means that the structure has not yet reached the limit.

[0051] Step S155: Evaluate the internal electrical operation characteristic parameters of the cable according to the internal hierarchical structure data of the cable;

[0052] In an embodiment of the present invention, a cable electrical structure list is called, and an electrical feature vector set is established based on parameters such as cable core structure, insulation material type, capacitive coupling structure, and current carrying capacity. For example, for a 3-core cable, the conductor resistance is 0.23Ω / km, the rated current is 450A, the core insulation thickness is 3mm, the insulation resistance is 1000MΩ·km, and the working voltage is 10kV. These parameters are used as input conditions, and the electrical simulation system (such as based on Ohm's law and the thermo-electric coupling heat transfer formula) is used to calculate the heat generation per unit length, the stable operation time, and the internal temperature rise change range when the cable is powered on for a long time, and the electrical operation feature set is obtained accordingly to describe the physical electrical performance of the cable during operation, which serves as one of the bases for internal structure evaluation.

[0053] Step S156: Evaluate the stability of the cable's internal structure based on the cable's internal electrical operation characteristic parameters and the cable's outer protective structure strength parameters;

[0054] According to the cable protection structure strength parameters output in step S154 and the electrical operation feature set obtained in step S155, a structural stability assessment logic is established. The stability of cables with high compressive strength, low conductor heat generation and large insulation resistance is assessed as high. The weighted factor method is used to comprehensively calculate the stability: stability parameter S_stab = W1×structural strength score + W2×electrical load-bearing score, where W1 and W2 are set according to different ratios of the line laying environment (such as underground humid areas and ventilation cable wells). The stability results are divided into threshold segments, corresponding to grade labels such as high stability, boundary critical and unstable, and recorded as cable stability evaluation fields, marked on the cable segment structure diagram for subsequent operation status judgment.

[0055] Step S157: Evaluate the cable operation status data based on the cable operation environment temperature and humidity data and the stability of the cable internal structure.

[0056] In an embodiment of the present invention, the operating environment temperature and humidity data obtained in step S153 and the structural stability level determined in step S156 are integrated to establish an operating status deduction function in a dual-parameter comparison manner. The operating status score is based on the basic stability parameters and is corrected according to the following formula: status score S_run = S_stab × (1-R_temp-R_hum), where R_temp is the stability correction rate based on the temperature fluctuation rate (high risk is judged as above 8°C / h), and R_hum is the correction rate under the humidity surge rate (when it is above 20%RH / h, the influence of moisture expansion stress is introduced). The final output result is a continuous value, which is divided into operating status levels according to empirical segmentation rules. At the same time, combined with the temperature and humidity fluctuation amplitude and stability level change trend during the data collection period, a predictive operating status trend graph is output. This graph is bound to the structural diagram to support subsequent aging assessment and diagnosis module calls.

[0057] Preferably, determining the cable power sudden demand data in step S2 includes:

[0058] Collect cable operation power demand data based on cable operation log data; perform interpolation and smoothing processing on the cable operation power demand data to obtain cable operation power demand smoothing data;

[0059] In this embodiment of the present invention, a cable monitoring system is used to collect operation logs. The cable monitoring system includes smart meters and sensors that can monitor electrical parameters such as voltage, current, and power in the power system in real time. Data collection devices are deployed at key locations in the power distribution system. The sensors sample once per second, recording multiple variables related to cable power demand, such as current intensity, voltage fluctuation, power value, and power factor. This collected data is transmitted in real time to a data collection center via a serial port or Ethernet via Modbus or DNP3 protocols and stored in a database. A PostgreSQL database is used for data storage, with each data item archived by timestamp. Fields include "timestamp," "voltage (kV)," "current (A)," "power (kW)," and "power factor." The data is stored in the original CSV or JSON file format and aggregated by daily, weekly, or monthly periods. All power demand data is saved as "raw cable power demand data," and each record is annotated to facilitate subsequent data analysis and backtracking. The data collection frequency and time accuracy can be adjusted according to project requirements to cover cable power demand under different workloads. After acquiring the raw power demand data, it needs to be preprocessed to eliminate noise and outliers. The raw data is then inspected for missing and outlier values. Missing values, caused by sensor failure or communication interruptions, are filled using interpolation. A five-point Lagrangian interpolation method is used to address missing data in the time series. Specifically, the data is taken from two points before and after the time point, and the value of the missing point is calculated using an interpolation formula. Furthermore, a sliding window variance method is used to screen for large outliers. When the power value of a point differs from that of its predecessor and successor by more than a set threshold (e.g., 20%), the point is considered an outlier and replaced with a neighboring normal point. The data is further smoothed using a Gaussian convolution kernel (with a window size of 11 and a standard deviation of 2.5) to create a continuous and smooth data curve. This processing results in "cable operation power demand smoothing data." The fluctuations in this data set are smoothed, eliminating the interference from transient spikes and transitions, and more accurately reflecting the long-term trend of power demand.

[0060] Construct a cable operation power demand fluctuation curve based on the cable operation power demand smoothing data; predict the nonlinear change trend of cable demand fluctuation based on the cable operation power demand fluctuation curve;

[0061] In this embodiment of the present invention, the smoothed power demand data is used to construct a power demand fluctuation curve. The data is arranged along a timeline, with time as the horizontal axis and power demand as the vertical axis. Matplotlib or a plotting tool is used to plot the power demand over time. The power demand value at each time point directly determines a point on the fluctuation curve. The second-order difference of the data is calculated—that is, the rate of change of power between every two adjacent points on the curve—to assess the changing trend of power demand. This helps identify acceleration or deceleration of power demand fluctuations. The curve generated by the differencing operation can provide more precise guidance for subsequent fluctuation analysis. Based on this data, a cable operation power demand fluctuation curve is generated, which can reflect the load fluctuations of the cable in different time periods. The graphical display can resample the data using time periods (e.g., hourly, daily, weekly), and by setting appropriate time windows, the readability of the fluctuation curve can be further refined. This fluctuation curve will serve as the basis for evaluating abnormal power demand fluctuations in subsequent analysis. Based on the power demand fluctuation curve, demand fluctuation trend prediction is initiated. Local time periods of the demand fluctuation curve are defined, and specific time intervals (e.g., every 10 minutes, every hour, etc.) are selected for local fluctuation analysis. By performing sliding window processing on the fluctuation curve, the fluctuation data within each time period is analyzed to obtain the rate of change in power demand within that time period. These fluctuation rates are smoothed using a weighted average method to obtain a weighted fluctuation trend for each time period. By comparing the fluctuation rates of adjacent time periods, nonlinear changes in power demand can be identified. Nonlinear changes typically manifest as sudden increases or decreases in power demand, which are common when load changes suddenly or system failures occur. By comparing similar fluctuation trends in historical data, trend extrapolation is used to predict future power demand changes and obtain nonlinear fluctuation trends in cable demand fluctuations. The core goal of this step is to capture sudden nonlinear fluctuations that differ from conventional fluctuation patterns.

[0062] When the similarity of the nonlinear change trend of cable demand fluctuation is greater than 0.85, nonlinear fluctuation similarity division is performed to obtain the cable demand fluctuation similarity curve dataset; the slope interval of the demand fluctuation curve is calculated using the cable demand fluctuation similarity curve dataset;

[0063] In this embodiment of the present invention, a dynamic time warping (DTW) algorithm is used for similarity calculation. Dynamic time warping can calculate the minimum distance between two time series of different lengths and is particularly applicable to time lags in fluctuation data. The resulting nonlinear fluctuation trend is compared with fluctuation patterns stored in a historical database to calculate their similarity. A similarity threshold is set at 0.85. When the DTW similarity between the current nonlinear fluctuation trend and the historical fluctuation trend is greater than 0.85, the pattern is considered similar. This method allows similar fluctuation patterns in the power demand fluctuation dataset to be classified into different categories, forming a "cable demand fluctuation similarity curve dataset." Each category contains similar fluctuation trends, and each fluctuation trend in the dataset is labeled with a corresponding category to facilitate subsequent analysis and classification. Based on the cable demand fluctuation similarity curve dataset, the slope of the fluctuation curve for each category is calculated. The slope is obtained by calculating the rate of change of the fluctuation curve within each time period, that is, by calculating the power difference between each two adjacent time points and dividing it by the time difference. Slope interval statistics are performed on each fluctuation curve, recording the maximum slope, minimum slope, and average slope. The slope of each curve is used to assess the intensity of power demand fluctuations. If a fluctuation curve has a slope greater than 1.2 kW / min, it is considered to be a high fluctuation trend, which is often associated with emergencies. All fluctuation curves with a slope greater than 1.2 kW / min are marked as high-slope fluctuations.

[0064] Identify the distribution law of power fluctuation curve based on the power demand fluctuation similarity curve dataset when the slope of the demand fluctuation curve is greater than 1.2kW / min;

[0065] In an embodiment of the present invention, all fluctuation curves marked as "high fluctuation" are centrally analyzed in combination with the slope interval data. Specifically, by statistically analyzing the time period distribution, frequency, power peak and other multi-dimensional information of these fluctuation curves, a distribution map of the fluctuation curve is drawn. Based on these distribution maps, statistical methods (such as kernel density estimation, KDE) are used to model the time-frequency characteristics of these fluctuations. The core of the distribution law is to identify which fluctuation patterns are recurring. These patterns are usually sudden fluctuations caused by system failures, load changes, etc. The distribution law of power fluctuations is extracted through statistical and pattern recognition methods.

[0066] Conduct normal distribution analysis on the distribution law of power fluctuation curve and obtain normal distribution law data;

[0067] In the embodiment of the present invention, a normal distribution analysis is performed on the identified power fluctuation distribution law. The Shapiro-Wilk test and other methods are used to determine whether the data conforms to the normal distribution. If the data conforms to the normal distribution, the mean (μ) and standard deviation (σ) are used as its characteristic parameters. If it does not conform to the normal distribution, the data is transformed (such as logarithmic transformation) to make it close to the normal distribution. The purpose of normal distribution analysis is to obtain a stable distribution model.

[0068] According to the distribution law of normal data and the distribution law of power fluctuation curve, the fluctuation trend linear regression analysis of cable power demand fluctuation curve is carried out to obtain the linear data of demand fluctuation trend;

[0069] In the embodiment of the present invention, the obtained normal distribution data is used as the baseline to perform trend analysis on the power fluctuation curve through linear regression analysis. The key time periods in the fluctuation curve are fitted by the least squares method to calculate the regression slope, intercept and goodness of fit R. 2 If the goodness of fit is greater than 0.95, it means that the fluctuation trend has strong linear characteristics. Through this process, the trend linear data obtained includes information such as regression slope, intercept, and goodness of fit.

[0070] Calculate the abnormal increase in demand fluctuations based on the linear data of demand fluctuation trends;

[0071] In the embodiment of the present invention, the trend linear data of the cable power demand fluctuation curve is obtained by linear regression analysis, including the regression slope, intercept and goodness of fit (R 2Based on these regression analysis results, the system calculates abnormal demand fluctuations. The predicted data from the regression line is obtained. Specifically, the slope and intercept of the regression equation are used to calculate the power demand forecast for each time period. Actual power demand values ​​are obtained and compared with the regression predictions. The deviation between the actual and predicted values—the difference between the actual and fitted values—is calculated to obtain the demand fluctuation deviation data. If the difference between the actual and predicted power demand in a given time period exceeds a set threshold (for example, ±15%), the fluctuation in that period is considered abnormal. By continuously monitoring the difference between the actual power demand and the regression prediction for each time period, all time periods with abnormal demand fluctuations are identified. Abnormal fluctuations are identified when the actual power demand increase exceeds the normal fluctuation range of the linear prediction model. This typically indicates significant abnormal system load fluctuations, caused by equipment failure, sudden load changes, or external disturbances. Each time period with abnormal fluctuations is recorded, and the absolute value of the increase is calculated based on the deviation value for that period, resulting in a specific data set of abnormal demand fluctuations. The dataset includes time periods, actual power demand values, predicted power demand values, and increase deviations, which can clearly reflect the magnitude and time points of abnormal demand fluctuations.

[0072] Determine the sudden demand data for cable power based on the abnormal increase in demand fluctuations and the linear data of demand fluctuation trends.

[0073] In this embodiment of the present invention, sudden fluctuations in cable power demand are identified by combining linear demand fluctuation trend data with demand fluctuation trend linear data. Time periods identified as abnormal increases are screened, and time periods with increases exceeding a threshold are selected and matched with the trends in the regression prediction data. By calculating the relationship between the abnormal increases in demand fluctuations and the linear trend data, it is further determined which abnormal fluctuations constitute sudden demand fluctuations. Sudden demand fluctuations generally refer to fluctuations in power demand that exceed the normal fluctuation range and exhibit a significant increase or sharp decrease within a short period of time. In specific operations, the power demand values ​​for each abnormal increase period are analyzed. The duration of the abnormal fluctuation period and the rate of demand increase are calculated to assess its suddenness. For example, if power demand suddenly increases by more than 20% of the current load within just a few minutes, this period is considered a sudden demand fluctuation. For sudden fluctuations, in addition to calculating the increase, it is necessary to compare the power demand with the power demand of the adjacent time periods before and after the period to determine whether the fluctuation is not the result of a continuous load change but rather caused by an unexpected event. This analysis can accurately distinguish between normal load fluctuations and sudden demand fluctuations, thereby generating "cable power sudden demand data." This dataset contains information such as the specific time period when sudden fluctuations occurred, the magnitude of the sudden fluctuations, and related regression prediction data. Sudden demand data is valuable for power system monitoring and fault prediction, enabling timely identification of abnormal changes in power demand.

[0074] Preferably, determining the cable non-uniform thermal coupling imbalance state in step S2 includes:

[0075] Determine the cable multi-link current redistribution status based on the cable operation status data according to the cable power sudden demand data;

[0076] In an embodiment of the present invention, the cable power sudden demand data is used in combination with the cable operating status data to perform a multi-link current redistribution analysis. The power sudden demand data reflects the current demand of the system when the load suddenly changes. The data includes power demand fluctuations, abnormal increases, and trends in power demand fluctuations. At this time, the power sudden demand data is compared with the original current distribution status of the cable, and the current redistribution algorithm is used to adjust the load changes of each current link in real time. The key operation of redistribution is to calculate the current distribution of each link of the cable under sudden power demand, which involves recalculating the current load of each line of the cable and redistributing the current according to the changes in sudden power demand. Through this operation, equipment overload or excessive temperature rise caused by excessive current load in some links is avoided. The multi-link current redistribution status provides a current distribution diagram of the cable under sudden demand, which becomes the basis for subsequent analysis of cable status changes.

[0077] Determine the cable load reconfiguration misalignment based on the cable multi-link current redistribution status and cable power sudden demand data;

[0078] In an embodiment of the present invention, the current redistribution status of the cable multi-link has been obtained through the current redistribution analysis in the previous stage. Next, based on this status, combined with the power sudden demand data, the reconstruction misalignment of the cable operating load is determined. The core of this step is to analyze whether the distribution of current on different links still meets the expected load requirements after the current redistribution. If the load is misaligned, that is, the load cannot be evenly distributed, and the current is excessively concentrated in certain links, while the link load is lighter, it indicates a load reconstruction misalignment. The method for calculating the load misalignment is to calculate the deviation between the current of each link and its standard load based on the current distribution result. By setting the current threshold, it is identified which links have loads that deviate from the standard load range, and then it is determined whether the load is misaligned. This operation can obtain the load status of each link by calculating the current deviation, and based on this, determine the severity of the load misalignment.

[0079] Calculate the cable running load reconstruction misalignment offset difference based on the cable running load reconstruction misalignment situation;

[0080] In an embodiment of the present invention, based on the load reconstruction misalignment situation, the cable operation load reconstruction misalignment offset difference is calculated. The load reconstruction misalignment offset difference refers to the deviation amplitude of the current of each link during the load distribution process, and is usually calculated by comparing the difference between the actual current of the link and its standard current. For each link, if its current exceeds the set threshold range, its offset difference needs to be calculated. This step obtains the degree of misalignment of the current load during the cable operation by statistically calculating the offset of each link current. Based on the offset difference, it is further determined whether there is excessive current concentration or uneven distribution during the load reconstruction process.

[0081] Determine the excessive current concentration in the local area of ​​the cable based on the cable operating load reconstruction misalignment offset difference;

[0082] In an embodiment of the present invention, based on this offset difference, the excessive concentration of local current in the cable is determined. Excessive concentration usually refers to the current overload of certain links, which can cause local overheating, equipment damage or further aging. In this step, based on the offset difference calculated above, the link whose current deviation value exceeds the preset threshold is identified, and then it is determined whether the link has the phenomenon of excessive current concentration. The method for determining excessive current concentration is: by comparing the offset difference with the standard load current of the link in the system. If the offset difference is large and exceeds the threshold range, it indicates that the current of the link is excessively concentrated. At this time, these links are marked as being in a state of excessive current concentration, and the specific current values ​​of these links and the degree of excessive concentration are recorded.

[0083] Utilize the excessive concentration of local current in the cable to detect the current surge conduction condition of the cable;

[0084] In an embodiment of the present invention, the excessive concentration of local current is determined, and then the information is used to detect the current shock conduction of the cable. Current shock conduction refers to whether the shock generated by abnormal current fluctuations can be transmitted to part of the line, which will cause a larger range of load changes and temperature rises. In this step, by analyzing the current fluctuation data of the cable, it is determined whether the current fluctuation has the characteristics of shock transmission. The specific operation is to observe whether the link with excessive current concentration causes current fluctuations in the link. If the excessively concentrated current begins to be transmitted to the surrounding links and causes current fluctuations in the link, it is determined to be a current shock conduction phenomenon. This detection monitors the propagation of current fluctuations in real time, analyzes the direction and amplitude of current changes, and determines whether the current is propagating in an impact manner.

[0085] Determine the degree of Joule heat surge during cable operation based on the excessive concentration of local current in the cable and the current impulse conduction in the cable;

[0086] In the embodiment of the present invention, the current surge conduction situation has been detected. Next, based on the excessive current concentration in the local cable and the current surge conduction situation, the degree of Joule heat surge during cable operation is measured. Joule heat is generated when the current flows through the conductor, and the resistance causes the electrical energy to be converted into thermal energy. Excessive current concentration and current surge conduction will cause local temperature rise, which in turn causes a surge in Joule heat. At this time, the degree of Joule heat surge is determined by calculating the current density, the resistance of the conductor, and the amplitude of the current fluctuation. Specifically, the degree of Joule heat surge is calculated using the formula:

[0087] Q=I 2 RT;

[0088] Where I is current, R is resistance, t is time, and Q is Joule heat. Based on the current fluctuation data obtained in the previous steps, the degree of Joule heat surge is calculated in real time to determine whether Joule heat has reached an excessive limit, thereby providing an early warning of cable temperature rise and potential damage.

[0089] Determine the cable's steady-state thermal capacity exceeding the limit based on the excessive concentration of local current in the cable and the degree of Joule heat increase during cable operation;

[0090] In an embodiment of the present invention, the degree of Joule heat surge is obtained, and then based on this data and the local current over-concentration condition, it is determined whether the steady-state thermal capacity of the cable is out of limit. Steady-state thermal capacity over-limit means that under the influence of excessive current concentration and Joule heat surge, the thermal capacity of the cable exceeds its designed safety threshold. This process usually determines whether it will cause overheating and aging by analyzing the thermal capacity and duration of the cable. During the specific operation, the heat accumulation of the cable is calculated by combining the current fluctuation and heat accumulation model, and compared with its designed heat capacity standard. If the heat accumulation exceeds the predetermined threshold, it indicates that the cable has entered a steady-state thermal capacity over-limit state, leading to problems such as accelerated aging and insulation damage.

[0091] The imbalance state of cable non-uniform thermal coupling is determined based on the degree of Joule heat surge during cable operation and the exceeding of steady-state heat capacity of the cable.

[0092] In an embodiment of the present invention, a comprehensive analysis of the cable's current distribution, heat accumulation, and current surge conduction is performed, combined with the cable's Joule heating surge and steady-state thermal capacity exceeding limits, to determine whether the cable is experiencing a non-uniform thermal coupling imbalance. This step aims to identify cable temperature imbalances caused by factors such as excessive current concentration and uneven local heat accumulation, thereby proactively identifying risks that could lead to cable aging or damage. Current distribution data is collected from the cable's real-time monitoring system. This data includes current density in different areas, particularly those affected by high loads or significant current fluctuations. Current distribution data is collected using sensors or current measurement modules, which can accurately capture the current intensity of each link. This current data reflects the current distribution characteristics of each section of the cable, including whether there are areas with localized excessively high current density. Combined with the calculation results from the above steps, a comprehensive analysis of temperature accumulation, Joule heating surge, and steady-state thermal capacity exceeding limits is performed for each area to determine whether the cable is experiencing a non-uniform thermal coupling imbalance. If the current load is concentrated in certain areas, causing the temperature accumulation in those areas to far exceed the designed thermal capacity, and if the heat in those areas is not promptly transferred to other areas, thermal coupling imbalance is determined in those areas. When uneven thermal coupling imbalance is detected in a cable, the monitoring system immediately generates early warning signals. These signals include the location of abnormally high temperature areas, areas with excessive current concentration, and damaged areas. The system combines this data with cable aging models to predict the potential degradation risk of these thermally imbalanced areas.

[0093] Preferably, the detection of coupling failure condition of the cable multilayer structure in step S2 includes:

[0094] Evaluate the complexity of the cable's external operating environment based on cable operating status data;

[0095] In an embodiment of the present invention, the environmental sensing data in the cable operation status monitoring system is used to evaluate the complexity of the external operating environment of the cable in actual work. Environmental data mainly include influencing factors such as temperature and humidity, airflow, chemical corrosion factors, electromagnetic interference, and external mechanical pressure. The real-time changes of the environment around the cable are monitored by multifunctional sensors installed on the outside of the cable (such as temperature and humidity sensors, acceleration sensors, gas sensors, etc.). The temperature and humidity changes outside the cable are obtained through temperature and humidity sensors. These factors will directly affect the material properties of the outer protective layer of the cable; the presence of corrosive gases (such as hydrogen sulfide or chloride, etc.) is monitored in real time through gas sensors, so as to evaluate whether the external environment poses a potential corrosion threat to the protective layer of the cable. The real-time data of these external environmental factors will help to judge the complexity of the external environment of the cable and provide basic data for subsequent degradation detection.

[0096] According to the complexity of the cable's external operating environment, the cable operating status data is used to detect the cable's external non-uniformity degradation;

[0097] In an embodiment of the present invention, the cable operating status data is further analyzed based on the assessed complexity of the external environment, with particular attention paid to the non-uniform degradation of the cable protective layer. By combining various external environmental factors such as temperature and humidity changes, corrosive gas concentrations, mechanical loads, and electromagnetic interference, it is determined whether the cable has experienced accelerated degradation in local areas under the influence of these environmental factors. For example, in an environment with drastic external temperature changes, certain areas of the cable may experience accelerated aging of the protective layer material due to excessive temperature differences, which manifests as the appearance of local cracks or hardening of the protective layer. By analyzing the cumulative effects of these environmental factors, it is determined whether the cable's external protective layer has experienced non-uniform degradation, providing a basis for subsequent analysis of plasticizer volatilization and microcrack growth.

[0098] Determine the volatilization status of the plasticizer in the cable protective layer based on the non-uniform degradation of the cable exterior and the non-uniform thermal coupling imbalance of the cable;

[0099] In an embodiment of the present invention, when the outer protective layer of the cable begins to deteriorate non-uniformly, especially in an environment such as high temperature or chemical gas corrosion, the plasticizer in the protective layer will volatilize. This volatilization usually causes the hardness of the protective layer to increase, the material to become brittle, and affects the toughness and compressive resistance of the protective layer. To this end, the Joule heat surge data is obtained through the thermal monitoring system of the cable, and the volatilization of the plasticizer in the cable protective layer is inferred by combining the data of the temperature, humidity, and gas sensors. In specific implementation, the temperature change data of different areas can be obtained by the sensor, and the influence of temperature and humidity on the volatilization of the plasticizer in the protective layer can be analyzed in combination with the complexity of the external environment. Furthermore, the chemical composition changes of the cable protective layer material are analyzed by thermal analysis technology (such as thermogravimetric analyzer, differential scanning calorimeter, etc.) to determine whether there is plasticizer volatilization.

[0100] Determine the degree of thickness reduction of local cable structure according to the volatilization of plasticizer in cable protective layer;

[0101] In an embodiment of the present invention, the volatilization of the plasticizer in the protective layer will cause the structure of the material to change, which is usually manifested as hardening and brittleness of the protective layer, and even the formation of microcracks after long-term operation. The decrease in the thickness of the protective layer is detected by using an ultrasonic thickness measuring instrument to measure at different positions to obtain data on the thickness of the protective layer. By comparing the thickness values ​​of different areas, the degree of decrease in the local structure of the protective layer can be determined. An initial standard thickness value is set, and real-time data is obtained through ultrasonic testing to evaluate whether there is local thickness thinning. Combined with the current load distribution data and temperature change data, it is further determined which areas of the protective layer are affected by the volatilization of the plasticizer, resulting in a change in thickness.

[0102] The growth of micro cracks in the cable protective layer is estimated by using the thickness reduction degree of the local cable structure and the volatilization status of the plasticizer in the cable protective layer;

[0103] In an embodiment of the present invention, based on the detection results of the decreasing thickness of the local structure of the cable and combined with the volatilization of the plasticizer, the growth of microcracks in the protective layer is estimated. After the protective layer hardens and loses its elasticity, the stress concentration in the local area can easily cause the expansion of microcracks. To quantify this process, a microscopic scanning technology (such as a scanning electron microscope (SEM) or X-ray imaging technology) is used to conduct a detailed analysis of the damaged area. Through a scanning electron microscope, it is observed whether microcracks appear on the surface of the protective layer, and combined with the data of the decreasing thickness of the structure, the growth of the microcracks is further inferred. For example, if the thickness of the protective layer of a certain part is significantly reduced, and the number of cracks in the area increases, it is inferred that the microcracks in the area will gradually destroy the overall structure of the protective layer during the expansion process.

[0104] The coupling failure of the cable multi-layer structure is detected based on the growth of microcracks in the cable protective layer and the thickness decrease of the local cable structure.

[0105] In an embodiment of the present invention, the coupling failure state of the multi-layer structure of the cable is comprehensively analyzed in combination with all the detected parameters (such as plasticizer volatilization, local thickness decrease, microcrack growth, etc.). The coupling performance between the cable protective layer, insulation layer and conductive layer is the key to the stable operation of the cable. If the microcracks in the protective layer gradually expand, resulting in coupling failure between the protective layer and the insulation layer, it will cause an electrical short circuit or serious fault. By using a multi-layer structure comprehensive analysis model, the thickness, crack distribution and material properties of each layer are combined with the electrical performance data to obtain the overall failure state of the cable. In this process, technical means such as finite element analysis and material mechanics simulation are used to further refine the structural performance changes of each layer to determine whether the cable has the risk of electrical failure caused by multi-layer structure failure.

[0106] Preferably, step S3 includes the following steps:

[0107] Step S31: detecting the damage status of the cable structure integrity according to the coupling failure status of the cable multi-layer structure;

[0108] In an embodiment of the present invention, by detecting the coupling failure condition of the cable's multi-layer structure, it is determined whether the structural integrity of the cable has been damaged. This process combines the data on the thickness, cracks, growth trends, etc. of the cable's protective layer, insulation layer, etc. detected in the previous steps to comprehensively evaluate the stability of its structure. During specific operations, sensors (such as temperature sensors, strain sensors, and acceleration sensors) are used to obtain dynamic data of the cable under different operating conditions, especially the impact of external loads, mechanical vibrations, and other factors on the cable. These data are combined with previously collected information such as the thickness of the cable's protective layer, plasticizer volatilization, and microcracks to analyze the coupling relationship between the layers and the resulting damage mode. For example, the strain data measured by the strain sensor can be used to determine whether the cable has undergone abnormal deformation due to external environmental pressure or uneven internal distribution. Based on these data, it can be determined whether the cable's multi-layer structure has been damaged to form an uneven stress area.

[0109] Step S32: evaluating the cable electrical insulation structure strength degradation trend based on the cable structural integrity damage status;

[0110] In an embodiment of the present invention, when damage to the cable structure is detected, the next step is to assess whether the strength of its electrical insulation structure has deteriorated. The insulation structure of a cable typically includes an insulating layer, a protective layer, and other layers, from the inside out. Structural damage directly affects the degradation of insulation performance. Therefore, electrical insulation testing equipment (such as a high-voltage insulation tester or an insulation resistance tester) is used to measure the electrical insulation impedance of the cable to monitor its insulation performance in real time. In practice, the integrity of the insulation structure is assessed by applying a specified voltage at various test points and measuring the current flowing through the insulation layer. If the cable's insulation layer is damaged by external environmental factors (such as excessive heat damage, chemical corrosion, etc.) or internal delamination cracks, its electrical insulation strength will gradually deteriorate. By repeatedly measuring and recording the electrical insulation impedance data from each test, the downward trend of the electrical insulation structure strength can be determined. For example, during a high-voltage insulation test, if the cable's electrical insulation impedance value gradually decreases, it indicates that the cable's insulation layer has begun to age or deteriorate, resulting in a degradation of electrical insulation performance. At this point, by comparing historical data, the downward trend can be assessed, and future changes in electrical insulation performance can be predicted using trend prediction models (such as regression analysis).

[0111] Step S33: Detecting an electrical safety boundary out-of-control condition based on a decreasing trend in cable electrical insulation strength and damage to cable structural integrity;

[0112] In this embodiment of the present invention, based on the declining trend of the electrical insulation structure and the damage to the cable's structural integrity, the next step is to detect whether the electrical safety margin is out of control. The electrical safety margin generally refers to the maximum voltage and current range allowed by the electrical system under normal operating conditions. Once this range is exceeded, the cable will fail due to overload or overheating. In this step, the risk of losing control of the electrical safety margin is assessed by dynamically monitoring the electrical load status, combining the data from the previous two steps (such as the decline in electrical insulation strength and the extent of cable structural damage). An online monitoring system obtains real-time voltage and current data and compares it with the known electrical safety margin. When the voltage or current exceeds the safe range, the system automatically marks it as out of control. For example, current sensors and voltage sensors are used to monitor the operating voltage and current of the cable in real time. If the current or voltage in certain areas exceeds the set safety range, accompanied by a decline in the strength of the electrical insulation structure and damage to the cable's multi-layer structure, the area is determined to be at risk of losing control of the electrical safety margin. At this time, by setting up an alarm mechanism, the system can promptly issue an alarm, prompting maintenance personnel to conduct further inspections or take protective measures.

[0113] Step S34: determining the cable load stability collapse condition based on the electrical safety margin out-of-control condition and the cable electrical insulation structure strength degradation trend.

[0114] In an embodiment of the present invention, a cable's load stability is determined to have collapsed based on the loss of electrical safety margins and the trend of declining electrical insulation strength. A cable's load stability is directly related to its ability to withstand prolonged current transmission without failure. If the electrical safety margin is lost and the electrical insulation strength continues to decline, it indicates that the cable's load capacity has significantly degraded. To monitor the cable's load stability, long-term monitoring of the cable's load conditions is performed, collecting data such as voltage and current. Combined with the trend of declining insulation strength, a load prediction model (such as a time series prediction method) is used to analyze changes in load capacity. When load stability collapses, the cable's electrical performance will decline sharply, and even faults such as short circuits or arcing may occur. For example, if the cable's insulation strength approaches zero when the voltage and current are at critical values, it is inferred that the cable's load stability has collapsed, and the risk of failure is extremely high. At this point, the real-time monitoring system issues an alarm based on a preset threshold and initiates automatic protection mechanisms, such as power outages or load limiting, to prevent accidents.

[0115] Preferably, step S31 includes the following steps:

[0116] Step S311: detecting shear dislocation of the cable structure according to the coupling failure condition of the cable multi-layer structure;

[0117] In an embodiment of the present invention, a high-resolution X-ray tomography (Micro-CT) imaging platform was constructed to address shear misalignment in multilayer cable structures. The platform was equipped with scanning parameters set to a 5-micron scanning accuracy, and structural slice images were acquired layer by layer along the cable cross-section. A 3D image reconstruction algorithm was used to calibrate the relative geometric center positions and boundaries between the conductor layer, insulation layer, shielding layer, and sheath layer, extracting the central axis trajectory of each layer in the scanned volume coordinate system. A vector comparison method based on polyline fitting was used to calculate the relative offset vectors between the central axes of each layer, and the presence of interlayer shear misalignment was determined based on the angular variation. A threshold was set such that shear misalignment was determined when the length of the offset vector between a layer and its upper or lower layer exceeded the original design tolerance of 0.3 mm and the corresponding angular variation exceeded 5 degrees. The output data was formatted as a CSV file, containing the offset direction, offset amount (mm), angular variation (degrees), and position coordinates (x, y, z) for subsequent adhesion assessment.

[0118] Step S312: detecting the degradation of the cable layer interface adhesion ability according to the coupling failure condition of the cable multi-layer structure;

[0119] In an embodiment of the present invention, after the shear dislocation detection of the cable structure is completed, the adhesion strength of the cable layer interface is tested using ultrasonic phased array (Phased Array Ultrasonic Testing, PAUT) technology. During the implementation operation, a 64-channel phased array probe with a frequency of 10MHz is selected to perform reflection energy detection along the longitudinal axis of the cable in a layer-by-layer scanning manner. After obtaining the original reflection waveform, time-amplitude analysis (A-Scan) is used to extract the intensity peak and delay time of the reflected echo of each layer interface, and calculate the echo energy ratio (dB value) caused by the interface acoustic impedance discontinuity. The initial adhesion strength reference value is set according to the cable manufacturing standard. When the reflected echo peak of a certain interface is detected to exceed the reference value by more than 15dB, and the echo delay time is shortened by more than 5μs, it is determined that the adhesion ability of the interface has deteriorated and an analysis report containing the detection layer number, echo energy, delay time, and acoustic impedance mutation ratio is formed as a quantitative basis for the adhesion ability degradation state.

[0120] Step S313: determining the cable structure delamination condition based on the cable delamination interface adhesion ability degradation condition and the cable delamination interface adhesion ability degradation condition;

[0121] In an embodiment of the present invention, after obtaining the shear dislocation data and adhesion degradation parameters, a comprehensive identification of the delamination and peeling condition of the cable structure is carried out. Specifically, a multi-parameter coupling judgment strategy is adopted, and the X-ray image data and the ultrasonic scanning results are spatially aligned through a spatial mapping algorithm to determine the failure area located in the same coordinate system. The judgment logic is set as follows: if a certain area has a structural offset exceeding 0.3mm and an echo energy ratio higher than 15dB, it is determined that delamination and peeling have occurred at that location. After combining the coordinate mapping, the spatial volume of the peeling area is reconstructed, and the total peeling area (mm 2 ) and peeling volume (mm 3 In addition, to improve the accuracy of peeling identification, laser profile scanning is used to check the degree of contour sinking of the outer sheath after peeling. If the sinking depth exceeds 0.2mm, it will be included in the peeling area statistics and a peeling status report will be output, including the location, volume, area of ​​the peeling area and its corresponding relationship with the structural layer.

[0122] Step S314: Identifying the degree of exposure of the cable metal layer based on the delamination status of the cable structure;

[0123] In the embodiment of the present invention, for the identified delamination area, infrared thermal imaging combined with Raman spectroscopy scanning method is used to identify the exposure degree of the cable metal layer. During operation, a high-precision infrared thermal imager (thermal sensitivity ≤ 0.03 ° C) is used to apply a stable constant current load of 5A to the delamination area to capture the temperature anomaly caused by metal exposure. Subsequently, a scanning Raman spectrometer (light source wavelength 532nm, resolution <1cm-1 ) Perform multi-point scanning on the surface of the high-heat zone, extract the ratio of the metal characteristic peak intensity to the non-metallic coating characteristic peak, and quantify the metal exposed area. -1 ) intensity ratio is greater than 0.8, and the temperature rise of the location in the thermal imaging is greater than 2.5 ° C, then the metal layer at that location is considered to be exposed. Output all exposed area data with three-dimensional coordinates, and calculate the area ratio of the entire peeling area and the total metal exposure area (unit: mm 2 ).

[0124] Step S315: determining the severity of corrosion of the cable metal layer according to the exposure degree of the cable metal layer;

[0125] In the embodiment of the present invention, after obtaining the exposure degree data of the metal layer, the corrosion aggravation situation is detected and the corrosion degree of the exposed area surface is evaluated using an electrochemical impedance spectroscopy (EIS) measuring device. 4 A sinusoidal AC signal with a range of 10-2 Hz to 10-2 Hz is measured, and its impedance modulus and phase angle changes are recorded. Equivalent circuit parameters are calculated using Nyquist and Bode plots, and the interfacial charge transfer resistance (Rct) and double layer capacitance (Cdl) are extracted. The corrosion rate (mm / year) is then derived based on the corrosion rate formula. The criteria are: if Rct is less than 100 Ω and Cdl is greater than 50 μF / cm 2 , indicating that the corrosion rate is in an aggravated state, output the corrosion rate curve and the electrochemical characteristic data of each exposed area, and summarize them into a "Metal Layer Corrosion Aggravation Detection Report" as input for subsequent integrity analysis.

[0126] Step S316: Detecting the damage status of the cable structure integrity based on the aggravated corrosion of the cable metal layer and the delamination of the cable structure.

[0127] In an embodiment of the present invention, based on the aforementioned delamination and metal layer corrosion conditions, cable structural integrity damage is detected. A finite area structural mechanics solution platform (such as ANSYS Workbench) is used to construct a three-dimensional multilayer material model that integrates the structural layer volume, metal exposure area, and corrosion rate. Equivalent load conditions are applied to the model, including an ambient temperature of 60°C, a humidity of 90%, and an alternating current load of 7A. Based on a material parameter database, the mechanical properties of each layer (such as elastic modulus, Poisson's ratio, and yield strength) are imported to perform static and thermal-electric coupling analysis. The maximum principal stress and equivalent strain in the stress concentration area of ​​the structure are extracted, and combined with material failure criteria (such as the Von Mises criterion), a determination is made as to whether structural instability or crack propagation trends are occurring. The judgment criteria are: if the principal stress in the delamination area exceeds 80% of the sheath material's yield strength, or if stress concentration in the corrosion area simulates a crack extending more than 1 mm, the structural integrity is considered damaged. Output results include a structural damage level assessment table, a failure location coordinate diagram, and various mechanical parameter values.

[0128] It is particularly important that step S32 includes the following steps:

[0129] Step S321: detecting abnormalities in the geometric symmetry of the insulation layer according to the damage status of the cable structure integrity;

[0130] In an embodiment of the present invention, the cable's structural integrity is determined by performing multi-channel infrared thermal imaging and X-ray non-destructive testing on multi-point scanned images of the cable's cross-section. This testing utilizes an industrial-grade cable XCT three-dimensional tomography device (model: VJ-CTX320), which boasts a 30μm resolution and captures at least 2,000 frames of structural data per meter of cable within a continuous span of 50mm. The captured structural images are then processed by an image processing unit. This process utilizes an algorithm based on structural boundary stretching and grayscale mean gradient analysis to extract a data sequence of the insulation layer's outer diameter and the core wire center distance. This data is then used to calculate the insulation layer thickness distribution at every 1° along the circumference. Geometric symmetry anomalies are determined based on the following criteria: a deviation between the theoretical geometric center of the insulation layer and the actual insulation sheath boundary exceeding ±3% is used as a threshold, or a maximum circumferential thickness difference exceeding the design tolerance (e.g., ±0.1mm) is used as an anomaly indicator. The geometric symmetry anomaly coordinate data determined in this manner will be used to guide subsequent identification of sheath uniformity violations.

[0131] Step S322: identifying the destruction of the insulation layer coating uniformity based on the abnormality of the geometric symmetry of the insulation layer;

[0132] In an embodiment of the present invention, the geometric offset distribution data of the insulating layer obtained in step S321 is used as the initial input, and the coating trajectory, extrusion rate and temperature control parameters recorded in the cable manufacturing process are combined to perform dynamic process consistency backtracking analysis. The process backtracking algorithm adopted is the time series alignment analysis method, which calculates the relative error rate by comparing it with the standard coating template second by second. The coating parameter log integrated in the industrial automation cable manufacturing monitoring system (model: SIEMENS SIMATIC S7 series) is used to extract key control quantities such as extrusion pressure, cooling average temperature, and die centering offset value in the same batch production process. If the centering deviation of the extrusion die corresponding to the geometric symmetry abnormal area exceeds ±0.5mm, or the cooling time of the coating layer is less than 30% of the standard value, it is determined that the coating uneven area is formed by superimposing the thermal distribution map and the thickness variation map to form a coating uniformity destruction location map, and the abnormal section position, range and severity level are marked for subsequent electric field concentration trend analysis.

[0133] Step S323: predicting the concentration trend of the cable electric field lines according to the uniformity damage status of the insulation layer;

[0134] In an embodiment of the present invention, a static electric field distribution simulation platform (equipment: Hioki 3930 insulation electric field distribution detector) is used to input physical parameters and perform simulation calculations on the coating uniformity destruction area determined in step S322. The potential boundary modeling is performed based on the thickness of the insulating material in each destruction area (in steps of 25 μm), the difference in dielectric constant (measured by a dielectric response spectrometer Keysight E4990A), and the voltage application conditions (for example, 10 kV industrial frequency AC). The calculation area is divided into finite difference grids, and the electric field strength vector is calculated at each point. If the local point electric field strength exceeds the safety limit under the original design (such as 30 kV / mm), and there is a continuous concentrated electric field high-density area within the spatial range (such as more than 100 mm 2 If the electric field exceeds 90% of the safety upper limit, the area is considered to have a significant concentration trend of electric field lines. The spatial coordinates, intensity gradient changes, and distribution density values ​​of this trend are output in a data table format, providing basic data for local breakdown risk prediction.

[0135] Step S325: predicting the local breakdown of the insulation layer based on the concentration trend of the cable electric field lines;

[0136] In an embodiment of the present invention, this step introduces a local withstand voltage measurement experiment in the aforementioned electric field concentration area, and combines the electrothermal coupling effect simulation to perform breakdown prediction. The experimental platform used is the Hubbell high-voltage withstand voltage test station. The test voltage is increased from 0kV to the design peak value (such as 15kV), stepping 250V per second, and recording the current mutation point. If a leakage current surge occurs in this area (such as a sudden change from 0.1μA to more than 2mA), it is determined to be the starting point of breakdown, and an infrared thermal imager (FlukeTiX580) is used to confirm the instantaneous thermal explosion point of breakdown. For areas where no breakdown occurs directly, the critical withstand voltage difference analysis method is used to calculate the critical distance of breakdown and the remaining breakdown margin (unit: kV / mm) in combination with the previous electric field strength data and dielectric constant fluctuations. The breakdown point distribution map, breakdown margin table, and current surge coordinates of each concentrated area are integrated into a local breakdown risk map to provide parameter input for the next strength assessment.

[0137] Step S326: Evaluate the decreasing trend of the cable electrical insulation structure strength based on the local breakdown of the insulation layer and the uniformity damage of the insulation layer coating.

[0138] In an embodiment of the present invention, the local breakdown point position, breakdown voltage, and thermal response value obtained in step S325 are analyzed one-to-one with the coating thickness anomaly rate and material degradation rate extracted in step S322. The effective insulation bearing capacity per unit length (with 1m as the segment unit) of each cable segment is calculated, and the original design insulation strength is adjusted using a statistical strength reduction factor (such as breakdown point density × average margin loss rate). For example, if the original design is 15kV / mm, if the detection finds that there are 3 breakdown points in the segment and the average margin loss is 2.5kV / mm, the strength reduction factor is 3×2.5 / 15=0.5, and the actual bearing capacity decreases by 50%. The above calculation results and the material aging index (thermal weight loss rate measured by thermogravimetric analyzer TGA) are used together as insulation structure strength decline trend evaluation parameters to draw a strength evolution curve over time, and output the insulation bearing safety margin value and predicted failure cycle of the current segment. The strength trend data is passed as input to the subsequent step S34 for electrical instability analysis.

[0139] It is particularly important that step S34 includes the following steps:

[0140] Step S341: determining the charge imbalance condition at the leading edge of the cable based on the decreasing trend of the cable electrical insulation strength and the damage condition of the cable structure integrity;

[0141] In an embodiment of the present invention, data on the downward trend of the strength of the cable electrical insulation structure is collected. The data is derived from the record of the voltage breakdown threshold change based on the local breakdown position of the insulation layer in step S326, and the electric field nonlinear distribution map caused by the change in coating thickness. The parameters obtained by detecting the structural integrity destruction characteristics in steps S321 to S323 are called, especially the insulation layer thickness offset coordinate mapping map and the material cracking position map. During implementation, an electrostatic potential imager (such as a scanning electrostatic potential microscope based on Kelvin probe technology) is used to perform segmented potential scanning on the front area of ​​the cable, and the spatial charge accumulation intensity of each segment is recorded. By comparing the surface charge distribution deviation between the normal area and the structural degradation area, the charge imbalance factor Δσ_front is extracted, and the unit is C / m 2 , which reflects the degree of charge density deviation at the leading edge of the cable. The leading edge charge imbalance is output as a spatial deviation vector matrix.

[0142] Step S342: Analyze the cable load current imbalance based on the cable front charge imbalance;

[0143] In an embodiment of the present invention, based on the charge imbalance factor Δσ_front extracted in the previous step and its spatial distribution diagram, the embodiment uses an impedance spectrum analyzer (LCR tester, frequency range 0.1Hz-10MHz) in conjunction with a three-phase balanced current sensor array to measure the current response of different phase conductors of the cable under stable current carrying conditions. During the measurement, under a fixed voltage output state, a constant current is injected into the three-phase conductors respectively, and the deviation amplitude of the local current waveform caused by Δσ_front is recorded. The measurement area covers the front structure, the middle section and the end of the cable. The current unbalance factor ΔI_unbalance is quantified as follows: ΔI_unbalance = max(|I_A-I_B|,|I_B-I_C|,|I_C-I_A|), in A. The higher the value, the more the cable deviates from the balanced current carrying state. The ΔI_unbalance value output by this step will be used to assess whether there is a non-uniform conduction section and provide a basis for short circuit prediction.

[0144] Step S343: estimating a local short circuit in the cable transmission according to the cable load current imbalance;

[0145] In an embodiment of the present invention, according to the ΔI_unbalance parameter obtained in step S342 and its changing trend along the cable direction, a thermal imaging device (mid-infrared thermal imager, resolution ≤ 0.05°C) is further used to perform dynamic temperature rise tracking detection on the cable. Since uneven current carrying often causes local resistance heat accumulation, the presence of a local short circuit point between conductors is identified by combining the overlap between the current imbalance position and the temperature rise abnormality area. The local cross impedance method is then used to perform auxiliary verification on the area, that is, a slight disturbance voltage is applied to the adjacent conductors in the suspected short circuit area, and the difference between the response voltage and the reflected current is measured. If the cross impedance coefficient Z_cross is less than the set safety threshold (such as 0.5Ω), the point is determined to be a candidate area for a local short circuit, and the local short circuit risk level (Low / Medium / High) and the corresponding Z_cross value are output, and a spatial position mapping is established for use in the next step.

[0146] Step S344: Analyze the oscillation of the entire cable link based on the local short circuit of the cable transmission and the cable load current imbalance;

[0147] In an embodiment of the present invention, a multi-node high-frequency voltage / current oscillation probe (such as a combination of a Rogowski coil and a voltage divider) is deployed for the identified short-circuit risk points and high ΔI_unbalance sections, and the transmission signal waveform in the frequency domain above 10 MHz is synchronously collected at different positions of the link (input end, middle section, and output end). The power spectrum density of the signal in each region is extracted by Fourier transform, and its spectrum deviation rate ΔPSD from the reference cable is evaluated to analyze the oscillation characteristics such as signal reflection and harmonic surge caused by short circuit or current fluctuation. Special attention is paid to the rapid growth area of ​​the reflection factor R (R = V_reflected / V_incident) in the high frequency band. If the R value is continuously higher than 0.3 and the oscillation frequency band bandwidth exceeds 10 kHz, it is marked as a significant oscillation section, and its specific position in the link is located. The output oscillation section number, bandwidth, and reflection factor R value provide a signal interference basis for the next load stability analysis.

[0148] Step S345: Determine the cable load stability collapse condition based on the cable load current imbalance condition.

[0149] In an embodiment of the present invention, based on the load current unbalance factor ΔI_unbalance obtained in step S342, and superimposed with the high-frequency oscillation position and R value information obtained in step S344, a load resistance dynamic response test is performed on each conductor phase line. A fast-switching resistor array (programmable electronic load device, dynamic response <1ms) is used to simulate dynamic load behavior under different load conditions on the load side, and the voltage sag / current jump monitoring module is used to record the response curve of each phase conductor in real time. During the test, the break point in the current-time curve, the voltage response delay value Δt_delay, and the nonlinearity of the current system load-response curve are analyzed. If it is detected that Δt_delay exceeds the set upper limit (such as 2ms) and is accompanied by a voltage drop of more than 20%, it is determined that the conductor path has a tendency to collapse. The collapse determination result of each phase line, the critical collapse response time, and the oscillation-affected section number are output in the form of a triplet, providing a basis for subsequent repair or isolation.

[0150] Preferably, step S4 includes the following steps:

[0151] Step S41: detecting a degradation in cable transmission power quality based on a breakdown in cable load stability;

[0152] In an embodiment of the present invention, real-time load fluctuation data during cable transmission is received. This data is acquired by a monitoring unit consisting of an access-type current transformer and a voltage sensor. The sampling period is set to 10ms, and the sampled data includes continuous time series data of current, voltage, and power factor. Next, statistical processing is performed on the data within a certain interval by setting a stability threshold (e.g., a fluctuation amplitude exceeding ±15% of the rated load current is considered a breakdown threshold). Specifically, a fixed time window sliding difference method is used to calculate the maximum and minimum current values ​​within the period. This statistical value is then compared with a preset stability standard. If the number of fluctuations exceeds a set value within a specified time period, a cable load stability breakdown event is determined. Combined with the voltage fluctuation curve corresponding to the time period of the event, a power quality analyzer performs Fourier transform processing to detect the distortion content of the power transmission, such as whether the total harmonic distortion (THD) exceeds a set threshold (e.g., 5%), thereby confirming the occurrence of a cable transmission power quality degradation.

[0153] Step S42: determining the growth degree of abnormal fluctuation of the cable operating voltage based on the degradation of the cable transmission power quality;

[0154] In an embodiment of the present invention, based on the confirmation of the decline in power quality, the voltage data is sampled at a high frequency (the sampling rate is not less than 10kHz) and the voltage change curve data is continuously recorded for at least 48 hours. The frequency domain analysis method is used to convert the voltage signal from the time domain to the frequency domain to identify non-steady-state behaviors such as instantaneous overshoot, undervoltage and voltage drop. An abnormal fluctuation threshold is set. For example, if the voltage exceeds ±10% of the rated value, the number of abnormal fluctuations per unit time is counted and its growth rate is calculated. Specifically, the interval linear regression analysis method is used to fit the fluctuation number sequence to obtain the fluctuation growth slope. The slope value is used as a quantitative indicator of the degree of abnormal fluctuation growth of the cable operating voltage to form a continuous operating voltage stability evaluation curve.

[0155] Step S43: identifying the cable transmission transient pulse situation based on the abnormal fluctuation growth degree of the cable operating voltage;

[0156] In an embodiment of the present invention, the voltage abnormal fluctuation growth data obtained in step S42 is used to further call a high-speed oscilloscope recording device to accurately capture voltage transients during the abnormal voltage period. The specific process includes setting a front and rear trigger window (5ms each) before and after the fluctuation peak, and recording the voltage changes within this time period point by point. The voltage curve is decomposed by wavelet transform analysis technology to extract the rapidly changing components therein, and its characteristic values ​​such as amplitude, duration, and frequency mutation are identified. If the defined transient pulse standard is met (such as the transient peak amplitude is more than 20% higher than the rated voltage and the duration is less than 5ms), the event is recorded as a transient pulse. Based on the time density, amplitude frequency distribution and other data of multiple pulse events, a cable transmission transient pulse condition parameter table is generated as input data for subsequent polarization imbalance detection.

[0157] Step S44: detecting the local polarization imbalance of the cable based on the abnormal fluctuation growth degree of the cable operating voltage and the transient pulse condition of the cable transmission;

[0158] In an embodiment of the present invention, based on the obtained voltage abnormal fluctuation growth data and transient pulse event data, the array electric field sensor deployed in combination with the cable local electric field monitoring module is used to sample the cable surface potential changes in real time. The sampling interval is set to 100μs, and the data is reconstructed after removing high-frequency noise through a low-pass filter to reconstruct the spatial potential distribution. The potential gradient difference method is used to extract the trend of polarization degree changes in the local area of ​​the cable. The time period of the transient pulse event is used as the reference window, and the time series map of the potential distribution within the period is compared. If a specific area is found to have a continuous potential offset after multiple pulses, and the offset exceeds the baseline polarization balance difference by more than 10%, it is recorded as a local polarization imbalance area, and the area is marked and located to generate a local polarization imbalance status report data set.

[0159] Step S45: detecting fatigue trend of cable transmission metal material according to the local polarization imbalance condition of the cable and the transient pulse condition of the cable transmission;

[0160] In an embodiment of the present invention, the local polarization imbalance area positioning result obtained in step S44 is superimposed with the transient pulse event distribution identified in step S43, and the potential change and conductor response of the same area under high-frequency stress are jointly analyzed through a time synchronization mechanism. Combined with the electromagnetic vibration sensor data (sampling frequency 50kHz) arranged on the surface of the cable metal conductor, the amplitude-frequency characteristic changes of the metal lattice response under the action of pulse stress are analyzed. The metal fatigue critical frequency band feature matching method is adopted to compare the monitoring data with the known metal fatigue response spectrum. If a resonance feature consistent with the fatigue critical state appears in the detection frequency band (such as a Q value change of more than 20% and an amplitude drop of more than 15%), the area is marked as a metal fatigue trend enhancement area. The fatigue trend distribution of each area is aggregated and calculated to form a complete fatigue trend distribution map of the cable metal material.

[0161] Step S46: performing cable aging diagnosis based on the fatigue trend of the cable transmission metal material to obtain cable aging diagnosis data.

[0162] In an embodiment of the present invention, based on the obtained fatigue trend map of the metal material, a multi-layer joint analysis is performed in combination with the local polarization imbalance area and the transient pulse density layer. A three-dimensional vector mapping algorithm is used to superimpose the spatial distribution information of different layers, and a regional fusion weighted algorithm is used to determine the high-risk aging area. The fatigue trend slope value, potential imbalance duration, and transient pulse density value of each high-risk area are used as input, and the parameters are normalized to construct an aging index parameter sequence. A threshold classification is set for each section of the aging index, and the aging level is divided by region (such as level I to level IV), and the data is output as cable aging diagnostic data. The aging level, typical characteristic parameters, and evolution trajectory of each area are stored in the database to form a data support interface for the diagnostic system.

[0163] The present invention further provides a cable aging diagnosis and monitoring system for executing the cable aging diagnosis and monitoring method described above. The cable aging diagnosis and monitoring system comprises:

[0164] The cable operation status data evaluation module is used to obtain cable operation log data and cable internal structure design data; collect cable structure distribution status based on cable operation log data; query cable operation environment parameters based on cable structure distribution status and cable operation log data; and evaluate cable operation status data based on cable operation environment parameters and cable internal structure design data;

[0165] The cable multi-layer structure failure detection module is used to determine the cable power sudden demand data based on the cable operation log data; determine the cable non-uniform thermal coupling imbalance state based on the cable operation status data; and detect the cable multi-layer structure coupling failure condition based on the cable operation status data and the cable non-uniform thermal coupling imbalance state;

[0166] The cable load stability detection module is used to detect the damage to the cable structure integrity based on the coupling failure of the cable multi-layer structure; evaluate the decline trend of the cable electrical insulation structure strength based on the damage to the cable structure integrity; and determine the collapse of the cable load stability based on the damage to the cable structure integrity and the decline trend of the cable electrical insulation structure strength;

[0167] The cable aging diagnosis module is used to detect the degradation of the cable transmission power quality based on the collapse of the cable load stability; determine the degree of abnormal fluctuation growth of the cable operating voltage based on the degradation of the cable transmission power quality; detect the fatigue trend of the cable transmission metal material based on the degree of abnormal fluctuation growth of the cable operating voltage; perform cable aging diagnosis based on the fatigue trend of the cable transmission metal material to obtain cable aging diagnosis data.

[0168] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A cable aging diagnosis and monitoring method, characterized in that: The following steps are involved: Step S1: Obtain cable operation log data and cable internal structure design data; Collect cable structure distribution status based on cable operation log data; Query cable operating environment parameters based on cable structure distribution and cable operation log data; Evaluate cable operation status data based on cable operation environment parameters and cable internal structure design data; Step S2: determining the cable power sudden demand data based on the cable operation log data; Determine the cable non-uniform thermal coupling imbalance state based on the cable operation state data according to the cable power sudden demand data; Detect cable multi-layer structure coupling failure based on cable operation status data and cable non-uniform thermal coupling imbalance status; Step S3: detecting the damage status of the cable structure integrity according to the coupling failure status of the cable multi-layer structure; Evaluate the cable electrical insulation strength degradation trend based on the cable structural integrity damage; determine the cable load stability collapse based on the cable structural integrity damage and the cable electrical insulation strength degradation trend; Step S4: detecting a degradation in the quality of power transmitted by the cable based on a breakdown in cable load stability; Determine the growth degree of abnormal fluctuation of cable operating voltage based on the degradation of cable transmission power quality; Detect the fatigue trend of cable transmission metal materials based on the increase in abnormal fluctuation of cable operating voltage; Cable aging diagnosis is performed based on the fatigue trend of cable transmission metal materials to obtain cable aging diagnosis data.

2. The cable aging diagnosis and monitoring method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining cable operation log data and cable internal structure design data; Step S12: collecting cable structure distribution status based on cable operation log data; Step S13: querying cable operation environment parameters according to the cable structure distribution status and cable operation log data; Step S14: collecting cable internal hierarchical structure data according to the cable internal structure design data; Step S15: Evaluate the cable operating status data based on the cable operating environment parameters and the cable internal hierarchical structure data.

3. The cable aging diagnosis and monitoring method according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: setting the temperature measurement range of the temperature sensor to 0-100°C, the minimum temperature change to 0.05°C, and the temperature sampling frequency to 10Hz; Step S152: Setting the humidity sensor's humidity measurement range to 0% to 100% RH, the minimum humidity change to 0.1% RH, and the humidity sampling frequency to 5 Hz; Step S153: using a humidity sensor and a temperature sensor to collect the temperature and humidity status of the cable operating environment parameters to obtain the temperature and humidity data of the cable operating environment; Step S154: Evaluate the cable outer layer protection structure strength parameters based on the cable internal layered structure data; Step S155: Evaluate the internal electrical operation characteristic parameters of the cable according to the internal hierarchical structure data of the cable; Step S156: Evaluate the stability of the cable's internal structure based on the cable's internal electrical operation characteristic parameters and the cable's outer protective structure strength parameters; Step S157: Evaluate the cable operation status data based on the cable operation environment temperature and humidity data and the stability of the cable internal structure.

4. The cable aging diagnosis and monitoring method according to claim 1, characterized in that: The determination of the cable power sudden demand data in step S2 includes: Collect cable operation power demand data based on cable operation log data; perform interpolation and smoothing processing on the cable operation power demand data to obtain cable operation power demand smoothing data; Construct a cable operation power demand fluctuation curve based on the cable operation power demand smoothing data; predict the nonlinear change trend of cable demand fluctuation based on the cable operation power demand fluctuation curve; When the similarity of the nonlinear change trend of cable demand fluctuation is greater than 0.85, nonlinear fluctuation similarity division is performed to obtain the cable demand fluctuation similarity curve dataset; the slope interval of the demand fluctuation curve is calculated using the cable demand fluctuation similarity curve dataset; Identify the distribution law of power fluctuation curve based on the power demand fluctuation similarity curve dataset when the slope of the demand fluctuation curve is greater than 1.2kW / min; Conduct normal distribution analysis on the distribution law of power fluctuation curve and obtain normal distribution law data; According to the distribution law of normal data and the distribution law of power fluctuation curve, the fluctuation trend linear regression analysis of cable power demand fluctuation curve is carried out to obtain the linear data of demand fluctuation trend; Calculate the abnormal increase in demand fluctuations based on the linear data of demand fluctuation trends; Determine the sudden demand data for cable power based on the abnormal increase in demand fluctuations and the linear data of demand fluctuation trends.

5. The cable aging diagnosis and monitoring method according to claim 1, characterized in that: The determination of the cable non-uniform thermal coupling imbalance state in step S2 includes: Determine the cable multi-link current redistribution status based on the cable operation status data according to the cable power sudden demand data; Determine the cable load reconfiguration misalignment based on the cable multi-link current redistribution status and cable power sudden demand data; Calculate the cable running load reconstruction misalignment offset difference based on the cable running load reconstruction misalignment situation; Determine the excessive current concentration in the local area of ​​the cable based on the cable operating load reconstruction misalignment offset difference; Utilize the excessive concentration of local current in the cable to detect the current surge conduction condition of the cable; Determine the degree of Joule heat surge during cable operation based on the excessive concentration of local current in the cable and the current impulse conduction in the cable; Determine the cable's steady-state thermal capacity exceeding the limit based on the excessive concentration of local current in the cable and the degree of Joule heat increase during cable operation; The imbalance state of cable non-uniform thermal coupling is determined based on the degree of Joule heat surge during cable operation and the exceeding of steady-state heat capacity of the cable.

6. The cable aging diagnosis and monitoring method according to claim 1, characterized in that: The detection of coupling failure condition of the cable multilayer structure in step S2 includes: Evaluate the complexity of the cable's external operating environment based on the cable's operating status data; According to the complexity of the cable's external operating environment, the cable operating status data is used to detect the cable's external non-uniformity degradation; Determine the volatilization status of the plasticizer in the cable protective layer based on the non-uniform degradation of the cable exterior and the non-uniform thermal coupling imbalance of the cable; Determine the degree of thickness reduction of local cable structure according to the volatilization of plasticizer in cable protective layer; The growth of micro cracks in the cable protective layer is estimated by using the thickness reduction degree of the local cable structure and the volatilization status of the plasticizer in the cable protective layer; The coupling failure of the cable multi-layer structure is detected based on the growth of microcracks in the cable protective layer and the thickness decrease of the local cable structure.

7. The cable aging diagnosis and monitoring method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: detecting the damage status of the cable structure integrity according to the coupling failure status of the cable multi-layer structure; Step S32: evaluating the cable electrical insulation structure strength degradation trend based on the cable structural integrity damage status; Step S33: Detecting an electrical safety boundary out-of-control condition based on a decreasing trend in cable electrical insulation strength and damage to cable structural integrity; Step S34: determining the cable load stability collapse condition based on the electrical safety margin out-of-control condition and the cable electrical insulation structure strength degradation trend.

8. The cable aging diagnosis and monitoring method according to claim 7, characterized in that: Step S31 includes the following steps: Step S311: detecting shear dislocation of the cable structure according to the coupling failure condition of the cable multi-layer structure; Step S312: detecting the degradation of the cable layer interface adhesion ability according to the coupling failure condition of the cable multi-layer structure; Step S313: determining the cable structure delamination condition based on the cable delamination interface adhesion ability degradation condition and the cable delamination interface adhesion ability degradation condition; Step S314: Identifying the degree of exposure of the cable metal layer based on the delamination status of the cable structure; Step S315: determining the severity of corrosion of the cable metal layer according to the exposure degree of the cable metal layer; Step S316: Detecting the damage status of the cable structure integrity based on the aggravated corrosion of the cable metal layer and the delamination of the cable structure.

9. The cable aging diagnosis and monitoring method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: detecting a degradation in cable transmission power quality based on a breakdown in cable load stability; Step S42: determining the growth degree of abnormal fluctuation of the cable operating voltage based on the degradation of the cable transmission power quality; Step S43: identifying the cable transmission transient pulse situation based on the abnormal fluctuation growth degree of the cable operating voltage; Step S44: detecting the local polarization imbalance of the cable based on the abnormal fluctuation growth degree of the cable operating voltage and the transient pulse condition of the cable transmission; Step S45: detecting fatigue trend of cable transmission metal material according to the local polarization imbalance condition of the cable and the transient pulse condition of the cable transmission; Step S46: performing cable aging diagnosis based on the fatigue trend of the cable transmission metal material to obtain cable aging diagnosis data.

10. A cable aging diagnosis and monitoring system, characterized in that: For executing the cable aging diagnosis and monitoring method according to claim 1, the cable aging diagnosis and monitoring system comprises: The cable operation status data evaluation module is used to obtain cable operation log data and cable internal structure design data; collect cable structure distribution status based on cable operation log data; query cable operation environment parameters based on cable structure distribution status and cable operation log data; and evaluate cable operation status data based on cable operation environment parameters and cable internal structure design data; The cable multi-layer structure failure detection module is used to determine the cable power sudden demand data based on the cable operation log data; determine the cable non-uniform thermal coupling imbalance state based on the cable operation status data; and detect the cable multi-layer structure coupling failure condition based on the cable operation status data and the cable non-uniform thermal coupling imbalance state; The cable load stability detection module is used to detect the damage to the cable structure integrity based on the coupling failure of the cable multi-layer structure; evaluate the decline trend of the cable electrical insulation structure strength based on the damage to the cable structure integrity; and determine the collapse of the cable load stability based on the damage to the cable structure integrity and the decline trend of the cable electrical insulation structure strength; The cable aging diagnosis module is used to detect the degradation of the cable transmission power quality based on the collapse of the cable load stability; determine the degree of abnormal fluctuation growth of the cable operating voltage based on the degradation of the cable transmission power quality; detect the fatigue trend of the cable transmission metal material based on the degree of abnormal fluctuation growth of the cable operating voltage; perform cable aging diagnosis based on the fatigue trend of the cable transmission metal material to obtain cable aging diagnosis data.

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