Method, system, electronic device and medium for monitoring the state of cable insulation

Abstract

The application relates to a cable insulation state monitoring method, system, electronic equipment and medium, which comprises the following steps: acquiring insulation state parameters of a main line, leakage currents and electric field intensities of branch joints; calculating branch current fluctuations and insulation resistances according to the same, positioning starting joints; extrapolating from the starting joints along the main line, comprehensively calculating section insulation deterioration probabilities according to the current fluctuations and the insulation state parameters; dynamically adjusting a collection frequency and updating the electric field intensities according to the probabilities; generating a fault propagation path diagram according to starting positions and gradients, simulating multi-scene operation data, and outputting global insulation state monitoring results. Therefore, the method realizes early identification and global evaluation of cable insulation deterioration, improves monitoring precision and early warning reliability by updating the collection frequency along the main line section in real time, comprehensively calculating section deterioration probabilities according to the insulation state parameters, and further generating a fault propagation path diagram and simulating multi-scene operation data.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, system, electronic device, and medium for monitoring the insulation status of cables. Background Technology

[0002] As urban electricity demand rises, cable network coverage continues to expand. Internally, cable networks typically exhibit a "tree-like" radial structure, with branch cables and trunk cables. Trunk cables are the power cables that bear the primary transmission function in the power grid, connecting upstream power sources to multiple branch cables; their cross-sectional area and rated capacity are larger than those of branch cables. Branch cables are power cables that extend from the trunk cables, supplying power to localized load areas; their cross-sectional area and rated capacity are smaller than those of the trunk cables. Therefore, the insulation performance at the connection point between branch and trunk cables is a critical factor affecting the reliability of the cable network system: if the insulation at this point weakens, localized faults may propagate along the line, threatening the overall stability of the power grid.

[0003] However, after in-depth research, the inventors discovered that current methods for monitoring the insulation status of cable networks still have significant limitations: existing practices mostly rely on fixed sensor monitoring or periodic manual inspections. These methods often focus on static data at a single point and do not fully consider the propagation characteristics of insulation defects throughout the cable network, thus failing to effectively track the trend of fault spread.

[0004] (1) When branch lines are frequently put into service or de-energized due to actual needs, the current in the main cable will be redistributed accordingly, which directly causes transient fluctuations in the electric field at the connection point between the main line and the branch. These fluctuations are not limited to the connection point itself, but may also extend to adjacent line sections, forming a wider electric field disturbance, which further increases the complexity of condition monitoring.

[0005] (2) As the most direct indicator of insulation performance, leakage current is significantly affected by various dynamic factors such as load fluctuations and operation mode adjustments, exhibiting strong nonlinear characteristics. If data is collected from a fixed point or a specific time period, it is difficult to accurately reflect the true state of cable network insulation. Summary of the Invention

[0006] In view of the above-mentioned deficiencies or disadvantages, the present invention provides a method for monitoring the insulation status of cables, which can solve at least one of the above technical problems.

[0007] This invention provides a method for monitoring the insulation condition of cables, comprising:

[0008] Obtain insulation status parameters of each section in each trunk line of the cable network, as well as leakage current data and electric field strength data of each branch connection point of the trunk cable.

[0009] Determine the current fluctuation amplitude and insulation resistance value of each branch connection based on leakage current data and electric field strength data;

[0010] The starting connection point of each main line is determined based on the insulation resistance value, electric field strength data and insulation status parameters.

[0011] The insulation degradation probability value of each section is calculated based on the current fluctuation amplitude and insulation status parameters in the direction of the section extending outward from the corresponding starting point along the main line.

[0012] The sampling frequency of each section is updated based on the probability value of each insulation degradation, and the updated electric field strength data is obtained based on the updated sampling frequency.

[0013] A fault propagation path diagram is generated based on the location of each starting point and the intensity gradient of the updated electric field intensity data.

[0014] Based on the fault propagation path diagram, simulated monitoring data of the cable network under various simulated operating scenarios are calculated to generate insulation status monitoring results of the cable network.

[0015] According to a second aspect, the present invention provides a cable insulation condition monitoring system, comprising:

[0016] The monitoring data acquisition module is used to acquire insulation status parameters of each section in each main line of the cable network, as well as leakage current data and electric field strength data of branch joints in the main cable.

[0017] The contact resistance calculation module is used to calculate the current fluctuation amplitude and insulation resistance value of each branch contact based on leakage current data and electric field strength data.

[0018] The starting contact determination module is used to determine the starting contact of each main line based on the insulation resistance value, electric field strength data and insulation status parameters.

[0019] The degradation probability calculation module is used to calculate the insulation degradation probability value of each section along the main line from the corresponding starting point outward, based on the current fluctuation amplitude and insulation status parameters in that section direction.

[0020] The electric field strength data gain module is used to update the sampling frequency of each section according to the probability value of each insulation degradation, and to obtain the updated electric field strength data based on the updated sampling frequency.

[0021] The propagation path construction module is used to generate a fault propagation path diagram based on the location of each starting point and the intensity gradient of the updated electric field intensity data.

[0022] The insulation condition analysis module is used to calculate the simulated monitoring data of the cable network under various simulated operating scenarios based on the fault propagation path diagram, so as to generate the insulation condition monitoring results of the cable network.

[0023] According to a third aspect, the present invention provides an electronic device comprising:

[0024] At least one processor; and

[0025] The memory that is communicatively connected to the at least one processor;

[0026] The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform any cable insulation state monitoring method in the embodiments of the present invention.

[0027] According to a fourth aspect, the present invention provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute a monitoring method for the insulation state of any cable in the embodiments of the present invention.

[0028] The technical solution of this invention utilizes the insulation status parameters of each section of the main line in the cable network, as well as the leakage current data and electric field strength data of each branch connection. First, the current fluctuation amplitude and insulation resistance value of the branch connection are determined by the leakage current data and electric field strength data. Then, the starting connection of the main line is determined based on the insulation resistance value, electric field strength data, and insulation status parameters. Subsequently, along the section direction extending outward from the starting connection of the main line, the insulation degradation probability value of each section is calculated by combining the current fluctuation amplitude and insulation status parameters. The section acquisition frequency is updated according to the probability value to obtain updated electric field strength data. Then, the intensity gradient of the updated electric field strength data is used to generate a fault propagation path diagram. Finally, based on the path diagram, simulated monitoring data under various operating scenarios are calculated and insulation status monitoring results are generated. This enables early detection of local insulation defects and accurate assessment of the overall insulation health status, achieving early warning and reliable monitoring of cable insulation status, and improving monitoring accuracy and early warning reliability. Attached Figure Description

[0029] Figure 1 This is a flowchart of a cable insulation condition monitoring method according to an embodiment of the present invention;

[0030] Figure 2 This is a structural block diagram of a cable insulation condition monitoring system according to an embodiment of the present invention;

[0031] Figure 3 This is a block diagram of an electronic device used to implement embodiments of the present invention. Detailed Implementation

[0032] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of the present invention, including various details to aid understanding. These details should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope of the invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0033] According to the first aspect of this invention, a method for monitoring the insulation condition of a cable is provided. This method can be applied to a cable insulation condition monitoring system for a cable network (hereinafter referred to as the "system"), such as... Figure 1 As shown, the method may include:

[0034] Step S110: Obtain the insulation status parameters of each section in each main line of the cable network, as well as the leakage current data and electric field strength data of each branch connection.

[0035] The main line is the continuous section of the main cable used to undertake the main power transmission task, and the branch connection is the connection point between the branch cable and the main line. Insulation status parameters may include temperature data, partial discharge data, dielectric constant and dielectric loss factor (dimensionless) of the corresponding section, which are updated by an online dielectric loss tester every 15 minutes; leakage current data is collected by a ring leakage current sensor at a frequency of once every 5 seconds, and electric field strength data is continuously measured by a non-contact electric field probe at a distance of 10 cm from the outer sheath surface of the branch connection, in kilovolts per meter.

[0036] Generally, the main trunk cable is equipped with distributed temperature-sensing optical fibers, which are laid spirally along the entire length of the main trunk cable's outer sheath and secured with insulating straps. Branch cables, however, do not have optical fibers; the system obtains their temperature data by scanning with an infrared thermometer.

[0037] For example, the system can acquire temperature data in real time (in degrees Celsius) along the main line through distributed temperature sensing optical fiber at sampling intervals of 50 meters, and collect the amplitude (in picoseconds) and frequency of partial discharge pulses at a sampling rate of 1 MHz per second through ultrasonic partial discharge sensors and high-frequency current transformers.

[0038] Step S120: Calculate the current fluctuation amplitude and insulation resistance value of each branch connection based on the leakage current data and electric field strength data.

[0039] Leakage current data can be determined by the circulating current (microampere level) on the surface of the branch contact sheath and its time-series curve as a function of load. Electric field strength data can be determined by the spatial electric field (kilovolts per meter) at a distance of 10 cm from the outer sheath of the contact and its distribution gradient along the radial direction of the cable.

[0040] The leakage current data may include: the sheath leakage current measured at branch contact B234 at 15:00 was 0.8 mA, fluctuating by ±0.05 mA within 30 seconds, corresponding to a decrease in load current from 180 A to 120 A.

[0041] Electric field strength data can include: at 10 cm from the outer sheath surface of the same connection point, the electric field strength measured at 0.5 m intervals along the radial direction of the cable is 3.2, 2.9, and 2.4 kV per meter, respectively, showing a decreasing gradient outwards.

[0042] For example, the system can first calculate the current fluctuation amplitude (in amperes) based on the difference between the maximum and minimum values ​​of the load current of the main line during the data collection period. Then, the system divides the leakage current value by the electric field strength value to obtain the initial resistance value (in megohms). If the ratio of the fluctuation amplitude to the historical average fluctuation amplitude for the same period exceeds 1.5, the reciprocal of this ratio is used as a correction factor to adjust the initial resistance value. The corrected result is the insulation resistance value at the branch connection.

[0043] Step S130: Determine the starting connection of each main line based on the insulation resistance value, electric field strength data and insulation status parameters.

[0044] Among these insulation condition parameters, the partial discharge data refers to the amplitude (picocells, pC), frequency (times / second), and phase distribution of transient pulse signals excited by insulation defects within branch junctions or main line sections. The system synchronously captures these pulses using an ultrasonic sensor and a high-frequency current transformer, recording the peak amplitude and occurrence time of each pulse.

[0045] Partial discharge data may include: 42 partial discharge pulses captured by the main node M103 during the period from 14:32 to 14:33 (IEC 60270 standard can be used, detection bandwidth 9 kHz–30 MHz, threshold 5 picoseconds, integration window 60 seconds), with a maximum amplitude of 520 picoseconds and an average frequency of 0.7 pulses / second; the pulse phase is concentrated in the first quadrant (0–90 degrees) at 50 Hz, indicating the presence of air gap discharge inside the insulation.

[0046] The starting point can be the first main node on the main line that simultaneously meets the triple criteria of "lowest insulation resistance, largest electric field intensity gradient and abnormal temperature rise" from the high-risk branch point outward. It is used to mark the precise starting point of the fault propagation from the branch to the main network and serves as the reference origin for subsequent steps to generate the fault propagation path diagram and calculate the degradation probability.

[0047] For example, the system can traverse all branch connections and mark those with insulation resistance values ​​below a preset threshold of 300 megohms and an outward diffusion trend in electric field strength distribution (i.e., the electric field strength decreases radially along the cable and the gradient value is large) as high-risk connections. Subsequently, the system searches for the set of main line nodes directly physically connected to the high-risk connections, calculates the electric field strength gradient (in kV / m²) at 0.5-meter intervals radially along the cable for each node, and simultaneously measures the difference between the node's real-time temperature and its historical reference temperature (in degrees Celsius). Next, the system selects the node with the largest electric field strength gradient and a temperature difference exceeding 10 degrees Celsius as the starting point of a potential propagation path and records the unique node number and geographical coordinates of this starting point.

[0048] Step S140: Along the main line extending outward from the corresponding starting point, calculate the insulation degradation probability value of each section based on the current fluctuation amplitude and insulation status parameters in that section.

[0049] Specifically, "the direction of the section extending outward from the corresponding starting point along each main line" can refer to the power flow direction that extends continuously outward from the starting point along the actual laying path (non-linear) of the main cable, with the starting point as the origin. The direction vector points from the starting point to the first monitoring section where the electric field strength gradient decreases most slowly and the current leakage continues to increase. For example, starting point M103 is located at the cable joint of tower No. 128 of the 10 kV main line. The direction of its outward extension extends 350 meters eastward along the cable to tower No. 131. In this section, the electric field strength decreases from 3.2 kV / m to 2.4 kV / m, and the current leakage increases from 0.8 mA to 1.1 mA.

[0050] For example, the system uses the initial connection point as the origin and divides the main line into monitoring sections every 100 meters, forming a continuous sequence of sections. Specifically, within each section, the system calculates the relative growth rate of leakage current (in percentage per kilometer, reflecting the drastic change in leakage current) and the average electric field strength (in kilovolts per meter), and then weights and sums these two values ​​after max-min normalization to obtain the section degradation index (ranging from 0 to 1, reflecting the combined degradation effect of temperature rise and electric field on insulation). Subsequently, the system can use Bayesian inference, with the number of historical faults as the prior probability and the aforementioned section degradation index as the likelihood function, to calculate the posterior probability value of insulation degradation in each section, forming an insulation degradation probability distribution map covering the entire cable network.

[0051] Furthermore, the insulation degradation probability value can be equal to the posterior probability of insulation failure in a given segment within a specified future period, calculated using Bayesian inference, after observing the current segment degradation index. The value ranges from 0 to 1, with values ​​closer to 1 indicating a higher risk. For example, the insulation degradation probability value could be: segment A23 has a leakage growth rate of 0.8% / km, an average electric field of 3.2 kV / m, and a normalized degradation index of 0.72; combined with a prior historical fault value of 0.3, the Bayesian posterior yields an insulation degradation probability value of 0.78, indicating a 78% probability of insulation failure in the segment within the following 30 days. The calculation method for the insulation degradation probability value references the condition assessment framework based on multi-feature fusion advocated by the International Conference on Large Electric Systems (CIGRE) and IEEE standards. Specifically, by normalizing and fusing real-time monitoring characteristics such as leakage current growth rate and electric field strength into a comprehensive degradation index, this index is used as a likelihood function. Combined with prior probabilities obtained from historical fault statistics, a Bayesian inference algorithm is applied to calculate the posterior fault probability, thereby achieving dynamic and personalized probabilistic risk assessment of cable insulation status. The aforementioned prior probabilities, normalization parameters, and weighting coefficients have been calibrated based on the operational history and field data of the specific cable network.

[0052] Step S150: Update the sampling frequency of each section according to the insulation degradation probability value, and obtain the updated electric field strength data based on the updated sampling frequency.

[0053] The updated electric field strength data refers to the time-series electric field strength values ​​(unit: kilovolt per meter) sampled at a high density every 0.5 meters along the main line after the sampling frequency was shortened from "original interval of 30 minutes" to "new interval of 10 minutes". Its time resolution is improved by 3 times, while the spatial resolution remains unchanged.

[0054] For example, the original electric field strength data recorded 2.8 kV / m in section A23 at 14:00; after the update, 2.81, 2.84, and 2.87 kV / m were recorded at 14:00, 14:10, and 14:20 respectively, capturing a subtle upward gradient of 0.03 kV / m, whereas the original data could only provide a single-point value.

[0055] Furthermore, the system can compare the insulation degradation probability value of each section with the preset degradation threshold of 0.75. If it is higher than the threshold, the data acquisition time interval of the section is shortened from the original 30 minutes to 10 minutes, and the acceleration factor 3 (3, which can represent the frequency being increased to 3 times the original) is recorded in the section configuration table. Then, the electric field strength data is re-acquired according to the updated acquisition frequency, and the gradient change rate (in kilovolts per meter per hour) obtained by dividing the difference in electric field strength between the current time and the previous time by the time interval is calculated. If the gradient change rate is positive and increasing for three consecutive acquisition times, the direction is marked as the expansion direction of the propagation path.

[0056] Step S160: Generate a fault propagation path diagram based on the location of each starting contact and the intensity gradient of the updated electric field intensity data.

[0057] The starting point can be the first trunk node in the cable network that simultaneously meets the following criteria: insulation resistance below 300 megohms, maximum electric field gradient, and temperature rise exceeding 10 degrees Celsius. The fault propagation path diagram can be a visual representation of cable network fault propagation centered on the starting point, bounded by the diffusion radius, using different line types to represent longitudinal / diagonal / lateral propagation directions, and labeling node sequences and arrows.

[0058] For example, the system can identify the farthest measurement point where the electric field strength continuously exceeds the safety threshold of 3 kV / m along the expansion direction, centered on the initial contact point, and calculate the cumulative line length from the initial contact point to that point as the diffusion radius (in meters). Subsequently, the system measures the angle between the expansion direction and the main line direction. If the angle is less than 30 degrees, it is classified as longitudinal propagation and represented by a solid line; if the angle is between 30 and 60 degrees, it is classified as oblique propagation and represented by a dashed line; if the angle is greater than 60 degrees, it is classified as lateral propagation and represented by a dotted line. Finally, all nodes within the diffusion range are connected on the cable network topology map with the corresponding line type, and arrows indicating the propagation direction are added to generate a fault propagation path map.

[0059] Step S170: Based on the fault propagation path diagram, calculate the simulated monitoring data of the cable network under various simulated operating scenarios to generate the insulation status monitoring results of the cable network.

[0060] Among these, multiple simulated operating scenarios can refer to the parallel simulation of the cable network insulation state under three fixed operating conditions: normal load, overload, and sudden load change. For example:

[0061] Under normal load conditions, the main current is maintained at 70% to 80% of the rated value for 15 minutes.

[0062] In an overload scenario, the main current rises to 100%–110% of the rated value for 30 minutes.

[0063] In a sudden load change scenario, the current jumps from 30% of the rated value to 90% within 5 seconds and is maintained for 10 minutes.

[0064] The main difference between the three scenarios is that the current amplitude, duration, and rate of change increase sequentially, and are used to evaluate the differences in insulation degradation response under daily, peak, and transient impact conditions.

[0065] For example, the system can extract the propagation node sequence based on the fault propagation path diagram and read the historical current data of each node under three operating scenarios: normal load, overload, and load change. It calculates the absolute value of the current difference between adjacent moments under each scenario as a current fluctuation amplitude sequence (in amperes) and extracts the maximum value of the sequence. Simultaneously, the system reads the historical insulation resistance data of each node, calculates the insulation resistance decay rate (in megaohms per year) and the average partial discharge intensity (in times per hour). Then, the system multiplies the normalized ratios of the decay rate to the baseline decay rate, the average discharge intensity to the baseline discharge intensity, and the maximum current fluctuation value to obtain a node degradation index (ranging from 0 to 1). The system then aggregates the node degradation indices of all nodes and calculates the average as a network health score (ranging from 0 to 100, with lower scores indicating higher risk). If the score is below 60, it is considered a high-risk state and triggers a red alert; if the score is between 60 and 80, it is considered a medium-risk state and triggers a yellow alert; otherwise, it is considered a low-risk state. The system can ultimately output insulation status monitoring results that include risk level, key node number and node deterioration index, enabling early warning and reliable monitoring of cable insulation status.

[0066] Therefore, according to the above implementation method, the system can utilize the insulation status parameters of each section of the main line in the cable network, as well as the leakage current data and electric field strength data of each branch connection. First, it determines the current fluctuation amplitude and insulation resistance value of the branch connection through the leakage current data and electric field strength data. Then, based on the insulation resistance value, electric field strength data, and insulation status parameters, it determines the starting connection of the main line. Subsequently, along the section direction extending outward from the starting connection of the main line, it calculates the insulation degradation probability value of each section by combining the current fluctuation amplitude and insulation status parameters. Based on this probability value, it updates the section acquisition frequency to obtain updated electric field strength data. Then, it uses the intensity gradient of the updated electric field strength data to generate a fault propagation path diagram. Finally, based on this path diagram, it calculates simulated monitoring data under various operating scenarios and generates insulation status monitoring results. This enables early detection of local insulation defects and accurate assessment of the overall insulation health status, achieving early warning and reliable monitoring of cable insulation status, and improving monitoring accuracy and early warning reliability.

[0067] In some embodiments, the current fluctuation amplitude and insulation resistance value of each branch contact are calculated based on leakage current data and electric field strength data, including:

[0068] Obtain load current data of the main line connected to the branch connection during the acquisition period.

[0069] The data acquisition period can be defined as a 15-minute sliding window. Load current data can be uploaded by the substation monitoring terminal every second, and the data sequence is denoted as I_t, t=1,2,…,900. Generally speaking, a substation monitoring terminal (SMT) is a field data acquisition and communication device installed in a substation (or terminal substation). It is a publicly available existing device responsible for uploading real-time operating information of primary equipment (transformers, circuit breakers, instrument transformers, etc.) and auxiliary equipment (temperature and humidity, security, fire protection, etc.) to the background monitoring system or remote dispatch / central control center after protocol conversion and processing, thereby realizing the substation's "telemetry, remote signaling, remote control, and remote adjustment" functions.

[0070] The load current data is used as the current data corresponding to the branch contact. The current fluctuation amplitude ΔI of each branch contact is obtained by calculating the difference between the maximum and minimum values ​​of each current data, in milliamperes.

[0071] For example, if the maximum load current of branch contact B234 is 180 amps and the minimum load current is 120 amps during the period from 15:00 to 15:15, then ΔI = 60 amps.

[0072] Obtain the historical average fluctuation amplitude of each branch node, denoted as Δ. This value can be obtained by calculating the arithmetic mean of ΔI over the same period of the past 30 days.

[0073] Furthermore, the system can calculate the fluctuation ratio k of each branch contact based on the amplitude of each current fluctuation and the corresponding historical average fluctuation amplitude. The calculation formula is k=ΔI / Δ .

[0074] For example, Δ of number B234 If the ampere is 40 amps, then k = 60 / 40 = 1.5.

[0075] The initial resistance value of each branch contact is calculated by dividing the leakage current value of each branch contact by the corresponding electric field strength value.

[0076] Specifically, the system can first obtain the leakage current value of each branch contact (hereinafter referred to as: The unit is milliampere (mA). Next, the electric field strength value E of each branch connection is obtained, in kilovolts per meter (kV / m). The leakage current value of each branch connection is divided by the corresponding electric field strength value to calculate the initial resistance value of each branch connection (hereinafter referred to as: The unit is megaohm. However, in actual calculations, attention must be paid to unit conversion. For example, 1mA / 1kV / m=1e-3 / 1e3=1e-6MΩ. In actual calculations, calibration must be performed according to the sensor's dimensions.

[0077] For example, number B234 was measured =0.8 mA, E=3.2 kV / m, then =0.8 / 3.2=0.25 megohms.

[0078] From the various fluctuation ratios, determine the first fluctuation ratio that exceeds the preset ratio threshold and the second fluctuation ratio that does not exceed the ratio threshold.

[0079] Specifically, the system can first determine a first fluctuation ratio k1 that exceeds a preset ratio threshold of 1.5 from each fluctuation ratio k, and a second fluctuation ratio k2 that does not exceed the ratio threshold of 1.5.

[0080] For example, if k=1.4 for number B234, which is less than the ratio threshold, the system will classify it as the second fluctuation ratio. Alternatively, if k=1.6 for number B233, which exceeds the ratio threshold, the system will classify it as the first fluctuation ratio.

[0081] The reciprocal of each first fluctuation ratio is used as a correction factor. The initial resistance value corresponding to each first fluctuation ratio is multiplied by the correction factor to calculate the corrected insulation resistance value.

[0082] Specifically, the system can first use the reciprocal of each first fluctuation ratio k1 as a correction coefficient c, i.e., c = 1 / k1. Then, the system multiplies the initial resistance value corresponding to each first fluctuation ratio by the correction coefficient to calculate the corrected insulation resistance value (hereinafter referred to as: (), the unit is megaohms.

[0083] For example, in number B234, c = 1 / 1.5 ≈ 0.67, then =0.25×0.67≈0.1667 (megaohms).

[0084] Next, the system can first set the initial resistance value corresponding to each second fluctuation ratio k2 (which can be simply referred to as: As the insulation resistance value that does not require correction (hereinafter referred to as: ).

[0085] Finally, the system can or These are respectively used as the insulation resistance values ​​for the corresponding branch contacts.

[0086] Therefore, according to the above implementation method, the system can suppress errors through correction mechanism when the load fluctuates drastically and retain the original accuracy when the load is stable, thereby obtaining more reliable branch contact insulation resistance values, providing accurate basis for subsequent high-risk contact identification and fault propagation path tracing.

[0087] In some embodiments, the insulation status parameters of each section include temperature data and partial discharge data for the corresponding section. The partial discharge data includes the discharge pulse amplitude and discharge pulse frequency for the corresponding main line. Determining the starting connection point of each main line based on the insulation resistance value, electric field strength data, and insulation status parameters includes:

[0088] Temperature distribution curves are generated based on the temperature data. Each temperature distribution curve is used to characterize the temperature values ​​distributed along the extension direction of each main line at a set sampling distance.

[0089] Among them, the temperature distribution curve can be based on the actual laying path of the main line as the horizontal axis, with a set sampling distance of 50 meters, recording the instantaneous temperature value of the corresponding point, forming a two-dimensional temperature sequence that is continuously distributed along the line direction, which is used to characterize the spatial differences in the thermal state of the main line.

[0090] Locations in each temperature distribution curve where the difference between the temperature value and the average temperature of the adjacent section exceeds a preset deviation value are marked as temperature anomaly points.

[0091] Specifically, the system can mark locations in each temperature distribution curve where the temperature value differs from the average temperature of the adjacent section by more than a preset deviation value of 5 degrees Celsius as temperature anomaly points.

[0092] For example, the temperature peak of section M101-M102 is 78 degrees Celsius 200 meters from the starting point, while the average temperature of the adjacent section is 71 degrees Celsius, a difference of 7 degrees Celsius. Therefore, this location is recorded as a temperature anomaly point.

[0093] Branch contacts whose current fluctuation amplitude exceeds the preset fluctuation threshold are marked as high-risk contacts.

[0094] Specifically, the system can mark branch contacts whose current fluctuation amplitude exceeds a preset fluctuation threshold of 30 amps as high-risk contacts. For example, the system can complete the marking by comparing the current ΔI with this threshold and write the unique number of the high-risk contact into the risk list.

[0095] Read the insulation resistance value and electric field strength data of each high-risk contact, and select the position with the largest electric field strength gradient change rate as the candidate contact according to the direction of decreasing electric field strength from the corresponding high-risk contact to the corresponding main line.

[0096] For example, the system can calculate the rate of change of electric field intensity gradient every 0.5 meters along the radial direction of the cable, in kilovolts per square meter per meter, and select the location with the largest rate of change of gradient as a candidate junction.

[0097] Candidate starting contacts are selected from all candidate contacts. Each candidate starting contact coincides with a temperature anomaly point and is a contact whose partial discharge data signal strength exceeds a preset signal threshold. The signal strength is determined by the amplitude and frequency of the discharge pulse.

[0098] Specifically, the selection criteria for obtaining candidate starting nodes from among the candidate nodes may include:

[0099] 1. The geographical location of the candidate junction coincides with the coordinates of the temperature anomaly point, and the coincidence tolerance is set to 2 meters;

[0100] 2. The signal strength of the partial discharge data exceeds the preset signal threshold of 1000 picocut.

[0101] Furthermore, the tolerance of 2 meters is based on the following: According to the State Grid Corporation of China's enterprise standard Q / GDW11262-2014 "Operating Procedures for Power Cable Lines", the allowable range of cable joint positioning error is ±2 meters; the spatial resolution of the field distributed fiber optic temperature sensing system (DTS) is 1 meter, and with the superimposed GIS measurement error of 0.5 meters, the total uncertainty does not exceed 1.5 meters. Taking 2 meters can cover all system errors and leave a 25% safety margin.

[0102] For example, candidate contact M104 satisfies both of the above conditions, and is therefore included in the candidate starting contact set.

[0103] The target starting point among the candidate starting points is determined as the starting point of the potential propagation path of each trunk line; the insulation resistance value of each target starting point is lower than the preset resistance threshold, and the intensity of the corresponding electric field decreases monotonically along the trunk line from the corresponding starting point outward.

[0104] Among them, the insulation resistance value of the target starting contact can be lower than the preset resistance threshold of 300 megohms, and the corresponding electric field strength decreases monotonically along the direction of the main line. The decreasing judgment rule is that the field strength of three consecutive sampling points decreases in sequence and the decreasing rate is not less than 0.1 kV per square meter.

[0105] For example, the insulation resistance of candidate starting contact M104 is 250 megohms, and its electric field strength along the main line direction decreases from 3.2 kV / m to 2.4 kV / m, which meets the monotonically decreasing requirement. Therefore, M104 is identified as the target starting contact.

[0106] Therefore, according to the above implementation method, the system can use the three-source information of temperature anomaly point, partial discharge intensity and electric field gradient to locate the physical starting point of fault propagation, and provide a high-precision benchmark for subsequent fault propagation path map generation and dynamic assessment of insulation health.

[0107] In some embodiments, after reading the insulation resistance value and electric field strength data of each high-risk contact, the method further includes:

[0108] Identify the main line nodes connected to the high-risk contact points and designate each main line node as a node to be tested.

[0109] Among them, the nodes to be tested refer to all backbone nodes that are directly connected to high-risk branch junctions by a single cable segment, as obtained through a Geographic Information System (GIS). The system stores their node numbers and coordinates in a list format.

[0110] The electric field intensity values ​​at each node to be tested are collected along the extension direction of the main line. The electric field intensity gradient of each node to be tested is calculated based on the set spacing between adjacent nodes and the electric field intensity values.

[0111] The sampling interval can be set to 0.5 meters, and the system can continuously measure the electric field using a non-contact electric field probe, with the unit being kilovolts per meter. The electric field intensity gradient of each measured node, calculated by the system based on the set spacing between adjacent measured nodes and the electric field intensity value, can be measured in kilovolts per square meter.

[0112] The temperature values ​​of each node to be measured are determined based on the temperature data of each section, and the temperature difference between each adjacent node to be measured is calculated based on the temperature values ​​of each node to be measured and the preset historical reference temperature values.

[0113] The temperature values ​​can be provided by distributed temperature sensing fiber optics or infrared thermometers, and the unit is degrees Celsius. The temperature difference between adjacent nodes, calculated by the system based on the temperature values ​​of each node under test and preset historical reference temperatures, is also measured in degrees Celsius.

[0114] The nodes to be tested that have an electric field intensity gradient exceeding a preset gradient threshold and a temperature difference exceeding a preset temperature threshold are marked as starting nodes.

[0115] Specifically, the system can mark the nodes under test that have an electric field intensity gradient exceeding a preset gradient threshold of 50 kV per square meter and a temperature difference exceeding a preset temperature threshold of 10 degrees Celsius as the starting point.

[0116] For example, node M104 has an electric field intensity gradient of 58 kV per square meter and a temperature difference of 12 degrees Celsius, so it is marked as the starting junction.

[0117] Along the extension direction of the main line, an initial propagation link is constructed based on the contact sequence and contact spacing from the high-risk contact to the starting contact.

[0118] The initial propagation link can be used to record the node number, the cable length between nodes (in meters), and the direction vector to form an ordered list; and the initial propagation link can be used as a reference for calculating the insulation degradation probability value and for inputting coordinate information for various simulated operation scenarios.

[0119] Therefore, according to the above implementation method, the system can accurately locate the starting point of the fault on the main line and provide a unified and traceable spatial benchmark for subsequent degradation probability assessment and operation scenario simulation in the form of the initial propagation link.

[0120] In some embodiments, along the main line extending outward from the corresponding starting contact, the insulation degradation probability value of each segment is calculated based on the current fluctuation amplitude and insulation state parameters in that segment direction, including:

[0121] Along each main line, extending outward from the corresponding starting point, the spatial distribution characteristics of the electric field intensity are determined based on the electric field intensity values ​​of each node to be tested in that section, and multiple decreasing directions of the electric field intensity are extracted from the spatial distribution characteristics of the electric field intensity.

[0122] The direction of decrease in electric field intensity is determined by comparing the electric field intensity values ​​of adjacent test nodes and selecting the direction of monotonically decreasing electric field intensity.

[0123] Furthermore, "spatial distribution characteristics of electric field intensity" can refer to the numerical sequence of electric field intensity continuously measured along the main line direction at sampling intervals of 0.5 meters; "direction of decreasing electric field intensity" can be determined by comparing the electric field intensity values ​​of adjacent measured nodes and selecting the direction of monotonically decreasing electric field intensity, that is, the electric field intensity at point i along the line direction. The electric field strength at point i+1 is greater than that at point i+1. If the decrease rate is not less than 0.1 kV per square meter, then the direction is marked as the decreasing direction.

[0124] Each pair of target nodes is identified where the difference in electric field intensity between adjacent test nodes is less than a preset threshold. The direction of decreasing electric field intensity corresponding to each pair of target node nodes is taken as the potential diffusion direction. Essentially, each pair of target node nodes is a pair of adjacent test nodes where the difference in electric field intensity between them is less than the preset threshold.

[0125] For example, the system can identify adjacent test nodes whose electric field intensity difference is less than a preset threshold of 0.2 kV / m, and use the decreasing direction of the electric field intensity corresponding to each adjacent test node as the potential diffusion direction. Then, the system can write this threshold into a configuration table to distinguish between gradual decay regions and abrupt decay regions.

[0126] Based on the current fluctuation amplitude of each target node pair along each potential diffusion direction, the relative growth rate of leakage current is calculated.

[0127] The relative growth rate of leakage current can be defined as:

[0128] ;

[0129] and These are the leakage current values ​​of adjacent nodes under test, and the relative growth rate of leakage current is expressed as a percentage per kilometer.

[0130] Based on the temperature difference between each pair of target nodes corresponding to each potential diffusion direction, and the average value of the electric field strength of each node to be tested, the section degradation index is calculated.

[0131] Specifically, the system can be based on the temperature difference ΔT between adjacent nodes under test corresponding to each potential diffusion direction, and the average electric field strength of each node under test (hereinafter referred to as: The section deterioration index DI was calculated; where ΔT is in degrees Celsius. The unit is kilovolts per meter, DI = 0.6 × normalized (ΔT) + 0.4 × normalized (ΔT) The value ranges from 0 to 1, and the normalized value (ΔT) can be the maximum historical ΔT value across the entire network. =20 degrees Celsius =4 kV per meter. Where, Δ This represents the maximum temperature difference between adjacent test nodes. This represents the highest electric field strength value among all adjacent nodes to be measured.

[0132] Based on the relative growth rate of leakage current, the section degradation index, and insulation condition parameters, the insulation degradation probability value of each section is calculated.

[0133] Specifically, the system can jointly substitute the relative growth rate of leakage current and the section degradation index into the Bayesian inference model, and use the dielectric loss factor in the insulation state parameters as a correction factor to output the posterior probability value, denoted as the insulation degradation probability value (hereinafter referred to as: ).

[0134] For example, the relative growth rate of leakage current in section S45 is 1.2% per kilometer (a dimensionless proportion), the section degradation index is 0.68, and the dielectric loss factor is 0.05. After Bayesian inference, =0.73, which means that the probability of insulation failure in this section within the next 30 days is 73%.

[0135] Furthermore, each section Bayesian posterior estimation can be used:

[0136] =P(fault|observation data)=[P(observation data|fault)×P(fault)]÷P(observation data);

[0137] The prior probability P(fault) can be set to 0.3 (30%). This prior probability is set with reference to the statistical data in Technical Brochure 379, "Transmission Cable System Reliability," published by the International Conference on Large Electric Systems (CIGRE). The statistical data shows that the average probability of insulation failure in medium-voltage XLPE cables within 5 to 10 years after commissioning is 0.28 to 0.32. This invention uses the upper limit of the interval, 0.30. The likelihood function P(observation data|fault) can adopt a Beta (α=7, β=3) distribution. The parameters are estimated from the sample proportion (70%) of "insulation degradation index DI ≥ 0.7 and eventual failure" in the 1000 km / year field data given in the aforementioned CIGRE report. That is, it is determined based on Table 4-2 of CIGRE TB 379, with a sample size of n = 1000 km / year and a confidence level of 95%. The normalization constant P (of the observed data) is calculated using the law of total probability, ensuring that the posterior probability falls within the interval of 0 to 1. The posterior probability of segment S45 is calculated as follows: =0.73.

[0138] Therefore, according to the above implementation method, the system can quickly lock the potential diffusion direction along the main line, and accurately quantify the insulation degradation probability of each section with the three-dimensional fusion index of leakage-temperature-electric field, providing a reliable basis for dynamically adjusting the monitoring frequency and generating fault propagation path map.

[0139] In some embodiments, a fault propagation path map is generated based on the location of each starting contact and the intensity gradient of the updated electric field intensity data, including:

[0140] Based on the location of each starting junction, each potential diffusion direction, and the intensity gradient of the updated electric field intensity data, multiple potential propagation paths are generated.

[0141] Among them, "potential propagation path" can refer to a continuous sequence of nodes extending outward along the direction of electric field intensity gradient with the starting point as the origin and the electric field intensity decreasing monotonically, which is used to characterize the possible propagation trajectory of the fault in the main line.

[0142] The corresponding target measurement points are determined along the expansion direction of each potential propagation path. Each target measurement point is the farthest measurement point where the electric field strength value in the current direction exceeds the preset safety threshold.

[0143] For example, the potential propagation path corresponding to the starting contact M103 extends eastward. The system collects the electric field strength every 0.5 meters in this direction. When the electric field strength first falls below 3 kV / m, the preceding position is marked as the target measurement point, denoted as (hereinafter referred to as: ).

[0144] Calculate the cumulative path length from the starting point of each potential propagation path to the corresponding target measurement point, and use each cumulative path length as the diffusion radius.

[0145] Specifically, the cumulative line length can be obtained by summing the actual cable lengths between adjacent nodes on the path, in meters.

[0146] For example, M103 to If the cumulative line length is 350 meters, then the diffusion radius R = 350 meters.

[0147] The corresponding fault propagation range is determined by taking the starting point of each potential propagation path as the center and the corresponding diffusion radius as the boundary.

[0148] Specifically, this range can be represented in a geographic information system (GIS) as a buffer zone along the line with the starting node as the center and the diffusion radius as the radius. All nodes within the buffer zone boundary are included in the fault-affected area.

[0149] The fault propagation direction of each potential propagation path is classified according to the angle between the expansion direction of each potential propagation path and the direction of the corresponding trunk line.

[0150] The included angle θ can be obtained through vector operations, and the classification rules can be as follows:

[0151] When θ is less than 30 degrees, it is denoted as longitudinal propagation;

[0152] When θ is between 30 degrees and 60 degrees, it is denoted as oblique propagation;

[0153] When θ is greater than 60 degrees, it is denoted as lateral propagation;

[0154] For example, M103— The path and the main route form an angle of 18 degrees, which the system classifies as longitudinal propagation.

[0155] Based on the established cable network topology, a fault propagation path map is generated according to the classification of each potential propagation path and the direction of fault propagation.

[0156] Specifically, the system can adopt a layered visualization method, with the bottom layer displaying the cable network topology, the middle layer overlaying the diffusion range, and the top layer indicating the corresponding fault propagation direction with solid lines, dashed lines, or dotted lines, and adding arrows between the starting point and the target measurement point to form a complete fault propagation path diagram.

[0157] Therefore, according to the above implementation method, the system can quickly generate multi-directional and multi-mode fault propagation path maps based on electric field intensity gradient and line topology, realize accurate visualization of fault propagation range and direction, and provide a reliable spatial reference for subsequent insulation degradation probability calculation and operation scenario simulation.

[0158] In some embodiments, the simulated monitoring data includes the current fluctuation amplitude and insulation resistance decay rate of each branch connection, as well as the partial discharge data of each section in each main line; based on the fault propagation path diagram, simulated monitoring data of the cable network under various simulated operating scenarios are calculated to generate the insulation status monitoring results of the cable network, including:

[0159] In the fault propagation path diagram, the node sequence in each main line is identified, starting from the initial contact and extending outward along the potential propagation direction, and the historical current data of each node under the preset operating scenario is read from each node sequence.

[0160] The preset operating scenarios include normal load scenarios, overload scenarios, and load change scenarios. The node sequence can refer to a list of all main node numbers arranged in the actual cable laying order, starting from the initial connection point; the list length is equal to the total number of nodes within the diffusion radius. Preset operating scenarios can include normal load scenarios, overload scenarios, and load change scenarios; the scenario identifier is uniformly encoded by the system as a scenario ID (scenario identifier). Historical current data can be considered as a typical type of operating data under the pre-approved operating scenarios.

[0161] The absolute value of the current difference between adjacent time points is calculated based on historical current data, and the current fluctuation amplitude of each node under each preset operating scenario is determined based on the absolute value of the current difference.

[0162] Specifically, the system can record the maximum absolute value of the current difference between adjacent times within a 15-minute sliding window as the current fluctuation amplitude ΔI of that node in that scenario, in amperes.

[0163] For example, the ΔI of node M105 under normal load scenario is 12 amps.

[0164] The initial insulation resistance value of each node is obtained. Based on the insulation resistance value, the initial insulation resistance value, and the monitoring time, the decay rate of the insulation resistance value of each node is calculated.

[0165] Specifically, the system can first obtain the initial value of the insulation resistance of each node (hereinafter referred to as: This value is the average insulation resistance measured within the first month of node commissioning, in megaohms. Next, the system uses the current insulation resistance value (hereinafter referred to as: ), Based on the monitoring duration T, the insulation resistance attenuation rate (DR) of each node is calculated, in megaohms per year, using the following formula:

[0166] .

[0167] For example, node M105's =500 megaohms =350 mega-ohms, T=2 years, then DR=75 mega-ohms per year.

[0168] The partial discharge data of each node is obtained, and the average discharge intensity of each node is calculated based on the total number and duration of each partial discharge pulse.

[0169] Next, the system can first acquire the partial discharge data of each node, and calculate the average discharge intensity (hereinafter referred to as AD) of each node based on the total number N of each partial discharge pulse and the pulse duration Δt, in units of times per hour, with the formula AD=N / Δt.

[0170] For example, if node M105 records 180 pulses in 1 hour, then AD = 180 pulses per hour.

[0171] The attenuation coefficient is calculated based on the ratio of the attenuation rate of the insulation resistance value of each node to the preset reference attenuation rate.

[0172] Specifically, the system can determine the DR of each node and the preset reference attenuation rate (hereinafter referred to as: ), =50 megaohms per year, the attenuation coefficient (hereinafter referred to as: );in, The formula for calculating can be shown below:

[0173] .

[0174] For example, node M105's =75 / 50=1.5.

[0175] The discharge coefficient is calculated based on the ratio of the average discharge intensity of each node to the preset reference discharge intensity.

[0176] Specifically, the system can adjust the AD of each node based on the preset reference discharge intensity (hereinafter referred to as: ), =The ratio of 100 times per hour is used to calculate the discharge coefficient (hereinafter referred to as: );in, The formula for calculating can be shown below:

[0177] .

[0178] For example, node M105's =180 / 100=1.8.

[0179] The node degradation index of each node is calculated based on the attenuation coefficient, discharge coefficient, and current fluctuation amplitude of each node.

[0180] Specifically, the system can be based on each node's... , The node degradation index (NDI) of the corresponding node is calculated using ΔI, and the formula is NDI = × × normalization(ΔI), where normalization(ΔI) is mapped to 0 to 1 using max-min normalization.

[0181] For example, if the normalized value of ΔI of M105 is 0.6, then NDI = 1.5 × 1.8 × 0.6 = 1.62, and after threshold clipping, it is taken as 1.0 (generally there is a preset threshold clipping rule: if NDI > 1, then take 1).

[0182] Insulation status monitoring results are generated based on the degradation index of each node.

[0183] For example, the system can take the arithmetic mean of the NDI of all nodes to obtain the network health score H, with a score range of 0 to 1.0; if H ≥ 0.8, it is judged as low risk, 0.6 ≤ H < 0.8 is judged as medium risk, and H < 0.6 is judged as high risk, and output a monitoring report in JSON format containing node number, NDI, risk level and scene ID.

[0184] Therefore, according to the above implementation method, the system can use real-time and historical data of branch contacts and main nodes to quantify the degree of insulation degradation in multiple operating scenarios, and output visualized and traceable insulation status monitoring results with node deterioration index as the core indicator, so as to achieve early warning and precise maintenance.

[0185] Figure 2 This is a structural block diagram of a cable insulation condition monitoring system according to an embodiment of the present invention.

[0186] like Figure 2 As shown, the cable insulation condition monitoring system includes:

[0187] The monitoring data acquisition module 210 is used to acquire the insulation status parameters of each section of each main line in the cable network, as well as the leakage current data and electric field strength data of the branch connection points of the main cable.

[0188] The contact resistance calculation module 220 is used to calculate the current fluctuation amplitude and insulation resistance value of each branch contact based on leakage current data and electric field strength data.

[0189] The starting contact determination module 230 is used to determine the starting contact of each main line based on the insulation resistance value, electric field strength data and insulation status parameters.

[0190] The degradation probability calculation module 240 is used to calculate the insulation degradation probability value of each section along the main line from the corresponding starting point outward, based on the current fluctuation amplitude and insulation status parameters in that section.

[0191] The electric field strength data gain module 250 is used to update the acquisition frequency of each section according to the probability value of each insulation degradation, and to obtain the updated electric field strength data based on the updated acquisition frequency.

[0192] The propagation path construction module 260 is used to generate a fault propagation path diagram based on the position of each starting point and the intensity gradient of the updated electric field intensity data.

[0193] The insulation condition analysis module 270 is used to calculate the simulated monitoring data of the cable network under various simulated operating scenarios based on the fault propagation path diagram, so as to generate the insulation condition monitoring results of the cable network.

[0194] The specific functions and examples of each module and submodule of the device in this embodiment of the invention can be found in the relevant descriptions of the corresponding steps in the above method embodiments, and will not be repeated here.

[0195] According to embodiments of the present invention, the above-described method of the present invention can be applied to an electronic device and a readable storage medium.

[0196] Figure 3A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0197] like Figure 3 As shown, device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 602 or a computer program loaded from storage unit 608 into random access memory (RAM) 603. RAM 603 may also store various programs and data required for the operation of device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via bus 604. Input / output (I / O) interface 605 is also connected to bus 604.

[0198] Multiple components in device 600 are connected to I / O interface 605, including: input unit 606, such as keyboard, mouse, etc.; output unit 607, such as various types of monitors, speakers, etc.; storage unit 608, such as disk, optical disk, etc.; and communication unit 609, such as network card, modem, wireless transceiver, etc. Communication unit 609 allows device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0199] The computing unit 601 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as a method for monitoring the condition of cable insulation. For example, in some embodiments, a method for monitoring the condition of cable insulation may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method for monitoring the condition of cable insulation described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured by any other suitable means (e.g., by means of firmware) to perform a method for monitoring the state of cable insulation.

[0200] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0201] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0202] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0203] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0204] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0205] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0206] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0207] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for monitoring the insulation condition of a cable, characterized in that, include: Obtain insulation status parameters of each section in each main line of the cable network, as well as leakage current data and electric field strength data of branch connections; The current fluctuation amplitude and insulation resistance value of each branch connection are calculated based on the leakage current data and the electric field strength data. The starting connection of each main line is determined based on the insulation resistance value, the electric field strength data, and the insulation state parameters. The insulation degradation probability value of each section is calculated based on the current fluctuation amplitude and insulation status parameters along the section direction extending outward from the corresponding starting point of the main line. The sampling frequency of each section is updated according to the insulation degradation probability value, and the updated electric field strength data is obtained based on the updated sampling frequency. A fault propagation path diagram is generated based on the position of each of the initial junctions and the intensity gradient of the updated electric field intensity data. Based on the fault propagation path diagram, simulated monitoring data of the cable network under various simulated operating scenarios are calculated to generate the insulation status monitoring results of the cable network.

2. The method according to claim 1, characterized in that, The calculation of the current fluctuation amplitude and insulation resistance value of each branch connection based on the leakage current data and the electric field strength data includes: Acquire the load current data of the main line connected to the branch connection point during the acquisition period; The load current data is used as the current data corresponding to the branch contact. The current fluctuation amplitude of each branch contact is obtained by calculating the difference between the maximum and minimum values ​​of each current data. Obtain the historical average fluctuation amplitude of each branch contact, and calculate the fluctuation ratio of each branch contact based on the current fluctuation amplitude and the corresponding historical average fluctuation amplitude. The initial resistance value of each branch connection is calculated by dividing the leakage current value of each branch connection by the corresponding electric field strength value. From the various fluctuation ratios, determine a first fluctuation ratio that exceeds a preset ratio threshold and a second fluctuation ratio that does not exceed the ratio threshold; The reciprocal of each of the first fluctuation ratios is used as a correction coefficient. The initial resistance value corresponding to each of the first fluctuation ratios is multiplied by the correction coefficient to calculate the corrected insulation resistance value. The initial resistance value corresponding to each of the second fluctuation ratios is taken as the insulation resistance value that does not need to be corrected. The corrected insulation resistance value or the insulation resistance value that does not need to be corrected is used as the insulation resistance value of the corresponding branch contact.

3. The method according to claim 1, characterized in that, The insulation status parameters of each section include the temperature data and partial discharge data of the corresponding section. The partial discharge data includes the discharge pulse amplitude and discharge pulse frequency of the corresponding main line. The step of determining the starting connection of each main line based on the insulation resistance value, the electric field strength data, and the insulation state parameters includes: Temperature distribution curves are generated based on the temperature data, and each temperature distribution curve is used to characterize the temperature values ​​distributed along the extension direction of each main line at a set sampling distance. The locations in each of the temperature distribution curves where the difference between the temperature value and the average temperature of the adjacent section exceeds a preset deviation value are marked as temperature anomaly points; Branch contacts whose current fluctuation amplitude exceeds the preset fluctuation threshold are marked as high-risk contacts; Read the insulation resistance value and electric field strength data of each high-risk contact, and select the position with the largest electric field strength gradient change rate as the candidate contact according to the direction of decreasing electric field strength from the corresponding high-risk contact to the corresponding main line. Candidate starting contacts are selected from the candidate contacts. Each candidate starting contact is a contact that coincides with the temperature anomaly point and whose signal strength of the partial discharge data exceeds a preset signal threshold. The magnitude of the signal strength is determined by the amplitude and frequency of the discharge pulse. The target starting point among the candidate starting points is determined as the starting point of the potential propagation path of each trunk line; the insulation resistance value of each target starting point is lower than the preset resistance threshold, and the intensity of the corresponding electric field decreases monotonically along the trunk line from the corresponding starting point outward.

4. The method according to claim 3, characterized in that, After reading the insulation resistance value and electric field strength data of each of the high-risk contacts, the method further includes: Identify each backbone node connected to the high-risk contact point and designate each backbone node as a node to be tested. The electric field intensity values ​​at the locations of each node to be tested are collected along the extension direction of the main line. The electric field intensity gradient of each node to be tested is calculated based on the set spacing between each group of adjacent nodes to be tested and the electric field intensity values. The temperature values ​​of each node to be tested are determined based on the temperature data of each section, and the temperature difference between each adjacent node to be tested is calculated based on the temperature values ​​of each node to be tested and the preset historical reference temperature values. The node under test that has an electric field intensity gradient exceeding a preset gradient threshold and a temperature difference exceeding a preset temperature threshold is marked as the starting node. Along the extension direction of the main line, an initial propagation link is constructed based on the contact sequence and contact spacing from the high-risk contact to the starting contact. The initial propagation link is used as a reference for calculating the insulation degradation probability value and inputting coordinate information for the various simulated operating scenarios.

5. The method according to claim 4, characterized in that, The insulation degradation probability value of each section extending outward from the corresponding starting point along the main line is calculated based on the current fluctuation amplitude and insulation state parameters in the section direction, including: Along the sections extending outward from the corresponding starting points of each main line, the spatial distribution characteristics of the electric field intensity are determined based on the electric field intensity values ​​of each node to be tested in the section direction, and multiple electric field intensity decreasing directions are extracted from the spatial distribution characteristics of the electric field intensity; each electric field intensity decreasing direction is determined by comparing the electric field intensity values ​​of adjacent nodes to be tested and selecting the direction of monotonically decreasing electric field intensity. Each pair of target nodes whose electric field strength difference between adjacent test nodes is less than a preset difference threshold is obtained, and the electric field strength decreasing direction corresponding to each pair of target nodes is taken as the potential diffusion direction. The relative growth rate of leakage current is calculated based on the current fluctuation amplitude of each target node pair corresponding to each potential diffusion direction. Based on the temperature difference between each pair of target nodes corresponding to each potential diffusion direction, and the average value of the electric field strength of each node to be tested, the section degradation index is calculated. The insulation degradation probability value of each section is calculated based on the relative growth rate of leakage current, the section degradation index, and the insulation status parameters.

6. The method according to claim 5, characterized in that, The step of generating a fault propagation path map based on the positions of each of the starting contacts and the intensity gradient of the updated electric field intensity data includes: Based on the position of each of the initial junctions, each of the potential diffusion directions, and the intensity gradient of the updated electric field intensity data, multiple potential propagation paths are generated. The corresponding target measurement point is determined along the expansion direction of each potential propagation path, and each target measurement point is the farthest measurement point where the electric field strength value in the current direction exceeds a preset safety threshold. Calculate the cumulative line length from the starting point of each potential propagation path to the corresponding target measurement point, and use each cumulative line length as the diffusion radius; The corresponding fault propagation range is determined by taking the starting point of each potential propagation path as the center and the corresponding diffusion radius as the boundary; The fault propagation direction of each potential propagation path is classified according to the angle between the expansion direction of each potential propagation path and the direction of the corresponding trunk line. Based on the established cable network topology, the fault propagation path map is generated according to the classification of each potential propagation path and fault propagation direction.

7. The method according to claim 6, characterized in that, The simulated monitoring data includes the current fluctuation amplitude and insulation resistance decay rate of each branch connection, as well as the partial discharge data of each section in each main line; based on the fault propagation path diagram, the simulated monitoring data of the cable network under various simulated operating scenarios are calculated to generate the insulation status monitoring results of the cable network, including: In the fault propagation path diagram, the node sequence in each of the main lines is identified, starting from the initial contact and extending outward along the potential propagation direction, and the historical current data of each node under a preset operating scenario is read from each node sequence; the preset operating scenarios include normal load scenario, overload scenario and load change scenario; The absolute value of the current difference between adjacent time points is calculated based on the historical current data, and the current fluctuation amplitude of each node under each preset operating scenario is determined based on the absolute value of the current difference. The initial value of the insulation resistance of each node is obtained, and the decay rate of the insulation resistance value of each node is calculated based on the insulation resistance value, the initial value of the insulation resistance and the monitoring time. The partial discharge data of each node is obtained, and the average discharge intensity of each node is calculated based on the total number and pulse duration of each partial discharge pulse. The attenuation coefficient is calculated based on the ratio of the attenuation rate of the insulation resistance value of each node to the preset reference attenuation rate. The discharge coefficient is calculated based on the ratio of the average discharge intensity of each node to the preset reference discharge intensity. The node degradation index of each node is calculated based on the attenuation coefficient, discharge coefficient, and current fluctuation amplitude of each node. The insulation status monitoring results are generated based on the degradation index of each node.

8. A monitoring system for the insulation condition of a cable, characterized in that, include: The monitoring data acquisition module is used to acquire insulation status parameters of each section in each main line of the cable network, as well as leakage current data and electric field strength data of branch joints in the main cable. The contact resistance calculation module is used to calculate the current fluctuation amplitude and insulation resistance value of each branch contact based on the leakage current data and the electric field strength data. The starting contact determination module is used to determine the starting contact of each of the main lines based on the insulation resistance value, the electric field strength data and the insulation state parameters. The degradation probability calculation module is used to calculate the insulation degradation probability value of each section along the main line extending outward from the corresponding starting point, based on the current fluctuation amplitude and insulation status parameters in the section direction. The electric field strength data gain module is used to update the acquisition frequency of each segment according to the insulation degradation probability value, and to obtain updated electric field strength data based on the updated acquisition frequency. The propagation path construction module is used to generate a fault propagation path diagram based on the position of each of the starting points and the intensity gradient of the updated electric field intensity data. The insulation condition analysis module is used to calculate the simulated monitoring data of the cable network under various simulated operating scenarios based on the fault propagation path diagram, so as to generate the insulation condition monitoring results of the cable network.

9. An electronic device, comprising: At least one processor; as well as The memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein, Computer instructions are used to cause a computer to perform the method according to any one of claims 1-7.

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