High-voltage cable fault location monitoring method and system based on environmental factor coupling

Abstract

This invention provides a method and system for high-voltage cable fault location monitoring based on environmental factor coupling, belonging to the field of cable monitoring technology. The method specifically involves: establishing a coupling relationship between environmental factors based on historical cable operation data and corresponding historical environmental parameters, mapping multidimensional environmental data along the cable line to corresponding equivalent environmental parameters; identifying abnormal cable operation events based on the equivalent environmental parameters and cable status information, and obtaining the corresponding initial fault location; determining the environmental coupling weights of each distributed measuring point using fuzzy inference and inverse distance weighting, thereby correcting the initial fault location; and outputting cable status early warning information based on the event identification results. This invention establishes a coupling relationship to map multidimensional environmental parameters to equivalent environmental parameters, and combines dynamic weights generated by fuzzy inference and inverse distance weighting to achieve adaptive correction of the location results to environmental changes and signal quality differences, thereby improving its accuracy.

Description

Technical Field

[0001] This invention relates to the field of cable monitoring technology, and in particular to a high-voltage cable fault location monitoring method and system based on environmental factor coupling. Background Technology

[0002] With the increasing prevalence of high-voltage cables in urban power grids, high-voltage cables have become a critical infrastructure for urban power transmission. The accuracy of fault location and the reliability of condition monitoring directly affect the efficiency of power grid restoration, the timeliness of operation and maintenance, and the overall safe and stable operation. Traveling wave location method, with its advantages of fast location speed and wide applicability, has become the primary method for locating faults in high-voltage cables.

[0003] Traditional two-end traveling wave location methods calculate the fault location by calculating the time difference between the propagation of a transient traveling wave generated at the fault point to both ends of the line, combined with known cable length and wave velocity parameters. The accuracy of this method is directly related to the stability of the wave velocity parameters. However, the propagation speed of the traveling wave in the cable is affected by environmental factors such as temperature and humidity. When environmental conditions change, the wave velocity also drifts, leading to inaccurate location results. Furthermore, this location method, which only uses information from both ends, cannot effectively monitor fault characteristics in the middle section of the cable. For long-distance cables or lines with complex branches, the location accuracy remains difficult to guarantee.

[0004] While distributed traveling wave positioning using multiple monitoring points can improve the positioning accuracy of long-distance cables or complex branch lines, this method still relies on the stability of wave velocity parameters and is susceptible to environmental factors. Furthermore, the environmental conditions at different monitoring points vary, and these environmental factors can be coupled, amplifying their impact and affecting positioning accuracy. Additionally, because the distances between monitoring points and fault points differ, the degree of signal attenuation and waveform distortion varies; data reliability decreases with distance. Distributed traveling wave positioning methods often use equal or fixed weights for data fusion from distributed monitoring points, which cannot adapt to the interference caused by these differences in propagation distance, resulting in relatively low positioning accuracy and reliability. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of cable fault location using traveling wave positioning, which cannot adapt to interference caused by environmental factors and differences in propagation distance, resulting in low accuracy of fault location. This invention provides a high-voltage cable fault location monitoring method and system based on environmental factor coupling. By establishing coupling relationships, multidimensional environmental parameters are mapped to equivalent environmental parameters. Combined with dynamic weights generated by fuzzy inference and inverse distance weighting, the location results are adaptively corrected to environmental changes and signal quality differences, thereby improving the accuracy and reliability of cable fault location under complex operating conditions.

[0006] The objective of this invention is achieved through the following technical solution: A high-voltage cable fault location and monitoring method based on environmental factor coupling includes: Simultaneously collect cable status information and multi-dimensional environmental data along the route from each distributed measurement point; Based on historical cable operation data and corresponding historical environmental parameters, establish the coupling relationship between environmental factors; Based on the established coupling relationship, multidimensional environmental data along the route are mapped to corresponding equivalent environmental parameters; Based on equivalent environmental parameters and cable status information, abnormal cable operation events are identified, and the corresponding initial fault locations are obtained. Based on equivalent environmental parameters and initial fault locations, the environmental coupling weights of each distributed measurement point are determined by fuzzy inference and inverse distance weighting. The initial fault location is corrected based on the environmental coupling weight, and the cable status early warning information is output by combining the identification results of abnormal cable operation events.

[0007] Furthermore, the process for setting up the distributed measurement points is as follows: Based on the laying type of high-voltage cables and the distribution of environmental parameters, the high-voltage cable lines are divided into several continuous basic monitoring sections. Based on the corresponding historical environmental data, the temporal correlation degree between any two environmental parameters in each basic monitoring section is calculated, and the corresponding environmental coupling value is obtained by weighted summation. The environmental coupling level of each basic monitoring section is set based on the corresponding environmental coupling value, and the density of measuring points in each basic monitoring section is matched according to the corresponding environmental coupling level. Using the starting point of the high-voltage cable as the reference measuring point, and combining the starting boundary, measuring point layout density, and node distribution information of each basic monitoring section, the measuring point positions within each basic monitoring section are set.

[0008] Furthermore, establishing the coupling relationship between environmental factors based on historical cable operation data and corresponding historical environmental parameters includes: Extract historical fault events corresponding to each distributed measurement point, along with their corresponding environmental parameters and fault locations, and construct a training dataset. By combining the corresponding preset coefficients to be optimized, a coupling function is constructed that includes measured environmental parameter terms, main effect terms of environmental parameters, and cross-coupling terms of environmental parameters; The goal is to minimize the fault location error, and the optimization coefficients of the coupling function are iteratively optimized based on the training dataset. The coefficients obtained by optimization iteration are substituted into the coupling function to establish the coupling relationship between environmental factors.

[0009] Furthermore, the process of identifying abnormal cable operation events based on equivalent environmental parameters and cable status information, and determining the corresponding initial fault location, includes: Environmental compensation correction is performed on cable status information based on equivalent environmental parameters, and abnormal events are identified in combination with preset steady-state operation thresholds. When an abnormal event is detected, the time difference of the fault traveling wave arriving at each distributed measuring point is obtained based on the cable status information, and the initial fault location is obtained by combining the location information of each distributed measuring point.

[0010] Furthermore, the determination of the environmental coupling weights of each distributed measurement point based on equivalent environmental parameters and initial fault locations through fuzzy inference and inverse distance weighting includes: Based on the parameter type, the equivalent environmental parameters of each distributed measurement point are divided into corresponding fuzzy subsets. Based on preset fuzzy inference rules, and with equivalent environmental parameters as input, the credibility of monitoring signals from each distributed measuring point is obtained after defuzzification processing. Based on the reliability of the corresponding monitoring signals and the initial fault location, the environmental coupling weight of each distributed measuring point is determined by combining inverse distance weighting.

[0011] Furthermore, the determination of the environmental coupling weights for each distributed measuring point based on the corresponding monitoring signal reliability and initial fault location, combined with inverse distance weighting, includes: Based on the preset laying mileage coordinates of each distributed measuring point, the cable mileage distance from each distributed measuring point to the initial fault location is obtained; Based on the inverse distance weighting rule, the inverse distance base weight of each distributed measuring point is calculated by pre-setting the attenuation index and the corresponding cable mileage distance. Using the reliability of the corresponding monitoring signal as a weighting coefficient, the environmental coupling weight of each distributed measurement point is obtained by multiplying it with the corresponding inverse distance basic weight.

[0012] Furthermore, the correction of the initial fault location based on environmental coupling weights includes: Obtain the fault location components corresponding to each distributed measurement point, and use the environmental coupling weight corresponding to each distributed measurement point as the weighting coefficient to perform weighted calculation on each fault location component. The weighted components of all fault locations are summed, and the mean is calculated by combining the sum of the environmental coupling weights of all distributed measurement points. The mean is then used as the corrected fault location.

[0013] Furthermore, when identifying abnormal cable operation events, the following steps are also performed: Based on the equivalent environmental parameters of each distributed measuring point, the corresponding external damage sensitive features are extracted, and external damage events are identified in combination with the preset external damage early warning threshold. Based on the identification results of abnormal cable operation events, early warning external damage events are selected from the external damage events and added to the abnormal cable operation events.

[0014] Furthermore, the cable status early warning information includes the type of abnormal cable operation event, the time of occurrence, and the location of the fault.

[0015] A high-voltage cable fault location and monitoring system based on environmental factor coupling, used to perform any of the above-mentioned location and monitoring methods, including: The data acquisition module is used to collect cable status information and multi-dimensional environmental data along the line from various distributed measurement points; The environmental parameter coupling module is used to establish the coupling relationship between environmental factors based on the cable's historical operating data and corresponding historical environmental parameters, so as to map multi-dimensional environmental data along the line into corresponding equivalent environmental parameters. The initial fault identification module is used to identify abnormal cable operation events based on equivalent environmental parameters and cable status information, and to obtain the corresponding initial fault location. The fault location correction module is used to determine the environmental coupling weight of each distributed measurement point based on equivalent environmental parameters and the initial fault location through fuzzy inference and inverse distance weighting, so as to correct the initial fault location. The early warning module is used to output cable status early warning information based on the corrected fault location and the identification results of abnormal cable operation events.

[0016] The beneficial effects of this invention are: By establishing the coupling relationship of multi-dimensional environmental factors and mapping them to equivalent environmental parameters, and combining fuzzy inference to automatically adjust the reliability of data from each distributed measurement point, the interference of environmental changes and environmental coupling effects on positioning accuracy is suppressed. At the same time, by reasonably reducing the weight of far-end attenuated signals through an inverse distance weighting mechanism, the positioning deviation caused by equal weight fusion can be avoided. This enables adaptive correction of positioning results to environmental changes and signal quality differences, thereby ensuring the accuracy and reliability of cable fault location. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a process of the present invention; Figure 2 This is a schematic diagram of the environmental coupling weight calculation process according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a structure according to an embodiment of the present invention. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Example: High-voltage cable fault location and monitoring methods based on environmental factor coupling, such as Figure 1 As shown, it includes: Simultaneously collect cable status information and multi-dimensional environmental data along the route from each distributed measurement point; Based on historical cable operation data and corresponding historical environmental parameters, establish the coupling relationship between environmental factors; Based on the established coupling relationship, multidimensional environmental data along the route are mapped to corresponding equivalent environmental parameters; Based on equivalent environmental parameters and cable status information, abnormal cable operation events are identified, and the corresponding initial fault locations are obtained. Based on equivalent environmental parameters and initial fault locations, the environmental coupling weights of each distributed measurement point are determined by fuzzy inference and inverse distance weighting. The initial fault location is corrected based on the environmental coupling weight, and the cable status early warning information is output by combining the identification results of abnormal cable operation events.

[0020] Utilizing a distributed positioning framework, cable status information and multi-dimensional environmental data along the cable route are collected synchronously from the measurement points of each distributed node. Cable status monitoring and fault location are performed based on this multi-dimensional monitoring data, ensuring the accuracy of the monitoring results. Furthermore, the coupling relationship between environmental factors is established by comparing historical cable operation data with corresponding historical environmental parameters to quantify the interactive coupling effects between different environmental factors.

[0021] Then, by using coupling relationships, multidimensional environmental data along the line are mapped to equivalent environmental parameters, transforming complex environmental coupling interference into standardized feature quantities, so as to effectively eliminate the nonlinear effects caused by multi-factor coupling and accurately quantify the impact of environmental factors on cable monitoring results.

[0022] Subsequently, the cable status information is compensated and corrected based on equivalent environmental parameters to accurately identify abnormal cable operation events and obtain the corresponding initial fault location, avoid abnormal misjudgments caused by environmental fluctuations, and ensure the basic accuracy of the initial positioning.

[0023] Then, based on equivalent environmental parameters, the reliability of the monitoring signal at each measuring point is obtained through fuzzy reasoning. The cable mileage distance between the measuring point and the fault point is calculated by combining the initial fault location and the inverse distance weighting calculation is completed. The reliability and inverse distance weights are integrated to determine the environmental coupling weights, while adapting to the signal attenuation and distortion problems caused by environmental coupling interference and propagation distance differences.

[0024] Finally, the initial fault location is corrected by weighted fusion based on environmental coupling weights. This strengthens the localization contribution of high-reliability, near-fault measurement points and weakens the interference of low-quality, long-distance measurement points, ensuring the accuracy of the corrected fault location. The final cable status warning information is output based on the corrected fault location and the corresponding cable operation anomaly event identification results, thereby improving the efficiency of cable fault handling and ensuring the safe operation of cable lines.

[0025] Considering the uneven distribution of environmental parameters such as temperature, humidity, and vibration along different sections of the cable, the varying degrees of coupling between environmental factors, and the different levels of fault traveling wave propagation attenuation and signal interference, a traditional uniform deployment method may result in insufficient measurement point density in sections with complex environmental coupling and strong interference. This leads to incomplete traveling wave signal acquisition and larger initial location errors. Conversely, redundant measurement points may be deployed in sections with stable environments and weak interference, resulting in wasted monitoring resources and reduced data processing efficiency. Furthermore, inappropriate measurement point locations directly affect the accuracy of calculating the cable mileage distance between the measurement point and the fault point, thus interfering with the accuracy of inverse distance weighting and environmental coupling weight calculations, and reducing the effectiveness of fault location correction.

[0026] Therefore, the distributed monitoring points should be set up differently according to the specific environmental conditions of the cable laying scenario to ensure the rational allocation of monitoring resources and improve the utilization efficiency of monitoring resources while ensuring the integrity of monitoring data.

[0027] Specifically, the process of setting up the distributed measurement points is as follows: Based on the laying type of high-voltage cables and the distribution of environmental parameters, the high-voltage cable lines are divided into several continuous basic monitoring sections. Based on the corresponding historical environmental data, the temporal correlation degree between any two environmental parameters in each basic monitoring section is calculated, and the corresponding environmental coupling value is obtained by weighted summation. The environmental coupling level of each basic monitoring section is set based on the corresponding environmental coupling value, and the density of measuring points in each basic monitoring section is matched according to the corresponding environmental coupling level. Using the starting point of the high-voltage cable as the reference measuring point, and combining the starting boundary, measuring point layout density, and node distribution information of each basic monitoring section, the measuring point positions within each basic monitoring section are set.

[0028] The laying types of the high-voltage cables include direct burial, duct laying, tunnel laying, and cable tray laying. The distribution of environmental parameters includes the temperature variation range and gradient along the line, the spatial difference of relative humidity, the distribution variation of soil parameters, and the intensity and frequency distribution of external vibration sources. Among them, soil parameters include resistivity, moisture content, and thermal resistance coefficient, and the external vibration sources include roads, construction areas, and railways.

[0029] The cable lines are divided into several continuous basic monitoring sections based on the rule that the laying method is consistent and the difference in the distribution of environmental parameters between adjacent locations does not exceed the corresponding difference threshold.

[0030] For each basic monitoring area, the corresponding historical environmental data are extracted, and the temporal correlation between temperature and humidity, temperature and vibration, temperature and soil, humidity and vibration, humidity and soil, and vibration and soil is calculated.

[0031] Specifically, temporal correlation can be calculated using methods such as the Pearson correlation coefficient to reflect the degree of coordinated change of different environmental factors over time, such as the high correlation between humidity and soil moisture content, and the weak correlation between temperature and vibration.

[0032] Then, the time series correlation degree of each group is weighted and summed to determine the environmental coupling value corresponding to each basic monitoring section, so as to quantify the interaction coupling strength of multiple environmental factors in the basic monitoring section. The higher the environmental coupling degree, the more significant the mutual influence between environmental factors in the section, that is, the stronger the interaction coupling strength, and the stronger the composite interference encountered by the traveling wave propagation in the section.

[0033] Based on the environmental coupling value of each segment, a corresponding environmental coupling level is set. Specifically, a threshold range for the environmental coupling value of each environmental coupling level can be preset. The environmental coupling level of each segment is determined by matching the environmental coupling value with the corresponding threshold range. The threshold range can be set according to actual needs.

[0034] Each environmental coupling level has a pre-set corresponding deployment density. Once the environmental coupling level is determined, the corresponding measurement point deployment density can be matched according to the environmental coupling level.

[0035] A high environmental coupling level indicates that the environmental factors in this section are complex and the coupling effect is strong, which has a significant impact on traveling wave positioning. Therefore, the density of measurement points should be higher, meaning more monitoring devices should be deployed per unit length. Conversely, a low environmental coupling level allows for a slightly lower density of measurement points to reduce system costs and data redundancy, and improve the utilization rate of monitoring resources.

[0036] Finally, using the starting end of the high-voltage cable as the reference measuring point, and combining the starting boundary coordinates of each basic monitoring section, the determined measuring point layout density, and the node distribution information along the cable, the specific installation location of the measuring points is determined section by section. The node distribution information includes the location of nodes such as manholes, cable joints, and grounding boxes along the line.

[0037] Furthermore, when setting the installation locations of distributed monitoring points, it is necessary to first determine the spacing between the status monitoring points used to collect cable status information within each monitoring section. This spacing is set based on the maximum allowable spacing corresponding to the effective attenuation distance of the traveling wave signal propagating in the cable, to ensure that at least two adjacent status monitoring points can receive a valid traveling wave signal when a fault occurs at any point. The specific maximum allowable spacing can be obtained by looking up a table. Then, using the spacing as a constraint, the installation locations of the corresponding status monitoring points are selected based on the node distribution information.

[0038] Based on the determined locations of the status monitoring points, additional environmental monitoring points are added, taking into account the starting boundary coordinates of each basic monitoring section and the already determined density of monitoring points.

[0039] For sections with high environmental coupling levels, environmental monitoring points are deployed more densely between status monitoring points, or environmental sensors are added at status monitoring points to meet the corresponding monitoring point density requirements and comprehensively capture rapid changes in temperature and humidity and coupling effects.

[0040] For sections with low environmental coupling levels, data collection mainly relies on the environmental sensors built into the status monitoring points, without additional points being deployed. Environmental monitoring points are only deployed between the status monitoring points when the number of status monitoring points within the initial boundary coordinate range cannot meet the corresponding measurement point deployment density requirements.

[0041] After determining the distributed monitoring points for all basic monitoring sections, install corresponding data acquisition equipment according to the type of monitoring point to synchronously collect corresponding cable status information and multi-dimensional environmental data along the route. For monitoring points only equipped with environmental data acquisition equipment, the cable status information collected by the nearest status monitoring point can be directly synchronized.

[0042] Based on the established distributed monitoring points, cable status information and multi-dimensional environmental data along the cable route are synchronously collected or acquired at the corresponding locations. The cable status information includes parameters reflecting the electrical operating status of the cable, such as load current signals, grounding circulating current signals, and main cable load signals. The multi-dimensional environmental data along the cable route includes parameters such as vibration signals, sound signals, temperature, humidity, and soil parameters.

[0043] Because the operating environment of outdoor cables is a complex system with strong coupling relationships between various environmental factors, temperature changes not only directly alter wave velocity but also affect the adsorption and desorption rates of moisture in the cable insulation medium. This results in a non-linear effect of humidity on the dielectric constant, which is strongly correlated with temperature. High humidity environments soften the cable sheath or soil medium, making it easier for external vibrations to couple to the cable body. Consequently, the transmission efficiency of vibration interference increases with rising humidity. Conversely, in low-temperature environments, the cable and soil become hard and brittle, and vibrations of the same intensity may lead to more severe signal distortion.

[0044] This demonstrates that temperature, humidity, vibration, and soil parameters along the cable route do not act independently. Instead, there is a nonlinear coupling effect between different environmental parameters such as temperature and humidity, and humidity and vibration. Changes in a single environmental parameter can amplify the interference on traveling wave propagation and signal acquisition. Even with optimized measurement point locations, the collected data remains discrete and uncorrelated, failing to reflect the interactive effects of multiple coupled factors, thus leading to positioning errors.

[0045] Therefore, after completing the optimized setup of distributed measuring points and data acquisition, we further establish the coupling relationship between environmental factors to accurately quantify the interactive effects of multidimensional environmental factors, and provide quantitative indicators for optimizing the interference of subsequent environmental interactions on monitoring signals and positioning results.

[0046] Specifically, establishing the coupling relationship between environmental factors based on historical cable operation data and corresponding historical environmental parameters includes: Extract historical fault events corresponding to each distributed measurement point, along with their corresponding environmental parameters and fault locations, and construct a training dataset. By combining the corresponding preset coefficients to be optimized, a coupling function is constructed that includes measured environmental parameter terms, main effect terms of environmental parameters, and cross-coupling terms of environmental parameters; The goal is to minimize the fault location error, and the optimization coefficients of the coupling function are iteratively optimized based on the training dataset. The coefficients obtained by optimization iteration are substituted into the coupling function to establish the coupling relationship between environmental factors.

[0047] Historical fault events corresponding to each distributed measurement point are extracted. Each historical fault event records the environmental parameters of each measurement point at the time of the event and the actual fault location of the event. The extracted data is aligned with the measurement points and time to construct a training dataset. Each sample in the training dataset contains a combination of environmental parameters from multiple measurement points at the same time and the corresponding fault location label.

[0048] Subsequently, a coupling function expression is constructed to describe the combined influence of multiple environmental factors on traveling wave propagation. The coupling function includes measured environmental parameter terms, main effect terms of environmental parameters, and cross-coupling terms of environmental parameters.

[0049] For each environmental parameter, the equivalent value is calculated in the same way. It is based on the measured value of the corresponding parameter and obtained by superimposing the corresponding main effect term and the cross-coupling term between the parameter and each other environmental parameter.

[0050] The main effect term is represented as a linear function of the difference between the measured value and the preset reference value of the parameter. Each cross-coupling term is represented as the product of the difference between the measured value and the corresponding reference value of another environmental parameter, multiplied by the measured value of the parameter itself. Each cross-coupling term is assigned a corresponding coupling coefficient to be optimized.

[0051] Specifically, in the coupling function expression, the coefficients to be optimized include the main effect coefficients corresponding to temperature, humidity, vibration and soil, and the coupling coefficients of temperature and humidity, temperature and vibration, temperature and soil, humidity and temperature, humidity and vibration, humidity and soil, vibration and temperature, vibration and humidity, vibration and soil, soil and temperature, soil and humidity, and soil and vibration.

[0052] For each historical fault event in the training dataset, the corresponding environmental parameters are substituted into the coupling function to obtain the corresponding equivalent environmental parameter value containing the coefficients to be optimized. Based on the equivalent environmental parameter value, the corresponding fault location estimate is calculated using the same processing method as the subsequent fuzzy inference and weighted localization process. This estimate is then compared with the actual fault location recorded in the training dataset to calculate the localization error, which can be either an absolute error or a root mean square error.

[0053] Using the average localization error of all training samples as the objective function, the coupling coefficient to be optimized is globally optimized iteratively using a genetic algorithm or particle swarm optimization algorithm until the objective function converges or the preset maximum number of iterations is reached.

[0054] Specifically, taking the genetic algorithm as an example, its global optimization iteration process is as follows: Initialize the population and randomly generate several sets of candidate coefficients, with each set of candidate coefficients constituting an individual.

[0055] For each individual in the population, the candidate coefficients corresponding to that individual are sequentially substituted into the coupling function. For each historical fault event in the training dataset, the corresponding environmental parameters are substituted into the coupling function to obtain the corresponding equivalent environmental parameter values. Based on the equivalent environmental parameter values, the corresponding fault location estimate is calculated using a processing method consistent with the subsequent fuzzy inference and weighted localization process. This estimate is compared with the actual fault locations recorded in the training dataset to calculate the localization error. This error can be either absolute error or root mean square error. Finally, the average localization error of all training samples is calculated, and this is used as the fitness value for the corresponding individual.

[0056] If the fitness value of the best individual in the current population no longer decreases for several consecutive generations, or if the preset maximum number of iterations has been reached, then optimization stops, and the current best individual is output as the final coupling coefficient. Otherwise, a preset proportion of the best individuals are selected based on their fitness values. The selected individuals are then subjected to single-point or uniform crossover according to a preset crossover probability to generate offspring individuals. The candidate coefficients in the offspring individuals are then randomly perturbed according to a preset mutation probability to generate a new generation of population. The fitness is then recalculated until convergence is determined or the preset maximum number of iterations has been reached.

[0057] After stopping optimization, the candidate coefficient combination corresponding to the best individual in the best iteration is taken as the coefficient set that minimizes the positioning error. The corresponding coefficients are substituted into the coupling function to obtain the coupling relationship between environmental factors.

[0058] However, the equivalent environmental parameters obtained through the coupling relationship between environmental factors are merely a quantification of the environmental state and cannot directly determine whether a cable fault has occurred. Normal fluctuations in environmental parameters can also lead to changes in the equivalent environmental parameters, but these changes do not necessarily indicate an electrical abnormality in the cable. Therefore, based on the equivalent environmental parameters, combining cable status information for abnormal event identification ensures the accuracy of abnormal situation identification and reduces the problem of misjudgment. Although traveling waves are affected by environmental factors, resulting in positioning deviations, traveling wave positioning analysis can still obtain a reliable coarse fault location. This serves as the basis for eliminating environmental factor interference in calculations, effectively avoiding blind searches or iterations for subsequent precise fault location and improving fault location efficiency.

[0059] Specifically, the step of identifying abnormal cable operation events based on equivalent environmental parameters and cable status information, and determining the corresponding initial fault location, includes: Environmental compensation correction is performed on cable status information based on equivalent environmental parameters, and abnormal events are identified in combination with preset steady-state operation thresholds. When an abnormal event is detected, the time difference of the fault traveling wave arriving at each distributed measuring point is obtained based on the cable status information, and the initial fault location is obtained by combining the location information of each distributed measuring point.

[0060] First, based on the equivalent environmental parameters of each distributed measurement point at the current moment, the effects of wave velocity drift, amplitude attenuation and waveform distortion caused by environmental factors are removed from the measured cable state signal to obtain the environmentally normalized state characteristic quantity.

[0061] Among them, environmental compensation correction can be achieved by using a pre-calibrated mapping relationship. Specifically, historical data or finite element simulation can be used, with equivalent temperature, equivalent humidity, equivalent vibration and equivalent soil parameters as inputs and corresponding signal distortion variables as outputs. The mapping relationship can be fitted by multinomial regression, neural network or table lookup interpolation.

[0062] The signal distortion can be wave velocity offset, amplitude attenuation coefficient, and waveform broadening coefficient, etc.

[0063] After obtaining the equivalent environmental parameters, the expected signal distortion under the current environmental conditions is determined according to the corresponding mapping relationship. These distortions are then compensated in reverse in the measured cable status signal. Specifically, the measured wave velocity is divided by the wave velocity correction coefficient to obtain the environmentally normalized wave velocity, and the measured amplitude is multiplied by the reciprocal of the attenuation compensation coefficient to obtain the normalized amplitude. The waveform is then subjected to inverse filtering to eliminate the broadening effect, etc.

[0064] The compensated and corrected state characteristic quantity is then compared with the preset steady-state operating threshold. The steady-state operating threshold is obtained based on the statistical data of the cable's historical normal operation under various typical environmental conditions, and reflects the normal fluctuation range of the cable's state characteristics under fault-free conditions.

[0065] If the current compensated state characteristic quantity continuously or instantaneously exceeds the steady-state operation threshold, it is determined that there is an abnormal cable operation event; otherwise, it is determined that there is environmental disturbance or normal operation.

[0066] When an abnormal event is detected, fault location is immediately triggered. The accurate time of arrival of the fault traveling wave at each distributed measuring point is extracted from the original or compensated cable status information. Specifically, the arrival time of the traveling wave front can be calibrated by wavelet transform or waveform peak detection. At the same time, the preset laying mileage coordinates of each distributed measuring point in the cable laying mileage topology are extracted. These coordinates are recorded and saved when the measuring points are set.

[0067] Based on the time difference of the traveling wave arriving at each measuring point and the position information of each measuring point, the initial position of the fault point is directly calculated using a wave velocity-independent algorithm.

[0068] The locations of the distributed measuring points on the high-voltage cable are respectively The distances from the traveling wave fault signal to each measuring point are respectively The arrival times of the traveling wave fault signal at each measuring point are as follows: For example, consider the following fault scenarios.

[0069] Taking the three-point measurement method as an example, the calculation expression for wave velocity independence in this fault scenario is as follows: ; in, Let the initial fault location be the one to be solved. These are three consecutive distributed measurement points in the nth group outside the fault interval. They are the first The, the The and the first The preset location coordinates of the distributed measurement points They are the first The, the The and the first The preset location coordinates of the distributed measurement points , , and The traveling wave fault signal arrives at the first The, the The, the The and the first The absolute time of each distributed measurement point The traveling wave fault signal arrives at the first and The absolute time of each distributed measurement point.

[0070] Sort all distributed measurement points in ascending order of arrival time. The earlier the arrival time, the closer the distributed measurement point is to the fault point. Select three consecutive distributed measurement points from the time series as a group. For each group of three consecutive distributed measurement points, substitute the time difference of the fault traveling wave arriving at each distributed measurement point and the corresponding location information of each distributed measurement point into the calculation expression to calculate the corresponding fault location estimate.

[0071] If the calculated fault location estimate is within the mileage range of the measurement points, the estimate is retained; otherwise, it is discarded.

[0072] The initial fault location is obtained by averaging all the retained fault location estimates.

[0073] When calculating the initial fault location, a wave velocity-independent algorithm is used, which can effectively avoid the wave velocity uncertainty introduced by changes in traveling wave velocity due to environmental factors, cable aging, and differences in laying methods. There is no need to pre-calibrate or correct the wave velocity parameters in real time, which can improve the accuracy of locating the initial fault location.

[0074] While the initial fault location was optimized by eliminating the influence of wave velocity variations using a wave velocity independence algorithm, the calculation process did not consider the differential interference caused by the actual environmental conditions of each distributed measurement point on signal quality. Different distributed measurement points operate under varying environmental conditions, and the coupling effects between environmental factors can cause varying degrees of distortion and attenuation in signal propagation. Furthermore, due to the different distances between each distributed measurement point and the fault point, the degree of signal attenuation during propagation also differs; the greater the distance, the lower the signal reliability.

[0075] Therefore, by further obtaining environmental coupling weights through fuzzy inference and inverse distance weighting, the data of different measurement points can be differentiated in subsequent positioning corrections, so that distributed measurement points with high signal quality and close to the fault point occupy a larger proportion in the fusion, thereby further improving the accuracy and robustness of the positioning results.

[0076] It should be noted that the differentiated processing of this environmental coupling weight is different from the compensation correction for anomaly identification. In the anomaly identification stage, the compensation correction of cable status information based on equivalent environmental parameters is applied to the original measurement signal of a single measuring point. The purpose is to eliminate the distortion caused by environmental factors to the signal of the distributed measuring point itself, so that the compensated signal can be accurately compared with the preset steady-state operating threshold, thereby reliably determining whether a fault event has occurred.

[0077] After obtaining the initial fault location, the environmental coupling weights of each measuring point are determined through fuzzy inference and inverse distance weighting. This process is applied to the data fusion process between multiple distributed measuring points. Even though the signal of each distributed measuring point has undergone independent compensation in the previous stage, the differences in environmental conditions at different measuring points and the residual errors of the compensation model still exist, resulting in different reliability of the remaining signals at each distributed measuring point. In addition, the physical attenuation of the traveling wave signal with increasing distance during propagation cannot be eliminated by signal-level compensation.

[0078] Among them, such as Figure 2 As shown, the determination of the environmental coupling weights of each distributed measurement point based on equivalent environmental parameters and initial fault locations through fuzzy inference and inverse distance weighting includes: Based on the parameter type, the equivalent environmental parameters of each distributed measurement point are divided into corresponding fuzzy subsets. Based on preset fuzzy inference rules, and with equivalent environmental parameters as input, the credibility of monitoring signals from each distributed measuring point is obtained after defuzzification processing. Based on the reliability of the corresponding monitoring signals and the initial fault location, the environmental coupling weight of each distributed measuring point is determined by combining inverse distance weighting.

[0079] The equivalent environmental parameters for each distributed measuring point, including equivalent temperature, equivalent humidity, equivalent vibration, and equivalent soil parameters, are divided into corresponding fuzzy subsets based on parameter type. For example, equivalent temperature is divided into three fuzzy subsets: low, medium, and high; equivalent humidity into three fuzzy subsets: dry, normal, and humid; equivalent vibration into three fuzzy subsets: weak, moderate, and strong; and equivalent soil parameters into three fuzzy subsets: low impact, moderate impact, and high impact. Each fuzzy subset has a predefined membership function, which can be in triangular or trapezoidal form, set according to actual needs, to calculate the membership degree of the input parameter to each subset.

[0080] Then, based on the preset fuzzy inference rule base, fuzzy inference is performed using the equivalent environmental parameters of each measuring point as input. The fuzzy inference rule base consists of several judgment rules. Each judgment rule outputs the fuzzy level of the credibility of the monitoring signal of the corresponding distributed measuring point according to different combinations of equivalent temperature, equivalent humidity, equivalent vibration, and equivalent soil parameters.

[0081] For example, if the equivalent temperature is high, the equivalent humidity is humid, the equivalent vibration is strong, and the equivalent soil parameter has a high impact, then the reliability of the output monitoring signal is extremely low. If the equivalent temperature is medium, the equivalent humidity is normal, the equivalent vibration is weak, and the equivalent soil parameter has a low impact, then the reliability of the output monitoring signal is extremely high.

[0082] The results of all rule triggers are synthesized and defuzzified using the centroid method or the maximum membership method to obtain the specific monitoring signal reliability value for each distributed measuring point. The obtained monitoring signal reliability value can reflect the comprehensive reliability of the data collected by the corresponding distributed measuring point under the current environmental conditions.

[0083] Considering that even if two distributed measurement points are in the same environmental conditions, the distributed measurement point farther from the fault point will still experience more severe signal amplitude attenuation and more obvious waveform broadening due to the longer traveling wave propagation path, and this attenuation effect cannot be eliminated by environmental compensation or fuzzy inference, based on the reliability of the acquired monitoring signal, the signal attenuation caused by the propagation distance is quantified by the initial fault location. By using the inverse distance weighting method, the differential effects of environmental interference and propagation attenuation are aggregated into the environmental coupling weight, thereby correcting the initial fault distance and improving the accuracy of the final fault location.

[0084] The determination of the environmental coupling weights for each distributed measurement point based on the corresponding monitoring signal reliability and initial fault location, combined with inverse distance weighting, includes: Based on the preset laying mileage coordinates of each distributed measuring point, the cable mileage distance from each distributed measuring point to the initial fault location is obtained; Based on the inverse distance weighting rule, the inverse distance base weight of each distributed measuring point is calculated by pre-setting the attenuation index and the corresponding cable mileage distance. Using the reliability of the corresponding monitoring signal as a weighting coefficient, the environmental coupling weight of each distributed measurement point is obtained by multiplying it with the corresponding inverse distance basic weight.

[0085] Obtain the preset laying mileage coordinates of each distributed measuring point, that is, the accurate length of the distributed measuring point from the starting point along the cable line. Combined with the calculated initial fault location, calculate the cable mileage distance between each distributed measuring point and the initial fault location. The cable mileage distance is the actual cable path length from the distributed measuring point to the initial fault location.

[0086] If there is a cable mileage distance of zero at one of the distributed measurement points, then its inverse distance base weight is set to the maximum value. The farther the distance, the smaller the weight value is calculated, thus reflecting the physical law that the traveling wave signal attenuates as the propagation distance increases.

[0087] For each distributed measurement point, its cable mileage distance is used as the independent variable. Its inverse distance basic weight value is calculated through a preset attenuation exponential function. Then, the corresponding monitoring signal reliability is used as the weighting coefficient, and the product operation is performed with the corresponding inverse distance basic weight to obtain the final environmental coupling weight.

[0088] The expression for the environmental coupling weight is: ; in, For the first Environmental coupling weights for distributed measurement points For the first Cable mileage distance for each distributed measurement point For the first Reliability of monitoring signals from distributed measurement points This is the preset attenuation index.

[0089] After obtaining the environmental coupling weight of each distributed measuring point, the initial fault location is further corrected according to the environmental coupling weight, so that the final positioning result is tilted towards the measuring point with high signal reliability and close to the fault point. This effectively suppresses the negative impact of remote measuring points with severe environmental interference and large signal attenuation on the positioning, and improves the accuracy and robustness of cable fault positioning under complex working conditions.

[0090] The step of correcting the initial fault location based on environmental coupling weights includes: Obtain the fault location components corresponding to each distributed measurement point, and use the environmental coupling weight corresponding to each distributed measurement point as the weighting coefficient to perform weighted calculation on each fault location component. The weighted components of all fault locations are summed, and the mean is calculated by combining the sum of the environmental coupling weights of all distributed measurement points. The mean is then used as the corrected fault location.

[0091] In the process of calculating the initial fault location, the fault location estimate of each group of three consecutive distributed measurement points has been calculated using the wave velocity independence algorithm. The corresponding fault location estimate is used as the fault location component of the corresponding distributed measurement point. If the same distributed measurement point participates in the calculation of multiple sets of estimates, the arithmetic mean of all the estimates in which the distributed measurement point participates is taken as the fault location component of the distributed measurement point, so as to ensure that each distributed measurement point contributes only one independent component value.

[0092] Using the environmental coupling weight corresponding to each distributed measurement point as the weighting coefficient, the fault location component of each distributed measurement point is weighted and calculated. That is, the fault location component of each distributed measurement point is multiplied by its environmental coupling weight to obtain the weighted fault location component of the distributed measurement point.

[0093] The weighted fault location components of all distributed measurement points are summed, and the environmental coupling weights of all distributed measurement points are summed. The corresponding weighted average value is calculated, and the result is the corrected fault location.

[0094] If the sum of all environmental coupling weights is zero, the initial fault location obtained previously through the wave speed independence algorithm is directly used as the correction result.

[0095] After weighted averaging by environmental coupling weights, measurement points with high signal reliability and close to the initial fault location can occupy a larger proportion in the final location result, thereby effectively suppressing the influence of interfering measurement points and outputting a more accurate and reliable fault location.

[0096] Considering that some external disturbances, such as vibrations from large construction machinery, may not yet cause cable insulation damage or electrical faults and cannot be triggered by cable status signals for anomaly identification, these external damage events will still affect cable operation. If they continue to occur, they may even lead to cable faults. Therefore, when identifying abnormal cable operation events, the following steps are also performed: Based on the equivalent environmental parameters of each distributed measuring point, the corresponding external damage sensitive features are extracted, and external damage events are identified in combination with the preset external damage early warning threshold. Based on the identification results of abnormal cable operation events, early warning external damage events are selected from the external damage events and added to the abnormal cable operation events.

[0097] The external rupture sensitive features include the time-domain peak value of the vibration signal, impact energy, frequency distribution characteristics, sound pressure level of the sound signal, and typical acoustic features such as blasting or excavation.

[0098] Based on the statistical distribution of external damage sensitivity features under historical normal operating conditions, preset external damage warning thresholds are established for each level. The external damage sensitivity features extracted at the current moment are compared with the preset warning thresholds. If the feature value is lower than the minimum threshold, it is determined that there is no external damage risk. If the feature value exceeds a certain threshold, it is determined that there is an external damage event, and the severity of the warning is determined according to the level of the threshold exceeded.

[0099] Then, by combining the results of abnormal cable operation events identified based on cable status signals, external damage events are filtered. If, when an external damage event occurs, the cable status signals of the corresponding distributed measuring point and adjacent distributed measuring points do not exceed the steady-state operation threshold, i.e., no electrical fault has occurred, then the external damage event is marked as a warning external damage event. If an abnormality is detected in the cable status signal at the same time, then it is marked as a fault external damage event.

[0100] For the selected early warning external damage events, they are included as new abnormal events in the original cable operation abnormal events. For fault external damage events, they are directly matched and bound to the original cable operation abnormal events.

[0101] Finally, based on the acquired abnormal cable operation events, corresponding cable status early warning information is generated, which includes the type of abnormal cable operation event, the time of occurrence, and the location of the fault.

[0102] If the abnormal event is identified based on cable status information, the event type is determined as either cable fault or cable and external damage fault, depending on whether it is linked to an external damage event. If it is a warning external damage event, the event type is determined as external damage risk.

[0103] Then, by combining the corresponding event identification event, the corresponding event is determined. Based on the corrected location result, the cable fault or cable fault is determined, or the fault location of the cable and external damage fault is determined based on the corrected location result and the location of the distributed measuring points that detected the external damage event, or the fault location of the external damage risk is determined based on the location of the distributed measuring points that detected the early warning external damage event.

[0104] By sending cable status warning information to maintenance personnel, timely on-site intervention can be initiated to avoid or mitigate potential cable faults or to address existing cable faults, thereby ensuring the safety and reliability of cable operation.

[0105] Another aspect of this embodiment also provides a high-voltage cable fault location and monitoring system based on environmental factor coupling, such as... Figure 3 As shown, it includes: The data acquisition module is used to collect cable status information and multi-dimensional environmental data along the line from various distributed measurement points; The environmental parameter coupling module is used to establish the coupling relationship between environmental factors based on the cable's historical operating data and corresponding historical environmental parameters, so as to map multi-dimensional environmental data along the line into corresponding equivalent environmental parameters. The initial fault identification module is used to identify abnormal cable operation events based on equivalent environmental parameters and cable status information, and to obtain the corresponding initial fault location. The fault location correction module is used to determine the environmental coupling weight of each distributed measurement point based on equivalent environmental parameters and the initial fault location through fuzzy inference and inverse distance weighting, so as to correct the initial fault location. The early warning module is used to output cable status early warning information based on the corrected fault location and the identification results of abnormal cable operation events.

[0106] The data acquisition module is connected to the environmental parameter coupling module, the anomaly initial identification module is connected to both the data acquisition module and the environmental parameter coupling module, the fault location correction module is connected to both the environmental parameter coupling module and the anomaly initial identification module, and the early warning module is connected to both the anomaly initial identification module and the fault location correction module.

[0107] The data acquisition module mainly includes cable status acquisition equipment and environmental data acquisition equipment. The cable status acquisition equipment includes traveling wave voltage sensors, traveling wave current sensors, grounding circulating current sensors, and load current sensors. The traveling wave sensors are high-frequency response sensors, such as capacitively coupled voltage sensors or Rogowski coil current sensors, and are installed on the cable body or grounding wire. The grounding circulating current sensors are installed in the grounding box or at the grounding lead, and the load current sensors are installed at the cable terminal or intermediate joint. All these sensors are installed without power interruption and employ double-layer magnetic shielding technology to eliminate electromagnetic interference. They are used to acquire cable status information such as load current signals and grounding circulating current signals.

[0108] The environmental data acquisition equipment includes vibration sensors, sound sensors, temperature sensors, humidity sensors, and soil parameter sensors. The vibration sensors, employing accelerometers, are mounted on the cable body or support to monitor external vibrations generated by construction machinery impacts, blasting, etc. The sound sensors use microphone arrays to identify typical acoustic signatures of excavators, pile drivers, etc. The temperature and humidity sensors are integrated into the same probe and installed on or near the cable surface in the air. The soil parameter sensors are mainly deployed in directly buried sections or duct sections, buried in the soil near the cable. These sensors are distributed along the cable line at preset measurement points, collecting multi-dimensional environmental data along the route, including vibration signals, sound signals, temperature, humidity, and soil parameters.

[0109] The environmental parameter coupling module, the initial anomaly identification module, the fault location correction module, and the early warning module are all located at the monitoring end, and can be devices such as computers and microprocessors with corresponding data processing capabilities.

[0110] Furthermore, the early warning module is equipped with a corresponding communication port, which can send the output cable status early warning information to the mobile terminal of the operation and maintenance personnel or the monitoring backend to realize fault early warning.

[0111] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Other variations and modifications are possible without departing from the technical solutions described in the claims.

Claims

1. A method for high voltage cable fault location monitoring based on environmental factors coupling, characterized in that, include: Simultaneously collect cable status information and multi-dimensional environmental data along the route from each distributed measurement point; Based on historical cable operation data and corresponding historical environmental parameters, establish the coupling relationship between environmental factors; Based on the established coupling relationship, multidimensional environmental data along the route are mapped to corresponding equivalent environmental parameters; Based on equivalent environmental parameters and cable status information, abnormal cable operation events are identified, and the corresponding initial fault locations are obtained. Based on equivalent environmental parameters and initial fault locations, the environmental coupling weights of each distributed measurement point are determined by fuzzy inference and inverse distance weighting. The initial fault location is corrected based on the environmental coupling weight, and the cable status early warning information is output by combining the identification results of abnormal cable operation events. The determination of environmental coupling weights for each distributed measurement point based on equivalent environmental parameters and initial fault locations, using fuzzy inference and inverse distance weighting, includes: Based on the parameter type, the equivalent environmental parameters of each distributed measurement point are divided into corresponding fuzzy subsets. Based on preset fuzzy inference rules, and with equivalent environmental parameters as input, the credibility of monitoring signals from each distributed measuring point is obtained after defuzzification processing. Based on the preset laying mileage coordinates of each distributed measuring point, the cable mileage distance from each distributed measuring point to the initial fault location is obtained; Based on the inverse distance weighting rule, the inverse distance base weight of each distributed measuring point is calculated by pre-setting the attenuation index and the corresponding cable mileage distance. Using the reliability of the corresponding monitoring signal as a weighting coefficient, the environmental coupling weight of each distributed measurement point is obtained by multiplying it with the corresponding inverse distance basic weight.

2. The environmental factor coupled high voltage cable fault location monitoring method of claim 1, wherein, The process for setting up the distributed measurement points is as follows: Based on the laying type of high-voltage cables and the distribution of environmental parameters, the high-voltage cable lines are divided into several continuous basic monitoring sections. Based on the corresponding historical environmental data, the temporal correlation degree between any two environmental parameters in each basic monitoring section is calculated, and the corresponding environmental coupling value is obtained by weighted summation. The environmental coupling level of each basic monitoring section is set based on the corresponding environmental coupling value, and the density of measuring points in each basic monitoring section is matched according to the corresponding environmental coupling level. Using the starting point of the high-voltage cable as the reference measuring point, and combining the starting boundary, measuring point layout density, and node distribution information of each basic monitoring section, the measuring point positions within each basic monitoring section are set.

3. The environmental factor coupled high voltage cable fault location monitoring method of claim 1, wherein, The establishment of coupling relationships between environmental factors based on historical cable operation data and corresponding historical environmental parameters includes: Extract historical fault events corresponding to each distributed measurement point, along with their corresponding environmental parameters and fault locations, and construct a training dataset. By combining the corresponding preset coefficients to be optimized, a coupling function is constructed that includes measured environmental parameter terms, main effect terms of environmental parameters, and cross-coupling terms of environmental parameters; The goal is to minimize the fault location error, and the optimization coefficients of the coupling function are iteratively optimized based on the training dataset. The coefficients obtained by optimization iteration are substituted into the coupling function to establish the coupling relationship between environmental factors.

4. The high-voltage cable fault location and monitoring method based on environmental factor coupling according to claim 1, characterized in that, The process of identifying abnormal cable operation events based on equivalent environmental parameters and cable status information, and determining the corresponding initial fault location, includes: Environmental compensation correction is performed on cable status information based on equivalent environmental parameters, and abnormal events are identified in combination with preset steady-state operation thresholds. When an abnormal event is detected, the time difference of the fault traveling wave arriving at each distributed measuring point is obtained based on the cable status information, and the initial fault location is obtained by combining the location information of each distributed measuring point.

5. The environmental factor coupled high voltage cable fault location monitoring method of claim 1, wherein, The correction of the initial fault location based on environmental coupling weights includes: Obtain the fault location components corresponding to each distributed measurement point, and use the environmental coupling weight corresponding to each distributed measurement point as the weighting coefficient to perform weighted calculation on each fault location component. The weighted components of all fault locations are summed, and the mean is calculated by combining the sum of the environmental coupling weights of all distributed measurement points. The mean is then used as the corrected fault location.

6. The high-voltage cable fault location and monitoring method based on environmental factor coupling according to claim 1, characterized in that, When identifying abnormal cable operation events, the following also applies: Based on the equivalent environmental parameters of each distributed measuring point, the corresponding external damage sensitive features are extracted, and external damage events are identified in combination with the preset external damage early warning threshold. Based on the identification results of abnormal cable operation events, early warning external damage events are selected from the external damage events and added to the abnormal cable operation events.

7. The environmental factor coupled high voltage cable fault location monitoring method of claim 1, wherein, The cable status early warning information includes the type of abnormal cable operation event, the time of occurrence, and the location of the fault.

8. A high voltage cable fault location monitoring system based on environmental factor coupling for performing the location monitoring method of any one of claims 1 to 7, characterized in that include: The data acquisition module is used to collect cable status information and multi-dimensional environmental data along the line from various distributed measurement points; The environmental parameter coupling module is used to establish the coupling relationship between environmental factors based on the cable's historical operating data and corresponding historical environmental parameters, so as to map multi-dimensional environmental data along the line into corresponding equivalent environmental parameters. The initial fault identification module is used to identify abnormal cable operation events based on equivalent environmental parameters and cable status information, and to obtain the corresponding initial fault location. The fault location correction module is used to determine the environmental coupling weight of each distributed measurement point based on equivalent environmental parameters and the initial fault location through fuzzy inference and inverse distance weighting, so as to correct the initial fault location. The early warning module is used to output cable status early warning information based on the corrected fault location and the identification results of abnormal cable operation events.

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