A method and system for accurate positioning of cable faults based on traveling wave signals

Abstract

The application discloses a kind of cable fault accurate positioning method and system based on travelling wave signal, it is related to cable fault positioning field, the method includes: using monitoring arrangement model to determine the layout point and quantity of monitoring equipment in high-voltage transmission cable monitoring area;Based on the trend of degradation and fatigue change, determine the travelling wave waveform when high-voltage transmission cable is in different fault types, and construct power characteristic quantity according to travelling wave waveform, determine the threshold range of fault type division;According to the layout point and quantity of monitoring equipment, monitoring equipment is deployed in high-voltage transmission cable monitoring area, and the zero sequence power signal output fault travelling wave signal of high-voltage transmission cable is obtained by using monitoring equipment, the accurate position point of high-voltage transmission cable fault is positioned, and the fault type of high-voltage transmission cable is determined according to the threshold range of fault type division.The present application greatly improves the operation and maintenance efficiency and safety guarantee ability of high-voltage transmission cable by means of travelling wave ranging theory positioning fault point.

Description

Technical Field

[0001] This invention relates to the field of cable fault location, and in particular to a method and system for accurate cable fault location based on traveling wave signals. Background Technology

[0002] A cable is a collection of conductive materials used to transmit electrical energy, signals, or data. It typically consists of a conductor, an insulation layer, and a sheath. High-voltage transmission cables are cables used for transmitting high-voltage electrical energy and are usually used for long-distance power transmission, such as from power plants to substations or urban power supply networks.

[0003] The purpose of precise cable fault location is to quickly and accurately determine the specific location of the fault point when a cable fault occurs, so as to take targeted maintenance measures, shorten power outage time, reduce maintenance costs, and ensure power supply safety and reliability.

[0004] However, existing cable fault location methods do not consider the costs of equipment procurement, installation, and operation and maintenance when deploying monitoring equipment. This leads to a deployment scheme that tends to be dense to ensure accuracy, ignoring the cost waste caused by redundant equipment. At the same time, they lack dynamic adaptability and cannot adjust the equipment density according to the condition of the cable, which reduces the accuracy and efficiency of the monitoring deployment.

[0005] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0006] To address the aforementioned problems, this invention proposes a method and system for precise cable fault location based on traveling wave signals, aiming to improve the operation and maintenance efficiency and safety assurance capabilities of high-voltage transmission cables.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a method for accurate cable fault location based on traveling wave signals, comprising:

[0009] Based on the results of the high-voltage transmission cable monitoring area division, a cable operation model was established, and environmental parameters were introduced into the cable operation model to analyze the degradation and fatigue change trends at various locations of the high-voltage transmission cable.

[0010] Based on the degradation and fatigue trends, a monitoring layout model with the goal of minimizing costs is established, and the layout model is used to determine the locations and number of monitoring devices within the high-voltage transmission cable monitoring area.

[0011] Based on the degradation and fatigue change trends, the traveling wave waveform of the high-voltage transmission cable under different fault types is determined, and the power characteristic quantity is constructed according to the traveling wave waveform to determine the threshold range for fault type classification.

[0012] According to the deployment points and number of monitoring devices, deploy monitoring equipment in the monitoring area of ​​high-voltage transmission cables, and use the monitoring equipment to obtain the zero-sequence power signal of the high-voltage transmission cable and output the fault traveling wave signal.

[0013] Based on the fault traveling wave signal and traveling wave ranging theory, the precise location of the fault in the high-voltage transmission cable is located, and the fault type of the high-voltage transmission cable is determined according to the threshold range of the fault type classification.

[0014] Preferably, a cable operation model is established based on the results of the high-voltage transmission cable monitoring area division, and environmental parameters are introduced into the cable operation model to analyze the degradation and fatigue change trends at various locations of the high-voltage transmission cable, including:

[0015] Based on the application location and operating status of high-voltage transmission cables, the high-voltage transmission cable lines are divided into several sub-regions with different monitoring requirements, and a cable operation model is established based on the structural parameters of the high-voltage transmission cable lines within the sub-regions.

[0016] The physical and chemical state of the cable operation model is calibrated based on the performance parameters of the high-voltage transmission cable line, and environmental parameters including meteorological and soil parameters are obtained. The simulation scenario is set using the environmental parameters.

[0017] The simulation scenario was loaded into the cable operation model in the form of quantitative boundary conditions, and the cable operation model was run to obtain the changes in partial discharge and mechanical strength attenuation rate at various locations of the high-voltage transmission cable under different simulation scenarios.

[0018] By statistically analyzing the changes in partial discharge and the mechanical strength attenuation rate, a quantitative relationship between environmental parameters and the condition of high-voltage transmission cables is established. Based on this quantitative relationship, the degradation and fatigue trends of high-voltage transmission cables at various locations within any given time period are analyzed.

[0019] Preferably, a monitoring layout model with the goal of minimizing costs is established based on the degradation and fatigue trends, and the layout model is used to determine the deployment points and number of monitoring devices within the high-voltage transmission cable monitoring area, including:

[0020] A multidimensional fault risk heat map is constructed based on the degradation and fatigue change trends within any time period. By analyzing the multidimensional fault risk heat map, the risk points and vulnerable sections of high-voltage transmission cables are output.

[0021] Based on the initial deployment scheme of monitoring equipment according to the risk points and vulnerable sections of high-voltage transmission cables, a multi-agent collaborative framework is constructed according to the deployment location to select a deployment scheme that meets the communication speed requirements.

[0022] Monte Carlo tree search technology is used to optimize the filtered deployment schemes in the time domain, determine the linear balance relationship between deployment location and fault detection probability, and then further filter the deployment schemes based on the balance relationship.

[0023] Based on the deployment scheme after secondary screening, a monitoring deployment model with the goal of minimizing costs is constructed. The monitoring deployment model is used to output the deployment points and number of monitoring equipment in the high-voltage transmission cable monitoring area.

[0024] Preferably, a multi-dimensional fault risk heat map is constructed based on the degradation and fatigue change trends over any time period. Analysis of the multi-dimensional fault risk heat map outputs the risk points and vulnerable sections of the high-voltage transmission cable, including:

[0025] Based on the combination of residual network and semantic segmentation network, an encoder-decoder network structure is constructed, and an attention mechanism is introduced into the encoder-decoder network structure to extract key points in the degradation and fatigue change trend process within any time period.

[0026] A heatmap is constructed using key points, and the offset of key points is predicted during the construction process to output the accuracy loss when extracting key points. The Hough voting technique is used to weight and aggregate the accuracy loss and offset to obtain a multi-dimensional fault risk heatmap.

[0027] By analyzing the multidimensional fault risk heat map through the encoding and decoding network structure, the analysis results are used to capture the fault risk change trend of each area of ​​the high-voltage transmission cable in the future time period and output the risk distribution.

[0028] Based on the risk distribution, the high-voltage transmission cable is divided into several risk level sections, and the risk points and vulnerable sections related to the health status of the high-voltage transmission cable are identified in the highest risk level section.

[0029] Preferably, the traveling wave waveform of the high-voltage transmission cable under different fault types is determined based on the degradation and fatigue change trends, and electrical characteristic quantities are constructed according to the traveling wave waveform to determine the threshold range for fault type classification, including:

[0030] Electromagnetic transient simulation is used to simulate the traveling wave waveforms of various faults under degradation and fatigue trends, and the dynamic characteristics of the traveling wave waveforms are analyzed. Based on the dynamic characteristics, time series analysis is introduced to capture the evolution law of the traveling wave waveforms with degradation and fatigue trends.

[0031] Based on the evolution law, a composite feature vector is constructed that integrates comprehensive power characteristics and degradation index. Fuzzy clustering technology is then used to perform pattern recognition on the comprehensive power characteristics and composite features to determine the threshold range for different fault types.

[0032] Secondly, the present invention also provides a cable fault precise location system based on traveling wave signals, the system comprising:

[0033] The cable variation simulation unit is used to establish a cable operation model based on the results of the high-voltage transmission cable monitoring area division, and to introduce environmental parameters into the cable operation model to analyze the degradation and fatigue variation trends of high-voltage transmission cables at various locations.

[0034] The monitoring deployment determination unit is used to establish a monitoring deployment model with the goal of minimizing costs based on the degradation and fatigue change trends, and to use the monitoring deployment model to determine the deployment points and number of monitoring equipment in the high-voltage transmission cable monitoring area.

[0035] The fault type classification unit is used to determine the traveling wave waveform of the high-voltage transmission cable under different fault types based on the degradation and fatigue change trends, and to construct power characteristic quantities based on the traveling wave waveform to determine the threshold range for fault type classification.

[0036] The traveling wave signal acquisition unit is used to deploy monitoring equipment in the monitoring area of ​​the high-voltage transmission cable according to the deployment points and number of monitoring equipment, and to use the monitoring equipment to acquire the zero-sequence power signal of the high-voltage transmission cable and output the fault traveling wave signal.

[0037] The fault precise location unit is used to locate the precise location of faults in high-voltage transmission cables based on fault traveling wave signals and traveling wave ranging theory, and to determine the fault type of the high-voltage transmission cable according to the threshold range of fault type classification.

[0038] Thirdly, the present invention also proposes an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the above-described method.

[0039] Fourthly, the present invention also provides a computer-readable storage medium on which a computer program is stored, the computer program implementing the above-described method when executed by a processor.

[0040] The beneficial effects of this invention are as follows:

[0041] 1. This invention digitizes and structures the cable operating status by introducing monitoring area division and environmental parameter modeling. It can dynamically reflect the degradation and fatigue change trends of different locations under various environmental influences, realizing a leap from static perception to dynamic prediction. Furthermore, it constructs a monitoring layout model with the goal of minimizing costs, thereby achieving optimal decision-making for the deployment of monitoring equipment. This not only improves the economy and resource allocation efficiency of the monitoring process but also enhances the coverage accuracy of key risk points. At the same time, the deployment of monitoring equipment is based on the optimal layout points and number. Combined with the high sensitivity characteristics of zero-sequence power signals, it achieves high-fidelity capture of fault traveling wave signals, making the entire fault location process have higher perception sensitivity and response speed.

[0042] 2. This invention constructs a multi-dimensional fault risk heat map based on the degradation and fatigue change trends of cables over any time period. It can comprehensively and dynamically reflect the health status of cables under different working conditions and accurately identify high-risk points and vulnerable sections of cables, providing strong data support for subsequent monitoring deployment. At the same time, the monitoring deployment model can output the most cost-effective deployment scheme, reducing unnecessary equipment investment and operation and maintenance costs, and ensuring the best monitoring effect in the monitoring area of ​​high-voltage transmission cables. Thus, it not only improves the accuracy and efficiency of monitoring deployment, but also provides a comprehensive solution in terms of cost control, resource optimization, and fault early warning.

[0043] 3. This invention uses traveling wave ranging theory to locate fault points and combines fault type threshold ranges for accurate classification, making the cable fault location process superior to traditional technologies in terms of accuracy, real-time performance, and intelligence. This not only significantly improves the operation and maintenance efficiency and safety assurance capabilities of high-voltage transmission cables. Attached Figure Description

[0044] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0045] Figure 1 This is a flowchart of a method for accurate cable fault location based on traveling wave signals according to an embodiment of the present invention;

[0046] Figure 2 This is a schematic diagram of a cable fault accurate location system based on traveling wave signals according to an embodiment of the present invention.

[0047] Figure 3 This is a schematic diagram of the hardware operating environment involved in the embodiments of the present invention;

[0048] Figure 4 This is a flowchart of step S1 in a cable fault accurate location method based on traveling wave signals according to an embodiment of the present invention;

[0049] Figure 5 This is a flowchart of step S2 in a cable fault accurate location method based on traveling wave signals according to an embodiment of the present invention.

[0050] In the picture:

[0051] 1. Cable change simulation unit; 2. Monitoring deployment determination unit; 3. Fault type classification unit; 4. Traveling wave signal acquisition unit; 5. Accurate fault location unit. Detailed Implementation

[0052] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0053] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0054] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments of the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. Furthermore, it should be understood that the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0055] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0056] Please see Figure 1 This invention provides a method for accurate cable fault location based on traveling wave signals, comprising:

[0057] Step S1: Establish a cable operation model based on the results of the high-voltage transmission cable monitoring area division, and introduce environmental parameters into the cable operation model to analyze the degradation and fatigue change trends of each location point of the high-voltage transmission cable.

[0058] Please see Figure 4 In one embodiment, establishing a cable operation model based on the high-voltage transmission cable monitoring area division results, and introducing environmental parameters into the cable operation model to analyze the degradation and fatigue change trends of various locations of the high-voltage transmission cable includes: dividing the high-voltage transmission cable line into several sub-regions with different monitoring needs according to the application location and operating status of the high-voltage transmission cable, and establishing a cable operation model based on the structural parameters of the high-voltage transmission cable line within the sub-regions; calibrating the physicochemical state of the cable operation model based on the performance parameters of the high-voltage transmission cable line, and obtaining environmental parameters including meteorological and soil parameters, and setting simulation scenarios using environmental parameters; loading the simulation scenarios into the cable operation model in the form of quantitative boundary conditions, and running the cable operation model to obtain the changes in partial discharge and mechanical strength attenuation rate of various locations of the high-voltage transmission cable under different simulation scenarios; statistically analyzing the changes in partial discharge and mechanical strength attenuation rate to establish a quantitative relationship between environmental parameters and the state of the high-voltage transmission cable, and analyzing the degradation and fatigue change trends of various locations of the high-voltage transmission cable in any time period based on the quantitative relationship.

[0059] It should be explained that the accurate analysis of cable degradation and fatigue trends mainly involves a comprehensive analysis of the area where the high-voltage transmission cable is located, dividing it into several sub-regions with different monitoring requirements. The specific sub-regions can be reasonably divided based on factors such as the cable's operating status, geographical environment, and load conditions. Since the cable line structure and environmental conditions are different in each sub-region, it is necessary to establish a cable operation model (i.e., a three-dimensional model of the high-voltage transmission cable) for each sub-region. This ensures that the cable operation model can accurately reflect the actual situation in different regions and improve the accuracy of predictions.

[0060] After establishing the cable operation model, the physical and chemical state of the cable is calibrated. Specifically, factors such as the cable's structure, materials, insulation performance, and aging degree need to be considered. The model parameters are adjusted by comparing them with actual measurement data. In addition, environmental parameters such as meteorological data (temperature, humidity, wind speed, etc.) and soil parameters (humidity, acidity, alkalinity, corrosivity, etc.) will also affect the cable's operating state. By collecting environmental data, simulation scenarios can be set up to further improve the cable operation model, making the cable operation model closer to the actual operating conditions.

[0061] After loading environmental parameters as quantitative boundary conditions into the cable operation model, a simulation scenario is run (the technical means used during operation can be continuous system simulation or discrete-time simulation) to simulate the cable's operation under different environmental conditions. Through simulation, the changes in partial discharge and mechanical strength attenuation rate of the cable under these conditions can be obtained. Partial discharge is a discharge phenomenon caused by the non-uniform electric field in the cable insulation layer, and its change is an important indicator of cable degradation. Mechanical strength attenuation rate characterizes the changes in the mechanical properties of the cable material during long-term operation. By calculating these two sets of indicators, the degradation process of the cable under various environments can be quantitatively described, and a mathematical relationship between environmental conditions and cable degradation and fatigue can be established. The degradation and fatigue change trends of each location point of the cable can be analyzed within any time period.

[0062] Suppose that in a certain high-voltage transmission cable, the cable line is divided into three sub-regions: urban area, mountain area and plain area. The cable line structure of each region is different. The cable load in the urban area is larger, the cable in the mountain area is more affected by wind and snow, and the cable in the plain area is affected by relatively stable climate conditions.

[0063] Assuming the cable design structure in the urban area is an XLPE (cross-linked polyethylene) insulated cable with a current carrying capacity of 500A, an operating ambient temperature of 35℃, and a relative humidity of 70%, a cable operation model is established based on the actual conditions of the area, and environmental parameters such as the average temperature and soil moisture of the area are introduced.

[0064] Meteorological data for the past 5 years was obtained from meteorological monitoring stations, showing that the temperature range was 20℃ to 40℃. Soil analysis revealed that the soil moisture content was 15%. Based on these environmental parameters, a simulation scenario was established and loaded into the cable operation model. During the simulation, the partial discharge of the cable increased under high temperature and humidity conditions. The partial discharge of the cable in the urban area was calculated to be 100 pC, and the mechanical strength attenuation rate was calculated to be 5%.

[0065] Based on the simulation results, a quantitative relationship between environmental parameters such as temperature, humidity, and load and the partial discharge and mechanical strength attenuation rate of the cable was established through statistical regression analysis. For example, for every 1°C increase in temperature, the partial discharge increases by 10 pC, and for every 5% increase in humidity, the mechanical strength attenuation rate increases by 1%. This allows for the prediction of changes in the cable's health status during future operation.

[0066] Step S2: Establish a monitoring layout model with the goal of minimizing costs based on the degradation and fatigue change trends, and use the monitoring layout model to determine the layout points and number of monitoring devices in the high-voltage transmission cable monitoring area.

[0067] Please see Figure 5 In one embodiment, a monitoring layout model with the goal of minimizing costs is established based on the degradation and fatigue trends. This model is then used to determine the locations and number of monitoring devices within the high-voltage transmission cable monitoring area, including:

[0068] A multidimensional fault risk heat map is constructed based on the degradation and fatigue change trends within any time period. By analyzing the multidimensional fault risk heat map, the risk points and vulnerable sections of high-voltage transmission cables are output.

[0069] Based on the initial deployment scheme of monitoring equipment according to the risk points and vulnerable sections of high-voltage transmission cables, a multi-agent collaborative framework is constructed according to the deployment location to select a deployment scheme that meets the communication speed requirements.

[0070] Monte Carlo tree search technology is used to optimize the filtered deployment schemes in the time domain, determine the linear balance relationship between deployment location and fault detection probability, and then further filter the deployment schemes based on the balance relationship.

[0071] Based on the deployment scheme after secondary screening, a monitoring deployment model with the goal of minimizing costs is constructed. The monitoring deployment model is used to output the deployment points and number of monitoring equipment in the high-voltage transmission cable monitoring area.

[0072] In one embodiment, a multidimensional fault risk heatmap is constructed based on the degradation and fatigue change trends over any time period. The analysis of this heatmap identifies risk points and vulnerable sections of the high-voltage transmission cable. This process includes: building an encoding-decoding network structure based on the combined results of a residual network and a semantic segmentation network; introducing an attention mechanism within this network structure to extract key points in the degradation and fatigue change trend process over any time period; constructing a heatmap using these key points; predicting the offset of key points during the construction process; outputting the accuracy loss during key point extraction; using Hough voting technology to weight and aggregate the accuracy loss and offset to obtain the multidimensional fault risk heatmap; analyzing the heatmap using the encoding-decoding network structure; capturing the fault risk change trends of various regions of the high-voltage transmission cable over future time periods based on the analysis results; and dividing the high-voltage transmission cable into several risk level sections based on the risk distribution, identifying the highest risk level section, and outputting the risk points and vulnerable sections related to the health status of the high-voltage transmission cable.

[0073] It should be explained that the input to the encoder-decoder network used to extract key points is the degradation trend sequence of each location point on the cable. The network structure includes an encoder, attention, and a decoder. The encoder is a ResNet34 used for pre-training, the attention module is a SENet channel attention mechanism, which can enhance the key degradation features, and the decoder is a UNet structure. The output of the encoder-decoder network is the confidence map of the key point location. During the training process, the training data is 1000 sets of cable degradation simulation data, and the loss function is MSE (mean squared error) and key point localization loss (Focal Loss).

[0074] In one embodiment, a heatmap is constructed using key points. During the construction process, the offset of key points is predicted, and the accuracy loss during key point extraction is output. The accuracy loss and offset are weighted and aggregated using Hough voting technology to obtain a multi-dimensional fault risk heatmap. This includes: setting key point regions centered on the peak points with a target radius based on the peak points; masking the key point regions using Gaussian filtering after setting the confidence level of the key point regions to a target threshold; and gradually attenuating the response values ​​from the center to the edge within the key point regions based on the masking results to generate an initial heatmap and record each pixel within the initial heatmap. The horizontal and vertical offsets from the point to its actual location are used to obtain the offset matrix. The initial heatmap and the offset matrix are used as input to construct a voting space. The voting weight of each key point is calculated, and the maximum value in the voting space is found to determine the accuracy loss of the key point in the extraction process. The accuracy loss and the offset matrix are weighted and aggregated to output the optimal position of the key point. The risk attributes of each key point are quantified and assigned to obtain the risk quantification value. The key point position information after the optimal position is output and the risk quantification value are mapped onto the initial heatmap. Through feature fusion, a multi-dimensional fault risk heatmap containing spatial distribution and risk level is generated.

[0075] It should be explained that in the process of outputting the risk points and vulnerable sections of high-voltage transmission cables, the degradation and fatigue change trend data of high-voltage transmission cables in any time period are input into an encoding and decoding network structure that is fused from a residual network (ResNet) and a semantic segmentation network (such as UNet). An attention mechanism is introduced into the network to enhance the attention to key degradation patterns and extract representative key points in the degradation and fatigue evolution process.

[0076] Using the peak value of the keypoint as the center, a fixed radius, such as 10 pixels, corresponding to an actual area of ​​5 meters, is set as the keypoint response zone. The confidence level of this zone is set as the target threshold. A Gaussian filter with standard σ=2 is used to perform spatial attenuation processing on the zone, forming a response value gradient from the center to the edge, generating an initial heatmap. The horizontal and vertical offsets of each pixel to the true position of the keypoint are marked, resulting in a set of offset matrices. The initial heatmap and offset matrices are used as input to construct the Hough voting space. In this space, each pixel in the keypoint region votes its response value to the position it believes to be the true keypoint. By aggregating the voting weights of each point, the peak position in the voting space is found. This peak value represents the most likely true position of the keypoint, thereby estimating the accuracy loss in the original keypoint extraction process. By weighting and aggregating the accuracy loss with the offsets, the optimal position of the keypoint can be output.

[0077] Each key point is assigned a risk quantification value. The risk quantification criteria include: degradation rate, such as the annual growth rate of partial discharge >12%; mechanical strength attenuation rate, such as annual attenuation exceeding 3%; and environmental sensitivity coefficient, such as the response coefficient of humidity or temperature changes to degradation >0.8. The final results are such as a risk value of 0.87 for key point A and 0.62 for key point B. These risk quantification values ​​are mapped back to the initial heat map along with the optimal location of the key points. A multi-channel feature fusion network is used to fuse spatial coordinates and risk attributes to generate a complete multi-dimensional fault risk heat map. This heat map contains information on the spatial risk distribution of the cable and the classification of each risk zone. For example, a risk value >0.8 is a red high-risk zone, 0.5~0.8 is an orange medium-risk zone, and <0.5 is a green low-risk zone.

[0078] By analyzing the heat map and statistically analyzing the risk density and trend changes in each region, we can identify several segments with the highest risk concentration, extract their corresponding spatial coordinates, and mark them as high-risk points and vulnerable sections of the cable.

[0079] Assuming a 10 kV high-voltage cable line is 500 meters long, divided into 5 sub-regions, each approximately 100 meters long, degradation data from the past year shows that the average annual growth rate of partial discharge in regions 2 and 4 is 15%, and the annual mechanical strength decay rate is 3.6%. Key points extracted through deep network analysis include joint 1 (located at 120m), joint 2 (located at 380m), and a corner point (at 250m). The radius of the key point region is set to 10m, corresponding to 10 pixels in the heatmap. After processing with a Gaussian filter, an initial heatmap is formed. An offset matrix records the spatial error between the key points and pixels. After Hough voting, the initial positioning error of joint 1 is found to be ±1.2m, and that of joint 2 is ±0.8m. Through weighted aggregation, the refined positions of the key points are output, and their risk quantification values ​​are calculated: 0.91 for joint 1, 0.76 for joint 2, and 0.83 for the corner point.

[0080] Risk heatmap analysis revealed that the area around 120m in Zone 2 and the area between 250m and 380m in Zone 4 are high-risk clusters. The recommended deployment of three traveling wave sensors at 120m, 250m, and 380m (as shown in Table 1) covers these high-risk sections. This ensures maximum risk perception coverage while meeting budget constraints, effectively guiding subsequent monitoring deployment optimization and fault warning strategy development. Furthermore, through refined modeling and intelligent identification, the accuracy of key monitoring point extraction is improved, and the targeted nature of risk location and the economic efficiency of monitoring resource allocation are significantly enhanced.

[0081] Table 1 Monitoring Equipment Deployment Scheme

[0082] Suggested deployment location (in meters) Coverage of high-risk sections (meters) Monitoring equipment type Main monitoring targets Remark 120 100–140 Traveling wave sensor Connector 1 and nearby wire segments Located at the starting point of the high-risk section in Zone 2 250 230–270 Traveling wave sensor Corner point and nearby line segments Covering the starting point of the high-risk section in Zone 4 380 360–400 Traveling wave sensor Connector 2 and nearby wire segments Located at the end of the high-risk section in Zone 4

[0083] It needs to be explained that in the process of screening deployment schemes, the location coordinates of high-risk points and vulnerable sections are extracted based on the multi-dimensional fault risk heat map, and initial deployment points are set. For example, an initial sensor position is set every 30 meters to generate an initial monitoring equipment deployment scheme. Since cable systems usually require multiple sensor nodes to synchronously capture traveling wave signals, communication speed (such as data reporting delay not exceeding 10ms) becomes one of the key constraints. Therefore, a multi-agent collaborative framework is constructed, where each agent represents a monitoring node. The nodes use local communication delay, bandwidth usage, and synchronization capability as cooperation indicators to execute a game-like strategy to screen deployment schemes in a coordinated manner. Finally, the deployment subset that can guarantee timeliness under network topology and data scheduling conditions is output.

[0084] It should be noted that the definition of an intelligent agent is that each monitoring device is an intelligent agent, and it has the following attributes: location coordinates, communication radius, monitoring radius, and status. At the same time, the interaction rules include communication synchronization, data sharing, and collaborative decision-making.

[0085] Simultaneously, a linear balance relationship between the failure detection probability of deployment locations is established, that is, the marginal contribution of each deployment point to the overall detection performance is evaluated, and an evaluation function is established in combination with its cost coefficient (such as equipment deployment cost, communication link cost, etc.). During the Monte Carlo tree search process, multiple sets of deployment scheme evolution paths are randomly simulated, and the expected detection probability improvement and cost gain ratio under each path are continuously backtracked and evaluated. Through policy pruning and iterative enhancement algorithms, those schemes that have high detection contribution around high-fault areas and can maintain communication constraints and cost lower limits are preferentially retained. Finally, the globally optimal or suboptimal equipment deployment scheme is output to determine the specific location and number of monitoring points.

[0086] In the Monte Carlo tree search optimization deployment scheme, each node represents a deployment scheme, which includes: a set of device locations, a set of coverage risk points, and total cost. Starting from the root node, child nodes are selected based on cumulative rewards and access counts. If a node is not fully expanded, a new device location is added. At the same time, random fault occurrences are simulated to evaluate the detection probability of the scheme. The cumulative rewards and access counts of all nodes on the path are updated until the maximum number of iterations is reached, and the deployment scheme corresponding to the node with the highest cumulative reward value is determined.

[0087] Assuming a high-voltage transmission cable is 600 meters long, risk heat map analysis identifies three high-risk points: P1 (110m from the starting point), P2 (305m from the starting point), and P3 (510m from the starting point), as well as two vulnerable sections: Zone A (90m~140m) and Zone B (480m~530m). Twelve deployment points are initially set up, spaced 50 meters apart. Based on the communication network topology and power dispatching requirements, the system requires inter-node communication delays to be no more than 10ms and synchronization accuracy better than ±1ms. The upper limit of local communication distance for each node is set to 150 meters, the transmission bandwidth to 2Mbps, and the minimum synchronization period to 20ms. Simulating the collaborative communication of each node, through collaborative game theory calculations, deployment points meeting the communication requirements are selected at 100m, 300m, 500m, and 600m. Correspondingly, nodes with severe bandwidth conflicts or unstable synchronization at 50m, 150m, 200m, and 350m are excluded.

[0088] In one embodiment, a monitoring deployment model with the objective of minimizing cost is constructed based on the deployment scheme after secondary screening. The deployment model outputs the deployment points and number of monitoring devices within the high-voltage transmission cable monitoring area, including: determining the installation center point of the monitoring devices according to the deployment scheme after secondary screening; establishing a Cartesian coordinate system based on the installation center point; deriving the monitoring coverage range of a single monitoring device using the coordinate system; setting constraints based on the monitoring coverage range, with the midpoint of the boundary of the high-voltage transmission cable monitoring area as the initial iteration position and complete coverage of the monitoring area as the termination condition; calculating the total cost of each scheme in the deployment scheme after secondary screening using the cost value, and setting the minimum total cost as the objective function; constructing the monitoring deployment model according to the combination of the objective function and constraint rules; using the number of monitoring devices and the plane coordinates of each monitoring device as decision variables, and the minimum total cost as the fitness function, iteratively searching for the optimal solution within the monitoring deployment model that satisfies all constraints, and outputting the coordinates and number of deployment points of monitoring devices within the high-voltage transmission cable monitoring area.

[0089] It needs to be explained that the installation center point of the monitoring equipment is determined based on the deployment plan after secondary screening. This center point is based on the location of risk points and vulnerable sections in the previous screening results, serving as the initial deployment plan candidate. The installation center point of each device has a clear location in the Cartesian coordinate system, which facilitates subsequent spatial calculations. By establishing the Cartesian coordinate system, the monitoring coverage range of each monitoring device can be derived more accurately. To ensure complete coverage of the high-voltage transmission cable monitoring area, it is necessary to set the initial iteration position and termination condition. The initial iteration position refers to starting the deployment from the midpoint of the boundary of the high-voltage transmission cable monitoring area. Based on the geometry of the monitoring area (such as a rectangular or circular area), the center of the area or other representative points are selected as the starting point to facilitate a more uniform equipment layout. Complete coverage of the monitoring area is the termination condition, which means that every point in each area must be within the monitoring range of at least one monitoring device.

[0090] The total cost of each deployment scheme is then calculated, and the minimization of the total cost is used as the optimization objective function. When constructing the monitoring deployment model, the number of monitoring devices and the planar coordinates of each device are used as decision variables. These variables need to be iteratively searched under given constraints. The goal of the iterative search is to find an optimal solution that minimizes the total cost while satisfying all constraints such as complete coverage of the monitoring area. In this process, optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) are used to continuously adjust the position and number of deployed devices until the optimal solution is found.

[0091] Assuming each monitoring device costs 20,000 yuan, the installation and maintenance cost is 10,000 yuan, and the data transmission link construction cost is 500 yuan per 100 meters, the total cost of each plan is calculated by comprehensively considering the monitoring equipment, equipment installation costs, and communication link costs. For example, in the initial deployment plan, the equipment cost for 5 monitoring devices is 100,000 yuan, the link construction cost is (2000 meters / 100 meters) × 500 yuan = 100,000 yuan, and the total cost is 200,000 yuan.

[0092] By iteratively searching and continuously adjusting the sensor positions, an optimal solution was determined: the monitoring equipment was deployed at positions P1, P3, and P4, and the deployment points P2 and P5 were deleted, ultimately forming three monitoring equipment deployment points, specifically at P1 100m, P3 800m, and P4 1800m. Thus, the solution not only achieved complete coverage of the monitoring area, but also reduced the total cost to 150,000 yuan, which was 50,000 yuan less than the initial solution.

[0093] By accurately deriving the coverage area of ​​the monitoring equipment, optimizing it in conjunction with the boundaries of the monitoring area, and arranging the equipment under the goal of minimizing costs, the resource utilization rate of the monitoring system is effectively improved, ensuring the optimal monitoring layout for the high-voltage transmission cable monitoring area.

[0094] Step S3: Based on the degradation and fatigue change trends, determine the traveling wave waveform of the high-voltage transmission cable under different fault types, and construct power characteristic quantities according to the traveling wave waveform to determine the threshold range for fault type classification.

[0095] In one embodiment, the traveling wave waveform of the high-voltage transmission cable under different fault types is determined based on the degradation and fatigue change trends, and electrical characteristic quantities are constructed according to the traveling wave waveform to determine the threshold range for fault type classification, including:

[0096] Electromagnetic transient simulation is used to simulate the traveling wave waveforms of various faults under degradation and fatigue trends, and the dynamic characteristics of the traveling wave waveforms are analyzed. Based on the dynamic characteristics, time series analysis is introduced to capture the evolution law of the traveling wave waveforms with degradation and fatigue trends.

[0097] Based on the evolution law, a composite feature vector is constructed that integrates comprehensive power characteristics and degradation index. Fuzzy clustering technology is then used to perform pattern recognition on the comprehensive power characteristics and composite features to determine the threshold range for different fault types.

[0098] In one embodiment, electromagnetic transient simulation is used to simulate the traveling wave waveforms of various faults under degradation and fatigue trends, and the dynamic characteristics of the traveling wave waveforms are analyzed. Based on the dynamic characteristics, time series analysis is introduced to capture the evolution law of the traveling wave waveform with degradation and fatigue trends. This includes: based on degradation and fatigue trends, electromagnetic transient simulation is used to simulate the propagation of electromagnetic waves inside the cable when it is affected by faults (such as short circuits, grounding, open circuits, etc.) and different degradation states (such as insulation aging, physical damage, etc.), generating traveling wave waveforms that reflect various fault types. Time series analysis is used to capture the evolution law of the traveling wave waveform with degradation and fatigue trends. As the cable degrades, the traveling wave waveform will exhibit phenomena such as amplitude decrease and waveform distortion. Time series analysis can extract time and frequency characteristics from the waveform data to reveal the law of the traveling wave waveform changing with cable degradation.

[0099] In one embodiment, a composite feature vector is constructed based on the evolution law, which integrates comprehensive power characteristics and degradation index. Fuzzy clustering technology is then used to perform pattern recognition on the comprehensive power characteristics and composite feature vector to determine the threshold range for different fault types. This includes: constructing comprehensive power characteristics based on the dynamic characteristics of traveling wave waveforms, such as waveform frequency components, amplitude changes, waveform period, etc., and combining them with the cable degradation index to form a comprehensive composite feature vector. The degradation index reflects the degradation state of the cable and is usually assessed by monitoring physical quantities such as partial discharge, insulation resistance, and external temperature of the cable. The composite feature vector includes power characteristics from the traveling wave waveform and a degradation index reflecting the degree of cable degradation, thus forming a multi-dimensional feature space. The comprehensive feature vector is then input into fuzzy clustering technology to dynamically identify the boundaries of different fault types (such as short circuit, grounding, open circuit, etc.) in the feature space, and the threshold range for different fault types is calculated based on the distribution of the feature vector.

[0100] Suppose a section of high-voltage transmission cable has reached 50% degradation and a short-circuit fault has occurred. Electromagnetic transient simulation was used to obtain traveling wave waveform data for this fault type. The simulation results show that the traveling wave amplitude under the short-circuit fault is 50A, and the frequency range is 20-50kHz. Simultaneously, the cable degradation index is 0.5, indicating that the cable's insulation performance has decreased by 50%. Time series analysis revealed that the waveform exhibits a trend of gradually decreasing amplitude and slight frequency shift during the degradation phase. The dynamic characteristics of this waveform, along with the degradation index, were used to construct a composite feature vector. The features include waveform amplitude (50A), frequency (30kHz), rise time (5ms), and degradation index (0.5). These features are used as input to a fuzzy clustering algorithm. Through training data, the threshold range of the short-circuit fault feature vector in the feature space is found to be: amplitude 50±5A, frequency 30±5kHz.

[0101] As shown in Table 2, the threshold ranges for different fault types were determined through fuzzy clustering analysis. For example, the criteria for short-circuit faults are: amplitude between 45-55A and frequency between 25-35kHz; the criteria for ground faults are: amplitude between 30-40A and frequency between 10-20kHz.

[0102] Table 2 Threshold Range for Cable Fault Type Identification Features

[0103] Fault type Traveling wave amplitude range (A) Frequency range (kHz) Rise time range (ms) Typical degradation index (0–1) Other characteristics (waveform distortion rate %) Remark Short circuit fault 45–55 25–35 4–6 0.4–0.6 10–20 High amplitude, medium frequency, concentrated energy Grounding fault 30–40 10–20 6–10 0.3–0.5 5–15 Medium amplitude, low frequency, and rapid attenuation. Open circuit fault 10–25 5–15 >10 0.5–0.7 20–40 Low amplitude, low frequency, obvious oscillation Insulation aging 5–20 30–50 indefinite 0.6–0.9 15–30 Low amplitude, high frequency, large discharge quantity

[0104] Furthermore, by combining electromagnetic transient simulation with time series analysis, the changing trend of cable fault waveforms with the degradation process is analyzed in depth. By constructing a composite feature vector with the degradation index and combining it with fuzzy clustering technology, the threshold range of different fault types can be effectively identified, thereby providing a more accurate basis for fault judgment.

[0105] Step S4: Deploy monitoring equipment in the high-voltage transmission cable monitoring area according to the deployment points and quantity of monitoring equipment, and use the monitoring equipment to obtain the zero-sequence power signal output fault traveling wave signal of the high-voltage transmission cable.

[0106] It should be explained that during the acquisition of fault traveling wave signals, the operating status of the cable is monitored online by monitoring equipment, and zero-sequence power signals are collected. Zero-sequence power signals are signals caused by the imbalance of three-phase current or voltage in the cable. They are often used to detect asymmetrical faults such as single-phase grounding. These signals fluctuate significantly and are accompanied by a fast high-frequency traveling wave at the moment of fault occurrence, thus becoming the signal source for traveling wave detection. The monitoring equipment continuously samples the zero-sequence current or voltage signals with a nanosecond-level response time. Once a fault occurs, its electrical disturbance propagates at high speed in the cable in the form of an electromagnetic traveling wave. The monitoring equipment simultaneously captures the leading edge of the traveling wave formed by the disturbance and sends it to the cable fault online monitoring and location system.

[0107] Specifically, several monitoring devices are installed along a high-voltage transmission cable line, each equipped with a wireless sensor. The high-voltage transmission cable line is divided into several segments. Each monitoring device synchronously collects and processes the zero-sequence signal of the high-voltage transmission cable to obtain the operating status of the high-voltage transmission cable. Each sensing node is usually in a resting state, meaning that it will not actively transmit high-voltage transmission cable information to the cable fault online monitoring and location system when it determines that the high-voltage transmission cable is in a normal state. When the zero-sequence voltage wireless network sensing node determines that the zero-sequence voltage of the cable has risen and exceeded the threshold, it will automatically send an alarm message to the cable fault online monitoring and location system. After receiving the fault alarm, the cable fault online monitoring and location system will turn on the power of each current IoT sensing node and obtain rich cable status information by summarizing the zero-sequence current data of each branch of the high-voltage transmission cable in real time.

[0108] Step S5: Based on the fault traveling wave signal and traveling wave ranging theory, locate the precise location of the fault in the high-voltage transmission cable, and determine the fault type of the high-voltage transmission cable according to the threshold range of the fault type classification.

[0109] It needs to be explained that the fault traveling wave signal is decomposed into discrete mode signals. The Teager energy operator is used to accurately track the instantaneous changes of non-stationary signals, enhancing the characteristics of transient fault signals. The first peak is calibrated as the moment the initial fault traveling wave front arrives at the measurement end. By real-time monitoring of the cable fault traveling wave dynamics, combined with fault location and fault line selection principles, the double-end traveling wave ranging and positioning technology is improved, eliminating the influence of wave velocity and fault type. Criteria for fault section determination are proposed. When calculating line fault parameters, the influence of wave velocity is eliminated to avoid the effect of wave velocity attenuation caused by different line parameters. A relative time difference is used to reduce the impact of time asynchrony, reducing the dependence on data synchronization and the accuracy of transmission line electrical parameters. During fault calculation, the arrival times of two initial traveling waves and the fault point reflected traveling wave at the endpoint are used to distinguish the near-fault end, avoiding the influence of attenuation of the reflected traveling wave at the opposite end.

[0110] It should be noted that the Teager energy operator is a nonlinear signal processing operator. Its core advantage lies in its ability to instantaneously track changes in the mechanical energy of a signal and its extremely high sensitivity to abrupt changes in fault traveling wave signals. Its input is a zero-sequence current signal and a sampling rate. The input is filtered, denoised, and normalized. Based on the processing results, a window length of 20 is set, and the Teager energy of the signal within the window is calculated. At the same time, a Hilbert transform is performed on the Teager energy to obtain an analytical signal and the energy envelope is calculated. Based on the energy envelope, a three-point moving average smoothing process is performed, and a dynamic threshold is set to detect the arrival time of the wavefront and plot the Teager energy curve to extract transient fault signal features.

[0111] Furthermore, by using distributed installation of online fault location monitoring equipment, the dynamics of cable fault traveling waves are monitored in real time. The synchronization unit collects time stamp information and combines it with the principles of fault location and fault line selection to accurately locate the fault. By strictly controlling the installation position of each monitoring device on site, the inherent mapping relationship between the direction of the fault traveling wave received by each monitoring device and the fault location is utilized, and combined with the traveling wave time difference of the whole ring network monitoring system, the source line of the fault is determined in real time.

[0112] Please see Figure 2 The present invention also provides a cable fault precise location system based on traveling wave signals, the system comprising:

[0113] Cable variation simulation unit 1 is used to establish a cable operation model based on the results of the high-voltage transmission cable monitoring area division, and to introduce environmental parameters into the cable operation model to analyze the degradation and fatigue variation trends of each location point of the high-voltage transmission cable;

[0114] The monitoring deployment determination unit 2 is used to establish a monitoring deployment model with the goal of minimizing costs based on the degradation and fatigue change trends, and to use the monitoring deployment model to determine the deployment points and number of monitoring equipment in the high-voltage transmission cable monitoring area.

[0115] The fault type classification unit 3 is used to determine the traveling wave waveform of the high-voltage transmission cable under different fault types based on the degradation and fatigue change trend, and to construct power characteristic quantities based on the traveling wave waveform to determine the threshold range for fault type classification.

[0116] The traveling wave signal acquisition unit 4 is used to deploy monitoring equipment in the monitoring area of ​​the high-voltage transmission cable according to the deployment points and number of monitoring equipment, and use the monitoring equipment to acquire the zero-sequence power signal of the high-voltage transmission cable and output the fault traveling wave signal.

[0117] The fault precise location unit 5 is used to locate the precise location of the fault in the high-voltage transmission cable based on the fault traveling wave signal and traveling wave ranging theory, and to determine the fault type of the high-voltage transmission cable according to the threshold range of the fault type classification.

[0118] Furthermore, the present invention also provides an electronic device. For example... Figure 3 The diagram illustrates the hardware operating environment of an electronic device, which may include: a processor (e.g., CPU), memory, a user interface, a network interface, and a communication bus. The communication bus is used to enable communication between components. The user interface may include a display screen and an input unit such as a keyboard; optionally, the user interface may also include a standard wired interface or a wireless interface. The network interface may optionally include a standard wired interface or a wireless interface. The memory may be high-speed RAM or stable non-volatile memory, such as disk storage. Alternatively, the memory may be a storage device independent of the aforementioned processor.

[0119] Those skilled in the art will understand that Figure 3 The electronic devices shown do not constitute a limitation on electronic devices and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0120] like Figure 3 As shown, a memory, as a type of computer storage medium, may include an operating system, a network communication module, a user interface module, and device management programs. The operating system is a program that manages and controls the hardware and software resources of electronic devices, supporting the operation of electronic devices and other software or programs. Figure 3 In the electronic device shown, the user interface is mainly used to connect to the terminal and communicate with the terminal, such as receiving user signaling data sent by the terminal; the network interface is mainly used to communicate with the backend server; the processor can be used to call the program stored in the memory and execute the steps of the method or system described above.

[0121] Furthermore, the present invention also proposes a computer-readable storage medium storing a device management program, which, when executed by a processor, implements the steps of the method or system described above.

[0122] The specific embodiments of the computer-readable storage medium of the present invention are basically the same as those of the above-described methods or systems, and will not be repeated here. Furthermore, to achieve the above objectives, the present invention also provides a computer program product, comprising: a computer program, which, when executed by a processor, implements the steps of the methods or systems described above.

[0123] Those skilled in the art will recognize that the units and algorithm steps described in conjunction with the embodiments herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0124] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for precise cable fault location based on traveling wave signals, characterized in that, include: Based on the results of the high-voltage transmission cable monitoring area division, a cable operation model was established, and environmental parameters were introduced into the cable operation model to analyze the degradation and fatigue change trends at various locations of the high-voltage transmission cable. Based on the degradation and fatigue trends, a monitoring layout model with the goal of minimizing costs is established, and the layout model is used to determine the locations and number of monitoring devices within the high-voltage transmission cable monitoring area. Based on the degradation and fatigue change trends, the traveling wave waveform of the high-voltage transmission cable under different fault types is determined, and the power characteristic quantity is constructed according to the traveling wave waveform to determine the threshold range for fault type classification. According to the deployment points and number of monitoring devices, deploy monitoring equipment within the monitoring area of ​​the high-voltage transmission cable, and use the monitoring equipment to obtain the zero-sequence power signal of the high-voltage transmission cable and output the fault traveling wave signal. Based on the fault traveling wave signal and traveling wave ranging theory, the precise location of the fault in the high-voltage transmission cable is located, and the fault type of the high-voltage transmission cable is determined according to the threshold range of the fault type classification.

2. The method for accurate cable fault location based on traveling wave signals according to claim 1, characterized in that, The establishment of a cable operation model based on the monitoring area division results of high-voltage transmission cables, and the introduction of environmental parameters into the cable operation model to analyze the degradation and fatigue change trends at various locations of the high-voltage transmission cable, includes: Based on the application location and operating status of high-voltage transmission cables, the high-voltage transmission cable lines are divided into several sub-regions with different monitoring requirements, and a cable operation model is established based on the structural parameters of the high-voltage transmission cable lines within the sub-regions. The physical and chemical state of the cable operation model is calibrated based on the performance parameters of the high-voltage transmission cable line, and environmental parameters including meteorological and soil parameters are obtained. The simulation scenario is set using the environmental parameters. The simulation scenario was loaded into the cable operation model in the form of quantitative boundary conditions, and the cable operation model was run to obtain the changes in partial discharge and mechanical strength attenuation rate at various locations of the high-voltage transmission cable under different simulation scenarios. By statistically analyzing the changes in partial discharge and the mechanical strength attenuation rate, a quantitative relationship between environmental parameters and the condition of high-voltage transmission cables is established. Based on this quantitative relationship, the degradation and fatigue trends of high-voltage transmission cables at various locations within any given time period are analyzed.

3. The method for accurate cable fault location based on traveling wave signals according to claim 1, characterized in that, The establishment of a monitoring deployment model based on degradation and fatigue trends with the goal of minimizing costs, and the determination of the deployment points and quantity of monitoring equipment within the high-voltage transmission cable monitoring area using the monitoring deployment model, includes: A multidimensional fault risk heat map is constructed based on the degradation and fatigue change trends within any time period. By analyzing the multidimensional fault risk heat map, the risk points and vulnerable sections of high-voltage transmission cables are output. Based on the risk points and vulnerable sections of high-voltage transmission cables, an initial deployment plan for monitoring equipment is generated. A multi-agent collaborative framework is constructed according to the deployment location to select a deployment plan that meets the communication speed requirements. Monte Carlo tree search technology is used to optimize the filtered deployment schemes in the time domain, determine the linear balance relationship between deployment location and fault detection probability, and then further filter the deployment schemes based on the balance relationship. Based on the deployment scheme after secondary screening, a monitoring deployment model with the goal of minimizing costs is constructed. The monitoring deployment model is used to output the deployment points and number of monitoring equipment in the high-voltage transmission cable monitoring area.

4. The method for accurate cable fault location based on traveling wave signals according to claim 3, characterized in that, The multi-dimensional fault risk heat map is constructed based on the degradation and fatigue change trends over any time period. Analysis of the multi-dimensional fault risk heat map outputs the risk points and vulnerable sections of the high-voltage transmission cable, including: Based on the combination of residual network and semantic segmentation network, an encoder-decoder network structure is constructed, and an attention mechanism is introduced into the encoder-decoder network structure to extract key points in the degradation and fatigue change trend process within any time period. A heatmap is constructed using key points, and the offset of key points is predicted during the construction process to output the accuracy loss when extracting key points. The Hough voting technique is used to weight and aggregate the accuracy loss and offset to obtain a multi-dimensional fault risk heatmap. By analyzing the multidimensional fault risk heat map through the encoding and decoding network structure, the analysis results are used to capture the fault risk change trend of each area of ​​the high-voltage transmission cable in the future time period and output the risk distribution. Based on the risk distribution, the high-voltage transmission cable is divided into several risk level sections, and the risk points and vulnerable sections related to the health status of the high-voltage transmission cable are identified in the highest risk level section.

5. The method for accurate cable fault location based on traveling wave signals according to claim 4, characterized in that, The process involves constructing a heatmap using key points, predicting the offset of key points during the construction process, and outputting the accuracy loss when extracting key points. The accuracy loss and offset are then weighted and aggregated using the Hough voting technique to obtain a multi-dimensional fault risk heatmap, including: Based on the peak value of the key point, a key point region is set with the peak point as the center and the radius as the target value. After setting the confidence of the key point region as the target threshold, the key point region is masked by Gaussian filtering. Based on the masking results, the response values ​​within the key point area are gradually attenuated from the center to the edge to generate an initial heatmap. The horizontal and vertical offsets of each pixel in the initial heatmap to its actual key point position are recorded to obtain the offset matrix. The initial heatmap and offset matrix are used as input to construct a voting space. The voting weight of each key point is calculated, and the maximum value in the voting space is found to determine the accuracy loss of the key points in the extraction process. The optimal position of the key point is output by weighting and aggregating the accuracy loss and the offset matrix, and the risk attribute of each key point is quantified and assigned to obtain the risk quantification value. The key point location information after the optimal location is output is mapped to the risk quantification value and then onto the initial heat map. A multi-dimensional fault risk heat map containing spatial distribution and risk level is generated through feature fusion.

6. The method for accurate cable fault location based on traveling wave signals according to claim 5, characterized in that, The monitoring deployment model, based on the deployment scheme after secondary screening, aims to minimize costs. The model outputs the deployment points and quantities of monitoring equipment within the high-voltage transmission cable monitoring area, including: The installation center point of the monitoring equipment is determined based on the deployment plan after secondary screening. A plane rectangular coordinate system is established based on the installation center point, and the monitoring coverage of a single monitoring device is derived using the coordinate system. Based on the monitoring coverage, the constraints are set with the midpoint of the boundary of the high-voltage transmission cable monitoring area as the initial iteration position and complete coverage of the monitoring area as the termination condition. The total cost of each scheme in the deployment scheme after secondary screening is calculated using cost values, and the minimum total cost is set as the objective function. A monitoring deployment model is constructed according to the combination of objective function and constraint rules. Using the number of monitoring devices and the planar coordinates of each monitoring device as decision variables, and the minimum total cost as the fitness function, the optimal solution that satisfies all constraints within the monitoring layout model is iteratively searched, and the coordinates and number of the deployment points of the monitoring devices in the high-voltage transmission cable monitoring area are output.

7. The method for accurate cable fault location based on traveling wave signals according to claim 1, characterized in that, The method of determining the traveling wave waveform of a high-voltage transmission cable under different fault types based on degradation and fatigue trends, and constructing electrical characteristic quantities based on the traveling wave waveform to determine the threshold range for fault type classification includes: Electromagnetic transient simulation is used to simulate the traveling wave waveforms of various faults under degradation and fatigue trends, and the dynamic characteristics of the traveling wave waveforms are analyzed. Based on the dynamic characteristics, time series analysis is introduced to capture the evolution law of the traveling wave waveforms with degradation and fatigue trends. Based on the evolution law, a composite feature vector is constructed that integrates comprehensive power characteristics and degradation index. Fuzzy clustering technology is then used to perform pattern recognition on the comprehensive power characteristics and composite features to determine the threshold range for different fault types.

8. A cable fault accurate location system based on traveling wave signals, used to implement the cable fault accurate location method based on traveling wave signals according to any one of claims 1-7, characterized in that, The system includes: The cable variation simulation unit is used to establish a cable operation model based on the results of the high-voltage transmission cable monitoring area division, and to introduce environmental parameters into the cable operation model to analyze the degradation and fatigue variation trends of high-voltage transmission cables at various locations. The monitoring deployment determination unit is used to establish a monitoring deployment model with the goal of minimizing costs based on the degradation and fatigue change trends, and to use the monitoring deployment model to determine the deployment points and number of monitoring equipment in the high-voltage transmission cable monitoring area. The fault type classification unit is used to determine the traveling wave waveform of the high-voltage transmission cable under different fault types based on the degradation and fatigue change trends, and to construct power characteristic quantities based on the traveling wave waveform to determine the threshold range for fault type classification. The traveling wave signal acquisition unit is used to deploy monitoring equipment in the monitoring area of ​​the high-voltage transmission cable according to the deployment points and number of monitoring equipment, and to use the monitoring equipment to acquire the zero-sequence power signal of the high-voltage transmission cable and output the fault traveling wave signal. The fault precise location unit is used to locate the precise location of faults in high-voltage transmission cables based on fault traveling wave signals and traveling wave ranging theory, and to determine the fault type of the high-voltage transmission cable according to the threshold range of fault type classification.

9. An electronic device, characterized in that, The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the cable fault accurate location method based on traveling wave signals as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the cable fault accurate location method based on traveling wave signals as described in any one of claims 1 to 7.

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