An intelligent monitoring and diagnosis method and device for a high voltage cable grounding system

Abstract

The present invention relates to the technical field of grounding system diagnosis, and in particular to an intelligent monitoring and diagnosis method and device for a high-voltage cable grounding system. The method comprises the following steps: performing three-dimensional scanning of the outer surface acoustic wave of the high-voltage cable and collecting acoustic wave pulse sensor signals; performing multi-path propagation effect analysis on the acoustic wave pulse sensor signals to obtain acoustic wave multi-path propagation effect data; performing regional unit classification on the acoustic wave multi-path propagation effect data to obtain cable regional unit data; obtaining the propagation trajectories of multiple cable parts based on the acoustic wave multi-path propagation effect data; performing spatial path morphological analysis on the propagation trajectories of multiple cable parts based on the cable regional unit data to construct a three-dimensional cable structure model; obtaining real-time resistance monitoring data of the grounding node, the grounding node temperature parameters and the grounding node structure diagram. The present invention realizes efficient and accurate intelligent monitoring and diagnosis of the grounding system.

Description

Technical Field

[0001] The present invention relates to the technical field of grounding system diagnosis, and in particular to an intelligent monitoring and diagnosis method and device for a high-voltage cable grounding system. Background Art

[0002] The high-voltage cable grounding system is an important component of the power transmission and distribution system, used to ensure the safe operation of power equipment and personal safety. However, during long-term operation, the high-voltage cable grounding system is affected by various internal and external factors, such as changes in grounding resistance, cable failures, ambient temperature fluctuations, etc. These factors lead to abnormal conditions and failures in the grounding system. The monitoring and diagnosis of traditional high-voltage cable grounding systems mainly rely on manual inspections, which often have problems with low monitoring efficiency and inaccuracy. Therefore, in order to promptly discover and solve problems in the grounding system and improve the reliability and safety of the system, an intelligent monitoring and diagnosis method is needed. Summary of the invention

[0003] The present invention proposes an intelligent monitoring and diagnosis method and device for a high-voltage cable grounding system to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides an intelligent monitoring and diagnosis method for a high-voltage cable grounding system, comprising the following steps:

[0005] Step S1: performing three-dimensional acoustic wave scanning processing on the outer surface of the high-voltage cable and collecting acoustic wave pulse sensor signals; performing multi-path propagation effect analysis on the acoustic wave pulse sensor signals to obtain acoustic wave multi-path propagation effect data; performing regional unit classification on the acoustic wave multi-path propagation effect data to obtain cable regional unit data;

[0006] Step S2: obtaining propagation trajectories of multiple cable locations based on the acoustic wave multipath propagation effect data; performing spatial path morphological analysis on the propagation trajectories of multiple cable locations based on the cable area unit data to construct a three-dimensional cable structure model;

[0007] Step S3: acquiring grounding node real-time resistance monitoring data, grounding node temperature parameters and grounding node structure diagram; performing global grounding uniformity analysis on the grounding node real-time resistance monitoring data based on the grounding node structure diagram to obtain grounding uniformity data;

[0008] Step S4: performing a grounding node thermal conductivity uniformity analysis on the grounding node temperature parameter to obtain grounding thermal conductivity uniformity data; performing dynamic rendering simulation processing on the cable three-dimensional structure model based on the grounding uniformity data and the grounding thermal conductivity uniformity data to obtain a cable digital twin model;

[0009] Step S5: performing response situation evolution analysis on the cable digital twin model to obtain cable response situation evolution data; performing abnormal trend analysis on the cable response situation evolution data to obtain grounding node abnormal trend data;

[0010] Step S6: extract abnormal grounding nodes based on the abnormal trend data of grounding nodes; perform abnormal trend diagnosis processing on the abnormal grounding nodes, and build an intelligent diagnosis optimization strategy to perform monitoring and diagnosis operations.

[0011] The present invention non-invasively obtains the acoustic wave information on the cable surface through three-dimensional acoustic wave scanning processing and acoustic wave pulse sensor signal collection, thereby avoiding disassembly or destructive detection of the cable. The multi-path propagation effect analysis can reveal the multi-path effect of the acoustic wave in the process of propagation in the cable, including reflection, refraction and other phenomena, providing a basis for subsequent data processing. The regional unit classification divides the cable area into different units, providing basic data for subsequent path morphology analysis and model construction. Through the acoustic wave multi-path propagation effect data analysis, the propagation trajectory of the acoustic wave in different parts of the cable is determined, and the characteristics and changes of the internal structure of the cable are revealed. The spatial path morphology analysis can further parse and analyze the propagation trajectory according to the cable regional unit data, obtain the three-dimensional structural information of the cable, including morphology, topology and other characteristics, obtain the real-time resistance monitoring data and temperature parameters of the grounding node, monitor the resistance change and temperature of the grounding system in real time, and provide data support for subsequent analysis and diagnosis. The grounding node structure diagram is used to analyze the real-time resistance monitoring data of the grounding system, evaluate the uniformity of the grounding, identify whether there is a poor or uneven grounding, and use the grounding node temperature parameter to analyze the grounding system The uniformity of the grounding is evaluated, and whether there is a poor or uneven grounding situation is identified. Analyze and evaluate the thermal conductivity uniformity of the grounding system, determine the temperature distribution of the grounding node, and find out whether there is a problem of uneven thermal conductivity. Apply the grounding uniformity data and the grounding thermal conductivity uniformity data to the cable three-dimensional structure model, perform dynamic rendering simulation processing, generate a digital twin model of the cable, and realize virtual simulation and simulation analysis of the cable status. By analyzing the response situation evolution of the cable digital twin model, simulate and predict the response changes of the cable in actual operation, including the change trends of parameters such as resistance and temperature, perform abnormal trend analysis on the cable response situation evolution data, identify the abnormal situation of the grounding node, including the trend change of abnormal resistance or temperature, and provide a basis for subsequent diagnosis and optimization. Through the analysis of the abnormal trend data of the grounding node, determine the abnormal grounding node, that is, the grounding node with abnormal resistance or temperature, perform abnormal trend diagnosis processing on the abnormal grounding node, identify the cause of the abnormality and the failure mode according to the characteristics and laws of the abnormal trend, and provide guidance for subsequent maintenance and optimization. Build an intelligent diagnosis optimization strategy, use machine learning and data analysis technologies to automatically perform monitoring and diagnosis operations, and improve the accuracy and efficiency of diagnosis.

[0012] Preferably, step S1 comprises the following steps:

[0013] Step S11: performing three-dimensional acoustic wave scanning on the outer surface of the high-voltage cable and collecting acoustic wave pulse sensing signals;

[0014] Step S12: performing multipath propagation effect analysis on the acoustic wave pulse sensing signal, thereby obtaining acoustic wave multipath propagation effect data;

[0015] Step S13: performing regional propagation attenuation analysis on the sound wave multipath propagation effect data to obtain sound wave regional propagation attenuation data;

[0016] Step S14: performing regional propagation velocity difference calculation on the acoustic wave multipath propagation effect data to obtain a regional propagation velocity difference value;

[0017] Step S15: performing regional unit classification based on the sound wave regional propagation attenuation data and the regional propagation velocity difference value to obtain cable regional unit data.

[0018] The present invention obtains the acoustic wave information of the cable surface through three-dimensional scanning of the outer surface acoustic wave, without disassembling or destructive testing the cable, thus realizing a non-invasive detection method, collecting the acoustic wave pulse sensor signal to provide the original data of the acoustic wave propagation in the cable, and providing a basis for subsequent analysis and processing. The multi-path propagation effect analysis reveals the multi-path effect of the acoustic wave in the process of propagation in the cable, including reflection, refraction and other phenomena. The acoustic wave multi-path propagation effect data provides the information of the acoustic wave propagation path, which is helpful to understand the internal structure of the cable and detect the existing abnormal conditions. The regional propagation attenuation analysis evaluates the attenuation of the acoustic wave in the process of propagation in different regions, and reveals the degree of loss of acoustic wave energy. The acoustic wave regional propagation attenuation data Providing information on the degree of sound wave propagation attenuation in different areas helps to determine the losses and faults in the cable. The regional propagation velocity difference calculation compares the differences in sound wave propagation velocities in different areas and detects velocity anomalies in the sound wave propagation process. The regional propagation velocity difference value provides quantitative information on the sound wave propagation velocity difference, which helps to determine the velocity anomalies or medium changes in the cable. The regional unit classification divides the cable into different regional units according to the sound wave regional propagation attenuation data and the regional propagation velocity difference value, which helps to analyze and model the cable structure. The cable regional unit data provides characteristic information of different regional units of the cable, providing basic data for subsequent path morphology analysis and model construction.

[0019] Preferably, step S2 comprises the following steps:

[0020] Step S21: reversely deduce the propagation path of the acoustic wave multipath propagation effect data, thereby obtaining the propagation trajectories of multiple cable locations;

[0021] Step S22: performing spatial path morphology analysis on the propagation trajectories of multiple cable locations to obtain cable path morphology data;

[0022] Step S23: reconstructing the cable path morphology data in space based on the cable area unit data to construct a three-dimensional cable structure model.

[0023] The present invention determines the path information of sound wave propagation by reverse deduction of the propagation path and analyzing the sound wave multi-path propagation effect data, and obtains the propagation trajectories of multiple cable parts to reveal the propagation path of the sound wave in the cable, which is helpful to locate potential faults or abnormal conditions. The spatial path morphology analysis converts the propagation trajectory into the path morphology data of the cable, including the shape, length, degree of curvature of the path and other information. The cable path morphology data provides the geometric structure information of the cable in space, which is helpful to detect the bending, stretching or other morphological anomalies of the cable. The spatial morphology reconstruction utilizes the cable area unit data and the cable path morphology data to map the path morphology to the three-dimensional structure of the cable. The cable three-dimensional structure model provides the overall spatial layout and geometric characteristics of the cable for visualization, analysis and simulation, which is helpful to understand the structure and performance of the cable.

[0024] Preferably, the specific steps of step S23 are:

[0025] Performing time series window segmentation on the acoustic wave multipath propagation effect data to obtain multipath propagation time series window data;

[0026] Performing timing shift difference analysis on multipath propagation timing window data, thereby obtaining timing shift difference data;

[0027] Reconstruct the three-dimensional spatial position distribution of the cable area unit data based on the time series displacement difference data to obtain the area unit spatial position data;

[0028] Performing overlapping structure analysis on the spatial position data of the regional unit to generate overlapping structure data of the regional unit;

[0029] Based on the overlapping structure data of regional units, spatial distribution analysis is carried out to obtain the spatial structure of regional units;

[0030] Based on the spatial position data of the regional unit, the cable path morphology data is spatially reconstructed to obtain an initial three-dimensional model;

[0031] The initial three-dimensional model is iteratively reshaped through a hierarchical structure through a regional unit space architecture to construct a three-dimensional structural model of the cable.

[0032] The present invention divides the sound wave multipath propagation effect data into continuous time series windows through time series window segmentation, each window contains propagation information within a period of time, and the multipath propagation time series window data provides the characteristics of sound wave propagation in each window, which is used for subsequent displacement difference analysis and spatial position reconstruction, and the time series displacement difference analysis compares the sound wave propagation displacement difference in different time series windows, which is used to detect displacement anomalies in the sound wave propagation process, and the time series displacement difference data provides quantitative information on the sound wave propagation displacement difference in different time series windows, which is helpful to judge the displacement situation existing in the cable, and the three-dimensional spatial position distribution reconstruction uses the time series displacement difference data to map the cable area unit to the position in the three-dimensional space, and the area unit spatial position data provides the position information of the cable area unit in the three-dimensional space, which is helpful to understand the spatial structure and layout of the cable, and the overlapping structure analysis compares the area unit spatial position data to detect the overlapping structure situation between different area units, that is, the mutual correlation and overlapping degree between the area units, and the area unit overlapping structure data provides information on the relationship between the area units. , which helps to understand the spatial architecture and connection method of the cable. The spatial distribution analysis uses the overlapping structure data of the regional units to determine the spatial distribution and connection relationship of the regional units and construct the spatial architecture of the cable. The regional unit spatial architecture provides the spatial layout and connection method between the cable regional units, which helps to understand the overall structure and function of the cable. The spatial morphology reconstruction uses the spatial position data of the regional units to map the cable path morphology data into three-dimensional space to generate an initial three-dimensional cable model. The initial three-dimensional model provides the overall spatial layout and geometric characteristics of the cable for visualization, analysis and simulation, which helps to understand the structure and performance of the cable. The hierarchical architecture iterative reshaping uses the regional unit spatial architecture to iteratively adjust and optimize the initial three-dimensional model to more accurately reconstruct the three-dimensional structure of the cable. The cable three-dimensional structure model provides detailed spatial layout and structural information of the cable system, including cable paths, connection nodes, the location and relationship of regional units, etc. The cable three-dimensional structure model is used for intelligent monitoring and diagnosis to help analyze the performance of the cable system, detect potential problems and optimize system operation.

[0033] Preferably, the specific steps of step S3 are:

[0034] Step S31: Acquire the real-time resistance monitoring data of the grounding node, the temperature parameters of the grounding node and the structure diagram of the grounding node;

[0035] Step S32: performing time series analysis on the real-time resistance monitoring data of the grounding node to generate time series resistance monitoring data;

[0036] Step S33: performing resistance distribution analysis on the grounding node structure diagram based on the sequential resistance monitoring data to obtain grounding node resistance distribution data;

[0037] Step S34: performing a global grounding uniformity analysis on the grounding node resistance distribution data to obtain grounding uniformity data.

[0038] The present invention provides the resistance value of the grounding node through real-time resistance monitoring data, which is used to evaluate the performance and status of the grounding system. The grounding node temperature parameter provides the temperature information of the grounding node, which is used to monitor the heat distribution and temperature change of the grounding system. The grounding node structure diagram provides the layout and connection method of the grounding system, which is helpful to understand the structure and components of the grounding system. The timing analysis processes and interprets the real-time resistance monitoring data of the grounding node, and extracts the timing resistance monitoring data. The timing resistance monitoring data provides information on the change of the grounding node resistance over time, which is used to detect the stability and change trend of the grounding system. The resistance distribution analysis uses the timing resistance monitoring data to analyze the grounding node structure diagram to derive the resistance distribution of the grounding node. The grounding node resistance distribution data provides the distribution information of the grounding node resistance in space, and understands the resistance size and spatial distribution of each node in the grounding system. The global grounding uniformity analysis comprehensively evaluates the grounding node resistance distribution data to judge the uniformity of the grounding system. The grounding uniformity data provides a uniformity index of the grounding system, which is used to evaluate the performance and quality of the grounding system and detect potential grounding problems.

[0039] Preferably, the specific steps of step S4 are:

[0040] Step S41: using a grounding node dynamic temperature change calculation formula to perform dynamic temperature change calculation on a grounding node temperature parameter to generate a node temperature change rate;

[0041] Step S42: performing temperature difference distribution analysis on the node temperature change rate to obtain temperature difference distribution data;

[0042] Step S43: performing a grounding node thermal conductivity uniformity analysis on the grounding node structure diagram based on the temperature difference distribution data to obtain grounding thermal conductivity uniformity data;

[0043] Step S44: performing node characteristic evolution analysis on the grounding uniformity data and the grounding thermal conductivity uniformity data to generate grounding node characteristic evolution data;

[0044] Step S45: Dynamically render and simulate the three-dimensional structure model of the cable through the grounding node feature evolution data to obtain a digital twin model of the cable.

[0045] The present invention uses the dynamic temperature change calculation formula of the grounding node to calculate the change rate of the grounding node temperature parameter. The node temperature change rate provides information about the change of the grounding node temperature over time, which is used to understand the temperature dynamics of the grounding system and predict the temperature change trend. The temperature difference distribution analysis processes and interprets the node temperature change rate, extracts the temperature difference distribution data, and provides the distribution of the grounding node temperature difference, which is used to evaluate the heat distribution, temperature gradient and thermal balance of the grounding system. The grounding node thermal conductivity uniformity analysis uses the temperature difference distribution data to analyze the grounding node structure diagram, evaluates the thermal conductivity uniformity of the grounding node, and the grounding The thermal uniformity data provides an indicator of the thermal conductivity of the grounding node, which is used to evaluate the thermal conductivity and thermal balance state of the grounding system. The node feature evolution analysis conducts a comprehensive analysis of the grounding uniformity data and the grounding thermal conductivity uniformity data, and extracts the node feature evolution data. The grounding node feature evolution data provides the evolution trend of the grounding system performance and characteristics, which is used to monitor the state changes of the grounding system and predict potential problems. The dynamic rendering simulation processing uses the grounding node feature evolution data to simulate and render the cable three-dimensional structure model. The cable digital twin model provides simulation and prediction of the grounding system behavior, which is used to optimize the grounding system design, predict system performance and guide maintenance decisions.

[0046] Preferably, the ground node dynamic temperature change calculation formula in step S41 is specifically:

[0047] ;

[0048]

[0049] Ming passed Indicates the relationship between the temperature change of the grounding node and the thermal power input rate of the cable. The greater the thermal power input rate, the faster the temperature changes; conversely, the smaller the thermal power input rate, the slower the temperature changes. By controlling the thermal power input rate, the temperature change of the grounding node can be adjusted. Represents the relationship between the temperature change of the grounding node and the thermal conductivity coefficient. The larger the thermal conductivity coefficient, the more sensitive the grounding node is to temperature changes; conversely, the smaller the thermal conductivity coefficient, the less sensitive the grounding node is to temperature changes. By controlling the thermal conductivity coefficient, the temperature response speed of the grounding node can be adjusted. The grounding node dynamic temperature change calculation formula provides a method for calculating the grounding node temperature change rate, and takes into account the influence of multiple factors on temperature change, including heat conduction, material properties, thermal expansion, ambient temperature, and heat exchange. Analyzing and predicting the temperature change of the grounding node through this formula helps to evaluate the thermal stability and safety of the grounding system.

[0050] Preferably, the specific steps of step S5 are:

[0051] Step S51: performing operation response simulation processing on the cable digital twin model to obtain cable operation response simulation data;

[0052] Step S52: performing response situation evolution analysis on the cable operation response simulation data, thereby obtaining cable response situation evolution data;

[0053] Step S53: performing forward time series prediction on the cable response situation evolution data to generate situation evolution prediction data;

[0054] Step S54: performing abnormal trend analysis on the situation evolution prediction data to obtain abnormal trend data of the grounding node.

[0055] The present invention uses a cable digital twin model to simulate the operation of a cable system through operation response simulation processing, and generates simulated cable operation response data. The cable operation response simulation data provides the response behavior of the cable system under different operating and load conditions, which is used to simulate the system operation status and evaluate the system performance. The response situation evolution analysis processes and interprets the cable operation response simulation data, and extracts the cable response situation evolution data. The cable response situation evolution data provides situation evolution information of the cable system response, which is used to analyze the changing trend of the cable behavior and detect abnormal conditions. The forward time series prediction uses the cable response situation evolution data to perform predictive analysis, and generates cable situation evolution prediction data. The situation evolution prediction data provides prediction information of the future behavior of the cable system, which is used to predict the operation trend of the cable system and warn of potential problems. The abnormal trend analysis processes and interprets the situation evolution prediction data, and extracts the abnormal trend data of the grounding node. The abnormal trend data of the grounding node provides an indicator of abnormal behavior of the grounding system, which is used to detect the abnormal state of the grounding node and predict potential problems.

[0056] Preferably, the specific steps of step S6 are:

[0057] Step S61: locating abnormal parts of the cable digital twin model based on the abnormal trend data of the grounding nodes, and extracting abnormal grounding nodes;

[0058] Step S62: performing abnormal trend diagnosis processing on the abnormal grounding node to obtain abnormal trend diagnosis data;

[0059] Step S63: Perform abnormal optimization decision analysis on the abnormal trend diagnosis data and build an intelligent diagnosis optimization strategy to execute monitoring and diagnosis operations.

[0060] The present invention locates the abnormal part and uses the abnormal trend data of the grounding node to perform positioning analysis on the cable digital twin model, determines the position of the abnormal grounding node, extracts the abnormal grounding node and provides the information of the abnormal grounding node, which is used to accurately locate the specific node with abnormality in the grounding system. The abnormal trend diagnosis processing analyzes and processes the abnormal grounding node and extracts the abnormal trend diagnosis data. The abnormal trend diagnosis data provides diagnostic information of the abnormal grounding node, which is used to judge the abnormal type, degree and change trend of the grounding node. The abnormal optimization decision analysis uses the abnormal trend diagnosis data for analysis and decision-making, and constructs an intelligent diagnosis optimization strategy. The intelligent diagnosis optimization strategy provides an optimized decision-making plan for abnormal grounding nodes, which is used to guide the execution of monitoring and diagnosis operations and provide an effective solution.

[0061] In this specification, an intelligent monitoring and diagnosis device for a high-voltage cable grounding system is provided, which is used to perform the intelligent monitoring and diagnosis method for the high-voltage cable grounding system as described above, including:

[0062] The regional unit module is used to perform three-dimensional scanning of the outer surface acoustic wave of the high-voltage cable and collect acoustic wave pulse sensor signals; perform multi-path propagation effect analysis on the acoustic wave pulse sensor signals to obtain acoustic wave multi-path propagation effect data; perform regional unit classification on the acoustic wave multi-path propagation effect data to obtain cable regional unit data;

[0063] The morphological analysis module is used to obtain the propagation trajectories of multiple cable parts based on the acoustic wave multipath propagation effect data; perform spatial path morphological analysis on the propagation trajectories of multiple cable parts based on the cable area unit data to construct a three-dimensional cable structure model;

[0064] The grounding uniformity module is used to obtain the real-time resistance monitoring data of the grounding node, the temperature parameters of the grounding node and the structure diagram of the grounding node; based on the structure diagram of the grounding node, the real-time resistance monitoring data of the grounding node is subjected to global grounding uniformity analysis to obtain the grounding uniformity data;

[0065] The twin model module is used to analyze the thermal conductivity uniformity of the grounding node based on the grounding node temperature parameters to obtain the grounding thermal conductivity uniformity data; based on the grounding uniformity data and the grounding thermal conductivity uniformity data, the three-dimensional structure model of the cable is dynamically rendered and simulated to obtain the digital twin model of the cable;

[0066] The abnormal trend module is used to analyze the response situation evolution of the cable digital twin model to obtain the cable response situation evolution data; perform abnormal trend analysis on the cable response situation evolution data to obtain the abnormal trend data of the grounding node;

[0067] The decision optimization module is used to extract abnormal grounding nodes based on the abnormal trend data of grounding nodes; perform abnormal trend diagnosis and processing on abnormal grounding nodes, and build an intelligent diagnosis optimization strategy to perform monitoring and diagnosis operations.

[0068] The present invention can obtain the acoustic wave information and sensor signals of the outer surface of the cable through three-dimensional scanning processing of the outer surface acoustic waves and acquisition of acoustic wave pulse sensor signals.

[0069] The multi-path propagation effect analysis is carried out by analyzing the acoustic pulse sensor signal to obtain the effect data of the multi-path propagation of acoustic waves in the cable.

[0070] Area unit classification classifies the acoustic wave multipath propagation effect data to obtain cable area unit data for further analysis of the cable structure and performance. Propagation trajectory acquisition determines the trajectory of acoustic wave propagation in multiple cable locations based on the acoustic wave multipath propagation effect data.

[0071] Spatial path morphology analysis uses cable area unit data to analyze the propagation trajectories of multiple cable locations, constructs a three-dimensional structural model of the cable, and provides geometric morphology information of the cable. Global grounding uniformity analysis analyzes the grounding uniformity of the grounding system based on the real-time resistance monitoring data of the grounding node and the grounding node structure diagram to obtain grounding uniformity data.

[0072] The grounding node thermal conductivity uniformity analysis analyzes the thermal conductivity uniformity of the grounding node based on the grounding node temperature parameters to obtain the grounding thermal conductivity uniformity data. The dynamic rendering simulation processing uses the grounding uniformity data and the grounding thermal conductivity uniformity data to perform dynamic rendering simulation processing on the cable three-dimensional structure model to generate a digital twin model of the cable. The response state evolution analysis analyzes the cable digital twin model to obtain the cable response state evolution data.

[0073] Abnormal trend analysis processes the cable response situation evolution data and extracts the abnormal trend data of the grounding node to detect the abnormal state and trend of the grounding node. Abnormal grounding node extraction determines the abnormal grounding node based on the abnormal trend data of the grounding node.

[0074] Abnormal trend diagnosis and processing analyzes and processes abnormal grounding nodes, and builds intelligent diagnosis optimization strategies to perform monitoring and diagnosis tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A schematic flow chart of the steps of an intelligent monitoring and diagnosis method for a high-voltage cable grounding system according to the present invention;

[0076] Figure 2 Detailed implementation flow chart of step S1;

[0077] Figure 3Detailed implementation flow chart of step S2;

[0078] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0079] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0080] The present application example provides an intelligent monitoring and diagnosis method and device for a high-voltage cable grounding system. The execution subjects of the intelligent monitoring and diagnosis method and system for a high-voltage cable grounding system include but are not limited to: mechanical equipment, data processing platform, cloud server node, network upload equipment, etc. equipped with the system can be regarded as general computing nodes of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0081] See also Figures 1 to 4 The present invention provides an intelligent monitoring and diagnosis method for a high-voltage cable grounding system, comprising the following steps:

[0082] Step S1: performing three-dimensional acoustic wave scanning processing on the outer surface of the high-voltage cable and collecting acoustic wave pulse sensor signals; performing multi-path propagation effect analysis on the acoustic wave pulse sensor signals to obtain acoustic wave multi-path propagation effect data; performing regional unit classification on the acoustic wave multi-path propagation effect data to obtain cable regional unit data;

[0083] Step S2: obtaining propagation trajectories of multiple cable locations based on the acoustic wave multipath propagation effect data; performing spatial path morphological analysis on the propagation trajectories of multiple cable locations based on the cable area unit data to construct a three-dimensional cable structure model;

[0084] Step S3: acquiring grounding node real-time resistance monitoring data, grounding node temperature parameters and grounding node structure diagram; performing global grounding uniformity analysis on the grounding node real-time resistance monitoring data based on the grounding node structure diagram to obtain grounding uniformity data;

[0085] Step S4: performing a grounding node thermal conductivity uniformity analysis on the grounding node temperature parameter to obtain grounding thermal conductivity uniformity data; performing dynamic rendering simulation processing on the cable three-dimensional structure model based on the grounding uniformity data and the grounding thermal conductivity uniformity data to obtain a cable digital twin model;

[0086] Step S5: performing response situation evolution analysis on the cable digital twin model to obtain cable response situation evolution data; performing abnormal trend analysis on the cable response situation evolution data to obtain grounding node abnormal trend data;

[0087] Step S6: extract abnormal grounding nodes based on the abnormal trend data of grounding nodes; perform abnormal trend diagnosis processing on the abnormal grounding nodes, and build an intelligent diagnosis optimization strategy to perform monitoring and diagnosis operations.

[0088] The present invention non-invasively obtains the acoustic wave information on the cable surface through three-dimensional acoustic wave scanning processing and acoustic wave pulse sensor signal collection, thereby avoiding disassembly or destructive detection of the cable. The multi-path propagation effect analysis can reveal the multi-path effect of the acoustic wave in the process of propagation in the cable, including reflection, refraction and other phenomena, providing a basis for subsequent data processing. The regional unit classification divides the cable area into different units, providing basic data for subsequent path morphology analysis and model construction. Through the acoustic wave multi-path propagation effect data analysis, the propagation trajectory of the acoustic wave in different parts of the cable is determined, and the characteristics and changes of the internal structure of the cable are revealed. The spatial path morphology analysis can further parse and analyze the propagation trajectory according to the cable regional unit data, obtain the three-dimensional structural information of the cable, including morphology, topology and other characteristics, obtain the real-time resistance monitoring data and temperature parameters of the grounding node, monitor the resistance change and temperature of the grounding system in real time, and provide data support for subsequent analysis and diagnosis. The grounding node structure diagram is used to analyze the real-time resistance monitoring data of the grounding system, evaluate the uniformity of the grounding, identify whether there is a poor or uneven grounding, and use the grounding node temperature parameter to analyze the grounding system The uniformity of the grounding is evaluated, and whether there is a poor or uneven grounding situation is identified. Analyze and evaluate the thermal conductivity uniformity of the grounding system, determine the temperature distribution of the grounding node, and find out whether there is a problem of uneven thermal conductivity. Apply the grounding uniformity data and the grounding thermal conductivity uniformity data to the cable three-dimensional structure model, perform dynamic rendering simulation processing, generate a digital twin model of the cable, and realize virtual simulation and simulation analysis of the cable status. By analyzing the response situation evolution of the cable digital twin model, simulate and predict the response changes of the cable in actual operation, including the change trends of parameters such as resistance and temperature, perform abnormal trend analysis on the cable response situation evolution data, identify the abnormal situation of the grounding node, including the trend change of abnormal resistance or temperature, and provide a basis for subsequent diagnosis and optimization. Through the analysis of the abnormal trend data of the grounding node, determine the abnormal grounding node, that is, the grounding node with abnormal resistance or temperature, perform abnormal trend diagnosis processing on the abnormal grounding node, identify the cause of the abnormality and the failure mode according to the characteristics and laws of the abnormal trend, and provide guidance for subsequent maintenance and optimization. Build an intelligent diagnosis optimization strategy, use machine learning and data analysis technologies to automatically perform monitoring and diagnosis operations, and improve the accuracy and efficiency of diagnosis.

[0089] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of an intelligent monitoring and diagnosis method for a high-voltage cable grounding system of the present invention. In this example, the steps of the intelligent monitoring and diagnosis method for a high-voltage cable grounding system include:

[0090] Step S1: performing three-dimensional acoustic wave scanning processing on the outer surface of the high-voltage cable and collecting acoustic wave pulse sensor signals; performing multi-path propagation effect analysis on the acoustic wave pulse sensor signals to obtain acoustic wave multi-path propagation effect data; performing regional unit classification on the acoustic wave multi-path propagation effect data to obtain cable regional unit data;

[0091] In this embodiment, an acoustic wave sensor or detector is used to scan the outer surface of the high-voltage cable. During the scanning process, parameters such as the intensity, frequency and phase of the acoustic wave signal are recorded. The acoustic wave signal collected by the sensor or detector forms a pulse sensing signal. The pulse sensing signal contains information about the propagation of the acoustic wave in the cable. The collected acoustic wave pulse sensing signal is analyzed, and the effect data of the multi-path propagation of the acoustic wave in the cable is obtained by analyzing the propagation path, multiple reflections, refractions and scattering effects of the acoustic wave signal. These effect data include information such as propagation time delay, signal attenuation, phase change, etc. The acoustic wave multi-path propagation effect data is classified to obtain cable area unit data. According to different propagation effect characteristics, the cable is divided into different area units, such as a conductor area, an insulation layer area, a shielding layer area, etc., and each area unit is marked for subsequent analysis and processing.

[0092] Step S2: obtaining propagation trajectories of multiple cable locations based on the acoustic wave multipath propagation effect data; performing spatial path morphological analysis on the propagation trajectories of multiple cable locations based on the cable area unit data to construct a three-dimensional cable structure model;

[0093] In this embodiment, the sound wave multipath propagation effect data is used to determine the propagation path of the sound wave in the cable, and the path and propagation mode of the sound wave in the cable are inferred based on the propagation time delay, phase change and other information of the sound wave signal. The cable area unit data is used to perform spatial path morphological analysis on the propagation trajectories of multiple cable parts. According to the classification and identification of the cable area units, the propagation path and position of the sound wave inside the cable are determined. Considering factors such as the cable's geometric shape, material properties and spatial distribution of area units, the geometric shape and spatial distribution characteristics of the cable propagation path are analyzed. Based on the geometric shape and spatial distribution characteristics of the cable propagation path obtained by analysis, a three-dimensional structural model of the cable is constructed, and the cable's area units and propagation paths are combined to form a complete cable model, including conductors, insulation layers, shielding layers and other parts. The shape, size and position of the cable are accurately represented and modeled through three-dimensional modeling technology.

[0094] Step S3: acquiring grounding node real-time resistance monitoring data, grounding node temperature parameters and grounding node structure diagram; performing global grounding uniformity analysis on the grounding node real-time resistance monitoring data based on the grounding node structure diagram to obtain grounding uniformity data;

[0095] In this embodiment, appropriate sensors or monitoring devices are used to obtain real-time resistance monitoring data of grounding nodes. These monitoring data are obtained through resistance measuring devices in grounding electrodes or grounding grids, and temperature parameters of grounding nodes are obtained. They are measured by temperature sensors or other temperature monitoring devices, and the structure diagram of the grounding system is obtained, including the layout, connection mode and other relevant structural information of the grounding electrodes or grounding grids. The structure diagram provides the topological structure and geometric layout of the grounding system, providing a basis for subsequent analysis. The grounding node structure diagram is used to divide the grounding system into different areas or units, and the real-time resistance monitoring data of the grounding nodes is analyzed. The grounding resistance values ​​of different areas or units are calculated, and the grounding resistance values ​​of different areas or units are compared. The grounding uniformity of the grounding system is evaluated, and statistical methods, graphic analysis or other appropriate technical means are used to analyze the grounding uniformity. According to the results of the global grounding uniformity analysis, the grounding uniformity data of the grounding system is obtained, and the grounding uniformity data includes the grounding resistance values, mean values, variances or other statistical indicators of different areas or units.

[0096] Step S4: performing a grounding node thermal conductivity uniformity analysis on the grounding node temperature parameter to obtain grounding thermal conductivity uniformity data; performing dynamic rendering simulation processing on the cable three-dimensional structure model based on the grounding uniformity data and the grounding thermal conductivity uniformity data to obtain a cable digital twin model;

[0097] In this embodiment, the grounding node temperature parameter is used to perform a grounding node thermal conductivity uniformity analysis. According to the temperature distribution of the grounding node, the thermal conductivity uniformity of the grounding system is evaluated. The thermal conductivity of the grounding node is analyzed by numerical simulation methods, heat conduction analysis and other technical means. According to the results of the grounding node thermal conductivity uniformity analysis, the thermal conductivity uniformity data of the grounding system is obtained. The grounding thermal conductivity uniformity data includes temperature distribution, uniformity index of the thermal conduction path, etc. The grounding uniformity data and the grounding thermal conductivity uniformity data are combined with the three-dimensional structure model of the cable to perform dynamic rendering simulation processing. In the simulation, factors such as the propagation path, resistance distribution, and thermal conductivity of the cable are considered. Computer graphics technology, physical simulation methods, etc. are used to perform dynamic rendering simulation processing to simulate the behavior and performance of the cable under actual working conditions. By performing dynamic rendering simulation processing on the three-dimensional structure model of the cable, a digital twin model of the cable is obtained. The digital twin model is a virtual simulation of the actual cable system, simulating the behavior, performance, and response of the cable under different working conditions.

[0098] Step S5: performing response situation evolution analysis on the cable digital twin model to obtain cable response situation evolution data; performing abnormal trend analysis on the cable response situation evolution data to obtain grounding node abnormal trend data;

[0099] In this embodiment, a digital twin model of the cable is used to perform a response situation evolution analysis. Factors such as the working conditions, environmental factors, and load changes of the cable are considered in the simulation, the evolution process of the cable response is simulated, and numerical simulation methods, simulation technologies, etc. are used to perform the response situation evolution analysis of the cable. According to the response situation evolution analysis results of the cable digital twin model, the response situation evolution data of the cable is obtained. The cable response situation evolution data includes changes in parameters such as current, voltage, and temperature over time. The cable response situation evolution data is used to perform an abnormal trend analysis to analyze the trend and change law of the cable response and detect whether there is an abnormal situation. Data analysis methods, statistical techniques, etc. are used to analyze and process the cable response situation evolution data. According to the results of the abnormal trend analysis, the abnormal trend data of the grounding node is obtained. The abnormal trend data includes changes in parameters such as the resistance value and temperature of the grounding node over time, which is used to detect the abnormal state of the grounding node.

[0100] Step S6: extract abnormal grounding nodes based on the abnormal trend data of grounding nodes; perform abnormal trend diagnosis processing on the abnormal grounding nodes, and build an intelligent diagnosis optimization strategy to perform monitoring and diagnosis operations.

[0101] In this embodiment, abnormal trend data of grounding nodes are used to extract abnormal grounding nodes, the abnormal trend data of grounding nodes are analyzed, and nodes that are significantly deviated from the normal state are identified. Data analysis methods, anomaly detection algorithms, etc. are used to extract abnormal grounding nodes. Abnormal trend diagnosis processing is performed on the extracted abnormal grounding nodes, the trend and change law of the abnormal grounding nodes are analyzed, the cause and influencing factors of the abnormality are determined, and fault diagnosis methods, expert systems, etc. are used to diagnose and process the abnormal grounding nodes. Based on the diagnosis results and related knowledge of the abnormal grounding nodes, an intelligent diagnosis optimization strategy is constructed. In combination with the experience of domain experts and data analysis methods, diagnostic models and rules are established to optimize the accuracy and efficiency of abnormal diagnosis of grounding nodes. Intelligent diagnosis optimization strategies are constructed using technical means such as machine learning and artificial intelligence. According to the constructed intelligent diagnosis optimization strategies, monitoring and diagnosis operations are performed, abnormal grounding nodes are monitored in real time, and related data and information are collected. According to the intelligent diagnosis optimization strategies, automated monitoring and diagnosis are performed to quickly and accurately identify and handle abnormal grounding node problems.

[0102] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0103] Step S11: performing three-dimensional acoustic wave scanning on the outer surface of the high-voltage cable and collecting acoustic wave pulse sensing signals;

[0104] Step S12: performing multipath propagation effect analysis on the acoustic wave pulse sensing signal, thereby obtaining acoustic wave multipath propagation effect data;

[0105] Step S13: performing regional propagation attenuation analysis on the sound wave multipath propagation effect data to obtain sound wave regional propagation attenuation data;

[0106] Step S14: performing regional propagation velocity difference calculation on the acoustic wave multipath propagation effect data to obtain a regional propagation velocity difference value;

[0107] Step S15: performing regional unit classification based on the sound wave regional propagation attenuation data and the regional propagation velocity difference value to obtain cable regional unit data.

[0108] In this embodiment, an acoustic wave scanning device is used to scan and process the outer surface of the high-voltage cable. The acoustic wave scanning device is an ultrasonic wave or other acoustic wave sensor, which is used to obtain acoustic wave signals on the cable surface. During the three-dimensional scanning of the acoustic wave, the pulse signal received by the acoustic wave sensor on the cable surface is collected. The pulse sensor signal contains the acoustic wave reflection and scattering information on the cable surface, which is used for subsequent analysis and processing. The collected acoustic wave pulse sensor signal is subjected to multipath propagation effect analysis. The multipath propagation effect refers to the phenomenon that the acoustic wave experiences multiple reflections, scattering and diffraction during the propagation process, resulting in complex changes in the signal. By analyzing the acoustic wave pulse sensor signal, the acoustic wave multipath propagation effect data is extracted. The multipath propagation effect data includes information such as the amplitude, phase, and propagation time delay of the acoustic wave. , used for subsequent analysis and processing, regional propagation attenuation analysis is performed on the sound wave multipath propagation effect data. Regional propagation attenuation refers to the weakening of signal strength due to absorption, scattering and other factors during the propagation of sound waves. By analyzing the sound wave multipath propagation effect data, the sound wave regional propagation attenuation data is extracted. The regional propagation attenuation data describes the attenuation degree of sound wave signals in different regions. It is used for subsequent analysis and processing, and the regional propagation speed difference is calculated for the sound wave multipath propagation effect data. The regional propagation speed difference refers to the difference in speed of sound waves during the propagation process in different regions. By analyzing the sound wave multipath propagation effect data, the regional propagation speed difference value is calculated. The regional propagation speed difference value describes the degree of difference in the propagation speed of sound waves in different regions, which is used for subsequent analysis and processing.

[0109] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0110] Step S21: reversely deduce the propagation path of the acoustic wave multipath propagation effect data, thereby obtaining the propagation trajectories of multiple cable locations;

[0111] Step S22: performing spatial path morphology analysis on the propagation trajectories of multiple cable locations to obtain cable path morphology data;

[0112] Step S23: reconstructing the cable path morphology data in space based on the cable area unit data to construct a three-dimensional structure model of the cable.

[0113] In this embodiment, the propagation path is reversely deduced using the sound wave multipath propagation effect data. The propagation path is reversely deduced based on the reception of the sound wave signal, and the path morphology data is regionally matched according to the regional unit to find the specific regional unit where the path is located; different shape functions are used to perform parameter fitting on each path to determine its position parameters within the regional unit; based on the starting point and end point of the path within the unit, its spatial position information and morphological characteristics are restored; the shape characteristics of the path, such as the bending radius and bifurcation angle, are digitally described; the continuous form of the path within each regional unit is reconstructed to restore its three-dimensional spatial morphological contour; within the regional unit grid, the shape function is used to reproduce the three-dimensional model of the path of each part of the cable; the path models of different parts are integrated, and a complete three-dimensional structural model of the cable is constructed based on the topological relationship.

[0114] In this embodiment, the specific steps of step S23 are:

[0115] Performing time series window segmentation on the acoustic wave multipath propagation effect data to obtain multipath propagation time series window data;

[0116] Performing timing shift difference analysis on multipath propagation timing window data, thereby obtaining timing shift difference data;

[0117] Reconstruct the three-dimensional spatial position distribution of the cable area unit data based on the time-series displacement difference data to obtain the area unit spatial position data;

[0118] Performing overlapping structure analysis on the spatial position data of the regional unit to generate overlapping structure data of the regional unit;

[0119] Based on the overlapping structure data of regional units, spatial distribution analysis is carried out to obtain the spatial structure of regional units;

[0120] Based on the spatial position data of the regional unit, the cable path morphology data is spatially reconstructed to obtain an initial three-dimensional model;

[0121] The initial three-dimensional model is iteratively reshaped through a hierarchical structure through a regional unit space architecture to construct a three-dimensional structural model of the cable.

[0122] In this embodiment, the sound wave multipath propagation effect data is divided according to time to obtain a series of time series windows. The time series window is the sound wave propagation data in a continuous time period. The time length of each time series window is determined and set according to actual needs. Considering the overlap between adjacent time series windows, in order to capture continuous sound wave propagation information, the multipath propagation data in each time series window is analyzed, and the sound wave displacement difference on different paths is calculated. The time series displacement difference data reflects the propagation difference of the sound wave on different paths. The three-dimensional spatial position distribution of the cable area unit is reconstructed by using the time series displacement difference data. By analyzing the displacement difference, the position distribution of the area unit in space is determined. According to the reconstructed spatial position distribution of the area unit, the spatial position data of the area unit is established. The spatial position data includes the three-dimensional coordinates, rotation angle and other information of each area unit. The spatial position data of the area unit is analyzed to detect the overlapping structure between the area units. The overlapping structure refers to the covering, contact or intersection relationship between the area units in space. Based on the results of the overlapping structure analysis, the overlapping structure data of the area unit is generated. The overlapping structure data describes the connection relationship, relative The spatial distribution of the regional units is analyzed by using the overlapping structural data of the regional units. The analysis includes the layout, aggregation, spatial relationship, etc. of the regional units. According to the results of the spatial distribution analysis, the spatial structure of the regional units is determined. The spatial structure of the regional units includes the relationship between each regional unit, the organizational structure and other information. The spatial position data of the regional units are used to reconstruct the spatial morphology of the cable path morphology data. According to the spatial position and connection relationship of the regional units, the morphology of the cable path in three-dimensional space is determined. According to the results of the spatial morphology reconstruction, an initial three-dimensional model of the cable is established. The initial three-dimensional model includes geometric information such as the path, shape, and size of the cable. Based on the spatial structure of the regional units, the initial three-dimensional model is iteratively reshaped in a hierarchical structure. The iterative reshaping is to adjust the layout and connection relationship of the cable path to make it conform to the spatial structure of the regional unit. According to the requirements of the spatial structure of the regional units, the layout of the cable path is optimized to make it more reasonable and compact. The optimization includes operations such as path adjustment and branch reconstruction. In the process of iterative reshaping of the hierarchical structure, the three-dimensional model is continuously updated to reflect the optimized cable structure. The updated three-dimensional model includes information such as cable path, connection relationship, shape, etc.

[0123] In this embodiment, reference Figure 4 The above is a schematic flow chart of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0124] Step S31: Acquire the real-time resistance monitoring data of the grounding node, the temperature parameters of the grounding node and the structure diagram of the grounding node;

[0125] Step S32: performing time series analysis on the real-time resistance monitoring data of the grounding node to generate time series resistance monitoring data;

[0126] Step S33: performing resistance distribution analysis on the grounding node structure diagram based on the sequential resistance monitoring data to obtain grounding node resistance distribution data;

[0127] Step S34: performing a global grounding uniformity analysis on the grounding node resistance distribution data to obtain grounding uniformity data.

[0128] In this embodiment, the resistance monitoring device installed on the grounding node will collect the resistance value of the grounding node in real time, and obtain and record the resistance value of the grounding node through a sensor or a monitoring system. The monitoring system also records the temperature parameters of the grounding node, which are obtained through temperature sensors and other devices. The temperature parameters are used to consider the influence of temperature on the grounding resistance in subsequent analysis. A structural diagram of the grounding node is obtained, including information such as the arrangement and connection relationship of the node. The structural diagram is a two-dimensional plane diagram or a three-dimensional model, which is used to analyze the resistance distribution and uniformity of the grounding node. A time series analysis is performed on the real-time resistance monitoring data of the grounding system, and the data is processed in chronological order. The time series characteristics of the resistance monitoring data are extracted using statistical methods, filtering methods, or time-frequency analysis techniques. On this basis, the resistance values ​​of each grounding node at different time points are obtained. The generated time-series resistance monitoring data reflects the change of grounding node resistance over time. The time-series resistance monitoring data is used to perform resistance distribution analysis on the grounding node structure diagram. The resistance distribution analysis derives the spatial distribution of grounding node resistance through calculation, interpolation or fitting methods. Based on the results of resistance distribution analysis, grounding node resistance distribution data is generated. The grounding node resistance distribution data includes the resistance value of each node and its spatial position information in the grounding system. The grounding uniformity of the grounding system is analyzed using the grounding node resistance distribution data. The grounding uniformity analysis evaluates the uniformity of the grounding system by calculating statistical indicators such as the mean, variance, and standard deviation of the grounding resistance.

[0129] In this embodiment, step S4 includes the following steps:

[0130] Step S41: using a grounding node dynamic temperature change calculation formula to perform dynamic temperature change calculation on a grounding node temperature parameter to generate a node temperature change rate;

[0131] Step S42: performing temperature difference distribution analysis on the node temperature change rate to obtain temperature difference distribution data;

[0132] Step S43: performing a grounding node thermal conductivity uniformity analysis on the grounding node structure diagram based on the temperature difference distribution data to obtain grounding thermal conductivity uniformity data;

[0133] Step S44: performing node characteristic evolution analysis on the grounding uniformity data and the grounding thermal conductivity uniformity data to generate grounding node characteristic evolution data;

[0134] Step S45: Dynamically render and simulate the three-dimensional structure model of the cable through the grounding node feature evolution data to obtain a digital twin model of the cable.

[0135] In this embodiment, according to the characteristics and environmental conditions of the grounding node, an appropriate dynamic temperature change calculation formula is selected. The dynamic temperature change calculation formula takes into account factors such as the heat capacity and heat conduction of the node to calculate the rate at which the node temperature changes over time. The selected dynamic temperature change calculation formula is used to input the initial temperature and environmental conditions of the grounding node to calculate the rate of change of the node temperature over time. The temperature difference distribution analysis is performed using the node temperature change rate data. The temperature difference distribution analysis derives the difference between the grounding node temperatures by calculation, interpolation or fitting. Based on the results of the temperature difference distribution analysis, temperature difference distribution data is generated to reflect the difference between the grounding node temperatures and their distribution characteristics. The temperature difference distribution data includes the temperature difference values ​​between each node and its spatial position information in the grounding system. The thermal conductivity uniformity analysis of the grounding node takes into account factors such as the size of the temperature difference between nodes and the uniformity of distribution to evaluate the thermal conductivity of the grounding system. The node characteristic evolution analysis is carried out based on the grounding uniformity data and the grounding thermal conductivity uniformity data. The node characteristic evolution analysis comprehensively considers the characteristics of the grounding system such as uniformity and thermal conductivity to obtain the data of the node characteristic evolution over time. Based on the results of the node characteristic evolution analysis, the grounding node characteristic evolution data is generated to reflect the changing trend of the grounding node characteristics over time. The grounding node characteristic evolution data is used to monitor the performance changes of the grounding system and provide a basis for prediction and early warning. The grounding node characteristic evolution data is associated with the three-dimensional structure model of the cable. During the simulation process, according to the changes in the grounding node characteristic evolution data, the parameters or properties of the relevant parts of the cable model are adjusted to simulate the dynamic change process of the actual grounding system. Through dynamic rendering simulation processing, the status and performance information of the cable at different time points are obtained. The generated cable digital twin model is used for real-time monitoring, prediction and diagnosis of key indicators such as the thermal stability and grounding performance of the cable.

[0136] In this embodiment, the calculation formula for the dynamic temperature change of the ground node in step S41 is specifically:

[0137] ;

[0138] The present invention is achieved by Indicates the relationship between the temperature change of the grounding node and the thermal power input rate of the cable. The greater the thermal power input rate, the faster the temperature changes; conversely, the smaller the thermal power input rate, the slower the temperature changes. By controlling the thermal power input rate, the temperature change of the grounding node can be adjusted. Represents the relationship between the temperature change of the grounding node and the thermal conductivity coefficient. The larger the thermal conductivity coefficient, the more sensitive the grounding node is to temperature changes; conversely, the smaller the thermal conductivity coefficient, the less sensitive the grounding node is to temperature changes. By controlling the thermal conductivity coefficient, the temperature response speed of the grounding node can be adjusted. The grounding node dynamic temperature change calculation formula provides a method for calculating the grounding node temperature change rate, and takes into account the influence of multiple factors on temperature change, including heat conduction, material properties, thermal expansion, ambient temperature, and heat exchange. Analyzing and predicting the temperature change of the grounding node through this formula helps to evaluate the thermal stability and safety of the grounding system.

[0139] In this embodiment, the specific steps of step S5 are:

[0140] Step S51: performing operation response simulation processing on the cable digital twin model to obtain cable operation response simulation data;

[0141] Step S52: performing response situation evolution analysis on the cable operation response simulation data, thereby obtaining cable response situation evolution data;

[0142] Step S53: performing forward time series prediction on the cable response situation evolution data to generate situation evolution prediction data;

[0143] Step S54: performing abnormal trend analysis on the situation evolution prediction data to obtain abnormal trend data of the grounding node.

[0144] In this embodiment, the time range and time step of the simulation are determined, and the boundary conditions and excitation conditions in the simulation process are set, such as the input current and ambient temperature of the cable. The operation response simulation processing of the cable is performed based on the digital twin model of the cable and the defined parameters. In the simulation process, factors such as the thermal conduction and electromagnetic field distribution of the cable are considered, and the temperature, electric potential and other responses at various positions of the cable are calculated. According to the results of the operation response simulation processing, the operation response simulation data of the cable is generated. The operation response simulation data includes the changes of the temperature and electric potential at various positions of the cable over time. The cable operation response simulation data is used to perform a response situation evolution analysis. The cable response data is processed and analyzed by means of statistics, data mining, visualization and other methods. Based on the results of the response situation evolution analysis, the response situation evolution data of the cable is generated. The potential evolution data reflects the overall trend and evolution characteristics of the cable response, such as temperature rise and fall, potential change, etc., determines the prediction time range and time step, sets the input conditions and model selection in the prediction process, such as using machine learning models, time series models, etc., based on the cable response situation evolution data, performs forward time series prediction processing, uses appropriate prediction models and algorithms, predicts the situation evolution in the future based on historical data, and generates cable situation evolution prediction data based on the results of forward time series prediction. The situation evolution prediction data includes the changes in the predicted values ​​of temperature, potential, etc. at each position of the cable over time. Based on the results of abnormal trend analysis, the abnormal trend data of the grounding node is generated. The abnormal trend data indicates whether there are abnormal changes in the grounding node within the prediction time range, such as abnormal temperature increase, abnormal potential change, etc.

[0145] In this embodiment, the specific steps of step S6 are:

[0146] Step S61: locating abnormal parts of the cable digital twin model based on the abnormal trend data of the grounding nodes, and extracting abnormal grounding nodes;

[0147] Step S62: performing abnormal trend diagnosis processing on the abnormal grounding node to obtain abnormal trend diagnosis data;

[0148] Step S63: Perform abnormal optimization decision analysis on the abnormal trend diagnosis data and build an intelligent diagnosis optimization strategy to execute monitoring and diagnosis operations.

[0149] In this embodiment, based on the cable digital twin model and the abnormal trend data of the grounding node, an abnormal part positioning algorithm is executed, and machine learning, data mining or expert system and other methods are used to determine the abnormal grounding node by comparing the abnormal trend data with the model prediction. According to the result of the abnormal part positioning, the abnormal grounding node is extracted. The abnormal grounding node is a node where a fault or abnormal situation occurs. Based on the abnormal grounding node data, an abnormal trend diagnosis algorithm is executed, and data analysis, signal processing or pattern recognition and other methods are used to diagnose and analyze the trend of the abnormal grounding node. According to the result of the abnormal trend diagnosis processing, abnormal trend diagnosis data is generated. The abnormal trend diagnosis data includes a description of the abnormal trend, the diagnosis result and the cause of the fault, etc. Based on the abnormal trend diagnosis data, an abnormal optimization decision analysis is performed, and an expert system, machine learning or optimization algorithm and other methods are used to perform decision analysis in combination with historical data and domain knowledge. According to the result of the abnormal optimization decision analysis, an intelligent diagnosis optimization strategy is constructed. The intelligent diagnosis optimization strategy includes troubleshooting suggestions, repair plans or guidance for further monitoring and diagnosis, etc. According to the constructed intelligent diagnosis optimization strategy, monitoring and diagnosis operations are performed.

[0150] In this embodiment, an intelligent monitoring and diagnosis device for a high-voltage cable grounding system is provided, which is used to execute the intelligent monitoring and diagnosis method for a high-voltage cable grounding system as described above, including:

[0151] The regional unit module is used to perform three-dimensional scanning of the outer surface acoustic wave of the high-voltage cable and collect acoustic wave pulse sensor signals; perform multi-path propagation effect analysis on the acoustic wave pulse sensor signals to obtain acoustic wave multi-path propagation effect data; perform regional unit classification on the acoustic wave multi-path propagation effect data to obtain cable regional unit data;

[0152] The morphological analysis module is used to obtain the propagation trajectories of multiple cable parts based on the acoustic wave multipath propagation effect data; perform spatial path morphological analysis on the propagation trajectories of multiple cable parts based on the cable area unit data to construct a three-dimensional cable structure model;

[0153] The grounding uniformity module is used to obtain the real-time resistance monitoring data of the grounding node, the temperature parameters of the grounding node and the structure diagram of the grounding node; based on the structure diagram of the grounding node, the real-time resistance monitoring data of the grounding node is subjected to global grounding uniformity analysis to obtain the grounding uniformity data;

[0154] The twin model module is used to analyze the thermal conductivity uniformity of the grounding node based on the grounding node temperature parameters to obtain the grounding thermal conductivity uniformity data; based on the grounding uniformity data and the grounding thermal conductivity uniformity data, the three-dimensional structure model of the cable is dynamically rendered and simulated to obtain the digital twin model of the cable;

[0155] The abnormal trend module is used to analyze the response situation evolution of the cable digital twin model to obtain the cable response situation evolution data; perform abnormal trend analysis on the cable response situation evolution data to obtain the abnormal trend data of the grounding node;

[0156] The decision optimization module is used to extract abnormal grounding nodes based on the abnormal trend data of grounding nodes; perform abnormal trend diagnosis and processing on abnormal grounding nodes, and build an intelligent diagnosis optimization strategy to perform monitoring and diagnosis operations.

[0157] The present invention can obtain the acoustic wave information and sensor signals of the outer surface of the cable through three-dimensional scanning processing of the outer surface acoustic waves and acquisition of acoustic wave pulse sensor signals.

[0158] The multi-path propagation effect analysis is carried out by analyzing the acoustic pulse sensor signal to obtain the effect data of the multi-path propagation of acoustic waves in the cable.

[0159] Area unit classification classifies the acoustic wave multipath propagation effect data to obtain cable area unit data for further analysis of the cable structure and performance. Propagation trajectory acquisition determines the trajectory of acoustic wave propagation in multiple cable locations based on the acoustic wave multipath propagation effect data.

[0160] Spatial path morphology analysis uses cable area unit data to analyze the propagation trajectories of multiple cable locations, constructs a three-dimensional structural model of the cable, and provides geometric morphology information of the cable. Global grounding uniformity analysis analyzes the grounding uniformity of the grounding system based on the real-time resistance monitoring data of the grounding node and the grounding node structure diagram to obtain grounding uniformity data.

[0161] The grounding node thermal conductivity uniformity analysis analyzes the thermal conductivity uniformity of the grounding node based on the grounding node temperature parameters to obtain the grounding thermal conductivity uniformity data. The dynamic rendering simulation processing uses the grounding uniformity data and the grounding thermal conductivity uniformity data to perform dynamic rendering simulation processing on the cable three-dimensional structure model to generate a digital twin model of the cable. The response state evolution analysis analyzes the cable digital twin model to obtain the cable response state evolution data.

[0162] Abnormal trend analysis processes the cable response situation evolution data and extracts the abnormal trend data of the grounding node to detect the abnormal state and trend of the grounding node. Abnormal grounding node extraction determines the abnormal grounding node based on the abnormal trend data of the grounding node.

[0163] Abnormal trend diagnosis and processing analyzes and processes abnormal grounding nodes, and builds intelligent diagnosis optimization strategies to perform monitoring and diagnosis tasks.

[0164] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0165] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. An intelligent monitoring and diagnostic method for a high-voltage cable grounding system, characterized in that: The following steps are involved: Step S1: performing three-dimensional acoustic wave scanning on the outer surface of the high-voltage cable and collecting acoustic wave pulse sensing signals; Performing multipath propagation effect analysis on the acoustic wave pulse sensor signal to obtain acoustic wave multipath propagation effect data; performing regional unit classification on the acoustic wave multipath propagation effect data to obtain cable regional unit data; Step S2: obtaining propagation trajectories of multiple cable locations based on the acoustic wave multipath propagation effect data; performing spatial path morphological analysis on the propagation trajectories of multiple cable locations based on the cable area unit data to construct a three-dimensional cable structure model; Step S3: acquiring grounding node real-time resistance monitoring data, grounding node temperature parameters and grounding node structure diagram; performing global grounding uniformity analysis on the grounding node real-time resistance monitoring data based on the grounding node structure diagram to obtain grounding uniformity data; The specific steps of step S3 are: Step S31: Acquire the real-time resistance monitoring data of the grounding node, the temperature parameters of the grounding node and the structure diagram of the grounding node; Step S32: performing time series analysis on the real-time resistance monitoring data of the grounding node to generate time series resistance monitoring data; Step S33: performing resistance distribution analysis on the grounding node structure diagram based on the sequential resistance monitoring data to obtain grounding node resistance distribution data; Step S34: performing a global grounding uniformity analysis on the grounding node resistance distribution data to obtain grounding uniformity data; Step S4: performing a grounding node thermal conductivity uniformity analysis on the grounding node temperature parameter to obtain grounding thermal conductivity uniformity data; performing dynamic rendering simulation processing on the cable three-dimensional structure model based on the grounding uniformity data and the grounding thermal conductivity uniformity data to obtain a cable digital twin model; The specific steps of step S4 are: Step S41: using a grounding node dynamic temperature change calculation formula to perform dynamic temperature change calculation on a grounding node temperature parameter to generate a node temperature change rate; Step S42: performing temperature difference distribution analysis on the node temperature change rate to obtain temperature difference distribution data; Step S43: performing a grounding node thermal conductivity uniformity analysis on the grounding node structure diagram based on the temperature difference distribution data to obtain grounding thermal conductivity uniformity data; Step S44: performing node characteristic evolution analysis on the grounding uniformity data and the grounding thermal conductivity uniformity data to generate grounding node characteristic evolution data; Step S45: dynamically rendering and simulating the cable three-dimensional structure model through the grounding node feature evolution data, thereby obtaining a cable digital twin model; Step S5: performing response situation evolution analysis on the cable digital twin model to obtain cable response situation evolution data; performing abnormal trend analysis on the cable response situation evolution data to obtain grounding node abnormal trend data; Step S6: extract abnormal grounding nodes based on the abnormal trend data of grounding nodes; perform abnormal trend diagnosis processing on the abnormal grounding nodes, and build an intelligent diagnosis optimization strategy to perform monitoring and diagnosis operations.

2. The intelligent monitoring and diagnosis method for a high-voltage cable grounding system according to claim 1 is characterized in that: The specific steps of step S1 are: Step S11: performing three-dimensional acoustic wave scanning on the outer surface of the high-voltage cable and collecting acoustic wave pulse sensing signals; Step S12: performing multipath propagation effect analysis on the acoustic wave pulse sensing signal, thereby obtaining acoustic wave multipath propagation effect data; Step S13: performing regional propagation attenuation analysis on the sound wave multipath propagation effect data to obtain sound wave regional propagation attenuation data; Step S14: performing regional propagation velocity difference calculation on the acoustic wave multipath propagation effect data to obtain a regional propagation velocity difference value; Step S15: performing regional unit classification based on the sound wave regional propagation attenuation data and the regional propagation velocity difference value to obtain cable regional unit data.

3. The intelligent monitoring and diagnosis method for a high-voltage cable grounding system according to claim 1 is characterized in that: The specific steps of step S2 are: Step S21: reversely deduce the propagation path of the acoustic wave multipath propagation effect data, thereby obtaining the propagation trajectories of multiple cable locations; Step S22: performing spatial path morphology analysis on the propagation trajectories of multiple cable locations to obtain cable path morphology data; Step S23: reconstructing the cable path morphology data in space based on the cable area unit data to construct a three-dimensional cable structure model.

4. The intelligent monitoring and diagnosis method for a high-voltage cable grounding system according to claim 3 is characterized in that: The specific steps of step S23 are: Performing time series window segmentation on the acoustic wave multipath propagation effect data to obtain multipath propagation time series window data; Performing timing shift difference analysis on multipath propagation timing window data, thereby obtaining timing shift difference data; Reconstruct the three-dimensional spatial position distribution of the cable area unit data based on the time series displacement difference data to obtain the area unit spatial position data; Performing overlapping structure analysis on the spatial position data of the regional unit to generate overlapping structure data of the regional unit; Based on the overlapping structure data of regional units, spatial distribution analysis is carried out to obtain the spatial structure of regional units; Based on the spatial position data of the regional unit, the cable path morphology data is spatially reconstructed to obtain an initial three-dimensional model; The initial three-dimensional model is iteratively reshaped through a hierarchical structure through a regional unit space architecture to construct a three-dimensional structural model of the cable.

5. The intelligent monitoring and diagnosis method for a high-voltage cable grounding system according to claim 1 is characterized in that: The calculation formula for the dynamic temperature change of the ground node in step S41 is specifically: ； For The rate of change of node temperature over time, is the initial temperature of the ground node, is the rate at which the cable thermal power is input to the ground node, is the thermal conductivity of the ground node, is the length of time over which the temperature change is calculated, is the density of ground nodes, is the specific heat capacity of the ground node, is the thermal expansion coefficient of the cable, is the ambient temperature, is the dielectric constant of the cable, is the convective heat to the ground node.

6. The intelligent monitoring and diagnosis method for a high-voltage cable grounding system according to claim 1 is characterized in that: The specific steps of step S5 are: Step S51: performing operation response simulation processing on the cable digital twin model to obtain cable operation response simulation data; Step S52: performing response situation evolution analysis on the cable operation response simulation data, thereby obtaining cable response situation evolution data; Step S53: performing forward time series prediction on the cable response situation evolution data to generate situation evolution prediction data; Step S54: performing abnormal trend analysis on the situation evolution prediction data to obtain abnormal trend data of the grounding node.

7. The intelligent monitoring and diagnosis method for a high-voltage cable grounding system according to claim 1 is characterized in that: The specific steps of step S6 are: Step S61: locating abnormal parts of the cable digital twin model based on the abnormal trend data of the grounding nodes, and extracting abnormal grounding nodes; Step S62: performing abnormal trend diagnosis processing on the abnormal grounding node to obtain abnormal trend diagnosis data; Step S63: Perform abnormal optimization decision analysis on the abnormal trend diagnosis data and construct an intelligent diagnosis optimization strategy to execute monitoring and diagnosis operations.

8. An intelligent monitoring and diagnostic device for a high-voltage cable grounding system, characterized in that: The intelligent monitoring and diagnosis method for a high-voltage cable grounding system according to claim 1 comprises: The regional unit module is used to perform three-dimensional scanning of the outer surface acoustic wave of the high-voltage cable and collect acoustic wave pulse sensor signals; perform multi-path propagation effect analysis on the acoustic wave pulse sensor signals to obtain acoustic wave multi-path propagation effect data; perform regional unit classification on the acoustic wave multi-path propagation effect data to obtain cable regional unit data; The morphological analysis module is used to obtain the propagation trajectories of multiple cable parts based on the acoustic wave multipath propagation effect data; perform spatial path morphological analysis on the propagation trajectories of multiple cable parts based on the cable area unit data to construct a three-dimensional cable structure model; The grounding uniformity module is used to obtain the real-time resistance monitoring data of the grounding node, the temperature parameters of the grounding node and the structure diagram of the grounding node; based on the structure diagram of the grounding node, the real-time resistance monitoring data of the grounding node is subjected to global grounding uniformity analysis to obtain the grounding uniformity data; The twin model module is used to analyze the thermal conductivity uniformity of the grounding node based on the grounding node temperature parameters to obtain the grounding thermal conductivity uniformity data; based on the grounding uniformity data and the grounding thermal conductivity uniformity data, the three-dimensional structure model of the cable is dynamically rendered and simulated to obtain the digital twin model of the cable; The abnormal trend module is used to analyze the response situation evolution of the cable digital twin model to obtain the cable response situation evolution data; perform abnormal trend analysis on the cable response situation evolution data to obtain the abnormal trend data of the grounding node; The decision optimization module is used to extract abnormal grounding nodes based on the abnormal trend data of grounding nodes; perform abnormal trend diagnosis and processing on abnormal grounding nodes, and build an intelligent diagnosis optimization strategy to perform monitoring and diagnosis operations.

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