A monitoring method, system, device and storage medium of a direct current wall bushing

Abstract

The application discloses a kind of monitoring method, system, equipment and storage medium of direct current wall bushing, applied to electric power equipment state monitoring field, including through real-time monitoring data driving multi-physics field coupling ideal simulation calculation, the electric field and temperature field distribution reflecting the real state inside equipment are obtained, that is, simulation distribution result;Real-time monitoring data and simulation distribution result are calculated in the feature deviation data of preset position, and dynamic threshold value is combined based on historical data to carry out fault monitoring, realize the complete diagnosis process from state perception to fault monitoring.The application realizes the monitoring of the whole field perception inside direct current wall bushing, significantly improves the accuracy of direct current wall bushing state monitoring and the timeliness of fault early warning.

Description

Technical Field

[0001] This invention relates to the field of power equipment condition monitoring technology, and in particular to a monitoring method, system, device and storage medium for DC through-wall bushings. Background Technology

[0002] With the rapid development of ultra-high voltage direct current (UHVDC) transmission technology, the continuous increase in voltage levels and transmission capacity has placed higher demands on the reliability of key equipment in converter stations. As a critical hub in UHVDC transmission systems, the operating status of DC wall bushings directly affects the reliability and security of the entire power grid. Therefore, developing precise and efficient condition monitoring and fault early warning technologies for such equipment is an important research direction in the field of intelligent operation and maintenance of power systems.

[0003] Currently, monitoring of DC through-wall bushings relies on external image sensors. These sensors collect limited external operating parameters and combine them with static thresholds for status assessment and early warning. This method struggles to fully cover the complex internal structure of the equipment, resulting in blind spots and hindering comprehensive monitoring by existing systems. Warnings are often triggered only in the middle to late stages of a fault, significantly increasing the risk of unplanned equipment downtime and catastrophic failures. Summary of the Invention

[0004] This invention provides a monitoring method, system, device, and storage medium for DC through-wall bushings to solve the technical problems of invisible multi-physics coupling states and difficulty in capturing fault evolution processes inside DC through-wall bushings, so as to achieve dynamic mapping of the internal state of DC through-wall bushings and early accurate warning and diagnosis of faults.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a method for monitoring DC through-wall bushings, comprising: Based on the real-time monitoring data of the target DC through-wall bushing, a multi-physics field coupled ideal simulation calculation is performed on the target DC through-wall bushing to obtain the target simulation distribution results; Calculate the characteristic deviation data between the real-time monitoring data and the target simulation distribution results; When the characteristic deviation data is greater than the dynamic threshold, a monitoring result of the fault of the target DC through-wall bushing is generated, wherein the dynamic threshold is determined based on the historical monitoring data of the target DC through-wall bushing.

[0006] As a preferred embodiment, before performing the multiphysics coupled ideal simulation calculation, the method further includes constructing a three-dimensional simulation model of the DC through-wall bushing. The construction process of the three-dimensional simulation model includes: The design drawings of the DC through-wall bushing are analyzed, and a three-dimensional geometric model is constructed based on the analysis results. Obtain the material parameters that constitute the three-dimensional geometric model and construct a material parameter database; By associating the three-dimensional geometric model with the material parameter database, a three-dimensional simulation model is obtained for performing the ideal calculation of the multiphysics coupling simulation.

[0007] As one preferred embodiment, the calculation of the characteristic deviation data between the real-time monitoring data and the target simulation distribution result includes: Determine the sensor monitoring position on the target DC through-wall bushing; Acquire real-time monitoring data of the target DC through-wall bushing at the sensor monitoring location, and extract simulation data of the target simulation distribution result at the corresponding sensor monitoring location; The difference between the real-time monitoring data and the simulation data is calculated to obtain the characteristic deviation data.

[0008] As one preferred embodiment, the process of the multiphysics coupled ideal simulation calculation includes: Based on the real-time monitoring data, the boundary conditions of the three-dimensional simulation model are determined; In the three-dimensional simulation model, thermal-fluid coupling calculations are performed to obtain a set of coupling equations; The coupled equations are iteratively calculated, and when the calculation result meets the convergence condition, the current calculation result is output as the first temperature field distribution result. Enter the electrothermal iterative update mode, update the electric field calculation parameters according to the first temperature field distribution result, and obtain the electric field distribution result based on the electric field calculation parameters; Based on the electric field distribution results, the temperature field calculation parameters are updated, and a second temperature field distribution result is obtained based on the temperature field calculation parameters. Repeat the electrothermal iterative update mode until both the temperature field distribution result and the electric field distribution result meet the preset conditions; The target simulation distribution result is obtained based at least on the corresponding temperature field distribution result and the corresponding electric field distribution result.

[0009] As one preferred embodiment, the process of determining the dynamic threshold includes: Extract the health operation data of the target DC through-wall bushing from the historical monitoring data; Statistical analysis is performed on the health operation data to calculate the probability distribution of the characteristic deviation under normal operating conditions; The dynamic threshold is determined based on the confidence interval of the probability distribution.

[0010] As a preferred embodiment, after obtaining the monitoring results of the fault of the target DC through-wall bushing, the method further includes: generating a risk field cloud map to indicate the risk location based on the monitoring results and the simulation distribution results, specifically including: The simulation distribution results are compressed and optimized to obtain field quantity data; The field quantity data is mapped to a color space to obtain the corresponding initial field quantity cloud map; Based on the monitoring results, the risk locations are marked in the initial field volume cloud map; The initial field quantity cloud map after annotation is rendered to obtain the risk field quantity cloud map.

[0011] Another embodiment of the present invention provides a monitoring system for DC through-wall bushings, comprising: The simulation module is used to perform multi-physics field coupled ideal simulation calculations on the target DC through-wall bushing based on real-time monitoring data of the target DC through-wall bushing, and obtain the target simulation distribution results; The calculation module is used to calculate the characteristic deviation data between the real-time monitoring data and the target simulation distribution results; The generation module is used to generate a monitoring result of the fault of the target DC through-wall bushing when the feature deviation data is greater than the dynamic threshold, wherein the dynamic threshold is determined based on the historical monitoring data of the target DC through-wall bushing.

[0012] As one preferred embodiment, the simulation module is further used for: The design drawings of the DC through-wall bushing are analyzed, and a three-dimensional geometric model is constructed based on the analysis results. Obtain the material parameters that constitute the three-dimensional geometric model and construct a material parameter database; By associating the three-dimensional geometric model with the material parameter database, a three-dimensional simulation model is obtained for performing the ideal calculation of the multiphysics coupling simulation.

[0013] As one preferred embodiment, the computing module is further configured to: Determine the sensor monitoring position on the target DC through-wall bushing; Acquire real-time monitoring data of the target DC through-wall bushing at the sensor monitoring location, and extract simulation data of the target simulation distribution result at the corresponding sensor monitoring location; The difference between the real-time monitoring data and the simulation data is calculated to obtain the characteristic deviation data.

[0014] As one preferred embodiment, the simulation module is further used for: Based on the real-time monitoring data, the boundary conditions of the three-dimensional simulation model are determined; In the three-dimensional simulation model, thermal-fluid coupling calculations are performed to obtain a set of coupling equations; The coupled equations are iteratively calculated, and when the calculation result meets the convergence condition, the current calculation result is output as the first temperature field distribution result. Enter the electrothermal iterative update mode, update the electric field calculation parameters according to the first temperature field distribution result, and obtain the electric field distribution result based on the electric field calculation parameters; Based on the electric field distribution results, the temperature field calculation parameters are updated, and a second temperature field distribution result is obtained based on the temperature field calculation parameters. Repeat the electrothermal iterative update mode until both the temperature field distribution result and the electric field distribution result meet the preset conditions; The target simulation distribution result is obtained based at least on the corresponding temperature field distribution result and the corresponding electric field distribution result.

[0015] As one preferred embodiment, the generation module is further configured to: Extract the health operation data of the target DC through-wall bushing from the historical monitoring data; Statistical analysis is performed on the health operation data to calculate the probability distribution of the characteristic deviation under normal operating conditions; The dynamic threshold is determined based on the confidence interval of the probability distribution.

[0016] As one preferred embodiment, the generation module is further configured to: The simulation distribution results are compressed and optimized to obtain field quantity data; The field quantity data is mapped to a color space to obtain the corresponding initial field quantity cloud map; Based on the monitoring results, the risk locations are marked in the initial field volume cloud map; The initial field quantity cloud map after annotation is rendered to obtain the risk field quantity cloud map.

[0017] Another embodiment of the present invention provides a monitoring device for a DC through-wall bushing, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the monitoring method for the DC through-wall bushing as described above.

[0018] In another embodiment of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, the monitoring method for DC through-wall bushings as described above is implemented.

[0019] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: (1) This invention uses real-time monitoring data to drive multi-physics field coupled ideal simulation calculations to obtain the electric field and temperature field distributions that reflect the actual internal state of the equipment, i.e., the simulation distribution results. By calculating the characteristic deviation data between the real-time monitoring data and the simulation distribution results at preset positions, and combining it with dynamic thresholds established based on historical data, fault monitoring is performed, realizing a complete diagnostic process from state perception to fault monitoring. This invention upgrades the traditional passive monitoring that relies on limited external measuring points to proactive early warning and accurate diagnosis based on the full state perception of the equipment, effectively improving the accuracy of DC through-wall bushing status assessment and the level of intelligence in operation and maintenance decision-making.

[0020] (2) This invention can keenly capture minute feature deviations caused by internal state anomalies that have not yet fully manifested in external monitoring data, thereby achieving early fault detection; through multi-physics field coupling simulation and dynamic threshold judgment, it accurately locates fault risks and assesses their severity; finally, through risk field quantity cloud maps, it intuitively displays the risk location and distribution, realizing real-time visualization of large-scale field quantities, greatly improving the intuitiveness of data and decision-making efficiency. This invention realizes in-depth state assessment and fault early warning based on multi-source data fusion, providing a powerful platform support for equipment condition maintenance. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a monitoring method for DC through-wall bushings in one embodiment of the present invention. Figure 2 This is a schematic diagram of a monitoring system for a DC through-wall bushing in one embodiment of the present invention; Figure 3 This is a structural block diagram of a monitoring device for a DC through-wall bushing in one embodiment of the present invention.

[0022] Figure label: The system includes a simulation module 11, a calculation module 12, a generation module 13, a processor 21, and a memory 22. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] In the description of this invention, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0025] In the description of this invention, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to communication within two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0026] In the description of this invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0027] One embodiment of the present invention provides a method for monitoring DC through-wall bushings. For details, please refer to [link / reference]. Figure 1 , Figure 1 The diagram shown is a flowchart illustrating a monitoring method for a DC through-wall bushing according to one embodiment of the present invention, which includes steps S1 to S3: S1: Based on the real-time monitoring data of the target DC through-wall bushing, perform multi-physics field coupled ideal simulation calculations on the target DC through-wall bushing to obtain the target simulation distribution results; Step S1 involves constructing a digital twin that is updated synchronously with the physical entity. This simulation method reveals the distribution of key physical fields inside the equipment that cannot be directly observed by external sensors, providing a dynamic benchmark for subsequent fault monitoring.

[0028] Preferably, in one embodiment of the present invention, before performing multiphysics coupled ideal simulation calculations, a three-dimensional simulation model of the DC through-wall bushing is constructed. The process of constructing the three-dimensional simulation model includes: The design drawings of DC through-wall bushings are analyzed, and a three-dimensional geometric model is constructed based on the analysis results. Obtain the material parameters that constitute the three-dimensional geometric model and construct a material parameter database; By linking the three-dimensional geometric model and the material parameter database, a three-dimensional simulation model is obtained for performing ideal calculations in multiphysics coupling simulation.

[0029] Performing multiphysics coupled ideal simulation calculations relies on a pre-constructed 3D simulation model corresponding to the target DC through-wall bushing. This model serves as the geometric and physical foundation for all subsequent simulation calculations. Specifically, its construction process includes: analyzing the design drawings of the target DC through-wall bushing, extracting the precise geometric dimensions and assembly relationships of the conductor, epoxy resin impregnation layer, silicone rubber skirt, metal flange, and various insulating air gaps, and constructing a high-fidelity structured 3D geometric model in the simulation software environment. Subsequently, through standard laboratory experiments, the multiphysics parameters of various materials constituting this 3D geometric model, including epoxy composite insulation materials, silicone rubber, conductive metals, and sulfur hexafluoride gas, under different operating conditions are obtained, including conductivity, dielectric constant, thermal conductivity, and specific heat capacity varying with temperature, and a structured material parameter database is established. Finally, the 3D geometric model is mapped to the material parameter database to ensure that each geometric component in the model is associated with its corresponding material physical properties, thus forming a parameterized 3D simulation model that can be used for multiphysics coupled simulation calculations.

[0030] After obtaining the 3D simulation model, the system begins to perform multiphysics coupled ideal simulation calculations on the target DC through-wall bushing based on real-time monitoring data. The real-time monitoring data is acquired in real-time by a multi-source sensor system deployed on the target DC through-wall bushing and its operating environment. The acquired real-time monitoring data mainly includes two categories: first, key electrical parameters, namely the load current flowing through the conductor rod and the conductor-to-ground voltage; second, thermodynamic and mechanical state parameters, including the outer surface temperature of structural components such as flanges and conductor rods, and the pressure of sulfur hexafluoride gas in the sealed gas chamber. All data is received and forwarded through the system's built-in unified data interaction interface, providing real-time input for subsequent multiphysics coupled ideal simulation calculations.

[0031] Preferably, in one embodiment of the present invention, the process of multiphysics coupled ideal simulation calculation includes: Based on the real-time monitoring data, the boundary conditions of the three-dimensional simulation model are determined; In the three-dimensional simulation model, thermal-fluid coupling calculations are performed to obtain a set of coupling equations; The coupled equations are iteratively calculated, and when the calculation result meets the convergence condition, the current calculation result is output as the first temperature field distribution result. Enter the electrothermal iterative update mode, update the electric field calculation parameters according to the first temperature field distribution result, and obtain the electric field distribution result based on the electric field calculation parameters; Based on the electric field distribution results, the temperature field calculation parameters are updated, and a second temperature field distribution result is obtained based on the temperature field calculation parameters. Repeat the electrothermal iterative update mode until both the temperature field distribution result and the electric field distribution result meet the preset conditions; The target simulation distribution result is obtained based at least on the corresponding temperature field distribution result and the corresponding electric field distribution result.

[0032] To accurately simulate the internal thermal state of a DC through-wall bushing, the system first needs to obtain an accurate initial temperature field distribution, i.e., the first temperature field distribution result. This result is the starting point for subsequent electrothermal iterative feedback calculations, and its accuracy is crucial to the convergence and reliability of the entire simulation process.

[0033] Specifically, real-time monitoring data is first obtained from a unified data interaction interface. This data serves as the boundary and initial conditions for the simulation calculations. Current and voltage data are used to calculate the heating power, surface temperature data is used to determine the boundary conditions, and gas pressure data is used to determine the fluid properties. Based on the data processing, the system calls a three-dimensional simulation model to perform coupled thermal-fluid calculations. This model defines the fluid domain, represented by the internal sulfur hexafluoride gas, and the geometric extent of all solid components.

[0034] The core of obtaining the first temperature field distribution lies in solving the complete thermal processes involving conduction, convection, and radiation inside the DC through-wall bushing. To achieve this comprehensive calculation, the system applies the finite volume method combined with a pressure correction algorithm to solve the mass and momentum conservation equations for gas flow and convective heat transfer, thereby calculating the velocity and pressure distribution of sulfur hexafluoride gas, providing a foundation for convective heat transfer. For heat conduction in the solid region and heat transfer at the solid-fluid coupling interface, the Galerkin finite element method is used, which can better handle complex geometries. For time progression, an implicit time discretization scheme is used, which achieves a good balance between computational efficiency and numerical stability.

[0035] Due to the complex internal structure of the bushing, with components such as the central guide rod, flange, and shielding cover obstructing each other, traditional simplified algorithms can introduce significant errors. The system requires fine meshing of the 3D simulation model surface of the DC through-wall bushing, generating a large number of triangular or quadrilateral elements. Subsequently, using an efficient obstruction detection technique, all effective surface element pairs that can "see" each other and undergo radiative heat transfer are quickly identified, while completely obstructed, unrelated element pairs are eliminated. For these effective surface element pairs, the Gaussian integral method is used to accurately calculate their radiation angle coefficients, ultimately establishing and solving the radiative heat transfer balance equation for the entire system. This process ensures that even under complex structures, the radiative heat transfer can be accurately calculated.

[0036] The formula for calculating its net radiative heat flux density is: In the formula, For surface emissivity, Here is the Stefan-Boltzmann constant, and its value is... W / (m2·K4), Surface temperature, The effective temperature of the surrounding surface, and is the angle coefficient, representing the fraction of the radiant energy emitted from surface j that falls onto surface i.

[0037] To achieve the coupled solution of the three heat transfer mechanisms, the system executes an internal iterative loop: In each iteration step, the radiation heat transfer term is first calculated using the current temperature field; then, this term is assembled with the heat conduction and convection terms into a global system equation set and solved to obtain an updated temperature field distribution; next, the radiation heat transfer term is recalculated using the updated temperature field, initiating the next iteration. This continues until the maximum relative change between the global temperature field results obtained from two adjacent iterations is less than one ten-thousandth, at which point the heat-fluid coupling calculation is considered to have converged. The final output is the first temperature field distribution result that has converged in this internal iteration. This result is the steady-state solution that comprehensively considers the three heat transfer mechanisms and will serve as an accurate physical initial condition, directly inputting into the electrothermal iterative update mode of subsequent steps.

[0038] After obtaining the initial temperature field distribution, the system enters the electrothermal iterative update mode. In this mode, the electric field calculation parameters, specifically the volume conductivity of the insulating medium, are updated first. Since the DC electric field distribution strongly depends on the material conductivity, and conductivity is extremely sensitive to temperature changes, a non-uniform temperature field will cause changes in the spatial distribution of conductivity, leading to electric field distortion. By introducing a temperature field correction for conductivity, the electric field distribution characteristics under DC conditions can be accurately simulated.

[0039] Specifically, the system uses the initial temperature field distribution as a key input, precisely mapping it to the electric field calculation module. For each location within the computational domain, especially in areas with insulating materials such as the epoxy core, the system retrieves the corresponding conductivity value from a pre-established material parameter database based on the temperature at that point. After obtaining the updated spatial conductivity distribution, a modified Poisson equation based on conductivity, suitable for DC conditions, is used to replace the equation based on dielectric constant under AC conditions. The resulting electric field distribution accurately reflects the distortion effect of temperature non-uniformity on the electric field.

[0040] After completing the electric field calculation based on the updated conductivity, the temperature field calculation parameters are updated according to the electric field distribution results, and a second temperature field distribution result is obtained based on these parameters. The temperature field calculation parameters describe the spatial distribution of power loss density within the insulating medium, i.e., the heat source intensity generated per unit volume due to the electric field. Because the heat source inside the DC through-wall bushing is not constant, under DC voltage, leakage current driven by the electric field will be generated inside the insulating medium, leading to power loss and constituting an important additional heat source. The magnitude of this power loss directly depends on the distribution of the electric field intensity; therefore, the electric field calculation results need to be fed back to the temperature field.

[0041] Specifically, the calculated spatial electric field distribution is first substituted into the formula for calculating the volumetric heating power of the insulating medium to obtain a new power loss. This heat source data is then fed back to step 2 to obtain an updated energy conservation equation, incorporating the newly added electric field-induced heat loss along with the original Joule heat source into the calculation. After updating the equation, the heat-fluid coupling solution unit restarts the calculation process, comprehensively solving the energy equation that considers convection, conduction, radiation, and the latest heat source distribution, thus obtaining an updated temperature field that more closely approximates the actual state—the second temperature field distribution result.

[0042] Repeatedly executing the electrothermal iterative update mode until both the temperature and electric field distributions meet preset conditions is a core step in ensuring the accuracy and reliability of multiphysics coupling simulation results. These preset conditions specifically refer to the convergence criteria for the electric and temperature fields. After each electrothermal iterative update, the maximum relative change in the current and previous global potential and temperature distributions is calculated, and both are required to be less than a threshold of 0.01%. This threshold, based on the characteristics of high-voltage DC equipment, ensures the accuracy of field strength calculations and accurately reflects the influence of temperature on material properties. This provides a solid and reliable benchmark for subsequent comparison with real-time monitoring data and for achieving accurate condition diagnosis.

[0043] After the electrothermal iterative update mode meets the convergence condition, the final physical field data is integrated and output to obtain the target simulation distribution results. Specifically, the target simulation distribution results include the final temperature field distribution and the final electric field distribution results after rigorous iteration until they stabilize, providing a reliable benchmark for subsequent characteristic deviation calculation and intelligent diagnosis. These two are direct products of electrothermal coupling calculation, accurately describing the thermal equilibrium state and insulation electric field distribution that should be achieved inside the bushing under given load and environmental conditions. In addition, the results also integrate the velocity field distribution calculated in parallel and continuously updated throughout the process by the thermal-fluid coupling solution unit. This velocity field reveals the flow path and velocity of internal insulating gases such as sulfur hexafluoride, which is key to analyzing the convective heat dissipation effect and identifying flow dead zones.

[0044] In one embodiment of the present invention, to further simulate the behavior of DC through-wall bushings under transient conditions such as polarity reversal and switching operations, the multiphysics coupling ideal simulation calculation also includes transient boundary condition processing. Specifically, upon receiving a real-time voltage command simulating transient conditions such as polarity reversal and switching operations of the DC through-wall bushing, the boundary conditions for electric field calculation are updated in real time according to the dynamic change characteristics of the voltage command. Within each calculation time step, the module couples the current temperature field distribution to quickly solve for the transient electric field distribution, and based on this, calculates the instantaneous power loss in the medium, feeding it back to the heat-fluid coupling calculation to update the heat source term in the energy conservation equation. To ensure the calculation accuracy and efficiency of the transient process, the embodiment employs an adaptive time step control algorithm. This algorithm first estimates the initial step size based on the voltage change rate and the characteristic time of the thermal diffusion process, taking the smaller value as the calculation starting point. During the solution process, the local truncation error is estimated by comparing the calculation results under different step sizes, and the next time step size is dynamically adjusted. When the calculation error exceeds the tolerance range, the step size is automatically reduced to ensure accuracy. When the error meets the requirements, the step size is appropriately increased to improve efficiency, so that the time step size is always kept within the preset reasonable range.

[0045] Preferably, in one embodiment of the present invention, calculating the characteristic deviation data between real-time monitoring data and target simulation distribution results includes: Determine the sensor monitoring location on the target DC through-wall bushing; Acquire real-time monitoring data of the target DC through-wall bushing at the sensor monitoring location, and extract simulation data of the target simulation distribution result at the corresponding sensor monitoring location; The difference between real-time monitoring data and simulation data is calculated to obtain characteristic deviation data.

[0046] The sensor monitoring location refers to the location of the physical sensors installed on the physical entity of the DC through-wall bushing. The simulation data refers to the physical quantity values ​​extracted from the converged field distribution results in the three-dimensional simulation model after multi-physics coupled ideal simulation calculations, at the coordinate points that completely correspond to the sensor monitoring locations, through interpolation and other methods.

[0047] Specifically, the sensor monitoring locations corresponding to the physical sensor placement points of the target DC through-wall bushing are first determined, such as fiber optic measuring points embedded in guide rods and pressure sensor interfaces for monitoring gas density. Then, real-time monitoring data for these locations is acquired through a unified data interaction interface. Simultaneously, simulation data for the corresponding spatial coordinates is extracted from the target simulation distribution results based on spatial mapping and interpolation algorithms. Finally, the difference between the real-time monitoring data and the simulation data for each monitoring location is calculated to obtain characteristic deviation data.

[0048] S3: When the characteristic deviation data is greater than the dynamic threshold, generate the monitoring results of the target DC through-wall bushing fault, wherein the dynamic threshold is determined based on the historical monitoring data of the target DC through-wall bushing; The dynamic threshold is a non-fixed, adaptively adjustable threshold used to determine whether the characteristic deviation data has become large enough to trigger a fault warning. The reason for using a dynamic threshold is that the operating status of DC through-wall bushings is affected by a combination of factors such as load and ambient temperature, and the characteristic deviation between the monitored data and the simulation data will naturally have a reasonable fluctuation range.

[0049] Specifically, the characteristic deviation data is compared with its corresponding dynamic threshold. When the characteristic deviation value of one or more sensor monitoring locations exceeds the upper limit of the dynamic threshold, it is determined to be an abnormal node. The system collects detailed information about the abnormal node, including but not limited to the specific physical quantity of the deviation, its exact location, the magnitude and sign of the deviation, the duration of the exceedance, and the rate of change of the deviation. The above information is then integrated to generate a standardized monitoring result for DC through-wall bushing faults.

[0050] Preferably, in one embodiment of the present invention, the process of determining the dynamic threshold includes: Extract the health operation data of the target DC through-wall bushing from historical monitoring data; Perform statistical analysis on health operation data and calculate the probability distribution of characteristic deviations under normal operating conditions; The dynamic threshold is determined based on the confidence interval of the probability distribution.

[0051] Specifically, the system first acquires a large amount of historical monitoring data and corresponding historical simulation data of the target DC through-wall bushing during its known healthy operating period. Using this data, characteristic deviation data sequences at various sensor monitoring locations within different historical time periods can be calculated. Subsequently, rigorous statistical analysis is performed on these historical deviation data under healthy conditions, such as calculating their mean and standard deviation, and determining their probability distribution model, such as a normal distribution or an empirical distribution based on actual data. Based on this, the system determines the upper and lower limits of the dynamic threshold according to a preset high confidence interval, such as a 95% or 99% confidence interval. Deviation values ​​outside this confidence interval are considered low-probability events, indicating a possible abnormality in the status.

[0052] Preferably, in one embodiment of the present invention, after obtaining the monitoring results of the target DC through-wall bushing fault, the method further includes: generating a risk field quantity cloud map to indicate the risk location based on the monitoring results and simulation distribution results, specifically including: The simulation distribution results are compressed and optimized to obtain field quantity data; Map the field quantity data to the color space to obtain the corresponding initial field quantity contour map; Based on the monitoring results, the risk locations were marked in the initial field volume cloud map; The initial field quantity cloud map after annotation is rendered to obtain the risk field quantity cloud map.

[0053] After obtaining the monitoring results of the target DC through-wall bushing fault, the system will generate a risk field quantity cloud map to intuitively indicate the risk location based on the monitoring results and the corresponding simulation distribution results.

[0054] Specifically, the complete simulation distribution results undergo data compression and optimization. Distributed computing is used to process the raw field data in blocks in parallel, and an adjustable dithering algorithm is applied to perform normalization, quantization, and dithering sequentially. This reduces the amount of data while maintaining visual smoothness, resulting in optimized field quantity data. Subsequently, the field quantity data is converted into color information using a predefined color mapping table, and GPU-accelerated rendering is used to generate an initial field quantity cloud map. Finally, based on the abnormal node information determined by fault monitoring results, visual annotations are added to the corresponding areas of the initial field quantity cloud map, highlighting risk areas with bright borders or flashing symbols. Finally, the annotation information is merged with the initial cloud map using a rendering engine to generate a final cloud map containing risk location information.

[0055] This method ensures visualization efficiency through data optimization and hierarchical rendering, while achieving accurate visualization and positioning of fault risks through intelligent annotation, enabling maintenance personnel to intuitively grasp the equipment status and risk distribution.

[0056] Another embodiment of the present invention provides a monitoring system for DC through-wall bushings. For details, please refer to [link to relevant documentation]. Figure 2 , Figure 2 The diagram shown illustrates a monitoring system for a DC through-wall bushing according to one embodiment of the present invention, which includes: Simulation module 11 is used to perform multi-physics field coupled ideal simulation calculations on the target DC through-wall bushing based on real-time monitoring data of the target DC through-wall bushing, and obtain the target simulation distribution results; Calculation module 12 is used to calculate the characteristic deviation data between real-time monitoring data and target simulation distribution results; The generation module 13 is used to generate the monitoring results of the target DC through-wall bushing fault when the feature deviation data is greater than the dynamic threshold, wherein the dynamic threshold is determined based on the historical monitoring data of the target DC through-wall bushing. The simulation module is also used for: Based on real-time monitoring data, the first temperature field distribution result is obtained; Enter the electrothermal iterative update mode, update the electric field calculation parameters based on the first temperature field distribution results, and obtain the electric field distribution results based on the electric field calculation parameters; Based on the electric field distribution results, the temperature field calculation parameters are updated, and the second temperature field distribution results are obtained based on the temperature field calculation parameters. Repeat the electrothermal iterative update mode until both the temperature field distribution result and the electric field distribution result meet the preset conditions. The target simulation distribution result is obtained based at least on the corresponding temperature field distribution result and the corresponding electric field distribution result.

[0057] Preferably, in one embodiment of the present invention, the simulation module is further configured to: The design drawings of DC through-wall bushings are analyzed, and a three-dimensional geometric model is constructed based on the analysis results. Obtain the material parameters that constitute the three-dimensional geometric model and construct a material parameter database; By linking the three-dimensional geometric model and the material parameter database, a three-dimensional simulation model is obtained for performing ideal calculations in multiphysics coupling simulation.

[0058] Preferably, in one embodiment of the present invention, the calculation module is further configured to: Determine the sensor monitoring location on the target DC through-wall bushing; Acquire real-time monitoring data of the target DC through-wall bushing at the sensor monitoring location, and extract simulation data of the target simulation distribution result at the corresponding sensor monitoring location; The difference between real-time monitoring data and simulation data is calculated to obtain characteristic deviation data.

[0059] Preferably, in one embodiment of the present invention, the simulation module is further configured to: Based on real-time monitoring data, the boundary conditions of the three-dimensional simulation model are determined; In the three-dimensional simulation model, thermal-fluid coupling calculations are performed to obtain a set of coupling equations; The coupled equations are iteratively calculated. When the calculation results meet the convergence condition, the current calculation result is output as the first temperature field distribution result. Enter the electrothermal iterative update mode, update the electric field calculation parameters based on the first temperature field distribution results, and obtain the electric field distribution results based on the electric field calculation parameters; Based on the electric field distribution results, the temperature field calculation parameters are updated, and the second temperature field distribution results are obtained based on the temperature field calculation parameters. Repeat the electrothermal iterative update mode until both the temperature field distribution result and the electric field distribution result meet the preset conditions. The target simulation distribution result is obtained based at least on the corresponding temperature field distribution result and the corresponding electric field distribution result.

[0060] Preferably, in one embodiment of the present invention, the generation module is further configured to: Extract the health operation data of the target DC through-wall bushing from historical monitoring data; Perform statistical analysis on health operation data and calculate the probability distribution of characteristic deviations under normal operating conditions; The dynamic threshold is determined based on the confidence interval of the probability distribution.

[0061] Preferably, in one embodiment of the present invention, the generation module is further configured to: The simulation distribution results are compressed and optimized to obtain field quantity data; Map the field quantity data to the color space to obtain the corresponding initial field quantity contour map; Based on the monitoring results, the risk locations were marked in the initial field volume cloud map; The initial field quantity cloud map after annotation is rendered to obtain the risk field quantity cloud map.

[0062] See Figure 3 This is a structural block diagram of a DC through-wall bushing monitoring device provided in an embodiment of the present invention. The DC through-wall bushing monitoring device 20 provided in this embodiment includes a processor 21, a memory 22, and a computer program stored in the memory 22 and configured to be executed by the processor 21. When the processor 21 executes the computer program, it implements the steps described in the above embodiment of a DC through-wall bushing monitoring method, for example... Figure 1The steps S1 to S3 described above; or, when the processor 21 executes the computer program, it implements the functions of each module in the above-described device embodiments, such as the simulation module 11.

[0063] For example, the computer program can be divided into one or more modules, which are stored in the memory 22 and executed by the processor 21 to complete the present invention. The one or more modules can be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the monitoring device 20 for the DC through-wall bushing. For example, the computer program can be divided into a simulation module 11, a calculation module 12, and a generation module 13, with the specific functions of each module as follows: Simulation module 11 is used to perform multi-physics field coupled ideal simulation calculations on the target DC through-wall bushing based on real-time monitoring data of the target DC through-wall bushing, and obtain the target simulation distribution results; Calculation module 12 is used to calculate the characteristic deviation data between real-time monitoring data and target simulation distribution results; The generation module 13 is used to generate monitoring results of the target DC through-wall bushing fault when the feature deviation data is greater than the dynamic threshold, wherein the dynamic threshold is determined based on the historical monitoring data of the target DC through-wall bushing.

[0064] The monitoring device 20 for the DC through-wall bushing may include, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that the schematic diagram is merely an example of the monitoring device for the DC through-wall bushing and does not constitute a limitation on the monitoring device 20. It may include more or fewer components than shown, or combine certain components, or different components. For example, the monitoring device 20 for the DC through-wall bushing may also include input / output devices, network access devices, buses, etc.

[0065] The processor 21 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 21 is the control center of the DC through-wall bushing monitoring device 20, connecting all parts of the monitoring device 20 via various interfaces and lines.

[0066] The memory 22 can be used to store the computer program and / or modules. The processor 21 implements various functions of the monitoring device 20 for the DC through-wall bushing by running or executing the computer program and / or modules stored in the memory 22 and calling the data stored in the memory 22. The memory 22 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory 22 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0067] The monitoring device 20 for the DC through-wall bushing, if integrated into a module and implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0068] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0069] Accordingly, embodiments of the present invention provide a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform steps in a monitoring method for DC through-wall bushings as described in the above embodiments, for example... Figure 1 Steps S1 to S3 as described above.

[0070] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: (1) This invention uses real-time monitoring data to drive multi-physics field coupled ideal simulation calculations to obtain the electric field and temperature field distributions that reflect the actual internal state of the equipment, i.e., the simulation distribution results. By calculating the characteristic deviation data between the real-time monitoring data and the simulation distribution results at preset positions, and combining it with dynamic thresholds established based on historical data, fault monitoring is performed, realizing a complete diagnostic process from state perception to fault monitoring. This invention upgrades the traditional passive monitoring that relies on limited external measuring points to proactive early warning and accurate diagnosis based on the full state perception of the equipment, effectively improving the accuracy of DC through-wall bushing status assessment and the level of intelligence in operation and maintenance decision-making.

[0071] (2) This invention can keenly capture minute feature deviations caused by internal state anomalies that have not yet fully manifested in external monitoring data, thereby achieving early fault detection; through multi-physics field coupling simulation and dynamic tolerance judgment, it accurately locates fault risks and assesses their severity; finally, through risk field quantity cloud maps, it intuitively displays the location and distribution of risks, realizing real-time, interactive visualization of large-scale field quantities, greatly improving the intuitiveness of data and decision-making efficiency. This invention realizes in-depth state assessment and fault early warning based on multi-source data fusion, providing a powerful platform support for equipment condition maintenance.

[0072] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for monitoring DC through-wall bushings, characterized in that, include: Based on the real-time monitoring data of the target DC through-wall bushing, a multi-physics field coupled ideal simulation calculation is performed on the target DC through-wall bushing to obtain the target simulation distribution results; Calculate the characteristic deviation data between the real-time monitoring data and the target simulation distribution results; When the characteristic deviation data is greater than the dynamic threshold, a monitoring result of the fault of the target DC through-wall bushing is generated, wherein the dynamic threshold is determined based on the historical monitoring data of the target DC through-wall bushing; Before performing the multiphysics coupled ideal simulation calculation, the method further includes constructing a three-dimensional simulation model of the DC through-wall bushing. The construction process of the three-dimensional simulation model includes: The design drawings of the DC through-wall bushing are analyzed, and a three-dimensional geometric model is constructed based on the analysis results. Obtain the material parameters that constitute the three-dimensional geometric model and construct a material parameter database; By associating the three-dimensional geometric model with the material parameter database, a three-dimensional simulation model is obtained for performing the ideal calculation of the multiphysics coupling simulation; The process of the multiphysics coupled ideal simulation calculation includes: Based on the real-time monitoring data, the boundary conditions of the three-dimensional simulation model are determined; In the three-dimensional simulation model, thermal-fluid coupling calculations are performed to obtain a set of coupling equations; The coupled equations are iteratively calculated, and when the calculation result meets the convergence condition, the current calculation result is output as the first temperature field distribution result. Enter the electrothermal iterative update mode, update the electric field calculation parameters according to the first temperature field distribution result, and obtain the electric field distribution result based on the electric field calculation parameters; Based on the electric field distribution results, the temperature field calculation parameters are updated, and a second temperature field distribution result is obtained based on the temperature field calculation parameters. Repeat the electrothermal iterative update mode until both the temperature field distribution result and the electric field distribution result meet the preset conditions; The target simulation distribution result is obtained based at least on the corresponding temperature field distribution result and the corresponding electric field distribution result.

2. The monitoring method for DC through-wall bushings as described in claim 1, characterized in that, The calculation of the characteristic deviation data between the real-time monitoring data and the target simulation distribution result includes: Determine the sensor monitoring position on the target DC through-wall bushing; Acquire real-time monitoring data of the target DC through-wall bushing at the sensor monitoring location, and extract simulation data of the target simulation distribution result at the corresponding sensor monitoring location; The difference between the real-time monitoring data and the simulation data is calculated to obtain the characteristic deviation data.

3. The monitoring method for DC through-wall bushings as described in claim 1, characterized in that, The process of determining the dynamic threshold includes: Extract the health operation data of the target DC through-wall bushing from the historical monitoring data; Statistical analysis is performed on the health operation data to calculate the probability distribution of the characteristic deviation under normal operating conditions; The dynamic threshold is determined based on the confidence interval of the probability distribution.

4. The monitoring method for DC through-wall bushings as described in claim 1, characterized in that, After obtaining the monitoring results of the fault of the target DC through-wall bushing, the method further includes: generating a risk field quantity cloud map to indicate the risk location based on the monitoring results and the simulation distribution results, specifically including: The simulation distribution results are compressed and optimized to obtain field quantity data; The field quantity data is mapped to a color space to obtain the corresponding initial field quantity cloud map; Based on the monitoring results, the risk locations are marked in the initial field volume cloud map; The initial field quantity cloud map after annotation is rendered to obtain the risk field quantity cloud map.

5. A monitoring system for DC through-wall bushings, characterized in that, include: The simulation module is used to perform multi-physics field coupled ideal simulation calculations on the target DC through-wall bushing based on real-time monitoring data of the target DC through-wall bushing, and obtain the target simulation distribution results; The calculation module is used to calculate the characteristic deviation data between the real-time monitoring data and the target simulation distribution results; A generation module is used to generate a monitoring result of the fault of the target DC through-wall bushing when the feature deviation data is greater than a dynamic threshold, wherein the dynamic threshold is determined based on the historical monitoring data of the target DC through-wall bushing. The simulation module is also used for: The design drawings of the DC through-wall bushing are analyzed, and a three-dimensional geometric model is constructed based on the analysis results. Obtain the material parameters that constitute the three-dimensional geometric model and construct a material parameter database; By associating the three-dimensional geometric model with the material parameter database, a three-dimensional simulation model is obtained for performing the ideal calculation of the multiphysics coupling simulation; The process of the multiphysics coupled ideal simulation calculation includes: Based on the real-time monitoring data, the boundary conditions of the three-dimensional simulation model are determined; In the three-dimensional simulation model, thermal-fluid coupling calculations are performed to obtain a set of coupling equations; The coupled equations are iteratively calculated, and when the calculation result meets the convergence condition, the current calculation result is output as the first temperature field distribution result. Enter the electrothermal iterative update mode, update the electric field calculation parameters according to the first temperature field distribution result, and obtain the electric field distribution result based on the electric field calculation parameters; Based on the electric field distribution results, the temperature field calculation parameters are updated, and a second temperature field distribution result is obtained based on the temperature field calculation parameters. Repeat the electrothermal iterative update mode until both the temperature field distribution result and the electric field distribution result meet the preset conditions; The target simulation distribution result is obtained based at least on the corresponding temperature field distribution result and the corresponding electric field distribution result.

6. A monitoring device for DC through-wall bushings, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the monitoring method for DC through-wall bushings as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the monitoring method for DC through-wall bushings as described in any one of claims 1 to 4.

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