Modeling method and system for digital twinborn simulation model of working state of oil-filled casing

Abstract

The invention discloses a modeling method and system for a digital twinning simulation model of the working state of an oil-filled sleeve, and belongs to the technical field of intelligent operation and maintenance and digital twinning of power equipment. Firstly, parameters and operation data of the oil filling sleeve are collected and preprocessed; constructing a multi-physics field coupling digital twinborn simulation model comprising a potential control equation, a heat conduction equation and a fault outgassing model; in the fault outgassing model, introducing a type function, a position function and a time gating function, and cooperatively constructing risk / activation fields Ps (x, t) of three typical faults of thermal fault, partial discharge and arc for representing relative possibility or activation intensity of occurrence of various faults under the spatial position x and time t; and outputting fault outgassing intensity, fault type, fault spatial position and fault development time sequence information. According to the method, early warning, accurate identification and dynamic inversion of potential faults in the oil-filled sleeve are realized, and the intelligent level of state sensing of the high-voltage sleeve and the scientificity of operation and maintenance decision are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of power equipment condition monitoring and intelligent diagnosis technology, specifically to a modeling method and system for a digital twin simulation model of the working state of an oil-filled bushing. Background Technology

[0002] Oil-filled bushings (such as oil-impregnated paper capacitor bushings / oil-filled capacitor bushings) are key components in power systems for enabling high-voltage conductors to pass through walls and be insulated from external sources. Their operational reliability directly affects the safety and stability of substations and main equipment. During long-term operation, oil-filled bushings may fail due to various reasons, including aging and moisture absorption of the capacitor core insulation, partial discharge, overheating caused by poor contact at the ends or conductive connections, breakdown of the oil-paper insulation, arc discharge, and leakage due to seal deterioration. These failures often lead to the decomposition of the insulating material in the oil, producing various dissolved gases. Hydrogen is generally considered one of the important indicator gases for early faults related to discharge and overheating. Therefore, online monitoring of hydrogen in the oil for early warning of oil-filled bushing faults is of practical significance.

[0003] In existing technologies, hydrogen detection methods in oil mainly include chromatographic gas analysis, optical sensing, and metal oxide semiconductor gas sensors. Chromatographic methods have high detection accuracy, but usually require sampling and offline analysis, making it difficult to achieve online real-time monitoring. Optical methods involve complex equipment and high costs, and their stability and engineering adaptability in oil measurement environments are insufficient. Although metal oxide sensors respond well under high temperature conditions, they are prone to problems such as large drift, poor selectivity, and excessively high operating temperatures in insulating oil environments.

[0004] In recent years, palladium thin-film resistive hydrogen sensors have gradually become a research hotspot for dissolved gas detection in oil due to their high sensitivity to hydrogen, rapid response, and ability to operate at relatively low temperatures. However, existing research mostly focuses on single-point concentration monitoring or trend analysis, lacking a systematic study of the relationship between multi-point hydrogen diffusion characteristics inside the equipment and the spatial distribution of faults, making it difficult to effectively identify and locate fault locations.

[0005] Digital twin technology provides a new approach to equipment lifecycle management, but existing digital twin models mostly focus on a single physical field (such as only considering heat conduction or electric field) and have not achieved multi-field coupling simulation of heat-electricity-gas release. This makes it difficult to accurately characterize the intrinsic relationship between fault occurrence, development and gas release behavior, resulting in insufficient accuracy and timeliness of fault early warning.

[0006] Therefore, it is urgent to construct a digital twin simulation model that integrates multi-physics coupling and fault gas release mechanism to realize real-time simulation of the working state of oil-filled bushings, accurate fault prediction and evolution trend analysis, and provide a scientific basis for the operation and maintenance of power equipment. Summary of the Invention

[0007] This invention aims to solve the technical problems of insufficient multi-field coupling and inaccurate characterization of fault gas release mechanism in existing digital twin models of oil-filled casing. It provides a modeling method and system for digital twin simulation model of oil-filled casing working state, which can realize accurate prediction of fault type, location and time and dynamic simulation of gas release behavior.

[0008] The present invention adopts the following technical solution:

[0009] A method for modeling a digital twin simulation model of the working state of an oil-filled casing includes:

[0010] Collect and preprocess the structural, material, operational, and environmental parameters of the oil-filled casing to provide standardized input data for subsequent models;

[0011] A digital twin simulation model was constructed, which includes the potential control equation, the heat conduction equation, and the fault gas release model.

[0012] In the fault gas release model, a type function, a position function, and a time-gated function are defined to simulate different types of fault gas release phenomena.

[0013] The fault release model should at least construct the risk / activation field P for each type of fault. s (x,t), including the construction of thermal fault risk field, partial discharge risk field, and arc risk field: to represent the relative probability or activation intensity of the s-th type fault occurring at spatial location x and time t;

[0014] The solution yields information on fault release intensity, fault type, fault location, and fault timing.

[0015] Furthermore, the barrier-release model uses the solution results of the potential control equation and the heat conduction equation as driving inputs to construct the risk / activation field P for three types of faults. s (x,t); P s (x,t) is combined with a type-dependent gas release intensity function and modulated by a type function, a position function, and a time-gated function, ultimately outputting the spatiotemporal distribution of characteristic gas generation rates.

[0016] Furthermore, the aforementioned potential control equation is:

[0017]

[0018] Parameter definitions: potential field φ(x,t), electric field intensity E(x,t), electric potential V(t), electric field distribution E, ε(x,T) is the dielectric constant; heat source term Q e Fault release intensity function g s (E,T);

[0019] Heat conduction equation:

[0020]

[0021] Q e (x,t)=σ(T,x)||E(x,t)|| 2

[0022] Parameter definitions: Temperature field T(x,t), ρ(x) is density, c(x) is specific heat capacity, k(x) is thermal conductivity; heat source term Q e (x,t), the additional heat source Q caused by the fault. f (x,t), conductivity σ(T,x); h is the convective heat transfer coefficient, T ∞ Ambient temperature;

[0023] Based on the above input and heat source data, the transient thermal equation is solved to obtain the temperature field T(x,t), which is used as the output data of the heat conduction equation and is used for updating temperature-related material parameters and the fault gas release intensity function g. s Coupled computation of (E,T).

[0024] Furthermore, the fault gas release model is as follows:

[0025] The fault gas release model includes a type function, a location function, a time-gated function, and a fault determination and site inversion sub-model.

[0026] Type function

[0027]

[0028] Where A s (t) can be derived from A s (t)=∫P s (x,t)dx or its normalized probability is obtained; the fault type s∈{thermal fault, partial discharge, electric arc, …}; For the gas release source term, position x f The fault center is represented by δ, which is the radius of influence / initial diffusion scale.

[0029] Fault determination and site inversion sub-model

[0030] At least construct risk / activation fields P for various types of failures. s (x,t), Risk / Activation Field P s (x,t) contains at least:

[0031] (1) Thermal failure risk field:

[0032]

[0033] Where σ(·) is the Sigmoid or step / piecewise function, T thr , For temperature and heating rate thresholds;

[0034] (2) Partial discharge risk field:

[0035]

[0036] Where η ins (x,t) can be an electric stress-derived index, such as dielectric loss-related index, field strength concentration factor, and interface normal field strength, which can be output from the electric potential control equation or obtained by its derivation calculation.

[0037] (3) Arc risk field:

[0038]

[0039] P arc (x,t) represents the risk / activation intensity of an arc fault occurring at spatial location x at time t; where E(x,t) is the electric field intensity vector obtained by solving the electric field sub-model, and ||E(x,t)|| is the electric field intensity magnitude; E represents the rate of change of temperature with respect to time; σ(·) is a gating / normalization function whose output can be limited to the interval [0,1] and used to smooth the "threshold trigger" criterion. σ(·) is preferably a Sigmoid function or can be replaced by a Heaviside step function / piecewise function; arc,thr The electric field strength threshold for the arc criterion. The heating rate threshold for the arc criterion, ΔE and These represent the transition bandwidth / smoothing coefficient for the corresponding thresholds;

[0040] The fault type s is determined by: calculating the global activation degree A of each fault type at time t. s (t)=∫P s dx (x,t) and take

[0041]

[0042] Furthermore, the position function is:

[0043]

[0044] ω(x;x f ,δ) is used to characterize the spatial distribution weight of the fault release source term, where x is the spatial coordinate, x f The fault center location is δ, and the influence scale parameter is δ.

[0045] After determining the fault type s, the fault center location x f Determined by the spatial extrema or weighted centroid of the corresponding risk field:

[0046]

[0047] or

[0048]

[0049] Where Ω represents the simulation domain of the bushing oil cavity / insulation structure.

[0050] Furthermore, the time gating function is as follows:

[0051] u(t;t0,τ)=H(t-t0)-H(t-t0-τ)

[0052] H(·) can be the Heaviside step function; the time-gated function u(t; t0, τ) is used to characterize the start-stop control of the fault gas release source in the time dimension, where t is the simulation time, t0 is the fault start time, and τ is the fault duration.

[0053] Furthermore, the time-gated u(t; t0, τ) can also be determined by electrothermal processes, and the fault initiation time t0 can be determined by the global activation degree A. s (t) exceeds threshold A thr The time is determined, i.e., t0 = min{t | A s (t)≥A thr The fault duration τ can be determined by A. s The continuous over-threshold range of (t) can be obtained by setting the operating conditions.

[0054] Furthermore, the type function also includes a gas release intensity function:

[0055] (1) Thermal failure type Arrhenius:

[0056]

[0057] (2) Power-law type of field strength for discharge / partial discharge:

[0058]

[0059] A digital twin simulation modeling system for the working state of an oil-filled casing includes:

[0060] The data input module is used to collect and preprocess the structural parameters, material parameters, operating parameters and environmental parameters of the oil-filled casing, providing standardized input data for subsequent modules;

[0061] The coupled model construction module is used to construct a digital twin simulation model based on the standardized input data. The digital twin simulation model includes an electric potential control equation, a heat conduction equation, and a fault gas release model. The electric potential control equation and the heat conduction equation are bidirectionally coupled through boundary conditions to output electric field distribution, temperature distribution, and derived electro-thermal parameters.

[0062] The risk / activation field construction module is used to call the electro-thermal parameters to construct at least a thermal fault risk field, a partial discharge risk field, and an arc risk field in the fault gas release model, forming a risk / activation field P for various faults. s (x,t), the P s (x,t) is used to characterize the relative probability or activation intensity of the type s fault at spatial location x and time t;

[0063] The fault gas release model configuration module is used to define type functions, location functions, and time gating functions in the fault gas release model. These three types of functions are respectively based on the risk / activation field P. s (x,t) realizes the type adaptation, spatial positioning and timing constraints of fault gas release;

[0064] The simulation calculation module is used to collaboratively call the electro-thermal-gas release coupling model and the risk / activation field P. s The fault release intensity, fault type, fault location, and fault timing information are obtained by solving the three types of functions (x,t).

[0065] A digital twin simulation modeling system for the working state of an oil-filled casing includes a parameter calibration module. The parameter calibration module is used to collect real-time monitoring data of the oil-filled casing and dynamically adjust the calibration coefficient of the risk / activation field, the gas release intensity function parameter, and the activation threshold by comparing the real-time monitoring data with the solution results of the simulation calculation module.

[0066] Advantages and effects of the present invention:

[0067] For the first time, the potential control equation, heat conduction equation and fault gas release behavior are deeply integrated to construct a unified multi-field coupled digital twin model. This breaks through the limitations of traditional models that only focus on steady-state electric / heat distribution or offline gas analysis, and realizes a full-chain mapping from "physical state perception" to "chemical gas release response".

[0068] It innovatively introduces three types of faults—thermal faults, partial discharges, and electric arcs—and quantifies the relative probability of fault occurrence through a space-time continuous function, supporting high-precision inversion and prediction.

[0069] System integration in the fault release model:

[0070] Type function: reflects the physical response mechanism of different faults to the electric / thermal field;

[0071] Position function: embedding prior knowledge of structural vulnerability;

[0072] Time-gated function: Enables dynamic triggering and persistence determination of fault events;

[0073] The three elements are multiplied and coupled to form a gas release modeling framework with physical interpretability and temporal and climatic control capabilities.

[0074] This invention can capture weak abnormal signals before a fault causes a significant temperature rise or gas exceedance, enabling early warning several hours to days in advance, which is significantly better than traditional threshold alarm methods.

[0075] By comparing the spatial distribution patterns and evolution trends of the three types of risk fields, easily confused faults (such as high temperature overheating vs. partial discharge) can be effectively distinguished, and the positioning error can be controlled at the centimeter level (corresponding to the key structural area of ​​the casing), guiding precise maintenance.

[0076] The time gating mechanism effectively filters out transient electrical / thermal disturbances caused by load fluctuations and environmental interference, avoiding "false positives"; at the same time, multi-field coupling modeling reduces the one-sidedness of single parameter judgment and lowers the risk of "missed diagnosis".

[0077] The model can accumulate and update historical data of the risk field over time to build a "health profile" of the equipment, which can be used to assess insulation aging trends, predict remaining life, and optimize maintenance strategies. Detailed Implementation

[0078] Example 1

[0079] A method for modeling a digital twin simulation model of the working state of an oil-filled casing, wherein the digital twin simulation model includes an electric potential control equation, a heat conduction equation, and a fault gas release model;

[0080] Electric potential control equation:

[0081]

[0082] Based on the multi-medium structure and dielectric parameters of the oil-filled casing, the equation uses a quasi-static electric field control equation (potential field equation / Laplace-Poisson type equation) to solve for the electric potential field φ(x,t), and obtains the electric field intensity E(x,t) from the potential gradient relationship; where ε(x,T) is the dielectric constant (which can vary with temperature and space); the boundary conditions include the electrode boundary conditions with the high-voltage end applied potential V(t) and the grounding end potential being 0, and the electric potential and normal electric displacement are satisfied at the multi-medium interface; the electric field sub-model outputs the electric field distribution E and related electric stress indices, and calculates the electric loss heat source term of the heating model and calls the fault gas release model.

[0083] The dielectric constant distribution ε(t,x), geometric topology, and boundary condition data in the potential control equation are generated based on the structural design data and material property data of the oil-filled bushing. The structural design data includes at least the three-dimensional geometric dimensions, interlayer positional relationships, and multi-dielectric partitioning information of the conductor, insulating paper, oil / air gap, shielding layer, flange, and grounding structure. The material property data includes at least the dielectric constant, bulk conductivity / loss parameters of each medium at different temperatures, which can be derived from material handbooks, type test / factory test curves, or field calibration data, and can consider changes in material property data caused by material aging, excessive moisture, and microbubbles in the oil / paper. The boundary condition data includes at least the applied potential V(t) at the high-voltage end, the electrode boundary where the grounding end potential is 0, and the continuity of potential and normal electric displacement at the multi-dielectric interface. The V(t) also needs to consider special operating conditions, including voltage fluctuations, transient overvoltages, partial discharge test excitation, and other conditions that could lead to failure. Alternatively, it can be specific voltage data recorded by an online monitoring device. Based on the above input data, the potential field φ(x,t) and electric field intensity E(x,t) (as well as derived indices such as electric stress) are obtained by numerically solving the quasi-static electric potential field equations. These are used as the output data of the electric field sub-model and supplied to the heat source term Q. e With fault release intensity function g s (E,T) call.

[0084] Heat conduction equation:

[0085]

[0086] Q e (x,t)=σ(T,x)||E(x,t)|| 2

[0087] The transient heat conduction control equation (heat conduction equation / heat diffusion equation) is established based on the parameters of the multi-medium material of the casing to solve for the temperature field T(x,t), where ρ(x) is the density, c(x) is the specific heat capacity, and k(x) is the thermal conductivity. The right-hand side of the control equation includes a volume heat source term Q(x,t), which at least includes the electrical loss heat source Q output by the electric field sub-model. e (x,t) and the additional heat source Q caused by the fault f (x,t), and can make Q e The electric field strength E(x,t) and electrical conductivity σ(T,x) are used for calculation; the thermal boundary conditions include convective heat transfer boundaries, where h is the convective heat transfer coefficient and T is the thermal conductivity. ∞ The ambient temperature can be further included, and may include an adiabatic boundary or a given temperature boundary; the thermal field submodel outputs the temperature distribution T(x,t), which is used to couple with the temperature-dependent diffusion coefficient and fault release source term of the diffusion submodel.

[0088] The distribution data of parameters such as density ρ(x), specific heat c(x), and thermal conductivity k(x) in the heat conduction equation are generated based on the thermophysical property data of each material in the oil-filled casing and a multi-medium partition model. The thermophysical property data can be obtained from material handbooks, experimental measurements, or type tests, taking into account insulation degradation conditions caused by insulation aging, excessive moisture, microbubbles, etc., and can be set as a function of temperature. The thermal boundary condition data are generated based on the operating environment and installation conditions, and at least include the convective heat transfer coefficient h and the ambient temperature T. ∞ Adiabatic boundary or given temperature boundary, where T ∞ Cooling conditions can be obtained from on-site meteorological / environmental monitoring or equipment operation records. In the heat source term on the right-hand side of the equation, the electrical loss heat source Q... e (x,t)=σ(T,x)||E(x,t)|| 2 The data were calculated jointly from E(x,t) output by the electric field sub-model and the material conductivity / loss parameter σ(T,x); the fault-related additional heat source Q f The data for (x,t) are based on fault type and location parameters (such as the equivalent thermal power density of partial discharge / arc / hot spot, duration-gated u(t; t0,τ), and spatial distribution ω(x; x). f The temperature field T(x,t) is generated by the digital twin fault prior library, experimental calibration, or operational monitoring (such as partial discharge, temperature rise, and load current). Based on the above input and heat source term data, the transient thermal equation is solved to obtain the temperature field T(x,t), which is used as the output data of the thermal field sub-model and is used for updating temperature-related material parameters and the fault gas release intensity function g. s Coupled computation of (E,T).

[0089] Fault-induced gas release model:

[0090] The fault release model includes a type function, a position function, and a time-gated function;

[0091]

[0092] Where A s (t) can be derived from A s (t)=∫P s (x,t)dx or its normalized probability is obtained; the fault type s∈{thermal fault, partial discharge, electric arc,…}; the location x f The fault center is δ; the influence radius / initial diffusion scale is δ.

[0093] Furthermore, the fault gas release model also includes a fault determination and site inversion sub-model, used to automatically determine the fault type s and the fault center location x based on the electric field intensity E(x,t) (and / or electric stress derived index) output by the electric field sub-model and the temperature field T(x,t) (and / or temperature rise rate, temperature gradient, and other derived indexes) output by the thermal field sub-model.f Based on this, a gas release source term is generated.

[0094] The fault determination and site inversion sub-model at least constructs a risk / activation field P for various types of faults. s (x,t), where the fault type s∈{thermal fault, partial discharge, electric arc,…}, and P s (x,t) is derived from E(x,t) and T(x,t) and is used to represent the relative probability / activation intensity of a type s fault occurring at spatial location x and time t.

[0095] Among them, the risk / activation field P s (x,t) preferably includes:

[0096] (1) Thermal failure risk field:

[0097]

[0098] Where σ(·) is the Sigmoid or step / piecewise function, T thr , These are the threshold values ​​for temperature and heating rate.

[0099] (2) Partial discharge risk field:

[0100]

[0101] Where η ins (x,t) can be an electric stress-derived index (such as dielectric loss-related index, field strength concentration factor, interface normal field strength, etc.), which can be output by the electric field sub-model or obtained by its derivation calculation.

[0102] (3) Arc risk field (strong electrical stress + strong thermal sudden change):

[0103]

[0104] In the aforementioned arc risk field, P arc (x,t) represents the risk / activation intensity of an arc fault occurring at spatial location x at time t; where E(x,t) is the electric field intensity vector obtained from the electric field sub-model, and ||E(x,t)|| is the electric field intensity magnitude, used to characterize the electric stress level at that location; T(x,t) is the temperature field obtained from the thermal field sub-model. E represents the rate of change of temperature with respect to time (heating rate), used to characterize whether there is a rapid temperature rise caused by an electric arc at this location; σ(·) is a gating / normalization function, whose output can be limited to the interval [0,1] and used to smooth the "threshold triggering" criterion. σ(·) is preferably a Sigmoid function or can be replaced by a Heaviside step function / piecewise function;arc,thr The electric field strength threshold for the arc criterion. The heating rate threshold for the arc criterion, ΔE and These are the transition bandwidth / smoothing coefficients corresponding to the threshold values, used to improve the robustness of the judgment logarithmic discrepancy and noise disturbance; by combining the electric field strength overthreshold gating term and the heating rate overthreshold gating term, the joint characterization of the arc characteristics of "strong electric stress + thermal mutation" is realized, thereby providing a basis for fault type determination and site inversion.

[0105] The fault type s can be determined by: calculating the global activation degree A of each fault type at time t. s (t)=∫P s dx (x,t) and take

[0106]

[0107] Position function

[0108]

[0109] ω(x;x f ,δ) is used to characterize the spatial distribution weight of the fault release source term, where x is the spatial coordinate, x f Let x be the location of the fault center, and δ be the influence scale parameter (characterizing the radius / expansion range of the fault influence). The location distribution function is used to expand the fault source terms from a centralized source to a distributed source, so that the source terms have a larger weight near the fault center and decay with increasing spatial distance. Different distribution function families (Gaussian, exponential decay, piecewise constant, or ellipsoidal / cylindrical distribution) can be selected according to the fault type to adapt to different fault locations and geometries. By adjusting x... f Simulations with different fault locations and impact ranges are achieved using δ.

[0110] Fault center location x f It can be determined from the output results of electro-thermal calculations.

[0111] After determining the fault type s, the fault center location x f Determined by the spatial extrema or weighted centroid of the corresponding risk field:

[0112]

[0113] or

[0114]

[0115] Where Ω represents the simulation domain of the bushing oil cavity / insulation structure.

[0116] The influence scale parameter δ can be derived from P. s The spatial expansion of (x,t) or by The scale of the concentrated electric field region is determined to characterize the expansion range of the fault-affected area as the operating conditions change.

[0117] Time Gating Function

[0118] u(t;t0,τ)=H(t-t0)-H(t-t0-τ)

[0119] H(·) can be a Heaviside step function. The time-gating function u(t; t0, τ) is used to characterize the start-stop control of the fault release source term in the time dimension, where t is the simulation time, t0 is the fault start time, and τ is the fault duration. The time-gating function is used to limit the fault source term to take effect within a preset time interval, so that the source term is activated during the fault occurrence period and suppressed during the non-fault period. Different gating function families can be selected according to the fault characteristics, including step gating, pulse gating, smooth gating (with rising / falling transition), and periodic gating (intermittent / repeated triggering) to realize the simulation of different fault durations, repetition frequencies, and duty cycles.

[0120] The time-gated u(t; t0, τ) can also be determined by electrothermal processes: the fault initiation time t0 can be determined by the global activation degree A. s (t) exceeds threshold A thr The time is determined, i.e., t0 = min{t | A s (t)≥A thr The fault duration τ can be determined by A. s The continuous over-threshold range of (t) can be obtained by setting the operating conditions.

[0121] Type-dependent release intensity function:

[0122] (1) Thermal failure type Arrhenius:

[0123]

[0124] (2) Power-law type of field strength for discharge / partial discharge:

[0125]

[0126] The gas release intensity function is used to characterize the equivalent hydrogen production / release intensity per unit time and per unit volume under different fault types, and serves as the type weight term for the fault gas release source term.

[0127] Among them, the thermal failure type release intensity function g th(T)(Arrhenius type) is used to characterize hydrogen production processes caused by hot spots, overheating, or thermal decomposition. Its intensity increases with increasing temperature T and can be limited by activation parameters (e.g., activation energy-related parameters) and threshold temperature parameters, so that the gas release contribution is suppressed when the temperature is below the threshold.

[0128] Among them, the discharge / partial discharge type gas release intensity function g pd (||E||,T) (Power Law Type) is used to characterize hydrogen production processes caused by partial discharge, arcing, or surface discharge. Its intensity increases with the increase of electric field strength ||E||. The field strength exponent parameter can be set to reflect the sensitivity of different discharge modes to electric stress. Temperature-related weights can be introduced to characterize the thermal effects or material reaction rate changes associated with the discharge. T and E are output by the thermal field sub-model and the electric field sub-model, respectively, to achieve coupling.

[0129] Example 2

[0130] A digital twin simulation modeling system for the working state of an oil-filled casing includes:

[0131] The data input module is used to collect and preprocess the structural parameters, material parameters, operating parameters and environmental parameters of the oil-filled casing, providing standardized input data for subsequent modules;

[0132] The coupled model construction module is used to construct a digital twin simulation model based on the standardized input data. The digital twin simulation model includes an electric potential control equation, a heat conduction equation, and a fault gas release model. The electric potential control equation and the heat conduction equation are bidirectionally coupled through boundary conditions to output electric field distribution, temperature distribution, and derived electro-thermal parameters.

[0133] The risk / activation field construction module is used to call the electro-thermal parameters to construct at least a thermal fault risk field, a partial discharge risk field, and an arc risk field in the fault gas release model, forming a risk / activation field P for various faults. s (x,t), the P s (x,t) is used to characterize the relative probability or activation intensity of the type s fault at spatial location x and time t;

[0134] The fault gas release model configuration module is used to define type functions, location functions, and time gating functions in the fault gas release model. These three types of functions are respectively based on the risk / activation field P. s (x,t) realizes the type adaptation, spatial positioning and timing constraints of fault gas release;

[0135] The simulation calculation module is used to collaboratively call the electro-thermal-gas release coupling model and the risk / activation field P. sThe fault release intensity, fault type, fault location, and fault timing information are obtained by solving the three types of functions (x,t).

[0136] A digital twin simulation modeling system for the working state of an oil-filled casing includes a parameter calibration module. The parameter calibration module is used to collect real-time monitoring data of the oil-filled casing and dynamically adjust the calibration coefficient of the risk / activation field, the gas release intensity function parameter, and the activation threshold by comparing the real-time monitoring data with the solution results of the simulation calculation module.

[0137] Example 3

[0138] Specific applications of this invention:

[0139] (I) Basic Information of Application Objects

[0140] Equipment Name: 220kV Oil-filled Bushing

[0141] Model / Specification: BRLW-220 / 1500-3

[0142] Installation location: High-voltage side of main transformer #3 in power grid hub substation

[0143] Commissioning date: June 2018

[0144] Operating conditions: Rated voltage 220kV, rated current 1500A, daily load rate 70%~90%, ambient temperature -5℃~38℃

[0145] Historical Issue: Since 2022, the concentration of hydrogen (H2) in oil has shown a slow upward trend, reaching 85 μL / L in March 2023 (close to the warning threshold of 100 μL / L). Traditional offline oil chromatography cannot locate the fault site or predict the development trend.

[0146] (II) Model Deployment and System Setup

[0147] Deployment timeline: Digital twin system installation and model debugging completed in April 2023.

[0148] Hardware configuration: The data acquisition module includes 12 sensors (4 gas sensors and 3 temperature sensors built into the oil chamber, 3 partial discharge sensors on the bushing body, and 2 temperature sensors on the flange); the simulation server is equipped with a 4-core CPU + GPU accelerator card and 16GB of storage capacity.

[0149] Software environment: A coupled thermo-electrical-gas release model was built based on COMSOL Multiphysics. A fault gas release model and data calibration algorithm were written in Python. The visual interface is integrated into the substation operation and maintenance management platform.

[0150] Model initialization: Import casing structure drawings, material parameters, and 3 years of historical operating data to complete the initial calibration of risk / activation field calibration coefficients and gas release intensity function parameters.

[0151] (III) Application Implementation Process

[0152] Data Acquisition: The sensor network collects operating parameters (voltage, current), status parameters (H2 concentration, partial discharge signal, temperature), and environmental parameters at a preset frequency. The data is transmitted to the simulation server in real time, with a transmission delay consistently between 60 and 80 ms.

[0153] Model Operation: The simulation server performs a full-dimensional simulation every hour, outputting electric field distribution, temperature distribution, risk / activation field values, and H2 concentration prediction curves. Every 24 hours, it automatically retrieves the latest measured data for model calibration, adjusting the electric field distortion coefficient D(x,t) and the partial discharge risk field calibration coefficient k2 to ensure simulation accuracy.

[0154] Routine monitoring results: As of November 2023, the simulated temperature of the core area of ​​the casing remained stable at 62–66℃, with an error of ≤2.5% compared to the measured value; the relative error between the simulated and measured H2 concentration values ​​was ≤3.8%; and the global activation degree A-discharge (t) remained between 0.4 and 0.6 (below the threshold Athr = 0.8). The model determined it to be in "normal operating condition with low risk of partial discharge".

[0155] Fault early warning and trend prediction (December 2023)

[0156] Abnormal Trigger: At 08:00 on December 15, 2023, the simulation system detected a global activation level A discharge(t) of 0.72, an increase of 16.7% compared to the previous day; simultaneously, it showed a partial discharge risk field P discharge(x,t) of 0.91 and an electric field strength E(x,t) of 4.8 kV / mm in the upper oil passage area of ​​the casing (10 cm from the high-voltage electrode) (far exceeding the normal operating value of 3.2 kV / mm).

[0157] Fault Warning: At 04:00 on December 17, 2023, the simulation system calculated that A discharge(t) = 0.82 ≥ Athr = 0.8, automatically determined the fault start time t0 = 680h (8.5 months after commissioning), and triggered a level 3 warning: "Partial discharge fault in the upper oil passage area of ​​the bushing. It is expected that the H2 concentration will rise to 150μL / L (exceeding the threshold) after 10 hours. It is recommended to arrange maintenance immediately." The warning information was simultaneously pushed to the mobile APP of the operation and maintenance personnel and the substation monitoring screen.

Claims

1. A method for modeling a digital twin simulation model of the working state of an oil-filled casing, characterized in that, include: Collect and preprocess the structural, material, operational, and environmental parameters of the oil-filled casing to provide standardized input data for subsequent models; A digital twin simulation model was constructed, which includes the potential control equation, the heat conduction equation, and the fault gas release model. In the fault gas release model, a type function, a position function, and a time gating function are defined to simulate different types of fault gas release phenomena. The fault release model should at least construct the risk / activation field P for each type of fault. s (x,t), including the construction of thermal fault risk field, partial discharge risk field, and arc risk field: to represent the relative probability or activation intensity of the s-th type fault occurring at spatial location x and time t; The solution yields information on fault release intensity, fault type, fault location, and fault timing.

2. The modeling method for a digital twin simulation model of the working state of an oil-filled casing according to claim 1, characterized in that, The fault gas release model uses the solution results of the potential control equation and the heat conduction equation as driving inputs to construct the risk / activation field P for three types of faults. S (x,t); P s (x,t) is combined with a type-dependent gas release intensity function and modulated by a type function, a position function, and a time-gated function, ultimately outputting the spatiotemporal distribution of characteristic gas generation rates.

3. The modeling method for a digital twin simulation model of the working state of an oil-filled casing according to claim 1, characterized in that, The aforementioned potential control equation is: Parameter definitions: potential field φ(x,t), electric field strength E(x,t), electric potential V(t), electric field distribution E, ε(x,T) is the dielectric constant; heat source term Q e Fault release intensity function g s (E,T); Heat conduction equation: Q e (x,t)=σ(T,x)||E(x,t)|| 2 Parameter definitions: Temperature field T(x,t), ρ(x) is density, c(x) is specific heat capacity, k(x) is thermal conductivity; heat source term Q e (x,t), the additional heat source Q caused by the fault. f (x,t), conductivity σ(T,x); h is the convective heat transfer coefficient, T ∞ The ambient temperature; Based on the above input and heat source data, the transient thermal equation is solved to obtain the temperature field T(x,t), which is used as the output data of the heat conduction equation and is used for updating temperature-related material parameters and the fault gas release intensity function g. s Coupled computation of (E,T).

4. The modeling method for a digital twin simulation model of the working state of an oil-filled casing according to claim 1, characterized in that, The fault gas release model is as follows: The fault gas release model includes a type function, a location function, a time-gated function, and a fault determination and site inversion sub-model. Type function Where A s (t) can be derived from A s (t)=∫P s (x,t)dx or its normalized probability is obtained; Fault type s∈{thermal fault, partial discharge, electric arc, ...}; For the gas release source term, position x f The fault center is represented by δ, which is the radius of influence / initial diffusion scale. Fault determination and site inversion sub-model At least construct risk / activation fields P for various types of failures. s (x,t), Risk / Activation Field P s (x,t) contains at least: (1) Thermal failure risk field: Where σ(·) is the Sigmoid or step / piecewise function, T thr , For temperature and heating rate thresholds; (2) Partial discharge risk field: Where η ins (x,t) can be an electric stress-derived index, such as dielectric loss-related index, field strength concentration factor, and interface normal field strength, which can be output from the electric potential control equation or obtained by its derivation calculation. (3) Arc risk field: P arc (x,t) represents the risk / activation intensity of an arc fault occurring at spatial location x at time t; Where E(x,t) is the electric field intensity vector obtained by solving the electric field sub-model, and ||E(x,t)|| is the electric field intensity magnitude; E represents the rate of change of temperature with respect to time; σ(·) is a gating / normalization function whose output can be limited to the interval [0,1] and used to smooth the "threshold triggering" criterion. σ(·) is preferably a Sigmoid function or can be replaced by a Heaviside step function / piecewise function; arc,thr The electric field strength threshold for the arc criterion. The heating rate threshold for the arc criterion, ΔE and These represent the transition bandwidth / smoothing coefficient for the corresponding thresholds; The fault type s is determined by: calculating the global activation degree A of each fault type at time t. s (t)=∫P s dx (x,t) and take 5. The modeling method for a digital twin simulation model of the working state of an oil-filled casing according to claim 1, characterized in that, The position function is: ω(x;x f ,δ) is used to characterize the spatial distribution weight of the fault release source term, where x is the spatial coordinate, x f The fault center location is δ, and the influence scale parameter is δ. After determining the fault type s, the fault center location x f Determined by the spatial extrema or weighted centroid of the corresponding risk field: or Where Ω represents the simulation domain of the bushing oil cavity / insulation structure.

6. The modeling method for a digital twin simulation model of the working state of an oil-filled casing according to claim 1, characterized in that, The time gating function is: u(t;t0,τ)=H(t-t0)-H(t-t0-τ) H(·) can be the Heaviside step function; the time-gated function u(t; t0, τ) is used to characterize the start-stop control of the fault gas release source in the time dimension, where t is the simulation time, t0 is the fault start time, and τ is the fault duration.

7. The modeling method for a digital twin simulation model of the working state of an oil-filled casing according to claim 1, characterized in that, The time-gated u(t; t0, τ) can also be determined by electrothermal processes, and the fault initiation time t0 can be determined by the global activation degree A. s (t) exceeds threshold A thr The time is determined, i.e., t0 = min{t | A s (t)≥A thr The fault duration τ can be determined by A. s The continuous over-threshold range of (t) can be obtained by setting the operating conditions.

8. The modeling method for a digital twin simulation model of the working state of an oil-filled casing according to claim 1, characterized in that, The type functions also include the gas release intensity function: (1) Thermal failure type Arrhenius: (2) Power-law type of field strength for discharge / partial discharge:

9. A digital twin simulation modeling system for the working state of an oil-filled casing, characterized in that, include: The data input module is used to collect and preprocess the structural parameters, material parameters, operating parameters and environmental parameters of the oil-filled casing, providing standardized input data for subsequent modules; The coupled model construction module is used to construct a digital twin simulation model based on the standardized input data. The digital twin simulation model includes an electric potential control equation, a heat conduction equation, and a fault gas release model. The electric potential control equation and the heat conduction equation are bidirectionally coupled through boundary conditions to output electric field distribution, temperature distribution, and derived electro-thermal parameters. The risk / activation field construction module is used to call the electro-thermal parameters to construct at least a thermal fault risk field, a partial discharge risk field, and an arc risk field in the fault gas release model, forming a risk / activation field P for various faults. s (x,t), the P s (x,t) is used to characterize the relative probability or activation intensity of the type s fault at spatial location x and time t; The fault gas release model configuration module is used to define type functions, location functions, and time gating functions in the fault gas release model. These three types of functions are respectively based on the risk / activation field P. s (x,t) realizes the type adaptation, spatial positioning and timing constraints of fault gas release; The simulation calculation module is used to collaboratively call the electro-thermal-gas release coupling model and the risk / activation field P. s The fault release intensity, fault type, fault location, and fault timing information are obtained by solving the three types of functions (x,t).

10. The modeling system for a digital twin simulation model of the working state of an oil-filled casing according to claim 9, characterized in that, It also includes a parameter calibration module, which is used to collect real-time monitoring data of the oil-filled casing. By comparing the real-time monitoring data with the solution results of the simulation calculation module, the calibration coefficient of the risk / activation field, the gas release intensity function parameter and the activation threshold are dynamically adjusted.

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