A substation state prediction method and system affected by stray current fluctuation

Abstract

This invention provides a method and system for predicting the state of a substation affected by stray current fluctuations. Based on train operation data, a digital twin traction power supply system model integrating train dynamics and traction arc characteristics is used to generate a stray current disturbance signal containing transient pulse components. Based on earth resistivity distribution data and the stray current disturbance signal, a three-dimensional finite element underground stray current diffusion model with adaptive mesh refinement is used to obtain the current component flowing into the substation grounding grid. Based on the current component and substation operation data, a digital twin model of the substation's electromagnetic-thermal coupling, explicitly modeled with the core lamination structure and oil-cooled convection channel, is used to make predictions, obtaining the substation's bias magnetization prediction results and temperature rise prediction results. This invention comprehensively considers multi-source heterogeneous data and combines simulation technology for the entire chain of subway traction, earth, and power grid substations, which can improve the risk prediction results of substations.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method and system for predicting the state of a substation affected by stray current fluctuations. Background Technology

[0002] Currently, urban subways use DC traction power supply. Some of the return current leaks to the ground through the track, forming stray currents. When the grounding grid of a nearby power system substation is shared with the subway structure, stray currents may flow into the neutral point of the main transformer, causing DC bias magnetization, which leads to core saturation, increased vibration, and localized overheating.

[0003] Existing studies often employ simplified circuit models or static finite element simulations to analyze stray current distribution or transformer bias effects, with these two typically modeled independently. However, such methods struggle to reflect the real-time impact of dynamic changes in subway operating conditions on power grid equipment, leading to significant discrepancies between assessment results and actual conditions, and failing to support accurate risk warnings and collaborative governance. Summary of the Invention

[0004] This invention provides a method and system for predicting the state of a substation affected by stray current fluctuations, in order to solve existing problems.

[0005] This invention provides a method for predicting the state of a substation affected by stray current fluctuations, comprising: Acquire data on earth resistivity distribution, train operation data, and substation operation data within the target area; Based on the train operation data, a stray current disturbance signal containing transient pulse components is generated using a digital twin traction power supply system model that integrates train dynamics and traction arc characteristics. Based on the earth resistivity distribution data and the stray current disturbance signal, the current component flowing into the substation grounding grid is obtained using a three-dimensional finite element underground stray current diffusion model with adaptive mesh refinement. Based on the current components and the substation operation data, a digital twin model of the substation electromagnetic-thermal coupling, which explicitly models the core lamination structure and the oil-cooled convection channel, is used to make predictions, and the substation bias prediction results and temperature rise prediction results are obtained.

[0006] Furthermore, based on the train operation data, the generation of a stray current disturbance signal containing transient pulse components using a digital twin traction power supply system model integrating train dynamics and traction arc characteristics includes: Based on the train timetable and current location in the train operation data, the train dynamics model in the digital twin traction power supply system model is used to calculate the traction force required by the train at present. Based on the traction force, the motor traction current waveform is calculated using the motor control logic in the digital twin traction power supply system model; Based on the current speed, current position, and catenary status parameters in the train operation data, the gap between the train's pantograph contactor and the catenary conductor of the traction power supply system is calculated using the pantograph contactor dynamic contact model in the digital twin traction power supply system model; based on the gap, it is determined whether the train has experienced a disconnection event. If so, then based on the arc model that introduces the arc volt-ampere characteristics into the digital twin traction power supply system model, an instantaneous pulse component is superimposed on the motor traction current waveform as a stray current source. The stray current source is mapped to the power grid injection point through the track-to-ground loop distribution coefficient to form a stray current disturbance signal.

[0007] Furthermore, the instantaneous pulse component includes a continuous negative current spike of a set amplitude; The arc voltage-current characteristic satisfies the following formula:

[0008] in, Arc voltage To maintain the voltage of the electric arc, Where I is the dynamic arc resistance and I is the arc current. Minimum holding current; The orbital geodetic loop allocation coefficient satisfies the following formula:

[0009] in, The orbital geodetic loop allocation coefficient. For the longitudinal resistance of the track, The current location of the train Equivalent resistivity of soil to ground; Determined based on earth resistivity distribution data.

[0010] Furthermore, based on the earth resistivity distribution data and the stray current disturbance signal, the current component flowing into the substation grounding grid is obtained using a three-dimensional finite element underground stray current diffusion model with adaptive mesh refinement, including: Based on the earth resistivity distribution data and the stray current disturbance signal, the underground potential distribution and the potential gradient of each element in the initial coarse grid are calculated using a three-dimensional finite element underground stray current diffusion model with adaptive grid refinement on the underground initial coarse grid. Cells with potential gradients exceeding a preset gradient threshold are subdivided into tetrahedral meshes to obtain non-uniformly refined meshes. Based on the non-uniform densified grid, the earth resistivity distribution data, and the stray current disturbance signal, the underground potential distribution is re-solved, and the current density on the surface of the grounding grid conductor is calculated according to the re-solved underground potential distribution. Integrating the current density over the surface of the grounding grid yields the current component flowing into the substation grounding grid.

[0011] Furthermore, the underground potential distribution satisfies the following formula:

[0012] in, This indicates calculating the gradient. The conductivity is obtained by converting the aforementioned earth resistivity distribution data. For the distribution of underground electric potential, The equivalent current source density mapped from the stray current disturbance signal to the track leak point is denoted by r, where r is the spatial coordinate.

[0013] Furthermore, the process of constructing the explicit modeling of the core lamination structure and the oil-cooled convection channel of the substation electromagnetic-thermal coupling digital twin model includes: The core of the main transformer in the substation is modeled as a hierarchical structure composed of multiple stacked silicon steel sheets, and an insulating boundary is set between each silicon steel sheet. The main transformer windings, oil tank and cold oil passage are modeled in three dimensions, and combined with the insulation boundary, to obtain the three-dimensional model of the substation. Based on the three-dimensional model of the substation, a nonlinear BH magnetization curve is introduced in the electromagnetic field domain, and natural convection and heat conduction equations are coupled in the thermal field domain to obtain an electromagnetic-thermal coupled digital twin model of the substation. The electromagnetic field satisfies the following formula:

[0014] in, This represents calculating the gradient, where A is the vector magnetic potential. The nonlinear reluctance is obtained by converting the BH curve of the iron core. The total current density is related to the substation operating data and the current components. The thermal field region satisfies the following formula:

[0015] in, This indicates the calculation of the gradient, where T is the temperature field. These are the density, specific heat capacity, and thermal conductivity of the cooling oil or solid material, respectively. For the oil flow velocity field, Joule heating for the core and windings This represents the eddy current loss power density of the core and windings.

[0016] Furthermore, the earth resistivity distribution data includes a dynamic three-dimensional resistivity field that takes into account seasonal water content variations and temperature dependence; The bias prediction results include the local magnetic flux density of the iron core in the substation and the proportion of the over-limit region relative to the saturation threshold. The temperature rise prediction results include the temperature of the hot spot in the iron core of the substation and its location coordinates.

[0017] This invention also provides a substation condition prediction system affected by stray current fluctuations, comprising: The data acquisition module is used to acquire data on the distribution of earth resistivity, train operation data, and substation operation data within the target area. The disturbance signal generation module is used to generate a stray current disturbance signal containing transient pulse components based on the train operation data and using a digital twin traction power supply system model that integrates train dynamics and traction arc characteristics. The current component generation module is used to obtain the current component flowing into the substation grounding grid based on the earth resistivity distribution data and the stray current disturbance signal, using a three-dimensional finite element underground stray current diffusion model with adaptive mesh encryption. The prediction module is used to make predictions based on the current component and the substation operation data, using an explicit modeling digital twin model of the substation's electromagnetic-thermal coupling of the core lamination structure and the oil-cooled convection channel, to obtain the substation's bias magnetization prediction results and temperature rise prediction results.

[0018] Furthermore, the disturbance signal generation module is specifically used for: Based on the train timetable and current location in the train operation data, the train dynamics model in the digital twin traction power supply system model is used to calculate the traction force required by the train at present. Based on the traction force, the motor traction current waveform is calculated using the motor control logic in the digital twin traction power supply system model; Based on the current speed, current position, and catenary status parameters in the train operation data, the gap between the train's pantograph contactor and the catenary conductor of the traction power supply system is calculated using the pantograph contactor dynamic contact model in the digital twin traction power supply system model; based on the gap, it is determined whether the train has experienced a disconnection event. If so, then based on the arc model that introduces the arc volt-ampere characteristics into the digital twin traction power supply system model, an instantaneous pulse component is superimposed on the motor traction current waveform as a stray current source. The stray current source is mapped to the power grid injection point through the track-to-ground loop distribution coefficient to form a stray current disturbance signal.

[0019] Furthermore, the instantaneous pulse component includes a continuous negative current spike of a set amplitude; The arc voltage-current characteristic satisfies the following formula:

[0020] in, Arc voltage To maintain the voltage of the electric arc, Where I is the dynamic arc resistance and I is the arc current. Minimum holding current; The orbital geodetic loop allocation coefficient satisfies the following formula:

[0021] in, The orbital geodetic loop allocation coefficient. For the longitudinal resistance of the track, The current location of the train Equivalent resistivity of soil to ground; Determined based on earth resistivity distribution data.

[0022] Furthermore, the current component generation module is specifically used for: Based on the earth resistivity distribution data and the stray current disturbance signal, the underground potential distribution and the potential gradient of each element in the initial coarse grid are calculated using a three-dimensional finite element underground stray current diffusion model with adaptive grid refinement on the underground initial coarse grid. Cells with potential gradients exceeding a preset gradient threshold are subdivided into tetrahedral meshes to obtain non-uniformly refined meshes. Based on the non-uniform densified grid, the earth resistivity distribution data, and the stray current disturbance signal, the underground potential distribution is re-solved, and the current density on the surface of the grounding grid conductor is calculated according to the re-solved underground potential distribution. Integrating the current density over the surface of the grounding grid yields the current component flowing into the substation grounding grid.

[0023] Furthermore, the underground potential distribution satisfies the following formula:

[0024] in, This indicates calculating the gradient. The conductivity is obtained by converting the aforementioned earth resistivity distribution data. For the distribution of underground electric potential, The equivalent current source density mapped from the stray current disturbance signal to the track leak point is denoted by r, where r is the spatial coordinate.

[0025] Furthermore, it also includes: a model building module, used for: The core of the main transformer in the substation is modeled as a hierarchical structure composed of multiple stacked silicon steel sheets, and an insulating boundary is set between each silicon steel sheet. The main transformer windings, oil tank and cold oil passage are modeled in three dimensions, and combined with the insulation boundary, to obtain the three-dimensional model of the substation. Based on the three-dimensional model of the substation, a nonlinear BH magnetization curve is introduced in the electromagnetic field domain, and natural convection and heat conduction equations are coupled in the thermal field domain to obtain an electromagnetic-thermal coupled digital twin model of the substation. The electromagnetic field satisfies the following formula: .

[0026] in, This represents calculating the gradient, where A is the vector magnetic potential. The nonlinear reluctance is obtained by converting the BH curve of the iron core. The total current density is related to the substation operating data and the current components. The thermal field region satisfies the following formula:

[0027] in, This indicates the calculation of the gradient, where T is the temperature field. These are the density, specific heat capacity, and thermal conductivity of the cooling oil or solid material, respectively. For the oil flow velocity field, Joule heating for the core and windings This represents the eddy current loss power density of the core and windings.

[0028] Furthermore, the earth resistivity distribution data includes a dynamic three-dimensional resistivity field that takes into account seasonal water content variations and temperature dependence; The bias prediction results include the local magnetic flux density of the iron core in the substation and the proportion of the over-limit region relative to the saturation threshold. The temperature rise prediction results include the temperature of the hot spot in the iron core of the substation and its location coordinates.

[0029] This application also provides an electronic device, which includes at least a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the steps of the substation state prediction method affected by stray current fluctuations as described above.

[0030] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the substation state prediction method affected by stray current fluctuations as described above.

[0031] This application also provides a computer program product, which includes: computer program code, and when the computer program code is run on a computer, causing the computer to perform the steps of any of the above-described methods for predicting the state of a substation affected by stray current fluctuations.

[0032] In this embodiment of the invention, the following steps are taken: First, the earth resistivity distribution data, train operation data, and substation operation data within the target area are acquired. Based on the train operation data, a digital twin traction power supply system model integrating train dynamics and traction arc characteristics is used to generate a stray current disturbance signal containing transient pulse components. Second, based on the earth resistivity distribution data and the stray current disturbance signal, a three-dimensional finite element underground stray current diffusion model with adaptive mesh refinement is used to obtain the current component flowing into the substation grounding grid. Third, based on the current component and the substation operation data, a digital twin model of the substation's electromagnetic-thermal coupling, explicitly modeled with the core lamination structure and oil-cooled convection channel, is used for prediction, yielding the substation's bias magnetization prediction and temperature rise prediction results. This invention comprehensively considers earth resistivity distribution data, train operation data, and substation operation data, and combines simulation technology across the entire chain of subway traction, earth, power grid, and substations. This enables simulation of the entire process evolution from stray current fluctuations to the substation's evolution, thereby improving the substation's risk prediction results and better supporting accurate risk warning and collaborative governance. Attached Figure Description

[0033] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 A flowchart illustrating a method for predicting the state of a substation affected by stray current fluctuations, provided in an embodiment of the present invention. Figure 2 A schematic diagram of a substation condition prediction system affected by stray current fluctuations provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0034] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0035] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical 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 invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0036] Example 1: Figure 1 This is a flowchart illustrating a method for predicting the state of a substation affected by stray current fluctuations, provided by an embodiment of the present invention. The process includes the following steps: S101: Acquire data on earth resistivity distribution, train operation, and substation operation within the target area.

[0037] S102: Based on train operation data, a stray current disturbance signal containing transient pulse components is generated using a digital twin traction power supply system model that integrates train dynamics and traction arc characteristics.

[0038] S103: Based on the earth resistivity distribution data and stray current disturbance signals, the current component flowing into the substation grounding grid is obtained using a three-dimensional finite element underground stray current diffusion model with adaptive mesh refinement.

[0039] S104: Based on current components and substation operation data, a digital twin model of the electromagnetic-thermal coupling of the substation with explicit modeling of the core lamination structure and oil-cooled convection channel is used to make predictions, and the substation bias prediction results and temperature rise prediction results are obtained.

[0040] In this embodiment of the invention, the earth resistivity distribution data, train operation data and substation operation data are comprehensively considered. Combined with the simulation technology of the entire chain of subway traction, earth, power grid and substation, the simulation of the entire process evolution from stray current fluctuation to substation can be realized, thereby improving the risk prediction results of substation and better supporting accurate risk warning and collaborative governance.

[0041] The substation status prediction method affected by stray current fluctuations provided in this invention is applied to electronic devices, such as PCs or servers.

[0042] The georesistivity distribution data in S101 above can represent the spatial distribution of conductivity characteristics of underground media in a target area, such as along urban rail transit lines and around substations. This data can be obtained through geophysical exploration methods such as geological survey reports, magnetotelluric sounding, or high-density electrical resistivity imaging, and stored in the form of a three-dimensional grid or a layered geoelectric model. For example, it can include resistivity values ​​and their horizontal distribution range corresponding to soil layers at different depths (such as topsoil, clay, gravel, and bedrock). This data reflects the influence of soil heterogeneity on stray current diffusion paths. Alternatively, it can include a dynamic three-dimensional resistivity field considering seasonal moisture content changes and temperature dependence. Compared to traditional static homogeneous soil models, dynamically coupling geology, climate, and conductivity can improve the prediction accuracy of stray current paths.

[0043] The train operation data in S101 above can be obtained from the subway operation dispatching system or onboard monitoring equipment, including but not limited to: real-time train location, running speed, acceleration, traction / braking status, timetable information, and pantograph working status. For example, the running trajectory and current command sequence of each train in the upward direction during the morning rush hour on a certain line can be obtained. This type of data can be used to drive a high-fidelity traction power supply system model to generate stray current disturbance sources that conform to actual operating conditions. In this embodiment of the invention, the train may include a subway system.

[0044] The substation operation data mentioned above in S101 can be obtained through the substation automation system or online monitoring equipment, including but not limited to: the load current of the main transformer, the neutral point grounding current, the top oil temperature, the winding temperature, the ambient temperature, and the operating status of the cooling system. For example, the load curve and grounding current waveform of the target substation over the past 24 hours can be collected.

[0045] By acquiring three types of multi-source heterogeneous data in S101 above, information connectivity across the entire chain of rail transit, underground propagation, and power facilities can be achieved, which can improve the accuracy of stray current path simulation and the reliability of transformer status prediction, and ensure the safe operation of the urban power grid.

[0046] To address the limitations of traditional solutions that simply treat the train as a constant current source or periodic load, this approach couples vehicle dynamics, mechanical offline events, and electrical transient responses during risk prediction. This generates stray current disturbance signals that not only include steady-state components but also reconstruct high-frequency, non-stationary transient pulses caused by pantograph offline, providing high-fidelity disturbance input for subsequent processing. In one implementation, step S102 includes the following steps: Based on the train timetable and current location in the train operation data, the train dynamics model in the digital twin traction power supply system model is used to calculate the traction force required by the train at present; Based on traction force, the motor traction current waveform is calculated using the motor control logic in the digital twin traction power supply system model; Based on the current speed, current position, and catenary status parameters in the train operation data, the gap between the train's pantograph contactor and the catenary conductor of the traction power supply system is calculated using the dynamic contact model of the pantograph contactor in the digital twin traction power supply system model; based on the gap, it is determined whether the train has experienced a disconnection event. If so, then based on the arc model that introduces the arc volt-ampere characteristics into the digital twin traction power supply system model, the instantaneous pulse component is superimposed on the motor traction current waveform as a stray current source. Stray current sources are mapped to the grid injection point through the track-to-ground loop distribution coefficient, forming a stray current disturbance signal.

[0047] In this implementation, the digital twin traction power supply system model integrates a train dynamics model, motor control logic, pantograph contact network dynamic contact model, and arc model.

[0048] For example, when calculating the required traction force of a train based on its timetable and current location in the train operation data, using the train dynamics model in the digital twin traction power supply system model, information such as the train timetable, current location, and operating speed can be extracted from the train operation data. Track gradient angles and wheel-rail resistance parameters can also be obtained from the track design database or a track GIS (Geographic Information System). Using the train dynamics model, the required traction force of the train under the current operating conditions can be dynamically calculated. The information extracted from the train operation data can be used to represent the precise spatiotemporal state of the train within the track network.

[0049] For example, the train dynamics model satisfies the following formula:

[0050] Where m is the equivalent mass of the train. The speed is t, and the time is t (which can be determined from the train timetable and the current location). The track slope angle, For wheel-rail resistance parameters, Let g be the traction force and g be the acceleration due to gravity. Wheel-rail resistance parameters include, but are not limited to, air resistance, rolling resistance, and curve resistance. By solving this formula, or using its discretized form, the traction force required to maintain the current operating speed and acceleration can be derived. By integrating a realistic train dynamics model into the electromagnetic interference problem of the power system, the impact of actual current fluctuations can be reflected more accurately.

[0051] The above-mentioned calculation of the motor traction current waveform based on traction force and utilizing the motor control logic in the digital twin traction power supply system model can convert the traction force into the corresponding motor traction current waveform as the basic current source by performing FOC (field-oriented control, vector control) or DTC (direct torque control) digital mapping through the motor control logic. In this example, the motor control logic is not a simple table lookup, but a digital mapping of the actual traction converter control strategy, thus reflecting the electrical behavior characteristics of the train under complex operating conditions. For example, using the motor control logic, the target torque of the train is calculated based on the required traction force and operating speed; the motor rotor position is obtained by integrating the operating speed; combined with the motor rotor position and flux linkage model, the stator current command is generated; then, considering the inverter switching frequency and modulation strategy, a continuous or discrete three-phase AC current waveform is output, which is then rectified or equivalently processed to convert it into a DC-side traction current waveform. The waveform generated in this way not only reflects the steady-state load demand, but also includes dynamic fluctuation components caused by acceleration / deceleration and gradient changes, ensuring adaptability to operating conditions.

[0052] The aforementioned overhead contact system parameters can be preset, including information such as conductor height, span, tension, and pantograph sliding plate material characteristics. When calculating the gap, based on the train's current speed and position, combined with preset overhead contact system geometric and mechanical parameters (such as conductor height, span, and tension), as well as the pantograph sliding plate material and lifting characteristics, a pantograph-overhead contact dynamic contact model sub-model is run within the digital twin traction power supply system. This sub-model, based on multibody dynamics or lumped parameter methods, solves in real-time the coupled response of pantograph frame vibration and overhead contact system elastic deformation, thereby calculating the instantaneous relative displacement between the sliding plate and the conductor, i.e., the dynamic gap. When determining whether a train has experienced a derailment event based on the gap, it can be determined whether the gap exceeds a preset gap threshold to identify any temporary derailment caused by track irregularities or high-speed cornering. For example, when a train passes through curves, switches, or sections of track with irregularities, the gap will increase due to mechanical disturbances; once it exceeds a physical critical value (such as a preset gap threshold), a derailment event is determined to have occurred. This process does not require pre-setting offline time; instead, it is derived dynamically through the evolution of operating conditions and structural parameters, significantly improving the authenticity and predictive reliability of stray current disturbance sources.

[0053] When an offline event is detected, the arc model can be activated. The arc volt-ampere characteristic introduced in the arc model is characterized by a piecewise nonlinear function representing the arc conduction behavior, including key parameters such as arc sustaining voltage, dynamic resistance, and minimum sustaining current. For example, the arc volt-ampere characteristic satisfies the following formula:

[0054] in, Arc voltage The arc sustaining voltage reflects the minimum energy required for arc combustion. The dynamic arc resistance reflects the conductivity of the arc channel, and I represents the arc current. To minimize the holding current, it is used to determine whether the arc is stable during the offline gap, thereby accurately capturing the millisecond-level high-amplitude current transients caused by pantograph offline. Based on this characteristic, by superimposing a transient pulse component on the motor traction current waveform, the current surge caused by real arc discharge can be simulated, thus forming a current source.

[0055] The transient pulse component includes a sustained negative current spike of a set amplitude, such as a high-amplitude negative transient pulse in the millisecond range, or a negative transient pulse with a duration of 1–10 ms and an amplitude of 1.5–3.0 times the rated current. This transient disturbance is one of the key causes of stray current fluctuations, which in turn leads to DC deviation in the transformer.

[0056] In the aforementioned step of mapping stray current sources to the grid injection point through the track-ground loop distribution coefficient to form stray current disturbance signals, the stray current sources can be distributed to the track return path and the ground leakage path according to the track-ground double loop current splitting principle, based on the track-ground loop distribution coefficient as the impedance ratio. Specifically, combining the known longitudinal resistance of the track (determined by the rail type and connection status), the soil conductivity distribution along the line (derived from the aforementioned ground resistivity data), and the grounding configuration of the substation and traction substation (such as the location and number of grounding electrodes), the equivalent leakage conductance to the ground for each track section is calculated. The stray current sources are split at each track node according to the track-ground loop distribution coefficient, forming a continuous leakage current density distributed along the line. Subsequently, through integration or finite element post-processing, the currents flowing to the ground and spreading in space from all leakage points are aggregated into the total DC component flowing into the grounding grid of the adjacent substation. For example, the track-ground loop distribution coefficient satisfies the following formula:

[0057] in, The orbital geodetic loop allocation coefficient. For the longitudinal resistance of the track, The current location of the train Equivalent resistivity of soil to ground; The formula is determined based on data on earth resistivity distribution. The coefficients in the formula are updated in real time with the train's location, reflecting the modulation effect of underground structures (such as tunnel linings, underground pipelines, and aquifers) on the current path.

[0058] In one implementation, step S103 above may include the following steps: Based on the earth resistivity distribution data and stray current disturbance signal, the underground potential distribution and the potential gradient of each element in the initial coarse grid are calculated using a three-dimensional finite element underground stray current diffusion model with adaptive mesh refinement on the underground initial coarse grid. Cells with potential gradients exceeding a preset gradient threshold are subdivided into tetrahedral meshes to obtain non-uniformly refined meshes. Based on the non-uniform densified grid, earth resistivity distribution data and stray current disturbance signal, the underground potential distribution is re-solved, and the current density on the surface of the grounding grid conductor is calculated based on the re-solved underground potential distribution. Integrating the current density over the surface of the grounding grid yields the current component flowing into the substation grounding grid.

[0059] In this implementation, the initial coarse underground network can cover subway lines, the subsoil layer, and substation areas. A three-dimensional finite element model for underground stray current diffusion using adaptive mesh refinement can simulate the propagation behavior of DC stray currents in non-uniform ground with high precision. For example, this model, based on the three-dimensional finite element method, discretizes the underground space into tetrahedral mesh elements and uses the measured or inverted earth resistivity distribution as the conductivity input to solve the governing equations describing current diffusion. This approach can reduce overall computational resource consumption while ensuring simulation accuracy in key areas, making it suitable for refined prediction of stray current paths and injection volumes under complex geological conditions.

[0060] For example, when calculating the underground electric potential distribution and the potential gradient of each cell in the initial coarse grid, the earth resistivity distribution data can be converted into a spatially related conductivity field on the initial underground coarse grid covering the subway line, soil strata, and substation area. The stray current disturbance signal can be mapped as an equivalent current source along the track, and the governing equations can be solved to obtain the preliminary underground electric potential distribution. At the same time, the potential gradient of each cell in the coarse grid is calculated to assess the degree of drastic change in field strength.

[0061] For example, the underground electric potential distribution satisfies the following formula:

[0062] in, This indicates calculating the gradient. The conductivity is derived from the earth resistivity distribution data. For the distribution of underground electric potential, Let r be the equivalent current source density mapped from stray current disturbance signal to the track leak point, and r be the spatial coordinate.

[0063] Cells with potential gradients exceeding a preset gradient threshold are typically concentrated at substation grounding electrodes, tunnel linings, or the boundaries of highly conductive strata. Performing tetrahedral mesh subdivision on such cells generates a higher-resolution non-uniform mesh, ensuring that these critical areas have sufficient geometric and physical details. This avoids leakage path distortion and underestimation of magnetic bias risk caused by coarse mesh.

[0064] In this implementation, when calculating the current density on the surface of the grounding grid conductor based on the re-solved underground potential distribution, the re-solved underground potential distribution serves as a high-precision underground potential field. The electric field strength is obtained by calculating the spatial gradient from this potential field, and then, combined with the conductivity of the soil or conductor in the target area, the current density vector at each point is calculated. Specifically, on the surface of the substation grounding grid conductor, since the conductor conductivity is much higher than the surrounding soil, the current mainly flows in or out along the normal direction; therefore, the vector distribution of this surface can be extracted.

[0065] In one implementation, the process of constructing the substation electromagnetic-thermal coupling digital twin model of the explicitly modeled iron core lamination structure and oil-cooled convection channel in S104 above may include: The core of the main transformer in the substation is modeled as a hierarchical structure composed of multiple stacked silicon steel sheets, and insulating boundaries are set between each silicon steel sheet to suppress cross-sheet eddy currents. The main transformer windings, oil tank and cooling oil passages are modeled in three dimensions, and combined with the insulation boundary to obtain the three-dimensional model of the substation, forming a complete natural convection channel for cooling oil. Based on the three-dimensional model of the substation, a nonlinear BH magnetization curve is introduced in the electromagnetic field domain, and the natural convection and heat conduction equations are coupled in the thermal field domain to obtain an electromagnetic-thermal coupled digital twin model of the substation.

[0066] For example, when using the electromagnetic-thermal coupled digital twin model of this substation for prediction, a nonlinear BH magnetization curve is introduced into the electromagnetic field domain. The current component is injected into the neutral point as a bias magnetization source to calculate the magnetic flux density distribution in the entire iron core region and identify the local magnetic saturation region caused by DC bias magnetization. Then, electromagnetic losses (including hysteresis, eddy currents, and additional losses) are mapped as heat sources to the thermal field model. Combined with substation operating data, fluid dynamics equations are coupled to simulate the natural convection process of cooling oil in the oil tank, thereby solving the three-dimensional temperature field and locating the hot spots and their temperature rise values ​​in the iron core or windings.

[0067] For example, the bias prediction results include the local magnetic flux density of the iron core in the substation and the proportion of the over-limit region relative to the saturation threshold, characterizing the degree and spatial distribution of the over-limit magnetic flux density of the iron core. The temperature rise prediction results include the hot spot temperature and its location coordinates in the iron core of the substation, characterizing the hot spot temperature and its evolution trend.

[0068] For example, the electromagnetic field satisfies the following formula: .

[0069] in, This represents calculating the gradient, where A is the vector magnetic potential. The nonlinear reluctance is obtained by converting the BH curve of the iron core. The total current density is related to substation operating data and current components. The thermal field satisfies the following formula:

[0070] in, This indicates the calculation of the gradient, where T is the temperature field. These are the density, specific heat capacity, and thermal conductivity of the cooling oil or solid material, respectively. For the oil flow velocity field, Joule heating for the core and windings This represents the eddy current loss power density of the core and windings.

[0071] Optionally, after obtaining the bias magnetic prediction results and temperature rise prediction results, this embodiment of the invention introduces a prediction result correction mechanism and a graded risk warning process to improve the reliability and practicality of the assessment.

[0072] When correcting the prediction results, the predicted values ​​of hot spot temperature and bias magnetic intensity output by the digital twin model can be compared with the top oil temperature, neutral point current and historical operating data collected in real time by the substation online monitoring system. If there is a systematic deviation, the simulation results can be corrected online through a lightweight machine learning proxy model (such as a neural network based on physical constraints) to achieve a closed-loop feedback of "simulation-actual measurement" and improve the long-term prediction stability.

[0073] When issuing risk warnings, a multi-level risk assessment can be performed based on the corrected (optional) prediction results, combined with a preset safety threshold system (including the core saturation critical magnetic flux density, the maximum allowable temperature of insulation materials, and the temperature rise rate limit). When the bias magnetization or temperature rise is within the normal range, the system maintains routine monitoring. When any indicator approaches the warning threshold, a yellow warning is triggered, suggesting enhanced inspections or adjustments to train scheduling strategies. When the indicator exceeds the emergency threshold or shows a rapid deterioration trend, a red warning is issued, and maintenance recommendations are pushed, such as activating DC blocking devices, starting auxiliary cooling, or restricting train operation in adjacent sections. Through tiered warnings and actionable recommendations, coordinated decision-making between power grid dispatching and rail transit operations can be effectively supported, reducing transformer failures caused by stray currents.

[0074] Example 2: Based on the same concept, Figure 2A schematic diagram of a substation condition prediction system affected by stray current fluctuations, provided as an embodiment of the present invention, includes: The data acquisition module is used to acquire data on the distribution of earth resistivity, train operation data, and substation operation data within the target area. The disturbance signal generation module is used to generate stray current disturbance signals containing transient pulse components based on train operation data and using a digital twin traction power supply system model that integrates train dynamics and traction arc characteristics. The current component generation module is used to obtain the current component flowing into the substation grounding grid based on the earth resistivity distribution data and stray current disturbance signal, using a three-dimensional finite element underground stray current diffusion model with adaptive mesh refinement. The prediction module is used to make predictions based on current components and substation operating data, using an explicit model of the electromagnetic-thermal coupling digital twin model of the substation's core lamination structure and oil-cooled convection channel, to obtain the substation's bias magnetization prediction results and temperature rise prediction results.

[0075] In one possible implementation, the disturbance signal generation module is specifically used for: Based on the train timetable and current location in the train operation data, the train dynamics model in the digital twin traction power supply system model is used to calculate the traction force required by the train at present; Based on traction force, the motor traction current waveform is calculated using the motor control logic in the digital twin traction power supply system model; Based on the current speed, current position, and catenary status parameters in the train operation data, the gap between the train's pantograph contactor and the catenary conductor of the traction power supply system is calculated using the dynamic contact model of the pantograph contactor in the digital twin traction power supply system model; based on the gap, it is determined whether the train has experienced a disconnection event. If so, then based on the arc model that introduces the arc volt-ampere characteristics into the digital twin traction power supply system model, the instantaneous pulse component is superimposed on the motor traction current waveform as a stray current source. Stray current sources are mapped to the grid injection point through the track-to-ground loop distribution coefficient, forming a stray current disturbance signal.

[0076] In one possible implementation, the instantaneous pulse component includes a continuous negative current spike of a set amplitude; The arc voltage-current characteristic satisfies the following formula:

[0077] in, Arc voltage To maintain the voltage of the electric arc, Where I is the dynamic arc resistance and I is the arc current. Minimum holding current; The orbital geodetic loop allocation coefficient satisfies the following formula:

[0078] in, The orbital geodetic loop allocation coefficient. For the longitudinal resistance of the track, The current location of the train Equivalent resistivity of soil to ground; Determined based on earth resistivity distribution data.

[0079] In one possible implementation, the current component generation module is specifically used for: Based on the earth resistivity distribution data and stray current disturbance signal, the underground potential distribution and the potential gradient of each element in the initial coarse grid are calculated using a three-dimensional finite element underground stray current diffusion model with adaptive mesh refinement on the underground initial coarse grid. Cells with potential gradients exceeding a preset gradient threshold are subdivided into tetrahedral meshes to obtain non-uniformly refined meshes. Based on the non-uniform densified grid, earth resistivity distribution data and stray current disturbance signal, the underground potential distribution is re-solved, and the current density on the surface of the grounding grid conductor is calculated based on the re-solved underground potential distribution. Integrating the current density over the surface of the grounding grid yields the current component flowing into the substation grounding grid.

[0080] In one possible implementation, the underground potential distribution satisfies the following formula:

[0081] in, This indicates calculating the gradient. The conductivity is derived from the earth resistivity distribution data. For the distribution of underground electric potential, Let r be the equivalent current source density mapped from stray current disturbance signal to the track leak point, and r be the spatial coordinate.

[0082] In one possible implementation, it further includes: a model building module, used for: The core of the main transformer in the substation is modeled as a hierarchical structure composed of multiple stacked silicon steel sheets, and an insulating boundary is set between each silicon steel sheet. The main transformer windings, oil tank and cold oil passage are modeled in three dimensions, and combined with the insulation boundary, to obtain the three-dimensional model of the substation. Based on the three-dimensional model of the substation, a nonlinear BH magnetization curve is introduced in the electromagnetic field domain, and the natural convection and heat conduction equations are coupled in the thermal field domain to obtain an electromagnetic-thermal coupled digital twin model of the substation. Electromagnetic fields satisfy the following formula: .

[0083] in, This represents calculating the gradient, where A is the vector magnetic potential. The nonlinear reluctance is obtained by converting the BH curve of the iron core. The total current density is related to substation operating data and current components. The thermal field satisfies the following formula:

[0084] in, This indicates the calculation of the gradient, where T is the temperature field. These are the density, specific heat capacity, and thermal conductivity of the cooling oil or solid material, respectively. For the oil flow velocity field, Joule heating for the core and windings This represents the eddy current loss power density of the core and windings.

[0085] In one possible implementation, the earth resistivity distribution data includes a dynamic three-dimensional resistivity field that takes into account seasonal water content variations and temperature dependence. The bias prediction results include the local magnetic flux density of the iron core in the substation and the proportion of the out-of-limit region relative to the saturation threshold; The temperature rise prediction results include the temperature of the hot spots in the iron core of the substation and their location coordinates.

[0086] Example 3: Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Based on the above embodiments, this embodiment of the present invention also provides an electronic device, including a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302 and the memory 303 communicate with each other through the communication bus 304. The memory 303 stores a computer program, which, when executed by the processor 301, causes the processor 301 to perform the steps in the substation state prediction method affected by stray current fluctuations as described in the above embodiment.

[0087] The communication bus mentioned in the above electronic devices can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0088] Communication interface 302 is used for communication between the above-mentioned electronic device and other devices.

[0089] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0090] The processors mentioned above can be general-purpose processors, including central processing units, network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits, field-programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0091] Example 4: Based on the above embodiments, this invention also provides a computer-readable storage medium storing a computer program, which is processed by a processor to provide the above-described substation state prediction method affected by stray current fluctuations.

[0092] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

[0093] The embodiments described in this invention are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0094] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A substation state prediction method affected by stray current fluctuation, characterized by, The method includes: Acquire data on earth resistivity distribution, train operation data, and substation operation data within the target area; Based on the train operation data, a stray current disturbance signal containing transient pulse components is generated using a digital twin traction power supply system model that integrates train dynamics and traction arc characteristics. Based on the earth resistivity distribution data and the stray current disturbance signal, the current component flowing into the substation grounding grid is obtained using a three-dimensional finite element underground stray current diffusion model with adaptive mesh refinement. Based on the current components and the substation operation data, a digital twin model of the substation electromagnetic-thermal coupling, which explicitly models the core lamination structure and the oil-cooled convection channel, is used to make predictions, and the substation bias prediction results and temperature rise prediction results are obtained.

2. The method of claim 1, wherein, Based on the train operation data, a stray current disturbance signal containing transient pulse components is generated using a digital twin traction power supply system model integrating train dynamics and traction arc characteristics, including: Based on the train timetable and current location in the train operation data, the train dynamics model in the digital twin traction power supply system model is used to calculate the traction force required by the train at present. Based on the traction force, the motor traction current waveform is calculated using the motor control logic in the digital twin traction power supply system model; Based on the current speed, current position, and catenary status parameters in the train operation data, the gap between the train's pantograph contactor and the catenary conductor of the traction power supply system is calculated using the pantograph contactor dynamic contact model in the digital twin traction power supply system model; based on the gap, it is determined whether the train has experienced a disconnection event. If so, then based on the arc model that introduces the arc volt-ampere characteristics into the digital twin traction power supply system model, an instantaneous pulse component is superimposed on the motor traction current waveform as a stray current source. The stray current source is mapped to the power grid injection point through the track-to-ground loop distribution coefficient to form a stray current disturbance signal.

3. The method of claim 2, wherein, The instantaneous pulse component includes a continuous negative current spike of a set amplitude; The arc voltage-current characteristic satisfies the following formula: wherein is the arc voltage, is the arc sustain voltage, is the dynamic arc resistance, I is the arc current, is the minimum sustain current; The orbital geodetic loop allocation coefficient satisfies the following formula: wherein, is a track ground return coefficient, is a track longitudinal resistance, is a current position of the train is a ground equivalent resistance of the soil at the current position of the train; is determined based on earth resistivity distribution data.

4. The method of claim 1, wherein, Based on the earth resistivity distribution data and the stray current disturbance signal, a three-dimensional finite element underground stray current diffusion model with adaptive mesh refinement is used to obtain the current component flowing into the substation grounding grid, including: Based on the earth resistivity distribution data and the stray current disturbance signal, the underground potential distribution and the potential gradient of each element in the initial coarse grid are calculated using a three-dimensional finite element underground stray current diffusion model with adaptive grid refinement on the underground initial coarse grid. Cells with potential gradients exceeding a preset gradient threshold are subdivided into tetrahedral meshes to obtain non-uniformly refined meshes. Based on the non-uniform densified grid, the earth resistivity distribution data, and the stray current disturbance signal, the underground potential distribution is re-solved, and the current density on the surface of the grounding grid conductor is calculated according to the re-solved underground potential distribution. Integrating the current density over the surface of the grounding grid yields the current component flowing into the substation grounding grid.

5. The method of claim 4, wherein, The underground electric potential distribution satisfies the following formula: wherein, denotes the gradient, is the conductivity converted from the distribution of the earth resistivity, is the distribution of the underground electric potential, is the equivalent current source density mapped from the stray current disturbance signal to the track leakage point, r is the spatial coordinate.

6. The method of claim 1, wherein, The process of constructing the explicit modeling of the core lamination structure and the oil-cooled convection channel of the substation electromagnetic-thermal coupling digital twin model includes: The core of the main transformer in the substation is modeled as a hierarchical structure composed of multiple stacked silicon steel sheets, and an insulating boundary is set between each silicon steel sheet. The main transformer windings, oil tank and cold oil passage are modeled in three dimensions, and combined with the insulation boundary, to obtain the three-dimensional model of the substation. Based on the three-dimensional model of the substation, a nonlinear BH magnetization curve is introduced in the electromagnetic field domain, and natural convection and heat conduction equations are coupled in the thermal field domain to obtain an electromagnetic-thermal coupled digital twin model of the substation. The electromagnetic field satisfies the following formula: wherein, denotes the gradient, A is the vector magnetic potential, is the non-linear magnetic reluctance converted from the core B-H curve, is the total current density, related to the substation operation data and the current components; The thermal field region satisfies the following formula: wherein, represents the gradient, T is the temperature field, are the density, specific heat capacity and thermal conductivity of the cooling oil or solid material, respectively, is the oil flow velocity field, is the Joule heat of the core and winding, is the eddy current loss power density of the core and winding.

7. The method according to any one of claims 1-6, characterized in that, The earth resistivity distribution data includes a dynamic three-dimensional resistivity field that takes into account seasonal water content variations and temperature dependence. The bias prediction results include the local magnetic flux density of the iron core in the substation and the proportion of the over-limit region relative to the saturation threshold. The temperature rise prediction results include the temperature of the hot spot in the iron core of the substation and its location coordinates.

8. A substation condition prediction system affected by stray current fluctuations, characterized in that, include: The data acquisition module is used to acquire data on the distribution of earth resistivity, train operation data, and substation operation data within the target area. The disturbance signal generation module is used to generate a stray current disturbance signal containing transient pulse components based on the train operation data and using a digital twin traction power supply system model that integrates train dynamics and traction arc characteristics. The current component generation module is used to obtain the current component flowing into the substation grounding grid based on the earth resistivity distribution data and the stray current disturbance signal, using a three-dimensional finite element underground stray current diffusion model with adaptive mesh encryption. The prediction module is used to make predictions based on the current component and the substation operation data, using an explicit modeling digital twin model of the substation's electromagnetic-thermal coupling of the core lamination structure and the oil-cooled convection channel, to obtain the substation's bias magnetization prediction results and temperature rise prediction results.

9. An electronic device, characterized in that, The electronic device includes at least a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the steps of the substation state prediction method affected by stray current fluctuations as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the substation state prediction method affected by stray current fluctuations as described in any one of claims 1 to 7.

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