A transformer dc bias evaluation method and device for a subway train

Abstract

This invention belongs to the field of power and discloses a method and device for evaluating the DC bias current of transformers in subway trains. The method includes: constructing a coupled simulation model of the subway line and the regional power grid; establishing a functional relationship between the train's operating position, speed, traction current, and time between two stations, and simulating the dynamic operating characteristics of a single train based on this functional relationship; simulating the dynamic operating conditions of multiple trains on the subway line based on the simulated single-train operating characteristics through time shifting and data overlay to generate dynamic operating characteristic curves containing the number, position, operating status, and traction current of trains; converting the train's travel distance into geographical coordinates based on linear interpolation and mapping methods, and outputting the traction current and coordinate information of all trains operating on the line at a specified time as multi-train dynamic operating data; inputting the multi-train dynamic operating data into the coupled simulation model to calculate and evaluate the DC bias current of the transformer in the regional power grid.

Description

Technical Field

[0001] This invention belongs to the field of power, and in particular relates to a method and apparatus for evaluating the DC bias magnetism of transformers in subway trains. Background Technology

[0002] With the continuous advancement of urbanization, rail transit has developed rapidly. The stray currents generated by subway operation are increasingly causing DC bias magnetization in transformers. As the return current structure of the subway system, the rails cannot be completely insulated from the ground, and insulation damage exists, causing some return current (i.e., stray current) to leak into the ground. These currents intrude into the transformer neutral point and flow into the windings, causing DC bias magnetization phenomena such as transformer vibration and increased noise, seriously threatening the safe and stable operation of the power grid and equipment.

[0003] In related technologies, research on DC bias current in transformers caused by stray currents in subways mainly focuses on building an equivalent DC resistance network model of the subway system and transformers based on the mechanism of DC bias current generation, and analyzing the distribution characteristics of the DC bias current. Furthermore, simulation software is used to build three-dimensional models of the subway and regional power grid to calculate the impact of specific lines on nearby transformers. However, with the development of subway lines, the impact of stray currents on urban AC power grids has expanded. Existing calculation and analysis methods cannot meet the requirements for calculating and evaluating the DC bias current of transformers under actual subway line and train dynamic operating conditions. To conform to the actual dynamic operating conditions of subways, the simulation calculation process needs to simulate the dynamic operating characteristics of actual trains. However, manually adding excitations results in low efficiency and accuracy in DC bias current simulation calculations, severely restricting the effective formulation of subsequent transformer DC bias current mitigation solutions. Summary of the Invention

[0004] In view of this, the present invention discloses a method and apparatus for evaluating the DC bias magnetism of transformers in subway trains, which can solve the shortcomings of related technologies.

[0005] To achieve the above objectives, the present invention discloses the following technical solution: According to a first aspect of the present invention, a method for evaluating the DC bias magnetism of transformers in subway trains is proposed, comprising: A subway system model is constructed based on the traction power supply system and return system of the subway system, and a regional power grid model is constructed based on the substation grounding grid, transformers and transmission lines of the regional power grid. Based on the relative spatial location of the subway line and the AC power grid, the subway system model and the regional power grid model are coupled into a coupled simulation model of the subway line and the regional power grid. Based on the train dynamics model, the functional relationship between the train's running position, speed, traction current and time between two stations is established, and the dynamic operating characteristics of a single train are simulated based on the functional relationship. Based on the simulated single-train operation characteristics, combined with subway line information, train stopping time and departure interval, the dynamic operation conditions of multiple trains on the subway line are simulated through time shifting and data overlay to generate dynamic operation characteristic curves that include the number of trains, their location, operating status and traction current. Based on linear interpolation and mapping, the train travel distance in the dynamic operation characteristic curve is converted into geographical coordinates, and the traction current and coordinate information of all trains running on the line at a specified time are output as multi-train dynamic operation data that matches the coupled simulation model. The dynamic operation data of the multiple trains are input into the coupled simulation model to calculate and evaluate the DC bias current of the transformer in the regional power grid.

[0006] According to a second aspect of the present invention, a transformer DC bias evaluation device for subway trains is provided, characterized in that it comprises: Construction Units: The subway system model is constructed based on the traction power supply system and return system of the subway system, and the regional power grid model is constructed based on the substation grounding grid, transformers and transmission lines of the regional power grid. Coupling unit: Based on the relative spatial position of the subway line and the AC power grid, the subway system model and the regional power grid model are coupled into a coupled simulation model of the subway line and the regional power grid; First simulation unit: Based on the train dynamics model, establish the functional relationship between the train's running position, speed, traction current and time between two stations, and simulate the dynamic operating characteristics of a single train based on the functional relationship; The second simulation unit: Based on the simulated single-train operation characteristics, combined with subway line information, train stopping time and departure interval, the dynamic operation conditions of multiple trains on the subway line are simulated through time shifting and data overlay to generate dynamic operation characteristic curves that include the number of trains, their positions, operating status and traction current. Output unit: Based on linear interpolation and mapping, the train travel distance in the dynamic operation characteristic curve is converted into geographical coordinates, and the traction current and coordinate information of all trains running on the line at a specified time are output as multi-train dynamic operation data that matches the coupled simulation model; Evaluation Unit: Input the dynamic operation data of the multiple trains into the coupled simulation model to calculate and evaluate the DC bias current of the transformer in the regional power grid.

[0007] According to a third aspect of the present invention, an electronic device is provided, comprising: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in the first aspect by running the executable instructions.

[0008] According to a fourth aspect of the invention, a computer-readable storage medium is provided having computer instructions stored thereon that, when executed by a processor, implement the steps of the method as described in the first aspect.

[0009] As can be seen from the above technical solutions, the present invention discloses a method and apparatus for evaluating the DC bias magnetism of transformers in subway trains. On the one hand, based on the relative positions of subway lines and substations, a coupled topology model of urban subway and regional power grid was established, combining the subway train operation simulation system with the coupled simulation model. A multi-train dynamic excitation simulation and output system was proposed, which can directly output the dynamic operation data of different trains on the line at a certain moment and input it into the simulation model. Combined with the simulation time, the DC bias current level of each transformer in the regional power grid can be calculated, thereby improving the efficiency and accuracy of transformer DC bias current simulation calculation. On the other hand, based on the train dynamics model and multi-train collaborative simulation, the dynamic operation scenario of multiple trains on the subway line during peak / off-peak periods can be accurately reproduced, making the simulation calculation closer to the actual operation of the subway and the evaluation results more meaningful for engineering guidance. In addition, the geographical coordinates of train excitation are automatically generated through linear interpolation and mapping methods and seamlessly integrated with the simulation model, realizing an automated process from dynamic operating condition simulation to bias current calculation. This facilitates batch evaluation of different lines and different operating schedules and has strong scalability. Attached Figure Description

[0010] Figure 1 This is a flowchart of an exemplary embodiment of a method for evaluating the DC bias magnetism of a transformer in a subway train; Figure 2 This is a schematic diagram of a non-autotransformer DC model of a transformer, provided in an exemplary embodiment. Figure 3 This is a schematic diagram of a DC model of an autotransformer provided in an exemplary embodiment; Figure 4 This is a schematic diagram of a ground wire and regional power grid coupling topology model provided in an exemplary embodiment; Figure 5 This is a flowchart of another method for evaluating the DC bias of a transformer in a subway train, provided in an exemplary embodiment. Figure 6 This is a schematic diagram illustrating the dynamic operating characteristics of a train during peak hours, provided in an exemplary embodiment. Figure 7This is a schematic diagram illustrating the characteristics of train excitation current variation as provided in an exemplary embodiment; Figure 8 This is a schematic diagram of a transformer DC bias current measurement provided in an exemplary embodiment; Figure 9 This is a schematic diagram of a simulated DC bias current value of a transformer provided in an exemplary embodiment; Figure 10 This is a schematic structural diagram of a device provided in an exemplary embodiment; Figure 11 This is a block diagram of an exemplary embodiment of a transformer DC bias evaluation device for subway trains. Detailed Implementation

[0011] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of the present invention as detailed in the appended claims.

[0012] It should be noted that in other embodiments, the corresponding methods are not necessarily performed in the order shown and described in this invention. The method comprises steps. In some other embodiments, the method may include more or fewer steps than those described in this invention. Furthermore, a single step described in this invention may be broken down into multiple steps in other embodiments; and multiple steps described in this invention may be combined into a single step in other embodiments.

[0013] With the global energy structure shifting towards cleaner energy sources, the large-scale development and utilization of offshore renewable energy sources such as deep-sea wind power has become an important direction. To effectively collect, utilize, and transmit offshore renewable energy over long distances, the concept of integrated offshore energy islands has emerged. These islands typically integrate multiple energy conversion and storage devices, such as wind power, energy storage, electrolytic hydrogen production, and ammonia production, forming a multi-energy coupled system of "electricity-hydrogen-ammonia-storage." However, the deep-sea environment is complex and variable, with highly uncertain wind speeds and waves, posing significant challenges to the planning, design, and stable operation of energy islands.

[0014] Existing marine energy system planning methods typically suffer from the following shortcomings: planning models are mostly based on deterministic assumptions, making it difficult to accurately characterize the multi-scale uncertainties and risks brought about by environmental factors such as wind and waves; equipment modeling parameters mostly use theoretical or empirical values, failing to fully consider the impact of long-term severe sea conditions on the actual operating characteristics of equipment, resulting in significant deviations between the model and the actual situation; the planning phase is disconnected from the operation phase, making it difficult for the capacity configuration scheme determined during planning to maintain optimal economy and safety in actual dynamic operation; and the system lacks the ability to self-correct and evolve based on long-term operational feedback.

[0015] To address the shortcomings in related technologies, this invention proposes a method and apparatus for evaluating the DC bias magnetism of transformers in subway trains.

[0016] Figure 1 This is a flowchart illustrating an exemplary embodiment of a method for evaluating the DC bias magnetism of a transformer in a subway train. Figure 1 As shown, the method may include the following steps: Step 101: Construct a subway system model based on the traction power supply system and return system of the subway system, and construct a regional power grid model based on the substation grounding grid, transformers and transmission lines of the regional power grid; Step 102: Based on the relative spatial positions of the subway line and the AC power grid, couple the subway system model and the regional power grid model into a coupled simulation model of the subway line and the regional power grid.

[0017] The subway system model mainly includes the traction power supply system and the return power system.

[0018] Specifically, the subway system model equates the up and down contact networks to a continuous longitudinal uniform conductor, and the up and down rails to a single longitudinal conductor. The radius and resistance per unit length of a single longitudinal conductor are determined by the parallel conductor equivalence method.

[0019] Since the up and down contact networks are interconnected by busbars at the traction substations to form a continuous structure, and the traction substations supply power to the trains through the contact networks, the traction substations are equivalent to uniform conductors connecting the contact networks and rails, and the up and down contact networks are equivalent to continuous longitudinal uniform conductors. The subway return flow system mainly consists of up and down rails, drainage networks, and structural reinforcement. The rails serve as the main return flow structure; the two up or down rails are connected by flow-equalizing lines to form a longitudinal parallel structure. To reduce the number of equivalent conductors in the simulation model, according to the parallel equal conductor equivalence method, the two up or down rails can be equivalent to a single longitudinal conductor. The radius and resistance per unit length of the equivalent conductor satisfy the following formula: ; ; In the formula, For the number of parallel conductors, For the radius of a single conductor, Resistance per unit length of a single conductor.

[0020] Based on the distribution of insulation structure between the rails and the track bed, the "rail-to-ground" transition resistance is simulated by setting an insulating coating that acts as an equivalent conductor for the rails. Furthermore, using the parallel conductor equivalence method, the drainage network and structural reinforcement are respectively equated to single longitudinal conductors.

[0021] Based on the flow path of stray currents in the subway AC power grid, an equivalent model of the regional power grid needs to be constructed. The equivalent model of the regional power grid mainly includes the substation grounding grid, transformers, and transmission lines. First, based on the actual engineering structure of the substation grounding grid and its grounding resistance value, it is equivalently represented as a conductor grid in the simulation model. Second, since the neutral point of 110kV and above transformers is usually directly grounded, except for autotransformers, there is only magnetic connection between the high-voltage and low-voltage windings of the transformer, with no DC path. The equivalent diagrams of transformers with different winding structures are shown below. Figure 2 and Figure 3 As shown, therefore, only the transformer windings connected to the transmission line are considered when modeling.

[0022] Transmission lines, as the main path for the propagation of DC bias current from transformers in the AC power grid, are equivalent to conductors connecting the grounding main transformer windings of substations, based on their topology and connection structure. Finally, based on the equivalent modeling methods for the metro system and regional power grid system described above, and considering the spatial location of the metro lines and their relative position to the AC power grid, a coupled topology model of the metro and regional power grid was constructed. To simulate the actual coupling conditions between the metro lines and the regional power grid, the model includes two metro lines and 13 substations, with the following topology: Figure 4 As shown.

[0023] Step 103: Based on the train dynamics model, establish the functional relationship between the train's running position, speed, traction current and time between the two stations, and simulate the dynamic operating characteristics of a single train based on the functional relationship.

[0024] The train dynamics model calculates the train acceleration based on the kinetic energy theorem and the law of conservation of energy, and uses the numerical integration method to iteratively solve the real-time speed and position of the train. Then, it combines the traction force and the DC voltage of the bus to calculate the train traction current. The acceleration of the train in traction acceleration, constant speed, and braking deceleration states is calculated based on the net value of traction force, braking force and running resistance, respectively.

[0025] Step 104: Based on the simulated single-train operation characteristics, combined with subway line information, train stopping time and departure interval, the dynamic operation conditions of multiple trains on the subway line are simulated through time shifting and data overlay to generate dynamic operation characteristic curves that include the number of trains, their positions, operating status and traction current.

[0026] The distribution of stray currents in subways is closely related to the traction characteristics of the subway, and the traction current changes dynamically with the train's operating state (such as traction, constant speed, and braking). To accurately assess the DC bias current of the regional power grid transformer, it is necessary to simulate the multi-train operating conditions of an actual subway line.

[0027] Specifically, such as Figure 5 As shown, firstly, a train dynamics model is constructed. The operation of a subway train between two stations can be decomposed into three stages: traction acceleration, constant speed, and braking deceleration. Based on the kinetic energy theorem and the law of conservation of energy, the train acceleration can be obtained. As shown below: ; In the formula, Indicates the total mass of the train. Indicates the slewing mass coefficient. The equivalent mass that constitutes the train. This represents the net net force during train operation.

[0028] Train net net force satisfies the equation; ; In the formula, Indicates the train's traction force. Indicates the train's braking force. This indicates the resistance experienced by the train.

[0029] Therefore, we can determine the specific acceleration of the train under traction acceleration, constant speed, and braking deceleration conditions. As shown below: ; Based on this, according to the numerical integration method, in the distance step length By iteratively solving for the train's real-time speed and position, the train's speed at each moment can be obtained. and location As shown below: ; ; At the same time, the train traction current The calculation formula is as follows: ; In the formula, Indicates the train's traction force. This indicates the DC voltage of the bus.

[0030] Based on the aforementioned dynamic operation simulation method between train stations, and combining the distance between stations and train dwell time, full-length operation data for a single unidirectional run of a train on the complete line can be generated. Subsequently, by combining train departure intervals during peak or off-peak hours, and through time shifting and data overlay, the dynamic operation characteristics of all trains on the subway line can be simulated. This yields the dynamic operation characteristic curves of a specific subway line during peak or off-peak hours, such as... Figure 6 and Figure 7 As shown in the figure, the diagram mainly includes the number of trains on the line at each moment, the train's position, and its operating status. In addition to the overall train excitation operation characteristic diagram, the traction current value of each train at different times can be obtained based on the aforementioned train dynamics model.

[0031] However, the train excitation dynamic operation characteristic diagram only shows the train's travel distance and its complete dynamic operation characteristics at a certain moment. Efficiently and accurately inputting train operation data into the coordinate system required by the CDEGS coupled simulation model is a challenge in calculating the transformer's DC bias current. Therefore, this invention, based on linear interpolation and mapping methods, combined with a subway and regional power grid coupled simulation model, proposes a transformation between train travel distance and coordinates to accurately and efficiently output the train's dynamic excitation coordinates and traction current.

[0032] Step 105: Based on linear interpolation and mapping, convert the train travel distance in the dynamic operation characteristic curve into geographical coordinates, and output the traction current and coordinate information of all trains running on the line at a specified time, as multi-train dynamic operation data matching the coupled simulation model.

[0033] Specifically, the step of converting the train travel distance in the dynamic operating characteristic curve into geographic coordinates based on linear interpolation and mapping includes: obtaining a table containing the distances of each inflection point of the subway line and their corresponding latitude and longitude, and locating the distance values ​​of two adjacent inflection points in the table based on the train's travel distance at the target time; calculating the ratio of the travel distance to the distance between the two inflection points, and substituting the ratio into the linear interpolation formula to calculate the latitude and longitude coordinates of the train's location at the target time.

[0034] During the initialization phase, the system loads a table containing the distances to subway lines and their corresponding latitude and longitude. To simplify model building, the lines between each inflection point are depicted as straight lines during the coupled model modeling process. Therefore, when any given time point is input, based on the aforementioned multi-train dynamic excitation simulation system, the train's travel distance at that time point can be determined. The system first locates the distances to the boundary inflection points on both sides and calculates the distance ratio at that point. As shown below.

[0035] ; In the formula, For the target mileage value, The distance to the inflection point of the upper boundary. This represents the distance to the lower boundary inflection point.

[0036] Then this distance ratio By substituting the values ​​into the standard linear interpolation formula, the precise latitude and longitude of the train's location on the track at that moment can be calculated, as shown below: ; ; In the formula, , These are the target longitude and latitude, respectively. , For the fine longitude and latitude of the upper boundary inflection point, , These are the longitude and latitude of the lower boundary inflection point, respectively.

[0037] To obtain the equivalent excitation information of multiple trains on the line at a certain simulation moment, the complete dynamic operation characteristics of each train on the line are first simulated based on the aforementioned multi-train dynamic excitation simulation system, combined with information such as the distance between stations on the line, train dwell time, and departure interval. A table file of subway line distances and corresponding latitude and longitude is constructed and input into the multi-train dynamic excitation simulation system for subsequent calculation and output of train excitation latitude and longitude.

[0038] Therefore, the system obtains the target time point. Then, trains that have not yet departed or have already finished running are excluded, and the trains at that time are selected. All trains currently running on the line, obtain the travel distance and operating status of each train ( , By calling the coordinate transformation method described above, the system converts all the train travel distances into precise latitude and longitude coordinates, outputs the traction current and coordinates of all trains into a table, and processes the output latitude and longitude coordinates in batches into (x, y, z) coordinate form.

[0039] According to the excitation setting method of the coupled simulation model, each train needs to be equipped with a corresponding equivalent train excitation conductor: two current excitations are set to represent a single train, one set on the contact wire at the train's location and the other set on the rail at the corresponding location. The absolute values ​​of these two current excitations are equal, but their directions are opposite. These are then input into the coupled simulation model in batches and the calculations are performed.

[0040] Traditional simulation models of subway-regional power grid coupling often rely on manually setting train excitations, and existing studies typically consider only a small number of trains, primarily for analyzing the flow characteristics of DC bias current. However, to accurately assess the DC bias current level of transformers in a regional power grid, it is necessary to simulate actual operating conditions with multiple trains and high density. In such complex scenarios, traditional manual setting methods struggle to balance the efficiency and accuracy of simulation calculations. Through the optimization and improvement of the aforementioned system, multi-train operating status data at corresponding time points can be output instantly and accurately, and directly input into the simulation model, ensuring the efficiency and accuracy of DC bias current calculation.

[0041] Step 106: Input the dynamic operation data of the multiple trains into the coupled simulation model, and calculate and evaluate the DC bias current of the transformer in the regional power grid.

[0042] Specifically, the calculation and evaluation of the DC bias current of transformers in the regional power grid includes: using a specific time period of subway operation that includes a complete train departure interval as the simulation period; within the simulation period, iteratively executing the operation of generating multi-train dynamic operation data and input coupling simulation model for calculation at a preset time step, so as to obtain the DC bias current values ​​of transformers in each substation at different times within the simulation period; and statistically analyzing the minimum, maximum and fluctuation range of the DC bias current of each transformer within the simulation period to evaluate its DC bias level.

[0043] This specific time period can be the morning or evening peak hours of subway operation.

[0044] Multi-physical quantity measurements were performed on transformers exhibiting DC bias in the urban AC power grid, covering DC bias current, noise, and vibration. Analysis of the measurement data revealed a high degree of consistency between the variation characteristics of these physical quantities and the subway line's operating schedule, confirming that the DC bias phenomenon in the transformers was caused by stray current intrusion from the subway. The variation range of the transformer's DC bias current is as follows: Figure 8 As shown, the DC bias current exhibits significant fluctuations, with its peak value consistently occurring during the morning and evening rush hours of the subway. This indicates that during peak periods with dense train operations, stray currents cause the most severe intrusion into the regional power grid, resulting in the DC bias current in the substation grounding transformer reaching its maximum value for the day.

[0045] Therefore, to quantitatively evaluate the DC bias current level of transformers in the regional power grid, this invention, based on the aforementioned coupled topology model of the ground and the regional power grid, and combined with a multi-train dynamic excitation simulation and output system, selects a departure interval during peak train operation periods (7:00-9:00 or 18:00-19:30) as the simulation period. Taking the established coupled model as an example, the train's running time between two stations in this model is mainly 120s-160s, and the departure intervals on different lines are 240s-300s. To include the train's operating status between different stations, the simulation step size is set to 10s. The specific evaluation calculation process is as follows: First, the initial multi-train dynamic excitation simulation and output system is input with a table containing the distance of the subway line and the corresponding latitude and longitude, and train operation data during peak hours is generated.

[0046] The train operation characteristic curve during peak hours was analyzed, and the total simulation duration was determined based on the maximum departure interval. The moment with the highest number of trains on the line during peak hours was selected as the starting point for the simulation.

[0047] The selected time points are input into the system. After calculation and filtering in step 105, the system outputs the operating data of all trains at that moment in real time. These data are then input into the coupled model in batches, and the DC bias current values ​​of each substation at that moment are recorded.

[0048] by ( The algorithm iterates through multiple trains' dynamic operation data and inputs them into a coupled simulation model using a sequence of steps (e.g., 1, 2, 3, ...) with a step size of 10 seconds. This process is repeated throughout the complete departure interval cycle. Then, all the current values ​​calculated during this period are statistically analyzed, and the minimum, maximum and fluctuation range of the DC bias current of each transformer in the regional power grid are analyzed to complete the evaluation.

[0049] Following the above calculation procedure, the DC bias current of the transformers in the constructed regional power grid was simulated. Figure 9 Taking the simulation results of a substation as an example, the variation characteristics of its DC bias current are consistent with the fluctuation trend of the actual operating state of subway trains. To further verify the accuracy of the model, the simulation results were compared with the aforementioned measured data. The results show that the relative error between the simulated peak value and the measured peak value is less than 16%, which meets the requirements of practical engineering applications. Furthermore, the traditional simulation method requires 900 minutes to complete all simulation calculations, while this method only takes 450 minutes. This confirms the effectiveness of the proposed coupled simulation model and the multi-train dynamic excitation simulation and output system, providing reliable data support for subsequent transformer DC bias assessment and mitigation.

[0050] The proposed method for evaluating the DC bias magnetism of transformers in subway trains addresses two main issues. First, it quantifies the reliability of multi-source data using a cross-source consistency entropy model and uses this as a basis for equipment parameter inversion. This makes the physical model upon which planning relies more closely resemble the actual operating characteristics of complex marine environments, improving the accuracy of the planning model. Second, it explicitly introduces exogenous disturbances such as wind speed and wave height into the energy flow model, establishing an uncertain energy flow propagation model. This model can quantitatively assess the risk exposure levels of different energy links, enabling the final capacity planning scheme to predict and avoid high-risk scenarios in advance, making the decision more robust. Furthermore, the constructed comprehensive objective function simultaneously considers construction costs, operating costs, and risk loss costs caused by sea state uncertainties, achieving a comprehensive optimization of economy and safety. This avoids the drawback of traditional planning methods that prioritize economy while neglecting operational risks.

[0051] Figure 10 This is a schematic structural diagram of a device provided in an exemplary embodiment. Please refer to... Figure 10 At the hardware level, the device includes a processor 1002, an internal bus 1004, a network interface 1006, memory 1008, and non-volatile memory 1010, and may also include other hardware required for its functions. One or more embodiments of the present invention can be implemented in software, for example, the processor 1002 reads the corresponding computer program from the non-volatile memory 1010 into the memory 1008 and then runs it. Of course, in addition to software implementation, one or more embodiments of the present invention do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0052] Please refer to Figure 11 A transformer DC bias evaluation device for subway trains can be applied to, for example... Figure 11 The device shown, in order to implement the technical solution of the present invention, includes: Construction unit 1101 is used to construct a subway system model based on the traction power supply system and return system of the subway system, and to construct a regional power grid model based on the substation grounding grid, transformer and transmission line of the regional power grid. The coupling unit 1102 is used to couple the metro system model and the regional power grid model into a coupled simulation model of metro lines and regional power grids based on the relative spatial positions of the metro lines and the AC power grid. The first simulation unit 1103 is used to establish the functional relationship between the train's running position, speed, traction current and time between two stations based on the train dynamics model, and to simulate the dynamic operating characteristics of a single train based on the functional relationship. The second simulation unit 1104 is used to simulate the dynamic operating conditions of multiple trains on the subway line based on the simulated single train operation characteristics, combined with subway line information, train stopping time and departure interval, through time shifting and data superposition, so as to generate dynamic operating characteristic curves including the number of trains, their positions, operating status and traction current. Output unit 1105 is used to convert the train travel distance in the dynamic operation characteristic curve into geographical coordinates based on linear interpolation and mapping method, and output the traction current and coordinate information of all trains running on the line at a specified time as multi-train dynamic operation data matching the coupled simulation model. Evaluation unit 1106 is used to input the dynamic operation data of the multiple trains into the coupled simulation model to calculate and evaluate the DC bias current of the transformer in the regional power grid.

[0053] Optionally, the subway system model equates the up and down contact networks to a continuous longitudinal uniform conductor, and equates the up and down rails to a single longitudinal conductor. The radius and resistance per unit length of the single longitudinal conductor are determined by the parallel conductor equivalence method.

[0054] Optionally, the output unit 1105 is specifically used for: Obtain a table containing the distances of each turning point of the subway line and their corresponding latitude and longitude, and locate the distance values ​​between two adjacent turning points in the table based on the train's travel distance at the target time. Calculate the ratio of the travel distance to the distance between the two inflection points, and substitute the ratio into the linear interpolation formula to calculate the latitude and longitude coordinates of the train's location at the target time.

[0055] Optionally, the evaluation unit 1106 is specifically used for: The simulation period is a specific time period in subway operation that includes a complete train departure interval. Within the simulation cycle, the operation of generating multi-train dynamic operation data and input coupling simulation model is iteratively executed at a preset time step to obtain the DC bias current value of each substation transformer at different times within the simulation cycle. The minimum, maximum, and fluctuation range of the DC bias current of each transformer during the simulation period were statistically analyzed to evaluate its DC bias level.

[0056] Furthermore, the specific time period refers to the morning or evening peak hours of subway operation.

[0057] Optionally, when inputting the multi-train dynamic operation data into the coupled simulation model in batches, the device further includes: The setting unit 1107 is used to set two equivalent current excitations for each train. One is set on the contact wire at the train position, and the other is set on the rail at the corresponding position. The absolute values ​​of the two current excitations are equal and their directions are opposite.

[0058] Optionally, the train dynamics model calculates the train acceleration based on the kinetic energy theorem and the law of conservation of energy, and uses the numerical integration method to iteratively solve the real-time speed and position of the train. Then, it combines the traction force and the DC voltage of the bus to calculate the train traction current. The acceleration of the train in traction acceleration, constant speed, and braking deceleration states is calculated based on the net value of traction force, braking force and running resistance, respectively.

[0059] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer, which can take the form of a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0060] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0061] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0062] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0063] For any other form of computer-readable medium (or computer-readable storage medium) as described above, computer instructions may be stored thereon, which, when executed by a processor, implement one or more of the above embodiments, thereby realizing the technical solution of the present invention.

[0064] The present invention also proposes a computer program that, when executed by a processor, implements one or more of the embodiments described above, thereby realizing the technical solution of the present invention. This computer program may be specifically recorded on the above-described or other computer-readable media, and the present invention does not impose any limitations on this.

[0065] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0066] The foregoing has described specific embodiments of the invention. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0067] The terminology used in one or more embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” used in one or more embodiments of the invention and in the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0068] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of the present invention, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of the present invention, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0069] The above description is merely a preferred embodiment of one or more embodiments of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of the present invention should be included within the protection scope of one or more embodiments of the present invention.

Claims

1. A method for evaluating DC bias of a transformer for a subway train, characterized in that, include: A subway system model is constructed based on the traction power supply system and return system of the subway system, and a regional power grid model is constructed based on the substation grounding grid, transformers and transmission lines of the regional power grid. Based on the relative spatial location of the subway line and the AC power grid, the subway system model and the regional power grid model are coupled into a coupled simulation model of the subway line and the regional power grid. Based on the train dynamics model, the functional relationship between the train's running position, speed, traction current and time between two stations is established, and the dynamic operating characteristics of a single train are simulated based on the functional relationship. Based on the simulated single-train operation characteristics, combined with subway line information, train stopping time and departure interval, the dynamic operation conditions of multiple trains on the subway line are simulated through time shifting and data overlay to generate dynamic operation characteristic curves that include the number of trains, their location, operating status and traction current. Based on linear interpolation and mapping, the train travel distance in the dynamic operation characteristic curve is converted into geographical coordinates, and the traction current and coordinate information of all trains running on the line at a specified time are output as multi-train dynamic operation data that matches the coupled simulation model. The dynamic operation data of the multiple trains are input into the coupled simulation model to calculate and evaluate the DC bias current of the transformer in the regional power grid.

2. The method of claim 1, wherein, The subway system model equates the up and down contact networks to a continuous longitudinal uniform conductor, and the up and down rails to a single longitudinal conductor. The radius and resistance per unit length of the single longitudinal conductor are determined by the parallel conductor equivalence method.

3. The method of claim 1, wherein, The method of converting the train travel distance in the dynamic operating characteristic curve into geographical coordinates based on linear interpolation and mapping includes: Obtain a table containing the distances of each turning point of the subway line and their corresponding latitude and longitude, and locate the distance values ​​between two adjacent turning points in the table based on the train's travel distance at the target time. Calculate the ratio of the travel distance to the distance between the two inflection points, and substitute the ratio into the linear interpolation formula to calculate the latitude and longitude coordinates of the train's location at the target time.

4. The method of claim 1, wherein, The calculation and evaluation of the DC bias current of transformers in the regional power grid includes: The simulation period is a specific time period in subway operation that includes a complete train departure interval. Within the simulation cycle, the operation of generating multi-train dynamic operation data and input coupling simulation model is iteratively executed at a preset time step to obtain the DC bias current value of each substation transformer at different times within the simulation cycle. The minimum, maximum, and fluctuation range of the DC bias current of each transformer during the simulation period were statistically analyzed to evaluate its DC bias level.

5. The method of claim 4, wherein, The specific time period refers to the morning or evening peak hours of subway operation.

6. The method of claim 1, wherein, When inputting the dynamic operation data of multiple trains into the coupled simulation model in batches, the method further includes: Two equivalent current excitations are set for each train: one is set on the contact wire at the train's location, and the other is set on the rail at the corresponding location. The absolute values ​​of the two current excitations are equal, but their directions are opposite.

7. The method according to claim 1, characterized in that, The train dynamics model calculates the train acceleration based on the kinetic energy theorem and the law of conservation of energy, and uses the numerical integration method to iteratively solve the real-time speed and position of the train. Then, it combines the traction force and the DC voltage of the bus to calculate the train traction current. The acceleration of the train in traction acceleration, constant speed, and braking deceleration states is calculated based on the net value of traction force, braking force and running resistance, respectively.

8. A transformer DC bias magnetism evaluation device for subway trains, characterized in that, include: Construction Units: The subway system model is constructed based on the traction power supply system and return system of the subway system, and the regional power grid model is constructed based on the substation grounding grid, transformers and transmission lines of the regional power grid. Coupling unit: Based on the relative spatial position of the subway line and the AC power grid, the subway system model and the regional power grid model are coupled into a coupled simulation model of the subway line and the regional power grid; First simulation unit: Based on the train dynamics model, establish the functional relationship between the train's running position, speed, traction current and time between two stations, and simulate the dynamic operating characteristics of a single train based on the functional relationship; The second simulation unit: Based on the simulated single-train operation characteristics, combined with subway line information, train stopping time and departure interval, the dynamic operation conditions of multiple trains on the subway line are simulated through time shifting and data overlay to generate dynamic operation characteristic curves that include the number of trains, their positions, operating status and traction current. Output unit: Based on linear interpolation and mapping, the train travel distance in the dynamic operation characteristic curve is converted into geographical coordinates, and the traction current and coordinate information of all trains running on the line at a specified time are output as multi-train dynamic operation data that matches the coupled simulation model; Evaluation Unit: Input the dynamic operation data of the multiple trains into the coupled simulation model to calculate and evaluate the DC bias current of the transformer in the regional power grid.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor implements the steps of the method as described in any one of claims 1-7 by running the executable instructions.

10. A computer-readable storage medium storing computer instructions thereon, characterized in that, When executed by the processor, this instruction implements the steps of the method as described in any one of claims 1-7.

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