Converter station simulation method, device and equipment, storage medium and program product

Abstract

The embodiment of the invention provides a simulation method and device of a converter station, equipment, a storage medium and a program product, relates to the technical field of electric power, and can improve the simulation accuracy of the converter station. The simulation method of the converter station comprises the following steps: acquiring operation data of the converter station; adjusting a circuit model of the converter station based on the operation data; adjusting a thermal field model and a magnetic field model of the converter station based on the adjusted circuit model; and performing simulation processing on the adjusted circuit model, thermal field model and magnetic field model to generate multi-physical field distribution information of the converter station under different operation conditions.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a simulation method, device, equipment, storage medium and program product of a converter station. Background Art

[0002] With the continuous development of the electric energy field, flexible direct current transmission technology has gradually become one of the important means of power transmission due to its advantages such as high efficiency, flexibility and reliability. As the core component of the flexible direct current transmission system, the flexible direct current converter station is responsible for the key tasks of power conversion and transmission. It can convert direct current into alternating current or alternating current into direct current, thereby realizing the interconnection between power systems of different voltage levels and frequencies. In addition, the flexible direct current converter station has the ability to quickly adjust power, which not only enhances the stability and reliability of the power grid, but also promotes the large-scale access of renewable energy, providing strong support for the development of clean energy.

[0003] The operation of flexible DC converter stations involves complex interactions of multiple physical fields, such as electric field, magnetic field, and thermal field. The distribution and changes of these physical fields are crucial to the overall performance and reliability of the converter station. The distribution of the electric field determines the insulation performance inside the converter station and the safe operation of the electrical equipment; the change of the magnetic field affects the electromagnetic compatibility of the converter station and the loss of the equipment; and the distribution of the thermal field is directly related to the service life and reliability of the equipment. In order to accurately evaluate the performance and reliability of the flexible DC converter station under different operating conditions, it is necessary to simulate and evaluate the flexible DC converter station based on multiple physical fields.

[0004] However, the simulation method of the converter station in the related art still has some shortcomings during use. On the one hand, during the simulation process, the model parameters and structure of the converter station are usually fixed, and lack the function of automatic adjustment according to the actual operating status, resulting in a certain gap between the simulation results and the actual situation. On the other hand, the coupling relationship between different physical fields is relatively simple, and it is difficult to achieve real-time collaborative simulation of multiple physical fields, making it difficult to comprehensively evaluate the performance and reliability of the converter station under complex working conditions. Summary of the invention

[0005] In order to solve the above problems, the present application provides a simulation method, device, equipment, storage medium and program product for a converter station.

[0006] In a first aspect, a simulation method for a converter station is provided, comprising:

[0007] Obtaining the operation data of the converter station;

[0008] Adjust the circuit model of the converter station based on the operating data;

[0009] Adjust the thermal field model and magnetic field model of the converter station based on the adjusted circuit model;

[0010] The adjusted circuit model, thermal field model and magnetic field model are simulated to generate multi-physical field distribution information of the converter station under different operating conditions.

[0011] Furthermore, the operation data includes: historical operation data, real-time operation data;

[0012] Adjust the circuit model of the converter station based on the operating data, including:

[0013] Determine load change information of the converter station based on historical operation data and real-time operation data;

[0014] Model parameters of the circuit model are adjusted based on the load change information so that the circuit model represents the current electrical characteristics of the converter station, and the model parameters include at least one of the following: resistance, inductance, and capacitance.

[0015] Further, adjusting the thermal field model and magnetic field model of the converter station based on the adjusted circuit model includes:

[0016] Obtaining model parameters after adjustment of the circuit model;

[0017] The thermal field model and the magnetic field model are adjusted based on the adjusted model parameters.

[0018] Further, adjusting the thermal field model and the magnetic field model based on the adjusted model parameters includes:

[0019] Determine current change information of the circuit in the converter station based on the adjusted model parameters;

[0020] Determining heat source term change information of the circuit in the converter station based on the current change information;

[0021] Adjust the temperature distribution in the thermal field model based on the change information of the heat source term.

[0022] Further, adjusting the thermal field model and the magnetic field model based on the adjusted model parameters includes:

[0023] Determine current change information of the circuit in the converter station based on the adjusted model parameters;

[0024] The magnetic field distribution in the magnetic field model is adjusted based on the current change information.

[0025] Furthermore, the adjusted circuit model, thermal field model and magnetic field model are simulated to generate multi-physical field distribution information of the converter station under different operating conditions, including:

[0026] Determine the solution algorithm based on the obtained simulation requirements;

[0027] Based on the solution algorithm, the adjusted circuit model, thermal field model and magnetic field model are coupled and solved to generate multi-physical field distribution information of the converter station under different operating conditions. The coupled solution includes: sequential coupled solution and direct coupled solution.

[0028] In a second aspect, the present application provides a simulation device for a converter station, comprising:

[0029] An acquisition module, used to acquire the operation data of the converter station;

[0030] A first adjustment module, used for adjusting a circuit model of a converter station based on operation data;

[0031] A second adjustment module, used for adjusting the thermal field model and the magnetic field model of the converter station based on the adjusted circuit model;

[0032] The processing module is used to simulate the adjusted circuit model, thermal field model and magnetic field model to generate multi-physical field distribution information of the converter station under different operating conditions.

[0033] Furthermore, the operation data includes: historical operation data, real-time operation data;

[0034] The first adjustment module includes:

[0035] A load determination unit, used to determine load change information of a converter station based on historical operation data and real-time operation data;

[0036] The circuit adjustment unit is used to adjust the model parameters of the circuit model based on the load change information so that the circuit model represents the current electrical characteristics of the converter station, and the model parameters include at least one of the following: resistance, inductance, and capacitance.

[0037] Furthermore, the second adjustment module includes:

[0038] A parameter acquisition unit, used to acquire the model parameters after the circuit model is adjusted;

[0039] The first adjustment unit is used to adjust the thermal field model and the magnetic field model based on the adjusted model parameters.

[0040] Furthermore, the first adjustment unit includes:

[0041] The thermal field adjustment unit is specifically used for:

[0042] Determine current change information of the circuit in the converter station based on the adjusted model parameters;

[0043] Determining heat source term change information of the circuit in the converter station based on the current change information;

[0044] Adjust the temperature distribution in the thermal field model based on the change information of the heat source term.

[0045] Furthermore, the first adjustment unit includes:

[0046] The magnetic field adjustment unit is specifically used for:

[0047] Determine current change information of the circuit in the converter station based on the adjusted model parameters;

[0048] The magnetic field distribution in the magnetic field model is adjusted based on the current change information.

[0049] Furthermore, the processing module comprises:

[0050] A solution determination unit, used to determine a solution algorithm based on the obtained simulation requirements;

[0051] The coupling solution unit is used to perform coupling solution processing on the adjusted circuit model, thermal field model and magnetic field model based on the solution algorithm to generate multi-physical field distribution information of the converter station under different operating conditions. The coupling solution processing includes: sequential coupling solution processing and direct coupling solution processing.

[0052] In a third aspect, the present application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the simulation method of the converter station when executing the program.

[0053] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned converter station simulation method.

[0054] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which implements the steps of the above-mentioned converter station simulation method when executed by a processor.

[0055] In this way, the circuit model of the converter station is adjusted through the operating data of the converter station, so that the circuit model can characterize the current electrical characteristics of the converter station. Furthermore, the thermal field model and magnetic field model associated with the adjusted circuit model are adjusted so that the above models can characterize the real-time operating status of the converter station, improve the accuracy of the simulation, and avoid deviations between the simulation results and the actual situation. Finally, the three adjusted models are simulated to generate multi-physical field distribution information of the converter station under different operating conditions, realizing the collaborative real-time simulation of multiple physical fields, accurately reflecting the interaction between different physical fields, and providing a more comprehensive and accurate reference for the design, operation and maintenance of the converter station. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate some embodiments of this specification or technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0057] Figure 1 A schematic diagram of a flow chart of a simulation method for a converter station provided in an embodiment of the present application;

[0058] Figure 2 A schematic flow chart of another converter station simulation method provided in an embodiment of the present application;

[0059] Figure 3 A schematic flow chart of another converter station simulation method provided in an embodiment of the present application;

[0060] Figure 4 A schematic flow chart of another converter station simulation method provided in an embodiment of the present application;

[0061] Figure 5 A schematic flow chart of another converter station simulation method provided in an embodiment of the present application;

[0062] Figure 6 A schematic flow chart of another converter station simulation method provided in an embodiment of the present application;

[0063] Figure 7 A schematic diagram of the structure of a simulation device for a converter station provided in an embodiment of the present application;

[0064] Figure 8 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in some embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on some embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0066] It should be noted that the terms "first", "second", etc. in the specification and claims of this document and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of this document described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment. It should be noted that the acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of relevant laws and regulations.

[0067] As described in the background technology, the current converter station simulation technology still has certain shortcomings in the actual application process. For example, in the current simulation technology, the model parameters and structure of the converter station are usually fixed, and cannot be adjusted according to the actual operating status of the converter station, resulting in a certain deviation between the final simulation results and the actual situation. In addition, during the operation of the converter station, multiple physical fields are involved, and in the current simulation technology, the coupling relationship between different physical fields is relatively simple, and it is impossible to achieve real-time collaborative simulation of multiple physical fields, resulting in the inability to fully evaluate the performance and reliability of the converter station under complex working conditions.

[0068] Based on this, the present application provides a simulation method for a converter station, the idea of ​​which is to adjust the power model of the converter station through the operation data of the converter station to avoid a certain deviation between the final simulation result and the actual situation. The adjusted power model is simulated to generate multi-physical field distribution information of the converter station under different operating conditions, and realize real-time collaborative simulation of multiple physical fields.

[0069] Figure 1 A flow chart of a simulation method for a converter station provided in an embodiment of the present application is provided, which is applied to a simulation device for a converter station, such as Figure 1 As shown, the following steps are included:

[0070] S101. Obtain operation data of a converter station.

[0071] S102. Adjust the circuit model of the converter station based on the operation data.

[0072] S103. Adjust the thermal field model and magnetic field model of the converter station based on the adjusted circuit model.

[0073] S104, simulating the adjusted circuit model, thermal field model and magnetic field model to generate multi-physical field distribution information of the converter station under different operating conditions.

[0074] In this way, the circuit model of the converter station is adjusted through the operating data of the converter station, so that the circuit model can characterize the current electrical characteristics of the converter station. Furthermore, the thermal field model and magnetic field model associated with the adjusted circuit model are adjusted so that the above models can characterize the real-time operating status of the converter station, improve the accuracy of the simulation, and avoid deviations between the simulation results and the actual situation. Finally, the three adjusted models are simulated to generate multi-physical field distribution information of the converter station under different operating conditions, realizing the collaborative real-time simulation of multiple physical fields, accurately reflecting the interaction between different physical fields, and providing a more comprehensive and accurate reference for the design, operation and maintenance of the converter station.

[0075] The following is an explanation of each of the above steps:

[0076] S101. Obtain operation data of a converter station.

[0077] In some embodiments, the operation data of the converter station is collected by sensors through a simulation device of the converter station.

[0078] The sensor includes at least one of the following: a voltage sensor, a current sensor, a temperature sensor, and a magnetic field strength sensor.

[0079] Specifically, multiple high-precision multi-type sensors can be installed at key equipment and locations of the converter station and arranged reasonably. For example, multiple current sensors are set at important busbar connections of the converter station to monitor current changes at different locations, and temperature sensors are arranged at key nodes of the converter valves of the converter station to monitor the temperature status of the converter valves. For another example, multiple temperature sensors and magnetic field strength sensors are set near the converter valves of the converter station to monitor the thermal and magnetic field changes of the converter valves during operation, and voltage sensors and current sensors are installed at the windings and cores of the transformer to monitor the electrical parameters of the transformer. In this way, by installing multiple sensors at key equipment and locations of the converter station, various operating data and changes of the converter station under different operating conditions can be obtained more accurately.

[0080] As a possible implementation manner, the collection frequency of the operating data may be set according to the characteristics of different physical parameters and the changing characteristics of different physical parameters.

[0081] For example, for physical parameters that may change rapidly, such as current, current and other physical parameters that may change transiently, a higher acquisition frequency can be set to ensure that the instantaneous changes of the physical parameters can be collected. For relatively stable physical parameters, such as temperature parameters in thermal fields, magnetic field strength parameters in magnetic fields, etc., a lower acquisition frequency can be set to reduce the amount of data and storage pressure, saving related resources.

[0082] In some embodiments, in order to ensure the accuracy of the operating data, in the process of acquiring the operating data, signal conditioning technology and data calibration technology are used to assist in collecting the operating data.

[0083] Exemplarily, weak sensor signals are amplified by signal amplifiers, and the accuracy of sensors is checked and adjusted through regular calibration procedures to ensure the accuracy of operation data collection. The processed operation data is transmitted to the simulation device of the converter station through data transmission protocols and data transmission channels, and stored in corresponding storage devices, and stored and managed through corresponding database management systems to improve the timeliness of operation data, ensure the integrity of operation data, and facilitate query and analysis of operation data.

[0084] In another exemplary embodiment, the collected operation data is filtered by a digital filter to remove high-frequency noise and improve the signal-to-noise ratio of the operation data. The accuracy of the sensor is checked and adjusted through a regular calibration procedure to ensure the accuracy of the collection of the operation data. The processed operation data is transmitted to the simulation device of the converter station through a data transmission protocol and a data transmission channel. For example, high-speed transmission of the operation data can be achieved through Ethernet or optical fiber communication technology. At the same time, a data check and error retransmission mechanism is used to ensure that the operation data is not lost or damaged during the transmission process, thereby ensuring the integrity of the operation data. The data is stored in a corresponding storage device and stored and managed through a corresponding database management system to facilitate query and analysis of the operation data. Data backup and recovery technology can also be used to prevent data loss.

[0085] In some embodiments, after the operation data is acquired, the operation data is preprocessed to ensure the reliability of the operation data and improve the data quality of the operation data.

[0086] Exemplarily, after the operation data is acquired, the operation data is subjected to denoising and outlier processing, and the noise interference in the operation data is removed by digital filtering, data smoothing and other technical means to ensure the accuracy and reliability of the data. For outliers in the operation data, identification and processing can be performed based on statistical analysis methods, such as correcting or eliminating data that exceeds a certain range based on the mean and standard deviation method. Alternatively, data that exceeds a certain range can be marked as outliers and further checked and processed.

[0087] S102. Adjust the circuit model of the converter station based on the operation data.

[0088] It should be noted that in order to avoid a certain deviation between the simulation results and the actual situation, it is necessary to adjust the circuit model according to the operation data of the converter station so that the circuit model represents the current electrical characteristics of the converter station.

[0089] S103. Adjust the thermal field model and magnetic field model of the converter station based on the adjusted circuit model.

[0090] It is understandable that in the converter station, there is a close interconnection between the circuit model and the thermal field model and the magnetic field model, and the three work together to affect the performance of the converter station. For example, the influence between the circuit model and the thermal field model is as follows: the resistors and other components in the circuit will generate power loss when the current passes through, and this part of the power loss will be converted into heat, affecting the temperature distribution of the converter station. The influence between the circuit model and the magnetic field model is as follows: the current in the circuit will generate a magnetic field, affecting the electromagnetic environment of the converter station. Based on this, by obtaining the model parameters after the circuit model is adjusted, adjusting the thermal field model and the magnetic field model, it is possible to capture the interaction between different physical fields, and make corresponding adjustments and simulations to achieve multi-physics collaborative simulation.

[0091] S104, simulating the adjusted circuit model, thermal field model and magnetic field model to generate multi-physical field distribution information of the converter station under different operating conditions.

[0092] Among them, the multi-physical field distribution information of the converter station under different operating conditions is the simulation result, which realizes the real-time collaborative simulation of multiple physical fields and improves the accuracy and reliability of the simulation.

[0093] In some embodiments, the operation data includes: historical operation data and real-time operation data. Figure 2 As shown, the above adjustment of the circuit model based on the operating data can be specifically implemented as the following steps:

[0094] S201. Determine load change information of a converter station based on historical operation data and real-time operation data.

[0095] As a possible implementation method, historical operation data and real-time operation data can be analyzed through a deep neural network model to determine the load change information of the converter station.

[0096] For example, the load change information of the converter station can be determined by learning and analyzing the historical operation data and the real-time operation data through the forward propagation process of the deep neural network model. The deep neural network model is implemented based on the following formula:

[0097] y=f(X;W,b)

[0098] Among them, y is the model output, f is the calculation function, which represents the calculation process of the neural network, X is the input data, W is the weight in the neural network, and b is the bias in the neural network.

[0099] S202: Adjust model parameters of the circuit model based on the load change information so that the circuit model represents the current electrical characteristics of the converter station.

[0100] The model parameters include at least one of the following: resistance, inductance, and capacitance.

[0101] Exemplarily, a relationship between the model parameters of the converter station and the circuit model is established through a deep neural network model. After the load change information is obtained, it is detected that the load of the converter station has changed, and the model parameters of the circuit model can be adjusted so that the circuit model represents the actual electrical characteristics of the converter station.

[0102] It is understandable that the deep neural network model can be trained through the mean square error loss function, and the weights and biases of the network can be continuously adjusted through the back propagation algorithm. The learning rate α controls the parameter update step size to ensure that the model can continuously learn and optimize. The specific formula is as follows:

[0103] Mean square error loss function:

[0104]

[0105] in, is the mean square error loss function, y is the vector of true labels, is the vector predicted by the model, n is the number of samples, y i is the true label of the t-th sample, and is the predicted value of the ith sample.

[0106] The calculation formula for parameter update is:

[0107]

[0108] Among them, W t is the weight at the tth iteration, b t is the bias at the tth iteration. t+1 is the weight at the t+1th iteration, b t+1 is the bias at the t+1th iteration, α is the learning rate, is the partial derivative of the loss function with respect to the weight, is the partial derivative of the loss function with respect to the bias.

[0109] In this way, the deep neural network model can be used to learn and analyze the operating data to determine the load changes of the converter station, so as to adjust the model parameters of the circuit model to more accurately represent the current electrical characteristics of the converter station. In actual applications, such as when the converter station is conducting different load tests, the deep neural network model can improve the efficiency and accuracy of adjusting the model parameters of the circuit model.

[0110] In some embodiments, Figure 3 As shown, adjusting the thermal field model and the magnetic field model based on the adjusted circuit model can be specifically implemented as the following steps:

[0111] S301, obtaining model parameters after adjustment of the circuit model.

[0112] It is understandable that in the converter station, there is a close interconnection between the circuit model and the thermal field model and the magnetic field model, and the three work together to affect the performance of the converter station. For example, the influence between the circuit model and the thermal field model is as follows: the resistors and other components in the circuit will generate power loss when the current passes through, and this part of the power loss will be converted into heat, affecting the temperature distribution of the converter station. The influence between the circuit model and the magnetic field model is as follows: the current in the circuit will generate a magnetic field, affecting the electromagnetic environment of the converter station. Based on this, by obtaining the model parameters after the circuit model is adjusted, adjusting the thermal field model and the magnetic field model, it is possible to capture the interaction between different physical fields, and make corresponding adjustments and simulations to achieve multi-physics collaborative simulation.

[0113] S302 , adjusting the thermal field model and the magnetic field model based on the adjusted model parameters.

[0114] In some embodiments, Figure 4 As shown, adjusting the thermal field model and the magnetic field model based on the adjusted model parameters can be specifically implemented as the following steps:

[0115] S401. Determine current change information of a circuit in a converter station based on adjusted model parameters.

[0116] It is understandable that when the current changes, the power loss generated by the circuit will also change, and the heat generated will also change. Therefore, it is necessary to determine the current change information in the circuit based on the model parameters in order to further adjust the thermal field model. For example, when the converter station is conducting a high load test, the current increases, the power loss generated in the circuit also increases, and the heat generated also increases.

[0117] S402: Determine heat source item change information of the circuit in the converter station based on the current change information.

[0118] Exemplarily, when a change occurs in a circuit, the power loss generated in the circuit is determined based on the current change information, and the power loss is used as a heat source item.

[0119] S403: Adjust the temperature distribution in the thermal field model based on the heat source term change information.

[0120] In some embodiments, the temperature distribution in the thermal field model is adjusted based on the information of the change of the heat source term through the heat conduction equation. The heat conduction equation is as follows:

[0121]

[0122] Where ρ is the density of the material, c is the specific heat capacity of the material, T is the temperature, and t is the time. is the gradient operator, k is the thermal conductivity, is the temperature gradient, is the heat flux and Q is the heat source term.

[0123] For example, based on the formula Combined with the change information of the heat source term, the changed temperature distribution is calculated, and then the temperature distribution of the thermal field model is adjusted according to the changed temperature distribution. Among them, Q is the heat source term, p is the power loss, and v is the volume.

[0124] It should be noted that in solving the heat conduction equation, adaptive grid technology is used to automatically adjust the density of the grid according to the temperature gradient to improve the calculation accuracy and efficiency. Among them, the material density ρ, the material specific heat capacity c and the thermal conductivity k are determined according to the physical characteristics of different materials in the converter station and can be set according to actual conditions.

[0125] In some embodiments, Figure 5 As shown, adjusting the thermal field model and the magnetic field model based on the adjusted model parameters can be specifically implemented as the following steps:

[0126] S501. Determine current change information of a circuit in a converter station based on adjusted model parameters.

[0127] It is understandable that the current in the circuit will generate a magnetic field, which will affect the electromagnetic environment of the converter station. Therefore, it is necessary to determine the current change information in the circuit based on the model parameters in order to further adjust the magnetic field model. For example, when the current in the circuit changes, its current value and spatial position relationship may change, and then the magnetic field distribution is adjusted according to the current change information.

[0128] S502: Adjust the magnetic field distribution in the magnetic field model based on the current change information.

[0129] As a possible implementation method, the magnetic field distribution in the magnetic field model is adjusted based on the current change information through the Max equations. The Max equations are as follows:

[0130]

[0131]

[0132] in, is the gradient operator, D is the electric displacement field, ρ is the charge density, B is the magnetic field, E is the electric field, H is the magnetic field intensity, J is the current density, and t is the time.

[0133] It should be noted that in order to ensure the accuracy of the magnetic field model, when solving Maxwell's equations, it is necessary to determine the boundary conditions of the magnetic field based on the actual structure of the converter station and the surrounding environment, such as the Dirichlet boundary conditions or the Neumann boundary conditions. The magnetic permeability depends on the magnetic properties of different materials in the converter station and needs to be determined based on actual conditions.

[0134] In some embodiments, Figure 6 As shown, the adjusted circuit model, thermal field model and magnetic field model are simulated to generate multi-physical field distribution information of the converter station under different operating conditions, which can be specifically implemented as follows:

[0135] S601. Determine a solution algorithm based on the obtained simulation requirements.

[0136] For example, the simulation requirement can be set according to the historical simulation data of the battery swap station. For example, if the accuracy of the historical simulation data of the battery swap station is too low, in order to achieve a more accurate simulation process, the simulation requirement can be set to require a high-precision simulation result. Alternatively, the simulation requirement can also be set according to the actual situation.

[0137] It should be noted that when determining the solution algorithm, it is not only necessary to determine it based on simulation requirements, but also to comprehensively consider the characteristics of different physical fields.

[0138] For example, if the electric field problem needs to deal with complex geometric shapes and inhomogeneous media, the finite element method can be used, and its governing equation is in, is the gradient operator, ∈ is the dielectric constant, is the charge density. For magnetic field problems, if the solution area has a complex boundary shape, the boundary element method can be selected. For thermal field problems. If conservation and stability need to be guaranteed, the finite volume method can be used, and its control equation is Where ρ is density, c is specific heat capacity, is the velocity vector, k is the thermal conductivity, and Q is the heat source term. At the same time, the compatibility and coupling between different solution methods are considered. For example, in the coupling of electric and magnetic fields, a solution method that can handle both electric and magnetic field equations is selected, or the calculation results of different methods are coupled through interfaces and conversion algorithms. The solution algorithm is also called a numerical calculation method.

[0139] S602: Based on the solution algorithm, the adjusted circuit model, thermal field model and magnetic field model are coupled and solved to generate multi-physical field distribution information of the converter station under different operating conditions.

[0140] The coupling solution processing includes: sequential coupling solution processing and direct coupling solution processing. The multi-physical field distribution information of the converter station under different operating conditions is the simulation result of the converter station.

[0141] For example, if a sequential coupled solution process is used, it is necessary to solve each physical field model separately, and then use the result of one physical field as the input of another physical field, and perform iterative solutions in sequence until the convergence conditions are met. For example, for the coupling of electric field and thermal field, it is necessary to first solve the circuit model, obtain the circuit distribution, calculate the Joule heat generated by the electric field as the heat source term, input it into the thermal field model to solve the temperature distribution, and then feed back the material parameter changes caused by the thermal field to the circuit model for updating. The calculation of the Joule heat generated by the electric field as the heat source term can be based on the following formula:

[0142] Q=J 2 / σ

[0143] Among them, Q is heat, J is current density, and σ is conductivity.

[0144] As another example, if direct coupling is used, the equations of multiple physical fields are solved simultaneously, and the coupling of multiple physical fields is achieved by establishing a unified set of equations. During the solution process, it is necessary to consider the coupling strength and time scale differences between different physical fields. For strongly coupled physical fields, tighter coupling methods and more frequent iterative updates are adopted. For physical fields with large time scale differences, different time steps are used for solution to improve computational efficiency.

[0145] In some embodiments, when performing coupled solution processing, the simulation tasks are assigned to multiple computing nodes for simultaneous calculation. According to the performance and load of the computing nodes, the load calculation formula is used to reasonably allocate tasks, and data communication and synchronization are performed. For example, parallel computing frameworks such as MPI or OpenMP are used for task allocation and data communication. Parallel computing technology can make full use of multi-core processors and distributed computing resources, decompose large-scale computing tasks into multiple small tasks for parallel execution, and effectively shorten the calculation time and improve the simulation efficiency. The load calculation formula is as follows:

[0146] L i (t+1)=L i (t)+T incoming -T completed

[0147] Among them, L i (t) represents the length of the task queue in time t, T incoming represents the number of tasks that enter the system between time t and t+1, T completed Represents the number of tasks completed between time t and t+1.

[0148] In some embodiments, after the multi-physical field distribution information of the converter station under different operating conditions is generated, it is analyzed and processed, and statistical analysis and trend analysis are performed.

[0149] For example, the statistical characteristics of different physical field parameters, such as mean, variance, maximum value, minimum value, etc., are calculated, the correlation and coupling relationship between physical field parameters are analyzed, and trend analysis is performed to predict the operation trend of the converter station in the future. Furthermore, the results can be visualized using a visualization algorithm based on feature extraction. For the visualization of electric and magnetic fields, the intensity, direction and other characteristics of the electric and magnetic fields are extracted, represented by different colors and arrows, and the color formula is used to characterize the color values ​​corresponding to different electric field intensities. The specific formula is as follows Among them, C(E) is the final color value, and E is the electric field strength. Arrows are used to represent the direction of the magnetic field. The direction and length of the arrows correspond to the direction and strength of the magnetic field, respectively. The visualization results can be presented in the form of three-dimensional charts, high-resolution images, dynamic animations, etc. Users can zoom in, zoom out, rotate, etc. through the interactive visualization interface, and select different physical field parameters for display, so as to observe the simulation details of the converter station more deeply.

[0150] As another example, statistical features such as mean and variance of different physical field parameters are calculated to analyze the correlation and coupling relationship between physical field parameters. Furthermore, the simulation results are visualized, and a visualization algorithm based on feature extraction is used to visualize the electric and magnetic fields, using different colors and arrows to indicate the strength and direction of the electric and magnetic fields.

[0151] In some embodiments, the simulation results can be displayed in a variety of visual forms. For example, three-dimensional charts, high-resolution images, dynamic animations, etc. Specifically, through three-dimensional charts, the parameter distribution of the flexible direct current converter station under different physical fields, such as electric field strength, magnetic field strength, temperature distribution, etc., can be intuitively displayed. High-resolution images can clearly display the details of the converter station equipment and the changes in the physical field. Dynamic animations can vividly display the dynamic changes of multiple physical fields in the operation of the converter station, allowing users to more intuitively understand the operating status of the converter station. The visualization interface has good interactivity. Users can zoom in, out, rotate, and other operations through the operation interface, and select different physical field parameters for display, so as to observe the details of the converter station more deeply.

[0152] In some embodiments, the simulation results can be subjected to in-depth data analysis. For example, statistical features can be calculated, correlation and coupling relationships can be analyzed, and trend analysis can be performed, and a data analysis report can be automatically generated based on the analysis results. The report content includes: multi-physics field characteristic analysis of the converter station, performance evaluation of key equipment, early warning of potential problems, etc. In addition, the report content can be customized according to user needs, and can also be output in a variety of formats, such as PDF, Word, etc., to facilitate users to consult and share, meeting the needs of different users.

[0153] It is understandable that outputting visualization results and data analysis reports to users can provide a basis for their decision-making. Furthermore, users can promptly discover problems in the converter station based on the visualization results and relevant content in the data analysis report, and take corresponding measures for optimization and maintenance. For example, if it is found that the temperature of a certain device is too high, measures such as strengthening heat dissipation and adjusting operating parameters can be taken. If it is found that the electric field strength or magnetic field strength exceeds the safe range, the layout of the equipment can be adjusted or shielding measures can be taken. In addition, visualization results and data analysis reports can also be integrated with other systems. For example, integration with the monitoring system can realize real-time monitoring and early warning of the converter station, and integration with the optimization design system can provide a reference for the optimization design of the converter station.

[0154] Exemplarily, the simulation method of the converter station provided in this application can be applied to the following scenarios: comprehensive performance testing and optimization adjustment are carried out before the newly built converter station is put into operation. Dynamic adaptive modeling and multi-physics coupling simulation technology can accurately simulate the multi-physics characteristics of the converter station under different working conditions. It provides a scientific basis for the debugging and optimization of the converter station. Parallel computing technology and diversified visualization result output improve the efficiency of simulation and the decision-making support ability of users, which helps to ensure that the converter station can work stably and efficiently after formal operation, and meet the power transmission needs under various complex working conditions.

[0155] It can be seen that the simulation method of the converter station provided in this application can conduct comprehensive performance testing and optimization adjustments before the newly built converter station is put into operation. The dynamic adaptive modeling and multi-physics coupling simulation technology can accurately simulate the multi-physics characteristics of the converter station under different working conditions, providing a scientific basis for the commissioning and optimization of the converter station. Parallel computing technology and diversified visualization result output improve the efficiency of simulation and the user's decision-making support capabilities, which helps to ensure that the converter station can work stably and efficiently after formal operation and meet the power transmission needs under various complex working conditions.

[0156] As another example, the simulation method of the converter station provided in this application can be applied to the following scenarios: A large-scale flexible direct current converter station undertakes key power conversion and transmission tasks in a complex power network. With the continuous growth of power demand and the increasing requirements for power system stability, the converter station needs to conduct a comprehensive and in-depth performance evaluation and optimization design to ensure its safe and stable operation under different operating conditions, while improving energy utilization efficiency and reducing maintenance costs.

[0157] It can be seen that the simulation method of the converter station provided in this application can realize comprehensive and accurate simulation and analysis of the multi-physical fields of the converter station. The dynamic adaptive modeling and multi-physical field coupling simulation technology can automatically adjust the model parameters and structure according to the actual operating status, realize the real-time collaborative simulation of multi-physical fields, and improve the accuracy and reliability of the simulation. Parallel computing technology and diversified visualization result output improve the efficiency of the simulation and the decision-making support ability of the user. On the whole, it provides a scientific and accurate technical basis for the design, operation and maintenance of the flexible direct current converter station, which helps to improve the performance and reliability of the converter station and ensure the safe and stable operation of the power system. At the same time, through the in-depth analysis of the multi-physical fields of the converter station, potential problems and optimization points can be found, which provides strong support for the optimal design and energy saving of the converter station.

[0158] In this way, the circuit model of the converter station is adjusted through the operating data of the converter station, so that the circuit model can characterize the current electrical characteristics of the converter station. Furthermore, the thermal field model and magnetic field model associated with the adjusted circuit model are adjusted so that the above models can characterize the real-time operating status of the converter station, improve the accuracy of the simulation, and avoid deviations between the simulation results and the actual situation. Finally, the three adjusted models are simulated to generate multi-physical field distribution information of the converter station under different operating conditions, realizing the collaborative real-time simulation of multiple physical fields, accurately reflecting the interaction between different physical fields, and providing a more comprehensive and accurate reference for the design, operation and maintenance of the converter station.

[0159] It should be noted that the information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0160] It should be noted that the technical solution provided in this application provides users with corresponding operation entrances for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0161] Figure 7Schematic diagram of the structure of a converter station simulation device provided in an embodiment of the present application, the device is used to execute the above-mentioned converter station simulation method, such as Figure 7 As shown, the simulation device of the converter station includes: an acquisition module, a first adjustment module, a second adjustment module and a processing module.

[0162] An acquisition module, used to acquire the operation data of the converter station;

[0163] A first adjustment module, configured to adjust the circuit model based on the operation data;

[0164] A second adjustment module, used for adjusting the thermal field model and the magnetic field model based on the adjusted circuit model;

[0165] The processing module is used to simulate the adjusted circuit model, thermal field model and magnetic field model to generate multi-physical field distribution information of the converter station under different operating conditions.

[0166] Furthermore, the operation data includes: historical operation data, real-time operation data;

[0167] The first adjustment module includes:

[0168] A load determination unit, used to determine load change information of a converter station based on historical operation data and real-time operation data;

[0169] The circuit adjustment unit is used to adjust the model parameters of the circuit model based on the load change information so that the circuit model represents the current electrical characteristics of the converter station, and the model parameters include at least one of the following: resistance, inductance, and capacitance.

[0170] Furthermore, the second adjustment module includes:

[0171] A parameter acquisition unit, used to acquire the model parameters after the circuit model is adjusted;

[0172] The first adjustment unit is used to adjust the thermal field model and the magnetic field model based on the adjusted model parameters.

[0173] Furthermore, the first adjustment unit includes:

[0174] The thermal field adjustment unit is specifically used for:

[0175] Determine current change information of the circuit in the converter station based on the adjusted model parameters;

[0176] Determining heat source term change information of the circuit in the converter station based on the current change information;

[0177] Adjust the temperature distribution in the thermal field model based on the change information of the heat source term.

[0178] Furthermore, the first adjustment unit includes:

[0179] The magnetic field adjustment unit is specifically used for:

[0180] Determine current change information of the circuit in the converter station based on the adjusted model parameters;

[0181] The magnetic field distribution in the magnetic field model is adjusted based on the current change information.

[0182] Furthermore, the processing module comprises:

[0183] A solution determination unit, used to determine a solution algorithm based on the obtained simulation requirements;

[0184] The coupling solution unit is used to perform coupling solution processing on the adjusted circuit model, thermal field model and magnetic field model based on the solution algorithm to generate multi-physical field distribution information of the converter station under different operating conditions. The coupling solution processing includes: sequential coupling solution processing and direct coupling solution processing.

[0185] In this way, the circuit model of the converter station is adjusted through the operating data of the converter station, so that the circuit model can characterize the current electrical characteristics of the converter station. Furthermore, the thermal field model and magnetic field model associated with the adjusted circuit model are adjusted so that the above models can characterize the real-time operating status of the converter station, improve the accuracy of the simulation, and avoid deviations between the simulation results and the actual situation. Finally, the three adjusted models are simulated to generate multi-physical field distribution information of the converter station under different operating conditions, realizing the collaborative real-time simulation of multiple physical fields, accurately reflecting the interaction between different physical fields, and providing a more comprehensive and accurate reference for the design, operation and maintenance of the converter station.

[0186] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer device, and specifically, the computer device may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0187] An embodiment of the present invention provides a computer device, including a memory and a processor, the memory is used to store information including program instructions, the processor is used to control the execution of the program instructions, and when the program instructions are loaded and executed by the processor, the steps of the embodiment of the simulation method of the above-mentioned converter station are implemented. For a specific description, please refer to the embodiment of the simulation method of the above-mentioned converter station.

[0188] Reference below Figure 8, which shows a schematic diagram of the structure of a computer device 800 suitable for implementing an embodiment of the present application.

[0189] like Figure 8 As shown, the computer device 800 includes a central processing unit (CPU) 801, which can perform various appropriate operations and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the computer device 800 are also stored. The CPU 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0190] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT), a liquid crystal feedback device (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 1005 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed, so that a computer program read therefrom is installed as needed as the storage section 808.

[0191] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program tangibly contained on a machine-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 809, and / or installed from the removable medium 811.

[0192] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0193] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0194] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0197] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0198] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0199] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0200] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0201] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0202] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0203] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A simulation method for a converter station, characterized in that: include: Obtaining the operation data of the converter station; adjusting a circuit model of the converter station based on the operation data; adjusting the thermal field model and the magnetic field model of the converter station based on the adjusted circuit model; The adjusted circuit model, thermal field model and magnetic field model are simulated to generate multi-physical field distribution information of the converter station under different operating conditions.

2. The method according to claim 1, characterized in that The operation data includes: historical operation data and real-time operation data; The adjusting the circuit model of the converter station based on the operation data comprises: Determining load change information of the converter station based on the historical operation data and the real-time operation data; The model parameters of the circuit model are adjusted based on the load change information so that the circuit model represents the current electrical characteristics of the converter station, and the model parameters include at least one of the following: resistance, inductance, and capacitance.

3. The method according to claim 2, characterized in that The step of adjusting the thermal field model and the magnetic field model of the converter station based on the adjusted circuit model includes: Obtaining adjusted model parameters of the circuit model; The thermal field model and the magnetic field model are adjusted based on the adjusted model parameters.

4. The method according to claim 3, characterized in that The step of adjusting the thermal field model and the magnetic field model based on the adjusted model parameters includes: Determining current change information of the circuit in the converter station based on the adjusted model parameters; Determining heat source term change information of the circuit in the converter station based on the current change information; The temperature distribution in the thermal field model is adjusted based on the heat source term change information.

5. The method according to claim 3, characterized in that: The step of adjusting the thermal field model and the magnetic field model based on the adjusted model parameters includes: Determining current change information of the circuit in the converter station based on the adjusted model parameters; The magnetic field distribution in the magnetic field model is adjusted based on the current change information.

6. The method according to claim 1, characterized in that The simulation processing of the adjusted circuit model, thermal field model and magnetic field model to generate multi-physical field distribution information of the converter station under different operating conditions includes: Determine the solution algorithm based on the obtained simulation requirements; Based on the solution algorithm, the adjusted circuit model, thermal field model and magnetic field model are coupled and solved to generate multi-physical field distribution information of the converter station under different operating conditions. The coupled solution includes: sequential coupled solution and direct coupled solution.

7. A simulation device for a converter station, characterized in that: include: An acquisition module, used to acquire the operation data of the converter station; A first adjustment module, configured to adjust a circuit model of the converter station based on the operation data; A second adjustment module, used to adjust the thermal field model and the magnetic field model of the converter station based on the adjusted circuit model; The processing module is used to simulate the adjusted circuit model, thermal field model and magnetic field model to generate multi-physical field distribution information of the converter station under different operating conditions.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the converter station simulation method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the converter station simulation method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the converter station simulation method according to any one of claims 1 to 6 are implemented.

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