Power distribution network simulation system based on digital twinning

Abstract

The invention relates to the technical field of power distribution network simulation, and provides a power distribution network simulation system based on digital twinning. The method comprises the following steps: acquiring equipment basic information of the power distribution network, wherein the equipment basic information comprises equipment model information and an equipment topological structure; constructing a power distribution network physical model according to the equipment model information and the equipment topological structure; obtaining a power distribution network simulation model; performing simulation analysis in the power distribution network simulation model to obtain a fault prediction result; formulating a fault response strategy according to the fault prediction result, wherein the fault response strategy comprises a plurality of fault repair schemes; obtaining an optimal repairing scheme; and performing fault repair of the power distribution network according to the optimal repair scheme. According to the method and the device, the technical problems of time and labor consumption and relatively poor repair effect of power distribution network fault repair in the prior art are solved, and the technical effects of improving the power distribution network fault repair efficiency, reducing the repair cost and improving the repair effect are achieved.

Description

Technical Field

[0001] This application relates to the field of power distribution network simulation technology, and in particular to a power distribution network simulation system based on digital twins. Background Technology

[0002] A distribution network refers to a power grid that receives electrical energy from the transmission network or regional power plants and distributes it locally or to various users according to voltage levels through distribution facilities. Nowadays, due to the increasing number of distribution networks, the rapid and accurate location and repair of distribution network faults have become problems that need to be solved. However, in the current technology, the workload of troubleshooting distribution network faults is high, which makes the repair of distribution network faults both time-consuming and labor-intensive.

[0003] In summary, this application solves the technical problems of time-consuming and labor-intensive fault repair in the prior art, and the repair effect is poor. Summary of the Invention

[0004] Therefore, it is necessary to provide a digital twin-based distribution network simulation system that can improve the efficiency of distribution network fault repair, reduce repair costs, and enhance repair effectiveness, addressing the aforementioned technical problems. This application solves the technical problems of time-consuming and labor-intensive distribution network fault repair with poor repair results in the prior art, achieving the technical effects of improving the efficiency of distribution network fault repair, reducing repair costs, and enhancing repair effectiveness.

[0005] In a first aspect, embodiments of this application provide a distribution network simulation method based on digital twins, comprising: acquiring basic equipment information of the distribution network, the basic equipment information including equipment model information and equipment topology; constructing a distribution network physical model based on digital twin technology according to the equipment model information and equipment topology; acquiring real-time operating condition data of the distribution network through sensing devices, integrating the real-time operating condition data with the distribution network physical model to obtain a distribution network simulation model; performing simulation analysis in the distribution network simulation model to obtain fault prediction results; formulating a fault response strategy based on the fault prediction results, the fault response strategy including multiple fault repair schemes; inputting the multiple fault repair schemes into the distribution network simulation model to perform fault repair simulation, evaluating the repair effect, and obtaining the optimal repair scheme; and performing fault repair of the distribution network according to the optimal repair scheme.

[0006] Secondly, embodiments of this application provide a distribution network simulation system based on digital twins, including: a distribution network equipment basic information acquisition module, which acquires basic equipment information of the distribution network, including equipment model information and equipment topology; a distribution network physical module construction module, which constructs a distribution network physical model based on digital twin technology according to the equipment model information and equipment topology; and a distribution network simulation model acquisition module, which acquires real-time operating condition data of the distribution network obtained through sensing devices, integrates the real-time operating condition data with the distribution network physical model, and acquires a distribution network simulation model. The system includes: a power grid simulation model; a fault prediction result acquisition module, which performs simulation analysis in the power grid simulation model to obtain fault prediction results; a fault response strategy formulation module, which formulates fault response strategies based on the fault prediction results, including multiple fault repair schemes; an optimal repair scheme acquisition module, which inputs the multiple fault repair schemes into the power grid simulation model for fault repair simulation, evaluates the repair effect, and obtains the optimal repair scheme; and a power grid fault repair module, which performs fault repair on the power grid based on the optimal repair scheme.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] First, basic equipment information of the distribution network is acquired, including equipment model information and equipment topology. Second, based on the equipment model information and equipment topology, a physical model of the distribution network is constructed using digital twin technology. Then, real-time operating condition data of the distribution network is acquired through sensing devices, and this real-time operating condition data is integrated with the physical model of the distribution network to obtain a distribution network simulation model. Simulation analysis is performed in the distribution network simulation model to obtain fault prediction results. Then, a fault response strategy is formulated based on the fault prediction results, and the fault response strategy includes multiple fault repair schemes. Next, the multiple fault repair schemes are input into the distribution network simulation model for fault repair simulation, the repair effect is evaluated, and the optimal repair scheme is obtained. Finally, the fault repair of the distribution network is carried out according to the optimal repair scheme. This application solves the technical problems of time-consuming and labor-intensive distribution network fault repair and poor repair effect in the prior art, and achieves the technical effects of improving the efficiency of distribution network fault repair, reducing repair costs, and improving repair effect.

[0009] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating a digital twin-based power distribution network simulation method in one embodiment.

[0011] Figure 2 This is a schematic diagram illustrating the process of obtaining fault prediction results using a digital twin-based power distribution network simulation method in one embodiment.

[0012] Figure 3 This is a block diagram of a distribution network simulation system based on digital twins in one embodiment.

[0013] Explanation of reference numerals in the attached diagram: Module 11 for obtaining basic information of distribution network equipment, Module 12 for constructing physical distribution network modules, Module 13 for obtaining distribution network simulation models, Module 14 for obtaining fault prediction results, Module 15 for formulating fault response strategies, Module 16 for obtaining the best repair scheme, and Module 17 for repairing distribution network faults. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0015] After introducing the basic principles of this application, the technical solutions in this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0016] like Figure 1 As shown, this application provides a distribution network simulation method based on digital twins, the method comprising:

[0017] S100: Obtain basic equipment information of the power distribution network, including equipment model information and equipment topology;

[0018] Specifically, digital twin is a concept that transcends reality, and can be viewed as a digital mapping system of one or more important, interdependent equipment systems. This application collects basic equipment information of a power distribution network, constructs a physical model of the power distribution network using digital twin technology, and then integrates this physical model with the real-time operating conditions of the power distribution network to obtain a power distribution network simulation model. Simulation analysis is then performed on this simulation model to obtain fault prediction results. Fault repair schemes are derived from the prediction results, and these schemes are evaluated to determine the optimal repair solution.

[0019] A distribution network refers to a power grid that receives electrical energy from a transmission network or regional power plants and distributes it locally or tiered according to voltage to various users through distribution facilities. Equipment basic information refers to the queryable basic information of the distribution network. Equipment topology refers to the structural connection required for interconnecting devices such as computers in the network; this connection method is called topology. In this application, it means that the devices in the distribution network are connected according to the aforementioned connection method to obtain the equipment topology. Obtaining the equipment basic information of the distribution network, including equipment model information and equipment topology, lays the foundation for subsequent construction of the distribution network physical model.

[0020] S200: Based on the equipment model information and equipment topology, construct a physical model of the power distribution network using digital twin technology;

[0021] Specifically, digital twin technology refers to a virtual model of a physical object. It spans the object's lifecycle and uses real-time data from sensors on the object to simulate behavior and monitor operations. A power distribution network physical model refers to a conceptual model established when studying and solving physics problems, discarding secondary factors and focusing on the primary ones. In this application, it refers to a physical model of the power distribution network based on the equipment model information and equipment topology. Based on the equipment model and topology, a power distribution network physical model is constructed using digital twin technology. Constructing the power distribution network physical model lays the groundwork for subsequent simulation model construction.

[0022] S300: Acquire real-time operating condition data of the power distribution network through sensing devices, integrate the real-time operating condition data with the physical model of the power distribution network, and obtain a power distribution network simulation model;

[0023] Specifically, a sensing device is essentially a detection device that can sense the measured information and transform it into electrical signals or other required forms of information output according to certain rules to meet the requirements of information transmission, processing, storage, display, recording, and control. In this application, it refers to collecting real-time data of the distribution network through a sensing device; real-time operating condition data refers to data on the real-time engineering status of the distribution network, such as power consumption and transmission data; integration refers to substituting the real-time operating condition data into the distribution network physical model. For example, if the transmission capacity of the distribution network at a certain moment is 10, then the transmission capacity is substituted into the distribution network physical model to form a real-time synchronized transmission capacity; the distribution network simulation model refers to a model made for studying the distribution network, which is one-to-one corresponding and restored from structure to data. By acquiring real-time operating condition data of the distribution network through a sensing device and integrating the real-time operating condition data with the distribution network physical model, a distribution network simulation model is obtained. Obtaining the distribution network simulation model lays the foundation for subsequent research on the faults and solutions of the distribution network.

[0024] S400: Perform simulation analysis in the power distribution network simulation model to obtain fault prediction results;

[0025] Specifically, simulation analysis refers to using specialized software to simulate an experimental environment and obtain experimental results. Since the experimental environment is "simulated" and "imitates a real-world scenario," it is called simulation. Analysis involves disassembling and studying the simulated experimental environment. Fault prediction results refer to the frequency of fault occurrence, fault propagation filters, and fault impact range obtained through operational studies of the distribution network simulation model. Simulation analysis is performed within the distribution network simulation model to obtain fault prediction results. By analyzing and running the distribution network simulation model, fault prediction results are obtained, laying the groundwork for subsequent fault repair simulation.

[0026] like Figure 2 As shown, the steps of this application further include:

[0027] S410: Determine the type of simulated fault;

[0028] S420: Set the corresponding simulation conditions and simulation parameters according to the simulated fault type;

[0029] S430: Change the input conditions according to the simulation conditions and simulation parameters, run the power distribution network simulation model, and obtain the simulation results;

[0030] S440: Perform fault analysis on the simulation results and obtain the fault prediction results.

[0031] Specifically, simulated fault types refer to the different types of faults that may occur during the operation of the distribution network. The data results differ depending on the time and location of each fault. These data results are input into the distribution network simulation model, constituting the simulated fault type. Simulation conditions and parameters refer to the structures and parameters set under the simulated fault types. Input conditions refer to the simulation conditions and parameters. Simulation results refer to the results obtained when the distribution network simulation model starts running under the simulation conditions and parameters. Fault analysis refers to analyzing the simulation results. Fault prediction results refer to the results obtained by fault allocation and matching when the simulation conditions and parameters are used in the distribution network simulation model. The process involves: determining the simulated fault type; setting corresponding simulation conditions and parameters based on the simulated fault type; changing the input conditions based on the simulation conditions and parameters; running the distribution network simulation model to obtain simulation results; performing fault analysis on the simulation results to obtain the fault prediction results. Obtaining the fault prediction results lays the groundwork for subsequently obtaining the optimal repair solution.

[0032] Furthermore, embodiments of this application also include:

[0033] S450: Obtain the simulation period and run the power distribution network simulation model within the simulation period;

[0034] S460: During the simulation, record the fault occurrence nodes, which include time nodes and location nodes;

[0035] S470: Based on the time and location nodes, mark the simulation running parameters and extract the fault parameters;

[0036] S480: Establish a mapping relationship between the time node, the location node, and the fault parameter to form a mapping relationship table of time node-location node-fault parameter, and add the mapping relationship table to the simulation run result.

[0037] Specifically, the simulation cycle refers to the running time of the power distribution network simulation system, such as one day, one hour, one minute, etc., which is set by the staff; the fault occurrence node refers to the time and location of the fault when the power distribution network simulation model is running within the simulation cycle; the simulation operation parameters refer to the parameters of each device and the time parameters when the power distribution network simulation model is running, such as the output power of the power distribution network simulation model in the first minute of the simulation cycle; the mapping relationship refers to a correspondence between two sets. In this application, it means that there is a one-to-one correspondence between the time node, the location node, and the fault parameters.

[0038] First, the simulation period is obtained, and the power distribution network simulation model is run within the simulation period. During the simulation, fault occurrence nodes are recorded, including time nodes and location nodes. Based on the time nodes and location nodes, fault parameters are extracted from the simulation parameters. A mapping relationship is established between the time nodes, location nodes, and fault parameters to form a time node-location node-fault parameter mapping table, which is then added to the simulation results. By adding the mapping table to the simulation results, a foundation is laid for subsequently determining the fault occurrence frequency, fault propagation path, and impact range.

[0039] Furthermore, the steps in this application also include:

[0040] S481: Determine the duration of the fault based on the time point of the fault occurrence and the corresponding fault parameters;

[0041] S482: Using the time node and the duration, perform frequency analysis to obtain the frequency of fault occurrence;

[0042] S483: Perform extreme value statistics on the simulation running parameters to obtain the extreme values ​​of the running parameters;

[0043] S484: Based on the abnormal location of extreme values ​​of operating parameters, perform fault propagation analysis in the distribution network to obtain the fault propagation path and impact range;

[0044] S485: The frequency of the fault occurrence, the fault propagation path, and the scope of impact are used as the fault prediction results.

[0045] Specifically, the corresponding fault parameters refer to the equipment parameters and power distribution parameters of the power distribution network during the time the fault occurs; the fault duration refers to the duration from the occurrence to the end of the fault; frequency analysis refers to the number of times the fault occurs within the simulation period; extreme value statistics refer to the statistics of the maximum and minimum values ​​in the simulation operating parameters; abnormal location refers to the location of the extreme value in the operating parameters that is not the location under normal operating conditions; fault propagation analysis refers to judging whether the fault has spread or the scope of its impact, for example, if the first device fails, and the devices are related, it may cause other related devices to fail as well; fault propagation path refers to the chain reaction that may occur between related devices if one device fails; and the scope of impact refers to the area affected by the fault.

[0046] Based on the time point of the fault occurrence and the corresponding fault parameters, the duration of the fault is determined. Frequency analysis is performed using the time point and duration to obtain the fault occurrence frequency. Extreme value statistics are conducted on the simulated operating parameters to obtain the extreme values ​​of the operating parameters. Based on the abnormal locations of the extreme values ​​of the operating parameters, fault propagation analysis is performed in the distribution network to obtain the fault propagation path and impact range. The fault occurrence frequency, fault propagation path, and impact range are used as the fault prediction result. Obtaining the fault prediction result lays the groundwork for subsequent fault response strategy development.

[0047] S500: Formulate a fault response strategy based on the fault prediction results, the fault response strategy including multiple fault repair schemes;

[0048] Specifically, a fault response strategy refers to the reaction method when a fault occurs during the operation of the distribution network simulation model, while a fault repair plan refers to the method of identifying the fault occurrence frequency, fault propagation path, and impact range when a fault occurs during the operation of the distribution network simulation model, and proposing a solution or repair method. A fault response strategy is formulated based on the fault prediction results, and this strategy includes multiple fault repair plans. Formulating a fault response strategy lays the groundwork for obtaining the optimal repair plan subsequently.

[0049] S600: Input the multiple fault repair schemes into the power distribution network simulation model to simulate fault repair, evaluate the repair effect, and obtain the best repair scheme;

[0050] Specifically, fault repair simulation refers to simulating the repair of a fault, that is, inputting the multiple fault repair schemes into the power distribution network simulation model to simulate the fault repair; repair effect refers to the effectiveness of the fault repair; the best repair scheme refers to the fault repair scheme that is most suitable among the above repair effects.

[0051] Furthermore, the steps in this application include:

[0052] S610: Apply each fault repair scheme to the power distribution network simulation model, simulate the actual fault repair process, and obtain the simulated fault repair results;

[0053] S620: Obtain assessment indicators and repair objectives for the repair effect;

[0054] S630: Based on the repair effect evaluation index, evaluate the simulated fault repair results and obtain multiple evaluation results of the multiple fault repair schemes;

[0055] S640: Based on the repair objective, filter the multiple evaluation results to obtain the best repair solution.

[0056] Specifically, simulating the actual fault repair process refers to performing fault repair in the power distribution network simulation model and obtaining the fault repair results for each fault repair scheme; the repair effect evaluation index refers to measuring the repair effect based on indicators, such as recovery time, degree of reliability improvement, equipment power recovery, number of power outages, cost, etc.; the repair objective refers to the repair effect that the staff most needs to achieve, such as some aiming for fast recovery time, others aiming for low cost, etc.; multiple evaluation results refer to the results obtained by evaluating the simulated fault repair results according to the staff's requirements; the best repair scheme refers to the scheme that best meets the repair objective by screening the multiple evaluation results.

[0057] Each fault repair scheme is applied to a distribution network simulation model to simulate the actual fault repair process and obtain simulated fault repair results. Repair effectiveness evaluation indicators and repair objectives are obtained. Based on the repair effectiveness evaluation indicators, the simulated fault repair results are evaluated to obtain multiple evaluation results for the multiple fault repair schemes. The multiple evaluation results are then filtered according to the repair objectives to obtain the optimal repair scheme. The optimal repair scheme provides the best choice for subsequent fault repairs in the distribution network.

[0058] S700: Perform fault repair on the distribution network according to the optimal repair plan.

[0059] Specifically, repairing the distribution network according to the optimal repair plan can save financial resources and improve the efficiency of distribution network fault repair.

[0060] Furthermore, the steps in this application include:

[0061] S710: Collects problem information in real time during the fault repair process;

[0062] S720: Upload the problem information to the power distribution network management system and generate adjustment suggestions;

[0063] S730: Update the optimal repair solution according to the adjustment recommendations.

[0064] Specifically, "problem information" refers to new problems that may arise during fault repair, such as the potential for excessive pressure on other equipment during power outage repair, leading to malfunctions. The distribution network management system refers to the system that manages the distribution network structure and real-time data. "Adjustment suggestions" refer to proposals to adjust the fault repair plan. Problem information is collected in real-time during fault repair; this information is uploaded to the distribution network management system to generate adjustment suggestions; and the optimal repair plan is updated based on these suggestions. By updating the optimal repair plan, a real-time updated feedback of the best repair plan can be obtained, making distribution network repair faster and more convenient. This application solves the technical problems of time-consuming and labor-intensive distribution network fault repair with poor repair results in the prior art, achieving the technical effects of improving the efficiency of distribution network fault repair, reducing repair costs, and improving repair effectiveness.

[0065] like Figure 3 As shown in the embodiments of this application, a distribution network simulation system based on digital twins is also provided, characterized in that it includes:

[0066] The distribution network equipment basic information acquisition module 11 is used to acquire the basic information of the equipment in the distribution network, including equipment model information and equipment topology.

[0067] Distribution network physical module construction module 12, the distribution network physical model construction module 12 is used to construct a distribution network physical model based on digital twin technology according to the equipment model information and equipment topology;

[0068] Distribution network simulation model acquisition module 13 is used to acquire real-time operating data of the distribution network obtained through sensing devices, integrate the real-time operating data with the distribution network physical model, and obtain the distribution network simulation model.

[0069] Fault prediction result acquisition module 14 is used to perform simulation analysis in the power distribution network simulation model to obtain fault prediction results.

[0070] The fault response strategy formulation module 15 is used to formulate a fault response strategy based on the fault prediction result, and the fault response strategy includes multiple fault repair schemes.

[0071] The optimal repair scheme acquisition module 16 is used to input the multiple fault repair schemes into the power distribution network simulation model to simulate fault repair, evaluate the repair effect, and obtain the optimal repair scheme.

[0072] Distribution network fault repair module 17, which is used to repair the distribution network faults according to the optimal repair scheme.

[0073] Furthermore, embodiments of this application also include:

[0074] A simulated fault type determination module, wherein the simulated fault type determination module is used to determine the simulated fault type;

[0075] A simulation condition setting module is used to set corresponding simulation conditions and simulation parameters according to the simulated fault type.

[0076] The simulation result acquisition module is used to change the input conditions according to the simulation conditions and simulation parameters, run the power distribution network simulation model, and acquire the simulation results.

[0077] The fault prediction result acquisition module is used to perform fault analysis on the simulation results and acquire the fault prediction results.

[0078] Furthermore, embodiments of this application also include:

[0079] A simulation cycle acquisition module is used to acquire the simulation cycle and run the power distribution network simulation model within the simulation cycle.

[0080] A fault occurrence node recording module is used to record fault occurrence nodes during simulation operation. The fault occurrence nodes include time nodes and location nodes.

[0081] The fault parameter extraction module is used to extract fault parameters by marking the time nodes and location nodes in the simulation operation parameters.

[0082] The mapping relationship adding module is used to establish mapping relationships between the time node, the location node, and the fault parameter, forming a mapping relationship table of time node-location node-fault parameter, and adding the mapping relationship table to the simulation run result.

[0083] Furthermore, embodiments of this application also include:

[0084] A fault duration determination module is used to determine the duration of the fault based on the time node where the fault occurred and the corresponding fault parameters.

[0085] A fault occurrence frequency acquisition module is used to perform frequency analysis using the time node and the duration period to acquire the fault occurrence frequency.

[0086] The module for obtaining extreme values ​​of operating parameters performs extreme value statistics on the simulation operating parameters to obtain the extreme values ​​of the operating parameters.

[0087] The distribution network fault propagation analysis module is used to perform fault propagation analysis in the distribution network based on the abnormal location of extreme values ​​of operating parameters, and to obtain the fault propagation path and the scope of impact.

[0088] The fault prediction result acquisition module is used to obtain the fault occurrence frequency, fault propagation path and impact range as the fault prediction result.

[0089] Furthermore, embodiments of this application also include:

[0090] The simulated fault repair result acquisition module is used to apply each fault repair scheme to the power distribution network simulation model, simulate the actual fault repair process, and obtain the simulated fault repair result.

[0091] A module for obtaining repair effectiveness evaluation indicators is used to obtain repair effectiveness evaluation indicators and repair targets.

[0092] An evaluation result acquisition module is used to evaluate the simulated fault repair results based on the repair effect evaluation index and acquire multiple evaluation results of the multiple fault repair schemes.

[0093] The optimal repair solution acquisition module is used to filter the multiple evaluation results according to the repair target and obtain the optimal repair solution.

[0094] Furthermore, embodiments of this application also include:

[0095] A problem information collection module is used to collect problem information in real time during the fault repair process;

[0096] An adjustment suggestion generation module is used to upload the problem information to the distribution network management system and generate adjustment suggestions.

[0097] The optimal solution update module is used to update the optimal repair solution according to the adjustment suggestions.

[0098] For specific embodiments of the digital twin-based distribution network simulation system, please refer to the embodiments of the digital twin-based distribution network simulation method described above, which will not be repeated here. The above modules can be embedded in hardware or independent of the processor in a computer device, or stored in software in the memory of a computer device, so that the processor can call and execute the operations corresponding to each module.

[0099] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0100] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A power distribution network simulation system based on digital twinning, characterized in that, The method comprises the following steps: An electrical power distribution network equipment basic information acquisition module is configured to acquire equipment basic information of an electrical power distribution network, wherein the equipment basic information comprises equipment model information and equipment topology structure; An electrical power distribution network physical model construction module is configured to construct an electrical power distribution network physical model based on digital twinning technology according to the equipment model information and the equipment topology structure; An electrical power distribution network simulation model acquisition module is configured to acquire real-time working condition data of an electrical power distribution network by a sensing device, integrate the real-time working condition data with the electrical power distribution network physical model, and acquire an electrical power distribution network simulation model; A fault prediction result acquisition module is configured to perform simulation analysis in the electrical power distribution network simulation model and acquire a fault prediction result; A fault response strategy formulation module is configured to formulate a fault response strategy according to the fault prediction result, wherein the fault response strategy comprises a plurality of fault repair schemes; An optimal repair scheme acquisition module is configured to input the plurality of fault repair schemes into the electrical power distribution network simulation model to perform fault repair simulation, evaluate repair effects, and acquire an optimal repair scheme; An electrical power distribution network fault repair module is configured to perform fault repair of an electrical power distribution network according to the optimal repair scheme.

2. The system of claim 1, wherein, The fault prediction result acquisition module comprises: A simulated fault type determination module is configured to determine a simulated fault type; A simulation condition setting module is configured to set corresponding simulation conditions and simulation parameters according to the simulated fault type; A simulation running result acquisition module is configured to change input conditions according to the simulation conditions and the simulation parameters, run an electrical power distribution network simulation model, and acquire a simulation running result; A fault prediction result acquisition module is configured to perform fault analysis on the simulation running result and acquire the fault prediction result.

3. The system of claim 2, wherein, The simulation running result acquisition module comprises: A simulation cycle acquisition module is configured to acquire a simulation cycle, wherein the electrical power distribution network simulation model is run in the simulation cycle; A fault occurrence node recording module is configured to record a fault occurrence node during simulation running, wherein the fault occurrence node comprises a time node and a location node; A fault parameter extraction module is configured to mark simulation running parameters according to the time node and the location node, and extract fault parameters; A mapping relationship adding module is configured to establish a mapping relationship among the time node, the location node and the fault parameters, form a mapping relationship table of time node-location node-fault parameter, and add the mapping relationship table to the simulation running result.

4. The system of claim 3, wherein, The fault prediction result acquisition module comprises: The fault duration determination module is configured to determine a duration of the fault according to a time node at which the fault occurs and a corresponding fault parameter; The fault occurrence frequency acquisition module is configured to perform frequency analysis by using the time node and the duration to acquire a fault occurrence frequency; The operating parameter extreme value obtaining module is configured to perform extreme value statistics on the simulation operating parameters to acquire an operating parameter extreme value; The power distribution network fault diffusion analysis module is configured to perform fault diffusion analysis in the power distribution network according to an abnormal position of the operating parameter extreme value to acquire a fault propagation path and an influence range; The fault prediction result acquisition module is configured to take the fault occurrence frequency, the fault propagation path and the influence range as the fault prediction result.

5. The system of claim 1, wherein, The optimal repair scheme acquisition module includes: The simulated fault repair result obtaining module is configured to apply each fault repair scheme to the power distribution network simulation model to simulate an actual fault repair process and acquire a simulated fault repair result; The repair effect evaluation index obtaining module is configured to acquire a repair effect evaluation index and a repair target; The evaluation result acquisition module is configured to evaluate the simulated fault repair result based on the repair effect evaluation index to acquire a plurality of evaluation results of the plurality of fault repair schemes; The optimal repair scheme acquisition module is configured to filter the plurality of evaluation results according to the repair target to acquire an optimal repair scheme.

6. The system of claim 1, wherein, The optimal repair scheme acquisition module includes: The problem information collection module is configured to collect problem information in real time during a fault repair process; The adjustment suggestion generation module is configured to upload the problem information to a power distribution network management system to generate an adjustment suggestion; The optimal scheme updating module is configured to update the optimal repair scheme according to the adjustment suggestion.

7. A power distribution network simulation method based on digital twinning, characterized in that, The method includes: Acquiring device basic information of a power distribution network, the device basic information including device model information and device topology structure; Constructing a power distribution network physical model based on digital twinning technology according to the device model information and the device topology structure; Integrating real-time working condition data of the power distribution network acquired by a sensing device with the power distribution network physical model to acquire a power distribution network simulation model; Performing simulation analysis in the power distribution network simulation model to obtain a fault prediction result; Formulating a fault response strategy including a plurality of fault repair schemes according to the fault prediction result; Inputting the plurality of fault repair schemes into the power distribution network simulation model to perform fault repair simulation and evaluate repair effects to acquire an optimal repair scheme; Performing fault repair of the power distribution network according to the optimal repair scheme.

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