New commercial power distribution network fault simulation method and system based on digital twinning

Abstract

The invention discloses a new commercial power distribution network fault simulation method and system based on digital twinning. The method comprises the following steps: S101, obtaining target power distribution network resource data; step S102, preprocessing and analyzing the collected data, and extracting useful information and features; s103, constructing a target power distribution network digital twinborn model according to the target power distribution network resource data; and S104, constructing an anticipated accident set according to the target power distribution network fault operation scene. According to the method, the digital twin model covering the new industry state elements of the power distribution network is constructed, so that the real state of the power distribution network can be reflected more accurately, the accuracy of a simulation result is improved, and the operation efficiency and reliability of the power distribution network are improved. And meanwhile, typical fault scenes are constructed according to different operation scenes, so that the comprehensiveness and practicability of the fault scenes are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system fault simulation, and in particular to a new business distribution network fault simulation method and system based on digital twins. Background Art

[0002] With the development of smart grids, the complexity of distribution networks is increasing. Traditional power system analysis and fault simulation methods can no longer meet the management needs of modern distribution networks. As an emerging technology, digital twin technology can achieve accurate simulation and prediction of physical entities and has broad application prospects in the operation and maintenance of distribution networks. However, how to apply digital twin technology to fault simulation of distribution networks to improve the operation stability and reliability of distribution networks is still a problem to be solved.

[0003] Existing fault simulation technologies mainly analyze the historical operation data of the distribution network and simulate possible fault scenarios to evaluate the operation risks of the distribution network and optimize the operation strategy of the distribution network. However, these technologies often ignore the new business elements of the distribution network, such as distributed power sources, electric vehicles, new loads, and multi-energy storage, or use simplified models and assumptions, which may cause a large deviation between the results of the fault simulation and the actual operation. In addition, the existing fault simulation technology can usually only simulate a single fault scenario and cannot comprehensively simulate various operation scenarios of the distribution network, which may affect the accuracy and practicality of the fault simulation. Therefore, how to fully consider the new business elements of the distribution network in the digital twin technology to improve the accuracy and practicality of the fault simulation is an important issue facing the current technology. Summary of the invention

[0004] In order to overcome the deficiencies in the prior art, the present invention provides a new business model distribution network fault simulation method and system based on digital twin, which realizes fault simulation of the new business model distribution network through digital twin technology.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted to solve the technical problem is as follows:

[0006] The present invention discloses a new business distribution network fault simulation method based on digital twin, comprising the following steps:

[0007] Step S101: Acquire target distribution network resource data;

[0008] Step S102: preprocessing and analyzing the collected data to extract useful information and features;

[0009] Step S103: constructing a digital twin model of the target distribution network according to the target distribution network resource data;

[0010] Step S104: constructing a set of anticipated accidents according to the target distribution network fault operation scenario.

[0011] Furthermore, in step S101, the target distribution network resource data includes topology information, ledger information, equipment parameters, historical fault data and basic grid operation data covering new elements of the distribution network.

[0012] Furthermore, in step S103, by comprehensively considering the data in the target distribution network resource data, a digital twin model is constructed, and the perceived multi-source data is digitally modeled and visualized to achieve a dynamic display of the power grid operation status.

[0013] Furthermore, in step S104, typical operating scenarios are analyzed based on the target distribution network resource data, including distributed power grid connection and absorption, electric vehicle and new load regulation, multi-energy storage participating in grid interactive regulation, and microgrid and off-grid regulation scenarios, to construct an anticipated accident set; the anticipated accident set includes fault type and fault impact range.

[0014] Furthermore, in step S104, the randomness and uncertainty of distributed energy output are comprehensively considered to affect the distribution network operation and fault simulation, and the input load samples are clustered using clustering technology to achieve that the input load samples in each cluster have a smaller fluctuation range, thereby obtaining load clustering and generating typical loads;

[0015] Clustering load samples, load samples can be expressed as:

[0016] L=[L1,L2,...,L i ,...,L m ]

[0017] Among them, L i The load at a certain time.

[0018] Furthermore, in order to obtain typical load samples, clustering technology is used to divide the input load samples into multiple clusters. The method includes the following steps:

[0019] (1) Randomly select the initial cluster center;

[0020] (2) Calculate the Euclidean distance from all cluster samples to each cluster center;

[0021] The distance between each cluster sample and the cluster center can be calculated by the following formula:

[0022]

[0023] Where d(l,j) is the Euclidean distance between the lth load sample and the jth cluster center;

[0024] (3) Calculate the mean of the load samples in each cluster and update the cluster center;

[0025] (4) Repeat steps (2) and (3) until the load samples in the cluster no longer change or the maximum number of repetitions is reached.

[0026] Furthermore, in order to ensure the clustering effect, the following method is used to determine the number of clusters and select the initial cluster center:

[0027] (1) Determination of the number of clusters

[0028] In order to select an appropriate value of K for clustering to ensure clustering effect and computational efficiency, an index for evaluating clustering effect, weighted average radius, is proposed, and its expression is:

[0029]

[0030] Where R is the weighted average radius of each cluster after clustering; p i is the ratio of the number of samples in the ith cluster to the total number of samples; r i is the radius of the i-th cluster;

[0031] When the number of clusters K is in a certain interval, the value of R decreases as K increases, and the clustering effect is significantly improved; when K reaches a certain value and then increases, the decreasing trend of the R value slows down, and the clustering effect is not obvious; selecting the value of K at this time as the number of clusters can not only ensure the clustering effect, but also reduce the calculation time;

[0032] (2) Selection of initial cluster centers

[0033] Clustering is optimized through preprocessing, and 10% of the samples are randomly selected for clustering. The cluster center of each cluster can reflect the distribution of all samples to a certain extent. At the same time, the calculation time is shorter when only 10% of the samples are selected for clustering, and the cluster center obtained by the pre-processed clustering can be used as the initial cluster center for clustering all samples.

[0034] Furthermore, during fault simulation, the influence of the distribution network automation configuration type on the power outage scope and power outage time is considered, and different fault isolation and power transfer durations corresponding to different automation configuration types are distinguished during faults. The method includes the following steps:

[0035] (1) First, consider the power outage caused by each device and make statistics on the impact range of upstream and downstream devices;

[0036] (2) Determine the fault location isolation time based on the upstream and downstream automation configuration types of the fault point;

[0037] (3) Determine the fault section and upstream power outage time based on the fault location isolation time and fault repair time;

[0038] (4) Determine the time for power transfer and power restoration based on whether there is a power transfer path downstream of the fault and the automation level of the interconnection switch;

[0039] (5) Finally, the power outage range and power outage time corresponding to different automation configuration types are obtained.

[0040] The present invention also discloses a new business distribution network fault simulation system based on digital twins, including a data input module, a data preprocessing module, a digital twin construction module, and a fault simulation module, wherein:

[0041] The data input module is used to obtain target distribution network resource data;

[0042] The digital preprocessing module is used to clean the target distribution network resource data, complete missing values ​​and process abnormal values;

[0043] The digital twin construction module is used to construct a digital twin model of the target distribution network according to the target distribution network resource data;

[0044] The fault simulation module is used to simulate various fault scenarios according to the real-time operating status of the power grid.

[0045] Furthermore, the data input module includes a substation master meter, a substation intelligent fusion terminal, a smart meter, a HPLC, and a fault recorder.

[0046] Furthermore, the target distribution network resource data includes topological information, ledger information, equipment parameters, historical fault data and basic grid operation data covering new elements of the distribution network, wherein:

[0047] The topology information includes the physical layout and connection mode of the distribution network;

[0048] The ledger information includes substation ledger, line ledger, distribution transformer ledger, distributed power source ledger, and flexible load ledger;

[0049] The equipment parameters include the model, rated voltage and rated current of the equipment;

[0050] The historical fault data includes fault type, fault time, fault location, fault impact range, fault risk level, etc.;

[0051] The basic operation data of the power grid include voltage, current and load.

[0052] Furthermore, the digital twin construction module monitors the operating status of the elements of the integrated new business grid, enables the virtual grid to operate synchronously with the physical grid, digitally models and visualizes the perceived multi-source data, and realizes dynamic display of the grid operation status.

[0053] Furthermore, the fault scenarios include distributed power grid-connected consumption scenarios, electric vehicle and new load regulation scenarios, multi-energy storage participating in grid interactive regulation scenarios, and microgrid on-grid and off-grid regulation scenarios; the anticipated accident set includes fault types and fault impact ranges.

[0054] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art:

[0055] 1. The present invention uses digital twin technology to digitally model the distribution network. The physical power grid and the virtual power grid correspond one to one and are closely integrated. Each element and state can be accurately matched and expressed. The received multi-source data is digitally modeled and visualized to achieve dynamic display of full-space information and real-time operation status of the power grid. By constructing a digital twin model covering new business elements of the distribution network, the real state of the distribution network can be more accurately reflected, the accuracy of the simulation results can be improved, and thus the operation efficiency and reliability of the distribution network can be improved.

[0056] 2. The present invention is based on a digital twin model. The present invention constructs a digital twin model by collecting various operating data of new business distribution network elements, including topology information, equipment parameters, operating data, historical fault data, etc. Based on the digital twin model, a variety of operating scenarios are simulated, including distributed power grid connection and absorption scenarios, electric vehicles and new load regulation scenarios, multi-energy storage participating in grid interactive regulation scenarios, and microgrid and off-grid regulation scenarios, to build an expected fault set. Integrate new business elements, improve fault simulation technology, enhance accuracy and practicality, and improve the stability and reliability of distribution network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0058] Figure 1 A schematic flow chart of a new business distribution network fault simulation method based on digital twins proposed in an embodiment of the present application;

[0059] Figure 2 A schematic diagram of a typical load generation process based on clustering technology provided in an embodiment of the present application;

[0060] Figure 3 A schematic diagram of a fault power outage impact analysis process in different automation configuration modes provided in an embodiment of the present application;

[0061] Figure 4 It is a structural schematic diagram of a new business distribution network fault simulation system based on digital twins provided in an embodiment of the present application;

[0062] Figure 5 It is a schematic diagram of an implementation method of a new business distribution network fault simulation method based on digital twins provided in an embodiment of the present application.

[0063]

Explanation of main symbols

[0064] 100 - power distribution fault simulation system based on digital twin; 110 - data input module; 120 - digital preprocessing module; 130 - digital twin model construction module; 140 - fault simulation module. DETAILED DESCRIPTION

[0065] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] Embodiment 1

[0067] like Figure 1 As shown, this embodiment discloses a new business distribution network fault simulation method based on digital twin, including the following steps:

[0068] Step S101: Acquire target distribution network resource data;

[0069] Specifically, in step S101, the target distribution network resource data includes topological information, ledger information, equipment parameters, historical fault data and basic grid operation data covering new elements of the distribution network. Among them, the topological structure information of the distribution network represents the physical layout and connection mode of the distribution network, that is, the connection mode of each node (such as substation, distribution room, etc.) and branch (such as transmission line, feeder, etc.) in the grid, the equipment parameters represent the technical indicators of each device in the distribution network, which may include the rated capacity of the transformer, etc., and the historical fault data may include the cause of the power outage, the scope of the power outage, the duration of the power outage, etc.

[0070] Step S102: preprocessing and analyzing the collected data to extract useful information and features;

[0071] Step S103: constructing a digital twin model of the target distribution network according to the target distribution network resource data;

[0072] Specifically, in step S103, by comprehensively considering the data in the target distribution network resource data, a digital twin model is constructed, and the perceived multi-source data is digitally modeled and visualized to achieve a dynamic display of the power grid operation status.

[0073] Step S104: constructing an anticipated accident set according to the target distribution network fault operation scenarios, wherein the anticipated accident set covers all typical fault operation scenarios of the target distribution network.

[0074] Specifically, in step S104, typical operating scenarios are analyzed according to the target distribution network resource data, including distributed power grid connection and absorption, electric vehicle and new load regulation, multi-energy storage participating in grid interactive regulation, microgrid connection and off-grid regulation, etc., to construct an anticipated accident set; the anticipated accident set includes fault type and fault impact range, etc.

[0075] In the above step S104, the randomness and uncertainty of the output of distributed energy (photovoltaic, wind power) are comprehensively considered to affect the distribution network operation and fault simulation, and the input load samples are clustered using clustering technology to achieve that the input load samples in each cluster have a smaller fluctuation range, thereby obtaining load clustering and generating typical loads;

[0076] Clustering load samples, load samples can be expressed as:

[0077] L=[L1,L2,...,L i ,...,L m ]

[0078] Among them, L i The load at a certain time.

[0079] Furthermore, in order to obtain typical load samples, clustering technology is used to divide the input load samples into multiple clusters, see Figure 2 , Figure 2 This is a typical load generation process diagram based on clustering technology provided by this application. The method includes but is not limited to the following steps:

[0080] (1) Randomly select the initial cluster center;

[0081] (2) Calculate the Euclidean distance from all cluster samples to each cluster center;

[0082] The distance between each cluster sample and the cluster center can be calculated by the following formula:

[0083]

[0084] Where d(l,j) is the Euclidean distance between the lth load sample and the jth cluster center;

[0085] (3) Calculate the mean of the load samples in each cluster and update the cluster center;

[0086] (4) Repeat steps (2) and (3) until the load samples in the cluster no longer change or the maximum number of repetitions is reached.

[0087] Furthermore, in order to ensure the clustering effect, the following method is used to determine the number of clusters and select the initial cluster center:

[0088] (1) Determination of the number of clusters

[0089] In order to select an appropriate value of K for clustering to ensure clustering effect and computational efficiency, an index for evaluating clustering effect, weighted average radius, is proposed, and its expression is:

[0090]

[0091] Where R is the weighted average radius of each cluster after clustering; p i is the ratio of the number of samples in the ith cluster to the total number of samples; r i is the radius of the i-th cluster;

[0092] When the number of clusters K is in a certain interval, the value of R decreases as K increases, and the clustering effect is significantly improved; when K reaches a certain value and then increases, the decreasing trend of the R value slows down, and the clustering effect is not obvious; selecting the value of K at this time as the number of clusters can not only ensure the clustering effect, but also reduce the calculation time;

[0093] (2) Selection of initial cluster centers

[0094] Clustering is optimized through preprocessing, and 10% of the samples are randomly selected for clustering. The cluster center of each cluster can reflect the distribution of all samples to a certain extent. At the same time, the calculation time is shorter when only 10% of the samples are selected for clustering, and the cluster center obtained by the pre-processed clustering can be used as the initial cluster center for clustering all samples.

[0095] Furthermore, when simulating faults, consider the impact of the distribution network automation configuration type on the scope and duration of power outages, and distinguish the different fault isolation and power transfer times corresponding to different automation configuration types during faults. Figure 3 , Figure 3 A schematic diagram of a power outage impact range analysis process under different automation configuration modes of a distribution network provided in an embodiment of the present application, the method comprising the following steps:

[0096] (1) First, consider the power outage caused by each device and make statistics on the impact range of upstream and downstream devices;

[0097] (2) Determine the fault location isolation time based on the upstream and downstream automation configuration types of the fault point;

[0098] (3) Determine the fault section and upstream power outage time based on the fault location isolation time and fault repair time;

[0099] (4) Determine the time for power transfer and power restoration based on whether there is a power transfer path downstream of the fault and the automation level of the interconnection switch;

[0100] (5) Finally, the power outage range and power outage time corresponding to different automation configuration types are obtained.

[0101] Embodiment 2

[0102] like Figure 4 As shown, the present invention also discloses a new business distribution network fault simulation system based on digital twins, including a data input module, a data preprocessing module, a digital twin construction module, and a fault simulation module, wherein:

[0103] The data input module is used to obtain target distribution network resource data;

[0104] Furthermore, the data input module includes a substation master meter, a substation intelligent fusion terminal, a smart meter, an HPLC, a fault recorder, etc. The target distribution network resource data includes topological information, ledger information, equipment parameters, historical fault data and basic grid operation data covering new elements of the distribution network, including:

[0105] The topology information includes the physical layout and connection mode of the distribution network;

[0106] The ledger information includes substation ledger, line ledger, distribution transformer ledger, distributed power source ledger, and flexible load ledger;

[0107] The equipment parameters include the model, rated voltage and rated current of the equipment;

[0108] The historical fault data includes fault type, fault time, fault location, fault impact range, fault risk level, etc.;

[0109] The basic operation data of the power grid include voltage, current, load, etc.

[0110] The digital preprocessing module is used to clean the target distribution network resource data, complete missing values ​​and process abnormal values;

[0111] The digital twin construction module is used to construct a digital twin model of the target distribution network according to the target distribution network resource data;

[0112] Furthermore, the digital twin construction module monitors the operating status of the elements of the integrated new business grid, enables the virtual grid to operate synchronously with the physical grid, digitally models and visualizes the perceived multi-source data, and realizes dynamic display of the grid operation status.

[0113] The fault simulation module is used to simulate various fault scenarios according to the real-time operating status of the power grid, including the specific power outage location (switches, switch stations, distribution stations) and the power outage range, simulate the fault location and isolation action after the power outage, and define the fault type (equipment failure, natural disasters, external force damage, etc.).

[0114] Furthermore, the fault scenarios include distributed power grid-connected consumption scenarios, electric vehicle and new load regulation scenarios, multi-energy storage participating in grid interactive regulation scenarios, and microgrid on-grid and off-grid regulation scenarios; the anticipated accident set includes fault types and fault impact scope, etc.

[0115] See also Figure 5 , Figure 5 It is a schematic diagram of an embodiment of a fault simulation method based on digital twins of new business distribution network elements provided in an embodiment of the present application.

[0116] This embodiment uses digital twin technology to simulate the faults of the new business distribution network and achieves the following effects:

[0117] (1) Constructing a digital twin of the distribution network: Based on the operating data of each new element of the distribution network, a digital twin of the distribution network is constructed. This digital twin can reflect the operating status of the distribution network in real time and provide an accurate basis for subsequent fault simulation.

[0118] (2) Fully consider the new business elements of the distribution network: In the digital twin technology, fully consider the new business elements of the distribution network, such as the grid-connected consumption of distributed power sources, the regulation of electric vehicles and new loads, the participation of multiple energy storage in grid interactive regulation, and the grid-connected and off-grid regulation of microgrids, so as to improve the accuracy and practicality of fault simulation.

[0119] (3) Constructing a set of anticipated faults: Based on the fault scenarios obtained by simulating various operation scenarios of the distribution network by the digital twin of the distribution network, a set of anticipated faults is constructed. This set of anticipated faults includes various possible fault conditions of the distribution network, providing a comprehensive set of fault scenarios for fault simulation.

[0120] (4) Optimizing the fault simulation process: By continuously optimizing the fault simulation process, the results of the fault simulation are made closer to the actual operating status of the distribution network, thereby improving the accuracy and practicality of the fault simulation.

[0121] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A new distribution network fault simulation method based on digital twin, characterized in that: The following steps are involved: Step S101: Acquire target distribution network resource data; Step S102: preprocessing and analyzing the collected data to extract useful information and features; Step S103: constructing a digital twin model of the target distribution network according to the target distribution network resource data; Step S104: constructing a set of anticipated accidents according to the target distribution network fault operation scenario.

2. According to a new business distribution network fault simulation method based on digital twins according to claim 1, it is characterized in that: In step S101, the target distribution network resource data includes topology information, ledger information, equipment parameters, historical fault data and basic grid operation data covering new elements of the distribution network.

3. According to a new business distribution network fault simulation method based on digital twins according to claim 1, it is characterized in that: In step S103, by comprehensively considering the data in the target distribution network resource data, a digital twin model is constructed, and the perceived multi-source data is digitally modeled and visualized to achieve a dynamic display of the power grid operation status.

4. According to a new business distribution network fault simulation method based on digital twins according to claim 1, it is characterized in that: In step S104, typical operation scenarios are analyzed based on the target distribution network resource data, including distributed power grid connection and absorption, electric vehicle and new load regulation, multi-energy storage participating in grid interactive regulation, microgrid and off-grid regulation scenarios, to construct an anticipated accident set; the anticipated accident set includes fault type and fault impact range.

5. According to a new business distribution network fault simulation method based on digital twins according to claim 1, it is characterized in that: In step S104, the randomness and uncertainty of distributed energy output are comprehensively considered to affect the distribution network operation and fault simulation, and the input load samples are clustered using clustering technology to achieve that the input load samples in each cluster have a smaller fluctuation range, thereby obtaining load clustering and generating typical loads; Clustering load samples, load samples can be expressed as: L=[L1,L2,...,L i ,...,L m ] Among them, L i The load at a certain time.

6. A new business distribution network fault simulation method based on digital twin according to claim 5, characterized in that: In order to obtain typical load samples, clustering technology is used to divide the input load samples into multiple clusters. The method includes the following steps: (1) Randomly select the initial cluster center; (2) Calculate the Euclidean distance from all cluster samples to each cluster center; The distance between each cluster sample and the cluster center can be calculated by the following formula: Where d(l,j) is the Euclidean distance between the lth load sample and the jth cluster center; (3) Calculate the mean of the load samples in each cluster and update the cluster center; (4) Repeat steps (2) and (3) until the load samples in the cluster no longer change or the maximum number of repetitions is reached.

7. A new business distribution network fault simulation method based on digital twin according to claim 6, characterized in that: In order to ensure the clustering effect, the following method is used to determine the number of clusters and select the initial cluster center: (1) Determination of the number of clusters In order to select an appropriate value of K for clustering to ensure clustering effect and computational efficiency, an index for evaluating clustering effect, weighted average radius, is proposed, and its expression is: Where R is the weighted average radius of each cluster after clustering; p i is the ratio of the number of samples in the ith cluster to the total number of samples; r i is the radius of the i-th cluster; When the number of clusters K is in a certain interval, the value of R decreases as K increases, and the clustering effect is significantly improved; when K reaches a certain value and then increases, the decreasing trend of the R value slows down, and the clustering effect is not obvious; selecting the value of K at this time as the number of clusters can not only ensure the clustering effect, but also reduce the calculation time; (2) Selection of initial cluster centers Clustering is optimized through preprocessing, and 10% of the samples are randomly selected for clustering. The cluster center of each cluster can reflect the distribution of all samples to a certain extent. At the same time, the calculation time is shorter when only 10% of the samples are selected for clustering, and the cluster center obtained by the pre-processed clustering can be used as the initial cluster center for clustering all samples.

8. A new business distribution network fault simulation method based on digital twin according to claim 5, characterized in that: During fault simulation, the influence of the distribution network automation configuration type on the power outage scope and power outage time is considered, and different fault isolation and power transfer durations corresponding to different automation configuration types are distinguished during faults. The method includes the following steps: (1) First, consider the power outage caused by each device and make statistics on the impact range of upstream and downstream devices; (2) Determine the fault location isolation time based on the upstream and downstream automation configuration types of the fault point; (3) Determine the fault section and upstream power outage time based on the fault location isolation time and fault repair time; (4) Determine the time for power transfer and power restoration based on whether there is a power transfer path downstream of the fault and the automation level of the interconnection switch; (5) Finally, the power outage range and power outage time corresponding to different automation configuration types are obtained.

9. A new distribution network fault simulation system based on digital twins, characterized in that: It includes data input module, data preprocessing module, digital twin construction module and fault simulation module, among which: The data input module is used to obtain target distribution network resource data; The digital preprocessing module is used to clean the target distribution network resource data, complete missing values ​​and process abnormal values; The digital twin construction module is used to construct a digital twin model of the target distribution network according to the target distribution network resource data; The fault simulation module is used to simulate various fault scenarios according to the real-time operating status of the power grid.

10. A new business distribution network fault simulation system based on digital twin according to claim 9, characterized in that: The data input module includes a substation master meter, a substation intelligent fusion terminal, a smart meter, a HPLC, and a fault recorder.

11. A new business distribution network fault simulation system based on digital twin according to claim 9, characterized in that: The target distribution network resource data includes topological information, ledger information, equipment parameters, historical fault data and basic grid operation data covering new elements of the distribution network, including: The topology information includes the physical layout and connection mode of the distribution network; The ledger information includes substation ledger, line ledger, distribution transformer ledger, distributed power source ledger, and flexible load ledger; The equipment parameters include the model, rated voltage and rated current of the equipment; The historical fault data includes fault type, fault time, fault location, fault impact range, fault risk level, etc.; The basic operation data of the power grid include voltage, current and load.

12. A new business distribution network fault simulation system based on digital twin according to claim 9, characterized in that: The digital twin construction module monitors the operating status of the elements of the integrated new business grid, enables the virtual grid to operate synchronously with the physical grid, digitally models and visualizes the perceived multi-source data, and realizes the dynamic display of the grid operation status.

13. A new business distribution network fault simulation system based on digital twin according to claim 9, characterized in that: The fault scenarios include distributed power grid-connected consumption scenarios, electric vehicle and new load regulation scenarios, multi-energy storage participating in grid interactive regulation scenarios, and microgrid grid-connected and off-grid regulation scenarios; the expected accident set includes fault types and fault impact scope.

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