Cascade fault analysis method based on digital twinning and physical power and related assembly

Abstract

The invention discloses a cascade fault analysis method based on digital twinning and physical power and related components. The method comprises the following steps: establishing a double-layer coupling network model through a digital twinning virtual network and a physical power network; setting a cascade fault propagation process through the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy so as to construct a multi-layer network cascade fault model; updating the multi-layer network cascade fault model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascade fault model; and performing cascade fault analysis on a specified power system by using the target cascade fault model. According to the method, the target cascade fault model is constructed based on the coupling symbiosis between the digital twin virtual network and the physical power network in combination with the communication delay, and the analysis accuracy and reliability of the cascade fault of the power system can be effectively improved through the target cascade fault model.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a cascading failure analysis method based on digital twins and physical power and related components. Background Art

[0002] Power equipment is a crucial foundation for ensuring safe grid operation, reliable power supply, and improved company operational performance. The digital transformation of power grid equipment refers to the use of information technology to build a modern equipment management system. This system ensures grid security, improves operational performance, supports service quality, and promotes technological upgrades. However, the digitalization of power networks also makes them more complex. If a small component in a power network fails, the complex nature of the network can potentially propagate the failure to a wider area through network coupling, leading to widespread or even global cascading failures. Once a large-scale cascading failure occurs, it can trigger adverse effects and cause immeasurable losses. Based on complex network theory, researchers have conducted extensive research on cascading failures in the power sector, aiming to effectively predict and control the propagation of power system failures. Currently, researchers have proposed various models to describe and analyze cascading failures in real-world systems, including self-organized criticality models and models based on complex network theory and cellular automata.

[0003] However, the complexity of power networks is not only reflected in the physical grid, but also in the coupling relationship between real and virtual scenarios. Therefore, the aforementioned models used to describe and analyze cascading failures in real-world systems have corresponding flaws, namely the lack of coupling between real and virtual scenarios, resulting in inaccurate and unreliable prediction results. Based on this, digital twin scenarios, which deeply integrate the concepts of next-generation information technology and digital models, have attracted widespread attention from industry and academia. This approach uses digital twins to depict the multidimensional attributes, actual behavior, and status of physical objects through the interaction between physical objects and virtual models, and analyzes the future development trends of physical objects, thereby realizing practical functional services and application needs such as monitoring, simulation, prediction, and optimization of physical objects.

[0004] However, the interactions between the digital twin virtual network and the physical power network can be probabilistic, or their impacts can occur with a delay, making the exact interactions often difficult to determine from the available data. Furthermore, because layers are interdependent, information about specific layers and the dynamics of cascading effects is often lost when aggregating network data, which can also affect the final results. Summary of the Invention

[0005] The embodiments of the present invention provide a cascading failure analysis method, apparatus, computer equipment, and storage medium based on digital twins and physical electricity, aiming to improve the accuracy and reliability of the analysis of cascading failures in power systems.

[0006] In a first aspect, an embodiment of the present invention provides a cascading failure analysis method based on digital twins and physical power, comprising:

[0007] Establish a two-layer coupled network model through digital twin virtual network and physical power network;

[0008] Based on the two-layer coupled network model, a cascading fault propagation process is set through the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy, thereby constructing a multi-layer network cascading fault model;

[0009] updating the multi-layer network cascading failure model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascading failure model;

[0010] The target cascading failure model is used to perform cascading failure analysis on a designated power system.

[0011] In a second aspect, an embodiment of the present invention provides a cascading failure analysis device based on digital twins and physical power, comprising:

[0012] A network coupling unit is used to establish a two-layer coupled network model through the digital twin virtual network and the physical power network;

[0013] A cascade setting unit is configured to set a cascade fault propagation process based on the two-layer coupled network model, using the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy, thereby constructing a multi-layer network cascade fault model;

[0014] a delay updating unit, configured to update the multi-layer network cascading failure model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascading failure model;

[0015] The cascade analysis unit is configured to perform cascade failure analysis on a designated power system using the target cascade failure model.

[0016] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the method for cascading failure analysis based on digital twins and physical power as described in the first aspect is implemented.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the cascading failure analysis method based on digital twins and physical power as described in the first aspect.

[0018] An embodiment of the present invention provides a cascading failure analysis method, device, computer equipment and storage medium based on digital twins and physical power. The method includes: establishing a two-layer coupled network model through a digital twin virtual network and a physical power network; based on the two-layer coupled network model, setting a cascading failure propagation process through the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy, thereby constructing a multi-layer network cascading failure model; updating the multi-layer network cascading failure model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascading failure model; and using the target cascading failure model to perform cascading failure analysis on a specified power system. The embodiment of the present invention takes into account the coupling symbiosis between the digital twin virtual network and the physical power network, constructs a two-layer coupled network model based on the coupling of the digital twin virtual network and the physical power network, and further sets the cascading fault propagation process through the network load, so that the two-layer coupled network model is upgraded to a multi-layer network cascading fault model. At the same time, combined with noise interference such as communication delay, the multi-layer network cascading fault model is updated to obtain a target cascading fault model with improved performance, so that the target cascading fault model can better simulate the cascading fault propagation process of the point network in the real scenario, thereby improving the analysis accuracy and reliability of the cascading faults of the power system and providing a basis for improving the robustness of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A schematic flow chart of a cascading failure analysis method based on digital twins and physical power provided in an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of network coupling in a cascading failure analysis method based on digital twins and physical power provided in an embodiment of the present invention;

[0022] Figure 3 A flowchart of cascading fault propagation for a cascading fault analysis method based on digital twins and physical power provided in an embodiment of the present invention;

[0023] Figure 4 A schematic block diagram of a cascading fault analysis device based on digital twins and physical power provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0025] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0026] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0027] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0028] See below Figure 1 , an embodiment of the present invention provides a cascading failure analysis method based on digital twins and physical power, which specifically includes: steps S101 to S104.

[0029] Step S101: Establish a double-layer coupled network model through the digital twin virtual network and the physical power network;

[0030] Step S102: Based on the two-layer coupled network model, a cascading fault propagation process is set by using the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy to construct a multi-layer network cascading fault model;

[0031] Step S103: updating the multi-layer network cascading failure model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascading failure model;

[0032] Step S104: Perform cascading failure analysis on the designated power system using the target cascading failure model.

[0033] In this embodiment, a two-layer coupled network model is first built based on the digital twin virtual network and the physical power network. Then, the cascading fault propagation process is designed by combining the load information and load redistribution strategy of the digital twin virtual network and the physical power network, thereby upgrading the two-layer coupled network model to a multi-layer network cascading fault model. Then, the parameters of the multi-layer network cascading fault model are set in combination with the communication delay, and finally a target cascading fault model that can be used for cascading fault analysis is obtained.

[0034] This embodiment takes into account the coupling and symbiosis between the digital twin virtual network and the physical power network, and constructs a two-layer coupled network model based on the coupling of the digital twin virtual network and the physical power network. The cascading fault propagation process is further set by the network load, so that the two-layer coupled network model is upgraded to a multi-layer network cascading fault model. At the same time, combined with noise interference such as communication delay, the multi-layer network cascading fault model is updated to obtain a target cascading fault model with improved performance. The target cascading fault model can better simulate the cascading fault propagation process of the point network in the real scenario, thereby improving the accuracy and reliability of the analysis of cascading faults in the power system and providing a basis for improving network robustness.

[0035] In one embodiment, establishing a two-layer coupled network model through a digital twin virtual network and a physical power network includes:

[0036] The digital twin virtual network is set as the upper layer, and the physical power network is set as the lower layer, and the network nodes of the digital twin virtual network and the physical power network are coupled one by one to establish a two-layer coupled network model; wherein the digital twin virtual network has a first initial load and a first load capacity, and the physical power network has a second initial load and a second load capacity.

[0037] like Figure 2 As shown, this embodiment proposes a two-layer coupled network model of physical power network-digital twin virtual network coupling. The two-layer coupled network model is mainly implemented by the construction of two-layer network structure and inter-layer coupling relationship. For example, a two-layer network can be constructed, and the upper network is a digital twin virtual network (VN): G V =(V V ,E V ), the lower layer is the physical power network (PN): G P =(V P ,E P ), there is a one-to-one coupling relationship between the nodes of the two-layer network, that is, each virtual node v i ∈V V With a physical node pi ∈V P One-to-one correspondence. Each virtual node v in the upper network i There is an initial load L V (v i ) and a load capacity C V (v i ), similarly, the lower physical layer node p i Define an initial load L P (p i ) and a load capacity C P (p i ).

[0038] In addition, from Figure 2 It can be seen that the network nodes of the digital twin virtual network and the physical power network can refer to various different power devices, such as sound devices, optoelectronic devices, thermal sensing devices, and chemical-related devices, etc. That is to say, the power system described in this embodiment can be different power devices such as sound devices, optoelectronic devices, etc. in actual applications.

[0039] In one embodiment, based on the two-layer coupled network model, a cascading fault propagation process is set through the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy to construct a multi-layer network cascading fault model, including:

[0040] The first and second initial loads are set according to the following formula:

[0041]

[0042] in, represents the first initial load, represents the second initial load, N represents the number of network nodes in the digital twin virtual network or physical power network, k i represents the degree of node i, α and β are adjustable parameters that control the initial load intensity of the node;

[0043] Set the first load capacity as follows:

[0044]

[0045] in, represents the first load capacity, λ1 represents the parameter of the maximum load that node i can accommodate in the digital twin virtual network;

[0046] Set the second load capacity as follows:

[0047]

[0048] in, represents the second load capacity, and λ2 represents a parameter of the maximum load that node i can accommodate in the physical power network.

[0049] This embodiment considers the coupling relationship between two layers of networks and constructs a multi-layer network cascading failure model. The cascading failure process of this model is as follows: Figure 3 As shown in the figure, node failures in the digital twin virtual network and / or the physical power network trigger a cascading failure process, which has its own unique load distribution process between the upper and lower layers. In particular, when a cascading failure occurs, communication delays between the two layers affect the fault propagation process between the virtual network (VN) and the physical power network (PN). In other words, the failure of a virtual node does not immediately cause the failure of the corresponding physical node, and vice versa.

[0050] Therefore, this embodiment first defines the initial load and load capacity of each network node. Assuming that the network is initially in a dynamic stable state, the nodes in the two layers of the network have the same initial load, and there are no faulty nodes at the initial moment, the initial risk load of node i is set to:

[0051]

[0052] Where N is the number of nodes in the network, k i is the degree of node i, α and β are adjustable parameters that control the initial load intensity of the node.

[0053] Generally, nodes in the digital twin virtual network can be set with a larger load capacity. However, due to cost reasons, the load capacity of nodes in the physical power network is limited. Therefore, the node load capacity of the two layers of the network is different. Therefore, the load capacity of the digital twin virtual node is set as:

[0054]

[0055] The load capacity of a node in the physical power network is set as:

[0056]

[0057] Among them, λ1 and λ2 (λ1, λ2>0) represent the parameters of the maximum load that node i can accommodate in the virtual network and the physical power network, respectively. The larger the values of λ1 and λ2, the stronger the load capacity of the node and the lower the possibility of failure.

[0058] In one embodiment, the method of setting a cascading fault propagation process based on the two-layer coupled network model by using the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy to construct a multi-layer network cascading fault model further includes:

[0059] Selecting any one of the network nodes of the digital twin virtual network or the physical power network as a target node, and determining whether the load of the target node is within a preset load capacity;

[0060] If it is determined that the load of the target node is within the preset load capacity, then the neighboring node of the target node is continued to be used as a new target node, and the load of the target node is further determined to be within the preset load capacity;

[0061] If it is determined that the load of the target node is not within the preset load capacity, the target node is set as the initial fault node and the corresponding initial time is recorded;

[0062] The load of the initial fault node is distributed to the neighboring nodes of the physical power network and the neighboring nodes of the digital twin virtual network respectively.

[0063] Combine Figure 3 First, we select any network node and determine whether its load is within its load capacity. If not, we determine that it has a fault. If not, we determine that it has no fault and continue to determine whether its neighboring nodes have faults. When a network node is determined to have a fault, we first distribute the load according to the load redistribution strategy of the physical power network, that is, distribute the load of the faulty node to its direct neighboring nodes. On the other hand, we distribute the load according to the load redistribution strategy of the virtual network, that is, evenly distribute it to other network nodes.

[0064] Specifically, distributing the load of the initial fault node to neighboring nodes of the physical power network and neighboring nodes of the digital twin virtual network respectively includes:

[0065] According to the following formula, the load of the initial fault node is distributed to the direct neighbor nodes of the physical power network according to the degree value:

[0066]

[0067]

[0068] in, represents the load of node j at time t=1 in the physical power network, represents the load of the node at the initial time t=0, ΔL i→j (0) represents the load that node j receives from the initial fault node i at the initial moment, represents the load of node i multiplied by the distribution ratio, V i represents the set of neighbor nodes of the initial faulty node, node j is one of the neighbor nodes, and α2 represents the adjustable parameter used to control the workload redistribution weight;

[0069] According to the following formula, the load of the initial fault node is evenly distributed to all other network nodes of the digital twin virtual network:

[0070]

[0071] in, represents the load of node j at time t=1 in the digital twin virtual network, represents the load of the node at the initial time t=0, It represents the load that node j receives from the initial fault node i at the initial moment.

[0072] In this embodiment, a node p in the physical power network can be selected. i As the initial fault node, record the fault time t = 0. Node p i The load is redistributed to neighboring nodes in the virtual network and the physical power network, respectively. The distribution strategy is as follows: In the physical power network, a local load redistribution strategy is adopted to take into account factors such as slow distribution speed and physical distance between equipment or power stations. The load of the faulty node is distributed only to its direct neighboring nodes according to the size of the degree. In the virtual network, issues such as transmission cost and transmission speed are ignored and a global load redistribution strategy is adopted. The load of the faulty node is evenly distributed to all other virtual nodes. Therefore, at time t = 1, the load of neighbor node j of node i in the two-layer network changes. The load of node j in the physical power network becomes:

[0073]

[0074] The load that node i distributes to a neighbor node j is set as:

[0075]

[0076] Among them, V i is the set of neighbor nodes of the failed node, node j is one of the neighbor nodes. α2 is an adjustable parameter used to control the workload redistribution weight.

[0077] The load of node j in the virtual network becomes:

[0078]

[0079] When the load in the network changes, the nodes in the two-layer network that exceed the load capacity will fail, and it is possible that they are different nodes in different layers of the network. Without considering the communication delay, the load of the nodes that have not failed in the digital twin virtual network changes with the load of the physical layer nodes, that is, Therefore, the node load on both upper and lower layers will increase. In this way, multi-layer coupled networks are more prone to cascading failures than single-layer networks, and the damage caused by cascading failures is greater.

[0080] In one embodiment, updating the multi-layer network cascading failure model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascading failure model includes:

[0081] Set the communication delay parameter τ between the digital twin virtual network and the physical power network P ;

[0082] When the initial fault node of the physical power network is obtained, the corresponding fault node of the digital twin virtual network is determined according to the following formula:

[0083]

[0084] in, Indicates the corresponding fault node, represents the initial fault node, t represents the initial time corresponding to the initial fault node;

[0085] Based on the initial fault node and the corresponding fault node, other network nodes in the multi-layer network cascading fault model are set:

[0086]

[0087] in, represents the load of node j at time t in the physical power network, Indicates the load of node j at time t-1 in the physical power network, ΔL i→j (t-1) represents the load that node j receives from the initial fault node i at time t-1 in the physical power network. represents the load capacity of node j in the physical power network, represents the load of node j at time t in the digital twin virtual network, represents the load of node j at time t-1 in the digital twin virtual network, In the digital twin virtual network, the load that node j receives from the initial fault node i at time t-1 is represented. Represents the load capacity of node j in the digital twin virtual network.

[0088] In this embodiment, considering the cascading failure process of inter-layer communication delay, we first define the communication delay parameter τ between the two layers of the network. P In the real network, when the node p in the physical power network i After the fault, after the delay time τ P, its corresponding virtual node v i Failure.

[0089]

[0090] To ensure that the network does not experience cascading failures at time t, any node j in the network needs to satisfy the following conditions:

[0091]

[0092] Where j is the set of neighbors of the faulty node i. The above formula can be used to calculate the minimum thresholds λ1 and λ2 that meet the conditions, thereby achieving network robustness at the lowest cost.

[0093] Specifically, the communication delay parameter τ P =C, where C is a constant and C≥0;

[0094] Alternatively, the communication delay parameter τ P =rand[0,m], where m is a random number greater than 0.

[0095] In this embodiment, the setting of the communication delay parameter can be divided into the following two cases:

[0096] (1)τ p =C, C is a constant, and C≥0. The communication delay time between the digital twin virtual network (VN) and the physical power network (PN) is fixed. Specifically, the failure of the physical node will also be delayed by a fixed time τ p This fixed delay simplifies the model while still capturing the impact of delay on the cascading failure process.

[0097] (2)τ P = rand[0,m], where m>0. The communication delay between the digital twin virtual network (VN) and the physical power network (PN) is no longer fixed but random. This randomness can better simulate the uncertainties in real systems, such as delay fluctuations caused by factors such as network congestion, differences in node processing capabilities, and external interference.

[0098] In general, the cascading failure analysis method based on digital twins and physical power provided in this embodiment has the following advantages:

[0099] (1) This embodiment proposes a coupling interaction process between a virtual network and a real network. Based on the concept of complex networks, a virtual and real double-layer coupling network is constructed in the digital twin scenario. The nodes and edges within the network and the coupling relationships between networks are designed. This fully considers the actual operating conditions and potential risks of the power system and improves the accuracy and reliability of fault prediction.

[0100] (2) This embodiment designs a cascading failure model in two networks, including initial load, load capacity, and load redistribution strategy. After a node failure occurs in the digital twin virtual network and / or the physical power network, a unique load distribution process is implemented at the upper and lower layers, namely, global distribution in the digital twin virtual network (VN) and local distribution in the physical power network (PN). This design can simulate the dynamic characteristics of the actual power system load redistribution strategy and more accurately reflect the propagation and impact of the fault in the power system.

[0101] (3) This embodiment takes into account the propagation process of cascading failures caused by communication delays in the coupled network and designs two different communication delay mechanisms. After the communication delay time, the node status of the initial fault node corresponding to the other layer of the network will also change. At the same time, the status of the nodes in the virtual network is updated according to the status of the physical power network nodes. This can more realistically simulate the cascading failure process in the actual power system and further improve the accuracy and reliability of fault prediction.

[0102] Figure 4 A schematic block diagram of a cascading failure analysis device 400 based on digital twins and physical power provided in an embodiment of the present invention includes:

[0103] A network coupling unit 401 is used to establish a two-layer coupled network model through a digital twin virtual network and a physical power network;

[0104] A cascade setting unit 402 is configured to set a cascade fault propagation process based on the two-layer coupled network model, using the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy, thereby constructing a multi-layer network cascade fault model;

[0105] a delay updating unit 403 , configured to update the multi-layer network cascading failure model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascading failure model;

[0106] The cascade analysis unit 404 is configured to perform cascade failure analysis on a designated power system using the target cascade failure model.

[0107] In one embodiment, the network coupling unit 401 includes:

[0108] A node coupling unit is used to set the digital twin virtual network as the upper layer and the physical power network as the lower layer, and to couple the network nodes of the digital twin virtual network and the physical power network one by one to establish a two-layer coupled network model; wherein the digital twin virtual network has a first initial load and a first load capacity, and the physical power network has a second initial load and a second load capacity.

[0109] In one embodiment, the cascade setting unit 402 includes:

[0110] The load setting unit is configured to set the first initial load and the second initial load according to the following formula:

[0111]

[0112] in, represents the first initial load, represents the second initial load, N represents the number of network nodes in the digital twin virtual network or physical power network, k i represents the degree of the ith node i, α and β represent the adjustable parameters that control the initial load intensity of the node;

[0113] The first capacity setting unit is configured to set the first load capacity according to the following formula:

[0114]

[0115] in, represents the first load capacity, λ1 represents the parameter of the maximum load that node i can accommodate in the digital twin virtual network;

[0116] The second capacity setting unit is configured to set the second load capacity according to the following formula:

[0117]

[0118] in, represents the second load capacity, and λ2 represents a parameter of the maximum load that node i can accommodate in the physical power network.

[0119] In one embodiment, the cascade setting unit 402 further includes:

[0120] A load judgment unit, configured to select any one of the network nodes of the digital twin virtual network or the physical power network as a target node, and to judge whether the load of the target node is within a preset load capacity;

[0121] A first determining unit is configured to, if it is determined that the load of the target node is within a preset load capacity, continue to use a neighboring node of the target node as a new target node, and continue to determine whether the load of the target node is within the preset load capacity;

[0122] a second determining unit, configured to set the target node as an initial fault node and record a corresponding initial time if it is determined that the load of the target node is not within a preset load capacity;

[0123] A load distribution unit is used to distribute the load of the initial fault node to the neighboring nodes of the physical power network and the neighboring nodes of the digital twin virtual network respectively.

[0124] In one embodiment, the load distribution unit includes:

[0125] A degree distribution unit is configured to distribute the load of the initial fault node to the direct neighboring nodes of the physical power network according to the degree according to the following formula:

[0126]

[0127] in, represents the load of node j at time t=1 in the physical power network, represents the load of the node at the initial time t=0, ΔL i→j (0) represents the load that node j receives from the initial fault node i at the initial moment, represents the load of node i multiplied by the distribution ratio, V i represents the set of neighbor nodes of the initial faulty node, node j is one of the neighbor nodes, and α2 represents the adjustable parameter used to control the workload redistribution weight;

[0128] A uniform distribution unit is used to evenly distribute the load of the initial fault node to all other network nodes of the digital twin virtual network according to the following formula:

[0129]

[0130] in, represents the load of node j at time t=1 in the digital twin virtual network, represents the load of the node at the initial time t=0, It represents the load that node j receives from the initial fault node i at the initial moment.

[0131] In one embodiment, the delay updating unit 403 includes:

[0132] A parameter setting unit, used to set the communication delay parameter τ between the digital twin virtual network and the physical power networkP ;

[0133] The node determination unit is configured to, when an initial fault node of the physical power network is obtained, determine a corresponding fault node of the digital twin virtual network according to the following formula:

[0134]

[0135] in, Indicates the corresponding fault node, represents the initial fault node, t represents the initial time corresponding to the initial fault node;

[0136] A node setting unit is used to set other network nodes in the multi-layer network cascading failure model based on the initial fault node and the corresponding fault node:

[0137]

[0138] in, represents the load of node j at time t in the physical power network, Indicates the load of node j at time t-1 in the physical power network, ΔL i→j (t-1) represents the load that node j receives from the initial fault node i at time t-1 in the physical power network. represents the load capacity of node j in the physical power network, represents the load of node j at time t in the digital twin virtual network, represents the load of node j at time t-1 in the digital twin virtual network, In the digital twin virtual network, the load that node j receives from the initial fault node i at time t-1 is represented. Represents the load capacity of node j in the digital twin virtual network.

[0139] In one embodiment, the communication delay parameter τ P =C, where C is a constant and C≥0;

[0140] Alternatively, the communication delay parameter τ P =rand[0,m], where m is a random number greater than 0.

[0141] Since the embodiments of the apparatus part correspond to the embodiments of the method part, please refer to the description of the embodiments of the method part for the embodiments of the apparatus part, and they will not be repeated here.

[0142] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When executed, the computer program can implement the steps provided in the above embodiments. The storage medium can include a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, among other media capable of storing program code.

[0143] The present invention also provides a computer device that may include a memory and a processor. The memory stores a computer program, and when the processor calls the computer program in the memory, the steps provided in the above embodiment can be implemented. Of course, the computer device may also include various network interfaces, a power supply, and other components.

[0144] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of this application, several improvements and modifications can be made to this application, and these improvements and modifications also fall within the scope of protection of the claims of this application.

[0145] It should also be noted that, in this specification, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

Claims

1. A cascading failure analysis method based on digital twins and physical power, characterized in that: include: Establish a two-layer coupled network model through digital twin virtual network and physical power network; Based on the two-layer coupled network model, a cascading fault propagation process is set through the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy, thereby constructing a multi-layer network cascading fault model; updating the multi-layer network cascading failure model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascading failure model; The target cascading failure model is used to perform cascading failure analysis on a designated power system.

2. The cascading failure analysis method based on digital twins and physical power according to claim 1 is characterized in that: The two-layer coupled network model is established by using the digital twin virtual network and the physical power network, including: The digital twin virtual network is set as the upper layer, and the physical power network is set as the lower layer, and the network nodes of the digital twin virtual network and the physical power network are coupled one by one to establish a two-layer coupled network model; wherein the digital twin virtual network has a first initial load and a first load capacity, and the physical power network has a second initial load and a second load capacity.

3. The cascading failure analysis method based on digital twins and physical power according to claim 2 is characterized in that: The method is based on the dual-layer coupled network model, and sets a cascading fault propagation process through the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy, thereby constructing a multi-layer network cascading fault model, including: The first and second initial loads are set according to the following formula: in, represents the first initial load, represents the second initial load, N represents the number of network nodes in the digital twin virtual network or physical power network, k i represents the degree of node i, α and β are adjustable parameters that control the initial load intensity of the node; Set the first load capacity as follows: in, represents the first load capacity, λ1 represents the parameter of the maximum load that node i can accommodate in the digital twin virtual network; Set the second load capacity as follows: in, represents the second load capacity, and λ2 represents a parameter of the maximum load that node i can accommodate in the physical power network.

4. The cascading failure analysis method based on digital twins and physical power according to claim 3 is characterized in that: The method further includes setting a cascading fault propagation process based on the double-layer coupled network model by using the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy to construct a multi-layer network cascading fault model. Select any one of the network nodes of the digital twin virtual network or the physical power network as a target node, and determine whether the load of the target node is within the preset load capacity; If it is determined that the load of the target node is within the preset load capacity, then the neighboring node of the target node is continued to be used as a new target node, and the load of the target node is further determined to be within the preset load capacity; If it is determined that the load of the target node is not within the preset load capacity, the target node is set as the initial fault node and the corresponding initial time is recorded; The load of the initial fault node is distributed to the neighboring nodes of the physical power network and the neighboring nodes of the digital twin virtual network respectively.

5. The cascading failure analysis method based on digital twins and physical power according to claim 4 is characterized in that: The distributing the load of the initial fault node to the neighboring nodes of the physical power network and the neighboring nodes of the digital twin virtual network respectively includes: According to the following formula, the load of the initial fault node is distributed to the direct neighbor nodes of the physical power network according to the degree value: in, represents the load of node j at time t=1 in the physical power network, represents the load of the node at the initial time t=0, ΔL i→j (0) represents the load that node j receives from the initial fault node i at the initial moment, represents the load of node i multiplied by the distribution ratio, V i Indicates the neighbor nodes of the initial fault node The set, node j is one of the neighbor nodes, and α2 represents the adjustable parameter used to control the workload redistribution weight; According to the following formula, the load of the initial fault node is evenly distributed to all other network nodes of the digital twin virtual network: in, represents the load of node j at time t=1 in the digital twin virtual network, represents the load of the node at the initial time t=0, It represents the load that node j receives from the initial fault node i at the initial moment.

6. The cascading failure analysis method based on digital twins and physical power according to claim 5 is characterized in that: The updating of the multi-layer network cascading failure model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascading failure model includes: Set the communication delay parameter τ between the digital twin virtual network and the physical power network P ; When the initial fault node of the physical power network is obtained, the corresponding fault node of the digital twin virtual network is determined according to the following formula: in, Indicates the corresponding fault node, represents the initial fault node, t represents the initial time corresponding to the initial fault node; Based on the initial fault node and the corresponding fault node, other network nodes in the multi-layer network cascading fault model are set: in, represents the load of node j at time t in the physical power network, Indicates the load of node j at time t-1 in the physical power network, ΔL i→j (t-1) represents the load that node j receives from the initial fault node i at time t-1 in the physical power network. represents the load capacity of node j in the physical power network, represents the load of node j at time t in the digital twin virtual network, represents the load of node j at time t-1 in the digital twin virtual network, In the digital twin virtual network, the load that node j receives from the initial fault node i at time t-1 is represented. Represents the load capacity of node j in the digital twin virtual network.

7. The cascading failure analysis method based on digital twins and physical power according to claim 6, characterized in that: The communication delay parameter τ P =C, where C is a constant and C≥0; Alternatively, the communication delay parameter τ P =rand[0,m], where m is a random number greater than 0.

8. A cascading failure analysis device based on digital twins and physical power, characterized in that: include: A network coupling unit is used to establish a two-layer coupled network model through the digital twin virtual network and the physical power network; A cascade setting unit is configured to set a cascade fault propagation process based on the two-layer coupled network model, using the load information of the digital twin virtual network and the physical power network and a preset load redistribution strategy, thereby constructing a multi-layer network cascade fault model; a delay updating unit, configured to update the multi-layer network cascading failure model according to the communication delay between the digital twin virtual network and the physical power network to obtain a target cascading failure model; The cascade analysis unit is configured to perform cascade failure analysis on a designated power system using the target cascade failure model.

9. A computer device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the cascading failure analysis method based on digital twins and physical power as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the cascading failure analysis method based on digital twins and physical power according to any one of claims 1 to 7.

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