Modeling method and device for cascading failures of power cyber-physical systems

Abstract

The present invention provides a method and device for modeling cascading failures of an electric power information-physical system. The method comprises: performing community division on the initial electric power network, establishing a modular electric power information network using the community division result, and constructing a modular electric power information-physical system network model based on the modular electric power information network and the initial electric power network; randomly selecting at least one electric power node or line in the initial electric power network to set an initial fault, and randomly selecting at least one electric power information node in the electric power information network to inject a virus; performing cascading failure simulation on the electric power information-physical network model based on a preset virus propagation model, obtaining the electric power information-physical network model after the cascading failure, and calculating the load loss rate. The present invention establishes a modular network model with functional differences of network nodes by considering the modularization of the electric power network community partition and the coupling between networks, thereby making up for the shortcomings of previous studies in considering the impact of modularity on electric power information-physical systems.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power information network, and in particular to a method and device for modeling cascading failures of an electric power information-physical system. Background Art

[0002] The rapid development and application of information and communication technologies (ICTs) have transformed traditional power systems into typical cyber-physical systems (CPSs). Serving as monitors and managers of the power grid, CPSs enable intelligent grid control and fault mitigation. Cascading failures, a common dynamic phenomenon in power systems, stem from the interdependencies between power components and the physical constraints of system operation. These failures typically begin with small-scale faults and often lead to large-scale power outages. However, the deep coupling with CPSs makes the evolution of cascading failures in power systems different from those in traditional power networks.

[0003] Information systems play a vital role in ensuring the safe operation of power systems. They transmit real-time data, such as grid measurements and equipment status, to the dispatch center. The dispatch center analyzes and makes decisions based on the data received, and then sends corresponding control commands back to the relevant equipment via the power information network. Furthermore, many distributed control systems also serve as local decision-making units, executing local control under the supervision of the dispatch center. When an information system suffers a virus attack, causing its components to malfunction, it disrupts the connection between the dispatch center and the power grid, rendering the grid unsafe.

[0004] During the implementation of this invention, the inventors discovered that current research, which primarily uses complex network approaches to study the various interlocking and interactive mechanisms in cyber-physical systems, fails to adequately consider the physical dynamic behavior and functional characteristics of power cyber-physical systems during modeling. Furthermore, the distributed nature of coupled systems and the impact of network virus propagation on the propagation of cascading failures are overlooked. In reality, the propagation of viruses in power cyber networks is affected by the modular topology of the network and often disrupts the actual monitoring and control functions of cyber systems during grid operation. Current research fails to reveal the dynamic mechanisms of cascading failures in coupled systems under the influence of virus propagation in power cyber networks. Summary of the Invention

[0005] The embodiments of the present invention provide a method and apparatus for modeling cascading failures of an electric power cyber-physical system, so as to solve the problem in the prior art that the dynamic mechanism of cascading failures of coupled systems under the influence of virus propagation in the electric power information network cannot be revealed.

[0006] In a first aspect, an embodiment of the present invention provides a method for modeling cascading failures in an electric power cyber-physical system, comprising:

[0007] Performing community division on the initial power network, establishing a modular power information network using the community division results, and constructing a modular power cyber-physical system network model based on the modular power information network and the initial power network;

[0008] Randomly selecting at least one power network node or line in the initial power network to set an initial fault, and randomly selecting at least one power information network node in the power information network to inject a virus;

[0009] Performing a cascading failure simulation on the power cyber-physical network model based on a preset virus propagation model to obtain a power cyber-physical network model after the cascading failure;

[0010] The load loss rate of the power network after the cascading failure relative to the initial power network is calculated.

[0011] In a second aspect, an embodiment of the present invention provides an apparatus for a method for modeling cascading failures of an electric power cyber-physical system, including:

[0012] A module establishment device, configured to divide the initial power network into communities, establish a modular power information network using the community division results, and construct a modular power cyber-physical system network model based on the modular power information network and the initial power network;

[0013] A virus injection device, configured to randomly select at least one power network node or line in the initial power network to set an initial fault, and randomly select at least one power information network node in the power information network to inject a virus;

[0014] A model updating device, configured to perform a cascading failure simulation on the electric power cyber-physical network model based on a preset virus propagation model to obtain a post-cascading failure electric power cyber-physical network model;

[0015] The calculation device is used to calculate the load loss rate of the power network after the cascading failure relative to the initial power network.

[0016] In a third aspect, an embodiment of the present invention provides a terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0018] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: the present invention divides the initial power network into communities, establishes a modular power information network using the community division results, and constructs a modular power information physical system network model based on the modular power information network and the initial power network; randomly selects at least one power network node or line in the initial power system network to set an initial fault, and randomly selects at least one power information network node in the power information network to inject a virus; simulates a cascading failure of the power information physical network model based on a preset virus propagation model to obtain a power information physical network model after the cascading failure; and calculates the load loss rate of the power network after the cascading failure relative to the initial power network. The embodiments of the present invention establish a coupled network model, and by considering the modularization of the power network community partition and the coupling between networks, establish a heterogeneous modular dependent network model that takes into account the functional differences of network nodes. This makes up for the shortcomings of previous studies in considering the impact of modularity on power information physical systems, and can be applied to the fields of power information physical system cascading failure analysis, network security analysis, and robustness assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only 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 This is a flowchart of an implementation method for modeling cascading failures of a power cyber-physical system provided by an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of a modular power cyber-physical system network model that takes functional differences into consideration, provided by an embodiment of the present invention;

[0022] Figure 3 This is a diagram of node state transitions in a power information network according to an embodiment of the present invention;

[0023] Figure 4 This is the result of the cascading failure of the power cyber-physical system when different information network virus propagation parameters are provided in the embodiment of the present invention;

[0024] Figure 5 This is another example of a power cyber-physical system cascading failure result when different information network virus propagation parameters are provided in an embodiment of the present invention;

[0025] Figure 6 The results of the power cyber-physical system cascading failures when different power information network community connection probabilities are provided in the embodiment of the present invention;

[0026] Figure 7 This is a flowchart of an implementation method for modeling cascading failures of a power cyber-physical system provided by an embodiment of the present invention;

[0027] Figure 8 A device for the power cyber-physical system cascading failure modeling method provided in an embodiment of the present invention;

[0028] Figure 9 It is a schematic diagram of a terminal device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0030] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0031] Figure 1 This is a flowchart of the implementation of the power cyber-physical system cascading failure modeling method provided by the embodiment of the present invention, referring to Figure 1 , as detailed below:

[0032] In step 101: the initial power network is divided into communities, a modular power information network is established using the community division results, and a modular power cyber-physical system network model is constructed based on the modular power information network and the initial power network.

[0033] In some embodiments, step 101 includes:

[0034] Divide the initial power network into communities based on the community discovery algorithm;

[0035] Based on the results of community division and scale-free network generation algorithm, a modular power information network model is established;

[0036] Based on the topological similarity between the initial power network and the power information network, the nodes of the initial power network and the power information network are coupled according to the result of the community division to obtain a modular power cyber-physical system network model.

[0037] For example, the cascading failure of the power information-physical system can be simulated based on the information-coupled IEEE 118 node system. The distribution of power network nodes is often regional, and the nodes in the region are more closely connected. The power network community division method can adopt the GN (Girvan-Newman) algorithm and modularity index. The division results are shown in Table 1. It should be understood that this application does not limit the type of node system used to perform cascading failures on the power information-physical system, nor does it limit the specific method of community division. Table 1 is only for illustration and does not limit the community division results.

[0038] Table 1 IEEE 118-node system community partition results

[0039]

[0040] A modeled power information network is established based on the results of power network division and scale-free network generation algorithm. Figure 2 A schematic diagram of a modular power cyber-physical system network model that considers functional differences, as provided in an embodiment of the present invention. Based on the functional differences of network nodes, power nodes can be divided into generation nodes, transmission nodes, and load nodes, and information nodes can be divided into scheduling nodes and routing nodes. This diagram illustrates the network structure characteristics of a power cyber-physical system. By considering the modularization of power network community partitioning and inter-network coupling, a heterogeneous modular interdependent network model is established that accounts for the functional differences of network nodes, addressing the shortcomings of previous research in considering the impact of modularity on power cyber-physical systems.

[0041] In step 102 : at least one power network node or line is randomly selected in the initial power network to set an initial fault, and at least one power information network node is randomly selected in the power information network to inject a virus.

[0042] In this embodiment, an initial fault is set in the power network by randomly selecting at least one power network node or line. The initial fault can be set in any of the following three ways: first, removing one or more power network nodes in the power network; second, removing one or more power network lines in the power network; and third, removing one or more power network nodes and one or more power network lines in the power network, without limitation. The purpose of setting the initial fault is to inject a virus and, when it propagates through the information network, affect the power network and cause a cascading failure.

[0043] In step 103: a cascading failure simulation is performed on the power cyber-physical network model based on a preset virus propagation model to obtain the power cyber-physical network model after the cascading failure.

[0044] In some embodiments, step 103 includes:

[0045] Step 1: Use the preset virus propagation model to simulate the dynamic propagation of the virus in the power information network;

[0046] Step 2: Identify whether there are overloaded power lines in the power network;

[0047] Step three: If yes, delete the overloaded power line, update the power information-physical system network model, and jump to the step of using the preset virus propagation model to simulate the dynamic propagation of the virus in the power information network; if not, output the power information-physical system network model after the chain failure.

[0048] In some application scenarios, after using a pre-defined virus propagation model to simulate the dynamic spread of viruses within the power information network, it is necessary to monitor the virus infection status of nodes in the power information network. Infected nodes lose their ability to monitor coupled power nodes, and the observability of power lines is further determined based on the effectiveness of the information nodes. In this embodiment, the virus propagation model includes two node states: a healthy state indicates that the information node is operating normally and monitoring the power grid; an infected state indicates that the information node is malfunctioning due to virus infection and cannot effectively monitor the power network. Nodes transition between states in discrete time steps, and each node can only be infected by its neighboring nodes. After an information node is infected, the information defense system upgrades the antivirus software based on the acquired virus information, reducing the probability of the node being infected again.

[0049] In some embodiments, the above step 2, identifying whether there is an overloaded power line in the power network, includes:

[0050] The frequency deviation is calculated based on the power distribution of the generator nodes and load nodes of the power network; and whether the frequency deviation exceeds the limit is determined based on the relationship between the frequency deviation and the preset frequency limit.

[0051] If the frequency deviation exceeds the limit, the power flow optimization strategy is executed based on the power network information identified by the power information network dispatching center; and the actual power flow of the power network is calculated using the results of the power flow optimization and the preset DC power flow model; based on the actual power flow of the power network, it is determined whether there are overloaded power lines in the power network.

[0052] If the frequency deviation does not exceed the limit, the power distribution of the generator node and the load node is adjusted based on the static power-frequency characteristics of the power network, and the grid flow is calculated according to the preset DC flow model; based on the grid flow, it is determined whether there is an observable overloaded power line in the power network; wherein, an observable power line is a power line in the power network in which at least one of the coupling information nodes corresponding to the two power nodes connected by the power line is in a healthy state.

[0053] If there are any overloaded power lines in the power network, a power flow optimization strategy is executed based on the power network information identified by the power information network dispatching center; the actual power flow of the power network is calculated using the results of the power flow optimization and a preset DC power flow model; and the presence of any overloaded power lines in the power network is determined based on the actual power flow of the power network;

[0054] If there is no appreciable overloaded power line in the power network, it is determined whether there is an overloaded power line in the power network based on the power grid flow.

[0055] In this embodiment, the distributed control of the power grid is described based on the static power-frequency characteristics of the power system. When the active power output of the generator in the system is greater than the active power consumed by the load, the system frequency will rise; when the active power of the generator in the system is less than the active power consumed by the load, the frequency will decrease. When the system frequency rises or falls, the frequency deviation of the system will be disturbed. When the frequency deviation exceeds the set frequency deviation limit, it will affect the stable operation of the system. When a large active power difference occurs, the dispatching center is used to centrally control the power grid. For example, the frequency deviation limit is set to ±0.5. When the frequency deviation exceeds 0.5, it is determined that the frequency deviation is out of limit. No limitation is made here.

[0056] When the frequency deviation does not exceed the limit, the distributed control of the power grid is described based on the static power-frequency characteristics of the power system. Whether the observable power line is overloaded is determined based on the calculated power grid flow. When overloaded, the information dispatching center is used to perform centralized control of the power network. The power information network has the function of monitoring and controlling the power network. The power information network collects the topological information and electrical variables of the power network and sends these real-time states to the information dispatching center. The information dispatching center generates control instructions based on the received data to achieve centralized control of the power grid. When the power line is overloaded due to a fault, in order to effectively avoid cascading failures, the generator is re-dispatched and, when necessary, the load is reduced to alleviate the overload pressure of the power grid. The present invention combines the distributed control based on the static power-frequency characteristics and the centralized control based on the dispatching center to describe the control function of the power grid, solving the problem of insufficient description of the actual monitoring function of the power grid by the information system.

[0057] In some embodiments, the above-mentioned determining whether there is a significant overloaded power line in the power network based on the power network flow includes:

[0058] If the power grid current of the power network exceeds the preset current limit, it is determined that there is a significant overloaded power line in the power network; otherwise, it is determined that there is no significant overloaded power line in the power network;

[0059] The above-mentioned determination of whether there is an overloaded power line in the power network based on the actual power flow of the power network includes:

[0060] If the actual power flow of the power network exceeds the preset power flow limit, it is determined that there is an overloaded power line in the power network; otherwise, it is determined that there is no overloaded power line in the power network.

[0061] For example, the DC power flow model is:

[0062] P=Bθ

[0063] F=(b×A)θ

[0064] Where P is the node injection power vector; B is the admittance matrix, B = A T ×b×A; θ is the bus voltage phase angle vector; F is the power flow vector of the power grid branch; b is the conductivity diagonal matrix, b=diag(1 / x1,1 / x2,...,1 / x m ); A is the node-line association matrix.

[0065] For example, the optimization objective of the power flow optimization strategy is:

[0066]

[0067] Among them, N P is the number of power nodes; ΔP Li is the load shedding amount of power node i.

[0068] For example, the constraints of the power flow optimization strategy are:

[0069]

[0070] -F lmax ≤F l ≤F lmax

[0071]

[0072]

[0073] Among them, F lmax is the branch power flow limit; F l is the power flow value of the transmission line; Load for normal node operation; Generator power generation capacity limit.

[0074] In this embodiment, a DC optimal power flow model is used as the information system control strategy. The optimal power flow is to find the power flow distribution that satisfies the specified constraints and obtains the optimal goal by controlling variables when the grid structure parameters and load parameters are known. The DC optimal power flow model takes the minimum load shedding as the optimization goal.

[0075] Specifically, the four constraints of the power flow optimization strategy refer to the constraints on the power balance of the power grid system, the constraints on the transmission line not exceeding the limit, the constraints on the power consumption adjustment range of the load node, and the constraints on the output power adjustment range of the generator node.

[0076] In some embodiments, the above-mentioned calculation of the frequency deviation based on the power distribution of the generator nodes and the load nodes of the power network includes:

[0077] Calculating a frequency deviation based on power distribution of a generator node and a load node of the power network and a first formula;

[0078] Among them, the first formula includes:

[0079]

[0080] Where Δf is the frequency deviation of the system after the disturbance occurs; P Gi is the output power of generator i; P Li is the power of load i; K Gi is the frequency regulation coefficient of generator i; K Li is the frequency adjustment coefficient of load i; Ω G is the set of system generators, Ω L is the system load collection;

[0081] Based on the static power-frequency characteristics of the power network and the second formula, adjust the power distribution of generator nodes and load nodes;

[0082] The second formula includes:

[0083]

[0084]

[0085] in, is the power regulation of the generator node; is the power regulation of the load node; P G0i is the initial generator output; P L0i is the initial load power.

[0086] In step 104: the load loss rate of the power network after the cascading failure relative to the initial power network is calculated.

[0087] In some embodiments, step 104 includes:

[0088] According to the third formula, the load loss rate of the power network after the cascading failure is calculated relative to the initial power network.

[0089] Among them, the third formula is:

[0090]

[0091] Where η is the load loss rate; P L1 is the initial load of the power grid; P L2 is the load on the power grid after the cascading failure.

[0092] In this embodiment, the load loss rate η describes the initial load of the power grid P L1 and the power grid load after the cascading failure P L2 The relationship between load loss rate and load loss rate reflects the severity of power grid failure caused by cascading failures. The greater the load loss rate, the more serious the blackout caused by cascading failures.

[0093] In summary, the embodiment of the present invention divides the initial power network into communities, establishes a modular power information network using the community division results, and constructs a modular power information-physical system network model based on the modular power information network and the initial power network; randomly selects at least one power network node or line in the initial power system network to set an initial fault, and randomly selects at least one power information network node in the power information network to inject a virus; simulates a cascading failure of the power information-physical network model based on a preset virus propagation model to obtain a power information-physical network model after the cascading failure; and calculates the load loss rate of the power network after the cascading failure relative to the initial power network. The embodiment of the present invention establishes a coupled network model, and by considering the modularization of the power network community partition and the coupling between networks, establishes a heterogeneous modular interdependent network model that takes into account the functional differences of network nodes. This makes up for the shortcomings of previous studies in considering the impact of modularity on power information-physical systems, and can be applied to fields such as power information-physical system cascading failure analysis, network security analysis, and robustness assessment.

[0094] In some embodiments, the viral spread model includes:

[0095]

[0096]

[0097]

[0098] Among them, s i (t+1) is the state of node i at time (t+1); s i (t) is the state of node i at time t, the upper horizontal line is the node state inversion; g is the state transition judgment function; z i (t) is the health status of node i at time t. When it is in a healthy state, z i (t)=0, otherwise, z i(t) = 1; α is the probability of an infected information node infecting its neighboring nodes; σ is the virus infection attenuation factor, that is, the probability of an information node being infected after the software upgrade becomes σ times the original value, and the value range is (0-1); q i (t) is the number of times the information node is infected; n i (t) represents the number of infected nodes connected to node i at time t; r is a random number uniformly distributed between (0, 1); β is the probability that the infected information node recovers to a healthy state; a ij is the adjacency matrix element of the power information network.

[0099] In this embodiment, reference Figure 3 , Figure 3 This is the state transition relationship diagram of the power information network node provided by this embodiment. α is the probability of an infected information node infecting a neighboring node, β is the probability of an infected information node recovering, σ (0<σ<1) is the virus infection attenuation factor, that is, after the software upgrade, the probability of an information node being infected becomes σ times the original, and q i (t) is the number of times an information node is infected. This embodiment of the present invention integrates the proposed virus propagation model into the information network topology to simulate the dynamic spread of information network viruses and the network defense upgrade process. The effectiveness of information nodes in monitoring the power grid is determined based on the node infection status, providing a method for introducing information network virus propagation into the study of cascading failures in power cyber-physical systems.

[0100] Taking the power network of a certain region as an example, the results of the power cyber-physical system cascading failure under different information network virus propagation parameters in the embodiment of the present invention are described as follows: Figure 4 and Figure 5 As shown, the figure shows the cumulative probability of occurrence of different grid load loss rates. Figure 4 Figure 2 shows the cascading failure results when the virus infection rate α changes. It can be seen that because virus infection causes some information nodes to fail, weakening the monitoring function of the power network, information network virus infection leads to more large-scale power outages in the coupled power grid compared to normal operation of the information network. When the information node recovery rate β and the virus infection attenuation factor σ remain unchanged, the severity of the cascading failure in the power grid increases with the value of the information node infection rate α. This is because as the information node infection rate increases, the proportion of virus-infected information nodes in the information network increases. The functional failure of infected information nodes reduces the information network's visibility to power nodes, thereby weakening the information network's regulatory function on the power grid, exacerbating the development of cascading failures, and increasing the power grid's load loss rate. Figure 5The results of cascading failures when the virus infection attenuation factor σ changes are shown. As the attenuation factor σ increases, the information defense system's ability to weaken repeated node virus infections decreases. The increase in the number of infected information nodes weakens the information network's regulatory function on the power grid, thereby increasing the load lost by the power system due to cascading failures.

[0101] The propagation of cascading failures in power cyber-physical systems considering the spread of information network viruses is affected by the modular topology of the network. Figure 6 This figure illustrates the cascading failure results of the power cyber-physical system under different power information network community connection probabilities in the embodiment of the present invention. It can be found that considering modular distribution leads to different cascading failure outcomes compared to completely ignoring the modular distribution of the coupled system. Furthermore, as the connection probability of information network communities increases, the grid load loss caused by cascading failures increases. This is because the number of inter-community connections increases with the connection probability, while simultaneously weakening the strength of the community structure of the information network. The infection rate of an information node is positively correlated with the number of infected nodes it connects to. The increase in inter-community connections enhances the virus transmission paths between different communities, increases the infection probability of information network nodes, and thus weakens the information network's monitoring function on the power grid. This prevents the information system from accurately regulating the power grid and increases the severity of the cascading failure results in the power system.

[0102] The above method is described below through a specific implementation example. Figure 7 . Figure 7 This is a flowchart of the implementation method of the power cyber-physical system cascading failure modeling method provided by an embodiment of the present invention. The specific implementation process is as follows:

[0103] Step 701 , establishing a power cyber-physical system network model: performing community division on the power network, establishing a modular power information network using the community division results, and constructing a modular power cyber-physical system network model based on the modular power information network and the power network.

[0104] Step 702, initial fault setting: randomly selecting at least one power network node or line in the power system network to set an initial fault, and randomly selecting at least one power information network node in the power information network to inject a virus.

[0105] Step 703: Power information network virus propagation: Use the virus propagation model to simulate the dynamic propagation of information network viruses and detect the virus infection status of information nodes. The infected nodes lose the monitoring function of the coupled power nodes, and the observability of the power line is further judged based on the effectiveness of the information nodes.

[0106] Step 704: Grid distributed control: Calculate the frequency deviation based on the power distribution of the generator nodes and load nodes of the power network. If the frequency deviation exceeds the deviation limit, go to step 706; otherwise, adjust the power of the generator and load nodes according to the static power-frequency characteristics of the power grid and go to step 705.

[0107] Step 705: Grid flow calculation: Calculate the grid flow based on the DC flow model and determine the overload status of the observable line. If the flow of the observable line exceeds the upper limit of the line's allowable flow, there is an observable overloaded line, and go to step 706; otherwise, directly delete the overloaded line according to the actual flow and go to step 703. If there is no overloaded line, output the power information physical network model after the fault and calculate the grid load loss rate.

[0108] Step 706: Centralized power grid control: The information network dispatching center executes the power flow optimization strategy based on the observed power grid information and executes step 707;

[0109] Step 707: Recalculate the grid flow: Recalculate the grid flow using the DC flow model based on the flow optimization results. If there is an overload line, delete the overload line and go to step 703; otherwise, output the power information physical network model and calculate the grid load loss rate.

[0110] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0111] The following are device embodiments of the present invention. For details not fully described therein, reference may be made to the corresponding method embodiments described above.

[0112] Figure 8 A schematic diagram of the structure of an apparatus for a method for modeling cascading failures of a power cyber-physical system provided by an embodiment of the present invention is shown. For ease of explanation, only the portion related to the embodiment of the present invention is shown, which is described in detail as follows:

[0113] like Figure 8 As shown, the device 80 of the method for modeling cascading failures of an electric power cyber-physical system includes: a module establishment device 81, a virus injection device 82, a model updating device 83 and a calculation device 84.

[0114] The module establishment device 81 is used to divide the power network into communities, establish a modular power information network based on the community division results, and construct a modular power cyber-physical system network model based on the modular power information network and the power network;

[0115] The virus injection device 82 is used to randomly select at least one power network node or line in the power system network to set an initial fault, and randomly select at least one power information network node in the power information network to inject a virus;

[0116] The model updating device 83 is used to simulate a cascading failure on the power cyber-physical network model based on a preset virus propagation model to obtain a power cyber-physical network model after the cascading failure;

[0117] The calculation device 84 is used to calculate the load loss rate of the power network after the cascading failure relative to the initial power network.

[0118] Optionally, the module establishing means 81 is configured to:

[0119] Divide the initial power network into communities based on the community discovery algorithm;

[0120] Based on the results of community division and scale-free network generation algorithm, a modular power information network model is established;

[0121] Based on the topological similarity between the initial power network and the power information network, the nodes of the initial power network and the power information network are coupled according to the results of community division to obtain a modular power cyber-physical system network model.

[0122] Optionally, the model updating device 83 is used to:

[0123] Use the preset virus propagation model to simulate the dynamic propagation of viruses in the power information network;

[0124] Identify whether there are overloaded power lines in the power network;

[0125] If so, delete the overloaded power line, update the power information-physical system network model, and jump to the step of using the preset virus propagation model to simulate the dynamic propagation of the virus in the power information network; if not, output the power information-physical system network model after the chain failure.

[0126] Optionally, the model updating device 83 is used to:

[0127] Calculate frequency deviation based on power distribution of generator nodes and load nodes of the power network;

[0128] According to the relationship between the frequency deviation and the preset frequency limit, determine whether the frequency deviation exceeds the limit;

[0129] If the frequency deviation exceeds the limit, the power flow optimization strategy is executed based on the power network information identified by the power information network dispatching center; the actual power flow of the power network is calculated using the power flow optimization results and the preset DC power flow model; and the actual power flow of the power network is used to determine whether there are overloaded power lines in the power network;

[0130] If the frequency deviation does not exceed the limit, the power distribution of the generator node and the load node is adjusted based on the static power-frequency characteristics of the power network, and the power grid flow is calculated according to the preset DC power flow model. The power grid flow is used to determine whether there is an observable overloaded power line in the power network. Among them, an observable power line is a power line in the power network where at least one of the coupling information nodes corresponding to the two power nodes connected by the power line is in a healthy state.

[0131] If there are any overloaded power lines in the power network, a power flow optimization strategy is executed based on the power network information identified by the power information network dispatching center; the actual power flow of the power network is calculated using the results of the power flow optimization and a preset DC power flow model; and the presence of any overloaded power lines in the power network is determined based on the actual power flow of the power network;

[0132] If there is no appreciable overloaded power line in the power network, it is determined whether there is an overloaded power line in the power network based on the power grid flow.

[0133] Optionally, the model updating device 83 is used to:

[0134] If the power grid current of the power network exceeds the preset current limit, it is determined that there is a significant overloaded power line in the power network; otherwise, it is determined that there is no significant overloaded power line in the power network;

[0135] Determine whether there are overloaded power lines in the power network based on the actual power flow of the power network, including:

[0136] If the actual power flow of the power network exceeds the preset power flow limit, it is determined that there is an overloaded power line in the power network; otherwise, it is determined that there is no overloaded power line in the power network;

[0137] The DC power flow model is:

[0138] P=Bθ

[0139] F=(b×A)θ

[0140] Where P is the node injection power vector; B is the admittance matrix, B = A T ×b×A; θ is the bus voltage phase angle vector; F is the power flow vector of the power grid branch; b is the conductivity diagonal matrix, b=diag(1 / x1,1 / x2,...,1 / x m); A is the node-line association matrix;

[0141] The optimization goal of the power flow optimization strategy is:

[0142]

[0143] Among them, N P is the number of power nodes; ΔP Li is the load shedding amount of power node i;

[0144] The constraints of the power flow optimization strategy are:

[0145]

[0146] -F lmax ≤F l ≤F lmax

[0147]

[0148]

[0149] Among them, F lmax is the branch power flow limit; F l is the power flow value of the transmission line; Load for normal node operation; Generator power generation capacity limit.

[0150] Optionally, the computing device 84 is further configured to:

[0151] Calculating a frequency deviation based on power distribution of a generator node and a load node of the power network and a first formula;

[0152] Among them, the first formula includes:

[0153]

[0154] Where Δf is the frequency deviation of the system after the disturbance occurs; P Gi is the output power of generator i; P Li is the power of load i; K Gi is the frequency regulation coefficient of generator i; K Li is the frequency adjustment coefficient of load i; Ω G is the set of system generators, Ω L is the system load set;

[0155] Adjust the power distribution of generator nodes and load nodes based on the static power-frequency characteristics of the power network, including:

[0156] Based on the static power-frequency characteristics of the power network and the second formula, adjust the power distribution of the generator node and the load node;

[0157] The second formula includes:

[0158]

[0159]

[0160] in, is the power regulation of the generator node; is the power regulation of the load node; P G0i is the initial generator output; P L0i is the initial load power.

[0161] Optionally, the computing device 84 is further configured to:

[0162] Viral spread models include:

[0163]

[0164]

[0165]

[0166] Among them, s i (t+1) is the state of node i at time (t+1); s i (t) is the state of node i at time t, the upper horizontal line is the node state inversion; g is the state transition judgment function; z i (t) is the health status of node i at time t. When it is in a healthy state, z i (t)=0, otherwise, z i (t) = 1; α is the probability of an infected information node infecting its neighboring nodes; σ is the virus infection attenuation factor, that is, the probability of an information node being infected becomes σ times the original after the software upgrade, and the value range is (0-1); q i (t) is the number of times the information node is infected; n i (t) represents the number of infected nodes connected to node i at time t; r is a random number uniformly distributed between (0, 1); β is the probability that the infected information node recovers to a healthy state; a ij is the adjacency matrix element of the power information network.

[0167] Calculate the load loss rate of the power network after the cascading failure relative to the initial power network, including:

[0168] According to the third formula, the load loss rate of the power network after the cascading failure is calculated relative to the initial power network.

[0169] Among them, the third formula is:

[0170]

[0171] Where η is the load loss rate; P L1 is the initial load of the power grid; P L2 is the load on the power grid after the cascading failure.

[0172] Figure 9 FIG is a schematic diagram of a terminal device provided by an embodiment of the present invention. Figure 9 As shown, the terminal device 9 of this embodiment includes: a processor 90, a memory 91, and a computer program 92 stored in the memory 91 and executable on the processor 90. When the processor 90 executes the computer program 92, the steps of each of the above-mentioned embodiments of the method for modeling cascading failures of a power cyber-physical system are implemented.

[0173] The computer program 92 may be divided into one or more modules / units, which are stored in the memory 91 and executed by the processor 90 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 92 in the terminal device 9.

[0174] The terminal device 9 can be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device can include, but is not limited to, a processor 90 and a memory 91. It can be understood by those skilled in the art that Figure 9 It is only an example of the terminal device 9 and does not constitute a limitation on the terminal device 9. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0175] The processor 90 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0176] The memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal device 9. Furthermore, the memory 91 may include both an internal storage unit of the terminal device 9 and an external storage device. The memory 91 is used to store the computer program and other programs and data required by the terminal device. The memory 91 may also be used to temporarily store data that has been output or is about to be output.

[0177] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0178] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0179] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0180] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0181] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0182] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0183] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned method embodiments. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0184] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A method for modeling cascading failures in a power cyber-physical system, characterized in that: include: Performing community division on the initial power network, establishing a modular power information network using the community division results, and constructing a modular power cyber-physical system network model based on the modular power information network and the initial power network; Randomly selecting at least one power network node or line in the initial power network to set an initial fault, and randomly selecting at least one power information network node in the power information network to inject a virus; Using a preset virus propagation model to simulate the dynamic propagation of the virus in the power information network; Identifying whether there is an overloaded power line in the power network; If so, the overloaded power line is deleted, the power cyber-physical system network model is updated, and the process jumps to the dynamic propagation step; If not, output the power cyber-physical system network model after the cascading failure; Calculating a load loss rate of the power network after the cascading failure relative to the initial power network; The step of identifying whether there is an overloaded power line in the power network includes: Calculating frequency deviation based on power distribution of generator nodes and load nodes of the power network; determining whether the frequency deviation exceeds a limit based on a relationship between the frequency deviation and a preset frequency limit; If so, executing a power flow optimization strategy based on the power network information identified by the power information network dispatching center; calculating the actual power flow of the power network using the results of the power flow optimization and a preset DC power flow model; and determining whether there are overloaded power lines in the power network based on the actual power flow of the power network; If not, then based on the static power-frequency characteristics of the power network, adjust the power distribution of the generator node and the load node, and calculate the power grid flow according to a preset DC power flow model; determine whether there is an overloaded power line in the power network based on the power grid flow; wherein the overloaded power line is a power line in the power network where at least one of the coupling information nodes corresponding to two power nodes connected by the power line is in a healthy state; If there is a significant overloaded power line, execute a power flow optimization strategy based on the power network information identified by the power information network dispatching center; calculate the actual power flow of the power network using the results of the power flow optimization and a preset DC power flow model; and determine whether there is an overloaded power line in the power network based on the actual power flow of the power network; If there is no appreciable overloaded power line, it is determined whether there is an overloaded power line in the power network according to the power grid flow.

2. The method according to claim 1, wherein Performing community division on the initial power network, establishing a modular power information network using the community division results, and constructing a modular power cyber-physical system network model based on the modular power information network and the initial power network, including: Performing community division on the initial power network based on a community discovery algorithm; Based on the community division results and the scale-free network generation algorithm, a modular power information network model is established; Based on the topological similarity between the initial power network and the power information network, the nodes of the initial power network and the power information network are coupled according to the result of the community division to obtain a modular power cyber-physical system network model.

3. The method according to claim 1, wherein Determining whether there is a significant overloaded power line in the power network according to the power network flow, including: If the grid power flow of the power network exceeds a preset power flow limit, it is determined that there is a significant overloaded power line in the power network; otherwise, it is determined that there is no significant overloaded power line in the power network; Determining whether there is an overloaded power line in the power network according to an actual power flow of the power network includes: If the actual power flow of the power network exceeds the preset power flow limit, it is determined that there is an overloaded power line in the power network; otherwise, it is determined that there is no overloaded power line in the power network; The DC power flow model is: in, Inject power vectors into nodes; is the admittance matrix, ; is the bus voltage phase angle vector; is the power flow vector of the power grid branch; is the conductance diagonal matrix, ; is the node-line association matrix; The optimization goal of the power flow optimization strategy is: in, is the number of power nodes; For power nodes Load shedding capacity; The constraints of the power flow optimization strategy are: in, is the branch power flow limit; is the power flow value of the transmission line; Load for normal node operation; Generator power generation capacity limit.

4. The method according to claim 1, wherein Calculating the frequency deviation according to the power distribution of the generator nodes and the load nodes of the power network includes: Calculating a frequency deviation based on power distribution of a generator node and a load node of the power network and a first formula; The first formula includes: in, is the frequency deviation of the system after the disturbance occurs; For generators Output power; is the load Power; For generators Frequency adjustment coefficient; For load Frequency adjustment coefficient; is a collection of system generators, is the system load collection; Adjusting the power distribution of the generator node and the load node based on the static power-frequency characteristics of the power network includes: Adjusting the power distribution of the generator node and the load node based on the static power-frequency characteristics of the power network and a second formula; The second formula includes: in, is the power regulation of the generator node; is the power regulation of the load node; is the initial generator output; is the initial load power.

5. The method according to any one of claims 1 to 4, characterized in that The virus propagation model includes: in, For nodes i In time ( t +1) status; For nodes In time The upper horizontal line indicates the node status is inverted; is the state transition judgment function; For nodes exist Always in a healthy state, when in a healthy state, =0, otherwise, =1; The probability that the infected information node infects the neighboring node; σ is the virus infection attenuation factor, that is, the probability of an information node being infected after the software upgrade becomes the original σ times, the value range is (0~1); is the number of times the information node is infected; To express Moments and Nodes i The number of connected infected nodes; is a random number uniformly distributed between (0, 1); The probability of restoring the infected information node to a healthy state; is the element of the adjacency matrix of the power information network; The calculating of the load loss rate of the power network after the cascading failure relative to the initial power network comprises: According to the third formula, the load loss rate of the power network after the cascading failure relative to the power network is calculated. Wherein, the third formula is: in, is the load loss rate; is the initial load of the power grid; is the load on the power grid after the cascading failure.

6. A device for modeling a cascading failure of an electric power cyber-physical system, characterized in that: include: A module establishment device, configured to divide the initial power network into communities, establish a modular power information network using the community division results, and construct a modular power cyber-physical system network model based on the modular power information network and the initial power network; A virus injection device, configured to randomly select at least one power network node or line in the initial power network to set an initial fault, and randomly select at least one power information network node in the power information network to inject a virus; A model updating device, configured to simulate the dynamic propagation of the virus in the power information network using a preset virus propagation model; Identifying whether there is an overloaded power line in the power network; If so, the overloaded power line is deleted, the power cyber-physical system network model is updated, and the process jumps to the dynamic propagation step; If not, output the power cyber-physical system network model after the cascading failure; a calculation device for calculating a load loss rate of the power network after the cascading failure relative to the initial power network; Wherein, the model updating device is used for: Calculating frequency deviation based on power distribution of generator nodes and load nodes of the power network; determining whether the frequency deviation exceeds a limit based on a relationship between the frequency deviation and a preset frequency limit; If so, executing a power flow optimization strategy based on the power network information identified by the power information network dispatching center; calculating the actual power flow of the power network using the results of the power flow optimization and a preset DC power flow model; and determining whether there are overloaded power lines in the power network based on the actual power flow of the power network; If not, then based on the static power-frequency characteristics of the power network, adjust the power distribution of the generator node and the load node, and calculate the power grid flow according to a preset DC power flow model; determine whether there is an overloaded power line in the power network based on the power grid flow; wherein the overloaded power line is a power line in the power network where at least one of the coupling information nodes corresponding to two power nodes connected by the power line is in a healthy state; If there is a significant overloaded power line, execute a power flow optimization strategy based on the power network information identified by the power information network dispatching center; calculate the actual power flow of the power network using the results of the power flow optimization and a preset DC power flow model; and determine whether there is an overloaded power line in the power network based on the actual power flow of the power network; If there is no appreciable overloaded power line, it is determined whether there is an overloaded power line in the power network according to the power grid flow.

7. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for modeling cascading failures of an electric cyber-physical system as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for modeling cascading failures of an electric cyber-physical system as described in any one of claims 1 to 5 are implemented.

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