CN116366349A - Modeling method and device for cascading failures of power information physical system - Google Patents
Modeling method and device for cascading failures of power information physical system Download PDFInfo
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Abstract
The invention provides a modeling method and device for cascading faults of an electric power information physical system. The method comprises the following steps: performing community division on the initial power network, establishing a modularized power information network by using a community division result, and constructing a modularized power information physical system network model based on the modularized power information network and the initial power network; randomly selecting at least one power node or line in an initial power network to set an initial fault, and randomly selecting at least one power information node in a power information network to inject viruses; and performing cascading failure simulation on the power information physical network model based on a preset virus propagation model to obtain the power information physical network model after cascading failure, and calculating the load loss rate. According to the invention, the modularization of the power network community partition and the coupling between networks is considered, and a modularization network model of the network node function difference is established, so that the defect of considering the modularity influence of the previous research on the power information physical system is overcome.
Description
Technical Field
The invention relates to the technical field of electric power information network safety, in particular to a modeling method and device for cascading faults of an electric power information physical system.
Background
The rapid development and application of the information communication technology enable the traditional power system to be evolved into a typical information physical system, and the information system is used as a monitoring and manager of the power grid, so that intelligent regulation and control and fault relief of the power grid are realized. Cascading failures are common dynamic phenomena in power systems, which stem from interdependencies between power elements and physical law constraints of system operation, are often caused by small-scale failures and often lead to large-scale outage events. However, deep coupling with the information system makes the evolution mechanism of power system cascading failures different from conventional power networks.
The information system plays a vital role in ensuring the safe operation of the power system, the information system transmits real-time data such as power grid measurement data, equipment state information and the like to the dispatching center, and the dispatching center analyzes and decides the received data and transmits corresponding regulation and control commands back to related equipment through the power information network. In addition, many distributed control systems also act as local decision units, performing local control under the supervision of a dispatch center. When the information system is attacked by viruses, the function of the information element is invalid, the connection between the dispatching center and the power grid is destroyed, and the power grid is in an unsafe running state.
In the process of realizing the invention, the inventor finds that the current related research is mostly based on a complex network method to research various linkage and interaction mechanisms in the information physical system, the physical dynamic behavior and the functional characteristics of the electric power information physical system are not fully considered in the modeling process, and the community distribution characteristics of the coupling system and the influence of network virus propagation in the linkage fault propagation process are ignored. In fact, the transmission of the power information network viruses is affected by the network modularized topological structure and often damages the actual monitoring and control functions of the information system in the power grid operation process, and the current research cannot reveal the coupling system cascading failure dynamic mechanism under the influence of the transmission of the power information network viruses.
Disclosure of Invention
The embodiment of the invention provides a modeling method and device for cascading faults of a power information physical system, which are used for solving the problem that a coupling system cascading fault dynamic mechanism under the influence of virus propagation of a power information network cannot be disclosed in the prior art.
In a first aspect, an embodiment of the present invention provides a modeling method for cascading failures of an electrical power information physical system, including:
performing community division on the initial power network, establishing a modularized power information network by using a community division result, and constructing a modularized power information physical system network model based on the modularized 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 initial faults, and randomly selecting at least one power information network node in the power information network to inject viruses;
performing cascading failure simulation on the power information physical network model based on a preset virus propagation model to obtain a cascading failure-based power information physical network model;
and calculating the load loss rate of the power network after cascading failures relative to the initial power network.
In a second aspect, an embodiment of the present invention provides an apparatus for modeling cascading failure of a power information physical system, including:
the module establishing device is used for carrying out community division on the initial power network, establishing a modularized power information network by utilizing a community division result, and establishing a modularized power information physical system network model based on the modularized power information network and the initial power network;
the virus injection device is used for randomly selecting at least one power network node or line to set initial faults in the initial power network, and randomly selecting at least one power information network node in the power information network to inject viruses;
The model updating device is used for performing cascading failure simulation on the electric power information physical network model based on a preset virus propagation model to obtain a cascading failure electric power information physical network model;
and the calculating device is used for calculating the load loss rate of the power network after cascading failure relative to the initial power network.
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, the processor implementing the steps of the method according to the first aspect or any one of the possible implementations of the first aspect when the computer program is executed.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.
Compared with the prior art, the embodiment of the invention has the beneficial effects that: the method comprises the steps of performing community division on an initial power network, establishing a modularized power information network by using a community division result, and establishing a modularized power information physical system network model based on the modularized power information network and the initial power network; randomly selecting at least one power network node or line in an initial power system network to set an initial fault, and randomly selecting at least one power information network node in a power information network to inject viruses; performing cascading failure simulation on the power information physical network model based on a preset virus propagation model to obtain a cascading failure-based power information physical network model; and calculating the load loss rate of the power network after cascading failures relative to the initial power network. According to the embodiment of the invention, the coupled network model is established, and the heterogeneous modularized dependent network model which takes the function difference of network nodes into account is established by considering the modularization of the power network community partition and the coupling between networks, so that the defect of considering the modularity influence of the power information physical system in the past research is overcome, and the method can be applied to the fields of power information physical system cascading failure analysis, network security analysis, robustness assessment and the like.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an implementation of a cascading failure modeling method for an electrical power information physical system provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of a network model of a modular power information physical system taking into account functional differences provided by an embodiment of the present invention;
fig. 3 is a state transition relationship diagram of a power information network node according to an embodiment of the present invention;
FIG. 4 is a power information physical system cascading failure result when different information network viruses propagate parameters provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of a power information physical system cascading failure result when another information network virus propagation parameter is different according to an embodiment of the present invention;
FIG. 6 is a power information physical system cascading failure result when different power information network communities are connected according to the embodiment of the present invention;
FIG. 7 is a flowchart of an implementation of a cascading failure modeling method for an electrical power information physical system according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a device for modeling cascading failure of an electrical power information physical system according to an embodiment of the present invention;
fig. 9 is a schematic diagram of a terminal device provided in an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, 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.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
Fig. 1 is a flowchart of an implementation of a cascading failure modeling method of an electrical power information physical system according to an embodiment of the present invention, and details are as follows with reference to fig. 1:
in step 101: and performing community division on the initial power network, establishing a modularized power information network by using a community division result, and constructing a modularized power information physical system network model based on the modularized power information network and the initial power network.
In some embodiments, step 101 comprises:
performing community division on the initial power network based on a community discovery algorithm;
establishing a modularized power information network model based on a community division result and a scaleless network generation algorithm;
based on the topological similarity of the initial power network and the power information network, coupling the nodes of the initial power network and the power information network according to the community division result to obtain a modularized power information physical system network model.
Illustratively, the power information physical system cascading failure may be simulated based on the information coupling IEEE 118 node system, with the power network node distribution often having regional areas with more tightly connected nodes. The power network community division method can adopt a GN (Girvan-Newman) algorithm and a modularity index, and the division result is shown in table 1. It should be understood that the present application does not limit what type of node system is used to perform cascading failures on the power information physical system, nor does it limit the specific manner in which communities are partitioned. Table 1 is for illustration only and is not limiting of the community division results.
Table 1IEEE 118 node System Community partition results
A modeled power information network is established based on the results of the power network partitioning and a scaleless network generation algorithm. Fig. 2 is a schematic diagram of a network model of a modularized power information physical system with consideration of functional differences, where according to the functional differences of network nodes, power nodes may be divided into power generation nodes, transmission nodes and load nodes, and information nodes may be divided into scheduling nodes and routing nodes. The figure illustrates the network architecture features of the power information physical system. By considering modularization of power network community partitions and coupling between networks, a heterogeneous modularization dependent network model which takes network node function differences into account is established, and the defect of considering the modularity influence of the previous research on the power information physical system is overcome.
In step 102: at least one power network node or line is randomly selected from the initial power network to set initial faults, and at least one power information network node is randomly selected from the power information network to inject viruses.
In this embodiment, the initial fault is set in the power network by randomly selecting at least one power network node or line. The initial fault set may be in any of three settings: first, removing one or more power network nodes in the power network; second, removing one or more lines in the power network; third, without limitation, one or more power network nodes in the power network are removed and one or more lines in the power network are removed. The function of setting the initial fault is that when the information network propagates after virus is injected, the power network can be affected and cascading faults occur.
In step 103: and performing cascading failure simulation on the power information physical network model based on a preset virus propagation model to obtain the power information physical network model after cascading failure.
In some embodiments, step 103 comprises:
step one, simulating dynamic propagation of viruses in an electric power information network by using a preset virus propagation model;
step two, identifying whether an overloaded power line exists in the power network;
if yes, deleting the overloaded power line, updating a power information physical system network model, and jumping to a step of simulating dynamic propagation of viruses in a power information network by using a preset virus propagation model; if not, outputting the network model of the power information physical system after cascading failure.
In some application scenarios, after a preset virus propagation model is utilized to simulate the dynamic propagation of viruses in the power information network, the virus infection state of the power information network node needs to be monitored, the infected node loses the monitoring function on the coupled power node, and the observability of the power line is further judged according to the validity of the information node. In this embodiment, the virus propagation model includes two node states, and the health state indicates that the information node can normally operate, so as to realize a monitoring function on the power grid; the infection status indicates that the information node is malfunctioning due to a virus infection and cannot effectively monitor the power network. Nodes transition states in discrete time steps and each node can only be infected by its neighbor nodes. After the information node is infected, the information defense system upgrades the antivirus software according to the grasped virus information, and the probability of the node being infected again is reduced.
In some embodiments, the step two, identifying whether there is an overloaded power line in the power network includes:
calculating frequency deviation according to power distribution of generator nodes and load nodes of the power network; and judging whether the frequency deviation is out of limit according to the relation between the frequency deviation and a preset frequency limit.
If the frequency deviation is out of limit, executing a power flow optimization strategy according to the information of the power network identified by the power information network dispatching center; calculating the actual power flow of the power network by using the power flow optimization result and a preset direct current power flow model; and judging whether an overloaded power line exists in the power network according to the actual power flow of the power network.
If the frequency deviation is not out of limit, based on the static power frequency characteristic of the power network, adjusting the power distribution of the generator node and the load node, and calculating the power flow of the power grid according to a preset direct current power flow model; judging whether an overload considerable power line exists in the power network according to the power flow of the power grid; the considerable power line is at least one power line in a health state, wherein the coupling information nodes correspond to two power nodes connected with the power line in the power network.
If an overload considerable power line exists in the power network, executing a power flow optimization strategy according to the information of the power network identified by the power information network dispatching center; calculating the actual power flow of the power network by using the power flow optimization result and a preset direct current power flow model; judging whether an overloaded power line exists in the power network according to the actual power flow of the power network;
And if no overload considerable power line exists in the power network, judging whether the overload power line exists in the power network according to the power flow of the power grid.
In the embodiment, the power grid distributed control is described based on the static power frequency characteristic of the power system, and when the active power output by the generator in the system is larger than the active power consumed by the load, the system frequency can rise; when the active power of the generator in the system is smaller than the active power consumed by the load, the frequency is reduced. When the frequency of the system rises or falls, the frequency deviation of the system can be disturbed, and when the frequency deviation exceeds a set frequency deviation limit, the stable operation of the system can be affected. When a larger active power difference occurs, the dispatching center is utilized to intensively regulate and control the power grid. For example, the frequency deviation limit is set to ±0.5, and when the frequency deviation exceeds 0.5, the frequency deviation is determined to be out of limit, which is not limited herein.
And describing the distributed control of the power grid based on the static power frequency characteristic of the power system when the frequency deviation is not over. Judging whether the considerable power line is overloaded according to the calculated power grid power flow, and carrying out centralized control on the power network by using an information dispatching center when the considerable power line is overloaded. The power information network has the functions of monitoring and controlling the power network, collects topology information and electrical variables of the power network, sends the real-time states to the information dispatching center, and the information dispatching center generates control instructions according to the received data to realize centralized control on the power grid. The invention combines the distributed control based on the static power frequency characteristic and the centralized control based on the dispatching center to describe the control function of the power grid, and solves the problem of insufficient description of the actual monitoring function of the power grid by the information system.
In some embodiments, the determining whether there is an overload on the power network according to the grid power flow of the power network includes:
if the grid power flow of the power network exceeds a preset power flow limit, determining that an overload considerable power line exists in the power network; otherwise, determining that the power network has no overload of the considerable power line;
the above-mentioned judging whether there is an overloaded power line in the power network according to the actual power flow of the power network includes:
if the actual power flow of the power network exceeds a preset power flow limit, determining that an overloaded power line exists in the power network; otherwise, it is determined that there is no overloaded power line to the power network.
Illustratively, the direct current power flow model is:
P＝Bθ
F＝(b×A)θ
wherein P is a node injection power vector; b is an admittance matrix, b=a T X b x a; θ is the bus voltage phase angle vector; f is a branch power flow vector of the power grid; b is a diagonal matrix of conductance, b=diag (1/x 1 ,1/x 2 ,...,1/x m ) The method comprises the steps of carrying out a first treatment on the surface of the A is the association matrix of the node line.
Illustratively, the optimization objective of the power flow optimization strategy is:
wherein N is P The number of power nodes; ΔP Li Is the load shedding amount for the power node i.
Exemplary constraints of the power flow optimization strategy are:
-F lmax ≤F l ≤F lmax
Wherein F is lmax Is a branch tidal current limit; f (F) l The current value is the current value of the transmission line;
the normal operation load of the node is set; />
The generator power generation capacity limit.
In the embodiment, a direct current optimal power flow model is adopted as an information system control strategy, the optimal power flow is the power flow distribution when the control variable finds out the power flow distribution simultaneously meeting the specified constraint condition and obtaining the optimal target under the condition that the power grid structure parameter and the load parameter are known, and the direct current optimal power flow model takes the minimum load removal amount as the optimal target.
Specifically, four constraint conditions of the power flow optimization strategy refer to constraint on power balance of a power grid system, constraint on no out-of-limit of a power transmission line, constraint on a power adjustment range of load node consumption and constraint on a power adjustment range of generator node output power respectively.
In some embodiments, calculating the frequency deviation from the power distribution of the generator node and the load node of the power network includes:
calculating frequency deviation according to power distribution of generator nodes and load nodes of the power network and a first formula;
wherein the first formula comprises:
wherein Δf is the frequency deviation of the system after disturbance; p (P) Gi The output power of the generator i; p (P) Li Is the power of load i; k (K) Gi A frequency adjustment coefficient for generator i; k (K) Li A frequency adjustment coefficient for load i; omega shape G Is a set of system generators, Ω L Is a system load set;
based on the static power frequency characteristic of the power network and a second formula, adjusting the power distribution of the generator node and the load node;
wherein the second formula comprises:
wherein,,
the power adjustment quantity of the generator node; />
The power adjustment quantity of the load node; p (P) G0i Output for the initial generator; p (P) L0i Is the initial load power.
In step 104: and calculating the load loss rate of the power network after cascading failures relative to the initial power network.
In some embodiments, step 104 comprises:
according to a third formula, calculating the load loss rate of the power network after cascading failure relative to the initial power network,
wherein, the third formula is:
wherein η is the load loss rate; p (P) L1 Initial load quantity for the power grid; p (P) L2 And the load capacity of the power grid after cascading failure.
In the present embodiment, the load loss rate η describes the initial load amount P of the grid L1 And grid load P after cascading failure L2 The relation of the load loss rate is larger, and the serious blackout caused by cascading failure is indicated.
In summary, the embodiment of the invention establishes a modularized power information network by performing community division on an initial power network and utilizing a community division result, and establishes a modularized power information physical system network model based on the modularized power information network and the initial power network; randomly selecting at least one power network node or line in an initial power system network to set an initial fault, and randomly selecting at least one power information network node in a power information network to inject viruses; performing cascading failure simulation on the power information physical network model based on a preset virus propagation model to obtain a cascading failure-based power information physical network model; and calculating the load loss rate of the power network after cascading failures relative to the initial power network. According to the embodiment of the invention, the coupled network model is established, and the heterogeneous modularized dependent network model which takes the function difference of network nodes into account is established by considering the modularization of the power network community partition and the coupling between networks, so that the defect of considering the modularity influence of the power information physical system in the past research is overcome, and the method can be applied to the fields of power information physical system cascading failure analysis, network security analysis, robustness assessment and the like.
In some embodiments, the virus propagation model comprises:
wherein s is i (t+1) is the state of node i at time (t+1); s is(s) i (t) is the state of the node i at time t, and the upper transverse line is the node state inverse; g is a state transition judging function; z i (t) is the health status of node i at time t, z when in health status i (t) =0, otherwise, z i (t) =1; alpha is the probability of the infection information node to infect the neighbor node; sigma is a virus infection attenuation factor, namely the probability of the infected information node after the software is upgraded is changed into sigma times of the original value, and the value range is (0-1); q i (t) is the number of inode infections; n is n i (t) is a number representing the number of infected nodes connected to node i at time t; r is a random number uniformly distributed between (0, 1); beta is the probability of the infected information node to recover the health state; a, a ij The matrix elements are contiguous for the power information network.
In this embodiment, referring to fig. 3, fig. 3 is a state transition relationship diagram of a power information network node provided in this embodiment. Where α is the probability of an infected information node infecting a neighboring node, β is the probability of an infected information node recovering health, σ (0<σ<1) Is a virus infection attenuation factor, i.e. the probability of the information node being infected after the software is upgraded is changed into sigma times and q times of the original i And (t) is the number of times of infection of the information node. The embodiment of the invention integrates the virus propagation model into the information network topology to simulate the dynamic propagation and network defense upgrading process of the information network virus, judges the effectiveness of the information node on the power grid monitoring function according to the node infection state, and provides a method for introducing the information network virus propagation into the chain fault research of the electric power information physical system.
The power network in a certain area is used for explaining the cascading failure results of the power information physical system when different information network viruses spread parameters in the embodiment of the invention, and the results are shown in fig. 4 and 5, wherein the cumulative occurrence probability of different power grid load loss rates is shown in the drawings. Fig. 4 shows the cascading failure results when the virus infection rate α changes, and it can be found that, due to virus infection, part of information nodes fail, so that the monitoring function on the power network is weakened, and compared with the normal operation condition of the information network, the virus infection of the information network can cause the coupling power grid to generate more large-scale outage events. When the recovery rate beta of the information node and the virus infection attenuation factor sigma are kept unchanged, the severity of cascading failure of the power grid increases with the increase of the infection rate alpha of the information node. The method is characterized in that as the infection rate of the information nodes increases, the infection rate of the information nodes infected by viruses in the information network increases, the function failure of the infected information nodes reduces the observability of the information network on the power nodes, further reduces the regulation and control functions of the information network on the power grid, aggravates the development of cascading failures and increases the load loss rate of the power grid. Fig. 5 shows the cascading failure result when the virus infection attenuation factor sigma changes, and as the attenuation factor sigma increases, the attenuation capability of the information defense system to the repeated infection virus of the nodes is reduced, and the increase of the number of the infected information nodes weakens the regulation and control functions of the information network to the power grid, so that the load of the power system lost due to the cascading failure is increased.
Considering that the cascading failure propagation of the electric power information physical system transmitted by the information network virus is influenced by the network modularization topological structure, fig. 6 is the cascading failure result of the electric power information physical system when different electric power information network communities are connected according to the embodiment of the invention. It can be found that considering the modular distribution causes different cascading failure results compared to not considering the modular distribution of the coupling system at all, and that as the probability of the information network community connection increases, the grid load loss caused by the cascading failure increases. The method is characterized in that the connecting edges between communities are increased along with the increase of the connection probability, the community structural strength of the information network is weakened, the infection rate of the information nodes is positively correlated with the number of the connected infection nodes, the propagation path of viruses among different communities is improved by the increase of the connecting edges between communities, the infection probability of the information network nodes is increased, the monitoring function of the information network on a power grid is further weakened, the information system cannot accurately regulate and control the power network, and the severity of the cascading failure result of the power system is increased.
The above method is described below by way of a specific example of implementation, with reference to fig. 7. Fig. 7 is a flowchart of an implementation of a cascading failure modeling method of an electrical power information physical system according to an embodiment of the present invention, and a specific implementation flow is as follows:
Step 703: power information network virus propagation: and simulating the dynamic virus transmission of the information network by using a virus transmission model, detecting the virus infection state of the information node, and further judging the observability of the power line according to the validity of the information node when the infected node loses the monitoring function on the coupled power node.
Step 704: grid distributed control: calculating a frequency deviation from the power distribution of the generator nodes and the load nodes of the power network, and if the frequency deviation exceeds a deviation limit, proceeding to step 706; otherwise, the power of the generator and the load node is adjusted by the static power frequency characteristic of the power grid, and the process goes to step 705.
Step 705: calculating power flow of a power grid: calculating the power grid power flow based on the direct current power flow model, judging the overload state of the considerable line, if the power flow of the observable line exceeds the upper limit of the allowable power flow of the line, existence of the considerable overload line, and turning to step 706; otherwise, the overload circuit is directly deleted according to the actual power flow and the step 703 is carried out, if the overload circuit is not present, the physical network model of the power information after the fault is output and the power grid load loss rate is calculated.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 8 is a schematic structural diagram of a device of the cascading failure modeling method of the power information physical system according to the embodiment of the present invention, and for convenience of explanation, only a portion relevant to the embodiment of the present invention is shown, which is described in detail below:
as shown in fig. 8, an apparatus 80 of the power information physical system cascading failure modeling method includes: a module creation means 81, a virus injection means 82, a model update means 83 and a calculation means 84.
Module establishing means 81 for performing community division on the power network, establishing a modularized power information network by using a community division result, and constructing a modularized power information physical system network model based on the modularized power information network and the power network;
virus injection means 82 for randomly selecting at least one power network node or line set initial fault in the power system network, and randomly selecting at least one power information network node in the power information network to inject virus;
the model updating device 83 is configured to perform cascading failure simulation on the power information physical network model based on a preset virus propagation model, so as to obtain a power information physical network model after cascading failure;
And the calculating device 84 is used for calculating the load loss rate of the power network after cascading failure relative to the initial power network.
Optionally, the module setting-up device 81 is configured to:
performing community division on the initial power network based on a community discovery algorithm;
establishing a modularized power information network model based on a community division result and a scaleless network generation algorithm;
based on the topological similarity of the initial power network and the power information network, coupling nodes of the initial power network and the power information network according to a community division result to obtain a modularized power information physical system network model.
Optionally, the model updating device 83 is configured to:
simulating dynamic propagation of viruses in the power information network by using a preset virus propagation model;
identifying whether an overloaded power line exists in the power network;
if yes, deleting the overloaded power line, updating a power information physical system network model, and jumping to a step of simulating dynamic propagation of viruses in the power information network by using a preset virus propagation model; if not, outputting the network model of the power information physical system after cascading failure.
Optionally, the model updating device 83 is configured to:
Calculating frequency deviation according to power distribution of generator nodes and load nodes of the power network;
judging whether the frequency deviation is out of limit according to the relation between the frequency deviation and a preset frequency limit;
if the frequency deviation is out of limit, executing a power flow optimization strategy according to the information of the power network identified by the power information network dispatching center; calculating the actual power flow of the power network by using the power flow optimization result and a preset direct current power flow model; judging whether an overloaded power line exists in the power network according to the actual power flow of the power network;
if the frequency deviation is not out of limit, based on the static power frequency characteristic of the power network, adjusting the power distribution of the generator node and the load node, and calculating the power flow of the power grid according to a preset direct current power flow model; judging whether an overload considerable power line exists in the power network according to the power flow of the power grid; the considerable power line is a power line in a health state at least of coupling information nodes corresponding to two power nodes connected with the power line in the power network;
if an overload considerable power line exists in the power network, executing a power flow optimization strategy according to the information of the power network identified by the power information network dispatching center; calculating the actual power flow of the power network by using the power flow optimization result and a preset direct current power flow model; judging whether an overloaded power line exists in the power network according to the actual power flow of the power network;
And if no overload considerable power line exists in the power network, judging whether the overload power line exists in the power network according to the power flow of the power grid.
Optionally, the model updating device 83 is configured to:
if the grid power flow of the power network exceeds a preset power flow limit, determining that an overload considerable power line exists in the power network; otherwise, determining that the power network has no overload of the considerable power line;
judging whether an overloaded power line exists in the power network according to the actual power flow of the power network, comprising:
if the actual power flow of the power network exceeds a preset power flow limit, determining that an overloaded power line exists in the power network; otherwise, determining that the power network has no overload power line;
the direct current power flow model is as follows:
P＝Bθ
F＝(b×A)θ
wherein P is a node injection power vector; b is an admittance matrix, b=a T X b x a; θ is the bus voltage phase angle vector; f is a branch power flow vector of the power grid; b is a diagonal matrix of conductance, b=diag (1/x 1 ,1/x 2 ,...,1/x m ) The method comprises the steps of carrying out a first treatment on the surface of the A is an incidence matrix of the node line;
the optimization targets of the tide optimization strategy are as follows:
wherein N is P The number of power nodes; ΔP Li Load shedding amount for the power node i;
constraint conditions of the tide optimization strategy are as follows:
-F lmax ≤F l ≤F lmax
Wherein F is lmax Is a branch tidal current limit; f (F) l The current value is the current value of the transmission line;
the normal operation load of the node is set; />
The generator power generation capacity limit.
Optionally, the computing device 84 is further configured to:
calculating frequency deviation according to power distribution of generator nodes and load nodes of the power network and a first formula;
wherein the first formula comprises:
wherein Δf is the frequency deviation of the system after disturbance; p (P) Gi The output power of the generator i; p (P) Li Is the power of load i; k (K) Gi A frequency adjustment coefficient for generator i; k (K) Li A frequency adjustment coefficient for load i; omega shape G Is a set of system generators, Ω L Is a system load set;
based on the static power frequency characteristics of the power network, adjusting the power distribution of the generator node and the load node, comprising:
based on the static power frequency characteristic of the power network and a second formula, adjusting the power distribution of the generator node and the load node;
wherein the second formula comprises:
wherein,,
the power adjustment quantity of the generator node; />
The power adjustment quantity of the load node; p (P) G0i Output for the initial generator; p (P) L0i Is the initial load power.
Optionally, the computing device 84 is further configured to:
the virus propagation model includes:
wherein s is i (t+1) is the state of node i at time (t+1); s is(s) i (t) is the state of the node i at time t, and the upper transverse line is the node state inverse; g is a state transition judging function; z i (t) is the health status of node i at time t, z when in health status i (t) =0, otherwise, z i (t) =1; alpha is the probability of the infection information node to infect the neighbor node; sigma is a virus infection attenuation factor, namely the probability of the infected information node after the software is upgraded is changed into sigma times of the original value, and the value range is (0-1); q i (t) is the number of inode infections; n is n i (t) is a number representing the number of infected nodes connected to node i at time t; r is a random number uniformly distributed between (0, 1); beta is the probability of the infected information node to recover the health state; a, a ij The matrix elements are contiguous for the power information network.
Calculating a load loss rate of the power network after cascading failure relative to the initial power network, comprising:
according to a third formula, calculating the load loss rate of the power network after cascading failure relative to the initial power network,
wherein, the third formula is:
wherein η is the load loss rate; p (P) L1 Initial load quantity for the power grid; p (P) L2 And the load capacity of the power grid after cascading failure.
Fig. 9 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 9, the terminal device 9 of this embodiment includes: a processor 90, a memory 91 and a computer program 92 stored in said memory 91 and executable on said processor 90. The processor 90, when executing the computer program 92, implements the steps of the various power information physical system cascading failure modeling method embodiments described above.
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 complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions describing the execution of the computer program 92 in the terminal device 9.
The terminal device 9 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor 90, a memory 91. It will be appreciated by those skilled in the art that fig. 9 is merely an example of the terminal device 9 and does not constitute a limitation of the terminal device 9, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The processor 90 may be a central processing unit (Central Processing Unit, CPU), other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or a 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 memory Card (Flash Card) or the like, which are provided on the terminal device 9. Further, the memory 91 may also include both an internal storage unit and an external storage device of the terminal device 9. The memory 91 is used for storing the computer program and other programs and data required by the terminal device. The memory 91 may also be used for temporarily storing data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of each functional unit and module is illustrated, and in practical application, the above-described functional allocation may be performed by different functional units and modules, i.e. the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (10)
1. The modeling method for the cascading failure of the power information physical system is characterized by comprising the following steps of:
performing community division on the initial power network, establishing a modularized power information network by using a community division result, and constructing a modularized power information physical system network model based on the modularized 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 initial faults, and randomly selecting at least one power information network node in the power information network to inject viruses;
performing cascading failure simulation on the power information physical network model based on a preset virus propagation model to obtain a cascading failure-based power information physical network model;
And calculating the load loss rate of the power network after cascading failures relative to the initial power network.
2. The method of claim 1, wherein the initial power network is socially partitioned, a modularized power information network is established using a result of the socialization, and a modularized power information physical system network model is constructed based on the modularized power information network and the initial power network, comprising:
performing community division on the initial power network based on a community discovery algorithm;
establishing a modularized power information network model based on the community division result and a scaleless network generation algorithm;
based on the topological similarity of the initial power network and the power information network, coupling nodes of the initial power network and the power information network according to the community division result to obtain a modularized power information physical system network model.
3. The method of claim 1, wherein performing cascading failure simulation on the power information physical network model based on a preset virus propagation model to obtain a cascading failure-derived power information physical network model comprises:
Simulating dynamic propagation of the viruses in the power information network by using a preset virus propagation model;
identifying whether an overloaded power line exists in the power network;
if yes, deleting the overloaded power line, updating a power information physical system network model, and jumping to a step of simulating dynamic propagation of the virus in the power information network by using a preset virus propagation model; if not, outputting the network model of the power information physical system after cascading failure.
4. A method according to claim 3, wherein identifying whether an overloaded power line is present in the power network comprises:
calculating frequency deviation according to power distribution of generator nodes and load nodes of the power network;
judging whether the frequency deviation is out of limit according to the relation between the frequency deviation and a preset frequency limit;
if the frequency deviation is out of limit, executing a power flow optimization strategy according to the information of the power network identified by the power information network dispatching center; calculating the actual power flow of the power network by using the power flow optimization result and a preset direct current power flow model; judging whether an overloaded power line exists in the power network according to the actual power flow of the power network;
If the frequency deviation is not out of limit, adjusting the power distribution of the generator node and the load node based on the static power frequency characteristic of the power network, and calculating the power grid power flow according to a preset direct current power flow model; judging whether an overload considerable power line exists in the power network according to the power flow of the power grid; the considerable power line is a power line with at least one coupling information node in a health state, wherein the coupling information node corresponds to two power nodes connected with the power line in the power network;
if overload considerable power lines exist in the power network, executing a power flow optimization strategy according to the information of the power network identified by the power information network dispatching center; calculating the actual power flow of the power network by using the power flow optimization result and a preset direct current power flow model; judging whether an overloaded power line exists in the power network according to the actual power flow of the power network;
and if no overload considerable power line exists in the power network, judging whether the overload power line exists in the power network according to the power flow of the power grid.
5. The method of claim 4, wherein determining whether an overload substantial power line exists in the power network based on a grid flow of the power network comprises:
If the grid power flow of the power network exceeds a preset power flow limit, determining that an overload considerable power line exists in the power network; otherwise, determining that there is no overload of the power network for a substantial power line;
judging whether an overloaded power line exists in the power network according to the actual power flow of the power network, comprising:
if the actual power flow of the power network exceeds a preset power flow limit, determining that an overloaded power line exists in the power network; otherwise, determining that the power network has no overload power line;
wherein, the direct current power flow model is:
P＝Bθ
F＝(b×A)θ
wherein P is a node injection power vector; b is an admittance matrix, b=a T X b x a; θ is the bus voltage phase angle vector; f is a branch power flow vector of the power grid; b is a diagonal matrix of conductance, b=diag (1/x 1 ,1/x 2 ,...,1/x m ) The method comprises the steps of carrying out a first treatment on the surface of the A is an incidence matrix of the node line;
the optimization targets of the tide optimization strategy are as follows:
wherein N is P The number of power nodes; ΔP Li Load shedding amount for the power node i;
constraint conditions of the tide optimization strategy are as follows:
-F lmax ≤F l ≤F lmax
wherein F is lmax Is a branch tidal current limit; f (F) l The current value is the current value of the transmission line;
the normal operation load of the node is set; />
The generator power generation capacity limit.
6. The method of claim 4, wherein calculating the frequency deviation from the power distribution of the generator nodes and the load nodes of the electrical power network comprises:
calculating frequency deviation according to power distribution of generator nodes and load nodes of the power network and a first formula;
wherein the first formula comprises:
wherein Δf is the frequency deviation of the system after disturbance; p (P) Gi The output power of the generator i; p (P) Li Is the power of load i; k (K) Gi A frequency adjustment coefficient for generator i; k (K) Li A frequency adjustment coefficient for load i; omega shape G Is a set of system generators, Ω L Is a system load set;
adjusting a power distribution of the generator node and the load node based on a static power frequency characteristic of the power network, comprising:
adjusting the power distribution of the generator node and the load node based on the static power frequency characteristic of the power network and a second formula;
wherein the second formula comprises:
wherein,,
the power adjustment quantity of the generator node; />
The power adjustment quantity of the load node; p (P) G0i Output for the initial generator; p (P) L0i Is the initial load power.
7. The method of any one of claims 1-6, wherein the virus propagation model comprises:
Wherein s is i (t+1) is the state of node i at time (t+1); s is(s) i (t) is the state of the node i at time t, and the upper transverse line is the node state inverse; g is a state transition judging function; z i (t) is the health status of node i at time t, z when in health status i (t) =0, the reverse，z i (t) =1; alpha is the probability of the infection information node to infect the neighbor node; sigma is a virus infection attenuation factor, namely the probability of the infected information node after the software is upgraded is changed into sigma times of the original value, and the value range is (0-1); q i (t) is the number of inode infections; n is n i (t) is a number representing the number of infected nodes connected to node i at time t; r is a random number uniformly distributed between (0, 1); beta is the probability of the infected information node to recover the health state; a, a ij Adjoining matrix elements for a power information network;
the calculating the load loss rate of the power network after cascading failure relative to the initial power network comprises the following steps:
according to a third formula, calculating the load loss rate of the power network after cascading failure relative to the power network,
wherein the third formula is:
wherein η is the load loss rate; p (P) L1 Initial load quantity for the power grid; p (P) L2 And the load capacity of the power grid after cascading failure.
8. An apparatus for modeling cascading failure of an electric power information physical system, comprising:
The module establishing device is used for carrying out community division on the initial power network, establishing a modularized power information network by utilizing a community division result, and establishing a modularized power information physical system network model based on the modularized power information network and the initial power network;
the virus injection device is used for randomly selecting at least one power network node or line to set initial faults in the initial power network, and randomly selecting at least one power information network node in the power information network to inject viruses;
the model updating device is used for performing cascading failure simulation on the electric power information physical network model based on a preset virus propagation model to obtain a cascading failure electric power information physical network model;
and the calculating device is used for calculating the load loss rate of the power network after cascading failure relative to the initial power network.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the power information physical system cascading failure modeling method of any one of the preceding claims 1 to 7 when the computer program is executed by the processor.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the power information physical system cascading failure modeling method of any one of the preceding claims 1 to 7.
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| CN117828243B (en) * | 2024-03-06 | 2024-05-14 | 国网上海能源互联网研究院有限公司 | FPGA tide parallel computing system and method |
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