CN116938737A - Power distribution Internet of things typical communication state simulation method based on novel interaction model - Google Patents
Power distribution Internet of things typical communication state simulation method based on novel interaction model Download PDFInfo
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Abstract
The invention discloses a power distribution Internet of things typical communication state simulation method based on a novel interaction model, which solves the defects of the prior art and comprises the following steps: step 1, modeling a power distribution Internet of things through a discrete time event system, and synchronizing information physical coupling event time points based on a global event list; step 2, generating a power distribution Internet of things communication state simulation method based on a network simulator, establishing a power distribution Internet of things information physical interaction model, and simulating a transmission process of a control flow in a communication network; step 3, introducing information time delay according to the control state information disturbance simulation technology of the power distribution Internet of things, and establishing an operation control method of the power distribution Internet of things in an abnormal communication state; and 4, simulating the whole power distribution Internet of things, recording parameters of a power system and a communication network in the power distribution Internet of things and influence relations between the power system and the communication network, and analyzing typical communication states of the power distribution Internet of things.
Description
Technical Field
The invention relates to the technical field of simulation, in particular to a power distribution Internet of things typical communication state simulation method based on a novel interaction model.
Background
Today, in order to achieve a more reliable and efficient electrical infrastructure, advanced electrical power networks increasingly rely on the use of information and communication technology (Informationand Communication Technology, ICT) to implement networked control to detect and control grid operation. Conventional power systems feature a power system that generates electricity centrally and is primarily on the passive distribution side, whereas operation and control of future power systems must accommodate renewable and distributed energy resources, and this requires active participation on the demand side. Millions of sensor nodes collect real-time measurement data and send the collected data to a control center through a communication network, and the control center analyzes the data and makes corresponding adjustments to the state of the system, for example, the system can respond quickly to fault conditions, so that the system is stable from the faults, and the like. Due to the intervention of communication network technology and computer technology, the existing power system is gradually changed into a Physical information system (CPS) to become an intelligent power system. There are many definitions about smart grids, the main focus of the european union definition being to propose that smart grids are all users of a power network that can intelligently integrate behavior and actions to ensure sustainable, economical and safe power supply. The definition of the united states department of energy indicates that smart grids use digital technology to improve the reliability, safety and efficiency of power systems. From these definitions it can be seen that the application of ICT in power systems is critical in order to make existing power systems evolve towards safe, reliable and efficient smart grids.
Considering the increasing role of communication networks in power system operation, it is important to know their interactions and interdependencies thoroughly. If a shared infrastructure, such as the internet, instead of a private communication network, is considered as a power system ICT infrastructure, the dependence of power system behaviour and status on communication network performance becomes more pronounced. This important interdependence of power and communication systems in the future requires their joint analysis and design. Because of the associated complexity and cost limitations, it is impractical to test various communication solutions in a practical system, and the operational tasks of each item in the power network are critical, which makes it necessary for the applications running on the sensors, controllers (e.g., applications how the system state data collected by the collection nodes is packaged and uploaded to the control center) to be strictly debugged, and the effects of some uncertainty factors in the network on the power system, such as packet delays, packet loss, and communication link failures, should be analyzed, digital analog simulation is the only viable solution, simulating the operation of both the power system and the communication network. However, for such advanced power system and communication network combined systems, there is no one simulator in the conventional simulator capable of simulating the entire system at the same time.
Because the novel power distribution Internet of things model is complex, equipment is numerous, the scale is huge, the simulation needs of the power distribution Internet of things cannot be met by the traditional simulation method and simulation tools, and the difficulties of data transmission, time sequence synchronization and information physical coupling event coordination exist in the existing simulation tools, so that the application range and efficiency of the simulation tools are limited.
Disclosure of Invention
The invention aims to overcome the defects that the power distribution Internet of things model is complex, equipment is numerous and large in scale, the traditional simulation method and simulation tool cannot meet the simulation requirement of the power distribution Internet of things, and the traditional simulation tool has difficulties in data transmission, time sequence synchronization and information physical coupling event coordination, and provides a power distribution Internet of things typical communication state simulation method based on a novel interaction model.
The invention aims at realizing the following technical scheme:
a power distribution Internet of things typical communication state simulation method based on a novel interaction model comprises the following steps:
step 1, modeling a power distribution Internet of things through a discrete time event system, and synchronizing information physical coupling event time points based on a global event list;
step 2, generating a power distribution Internet of things communication state simulation method based on a network simulator, establishing a power distribution Internet of things information physical interaction model, and simulating a transmission process of a control flow in a communication network;
step 3, introducing information time delay according to the control state information disturbance simulation technology of the power distribution Internet of things, and establishing an operation control method of the power distribution Internet of things in an abnormal communication state;
and 4, simulating the whole power distribution Internet of things according to the steps 1 to 3, recording parameters of a power system, communication network parameters and influence relations between the power system and the communication network in the power distribution Internet of things, and analyzing typical communication states of the power distribution Internet of things.
Preferably, in the step 1, the synchronization of the information physical coupling event time point based on the global event list is specifically: each iteration is used as a discrete event, and a time stamp is attached; a global event scheduler is designed to be used as a global event reference and coordinator, a global event list is arranged in the global event scheduler and is used for recording events in the whole joint simulation, and the iterative events and the events in the communication network are sequentially mixed and arranged in the global event list according to the time stamp of the iterative events and the events in the communication network and the time sequence; each time the global event scheduler takes the previous event from the global event list to execute, only one event will be executed at the same time, so the joint simulation running process of the two simulators is described by a time axis.
Preferably, the network simulator in step 2 is a mini communication network simulator.
Preferably, in the step 4, the method for detecting the abnormal communication state includes:
the method for detecting the information abnormality based on the event chain correlation is provided by utilizing the correlation coefficient, and comprises the following steps:
1) Electrical network and information network event chain representation and acquisition methods;
for a particular scenario Φ, electrical network event chain E p The formula (phi) is shown as follows,
wherein ,t, for a set of simultaneous events i For the moment of occurrence of an event, n e Is the event chain length; further, the +.A. is expressed by the event source device name>As shown in the following formula,
wherein ,mi The number of the simultaneous events is d, and the name of the electrical element with the event is the name of the electrical element with the event;
information network event chain E c (phi) is represented by the following formula;
in the aboveRepresenting a set of data arrival events in a form represented by the formula; wherein k is i To simultaneously generate event numbersOrder, v i Representing an information network device name;
2) The information anomaly detection method comprises the following steps:
a) Selecting a reference
Determining a set of typical fault scenarios Φ from all possible fault states of an electrical network s ；Φ s Element phi in (a) si (i＝1,2,…,n f ) Representing a fault scenario, n f The number of typical fault scenes; in practical application, the selection of a typical fault scene is related to the electric network structure and the position of a measuring unit;
b) Calculating distance
The "distance" between different fault scenarios can be measured by the correlation between discrete event chains; the discrete event chain is a special discrete time symbol sequence, and the index for measuring the correlation of the discrete event chain is Minkowski distance, pearson correlation coefficient or Hamming distance; defining the correlation between event chains from the point of view of topological association, wherein the calculation formula is shown in the following formula;
wherein x and y represent two event chains; the above is identical in form to the Pearson correlation coefficient; the larger the value of the correlation coefficient r is, the larger the correlation is;
c) Judging abnormality
Further, a scene phi to be detected of the power grid is set r The method comprises the steps of carrying out a first treatment on the surface of the Analysis of phi r Corresponding electrical and information network event chain Φ s The correlation of each scene corresponding to the event chain to obtain a correlation coefficient matrix R with the dimension of 4 multiplied by n f The method comprises the steps of carrying out a first treatment on the surface of the Each element in R is as follows:
i＝1，2，…，n f
the correlation coefficient matrix R needs to be normalized toThat is, in units of rows, reassigns according to the sizes of the respective elements, and the largest assignment is n f The smallest value is assigned to 1, and the rest are analogized in turn; in normalized correlation coefficient matrix->On the basis of the above, the difference between each row is analyzed to obtain a communication dynamic abnormality index D nr ；
wherein nr Is the number of rows of the normalized correlation coefficient matrix taken into account; ideally, D is absent when there is no information intrusion nr =0; taking into account that the metrology system may be subject to various disturbances, normally D nr Is a smaller rational number; d upon occurrence of information invasion nr Becomes very large; thus, the communication dynamic anomaly index may measure the likelihood that the electrical network is subject to information intrusion.
Preferably, the operation control method of the power distribution Internet of things operates an active power distribution network control model based on consideration of information time delay, wherein the active power distribution network control model comprises a primary system hybrid system model, a control information conversion model and a layered distribution control system model.
The beneficial effects of the invention are as follows: according to the power distribution Internet of things typical communication state simulation method based on the novel interaction model, the power distribution system and the communication system are considered as a whole in an inter-related system, the mutual influence between the power distribution system and the communication system is fully considered, and further the simulation result can correctly reflect the influence of the communication environment on the operation control state of the power distribution Internet of things, so that the use efficiency of a simulation tool is improved.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of an event driven based physical interaction timing synchronization mechanism for information;
FIG. 3 is a network topology of a communication system under certain scenarios;
fig. 4 is a control information execution situation contrast chart.
Detailed Description
The invention is further described below with reference to the drawings and examples.
Examples:
a power distribution Internet of things typical communication state simulation method based on a novel interaction model is shown in fig. 1, and comprises the following steps:
step 1, modeling a power distribution Internet of things through a discrete time event system, and synchronizing information physical coupling event time points based on a global event list;
step 2, generating a power distribution Internet of things communication state simulation method based on a network simulator, establishing a power distribution Internet of things information physical interaction model, and simulating a transmission process of a control flow in a communication network;
step 3, introducing information time delay according to the control state information disturbance simulation technology of the power distribution Internet of things, and establishing an operation control method of the power distribution Internet of things in an abnormal communication state;
step 4, simulating the whole power distribution Internet of things according to the steps 1 to 3, and recording the parameters of the power system and the communication network in the power distribution Internet of things and the influence relationship between the power system and the communication network, wherein the parameters are used for analyzing the typical communication state of the power distribution Internet of things;
in the step 1, the synchronization of the information physical coupling event time point based on the global event list is specifically:
as shown in fig. 2, each iteration acts as a discrete event, with a time stamp attached; a global event scheduler is designed to be used as a global event reference and coordinator, a global event list is arranged in the global event scheduler and is used for recording events in the whole joint simulation, and the iterative events and the events in the communication network are sequentially mixed and arranged in the global event list according to the time stamp of the iterative events and the events in the communication network and the time sequence; each time the global event scheduler takes the previous event from the global event list to execute, only one event will be executed at the same time, so the joint simulation running process of the two simulators is described by a time axis.
In addition, the global event scheduler checks the global event list, in order to determine whether the next event is an iteration event in the power system or an event in the communication network, if the next event is the power system iteration event, the global event scheduler firstly pauses the network simulator, gives control rights to the power system simulator to run the iteration event, after the power system iteration event is finished, the power system simulator gives up the control rights, returns the control rights to the global event scheduler, if the next event is the network event, the global event scheduler gives the control rights to the communication network simulator, the communication network simulator runs the network event, and when the communication network event is finished, the simulation control rights are returned to the global event simulator, the global event scheduler continues to perform event checking, and the control rights are handed over until the simulation is finished.
The network simulator in the step 2 is a Mininet communication network simulator.
Mininet is a virtual SDN network emulation orchestration system. Mininet was developed by Stanford university based on the Linux Container architecture, which was written in Python language (invoking part of the C++ language code), running a series of end hosts, switches, routers and links on a single Linux kernel. It uses a lightweight virtualization method, can compose a complete network in a single system, and can run the same kernel, system and user code on each virtual host. The Mininet host behaves like a real machine, and the user can enter it through ssh and run arbitrary programs (including programs installed on the underlying Linux system). The running program can send data packets through the simulated Ethernet interface, with given link speed and delay; while the packets are handled by an emulated ethernet switch, router, or middlebox and have a given number of queues. When the programs on two virtual hosts communicate through the mini, the measured performance should match the measured results when the same program is running on both local machines. In short, virtual hosts, switches, links and controllers in Mininet can be considered to exist virtually, except that they are created using software rather than hardware, but their behavior is largely analogous to discrete hardware elements. A Mininet network, similar to a hardware network, or a hardware network, similar to a Mininet network, can typically be created and the same binary code and applications run on either platform.
In an actual distribution internet of things, each host, switch or router, link, controller, etc. in a communication system has a specific physical layout, i.e. a network topology. These complex network topologies may be built using the functionality of the mini custom topology (mini-WiFi equivalent), and each host may have a separate interface.
A simplest scenario is assumed to describe a typical communication process for the distribution internet of things. In the distribution Internet of things, 1 monitoring device and 1 execution device are arranged at a certain bus, and are connected to the same switch due to the fact that the bus is close to the monitoring device in physical space; and the remote control device is connected to its local exchange; and a corresponding communication network is arranged between the two switches. Assuming that there is a direct connection of the communication links between the two switches (other information paths are not considered for the moment), the network topology of the communication system in this scenario can be abstracted to fig. 3.
In fig. 3, c0 represents a controller of an SDN architecture communication network, h1, h2, h3 represent hosts, s1, s2 represent switches, and L1, L2, L3, L4 represent communication links. And setting monitoring equipment of the bus corresponding to h1, executing equipment of the bus corresponding to h2, remote control equipment corresponding to h3, a switch at the bus corresponding to s1 and a switch at the control equipment corresponding to s 2. Assuming that in a certain situation, the monitoring device h1 samples the bus voltage value, detects that the bus voltage is reduced and is lower than the reference value, h1 needs to upload the bus voltage sampling value to h3 through the paths L1-s1-L4-s2-L3 in the form of a data packet; h3, collecting the busbar voltage sampling value as input for running a control program of the busbar voltage sampling value, and obtaining a control strategy, such as a control instruction of a capacitor bank at the busbar; then, h3 needs to transmit the control command to h2 through the paths L3-s2-L4-s1-L2 in the form of a data packet; h2, receiving a control instruction and executing the control instruction locally; and h1, sampling the voltage value of the bus again, and completing a control process when the bus voltage is monitored to be recovered. And the h1 and the h2 can also communicate with the corresponding communication links L1 and L2 through the s1, so that information interaction required by implementing a local control strategy at the bus is facilitated.
In the step 4, the method for detecting the abnormal communication state includes:
the method for detecting the information abnormality based on the event chain correlation is provided by utilizing the correlation coefficient, and comprises the following steps:
1) Electrical network and information network event chain representation and acquisition methods;
wherein ,t, for a set of simultaneous events i For the moment of occurrence of an event, n e Is the event chain length; further, the +.A. is expressed by the event source device name>As shown in the following formula,
wherein ,mi The number of the simultaneous events is d, and the name of the electrical element with the event is the name of the electrical element with the event;
information network event chain E c (phi) is represented by the following formula;
in the aboveRepresenting a set of data arrival events in a form represented by the formula; wherein k is i To simultaneously occur the number of events, v i Representing an information network device name;
the event chain can be obtained from the actual information physical coupling power network monitoring data and the joint simulation result. During operation of the coupled grid, both electrical network alarm events and information network arrival event related data are recorded and aggregated. The event chain may be obtained from a history of grid faults or test generation. If the sampling period of the measurement system is larger, multiple events occur simultaneously in the obtained event chain. Part of the serious faults rarely occur and are difficult to test, so that an event chain of the serious faults needs to be obtained through a joint simulation method. The information physical power network joint simulation can utilize event occurrence driving simulation on the basis of a unified event axis. After the event occurs, the respective known event sets of the electrical and information networks are updated, and then the next event is processed in time sequence. By reducing the simulation step length, the front-back relation between events with similar occurrence time can be determined, and m is further obtained i≡1 and ki Chain of electrical network events of≡1.
2) The information anomaly detection method comprises the following steps:
a) Selecting a reference
From electricityDetermining a set of typical fault scenarios Φ among all possible fault states of the gas network s ；Φ s Element phi in (a) si (i＝1,2,…,n f ) Representing a fault scenario, n f The number of typical fault scenes; in practical application, the selection of a typical fault scene is related to the electric network structure and the position of a measuring unit;
b) Calculating distance
The "distance" between different fault scenarios can be measured by the correlation between discrete event chains; the discrete event chain is a special discrete time symbol sequence, and the index for measuring the correlation of the discrete event chain is Minkowski distance, pearson correlation coefficient or Hamming distance; defining the correlation between event chains from the point of view of topological association, wherein the calculation formula is shown in the following formula;
wherein x and y represent two event chains; the above is identical in form to the Pearson correlation coefficient; the larger the value of the correlation coefficient r is, the larger the correlation is;
c) Judging abnormality
Further, a scene phi to be detected of the power grid is set r The method comprises the steps of carrying out a first treatment on the surface of the Analysis of phi r Corresponding electrical and information network event chain Φ s The correlation of each scene corresponding to the event chain to obtain a correlation coefficient matrix R with the dimension of 4 multiplied by n f The method comprises the steps of carrying out a first treatment on the surface of the Each element in R is as follows:
i＝1，2，…，n f
the correlation coefficient matrix R needs to be normalized toThat is, in units of rows, reassigns according to the sizes of the respective elements, and the largest assignment is n f The smallest value is assigned to 1, and the rest are analogized in turn; in normalized correlation coefficient matrix->On the basis of the above, the difference between each row is analyzed to obtain a communication dynamic abnormality index D nr ；
wherein nr Is the number of rows of the normalized correlation coefficient matrix taken into account; ideally, D is absent when there is no information intrusion nr =0; taking into account that the metrology system may be subject to various disturbances, normally D nr Is a smaller rational number; d upon occurrence of information invasion nr Becomes very large; thus, the communication dynamic anomaly index may measure the likelihood that the electrical network is subject to information intrusion.
The operation control method of the power distribution Internet of things operates an active power distribution network control model based on consideration of information time delay, wherein the active power distribution network control model comprises a primary system hybrid system model, a control information conversion model and a layered distribution control system model.
Mixed system model of primary system
Let the control logic variable be delta (t), the recursive model shown in the lower pattern is a mixed logic dynamic model converted from the general control model through setting logic variable, and reflects the dynamic evolution state of the controlled object.
x(t+Δt)＝Ax(t)+Bδ(t)
Considering the information process of a hierarchical distributed control system, Δt is chosen according to the information system situation, since the time consumed for the information system processing process is typically less than the time interval that can be tolerated by a single system control.
If the control scenario represented by the above formula has j controllable switching states, such as a power distribution mode of feeder power control, and a flexible load independent operation state or an operation combination mode of a load group. Then, in order to ensure that there is and only one mode is involved in the control at the same time, the vector δ (t) = [ δ ] consisting of logical variables 1 (t)，δ 2 (t)，…，δ j (t)]' the equation constraint that satisfies the following equation is required:
after the control process is performed in steps delta (t), the next moment will be controlled in accordance with the new logical variable delta (t + deltat). In feeder power control and flexible load control, since j control modes in δ (t) are possible to be selected at each time instant, there are still j possible transitions from δ (t) to δ (t+Δt).
Control information conversion model
An example of a timeline scenario is illustrated in fig. 4. The controlled device receives 2 times of control in the control period set at=20Δt. Under the ideal condition of not considering information time delay, the control process is equidistantly performed according to the time interval of 10 delta t, and the control quantity acts on the controlled equipment immediately after being generated. Under the condition of considering information time delay, the first control information U1 starts from t=0, reaches the controlled equipment at t=11Δt and executes a control signal; while the second control information U2 leaves the controller from t=4Δt, reaches the controlled device at t=13Δt, but does not perform control until t=19Δt.
Thus, in the case of considering the information delay, the two control information are respectively delayed and advanced by one step compared with the ideal case of not considering the information delay. Meanwhile, this example also indicates a case where the control information arrives at the controlled side, which does not mean that the control is performed, but may wait for a number of times at the controlled side; and the control information to start execution may also last for several times. Both of these cases will be described in modeling by constraints.
If the execution time of the control information is defined as the conversion time of the control information, then the conversion from U1 to U2 occurs at t=19Δt. At any time from t=12Δt to t=19Δt, the control signal may be switched or stay in the original state.
For this procedure, a set of target states L is defined for the control information conversion s (t) the control information indicating the time t may be unchanged at the time t+Δt or may be transferred to the next control information. The following logical relationship can be obtained.
s(t)＝[s 1 (t)，s 2 (t)…s m (t)…s s (t)]′
Wherein s (t) is a control information vector at time t, the control information vector contains state changes of s pieces of control information set in one control period, and s m (t) ∈ {0,1}. Then when the mth control information participates in the control, only s out of all s elements of s (t) m (t) =1. The above formula represents the relationship of the control information at two moments as the implication relationship of the union of the control information at the previous moment and the possible control information at the next moment. The above equation can be converted into the following inequality according to the logical conversion relation:
if the transition from s (t) to s (t+Δt) is represented by an association matrix, that is, the target state set L at each time is described by the association matrix s (t) the above formula can be rewritten as follows:
wherein Is an s x s dimension association matrix whose elements +.>
Since at the same time there is only one control information possible to be executed on the controlled device, and s m (t) ∈ {0,1}, and can be deduced
3) Hierarchical distributed control system model
In an active power distribution network hierarchical distribution control system, information flows reach controlled devices after passing through a plurality of nodes. If the time step is Δt, each control information s m Determining the next time s according to the position of the previous control information and the processing capacity of each information system node at the intervals of deltat m At the location of the device.
For this information flow process, a node conversion target set L of the hierarchical distributed control system is defined c (t) the control information s at time t m Starting from the current control system node, a new control system node which may arrive at time t+Δt or stay at the present node. Accordingly, the following logical relationship can be obtained.
Where c (t) is a vector of c control system node states at time t. Element c in c (t) n (t) is an s-dimensional vector representing the state of each control information at the nth control node at time t, and c nm (t) ∈ {0,1}, c only when the mth control information is at the nth control node nm (t)＝1。
c(t)＝[c 1 (t)，c 2 (t)…c n (t)…c c (t)]′
c n (t)＝[c n1 (t)，c n2 (t)…c nm (t)…c ns (t)]′
The above can also be converted according to logic rules, and a c×c-order incidence matrix describing the node connection relation of the control system is setThe following form of inequality is formed:
considering the information processing capability of each control link, if at the same time, except the control information generating node and the executing node, the nth control node can process w pieces of control information at the same time at most, then the following can be obtained:
for the aforementioned problem that the arrival time of the control information to the controlled device and the execution time of the controlled device are different, provision needs to be made by:
[s m (t)＝1]→[c cm (t)＝1]
when the mth control information is executed, the information necessarily reaches an execution node c of the control system, otherwise, the expression is a false proposition; and when the mth control information is not executed, there is a possibility that the information reaches the c-node. After the fine logic conversion, the following inequality can be obtained:
s m (t)≤c nm (t)
all logic elements of a 'physical process and information process fusion' model of the active power distribution network are obtained, and the transfer and conversion rules of the control system, the time and space of the control information analyzed in the previous step can be reflected.
In order to form s pieces of control information in one control period with the aforementioned control amount delta (t)In linkage, the progressive relationship and the control information transfer state s are required to be transferred m The association is made to determine that the logical variable at a certain moment can be executed as control information:
the complete active power distribution network hybrid system control model considering the information time delay can be obtained as follows:
the above-described embodiment is only a preferred embodiment of the present invention, and is not limited in any way, and other variations and modifications may be made without departing from the technical aspects set forth in the claims.
Claims (5)
1. The power distribution Internet of things typical communication state simulation method based on the novel interaction model is characterized by comprising the following steps of:
step 1, modeling a power distribution Internet of things through a discrete time event system, and synchronizing information physical coupling event time points based on a global event list;
step 2, generating a power distribution Internet of things communication state simulation method based on a network simulator, establishing a power distribution Internet of things information physical interaction model, and simulating a transmission process of a control flow in a communication network;
step 3, introducing information time delay according to the control state information disturbance simulation technology of the power distribution Internet of things, and establishing an operation control method of the power distribution Internet of things in an abnormal communication state;
and 4, simulating the whole power distribution Internet of things according to the steps 1 to 3, recording parameters of a power system, communication network parameters and influence relations between the power system and the communication network in the power distribution Internet of things, and analyzing typical communication states of the power distribution Internet of things.
2. The simulation method of the typical communication state of the power distribution internet of things based on the novel interaction model according to claim 1, wherein in the step 1, the synchronization of the information physical coupling event time points based on the global event list is specifically:
each iteration is used as a discrete event, and a time stamp is attached; a global event scheduler is designed to be used as a global event reference and coordinator, a global event list is arranged in the global event scheduler and is used for recording events in the whole joint simulation, and the iterative events and the events in the communication network are sequentially mixed and arranged in the global event list according to the time stamp of the iterative events and the events in the communication network and the time sequence; each time the global event scheduler takes the previous event from the global event list to execute, only one event will be executed at the same time, so the joint simulation running process of the two simulators is described by a time axis.
3. The simulation method of typical communication states of the power distribution internet of things based on the novel interaction model according to claim 1, wherein the network simulator in the step 2 is a mini communication network simulator.
4. The simulation method of the typical communication state of the power distribution internet of things based on the novel interaction model according to claim 1, wherein in the step 4, the detection method of the abnormal communication state is as follows:
the method for detecting the information abnormality based on the event chain correlation is provided by utilizing the correlation coefficient, and comprises the following steps:
1) Electrical network and information network event chain representation and acquisition methods;
for a particular scenario Φ, electrical network event chain E p The formula (phi) is shown as follows,
wherein ,t, for a set of simultaneous events i For the moment of occurrence of an event, n e Is the event chain length; further, the +.A. is expressed by the event source device name>As shown in the following formula,
wherein ,mi The number of the simultaneous events is d, and the name of the electrical element with the event is the name of the electrical element with the event;
information network event chain E c (phi) is represented by the following formula;
in the aboveRepresenting a set of data arrival events in a form represented by the formula; wherein k is i To simultaneously occur the number of events, v i Representing an information network device name;
2) The information anomaly detection method comprises the following steps:
a) Selecting a reference
Determining a set of typical fault scenarios Φ from all possible fault states of an electrical network s ；Φ s Element phi in (a) si (i＝1,2,…,n f ) Representing a fault scenario, n f The number of typical fault scenes; in practical application, selection of typical fault scene, electric network structure and electric networkMeasuring the position of the unit;
b) Calculating distance
The "distance" between different fault scenarios can be measured by the correlation between discrete event chains; the discrete event chain is a special discrete time symbol sequence, and the index for measuring the correlation of the discrete event chain is Minkowski distance, pearson correlation coefficient or Hamming distance; defining the correlation between event chains from the point of view of topological association, wherein the calculation formula is shown in the following formula;
wherein x and y represent two event chains; the above is identical in form to the Pearson correlation coefficient; the larger the value of the correlation coefficient r is, the larger the correlation is;
c) Judging abnormality
Further, a scene phi to be detected of the power grid is set r The method comprises the steps of carrying out a first treatment on the surface of the Analysis of phi r Corresponding electrical and information network event chain Φ s The correlation of each scene corresponding to the event chain to obtain a correlation coefficient matrix R with the dimension of 4 multiplied by n f The method comprises the steps of carrying out a first treatment on the surface of the Each element in R is as follows:
i＝1，2，…，n f
the correlation coefficient matrix R needs to be normalized toThat is, in units of rows, reassigns according to the sizes of the respective elements, and the largest assignment is n f The smallest value is assigned to 1, and the rest are analogized in turn; in normalized correlation coefficient matrix->On the basis of the above, the difference between each row is analyzed to obtain a communication dynamic abnormality index D nr ；
wherein nr Is the number of rows of the normalized correlation coefficient matrix taken into account; ideally, D is absent when there is no information intrusion nr =0; taking into account that the metrology system may be subject to various disturbances, normally D nr Is a smaller rational number; d upon occurrence of information invasion nr Becomes very large; thus, the communication dynamic anomaly index may measure the likelihood that the electrical network is subject to information intrusion.
5. The method for simulating the typical communication state of the power distribution Internet of things based on the novel interaction model according to claim 4, wherein the power distribution Internet of things operation control method operates an active power distribution network control model based on consideration of information time delay, and the active power distribution network control model based on consideration of information time delay comprises a primary system hybrid system model, a control information conversion model and a layered distribution control system model.
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