Electric power information physical system modeling method, device and equipment based on digital twinning and cross-domain joint simulation and medium

Abstract

The invention belongs to the technical field of electric power system information physical fusion, and discloses an electric power information physical system modeling method and device based on digital twinborn and cross-domain joint simulation, equipment and a medium. The device comprises a physical layer used for acquiring basic data of a power network; the communication layer is used for constructing a communication network model and simulating communication among different subsystems in the power network; the digital twin layer is used for constructing a protocol conversion gateway to align semantics of the physical layer data and the communication layer data, and performing state estimation of the physical layer and the communication layer by adopting digital twin virtual-real interaction based on the physical layer data and the communication layer data which are subjected to semantic alignment; performing joint simulation of the physical layer and the communication layer in different communication time delays and communication fault risk conditions to obtain a simulation result; and the application layer is used for obtaining critical time delay and time delay-frequency sensitivity based on the simulation result.

Description

Technical Field

[0001] The present invention belongs to the technical field of information-physical fusion of power systems, and in particular relates to a method, device, equipment and medium for modeling power information-physical systems based on digital twins and cross-domain joint simulation. Background Art

[0002] Existing technologies propose a power CPS modeling framework based on layered Petri nets, but this does not address the cross-layer coupling problem of multi-voltage grids. In the communications network sector, some studies have used game theory to quantify false data injection attacks, but have not analyzed cascading failures at the physical layer. At the distribution network level, some studies have constructed digital twins of distribution networks, but have not addressed the issue of cross-protocol data interaction. In simulation, there are achievements in 5G-based joint simulation platforms for new energy grid connection, but these do not cover the coordinated optimization of multiple links between sources, grids, loads, and storage. In terms of risk assessment, literature has reviewed cross-domain collaborative control of power CPSs, but lacks dynamic risk assessment methods.

[0003] The existing power cyber-physical system modeling has the following technical problems: (1) The heterogeneity of power physical equipment and communication network data leads to low efficiency of cross-domain collaborative simulation; (2) The threshold for the impact of communication anomalies (such as delays and DDoS attacks) on grid stability is unclear; (3) There is a lack of a real-time correlation model between the dynamic response of multiple links of source, network, load and storage and communication QoS. Summary of the Invention

[0004] The purpose of the present invention is to provide a modeling method, device, equipment and medium for power information-physical systems based on digital twins and cross-domain joint simulation, aiming to achieve cross-protocol data fusion and virtual-reality synchronous mapping of power physical equipment and communication networks, construct a cross-domain risk transmission quantitative model of communication anomalies and physical failures, provide dynamic risk assessment and active defense strategies, and ensure the safe operation of new power systems.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a power cyber-physical system modeling device based on digital twins and cross-domain joint simulation, comprising: The physical layer is used to obtain basic data of the power network; The communication layer is used to build a communication network model to simulate the communication between different subsystems in the power network; The digital twin layer is used to build a protocol conversion gateway to semantically align physical and communication layer data. Based on the semantically aligned physical and communication layer data, the digital twin virtual-real interaction is used to estimate the state of the physical and communication layers. The simulation results are obtained by jointly simulating the physical and communication layers under different communication delays and communication failure risks. The application layer is used to obtain critical delay and delay-frequency sensitivity based on simulation results. A further improvement of the present invention is that the digital twin engine adopts the Spring Cloud microservice framework, and the protocol conversion gateway is implemented based on Apache Camel.

[0006] A further improvement of the present invention is that the basic data of the power network specifically includes: generator equipment status data on the power supply side, status data of grid-side equipment, user-side smart meter data and meteorological data.

[0007] A further improvement of the present invention is that the communication network model includes communication equipment, communication mode, delay characteristics and QoS guarantee mechanism.

[0008] A further improvement of the present invention is that the digital twin layer constructs a protocol conversion gateway to align the semantics of physical layer data and communication layer data, specifically including: Real-time fusion of multi-source data is achieved through Apache Kafka stream processing middleware, and a sliding window compensation algorithm is used to align timestamps. The window size is 100ms, and the compensation formula is:

[0009] in, t sync is the timestamp after compensation, t raw is the original timestamp, n is the number of times contained in the window, t i For the i A time, is the time mean within the window.

[0010] A further improvement of the present invention is that in the step of using digital twin virtual-real interaction to perform state estimation of the physical layer and communication layer, the state estimation equation is:

[0011] in, is the estimated value of the device state at time k at time k-1, is the estimated value of the device state at the current k moment, is the direct collection value of some equipment status data at time k, is the Kalman gain, H is the observation matrix.

[0012] The further improvement of the application is that the state estimation is to improve the accuracy of the modeling of the physical layer and the communication layer, and subsequent joint simulation is based on the physical layer and information layer model obtained by the state estimation.

[0013] The further improvement of the application is that the digital twin layer performs joint simulation of the physical layer and the communication layer under different communication time delays and communication fault risk conditions, and in the step of obtaining the simulation result, the joint simulation is the frequency deviation of the physical layer under the condition of different communication time delays and communication fault risk conditions.

[0014] The further improvement of the application is that in the step of obtaining the critical time delay and the time delay-frequency sensitivity based on the simulation result, the time delay-frequency sensitivity is:

[0015] wherein, , , is the critical time delay; The critical time delay is obtained by solving the following optimization model by using a mixed integer linear programming method : Objective function: max ; Constraint condition:

[0016] wherein, is the frequency deviation of the physical layer, is the time delay of the automatic generation control AGC, is the load loss.

[0017] In the second aspect, the application provides a power information physical system modeling method based on digital twin and cross-domain joint simulation, comprising: constructing a physical layer and a communication layer device model; constructing a protocol conversion gateway to align the semantics of the physical layer data and the communication layer data; based on the semantics-aligned physical layer data and communication layer data, using digital twin virtual-real interaction to perform state estimation of the physical layer and the communication layer; performing joint simulation of the physical layer and the communication layer under different communication time delays and communication fault risk conditions to obtain simulation results; based on the simulation results, obtaining the critical time delay and the time delay-frequency sensitivity.

[0018] In the third aspect, the application provides an electronic device comprising a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to realize the power information physical system modeling method based on digital twin and cross-domain joint simulation.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium, which stores at least one instruction. When the at least one instruction is executed by a processor, it implements the power information-physical system modeling method based on digital twins and cross-domain joint simulation.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides an electric information-physical system modeling device based on digital twin and cross-domain joint simulation, including: a physical layer for acquiring basic data of the electric power network; a communication layer for building a communication network model to simulate the communication between different subsystems in the electric power network; a digital twin layer for building a protocol conversion gateway to semantically align the physical layer data and the communication layer data, and based on the semantically aligned physical layer data and the communication layer data, use digital twin virtual-real interaction to perform state estimation of the physical layer and the communication layer; perform joint simulation of the physical layer and the communication layer under different communication delays and communication failure risk conditions to obtain simulation results; an application layer for obtaining critical delay and delay-frequency sensitivity based on the simulation results. The calculation results of critical delay and delay-frequency sensitivity can provide a quantitative scientific basis for the real-time requirements of the communication layer, guide the optimization design of the communication layer, and ultimately support the frequency stability of the physical power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings: Figure 1 A schematic diagram of the architecture of an electric power cyber-physical system based on digital twins and cross-domain joint simulation provided by an embodiment of the present invention; Figure 2 A flow chart of a modeling method for an electric power cyber-physical system based on digital twins and cross-domain joint simulation provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a modeling device for an electric power cyber-physical system based on digital twin and cross-domain joint simulation provided by an embodiment of the present invention; Figure 4 The figure is a schematic structural diagram of an electronic device of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0023] The following detailed description is an exemplary description and is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention.

[0024] See also Figure 1 As shown, the embodiment of the present invention provides a modeling method for power cyber-physical systems based on digital twins and cross-domain joint simulation. Based on digital twin technology, a four-layer fusion architecture of "physical layer-communication layer-digital twin layer-application layer" is constructed, and collaborative modeling and risk assessment are achieved through the following core mechanisms: (1) Virtual-to-real mapping: Using the digital twin engine to fully model the power network (generators, transformers, switches, lines) and the communication network (5G, optical fiber) to form the physical layer and communication layer; In a specific implementation, this step is to model the power network equipment and communication network equipment, and the modeling tool adopts a mature digital twin engine, such as Unity, Siemens MindSphere, PTC ThingWorx and other tools.

[0025] (2) Cross-domain simulation: Deducing the impact of communication anomalies on power grid stability based on the "OPNET+Matlab / Simulink" joint simulation platform; (3) Dynamic assessment: Combining the attack tree model with the dynamic Bayesian network (DBN) to quantify the risk threshold and trigger the active control strategy. An embodiment of the present invention provides a method for modeling an electric power cyber-physical system based on digital twins and cross-domain joint simulation, which specifically includes the following steps: S1. Build a multi-level fusion architecture: Physical layer: Deploy intelligent terminals (PMUs and RTUs) to collect device status (voltage, current, and power) and integrate data from meteorological sensors (irradiance and wind speed) and user-side smart meters. RTUs collect generator status data from the power source, PMUs collect status data from transformers, switches, and lines on the grid, and user-side smart meters collect data on voltage, current, power, and energy consumption.

[0026] Communication layer: Builds a communication network model, including communication equipment, communication methods, latency characteristics, and QoS assurance mechanisms. Communication methods include fiber optics, 5G, and power carriers. Fiber optic communication has a latency of 1-5ms, 5G communication has a latency of 10-50ms, and power carrier communication has a latency of 80-200ms. The QoS assurance mechanism is implemented through a traffic shaping algorithm, ensuring that the latency of key control instructions is ≤20ms.

[0027] Digital twin layer: Dynamic entity modeling uses the Modelica language to define device interface standards, implement physical layer and communication layer device modeling, and build an association model between physical layer devices, communication layer devices, and communication methods. Data synchronization: The data synchronization process in data acquisition and communication uses Apache Kafka stream processing middleware to achieve real-time fusion of multi-source data, and uses a sliding window compensation algorithm to align timestamps. The window size is 100ms, and the compensation formula is:

[0028] Among them, t sync is the timestamp after compensation, is the original timestamp, n is the number of times contained in the window, t i For the i A time, is the time mean within the window.

[0029] Application layer: The application layer quantifies risk thresholds based on the attack tree model and dynamic Bayesian network (DBN) to achieve dynamic risk assessment. The following steps are included: (1) Aggressive behavior modeling: The attack tree model is used to characterize the DDoS attack strategy, defining the leaf node as the attack entry (exposed DTU port), the intermediate node as the attack steps (port scanning - vulnerability exploitation - traffic flooding), and the root node as the attack target (blocking the communication of the scheduling master station).

[0030] The attack success probability is calculated based on the dynamic Bayesian network (DBN), and the prior probability distribution is updated by combining historical attack data and vulnerability scanning results.

[0031]

[0032] in, for S i The parent node of For the parent node Transfer to state S i probability.

[0033] (2) Quantification of the impact on grid stability: Establish a correlation function between the communication performance index (delay) and the grid stability parameter (frequency deviation). Define the delay-frequency sensitivity coefficient:

[0034] in, is the critical time delay that causes frequency loss of control.

[0035] S2. Cross-domain data model and protocol adaptation: Unified Data Model: Based on the Common Information Model (CIM), the new energy power generation and communication equipment entity classes are expanded, with a new PhotovoltaicUnit subclass defining its attribute sets and relationships. The PV unit attribute set includes PV panel tilt, inverter efficiency, and communication interface bandwidth. Its relationships include the PV unit-busbar connection relationship and the PV unit-communication node mapping relationship.

[0036] Protocol conversion: Build a protocol conversion gateway to support automatic parsing and conversion of communication protocols such as IEC 61850, Modbus, and MQTT, convert register data in the communication protocol into CIM-compatible JSON-LD format, and achieve semantic alignment with communication layer data.

[0037] S3. Digital Twin Virtual-Real Interaction: InfluxDB time series database is deployed in the cloud to store historical data streams (time resolution 100ms); The edge side synchronizes device status in real time through the OPC UA protocol, and the subscription / publish mode adopts the MQTT protocol (QoS=2); The model error correction adopts the Kalman filter algorithm, and the state estimation equation is:

[0038] in, is the estimated value of the device state at time k at time k-1, is the estimated value of the device state at the current k moment, is the direct collection value of some equipment status data at time k, is the Kalman gain, H is the observation matrix.

[0039] S4. Build OPNET+Matlab / Simulink joint simulation interface: OPNET simulates communication network traffic, and Matlab / Simulink simulates power grid dynamic response; Cross-domain data interaction is implemented through the TCP / IP protocol, and the interface specifications are defined as follows: [Simulink→OPNET] Delay injection instruction format: { "type": "delay", "value": 50, "unit": "ms"} [OPNET→Simulink] Communication status feedback format: { "bandwidth": "10Gbps", "loss_rate": 0.05}; The above four steps ultimately lay the foundation for S5. S1 proposes an overall architecture and establishes device models for the physical and communication layers. This foundation is achieved through cross-domain data model and protocol adaptation (S2) and digital twin virtual-real interaction (S3). In particular, timestamp alignment and state estimation improve fusion accuracy. This allows for S4, joint simulation of the physical and communication layers. This simulation examines the dynamic response of the physical power grid, namely, frequency deviation, under varying communication delays and the risk of communication failures (ultimately leading to longer communication delays). Based on the simulation results of S4, critical delay calculations and delay-frequency sensitivity calculations are performed to support S5. These critical delay and delay-frequency sensitivity calculations provide a quantitative, scientific basis for the real-time requirements of the communication layer, guiding the optimized design of the communication layer and ultimately supporting frequency stability in the physical power grid.

[0040] S5. Cyber-physical security risk assessment: Attack modeling: Construct an attack tree model, with leaf nodes representing attack steps (e.g., "scanning DTU ports") and the root node representing the attack target ("blocking communication with the dispatching master station"). Calculate the probability of attack success using a dynamic Bayesian network (DBN):

[0041] in, is the parent node status, For the parent node Transfer to state S i probability.

[0042] (1) Establish delay-frequency sensitivity function:

[0043] in, , , is the critical delay; the critical delay is calculated according to the next step. (2) Calculation of critical delay: Solving critical time delay using mixed integer linear programming method , that is, solving the following optimization model: Objective function: max

[0044] The constraints are as follows:

[0045] in, 、 、 They are the frequency deviation, automatic generation control (AGC) delay, and load loss of the physical power grid, which are obtained by joint simulation of the physical layer and the information layer. 0.5Hz is the frequency deviation requirement, 50ms is the AGC delay requirement, and 5% is the load loss requirement.

[0046] The method of the present invention is applied to a specific embodiment: a DDoS attack scenario of a regional power grid (10Gbps traffic peak); (1) Attack occurs: • 5G base station bandwidth utilization rate >90%, latency soars from 35ms to 210ms; • The digital twin layer detects that the delay of key control instructions (AGC frequency modulation signal) exceeds the threshold (>82ms), triggering a level 3 alarm.

[0047] (2) Risk deduction: • The digital twin, through joint simulation, predicted that the frequency deviation would reach 0.45Hz within 2 minutes; • The application layer decision module issues a command to switch the energy storage system to island mode and cut off 50MW of non-critical load.

[0048] (3) Effect verification: • The actual grid frequency deviation is stable at 0.12Hz (safety threshold ±0.5Hz); • Load loss rate is only 4.8%.

[0049] The method of the present invention has the following beneficial effects: (1) Collaborative perception accuracy: • Bus voltage error ≤ 0.3%; • Communication delay error ≤7%.

[0050] (2) Risk assessment performance: • In DDoS attack scenarios, the load loss rate is reduced from 15.2% of the traditional method to 4.8%; • In the scenario of sudden delay change, the model warning time is shortened to 3 seconds.

[0051] (3) Computational efficiency: • The online calculation time of the model is ≤3.0 seconds; • The computation time for Monte Carlo simulation of 1000 delay mutations is ≤ 10 minutes.

[0052] See also Figure 1 As shown, an embodiment of the present invention provides a power cyber-physical system modeling device based on digital twin and cross-domain joint simulation, including: The physical layer is used to obtain basic data of the power network; a communication layer for constructing a communication network model to simulate the communication between different subsystems in the power network; a digital twin layer for constructing a protocol conversion gateway to align the semantics of the physical layer data and the communication layer data, based on the semantics-aligned physical layer data and the communication layer data, using digital twin virtual-real interaction to perform state estimation of the physical layer and the communication layer; performing joint simulation of the physical layer and the communication layer under different communication time delays and communication fault risks to obtain simulation results; an application layer for obtaining critical time delays and time delay-frequency sensitivities based on the simulation results. In a specific embodiment, the basic data of the power network specifically includes: generator equipment state data on the power supply side, state data of equipment on the power grid side, smart meter data on the user side, and meteorological data.

[0053] In a specific embodiment, the communication network model includes communication equipment, communication methods, time delay characteristics, and QoS guarantee mechanisms.

[0054] In a specific embodiment, the digital twin layer, in the step of constructing a protocol conversion gateway to align the semantics of the physical layer data and the communication layer data, specifically includes: Real-time fusion of multi-source data is achieved through Apache Kafka stream processing middleware, and a sliding window compensation algorithm is used to align the timestamps, with a window size of 100 ms and a compensation formula of:

[0055] wherein, t sync is the compensated timestamp, t raw is the original timestamp, n is the number of times contained in the window, t i is the first time, i is the average time in the window.

[0056] In a specific embodiment, in the step of using digital twin virtual-real interaction to perform state estimation of the physical layer and the communication layer in the digital twin layer, the state estimation equation is:

[0057] wherein, is the estimated value of the device state at time k-1 at time k, is the estimated value of the current device state at time k at time k, is the direct collection value of the device partial state data at time k, is the Kalman gain, H ​is the observation matrix.

[0058] In a specific embodiment, the digital twin layer performs a joint simulation of the physical layer and the communication layer under different communication delays and communication failure risks. In the step of obtaining the simulation results, the joint simulation is the frequency deviation of the physical layer under different communication delays and communication failure risks as the simulation result.

[0059] In a specific embodiment, the application layer obtains the critical delay and delay-frequency sensitivity based on the simulation results. for:

[0060] in, , , is the critical delay; The mixed integer linear programming method is used to solve the following optimization model to obtain the critical delay : Objective function: max ; Constraints:

[0061] in, is the frequency deviation of the physical layer, is the time delay of the automatic generation control AGC, For load loss.

[0062] See also Figure 2 As shown, an embodiment of the present invention provides a method for modeling a power cyber-physical system based on digital twins and cross-domain joint simulation, including: S100, build physical layer and communication layer device models; S200, building a protocol conversion gateway to align the semantics of physical layer data and communication layer data; S300, based on semantically aligned physical layer data and communication layer data, uses digital twin virtual-real interaction to estimate the state of the physical layer and communication layer; S400, performing joint simulation of the physical layer and the communication layer under different communication delays and communication failure risk conditions to obtain simulation results; S500: Based on the simulation results, obtain the critical delay and delay-frequency sensitivity.

[0063] In a specific embodiment, the basic data of the power network specifically includes: generator equipment status data on the power supply side, status data of grid-side equipment, user-side smart meter data and meteorological data.

[0064] In an embodiment, the communication network model comprises a communication device, a communication mode, a time delay characteristic and a QoS guarantee mechanism. The communication mode supports optical fiber, 5G and power carrier. The time delay of optical fiber communication is 1-5 ms, the time delay of 5G communication is 10-50 ms, and the time delay of power carrier communication is 80-200 ms. The QoS guarantee mechanism is realized by a traffic shaping algorithm to ensure that the time delay of critical control instructions is less than or equal to 20 ms.

[0065] In an embodiment, the step of constructing a protocol conversion gateway to align the semantics of physical layer data and communication layer data comprises: Real-time fusion of multi-source data is realized by Apache Kafka stream processing middleware, and a sliding window compensation algorithm is used to align the timestamps. The window size is 100 ms, and the compensation formula is:

[0066] wherein, t sync is the compensated timestamp, t raw is the original timestamp, n is the number of times contained in the window, t i is the first time, i is the average time in the window.

[0067] In an embodiment, in the step of using digital twin virtual-real interaction to estimate the state of the physical layer and the communication layer, the state estimation equation is:

[0068] wherein, is the estimated value of the device state at time k-1 for time k, is the estimated value of the current device state at time k for time k, is the direct collection value of the device partial state data at time k, is the Kalman gain, H is the observation matrix.

[0069] In an embodiment, in the step of obtaining simulation results by jointly simulating the physical layer and the communication layer under different communication time delays and communication failure risks, the joint simulation is the frequency deviation of the physical layer under different communication time delays and communication failure risks, as the simulation result.

[0070] In an embodiment, based on the simulation results, the step of obtaining the critical time delay and the time delay-frequency sensitivity is: ​​

[0071] in, , , is the critical delay; The mixed integer linear programming method is used to solve the following optimization model to obtain the critical delay : Objective function: max ; Constraints:

[0072] in, is the frequency deviation of the physical layer, is the time delay of the automatic generation control AGC, For load loss.

[0073] See also Figure 3 As shown, an embodiment of the present invention provides a power cyber-physical system modeling device based on digital twin and cross-domain joint simulation, including: Building blocks for constructing physical layer and communication layer device models; The semantic alignment module is used to build a protocol conversion gateway to align the semantics of physical layer data and communication layer data; The digital twin module is used to estimate the physical and communication layer states based on semantically aligned physical and communication layer data, using digital twin virtual-real interaction. The simulation module is used to perform joint simulation of the physical layer and the communication layer under different communication delays and communication failure risk conditions to obtain simulation results; The risk assessment module is used to obtain critical delay and delay-frequency sensitivity based on simulation results.

[0074] In a specific embodiment, the basic data of the power network specifically includes: generator equipment status data on the power supply side, status data of grid-side equipment, user-side smart meter data and meteorological data.

[0075] In one specific embodiment, the communication network model includes communication equipment, communication methods, latency characteristics, and a QoS guarantee mechanism. The communication methods support fiber, 5G, and power carrier communication. The latency for fiber communication is 1-5ms, the latency for 5G communication is 10-50ms, and the latency for power carrier communication is 80-200ms. The QoS guarantee mechanism is implemented through a traffic shaping algorithm to ensure that the latency of key control instructions is ≤20ms.

[0076] In a specific embodiment, the steps of constructing a protocol conversion gateway to align the semantics of physical layer data and communication layer data specifically include: Real-time fusion of multi-source data is achieved through Apache Kafka stream processing middleware, and a sliding window compensation algorithm is used to align timestamps. The window size is 100ms, and the compensation formula is:

[0077] in, t sync is the timestamp after compensation, t raw is the original timestamp, n is the number of times contained in the window, t i For the i A time, is the time mean within the window.

[0078] In a specific embodiment, in the step of using digital twin virtual-real interaction to perform state estimation of the physical layer and communication layer, the state estimation equation is:

[0079] in, is the estimated value of the device state at time k at time k-1, is the estimated value of the device state at the current k moment, is the direct collection value of some equipment status data at time k, is the Kalman gain, H is the observation matrix.

[0080] In a specific embodiment, a joint simulation of the physical layer and the communication layer is performed under different communication delays and communication failure risks. In the step of obtaining the simulation results, the joint simulation is the frequency deviation of the physical layer under different communication delays and communication failure risks as the simulation result.

[0081] In a specific embodiment, in the step of obtaining the critical delay and the delay-frequency sensitivity based on the simulation results, the delay-frequency sensitivity for:

[0082] in, , , is the critical delay; Solving critical time delay using mixed integer linear programming method , that is, solving the following optimization model: Objective function: max

[0083] The constraints are as follows:

[0084] in, 、 、 They are the frequency deviation, automatic generation control (AGC) delay, and load loss of the physical power grid, which are obtained by joint simulation of the physical layer and the information layer. 0.5Hz is the frequency deviation requirement, 50ms is the AGC delay requirement, and 5% is the load loss requirement.

[0085] See also Figure 4 As shown, an embodiment of the present invention provides an electronic device 100 for implementing a method for modeling an electric power information-physical system based on digital twins and cross-domain joint simulation; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0086] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the method for modeling the power information-physical system based on digital twin and cross-domain joint simulation described in the embodiment by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area may store data (such as audio data) created according to the use of the electronic device 100. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0087] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100 and connects various parts of the entire electronic device 100 using various interfaces and lines.

[0088] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a method for modeling a power cyber-physical system based on digital twins and cross-domain joint simulation. The processor 102 may execute the plurality of instructions to implement: Build physical layer and communication layer device models; Build a protocol conversion gateway to align the semantics of physical layer data and communication layer data; Based on semantically aligned physical layer data and communication layer data, digital twin virtual-real interaction is used to estimate the state of the physical layer and communication layer; Conduct joint simulation of the physical layer and communication layer under different communication delays and communication failure risk conditions to obtain simulation results; Based on the simulation results, the critical delay and delay-frequency sensitivity are obtained.

[0089] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0090] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0094] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. The power cyber-physical system modeling device based on digital twin and cross-domain joint simulation is characterized by: include: The physical layer is used to obtain basic data of the power network; The communication layer is used to build a communication network model to simulate the communication between different subsystems in the power network; The digital twin layer is used to build a protocol conversion gateway to semantically align physical and communication layer data. Based on the semantically aligned physical and communication layer data, the digital twin virtual-real interaction is used to estimate the state of the physical and communication layers. The simulation results are obtained by jointly simulating the physical and communication layers under different communication delays and communication failure risks. The application layer is used to obtain critical delay and delay-frequency sensitivity based on simulation results.

2. The power cyber-physical system modeling device based on digital twin and cross-domain joint simulation according to claim 1 is characterized in that: The basic data of the power network specifically includes: generator equipment status data on the power supply side, status data of grid-side equipment, user-side smart meter data and meteorological data.

3. The power cyber-physical system modeling device based on digital twin and cross-domain joint simulation according to claim 1 is characterized in that: The communication network model includes communication equipment, communication mode, delay characteristics and QoS guarantee mechanism.

4. The power cyber-physical system modeling device based on digital twin and cross-domain joint simulation according to claim 1 is characterized in that: The digital twin layer involves building a protocol conversion gateway to align the semantics of physical layer data with communication layer data. Specifically, the steps include: Real-time fusion of multi-source data is achieved through Apache Kafka stream processing middleware, and a sliding window compensation algorithm is used to align timestamps. The window size is 100ms, and the compensation formula is: in, t sync is the timestamp after compensation, t raw is the original timestamp, n is the number of times contained in the window, t i For the i A time, is the time mean within the window.

5. The power cyber-physical system modeling device based on digital twin and cross-domain joint simulation according to claim 1 is characterized in that: In the digital twin layer, the state estimation step of the physical layer and communication layer is performed using the digital twin virtual-real interaction. The state estimation equation is: in, is the estimated value of the device state at time k at time k-1, is the estimated value of the device state at the current k moment, is the direct collection value of some equipment status data at time k, is the Kalman gain, H is the observation matrix.

6. The power cyber-physical system modeling device based on digital twin and cross-domain joint simulation according to claim 1 is characterized in that: The digital twin layer performs a joint simulation of the physical layer and the communication layer under different communication delays and communication failure risks. In the step of obtaining the simulation results, the joint simulation is the frequency deviation of the physical layer under different communication delays and communication failure risks as the simulation result.

7. The power cyber-physical system modeling device based on digital twin and cross-domain joint simulation according to claim 1 is characterized in that: Application layer, based on simulation results, in the step of obtaining critical delay and delay-frequency sensitivity, delay-frequency sensitivity for: in, , , is the critical delay; The mixed integer linear programming method is used to solve the following optimization model to obtain the critical delay : Objective function: max ; Constraints: in, is the frequency deviation of the physical layer, is the time delay of the automatic generation control AGC, For load loss.

8. The power cyber-physical system modeling method based on digital twin and cross-domain joint simulation is characterized by: include: Build physical layer and communication layer device models; Build a protocol conversion gateway to align the semantics of physical layer data and communication layer data; Based on semantically aligned physical layer data and communication layer data, digital twin virtual-real interaction is used to estimate the state of the physical layer and communication layer; Conduct joint simulation of the physical layer and communication layer under different communication delays and communication failure risk conditions to obtain simulation results; Based on the simulation results, the critical delay and delay-frequency sensitivity are obtained.

9. An electronic device, characterized in that: It includes a processor and a memory, and the processor is used to execute the computer program stored in the memory to implement the power information-physical system modeling method based on digital twin and cross-domain joint simulation as described in claim 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, it implements the power information-physical system modeling method based on digital twin and cross-domain joint simulation as described in claim 8.

Images