Electric power production data private network digital twinborn body construction method

Abstract

The invention discloses an electric power production data private network digital twinborn body construction method, which comprises the following steps: 1) establishing a geometric model, a physical model, a behavior model and a rule model according to geometric information, physical information, operation behaviors and operation rules of an electric power production data private network, a geometric model, a physical model, a behavior model and a rule model are integrated into a unified digital twin model; 2) constructing an electric power production data private network digital twinborn real-time data acquisition framework which comprises a sensing layer, a transmission layer and a feedback layer; and 3) sending a control instruction to the physical entity according to the decision instruction and the feedback, and operating and adjusting the control instruction by the virtual model to realize digital twinborn construction of the power production data private network. According to the method, the system is monitored in real time, the system decision strategy is optimized, the processing requirement for mass heterogeneous data under the background of an intelligent power grid is met, and the system efficiency is improved.

Description

Technical Field

[0001] The present invention relates to smart grid technology, and in particular to a method for constructing a digital twin of a power production data private network. Background Art

[0002] The digitization of the power data private network is one of the important directions for the development of the power system in recent years. It mainly establishes a digital model of the power production data private network through digital technology to reflect the situation of the private network, and then conducts simulation operation and simulation analysis on the power data private network through simulation technology to help operation and maintenance personnel better understand the situation and existing problems of the private network. However, although the existing digital models of power production data private networks have made certain progress, there are still some challenges and problems. Facing the processing requirements of massive heterogeneous data in the context of smart grids, the existing digital models of power production data private networks have technical problems such as insufficient real-time performance, low accuracy, and poor efficiency. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for constructing a digital twin of a power production data private network in view of the defects in the prior art.

[0004] The technical solution adopted by the present invention to solve its technical problems is as follows: A method for constructing a digital twin of a power production data private network includes the following steps:

[0005] 1) According to the geometric information, physical information, operating behavior, and operating rules of the power production data private network, establish a geometric model, a physical model, a behavior model, and a rule model, and integrate the geometric, physical, behavior, and rule models into a unified digital twin model;

[0006] 2) Construct a real-time data acquisition framework for the digital twin of the power production data private network, including a sensing layer, a transmission layer, and a feedback layer;

[0007] The sensing layer obtains the real-time data of the power production data private network in real time by deploying various sensing devices, associates the real-time data with the model to ensure that the digital model can accurately reflect the real-time state of the physical entity, and conveys these data to the corresponding feedback modules in the feedback layer through the data flow network constructed by the transmission layer, and then the feedback modules make decision instructions and feedback on the real-time data;

[0008] 3) Send control instructions to the physical entity according to the decision instructions and feedback, perform operations and adjustments on it by the virtual model, and realize the construction of the digital twin of the power production data private network.

[0009] Make decision instructions and feedback on real-time data, and send the generated instructions to physical entities through an OPC UA client. Then, the control instructions are passed to physical entities through an OPC UA server to realize the operation and adjustment of the virtual model on them.

[0010] According to the above solution, the sensing layer in the digital twin data acquisition framework includes sensors deployed on power production equipment for real-time monitoring of operating status and temperature parameters, such as generators, transformers, and switchgear; environmental monitoring sensors for monitoring surrounding environmental conditions, including air temperature, humidity, and wind speed; and energy metering equipment for measuring real-time power, current, and voltage parameters of power production equipment.

[0011] According to the above solution, the transmission layer in the digital twin data acquisition framework includes: the OPC UA protocol, a data storage component, and a data encryption and security component;

[0012] After the OPC UA client based on the OPC UA protocol reads real-time data from the sensing layer, a distributed database system or a cloud storage solution is used to cache and store the real-time data for backup and analysis; the data encryption and security component ensures that the data is encrypted during data transmission to prevent unauthorized access and data tampering.

[0013] According to the above solution, the feedback layer in the digital twin data acquisition framework includes a feedback module, a decision instruction generation component, and an alarm and notification system;

[0014] After receiving the real-time data collected by the transmission layer to the feedback layer, the feedback module processes the data through artificial intelligence algorithms or machine learning model methods, and then the decision instruction generation component generates corresponding instructions, such as adjusting the output of the generator, etc.; if the data is abnormal, the decision instruction generation component generates corresponding abnormal status information, and the alarm and notification system promptly notifies relevant personnel or system administrators of the abnormal status of the power production system.

[0015] The beneficial effects produced by the present invention are:

[0016] The present invention provides a method for constructing a "geometry-physics-behavior-rule" multi-dimensional digital twin of a power production data private network based on entity objects in a power production private network, realizing real-time monitoring of the system and optimizing the system decision-making strategy to meet the processing requirements of massive heterogeneous data in the context of a smart grid and improve system efficiency, and solving the technical problems of insufficient real-time performance, low accuracy, and poor efficiency existing in existing digital models of power production data private networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the drawings and embodiments. In the drawings:

[0018] Figure 1 is the flowchart of the method according to an embodiment of the present invention;

[0019] Figure 2 is the schematic diagram of the real-time data acquisition framework according to an embodiment of the present invention. Detailed implementation manners

[0020] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] As Figure 1 shown, a method for constructing a digital twin of a power production data private network includes the following steps:

[0022] 1) According to the geometric information, physical information, operating behavior and operating rules of the power production data private network, establish a geometric model, a physical model, a behavior model and a rule model, and integrate the geometric, physical, behavior and rule models into a unified digital twin model;

[0023] The construction of the digital twin model of the power production data private network needs to be described and characterized in multiple dimensions: geometric description, using geometric information such as the size and network topology of the power data private network to construct a geometric model; physical description, using physical parameters related to power equipment to construct a physical model to express the working principle and performance of power equipment; behavior description, describing the operating behavior of the power system, including operations such as starting, stopping, and overloading of equipment and the interaction between equipment; rule description, defining the rules and constraints for system operation to ensure compliance with safety requirements.

[0024] 2) Construct a real-time data acquisition framework for the digital twin of the power production data private network, including a sensing layer, a transmission layer and a feedback layer;

[0025] The sensing layer obtains the real-time data of the power production data private network in real time by deploying various sensing devices, associates the real-time data with the model to ensure that the digital model can accurately reflect the real-time state of the physical entity, and conveys these data to the corresponding feedback modules in the feedback layer through the data flow network constructed by the transmission layer, and then the feedback modules make decision instructions and feedback on the real-time data;

[0026] As Figure 2, the perception layer includes sensors deployed on power production equipment for real-time monitoring of operating status and temperature parameters, such as generators, transformers, and switchgear; environmental monitoring sensors for monitoring ambient environmental conditions, including temperature, humidity, and wind speed; and energy metering equipment for measuring real-time power, current, and voltage parameters of power production equipment.

[0027] The transmission layer includes: the OPC UA protocol, a data storage component, and a data encryption and security component;

[0028] After the OPC UA client based on the OPC UA protocol reads real-time data from the perception layer, it caches and stores the real-time data using a distributed database system or a cloud storage solution for backup and analysis; the data encryption and security component ensures that the data is encrypted during data transmission to prevent unauthorized access and data tampering.

[0029] The feedback layer includes a feedback module, a decision instruction generation component, and an alarm and notification system; after receiving the real-time data collected by the transmission layer to the feedback layer, the feedback module processes the data through artificial intelligence algorithms or machine learning model methods, and then the decision instruction generation component generates corresponding instructions, such as adjusting the output of the generator, etc.; if the data is abnormal, the decision instruction generation component generates corresponding abnormal status information and the alarm and notification system promptly notifies relevant personnel or system administrators of the abnormal status of the power production system.

[0030] 3) Send control instructions to the physical entity according to the decision instruction and feedback, perform operations and adjustments on it by the virtual model, and realize the construction of the digital twin of the power production data private network.

[0031] Make decision instructions and feedback on the real-time data, and the generated instructions are sent to the physical entity through the OPC UA client, and then the control instructions are passed to the physical entity through the OPC UA server, realizing the operation and adjustment of the virtual model on it.

[0032] The present invention establishes a virtual-real interaction mechanism for the power production data private network based on the OPC UA protocol. In the "real-virtual" direction, the OPC UA server is connected to the physical entity of the power production data private network to be responsible for connecting and providing real-time data, and the OPC UA client is embedded in the virtual environment to support real-time data acquisition and monitoring. In the "virtual-real" direction, control instructions are sent to the physical entity through the OPC UA client, and then the control instructions are passed to the physical entity through the OPC UA server. Finally, the construction of the digital twin of the power production data private network is realized, providing an efficient and feasible solution for the current problems of insufficient real-time performance and low transmission efficiency of data transmission, meeting the processing requirements of massive heterogeneous data in the context of the smart grid, and improving the efficiency and reliability of the power system.

[0033] It should be understood that those of ordinary skill in the art can make improvements or modifications based on the above description, and all such improvements and modifications shall fall within the protection scope of the appended claims of the present invention.

Claims

1. A method for constructing a digital twin of a power production data private network, characterized in that: The following steps are involved: 1) According to the geometric information, physical information, operation behavior and operation rules of the power production data private network, a geometric model, a physical model, a behavioral model and a rule model are established, and the geometric, physical, behavioral and rule models are integrated into a unified digital twin model; 2) Build a real-time data acquisition framework for the digital twin of the power production data private network, including the perception layer, transmission layer, and feedback layer; The perception layer acquires real-time data from the power production data network by deploying various types of perception devices, associates the real-time data with the model, ensures that the digital model can accurately reflect the real-time status of the physical entity, and transmits these data to the corresponding feedback modules in the feedback layer through the data flow network constructed by the transmission layer. The feedback modules then make decision instructions and feedback on the real-time data. 3) Send control instructions to the physical entity based on the decision instructions and feedback, operate and adjust the virtual model, and realize the construction of the digital twin of the power production data network.

2. The method for constructing a digital twin of a power production data private network according to claim 1, characterized in that: The perception layer in the digital twin data acquisition framework includes sensors deployed on power production equipment for real-time monitoring of operating status and temperature parameters; environmental monitoring sensors for monitoring surrounding environmental conditions, including air temperature, humidity, and wind speed; and energy metering equipment for measuring real-time power, current, and voltage parameters of power production equipment.

3. The method for constructing a digital twin of a power production data private network according to claim 1, characterized in that: The transport layer in the digital twin data acquisition framework includes: OPC UA protocol, data storage components, and data encryption and security components; After the OPC UA client based on the OPC UA protocol reads the real-time data from the perception layer, it uses a distributed database system or cloud storage solution to cache and store the real-time data for backup and analysis; Data encryption and security components ensure that data is encrypted during data transmission to prevent unauthorized access and data tampering.

4. The method for constructing a digital twin of a power production data private network according to claim 1, characterized in that: The feedback layer in the digital twin data acquisition framework includes a feedback module, a decision instruction generation component, and an alarm and notification system; After the receiving transmission layer sends the collected real-time data to the feedback layer, the feedback module processes the data through artificial intelligence algorithms or machine learning model methods, and then the decision-making instruction generation component generates corresponding instructions; if the data is abnormal, the decision-making instruction generation component generates corresponding abnormal status information, and the alarm and notification system promptly notifies relevant personnel or system administrators about the abnormal status of the power production system.

5. An electronic device, characterized in that: include: one or more processors; as well as a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the method according to any one of claims 1 to 4.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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