CN117436215A - Distribution network digital twin body rapid construction method integrating electrical topology - Google Patents
Distribution network digital twin body rapid construction method integrating electrical topology Download PDFInfo
- Publication number
- CN117436215A CN117436215A CN202311550574.1A CN202311550574A CN117436215A CN 117436215 A CN117436215 A CN 117436215A CN 202311550574 A CN202311550574 A CN 202311550574A CN 117436215 A CN117436215 A CN 117436215A
- Authority
- CN
- China
- Prior art keywords
- data
- digital twin
- distribution network
- model
- parameters
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 45
- 238000010276 construction Methods 0.000 title claims abstract description 24
- 238000004364 calculation method Methods 0.000 claims abstract description 34
- 238000000034 method Methods 0.000 claims abstract description 24
- 238000005516 engineering process Methods 0.000 claims abstract description 14
- 238000005457 optimization Methods 0.000 claims abstract description 9
- 238000012795 verification Methods 0.000 claims abstract description 8
- 238000004088 simulation Methods 0.000 claims abstract description 5
- 238000013480 data collection Methods 0.000 claims abstract description 4
- 239000002245 particle Substances 0.000 claims description 27
- 238000004891 communication Methods 0.000 claims description 21
- 238000012545 processing Methods 0.000 claims description 12
- 238000007405 data analysis Methods 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000002347 injection Methods 0.000 claims description 3
- 239000007924 injection Substances 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 230000005611 electricity Effects 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/25—Design optimisation, verification or simulation using particle-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/04—Power grid distribution networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- Computer Hardware Design (AREA)
- Power Engineering (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Mathematical Optimization (AREA)
- Computer Networks & Wireless Communication (AREA)
- Mathematical Analysis (AREA)
- Health & Medical Sciences (AREA)
- Computational Mathematics (AREA)
- Artificial Intelligence (AREA)
- Biomedical Technology (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention relates to the technical field of electricity, and discloses a rapid construction method of a distribution network digital twin body fusing electrical topology, which comprises the following steps: s1, data collection: collecting related data of a power distribution network; s2, establishing a model: based on the collected data, establishing a power flow calculation model of the power distribution network; s3, based on an internet of things (IoT) technology, acquiring real-time data of the power distribution network into a digital twin platform; s4, parameter calibration: calibrating and adjusting parameters of the digital model by comparing with actual operation data; s5, simulation and optimization: and simulating and optimizing different scenes by using the established digital model. According to the rapid construction method of the distribution network digital twin body fused with the electrical topology, the digital twin body can be rapidly constructed by utilizing the existing power grid topology information through the method of fusing the electrical topology, and the time of data acquisition and model verification can be reduced through modeling the power grid topology, so that the construction speed of the digital twin body is increased.
Description
Technical Field
The invention relates to the technical field of electricity, in particular to a rapid construction method of a distribution network digital twin body fused with an electrical topology.
Background
The digital twin is to fully utilize data such as physical models, sensor updating, operation histories and the like, integrate simulation processes of multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities, and complete mapping in a virtual space, thereby reflecting the whole life cycle process of corresponding entity equipment, and the whole formed by various voltage power substations and power transmission and distribution lines in a power system is called a power grid.
The traditional distribution network digital twin body construction method generally needs complex data acquisition, model establishment and verification processes, the data acquisition and integration are complex tasks, and challenges such as inconsistent data formats, data quality problems and isomerism of data sources are required to be solved, so that the distribution network digital twin body rapid construction method integrating electrical topology is provided to solve the problems.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a rapid construction method of a distribution network digital twin body fused with electrical topology, which has the advantages of rapid construction and the like, and solves the problem that the traditional construction method of the distribution network digital twin body generally needs complex data acquisition, model establishment and verification processes.
(II) technical scheme
In order to achieve the above purpose, the present invention provides the following technical solutions: a rapid construction method of a distribution network digital twin body integrating electrical topology comprises the following steps:
s1, data collection: collecting related data of a power distribution network;
s2, establishing a model: based on the collected data, establishing a power flow calculation model of the power distribution network;
s3, based on an internet of things (IoT) technology, acquiring real-time data of the power distribution network into a digital twin platform;
s4, parameter calibration: calibrating and adjusting parameters of the digital model by comparing with actual operation data;
s5, simulation and optimization: simulating and optimizing different scenes by using the established digital model;
s6, updating in real time: and updating the real-time monitoring data and the digital model in real time, and keeping the consistency of the digital twin body and an actual system.
Preferably, the data in S1 includes electrical topology information, device parameters, load data, and measurement data.
Preferably, the scenario in S5 includes a load change and a fault condition.
Preferably, the specific steps of collecting the real-time data of the power distribution network to the digital twin platform based on the IoT technology in S3 are as follows:
s3.1, sensor deployment: deploying sensor devices at appropriate locations in the distribution network;
s3.2, data transmission: transmitting real-time data acquired by a sensor to a digital twin platform by using a Modbus communication technology;
s3.3, data processing and storage: in the digital twin platform, a data receiving and processing module is arranged and used for receiving, processing and storing data sent by a sensor;
s3.4, data synchronization and updating: synchronizing and updating the data acquired in real time with the digital twin model;
s3.5, data analysis and application: in a digital twin platform, data analysis and application may be performed.
Preferably, the specific steps of calibrating and adjusting the parameters of the power flow calculation model by comparing with the actual operation data in S4 are as follows:
s4.1, calculating actual operation data by using a power flow calculation initial model to obtain a prediction result of the model;
s4.2, comparing the prediction result with actual operation data, and finding out the difference between the model and the actual data;
s4.3, adjusting parameters of the power flow calculation model according to the comparison result, and optimizing the parameters by using a particle swarm algorithm;
s4.4, after parameters are adjusted, carrying out power flow calculation again by using the adjusted model, comparing the calculation result with actual operation data again, and repeating the steps until the prediction result of the model and the actual data reach a certain degree of proximity;
and S4.5, updating and calibrating the tide calculation model regularly along with the change of actual operation data.
Preferably, the actual operation data in S4.1 includes voltage, power injection value, load data and branch parameters of each node of the power system.
Preferably, the specific steps of transmitting the real-time data acquired by the sensor to the digital twin platform by using the Modbus communication technology in S3.2 are as follows:
s3.21, determining the interface and the communication requirement of the digital twin platform;
s3.22, configuring equipment supporting Modbus communication according to the requirements of the digital twin platform;
s3.23, connecting the sensor with Modbus communication equipment;
s3.24, configuring parameters of Modbus equipment according to the specification of the sensor and the requirements of the Modbus communication equipment;
s3.25, writing a data reading program by using Python, and reading real-time data from the sensor through a Modbus protocol;
s3.26, transmitting the data read from the sensor to a digital twin platform;
and S3.27, analyzing and processing the transmitted data on the digital twin platform.
Preferably, the parameters configuring the Modbus device in S3.24 include a communication rate, a data bit number, and a verification manner.
Preferably, the specific steps of parameter optimization of the power flow calculation model in S4.3 by the particle swarm algorithm are as follows:
s4.31, determining parameters to be optimized, and defining an adaptability function for evaluating the performance of each particle in a parameter space;
s4.32, randomly generating a group of particles, wherein each particle represents a parameter vector, randomly distributing an initial position and a speed for each particle, and setting an initial individual optimal position and a global optimal position for each particle;
s4.33, gradually optimizing parameters by iteratively updating the positions and the speeds of the particles, carrying out load flow calculation according to new parameter values for each particle, and calculating the value of the fitness function;
s4.34, setting a termination condition, ending iteration if the termination condition is met, otherwise returning to the step S4.33 to continue iteration update
And S4.35, extracting a parameter value corresponding to the global optimal position as an optimized parameter after iteration is finished.
Preferably, the parameters in S4.31 are various coefficients, initial values and other adjustable parameters in the power flow calculation model.
(III) beneficial effects
Compared with the prior art, the invention provides a rapid construction method of a distribution network digital twin body fusing electrical topology, which has the following beneficial effects:
according to the rapid construction method of the distribution network digital twin body fused with the electrical topology, the existing power grid topology information can be utilized to rapidly construct the digital twin body through the method of fusing the electrical topology, the time for data acquisition and model verification can be reduced through modeling of the power grid topology, so that the construction speed of the digital twin body is increased, the method of fusing the electrical topology mainly depends on the power grid topology information, compared with the traditional method which requires a large amount of real-time data, the data requirement is relatively low, the difficulty and cost of data acquisition can be reduced, and particularly, the available digital twin body can be constructed under the condition that some data acquisition is difficult or incomplete, meanwhile, the electrical topology is a basic component of a power grid, the method of fusing the electrical topology can be better suitable for power grid systems with different scales and complexities, the expandability and universality of the method are improved, in addition, the electrical topology information can provide the structure and the connection relation of the power grid system, the behavior of the power grid system can be described more accurately through fusing the electrical topology, the accuracy of the model of the digital twin body is improved, and the actual running state and the behavior of the power grid can be reflected better.
Drawings
Fig. 1 is a flowchart of a rapid construction method of a distribution network digital twin body integrating an electrical topology.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
A rapid construction method of a distribution network digital twin body integrating electrical topology comprises the following steps:
s1, data collection: collecting related data of a power distribution network, including electrical topology information, equipment parameters, load data and measurement data, wherein the data are acquired from existing power system data, sensors and monitoring equipment;
s2, establishing a model: based on the collected data, establishing a power flow calculation model of the power distribution network;
s3, based on an internet of things (IoT) technology, acquiring real-time data of the power distribution network into a digital twin platform;
s4, parameter calibration: the parameters of the digital model are calibrated and adjusted by comparing with actual operation data so as to ensure the accuracy and reliability of the digital model;
s5, simulation and optimization: simulating and optimizing different scenes by using the established digital model, including load change and fault conditions, evaluating the running condition of the system, and carrying out optimization design;
s6, updating in real time: and updating the real-time monitoring data and the digital model in real time, keeping the consistency of the digital twin body and an actual system, discovering system abnormality and faults in the process of machine time, and taking corresponding measures.
Specifically, the specific content of the power flow calculation model comprises the steps of determining nodes in the power system, wherein each node usually represents one bus or load node in the power system, for each node, establishing a node power balance equation according to the basic principle of power flow calculation, establishing a branch model according to the electrical topology and equipment parameters of the power system, and solving the power flow calculation model by a Newton-Lafson method in consideration of the operation constraint condition of the power system.
Specifically, the branch model includes the impedance, admittance, and the relationship between current and voltage of the branch, and the operating constraints include voltage limits and power limits.
Specifically, the specific steps of collecting real-time data of the power distribution network to the digital twin platform based on the IoT technology in S3 are as follows:
s3.1, sensor deployment: sensor equipment is deployed at a proper position in the power distribution network and used for monitoring various parameters of the power system in real time, including current, voltage, power and temperature;
s3.2, data transmission: transmitting real-time data acquired by a sensor to a digital twin platform by using a Modbus communication technology;
s3.3, data processing and storage: in the digital twin platform, a data receiving and processing module is arranged and used for receiving, processing and storing data sent by a sensor, including data analysis, data cleaning and data aggregation processes, and a real-time database is used for storing and managing the acquired data;
s3.4, data synchronization and updating: synchronizing and updating the data acquired in real time with the digital twin model by realizing a data interface and a data synchronization mechanism in the digital twin platform, wherein when the real-time data changes, the digital twin model can be updated in time so as to reflect the state of a real system;
s3.5, data analysis and application: in the digital twin platform, data analysis and application, such as real-time monitoring of system state, fault diagnosis and predictive analysis, can be performed, and decision support and optimization suggestions can be provided through analysis of real-time data, so as to help to improve the operation efficiency and reliability of the power distribution network.
Specifically, the specific steps of calibrating and adjusting parameters of the power flow calculation model by comparing with actual operation data in S4 are as follows:
s4.1, calculating actual operation data by using a power flow calculation initial model to obtain a prediction result of the model, wherein the actual operation data comprises voltage, power injection value, load data and branch parameters of each node of the power system;
s4.2, comparing the prediction result with actual operation data, and finding out the difference between the model and the actual data;
s4.3, adjusting parameters of the power flow calculation model according to the comparison result, and optimizing the parameters by using a particle swarm algorithm;
s4.4, after parameters are adjusted, carrying out power flow calculation again by using the adjusted model, comparing the calculation result with actual operation data again, and repeating the steps until the prediction result of the model and the actual data reach a certain degree of proximity;
and S4.5, updating and calibrating the tide calculation model regularly along with the change of actual operation data.
Specifically, the specific steps of transmitting the real-time data acquired by the sensor to the digital twin platform by using the Modbus communication technology in S3.2 are as follows:
s3.21, determining the interface and the communication requirement of the digital twin platform;
s3.22, configuring equipment supporting Modbus communication according to the requirements of the digital twin platform;
s3.23, connecting the sensor with Modbus communication equipment;
s3.24, configuring parameters of Modbus equipment, including communication rate, data bit number and verification mode, according to the specification of the sensor and the requirements of the Modbus communication equipment;
s3.25, writing a data reading program by using Python, and reading real-time data from the sensor through a Modbus protocol;
s3.26, transmitting the data read from the sensor to a digital twin platform;
and S3.27, analyzing and processing the transmitted data on the digital twin platform.
Specifically, the specific steps of parameter optimization of the power flow calculation model through the particle swarm algorithm in S4.3 are as follows:
s4.31, determining parameters to be optimized, defining an adaptability function, and evaluating the performance of each particle in a parameter space, wherein the parameters to be optimized are various coefficients, initial values and other adjustable parameters in a tide calculation model;
s4.32, randomly generating a group of particles, wherein each particle represents a parameter vector, randomly distributing an initial position and a speed for each particle, and setting an initial individual optimal position and a global optimal position for each particle;
s4.33, gradually optimizing parameters by iteratively updating the positions and the speeds of the particles, carrying out load flow calculation according to new parameter values for each particle, and calculating the value of the fitness function;
s4.34, setting a termination condition, ending iteration if the termination condition is met, otherwise returning to the step S4.33 to continue iteration update
And S4.35, extracting a parameter value corresponding to the global optimal position as an optimized parameter after iteration is finished.
In summary, the method for quickly constructing the digital twin of the distribution network by fusing the electrical topology can quickly construct the digital twin by utilizing the existing power grid topology information through the method for fusing the electrical topology, the time for data acquisition and model verification can be reduced through modeling the power grid topology, so that the construction speed of the digital twin is increased, the method for fusing the electrical topology mainly depends on the power grid topology information, compared with the traditional method which needs a large amount of real-time data, the data requirement is relatively less, the difficulty and cost of data acquisition can be reduced, and particularly, the available digital twin can be constructed under the condition that some data acquisition is difficult or incomplete, meanwhile, the electrical topology is a basic component of a power grid, the method for fusing the electrical topology can better adapt to power grid systems with different scales and complexity, the expandability and the universality of the method are improved, in addition, the electrical topology information can provide the structure and the connection relation of the power grid system, the behavior of the system can be more accurately described through fusing the electrical topology, the accuracy of the model of the digital twin is improved, the situation that the model better reflects the actual running state and the actual running state of the power grid can be better, and the problem of the traditional method for constructing the digital twin requires the data acquisition model is solved.
The related modules involved in the system are all hardware system modules or functional modules in the prior art combining computer software programs or protocols with hardware, and the computer software programs or protocols involved in the functional modules are all known technologies for those skilled in the art and are not improvements of the system; the system is improved in interaction relation or connection relation among the modules, namely, the overall structure of the system is improved, so that the corresponding technical problems to be solved by the system are solved.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The rapid construction method of the distribution network digital twin body integrating the electrical topology is characterized by comprising the following steps of:
s1, data collection: collecting related data of a power distribution network;
s2, establishing a model: based on the collected data, establishing a power flow calculation model of the power distribution network;
s3, based on an internet of things (IoT) technology, acquiring real-time data of the power distribution network into a digital twin platform;
s4, parameter calibration: calibrating and adjusting parameters of the digital model by comparing with actual operation data;
s5, simulation and optimization: simulating and optimizing different scenes by using the established digital model;
s6, updating in real time: and updating the real-time monitoring data and the digital model in real time, and keeping the consistency of the digital twin body and an actual system.
2. The rapid construction method of a distribution network digital twin body fusing electrical topology according to claim 1, wherein the data in S1 includes electrical topology information, equipment parameters, load data and measurement data.
3. The rapid construction method of a distribution network digital twin body fusing electrical topology according to claim 1, wherein the scene in S5 includes load change and fault conditions.
4. The method for quickly constructing a digital twin body of a distribution network with integrated electrical topology according to claim 1, wherein the specific steps of collecting real-time data of the distribution network to the digital twin platform based on the IoT technology in S3 are as follows:
s3.1, sensor deployment: deploying sensor devices at appropriate locations in the distribution network;
s3.2, data transmission: transmitting real-time data acquired by a sensor to a digital twin platform by using a Modbus communication technology;
s3.3, data processing and storage: in the digital twin platform, a data receiving and processing module is arranged and used for receiving, processing and storing data sent by a sensor;
s3.4, data synchronization and updating: synchronizing and updating the data acquired in real time with the digital twin model;
s3.5, data analysis and application: in a digital twin platform, data analysis and application may be performed.
5. The rapid construction method of a distribution network digital twin body fused with an electrical topology according to claim 1, wherein the specific steps of calibrating and adjusting parameters of the power flow calculation model by comparing with actual operation data in S4 are as follows:
s4.1, calculating actual operation data by using a power flow calculation initial model to obtain a prediction result of the model;
s4.2, comparing the prediction result with actual operation data, and finding out the difference between the model and the actual data;
s4.3, adjusting parameters of the power flow calculation model according to the comparison result, and optimizing the parameters by using a particle swarm algorithm;
s4.4, after parameters are adjusted, carrying out power flow calculation again by using the adjusted model, comparing the calculation result with actual operation data again, and repeating the steps until the prediction result of the model and the actual data reach a certain degree of proximity;
and S4.5, updating and calibrating the tide calculation model regularly along with the change of actual operation data.
6. The method for quickly constructing a distribution network digital twin body with integrated electrical topology according to claim 5, wherein the actual operation data in S4.1 comprises voltage, power injection value, load data and branch parameters of each node of the power system.
7. The rapid construction method of a distribution network digital twin body fused with electrical topology according to claim 4, wherein the specific steps of transmitting real-time data acquired by a sensor to a digital twin platform by using a Modbus communication technology in S3.2 are as follows:
s3.21, determining the interface and the communication requirement of the digital twin platform;
s3.22, configuring equipment supporting Modbus communication according to the requirements of the digital twin platform;
s3.23, connecting the sensor with Modbus communication equipment;
s3.24, configuring parameters of Modbus equipment according to the specification of the sensor and the requirements of the Modbus communication equipment;
s3.25, writing a data reading program by using Python, and reading real-time data from the sensor through a Modbus protocol;
s3.26, transmitting the data read from the sensor to a digital twin platform;
and S3.27, analyzing and processing the transmitted data on the digital twin platform.
8. The method for quickly constructing a distribution network digital twin body with integrated electrical topology according to claim 7, wherein the parameters of the Modbus device configured in S3.24 include a communication rate, a data bit number and a verification mode.
9. The rapid construction method of a distribution network digital twin body fused with electrical topology according to claim 5, wherein the specific steps of parameter optimization of the power flow calculation model by the particle swarm algorithm in S4.3 are as follows:
s4.31, determining parameters to be optimized, and defining an adaptability function for evaluating the performance of each particle in a parameter space;
s4.32, randomly generating a group of particles, wherein each particle represents a parameter vector, randomly distributing an initial position and a speed for each particle, and setting an initial individual optimal position and a global optimal position for each particle;
s4.33, gradually optimizing parameters by iteratively updating the positions and the speeds of the particles, carrying out load flow calculation according to new parameter values for each particle, and calculating the value of the fitness function;
s4.34, setting a termination condition, ending iteration if the termination condition is met, otherwise returning to the step S4.33 to continue iteration update
And S4.35, extracting a parameter value corresponding to the global optimal position as an optimized parameter after iteration is finished.
10. The method for quickly constructing a distribution network digital twin body with integrated electrical topology according to claim 9, wherein the parameters in S4.31 are various coefficients, initial values and other adjustable parameters in a power flow calculation model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311550574.1A CN117436215A (en) | 2023-11-20 | 2023-11-20 | Distribution network digital twin body rapid construction method integrating electrical topology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311550574.1A CN117436215A (en) | 2023-11-20 | 2023-11-20 | Distribution network digital twin body rapid construction method integrating electrical topology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117436215A true CN117436215A (en) | 2024-01-23 |
Family
ID=89553391
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311550574.1A Pending CN117436215A (en) | 2023-11-20 | 2023-11-20 | Distribution network digital twin body rapid construction method integrating electrical topology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117436215A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117891644A (en) * | 2024-03-11 | 2024-04-16 | 南京市计量监督检测院 | Data acquisition system and method based on digital twin technology |
| CN118017509A (en) * | 2024-04-10 | 2024-05-10 | 国网江苏省电力有限公司南通供电分公司 | Large-scale power distribution network parallel optimization method based on digital twin space |
-
2023
- 2023-11-20 CN CN202311550574.1A patent/CN117436215A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117891644A (en) * | 2024-03-11 | 2024-04-16 | 南京市计量监督检测院 | Data acquisition system and method based on digital twin technology |
| CN117891644B (en) * | 2024-03-11 | 2024-06-04 | 南京市计量监督检测院 | Data acquisition system and method based on digital twin technology |
| CN118017509A (en) * | 2024-04-10 | 2024-05-10 | 国网江苏省电力有限公司南通供电分公司 | Large-scale power distribution network parallel optimization method based on digital twin space |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117436215A (en) | Distribution network digital twin body rapid construction method integrating electrical topology | |
| CN112331941A (en) | Cloud auxiliary battery management system and method | |
| CN103577926B (en) | A kind of method that realizes the calculating in real time of large scale electric network theory wire loss and high accuracy | |
| CN113254382B (en) | Data processing system for constructing digital numerical value fusion device based on supercomputer | |
| CN109218048A (en) | To the performance test methods and device of an industrial system being deployed on cloud | |
| US7957951B2 (en) | Address translation system for use in a simulation environment | |
| CN113093017A (en) | Online construction method for lithium ion battery equivalent circuit model | |
| CN115512777B (en) | Electrochemical model parameter identification method and system based on capacity change rate | |
| CN104881982A (en) | Intelligent meter data acquiring system having self-adaptive meter reading strategy and acquiring method of intelligent meter data acquiring system | |
| CN117060591A (en) | Digital power grid system based on digital twin | |
| CN115146538A (en) | Power system state estimation method based on message passing graph neural network | |
| Herget et al. | Design space exploration for distributed cyber-physical systems: State-of-the-art, challenges, and directions | |
| CN115186555A (en) | Drying equipment live simulation method based on digital twin and related equipment | |
| CN114925973A (en) | Power grid running state obtaining method and system based on multi-source data integration | |
| CN118017509A (en) | Large-scale power distribution network parallel optimization method based on digital twin space | |
| CN114048525A (en) | Design system of energy storage battery system and preparation of energy storage | |
| Akkaya et al. | Modeling and simulation of network aspects for distributed cyber-physical energy systems | |
| CN116578845B (en) | Risk identification method and system for batch identification data learning | |
| CN108574290A (en) | Oscillation source localization method, device, terminal and the readable storage medium storing program for executing of forced oscillation | |
| CN115375867B (en) | Method, system, equipment and medium for calculating geothermal resource quantity by using grid model | |
| CN116165547A (en) | Method, system, device and storage medium for calculating and correcting voltage of lithium battery | |
| CN116502024A (en) | Power distribution network line parameter identification method based on model driving | |
| KR20200075247A (en) | OPC UA based Platform Configuration Method for Efficient Management of Heterogeneous Quality Data | |
| CN114609969A (en) | Numerical control machine tool track error compensation method based on cloud computing | |
| CN114624791A (en) | Rainfall measurement method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |