CN112751334A - Power grid online modeling method and system based on memory computing architecture - Google Patents
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Abstract
The application discloses a power grid online modeling method and system based on a memory computing architecture. Wherein, the method comprises the following steps: establishing different types of object models according to different types of power grid data, wherein the different types comprise a generator, a load, a line and a transformer; performing hash mapping on the object models of different types and the memory computing nodes; and according to the memory computing nodes, carrying out distributed parallel memory computing on the different types of object models in a memory data network.
Description
Technical Field
The application relates to the technical field of large power grid safety, in particular to a power grid online modeling method and system based on a memory computing architecture.
Background
The scale of modern power grids is gradually enlarged, extra-high voltage alternating current and direct current transmission lines are gradually connected into the power grids, FACTS facilities are widely applied, and the operation modes and characteristics of the power grids are gradually complicated. The wind power and photovoltaic new energy is connected into a power grid in a large scale, response resources of various demand sides are increasingly abundant at a power utilization end, and the electric automobile is rapidly developed, so that the randomness and the volatility of a power system are greatly enhanced.
The operation mode and the dynamic characteristic of the power grid become more and more complex and changeable, and the power system dispatching department and the operation personnel hope to acquire the operation state of the power grid in a shorter time interval and acquire the real-time tide of the system so as to further dynamically evaluate and control the power grid, thereby ensuring the safety and the economy of the power system. The state estimation of the power system is the most main way and way for acquiring the real-time state of the power grid at present, but the modern power grid urgently needs to advance the monitoring and control of the power system from a minute level to a second level or even a ten millisecond level, the dynamic power flow (state estimation) based on PMU is an important way for realizing the goal, and in addition, the accurate real-time state and model parameters of the power grid are needed to further carry out the work of stable analysis, decision control and the like.
The main flow of the current online data analysis is shown in fig. 1. And (4) forming a power flow section by carrying out SCADA and state estimation processing on the power grid measurement information (RTU). At present, the arrival of the D5000 platform flow section at a data integration layer in a graph has minute-scale delay, which is one of the main limitations influencing the increase of the response speed of online analysis. And then, the tidal current section is subjected to a data integration and calculation process control layer to drive prevention control auxiliary decision-making online analysis and application, including static safety, transient stability, dynamic stability, static voltage stability and frequency stability. In these online analytical applications, static safety, static voltage stabilization, and frequency stabilization are solved based on algebraic equations, and transient stability and dynamic stability are solved based on differential equations.
Disclosure of Invention
The embodiment of the disclosure provides a method and a system for online modeling of a power grid based on a memory computing architecture, so as to at least solve the technical problems in the prior art.
According to an aspect of the embodiments of the present disclosure, there is provided a method for online modeling of a power grid based on a memory computing architecture, including: establishing different types of object models according to different types of power grid data, wherein the different types comprise a generator, a load, a line and a transformer; performing Hash mapping on different types of object models and memory computing nodes; and according to the memory computing nodes, carrying out distributed parallel memory computing on different types of object models in the memory data network.
According to another aspect of the embodiments of the present disclosure, there is also provided a system for online modeling of a power grid based on a memory computing architecture, including: the model building module is used for building different types of object models according to different types of power grid data, wherein the different types comprise a generator, a load, a line and a transformer; the Hash mapping module is used for carrying out Hash mapping on different types of object models and memory computing nodes; and the memory computing module is used for performing distributed parallel memory computing on different types of object models in the memory data network according to the memory computing nodes.
According to the method and the device, the problem that the integration time of the tidal current section data of the complex power grid is delayed by minutes can be effectively solved, and conditions are provided for researching on-line analysis and faster simulation analysis of the complex power grid. Therefore, the method is applied to the field of safety online analysis and early warning of large power grids, the time of early warning analysis of the power grids is shortened, and the safe operation of the power grids is ensured.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
FIG. 1 is a flow diagram of online analysis data in the background art;
fig. 2 is a schematic flowchart of a method for online modeling of a power grid based on a memory computing architecture according to an embodiment of the present disclosure;
fig. 3 is a modeling diagram of an online analysis object of a power grid based on a memory computing architecture according to an embodiment of the present disclosure;
FIG. 4 is a diagram of the effect of on-line analysis of an architecture based on memory computing.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
According to a first aspect of the application, a method for online modeling of a power grid based on a memory computing architecture is provided. Referring to fig. 2, the method includes:
s202, establishing different types of object models according to different types of power grid data, wherein the different types comprise a generator, a load, a line and a transformer;
s204, performing Hash mapping on different types of object models and memory computing nodes;
and S206, according to the memory computing nodes, carrying out distributed parallel memory computing on different types of object models in the memory data network.
Specifically, the method, as shown in fig. 3, goes through three steps:
the complex grid is first objectified. The complex power grid object is to establish different models for power grid data according to different types, wherein the different types comprise a generator, a load, a line, a transformer and the like.
Different types of models are hashed. Hash mapping is the correspondence between complex power grid objects and memory computing nodes, and network objects (generators, loads, buses and the like) in the complex power grid are mapped to one or more memory computing nodes in an organized manner through hash functions corresponding to key values.
And (4) memory calculation of a distributed memory network. Different complex power grid object models are subjected to distributed parallel memory calculation in the memory data network, so that the analysis time of the power grid model is greatly shortened, and the real-time updating time of the power grid model can be shortened from minute level to millisecond level.
Referring to fig. 4, the present invention provides a complex grid online analysis object modeling based on a memory computing architecture, and is applied to an online analysis D5000 system.
(1) The complex power grid object model is carried in a data grid
The real-time updating efficiency of the object model has a decisive influence on the overall response speed of the online analysis platform. A4-ten-thousand-node scale power grid model of a national grid is taken as a test case, and the updating performance of a power grid object model is comprehensively tested in the research. The test result shows that: the analysis model can efficiently process RTU measurement information and update the RTU measurement information by self so as to track the change of the running state of the power grid, and the tracking delay is millisecond level.
The power grid object model is modeled in a data grid by adopting an object-oriented design and Key Value (Key-Value) based method. The power grid object model Modeling adopts a model-driven Modeling technology Eclipse Modeling Framework (EMF), a code automatic generation technology and a memory data grid technology. And developing an adaptive integration tool of an actual power grid regulation and control system, and performing data interaction with a D5000 platform real-time library, CIM/E QS format files and PSASP LF format files. The power grid object model supports memory calculation and seamless integration with the CEP engine.
The power grid object model is a physical and calculation integrated model and consists of a physical model (a node/switch model) and a calculation model (a bus/branch model). And caching the incidence relation between the physical model and the calculation model in a data grid memory, and establishing the mapping and automatic collaborative updating relation of the two parts through topological analysis.
(2) Model-based memory computation
The power grid object model supports the realization of a simulation calculation algorithm based on memory calculation. The main idea of memory calculation is "move algorithm rather than move data", which was originally proposed to solve the problem of data processing efficiency reduction caused by data movement in the case of big data processing. The memory calculation method is applied to power grid object model modeling and power grid simulation calculation, and the online analysis data processing efficiency and the overall response speed of the system can be improved. In the method, a grid object model is carried in a data grid, and simulation data is stored in a ready state according to grid simulation calculation requirements before a simulation calculation request is received. The simulation calculation algorithm processes data by direct memory access, minimizing data movement during the simulation calculation process. In a conventional online analysis system, data representing the current grid operating state is usually first exported from a certain storage node, and a simulation calculation initial profile file is created. The profile file is then sent to an application or group of applications as simulation calculation input data. The data exchange is usually completed through a data file form, and the online analysis data processing efficiency and the overall response speed of the system are greatly influenced.
Therefore, the problem that the integration time of the tidal current section data of the complex power grid is delayed by minutes can be effectively solved, and conditions are provided for researching on-line analysis and faster simulation analysis of the complex power grid. Therefore, the method is applied to the field of safety online analysis and early warning of large power grids, the time of early warning analysis of the power grids is shortened, and the safe operation of the power grids is ensured.
Optionally, performing hash mapping on different types of object models and memory computing nodes, including:
and mapping the object models of different types to the memory computing node through a hash function corresponding to the key value.
Optionally, the method further comprises: and carrying out data interaction on different types of object models, a D5000 platform real-time library, CIM/E QS format files and PSASPL format files.
Optionally, before performing distributed parallel memory computation on different types of object models in the memory data network according to the memory computation node, the method includes: and storing the simulation data according to the simulation calculation requirements of the power grid.
Optionally, the object model is an integrated model and is composed of a physical model and a computational model, the association relationship between the physical model and the computational model is cached in a data grid memory, and the mapping and automatic collaborative updating relationship between the physical model and the computational model is established through topology analysis.
According to another aspect of the application, a system for online modeling of a power grid based on a memory computing architecture is provided. The system comprises: the model building module is used for building different types of object models according to different types of power grid data, wherein the different types comprise a generator, a load, a line and a transformer; the Hash mapping module is used for carrying out Hash mapping on different types of object models and memory computing nodes; and the memory computing module is used for performing distributed parallel memory computing on different types of object models in the memory data network according to the memory computing nodes.
Optionally, the hash mapping module includes: and the mapping submodule is used for mapping the object models of different types to the memory computing node through the hash function corresponding to the key value.
Optionally, the system further comprises: and the interaction module is used for performing data interaction on the different types of object models, the D5000 platform real-time library, CIM/E QS format files and PSASP LF format files.
Optionally, the memory computing module includes: and the storage submodule is used for storing the simulation data according to the simulation calculation requirement of the power grid.
Optionally, the object model is an integrated model and is composed of a physical model and a computational model, the association relationship between the physical model and the computational model is cached in a data grid memory, and the mapping and automatic collaborative updating relationship between the physical model and the computational model is established through topology analysis.
The system for online modeling of a power grid based on a memory computing architecture in the embodiment of the present invention corresponds to the method for online modeling of a power grid based on a memory computing architecture in another embodiment of the present invention, and is not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A method for online modeling of a power grid based on a memory computing architecture is characterized by comprising the following steps:
establishing different types of object models according to different types of power grid data, wherein the different types comprise a generator, a load, a line and a transformer;
performing hash mapping on the object models of different types and the memory computing nodes;
and according to the memory computing nodes, carrying out distributed parallel memory computing on the different types of object models in a memory data network.
2. The method of claim 1, wherein hashing the different types of object models with memory compute nodes comprises:
and mapping the object models of different types to the memory computing node through a hash function corresponding to the key value.
3. The method of claim 1, further comprising:
and carrying out data interaction on the different types of object models, a D5000 platform real-time library, CIM/E QS format files and PSASPL format files.
4. The method of claim 1, wherein prior to performing distributed parallel memory computation on the different types of object models in the memory data network according to the memory computation node, the method comprises:
and storing the simulation data according to the simulation calculation requirements of the power grid.
5. The method of claim 1,
the object model is an integrated model and consists of a physical model and a calculation model, the incidence relation between the physical model and the calculation model is cached in a data grid memory, and the mapping and automatic collaborative updating relation between the physical model and the calculation model is established through topology analysis.
6. A system for online modeling of a power grid based on a memory computing architecture is characterized by comprising:
the model establishing module is used for establishing different types of object models according to different types of power grid data, wherein the different types comprise a generator, a load, a line and a transformer;
the Hash mapping module is used for carrying out Hash mapping on the object models of different types and the memory computing nodes;
and the memory computing module is used for performing distributed parallel memory computing on the object models of different types in the memory data network according to the memory computing nodes.
7. The system of claim 6, wherein the hash mapping module comprises:
and the mapping submodule is used for mapping the object models of different types to the memory computing node through a hash function corresponding to the key value.
8. The system of claim 6, further comprising:
and the interaction module is used for performing data interaction on the different types of object models, the D5000 platform real-time library, CIM/E QS format files and PSASP LF format files.
9. The system of claim 6, wherein the memory computation module comprises:
and the storage submodule is used for storing the simulation data according to the simulation calculation requirement of the power grid.
10. The system of claim 6,
the object model is an integrated model and consists of a physical model and a calculation model, the incidence relation between the physical model and the calculation model is cached in a data grid memory, and the mapping and automatic collaborative updating relation between the physical model and the calculation model is established through topology analysis.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115390827A (en) * | 2022-10-28 | 2022-11-25 | 中国电力科学研究院有限公司 | Power grid digital twin body construction method and platform for supporting debugging operation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104239686A (en) * | 2014-08-12 | 2014-12-24 | 浪潮通用软件有限公司 | Method for realizing formula computation in comprehensive budget based on memory computation |
| CN104599032A (en) * | 2014-11-28 | 2015-05-06 | 国家电网公司 | Distributed memory power grid construction method and system for resource management |
| CN108647025A (en) * | 2018-05-15 | 2018-10-12 | 浙江口碑网络技术有限公司 | Processing method and processing device, electronics and the storage device of DOM Document Object Model interior joint |
| CN109063980A (en) * | 2018-07-16 | 2018-12-21 | 北京科东电力控制系统有限责任公司 | Memory calculation method and system suitable for electrical network analysis |
-
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- 2020-12-21 CN CN202011518422.XA patent/CN112751334A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104239686A (en) * | 2014-08-12 | 2014-12-24 | 浪潮通用软件有限公司 | Method for realizing formula computation in comprehensive budget based on memory computation |
| CN104599032A (en) * | 2014-11-28 | 2015-05-06 | 国家电网公司 | Distributed memory power grid construction method and system for resource management |
| CN108647025A (en) * | 2018-05-15 | 2018-10-12 | 浙江口碑网络技术有限公司 | Processing method and processing device, electronics and the storage device of DOM Document Object Model interior joint |
| CN109063980A (en) * | 2018-07-16 | 2018-12-21 | 北京科东电力控制系统有限责任公司 | Memory calculation method and system suitable for electrical network analysis |
Non-Patent Citations (1)
| Title |
|---|
| 周二专等: "内存计算技术及在电网分析中的应用", 《电力系统自动化》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115390827A (en) * | 2022-10-28 | 2022-11-25 | 中国电力科学研究院有限公司 | Power grid digital twin body construction method and platform for supporting debugging operation |
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