CN115000949A - Power distribution network modeling method, device, equipment and storage medium - Google Patents
Power distribution network modeling method, device, equipment and storage medium Download PDFInfo
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- 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
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- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
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Abstract
The invention discloses a power distribution network modeling method, a device, equipment and a storage medium, wherein the method comprises the following steps: performing data relevance analysis on the calculation data related to each component unit in the original power distribution network model of the target power distribution network to obtain a data analysis result; adjusting an original topological structure in the original power distribution network model according to the data analysis result to obtain a target topological structure; and optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model. Through the scheme, the power distribution network model can be effectively simplified, the memory space of the power distribution network model is saved, and the data analysis and calculation efficiency of the power distribution network model is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of power systems, in particular to a power distribution network modeling method, device, equipment and storage medium.
Background
In recent years, with the rapid promotion of national economy of China, the power distribution network response power consumption demand is rapidly developed, the requirements on scale and power supply quality are higher and higher, and under the condition, various analysis and calculation are required to be carried out on the power distribution network so as to complete daily operations such as maintenance and power supply adjustment of the power distribution network under the condition that power supply is not influenced. When the distribution network is analyzed through the distribution network model at the present stage, the problem of low analysis efficiency exists, and therefore how to improve the analysis efficiency of the distribution network model is the problem to be solved.
Disclosure of Invention
The invention provides a power distribution network modeling method, a device, equipment and a storage medium, which can effectively simplify a power distribution network model, save the memory space of the power distribution network model and improve the data analysis and calculation efficiency of the power distribution network model.
According to an aspect of the present invention, there is provided a power distribution network modeling method, including:
performing data relevance analysis on the calculation data related to each component unit in the original power distribution network model of the target power distribution network to obtain a data analysis result;
adjusting an original topological structure in the original power distribution network model according to the data analysis result to obtain a target topological structure;
and optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model.
According to another aspect of the present invention, there is provided a power distribution network modeling apparatus, the apparatus including:
the data correlation analysis module is used for carrying out data correlation analysis on the calculation data correlated with each component unit in the original power distribution network model of the target power distribution network to obtain a data analysis result;
the target topological structure obtaining module is used for adjusting an original topological structure in the original power distribution network model according to the data analysis result to obtain a target topological structure;
and the target power distribution network model acquisition module is used for optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method of modeling a power distribution grid according to any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the method for modeling a power distribution network according to any of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, data relevance analysis is carried out on the calculation data related to each component unit in the original power distribution network model of the target power distribution network to obtain a data analysis result; adjusting an original topological structure in the original power distribution network model according to the data analysis result to obtain a target topological structure; and optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model. By means of the scheme, the problem that due to the fact that the power distribution network data contained in the original power distribution network model are too large, a large amount of redundant data exist in the original power distribution network model, a large amount of memory in the original power distribution network model is occupied, and therefore analysis efficiency is low when constituent units of the power distribution network in the original power distribution network model are analyzed is solved. The original power distribution network model is optimized, the optimized original power distribution network model can aggregate data, and the problem of low search efficiency caused by data dispersion is solved. The memory space of the power distribution network model is released, and the analysis and calculation efficiency of the power distribution network model is improved.
It should be understood that the statements in this section are not intended to identify key or critical features of the embodiments of the present invention, nor are they intended to limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a power distribution network modeling method according to an embodiment of the present invention;
fig. 2 is a flowchart of a power distribution network modeling method according to a second embodiment of the present invention;
fig. 3 is a flowchart of a power distribution network modeling method according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a power distribution network modeling apparatus according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It is to be understood that the terms "current," "target," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in other sequences than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a power distribution network modeling method according to an embodiment of the present invention, which is applicable to a case of building a power distribution network model. The method can be executed by a power distribution network modeling device, which can be implemented in hardware and/or software, and can be configured in the electronic equipment. As shown in fig. 1, the method includes:
and S110, performing data relevance analysis on the calculation data related to each component unit in the original power distribution network model of the target power distribution network to obtain a data analysis result.
The power distribution network refers to a power network which receives electric energy from a transmission network or a regional power plant and distributes the electric energy to various users on site through power distribution facilities or step by step according to voltage. The power distribution network is composed of overhead lines, cables, towers, distribution transformers, isolating switches, reactive power compensators, a plurality of accessory facilities and the like, and plays a role in distributing electric energy in the power network. The target power distribution network refers to a power distribution network needing to be modeled. The original distribution network model refers to a distribution network model obtained by modeling according to the related data of the distribution network stored in the distribution network database through IEC61970 and IEC61968 standards. The power distribution network model comprises a conductive equipment model, a feeder line model, a switch model, a transformer model, an endpoint model, a connection node model, an acquisition device model, a control terminal model, a user model and an electric energy metering model. The calculation data related to the composition units refers to data applied by the composition units when the composition units perform analysis calculation on the power distribution network model. Data relevance refers to the existence of a direct association relationship between data or an indirect association relationship between data.
In this embodiment, the modeling of the power distribution network refers to constructing a power distribution network model composed of power distribution network constituent units such as a transformer, a power transmission line, a load and the like, and can be used for representing a power distribution system composed of a distributed power supply, a load, an energy storage system and a control device.
Specifically, modeling is performed according to the related data of the power distribution network stored in the power distribution network database through IEC61970 and IEC61968 standards, and an original power distribution network model is obtained. And determining each component unit in the original power distribution network model, and respectively determining the associated calculation data of each component unit. And performing data relevance analysis on the calculation data related to each composition unit to obtain a data analysis result of whether each composition unit has relevance.
And S120, adjusting the original topological structure in the original power distribution network model according to the data analysis result to obtain the target topological structure.
The topological structure refers to nodes and branches contained in the power distribution network model. The original topological structure refers to original nodes and original branches contained in an original power distribution network model. The target topological structure is obtained by optimizing an original topological structure and accords with an expected topological structure.
Specifically, whether the association exists in each component unit is determined according to the data analysis result. And if the related constituent units exist in the original power distribution network model according to the data analysis result, determining the related constituent units as the related units. If the association units are completely consistent constituent units, the original topological structure corresponding to one association unit in the original power distribution network model is reserved, and the original topological structures corresponding to other association units in the original power distribution network model are deleted. And taking the adjusted topological structure in the original power distribution network model as a target topological structure.
For example, the method of obtaining the target topology may be: and according to the data analysis result, determining a power transmission connection point, a terminal connection point and a bus connection point of an actual bus in the original power distribution network model, and simultaneously acquiring branches corresponding to the feeder system and the double-end equipment. Power transmission connection points, terminal connection points, bus connection points, and branches are taken as target topologies.
Specifically, a node list is constructed according to the power transmission connection point, the terminal connection point and the bus connection point of the actual bus; and constructing a branch list according to the node list and branches corresponding to the feeder system and the double-ended equipment.
The node list is a list for storing a node position and a node ID (Identity document) corresponding to the target topology. The branch list is a list for storing branches in the target topology.
Illustratively, all actual buses in the target topology are traversed, the actual buses are used as nodes of the target power distribution network model, the nodes are added into a node list, and the bus connection point ID of the actual buses is recorded in a branch list.
Traversing all bus connection points, if the number of connected terminals exceeds two bus connection points, adding the bus connection points as terminal connection points into a node list, and recording the ID of the terminal connection points.
And traversing all single-ended equipment with power output or power consumption, such as power generation equipment, load equipment, compensation equipment and the like, adding a connection point connected with a single-ended equipment terminal into the node list as a power transmission connection point, and recording the ID of the power transmission connection point.
And traversing all feeder systems, and if the terminal connection points on the left side and the right side of the feeder system are both in the node list, correspondingly storing the node IDs of the feeder system and the terminal connection points on the left side and the right side of the feeder system in the branch list. A feeder system is a system for transmitting signals between an antenna and a transceiver. The feeder system is formed by connecting various microwave components such as a waveguide, a rotary joint, a receiving and transmitting switch and the like. A feeder system is connected between the transmitter, the receiver and the antenna.
Traversing all the double-end equipment, and if the terminal connection points on the left side and the right side of the double-end equipment are both in the node list, correspondingly storing the node IDs of the double-end equipment and the terminal connection points on the left side and the right side of the double-end equipment in the branch list.
If there is a terminal connection point that is not in the node list among the terminal connection points on the left and right sides of the dual-ended device, the terminal connection point that is not in the node list is regarded as a node, and the node ID of the node is recorded in the node list. And correspondingly storing the node IDs of the double-end equipment and the terminal connection points at the left side and the right side of the double-end equipment in a branch list.
And taking the node corresponding to the node ID stored in the node list and the branch stored in the branch list as a target topological structure.
And S130, optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model.
Specifically, a target power distribution network model is established based on the target topological structure, so that the target topological structure is converted into a node-branch simplified model. In the node-branch simplified model, one node may connect a plurality of branches. All actual buses, power transmission connection points and terminal connection points corresponding to the target topological structure are used as nodes in the target power distribution network model, and equipment connected with different nodes is used as equivalent branches of the nodes connected with the equipment. Directly related and indirectly related information is organized together, so that not only is the data required by one computing unit complete, but also the independence among different computing units is ensured so as to facilitate parallel computing.
The actual bus refers to a bus corresponding to a conductor actually existing in the target topological structure; the power transmission connection point refers to a connection point with power input or power output in a target topological structure; a terminal connection point refers to a connection point in a target topology that connects more than two terminals.
It should be noted that the original distribution network model includes an original topology structure corresponding to the actual bus and an original topology structure corresponding to the virtual bus. The virtual bus is not an actual bus, but a virtual bus constructed for the convenience of model calculation.
Illustratively, the original power distribution network model can be optimized to obtain the target power distribution network model through the following steps.
S1301, determining the relevance between the target topological structures according to the data analysis result.
Specifically, the relevance between the target topological structures is determined according to the correspondence between the branches and the nodes stored in the branch list.
For example, it may be determined that the feeder device has an association with the terminal connection points on the left and right sides according to the branch corresponding to the feeder device in the branch list.
S1302, optimizing model nodes in the original power distribution network model according to the target topological structure.
Specifically, according to the node position and the node ID corresponding to the target topology structure in the node list, the node to be deleted in the original power distribution network model and the node to be added in the original power distribution network model are determined. And deleting redundant nodes in the original power distribution network model, and adding missing nodes into the original power distribution network model to optimize model nodes in the original power distribution network model.
And S1303, optimizing branch states in the original power distribution network model according to the relevance.
Specifically, a target branch in the target topological structure is determined according to the branch list, and the target branch is determined from the original power distribution network model. And reserving the target branch in the original power distribution network model, and deleting the redundant branch except the target branch in the original power distribution network model. Meanwhile, target branches which do not exist in the original distribution network model are added into the original distribution network model, so that the branch state in the original distribution network model is optimized.
And S1305, determining a target power distribution network model according to the optimized model nodes and the optimized branch states.
Specifically, the target topological structure is constructed into a node-branch model according to the model nodes after the cost is high and the optimized branch state, and the constructed node-branch model is used as a target power distribution network model.
It can be understood that branch states in the original distribution network model are optimized according to the correlation between the target topologies to determine the target distribution network model. The data in the original power distribution network can be aggregated, and the problem of low analysis efficiency caused by data dispersion is solved.
By the technical scheme, the problem that due to the fact that the power distribution network data contained in the original power distribution network model are too large, a large amount of redundant data exist in the original power distribution network model, a large amount of memory in the original power distribution network model is occupied, and therefore analysis efficiency is low when constituent units of the power distribution network in the original power distribution network model are analyzed is solved.
According to the technical scheme provided by the embodiment, data relevance analysis is carried out on the calculation data related to each component unit in the original power distribution network model of the target power distribution network to obtain a data analysis result; adjusting an original topological structure in the original power distribution network model according to the data analysis result to obtain a target topological structure; and optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model. According to the scheme, the original power distribution network model is optimized, the optimized original power distribution network model can aggregate data, and the problem of low search efficiency caused by data dispersion is solved. The memory space of the power distribution network model is released, and the analysis and calculation efficiency of the power distribution network model is improved.
Preferably, on the basis of the above embodiment, after the model nodes in the original power distribution network model are optimized according to the target topology structure, the remote traffic model and the measurement model in the optimized original power distribution network model can be further associated with the nodes in the optimized original power distribution network model.
The remote traffic model refers to a protection device, such as a switching device, in the power distribution network, which has only two states of on and off. The measurement model refers to a model of a standard measurement device in the power distribution network for measuring parameters in the power distribution network, and may be a current measurement instrument model, for example.
Specifically, model IDs of all remote signalling quantity models in an original power distribution network model are collected and obtained, and the model IDs comprise switch IDs corresponding to switches and disconnecting link IDs corresponding to disconnecting links. And determining the branch corresponding to the model ID in the optimized original power distribution network model.
And if the measuring equipment corresponding to the measuring model is active equipment, reactive equipment and current measuring equipment, and the terminal of the measuring model belongs to impedance equipment or cut-off equipment. And if the terminal of the measurement model belongs to the impedance equipment or the cut-off equipment, directly associating the terminal with the branch corresponding to the model ID of the measurement model, wherein the branch state of the branch is a closed state.
And if the measuring equipment corresponding to the measuring model does not meet the requirements of any one of the active equipment, the reactive equipment and the current measuring equipment, adjusting the branch state corresponding to the measuring model to be a disconnection state.
And if the measuring equipment corresponding to the measuring model is voltage measuring equipment, the terminal node corresponding to the terminal of the measuring model is a bus node. And determining the node ID of the bus node corresponding to the terminal node of the measurement model, and determining branches between the node and other nodes according to the node ID and the branch list, so that the node and other nodes are related through the branches.
And if the measuring equipment corresponding to the measuring model is active equipment, reactive equipment and current measuring equipment, and the terminal of the measuring model belongs to load equipment, associating the branch corresponding to the measuring model with the terminal of the measuring model.
For example, if there is no measurement model on a branch in the optimized original power distribution network model, but there is current data in the branch, and terminals corresponding to terminal nodes on both sides of the branch belong to load devices, a power node containing power corresponding to the current data is constructed in the optimized original power distribution network model, and the power node and the node in the optimized original power distribution network model are associated through the branch. The corresponding power of the current data comprises active electric quantity and reactive electric quantity. The reactive electric quantity means that energy is continuously interacted, but no energy loss is generated, namely, the energy is not reduced or increased. Active electricity is energy loss or new energy generation.
It can be understood that by associating the remote traffic model and the measurement model in the optimized original power distribution network model with the nodes in the optimized original power distribution network model, a more complete target power distribution network model can be obtained.
Example two
Fig. 2 is a flowchart of a power distribution network modeling method according to a second embodiment of the present invention, which is optimized based on the above embodiments, and provides a preferred embodiment of performing data association analysis on calculation data associated with each constituent unit in an original power distribution network model of a target power distribution network to obtain a data analysis result. Specifically, as shown in fig. 2, the method includes:
s210, analyzing the calculation data associated with each component unit according to the internal component structure of each component unit in the original distribution network model of the target distribution network to obtain a minimum data set corresponding to each component unit.
The minimum data set refers to a set of necessary and sufficient data for analyzing the target power distribution network in each constituent unit. The internal composition structure of each composition unit is a structure composition used for analyzing the target power distribution network, each internal composition structure needs to analyze the target power distribution network through a certain analysis mode and analysis data, and the analysis data applied to all the internal composition structures of a certain composition unit is calculation data related to the composition units. The data analysis results of a certain constituent unit can be determined by integrating the data analysis results of all internal constituent structures of the constituent unit.
Specifically, all the constituent units contained in a raw water distribution network model of the target distribution network are determined. And respectively carrying out data analysis on the composition structures in the composition units by adopting a longitudinal analysis method so as to obtain analysis data applied when each composition structure is used for analyzing the target distribution network. The minimum data set for a certain constituent unit can be obtained by integrating the analysis data of all constituent structures in the constituent unit. The longitudinal analysis method is an analysis method for analyzing different constituent structures in the same constituent unit.
And S220, performing relevance analysis on the minimum data set corresponding to each composition unit to obtain a data analysis result.
Specifically, a correlation analysis is performed on the minimum data set corresponding to each constituent unit by using a transverse analysis method, so as to obtain a data analysis result.
Illustratively, the necessity of the internal composition structure of each composition unit can be determined by performing correlation analysis on the minimum data set corresponding to each composition unit, and the dependency and structural repeatability between the composition units can be determined. Specifically, the method can be realized by the following substeps:
s2201, determining the necessity of original nodes and original branches of the original power distribution network model in the data analysis result according to the minimum data set corresponding to each composition unit.
The necessity of the internal composition structure means whether the internal composition structure is a structure that the composition unit must have when the composition unit analyzes the target distribution network.
Specifically, according to the minimum data set corresponding to each constituent unit, the original node of the terminal and the original branch of the connection terminal corresponding to the minimum data set are determined. The original node of the terminal corresponding to the minimum data set and the original branch connecting the terminal node are necessary.
S2202, determining the dependency and structural repeatability among the composition units in the data analysis result according to the coincidence data among the minimum data sets corresponding to different composition units.
Specifically, it may be determined whether two or more minimum data sets corresponding to each constituent unit have overlapping data according to the data analysis result. If coincidence data exists between two or more minimum data sets, it can be determined that dependency and structural repeatability exist between the constituent units corresponding to the minimum data sets in which the coincidence data exists.
For example, the structural repeatability between the constituent units in the data analysis result may also be determined according to the variation of the overlapping data between the minimum data sets corresponding to different constituent units.
It should be noted that the data contained in the minimum data set may be dynamically changed data or may be invariable data; meanwhile, the minimum data set may be a constant data set or a dynamically changing data set. Accordingly, the data included in the coincidence data may be dynamically changing data or may be constant data.
Optionally, if the coincidence data between the minimum data sets corresponding to different constituent units is fixed data, it is determined that structural repeatability exists between the different constituent units corresponding to the coincidence data, and a common structure between the internal constituent structures of the different constituent units corresponding to the coincidence data is determined.
The fixed data is data with a fixed data structure and a fixed data size.
Specifically, if it is determined that there is a minimum data set containing coincidence data, it is determined whether the coincidence data is fixed data or dynamically changing data. If the coincidence data contained in the minimum data set are all fixed data, determining that structural repeatability exists between the constituent units corresponding to the minimum data set where the coincidence data are located; and further determining that the internal composition structure corresponding to the superposition data is a common structure.
And if dynamically changed data exists in the superposed data contained in the minimum data set, determining that structural repeatability does not exist between the constituent units corresponding to the minimum data set where the superposed data is located.
Optionally, if the coincidence data between the minimum data sets corresponding to different constituent units is dynamically changing data, the coincidence data in the constituent units containing the coincidence data is used as copy data, and endpoints and connection nodes of the copy data in the power distribution network model are determined.
It can be understood that the necessity of determining the original nodes and original branches of the original power distribution network model according to the minimum data set and the dependency and structural repeatability among all the constituent units are determined, the redundant constituent units in the original power distribution network model can be determined, and the redundant internal constituent structures in the constituent units in the original power distribution network can be determined, so that an accurate target topological structure is obtained.
And S230, adjusting the original topological structure in the original power distribution network model according to the data analysis result to obtain the target topological structure.
And S240, optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model.
According to the technical scheme of the embodiment, the calculation data associated with each component unit is analyzed according to the internal component structure of each component unit in the original power distribution network model of the target power distribution network, and a minimum data set corresponding to each component unit is obtained; performing relevance analysis on the minimum data set corresponding to each composition unit to obtain a data analysis result; adjusting an original topological structure in the original power distribution network model according to the data analysis result to obtain a target topological structure; and optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model. According to the scheme, each component unit in the original power distribution network model can be analyzed through a longitudinal analysis method, and a minimum data set is obtained; and performing relevance analysis on each composition unit according to the minimum data set, and determining a target topological structure according to an analysis result. And adjusting the power distribution network model according to the target topological structure, so that the redundant topological structure in the original power distribution network model can be removed, and the optimized target power distribution network model is obtained. The analysis efficiency can be improved by analyzing the constituent units in the target power distribution network model through the target power distribution network model.
EXAMPLE III
Fig. 3 is a flowchart of a power distribution network modeling method according to a third embodiment of the present invention, which is optimized based on the foregoing embodiments in this embodiment, and provides a preferred implementation manner for selecting a target application scenario from selectable application scenarios according to an application scenario use request. Specifically, as shown in fig. 3, the method includes:
and S310, analyzing the calculation data associated with each component unit according to the internal component structure of each component unit in the original power distribution network model of the target power distribution network to obtain a minimum data set corresponding to each component unit.
And S320, determining the necessity of the original nodes and original branches of the original power distribution network model in the data analysis result according to the minimum data set corresponding to each component unit.
S330, determining the dependency and structural repeatability among the composition units in the data analysis result according to the coincidence data among the minimum data sets corresponding to different composition units.
And S340, determining a target composition structure according to the necessity of the internal composition structure of each composition unit in the data analysis result and the structural repeatability among the composition units.
Specifically, if structural repeatability exists among the constituent units, the internal constituent structure corresponding to the coincidence data is determined to be a common structure. And determining each original topological structure corresponding to the common structure in the original power distribution network model, and taking the original topological structure corresponding to one common structure as a target composition structure.
And S350, adjusting the original topological structure in the original power distribution network model based on the target composition structure.
Specifically, a target composition structure is determined from an original topological structure in an original power distribution network model, and the target composition structure is reserved. And deleting other common structures except the target composition structure in the original topological structure.
And S360, taking the adjusted original topological structure as a target topological structure.
And S370, optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model.
According to the technical scheme of the embodiment, the calculation data associated with each component unit is analyzed according to the internal component structure of each component unit in the original power distribution network model of the target power distribution network, and a minimum data set corresponding to each component unit is obtained; determining the necessity of an original node and an original branch of an original power distribution network model in a data analysis result according to a minimum data set corresponding to each component unit; determining the dependency and structural repeatability among all the composition units in the data analysis result according to the coincidence data among the minimum data sets corresponding to different composition units; determining a target composition structure according to the necessity of the internal composition structure of each composition unit and the structural repeatability among the composition units in the data analysis result; adjusting an original topological structure in the original power distribution network model based on the target composition structure; taking the adjusted original topological structure as a target topological structure; and optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model. According to the scheme, the necessity of the internal composition structure of each composition unit is determined according to the minimum data set; and determining the dependency and structural repeatability among all the composition units according to the superposition data among the minimum data sets, thereby determining the target composition structure. The original composition units and the original composition structures in the original power distribution network model are optimized, and the optimized target power distribution network model is obtained. The analysis of the constituent units in the target power distribution network model is performed through the target power distribution network model, and the analysis efficiency can be improved.
Example four
Fig. 4 is a schematic structural diagram of a power distribution network modeling apparatus according to a fourth embodiment of the present invention. The present embodiment is applicable to the case of building a distribution network model. As shown in fig. 4, the power distribution network modeling apparatus includes: the system comprises a data correlation analysis module 410, a target topological structure acquisition module 420 and a target power distribution network model acquisition module 430.
The data association analysis module 410 is configured to perform data association analysis on calculation data associated with each component unit in an original power distribution network model of a target power distribution network to obtain a data analysis result;
the target topological structure obtaining module 420 is configured to adjust an original topological structure in the original power distribution network model according to the data analysis result, and obtain a target topological structure;
and the target power distribution network model obtaining module 430 is configured to optimize the original power distribution network model according to the data analysis result and the target topology structure, and obtain a target power distribution network model.
According to the technical scheme provided by the embodiment, data relevance analysis is carried out on the calculation data relevant to each component unit in the original power distribution network model of the target power distribution network to obtain a data analysis result; adjusting an original topological structure in the original power distribution network model according to the data analysis result to obtain a target topological structure; and optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model. By means of the scheme, the problem that due to the fact that the power distribution network data contained in the original power distribution network model are too large, a large amount of redundant data exist in the original power distribution network model, a large amount of memory in the original power distribution network model is occupied, and therefore analysis efficiency is low when constituent units of the power distribution network in the original power distribution network model are analyzed is solved. The original power distribution network model is optimized, the optimized original power distribution network model can aggregate data, and the problem of low search efficiency caused by data dispersion is solved. The memory space of the power distribution network model is released, and the analysis and calculation efficiency of the power distribution network model is improved.
The data association analysis module 410 includes:
the minimum data set acquisition unit is used for analyzing the calculation data related to each component unit according to the internal component structure of each component unit in the original power distribution network model of the target power distribution network to obtain a minimum data set corresponding to each component unit;
and the data analysis unit is used for carrying out relevance analysis on the minimum data set corresponding to each composition unit to obtain a data analysis result.
Further, the data analysis unit includes:
the necessity determining subunit is used for determining the necessity of an original node and an original branch of the original power distribution network model in the data analysis result according to the minimum data set corresponding to each component unit;
and the repeatability determining subunit is used for determining the dependency and structural repeatability among the composition units in the data analysis result according to the coincidence data among the minimum data sets corresponding to the different composition units.
Illustratively, the repeatability determining subunit further comprises:
and the data change determining subunit is used for determining the structural repeatability among the composition units in the data analysis result according to the change condition of the superposed data among the minimum data sets corresponding to different composition units.
Illustratively, the data change determining subunit is specifically configured to:
and if the coincidence data between the minimum data sets corresponding to the different composition units is fixed data, determining that the different composition units corresponding to the coincidence data have structural repeatability, and determining a common structure between the internal composition structures of the different composition units corresponding to the coincidence data.
Illustratively, the target topology obtaining module 420 is specifically configured to:
determining a target composition structure according to the necessity of the internal composition structure of each composition unit in the data analysis result and the structural repeatability among the composition units;
adjusting an original topological structure in the original power distribution network model based on the target composition structure;
and taking the adjusted original topological structure as a target topological structure.
Illustratively, the target distribution network model obtaining module 430 is specifically configured to:
determining the relevance among the target topological structures according to the data analysis result;
optimizing model nodes in the original power distribution network model according to the target topological structure;
optimizing branch states in the original power distribution network model according to the relevance;
and determining a target power distribution network model according to the optimized model nodes and the optimized branch state.
The power distribution network modeling device provided by the embodiment can be applied to the power distribution network modeling method provided by any embodiment, and has corresponding functions and beneficial effects.
EXAMPLE five
FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the power distribution network modeling method.
In some embodiments, the power distribution network modeling method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the above described power distribution network modeling method may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the power distribution grid modeling method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable power distribution network modeling apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A modeling method for a power distribution network is characterized by comprising the following steps:
performing data relevance analysis on the calculation data related to each component unit in the original power distribution network model of the target power distribution network to obtain a data analysis result;
adjusting an original topological structure in the original power distribution network model according to the data analysis result to obtain a target topological structure;
and optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model.
2. The method of claim 1, wherein performing data association analysis on the calculation data associated with each component unit in the original distribution network model of the target distribution network to obtain a data analysis result comprises:
analyzing the calculation data associated with each component unit according to the internal component structure of each component unit in the original power distribution network model of the target power distribution network to obtain a minimum data set corresponding to each component unit;
and performing relevance analysis on the minimum data set corresponding to each composition unit to obtain a data analysis result.
3. The method according to claim 2, wherein the performing the correlation analysis on the minimum data set corresponding to each constituent unit to obtain a data analysis result comprises:
determining the necessity of an original node and an original branch of the original distribution network model in a data analysis result according to the minimum data set corresponding to each component unit;
and determining the dependency and structural repeatability among the composition units in the data analysis result according to the coincidence data among the minimum data sets corresponding to different composition units.
4. The method of claim 3, wherein determining structural repetitiveness among the constituent units in the data analysis results according to coincidence data between the minimum data sets corresponding to different constituent units comprises:
and determining the structural repeatability among the constituent units in the data analysis result according to the change condition of the superposition data among the minimum data sets corresponding to different constituent units.
5. The method of claim 4, wherein determining structural repeatability among the constituent units in the data analysis results according to variation of the coincidence data among the minimum data sets corresponding to different constituent units comprises:
and if the coincidence data between the minimum data sets corresponding to the different composition units are fixed data, determining that structural repeatability exists between the different composition units corresponding to the coincidence data, and determining a common structure between the internal composition structures of the different composition units corresponding to the coincidence data.
6. The method of claim 3, wherein adjusting the original topology in the original power distribution network model to obtain the target topology according to the data analysis result comprises:
determining a target composition structure according to the necessity of the original node and the original branch of the original distribution network model in the data analysis result and the structural repeatability among the composition units;
adjusting an original topological structure in the original power distribution network model based on the target composition structure;
and taking the adjusted original topological structure as a target topological structure.
7. The method of claim 1, wherein optimizing the original power distribution network model based on the data analysis and the target topology to obtain a target power distribution network model comprises:
determining the relevance among the target topological structures according to the data analysis result;
optimizing model nodes in the original power distribution network model according to the target topological structure;
optimizing branch states in the original power distribution network model according to the relevance;
and determining a target power distribution network model according to the optimized model nodes and the optimized branch state.
8. A modeling apparatus for a power distribution network, comprising:
the data relevance analysis module is used for carrying out data relevance analysis on the calculation data related to each component unit in the original power distribution network model of the target power distribution network to obtain a data analysis result;
the target topological structure obtaining module is used for adjusting an original topological structure in the original power distribution network model according to the data analysis result to obtain a target topological structure;
and the target power distribution network model acquisition module is used for optimizing the original power distribution network model according to the data analysis result and the target topological structure to obtain a target power distribution network model.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of modeling a power distribution grid as claimed in any of claims 1-7.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the method of modeling a power distribution grid of any of claims 1-7 when executed.
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| CN116094169B (en) * | 2023-01-28 | 2024-04-12 | 国网江苏省电力有限公司连云港供电分公司 | Power distribution network topology model generation method and terminal equipment |
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