WO2023115842A1 - 一种数据驱动的离线在线一体化配电网仿真系统及方法 - Google Patents

一种数据驱动的离线在线一体化配电网仿真系统及方法 Download PDF

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WO2023115842A1
WO2023115842A1 PCT/CN2022/098701 CN2022098701W WO2023115842A1 WO 2023115842 A1 WO2023115842 A1 WO 2023115842A1 CN 2022098701 W CN2022098701 W CN 2022098701W WO 2023115842 A1 WO2023115842 A1 WO 2023115842A1
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data
risk
node
level
power
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PCT/CN2022/098701
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English (en)
French (fr)
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盛万兴
刘科研
孟晓丽
何开元
贾东梨
叶学顺
王晨钟
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中国电力科学研究院有限公司
国家电网有限公司
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Priority to CA3242113A priority Critical patent/CA3242113A1/en
Publication of WO2023115842A1 publication Critical patent/WO2023115842A1/zh

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    • 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
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]

Definitions

  • the present application relates to the field of power supply or distribution, in particular to a data-driven off-line and on-line integrated distribution network simulation system and method.
  • the new power system has the characteristics of large scale, many uncertain factors and wide fields.
  • relevant commercial software used in power system planning and design at home and abroad such as: PSASP, BPA, PSS/E, ETAP, PSAPAC, DigSilent and NETOMAC, etc.
  • PSASP power system planning and design at home and abroad
  • PSS/E PSS/E
  • ETAP PSAPAC
  • DigSilent and NETOMAC etc.
  • these softwares provide favorable tools for distribution network simulation calculation and analysis, they are not directly aimed at the planning, operation, maintenance and overhaul of new power systems, and lack distribution network operation status evaluation systems, comprehensive monitoring methods and auxiliary decision-making methods.
  • change the "passive repair" to "active monitoring” shorten the fault recovery time, improve the service level
  • offline and online integrated distribution network simulation system is an indispensable tool.
  • the off-line and on-line integrated distribution network simulation system integrates advanced perception, computing, communication, control and other information technologies and automatic control technologies to build a mutual mapping of human, machine, object, environment, information and other elements in physical space and information space, and timely A complex system of interaction and efficient collaboration, realizing on-demand response, fast iteration, and dynamic optimization of resource allocation and operation within the system.
  • the core of the offline and online integrated distribution network simulation system is to establish a data model of the power system and its application services, and propose data-driven modeling, analysis, prediction, simulation and control methods.
  • the offline and online integrated distribution network simulation system consists of two parts: online simulation and offline simulation.
  • the operating data of the offline simulation part such as load power
  • the operating data of the offline simulation part is generally entered directly by the user through the device attributes; but for online simulation, the actual distribution network has many lines.
  • Large scale, large number of loads, and long simulation time period the work of entering operation data using offline simulation is too large, and it cannot support advanced business applications of distribution network automation. Therefore, it is necessary to import the online operation data of the distribution network into the offline and online integrated distribution network simulation system through data synchronization.
  • the power system modeling method first needs to realize the separation of data, model, and algorithm. There are similar technologies in the software industry, and they already have complete theories and many mature applications. However, the power software system has always been known for its strict data structure and procedural processing methods, which have high computing speed, but it brings serious shortage of flexibility and reusability. In order to meet the application requirements of new power systems, a data-driven power system modeling method is urgently needed.
  • Multi-source data integration and data support Data storage generally has the characteristics of distribution and heterogeneity, which brings more complexity to the realization of data synchronization; in addition, due to the complexity of distribution network business, data filtering and time synchronization are required in the process of synchronizing data. Synchronization, data synchronization should have good scalability to meet the diverse needs of users. There are two main solutions to the data synchronization problem in the industry.
  • the first one is a dedicated data synchronization module, which supports the system’s own business remote collection, supports the data format required by itself, and supports the expansion of the same data format system;
  • Incremental real-time synchronization system is the representative, and its characteristic is that it only supports real-time incremental synchronization of the database, and does not support other types of data synchronization, and its versatility is limited; its use is also very complicated, and it is difficult for non-professionals to get started quickly, which limits the user group , the secondary development needs to be carried out on the basis of the source code, and its application is highly restricted.
  • the second solution is developed based on the stream processing framework, which has many excellent features, such as high scalability, distributed support, etc.; but there is a big difference between stream processing and data synchronization, and users need to develop data synchronization on top of stream processing by themselves Solve problems such as resuming uploads from breakpoints, incremental data acquisition, and distribution; although this method has higher scalability, the development cost is also higher.
  • the traditional data synchronization scheme can no longer meet all the business requirements of the offline and online integrated distribution network simulation system.
  • Graph database is a new type of NoSQL database based on graph theory. Relationships are the most important elements in graph databases, through which nodes can be related to each other to build complex models closely related to problem domains.
  • the graph database supports a very flexible and fine-grained data model. It can model and manage data applications in a simple and intuitive way, and can more easily miniaturize and standardize data units. At the same time, it can also realize rich relationship connections. When querying data, you can perform query operations in any imaginable way.
  • the data synchronization of the offline and online integrated power distribution simulation system based on the graph database is realized. On the one hand, it can be based on the node-relationship model of the graph database, which has excellent scalability and is easy to realize data integration; on the other hand, by using the graph database for data management, no matter No matter the number or depth of relationships ensures fast searches and queries with zero latency.
  • Dynamic visualization of distribution network There are many types and quantities of network visualization tools, such as Gephi, GraphViz, etc. These tools are rich in functions and strong in applicability, and are not limited to specific purposes.
  • Gephi is an open source, free cross-platform, JVM-based complex network analysis software, mainly used for various networks and complex systems, interactive visualization and detection open source tools for dynamic and hierarchical graphs.
  • Gephi provides various representative graph layout methods and allows users to set the layout, supports time-varying network data visualization and supports users to filter the network in real time, and build a new network from the filtering results.
  • Gephi uses clustering and hierarchical graph methods to process larger-scale graphs, explore multi-layer graphs by accelerating exploration and editing of large hierarchical graphs; aggregate graph networks using data attributes and built-in clustering algorithms.
  • GraphViz is an open source graphics drawing tool designed by Bell Labs. It supports various operating systems such as Windows, Linux and Mac. It uses a specific DSL (Domain Specific Language) - dot as a scripting language, and uses a layout engine to parse this Script that provides the auto layout algorithm.
  • GraphViz's dot scripting language is very simple and convenient, and provides a large number of automatic layout algorithms and rich export formats for users to choose.
  • this application proposes a radial power distribution network adaptive topology visualization method, including:
  • Step S1 extract the topology data of the distribution network
  • Step S2 Determine the zero-level backbone based on the topological data of the distribution network, and divide the display view of the topological data of the distribution network based on the zero-level backbone;
  • Step S3 Traverse all the next-level backbones and visualize them.
  • step S1 includes: performing connectivity analysis on the power distribution network based on the connection relationship between device nodes, eliminating isolated island devices, and establishing a topological device set.
  • determining the backbone based on the topology data of the distribution network includes: starting from the power node, performing a breadth-first search on the topology device set, using a bidirectional chain tree data structure for storage, generating a topology tree, and Save the level of each node in the search; obtain the path with the largest number of search layers, and then select the path with the largest sum of the number of path nodes from the path with the largest number of search layers as the backbone; wherein the backbone includes a zero-level backbone.
  • the display view is divided into upper and lower regions with equal areas.
  • the path with the largest sum of the number of nodes in the screening path is the trunk, including: the path with the largest sum of the number of nodes of a specific type in the screening path nodes is the trunk.
  • the specific type of node is a device node selected by the user.
  • the initial device node with the largest number of device nodes in each sub-topology device set corresponds to a sub-topology device set.
  • an adaptive topology visualization system for a radial power distribution network which includes:
  • the data extraction module is used to extract the topological data of the distribution network
  • a zero-level backbone analysis module configured to determine a zero-level backbone based on the topological data of the distribution network, and divide the display view of the topological data of the distribution network based on the zero-level backbone;
  • An iterative analysis module for traversing and visualizing all next-level backbones.
  • the data extraction module is configured to: analyze the connectivity of the power distribution network based on the connection relationship between the device nodes, eliminate isolated island devices, and establish a topological device set.
  • the zero-level trunk analysis module is configured to: start from the power supply node, perform a breadth-first search on the topological device set, use a bidirectional chain tree data structure for storage, generate a topological tree, and save each node Hierarchy in the search; obtain the path with the largest number of search layers, and then select the path with the largest sum of path node numbers from the paths with the largest number of search layers as the backbone.
  • the zero-level trunk analysis module is further configured to: take the path of the trunk as the X-axis, and divide the display view into upper and lower regions with equal areas.
  • the present application provides a processor for running a program, wherein the above method is executed when the program is running.
  • the present application provides an execution device, including a processor, the processor is coupled to a memory, the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the above-mentioned method.
  • the present application provides a computer-readable storage medium, including a program, which, when run on a computer, causes the computer to execute the above method.
  • the embodiment of the present application also provides a data-driven offline and online integrated distribution network simulation system, the system includes a data layer, a platform layer and an application layer; wherein, the data layer, including a graphics library and an attribute database, is stored in In Oracle db/Access, basic support systems and basic general data services are provided for calling by the platform layer; the platform layer includes an application platform operation framework and a development platform modeling tool; wherein, the application platform operation framework includes at least the following components At least one of: running basic components, interface display components, icon display components, graphic display components, application integration components, and portal integration components; the development platform modeling tool includes at least one of the following components: business modeling Components, report definition components, authority configuration components, tool integration components, process definition components, and basic framework components; the application layer includes at least one of the following modules: data management module, risk scanning module, fault simulation module, grid Optimization module, system configuration module, monthly report analysis module.
  • the data layer including a graphics library and an attribute database, is stored in In Oracle db/Access, basic support systems and basic
  • the data management module is used to implement data integration, data query, data quality evaluation analysis and/or data processing.
  • the risk scanning module is used to implement risk batch scanning and weak point analysis of distribution network, risk source statistics and rating and/or risk early warning.
  • the risk scanning module is used to: perform risk analysis based on ledgers, topological relationships, operating data, risk analysis conditions, and parameter evaluation, and obtain equipment risk results, topology analysis results, and power flow calculation results; wherein, the The equipment risk results include equipment aging risk and equipment quality risk; the topology analysis results include single power supply users, ring network operation lines and power-off users; the power flow calculation results include feeder power flow distribution, distribution transformer power flow distribution and node power flow distribution; Risk grading is based on the risk analysis results; among them, risk grading includes power maintenance risk, ring network operation risk, power supply reliability risk, power failure load (MW), power loss power (MWH), overload risk, overload risk, Grading of overvoltage risk, low voltage risk and/or capacity margin risk; risk statistics based on risk grading results; among them, risk statistics include loss of power load (MW), power loss (MWH), overvoltage of area and feeder Load risk, heavy load risk, overvoltage risk, low voltage risk, capacity margin risk, power supply risk, ring network operation
  • the fault simulation module is used to implement fault prediction, multiple fault simulation, and emergency repair decision-making assistance based on the expansion of the fault analysis function in the current advanced application of distribution automation.
  • the fault simulation module is used to: perform basic parameter setting based on the managed data; wherein the managed data includes grid information management, ledger data management, operation data management, equipment level management, meteorological information management and/or or maintenance plan management; the basic parameter setting includes scanning range setting, maintenance plan setting, weather information setting, load parameter setting and/or equipment status setting; fault prediction is performed based on the basic parameter setting; wherein, the fault prediction includes regional Pre-arranged power outage fault level, 10kv power distribution facility fault level, low-voltage facility fault level and/or power generation facility fault level, and feeder pre-arranged power outage fault level, 10kv power distribution facility fault level, 10kv and above power transmission and transformation facility fault level and power generation facility failure levels, as well as the plant’s 10kv power distribution facility failure level, 10kv and above power transmission and transformation facility failure level, low-voltage facility failure level, and power generation facility failure level; the result analysis is based on the fault prediction results; wherein, the results Analysis includes fault level analysis, fault location analysis and transfer analysis;
  • Formulate a scheme based on the analysis of the results; wherein, the formulation of the scheme includes the setting of the emergency repair station, the setting of the emergency repair scheme, the formulation of the transfer scheme, and the formulation of the optimization scheme.
  • the network frame optimization module performs the following operational evaluations: inrush current analysis, protection setting check, power supply capacity analysis, and voltage quality evaluation , loop closure analysis, loop solution analysis, transfer analysis and/or reactive power optimization.
  • system configuration module is used to complete automatic risk analysis and calculation setting and risk analysis and evaluation parameter setting.
  • the monthly report analysis module is used to complete the query, generation and editing of the monthly risk report, the monthly failure report and the monthly grid report.
  • the embodiment of the present application also provides a data-driven offline and online integrated distribution network simulation model establishment method, the method includes: modeling the data of the power system, including: the data model of the power system is represented by an XML file ;
  • the power system XML file includes a front part and a main part; the front part is an element representing a dependency relationship, and each dependent element points to an XML file, which needs to be loaded first when used; the main part is an element representing a device;
  • the input of the power system XML file is converted into a root QHash data structure for use, and the QHash is composed of nested QHash; wherein, the method of using the pre-file is: the root QHash first layer Key is the type, and the value is QHash,
  • the second-level key is ID, and the value is the use object in the form of QHash; its data can be used based on the type and ID;
  • the method of using the reference device is: the root QHash first-level key contains the device
  • the relevant calculation results are returned to the corresponding device algorithm model;
  • the output model is defined as the name of the output data in the algorithm model class and algorithm class, Special data needs to provide corresponding method support in the data class; generate output data files based on existing data and output models.
  • the modification on the visual interface is updated to the root QHash when determined.
  • an interpreter is connected between the root QHash and the network model; for a given device, the interpreter reads the directly contained device, and draws its network to interface.
  • the graphic element of the device uses a shape collection and its relative position, line and fill, the default shape is a straight line, the default line is a black solid line, and the default fill is none; and/or, the position of the device Determined by the node data, any device has one and only two nodes, and there are multiple connection terminals on a node; and/or, the connection relationship of the device is only related to nodes in the drawing, and there is no node element in the input file
  • nodes are generated according to the device connection data, one node is generated for one connection point, and two node elements are added for the device.
  • all node data is stored in an independent XML for pre-loading.
  • an interpreter is connected between the root QHash and the attribute model; the attribute model defines the following information: the attributes to be displayed by the device, the type of display, and the type of editor; for a given device, the interpreter generates a grid-like In the attribute interface of the layout, label and editor pairs are added sequentially according to the method of adding columns in the attribute model, and the matching data is bound, in which the label displays the attribute name, and the editor reads and writes the attribute value.
  • each independent element in the power system XML file contains type and ID attributes, and the main part of the power system XML file uses the dependent element through the type and ID; the dependent file can be used to represent a specific model of equipment and its parameters, associated Device and enumerated types, etc.
  • the main part of the power system XML file is the element representing the equipment.
  • the data of the equipment includes name, ID, type, included equipment, belonging equipment, equipment parameters, graphic information and model. Each item of data uses an element and its Nested element representation.
  • the device is an entity device or an abstract device set.
  • the properties of the basic element include name, value, type and editor, the name is a required attribute, the default value of the value is 0, the default type of the type is a string,
  • the default object of the editor is a text box; for extended type data, it is represented by multiple nested elements, and its final leaf element is the basic element; for reference type data, it contains the following elements: name, type, reference type, reference ID and editor, the default editor is button.
  • the embodiment of the present application also provides a data synchronization method for an offline and online integrated distribution network simulation system, including: establishing a distribution network simulation mathematical model centered on operating data, and realizing data description of primary and secondary equipment correlations; The corresponding relationship between the mathematical model and the data source data is estimated and optimized for multi-source data; the mathematical model of the distribution network simulation is transformed into a node-relationship model of the graph database, and the online data is stored in the graph database; In order to support the platform, real-time data synchronization is realized based on fast search, so that the offline simulation function can directly provide online auxiliary decision-making services for the real-time operation of the distribution network.
  • the data members of the distribution network simulation mathematical model include measurement, connection terminals and equipment; wherein, the measurement represents secondary equipment, the connection terminal represents the connection point between primary equipment, and the equipment Represents a primary device.
  • the measurement data members include at least one of the following: identification, phase, international unit code, unit multiplier, unit symbol, connection terminal, calculation method level, protection action adjustment, measurement type; data members of the connection terminal Include at least one of the following: identification, phase, device, connection node, regulation control, connection, serial number; the data members of the device include at least one of the following: identification, protection action regulation, weather station, configuration event, control, quantity Measurement, resource type, aggregation, service status, for network analysis, normal service status.
  • connection terminals, identification, phase, international unit code, unit multiplier, unit symbol, calculation method level, protection action adjustment, connection node, adjustment control, weather Stations, configuration events, control and/or resource types are modeled as nodes; where connection terminals, identification, phase, SI code, unit multiplier, unit symbol, calculation method hierarchy and/or protection action regulation nodes are respectively related to measurement Node association, equipment, identification, phase, connection node and/or regulation control node are respectively associated with connection terminal nodes, measurement, identification, protection action regulation, weather station, configuration event, control and/or resource type nodes are respectively associated with device nodes Association; the measurement type is the data member of the measurement node, the connection and serial number are the data members of the connection terminal, and the aggregation, service status, network analysis and/or normal service status are the data members of the device.
  • the offline and online integrated power distribution simulation system use the equipment-measurement-connection terminal structure or the equipment-connection terminal-measurement structure to use the data in the graph database; wherein, in the equipment-measurement-connection terminal structure , starting from the device, searching for measurement based on the association relationship, and searching for the connection terminal based on the measurement, so as to obtain the operation data of a full set of distribution network simulation; in the equipment-connection terminal-measurement structure, starting from the device, based on the association relationship Search for connection terminals, and search for measurements based on connection terminals, so as to obtain the operation data of a full set of distribution network simulation.
  • Fig. 1 is the architecture diagram of the offline online integrated distribution network simulation system provided by the embodiment of the present application.
  • Fig. 2 is the flow chart of realizing the functions of the off-line and on-line integrated distribution network simulation system provided by the embodiment of the present application;
  • FIG. 3 is a data integration structure diagram of the offline online integrated distribution network simulation system provided by the embodiment of the present application.
  • FIG. 4 is a schematic diagram of the implementation process of the risk scanning module provided by the embodiment of the present application.
  • Fig. 5 is a schematic diagram of the implementation flow of the fault simulation module provided by the embodiment of the present application.
  • FIG. 7 is a schematic diagram of a distribution network simulation primary and secondary equipment operation data mathematical model provided by the embodiment of the present application.
  • FIG. 8 is a schematic diagram of a node-relationship model of a distribution network simulation graph database provided in an embodiment of the present application.
  • FIG. 9 is a schematic diagram of a quick search of distribution network simulation data provided by an embodiment of the present application.
  • FIG. 10 is a visual flow chart of the self-adaptive topology of the radial power distribution network of the present application.
  • FIG. 11 is a schematic diagram of the distribution of node subtrees in this application.
  • This application is aimed at a new type of power system, and proposes an offline and online integrated simulation auxiliary decision-making system for distribution networks that integrates functions such as data quality assessment, risk scanning analysis, fault prediction simulation, and grid optimization.
  • functions such as data quality assessment, risk scanning analysis, fault prediction simulation, and grid optimization.
  • a data-driven power system modeling method is proposed: relying on power system modeling tools, the data model of the power system is established, and the input method of power data is proposed.
  • the usage method of electric power data includes interface interaction method, algorithm usage method and data output method.
  • the power system modeling tool has high flexibility and reusability. It can not only provide data support for advanced applications of offline and online integrated power distribution simulation systems, but also provide unit-level module support for offline and online integrated power distribution simulation systems.
  • a distribution network simulation mathematical model centered on operating data is established to realize the data description of primary and secondary equipment correlations; based on the relationship between the simulation mathematical model and data source data The corresponding relationship between the multi-source data is estimated and optimized; the mathematical model of the distribution network simulation is transformed into the node-relationship model of the graph database, and the online data is stored in the graph database; finally, the graph database is used as the supporting platform, based on the fast The search realizes real-time data synchronization, so that the offline simulation function directly provides online auxiliary decision-making services for the real-time operation of the distribution network.
  • the overall topology of the network is improved by using a variety of layout methods, and how to determine and allocate the specific positions of the nodes and edges of the mapping geometry in the limited display space through the topology layout.
  • the dynamic topology visualization method of the radial power distribution network avoids the display overlap of the topological structure diagram, reduces the crossing between edges in the topological structure diagram, and displays the distribution network topology structure more beautifully and generously.
  • Figure 1 is the architecture diagram of the data-driven offline and online integrated distribution network simulation system provided by the embodiment of the present application.
  • the data-driven offline and online integrated distribution network simulation system includes: data layer, platform layer and application layer; where,
  • the data layer includes a graphics library and an attribute database, which are stored in Oracle db/Access, provide basic support systems and basic general data services, data persistence, and database access capabilities for calling by the platform layer;
  • the platform layer includes the application platform operation framework and development platform modeling tools, including platform components such as workflow, transaction processing and security system; among them, the application platform operation framework includes at least the operation basis, interface display, icon display, graphic display, application integration and /or portal integration and other components; the development platform modeling tools include at least business modeling, report definition, authority configuration, tool integration, process definition and/or basic framework and other components.
  • platform components such as workflow, transaction processing and security system
  • the application platform operation framework includes at least the operation basis, interface display, icon display, graphic display, application integration and /or portal integration and other components
  • the development platform modeling tools include at least business modeling, report definition, authority configuration, tool integration, process definition and/or basic framework and other components.
  • the application layer is at least divided into modules such as data management, risk scanning, fault simulation, grid optimization, system configuration and/or monthly report analysis, etc., and provides charts, reports, and topological maps to realize multi-level units such as companies, cities, and counties. Department users interact with each other.
  • Fig. 2 is a flow chart of realizing the functions of the offline and online integrated distribution network simulation system provided by the embodiment of the present application, as shown in Fig. 2:
  • the data management module implements data integration, data query, data quality assessment analysis and/or data processing; wherein, the data includes archive data, operation data, topological data, meteorological data and/or city maps, etc.
  • Fig. 3 is a schematic diagram of the data integration structure of the offline and online integrated distribution network simulation system provided by the embodiment of the present application.
  • the offline and online integrated distribution network simulation system is oriented to PMS data, GIS platform, power quality monitoring system/power consumption information collection system, dispatching automation system, OMS/SCADA system, meteorological system and marketing business application system, and extracts the power grid structure through the data interface data, equipment archive data and operating data, and transform them into structured simulation data through data matching services.
  • Fig. 4 is a schematic diagram of the implementation process of the risk scanning module provided by the embodiment of the present application, as shown in Fig.
  • topological relationship includes topological relationship of tower, cable joint and cable terminal
  • operation data includes feeder outlet voltage, public transformer load, special transformer load and switch status, etc.
  • risk analysis conditions include time range , maintenance plan, social information and meteorological information, etc.
  • evaluation parameters include component failure probability, geographical information, equipment defects, equipment age, meteorological factors, time factors, tidal current influence and risk rating;
  • Risk grading is based on risk analysis; among them, risk grading includes power maintenance risk, ring network operation risk, power supply reliability risk, power failure load (MW), power loss power (MWH), overload risk, overload risk, Rating of voltage risk, low voltage risk and/or capacity margin risk;
  • risk grading includes power maintenance risk, ring network operation risk, power supply reliability risk, power failure load (MW), power loss power (MWH), overload risk, overload risk, Rating of voltage risk, low voltage risk and/or capacity margin risk;
  • risk statistics include area and feeder loss of power load (MW), loss of power (MWH), overload risk, heavy load risk, overvoltage risk, low voltage risk, capacity margin risk , power maintenance risk, ring network operation risk and/or power supply reliability risk, as well as power loss load (MW), power loss (MWH), overload risk, heavy load risk, overvoltage risk, low voltage risk and /or capacity margin risk, and loss of power load (MW), power loss (MWH), overload risk, heavy load risk, overvoltage risk, low voltage risk, power supply reliability risk and/or power retention risk in the station area ;
  • Risk early warning, risk measure analysis and pre-control plan formulation based on risk statistical results Risk early warning, risk measure analysis and pre-control plan formulation based on risk statistical results.
  • the fault simulation module is based on the expansion of the fault analysis function in the current advanced application of distribution automation, and realizes functions such as fault prediction, multiple fault simulation and emergency repair decision-making assistance;
  • Fig. 5 is the fault simulation module provided by the embodiment of the present application
  • the schematic diagram of the implementation process, as shown in Figure 5, sets basic parameters based on the management data; wherein, the management data includes grid information management, ledger data management, operation data management, equipment level management, meteorological information management and/or maintenance Plan management; that is, basic parameter setting based on data management results, the managed data includes grid information, ledger data, operating data, equipment level, weather information and/or maintenance plan; the basic parameter setting includes scanning range Settings, maintenance plan settings, weather information settings, load parameter settings and/or equipment status settings; fault prediction based on basic parameter settings, including regional pre-arranged power outage fault levels, 10kv power distribution facility fault levels, low-voltage facility fault levels and/or or the fault level of power generation facilities, as well as the fault level of the pre-arranged power outage of the feeder,
  • the grid optimization module mainly takes the minimum load loss as the goal, and the grid operation safety and voltage quality as constraints, to carry out inrush current analysis, protection setting check, power supply capacity analysis, voltage quality evaluation, loop closure analysis, and loop solution Operational evaluation such as analysis, transfer for analysis and/or reactive power optimization;
  • the system configuration module mainly completes the risk automatic analysis and calculation setting and the risk analysis and evaluation parameter setting
  • the monthly report analysis module mainly completes the query, generation and editing of risk monthly report, fault monthly report and grid monthly report.
  • the data model of the power system is represented by an XML file; the XML file of the power system includes a front part and a main part.
  • the front part of the power system XML file is an element representing the dependency relationship, and each dependency element points to an XML file, which needs to be loaded first when used.
  • Each independent element in the dependent file that is, the power system XML file pointed to by the dependent element
  • Dependency files can be used to represent specific models of devices and their parameters, associated devices and enumerated types, etc.
  • the main part of the power system XML file is the element representing the equipment.
  • the equipment contains data such as name, ID, type, included equipment, belonging equipment, equipment parameters, graphic information and models, and each item of data is represented by an element and its nested elements, as follows:
  • the attributes of the basic element include name, value, type, and editor.
  • the name is a mandatory attribute, and the default value is 0.
  • the province type is a string, and the default object of the editor is a text box.
  • complex numbers include real and imaginary parts
  • vectors include vector dimensions , type, and the value of each vector element
  • regular matrix includes row number, column number, type, and matrix element value
  • sparse matrix includes row number, column number, type, cell number, and matrix element, where the matrix element includes row number, column number, and The value has three elements; the enumeration includes the number of members, the value of each member and an editor, and the default editor is a combo box.
  • reference type data such as specific model equipment and its parameters, associated equipment and models, it is characterized by elements such as name, type, reference type, reference ID and editor, and the default editor is a button.
  • the reference object can be found through the reference type and reference ID. Note that the object is not defined in the device.
  • Devices can represent physical devices, such as power supplies, switches, transformers, and loads, etc.; they can also represent abstract device collections, such as networks, feeders, plants, and areas.
  • the method of using the pre-file is: the root QHash first-level Key is the type, the value is QHash, the second-level Key is ID, and the value is the use object in the form of QHash; the data can be used based on the type and ID.
  • the method of using the reference device is: the first layer Key of the root QHash contains the device ID, which can be found and used according to the reference ID.
  • Visual interface interaction method that is, modeling the visual interface interaction, including:
  • Visual interface interaction is triggered by events, and the model elements of the object define its display effect.
  • the same object can have multiple available network models and attribute models, but only one model can be used at a time.
  • the modification on the interface will be updated to the root QHash when confirmed. Connecting between the root QHash and the model is the interpreter.
  • the network model is used to interactively display the power system network connection relationship.
  • the interpreter reads its directly contained devices, and draws its network on the interface according to the primitives, positions and connection relationships of the contained devices.
  • the primitive needs to use related data such as shape collection and its relative position, line and fill.
  • the default shape is straight line, the default line is black solid line, and the default fill is none.
  • the position is determined by the node data, any device has only two nodes, and one node can have multiple connection terminals.
  • the connection relationship is only related to nodes in the drawing.
  • nodes need to be generated according to the device connection data, one node is generated for one connection point, and two node elements are added for the device; in addition, all nodes need to be Data is stored in a separate XML for pre-loading.
  • the attribute model is used to display and edit device attributes.
  • the attribute model defines the attributes that the device needs to display, the type of display, the type of editor and other information.
  • the interpreter For a given device, the interpreter generates a property interface with a grid-like layout, adds labels and editor pairs in sequence according to the column-by-column increase of the property model, and binds matching data, where the label displays the property name, and the editor reads and writes the property value .
  • the output model is defined as the name of the output data in the algorithm model class and the algorithm class, and the special data needs to provide corresponding method support in the data class; the output data file is generated according to the existing data and the output model.
  • the main data members of the distribution network simulation mathematical model mainly include three parts: measurement, connection terminal and equipment.
  • Measurements represent secondary equipment and can represent any measured, calculated or unmeasured, uncalculated quantity.
  • Any equipment may contain measurements, for example, a substation may contain temperature measurements and door opening indications, and a transformer may contain oil temperature and Tank pressure measurements, circuit breakers may contain switch state measurements.
  • a connection terminal represents a connection point between primary equipment, such as an AC electrical connection point to a conductive device, and is generally connected at a physical connection point called a connection node.
  • Equipment represents primary equipment such as AC terminals or components of a system connected by conduction.
  • Fig. 7 is a schematic diagram of the distribution network simulation primary and secondary equipment operation data mathematical model provided by the embodiment of the present application, as shown in Fig. 7:
  • the measurement data members include at least one of the following: identification, phase, international unit code, unit multiplier, unit symbol, connection terminal, calculation method level, protection action adjustment, and measurement type.
  • Identity provides a common identity for all classes that require an identity and named properties.
  • Phase is the measurement phase. If the attribute is missing, the default is ABC three-phase.
  • SI codes are string values for units and multipliers in the list maintained by the International Organization of Units, as described in "Code for Units of Measurement Used in International Trade".
  • the unit multiplier is the unit multiplier for the measured quantity.
  • a unit symbol is a unit of measurement for a quantity.
  • the connection terminal is the position associated with the measurement, and a measurement is associated with at most one connection terminal.
  • the calculation method hierarchy indicates the calculation method hierarchy applicable to this analog quantity.
  • Protection action adjustment when the protection action adjustment is activated, the data of the measuring equipment changes.
  • the measurement type specifies whether the measurement represents indoor temperature, outdoor temperature, bus voltage, line flow, etc.; when set to Specialized, the measurement type is defined in more detail by a specialized class that inherits from the measurement.
  • connection terminal includes at least one of the following: identification, phase, device, connection node, regulation control, connection, serial number.
  • identification, phase, device, connection node, regulation control, connection, serial number The meanings of the marks and phases are the same as above and will not be described in detail.
  • a device is the primary device to which the connection terminal belongs.
  • a connection terminal has one and only one corresponding device, and the device connects to other devices through the connection terminal.
  • the connection node is the topological node of the power distribution network, and it is the connection node where the terminals are connected with zero impedance. Regulatory control refers to the controller that controls this terminal. Connection is True to indicate that the terminal is connected to the relevant topology node, and false to indicate that it is not connected.
  • the serial number is used to indicate the terminal connection position of the multi-terminal conductive equipment; the serial number starts from 1, and the additional terminals are arranged in ascending order.
  • the data members of the device include at least one of the following: identification, protection action adjustment, weather station, configuration event, control, measurement, resource type, aggregation, service status, for network analysis, and normal service status.
  • identification and protection action adjustment are the same as above and will not be repeated.
  • the weather station stores the weather station to which the device belongs geographically. Configuration events are used to report details of the creation, change or deletion of an entity or its configuration.
  • the control output is used for the actual control of regulating devices, such as synchronous motors or the magnetization of capacitor bank circuit breaker actuators. Measurement refers to the measuring equipment installed on the device, and multiple measuring devices can be installed on one device.
  • Resource type that is, the equipment type of the self-defined classification of the distribution network.
  • Aggregate is used to identify whether a single instance of the device is multiple devices that have been modeled together as a collection; for example, a power transformer or synchronous machine running in parallel, modeled as a single aggregate power transformer or synchronous machine.
  • Service Status If true, the device is in use. For Network Analysis The default value is true, indicating that the device can participate in network analysis. Normal Service Status If true, the device is functioning normally.
  • f is the objective function
  • x is the estimated value
  • z i is the data value in the i-th data source
  • r i is the error variance representing the data value in the i-th data source
  • the default value is 1.
  • the expected value of the residual is 1. If the residual value ci is greater than the set threshold, the data value zi in the i-th data source is bad data. At this time, zi should be removed from the data source, and the formula (1) model should be solved again.
  • connection terminal, equipment, identification, phase, international unit code, unit multiplier, unit symbol, calculation method level, protection action adjustment, connection node, adjustment control, weather station, configuration Event, control and/or resource types are modeled as nodes.
  • the connection terminal, identification, phase, SI code, unit multiplier, unit symbol, calculation method level and/or protection action adjustment node are respectively associated with the measurement node
  • the equipment, identification, phase, connection node and/or adjustment control Nodes are respectively associated with connection terminal nodes
  • measurement, identification, protection action regulation, weather station, configuration event, control and/or resource type nodes are respectively associated with device nodes.
  • the measurement type is the data member of the measurement node
  • the connection and serial number are the data members of the connection terminal
  • the aggregation, service status, network analysis and/or normal service status are the data members of the device.
  • Each group of instance association relationships of 16 nodes of type is expressed as an identifier in two tables in the relational database.
  • the same identifier means that the corresponding two data are related to each other; a matching query is performed when the data is imported, and then Using the association relationship of graph data to represent, the matching query is no longer needed in subsequent use, and the use efficiency is greatly improved.
  • the operation data of a complete set of distribution network simulation can be obtained.
  • the measurement can be regarded as a sub-node of the equipment, or a leaf node of the distribution network equipment tree, which is easy for users to understand; it is worth mentioning that it is recommended to check the equipment associated with the connection terminal to ensure consistency.
  • the operation data of a complete set of distribution network simulation can be obtained. If you use this structure in a relational database, you need to use the device to search the connection terminal, and then use the terminal to search the measurement multiple times, which takes a long time to search; but through the graph database association relationship, you only need to directly pass the relationship terminal You can find the starting point as a terminal or measurement, which is very fast.
  • a dynamic topology visualization method for radial power distribution networks specifically including the following:
  • FIG. 10 is a schematic diagram of the implementation flow of the radial power distribution network adaptive topology visualization method provided in the embodiment of the present application. As shown in FIG. 10 , the method includes The following steps:
  • Step S1 Extract the topology data of the distribution network.
  • the extracting the topology data of the distribution network includes: analyzing the connectivity of the distribution network based on the connection relationship between the device nodes, eliminating isolated devices, and establishing a topology device set.
  • the topology data refers to the data representing the connection relationship (that is, the connection node, if different devices have the same connection node, there is a connection relationship between the devices); each device has topology data, and also includes other non-topological data.
  • Topology data is managed by devices.
  • Topology device collection is a collection of devices excluding isolated devices. Each device in the topology device collection holds its own topology data.
  • the largest sub-topology device set is selected as the topological device set by setting a ratio threshold; wherein the largest sub-topology device set refers to the set with the largest number of elements.
  • the ratio threshold is 95% ratio.
  • the establishment of one or more sub-topology device sets corresponding to each initial device node specifically includes the following steps SUB11 to SUB15:
  • Step SUB11 select an unanalyzed initial device node
  • Step SUB12 mark the unanalyzed initial device node as processed, start from the initial device node, determine the device nodes that have a direct connection relationship with the initial device node, acquire and store the device nodes that have a direct connection relationship attribute information;
  • Step SUB13 put the device node with the direct connection relationship into the sub-topology device set associated with the unanalyzed initial device node;
  • Step SUB14 select an unprocessed device node from the sub-topology device set, mark the unprocessed device node as processed, and determine that there is a direct connection relationship with the marked device node starting from the marked device node device node, obtain and store the attribute information of the device node that has a direct connection relationship with the marked device node, and return to step SUB13 to continue processing (that is, the device that will have a direct connection relationship with the marked device node Putting the node into the sub-topology device set associated with the initial device node, and continuing to step SUB14), until all the device nodes in the sub-topology device set have been processed, enter step SUB15;
  • Step SUB15 output the initial device node and its corresponding sub-topology device set; at this time, the connection relationship starting from the initial device node has been analyzed, and the processing of the next unanalyzed initial device node is continued. If all the initial equipment nodes are analyzed, then end, otherwise, return to step SUB11, and continue the analysis of the next unanalyzed initial equipment node.
  • the working status of the equipment is not considered, for example, the opening and closing of switches are not considered.
  • Step S2 Determine the zero-level backbone based on the topological data of the distribution network, and divide the display view of the topological data of the distribution network based on the zero-level backbone.
  • step S2 can be implemented in this way, including: starting from the power supply node, performing a breadth-first search on the topology device set, using a bidirectional chain tree data structure for storage, generating a topology tree, and saving each node Hierarchy in the search; obtain the path with the largest number of search layers, and then select the path with the largest sum of path node numbers from the paths with the largest number of search layers as the backbone.
  • the trunk path as the X axis, the display view of the topology data of the distribution network is divided into upper and lower regions with equal areas.
  • the backbones include zero-level backbones.
  • the path with the largest sum of the number of nodes in the screening path is the trunk, including: the path with the largest sum of the number of nodes of a specific type in the screening path nodes is the trunk.
  • the specific type of node is a device node selected by the user.
  • the determination process of all n-level backbones is the same.
  • Step S3 traverse all the next-level backbones and visualize them
  • the traversing and visualizing all the next-level backbones specifically includes the following steps SUB31 to SUB36:
  • Step SUB31 Determine the next-level trunk; and extract the subtree, including: traversing the nodes of the trunk, for the node n of the trunk, based on the topology tree of node n, ignoring the trunk branches and obtaining all the connection branches of the trunk node n, for The topology tree corresponding to each branch is pruned to generate first-level subtrees; and so on, until the layout of m first-level subtrees of node n is completed; two sets of first-level subtrees g 1 and g are formed on the upper and lower sides of the trunk 2 ;
  • the objects to be traversed are the nodes on the zero-level backbone.
  • the process of determining the next-level backbone is the same as the zero-level backbone.
  • node n is a node of the trunk of the level
  • the topology tree is a branch of the trunk of the level
  • node n is the connection point of the trunk of the level and the branch topology tree, and there may be multiple branches on a node.
  • Step SUB32 traverse the next-level trunk; specifically: set the number of first-level subtrees of node n as m; then obtain the maximum lengths of each first-level subtree ⁇ l 1 , l 2 , l 3 ,...,l m ⁇ , sort and number the m first-level subtrees according to the length from large to small;
  • the basic principle of m first-level subtree layout is that two long-length first-level subtrees form upper and lower pairs, and their positions tend to in the middle;
  • first-level subtree if the maximum length first-level subtree layout of node n-1 is at the bottom, then the maximum length first-level subtree of node n is at the top; if the number of first-level subtrees of node n-1 is 0, Then the reference node is n-2, and so on; if there is no reference node, the position of the first-level subtree with the maximum length of node n can be freely selected up and down.
  • No. 2 first-level subtree is at the same position on the opposite side of No. 1 first-level subtree.
  • the first-level subtree No. 3 is on the same side as the first-level subtree No. 2, and is closest to the central position (that is, the first-level subtree No. 2); if there are two positions with the same distance to the middle position, the reference node n is in the trunk If node n is close to node 1, then No. 3 primary subtree is in the direction of node n-1 (as shown in Figure 11 on the left side), otherwise in the opposite direction, equal can be randomly selected.
  • No. 4 first-level subtree is at the same position on the opposite side of No. 3 first-level subtree.
  • the first-level subtree No. 5 is on the same side as the first-level subtree No. 4, and is closest to the central position (that is, the first-level subtree No. 1).
  • No. 6 first-level subtree is at the same position on the opposite side of No. 5 first-level subtree.
  • the first-level subtree No. 7 is on the same side as the first-level subtree No. 6, and is closest to the central position (that is, the first-level subtree No. 2), and the first-level subtree No. 7 at the reference node n position is on the left.
  • the X-axis width is equal to max(l 1 ,l 2 )+max(l 3 ,l 4 )+max(l 5 ,l 6 )+l 7 .
  • Step SUB33 Traversing the next-level node; generating the next-level subtree of the node and calculating its length; laying out the next-level subtree of the node based on the length; determining the drawing range of the node, and The next-level subtree of the node is allocated a drawing range;
  • the traversing the next-level nodes includes: traversing all the first-level subtrees t, traversing all the backbone nodes of the first-level subtree t, and ignoring the main node o based on the topology tree of node o Get all the connected branches of node o after the dry branch, prune the topology tree corresponding to each branch, and generate secondary subtrees respectively, set the number of secondary subtrees of node o as p; then obtain the secondary subtrees respectively
  • the maximum length of the tree is ⁇ r 1 ,r 2 ,r 3 ,...,r p ⁇ , and the p secondary subtrees are sorted and numbered in descending order of length.
  • the basic principle of the layout of p secondary subtrees is that two long secondary subtrees form left and right pairs, and their positions tend to be in the middle; the specific process is similar to step SUB32, and only the subtrees in Figure 11 and The left-right layout of the tree pair is changed to the top-bottom layout for the secondary sub-tree pair, which will not be repeated here.
  • two sets of primary subtrees h 1 and h 2 are formed on the left and right sides of the primary trunk.
  • Step SUB34 Determine whether all lower-level nodes have been traversed, if yes, enter the next step SUB35; otherwise, return to step SUB33;
  • Step SUB35 further determine whether to complete the traversal of the backbone nodes, if so, enter the next step SUB36; otherwise, return to step SUB32; if u is an odd number, its process is the same as step SUB33; wherein, u represents the number of stages of the tree or subtree, such as 0, 1, 2, . . . .
  • Step SUB36 further determine whether there is a next-level tree, if yes, organize all the next-level trunks, and return to step SUB32; otherwise, end the entire process, at this time, for all u-level subtrees v, face the u-level subtree All backbone nodes of v; assign layout positions to the next-level subtrees of all backbone nodes, and determine the visualization range for this node; if u is an even number, the process is the same as step SUB32; the self-adaptive topology visualization of radial power distribution network ends.
  • the present application provides a processor for running a program, wherein the above method is executed when the program is running.
  • the present application provides an execution device, including a processor, the processor is coupled to a memory, the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the above-mentioned method.
  • the present application provides a computer-readable storage medium, including a program, which, when run on a computer, causes the computer to execute the above method.
  • an adaptive topology visualization system for a radial power distribution network which includes:
  • the data extraction module is used to extract the topology data of the distribution network; in some embodiments, the data extraction module is used to: perform connectivity analysis on the distribution network based on the connection relationship between device nodes, eliminate isolated island devices, and establish collection of topological devices;
  • a zero-level backbone analysis module configured to determine a zero-level backbone based on the topological data of the distribution network, and divide the display view of the topological data of the distribution network based on the zero-level backbone; in some embodiments Among them, the zero-level backbone analysis module is used to: start with the power node as the starting point, perform a breadth-first search on the set of topological devices, use a bidirectional chain tree data structure for storage, generate a topological tree, and save each node in the search levels; obtain the path with the largest number of search layers, and then select the path with the largest sum of path node numbers from the paths with the largest number of search layers as the backbone. For example, with the trunk path as the X axis, the display view of the topology data of the distribution network is divided into upper and lower regions with equal areas. At this time, the backbone includes a zero-level backbone;
  • An iterative analysis module for traversing and visualizing all next-level backbones.
  • the method for determining the backbone of each level is the same.
  • the present application also provides a radial power distribution network adaptive topology visualization server; the server is used for setting the above-mentioned radial power distribution network adaptive topology visualization system.
  • the new distribution network and its equipment model have high flexibility and reusability, which can provide data support for advanced applications of offline and online integrated power distribution simulation systems, and can also provide unit-level modules for offline and online integrated power distribution simulation systems support.
  • the embodiments of the present application may be provided as methods, systems, or computer program products. 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, etc.) having computer-usable program code embodied therein.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions
  • the device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

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Abstract

提供一种数据驱动的离线在线一体化配电网仿真系统及方法。辐射型配电网络自适应拓扑可视化方法包括：步骤S1：提取配电网拓扑数据；步骤S2：基于配电网的拓扑数据确定零级主干，并基于零级主干，将配电网的拓扑数据的显示视图进行视图划分；步骤S3：遍历所有下一级主干并进行可视化。考虑新型配电网网络结构特点，使用多样化的布局方式改良网络整体拓扑结构，通过拓扑布局决定在有限显示空间内如何确定和分配映射几何图形的节点和边的具体位置；充分利用了辐射型配电网结构特征，一次生成所有布局和位置，后续拓扑过程中不需要调整，拓扑可视化速度快。

Description

一种数据驱动的离线在线一体化配电网仿真系统及方法
相关申请的交叉引用
本申请基于申请号为202111594314.5、申请日为2021年12月24日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此以全文引入的方式引入本申请。
技术领域
本申请涉及供电或配电领域，具体涉及一种数据驱动的离线在线一体化配电网仿真系统及方法。
背景技术
新型电力系统具有规模大、不确定因素多和涉及领域广的特点。目前，国内外应用于电力系统规划设计的相关商业软件如：PSASP、BPA、PSS/E、ETAP、PSAPAC、DigSilent和NETOMAC等。这些软件虽然为配电网仿真计算分析提供了有利工具，但并不直接针对新型电力系统规划、运行、维护和检修，缺乏配电网运行状态评估体系、综合监测手段和辅助决策手段。为有效提高配电网运行监测、控制能力，快速实现配电网可观可控，变“被动报修”为“主动监控”，缩短故障恢复时间，提升服务水平，离线在线一体化配电网仿真系统是不可或缺的工具。
离线在线一体化配电网仿真系统通过集成先进的感知、计算、通信、控制等信息技术和自动控制技术，构建物理空间与信息空间中人、机、物、环境、信息等要素相互映射、适时交互、高效协同的复杂系统，实现系统内资源配置和运行的按需响应、快速迭代、动态优化。可以通过相关技术，实现电力系统的安全稳定经济运行，并为用户提供按需定制、快速响应、全面覆盖的优质服务。离线在线一体化配电网仿真系统的核心在于建立电力系统及其应用服务的数据模型，提出数据驱动的建模、分析、预测、仿真与控制方法。
离线在线一体化配电网仿真系统由在线仿真和离线仿真两部分组成，离线仿真部分运行数据如负荷功率等一般通过设备属性由用户直接录入；但对于在线仿真来说，实际配电网线路多、规模大、负荷数量多、仿真时间段长，使用离线仿真的方式录入运行数据工作过于庞大，且不能支撑配电网自动化高级业务应用。因此，需要通过数据同步的方式将配电网在线运行数据导入离线在线一体化配电网仿真系统。
设备动态建模方面。电力系统建模方法首先需要实现数据、模型、算法三者的分离，在软件行业中已有相似技术，已经具有完备的理论和许多成熟的应用。但电力软件系统一向以严格的数据结构、过程化的处理方式著称，这样具有高效的计算速率，但却带来灵活性与可重用性的严重不足。为了满足新型电力系统的应用需求，急需一种数据驱动的电力系统建模方法。
多源数据集成与数据支撑方面。数据存储一般具有分布性和异构性的特点，这就为数据同步实现带来了更多的复杂性；另外由于配电网业务的复杂性，在同步数据的过程中需要进行数据过滤和时间同步，数据同步应该具有良好的扩展性才能满足用户多种多样的需求。工业界中数据同步问题的解决方案主要有两种，第一种是专用的数据同步模块，支持系统自身的业务遥辑，支持自身要求的数据格式，支持同一种数据格式系统的扩展；以数据库増量实时同步系统为代表，其特点是只支持数据库的实时增量同步，不支持其他类型的数据同步，通用性有限；其使用亦非常复杂，非专业人士难以快速上手，限制了用户的使用群体，二次开发需要在源码的基础上进行，其应用受限程度大。第二种解决方案是基于流处理框架开发，具有许多优良特征，比如高扩展性、分布式支持等等；但是流处理与数据同步有很大区别，用户在流处理之上开发数据同步需要自己解决断点续传、增量数据获取、分布式等难题；这种方法虽然扩展性更高，但是开发成本也更高。综上所述，传统数据同步方案已经不能满足离线在线一体化配电网仿真系统所有的业务需求。图数据库是基于图论实现的一种新型NoSQL数据库。关系是图数据库中最重要的元素，通过关系能够将节点相互关联起来构建问题领域密切相关的复杂模型。图数据库支持非常灵活和细粒度的数据模型，可以用简单直观的方式对数据应用进行建模和管理，可以更方便地将数据单元小型化、规范化；同时还能实现丰富的关系连接，这样在对数据查询时可以用任何可想象到的方式进行查询操作。基于图数据库实现离线在线一体化配电仿真系统数据同步，一方面可以基于图数据库的节点‐关系 模型，具有优秀的扩展性能，易于实现数据集成；另一方面通过使用图数据库进行数据管理，无论关系的数量或深度如何都能确保零延时的快速搜索和查询。
配电网动态可视化方面。网络可视化工具种类、数量繁多，如Gephi、GraphViz等，这些工具功能丰富、适用性强，并不局限于特定的使用目的。Gephi是一款开源、免费跨平台、基于JVM的复杂网络分析软件，主要用于各种网络和复杂系统，动态和分层图的交互可视化与探测开源工具。Gephi提供了各类代表性图布局方法并允许用户进行布局设置，支持时变网络数据可视化并支持用户实时过滤网络，从过滤结果建立新网络。Gephi使用聚类和分层图的方法处理较大规模的图，通过加速探索编辑大型分层结构图来探究多层图；利用数据属性和内置的聚类算法聚合图网络。GraphViz是由贝尔实验室设计的一个开源的图形绘制工具，支持Windows、Linux和Mac等各种操作系统，使用一个特定的DSL(领域特定语言)——dot作为脚本语言，并使用布局引擎解析此脚本，提供自动布局算法。GraphViz的dot脚本语言非常简单方便，而且提供了大量的自动布局算法和丰富的导出格式供用户选择。虽然这些工具或软件包都有各自的功能特点，但在可扩展性、大规模数据处理、表现方式优化、自主可控等方面仍然存在诸多缺陷。
发明内容
为了解决现有技术中所存在的问题，本申请提出了一种辐射型配电网络自适应拓扑可视化方法，包括：
步骤S1：提取配电网的拓扑数据；
步骤S2：基于所述配电网的拓扑数据确定零级主干，并基于所述零级主干，将所述配电网的拓扑数据的显示视图进行视图划分；
步骤S3：遍历所有下一级主干并进行可视化。
进一步的，所述步骤S1包括：基于设备节点之间的连接关系，对配电网络进行连通性分析，剔除孤岛设备，建立拓扑设备集合。
进一步的，基于所述配电网的拓扑数据确定主干，包括：以电源节点为始点，对所述拓扑设备集合进行宽度优先搜索，使用双向链式树状数据结构进行存储，生成拓扑树，并保存各节点在搜索中的层级；获取搜索层数最多的路径，然后从搜索层数最多的路径中筛选路径节点数量之和最大的路径为所述主干；其中，所述主干包括零级主干。
进一步的，以所述主干的路径为X轴，将所述显示视图划分为面积相等的上下两个区域。
进一步的，所述筛选路径节点数量之和最大的路径为所述主干，包括：筛选路径节点中的特定类型节点数量之和最大的路径为所述主干。
进一步的，所述特定类型节点为用户选择的设备节点。
进一步的，所述对配电网络进行连通性分析，包括：获取设备节点的总数NALL，随机选择X＝A*NALL个初始设备节点，基于设备节点之间的连接关系，从每个初始设备节点出发建立其对应的一个或者多个子拓扑设备集合；将子拓扑设备集合中设备节点最多的子拓扑设备集合作为所述拓扑设备集合；其中：A为预设值。
进一步的，每个子拓扑设备集合中设备节点最多的初始设备节点对应一个子拓扑设备集合。
基于同一发明构思，本申请还提供一种辐射型配电网络自适应拓扑可视化系统，所述系统包括：
数据提取模块，用于提取配电网的拓扑数据；
零级主干分析模块，用于基于所述配电网的拓扑数据确定零级主干，并基于所述零级主干，将所述配电网的拓扑数据的显示视图进行视图划分；
迭代分析模块，用于遍历所有下一级主干并进行可视化。
进一步的，所述数据提取模块，用于：基于设备节点之间的连接关系，对配电网络进行连通性分析，剔除孤岛设备，建立拓扑设备集合。
进一步的，所述零级主干分析模块，用于：以电源节点为始点，对所述拓扑设备集合进行宽度优先搜索，使用双向链式树状数据结构进行存储，生成拓扑树，并保存各节点在搜索中的层级；获取搜索层数最多的路径，然后从搜索层数最多的路径中筛选路径节点数量之和最大的路径为主干。
进一步的，所述零级主干分析模块，还用于：以所述主干的路径为X轴，将所述显示视图划分为面积相等的上下两个区域。
基于同一发明构思，本申请提供一种处理器，所述处理器用于运行程序，其中，所述程序运行时执行上述方法。
基于同一发明构思，本申请提供一种执行设备，包括处理器，所述处理器和存储器耦合，所述存储器存储有程序指令，当所述存储器存储的程序指令被所述处理器执行时实现上述方法。
基于同一发明构思，本申请提供一种计算机可读存储介质，包括程序，当其在计算机上运行时，使得计算机执行上述方法。
本申请实施例还提供一种数据驱动的离线在线一体化配电网仿真系统，所述系统包括数据层、平台层和应用层；其中，所述数据层，包括图形库和属性数据库，存储于Oracle db/Access内，提供基础支撑系统与基础通用数据服务供所述平台层调用；所述平台层包括应用平台运行框架和开发平台建模工具；其中，所述应用平台运行框架至少包括以下组件中的至少之一：运行基础组件、界面展示组件、图标展示组件、图形展示组件、应用集成组件、门户集成组件；所述开发平台建模工具至少包括以下组件中的至少之一：业务建模组件、报表定义组件、权限配置组件、工具集成组件、流程定义组件、基础框架组件；所述应用层至少包括以下模块中的至少之一：数据管理模块、风险扫描模块、故障仿真模块、网架优化模块、系统配置模块、月报分析模块。
进一步的，所述数据管理模块，用于实现数据集成、数据查询、数据质量评估分析和/或数据处理。
进一步的，所述风险扫描模块，用于实现配电网风险批量扫描和薄弱点分析、风险源统计与评级和/或风险预警。
进一步的，所述风险扫描模块，用于：基于台账、拓扑关系、运行数据、风险分析条件和参数评估，进行风险分析，得到设备风险结果、拓扑分析结果和潮流计算结果；其中，所述设备风险结果包括设备老化风险和设备质量风险；所述拓扑分析结果包括单电源用户、环网运行线路和失电用户；所述潮流计算结果包括馈线潮流分布、配变潮流分布和节点潮流分布；基于风险分析结果进行风险定级；其中，风险定级包括保电风险、环网运行风险、供电可靠性风险、失电负荷(MW)、失电量(MWH)、过负荷风险、重载风险、过电压风险、低电压风险和/或容量裕度风险的定级；基于风险定级结果进行风险统计；其中，风险统计包括区域和馈线的失电负荷(MW)、失电量(MWH)、过负荷风险、重载风险、过电压风险、低电压风险、容量裕度风险、保电风险、环网运行风险和/或供电可靠性风险，以及厂站的失电负荷(MW)、失电量(MWH)、过负荷风险、重载风险、过电压风险、低电压风险和/或容量裕度风险，以及台区的失电负荷(MW)、失电量(MWH)、过负荷风险、重载风险、过电压风险、低电压风险、供电可靠性风险和/或保电风险；基于风险统计结果进行风险预警、风险措施分析和预控方案制定。
进一步的，所述故障仿真模块，用于基于当前配电自动化高级应用中故障分析功能的扩展，实现故障预测、多重故障模拟和抢修决策辅助决策。
进一步的，所述故障仿真模块，用于：基于管理的数据进行基本参数设置；其中，管理的数据包括网架信息管理、台账数据管理、运行数据管理、设备水平管理、气象信息管理和/或检修计划管理；所述基本参数设置包括扫描范围设置、检修计划设置、气象信息设置、 负荷参数设置和/或设备状态设置；基于基本参数设置进行故障预测；其中，所述故障预测包括区域的预安排停电故障等级、10kv配电设施故障等级、低压设施故障等级和/或发电设施故障等级，以及馈线的预安排停电故障等级、10kv配电设施故障等级、10kv及以上输变电设施故障等级和发电设施故障等级，以及厂站的10kv配电设施故障等级、10kv及以上输变电设施故障等级、低压设施故障等级和发电设施故障等级；基于故障预测结果进行结果分析；其中，所述结果分析包括故障等级分析、故障位置分析和转供分析；
基于所述结果分析进行方案制定；其中，所述方案制定包括抢修驻点设置、抢修方案设置、转供方案制定和优化方案制定。
进一步的，所述网架优化模块，以负荷损失最小为目标，以电网运行安全和电压质量为约束条件，进行以下操作评估：冲击电流分析、保护定值校核、供电能力分析、电压质量评估、合环分析、解环分析、转供分析和/或无功优化。
进一步的，所述系统配置模块，用于完成风险自动分析计算设置和风险分析评估参数设置。
进一步的，所述月报分析模块，用于完成风险月报、故障月报以及网架月报的查询、生成和编辑。
本申请实施例还提供一种数据驱动的离线在线一体化配电网仿真模型建立方法，所述方法包括：对电力系统的数据进行建模，包括：所述电力系统的数据模型用XML文件表示；电力系统XML文件包括前置部分和主体部分；所述前置部分为表示依赖关系的元素每一个依赖元素指向一个XML文件，使用时需首先载入；所述主体部分为表示设备的元素；将所述电力系统XML文件输入转化为根QHash数据结构以备使用，该QHash由嵌套的QHash组成；其中，前置文件的使用方法是：根QHash第一层Key为类型，值为QHash，第二层Key为ID，值为QHash形式的使用对象；基于类型和ID能够使用其数据；引用设备的使用方法是：根QHash第一层Key包含设备ID，根据引用ID即可找到并使用；对可视化界面交互进行建模，其中，可视化界面交互由事件触发，对象的模型元素定义其显示效果，同一对象具有多个可用的网络模型和属性模型；所述网络模型用来交互式地显示电力系统网络连接关系；所述属性模型用来显示和编辑设备属性；对算法使用和数据输出进行建模，其中，以计算对象为始点，以包含设备为搜索方向，进行迭代搜索，得到所有相关设备；对于每一个设备，根据其算法模型，建立算法模型类并关联相关数据；类内部包含对数据进行处理和计算的各种方法；以计算对象模型为参数，构造算法类，通过设备算法模型的方法调用获取输入，计算过程中与设备算法模型进行动态交互，完成计算后，将相关计算结果返回到对应的设备算法模型中；输出模型定义为算法模型类与算法类中需要输出数据的名称，特别数据需要提供数据类中有相应的方法支撑；根据已有数据和输出模型生成输出数据文件。
进一步的，所述可视化界面上的修改在确定时更新到根QHash中。
进一步的，连接根QHash和所述网络模型之间的是解释器；对于给定设备，解释器读取其直接包含设备，并根据包含设备的图元、位置和连接关系，将其网络绘制到界面上。
进一步的，所述设备的图元使用形状集合及其相对位置、线条和填充，缺省形状为直线，缺省线条为黑色实线，缺省填充为无；和/或，所述设备的位置由节点数据确定，任意设备有且仅有两个节点，一个节点上有多个连接端子；和/或，所述设备的连接关系在绘图中仅与节点有关，对于输入文件中没有节点元素的情况，根据设备连接数据生成节点，一个连接点生成一个节点，并为设备添加两个节点元素。
进一步的，所有节点数据存入一个独立XML中，以供前置部分载入。
进一步的，连接根QHash和所述属性模型之间的是解释器；属性模型定义以下信息：设备需显示的属性、显示的类型、编辑器的种类；对于给定设备，解释器生成网格状布局的属性界面，按照属性模型按列增加的方式依次添加标签与编辑器对，并绑定匹配数据，其中 标签显示属性名称，编辑器读写属性值。
进一步的，电力系统XML文件中的每一个独立元素均包含类型与ID属性，电力系统XML文件的主体部分通过类型与ID来使用依赖元素；依赖文件可以用来表示特定型号设备及其参数、关联设备和枚举类型等。
进一步的，电力系统XML文件的主体部分为表示设备的元素，设备的数据包括名称、ID、类型、包含设备、所属设备、设备参数、图形信息和模型，每一项数据均用一个元素及其嵌套元素表示。
进一步的，所述设备为实体设备或抽象设备集合。
进一步的，对于基本类型数据，使用一个基本元素表示，基本元素的属性包括名称、值、类型和编辑器，名称为必有属性，值的缺省值0，类型的缺省种类为字符串，编辑器的缺省对象为文本框；对于扩展类型数据，使用多个嵌套的元素表示，其最终叶元素为基本元素；对于引用类型数据，包含以下元素：名称、类型、引用类型、引用ID和编辑器，缺省编辑器为按钮。
本申请实施例还提供一种离线在线一体化配电网仿真系统数据同步方法，包括：建立以运行数据为中心的配电网仿真数学模型，实现一二次设备关联关系的数据描述；基于仿真数学模型与数据源数据之间的对应关系，对多源数据进行估计优化处理；将配电网仿真数学模型转化为图数据库的节点-关系模型，把在线数据存储到图数据库中；以图数据库为支撑平台，基于快速搜索实现实时数据同步，使离线仿真功能直接为配电网实时运行提供在线辅助决策服务。
进一步的，所述配电网仿真数学模型的数据成员包括量测、连接终端和设备；其中，所述量测代表二次设备，所述连接终端代表一次设备之间的连接点，所述设备代表一次设备。
进一步的，量测的数据成员至少包括以下至少之一：标识、相、国际单位代码、单位乘子、单位符号、连接终端、计算方法层次、保护动作调节、量测类型；连接终端的数据成员至少包括以下至少之一：标识、相、设备、连接节点、调节控制、连接、序列号；设备的数据成员至少包括以下至少之一：标识、保护动作调节、气象站、配置事件、控制、量测、资源类型、聚合、服务状态、用于网络分析、正常服务状态。
进一步的，图数据库的节点-关系模型中，量测、连接终端、设备、标识、相、国际单位代码、单位乘子、单位符号、计算方法层次、保护动作调节、连接节点、调节控制、气象站、配置事件、控制和/或资源类型建模为节点；其中，连接终端、标识、相、国际单位代码、单位乘子、单位符号、计算方法层次和/或保护动作调节节点分别与量测节点关联，设备、标识、相、连接节点和/或调节控制节点分别与连接终端节点关联，量测、标识、保护动作调节、气象站、配置事件、控制和/或资源类型节点分别与设备节点关联；量测类型为量测节点的数据成员，连接、序列号为连接终端的数据成员，聚合、服务状态、用于网络分析和/或正常服务状态为设备的数据成员。
进一步的，在离线在线一体化配电仿真系统中，使用设备-量测-连接终端结构或设备-连接终端-量测结构使用图数据库中数据；其中，在设备-量测-连接终端结构中，以设备为起点，基于关联关系搜索量测，基于量测搜索连接终端，从而获取全套配电网仿真的运行数据；在设备-连接终端-量测结构中，以设备为起点，基于关联关系搜索连接终端，基于连接终端搜索量测，从而获取全套配电网仿真的运行数据。
与现有技术相比，本申请的有益效果为：
1、考虑新型配电网网络结构特点，使用多样化的布局方式改良网络整体拓扑结构，通过拓扑布局决定在有限显示空间内如何确定和分配映射几何图形的节点和边的具体位置；充分利用了辐射型配电网结构特征，一次生成所有布局和位置，后续拓扑过程中不需要调整，拓扑可视化速度快。
2、避免拓扑结构图的显示重叠，减少拓扑结构图中边之间的交叉，更加美观大方地展示配电网络拓扑结构。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本申请。
附图说明
图1为本申请实施例提供的离线在线一体化配电网仿真系统架构图；
图2为本申请实施例提供的离线在线一体化配电网仿真系统功能实现流程图；
图3为本申请实施例提供的离线在线一体化配电网仿真系统数据集成结构图；
图4为本申请实施例提供的风险扫描模块的实现流程示意图；
图5为本申请实施例提供的故障仿真模块的实现流程示意图；
图6为本申请实施例提供的n＝1配电对等网络的基本结构示意图；
图7为本申请实施例提供的配电网仿真一二次设备运行数据数学模型示意图；
图8为本申请实施例提供的配电网仿真图数据库的节点-关系模型示意图；
图9为本申请实施例提供的配电网仿真数据快速搜索示意图；
图10为本申请的辐射型配电网络自适应拓扑可视化流程图；
图11为本申请的节点子树分布示意图。
具体实施方式
为了更好地理解本申请，下面结合说明书附图和实例对本申请的内容做进一步的说明。
本申请面向新型电力系统，提出一种集成数据质量评估、风险扫描分析、故障预测仿真和网架优化等功能的配电网离线在线一体化仿真辅助决策系统。通过对城市区域配电网实现全局风险扫描、故障模拟和推演和运行方式优化调整，实现对配电网可靠性薄弱点、高损耗点、安全隐患点精准定位和分级；对配电网疲劳老化、质量隐患设备快速梳理，全面把握运维检修重点线路和设备，为配电网技改方案提供指导依据；实现有针对性的抢修力量部署，减少供电恢复时间，最小化或避免用户供电中断；指导大规模复杂配电网网架向高可靠性、经济化运行方向演化重构，满足客户用电需求。
针对配电网及其设备柔性建模困难的问题，提出一种数据驱动的电力系统建模方法：以电力系统建模工具为依托，建立电力系统的数据模型，提出电力数据的输入方法，提出电力数据的使用方法包括界面交互方法、算法使用方法与数据输出方法。该电力系统建模工具灵活性与可重用性高，不仅能够为离线在线一体化配电仿真系统高级应用提供数据支撑，还可以为离线在线一体化配电仿真系统提供单元级模块支撑。
针对离线在线一体化配电仿真系统数据同步中存在的难题，建立以运行数据为中心的配电网仿真数学模型，实现一二次设备关联关系的数据描述；基于仿真数学模型与数据源数据之间的对应关系，对多源数据进行估计优化处理；将配电网仿真数学模型转化为图数据库的节点-关系模型，把在线数据存储到图数据库中；最后以图数据库为支撑平台，基于快速搜索实现实时数据同步，使离线仿真功能直接为配电网实时运行提供在线辅助决策服务。
面向新型配电网动态可视化的迫切需求，使用多样化的布局方式改良网络整体拓扑结构，通过拓扑布局决定在有限显示空间内如何确定和分配映射几何图形的节点和边的具体位置，提出一种辐射型配电网络动态拓扑可视化方法，避免拓扑结构图的显示重叠，减少拓扑结构图中边之间的交叉，更加美观大方地展示配电网络拓扑结构。
1、图1为本申请实施例提供的数据驱动的离线在线一体化配电网仿真系统架构图，如图1所示，数据驱动的离线在线一体化配电网仿真系统包括：数据层、平台层和应用层；其中，
数据层包括图形库和属性数据库，存储于Oracle db/Access内，提供基础支撑系统与基础通用数据服务，数据持久化、数据库访问能力，供平台层调用；
平台层包括应用平台运行框架和开发平台建模工具，包含工作流、事物处理和安全体系 等平台组件；其中，应用平台运行框架至少包括运行基础、界面展示、图标展示、图形展示、应用集成和/或门户集成等组件；开发平台建模工具至少包括业务建模、报表定义、权限配置、工具集成、流程定义和/或基础框架等组件。
应用层至少分为数据管理、风险扫描、故障仿真、网架优化、系统配置和/或月报分析等模块，提供图表、报表报告以及拓扑地图等展示，实现公司、市、县等多级单位部门用户交互使用。
图2为本申请实施例提供的离线在线一体化配电网仿真系统功能实现流程图，如图2所示：
(1)数据管理模块实现数据集成、数据查询、数据质量评估分析和/或数据处理；其中，数据包括档案数据、运行数据、拓扑数据、气象数据和/或城市地图等等。
图3为本申请实施例提供的离线在线一体化配电网仿真系统数据集成结构示意图。离线在线一体化配电网仿真系统面向PMS数据、GIS平台、电能质量监测系统/用电信息采集系统、调度自动化系统、OMS/SCADA系统、气象系统和营销业务应用系统，通过数据接口提取电网结构数据、设备档案数据和运行数据，并通过数据匹配服务转化为结构化的仿真数据。
(2)风险扫描模块实现配电网风险批量扫描和薄弱点分析、风险源统计与评级和/或风险预警；其中，图4为本申请实施例提供的风险扫描模块的实现流程示意图，如图4所示，基于台账、拓扑关系、运行数据、风险分析条件和参数评估，进行风险分析，得到设备风险结果、拓扑分析结果和潮流计算结果；其中，设备风险结果包括设备老化风险和设备质量风险；拓扑分析结果包括单电源用户、环网运行线路和失电用户；潮流计算结果包括馈线潮流分布、配变潮流分布和节点潮流分布；台账包括馈线、配电变压器、并联电容器、断路器、分段开关和联络开关的台账；拓扑关系包括杆塔、电缆接头和电缆终端的拓扑关系；运行数据包括馈线出口电压、公变负荷、专变负荷和开关状态等；风险分析条件包括时间范围、检修计划、社会信息和气象信息等；评估参数包括元件失效概率、地域信息、设备缺陷、设备年限、气象因素、时间因素、潮流影响和风险定级；
基于风险分析进行风险定级；其中，风险定级包括保电风险、环网运行风险、供电可靠性风险、失电负荷(MW)、失电量(MWH)、过负荷风险、重载风险、过电压风险、低电压风险和/或容量裕度风险的定级；
基于风险定级进行风险统计；其中，风险统计包括区域和馈线的失电负荷(MW)、失电量(MWH)、过负荷风险、重载风险、过电压风险、低电压风险、容量裕度风险、保电风险、环网运行风险和/或供电可靠性风险，以及厂站的失电负荷(MW)、失电量(MWH)、过负荷风险、重载风险、过电压风险、低电压风险和/或容量裕度风险，以及台区的失电负荷(MW)、失电量(MWH)、过负荷风险、重载风险、过电压风险、低电压风险、供电可靠性风险和/或保电风险；
基于风险统计结果进行风险预警、风险措施分析和预控方案制定。
(3)故障仿真模块基于当前配电自动化高级应用中故障分析功能的扩展，实现故障预测、多重故障模拟和抢修决策辅助决策等功能；其中，图5为本申请实施例提供的故障仿真模块的实现流程示意图，如图5所示，基于管理的数据进行基本参数设置；其中，管理的数据包括网架信息管理、台账数据管理、运行数据管理、设备水平管理、气象信息管理和/或检修计划管理；即，基于数据管理结果进行基本参数设置，所述管理的数据包括网架信息、台账数据、运行数据、设备水平、气象信息和/或检修计划；所述基本参数设置包括扫描范围设置、检修计划设置、气象信息设置、负荷参数设置和/或设备状态设置；基于基本参数设置进行故障预测，包括区域的预安排停电故障等级、10kv配电设施故障等级、低压设施故障等级和/或发电设施故障等级，以及馈线的预安排停电故障等级、10kv配电设施故障等 级、10kv及以上输变电设施故障等级和发电设施故障等级，以及厂站的10kv配电设施故障等级、10kv及以上输变电设施故障等级、低压设施故障等级和发电设施故障等级；之后，基于故障预测结果进行结果分析，包括故障等级分析、故障位置分析和转供分析；基于结果分析进行方案制定；其中，方案制定包括抢修驻点设置、抢修方案设置、转供方案和优化方案。
(4)网架优化模块主要以负荷损失最小为目标，电网运行安全、电压质量为约束条件，开展冲击电流分析、保护定值校核、供电能力分析、电压质量评估、合环分析、解环分析、转供分析和/或无功优化等操作评估；
(5)系统配置模块主要完成风险自动分析计算设置和风险分析评估参数设置；
(6)月报分析模块主要完成风险月报、故障月报以及网架月报的查询、生成和编辑。
2、建立数据驱动的新型配电网仿真模型，具体包括以下内容：
(1)电力系统的数据建模。
1)电力系统数据模型用XML文件表示；电力系统XML文件包括前置部分和主体部分。
2)电力系统XML文件的前置部分为表示依赖关系的元素，每一个依赖元素指向一个XML文件，使用时需首先载入。依赖文件(即依赖元素指向的电力系统XML文件)中的每一个独立元素均包含类型与ID属性，电力系统主体部分通过类型与ID来使用依赖元素。依赖文件可以用来表示特定型号设备及其参数、关联设备和枚举类型等。
3)电力系统XML文件的主体部分为表示设备的元素。设备包含名称、ID、类型、包含设备、所属设备、设备参数、图形信息和模型等数据，每一项数据均用一个元素及其嵌套元素表示，具体如下：
①对于字符串、整数、实数等基本类型数据，使用一个基本元素表示，基本元素的属性包括名称、值、类型和编辑器等，名称为必有属性，值的缺省值0，类型的缺省种类为字符串，编辑器的缺省对象为文本框。
②对于复数、向量、常规矩阵、稀疏矩阵和枚举等扩展类型数据，使用多个嵌套的元素表示，其最终叶元素为基本元素；如复数包括实部和虚部；向量包括向量维数、类型及各向量元素值；常规矩阵包括行数、列数、类型及矩阵元素值；稀疏矩阵包括行数、列数、类型、单元数量及矩阵单元，其中矩阵单元包含行号、列号和值三个元素；枚举包括成员数量、各成员值及编辑器，其中缺省编辑器为组合框。
③对于特定型号设备及其参数、关联设备和模型等引用类型数据，其特征为包含名称、类型、引用类型、引用ID和编辑器等元素，缺省编辑器为按钮。通过引用类型与引用ID能够找到引用对象，注意该对象并不在该设备内进行定义。
设备既可以表示实体设备，如电源、开关、变压器和负荷等；也可以表示抽象设备集合，如网络、馈线、厂站和区域等。
(2)电力数据输入方法。使用QHash的优点在于避免了根据关联数据进行搜索获取数据，易于使用且效率大幅提升。
1)将电力系统XML文件输入转化为根QHash数据结构以备使用，该QHash由嵌套的QHash组成；其Key为字符串；其值为QHash或字符串；QHash为分支，字符串为叶，在程序中均可使用var类型表示。将XML转化为QHash，每一个元素生成一个QHash，属性对应一组键值对，是末端叶节点。
2)前置文件的使用方法是：根QHash第一层Key为类型，值为QHash，第二层Key为ID，值为QHash形式的使用对象；基于类型和ID能够使用其数据。
3)引用设备的使用方法是：根QHash第一层Key包含设备ID，根据引用ID即可找到并使用。
(3)可视化界面交互方法，即对可视化界面交互进行建模，包括：
1)可视化界面交互由事件触发，对象的模型元素定义其显示效果。可视化界面交互类型有网络与属性两大类别，同一对象可以有多个可用的网络模型和属性模型，但同一时间仅能使用一种模型。界面上的修改会在确定时更新到根QHash中。连接根QHash和模型之间的是解释器。
2)网络模型用来交互式地显示电力系统网络连接关系。对于给定设备，解释器读取其直接包含设备，并根据包含设备的图元、位置和连接关系，将其网络绘制到界面上。图元需要使用形状集合及其相对位置、线条和填充等相关数据，缺省形状为直线，缺省线条为黑色实线，缺省填充为无。位置由节点数据确定，任意设备都有且仅有两个节点，一个节点上可以有多个连接端子。连接关系在绘图中仅与节点有关，对于输入文件中没有节点元素的情况，需要根据设备连接数据生成节点，一个连接点生成一个节点，并为设备添加两个节点元素；另外，需要将所有节点数据存入一个独立XML中，以供前置部分载入。
3)属性模型用来显示和编辑设备属性。属性模型定义了设备需要显示的属性，显示的类型，编辑器的种类等信息。对于给定设备，解释器生成网格状布局的属性界面，按照属性模型按列增加的方式依次添加标签与编辑器对，并绑定匹配数据，其中标签显示属性名称，编辑器读写属性值。
(4)算法使用方法与数据输出方法。
1)以计算对象为始点，以包含设备为搜索方向，进行迭代搜索，得到所有相关设备。对于每一个设备，根据其算法模型，建立算法模型类并关联相关数据；类内部包含对数据进行处理和计算的各种方法。
2)以计算对象模型为参数，构造算法类，通过设备算法模型的方法调用获取输入，计算过程中与设备算法模型进行动态交互，完成计算后，将相关计算结果返回到对应的设备算法模型中。
3)输出模型定义为算法模型类与算法类中需要输出数据的名称，特别数据需要提供数据类中有相应的方法支撑；根据已有数据和输出模型生成输出数据文件。
3、离线在线一体化配电网仿真系统数据同步方法，图6为本申请实施例提供的n＝1配电对等网络的基本结构示意图，如图6所示，具体包括以下步骤：
(1)建立以运行数据为中心的配电网仿真数学模型，实现一二次设备关联关系的数据描述，有数据表明一二次设备关联关系，则不再需要手动对点表，提升离线在线一体化配电仿真建模效率。
配电网仿真数学模型主要数据成员主要包括量测、连接终端和设备三大部分。量测代表二次设备，可代表任何测量的、计算的或未测量的、未计算的量，任何设备都可能包含测量值，例如，变电站可能包含温度测量值和开门指示，变压器可能包含油温和油箱压力测量值，断路器可能包含开关状态测量值。连接终端代表一次设备之间的连接点，如与导电设备连接的交流电气连接点，一般在称为连接节点的物理连接点处连接。设备代表一次设备，如通过传导方式连接的系统的交流终端或部件。
图7为本申请实施例提供的配电网仿真一二次设备运行数据数学模型示意图，如图7所示：
量测的数据成员至少包括以下至少之一：标识、相、国际单位代码、单位乘子、单位符号、连接终端、计算方法层次、保护动作调节、量测类型。标识为所有需要标识和命名属性的类提供公共标识。相即量测相，如果属性缺失，则默认为ABC三相。国际单位代码即国际单位组织维护的清单中单位和乘数的字符串值，如“国际贸易中使用的计量单位代码”所述。单位乘子为测量数量的单位乘数。单位符号为量数量的计量单位。连接终端即该量测关联的位置，一个量测最多关联一个连接终端。计算方法层次表示适用于该模拟量的计算方法层次结构。保护动作调节，当保护动作调整激活时，量测设备的数据发生变化。量测类型指 定测量值是否表示室内温度、室外温度、母线电压以及线流量等；当设置为“特化”时，度量类型由从量测继承的专用类更详细地定义。
连接终端的数据成员至少包括以下至少之一：标识、相、设备、连接节点、调节控制、连接、序列号。标识和相的含义同上不再赘述。设备即该连接终端所属的一次设备，一个连接终端有且只有一个对应的设备，设备通过连接终端连接其它设备。连接节点即配电网络拓扑节点，是终端以零阻抗连接的连接节点。调节控制是指控制这个终端的控制器。连接为True表示终端已连接到相关拓扑节点，false表示未连接。序列号用来表示多端子导电设备的端子连接位置；序列号从1开始，附加端子按递增顺序排列。
设备的数据成员至少包括以下至少之一：标识、保护动作调节、气象站、配置事件、控制、量测、资源类型、聚合、服务状态、用于网络分析、正常服务状态。标识和保护动作调节的含义同上不再赘述。气象站存储该设备地域上属于的气象站。配置事件用于报告实体或其配置的创建、更改或删除的详细信息。控制输出用于实际控制调节装置，例如同步电机或电容器组断路器致动器的磁化。量测即为该设备上安装的量测设备，一台设备上可以安装多台量测设备。资源类型，即该配电网自定义分类的设备类型。聚合用于标识该设备的单个实例是否已作为一个集合一起建模的多个设备；例如，电力变压器或同步机并联运行，建模为单个聚合电力变压器或聚合同步机。服务状态如果为真，则设备正在使用中。用于网络分析默认值为真，表示设备可以参与网络分析。正常服务状态如果为真，则设备正常工作。
(2)基于仿真数学模型与数据源数据之间的对应关系，对多源数据进行估计优化处理。若数据源与仿真数学模型数据之间为n对1的对应关系，则在离线在线一体化配电仿真系统中使用估计值，多源数据的估计优化处理模型如式(1)：
Figure PCTCN2022098701-appb-000001
式中，f为目标函数；x即为估计值；z i为第i个数据源中的数据值；r i为表示第i个数据源中数据值的误差方差，缺省值为1。可以用多种优化求解方法对公式(1)模型进行计算，计算时x的初始值如式(2)：
Figure PCTCN2022098701-appb-000002
求解出估计值x后，使用残差值对x进行校验；残差值的计算公式如式(3)：
Figure PCTCN2022098701-appb-000003
一般情况下残差期望值为1。若残差值c i大于设定阈值，则第i个数据源中的数据值z i为坏数据，此时应从数据源中去掉z i，重新求解公式(1)模型。
(3)将配电网仿真数学模型转化为图数据库的节点-关系模型，把在线数据存储到图数据库中。基于图数据库可快速搜索节点关系的特性，针对以运行数据为中心的配电网仿真数学模型，将模型中的对象转化为节点，数据成员当作一种关联关系，生成的图数据库的节点-关系模型如附图8所示。
图数据库的节点-关系模型中，量测、连接终端、设备、标识、相、国际单位代码、单位乘子、单位符号、计算方法层次、保护动作调节、连接节点、调节控制、气象站、配置事件、控制和/或资源类型建模为节点。其中，连接终端、标识、相、国际单位代码、单位乘 子、单位符号、计算方法层次和/或保护动作调节节点分别与量测节点关联，设备、标识、相、连接节点和/或调节控制节点分别与连接终端节点关联，量测、标识、保护动作调节、气象站、配置事件、控制和/或资源类型节点分别与设备节点关联。量测类型为量测节点的数据成员，连接、序列号为连接终端的数据成员，聚合、服务状态、用于网络分析和/或正常服务状态为设备的数据成员。
数据导入时，量测、连接终端、设备、标识、相、国际单位代码、单位乘子、单位符号、计算方法层次、保护动作调节、连接节点、调节控制、气象站、配置事件、控制、资源类型16个节点的每一组实例关联关系，在关系数据库中两个表中均表示为标识符，相同的标识符意味这相应的两条数据相互关联；在数据导入时进行一次匹配查询，之后使用图数据的关联关系进行表示，则后续使用中不再需要匹配查询，使用效率大幅提升。
(4)最后以图数据库为支撑平台，基于快速搜索实现实时数据同步，使离线仿真功能直接为配电网实时运行提供在线辅助决策服务。在离线在线一体化配电仿真系统中，量测一般不作为单独使用的对象；可以使用两种方式使用图数据库中数据，一是设备-量测-连接终端结构，另一是设备-连接终端-量测结构，如附图9所示。
在设备-量测-连接终端结构中，以设备为起点，基于关联关系搜索量测，然后基于量测搜索连接终端，即可获取全套配电网仿真的运行数据。该结构中可以将量测看作设备的子节点，或者配电网设备树的叶节点，易于使用者理解；值得一提的是，最后建议对连接终端关联的设备进行校核，进而保证数据一致性。
在设备-连接终端-量测结构中，以设备为起点，基于关联关系搜索连接终端，然后基于连接终端搜索量测，即可获取全套配电网仿真的运行数据。如果在关系数据库中使用该结构，则需要使用设备对连接终端进行搜索，然后分别使用终端对量测进行多次搜索，搜索耗时较长；但通过图数据库关联关系，只需直接通过关系终点即可找到作为终端或量测的起点，十分快捷。
4、辐射型配电网络动态拓扑可视化方法，具体包含以下内容：
本申请提供一种辐射型配电网络自适应拓扑可视化方法，图10为本申请实施例提供的辐射型配电网络自适应拓扑可视化方法的实现流程示意图，如图10所示，所述方法包含以下步骤：
步骤S1：提取配电网的拓扑数据。
在一些实施例中，所述提取配电网的拓扑数据，包括：基于设备节点之间的连接关系，对配电网络进行连通性分析，剔除孤岛设备，建立拓扑设备集合。需要说明的是，拓扑数据是指表示连接关系的数据(即连接节点，如果不同设备具有相同的连接节点，则该设备之间存在连接关系)；每个设备都有拓扑数据，同时还包括其它非拓扑数据。拓扑数据是通过设备来管理的，拓扑设备集合是剔除孤岛设备的设备集合；拓扑设备集合中每个设备都持有自身的拓扑数据。
进一步地，在一些实施例中，所述对配电网络进行连通性分析，包括：获取设备节点的总数NALL，随机选择X＝A*NALL个初始设备节点，基于设备节点之间的连接关系，从每个初始设备节点出发建立其对应的一个或者多个子拓扑设备集合；将子拓扑设备集合中设备节点最多的子拓扑设备集合作为所述拓扑设备集合。
优选的，在一些实施例中，A是随机系数，例如A＝5％。
优选的，在一些实施例中，通过设置比例阈值的方式选择最大的子拓扑设备集合作为所述拓扑设备集合；其中所述最大的子拓扑设备集合是指元素个数最多的集合。
示例性地，所述比例阈值为95％比例。
更进一步地，在一些实施例中，所述从每个初始设备节点出发建立其对应的一个或者多个子拓扑设备集合，具体包括如下步骤SUB11至步骤SUB15：
步骤SUB11：选择一未分析的初始设备节点；
步骤SUB12：标记所述一未分析的初始设备节点为已处理，从该初始设备节点出发，确定和该初始设备节点存在直接连接关系的设备节点，获取并存储所述存在直接连接关系的设备节点的属性信息；
步骤SUB13：将所述存在直接连接关系的设备节点放入和所述一未分析的初始设备节点关联的子拓扑设备集合中；
步骤SUB14；从所述子拓扑设备集合中选择一个未处理设备节点，标记所述未处理设备节点为已处理，从标记的该设备节点出发，确定和所述标记的该设备节点存在直接连接关系的设备节点，获取并存储与所述标记的该设备节点存在直接连接关系的设备节点的属性信息，并返回步骤SUB13继续处理(也就是将与所述标记的该设备节点存在直接连接关系的设备节点放入和所述初设设备节点关联的子拓扑设备集合中，并继续步骤SUB14)，直到子拓扑设备集合中的设备节点均为已处理为止，进入步骤SUB15；
步骤SUB15：输出所述初始设备节点及其对应的子拓扑设备集合；此时从该初始设备节点出发的连通关系已经分析完毕，继续下一未分析的初始设备节点的处理。若所有初始设备节点均分析完毕，则结束，否则，返回步骤SUB11，继续下一未分析的初始设备节点的分析。
优选的：在提取配电网的拓扑数据时，不考虑设备工作状态，例如：不考虑开关分合等。
步骤S2：基于所述配电网的拓扑数据确定零级主干，并基于所述零级主干，将所述配电网的拓扑数据的显示视图进行视图划分。
在一些实施例中，可以这样实现步骤S2，包括：以电源节点为始点，对所述拓扑设备集合进行宽度优先搜索，使用双向链式树状数据结构进行存储，生成拓扑树，并保存各节点在搜索中的层级；获取搜索层数最多的路径，然后从搜索层数最多的路径中筛选路径节点数量之和最大的路径为主干。以主干路径为X轴，将所述配电网的拓扑数据的显示视图划分为面积相等的上下两个区域。所述主干包括零级主干。
可替换的：所述筛选路径节点数量之和最大的路径为主干，包括：筛选路径节点中的特定类型节点数量之和最大的路径为主干。
优选的：所述特定类型节点为用户选择的设备节点。
在一些实施例中，所有的n级主干的确定过程是相同的。
步骤S3：遍历所有下一级主干并进行可视化；
在一些实施例中，所述遍历所有下一级主干并进行可视化，具体包括如下步骤SUB31至步骤步骤SUB36：
步骤SUB31：确定下一级主干；以及提取子树，包括：遍历主干的节点，对于主干的节点n，基于节点n的拓扑树，忽略主干支路后获取主干节点n的所有连接支路，对每条支路对应的拓扑树进行剪枝分别生成一级子树；以此类推，直到将节点n的m个一级子树布局完毕；主干上下侧形成两组一级子树g 1和g 2；
首次确定下一级主干时，遍历对象是零级主干上的节点。确定下一级主干的过程同零级主干。
可以理解地，节点n是该级主干的一个节点，拓扑树是该级主干的分支，节点n是该级主干和分支拓扑树的连接点，一个节点上可以有多个分支。
步骤SUB32：遍历所述下一级主干；具体为：设节点n的一级子树数量为m；然后分别获取各一级子树的最大长度{l 1,l 2,l 3,…,l m}，按照长度从大到小对m个一级子树进行排序并依次编号；m个一级子树布局的基本原则为长度长的两个一级子树组成上下对，且其位置趋向于中部；
具体排列布局如附图11所示：
对于1号一级子树：若节点n-1的最大长度一级子树布局在下，则节点n的最大长度一级子树在上；若节点n-1的一级子树数量为0，则参考节点为n-2，以此类推；若没有可参考的节点，节点n的最大长度一级子树位置可在上下随意选择。
2号一级子树在1号一级子树的对侧相同位置。
3号一级子树在2号一级子树的同侧，且距离中央位置(即2号一级子树)最近；若有两个位置到中间位置距离相等，则参考节点n在主干中的位置；若节点n离节点1端近，则3号一级子树在节点n-1方向(如附图11为左侧)，反之则在相反方向，相等可随机选择。
4号一级子树在3号一级子树的对侧相同位置。
5号一级子树在4号一级子树的同侧，且距离中央位置(即1号一级子树)最近。
6号一级子树在5号一级子树的对侧相同位置。
7号一级子树在6号一级子树的同侧，且距离中央位置(即2号一级子树)最近，参考节点n位置7号一级子树在左侧。
如附图11所示，主干上下侧形成两组一级子树g 1和g 2；有g 1＝{…，4，1，5，…}，g 2＝{…，7，3，2，6，…}。
综上所述，则节点n的X轴宽度为所有一级子树对的最大长度之和，即
Figure PCTCN2022098701-appb-000004
当i值大于m时，l i＝0；各个一级子树的位置与其Σ项max(l 2i-1,l 2i)一一对应。以附图11绘出子树为例，则X轴宽度等于max(l 1,l 2)+max(l 3,l 4)+max(l 5,l 6)+l 7。
步骤SUB33：遍历该下一级节点；生成节点的下一级子树并计算其长度；基于所述长度对所述节点的下一级子树进行布局；确定所述节点的绘图范围，并为所述节点的下一级子树分配绘图范围；
在一些实施例中，所述遍历该下一级节点，包括：遍历所有一级子树t，遍历一级子树t的所有主干节点，对于其节点o，基于节点o的拓扑树，忽略主干支路后获取节点o的所有连接支路，对每条支路对应的拓扑树进行剪枝，分别生成二级子树，设节点o二级子树数量为p；然后分别获取各二级子树的最大长度{r 1,r  2,r  3,…,r p}，按照长度从大到小对p个二级子树进行排序并依次编号。
优选的，p个二级子树布局的基本原则为长度长的两个二级子树组成左右对，且其位置趋向于中部；其具体过程与步骤SUB32相似，仅需将图11中以及子树对的左右布局修改为针对二级子树对上下布局，在此不再赘述。直到将节点o的p个二级子树布局完毕，一级主干左右侧形成两组一级子树h 1和h 2。
则节点o的Y轴高度为
Figure PCTCN2022098701-appb-000005
当j值大于p时，r j＝0；各个二级子树的位置与其Σ项max(r 2j-1,r 2j)一一对应。
步骤SUB34：确定是否所有下一级节点均遍历完毕，如果是，进入下一步骤SUB35；否则，返回步骤SUB33；
步骤SUB35：进一步确定是否完成主干节点的遍历，如果是，进入下一步骤SUB36；否则，返回步骤SUB32；若u为奇数其过程同步骤SUB33；其中，u表示树或子树的级数，如0，1，2，······。
步骤SUB36：进一步确定是否存在下一级树，如果是，则整理所有下一级主干，并返回步骤SUB32；否则，结束整个流程，此时，针对所有u级子树v，面向u级子树v的所有主干节点；为其所有主干节点的下一级子树分配布局位置，并为该节点确定可视化范围；若 u为偶数其过程同步骤SUB32；辐射型配电网络自适应拓扑可视化结束。
基于同一发明构思，本申请提供一种处理器，所述处理器用于运行程序，其中，所述程序运行时执行上述方法。
基于同一发明构思，本申请提供一种执行设备，包括处理器，所述处理器和存储器耦合，所述存储器存储有程序指令，当所述存储器存储的程序指令被所述处理器执行时实现上述方法。
基于同一发明构思，本申请提供一种计算机可读存储介质，包括程序，当其在计算机上运行时，使得计算机执行上述方法。
基于同一发明构思，本申请还提供一种辐射型配电网络自适应拓扑可视化系统，所述系统包括：
数据提取模块，用于提取配电网的拓扑数据；在一些实施例中，数据提取模块，用于：基于设备节点之间的连接关系，对配电网络进行连通性分析，剔除孤岛设备，建立拓扑设备集合；
零级主干分析模块，用于基于所述配电网的拓扑数据确定零级主干，并基于所述零级主干，将所述配电网的拓扑数据的显示视图进行视图划分；在一些实施例中，零级主干分析模块，用于：以电源节点为始点，对所述拓扑设备集合进行宽度优先搜索，使用双向链式树状数据结构进行存储，生成拓扑树，并保存各节点在搜索中的层级；获取搜索层数最多的路径，然后从搜索层数最多的路径中筛选路径节点数量之和最大的路径为主干。例如，以主干路径为X轴，将所述配电网的拓扑数据的显示视图划分为面积相等的上下两个区域。此时所述主干包括零级主干；
迭代分析模块，用于遍历所有下一级主干并进行可视化。
在一些实施例中，每一级主干的确定方法是相同的。
基于同一发明构思，本申请还提供一种辐射型配电网络自适应拓扑可视化服务器；所述服务器用于设置上述一种辐射型配电网络自适应拓扑可视化系统。
在本申请实施例中：
(1)完成新型配电网的差异性仿真评估分析，实现对配电网风险区域和故障区域的精准定位和分级，指导大规模复杂配电网网架向高可靠性、经济化运行方向演化重构，为城市配网提供定制化的运维辅助决策。
(2)新型配电网及其设备模型灵活性与可重用性高，能够为离线在线一体化配电仿真系统高级应用提供数据支撑，还可以为离线在线一体化配电仿真系统提供单元级模块支撑。
(3)实现数据的自动读取、过滤和装载，解决使用离线仿真的方式录入运行数据工作过于庞大的问题，实现高可用、高性能、高可扩展的数据同步；离线在线一体化仿真系统使用人员可便捷的将关系型数据库中的历史数据和线上业务系统产生的实时增量数据导入到图数据库中；基于图数据库的数据同步平台可扩展性强，可支撑多种配电网辅助决策与自动化高级业务应用。
(4)充分利用了辐射型配电网结构特征，一次生成所有布局和位置，后续拓扑过程中不需要调整，拓扑可视化速度快；避免了拓扑结构图的显示重叠，拓扑结构图中边之间的武交叉，更加美观大方地展示配电网络拓扑结构。
显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其它实施例，都属于本申请保护的范围。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储 介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上仅为本申请的实施例而已，并不用于限制本申请，凡在本申请的精神和原则之内，所做的任何修改、等同替换、改进等，均包含在申请待批的本申请的权利要求范围之内。

Claims (44)

  1. 一种辐射型配电网络自适应拓扑可视化方法，包括：
    步骤S1：提取配电网的拓扑数据；
    步骤S2：基于所述配电网的拓扑数据确定零级主干，并基于所述零级主干，将所述配电网的拓扑数据的显示视图进行视图划分；
    步骤S3：遍历所有下一级主干并进行可视化。
  2. 如权利要求1所述的方法，其中，所述步骤S1包括：基于设备节点之间的连接关系，对配电网络进行连通性分析，剔除孤岛设备，建立拓扑设备集合。
  3. 如权利要求2所述的方法，其中，基于所述配电网的拓扑数据确定主干，包括：以电源节点为始点，对所述拓扑设备集合进行宽度优先搜索，使用双向链式树状数据结构进行存储，生成拓扑树，并保存各节点在搜索中的层级；获取搜索层数最多的路径，然后从搜索层数最多的路径中筛选路径节点数量之和最大的路径为所述主干；其中，所述主干包括零级主干。
  4. 如权利要求3所述的方法，其中，基于所述主干，将所述配电网的拓扑数据的显示视图进行视图划分，包括：
    以所述主干的路径为X轴，将所述显示视图划分为面积相等的上下两个区域。
  5. 如权利要求3所述的方法，其中，筛选路径节点数量之和最大的路径为所述主干，包括：
    筛选路径节点中的特定类型节点数量之和最大的路径为所述主干。
  6. 如权利要求5所述的方法，其中，所述特定类型节点为用户选择的设备节点。
  7. 如权利要求3所述的方法，其中，所述对配电网络进行连通性分析，包括：
    获取设备节点的总数NALL，随机选择X＝A*NALL个初始设备节点，基于设备节点之间的连接关系，从每个初始设备节点出发建立其对应的一个或者多个子拓扑设备集合；将子拓扑设备集合中设备节点最多的子拓扑设备集合作为所述拓扑设备集合；其中：A为预设值。
  8. 如权利要求7所述的方法，其中，每个子拓扑设备集合中设备节点最多的初始设备节点对应一个子拓扑设备集合。
  9. 如权利要求7所述的方法，其中，所述从每个初始设备节点出发建立其对应的一个或者多个子拓扑设备集合，包括：
    选择一未分析的初始设备节点；
    标记所述一未分析的初始设备节点为已处理，从所述一未分析的初始设备节点出发，确定和所述一未分析的初始设备节点存在直接连接关系的设备节点，获取并存储所述存在直接连接关系的设备节点的属性信息；
    将所述存在直接连接关系的设备节点放入和所述一未分析的初始设备节点关联的子拓扑设备集合中；
    从所述子拓扑设备集合中选择一个未处理设备节点，标记所述未处理设备节点为已处理，从标记的该设备节点出发，确定和所述标记的该设备节点存在直接连接关系的设备节点，获取并存储与所述标记的该设备节点存在直接连接关系的设备节点的属性信息，以及将与所述标记的该设备节点存在直接连接关系的设备节点放入和所述一未分析的初设设备节点关联的子拓扑设备集合中，直到子拓扑设备集合中的设备节点均为已处理为止；
    输出所述一未分析的初始设备节点及其对应的子拓扑设备集合；
    从所述一未分析的初始设备节点出发的连通关系已经分析完毕，继续下一未分析的初始设备节点的处理。若所有初始设备节点均分析完毕，则结束，否则，继续下一未分析的初始设备节点的分析。
  10. 如权利要求1所述的方法，其中，所述遍历所有下一级主干并进行可视化，包括：
    确定下一级主干；遍历所述下一级主干；遍历该下一级节点；基于确定所有下一级节点均遍历完毕，以及完成主干节点的遍历，以及存在下一级树，则整理所有下一级主干，返回执行遍历下下一级主干。
  11. 根据权利要求10所述的方法，其中，所述方法还包括：
    基于确定所述下一级节点没有遍历完毕，遍历下一级节点。
  12. 根据权利要求10所述的方法，其中，所述方法还包括：
    基于确定没有完成主干节点的遍历，返回执行遍历下下一级主干，直至遍历完成。
  13. 根据权利要求10所述的方法，其中，所述方法还包括：
    基于确定不存在下一级树，针对所有u级子树v，面向u级子树v的所有主干节点；为其所有主干节点的下一级子树分配布局位置，并为该节点确定可视化范围。
  14. 一种辐射型配电网络自适应拓扑可视化系统，所述系统包括：
    数据提取模块，用于提取配电网的拓扑数据；
    零级主干分析模块，用于基于所述配电网的拓扑数据确定零级主干，并基于所述零级主干，将所述配电网的拓扑数据的显示视图进行视图划分；
    迭代分析模块，用于遍历所有下一级主干并进行可视化。
  15. 如权利要求14所述的系统，其中，所述数据提取模块，用于：基于设备节点之间的连接关系，对配电网络进行连通性分析，剔除孤岛设备，建立拓扑设备集合。
  16. 如权利要求15所述的系统，其中，所述零级主干分析模块，用于：以电源节点为始点，对所述拓扑设备集合进行宽度优先搜索，使用双向链式树状数据结构进行存储，生成拓扑树，并保存各节点在搜索中的层级；获取搜索层数最多的路径，然后从搜索层数最多的路径中筛选路径节点数量之和最大的路径为主干；其中所述主干包括零级主干。
  17. 如权利要求16所述的系统，其中，所述零级主干分析模块，还用于：以所述主干的路径为X轴，将所述显示视图划分为面积相等的上下两个区域。
  18. 一种处理器，所述处理器用于运行程序，其中，所述程序运行时执行权利要求1-13中任一项所述的方法。
  19. 一种执行设备，包括处理器，所述处理器和存储器耦合，所述存储器存储有程序指令，当所述存储器存储的程序指令被所述处理器执行时实现权利要求1-13中任一项所述的方法。
  20. 一种计算机可读存储介质，包括程序，当其在计算机上运行时，使得计算机执行如权利要求1-13中任一项所述的方法。
  21. 一种数据驱动的离线在线一体化配电网仿真系统，所述系统包括数据层、平台层和应用层；其中，
    所述数据层，包括图形库和属性数据库，存储于Oracle db/Access内，提供基础支撑系统与基础通用数据服务供所述平台层调用；
    所述平台层包括应用平台运行框架和开发平台建模工具；其中，所述应用平台运行框架至少包括以下组件中的至少之一：运行基础组件、界面展示组件、图标展示组件、图形展示组件、应用集成组件、门户集成组件；所述开发平台建模工具至少包括以下组件中的至少之一：业务建模组件、报表定义组件、权限配置组件、工具集成组件、流程定义组件、基础框架组件；
    所述应用层至少包括以下模块中的至少之一：数据管理模块、风险扫描模块、故障仿真模块、网架优化模块、系统配置模块、月报分析模块。
  22. 根据权利要求21所述的系统，其中，所述数据管理模块，用于实现数据集成、数据查询、数据质量评估分析和/或数据处理。
  23. 根据权利要求21所述的系统，其中，所述风险扫描模块，用于实现配电网风险批 量扫描和薄弱点分析、风险源统计与评级和/或风险预警。
  24. 根据权利要求23所述的系统，其中，所述风险扫描模块，用于：
    基于台账、拓扑关系、运行数据、风险分析条件和参数评估，进行风险分析，得到设备风险结果、拓扑分析结果和潮流计算结果；其中，所述设备风险结果包括设备老化风险和设备质量风险；所述拓扑分析结果包括单电源用户、环网运行线路和失电用户；所述潮流计算结果包括馈线潮流分布、配变潮流分布和节点潮流分布；
    基于风险分析结果进行风险定级；其中，风险定级包括保电风险、环网运行风险、供电可靠性风险、失电负荷、失电量、过负荷风险、重载风险、过电压风险、低电压风险和/或容量裕度风险的定级；
    基于风险定级结果进行风险统计；其中，风险统计包括区域和馈线的失电负荷、失电量、过负荷风险、重载风险、过电压风险、低电压风险、容量裕度风险、保电风险、环网运行风险和/或供电可靠性风险，以及厂站的失电负荷、失电量、过负荷风险、重载风险、过电压风险、低电压风险和/或容量裕度风险，以及台区的失电负荷、失电量、过负荷风险、重载风险、过电压风险、低电压风险、供电可靠性风险和/或保电风险；
    基于风险统计结果进行风险预警、风险措施分析和预控方案制定。
  25. 根据权利要求21所述的系统，其中，所述故障仿真模块，用于基于当前配电自动化高级应用中故障分析功能的扩展，实现故障预测、多重故障模拟和抢修决策辅助决策。
  26. 根据权利要求25所述的系统，其中，所述故障仿真模块，用于：
    基于管理的数据进行基本参数设置；其中，管理的数据包括网架信息管理、台账数据管理、运行数据管理、设备水平管理、气象信息管理和/或检修计划管理；所述基本参数设置包括扫描范围设置、检修计划设置、气象信息设置、负荷参数设置和/或设备状态设置；
    基于基本参数设置进行故障预测；其中，所述故障预测包括区域的预安排停电故障等级、10kv配电设施故障等级、低压设施故障等级和/或发电设施故障等级，以及馈线的预安排停电故障等级、10kv配电设施故障等级、10kv及以上输变电设施故障等级和发电设施故障等级，以及厂站的10kv配电设施故障等级、10kv及以上输变电设施故障等级、低压设施故障等级和发电设施故障等级；
    基于故障预测结果进行结果分析；其中，所述结果分析包括故障等级分析、故障位置分析和转供分析；
    基于所述结果分析进行方案制定；其中，所述方案制定包括抢修驻点设置、抢修方案设置、转供方案制定和优化方案制定。
  27. 根据权利要求21所述的系统，其中，所述网架优化模块，以负荷损失最小为目标，以电网运行安全和电压质量为约束条件，进行以下操作评估：冲击电流分析、保护定值校核、供电能力分析、电压质量评估、合环分析、解环分析、转供分析和/或无功优化。
  28. 根据权利要求21所述的系统，其中，所述系统配置模块，用于完成风险自动分析计算设置和风险分析评估参数设置。
  29. 根据权利要求21所述的系统，其中，所述月报分析模块，用于完成风险月报、故障月报以及网架月报的查询、生成和编辑。
  30. 一种数据驱动的离线在线一体化配电网仿真模型建立方法，所述方法包括：
    对电力系统的数据进行建模，包括：所述电力系统的数据模型用XML文件表示；电力系统XML文件包括前置部分和主体部分；所述前置部分为表示依赖关系的元素每一个依赖元素指向一个XML文件，使用时需首先载入；所述主体部分为表示设备的元素；
    将所述电力系统XML文件输入转化为根QHash数据结构以备使用，该QHash由嵌套的QHash组成；其中，前置文件的使用方法是：根QHash第一层Key为类型，值为QHash，第二层Key为ID，值为QHash形式的使用对象；基于类型和ID能够使用其数据；引用设 备的使用方法是：根QHash第一层Key包含设备ID，根据引用ID即可找到并使用；
    对可视化界面交互进行建模，其中，可视化界面交互由事件触发，对象的模型元素定义其显示效果，同一对象具有多个可用的网络模型和属性模型；所述网络模型用来交互式地显示电力系统网络连接关系；所述属性模型用来显示和编辑设备属性；
    对算法使用和数据输出进行建模，其中，以计算对象为始点，以包含设备为搜索方向，进行迭代搜索，得到所有相关设备；对于每一个设备，根据其算法模型，建立算法模型类并关联相关数据；类内部包含对数据进行处理和计算的各种方法；以计算对象模型为参数，构造算法类，通过设备算法模型的方法调用获取输入，计算过程中与设备算法模型进行动态交互，完成计算后，将相关计算结果返回到对应的设备算法模型中；输出模型定义为算法模型类与算法类中需要输出数据的名称，特别数据需要提供数据类中有相应的方法支撑；根据已有数据和输出模型生成输出数据文件。
  31. 根据权利要求30所述的方法，其中，所述可视化界面上的修改在确定时更新到根QHash中。
  32. 根据权利要求30所述的方法，其中，连接根QHash和所述网络模型之间的是解释器；对于给定设备，解释器读取其直接包含设备，并根据包含设备的图元、位置和连接关系，将其网络绘制到界面上。
  33. 根据权利要求32所述的方法，其中，所述设备的图元使用形状集合及其相对位置、线条和填充，缺省形状为直线，缺省线条为黑色实线，缺省填充为无；和/或，
    所述设备的位置由节点数据确定，任意设备有且仅有两个节点，一个节点上有多个连接端子；和/或，
    所述设备的连接关系在绘图中仅与节点有关，对于输入文件中没有节点元素的情况，根据设备连接数据生成节点，一个连接点生成一个节点，并为设备添加两个节点元素。
  34. 根据权利要求33所述的方法，其中，所有节点数据存入一个独立XML中，以供前置部分载入。
  35. 根据权利要求30所述的方法，其中，连接根QHash和所述属性模型之间的是解释器；属性模型定义以下信息：设备需显示的属性、显示的类型、编辑器的种类；对于给定设备，解释器生成网格状布局的属性界面，按照属性模型按列增加的方式依次添加标签与编辑器对，并绑定匹配数据，其中标签显示属性名称，编辑器读写属性值。
  36. 根据权利要求30所述的方法，其中，电力系统XML文件中的每一个独立元素均包含类型与ID属性，电力系统XML文件的主体部分通过类型与ID来使用依赖元素；依赖文件可以用来表示特定型号设备及其参数、关联设备和枚举类型等。
  37. 根据权利要求30所述的方法，其中，电力系统XML文件的主体部分为表示设备的元素，设备的数据包括名称、ID、类型、包含设备、所属设备、设备参数、图形信息和模型，每一项数据均用一个元素及其嵌套元素表示。
  38. 根据权利要求37所述的方法，其中，所述设备为实体设备或抽象设备集合。
  39. 根据权利要求37所述的方法，其中，
    对于基本类型数据，使用一个基本元素表示，基本元素的属性包括名称、值、类型和编辑器，名称为必有属性，值的缺省值0，类型的缺省种类为字符串，编辑器的缺省对象为文本框；
    对于扩展类型数据，使用多个嵌套的元素表示，其最终叶元素为基本元素；
    对于引用类型数据，包含以下元素：名称、类型、引用类型、引用ID和编辑器，缺省编辑器为按钮。
  40. 一种离线在线一体化配电网仿真系统数据同步方法，包括：
    建立以运行数据为中心的配电网仿真数学模型，实现一二次设备关联关系的数据描述；
    基于仿真数学模型与数据源数据之间的对应关系，对多源数据进行估计优化处理；
    将配电网仿真数学模型转化为图数据库的节点-关系模型，把在线数据存储到图数据库中；
    以图数据库为支撑平台，基于快速搜索实现实时数据同步，使离线仿真功能直接为配电网实时运行提供在线辅助决策服务。
  41. 根据权利要求40所述的方法，其中，所述配电网仿真数学模型的数据成员包括量测、连接终端和设备；其中，所述量测代表二次设备，所述连接终端代表一次设备之间的连接点，所述设备代表一次设备。
  42. 根据权利要求41所述的方法，其中，
    量测的数据成员至少包括以下至少之一：标识、相、国际单位代码、单位乘子、单位符号、连接终端、计算方法层次、保护动作调节、量测类型；
    连接终端的数据成员至少包括以下至少之一：标识、相、设备、连接节点、调节控制、连接、序列号；
    设备的数据成员至少包括以下至少之一：标识、保护动作调节、气象站、配置事件、控制、量测、资源类型、聚合、服务状态、用于网络分析、正常服务状态。
  43. 根据权利要求42所述的方法，其中，
    图数据库的节点-关系模型中，量测、连接终端、设备、标识、相、国际单位代码、单位乘子、单位符号、计算方法层次、保护动作调节、连接节点、调节控制、气象站、配置事件、控制和/或资源类型建模为节点；
    其中，连接终端、标识、相、国际单位代码、单位乘子、单位符号、计算方法层次和/或保护动作调节节点分别与量测节点关联，设备、标识、相、连接节点和/或调节控制节点分别与连接终端节点关联，量测、标识、保护动作调节、气象站、配置事件、控制和/或资源类型节点分别与设备节点关联；量测类型为量测节点的数据成员，连接、序列号为连接终端的数据成员，聚合、服务状态、用于网络分析和/或正常服务状态为设备的数据成员。
  44. 根据权利要求42所述的方法，其中，
    在离线在线一体化配电仿真系统中，使用设备-量测-连接终端结构或设备-连接终端-量测结构使用图数据库中数据；其中，
    在设备-量测-连接终端结构中，以设备为起点，基于关联关系搜索量测，基于量测搜索连接终端，从而获取全套配电网仿真的运行数据；
    在设备-连接终端-量测结构中，以设备为起点，基于关联关系搜索连接终端，基于连接终端搜索量测，从而获取全套配电网仿真的运行数据。
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Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116778856A (zh) * 2023-08-18 2023-09-19 深圳市巴科光电科技股份有限公司 一种应用于电力系统的智能化led显示装置及方法
CN117057593A (zh) * 2023-10-11 2023-11-14 中国长江电力股份有限公司 一种多约束条件下水电站检修策略动态优化方法
CN117078385A (zh) * 2023-10-18 2023-11-17 江苏银服智能装备有限公司 一种银行网点设备的集成管理方法及系统
CN117112630A (zh) * 2023-09-15 2023-11-24 国网江苏省电力有限公司信息通信分公司 电网数据的拓扑结构生成方法、装置、设备及存储介质
CN117132009A (zh) * 2023-09-07 2023-11-28 云南电网有限责任公司电力科学研究院 一种基于配网智慧运维的临近环网线路规划方法
CN116973694B (zh) * 2023-09-22 2023-12-12 国网浙江宁波市鄞州区供电有限公司 配电网故障诊断优化方法及系统
CN117313189A (zh) * 2023-12-01 2023-12-29 国网山东省电力公司淄博供电公司 光纤二次回路可视化分析方法、系统、终端及存储介质
CN117354164A (zh) * 2023-12-05 2024-01-05 长沙先度科技有限公司 一种以太网络拓扑结构的自动生成方法
CN117579538A (zh) * 2024-01-17 2024-02-20 珠海市捷锐科技有限公司 一种应用于数字化工厂的大数据分析系统及方法
CN117668418A (zh) * 2023-12-11 2024-03-08 中国建筑西南设计研究院有限公司 建筑工程供配电系统的单相接地故障电流计算方法及系统
CN117827933A (zh) * 2024-01-02 2024-04-05 北京易成慧众能源科技有限公司 一种基于电力交易数据图形生成方法及系统
CN117874984A (zh) * 2024-03-13 2024-04-12 武汉易晨创想科技有限公司 基于cim模型的配网拓扑图形生成方法及装置
CN117992644A (zh) * 2024-04-07 2024-05-07 国网山东省电力公司沂水县供电公司 一种35kV及以下电力系统故障定位软件系统
CN118095195A (zh) * 2024-04-17 2024-05-28 中建安装集团有限公司 一种大型建筑安装强弱电布线设计方法、介质及系统

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114448087A (zh) * 2021-12-24 2022-05-06 中国电力科学研究院有限公司 一种辐射型配电网络动态拓扑可视化方法和系统
CN115241976B (zh) * 2022-07-27 2023-11-17 国网江苏省电力有限公司电力科学研究院 一种配电网监测数据可视化方法、计算机设备及存储介质

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120316696A1 (en) * 2011-06-08 2012-12-13 Alstom Grid Multi-level topologytopography for electrical distribution grid control
CN103150425A (zh) * 2013-02-06 2013-06-12 上海交通大学 基于拓扑分层的配电网单线图自动生成方法
CN103699664A (zh) * 2013-12-27 2014-04-02 国家电网公司 一种配电网动态拓扑分析方法
CN104123675A (zh) * 2013-04-27 2014-10-29 国家电网公司 基于全网数据的配电网仿真研究分析系统及方法
CN109816161A (zh) * 2019-01-14 2019-05-28 中国电力科学研究院有限公司 一种配电网运行辅助决策分析系统及其应用方法
CN112579827A (zh) * 2020-11-23 2021-03-30 中国电力科学研究院有限公司 一种数据驱动的电力系统数据处理方法及系统
CN114448087A (zh) * 2021-12-24 2022-05-06 中国电力科学研究院有限公司 一种辐射型配电网络动态拓扑可视化方法和系统

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120316696A1 (en) * 2011-06-08 2012-12-13 Alstom Grid Multi-level topologytopography for electrical distribution grid control
CN103150425A (zh) * 2013-02-06 2013-06-12 上海交通大学 基于拓扑分层的配电网单线图自动生成方法
CN104123675A (zh) * 2013-04-27 2014-10-29 国家电网公司 基于全网数据的配电网仿真研究分析系统及方法
CN103699664A (zh) * 2013-12-27 2014-04-02 国家电网公司 一种配电网动态拓扑分析方法
CN109816161A (zh) * 2019-01-14 2019-05-28 中国电力科学研究院有限公司 一种配电网运行辅助决策分析系统及其应用方法
CN112579827A (zh) * 2020-11-23 2021-03-30 中国电力科学研究院有限公司 一种数据驱动的电力系统数据处理方法及系统
CN114448087A (zh) * 2021-12-24 2022-05-06 中国电力科学研究院有限公司 一种辐射型配电网络动态拓扑可视化方法和系统

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
LI, WENBO ET AL: "Research on Power Equipment Quality Integrated Management System Based on Graph Database", DISTRIBUTION & UTILIZATION, vol. 38, no. 11, 5 November 2021 (2021-11-05), pages 48 - 54, XP009547797, ISSN: 1006-6357, DOI: 10.19421/j.cnki.1006-6357.2019.11.007 *
ZHANG, WEN ET AL: "Architecture and Technology Implementation of Massive Data Based Distribution Network Operation Analysis System", AUTOMATION OF ELECTRIC POWER SYSTEMS, NANJING ZIDONGHUA YANJIUSUO, NANJING, CN, vol. 44, no. 3, 5 December 2019 (2019-12-05), CN , pages 147 - 153, XP009547795, ISSN: 1000-1026, DOI: 10. 7500/AEPS20190520009 *

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116778856A (zh) * 2023-08-18 2023-09-19 深圳市巴科光电科技股份有限公司 一种应用于电力系统的智能化led显示装置及方法
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