Large power grid imaging system and its architecture system

Abstract

A large power grid imaging system and its architecture, the large power grid imaging system comprising: a measurement data access function module, a real-time imaging power grid function module, an imaging model synchronous evolution function module, a response-driven intelligent analysis and decision-making function module, and a multivariate information visualization function module. The large power grid imaging system and its architecture provided by the embodiment of the present invention can realize unified data modeling and efficient information extraction, and can support real-time power grid analysis and calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation analysis and calculation, and in particular to a large power grid imaging system and an architecture system thereof. Background Art

[0002] The development and continuation of the physical power grid and the upgrading and replacement of power grid analysis methods are inseparable, mutually reinforcing and evolving together. Accurately, quickly and comprehensively reflecting the characteristics of the ever-changing objective physical power grid is the breakthrough direction of power grid analysis tools. For a long time, with the expansion of the scale of the power grid and the increase in dynamic components, traditional dynamic simulation, digital simulation and other technologies have continued to improve, supporting the development needs of the power grid.

[0003] At present, with the large-scale grid connection of new energy and the prominent power electronics characteristics of power systems, the uncertainty of power grid operation and the complexity of dynamic behavior are facing unprecedented changes. The traditional simulation-based plan-based power grid analysis method faces problems such as lagging simulation model update speed, slow simulation analysis speed, and low intelligence level of analysis means, which will seriously restrict the ability of power grid analysis and operation personnel to understand and control the power grid. Summary of the invention

[0004] In view of this, the present invention proposes a large power grid imaging system and its architecture system, aiming to solve the problems of slow updating and analysis speed and low intelligence level of power grid simulation model in the prior art.

[0005] In a first aspect, an embodiment of the present invention provides a large power grid imaging system, including: a measurement data access function module, which is used to acquire data in real time and send the acquired real-time data to the real-time imaging power grid function module; a real-time imaging power grid function module, which is used to process the real-time data sent by the measurement data access function module to obtain continuous time period data and store the continuous time period data; an imaging model synchronous evolution function module, which is used to acquire the continuous time period data stored in the real-time imaging power grid function module, form a power grid imaging model according to the continuous time period data, and send the power grid imaging model to the real-time imaging power grid function module for storage; a response-driven intelligent analysis and decision-making function module, which is used to acquire the power grid imaging model stored in the real-time imaging power grid function module, analyze, make decisions and verify based on the power grid imaging model, and send the results of the analysis, decision and verification to the real-time imaging power grid function module for storage; a multivariate information visualization function module, which is used to acquire and display the power grid imaging model stored in the real-time imaging power grid function module and the results of the analysis, decision and verification based on the power grid imaging model.

[0006] Furthermore, the measurement data access function module is used to: acquire PMU data and RTU data in real time through dynamic data access and steady-state data access respectively; buffer the acquired PMU data and RTU data; and perform streaming data processing on the buffered data and send it to the real-time power grid imaging function module.

[0007] Furthermore, the acquired PMU data and RTU data are buffered, including: sending the PMU data and RTU data to a distributed message queue Kafka for data high-speed buffering.

[0008] Furthermore, the real-time grid imaging function module is also used to: based on the grid imaging model, improve the real-time data sent by the measurement data access function module.

[0009] Furthermore, the real-time power grid imaging function module is also used to: determine the data collection time period according to a predetermined window for the real-time data sent by the measurement data access function module, and process missing or erroneous measurement point measurement data based on the power grid imaging model to form usable continuous time period data; store the usable continuous time period data in a distributed memory data grid and a time series database, and transfer the data in the memory data grid to the distributed relational database for persistent storage according to predetermined conditions.

[0010] Furthermore, the image model synchronous evolution functional module is also used to: form a power grid image model by using fast observability calculation to achieve synchronous evolution of the power grid image model and the actual power grid.

[0011] Furthermore, the response-driven intelligent analysis and decision-making function module includes: a response-driven analysis module, which is used to integrate various response-driven and intelligent enhancement model algorithms based on the power grid image model, calculate the stability characteristics and identify the stability properties, and send the stability characteristics and stability properties to the response-driven control strategy generation module; a response-driven control strategy generation module, which is used to generate and verify the control strategy according to the stability characteristics and stability properties; and an intelligent enhancement analysis module, which is used to optimize the algorithms or models in the response-driven analysis module and the response-driven control strategy generation module.

[0012] Furthermore, the data structure of the power grid image model includes: a case set table, a node set table, a component set table, a topology information table, a model parameter information table, an operation status information table, an operation parameter information table, a model parameter definition table and an operation parameter definition table.

[0013] Furthermore, the data structure of the power grid image model is obtained by adopting inheritance and derivation mechanism.

[0014] In a second aspect, an embodiment of the present invention further provides an architecture system of a large power grid imaging system, which is applied to the large power grid imaging system provided by each of the above embodiments, and the architecture system includes: an object layer, a scene layer, a data layer, an imaging model, an algorithm layer, and an application layer connected in sequence; wherein the object layer is used to collect actual physical power grid data in real time, obtain real-time measurement data, and transmit the real-time measurement data to the scene layer; the scene layer is used to shunt the real-time measurement data output by the object layer, and transmit the shunted data to the data layer; the data layer is used to construct an imaging model basic database for the shunted data, and transmit the basic data in the imaging model basic database to the imaging model; the imaging model is used to perform multi-source data fusion processing on the basic data to obtain fused data, and transmit the fused data to the algorithm layer; the algorithm layer is used to perform analysis and calculation of the fused data on the imaging model to obtain analysis and calculation result data, and transmit the analysis and calculation result data to the application layer; the application layer is used to perform customized applications on the analysis and calculation result data.

[0015] The large power grid imaging system and its architecture system provided by the embodiment of the present invention establish non-simulation analysis and decision-making methods based on real-time measurement, and combine them with simulation analysis methods to achieve "real-time calculation, real-time analysis" to quickly change the operation boundary and adjust the operation mode until the disturbance is controlled in real time. It can realize unified data modeling and efficient information extraction, support real-time power grid analysis and calculation, adapt to real-time, offline, experimental, planning and other application scenarios, and support different professions of control centers such as power dispatching, mode, planning, and market.

[0016] The large power grid imaging system and its architecture system provided by some embodiments of the present invention can be deployed to the national, branch and provincial dispatching terminals, access PMU and RTU data from the real-time data platform of the new generation control system control cloud, and can expand other dynamic measurement data. The system continuously and rapidly generates dynamically changing full-network operation information at the millisecond level, can support simulation, analysis, and AI calculation, and is used for analysis and decision-making scenarios such as real-time monitoring and early warning of short-circuit ratio of multiple new energy stations, frequency stability judgment and control, power angle stability judgment and control, and voltage safety and stability monitoring and control. It can continuously and rapidly analyze the real-time system disturbances and oscillations, calculate stability characteristics, identify stability properties, give control strategies, and perform multi-angle visualization, providing technical support for dispatchers to provide real-time updates of control strategies, optimization and adjustment of new energy output, etc., and effectively reduce the operation risks of power grids with high penetration rates of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram showing the logical structure of an image model according to an embodiment of the present invention is shown;

[0018] Figure 2A schematic structural diagram of a large power grid imaging system according to an embodiment of the present invention is shown;

[0019] Figure 3 A schematic diagram showing a data structure according to an embodiment of the present invention;

[0020] Figure 4 A schematic diagram of the structure of data access processing according to an embodiment of the present invention is shown;

[0021] Figure 5 A schematic diagram of the structure of an architecture system of a large power grid imaging system according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0022] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0023] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0024] The establishment of a new power grid operation analysis system requires many aspects of work, the most important of which is the design of a new computing model. Based on this model, the data and computing technologies required to meet various needs are unified under one system to solve the problems faced by the analysis and decision-making of the new power system.

[0025] There are three main types of calculations closely related to power grid operation: simulation calculations, analytical calculations, and AI calculations. At present, the existing calculation models for power grid operation analysis are mainly simulation models, including two parts: network model and dynamic component model, which support steady-state and dynamic calculations. According to the time scale of the calculation and the level of detail of the model, it can be divided into electromechanical transient model and electromagnetic transient model.

[0026] For analytical calculations, the simulation calculation model contains the information required by the algorithm, but it also contains a lot of redundant information. This useless information also needs to be maintained synchronously, which reduces the practicality of the solution. In recent years, with the development of artificial intelligence technology, some AI algorithms for power grid simulation analysis and operation control have been established. These AI algorithms are usually also based on simulation models for research and application verification.

[0027] The new computing model is a major change to the existing computing system. Based on measurement data and component models, the present invention supports the real-time computing, analysis, verification, and decision-making process of future simulation, analysis, and AI. The embodiment of the present invention is called "image model". Figure 1 A schematic diagram of the logical structure of an image model according to an embodiment of the present invention is shown.

[0028] The so-called "image" refers to the reflection of objective things in the human brain. The same thing will leave different images in different people's minds. The image model must first be based on the dynamic measurement information of the system, combined with static measurement, component model and parameter data information, without dynamic simulation calculation, directly generate the system's power grid image that changes continuously over time in real time, realistically reflect the system status and characteristics, and further support various types of calculation and analysis on this basis.

[0029] At the same time, for the operation and control of the power grid, the same power grid and its operation status may correspond to different models for different types of calculation and analysis and different analysis purposes. Therefore, the image model is not a mathematical model, but a set of models. For example, the mathematical model used for simulation calculation contains topology, static and dynamic component models, and parameters, but the bus voltage measured by the PMU is generally not used as an input quantity, because the bus voltage is the output result of the simulation; the mathematical model used for response control analytical calculation contains topology and dynamic measurement results, and generally does not require static or dynamic component models and parameters; the mathematical model used for artificial intelligence calculation, the content of which is determined by the specific AI model, usually does not require the dynamic model and parameters of the component.

[0030] In order to solve the problems of measurement error, missing measurement points, alignment of dynamic measurement results, etc. involved in the process of forming the power grid image, parameter calculation can be performed based on the information obtained, which is called "observability" calculation. Through observability calculation, a complete power grid image is formed, and at the same time, the foundation is laid for forming power flow calculation data and supporting simulation calculation.

[0031] The image model can follow the current operating status of the system, and can evolve towards future scenarios when combined with prediction information. It can also evolve towards historical scenarios based on the system image of historical records.

[0032] Figure 2 A schematic structural diagram of a large power grid imaging system according to an embodiment of the present invention is shown.

[0033] like Figure 2 As shown, the large power grid imaging system 20 includes:

[0034] The measurement data access function module 201 is used to acquire data in real time and send the acquired real-time data to the real-time power grid imaging function module.

[0035] Furthermore, the measurement data access function module 201 is used to:

[0036] Acquire PMU data and RTU data in real time through dynamic data access and steady-state data access respectively;

[0037] Buffering the acquired PMU data and RTU data;

[0038] The buffered data is processed in a streaming manner and then sent to the real-time imaging power grid function module.

[0039] Furthermore, the acquired PMU data and RTU data are buffered, including:

[0040] The PMU data and RTU data are sent to the distributed message queue Kafka for data high-speed buffering.

[0041] Specifically, the measurement data access function module is used to: obtain PMU data in real time from the D5000 platform message bus of the smart grid dispatching technical support system in a production-consumption multi-threaded manner through dynamic data access. Through steady-state data access, the data acquisition interface of the control cloud or the data access interface provided by the SCADA real-time database is used to obtain RTU data in real time. After data high-speed buffering, high-quality steady-state and / or dynamic data services are provided to the large power grid imaging system in real time through streaming data processing.

[0042] The real-time imaging power grid function module 202 is used to process the real-time data sent by the measurement data access function module, obtain the continuous time period data, and store the continuous time period data;

[0043] Furthermore, the real-time grid imaging function module 202 is also used for:

[0044] Based on the power grid image model, improve the real-time data sent by the measurement data access function module.

[0045] Furthermore, the real-time grid imaging function module 202 is also used for:

[0046] For the real-time data sent by the measurement data access function module, the data collection time period is determined according to the predetermined window, and based on the power grid image model, the missing or erroneous measurement point measurement data is processed to form continuous time period data for use;

[0047] The available continuous time period data is stored in the distributed memory data grid and the time series database, and the data in the memory data grid is transferred to the distributed relational database for persistent storage according to predetermined conditions.

[0048] Specifically, the real-time power grid imaging function module is used to: obtain all the steady-state / dynamic measurement data with time series characteristics that continuously flow in from the flow data processing results of the measurement data access module, determine the data collection time period according to the predetermined window, process the missing measurement point measurement data, form continuous time period data that can be used for analysis and calculation, store it in the distributed memory data grid and time series database, and transfer the data in the memory to the distributed relational database for persistent storage according to predetermined conditions.

[0049] The image model synchronous evolution function module 203 is used to obtain the continuous time period data stored in the real-time image power grid function module, form a power grid image model according to the continuous time period data, and send the power grid image model to the real-time image power grid function module for storage.

[0050] Furthermore, the image model synchronous evolution function module 203 is also used for:

[0051] The grid image model is formed by using fast observability calculation to achieve synchronous evolution of the grid image model and the actual grid.

[0052] Specifically, the image model synchronous evolution function module is used to: use methods such as fast observable calculation to deal with problems such as measurement clock asynchrony, incomplete measurement points, data pollution errors, etc., quickly form generalized dynamic power flows, quickly form power grid images, and realize the synchronous evolution of the power grid image model and the actual power grid.

[0053] Among them, the simultaneous section generation, aimed at the inaccurate observation of the power grid caused by the asynchrony of the dynamic measurement time of the power grid, proposes a simultaneous section generation technology based on a unified virtual clock to solve the problem of inaccurate observation caused by the asynchrony of measurement. It can adopt the difference method, time series measurement to obtain the intermediate value; the trend of historical changes; neural network according to the correlation between the measurement points and the change trend of the measurement point data; observability calculation and other methods;

[0054] Among them, observability calculation is performed through observability calculation based on AI or observability calculation based on circuit theorems KCL and KVL, and fast observability calculation is used to form a power grid image model, and the power grid image model is sent to the real-time image power grid function module to correct or complete measurement information errors or incompleteness.

[0055] The response-driven intelligent analysis and decision-making function module 204 is used to obtain the power grid image model stored in the real-time image power grid function module, perform analysis, decision and verification based on the power grid image model, and send the results of the analysis, decision and verification to the real-time image power grid function module for storage.

[0056] Furthermore, the response-driven intelligent analysis and decision-making function module 204 includes:

[0057] The response drive analysis module 2041 is used to integrate various response drive and intelligent enhancement model algorithms based on the power grid image model, calculate the stability characteristics and identify the stability properties, and send the stability characteristics and stability properties to the response drive control strategy generation module;

[0058] A response driven control strategy generation module 2042, for generating and verifying a control strategy based on the stability characteristics and stability properties;

[0059] The intelligent enhancement analysis module 2043 is used to optimize the algorithms or models in the response drive analysis module 2041 and the response drive control strategy generation module 2042 .

[0060] Specifically, the response-driven analysis module is based on the real-time power grid image model, and integrates various response-driven and intelligent enhanced model algorithms such as work angle, voltage, frequency, short-circuit ratio, oscillation source, etc., to perform continuous second-level rapid analysis, calculate stability characteristics, and identify stability properties; the response-driven control strategy generation module implements response-driven control strategy generation configuration management, reads in stability judgment, control strategy, adjustable information and other information, generates control strategy, outputs and displays control strategy, and supports historical data storage and query; the intelligent enhanced analysis module is used to correct and enhance the above-mentioned algorithm or model parameters to improve calculation accuracy.

[0061] The multivariate information visualization function module 205 is used to obtain and display the power grid image model stored in the real-time power grid image function module and the results of analysis, decision-making and verification based on the power grid image model.

[0062] Specifically, the multivariate information visualization function module is used to: mainly display the results of power grid images and various algorithm calculations and analyses based on image models.

[0063] Furthermore, the data structure of the power grid image model includes: a case set table, a node set table, a component set table, a topology information table, a model parameter information table, an operation status information table, an operation parameter information table, a model parameter definition table and an operation parameter definition table.

[0064] Furthermore, the data structure of the power grid image model is obtained by adopting inheritance and derivation mechanism.

[0065] Specifically, the data structure design of the image model is the basis for the implementation of the image model. Figure 3 FIG. 4 is a schematic diagram showing a data structure according to an embodiment of the present invention. Figure 3As shown, considering the demand for information recording in different time scale evolution processes such as power system development and construction, normal operation, fault disturbance, etc., the embodiment of the present invention adopts an equipment model relationship oriented to the physical description of power system equipment components, and can establish an image model data structure composed of 9 types of data tables including case set, node set, component set, topology structure, model parameters, operating status, operating parameters, model parameter definition, and operating parameter definition, which supports lossless and non-redundant recording of multiple information at multiple time scales that exist simultaneously in the same set of data.

[0066] Among them, the case collection table is used to record the basic information of all cases, the node collection table is used to record the basic information of all nodes, the component collection table is used to record the basic information of all components, the topology information table is used to record the topology information of all components by time, the model parameter information table is used to record the model parameter information of all components by time, the operating status information table is used to record the operating status information of all components by time, the operating parameter information table is used to record the operating parameter information of all components by time, the model parameter definition table is used to define the equipment model parameter list, and the operating parameter definition table is used to define the equipment operating parameter list.

[0067] The data structure is extended from describing the single state information of the power system at a certain point in time to describing the information of the evolution process of the power system in a certain period of time. At the same time, it is compatible with the ability to describe the single state information of the power system at a certain point in time. It breaks through the limitation that the topology structure, model parameters, operating status and parameters of the power system are fixed in the same set of data, and realizes the description of the evolution process of the power system based on the same set of data. It has the following characteristics:

[0068] (1) Reduce data redundancy: Since it is not necessary to use multiple sets of independent calculation data to describe the evolution of the power system, it avoids recording a large amount of repeated information on the same topology, model parameters, operating status and parameters. The basic data does not grow significantly over time, and the data that changes over time only records the change information. The inheritance-derivation mechanism can significantly reduce the duplication of data between different cases, which not only saves data storage costs, but also helps improve data extraction and analysis efficiency.

[0069] (2) Adapt to multi-time scale evolution processes: In the topology information table, model parameter information table, operation status information table, and operation parameter information, the judgment rules for whether each component needs data update are independent of each other. Each component in each table and each case has an independent data sequence with a time stamp, which can fully adapt to the information recording needs of different time scale evolution processes from power system development and construction to fault disturbances, switch operations, etc., and support lossless and non-redundant recording of different information at different time scales that exist simultaneously in the same set of data.

[0070] (3) Accurately describe topology changes: Nodes are the basis of the topology of the power system, and the topology information table is specifically used to describe the connection relationship between components and nodes. For any component included in the case, there must be at least one record in the table to describe its initial topology information; when the topology of a component changes at a certain moment, such as a switch action, a corresponding record must be added to record the change. This mechanism for describing topology changes is accurate and non-redundant.

[0071] Specifically, the image system needs to quickly process a large amount of measurement data and continuously update the image model content. For the operation status of the power grid, the most commonly used measurement is PMU / RTU. Figure 4 FIG. 2 shows a schematic diagram of a data access process according to an embodiment of the present invention. Figure 4 As shown in the figure, the received PMU data is parsed in the format sent by the message bus through multi-threading to form real-time data in the format of the real-time image power grid, and sent to the specified Topic in the distributed message queue Kafka for data high-speed buffering according to the telemetry and telesignaling data types. The received RTU data is written to the distributed message queue Kafka and the time series database InfluxDB of the real-time image power grid. After that, the image system uses the message bus to receive events from the fixed channel and writes them to the distributed message queue Kafka for data high-speed buffering.

[0072] After data caching, streaming data processing is used to provide high-quality steady-state / dynamic data services for real-time power grid imaging. All steady-state / dynamic measurement data with time series characteristics that are continuously flowing in are obtained from streaming data processing through measurement data access. The data collection time period is determined according to the predetermined window, and the missing measurement point measurement data is processed to form continuous time period data that can be used for response control analysis and calculation. The data is stored in the distributed column relational database ClickHouse and the time series database InfluxDB, achieving 1s measurement data and completing the synchronous update of the imaging model in about 700ms.

[0073] The high-speed data processing method proposed in the above embodiment can access and process measurement data in real time and efficiently.

[0074] The large power grid imaging system provided in the above-mentioned embodiment establishes non-simulation analysis and decision-making methods based on real-time measurement, and combines them with simulation analysis methods to achieve "real-time calculation, real-time analysis" to quickly change operating boundaries and adjust operating modes until disturbances are controlled in real time. It can achieve unified data modeling and efficient information extraction, support real-time power grid analysis and calculation, adapt to real-time, offline, experimental, planning and other application scenarios, and support different professions of control centers such as power dispatching, methods, planning, and markets.

[0075] The large power grid imaging system provided by some of the above embodiments can be deployed to the national, branch, and provincial dispatching terminals, access PMU and RTU data from the real-time data platform of the new generation of control system control cloud, and can expand other dynamic measurement data. The system continuously and rapidly generates dynamically changing full-network operation information at the millisecond level, which can support simulation, analysis, and AI calculation, and is used for analysis and decision-making scenarios such as real-time monitoring and early warning of short-circuit ratio of multiple new energy stations, frequency stability judgment and control, power angle stability judgment and control, and voltage safety and stability monitoring and control. It can continuously and rapidly analyze the real-time system disturbances and oscillations, calculate stability characteristics, identify stability properties, give control strategies, and perform multi-angle visualization, providing technical support for dispatchers to provide real-time updates of control strategies, optimization and adjustment of new energy output, etc., and effectively reduce the operation risks of power grids with high penetration rates of new energy.

[0076] Figure 5 A structural schematic diagram of an architecture system of a large power grid imaging system according to an embodiment of the present invention is shown.

[0077] like Figure 5 As shown, the architecture system is applied to the large power grid imaging system provided in each of the above embodiments, including:

[0078] The object layer 501, the scene layer 502, the data layer 503, the image model 504, the algorithm layer 505, and the application layer 506 are connected in sequence; wherein,

[0079] The object layer 501 is used to collect the actual physical power grid data in real time, obtain the real-time measurement data, and transmit the real-time measurement data to the scene layer 502;

[0080] The scene layer 502 is used to perform diversion processing on the real-time measurement data output by the object layer and transmit the diverted data to the data layer 503;

[0081] The data layer 503 is used to construct the image model basic database with the diverted data, and transmit the basic data in the image model basic database to the image model 504;

[0082] The image model 504 is used to perform multi-source data fusion processing on the basic data to obtain fused data, and transmit the fused data to the algorithm layer 505;

[0083] The algorithm layer 505 is used to analyze and calculate the fusion data using the image model to obtain the analysis and calculation result data, and transmit the analysis and calculation result data to the application layer 506;

[0084] The application layer 506 is used to customize the analysis and calculation result data.

[0085] Specifically, the various levels of the system architecture of the image model are as follows:

[0086] Object layer: used to collect real-time data of the actual physical power grid. Its characteristics are to configure measurements widely according to the analysis requirements of different application scenarios of the actual power grid, and output the measurement data of the actual power grid to the scenario layer;

[0087] Scenario layer: used to shunt the real-time measurement data output by the object layer to obtain measurement data that meets the requirements of four different power grid analysis scenarios, including real-time, offline, experimental, and planning, and transmit the shunt data to the data layer;

[0088] Data layer: used to construct the basic database of the image model with the diverted data, which can access online, offline, experimental, planning and other data, involving state measurement, control and protection, meteorological environment, market behavior, policies and regulations, and transmit the basic data to the image model layer;

[0089] Image model: used to perform multi-source data fusion processing on basic data, realize digital mapping of physical objects by building twins online, use real-time evolution mechanism to ensure real-time synchronization of physical and digital, obtain fused data, and transmit the fused data to the algorithm layer;

[0090] Algorithm layer: Algorithm support and computing engine for advanced functions such as analysis, control, and evolution of image models using fused data. It mainly includes various intelligent algorithms, memory computing, serial / parallel simulation computing, embedded computing, and ultra-real-time computing, etc., to obtain analysis and settlement result data, and transmit the analysis and calculation result data to the application layer;

[0091] Application layer: used to customize the analysis and calculation result data to realize advanced application functions including static / dynamic real-time analysis, future evolution analysis, etc. The main functions include: real-time perception of operating status, synchronous update of cognition, intelligent deduction of operating status, closed-loop interaction of control information and automatic control closed loop.

[0092] The architecture system of the large power grid imaging system provided in the above-mentioned embodiment establishes non-simulation analysis and decision-making methods based on real-time measurement, and combines them with simulation analysis methods to achieve "real-time calculation, real-time analysis" to quickly change the operating boundaries and adjust the operating mode until the disturbance is controlled in real time. It can realize unified data modeling and efficient information extraction, support real-time power grid analysis and calculation, adapt to real-time, offline, experimental, planning and other application scenarios, and support different professions of control centers such as power dispatching, methods, planning, and markets.

[0093] The invention has been described above with reference to a few embodiments. However, it is readily apparent to a person skilled in the art that other embodiments than the ones disclosed above are equally within the scope of the invention, as defined by the appended patent claims.

[0094] Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a / said / the [means, components, etc.]" are to be openly interpreted as at least one instance of the means, components, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not necessarily have to be performed in the exact order disclosed, unless explicitly stated otherwise.

[0095] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A large power grid imaging system, characterized in that: include: The measurement data access function module is used to obtain data in real time and send the obtained real-time data to the real-time imaging power grid function module; A real-time power grid imaging function module is used to process the real-time data sent by the measurement data access function module to obtain continuous time period data and store the continuous time period data; An image model synchronous evolution function module is used to obtain the continuous time period data stored in the real-time image power grid function module, form a power grid image model according to the continuous time period data, and send the power grid image model to the real-time image power grid function module for storage; A response-driven intelligent analysis and decision-making function module is used to obtain the power grid image model stored in the real-time image power grid function module, perform analysis, decision and verification based on the power grid image model, and send the results of the analysis, decision and verification to the real-time image power grid function module for storage; A multivariate information visualization function module is used to obtain and display the power grid image model stored in the real-time image power grid function module and the results of analysis, decision-making and verification based on the power grid image model; Among them, the power grid image model is a model that revolves around the power grid and its operating status, based on measurement data and component models, and supports real-time calculation, analysis, verification and decision-making processes of future simulation, analysis and AI.

2. The large power grid imaging system according to claim 1, characterized in that: The measurement data access function module is used to: Acquire PMU data and RTU data in real time through dynamic data access and steady-state data access respectively; Buffering the acquired PMU data and RTU data; The buffered data is processed in a streaming manner and then sent to the real-time imaging power grid function module.

3. The large power grid imaging system according to claim 2, characterized in that: Buffer the acquired PMU data and RTU data, including: The PMU data and RTU data are sent to the distributed message queue Kafka for data high-speed buffering.

4. The large power grid imaging system according to claim 1, characterized in that: The real-time image power grid function module is also used for: Based on the power grid image model, the real-time data sent by the measurement data access function module is improved.

5. The large power grid imaging system according to claim 1, characterized in that: The real-time image power grid function module is also used for: For the real-time data sent by the measurement data access function module, the data collection time period is determined according to the predetermined window, and based on the power grid image model, the missing or erroneous measurement point measurement data is processed to form continuous time period data for use; The available continuous time period data is stored in a distributed memory data grid and a time series database, and the data in the memory data grid is transferred to a distributed relational database for persistent storage according to predetermined conditions.

6. The large power grid imaging system according to claim 1, characterized in that: The image model synchronous evolution functional module is also used for: The grid image model is formed by using fast observability calculation to achieve synchronous evolution of the grid image model and the actual grid.

7. The large power grid imaging system according to claim 1, characterized in that: The response-driven intelligent analysis and decision-making functional module includes: The response drive analysis module is used to integrate various response drive and intelligent enhancement model algorithms based on the power grid image model, calculate the stability characteristics and identify the stability properties, and send the stability characteristics and stability properties to the response drive control strategy generation module; Response-driven control strategy generation module, used to generate and verify control strategies based on stability characteristics and stability properties; The intelligent enhanced analysis module is used to optimize the algorithms or models in the response driven analysis module and the response driven control strategy generation module.

8. The large power grid imaging system according to claim 1, characterized in that: The data structure of the power grid image model includes: a case set table, a node set table, a component set table, a topology information table, a model parameter information table, an operation status information table, an operation parameter information table, a model parameter definition table and an operation parameter definition table.

9. The large power grid imaging system according to claim 1, characterized in that: The data structure of the power grid image model is obtained by adopting inheritance and derivation mechanism.

10. An architecture system for a large power grid imaging system, characterized in that: Applied to the large power grid imaging system as claimed in any one of claims 1 to 9, the architecture system comprises: The object layer, scene layer, data layer, image model, algorithm layer, and application layer are connected in sequence; among them, The object layer is used to collect actual physical power grid data in real time, obtain real-time measurement data, and transmit the real-time measurement data to the scene layer; The scene layer is used to perform shunt processing on the real-time measurement data output by the object layer and transmit the shunt data to the data layer; The data layer is used to construct an image model basic database with the diverted data, and transmit the basic data in the image model basic database to the image model; The image model is used to perform multi-source data fusion processing on the basic data to obtain fused data, and transmit the fused data to the algorithm layer; The algorithm layer is used to analyze and calculate the fusion data using the image model to obtain the analysis and calculation result data, and transmit the analysis and calculation result data to the application layer; The application layer is used to customize the application of the analysis and calculation result data.

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