WO2019218677A1 - 电网仿真分析数据存储方法、装置及电子设备 - Google Patents

电网仿真分析数据存储方法、装置及电子设备 Download PDF

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WO2019218677A1
WO2019218677A1 PCT/CN2018/123608 CN2018123608W WO2019218677A1 WO 2019218677 A1 WO2019218677 A1 WO 2019218677A1 CN 2018123608 W CN2018123608 W CN 2018123608W WO 2019218677 A1 WO2019218677 A1 WO 2019218677A1
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time series
representation information
simulation analysis
power grid
preset
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PCT/CN2018/123608
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English (en)
French (fr)
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周二专
赵林
冯东豪
黄鹏
陈捷
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北京科东电力控制系统有限责任公司
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Publication of WO2019218677A1 publication Critical patent/WO2019218677A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation

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  • the present disclosure relates to the field of power technologies, and in particular, to a power grid simulation analysis data storage method, apparatus, and electronic device.
  • One of the objectives of the present disclosure is to provide a power grid simulation analysis data storage method, apparatus, and electronic device to at least partially improve the above problems.
  • the technical solution adopted by the present disclosure is as follows:
  • the present disclosure provides a power grid simulation analysis data storage method applied to an electronic device, the method comprising: acquiring power grid simulation analysis data, where the power grid simulation analysis data includes original simulation results; and performing the original simulation result Characterizing the description, obtaining multi-level model representation information corresponding to the original simulation result; distributing the multi-level model representation information to a preset data warehouse.
  • the present disclosure provides a power grid simulation analysis data storage device, running on an electronic device, the device comprising: an acquisition unit, a characterization unit, and a storage unit.
  • the acquisition unit is configured to acquire grid simulation analysis data, the grid simulation analysis data including original simulation results.
  • the characterization unit is configured to perform characterization description on the original simulation result, and obtain multi-level model characterization information corresponding to the original simulation result.
  • the storage unit is configured to distribute the multi-level model representation information in a predetermined data warehouse.
  • FIG. 1 is a structural block diagram of an electronic device that can be used in the present disclosure
  • FIG. 2 is a flowchart of a method for storing a power grid simulation analysis data provided by the present disclosure
  • FIG. 3 is a schematic diagram showing a SAX method in a method for storing data of a power grid simulation analysis provided by the present disclosure
  • FIG. 4 is a schematic diagram of symbolic representation information corresponding to original simulation results in a power grid simulation analysis data storage method provided by the present disclosure
  • FIG. 5 is a Bitmap diagram corresponding to an original simulation result in a power grid simulation analysis data storage method provided by the present disclosure
  • FIG. 6 is a schematic diagram of a single-section simulation result of a power system in a stable working condition in a raw simulation result in a power grid simulation analysis data storage method provided by the present disclosure
  • FIG. 7 is a schematic diagram of a single-section simulation result of a power system in an unstable condition in an original simulation result in a power grid simulation analysis data storage method provided by the present disclosure
  • FIG. 8 is a structural block diagram of a power grid simulation analysis data storage apparatus according to an example of the present disclosure.
  • FIG. 1 shows a block diagram of a structure of an electronic device 100 that can be applied to the present disclosure.
  • the electronic device 100 may be a server running a power system, or may be a terminal device that is in communication with a server running the power system. This embodiment is not limited thereto.
  • the electronic device 100 can include a memory 102, a memory controller 104, one or more (only one shown in FIG. 1) processor 106, a peripheral interface 108, an input and output module 110, an audio module 112, The display module 114, the radio frequency module 116, and the grid simulation analysis data storage device.
  • the memory 102, the memory controller 104, the processor 106, the peripheral interface 108, the input and output module 110, the audio module 112, the display module 114, and the RF module 116 are electrically connected directly or indirectly to each other to implement data transmission or Interaction.
  • these components can be electrically connected by one or more communication buses or signal buses.
  • the grid simulation analysis data storage method includes at least one software function module that can be stored in the memory 102 in the form of software or firmware, such as a software function module or a computer program included in the grid simulation analysis data storage device.
  • the memory 102 can be configured to store various software programs and modules, such as the grid simulation analysis data storage method provided by the present disclosure and program instructions/modules corresponding to the apparatus.
  • the processor 106 executes various functional applications and data processing by running software programs and modules stored in the memory 102, such as the power grid simulation analysis data storage method in the present disclosure.
  • the memory 102 may include, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read only memory (PROM), and an erasable memory. Erasable Programmable Read-Only Memory (EPROM), Electric Erasable Programmable Read-Only Memory (EEPROM), and the like.
  • RAM random access memory
  • ROM read only memory
  • PROM programmable read only memory
  • EPROM Erasable Programmable Read-Only Memory
  • EEPROM Electric Erasable Programmable Read-Only Memory
  • Processor 106 can be an integrated circuit chip with signal processing capabilities.
  • the processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP processor, etc.), or a digital signal processor (DSP) or an application specific integrated circuit (ASIC). ), Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
  • CPU central processing unit
  • NP processor network processor
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA Field Programmable Gate Array
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • peripheral interface 108 couples various input/output devices to the processor 106 and to the memory 102.
  • peripheral interface 108, processor 106, and memory controller 104 can be implemented in a single chip. In other examples, they can be implemented by separate chips.
  • the input output module 110 is configured to provide input data to the user to enable user interaction with the electronic device 100.
  • the input and output module 110 can be, but is not limited to, a mouse, a keyboard, and the like.
  • the audio module 112 provides an audio interface to the user, which may include one or more microphones, one or more speakers, and audio circuitry.
  • the display module 114 provides an interactive interface (eg, a user interface) between the electronic device 100 and the user or for displaying image data to the user for reference.
  • the display module 114 can be a liquid crystal display or a touch display.
  • a touch display it can be a capacitive touch screen or a resistive touch screen that supports single-point and multi-touch operations. Supporting single-point and multi-touch operations means that the touch display can sense simultaneous touch operations from one or more locations on the touch display, and the touch operation is transferred to the processor 106. Perform calculations and processing.
  • the radio frequency module 116 is configured to receive and transmit electromagnetic waves to effect mutual conversion of electromagnetic waves and electrical signals to communicate with a communication network or other device.
  • FIG. 1 is merely illustrative, and the electronic device 100 may further include more or less components than those shown in FIG. 1, or have a different configuration than that shown in FIG.
  • the components shown in Figure 1 can be implemented in hardware, software, or a combination thereof.
  • the electronic device 100 can function as a user terminal or as a server.
  • the user terminal may be a terminal device such as a PC (personal computer) computer, a tablet computer, a mobile phone, a notebook computer, a smart TV, a set top box, or an in-vehicle terminal.
  • the present disclosure provides a grid simulation analysis data storage method, which can be applied to the electronic device shown in FIG. 1, and various steps included in the method will be described below.
  • Step S200 Acquire grid simulation analysis data, where the grid simulation analysis data includes original simulation results.
  • the original simulation result may be a simulation result of the power system.
  • Step S210 Perform characterization description on the original simulation result, and obtain multi-level model characterization information corresponding to the original simulation result.
  • the original simulation result includes a time series of a plurality of state variables and algebraic variables
  • the multi-level model representation information includes symbolized representation information and a Bitmap map
  • step S210 may include the following steps.
  • the time series is characterized based on a preset time series symbolization method to obtain symbolized representation information corresponding to the time series.
  • step one may include the following sub-steps:
  • the symbol corresponding to each of the second number of target portions is chronologically composed of symbolized representation information of the time series.
  • symbolized representation information is rendered based on a Bitmap rendering method to obtain a Bitmap map corresponding to the time series.
  • the foregoing step 2 may include the following sub-steps:
  • the number of occurrences of the preset characters in the symbolized representation information is counted, and the number of times is filled into the matrix as pixel values to obtain the Bitmap map.
  • the preset time series symbolization method may be a SAX (Simple API for XML) method.
  • the simulation of the power system is usually to verify the different fault types of the grid under different operating conditions. Since the simulation results are composed of the operating conditions and the fault types, the number of simulation results is large.
  • the result of each simulation also contains a time series of multiple state variables and algebraic variables. Taking the actual system as an example, the result of each simulation may contain thousands of time series of state variables and algebraic variables. If 20s is simulated in steps of 0.01s, the length of the time series is 2000. Therefore, the simulation result of the power system is actually a high-dimensional matrix.
  • the dynamic trajectories of power systems can be divided into two categories, one of which is the dynamic simulation results of state variables or algebraic variables obtained by simulation.
  • the other is a Wide Area Measurement System (WAMS) system based on PMU (Phasor Measurement Unit) or data acquisition and monitoring based on Industrial Remote Control Unit (RTU) (SCADA, Supervisory Control And Data Acquisition)
  • WAMS Wide Area Measurement System
  • RTU Industrial Remote Control Unit
  • SCADA Supervisory Control And Data Acquisition
  • the power system dynamic waveform that the PMU-based WAMS system can provide is microsecond, and the sampling interval is about 0.01s.
  • the dynamic waveform of the RTU-based SCADA system is second, and the sampling interval is about 1 s.
  • the SAX method combined with the mapping of Bitmap can obtain the picture representation of the single measurement curve of the power system.
  • the SAX method is a very classic time series symbolization method. First, it discretizes the value of the vertical axis of a time series curve into M parts, where M is called alphabetasize, which is used as a symbol in the SAX method. Number. M is the first quantity mentioned above. Then the time axis is equally divided into N parts, and then the average of the time series in each time period is taken as the value of the part. N is the second quantity mentioned above. In this way, the dimensionality reduction of the curve represented by N symbols can be obtained, wherein each symbol has M kinds of values, as shown in FIG. 3 below.
  • a time series of 1200 points is drawn using a random walk.
  • the curve is characterized by the SAX method, the alphabetasize is taken as 8, that is, the vertical axis of the time series is divided into 8 parts.
  • the time series is divided into 60 small segments. Find the average of the time series on each segment.
  • the Chao Game theorem which is widely used in DNA sequence representation, can be characterized to map time series of Bitmap images. According to the Chao Game theorem, after the Alphabetasize is determined, a picture can be divided into blocks, and the precision of each cell can be continuously improved.
  • the number of occurrences of the substring in the string representing the time series is statistically calculated and filled into the matrix of the Bitmap.
  • a time series Bitmap diagram can be obtained.
  • the curve in the 39-node simulation example of the power system 10 in the original simulation results is selected to demonstrate the example.
  • the voltage oscillation data of 0.2s before the fault of one bus and 1s after the fault is intercepted. It was characterized by the SAX method, as shown in Figure 4. And according to the above method, draw a Bitmap diagram. In the image characterized by Figure 4, the Alphabetasize is taken as 8, which means that the vertical axis is divided into 8 parts. It can be found from the analysis of Fig. 3 and Fig. 4 that when the Alphabetasize is taken as 8, the power system can be better.
  • the dynamic waveform is characterized.
  • the precision level is divided into 2 levels, and the number of occurrences of adjacent letters (ie, the preset string) in the string of the time series curve represented by the SAX method is counted, and multiplied by 10, The resulting bitmap is shown in Figure 5.
  • the dynamic trajectory after the disturbance is actually a time series of multiple variables. If a matrix is created in which each column represents a time series, each row representing a different state variable, then the matrix can contain the oscillation information of the power system within a particular time window. For such data, it can be described by a time-series chromatic aberration diagram.
  • the simulation after the failure is performed. Sampling is performed in the manner of 0.01s, and the data of 0.2s before the fault and the data of 1s after the fault are selected, and different simulation results are compared, and FIG. 6 and FIG. 7 can be obtained.
  • the state variables numbered 40 to 78 have very significant differences in the stable and unstable simulation pictures. After searching, it is found that the state variable of this part number is the angle of the busbar, and also includes the power angle of the generator. It has good consistency with the actual system using the generator power angle to distinguish the transient stability of the power grid.
  • Step S220 Distributedly storing the multi-level model representation information into a preset data warehouse.
  • step S220 may include:
  • the multi-level model representation information is distributedly stored in a metadata format to a preset data warehouse.
  • the simulation results of the power system in the original simulation results mainly include two parts: static results and dynamic results.
  • the static simulation results are derived from power flow calculation and stable calculation.
  • the typical file format is BPA power flow calculation file (.dat) and BPA stability calculation file (.swi).
  • the power flow calculation file stores the calculation results of voltage, power angle, active power and reactive power of each node in the simulation time, while the BPA stability calculation file is based on the power flow calculation, through static safety analysis, small interference analysis and transient stability. Analyze and other means to calculate the safety and stability of the system.
  • the power flow calculation file stores the calculation result in the form of a data card, and associates the calculation result with the network topology through the connection relationship between the component and the bus.
  • the data organized in tabular form is convenient for structured storage, it has difficulty in supporting unstructured data, which is not conducive to the expansion and contraction of data.
  • the linear storage structure is also not conducive to the improvement of data search matching speed. Therefore, the present disclosure proposes a metadata-based power system static data storage mechanism to achieve efficient mass static data search.
  • the dynamic simulation result of the power system includes a time series of multiple state variables. For the actual grid, a single simulation result contains simulation results for tens of thousands of state variables and algebraic variables.
  • the result of a single simulation will be a high dimensional matrix.
  • the power system has a variety of complex operating conditions, and the types of faults are various.
  • the amount of data is usually above the TB level. If the traditional method is used for storage, it is not conducive to the analysis method of big data. Therefore, you need to use proprietary big data storage frameworks such as Hadoop and Spark to better analyze big data.
  • Any power data source is a numeric and analytical data value.
  • the semantic field is composed of a certain description syntax.
  • a medium, such as CIM XML is a file formed by the XML syntax in terms of grid parameter values and fields that interpret the meaning of the parameters.
  • Metadata is a data storage format organized by a semantic field as a key (Key), a data value (Value), and a key-value pair mapping relationship.
  • Key a semantic field
  • Value a data value
  • key-value pair mapping relationship a key-value pair mapping relationship
  • the default data warehouse is Hadoop Distributed File System (HDFS).
  • the original simulation results are characterized as the multi-level model representation information stored in Hadoop Distributed File System HDFS (Hadoop Distributed File System).
  • HDFS Hadoop Distributed File System
  • the data warehouse interacts with data messages and integration through data buses (BUSs), services, and other components.
  • Hadoop HDFS is a distributed file system that runs on Commodity Hardware and is a highly fault-tolerant system suitable for deployment on inexpensive machines.
  • HDFS provides high-throughput data access, is ideal for applications on large-scale data sets, and supports distributed implementation of MapReduce algorithms.
  • the grid simulation result files expressed by the multi-level model are distributed and stored in Hadoop HDFS.
  • the method may further include:
  • the data warehouse is searched to obtain the retrieval result, so as to realize the power grid simulation analysis knowledge mining.
  • the preset power grid simulation analysis result retrieval method may include performing retrieval according to the simulation auxiliary information, performing retrieval according to a key indicator of the power grid in the simulation result, or performing retrieval according to a specific component electrical state change mode.
  • the retrieval is performed according to the simulation auxiliary information, such as retrieving corresponding simulation examples and results according to the simulation object, time, fault type, algorithm, operator, annotation, etc.; according to the simulation results, the key indicators of the power grid are retrieved, such as according to system stability Sex, voltage levels, and other metrics retrieve all relevant studies and results; search based on specific component electrical state change patterns, such as tidal current change patterns based on multiple lines, balance changes in critical tidal sections, or even specific clusters
  • the oscillating mode performs a retrieval of simulation examples and results.
  • the grid simulation analysis result cache library is established to realize the fast retrieval and acquisition of simulation analysis results.
  • the memory data grid technology and MapReduce algorithm are combined to develop and implement the simulation image data fast retrieval method.
  • the workflow engine can reduce the programming cost of data processing and improve the ability of the system to handle a large number of tasks concurrently.
  • Task scheduling is a core part of the workflow management system.
  • Workflow task scheduling mainly includes task decomposition, resource location, resource selection and optimization.
  • static scheduling algorithms and dynamic scheduling algorithms. This sub-topic will implement these two workflow scheduling algorithms to support the selection of static or dynamic workflow scheduling algorithms for workflow patterns in different application scenarios.
  • the power grid simulation analysis data may further include a feature quantity corresponding to the original simulation result, where the feature quantity may include a feature quantity having an explicit physical concept.
  • the method may further include:
  • the key state variables in the original simulation results are screened based on the feature quantities with explicit physical concepts.
  • the feature quantity of the physical system having the physical concept may include two different types, one of which is a state variable selected by using the feature selection algorithm, for example, based on the improved Relief algorithm.
  • the resulting data is analyzed.
  • Ten key state variables can be selected in the 10-node 39-node example. As shown in Table 1, Key Variable is the key state variable.
  • the feature quantity includes a feature quantity without a physical concept.
  • the original simulation results can be extracted by using methods such as data dimensionality reduction to obtain feature quantities without physical concepts.
  • the present embodiment utilizes the method of the semantic network to organize the knowledge and semantics of the power grid by establishing a knowledge map.
  • the present disclosure provides a power grid simulation analysis data storage method, which is applied to an electronic device, the method includes acquiring power grid simulation analysis data, and the grid simulation analysis data includes original simulation results, and characterizing the original simulation results. Obtaining multi-level model representation information corresponding to the original simulation result, and then distributing the multi-level model representation information to a preset data warehouse. This method saves storage capacity and implements an effective data management mechanism.
  • the present disclosure provides a power grid simulation analysis data storage device 400 that operates on an electronic device.
  • the device 400 includes an acquisition unit 410 , a characterization unit 420 , and a storage unit 430 .
  • the acquisition unit 410 is configured to acquire grid simulation analysis data, the grid simulation analysis data including original simulation results.
  • the power grid simulation analysis data further includes a feature quantity corresponding to the original simulation result, the feature quantity includes a feature quantity having an explicit physical concept, and the acquiring unit 410 is further configured to filter the feature quantity based on the feature quantity with an explicit physical concept.
  • the key state variables in the original simulation results are further configured to filter the feature quantity based on the feature quantity with an explicit physical concept.
  • the characterization unit 420 is configured to perform characterization description on the original simulation result, and obtain multi-level model characterization information corresponding to the original simulation result.
  • the original simulation result includes a time series of a plurality of state variables and algebraic variables, the multi-level model representation information including symbolized representation information and a Bitmap map.
  • the characterization unit 420 may be configured to: characterization the time series based on a preset time series symbolization method for a time sequence of each of the plurality of state variables and algebraic variables, Obtaining symbolized representation information corresponding to the time series; drawing the symbolized representation information according to a Bitmap rendering method to obtain a Bitmap map corresponding to the time series.
  • the time series of any one of the plurality of state variables and algebraic variables is a curve of the variable with respect to time.
  • the characterization unit 420 may perform the characterization of the time series based on a preset time series symbolization method: dividing the value range of the variable into a first number of parts, each part corresponding to one symbol; The time variation range of the variable is divided into a second number of time segments; for each time segment, an average value of the variable in the time period is calculated, and the average value belongs to the first number of parts a target portion, the second number of target portions are obtained; and symbols corresponding to the second plurality of target portions are chronologically composed of symbolized representation information of the time series.
  • the characterization unit 420 renders the symbolized characterization information based on the Bitmap rendering method, and the manner of obtaining the Bitmap map corresponding to the time series may be: establishing a matrix of the Bitmap; and counting the symbolized characterization information. The number of times the preset character appears, and the number of times is filled into the matrix as a pixel value to obtain the Bitmap map.
  • the storage unit 430 is configured to distribute the multi-level model representation information in a preset data warehouse.
  • the storage unit 430 is configured to distribute the multi-level model representation information in a format of metadata to a preset data warehouse.
  • the device 400 can also include:
  • the retrieval unit 440 is configured to perform a retrieval in the data warehouse based on a preset grid simulation analysis result retrieval method, and obtain a retrieval result to implement power grid simulation analysis knowledge mining.
  • the preset power grid simulation analysis result retrieval method comprises: performing retrieval according to the simulation auxiliary information, performing retrieval according to a key indicator of the power grid in the simulation result, or performing retrieval according to a specific component electrical state change mode.
  • Each of the above units may be implemented by software code.
  • each unit described above may be stored in the memory 102.
  • the above units can also be implemented by hardware such as an integrated circuit chip.
  • each block of the flowchart or block diagram can represent a module, a program segment, or a portion of code that includes one or more of the Executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the blocks may also occur in a different order than those illustrated in the drawings.
  • each block of the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented in a dedicated hardware-based system that performs the specified function or function. Or it can be implemented by a combination of dedicated hardware and computer instructions.
  • each functional module in various embodiments of the present disclosure may be integrated to form a separate part, or each module may exist separately, or two or more modules may be integrated to form a separate part.
  • the functions, if implemented in the form of software functional modules and sold or used as separate products, may be stored in a computer readable storage medium.
  • a computer readable storage medium including: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like.
  • the power grid simulation analysis data storage method, device and electronic device provided by the disclosure reduce the capacity of the required storage device and realize an effective data management mechanism.

Abstract

提供了一种电网仿真分析数据存储方法、装置及电子设备，涉及电力技术领域。方法应用于一电子设备，所述方法包括获取电网仿真分析数据，所述电网仿真分析数据包括原始仿真结果后，对所述原始仿真结果进行表征描述，获得所述原始仿真结果对应的多层次模型表征信息，然后将所述多层次模型表征信息分布式存储至预设的数据仓库中。该方法节省存储容量，实现有效的数据管理机制。

Description

电网仿真分析数据存储方法、装置及电子设备
相关申请的交叉引用
本申请要求于2018年05月14日提交中国专利局的申请号为2018104595387，名称为“电网仿真分析数据存储方法及装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本公开涉及电力技术领域，具体而言，涉及一种电网仿真分析数据存储方法、装置及电子设备。
背景技术
随着特高压输电技术的全面推广，大规模交直流混联电网已经成为我国电网输电的基本格局。交直流混联大电网形成后，特高压直流送受端系统相互耦合、交直流系统相互作用、特高压与超高压系统相互制约的问题更加明显，电网调度运行特性由此发生深刻变化，这对电力系统运行的精细化调控和一体化统筹管理水平提出了更高要求。
发明内容
本公开的目的之一在于提供一种电网仿真分析数据存储方法、装置及电子设备，以至少部分地改善上述问题。为了实现上述目的，本公开采取的技术方案如下：
第一方面，本公开提供了一种电网仿真分析数据存储方法应用于一电子设备，所述方法包括获取电网仿真分析数据，所述电网仿真分析数据包括原始仿真结果；对所述原始仿真结果进行表征描述，获得所述原始仿真结果对应的多层次模型表征信息；将所述多层次模型表征信息分布式存储至预设的数据仓库中。
第二方面，本公开提供了一种电网仿真分析数据存储装置，运行于一电子设备，所述装置包括：获取单元、表征单元和存储单元。获取单元配置成获取电网仿真分析数据，所述电网仿真分析数据包括原始仿真结果。表征单元配置成对所述原始仿真结果进行表征描述，获得所述原始仿真结果对应的多层次模型表征信息。存储单元配置成将所述多层次模型表征信息分布式存储至预设的数据仓库中。
本公开的其他特征和优点将在随后的说明书阐述，并且，部分地从说明书中变得显而易见，或者通过实施本公开了解。本公开的目的和其他优点可通过在所写的说明书、权利要求书、以及附图中所特别指出的结构来实现和获得。
附图说明
为了更清楚地说明本公开的技术方案，下面将对实施例中所需要使用的附图作简单地 介绍，应当理解，以下附图仅示出了本公开的某些实施例，因此不应被看作是对范围的限定，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1为一种可用于本公开提供的电子设备的结构框图；
图2为本公开提供的电网仿真分析数据存储方法的流程图；
图3为本公开提供的电网仿真分析数据存储方法中SAX方法的演示示意图；
图4为本公开提供的电网仿真分析数据存储方法中原始仿真结果对应的符号化表征信息示意图；
图5为本公开提供的电网仿真分析数据存储方法中原始仿真结果对应的Bitmap图；
图6为本公开提供的电网仿真分析数据存储方法中原始仿真结果中稳定工况的电力系统单断面仿真结果示意图；
图7为本公开提供的电网仿真分析数据存储方法中原始仿真结果中不稳定工况的电力系统单断面仿真结果示意图；
图8为本公开例提供的电网仿真分析数据存储装置的结构框图。
具体实施方式
为使本公开的目的、技术方案和优点更加清楚，下面将结合本公开中的附图，对本公开中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本公开一部分实施例，而不是全部的实施例。通常在此处附图中描述和示出的本公开的组件可以以各种不同的配置来布置和设计。因此，以下对在附图中提供的本公开的实施例的详细描述并非旨在限制要求保护的本公开的范围，而是仅仅表示本公开的选定实施例。基于本公开中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本公开保护的范围。
应注意到：相似的标号和字母在下面的附图中表示类似项，因此，一旦某一项在一个附图中被定义，则在随后的附图中不需要对其进行进一步定义和解释。同时，在本公开的描述中，术语“第一”、“第二”等仅用于区分描述，而不能理解为指示或暗示相对重要性。
图1示出了一种可应用于本公开中的电子设备100的结构框图。电子设备100可以是运行有电力系统的服务器，也可以是与运行有电力系统的服务器通信连接的终端设备，本实施例对此没有限制。如图1所示，电子设备100可以包括存储器102、存储控制器104、一个或多个(图1中仅示出一个)处理器106、外设接口108、输入输出模块110、音频模块112、显示模块114、射频模块116和电网仿真分析数据存储装置。
存储器102、存储控制器104、处理器106、外设接口108、输入输出模块110、音频 模块112、显示模块114、射频模块116各元件之间直接或间接地电连接，以实现数据的传输或交互。例如，这些元件之间可以通过一条或多条通讯总线或信号总线实现电连接。电网仿真分析数据存储方法分别包括至少一个可以以软件或固件(firmware)的形式存储于存储器102中的软件功能模块，例如所述电网仿真分析数据存储装置包括的软件功能模块或计算机程序。
存储器102可以配置成存储各种软件程序以及模块，如本公开提供的电网仿真分析数据存储方法及装置对应的程序指令/模块。处理器106通过运行存储在存储器102中的软件程序以及模块，从而执行各种功能应用以及数据处理，例如现本公开中的电网仿真分析数据存储方法。
存储器102可以包括，但不限于，随机存取存储器(Random Access Memory，RAM)，只读存储器(Read Only Memory，ROM)，可编程只读存储器(Programmable Read-Only Memory，PROM)，可擦除只读存储器(Erasable Programmable Read-Only Memory，EPROM)，电可擦除只读存储器(Electric Erasable Programmable Read-Only Memory，EEPROM)等。
处理器106可以是一种集成电路芯片，具有信号处理能力。上述处理器可以是通用处理器，包括中央处理器(Central Processing Unit，简称CPU)、网络处理器(Network Processor，简称NP)等；还可以是数字信号处理器(DSP)、专用集成电路(ASIC)、现场可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。其可以实现或者执行本公开中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述外设接口108将各种输入/输出装置耦合至处理器106以及存储器102。在一些示例中，外设接口108、处理器106以及存储控制器104可以在单个芯片中实现。在其他一些示例中，他们可以分别由独立的芯片实现。
输入输出模块110配置成提供给用户输入数据，以实现用户与电子设备100的交互。所述输入输出模块110可以是，但不限于，鼠标和键盘等。
音频模块112向用户提供音频接口，其可包括一个或多个麦克风、一个或者多个扬声器以及音频电路。
显示模块114在电子设备100与用户之间提供一个交互界面(例如用户操作界面)或用于显示图像数据给用户参考。在本实施例中，所述显示模块114可以是液晶显示器或触控显示器。若为触控显示器，其可为支持单点和多点触控操作的电容式触控屏或电阻式触控屏等。支持单点和多点触控操作是指触控显示器能感应到来自该触控显示器上一个或多个位置处同时产生的触控操作，并将该感应到的触控操作交由处理器106进行计算和处理。
射频模块116配置成接收以及发送电磁波，实现电磁波与电信号的相互转换，从而与通信网络或者其他设备进行通信。
可以理解，图1所示的结构仅为示意，电子设备100还可包括比图1中所示更多或者更少的组件，或者具有与图1所示不同的配置。图1中所示的各组件可以采用硬件、软件或其组合实现。
于本公开中，电子设备100可以作为用户终端，或者作为服务器。其中，用户终端可以为PC(personal computer)电脑、平板电脑、手机、笔记本电脑、智能电视、机顶盒、车载终端等终端设备。
请参阅图2，本公开提供了一种电网仿真分析数据存储方法，可以应用于图1中示出的电子设备，下面将对该方法包括的各个步骤进行描述。
步骤S200：获取电网仿真分析数据，所述电网仿真分析数据包括原始仿真结果。
在本实施例中，原始仿真结果可以是电力系统的仿真结果。
步骤S210：对所述原始仿真结果进行表征描述，获得所述原始仿真结果对应的多层次模型表征信息。
可选地，所述原始仿真结果包括多个状态变量和代数变量的时间序列，所述多层次模型表征信息包括符号化表征信息和Bitmap图，步骤S210可以包括以下步骤。
第一，针对所述多个状态变量和代数变量中每一变量的时间序列，基于预设的时间序列符号化方法对所述时间序列进行表征，获得所述时间序列对应的符号化表征信息。
其中，上述步骤一可以包括以下子步骤：
将所述变量的取值范围划分为第一数量个部分，每个部分对应一个符号；将所述变量的时间变化范围划分为第二数量个时间段；
针对每一时间段，计算所述变量在该时间段内的平均值，从所述第一数量个部分中确定该平均值所属的目标部分，得到所述第二数量个目标部分；
将所述第二数量个目标部分各自对应的符号按照时间顺序组成所述时间序列的符号化表征信息。
第二，基于Bitmap绘制方法对所述符号化表征信息进行绘制，获得所述时间序列对应的Bitmap图。
可选地，上述步骤二可以包括以下子步骤：
建立Bitmap的矩阵；
统计所述符号化表征信息中预设字符出现的次数，将该次数作为像素值填充到所述矩阵中，得到所述Bitmap图。
在本实施例中，所述预设的时间序列符号化方法可以为SAX(Simple API for XML)方法。电力系统的仿真，通常是对不同运行工况下的电网进行不同故障类型的校验。由于仿真结果由运行工况和故障类型组合而成，所以仿真结果数量较多。每次仿真的结果，也包含了多个状态变量和代数变量的时间序列。以实际系统为例，每次仿真的结果可能会包含几千个状态变量和代数变量的时间序列。如果以0.01s的步长仿真20s，那么时间序列的长度为2000。因此，电力系统的仿真结果实际上是一个高维的矩阵。而在对电力系统的仿真结果进行分析的时候，如果直接对高维矩阵进行处理，数据的规模较大而且较为抽象，不太便于直接分析处理和探索发现不同仿真结果之间的本质区别，因此，为了更好地对电网的特性进行挖掘，需要探索仿真结果的其它表征方法。
电力系统的动态轨迹可分为两类，其中一类为仿真得到的状态变量或者代数变量的动态仿真结果。另外一类为基于同步向量测量装置(PMU，Phasor Measurement Unit)的广域测量系统(WAMS，Wide Area Measurement System)系统或者基于工业控制系统(RTU，Remote Terminal Unit)的数据采集及监控(SCADA，Supervisory Control And Data Acquisition)系统量测得到的实际电力系统的动态波形。其中基于PMU的WAMS系统能够提供的电力系统动态波形是微秒级的，采样间隔在0.01s左右。而基于RTU的SCADA系统的动态波形是秒级的，采样间隔在1s左右。利用SAX方法结合Bitmap的绘制可以得到电力系统的单条量测曲线的图片表征。SAX方法是一种很经典的时间序列符号化方法，首先它将一条时间序列曲线的纵轴的取值进行离散化，分成M个部分，其中M被称为alphabetasize，作为SAX方法中表征符号的个数。M即为上述的第一数量。然后再将时间轴等分为N个部分，然后求取每个时间段内时间序列的平均值将其作为该部分的值。N即为上述的第二数量。这样，可以得到用N个符号表示的曲线降维，其中每个符号存在M种取值，如下图3所示。
在图3中，利用随机游走的方法绘制了一条包含1200个点的时间序列。在利用SAX方法对曲线进行表征的时候，alphabetasize取为8，也就是说，将时间序列的纵轴分成了8份。在此基础上，将时间序列划分为了60个小段。求取每个小段上时间序列的平均值。在利用SAX方法得到时间序列的表征之后，可利用广泛运用于DNA序列表示的Chao Game定理，对其进行表征，从而绘制出时间序列的Bitmap图像。根据Chao Game定理，在确定了Alphabetasize之后，对一张图片可以进行逐划分，每个小格的精度可以不断提高。根据绘制Bitmap图的基本引理，统计表征时间序列的字符串中子串出现的次数，并将其填写到Bitmap的矩阵中，作为像素点的表征，即可得到时间序列的Bitmap图。
选取原始仿真结果中电力系统10机39节点仿真算例中的曲线进行算例演示。截取 其中一条母线故障前0.2s和故障后1s内的电压振荡数据。利用SAX方法对其进行表征，如图4所示。并按照以上方法，绘制出Bitmap图。在图4表征的图像中，Alphabetasize取为8，这意味着纵轴被分成了8份，通过对图3、图4的分析可以发现，当Alphabetasize取为8时，能够较好地对电力系统的动态波形进行表征。在绘制Bitmap图片的时候，精度层级划分到2级，统计利用SAX方法表征得到的时间序列曲线的字串中相邻字母(即：所述预设字符串)出现的次数，并乘以10，最终绘制出的Bitmap图如图5所示。
对于原始仿真结果中特定工况下的电力系统，在受到扰动之后的动态轨迹实际上为多个变量的时间序列。如果建立一个矩阵，其中每一列代表一条时间序列，每一行代表不同的状态变量，那么这个矩阵即可包含了特定时间窗内电力系统的振荡信息。对此类数据，可以用时序色差图进行描述。在10机39节点中，进行故障后的仿真。以0.01s的方式进行采样，选取故障前0.2s的数据和故障后1s的数据，比较不同的仿真结果，可以得到图6和图7。从图6和图7可以看出，编号为40到78的状态变量在稳定和不稳定的仿真图片中，具有非常显著的区别。经过查找发现，这部分编号的状态变量为母线的角度，也包含了发电机的功角，与实际系统中利用发电机功角对电网的暂态稳定进行区分具有较好的一致性。
步骤S220：将所述多层次模型表征信息分布式存储至预设的数据仓库中。
可选地，步骤S220可以包括：
将所述多层次模型表征信息以元数据的格式分布式存储至预设的数据仓库中。
在本实施例中，原始仿真结果中电力系统的仿真结果主要包含了静态结果和动态结果两大部分。其中，静态的仿真结果来源于潮流计算和稳定计算，典型的文件格式为BPA潮流计算文件(.dat)和BPA稳定计算文件(.swi)。其中，潮流计算文件存储了仿真时间内各节点电压、功角、有功、无功的计算结果，而BPA稳定计算文件则在潮流计算的基础上，通过静态安全分析、小干扰分析和暂态稳定分析等手段，计算出系统的安全稳定状态。
潮流计算文件以数据卡的形式存储计算结果，并通过元件与母线的连接关系将计算结果跟网络拓扑关联起来。这种以表格形式组织的数据虽然便于结构化存储，但对于非结构化数据的支持存在困难，不利于数据的伸缩扩展，线性的存储结构也不利于数据的搜索匹配速度的提升。因此，本公开提出了一种基于元数据的电力系统静态数据存储机制，以实现高效的海量静态数据搜索匹配电力系统的动态仿真结果包含了多个状态变量的时间序列。对于实际电网，单次的仿真结果就包含了上万个状态变量和代数变量的仿真结果。如果仿真的步长为0.01s，对电力系统进行了20秒的仿真，那么单次仿真的结果将是一个高维的矩阵。而电力系统具有多种复杂的运行工况，故障类型也多种多样，数据量通常在TB 量级以上，如果利用传统的方法进行存储，不太利于大数据的分析方法。因此，需要利用Hadoop和Spark等专有的大数据存储框架，从而能够更好地进行大数据的分析任何电力的数据源都是数值型和解析数据值的语义字段按照一定的描述语法组成的数据介质，例如CIM XML就是电网参数值和解释参数含义的字段按照XML语法形成的文件。元数据，就是以语义字段为键(Key)，以数据值为值(Value)，用键值对的映射关系组织起来的数据存储格式。元数据存储最大的优势在于消除了电力数据描述语法的差异，简化了语义字段的描述方式，使得不同结构乃至不同来源的电力系统数据都能在统一的形式下存储和表达。这样可以极大丰富仿真数据的来源，提升数据存储的灵活性和伸缩性。
在本实施例中，预设的数据仓库为Hadoop的分布式文件系统HDFS(Hadoop Distributed File System)。将原始仿真结果表征为所述多层次模型表征信息存于Hadoop的分布式文件系统HDFS(Hadoop Distributed File System)中。数据仓库通过数据总线(BUS)、服务(Service)和其他组件交互数据消息及集成。Hadoop HDFS是一个可以在通用硬件(Commodity Hardware)上运行的分布式文件系统，是一个具有高度容错性的系统，适合部署在廉价的机器上。HDFS能提供高吞吐量的数据访问，非常适合大规模数据集上的应用，并支持MapReduce算法的分布式实现。在电网仿真数据存储实施中，将多层次模型表述的电网仿真结果文件分布式存储于Hadoop HDFS中。在Hadoop HDFS基础上，定义海量数据仓库的高可用部署方案，包括节点资源配置、网络拓扑和应用服务部署方案等，保障数据存储和应用系统高效性和可靠性。针对电网分析海量数据仓库的监控和调度技术，实施有效的数据管理机制，有效地对平台中的数据存储、访问、迁移、处理等过程进行监控，并对各类硬件和服务资源进行动态监测和优化调度。
步骤S220之后，所述方法还可以包括：
基于预设的电网仿真分析结果检索方法，在所述数据仓库中进行检索，获得检索结果，以实现电网仿真分析知识挖掘。
可选地，所述预设的电网仿真分析结果检索方法可以包括根据仿真辅助信息进行检索、根据仿真结果中电网关键指标进行检索或根据特定的元件电气状态改变模式进行检索。
详细地，根据仿真辅助信息进行检索，如根据仿真对象、时间、故障类型、算法、操作者、批注等检索相应的仿真算例和结果；根据仿真结果中电网关键指标进行检索，如根据系统稳定性、电压水平和其他指标检索所有相关的算例和结果；根据特定的元件电气状态改变模式进行检索，如根据多条线路的潮流改变模式、关键潮流断面的平衡变化情况，甚至是特定机群间震荡模式进行仿真算例和结果的检索。基于研发的内存数据网格技术，建立电网仿真分析结果缓存库，以实现仿真分析结果快速检索和获取，结合内存数据网格 技术和MapReduce算法开发、实施仿真影像数据快速检索方法。开发适用于电网分析数据仓库的工作流引擎。为了实现电网仿真分析知识挖掘，需要依据所存储的仿真分析数据在仿真知识库中开展大量的数据检索和模式识别的作业。工作流引擎可以减少数据处理的编程代价，提高系统并发处理大量任务的能力。任务调度是工作流管理系统的核心部分。工作流任务调度，主要包括任务的分解、资源的定位、资源选择与优化等。工作流调度方法目前有两类：静态调度算法和动态调度算法。本子课题将实施这两种工作流调度算法，支持针对不同应用场景的工作流形态选择静态或者动态工作流调度算法。
可选地，所述电网仿真分析数据还可以包括原始仿真结果对应提取的特征量，所述特征量可以包括具有显式物理概念的特征量，在此情况下，所述方法还可以包括：
基于具有显式物理概念的特征量，筛选所述原始仿真结果中的关键状态变量。
在本实施例中，电力系统有物理概念的特征量可以包括两种不同的类型，其中一种是，利用了特征选取算法得到的，筛选出的状态变量，例如，基于改进的Relief算法对仿真结果数据进行分析，可在10机39节点的算例中选取10个关键的状态变量，如表1所示，Key Variable为关键状态变量。
表1筛选出的电网关键状态变量
Figure PCTCN2018123608-appb-000001
可选地，所述特征量包括无物理概念的特征量。可以利用数据降维数等方法对原始仿真结果进行特征提取，获得无物理概念的特征量。
此外，本实施例利用语义网络的方法，通过建立知识图谱，对电网的知识语义进行了整理。
本公开提供了一种电网仿真分析数据存储方法，应用于一电子设备，所述方法包括获取电网仿真分析数据，所述电网仿真分析数据包括原始仿真结果后，对所述原始仿真结果 进行表征描述，获得所述原始仿真结果对应的多层次模型表征信息，然后将所述多层次模型表征信息分布式存储至预设的数据仓库中。该方法节省存储容量，实现有效的数据管理机制。
请参阅图8，本公开提供了一种电网仿真分析数据存储装置400，运行于一电子设备，所述装置400包括获取单元410、表征单元420和存储单元430。
获取单元410配置成获取电网仿真分析数据，所述电网仿真分析数据包括原始仿真结果。
所述电网仿真分析数据还包括原始仿真结果对应提取的特征量，所述特征量包括具有显式物理概念的特征量，获取单元410，还用于基于具有显式物理概念的特征量，筛选所述原始仿真结果中的关键状态变量。
表征单元420配置成对所述原始仿真结果进行表征描述，获得所述原始仿真结果对应的多层次模型表征信息。
所述原始仿真结果包括多个状态变量和代数变量的时间序列，所述多层次模型表征信息包括符号化表征信息和Bitmap图。在此情况下，所述表征单元420具体可以配置成：针对所述多个状态变量和代数变量中每一变量的时间序列，基于预设的时间序列符号化方法对所述时间序列进行表征，获得所述时间序列对应的符号化表征信息；基于Bitmap绘制方法对所述符号化表征信息进行绘制，获得所述时间序列对应的Bitmap图。
可选地，在本实施例中，所述多个状态变量和代数变量中任意一个变量的时间序列为该变量相对于时间的变化曲线。所述表征单元420基于预设的时间序列符号化方法对所述时间序列进行表征的方式可以为：将所述变量的取值范围划分为第一数量个部分，每个部分对应一个符号；将所述变量的时间变化范围划分为第二数量个时间段；针对每一时间段，计算所述变量在该时间段内的平均值，从所述第一数量个部分中确定该平均值所属的目标部分，得到所述第二数量个目标部分；将所述第二数量个目标部分各自对应的符号按照时间顺序组成所述时间序列的符号化表征信息。
可选地，所述表征单元420基于Bitmap绘制方法对所述符号化表征信息进行绘制，获得所述时间序列对应的Bitmap图的方式可以为：建立Bitmap的矩阵；统计所述符号化表征信息中预设字符出现的次数，将该次数作为像素值填充到所述矩阵中，得到所述Bitmap图。
存储单元430配置成将所述多层次模型表征信息分布式存储至预设的数据仓库中。
存储单元430配置成将所述多层次模型表征信息以元数据的格式分布式存储至预设的数据仓库中。
所述装置400还可以包括：
检索单元440配置成基于预设的电网仿真分析结果检索方法，在所述数据仓库中进行检索，获得检索结果，以实现电网仿真分析知识挖掘。
可选地，所述预设的电网仿真分析结果检索方法包括根据仿真辅助信息进行检索、根据仿真结果中电网关键指标进行检索或根据特定的元件电气状态改变模式进行检索。
以上各单元可以是由软件代码实现，此时，上述的各单元可存储于存储器102内。以上各单元同样可以由硬件例如集成电路芯片实现。
本公开提供的电网仿真分析数据存储装置400，其实现原理及产生的技术效果和前述方法实施例相同，为简要描述，装置实施例部分未提及之处，可参考前述方法实施例中相应内容。
在本公开所提供的几个实施例中，应该理解到，所揭露的装置和方法，也可以通过其它的方式实现。以上所描述的装置实施例仅仅是示意性的，例如，附图中的流程图和框图显示了根据本公开的多个实施例的装置、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分，所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现方式中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
另外，在本公开各个实施例中的各功能模块可以集成在一起形成一个独立的部分，也可以是各个模块单独存在，也可以两个或两个以上模块集成形成一个独立的部分。
所述功能如果以软件功能模块的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本公开的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本公开各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在 任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
以上所述仅为本公开的优选实施例而已，并不用于限制本公开的保护范围，对于本领域的技术人员来说，本公开可以有各种更改和变化。凡在本公开的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本公开的保护范围之内。
以上所述，仅为本公开的具体实施方式，但本公开的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本公开揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本公开的保护范围之内。因此，本公开的保护范围应所述以权利要求的保护范围为准。
工业实用性
本公开提供的电网仿真分析数据存储方法、装置及电子设备，减少了所需的存储设备的容量，实现了有效的数据管理机制。

Claims (15)

  1. 一种电网仿真分析数据存储方法，应用于一电子设备，其特征在于，所述方法包括：
    获取电网仿真分析数据，所述电网仿真分析数据包括原始仿真结果；
    对所述原始仿真结果进行表征描述，获得所述原始仿真结果对应的多层次模型表征信息；
    将所述多层次模型表征信息分布式存储至预设的数据仓库中。
  2. 根据权利要求1所述的方法，其特征在于，所述原始仿真结果包括多个状态变量和代数变量的时间序列，所述多层次模型表征信息包括符号化表征信息和Bitmap图，对所述原始仿真结果进行表征描述，获得所述原始仿真结果对应的多层次模型表征信息，包括：
    针对所述多个状态变量和代数变量中每一变量的时间序列，基于预设的时间序列符号化方法对所述时间序列进行表征，获得所述时间序列对应的符号化表征信息；
    基于Bitmap绘制方法对所述符号化表征信息进行绘制，获得所述时间序列对应的Bitmap图。
  3. 根据权利要求2所述的方法，其特征在于，所述多个状态变量和代数变量中任意一个变量的时间序列为该变量相对于时间的变化曲线；
    基于预设的时间序列符号化方法对所述时间序列进行表征，获得所述时间序列对应的符号化表征信息，包括：
    将所述变量的取值范围划分为第一数量个部分，每个部分对应一个符号；将所述变量的时间变化范围划分为第二数量个时间段；
    针对每一时间段，计算所述变量在该时间段内的平均值，从所述第一数量个部分中确定该平均值所属的目标部分，得到所述第二数量个目标部分；
    将所述第二数量个目标部分各自对应的符号按照时间顺序组成所述时间序列的符号化表征信息。
  4. 根据权利要求2或3所述的方法，其特征在于，基于Bitmap绘制方法对所述符号化表征信息进行绘制，获得所述时间序列对应的Bitmap图，包括：
    建立Bitmap的矩阵；
    统计所述符号化表征信息中预设字符出现的次数，将该次数作为像素值填充到所述矩阵中，得到所述Bitmap图。
  5. 根据权利要求1-4中任意一项所述的方法，其特征在于，将所述多层次模型表征信息分布式存储至预设的数据仓库中，包括：
    将所述多层次模型表征信息以键值对的格式分布式存储至预设的数据仓库中。
  6. 根据权利要求1-5中任意一项所述的方法，其特征在于，在将所述多层次模型表征信息分布式存储至预设的数据仓库中之后，所述方法还包括：
    基于预设的电网仿真分析结果检索规则，在所述数据仓库中进行检索，获得检索结果，以实现电网仿真分析知识挖掘。
  7. 根据权利要求6所述的方法，其特征在于，所述预设的电网仿真分析结果检索规则包括根据仿真辅助信息进行检索、根据仿真结果中电网关键指标进行检索或根据特定的元件电气状态改变模式进行检索。
  8. 根据权利要求1-7中任意一项所述的方法，其特征在于，所述电网仿真分析数据还包括原始仿真结果对应提取的特征量，所述特征量包括具有显式物理概念的特征量，所述方法还包括：
    基于具有显式物理概念的特征量，筛选所述原始仿真结果中的关键状态变量。
  9. 一种电网仿真分析数据存储装置，其特征在于，运行于一电子设备，所述装置包括：
    获取单元，配置成获取电网仿真分析数据，所述电网仿真分析数据包括原始仿真结果；
    表征单元，配置成对所述原始仿真结果进行表征描述，获得所述原始仿真结果对应的多层次模型表征信息；
    存储单元，配置成将所述多层次模型表征信息分布式存储至预设的数据仓库中。
  10. 根据权利要求9所述的装置，其特征在于，所述原始仿真结果包括多个状态变量和代数变量的时间序列，所述多层次模型表征信息包括符号化表征信息和Bitmap图，所述表征单元，具体配置成：针对所述多个状态变量和代数变量中每一变量的时间序列，基于预设的时间序列符号化方法对所述时间序列进行表征，获得所述时间序列对应的符号化表征信息；基于Bitmap绘制方法对所述符号化表征信息进行绘制，获得所述时间序列对应的Bitmap图。
  11. 根据权利要求10所述的装置，其特征在于，所述多个状态变量和代数变量中任意一个变量的时间序列为该变量相对于时间的变化曲线；所述表征单元基于预设的时间序列符号化方法对所述时间序列进行表征，获得所述时间序列对应的符号化表征信息的方式为：将所述变量的取值范围划分为第一数量个部分，每个部分对应一个符号；将所述变量的时间变化范围划分为第二数量个时间段；针对每一时间段，计算所述变量在该时间段内的平均值，从所述第一数量个部分中确定该平均值所属的目标部分，得到所述第二数量个目标部分；将所述第二数量个目标部分各自对应的符号按照时间顺序组成所述时间序列的符号化表征信息。
  12. 根据权利要求10或11所述的装置，其特征在于，所述表征单元基于Bitmap绘制方法对所述符号化表征信息进行绘制，获得所述时间序列对应的Bitmap图的方式为：建立Bitmap的矩阵；统计所述符号化表征信息中预设字符出现的次数，将该次数作为像素值填充到所述矩阵中，得到所述Bitmap图。
  13. 根据权利要求9-12中任意一项所述的装置，其特征在于，所述存储单元，还配置成将所述多层次模型表征信息以元数据的格式分布式存储至预设的数据仓库中。
  14. 根据权利要求9-13中任意一项所述的装置，其特征在于，所述装置还包括：
    检索单元，配置成基于预设的电网仿真分析结果检索方法，在所述数据仓库中进行检索，获得检索结果，以实现电网仿真分析知识挖掘。
  15. 一种电子设备，其特征在于，包括处理器及存储器，该存储器上存储有机器可执行指令，该机器可执行指令被执行时促使处理器实现权利要求1-8中任意一项所述的方法。
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