Power grid data analysis and calculation architecture establishment system

Abstract

The invention, which relates to the technical field of the power grid, discloses a power grid data analysis and calculation architecture establishment system comprising a data integration and processing engine module, a user interface module, a property interface module, a data acquisition module, a system anomaly detection module and a cloud database module. The data acquisition module comprises a user private data module and a community public data module; the system anomaly detection module is connected with an electric quantity data acquisition module, and the electric quantity data acquisition module is connected with a plurality of calculation channels. According to the method, the data can be efficiently and accurately analyzed, decision support is provided for operation, maintenance and management of the intelligent power grid, and the optimal power utilization experience can be provided for a user.

Description

Technical Field

[0001] The present invention relates to the field of power grid technology, and in particular to a system for establishing a power grid data analysis and computing architecture. Background Art

[0002] The power grid system is an electric energy production and consumption system consisting of power plants, transmission and transformation lines, power distribution stations and power users. Its function is to convert primary energy in nature into electric energy through power generation devices, and then supply electric energy to various users through transmission, transformation and distribution. To achieve this function, the power grid system also has corresponding information and control systems at various links and levels to measure, regulate, control, protect, communicate and dispatch the production process of electric energy.

[0003] With the rapid development of smart grid construction, massive amounts of power data are constantly being generated. Failure to efficiently and accurately analyze these data, provide decision support for the operation, maintenance and management of smart grids, and provide users with an optimal electricity experience is impossible. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the present invention provides a system for establishing a power grid data analysis computing architecture, which can solve the problems mentioned in the background technology.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a system for establishing a power grid data analysis and computing architecture, comprising: a data warehouse module, which is used for subject-oriented, integrated and non-updatable data storage to support high-level decision analysis; an online processing module, which is used to convert raw data and perform fast, consistent and exchangeable access information; a data visualization module, which is used to display multiple groups of data in 2D or 3D graphics according to different classification dimensions; a data acquisition module, which is used to collect various types of current data from a variety of smart meters and accessory devices; a data storage module, which is used to store feedback data in a database; a data analysis module, which is used to analyze and extract stored data, including data cleaning, data format conversion, data normalization and data dimensionality reduction; a data processing module, which is used to process the analysis results and generate reports and charts; a decision support module, which is used to provide decision support for the power grid status, provide the information analyzed by the data analysis layer to users, and provide load forecasting, fault diagnosis, energy management and other application services.

[0007] As a preferred solution for establishing a system for the power grid data analysis and computing architecture described in the present invention, each subject in the data warehouse module corresponds to a macro analysis field, and the data undergoes data processing and integration before entering the data warehouse. The data warehouse reflects the content of historical data and is a collection of data at different times, and its data retention period meets the needs of decision analysis.

[0008] As a preferred solution for establishing a system for power grid data analysis and computing architecture described in the present invention, the user response time of the online processing module is within 5S, and it can handle any logical analysis and statistical analysis related to the application, so that users can perform data analysis on the OLAP platform.

[0009] As a preferred solution for establishing a system for power grid data analysis and computing architecture described in the present invention, the data visualization module realizes observation of curve change rules through rotation and perspective.

[0010] As a preferred solution for establishing a system for the power grid data analysis and computing architecture described in the present invention, the data acquisition module includes sensors, remote metering equipment, communication equipment and a background database, constituting a data acquisition and transmission system; the data storage module stores the data collected by the data acquisition system in a background database for subsequent analysis and processing.

[0011] As a preferred solution for establishing a system for the power grid data analysis and computing architecture described in the present invention, the data processing module uses graph theory technology, including graph traversal, graph search, graph optimization and graph visualization, to find the shortest path, the longest path and the minimum spanning tree.

[0012] As a preferred solution for establishing a system for the power grid data analysis and computing architecture described in the present invention, the data acquisition module extracts information from the data by applying data analysis technology, performs virtual data analysis through time series, implements permission management by accessing ACL, tracks data by recording and monitoring data access logs, and uses an ARIMA model for prediction.

[0013] To further solve the above technical problems, the present invention provides the following technical solutions: a method for establishing a power grid data analysis computing architecture, comprising: constructing a data warehouse module and an online processing module; wherein the data warehouse module stores historical data and classifies them by subject, and the online processing module responds to user analysis requirements within 5 seconds; constructing a data visualization module, through which the data visualization module displays the data in 2D or 3D graphics; constructing a data acquisition module, a data storage module, a data analysis module, a data processing module and a decision support module; wherein the data acquisition module collects smart meter data, the data storage module stores the smart meter data, the data analysis module cleans, converts the format and normalizes the smart meter data, the data processing module generates an analysis result report, and the decision support module provides load forecasting and fault diagnosis.

[0014] A computer device includes a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of establishing a system for power grid data analysis and computing architecture as described above when executing the computer program.

[0015] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of establishing a system for power grid data analysis and computing architecture as described above are implemented.

[0016] Beneficial effects of the present invention: The present invention can analyze data efficiently and accurately, provide decision support for the operation, maintenance and management of smart grids, and provide users with an optimal electricity consumption experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 It is the overall system structure diagram of the present invention.

[0019] Figure 2 A diagram of a computer device according to the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Example 1, reference Figure 1 , as an embodiment of the present invention, provides a system for establishing a power grid data analysis and computing architecture.

[0023] See also Figure 1 The present invention provides a system for establishing a power grid data analysis and computing architecture, comprising the following steps:

[0024] Step 1: Construct data warehouse module and online processing module;

[0025] Step 2: Build a data visualization module;

[0026] Step 3: Construct data acquisition module, data storage module, data analysis module, data processing module and decision support module.

[0027] The data warehouse module constructed in the step 1 is subject-oriented, integrated and non-updatable. It can change over time and is used to support high-level decision analysis. Each subject corresponds to a macro analysis field. Before data enters the data warehouse, it must undergo data processing and integration. The data warehouse reflects the content of historical data. The data warehouse is a collection of data at different times. It requires that the data storage period in the data warehouse can meet the needs of decision analysis. It must be based on a more comprehensive and complete information application. A topic is a standard for classifying data at a higher level, and each topic corresponds to a macro-analysis field. Before the integrated data enters the data warehouse, it must undergo data processing and integration, which is a key step in establishing a data warehouse. First, the contradictions in the original data must be unified, and the original data structure must be transformed from application-oriented to topic-oriented. The data warehouse reflects the content of historical data, not the data generated by daily transaction processing. After the data is processed and integrated into the data warehouse, it is rarely or not modified at all. It changes over time. The data warehouse is a collection of data at different times. It requires that the data in the data warehouse must be kept for a period of time to meet the needs of decision analysis, and the data in the data warehouse must indicate the historical period of the data. The data in the data warehouse is not the latest or proprietary, but comes from other databases. The establishment of a data warehouse is not to replace the database. It must be based on a more comprehensive and complete information application to support high-level decision analysis, and the transaction processing database undertakes daily operational tasks in the enterprise's information environment.

[0028] The online processing module in step 1 can be converted from the original data, and the online processing module performs fast, consistent and exchanged access information. Users have high requirements for the rapid response capability of OLAP. The system should be able to respond to most of the user's analysis requirements within 5S. The OLAP system should be able to handle any logical analysis and statistical analysis related to the application, and users can perform data analysis on the OLAP platform. Users have high requirements for the rapid response capability of OLAP. The system should be able to respond to most of the user's analysis requirements within 3S, establish a special data storage format, a large number of pre-calculations, special hardware design, etc.; the OLAP system should be able to handle any logical analysis and statistical analysis related to the application, and users can define new special calculations without programming, use them as part of the analysis, and give reports in the way the user wants. Users can perform data analysis on the OLAP platform, and can also connect to other external analysis tools, such as time series analysis tools, cost allocation tools, accidental alarms, data mining, etc.; no matter how large the data volume is, no matter where the data is stored, the OLAP system should be able to obtain information in a timely manner and manage large amounts of information.

[0029] The data visualization module in step 2 uses data visualization technology to display multiple groups of data in 2D or 3D graphics according to different classification dimensions. Rotation and perspective can be used to observe the changing patterns of the curves.

[0030] The data acquisition module in step three collects various current data from a variety of smart meters and accessory devices, and feeds back to the data acquisition module. The data acquisition system refers to a data acquisition and transmission system composed of sensors, remote metering equipment, communication equipment, and background databases. The data acquisition system refers to a data acquisition and transmission system composed of sensors, remote metering equipment, communication equipment, and background databases. The main functions of the data acquisition system include remote metering technology, which refers to the use of communication technology to collect and transmit electrical parameter data from each monitoring point in the power grid to the control center. Sensor technology refers to the use of various sensor elements to convert electrical parameters into electrical signals or digital signals for data acquisition and transmission.

[0031] In the step three, the data storage module stores the feedback data in a database, and the data acquisition system stores the collected data in a background database for subsequent analysis and processing.

[0032] The data analysis module in step three analyzes and extracts the stored data, mainly including steps such as data cleaning, data format conversion, data normalization and data dimension reduction. Data normalization is to convert data of different dimensions into the same dimension to ensure the comparability of data. Data preprocessing is the basis of smart grid data analysis, mainly including steps such as data cleaning, data format conversion, data normalization and data dimension reduction. Data cleaning is to remove noise, outliers and missing values ​​in the data to ensure the integrity and accuracy of the data. Data format conversion is to convert the data into a unified format for subsequent analysis and processing. Data normalization is to convert data of different dimensions into the same dimension to ensure the comparability of data. Data dimension reduction is to reduce high-dimensional data to low-dimensional data to reduce the complexity and amount of calculation of the data.

[0033] The data processing module in step three processes the analysis results and generates reports and charts. Graph theory is a discipline that studies the properties and applications of graphs composed of vertices and edges. Power grid graph theory technology mainly includes graph traversal, graph search, graph optimization and graph visualization, etc., which are used to find the shortest path, the longest path and the minimum spanning tree. Energy grid graph theory technology mainly includes graph traversal, graph search, graph optimization and graph visualization. Graph traversal is the process of accessing all vertices or edges in the graph, which is used to find paths, rings and connected components in the graph. Graph search is the process of searching for vertices or edges that meet certain conditions in the graph, which is used to find the shortest path, the longest path and the minimum spanning tree. Graph optimization is the process of solving the optimal value of certain objective functions in the graph, which is used to solve problems such as power system flow calculation and power equipment selection. Graph visualization is to represent the graph in a graphical way to facilitate understanding and analysis of the graph.

[0034] The decision support module provides decision support for the power grid status and is mainly responsible for providing the information analyzed by the data analysis layer to users and providing various application services such as load forecasting, fault diagnosis, energy management, etc.

[0035] The data acquisition module extracts information from the data by applying data analysis technology, analyzes the virtual data contained in the power grid data through time series, implements permission management by accessing ACL, tracks data by recording and monitoring data access logs, and specifically uses ARIMA model prediction.

[0036] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0037] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0038] In summary, the present invention can analyze data efficiently and accurately, provide decision support for the operation, maintenance and management of smart grids, and provide users with an optimal electricity consumption experience.

[0039] Embodiment 2 is an embodiment of the present invention, which provides a method for establishing a power grid data analysis and computing architecture, including:

[0040] S1: Build a data warehouse module and an online processing module; the data warehouse module stores historical data and classifies it by subject, and the online processing module responds to user analysis requests within 5 seconds;

[0041] S2: construct a data visualization module, and display the data in 2D or 3D graphics through the data visualization module;

[0042] S3: Construct data acquisition module, data storage module, data analysis module, data processing module and decision support module; the data acquisition module collects smart meter data, the data storage module stores smart meter data, the data analysis module cleans, converts and normalizes smart meter data, the data processing module generates analysis result reports, and the decision support module provides load forecasting and fault diagnosis.

[0043] Example 3, reference Figure 2 , is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0044] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0045] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0046] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0047] Example 4 is an embodiment of the present invention, which provides a system for establishing a power grid data analysis and computing architecture. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0048] In order to verify the effectiveness and advancement of the present invention, the present invention designed a set of control experiments, using the power grid data analysis computing architecture system of the present invention and the existing technical solutions to compare the efficiency of power grid data analysis. The experiment selected the actual operation data of a provincial power grid company from January to December 2023 as the test sample, including multi-dimensional data such as power load, voltage fluctuation, fault warning, etc., totaling about 5 million data records. The experimental environment uses the same hardware configuration: Intel XeonE5-2680 v4 processor, 256GB RAM, and the database uses PostgreSQL 13.0 with a distributed architecture.

[0049] During the experiment, four key indicators, data processing efficiency, prediction accuracy, fault diagnosis accuracy and system response time, were investigated. The present invention uses an improved ARIMA model for load forecasting, combines graph theory technology for fault diagnosis, and improves system performance through optimized data warehouse structure and online analytical processing technology. The experiment was tested under normal load (50%) and high load (90%). Each group of tests was repeated 10 times to take the average value, and the comparability of the data was ensured through standardized processing.

[0050] Table 1: Comparison with existing technology

[0051] Test indicators Load conditions The present invention Traditional Solution A Traditional Solution B Performance improvement rate Data processing speed (10,000 records / second) Normal load 8.56 5.23 4.87 63.67％ Data processing speed (10,000 records / second) High load 6.89 3.92 3.56 75.77％ Load prediction accuracy (%) Normal load 97.23 92.45 91.87 5.61％ Load prediction accuracy (%) High load 95.67 89.34 88.92 7.08％ Fault diagnosis accuracy (%) Normal load 96.89 91.23 90.78 6.21％ Fault diagnosis accuracy (%) High load 94.56 87.45 86.92 8.13％ System response time (ms) Normal load 267 456 487 45.17％ System response time (ms) High load 342 678 712 51.97％

[0052] Through the analysis of experimental data, it can be seen that the present invention is significantly superior to the existing technical solutions in all key performance indicators. Specifically, in terms of data processing speed, the present invention can reach 85,600 pieces / second under normal load, which is 63.67% higher than the traditional solution on average; the performance advantage is more obvious under high load conditions, with an improvement of 75.77%. In terms of load prediction accuracy, the present invention reaches 97.23% and 95.67% under normal load and high load conditions, respectively, which is 5.61% and 7.08% higher than the traditional solution. The fault diagnosis accuracy shows a similar improvement trend, and the improvement is more significant under high load conditions, reaching 8.13%. The system response time has been greatly optimized, only 267ms under normal load, which is 45.17% shorter than the traditional solution. The response time optimization effect under high load conditions is more prominent, and the performance improvement rate reaches 51.97%. These data fully prove that the present invention has significant technical advantages and practical value in practical applications.

[0053] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A system for establishing a power grid data analysis and computing architecture, characterized in that: include: Data warehouse module, for subject-oriented, integrated and non-updatable data storage, supporting high-level decision analysis; Online processing module for converting raw data and accessing information quickly, consistently and interchangeably; Data visualization module, used to display multiple sets of data in 2D or 3D graphics according to different classification dimensions; Data acquisition module, used to collect various current data from various devices; A data storage module, used to store the feedback data into a database; Data analysis module, used to analyze and extract stored data, including data cleaning, data format conversion, data normalization and data dimension reduction; Data processing module, used to process the analysis results and generate reports and charts; The decision support module is used to provide decision support for the power grid status, provide the information analyzed by the data analysis layer to users, and provide application services.

2. The power grid data analysis and computing architecture establishment system according to claim 1, characterized in that: Each topic in the data warehouse module corresponds to a macro analysis field. Data is processed and integrated before entering the data warehouse. The data warehouse reflects the content of historical data and is a collection of data at different times. The data retention period meets the needs of decision-making analysis.

3. The power grid data analysis and computing architecture establishment system according to claim 2, characterized in that: The user response time of the online processing module is within 5 seconds, and it can process any logical analysis and statistical analysis related to the application, allowing users to perform data analysis on the OLAP platform.

4. The power grid data analysis and computing architecture establishment system according to claim 3, characterized in that: The data visualization module realizes the observation of the curve change law through rotation and perspective.

5. The power grid data analysis and computing architecture establishment system according to claim 4, characterized in that: The data acquisition module includes sensors, remote metering equipment, communication equipment and a background database, forming a data acquisition and transmission system; the data storage module stores the data collected by the data acquisition system in the background database for subsequent analysis and processing.

6. The power grid data analysis and computing architecture establishment system according to claim 5, characterized in that: The data processing module uses graph theory techniques, including graph traversal, graph search, graph optimization and graph visualization, to find the shortest path, the longest path and the minimum spanning tree.

7. The power grid data analysis and computing architecture establishment system according to claim 6, characterized in that: The data acquisition module extracts information from data by applying data analysis technology, performs virtual data analysis through time series, implements permission management through access ACL, tracks data by recording and monitoring data access logs, and uses ARIMA model for prediction.

8. A method for establishing a power grid data analysis and computing architecture, based on the power grid data analysis and computing architecture establishment system according to any one of claims 1 to 7, characterized in that: include, Construct data warehouse module and online processing module; The data warehouse module stores historical data and categorizes them by subject, and the online processing module responds to user analysis requests within 5 seconds; Constructing a data visualization module, through which the data is displayed in 2D or 3D graphics; Construct a data acquisition module, a data storage module, a data analysis module, a data processing module and a decision support module; wherein the data acquisition module collects smart meter data, the data storage module stores the smart meter data, the data analysis module cleans, converts the format and normalizes the smart meter data, the data processing module generates an analysis result report, and the decision support module provides load forecasting and fault diagnosis.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of establishing a system for power grid data analysis and computing architecture according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of establishing a system for power grid data analysis and computing architecture according to any one of claims 1 to 7 are implemented.

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