A distribution network operation auxiliary decision-making analysis system and its application method

Abstract

An auxiliary decision-making analysis system for distribution network operation and its application method, including: Data layer: used to classify and store the acquired external data; Platform layer: used to build operation modules according to the types of the data classified and stored in the data layer; Application layer: used to perform risk analysis on the distribution network and provide optimization solutions based on the operation modules built by the platform layer and the data classified and stored in the data layer, and is also used to display the result information of each operation module. Considering the differences of urban distribution networks from the overall architecture design to the specific business function design, it directly conducts operation and maintenance on the distribution network, realizes intelligent auxiliary decision-making analysis of the distribution network, puts forward corresponding measures pertinently, and reduces the probability of distribution network failures and risks.

Description

Technical Field

[0001] The present invention relates to the technical field of urban distribution network operation simulation, and particularly relates to a distribution network operation auxiliary decision-making analysis system and an application method thereof. Background Art

[0002] With the popularization and application of application systems such as distribution automation and power consumption information collection, an exponential growth of massive heterogeneous and high-dimensional multi-state data is generated in the distribution network. Due to the late start of distribution network informatization, the data quality, data types and data perfection are poor, and it is necessary to investigate and analyze the relevant information systems of the distribution network, and perform data preprocessing based on the existing and reliable distribution network data information. How to flexibly apply the general methods of big data in the information field to distribution network big data analysis, realize accurate prediction of distribution network faults and scanning of potential risks, and find out weak links for improvement has become an urgent problem to be solved currently.

[0003] The operation and maintenance work of the distribution network is a complex systematic project, which is characterized by large scale, many uncertain factors and wide involved fields. Traditional operation and maintenance means are difficult to meet the requirements. Relevant personnel not only need to collect historical data on the development of the target distribution network, fully analyze the characteristics of the distribution network for in-depth research, but also have a relatively comprehensive understanding of the operation health status of the distribution network and the weak links of distribution business. In order to effectively improve the operation monitoring and control capabilities of the distribution network, quickly realize the observable and controllable of the distribution network, change "passive repair" to "active monitoring", shorten the fault recovery time and improve the service level, the computer operation simulation auxiliary decision-making system has become an indispensable tool for modern distribution network planning and construction. Currently, relevant commercial software applied to distribution network operation and maintenance management and control includes: PSASP, BPA, PSS / E, ETAP, PSAPAC, DigSilent and NETOMAC, etc. Although these software provide favorable tools for distribution network simulation calculation and analysis, they do not directly target distribution network operation and maintenance.

[0004] A large number of distributed power sources, microgrids, large-capacity chargers, energy storage systems, etc. are connected to the distribution network, which makes the distribution network transform from a traditional passive distribution network to a more active active distribution network. Its complex topological structure, flexible operation mode, bidirectional and variable power flow, and uncertain output characteristics lead to great differences in the fault characteristics between the distribution network and the traditional distribution network. The weak characteristics and high-frequency transient characteristics presented by the distribution network faults are becoming more and more obvious, the difficulty of distribution network fault diagnosis and treatment increases, the existing relay protection and fault diagnosis performance of the distribution network are reduced, and misoperation and misjudgment are likely to occur, seriously threatening the safe operation of the distribution network. Summary of the Invention

[0005] In order to solve the problems existing in the prior art, the present invention provides a distribution network operation auxiliary decision-making analysis system and an application method thereof.

[0006] The technical solution provided by the present invention is as follows:

[0007] A distribution network operation auxiliary decision-making analysis system, the system comprising:

[0008] Data layer: used for classifying and storing the acquired external data;

[0009] Platform layer: used for building operation modules according to the types of the data classified and stored in the data layer;

[0010] Application layer: used for performing risk analysis on the distribution network and providing optimization solutions according to the operation modules built by the platform layer and the data classified and stored in the data layer, and is also used for displaying the result information of each operation module.

[0011] Preferably, the data layer includes: an acquisition module, a data integration module, and a data storage module;

[0012] The acquisition module is used for acquiring data from PMS2.0, power grid GIS platform, marketing business application system, power consumption information acquisition system, equipment operation and maintenance lean management system, OMS / SCADA system, and distribution network automation;

[0013] The data integration module is used for integrating the data acquired by the acquisition module into operation data, archive data, ledger data, simulation data, and topology data according to types;

[0014] The data storage module is used for storing the data acquired by the acquisition module in the corresponding data platform according to the data type; and is also used for storing the data integrated by the data integration module according to types into the database.

[0015] Preferably, the data integration module is specifically used for:

[0016] Integrating the data sets of the marketing basic data platform, massive data platform, and power grid GIS data platform into operation data, archive data, ledger data, simulation data, and topology data.

[0017] Preferably, the storage module includes: a marketing business application system data storage unit, a power consumption information acquisition system data storage unit, an equipment operation and maintenance lean management system data storage unit, a power grid GIS platform data storage unit, and an OMS / SCADA system data storage unit;

[0018] The marketing business application system data storage unit is used for real-time updating of the marketing business application system users, metering points, meter attributes, and metering boxes of the acquired marketing business application system data to the marketing basic data platform through the OGG method;

[0019] The data storage unit of the power consumption information acquisition system is used to store the meter reading at the bottom of the electric energy meter and the power consumption data of the power consumption information acquisition system into the mass data platform through the standard interface mode of the mass platform;

[0020] The data storage unit of the equipment operation and maintenance lean management system is used to store the standard coding, organizational structure, equipment files and parameters, file relationships, maintenance plans and defect information of the equipment operation and maintenance lean management system into the mass data platform through the technical route of the data center;

[0021] The data storage unit of the power grid GIS platform is used to store the power grid spatial information service data of the power grid GIS platform into the power grid GIS data platform in a timed or instant manner;

[0022] The data storage unit of the OMS / SCADA system is used to convert the data of the OMS / SCADA system into the CIM / E or XML file format and then store it into the mass data platform;

[0023] Preferably, the data storage unit of the OMS / SCADA system is used for:

[0024] When new power grid structure data is added to the OMS / SCADA system, convert the added data into a power grid structure file in the CIM / E or XML file format;

[0025] Analyze the added power grid structure file and store the analyzed data into the mass data platform.

[0026] Preferably, the storage module further includes: an integrated data storage unit;

[0027] The integrated data storage unit is used to store operation data, file data, ledger data, simulation data and topology data into the database.

[0028] Preferably, the database includes: Oracle, db, SAL and Acess

[0029] Preferably, the platform layer includes: a general data / service interface module, an application platform operation module and a development platform modeling tool module;

[0030] The general data / service interface module is used to obtain and store data from the database by type and provide the data to the application platform operation module and the development platform modeling tool module through the service bus;

[0031] The application platform operation module is used to provide a running framework, icons, and a data classification integration framework and instructions for the data layer for each model in the application layer;

[0032] The development platform modeling tool module is used to model the application layer according to the data layer data by type, configure the permissions of power grid devices, and generate reports on the distribution network topology structure.

[0033] Preferably, the general data / service interface module includes an acquisition unit and a transmission unit.

[0034] The acquisition unit is used to acquire the current latest marketing data in the way of JDBC, acquire the electricity meter bottom reading and electricity quantity data in the way of the massive platform standard interface, acquire the power grid topology and equipment ledger information in the way of ETL, acquire the power grid GIS data, OMS / SCADA system data, and data in the database.

[0035] The transmission unit is used to transmit the data classification and integration instructions of the application platform operation module to the data layer.

[0036] Preferably, the application layer includes a data management module, a risk scanning module, a fault simulation module, a grid framework optimization module, a system configuration module, and a monthly report analysis module.

[0037] The data management module is used to analyze and manage the archive data, operation data, and topology data of the data integration module, and provide visual display for the distribution network topology structure data.

[0038] The risk scanning module is used to scan the distribution network risk faults according to the ledger data, topology data, and operation data of the data integration module, lock the risk targets, and deduce the risk targets; it is also used to provide risk warnings and corresponding risk measures.

[0039] The fault simulation module is used to set the risk scanning range, maintenance plan, meteorological information, load parameters, and equipment status according to the simulation data of the data integration module; it is also used to conduct risk prediction according to the risk targets of the risk scanning module, analyze the prediction results, and formulate corresponding optimization plans.

[0040] The grid framework optimization module is used to calculate and evaluate the risk measures of the risk scanning module and the optimization plans of the fault simulation module with the goal of minimizing load loss and subject to the constraints of power grid operation safety and voltage quality.

[0041] The system configuration module is used to configure the calculation time interval of the distribution network risk, the upper and lower limits of transformer and line overload, and the upper and lower limits of voltage for each voltage level; it is also used to set component failure probability parameters, power flow impact factor library, meteorological factor impact factor, geographical factor impact factor, time factor impact factor, equipment defect impact factor, equipment age impact factor, risk grading threshold, and risk measures.

[0042] The monthly report analysis module is used to query, generate, and edit the risk scan monthly report, fault prediction monthly report, and grid optimization monthly report based on the results of the risk scan module, fault prediction module, and grid optimization module, as well as the data in the data layer, and perform online html display, import, export, and automatic email distribution.

[0043] Preferably, the fault simulation module includes: a sample data unit, a prediction unit, and a selection unit;

[0044] The sample data unit is used to split the fault data in the data layer into training data and prediction data according to a ratio of 7:3;

[0045] The prediction unit is used to train the training data on the random forest model, neural network model, support vector machine model, and ensemble learning model respectively, and use the prediction data to detect the random forest model, neural network model, support vector machine model, and ensemble learning model;

[0046] The selection unit is used to select the optimal model in the prediction models to perform online prediction on the distribution network faults.

[0047] A method for auxiliary decision-making analysis of distribution network operation, the method includes:

[0048] The data layer obtains external data and stores it classified;

[0049] The platform layer analyzes the data stored classified by the data layer and builds an operation module for the application layer;

[0050] The application layer performs risk analysis on the distribution network based on the operation module built by the platform layer and provides optimization solutions, and displays the result information of the operation module.

[0051] Preferably, the data layer obtains external data and stores it classified, including:

[0052] The data layer obtains data from PMS2.0, power grid GIS platform, marketing business application system, power consumption information collection system, equipment operation and maintenance lean management system, OMS / SCADA system, and distribution network automation;

[0053] Based on the obtained data, it is integrated into operation data, archive data, ledger data, simulation data, and topology data according to types;

[0054] The obtained data is respectively stored in the corresponding data platforms, and the classified and integrated data is stored in the database.

[0055] Preferably, the platform layer analyzes the data information stored classified by the data layer and builds an operation module for the application layer, including:

[0056] The platform layer obtains the data of the data layer based on the general data / service interface and issues data layer classification storage instructions.

[0057] The platform layer builds an operation framework, icons, and a data layer data classification integration framework for the application layer based on the obtained data.

[0058] The platform layer classifies and integrates the data of the data layer to configure the permissions of power grid equipment and generate reports on the distribution network topology structure.

[0059] Preferably, the application layer performs risk analysis on the distribution network based on the operation modules built by the platform layer, provides optimization solutions, and displays the result information of the operation modules, including:

[0060] The application layer scans the distribution network for risk faults based on account data, topology data, and operation data, locks the risk targets, and deduces the risk targets.

[0061] Provides risk warnings and corresponding risk measures based on the deduction results.

[0062] Performs risk prediction based on the risk targets and formulates optimization solutions.

[0063] Evaluates based on the risk measures and optimization solutions with the goal of minimizing load loss and subject to the constraints of power grid operation safety and voltage quality, and displays all risk measures and optimization solutions.

[0064] Compared with the prior art, the beneficial effects of the present invention are:

[0065] 1. A distribution network operation auxiliary decision-making analysis system and its application method provided by the present invention. The data layer is used to classify and store the obtained external data; the platform layer is used to build operation modules according to the types of the data classified and stored by the data layer; the application layer is used to perform risk analysis on the distribution network based on the operation modules built by the platform layer and the data classified and stored by the data layer, provide optimization solutions, and is also used to display the result information of each operation module, directly perform operation and maintenance on the distribution network, and specifically proposes corresponding measures for intelligent auxiliary decision-making analysis of the distribution network, reducing the probability of distribution network failures and risks.

[0066] 2. The present invention fully considers the differential characteristics of urban distribution networks from the overall architecture design to the specific business function design. Based on the distribution network big data platform and GIS platform, it constructs an urban distribution network operation simulation auxiliary decision-making system integrating functions such as data quality assessment, risk scanning analysis, fault prediction, and grid frame optimization by adopting advanced computer technology and distribution network operation simulation professional technology.

[0067] 3. The distribution network operation auxiliary decision-making analysis system provided by the present invention. The data management module acquires relevant data of the distribution network for multi-source data fusion and mining, analyzes and manages the archive data, operation data, and topology data respectively, provides data support for the realization of the fault prediction and risk scanning functions, completes the configuration of the distribution network operation simulation parameters, and realizes the construction of the distribution network operation simulation model.

[0068] 4. The distribution network operation auxiliary decision-making analysis system provided by the present invention. The fault simulation module can present the overall indicators of distribution network fault power outages, fault areas, historical statistics of fault occurrences, and future trends, etc., realizes the accurate prediction of faults, completes the fault simulation, deduces the distribution network faults, and gives the fault recovery plan.

[0069] 5. The distribution network operation auxiliary decision-making analysis system provided by the present invention. The network structure optimization module realizes the transfer supply analysis, loop opening analysis, and reactive power optimization analysis, provides possible optimization plans for the distribution network under different objectives such as the minimum network loss, minimum voltage deviation, and minimum comprehensive deviation, and realizes the visual presentation of each optimization plan.

[0070] 6. The distribution network operation auxiliary decision-making analysis system provided by the present invention. The monthly report analysis module realizes the summary, report generation, editing, and printing of risk scanning, fault prediction, and network structure health index conditions.

[0071] 7. The distribution network operation auxiliary decision-making analysis system provided by the present invention finally completes the differential simulation evaluation analysis of the urban distribution network, realizes the accurate positioning and grading of the risk areas and fault areas of the distribution network, supports the evolution and reconstruction of the complex distribution network structure towards high reliability and economical operation, and provides customized operation and maintenance auxiliary decision-making for the urban distribution network.

[0072] 8. The distribution network operation auxiliary decision-making analysis system provided by the present invention. The risk scanning module correlates geographical information data and meteorological information data, completes the statistics of risk trends of different risk types such as overloading risk, heavy load risk, and overvoltage risk of the distribution network, conducts risk level assessment and renders the risk degree in the form of a heat map, and realizes the visual presentation of the risk hot spots area. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 is the system architecture diagram of the distribution network operation auxiliary decision-making analysis system of the present invention;

[0074] Figure 2 is the data integration structure diagram of the distribution network information system of the present invention;

[0075] Figure 3 is the data integration structure diagram with the OMS / SCADA system of the present invention;

[0076] Figure 4 It is the flow chart for realizing the system functions of the present invention;

[0077] Figure 5 It is the data management flow chart of the distribution network information system of the present invention;

[0078] Figure 6 It is the flow chart for realizing risk scanning of the present invention;

[0079] Figure 7 It is the flow chart for realizing fault simulation of the present invention;

[0080] Figure 8 The specific implementation flow chart of fault prediction based on machine learning. Specific implementation manners

[0081] To better understand the present invention, the content of the present invention will be further described below in conjunction with the attached drawings of the specification and examples.

[0082] Example 1:

[0083] The present invention provides an intelligent operation assistance decision-making analysis system and method for a distribution network. The system adopts a three-layer technical architecture system setting and a system function architecture, covering modules such as data management, risk scanning, fault simulation, network framework optimization, system configuration, monthly report analysis, etc., to realize differential simulation analysis of the target distribution network and support efficient and accurate operation and maintenance of the distribution network. The design descriptions of the system technical architecture and function architecture are as follows:

[0084] The technical architecture of the operation assistance decision-making analysis system for the distribution network is set as Figure 1 shown.

[0085] Data layer: used to classify and store the acquired external data;

[0086] Platform layer: used to build operation modules according to the types of the data classified and stored in the data layer;

[0087] Application layer: used to perform risk analysis on the distribution network and provide optimization solutions according to the operation modules built in the platform layer and the data classified and stored in the data layer, and is also used to display the result information of each operation module.

[0088] Data layer. Deeply analyze the data types, formats, specifications, etc. of multiple external systems such as the to-be-connected PMS 2.0, power consumption information collection system, GIS platform, D5000 / SCADA system, and distribution automation system. Through data integration, data cleaning, data mining, etc., according to the actual requirements of distribution network operation simulation, deeply integrate operation and maintenance auxiliary decision-making data such as power grid fault data, operation data, meteorological information data, and geographic information data, and realize the serial acquisition of multi-service data systems. This layer consists of a graphics library and an attribute database, stored in Oracle / Acess, providing a basic support system and basic general data services, data persistence, and database access capabilities for the platform layer to call.

[0089] Data integration part

[0090] The data integration implementation process between the distribution network operation auxiliary decision-making analysis system and some distribution network information systems is as Figure 2 shown.

[0091] (1) Data integration design with marketing business application

[0092] The data integration requirements between the distribution network operation auxiliary decision-making analysis system and the marketing business application system mainly include organization structure, standard codes, archive information such as customers, metering points, meters, distribution areas, distribution transformers, etc., and information such as the relationship of distribution network equipment. Through the analysis of elements such as the amount of transmitted data and transmission frequency of the data integration requirements with the marketing professional system, for the horizontal integration data requirements between the distribution network operation auxiliary decision-making analysis system and the provincial (municipal) level, adopt the technical route of the marketing basic data platform.

[0093] Provincial marketing business application data is replicated to the provincial marketing basic data platform through the OGG method. The distribution network operation auxiliary decision-making analysis system obtains data from the marketing basic data platform in the data center. When new, changed, or deleted information occurs for users, metering point attributes, meter attributes, metering boxes, and the relationship between metering boxes and metering points, the marketing basic data platform processes the data from the marketing business application system through the OGG method as needed, and the distribution network operation auxiliary decision-making analysis system calls the current latest detailed data through the JDBC method according to the update time.

[0094] (2) Data integration design with power consumption information collection system

[0095] The data integration requirements between the distribution network operation auxiliary decision-making analysis system and the power consumption information collection system mainly include information such as 96-point current, voltage, power, power factor and other curve data. The power consumption information collection system writes information such as the bottom reading of the electric energy meter and power consumption data into the mass data platform through the standard interface of the mass platform. The provincial company deploys the calculation service module of the distribution network operation auxiliary decision-making analysis system and extracts the bottom reading of the meter for calculation through the standard interface of the mass platform.

[0096] (3) Data Integration Design with the Equipment (Asset) Operation and Maintenance Lean Management System

[0097] The data integration requirements between the Distribution Network Operation Assistant Decision-making Analysis System and the Equipment (Asset) Operation and Maintenance Lean Management System mainly include information such as standard coding, organizational structure, equipment archives and parameters, archive relationships, maintenance plans, and defects. By analyzing elements such as the data volume and transmission frequency of the data integration requirements with the operation and inspection professional system, for the horizontal integration data requirements between the Distribution Network Operation Assistant Decision-making Analysis System and the provincial (municipal) level, the technical route of the data center is adopted. The Equipment (Asset) Operation and Maintenance Lean Management System pushes the information of the transmission part, distribution part, low-voltage part, and electrical system nameplate operation library to the provincial data center, and the Distribution Network Operation Assistant Decision-making Analysis System extracts the grid topology and equipment ledger information required by the system through the ETL method.

[0098] (4) Data Integration Design with the Grid GIS Platform

[0099] The data integration requirements between the Distribution Network Operation Assistant Decision-making Analysis System and the integrated GIS system mainly include information such as the "substation-line-transformer-access point-meter box" operation and distribution connection relationship, etc. Data can be accessed through two methods: regular / immediate. The Distribution Network Operation Assistant Decision-making Analysis System mainly requires the integrated GIS system to provide application services such as basic maps, grid equipment management, thematic map display, and grid power flow map display.

[0100] 1) Operation and distribution connection data integration: For low-voltage users, a corresponding relationship is established with the grid end equipment (access point) with the low-voltage metering box as the integration point; for high-voltage users, a corresponding relationship is established with the grid end equipment with the combination of electricity user number, power supply (distributed power source), and metering point as the integration point. The grid equipment information such as substations, lines, public transformers, and low-voltage lines above the integration point is subject to the GIS system; the medium-voltage user and low-voltage user archive information below the integration point is subject to the marketing system. The Distribution Network Operation Assistant Decision-making Analysis System realizes the acquisition of high-voltage special transformer user mapping, public transformer (distribution room) mapping, and low-voltage meter box - access point mapping data by calling the operation and distribution connection data interface service provided by the grid GIS platform.

[0101] 2) Business application service integration: The Distribution Network Operation Assistant Decision-making Analysis System realizes relevant business applications by calling various grid spatial information services (grid graphic browsing service, grid graphic query and positioning service, grid analysis service, etc.) provided by the grid GIS platform.

[0102] The applications of the grid GIS platform in the Distribution Network Operation Assistant Decision-making Analysis System mainly include: basic maps, grid equipment management, thematic map display, and grid power flow map display.

[0103] 1) Provision of basic maps

[0104] The power grid GIS platform provides basic map data and power-related layer data (such as the power grid framework structure diagram, etc.). It also provides basic graphic operation functions, such as zoom in, zoom out, pan, front and back views, full map, highlight display, clear highlight display, bird's-eye view, etc. It can also provide graphic element hotspot and event trigger functions.

[0105] 2) Power grid equipment management

[0106] Through the integration with GIS functions, the positioning of equipment such as substations, lines, public transformers, special transformers, and users on the equipment tree is realized, and the grid resource information can be queried by dragging a box on the map, making the management and query of power grid equipment more convenient, clear, and intuitive.

[0107] 3) Thematic map display

[0108] Thematic map display is the main function of the power grid GIS platform, which can be used to view and obtain thematic maps such as the in-station primary wiring diagram, cable burial profile diagram, system diagram, single-line diagram, cable well profile diagram, and buffer generation diagram, etc.

[0109] 4) Power grid power flow diagram display

[0110] The power grid GIS platform provides secondary development interfaces for realizing the power grid power flow diagram display function, such as the power grid framework diagram, graphic element hotspot and trigger functions, and graphic element coordinate positions.

[0111] (5) Data integration design with OMS / SCADA systems

[0112] The system integrates the power grid equipment and topological information within the jurisdiction of the city company, and the grid connection points in the plant substation. The control center generates CIM / E or XML format files (preferably in CIM / E format), and the provincial data center is responsible for data parsing. The data integration structure with OMS / SCADA systems is as Figure 3 shown.

[0113] When there are new additions to the power grid structure model, the control center generates corresponding CIM / E or XML power grid structure model files. The data center regularly starts the power grid structure data access service and calls the CIM / E or XML power grid structure model parsing service deployed in the distribution network operation auxiliary decision-making analysis system to start data parsing operations. When the distribution network operation auxiliary decision-making analysis system parses the power grid structure data, it first scans the file sharing area, starts parsing for the newly added CIM / E or XML model files, and then writes the parsed result data to the BUFFER area of the structured data area of the data center through the JDBC method. After the processing is completed, the original CIM / E or XML power grid structure model files will be moved to the processed area. Finally, the data center regularly writes the data in the BUFFER area to the CIM area every day for sharing by the distribution network operation auxiliary decision-making analysis system and other systems.

[0114] When the power grid structure model changes, the control center regenerates the corresponding CIM / E or XML power grid structure model file for sharing. Before writing to the buffer area, it is the same as the new processing operation, but when writing to the CIM area, data is updated according to the table key fields.

[0115] Platform layer. The platform layer accesses external system data from the data layer through general data / service interfaces and uses technologies such as Webservice and ETL; this layer consists of the operation framework construction and the development platform modeling, including open-source components of the platform such as workflow, transaction processing, and security system. The application platform operation framework consists of an operation basic framework, an interface display framework, an icon display framework, an application integration framework, etc. The development platform modeling tool consists of business modeling, report definition, permission configuration, etc.

[0116] Application layer. The bottom layer uses programming languages such as Java, C, and C++. Based on the Echatrs chart library, it conducts front-end data visualization design, which is divided into six modules: data management, risk scanning, fault simulation, network framework optimization, system configuration, and monthly report analysis. It provides displays such as charts, reports, and topological maps, and realizes interactive use by users of multi-level unit departments.

[0117] The core functions of the invention of the distribution network operation auxiliary decision-making analysis system are reflected through the six modules of the application layer. The system function architecture is as Figure 4 shown. The system covers business modules such as data management, risk scanning, fault simulation, network framework optimization, system configuration, and monthly report analysis. Data management module: realizes data integration, data query, data quality assessment analysis, and data processing. Risk scanning module: realizes batch scanning of distribution network risks, analysis of weak points, statistics and rating of risk sources, and risk early warning. Fault simulation module: based on the extension of the fault analysis function in the current distribution automation advanced application, realizes functions such as fault prediction, multiple fault simulation, and auxiliary decision-making for emergency repair decision-making. Network framework optimization module: with the goal of minimizing load loss and subject to the constraints of power grid operation safety and voltage quality, conducts operation evaluations such as inrush current analysis, protection setting verification, power supply capacity analysis, voltage quality assessment, loop closing analysis, loop opening analysis, transfer analysis, and reactive power optimization, and forms transfer plans, optimization plans, and equipment update plans. System configuration module: mainly completes the settings of automatic risk analysis calculation and risk analysis and evaluation parameters. Monthly report analysis module: mainly completes the query, generation, and editing of risk monthly reports, fault monthly reports, and network framework monthly reports.

[0118] Data management part

[0119] Data management realizes data integration, data processing, data query, and data quality assessment analysis. The implementation process of data management in the distribution network information system is as Figure 5 shown.

[0120] (1) Data access: Manage the data access of PMS data, GIS platform, power quality monitoring system / electricity consumption information collection system, dispatching automation system, OMS / SCADA system, meteorological system, and marketing business application system.

[0121] (2) Data management: Analyze and manage archive data, operation data, and topology data respectively. It includes performing data quality analysis and management on the archive data of distribution lines, pole-mounted transformers, distribution transformers, switchgear, switch stations, ring main units, and other equipment; performing data quality analysis and management on operation data to complete abnormal load diagnosis, load collection continuity check, distribution line outlet voltage statistics, and operation mode diagnosis; performing data quality analysis and management on topology data to achieve visual display of the distribution network topology structure.

[0122] Risk scanning part

[0123] Module interface design:

[0124] (1) Risk home page: Display the risk situation in the form of risk indicators and risk maps. It includes risk block diagrams, overall indicators, regional risk ratios, risk rankings, historical statistics, future trends, maintenance situations, and rolling reminders of risks. The map display includes classification by region, substation, feeder, and distribution area, showing risk information according to time changes and risk warnings of risk heat maps.

[0125] (2) Risk analysis: Include risk calculation scan range setting, risk calculation maintenance plan setting, risk calculation meteorological information setting, risk calculation load parameter setting, regional risk result information, substation risk result information, feeder risk result information, distribution area risk result information, risk warning information, and risk measure information.

[0126] (3) Risk query: Query risk indicators and their measures according to dimensions such as region, substation, feeder, distribution area, equipment, risk type, and time.

[0127] Implementation of the risk scanning module

[0128] The risk scanning realizes batch scanning of distribution network risks, weak point analysis, risk rating, and early warning. The implementation process of the risk scanning module is as Figure 6 shown.

[0129] Based on the distribution network ledger data, topological relationship, meteorological information and operation data, the risk influencing factors of the distribution network are analyzed. Risk settings are made according to meteorology, maintenance, load and scope, and risk target locking and network model construction are completed; power flow calculation and topological analysis are adopted to calculate the risk indicators, risk grading, risk scope fitting and risk dynamic deduction of regions, substations, feeders and distribution areas; early warnings for various types of risks at the regional level, substation level, feeder level and distribution area level are realized, and corresponding risk measures are given for overload risks, overvoltage risks, low voltage risks, load loss risks, loop network operation risks, heavy load risks, power protection risks, etc., including loop breaking schemes, transfer supply schemes, loop closing schemes, reactive power optimization schemes, etc.; it provides important support for the formulation of distribution network emergency transfer supply schemes and optimization schemes.

[0130] Fault simulation module part

[0131] The home page of the fault simulation module displays various indicators in the fault prediction process and results, including: fault block diagram, key power outage areas, overall indicators, fault area conditions, historical statistics, future trends, fault levels and power outage situations, etc. This module is based on the extension of the fault analysis function in the current advanced application of distribution automation, and makes fault settings according to meteorology, maintenance, load, scope and equipment status; conducts power outage cause analysis, power outage scope analysis, and power outage level analysis; calculates the fault levels of various faults in regions, substations and feeders; and conducts fault level analysis, fault location analysis, transfer supply analysis and optimization analysis on the results; realizes functions such as fault prediction, multiple fault simulation, and emergency repair decision-making assistance. The design process of the fault simulation module is as Figure 7 shown.

[0132] (1) Data management stage. Preprocess the collected distribution network load data, ledger data, weather data and fault data, such as data integration, cleaning and outlier sample removal, and analyze the data that may be related to distribution network faults.

[0133] (2) Basic parameter setting stage. Set the scanning range, maintenance plan, meteorological information, load parameters and equipment parameters, select the distribution network fault prediction range and the required fault feature set, determine the basis for dividing the distribution network fault levels, and divide the faults into levels. Calculate and sort the weights of fault features based on the feature selection algorithm in data mining, select the features with strong classification ability, and obtain the optimal fault feature set.

[0134] (3) Fault prediction stage. Conduct distribution network fault prediction based on machine learning methods. The specific implementation process is as Figure 8As shown below. First, determine and import the distribution network fault sample set, and perform dimensionless processing on the sample data through "normalization"; split the fault sample data, randomly split it according to a ratio of 7:3, with 70% of the data used to train the model and 30% of the data used for prediction; select classification algorithms such as random forest, neural network, support vector machine, and ensemble learning in machine learning to predict the distribution network faults respectively; compare the precision and recall rates of the prediction, determine the algorithm that produces the best results to train the model, and perform online prediction of the distribution network fault situation.

[0135] (4) Fault analysis and solution formulation stage. Set general fault types and complex multiple fault types on the bus or line. According to the set fault objects and fault types, analyze the minimum range affected by the fault, change the boundary circuit breaker whose affected range is changed from running to disconnected, start the transfer power analysis, analyze the fault recovery solutions, calculate the risk of each recovery, sort all the recovery solutions, and realize fault simulation and deduction.

[0136] Grid framework optimization part

[0137] With the goal of minimizing load loss and with the grid operation safety and voltage quality as constraints, carry out operation evaluations such as inrush current analysis, protection setting verification, power supply capacity analysis, voltage quality assessment, loop closing analysis, loop opening analysis, transfer power analysis, and reactive power optimization.

[0138] (1) Transfer power analysis: Set overloaded, under maintenance, or faulty lines, and search for all transfer power solutions according to the transfer power solution search method, and display the transfer power solutions in a list.

[0139] (2) Reactive power optimization: Automatically analyze the nodes that require reactive power compensation optimization according to the overvoltage and low voltage risks, and calculate the reactive power compensation input amount at the reactive power compensation optimization points.

[0140] System configuration part

[0141] The system configuration module mainly completes the settings for automatic risk analysis calculation and the settings for risk analysis and evaluation parameters.

[0142] (1) Automatic risk analysis settings include calculation time intervals, upper and lower limits for overloading of transformers and lines, upper and lower voltage limits for each voltage level, etc.

[0143] (2) Set risk analysis and evaluation parameters, including component failure probability parameters, power flow influence factor library, meteorological factor influence factors, geographical factor influence factors, time factor influence factors, equipment defect influence factors, equipment age influence factors, risk grading thresholds, and risk measure libraries, etc.

[0144] Monthly report analysis part

[0145] The monthly report analysis module mainly completes the query, generation, and editing of risk monthly reports, fault monthly reports, and grid monthly reports.

[0146] (1) Risk scanning report: Completes the query, generation, editing, etc. of risk monthly reports, and can achieve online html display, import / export, automatic email sending, etc.

[0147] (2) Fault prediction report: Completes the query, generation, editing, etc. of fault monthly reports, and can achieve online html display, import / export, automatic email sending, etc.

[0148] (3) Grid health index report: Completes the query, generation, editing, etc. of grid monthly reports, and can achieve online html display, import / export, automatic email sending, etc.

[0149] Embodiment 2:

[0150] Based on the same inventive concept, the present invention also provides a method for auxiliary decision-making analysis of distribution network operation, and the method includes:

[0151] The data layer obtains external data and stores it classified;

[0152] The platform layer analyzes the data stored classified by the data layer and builds an operation module for the application layer;

[0153] The application layer performs risk analysis on the distribution network based on the operation module built by the platform layer and provides an optimization plan, and displays the result information of the operation module.

[0154] Preferably, the data layer obtains external data and stores it classified, including:

[0155] The data layer obtains data from PMS2.0, power grid GIS platform, marketing business application system, power consumption information collection system, equipment operation and maintenance lean management system, OMS / SCADA system, and distribution network automation;

[0156] Based on the obtained data, it is integrated into operation data, archive data, ledger data, simulation data, and topology data by type;

[0157] The obtained data is respectively stored in the corresponding data platforms, and the classified and integrated data is stored in the database.

[0158] Preferably, the platform layer analyzes the data information stored classified by the data layer and builds an operation module for the application layer, including:

[0159] The platform layer obtains the data of the data layer and issues data layer classification storage instructions based on the general data / service interface;

[0160] The platform layer builds an operation framework, icons, and a data classification and integration framework for the data layer based on the obtained data;

[0161] The platform layer configures the permissions of power grid equipment based on the data classification and integration of the data layer, and makes reports on the distribution network topology structure.

[0162] Preferably, the application layer performs risk analysis on the distribution network based on the operation module built by the platform layer and provides optimization solutions, and displays the result information of the operation module, including:

[0163] The application layer scans the risk faults of the distribution network based on the account data, topology data, and operation data, locks the risk targets, and deduces the risk targets;

[0164] Provides risk warnings and corresponding risk measures based on the deduction results;

[0165] Performs risk prediction based on the risk targets and formulates optimization solutions;

[0166] Based on the risk measures and optimization solutions, evaluates with the minimum load loss as the goal and the grid operation safety and voltage quality as the constraints, and displays all the risk measures and optimization solutions.

[0167] Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0168] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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.

[0169] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1 each process or multiple processes and / or blocksFigure 1 means for the functions specified in one or more boxes.

[0170] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions specified in one or more of the procedures Figure 1 one or more procedures and / or boxes Figure 1 means for the functions specified in one or more boxes.

[0171] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, such that a series of operational steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in one or more of the procedures Figure 1 one or more procedures and / or boxes Figure 1 means for the functions specified in one or more boxes.

[0172] The above are only embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention are included within the scope of the claims of the present invention pending approval of the application.

Claims

1. An auxiliary decision-making analysis system for distribution network operation, characterized in that, The system includes: Data layer: used to classify and store the obtained external data; Platform layer: used to build operation modules according to the types of the data classified and stored in the data layer; Application layer: used to perform risk analysis on the distribution network and provide optimization solutions based on the operation modules built by the platform layer and the data classified and stored in the data layer, and is also used to display the result information of each operation module; The data layer includes: an acquisition module, a data integration module, and a data storage module; The application layer includes: a data management module, a risk scanning module, a fault simulation module, a network framework optimization module, a system configuration module, and a monthly report analysis module; The fault simulation module includes: a sample data unit, a prediction unit, and a selection unit.

2. The auxiliary decision-making analysis system for distribution network operation according to claim 1, characterized in that, The acquisition module is used to acquire data from PMS2.0, the power grid GIS platform, the marketing business application system, the power consumption information collection system, the equipment operation and maintenance lean management system, the OMS / SCADA system, and the distribution network automation; The data integration module is used to integrate the data acquired by the acquisition module into operation data, archive data, ledger data, simulation data, and topology data according to types; The data storage module is used to store the data acquired by the acquisition module in the corresponding data platform according to the data type; it is also used to store the data integrated by the data integration module according to types into the database.

3. The auxiliary decision-making analysis system for distribution network operation according to claim 2, characterized in that, The data integration module is specifically used for: Integrating the data sets of the marketing basic data platform, the massive data platform, and the power grid GIS data platform into operation data, archive data, ledger data, simulation data, and topology data.

4. The auxiliary decision-making analysis system for distribution network operation according to claim 2, characterized in that, The storage module includes: a marketing business application system data storage unit, a power consumption information collection system data storage unit, an equipment operation and maintenance lean management system data storage unit, a power grid GIS platform data storage unit, and an OMS / SCADA system data storage unit; The marketing business application system data storage unit is used to update the users of the marketing business application system, metering points, meter attributes, and metering boxes of the marketing business application system data obtained in real time to the marketing basic data platform through the OGG method; The power consumption information collection system data storage unit is used to store the electric energy meter reading and power consumption data of the power consumption information collection system into the massive data platform through the standard interface method of the massive platform; The equipment operation and maintenance lean management system data storage unit is used to store the standard codes, organizational structures, equipment archives and parameters, archive relationships, maintenance plans, and defect information of the equipment operation and maintenance lean management system into the massive data platform through the technical route of the data center; The power grid GIS platform data storage unit is used to store the power grid spatial information service data of the power grid GIS platform into the power grid GIS data platform in a timed or immediate manner; The OMS / SCADA system data storage unit is used to convert the OMS / SCADA system data into the CIM / E or XML file format and then store it into the massive data platform.

5. The auxiliary decision-making analysis system for distribution network operation according to claim 4, characterized in that, The OMS / SCADA system data storage unit is used for: When new power grid structure data is added to the OMS / SCADA system, convert the added data into a power grid structure file in CIM / E or XML file format; Parse the added power grid structure file and store the parsed data in the mass data platform.

6. The auxiliary decision-making analysis system for distribution network operation according to claim 4, characterized in that, The storage module further includes: an integrated data storage unit; The integrated data storage unit is used to store operation data, archive data, ledger data, simulation data, and topology data in a database.

7. The auxiliary decision-making analysis system for distribution network operation according to claim 2 or 6, characterized in that, The database includes: Oracle, db, SAL, and Acess.

8. The auxiliary decision-making analysis system for distribution network operation according to claim 1, characterized in that, The platform layer includes: a general data / service interface module, an application platform operation module, and a development platform modeling tool module; The general data / service interface module is used to obtain and store data from the database by type, and provide the data to the application platform operation module and the development platform modeling tool module through a service bus; The application platform operation module is used to provide a running framework, icons, and a data layer data classification integration framework and instructions for each model in the application layer; The development platform modeling tool module is used to model the application layer according to the data in the data layer by type, configure the permissions of power grid devices, and generate reports for the distribution network topology structure.

9. The operation auxiliary decision-making analysis system for a distribution network according to claim 8, characterized in that, The general data / service interface module includes: an obtaining unit and a transmitting unit; The obtaining unit is used to obtain the current latest marketing data in the way of JDBC, obtain the electricity meter bottom reading and electricity quantity data in the way of the mass platform standard interface, obtain the power grid topology and equipment ledger information in the way of ETL, obtain the power grid GIS data, OMS / SCADA system data, and data in the database; The transmitting unit is used to transmit the data classification integration instructions of the application platform operation module to the data layer.

10. The operation auxiliary decision-making analysis system for a distribution network according to claim 3, characterized in that, The data management module is used to analyze and manage the archive data, operation data, and topology data of the data integration module, and provide a visual display for the distribution network topology structure data; The risk scanning module is used to scan the distribution network risk faults according to the ledger data, topology data, and operation data of the data integration module, lock the risk targets, and deduce the risk targets; It is also used to provide risk warnings and corresponding risk measures; The fault simulation module is used to set the risk scanning range, maintenance plan, meteorological information, load parameters, and equipment status according to the simulation data of the data integration module; It is also used to perform risk prediction according to the risk targets of the risk scanning module, analyze the prediction results, and formulate corresponding optimization plans; The grid framework optimization module is used to calculate and evaluate the risk measures of the risk scanning module and the optimization plans of the fault simulation module with the goal of minimizing load loss and subject to the constraints of power grid operation safety and voltage quality; The system configuration module is used to configure the calculation time interval of the distribution network risk, transformers, upper and lower limits of line overload, and upper and lower limits of voltage for each voltage level; it is also used to set component failure probability parameters, power flow impact factor library, meteorological factor impact factors, geographical factor impact factors, time factor impact factors, equipment defect impact factors, equipment age impact factors, risk grading thresholds, and risk measures. The monthly report analysis module is used to query, generate, and edit the risk scan monthly report, fault prediction monthly report, and grid optimization monthly report according to the results of the risk scan module, fault prediction module, and grid optimization module, as well as the data in the data layer, and perform online html display, import, export, and automatic email distribution.

11. The operation auxiliary decision-making analysis system for a distribution network according to claim 10, characterized in that, The sample data unit is used to split the fault data in the data layer into training data and prediction data according to a ratio of 7:

3. The prediction unit is used to train the training data on the random forest model, neural network model, support vector machine model, and ensemble learning model respectively, and use the prediction data to detect the random forest model, neural network model, support vector machine model, and ensemble learning model. The selection unit is used to select the optimal model in the prediction model for online prediction of distribution network faults.

12. An operation auxiliary decision-making analysis method for a distribution network, characterized in that, The method includes: The data layer obtains external data and stores it classified. The platform layer analyzes the data stored classified by the data layer and builds an operation module for the application layer. The application layer performs risk analysis on the distribution network based on the operation module built by the platform layer and provides an optimization plan, and displays the result information of the operation module. The data layer obtains external data and stores it classified, including: The data layer obtains data from PMS2.0, power grid GIS platform, marketing business application system, power consumption information collection system, equipment operation and maintenance lean management system, OMS / SCADA system, and distribution network automation. Based on the obtained data, it is integrated by type into operation data, archive data, ledger data, simulation data, and topology data. The obtained data is stored in the corresponding data platforms respectively, and the classified and integrated data is stored in the database. The application layer performs risk analysis on the distribution network based on the operation module built by the platform layer and provides an optimization plan, and displays the result information of the operation module, including: The application layer scans the distribution network risk faults based on the ledger data, topology data, and operation data, locks the risk targets, and deduces the risk targets. Based on the deduction results, it provides risk warnings and corresponding risk measures. Performs risk prediction based on the risk targets and formulates an optimization plan. Based on the risk measures and optimization plan, it evaluates with the minimum load loss as the goal and the grid operation safety and voltage quality as the constraint conditions, and displays all the risk measures and optimization plans.

13. The operation auxiliary decision-making analysis method for a distribution network according to claim 12, characterized in that, The platform layer analyzes the data information stored classified by the data layer and builds an operation module for the application layer, including: The platform layer obtains the data of the data layer through the general data / service interface and issues instructions for classifying and storing the data layer. The platform layer builds an operation framework, icons, and a data classification and integration framework for the data layer for the application layer based on the obtained data; The platform layer configures the permissions of power grid equipment based on the data classification and integration data of the data layer, and generates reports on the distribution network topology structure.

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