A Visual Prediction Method and System for Low Voltage in Distribution Networks

Abstract

This invention proposes a visual prediction method and system for low voltage in distribution networks. The method includes the following steps: S1: Data acquisition, acquiring usage data from the main grid OCS system, distribution network OCS system, GIS system, marketing system, and metering automation system; S2: Main and distribution model stitching: performing integrated map and model stitching processing to obtain an integrated model; S3: Data mapping, implementing data mapping between various systems within the integrated model, integrating all systems to form an integrated panoramic model; S4: State estimation, establishing a state estimation analysis result tree; performing abnormal data detection and identification on the panoramic model based on the state estimation analysis result tree; S5: Power source tracking; S6: Low voltage range judgment and alarm, judging and alarming devices in the low voltage range based on the voltage values ​​of devices along the power source tracking path. This invention provides technicians with a highly operable low voltage management and prediction solution.

Description

Technical Field

[0001] This invention relates to the field of power distribution network technology, and in particular to a method and system for visually predicting low voltage in power distribution networks. Background Technology

[0002] With the advancement of my country's new urbanization process, the urban and rural economies have entered a phase of rapid development, leading to a significant increase in social electricity demand. In recent years, my country has gradually increased its investment in power grid construction, resulting in substantial progress in the grid. However, in the process of power grid development, there has been an overemphasis on high-voltage transmission network construction, neglecting the related configuration of the distribution network, leading to increasingly prominent low-voltage problems in the distribution network.

[0003] Currently, monitoring systems for medium and low voltage distribution network equipment are decentralized, and the real-time correlation of equipment operation data is poor. When abnormal phenomena such as single-phase grounding, low voltage at the end of the distribution line, low voltage in the transformer area, and overload occur, they fail to be immediately reflected in the distribution network OCS system. This highlights the problems of insufficient timeliness and speed in "load monitoring, low voltage monitoring, power outage monitoring, and risk monitoring" in medium and low voltage distribution networks. At the same time, the large number of distribution network equipment and the excessive workload of operation and maintenance seriously affect the timeliness of rapid emergency response to line abnormalities, further prolonging the time for lines to restore normal power supply, resulting in a decrease in power supply reliability and customer satisfaction.

[0004] To address technical factors contributing to low voltage phenomena, such as outdated equipment, insufficient reactive power compensation, and power supply radius exceeding guidelines, these issues can be partially or completely resolved through subsequent distribution network planning at the power grid planning level. However, at the power grid operation level, rapid identification of potential low voltage areas before user feedback is crucial. Therefore, there is an urgent need to develop a rapid voltage anomaly retrieval tool for distribution networks based on the collaboration of main distribution automation, metering automation, and a marketing system big data platform. This tool should deeply explore real-time monitoring and analysis applications for medium and low voltage distribution areas, tackling the problem of low fault handling efficiency caused by the inefficient linkage of voltage alarm signals in multi-dimensional systems. It should achieve real-time monitoring and intelligent analysis of big data in medium and low voltage distribution networks, making the operation and management of distribution networks transparent, constructing an intelligent operation and maintenance system for distribution networks, and effectively optimizing medium and low voltage fault prediction, low voltage management, and three-phase imbalance management. To this end, this invention studies user low voltage prediction methods and proposes a visual prediction method and system for distribution networks. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a visual prediction method and system for low voltage in power distribution networks, which can provide technicians with a highly operable low voltage management and prediction solution.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] This invention provides a method for visually predicting low voltage in a power distribution network, comprising the following steps:

[0008] S1: Data Acquisition, acquiring usage data from the main network OCS system, distribution network OCS system, GIS system, marketing system, and metering automation system respectively;

[0009] S2: Main and auxiliary model assembly;

[0010] The GIS system and the main network OCS system are integrated to form an integrated GIS-main network OCS system model;

[0011] The main network OCS system and the distribution network OCS system are integrated and spliced ​​together to form an integrated model of main network OCS-distribution network OCS system;

[0012] The distribution network OCS system and the metering automation system are integrated to form an integrated model of distribution network OCS-metering system;

[0013] The integrated model is obtained by combining the integrated model of the GIS-main network OCS system, the integrated model of the main network OCS-distribution network OCS system, and the integrated model of the distribution network OCS-metering system.

[0014] S3: Data mapping, which realizes data mapping of various systems within the integrated model and integrates all systems to form an integrated panoramic model; the panoramic model is a panoramic model composed of several nodes; the nodes represent the 10kV-400V equipment in the entire network, and each node can display the voltage measurement data of the corresponding equipment; the equipment can be busbars, low-voltage transformers, etc.

[0015] S4: State estimation, setting statistical analysis rules, and building a state estimation analysis result tree based on the statistical analysis rules; performing bad data detection and identification on the panoramic model based on the state estimation analysis result tree;

[0016] S5: Power supply point tracking. Starting from the distribution transformer belonging to a user specified in the panoramic model, power supply tracking is performed until the power supply point equipment is obtained; the voltage data of all nodes along the tracking path is stored in a table; after connecting all nodes along the tracking path, a power supply tracking diagram is formed, and the voltage value of the equipment is marked on the power supply tracking diagram.

[0017] S6: Low voltage range detection and alarm. Based on the voltage values ​​of devices on the power point tracking path, determine the devices in the low voltage range and issue an alarm.

[0018] Furthermore, the data used by the autonomous grid OCS system includes: power grid model files, graphic files, real-time data, and historical data;

[0019] The data used by the distribution network OCS system includes: real-time data, historical data, and automatic switch information;

[0020] The data used by the GIS system includes: feeder model files and feeder graphic files;

[0021] The marketing system uses the following data: user data and customer relationship data.

[0022] The data used by the automated metering system is: variable measurement data.

[0023] Furthermore, in step S2, the specific process of forming an integrated model of the main network OCS-distribution network OCS system includes the following steps:

[0024] Starting from the feeder outlet switch of the main grid OCS system, a depth-first search is performed to obtain switches and disconnectors in normal state imported from the GIS system. The search stops when a public transformer or distribution transformer is found. Each feeder forms a tree with the substation feeder outlet switch as the root. Each switch information is a node in the tree. The automatically generated tree structure is an integrated model of the main grid OCS and distribution network OCS systems.

[0025] Furthermore, in step S2, the specific process of forming an integrated model of the distribution network OCS-metering system includes the following steps:

[0026] Power supply path tracing is performed on the equipment in the main grid OCS system and the medium-voltage and low-voltage equipment in the distribution network OCS system. The connected switches and line equipment are searched through the load equipment, the feeders are identified through the line equipment, and the feeders are matched with the feeder outlet switches of the substation. The panoramic power supply path information is displayed as an integrated model of distribution network OCS-metering system.

[0027] Furthermore, in step S2, the integrated splicing to form the distribution network OCS-metering system integrated model includes the following steps:

[0028] The distribution network OCS system connects the distribution network feeder model with the disconnect switches that are connected to the load; the metering automation system applies topology mapping analysis technology and data depth calculation to achieve efficient calculation and sharing of distribution transformer GIS ID-metering measurement data wide table.

[0029] Furthermore, the integrated graphic model splicing process is as follows:

[0030] The device topology model of the main network OCS system is stored in the memory of the SCADA server of the distribution network OCS system. The topology model of the low-voltage equipment in the distribution network OCS system is cached in the main and distribution equipment splicing model file. The mapping relationship between the medium and low voltage boundary equipment of the main network OCS system and the distribution network OCS system and the corresponding equipment is recorded in memory.

[0031] Furthermore, the specific process of step S3 is as follows:

[0032] (1) Map the distribution transformer data of the metering automation system to the distribution transformer data of the GIS system, obtain user and user transformer relationship data, display the distribution of distribution transformers and users, and display real-time measurement data;

[0033] (2) The main grid OCS system and the distribution network OCS system collect data and map them with GIS equipment to obtain voltage information of each node in the power grid and display the status of main and distribution network switches, bus voltage and real-time measurement data of voltage of each device.

[0034] (3) Integrate models from multiple systems to form a panoramic model that integrates GIS, main network OCS, distribution network OCS, and metering data.

[0035] Furthermore, the statistical analysis rules include statistical analysis of suspicious parameters, statistical analysis of suspicious data collection, statistical analysis of bad data, and statistical analysis of suspicious data.

[0036] The statistical analysis of suspicious parameters is used to further investigate data where equipment parameters do not conform to normal conditions.

[0037] The collection and statistical analysis of suspicious data involves identifying instances where errors have occurred in the collected data and then selecting data for further investigation.

[0038] Bad data statistical analysis involves setting the state estimation results and voltage value range of the voltage measurement quantity, and filtering out data that differ from the state estimation results and corresponding voltage value range of the voltage measurement quantity.

[0039] Suspicious data statistical analysis: Set a threshold value; filter out data whose state estimation results differ from the voltage value by more than the threshold value for further investigation.

[0040] A low-voltage visualization prediction system for a power distribution network includes: a data acquisition module, a data analysis module, a data storage module, and a result display module;

[0041] The data acquisition module acquires usage data from the main network OCS system, distribution network OCS system, GIS system, marketing system, and metering automation system, respectively.

[0042] The data analysis module performs main and auxiliary model splicing, data mapping, state estimation, power point tracking, low voltage range judgment and alarm based on the usage data.

[0043] The data storage module stores device information and voltage information along the path;

[0044] The results display module displays the device information along the power supply tracking path and the results of low voltage range judgment and alarm in a table and a visual chart.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] This invention is beneficial for timely detection of low voltage ranges, enabling users to quickly predict low voltage levels. It is also beneficial for evaluating the performance of low-voltage distribution networks in terms of power supply capacity, power supply security, and power supply quality, and helps users identify weak links in low-voltage distribution networks. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart illustrating a method for visually predicting low voltage in power distribution networks.

[0049] Figure 2 This is a structural diagram of a low-voltage visualization and prediction system for power distribution networks.

[0050] Figure 3 This is a flowchart of a power distribution network voltage anomaly retrieval method, which is a visual prediction method for low voltage in power distribution networks. Detailed Implementation

[0051] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0052] The following specific examples illustrate the embodiments disclosed in this invention. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. This invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this invention. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. All other embodiments obtained by those skilled in the art based on the embodiments in this disclosure without creative effort are within the scope of protection of this disclosure. Example

[0053] In the first embodiment of the present invention, the present invention provides a method for visually predicting low voltage in a distribution network, such as... Figure 1 As shown, it includes the following steps:

[0054] S1: Data Acquisition

[0055] Usage data was obtained from the main network OCS system, distribution network OCS system, GIS system, marketing system, and metering automation system. The usage data is shown in Table 1.

[0056] Table 1. Relationship between various systems in the power distribution network and usage data.

[0057] System Name Using data Mainnet OCS System Power grid model files, graphic files, real-time data Distribution network OCS system Real-time data, automated switch information GIS system Feeder model file, feeder graphic file Marketing System User data, household relationship data Metering system Matching variable measurement data

[0058] S2: Main and auxiliary model assembly:

[0059] To achieve the splicing of the distribution network GIS feeder with the main grid OCS system, locate the 10kV load under the main grid substation that has the same station name and feeder number as the distribution network and 10kV incoming line, and then connect the distribution network 10kV incoming line to the disconnect switch connected to the load.

[0060] The specific model assembly process is as follows:

[0061] (1) Starting from the feeder outlet switch of the main grid OCS system, a depth-first search is performed to obtain the switch and disconnector status from the "normal status" imported from GIS. The search stops when a public transformer or distribution transformer is found. Each feeder forms a tree with the substation feeder outlet switch as the root, and each switch information is a node in the tree, automatically generating the tree structure. Finally, the main grid OCS system collects substation feeders and 10kV feeders in the GIS system that have the same substation name + 10kV feeder number, forming an integrated model of GIS-main grid OCS system.

[0062] (2) Power supply path tracking is performed on the main grid, distribution network medium voltage and distribution network low voltage equipment. Through the load equipment, the connected switches and line equipment are searched. The feeder is determined through the line equipment. The feeder is matched with the feeder outlet switch of the substation. The panoramic power supply path information from 500kV to 10kV is displayed to form an integrated model of the main grid OCS-distribution network OCS system.

[0063] (3) The distribution network OCS system splices the 10kV feeder model of the distribution network with the disconnect switches connected to the load. Based on the cloud data platform of the distribution automation system, topology mapping analysis technology and data depth calculation are applied to realize the efficient calculation and sharing of distribution transformer GIS ID-metering measurement data wide table, forming an integrated model of distribution network OCS-metering system.

[0064] (4) The integrated model is obtained by splicing the integrated model of GIS-main network OCS system, main network OCS-distribution network OCS system, and distribution network OCS-metering system.

[0065] The process of integrating graphic model splicing is as follows:

[0066] The device topology model of the main network OCS system is stored in the memory of the SCADA server of the distribution network OCS system. The topology model of the low-voltage equipment in the distribution network OCS system is cached in the main and distribution equipment splicing model file. The mapping relationship between the medium and low voltage boundary equipment of the main network OCS system and the distribution network OCS system and the corresponding equipment is recorded in memory.

[0067] S3: Data Mapping

[0068] Within the integrated model, data mapping is achieved across various systems, integrating all systems to form a unified panoramic model. The panoramic model consists of several nodes. Each node represents 10kV-400V equipment across the entire network, and each node can display the voltage measurement data of the corresponding equipment; the equipment can be busbars, low-voltage transformers, etc.

[0069] The specific process is as follows:

[0070] (1) Map the distribution transformer data of the metering automation system to the distribution transformer data of the GIS system, obtain user and user transformer relationship data, display the distribution of distribution transformers and users, and display real-time measurement data;

[0071] (2) The main grid OCS system and the distribution network OCS system collect data and map them with GIS equipment to obtain voltage information of each node in the power grid and display the status of main and distribution network switches, bus voltage and real-time measurement data of voltage of each device.

[0072] (3) Integrate models from multiple systems to form a panoramic model that integrates GIS, main network OCS, distribution network OCS, and metering data.

[0073] S4: State Estimation

[0074] Set statistical analysis rules and build a state estimation analysis result tree based on the statistical analysis rules; perform bad data detection and identification on the panoramic model based on the state estimation analysis result tree.

[0075] Statistical analysis rules include statistical analysis of suspicious parameters, statistical analysis of suspicious data collection, statistical analysis of bad data, and statistical analysis of suspicious data.

[0076] The statistical analysis of suspicious parameters is used to further investigate data where equipment parameters do not conform to normal conditions.

[0077] The collection and statistical analysis of suspicious data involves identifying instances where errors have occurred in the collected data and then selecting data for further investigation.

[0078] Bad data statistical analysis involves setting the state estimation results and voltage value range of the voltage measurement quantity, and filtering out data that differ from the state estimation results and corresponding voltage value range of the voltage measurement quantity.

[0079] Suspicious data statistical analysis: Set a threshold value; filter out data whose state estimation results differ from the voltage value by more than the threshold value for further investigation.

[0080] S5: Power Point Tracking

[0081] Starting from a specified user's distribution transformer in the panoramic model, power supply tracing is performed until the power point equipment is acquired. Voltage data of all nodes along the tracing path are stored in a table; after connecting all nodes along the tracing path, a power supply tracing diagram is formed, and the voltage values ​​of the equipment are marked on the power supply tracing diagram.

[0082] Based on the telemetry and telecontrol status, the system checks the switch status to ensure it is correct and generates suspicious status quantities. Then, it identifies erroneous switch / disconnector statuses, and if automatic correction is required, it automatically corrects the measurements in the memory database.

[0083] (1) Based on network topology analysis, start power supply tracing from the distribution transformer to which the user belongs, and continue searching up to 500kV.

[0084] (2) Store the voltage data of all nodes on the tracking path in a table. In this embodiment of the invention, the table is named Route_Dev_Mea. The field type table of the voltage data storage table is designed as shown in Table 2. The voltage data storage table can be designed according to this field type table.

[0085] Table 2 Field Types for Voltage Data Storage

[0086] Fields type Explanation DEVID varchar(32) Device ID DEVDESC varchar(60) Equipment Description P float Merit (MVA) Q float Reactive power (MVA) I float Current (A) U float Voltage (kV)

[0087] Create a power supply tracking diagram and mark the measured (voltage) values ​​of the devices on the diagram.

[0088] S6: Low voltage range detection and alarm

[0089] Based on the measurement values ​​of devices along the power point tracing path, determine the low voltage range and issue an alarm. The specific methods and steps are as follows:

[0090] 1. Monitoring and alarm methods:

[0091] (1) Retrieve voltage over-limit range:

[0092] (2) Monitor whether the power distribution terminal reports “zero-sequence voltage over-limit alarm” (remote signaling alarm), “current over-limit alarm” (remote measurement alarm), and “voltage over-limit alarm” (remote measurement alarm). Based on the above information, retrieve the over-limit range.

[0093] (3) Monitor whether there is an "over-limit alarm" on the 10kV bus voltage of the substation and retrieve the over-limit range.

[0094] The system penetrates every line from the 10kV-400V power grid, precisely locating the topology from branch line switches to main line distribution automation switches to public (private) transformers. It monitors for voltage over-limit alarms, pinpointing the nearest switch to the last distribution automation switch before the public (private) transformer.

[0095] (4) If the last distribution automation switch on the branch line, i.e. the switch closest to the public (dedicated) transformer, has a "voltage limit exceeded" alarm, the program will start, apply the previous day's section, and evaluate the application through historical data / transformer capacity measurement.

[0096] (5) Three-phase unbalanced voltage monitoring: The analog quantity is calculated at the 10kV outgoing line switch. If it exceeds the limit, it is judged as exceeding the limit. The zero-sequence voltage 3U0 is monitored at the distribution terminal. If it exceeds the limit, it is judged as exceeding the limit.

[0097] 2. Cross-sectional calculation method:

[0098] (1) A single cross section can be set by the user, which can be the cross section value of the most recent 15 minutes or the cross section value of the same time the previous day.

[0099] (2) The average cross-section can be specified by the user within a time range, such as the average of the previous day or the average of the last 7 days.

[0100] (3) Static value, evaluated by transformer capacity P=(Pcapacity / Ptotal Capacity [Total Transformer Capacity])*PSwitch.

[0101] Calculate the current cross-sectional value based on historical cross-sectional values ​​and topological relationships:

[0102] History: Switches P_total, U_total, I_total

[0103] The transformer supplied by this switch is: P1...Pn, U1...Un, I1...In

[0104] The percentage of a specific transformer: Pxper = Px / Ptotal * 100%, IxPer = Ix / Itotal * 100%

[0105] current:

[0106] For a certain transformer: Px = PXPerx * Ptotal * 100%, Ix = IxPer / Itotal * 100%

[0107] Calculate U based on P and I.

[0108] (4) Based on the topology and transformer matching and metering data, calculate the relevant data of the upper-level switch and output the list of transformers that exceed the limit (which can be sorted, and the overload and over-limit can be highlighted separately and highlighted on the single line diagram). If the metering data of some transformers is not received in time, the most recent 15-minute cross-sectional value or the cross-sectional value at the same time the previous day can be used to replace the calculation.

[0109] 3. Display method:

[0110] Identify low-voltage equipment and highlight it in tables and graphs to create an over-limit list including: substation name, branch switch, and public (private) information. Example

[0111] Reference Figure 2 , Figure 3 This is the second embodiment of the present invention. This embodiment provides a system for implementing a low voltage visualization prediction method for distribution networks as described in Embodiment 1. The system includes a data acquisition module 100, a data analysis module 200, a data storage module 300, and a result display module 400.

[0112] The data acquisition module 100 acquires power grid model files, graphic files, and real-time data from the main grid OCS system; real-time data and automated switch information from the distribution network OCS system; feeder model files and feeder graphic files from the GIS system; user data and customer-transformer relationship data from the marketing system; and distribution transformer measurement data from the metering system.

[0113] The data analysis module 200 requires two types of analysis: first, state estimation analysis, which supplements the measurement information of missing measurement devices; and second, network topology analysis, which analyzes the power supply path from the user's distribution transformer to the main grid power station.

[0114] The data storage module 300 stores the device information and voltage information retrieved from the power supply path in a table for easy access by the user.

[0115] The results display module 400 displays the power supply path information in two ways: first, it displays the equipment information on the power supply path in a table, and second, it displays the path information in a graphical visualization, highlighting low-voltage nodes. Example

[0116] This is the third embodiment of the present invention, which differs from the first two embodiments in that:

[0117] This embodiment takes the power grid data of a certain province in my country as an example. It searches for the power supply tracking map of a certain distribution transformer. The visualized map information is all on an SVG map. The power supply path from the distribution transformer to the main transformer on the main grid side can be seen very intuitively on the map, and the measurements (voltage) of key equipment on the power supply path are marked on the map.

[0118] This invention proposes a visual prediction method and system for low voltage in power distribution networks, particularly suitable for scenarios with small sample data and edge computing. It can provide a basis for equipment fault diagnosis or further enable predictive maintenance of equipment. Intelligent monitoring of abnormal states is an interdisciplinary application of computer science, communication, and electronic and electrical engineering, and can be widely used in building energy conservation, smart cities, and smart grids.

[0119] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for visually predicting low voltage in a power distribution network, characterized in that, Includes the following steps: S1: Data Acquisition, acquiring usage data from the main network OCS system, distribution network OCS system, GIS system, marketing system, and metering automation system respectively; S2: Main and auxiliary model assembly; The GIS system and the main network OCS system are integrated and stitched together to form an integrated GIS-main network OCS system model; The main network OCS system and the distribution network OCS system are integrated and spliced ​​together to form an integrated model of main network OCS-distribution network OCS system; The distribution network OCS system and the metering automation system are integrated to form an integrated model of distribution network OCS-metering system; The integrated model is obtained by combining the integrated model of the GIS-main network OCS system, the integrated model of the main network OCS-distribution network OCS system, and the integrated model of the distribution network OCS-metering system. In step S2, the specific process of forming an integrated model of the main network OCS and distribution network OCS system includes the following steps: Starting from the feeder outlet switch of the main grid OCS system, a depth-first search is performed to obtain switches and disconnectors in normal status imported from the GIS system. The search stops when a public transformer or distribution transformer is found. Each feeder forms a tree with the substation feeder outlet switch as the root. Each switch information is a node in the tree. The automatically generated tree structure is an integrated model of the main grid OCS-distribution network OCS system. S3: Data mapping, which realizes data mapping of various systems within the integrated model and integrates all systems to form an integrated panoramic model; the panoramic model is a panoramic model composed of several nodes; the nodes represent the 10kV-400V devices in the entire network, and each node can display the voltage measurement data of the corresponding device. S4: State estimation, setting statistical analysis rules, and building a state estimation analysis result tree based on the statistical analysis rules; performing bad data detection and identification on the panoramic model based on the state estimation analysis result tree; S5: Power supply point tracking. Starting from the distribution transformer belonging to a user specified in the panoramic model, power supply tracking is performed until the power supply point equipment is obtained; the voltage data of all nodes along the tracking path is stored in a table; after connecting all nodes along the tracking path, a power supply tracking diagram is formed, and the voltage value of the equipment is marked on the power supply tracking diagram. S6: Low voltage range detection and alarm. Based on the voltage values ​​of devices on the power point tracking path, determine the devices in the low voltage range and issue an alarm.

2. The method for visually predicting low voltage in a distribution network according to claim 1, characterized in that, The data used by the autonomous grid OCS system includes: power grid model files, graphic files, real-time data, and historical data; The data used by the distribution network OCS system includes: real-time data, historical data, and automatic switch information; The data used by the GIS system includes: feeder model files and feeder graphic files; The marketing system uses the following data: user data and customer relationship data. The data used by the automated metering system is: variable measurement data.

3. The method for visually predicting low voltage in a distribution network according to claim 1, characterized in that, In step S2, the specific process of forming an integrated model of the distribution network OCS-metering system includes the following steps: Power supply path tracing is performed on the equipment in the main grid OCS system and the medium-voltage and low-voltage equipment in the distribution network OCS system. The connected switches and line equipment are searched through the load equipment, the feeders are identified through the line equipment, and the feeders are matched with the feeder outlet switches of the substation. The panoramic power supply path information is displayed as an integrated model of distribution network OCS-metering system.

4. The method for visually predicting low voltage in a distribution network according to claim 1, characterized in that, In step S2, the integrated splicing to form the integrated model of the distribution network OCS-metering system includes the following steps: The distribution network OCS system connects the distribution network feeder model with the disconnect switches that are connected to the load; the metering automation system applies topology mapping analysis technology and data depth calculation to achieve efficient calculation and sharing of distribution transformer GIS ID-metering measurement data wide table.

5. The method for visually predicting low voltage in a distribution network according to claim 1, characterized in that, The process of integrating the graphic model splicing is as follows: The device topology model of the main network OCS system is stored in the memory of the SCADA server of the distribution network OCS system. The topology model of the low-voltage equipment in the distribution network OCS system is cached in the main and distribution equipment splicing model file. The mapping relationship between the medium and low voltage boundary equipment of the main network OCS system and the distribution network OCS system and the corresponding equipment is recorded in memory.

6. The method for visually predicting low voltage in a distribution network according to claim 1, characterized in that, The specific process of step S3 is as follows: (1) Map the distribution transformer data of the metering automation system to the distribution transformer data of the GIS system, obtain user and user transformer relationship data, display the distribution of distribution transformers and users, and display real-time measurement data; (2) The main grid OCS system and the distribution network OCS system collect data and map them with GIS equipment to obtain voltage information of each node in the power grid and display the status of main and distribution network switches, bus voltage and real-time measurement data of voltage of each device. (3) Integrate models from multiple systems to form a panoramic model that integrates GIS, main network OCS, distribution network OCS, and metering data.

7. The method for visually predicting low voltage in a distribution network according to claim 1, characterized in that, The statistical analysis rules include statistical analysis of suspicious parameters, statistical analysis of suspicious data collection, statistical analysis of bad data, and statistical analysis of suspicious data. The statistical analysis of suspicious parameters is used to further investigate data where equipment parameters do not conform to normal conditions. The collection of suspicious statistical analysis is used to analyze situations where errors occur in the collected data. The data will be selected for further investigation. Bad data statistical analysis involves setting the state estimation results and voltage value range of the voltage measurement quantity, and filtering out data that differ from the state estimation results and corresponding voltage value range of the voltage measurement quantity. Suspicious data statistical analysis: setting threshold values; Data whose state estimation results differ from voltage values ​​by more than a threshold value are screened out for further investigation.

8. A low-voltage visualization prediction system for a power distribution network, used to implement the visualization prediction method according to any one of claims 1-7, comprising: a data acquisition module, a data analysis module, a data storage module, and a result display module; The data acquisition module acquires usage data from the main network OCS system, distribution network OCS system, GIS system, marketing system, and metering automation system, respectively. The data analysis module performs main and auxiliary model splicing, data mapping, state estimation, power point tracking, low voltage range judgment and alarm based on the usage data. The data storage module stores device information and voltage information along the path; The results display module displays the device information along the power supply tracking path and the results of low voltage range judgment and alarm in a table and a visual chart.

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