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CN-122017670-A - Medium-low voltage transformer area electric leakage identification system and identification method thereof

CN122017670ACN 122017670 ACN122017670 ACN 122017670ACN-122017670-A

Abstract

The invention belongs to the field of electric leakage identification, and particularly relates to a medium-low voltage transformer area electric leakage identification system which comprises a data acquisition module, a data preprocessing module, a system control module, a fault sensing module, a knowledge graph construction module, an event processing module, a grading early warning module and a system optimization module. The system acquires electric quantity, topology and power failure reporting data in real time through the data acquisition module, and after the data acquisition module is cleaned and calibrated through the preprocessing module, a power failure event is quickly judged by combining a topological relation through the fault sensing module, and then the knowledge graph is utilized to construct the wide area association of equipment and the event, so that the accurate positioning of the fault position from the station area to the cell unit is realized. The event processing module integrates massive power failure data, repeated alarms are reduced, the emergency repair supporting module is linked with the grading early warning mechanism, important user early warning and emergency repair information is pushed in a targeted mode, fault positioning time is greatly shortened, site treatment efficiency is improved, and full-flow closed loop control of low-voltage faults from perception to treatment is ensured.

Inventors

  • LI HUABIN

Assignees

  • 连云港云讯网络科技有限公司

Dates

Publication Date
20260512
Application Date
20260204

Claims (10)

  1. 1. The medium-low voltage transformer area electric leakage identification system comprises a data acquisition module (1), a data preprocessing module (2), a system control module (3), a fault perception module (4), a knowledge graph construction module (5), an event processing module (6), a grading early warning module (7), a system optimization module (8), a data management module (9) and a data center module (10), and is characterized in that the data acquisition module (1) is connected with the data preprocessing module (2), the data preprocessing module (2) is connected with the system control module (3), the system control module (3) is connected with the fault perception module (4), the system control module (3) is connected with the knowledge graph construction module (5), the system control module (3) is connected with the grading early warning module (7), the system control module (3) is connected with the system optimization module (8), and the system control module (3) is connected with the data management module (9), and the data management module (9) is connected with the data center module (10).
  2. 2. The medium-low voltage transformer area electricity leakage identification system according to claim 1 is characterized in that the data acquisition module (1) comprises a real-time measurement module (11), a power failure reporting module (12), a topology data module (13) and a historical operation data module (14), the real-time measurement module (11) is connected with the power failure reporting module (12), the power failure reporting module (12) is connected with the topology data module (13), the topology data module (13) is connected with the historical operation data module (14), the real-time measurement module (11) is used for acquiring real-time electric quantity data such as transformer area voltage and current and providing real-time basic data support for fault analysis, the power failure reporting module (12) is used for collecting power failure information reported by users or equipment and realizing quick sensing and reporting of power failure events, the topology data module (13) is used for acquiring transformer area power grid topology structure data and providing network connection relation basis for fault location, and the historical operation data module (14) is used for acquiring power failure history and equipment operation data and fault pattern analysis and mining rules.
  3. 3. The medium-low voltage transformer area leakage identification system according to claim 1, wherein the data preprocessing module (2) comprises a cleaning and filtering module (21), a time scale correction module (22), an anomaly correction module (23) and a metering optimization module (24), the cleaning and filtering module (21) is connected with the time scale correction module (22), the time scale correction module (22) is connected with the anomaly correction module (23), the anomaly correction module (23) is connected with the metering optimization module (24), the cleaning and filtering module (21) is used for eliminating noise and invalid data, improving data quality and guaranteeing accuracy of subsequent analysis, the time scale correction module (22) is used for calibrating data time stamp consistency and avoiding fault analysis errors caused by time deviation, the anomaly correction module (23) is used for correcting anomaly information in a topology ledger and guaranteeing accuracy of power grid structure data, and the metering optimization module (24) is used for optimizing metering data and improving reliability and usability of the metering data.
  4. 4. The medium-low voltage transformer area leakage identification system according to claim 1 is characterized in that the fault perception module (4) comprises a power failure judgment module (41), a relation analysis module (42), a dead point shielding module (43) and a feature extraction module (44), the power failure judgment module (41) is connected with the relation analysis module (42), the relation analysis module (42) is connected with the dead point shielding module (43), the dead point shielding module (43) is connected with the feature extraction module (44), the power failure judgment module (41) is used for judging whether a power failure event occurs according to measured data to realize primary identification of a power failure fault, the relation analysis module (42) is used for combining a topological structure to identify a suspected power failure area to reduce a fault investigation range, the dead point shielding module (43) is used for accurately shielding dead point data to avoid interference of fault investigation, and the feature extraction module (44) is used for extracting features such as zero sequence current from an electrical quantity to realize special identification of the power failure.
  5. 5. The medium-low voltage transformer area electricity leakage identification system according to claim 1 is characterized in that the knowledge graph construction module (5) comprises a topology knowledge module (51), a correlation construction module (52), a graph optimization module (53) and a model splicing module (54), wherein the topology knowledge module (51) is connected with the correlation construction module (52), the correlation construction module (52) is connected with the graph optimization module (53), the graph optimization module (53) is connected with the model splicing module (54), the topology knowledge module (51) is used for constructing a medium-low voltage distribution network integrated knowledge system based on topology data to form structural topology cognition, the correlation construction module (52) is used for establishing wide area correlation of equipment, topology and outage events to achieve multidimensional data linkage analysis, the graph optimization module (53) is used for optimizing knowledge graph structure and content to improve supporting capacity for fault analysis, and the model splicing module (54) is used for splicing and fusing different data models to form a complete fault analysis data model system.
  6. 6. The medium-low voltage transformer area leakage identification system of claim 1, wherein the event processing module (6) comprises a power failure merging module (61), a merging module (62), an address matching module (63) and a cell identification module (64), the power failure merging module (61) is connected with the merging module (62), the merging module (62) is connected with the address matching module (63), the address matching module (63) is connected with the cell identification module (64), the power failure merging module (61) is used for merging massive power failure data in a kneading way to realize event management and reduce repeated alarms, the merging module (62) is used for establishing a fault determination event merging model and improving the intelligent level of power failure event processing, the address matching module (63) is used for matching user address information and realizing accurate positioning and description of a fault position, and the cell identification module (64) is used for identifying specific cell and unit user numbers and providing accurate position guide for on-site rush repair.
  7. 7. The medium-low voltage transformer area leakage identification system of claim 1 is characterized in that the grading early warning module (7) comprises a user early warning module (71), a frequent early warning module (72), a grading layering module (73) and a first-aid repair supporting module (74), the user early warning module (71) is connected with the frequent early warning module (72), the frequent early warning module (72) is connected with the grading layering module (73), the grading layering module (73) is connected with the first-aid repair supporting module (74), the user early warning module (71) is used for pushing early warning information to important users involved in power failure in a targeted mode to improve emergency response efficiency, the frequent early warning module (72) is used for generating frequent power failure early warning and power pressure-aid pressure-drop complaint work orders based on a power failure information pool, the grading layering module (73) is used for grading layering power failure events to achieve differential control of faults, and the first-aid repair supporting module (74) is used for providing information such as fault positions and influence ranges for first-aid repair personnel to improve first-aid repair efficiency.
  8. 8. The medium-low voltage transformer area leakage identification system according to claim 1, wherein the system optimization module (8) comprises a feeder optimization module (81), a grading protection module (82), a signal processing module (83) and an abnormal operation side module (84), the feeder optimization module (81) is connected with the grading protection module (82), the grading protection module (82) is connected with the signal processing module (83), the signal processing module (83) is connected with the abnormal operation side module (84), the feeder optimization module (81) is used for optimizing a feeder automation function and improving fault isolation and recovery efficiency, the grading protection module (82) is used for optimizing grading protection configuration and improving fault protection accuracy and reliability, the signal processing module (83) is used for optimizing signal processing such as in-station and feeder and improving signal transmission and identification accuracy, and the abnormal operation side module (84) is used for processing a power grid abnormal operation mode and guaranteeing power grid operation stability and safety.
  9. 9. The medium-low voltage transformer area leakage identification system according to claim 1, wherein the data management module (9) comprises an information pool management module (91), a historical data module (92), a data security module (93) and an interface management module (94), the information pool management module (91) is connected with the historical data module (92), the historical data module (92) is connected with the data security module (93), the data security module (93) is connected with the interface management module (94), the information pool management module (91) is used for managing power failure information pool data and providing unified data storage and calling for fault analysis, the historical data module (92) is used for storing historical operation data and providing data support for fault analysis and trend prediction, the data security module (93) is used for guaranteeing system data security and preventing data leakage and illegal tampering, and the interface management module (94) is used for managing an external interface of a system and achieving data interaction and sharing with other systems.
  10. 10. The identification method of the medium-low voltage transformer area leakage identification system according to any one of claims 1to 9, wherein the specific steps include: Step S101, obtaining electric quantity of a station area, power failure information, power grid topology and historical operation data through a real-time measurement, power failure reporting, topology relation and historical operation data acquisition module, removing invalid data, calibrating a time stamp and correcting topology abnormality through data cleaning and filtering, time scale synchronous calibration, station account abnormality correction and metering data optimization, and providing an accurate data basis for subsequent analysis; Step S102, based on the preprocessing data, a power failure event is identified by utilizing a power failure event judging module, a suspected power failure area is positioned by combining a topological relation analyzing module, interference data is eliminated by a fault data shielding module, and meanwhile, leakage characteristics such as zero sequence current and the like are extracted from the electric quantity, so that preliminary positioning of the power failure and special identification of the leakage characteristics are realized; Step 103, constructing a medium-low voltage distribution network integrated topology knowledge by means of topology data, establishing a wide-area association relation between equipment, topology and power failure events, improving the accuracy of a map by a knowledge map optimizing module, splicing and fusing different data models, and forming a structured and associated fault analysis knowledge system; step S104, kneading massive power failure data by using a power failure event merging module, and realizing power failure event management by combining an event merging model; Step 105, aiming at important users related to power failure and historical repair sensitive users, targeted pushing information is provided through an important user early warning module, frequent power failure grading early warning events are generated based on a power failure information pool, and a grading layering early warning mechanism is combined to realize fault differentiation management and control, and meanwhile support information such as a fault influence range is provided for emergency repair personnel; And step S106, module performances such as feeder automation, grading protection and the like are optimized regularly, the problems of signals, abnormal operators and the like in the station are solved, and the unified storage, safe interaction and sharing of data and continuous and stable operation of the support system are realized through power failure information pool management, historical data storage, data security assurance and interface management.

Description

Medium-low voltage transformer area electric leakage identification system and identification method thereof Technical Field The invention relates to the technical field of leakage identification, in particular to a medium-low voltage transformer area leakage identification system and an identification method thereof. Background The medium-low voltage transformer area electricity leakage identification system realizes second-level accurate judgment, automatic identification of influence range and frequent power failure layering early warning of low-voltage faults by constructing an intelligent technical system, improves fault positioning and disposal efficiency, accurately identifies electricity leakage characteristics, supports rush repair resource targeting scheduling, effectively reduces pressure drop and frequent power failure complaints, and comprehensively improves power supply service quality and intelligent management level of a power grid. In the prior art, the medium-low voltage transformer area electricity leakage identification technology has the obvious defects that a high-efficiency filtering mechanism is lacked in massive electricity failure data, invalid information is difficult to be removed by combining with a topological relation, so that fault positioning efficiency is low, bad point data is seriously interfered, topology association analysis capability is weak, a power failure event cannot be accurately judged, a standardized merging mechanism is lacked in the power failure event, a repeated alarm problem is prominent, frequent electricity failure management and control depends on manual analysis, grading early warning cannot be automatically generated, important user electricity failure early warning lacks targeted pushing, and information transmission is inaccurate during multi-professional collaborative repair. In addition, the special extraction capability of the leakage characteristic is weak, the knowledge graph construction and the data fusion are insufficient, and the support of the integrated fault research and judgment is difficult. Therefore, it is necessary to design a medium-low voltage transformer area leakage recognition system and a recognition method thereof. Disclosure of Invention The invention aims to solve the problems and provide a medium-low voltage transformer area electric leakage identification system and an identification method thereof, which solve the problems in the background art. In order to solve the technical problems, the medium-low voltage transformer area electric leakage identification system provided by the invention comprises a data acquisition module, a data preprocessing module, a system control module, a fault perception module, a knowledge graph construction module, an event processing module, a grading early warning module, a system optimization module, a data management module and a data center module, wherein the data acquisition module is connected with the data preprocessing module, the data preprocessing module is connected with the system control module, the system control module is connected with the fault perception module, the system control module is connected with the knowledge graph construction module, the system control module is connected with the event processing module, the system control module is connected with the grading early warning module, the system control module is connected with the system optimization module, the system control module is connected with the data center module, and the data management module is connected with the data center module. Preferably, the data acquisition module comprises a real-time measurement module, a power failure reporting module, a topology data module and a historical operation data module, wherein the real-time measurement module is connected with the power failure reporting module, the power failure reporting module is connected with the topology data module, and the topology data module is connected with the historical operation data module; The system comprises a power failure analysis module, a power failure reporting module, a real-time measurement module, a topology data module and a historical operation data module, wherein the real-time measurement module is used for collecting real-time electric quantity data such as voltage and current of a transformer area and providing real-time basic data support for failure analysis, the power failure reporting module is used for collecting power failure information reported by a user or equipment and realizing quick sensing and reporting of a power failure event, the topology data module is used for obtaining topology structure data of a transformer area power grid and providing a network connection relation basis for failure positioning, and the historical operation data module is used for collecting historical power failure and equipment operation data and is used for failure mode analysis and historical rule mining. Preferably, t