Distribution network daily line loss abnormity identification and tracking system
By constructing a daily line loss anomaly identification and tracking system for distribution networks, the problems of data silos and insufficient real-time monitoring in existing distribution network line loss management systems have been solved. This system enables automated calculation, anomaly identification, and closed-loop processing of daily line losses in distribution network areas, thereby improving the accuracy and efficiency of line loss management.
Patent Information
- Application Number
- CN202511895506.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-16
- Publication Date
- 2026-03-20
AI Technical Summary
Existing power distribution network line loss management systems suffer from problems such as data silos, lack of real-time monitoring and automated processing, inaccurate line loss calculations, incomplete anomaly identification, difficulty in verifying topological relationships, difficulty in timely detection of metering device failures, and low efficiency in identifying electricity theft, resulting in low efficiency and poor accuracy in line loss management.
It employs a multi-source data real-time access and fusion engine, a daily line loss intelligent calculation and anomaly screening module, an anomaly cause intelligent diagnosis engine, a multi-cause collaborative attribution and priority ranking module, an anomaly work order automatic generation and closed-loop tracking module, as well as a visual interactive interface and mobile terminal support module to achieve a unified data model, dynamic baseline loss range, automated diagnosis and closed-loop processing.
It improved the timeliness and accuracy of detecting line loss anomalies, optimized the anomaly handling process, reduced manpower costs, improved management efficiency, ensured the timeliness and consistency of data, promptly detected and handled topology problems, accurately identified electricity theft, clarified the main causes of line loss anomalies, and formed a closed-loop management system for the entire process.
Smart Images
Figure CN121705883A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of distribution network line loss management technology, and more specifically, to a daily line loss anomaly identification and tracking system for distribution networks. Background Technology
[0002] In the operation of power systems, line loss management in distribution network areas is a crucial link in ensuring the economic benefits and power supply reliability of power companies. As an important indicator for measuring the operational efficiency and management level of power systems, the accurate calculation and effective control of line loss rate are essential for power companies. However, current technologies for line loss management in distribution network areas have several shortcomings, as follows: Existing line loss management systems typically rely on multiple different business systems, such as marketing systems, metering automation systems, production management systems (PMS), work order systems, and GIS systems. These systems operate independently, with inconsistent data standards and formats, and lack an effective data integration mechanism. This necessitates manual data collection, processing, and conversion during line loss calculation and analysis, which is not only labor-intensive and inefficient but also prone to data errors and inconsistencies. Furthermore, untimely data updates fail to reflect the actual operating status of the power grid in real time, impacting the accuracy and reliability of line loss calculation results.
[0003] The existence of data silos between various business systems, and the lack of cross-system data association and sharing mechanisms, makes it difficult to comprehensively utilize multi-source data when conducting line loss analysis. For example, when analyzing the causes of abnormal line loss, it is impossible to effectively integrate user electricity consumption data, metering equipment operation data, and power grid topology data, making it difficult to comprehensively and accurately identify the root cause of abnormal line loss.
[0004] Traditional line loss calculation methods typically use fixed line loss rate indicators for evaluation, without taking into account the dynamic changes in power grid operation and the impact of seasonal factors. The load characteristics of the power grid vary greatly in different seasons and time periods, and fixed line loss rate indicators cannot accurately reflect the actual line loss situation, which can easily lead to misjudgment and omission of line loss anomalies.
[0005] Existing methods for identifying line loss anomalies often use a uniform threshold for judgment, failing to consider differences in urban / rural characteristics, transformer capacity, and photovoltaic (PV) grid connection status. For some special transformer areas, such as rural areas or areas with a large number of distributed PV grid connections, a uniform threshold may not be suitable for their actual operating conditions, resulting in low accuracy in identifying line loss anomalies.
[0006] In power distribution networks, the topological relationships between users and transformers are complex and prone to change. Existing methods for verifying topological relationships mainly rely on manual on-site surveys and data entry, which are not only labor-intensive and inefficient but also prone to errors. Furthermore, the lack of real-time monitoring and automatic verification mechanisms means that changes in topological relationships cannot be detected and corrected in a timely manner, leading to inaccurate line loss calculations.
[0007] The operational status of metering devices directly affects the accuracy of line loss calculations. Existing fault diagnosis methods for metering devices mainly rely on periodic inspections and post-incident maintenance, lacking real-time monitoring and early warning mechanisms for the operational status of metering devices. When metering devices malfunction, they often fail to be detected and addressed promptly, leading to persistent abnormal line losses. Existing methods for identifying electricity theft primarily depend on manual experience and traditional monitoring techniques, such as checking meter seals and observing load curves. These methods are inefficient, inaccurate, and struggle to detect concealed electricity theft. Furthermore, with the continuous development of electricity theft techniques, traditional monitoring methods are no longer sufficient to meet practical needs.
[0008] Existing line loss anomaly handling processes typically rely on manual work order dispatch and paper-based records, lacking automated and information-based management methods. Problems such as untimely information transmission, difficulties in tracking progress, and unclear division of responsibilities easily arise during work order processing, leading to low efficiency in anomaly handling and unresolved issues. Line loss anomalies are often caused by the combined effects of multiple factors, but existing line loss management systems lack the ability to perform collaborative analysis of these factors. When handling line loss anomalies, they often focus only on a single factor, ignoring the interrelationships and combined effects of various factors, making it difficult to fundamentally solve the line loss problem. Therefore, this paper proposes a daily line loss anomaly identification and tracking system for distribution networks. Summary of the Invention
[0009] The purpose of this invention is to address the problems raised in the existing background technology. To achieve the above-mentioned objective, this invention provides the following technical solution: a distribution network daily line loss anomaly identification and tracking system, comprising a multi-source data real-time access and fusion engine, a daily line loss intelligent calculation and anomaly initial screening module, an anomaly cause intelligent diagnosis engine, a multi-factor collaborative attribution and priority ranking module, an anomaly work order automatic generation and closed-loop tracking module, and a visual interactive interface and mobile terminal support module; the distribution network daily line loss anomaly identification and tracking system integrates multi-source data from a distribution network daily line loss anomaly identification and tracking system, a metering automation system, a PMS, work orders, and a GIS system; The multi-source data real-time access and fusion engine constructs a unified data model, which is based on the meter asset number. ,user It enables cross-system primary key matching and adopts an incremental synchronization mechanism to complete a full data refresh before 06:00 every day; Let the set of data to be updated on day t be... Historical data has been synchronized. Then the incremental synchronization process satisfies: This ensures data consistency while reducing communication load and processing latency.
[0010] As a preferred technical solution of the present invention, the daily line loss intelligent calculation and anomaly screening module is based on historical data. Sliding window data Combined with seasonal adjustment factors and external influence factor weight vector Establish a dynamic baseline loss range: Daily line loss or A level 1 alarm is triggered at any time, and it supports classification by urban / rural attribute (C) and transformer capacity. Photovoltaic grid connection status Configure differentiated thresholds .
[0011] As a preferred technical solution of the present invention, the intelligent diagnostic engine for abnormal causes includes an automatic topological relationship verification module, which utilizes the phase angle acquired by the HPLC communication module. or carrier signal attenuation value Inverse inference of the concentrator c to which user u belongs and its transformer Define the user registration area as The actual communication location is If the following conditions are met for three consecutive days: If the error is detected, the user-transformer relationship will be incorrect, and a topology anomaly alarm will be generated.
[0012] As a preferred technical solution of the present invention, the intelligent diagnostic engine for abnormal causes includes a metering device health assessment module, which is based on the electricity meter operating parameter set. A health score model is constructed using the equipment's service life Y and the number of historical failures F.
[0013] As a preferred technical solution of the present invention, the intelligent diagnostic engine for abnormal causes includes an intelligent identification module for electricity theft behavior, which employs... Deep learning model, input sequence Attention mechanism calculates the output: Ultimately, the electricity theft risk level is output through the fully connected layer. , , And suspicious time periods.
[0014] As a preferred technical solution of the present invention, the multi-cause collaborative attribution and priority ranking module constructs a knowledge graph of line loss anomalies G=(V,E), where the node set V includes transformer areas, users, equipment, work orders, and events, and the edge set E represents causal relationships, including data interruption during table replacement; a graph neural network (GNN) is applied for message passing. After K-layer propagation, the contribution score of each factor to the line loss anomaly is calculated: Output the causal chain and its sorting.
[0015] As a preferred technical solution of the present invention, the automatic generation and closed-loop tracking module for abnormal work orders is equipped with a timeout warning mechanism; assuming the work order creation time is... The processing time is Given a current time t, when the following conditions are met: The daily line loss anomaly identification and tracking system for the distribution network automatically upgrades and alerts management, with the push frequency increasing exponentially with the timeout period. To prevent problems from accumulating.
[0016] As a preferred technical solution of the present invention, the visual interactive interface and mobile terminal support module provide a large-screen display function on a PC, displaying a heat map of line loss in the transformer area. ,in This refers to GIS coordinates; the mobile app supports OCR recognition of the table code. Voice notes converted to text And achieve one-click closed-loop confirmation: Improve on-site operation efficiency and the timeliness of information feedback.
[0017] As a preferred technical solution of the present invention, the content displayed on the large screen of the PC terminal of the visualization interactive interface and the mobile terminal support module includes heat map of line loss in the transformer area, anomaly ranking, and trend analysis; the mobile app supports front-line personnel to receive tasks, upload and process photos, confirm the closed loop, and supports voice input notes and photo OCR recognition of table codes.
[0018] As a preferred technical solution of the present invention, the feature extraction rules of the intelligent identification module for electricity theft include: suspected circuit breaker tripping due to constant low load at night; bypass electricity theft due to zero-ground current deviation; and judgment based on data from Hall sensors caused by magnetic field interference causing metering to stop.
[0019] As a preferred technical solution of the present invention, the causal chain output by the multi-cause collaborative attribution and priority ranking module can clearly define the primary and secondary relationships of each factor, and the main reason for the increase in line loss is that newly added photovoltaic users are not included in the electricity sales statistics.
[0020] Compared with existing technologies, the advantages of this invention are as follows: This invention utilizes a multi-source data real-time access and fusion engine to construct a unified data model. It achieves cross-system primary key matching through meter asset numbers and user IDs, employing an incremental synchronization mechanism to complete a full data refresh before 06:00 daily. This ensures data timeliness and consistency, avoids errors and delays that may occur during manual data processing, and provides a solid and reliable data foundation for subsequent line loss calculation, analysis, and diagnosis. The daily line loss intelligent calculation and anomaly screening module establishes a dynamic baseline line loss range based on historical sliding window data, combined with a seasonal adjustment method, and introduces external factor correction. When line loss exceeds the normal range, an alarm is automatically triggered, and differentiated thresholds can be configured according to different attributes. This automated calculation and screening mechanism significantly saves manpower and time costs, and improves the timeliness and accuracy of line loss anomaly detection.
[0021] The automatic anomaly work order generation and closed-loop tracking module automatically generates processing work orders based on diagnostic results and intelligently dispatches them to the corresponding responsible work teams. Work order status is transmitted in real-time via mobile devices, forming a closed-loop management system with a timeout warning mechanism. This makes the anomaly handling process more standardized and efficient, reducing problem-solving time and buck-passing, and improving overall management efficiency. The anomaly cause intelligent diagnostic engine's automatic topology verification module uses the phase recognition capability or carrier signal attenuation characteristics of the HPLC communication module to infer the actual transformer belonging to the user and compares it with the topology relationship registered in the GIS system. This can promptly detect errors in the user-transformer relationship and generate alarms, helping to quickly locate and resolve line loss anomalies caused by topology problems.
[0022] The metering device health assessment module constructs a health scoring model based on multiple operational data points from the electricity meter, combined with the device's operational years and historical fault records. When the score falls below a set threshold, a suspected metering fault is indicated, and verification or replacement is recommended. This allows for early detection of potential problems with the metering device, preventing inaccurate line loss due to metering malfunctions. A deep learning model extracts abnormal electricity consumption pattern features from multi-dimensional input data and outputs the electricity theft risk level and suspicious time periods. Simultaneously, it clarifies feature extraction rules for various electricity theft behaviors, facilitating precise crackdowns on electricity theft and reducing electricity loss. The multi-factor collaborative attribution and priority ranking module constructs a knowledge graph of line loss anomalies, applying a graph neural network (GNN) for multi-factor joint reasoning. It comprehensively evaluates the contribution of each factor and outputs the most likely causal chain and its weight ranking. This clarifies the primary and secondary relationships of each factor, helping managers quickly identify the main causes of increased line loss and take targeted measures for remediation. Attached Figure Description
[0023] Figure 1 The system module data block diagram provided by this invention; Figure 2 This is a data block diagram of the abnormal cause diagnosis submodule provided by the present invention; Figure 3 A data block diagram illustrating the characteristics of electricity theft provided by this invention; Figure 4 This is a flowchart of the work order processing process provided by the present invention. Detailed Implementation
[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0025] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely illustrates some embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. It should be noted that, in the absence of conflict, the embodiments and features and technical solutions in the embodiments of the present invention can be combined with each other. It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0026] Example 1: A distribution network daily line loss anomaly identification and tracking system, including a multi-source data real-time access and fusion engine, a daily line loss intelligent calculation and anomaly initial screening module, an anomaly cause intelligent diagnosis engine, a multi-factor collaborative attribution and priority ranking module, an anomaly work order automatic generation and closed-loop tracking module, and a visual interactive interface and mobile terminal support module; the distribution network daily line loss anomaly identification and tracking system integrates multi-source data from marketing systems, metering automation distribution network daily line loss anomaly identification and tracking systems, PMS, work orders, and GIS systems; Real-time access to multi-source data and the fusion engine build a unified data model, using meter asset numbers. ,user It enables cross-system primary key matching and adopts an incremental synchronization mechanism to complete a full data refresh before 06:00 every day; Let the set of data to be updated on day t be... Historical data has been synchronized. Then the incremental synchronization process satisfies: This ensures data consistency while reducing communication load and processing latency.
[0027] The daily line loss intelligent calculation and anomaly screening module is based on historical data. Sliding window data Combined with seasonal adjustment factors and external influence factor weight vector Establish a dynamic baseline loss range: Daily line loss or A level 1 alarm is triggered at any time, and it supports classification by urban / rural attribute (C) and transformer capacity. Photovoltaic grid connection status Configure differentiated thresholds .
[0028] The intelligent diagnostic engine for anomalies includes an automatic topology verification module, which utilizes the phase angle data acquired by the HPLC communication module. or carrier signal attenuation value Inverse inference of the concentrator c to which user u belongs and its transformer Define the user registration area as The actual communication location is If the following conditions are met for three consecutive days: If the error is detected, the user-transformer relationship will be incorrect, and a topology anomaly alarm will be generated.
[0029] The intelligent diagnostic engine for anomalies includes a metering device health assessment module, which is based on a set of electricity meter operating parameters. A health score model is constructed using the equipment's service life Y and the number of historical failures F.
[0030] The intelligent diagnostic engine for anomalies includes an intelligent identification module for electricity theft, which employs... Deep learning model, input sequence Attention mechanism calculates the output: Ultimately, the electricity theft risk level is output through the fully connected layer. , , And suspicious time periods.
[0031] The multi-cause collaborative attribution and prioritization module constructs a knowledge graph G=(V,E) for line loss anomalies. The node set V includes transformer areas, users, equipment, work orders, and events, while the edge set E represents causal relationships, including data interruptions during table replacement. A graph neural network (GNN) is applied for message passing. After K-layer propagation, the contribution score of each factor to the line loss anomaly is calculated: Output the causal chain and its sorting.
[0032] The automatic generation and closed-loop tracking module for abnormal work orders has a timeout warning mechanism; the work order creation time is set to... The processing time is Given a current time t, when the following conditions are met: The daily line loss anomaly identification and tracking system for the distribution network automatically upgrades and alerts management, with the push frequency increasing exponentially with the timeout period. To prevent problems from accumulating.
[0033] The visual interactive interface and mobile terminal support module provide PC-side large-screen display functionality, showing the heat map of line loss in the transformer area. ,in This refers to GIS coordinates; the mobile app supports OCR recognition of the table code. Voice notes converted to text And achieve one-click closed-loop confirmation: Improve on-site operation efficiency and the timeliness of information feedback.
[0034] A daily line loss anomaly identification and tracking system for power distribution networks includes a visual interactive interface and a mobile terminal support module. The PC-based large screen displays content such as heat maps of line losses in the distribution area, anomaly rankings, and trend analysis. The mobile app allows frontline personnel to receive tasks, upload processed photos, and confirm closed loops. It also supports voice input for notes and photo OCR recognition of form codes.
[0035] The intelligent electricity theft identification module's feature extraction rules include: suspected circuit breaker tripping due to constant low load at night, illegal wiring; deviation of neutral-to-ground current indicating bypass electricity theft; and magnetic field interference causing metering stoppage, combined with Hall sensor data for judgment. The multi-cause collaborative attribution and priority ranking module's output causal chain can clearly define the primary and secondary relationships of each factor, with the main reason for increased line loss being that newly added photovoltaic users were not included in the electricity sales statistics.
[0036] Daily loss calculation and dynamic threshold alarm (including seasonal adjustment) import numpy as np import pandas as pd from scipy import stats def compute_daily_loss(data,history_days=30): Calculate the daily stop loss and determine if it is abnormal (±2σ). data:DataFrame with['date','supply_energy','sale_energy'] data['loss_rate']=(data['supply_energy']-data['sale_energy']) / data['supply_energy'] Sliding window + seasonal adjustment factors (such as temperature, holidays) recent=data.tail(history_days) base_mean=recent['loss_rate'].mean base_std=recent['loss_rate'].std External factor correction (example: temperature effect coefficient) temp_factor=0.02 Assuming high temperature increases losses holiday_factor=0.05 if is_holiday(today)else 0 adjusted_mean=base_mean+temp_factor+holiday_factor upper=adjusted_mean+2*base_std lower=adjusted_mean-2*base_std today_loss=data.iloc[-1]['loss_rate'] return today_loss>upper or today_loss The system includes a multi-source data real-time access and fusion engine, a daily line loss intelligent calculation and anomaly screening module, an anomaly cause intelligent diagnosis engine, a multi-factor collaborative attribution and priority ranking module, an anomaly work order automatic generation and closed-loop tracking module, and a visual interactive interface and mobile terminal support module. Among these, the system integrates multi-source data from the marketing system, metering automation system, production management system (PMS), work order system, and GIS system to achieve automatic calculation, anomaly identification, intelligent diagnosis, and closed-loop processing of daily line losses in distribution network areas.
[0037] The multi-source data real-time access and fusion engine builds a unified data model, realizes cross-system primary key matching through meter asset number and user ID, and adopts an incremental synchronization mechanism to complete the full data refresh before 06:00 every day to ensure data timeliness and consistency.
[0038] Automatic verification of household registration relationship (HPLC communication inference) def detect_topology_error(user_comm_data,days=3): user_comm_[{'date':'2024-04-01','hplc_phase':A,'gis_transformer':T1,'actual_trans':T2},] errors for user_id,records in user_comm_data.groupby('user_id'): if len(records) >= days: gis_tx=records.iloc[0]['gis_transformer'] actual_txs=records['actual_trans'].tolist() if all(tx!=gis_tx for tx in actual_txs[-days:]): errors.append({'user_id':user_id,'reason':'Incorrect account transfer relationship'}) Errors returned.
[0039] The daily line loss intelligent calculation and anomaly screening module establishes a dynamic baseline line loss range based on historical 7-30 day sliding window data and seasonal adjustment method, and introduces external factors such as weather, holidays, and load growth rate for correction; when the daily line loss exceeds the ±2σ normal range, a first-level alarm is triggered, and it supports configuring differentiated thresholds according to urban and rural attributes, transformer area capacity, and photovoltaic access status.
[0040] The intelligent diagnostic engine for anomalies includes an automatic topology verification module. This module uses the phase recognition capability or carrier signal attenuation characteristics of the HPLC communication module to infer the actual transformer to which the user belongs and compares it with the topology relationship registered in the GIS system. If a user successfully communicates for 3 consecutive days but is not under the same transformer, it is determined that the user-transformer relationship is incorrect and an alarm is generated.
[0041] The intelligent diagnostic engine for anomalies includes a metering device health assessment module. This module builds a health scoring model based on daily frozen power fluctuations, voltage loss, current imbalance, and abnormal power factor operation data of the electricity meter, combined with the equipment's years of operation and historical fault records. When the score is lower than a set threshold, it indicates a suspected metering fault and recommends verification or replacement.
[0042] The intelligent diagnostic engine for abnormal causes includes an intelligent electricity theft identification module. This module uses an LSTM+Attention deep learning model. The input includes daily electricity consumption sequence, voltage / current waveform, live and neutral current difference, and minimum load at night. It extracts abnormal electricity consumption pattern features and outputs the electricity theft risk level (low / medium / high) and suspicious time periods. Among them, the constant low load at night is used to identify the behavior of illegally connected wires, the large deviation of the neutral and ground current is used to judge bypass electricity theft, and the metering stoppage caused by magnetic field interference is detected by combining Hall sensor data.
[0043] Automatic generation and dispatch logic for abnormal work orders def generate_work_order(anomaly_type, details): mapping={'topology_error':'Low-pressure customer service team', 'meter_fault':'Metering Operation and Maintenance Team', 'theft_suspect':'Electricity Inspection Team'} return{order_type':anomaly_type,'assign_to':mapping.get(anomaly_type,'universal team'),'content':details,'create_time':pd.Timestamp.now(), 'status':'Pending processing'} The multi-factor collaborative attribution and priority ranking module constructs a knowledge graph of line loss anomalies. Nodes include transformer areas, users, equipment, work orders, and events, and edges represent causal relationships. A graph neural network (GNN) is applied to perform multi-factor joint reasoning, comprehensively evaluate the contribution of each factor, and output the most likely causal chain and its weight ranking.
[0044] The abnormal work order automatic generation and closed-loop tracking module automatically generates processing work orders based on the diagnostic results and intelligently dispatches them to the corresponding responsible teams according to the abnormality type: topology problems are pushed to the low-voltage customer service team, metering faults are pushed to the metering operation and maintenance team, and suspected electricity theft is pushed to the electricity inspection team; the work order status is transmitted back in real time through the mobile terminal, supporting QR code check-in, photo upload and processing confirmation, forming a closed-loop management of the entire process.
[0045] The automatic generation and closed-loop tracking module for abnormal work orders has a timeout warning mechanism. When a work order is not processed within the specified time limit, the system automatically escalates the reminder to the team leader or management to prevent problems from accumulating.
[0046] The visual interactive interface and mobile terminal support module provide PC-side large-screen display functions, including heat maps of transformer area line loss, anomaly ranking, and trend analysis charts; it is also equipped with a mobile app, which supports frontline personnel to receive tasks, input notes by voice, recognize form codes by photo OCR, and realize one-click closed-loop submission of on-site processing results.
[0047] The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described in the present invention. Although the present invention has been described in detail with reference to the above embodiments, the present invention is not limited to the specific embodiments described above. Therefore, any modifications or substitutions to the present invention, and all technical solutions and improvements that do not depart from the spirit and scope of the invention, are covered within the scope of the claims of the present invention.
Claims
1. A system for identifying and tracking daily line loss anomalies in a power distribution network, characterized in that, It includes a multi-source data real-time access and fusion engine, a daily line loss intelligent calculation and anomaly screening module, an anomaly cause intelligent diagnosis engine, a multi-cause collaborative attribution and priority ranking module, an anomaly work order automatic generation and closed-loop tracking module, as well as a visual interactive interface and mobile terminal support module. The daily line loss anomaly identification and tracking system for power distribution networks integrates multi-source data from marketing systems, metering automation systems, PMS, work order systems, and GIS systems. Real-time access to multi-source data and the fusion engine build a unified data model, using meter asset numbers. ,user The daily loss anomaly identification and tracking system uses primary key matching and adopts an incremental synchronization mechanism to complete the full data refresh before 06:00 every day. Let the set of data to be updated on day t be... Historical data has been synchronized. Then the incremental synchronization process satisfies: This ensures data consistency while reducing communication load and processing latency. The daily line loss intelligent calculation and anomaly screening module is based on historical data. Sliding window data Combined with seasonal adjustment factors and external influence factor weight vector Establish a dynamic baseline loss range: Daily line loss or A level 1 alarm is triggered at any time, and it supports classification by urban / rural attribute (C) and transformer capacity. Photovoltaic grid connection status Configure differentiated thresholds .
2. The distribution network daily line loss anomaly identification and tracking system according to claim 1, characterized in that, The intelligent diagnostic engine for anomalies includes an automatic topology verification module, which utilizes the phase angle acquired by the HPLC communication module. or carrier signal attenuation value Inverse inference of the concentrator c to which user u belongs and its transformer Define the user registration area as The actual communication location is If the following conditions are met for three consecutive days: If the error is detected, the user-transformer relationship will be incorrect, and a topology anomaly alarm will be generated.
3. The daily line loss anomaly identification and tracking system for a distribution network according to claim 2, characterized in that, The intelligent diagnostic engine for the causes of anomalies includes a metering device health assessment module, which is based on a set of electricity meter operating parameters. A health score model is constructed using the equipment's service life Y and the number of historical failures F.
4. The daily line loss anomaly identification and tracking system for a distribution network according to claim 3, characterized in that, The intelligent diagnostic engine for anomalies includes an intelligent identification module for electricity theft, which employs... Deep learning model, input sequence Attention mechanism calculates the output: Ultimately, the electricity theft risk level is output through the fully connected layer. , , And suspicious time periods.
5. A distribution network daily line loss anomaly identification and tracking system according to claim 4, characterized in that, The multi-cause collaborative attribution and priority ranking module constructs a knowledge graph of line loss anomalies G=(V,E), where the node set V includes transformer areas, users, equipment, work orders, and events, and the edge set E represents causal relationships, including data interruption during table replacement; a graph neural network (GNN) is applied for message passing. After K-layer propagation, the contribution score of each factor to the line loss anomaly is calculated: Output the causal chain and its sorting.
6. A distribution network daily line loss anomaly identification and tracking system according to claim 5, characterized in that, The automatic generation and closed-loop tracking module for abnormal work orders is equipped with a timeout warning mechanism; the work order creation time is set to... The processing time is Given a current time t, when the following conditions are met: The daily line loss anomaly identification and tracking system for the distribution network automatically upgrades and alerts management, with the push frequency increasing exponentially with the timeout period.
7. A distribution network daily line loss anomaly identification and tracking system according to claim 6, characterized in that, The visual interactive interface and mobile terminal support module provide PC-side large-screen display functionality, showing the heat map of line loss in the transformer area. ,in This refers to GIS coordinates; the mobile app supports OCR recognition of the table code. Voice notes converted to text And achieve one-click closed-loop confirmation: On-site operational efficiency and timeliness of information feedback.
8. The daily line loss anomaly identification and tracking system for a distribution network according to claim 7, characterized in that, The visual interactive interface and mobile terminal support module's PC-side large screen display content includes transformer area line loss heat map, anomaly ranking, and trend analysis; the mobile app supports frontline personnel to receive tasks, upload processed photos, confirm closed loops, and supports voice input notes and photo OCR recognition of table codes.
9. A distribution network daily line loss anomaly identification and tracking system according to claim 8, characterized in that, The feature extraction rules of the intelligent identification module for electricity theft include: suspected circuit breaker tripping and illegal wiring after a constant low load at night; bypass electricity theft due to zero-ground current deviation; and judgment based on data from Hall sensors caused by magnetic field interference that causes metering to stop.
10. A distribution network daily line loss anomaly identification and tracking system according to claim 9, characterized in that, The causal chain output by the multi-cause collaborative attribution and priority ranking module can clearly define the primary and secondary relationships of each factor.