CN117129796A - Power grid fault identification system based on big data - Google Patents
Power grid fault identification system based on big data Download PDFInfo
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- CN117129796A CN117129796A CN202311073635.XA CN202311073635A CN117129796A CN 117129796 A CN117129796 A CN 117129796A CN 202311073635 A CN202311073635 A CN 202311073635A CN 117129796 A CN117129796 A CN 117129796A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
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Abstract
The invention discloses a power grid fault recognition system based on big data, which comprises a grid fault recognition system, wherein the grid fault recognition system comprises a data acquisition unit, a data storage and management unit, a feature extraction unit, an abnormality and fault recognition unit, a fault alarm unit, a data visualization unit and a report generation unit; the data acquisition unit is used for processing and converting the data acquired by the sensor and transmitting the data to the data storage unit. The invention utilizes the matched arrangement mode of the feature extraction unit and the fault alarm unit to improve the intelligent fault early warning event capability of the power grid and the early warning recognition capability of the power grid power failure fault, so that signals are instantly forwarded to related personnel and unrelated personnel to finish data monitoring, transmission, reporting, analysis, early warning and release in a short time, power failure emergency repair is driven by customer telephone repair and complaint, and the power failure emergency repair is changed into active response of a power supply enterprise, and quick and accurate power restoration is realized.
Description
Technical Field
The invention relates to the technical field of power grid fault identification, in particular to a power grid fault identification system based on big data.
Background
With the continuous promotion of electric power digital transformation construction and optimization of commercial environment work, the requirements on customer service level are also higher and higher. Because the power system is not yet realized and is distributed and communicated, when facing various sudden fault power failure events, most of clients feed back the power failure events, urgent repair personnel receive work orders and go to field processing, the urgent repair response time is long, the overall power failure event processing timeliness is insufficient, customer service experience is not facilitated, meanwhile, the urgent repair personnel can not know the real-time condition of the fault field at the first time, and high-efficiency maintenance cannot be correspondingly carried out.
Disclosure of Invention
The invention aims to provide a power grid fault identification system based on big data so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the power grid fault recognition system based on big data comprises a power grid fault recognition system, wherein the power grid fault recognition system comprises a data acquisition unit, a data storage and management unit, a feature extraction unit, an abnormality and fault recognition unit, a fault alarm unit, a data visualization unit and a report generation unit;
the data acquisition unit is used for processing and converting the data acquired by the sensor and transmitting the data to the data storage unit;
the data storage and management unit is used for storing the acquired real-time data into a database or a data warehouse and managing and sorting the data;
the feature extraction unit is used for directionally extracting specific features from the real-time data and the video data;
the abnormality and fault identification unit is used for monitoring the positions of faults and abnormal circuits in real time;
the fault alarm unit is used for monitoring the running state of the power grid in real time, and sending alarm information to related personnel in time once abnormal conditions are found;
the data visualization unit is used for performing visual display on the analyzed and processed data in the forms of charts, reports and the like;
and the report generating unit is used for generating a detailed abnormal behavior report.
Preferably, the data acquisition unit comprises a sensor module, a video monitoring module, a data quality control module, a data security module and a data analysis module;
the sensor module is used for collecting real-time data such as voltage, current, frequency and the like of each node of the power grid;
the video monitoring module is used for collecting video data of the power operation site and providing visual information;
the data quality control module is used for monitoring the quality of data and detecting abnormal data and data consistency problems;
the data security module is used for protecting the communication security between the monitoring control unit and the power grid, and comprises data encryption, access control and the like;
the data analysis module is used for analyzing the stored fault signals, extracting fault characteristics, and assisting in fault positioning and fault cause analysis.
Preferably, the data storage and management unit comprises a data storage module, a data management module and a data preprocessing module;
the data storage module is used for storing the acquired real-time data into a database;
the data management module is used for managing and arranging the stored data and providing convenient data inquiry and retrieval functions;
the data preprocessing module is used for cleaning the acquired data, removing abnormal values and supplementing missing values.
Preferably, the data preprocessing module comprises a data cleaning sub-module, a data conversion sub-module and a monitoring point state marking sub-module;
the data cleaning sub-module is used for removing abnormal data, supplementing missing data and the like;
the data conversion sub-module is used for converting and standardizing the original data;
and the monitoring point state marking sub-module is used for monitoring the whole fault and maintenance process in real time through a monitor and is used for carrying out screenshot marking on key events in the fault process or maintenance.
Preferably, the feature extraction unit comprises a time domain feature extraction module, a frequency domain feature extraction module and an image feature extraction module;
the time domain feature extraction module is used for extracting time domain features acquired from the real-time data;
the frequency domain feature extraction module is used for extracting frequency domain features acquired from real-time data;
and the image feature extraction module is used for extracting key visual features from the video data.
Preferably, the abnormality and fault recognition unit comprises a behavior pattern modeling module, an abnormal behavior recognition module, an abnormality and fault positioning module, an event sequence recording module and an event recall recording module;
the behavior pattern modeling module is used for modeling normal and abnormal behaviors through a machine learning or deep learning algorithm and classifying and identifying faults possibly occurring in the power grid according to the extracted characteristics;
the abnormal behavior identification module is used for comparing the real-time data with the behavior model and identifying and classifying abnormal behaviors;
the abnormality and fault positioning module is used for determining the specific position of the fault according to the identified fault information, diagnosing and analyzing the fault of the power system and helping to determine the type and position of the fault;
the event sequence recording module is used for marking the key event sequence in the fault process and recording according to the time sequence;
and the event recall recording module is used for completely recording the whole fault event.
Preferably, the fault alarm unit comprises a fault ground alarm module, a remote control ground alarm module, a diversified alarm module and a stepped alarm module;
the fault ground alarm module is used for carrying out fault ground alarm on the identified faults, reminding irrelevant personnel of keeping away, reminding relevant personnel of fault positions, reminding relevant personnel of maintenance or treatment and recovering the normal operation of the power grid;
the remote control ground alarm module is used for timely sending alarm information to a system administrator, an operation and maintenance personnel and an operator of a remote control ground when the power grid is failed by the failed ground, so as to realize remote control outage of the power line of the failed ground;
the diversified alarm module is used for carrying out corresponding classification according to the flickering color and flickering mode of the alarm lamp corresponding to the type of the accident in visual aspect, and carrying out corresponding classification according to the ringing mode and rhythm of the alarm bell corresponding to the type of the accident in auditory aspect;
the step type alarm module is divided into three steps type alarms according to the degree of faults under the category of the accident, wherein the first step alarm is an alarm lamp flashing alarm, the second step alarm is an alarm bell playing bell alarm, and the third step alarm is an alarm lamp flashing alarm and an alarm bell playing bell alarm.
The invention has the technical effects and advantages that:
the invention utilizes the matched setting mode of the feature extraction unit and the fault alarm unit to improve the intelligent fault early warning event capability of the power grid, strengthen the perception monitoring of the running state of the power grid from multiple dimensions, improve the early warning recognition capability of the power grid power failure fault, enable signals to be instantly forwarded to related personnel and unrelated personnel, finish data monitoring, transmission, reporting, analysis, early warning and issuing in a short time, enable power failure emergency repair to be driven by customer telephone repair and complaint, become the active response of a power supply enterprise, realize quick and accurate power failure emergency repair, simultaneously reduce the public opinion influence caused by various sudden faults, continuously improve customer service experience and strengthen the comprehensive service quality of a company.
Drawings
FIG. 1 is a block diagram of a power grid fault identification system of the present invention.
FIG. 2 is a block diagram of a data acquisition unit according to the present invention.
FIG. 3 is a block diagram of a data storage and management unit of the present invention.
FIG. 4 is a block diagram of a data preprocessing module according to the present invention.
Fig. 5 is a block diagram of a feature extraction unit of the present invention.
Fig. 6 is a block diagram of an anomaly and fault recognition unit of the present invention.
Fig. 7 is a block diagram of a fault alerting unit of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a big data-based power grid fault recognition system as shown in fig. 1-7, which comprises a grid fault recognition system, wherein the grid fault recognition system comprises a data acquisition unit, a data storage and management unit, a feature extraction unit, an abnormality and fault recognition unit, a fault alarm unit, a data visualization unit and a report generation unit;
the data acquisition unit is used for processing and converting the data acquired by the sensor and transmitting the data to the data storage unit;
the data storage and management unit is used for storing the acquired real-time data into a database or a data warehouse and managing and sorting the data;
the feature extraction unit is used for directionally extracting specific features from the real-time data and the video data;
the abnormal and fault identification unit is used for monitoring the positions of faults and abnormal circuits in real time and monitoring line faults, overload and short circuits in the power system;
the fault alarm unit is used for monitoring the running state of the power grid in real time, and sending alarm information to related personnel in time once abnormal conditions are found;
the data visualization unit is used for performing visual display on the analyzed and processed data in the forms of charts, reports and the like, so that a user can conveniently perform data analysis and decision;
and the report generating unit is used for generating a detailed abnormal behavior report and comprises information such as the identified abnormal type, time, position and the like.
Further, the data acquisition unit comprises a sensor module, a video monitoring module, a data quality control module, a data security module and a data analysis module;
the sensor module is used for collecting real-time data such as voltage, current, frequency and the like of each node of the power grid;
the video monitoring module is used for collecting video data of the power operation site and providing visual information;
the data quality control module is used for monitoring the quality of data and detecting abnormal data and data consistency problems;
the data security module is used for protecting the communication security between the monitoring control unit and the power grid, and comprises data encryption, access control and the like;
the data analysis module is used for analyzing the stored fault signals, extracting fault characteristics, and assisting in fault positioning and fault cause analysis.
Further, the data storage and management unit comprises a data storage module, a data management module and a data preprocessing module;
the data storage module is used for storing the acquired real-time data into a database;
the data management module is used for managing and arranging the stored data and providing convenient data inquiry and retrieval functions;
the data preprocessing module is used for cleaning the acquired data, removing abnormal values and supplementing missing values.
Further, the data preprocessing module comprises a data cleaning sub-module, a data conversion sub-module and a monitoring point state marking sub-module;
the data cleaning sub-module is used for removing abnormal data, supplementing missing data and the like;
the data conversion sub-module is used for converting and standardizing the original data so as to adapt to the subsequent analysis and modeling requirements;
and the monitoring point state marking sub-module is used for monitoring the whole fault and maintenance process in real time through a monitor and is used for carrying out screenshot marking on key events in the fault process or maintenance.
Further, the feature extraction unit comprises a time domain feature extraction module, a frequency domain feature extraction module and an image feature extraction module;
the time domain feature extraction module is used for extracting time domain features such as maximum value, minimum value, average value and the like obtained from the real-time data;
the frequency domain feature extraction module is used for extracting frequency domain features, such as frequency spectrum features, power spectrum density and the like, obtained from the real-time data;
and the image feature extraction module is used for extracting key visual features such as personnel actions, equipment states and the like from the video data.
Further, the abnormality and fault recognition unit comprises a behavior pattern modeling module, an abnormal behavior recognition module, an abnormality and fault positioning module, an event sequence recording module and an event recall recording module;
the behavior pattern modeling module is used for modeling normal and abnormal behaviors through a machine learning or deep learning algorithm and classifying and identifying faults possibly occurring in the power grid according to the extracted characteristics;
the abnormal behavior identification module is used for comparing the real-time data with the behavior model and identifying and classifying abnormal behaviors;
the abnormality and fault positioning module is used for determining the specific position of the fault according to the identified fault information, diagnosing and analyzing the fault of the power system and helping to determine the type and position of the fault;
the event sequence recording module is used for marking the key event sequence in the fault process and recording according to the time sequence;
and the event recall recording module is used for completely recording the whole fault event.
Further, the fault alarm unit comprises a fault ground alarm module, a remote control ground alarm module, a diversified alarm module and a stepped alarm module;
the fault ground alarm module is used for carrying out fault ground alarm on the identified faults, reminding irrelevant personnel of keeping away, reminding relevant personnel of fault positions, reminding relevant personnel of maintenance or treatment and recovering the normal operation of the power grid;
the remote control ground alarm module is used for timely sending alarm information to a system administrator, operation and maintenance personnel and operators of a remote control ground when a power grid fails, so that remote control power failure of a power line of the failed ground is realized, the electric leakage phenomenon caused by the failure is prevented, and the health of related personnel or unrelated personnel is threatened;
the diversified alarm module is used for carrying out corresponding classification according to the flickering color and flickering mode of the alarm lamp corresponding to the type of the accident in visual aspect, and carrying out corresponding classification according to the ringing mode and rhythm of the alarm bell corresponding to the type of the accident in auditory aspect;
the step type alarm module is divided into three steps type alarms according to the degree of faults under the category of the accident, wherein the first step alarm is an alarm lamp flashing alarm, the second step alarm is an alarm bell playing bell alarm, and the third step alarm is an alarm lamp flashing alarm and an alarm bell playing bell alarm.
Finally, it should be noted that: the foregoing description is only illustrative of the preferred embodiments of the present invention, and although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements or changes may be made without departing from the spirit and principles of the present invention.
Claims (7)
1. The power grid fault recognition system based on big data comprises a grid fault recognition system and is characterized by comprising a data acquisition unit, a data storage and management unit, a feature extraction unit, an abnormality and fault recognition unit, a fault alarm unit, a data visualization unit and a report generation unit;
the data acquisition unit is used for processing and converting the data acquired by the sensor and transmitting the data to the data storage unit;
the data storage and management unit is used for storing the acquired real-time data into a database or a data warehouse and managing and sorting the data;
the feature extraction unit is used for directionally extracting specific features from the real-time data and the video data;
the abnormality and fault identification unit is used for monitoring the positions of faults and abnormal circuits in real time;
the fault alarm unit is used for monitoring the running state of the power grid in real time, and sending alarm information to related personnel in time once abnormal conditions are found;
the data visualization unit is used for visually displaying the analyzed and processed data in the form of charts and reports;
and the report generating unit is used for generating a detailed abnormal behavior report.
2. The big data based power grid fault identification system of claim 1, wherein the data acquisition unit comprises a sensor module, a video monitoring module, a data quality control module, a data security module, a data analysis module;
the sensor module is used for collecting real-time data of voltage, current and frequency of each node of the power grid;
the video monitoring module is used for collecting video data of the power operation site and providing visual information;
the data quality control module is used for monitoring the quality of data and detecting abnormal data and data consistency problems;
the data security module is used for protecting the communication security between the monitoring control unit and the power grid and comprises data encryption and access control;
the data analysis module is used for analyzing the stored fault signals, extracting fault characteristics, and assisting in fault positioning and fault cause analysis.
3. The big data based power grid fault identification system of claim 1, wherein the data storage and management unit comprises a data storage module, a data management module, a data preprocessing module;
the data storage module is used for storing the acquired real-time data into a database;
the data management module is used for managing and arranging the stored data and providing convenient data inquiry and retrieval functions;
the data preprocessing module is used for cleaning the acquired data, removing abnormal values and supplementing missing values.
4. The big data based power grid fault identification system of claim 3, wherein the data preprocessing module comprises a data cleaning sub-module, a data conversion sub-module and a monitoring point state marking sub-module;
the data cleaning sub-module is used for removing abnormal data and supplementing missing data;
the data conversion sub-module is used for converting and standardizing the original data;
and the monitoring point state marking sub-module is used for monitoring the whole fault and maintenance process in real time through a monitor and is used for carrying out screenshot marking on key events in the fault process or maintenance.
5. The big data based power grid fault identification system of claim 1, wherein the feature extraction unit comprises a time domain feature extraction module, a frequency domain feature extraction module, and an image feature extraction module;
the time domain feature extraction module is used for extracting time domain features acquired from the real-time data;
the frequency domain feature extraction module is used for extracting frequency domain features acquired from real-time data;
and the image feature extraction module is used for extracting key visual features from the video data.
6. The big data based power grid fault identification system of claim 1, wherein the anomaly and fault identification unit comprises a behavior pattern modeling module, an anomaly behavior identification module, an anomaly and fault location module, an event sequence recording module, an event recall recording module;
the behavior pattern modeling module is used for modeling normal and abnormal behaviors through a machine learning or deep learning algorithm and classifying and identifying faults possibly occurring in the power grid according to the extracted characteristics;
the abnormal behavior identification module is used for comparing the real-time data with the behavior model and identifying and classifying abnormal behaviors;
the abnormality and fault positioning module is used for determining the specific position of the fault according to the identified fault information, diagnosing and analyzing the fault of the power system and helping to determine the type and position of the fault;
the event sequence recording module is used for marking the key event sequence in the fault process and recording according to the time sequence;
and the event recall recording module is used for completely recording the whole fault event.
7. The big data based power grid fault identification system of claim 1, wherein the fault alarm unit comprises a fault ground alarm module, a remote control ground alarm module, a diversity alarm module, a ladder alarm module;
the fault ground alarm module is used for carrying out fault ground alarm on the identified faults, reminding irrelevant personnel of keeping away, reminding relevant personnel of fault positions, reminding relevant personnel of maintenance or treatment and recovering the normal operation of the power grid;
the remote control ground alarm module is used for timely sending alarm information to a system administrator, an operation and maintenance personnel and an operator of a remote control ground when the power grid is failed by the failed ground, so as to realize remote control outage of the power line of the failed ground;
the diversified alarm module is used for carrying out corresponding classification according to the flickering color and flickering mode of the alarm lamp corresponding to the type of the accident in visual aspect, and carrying out corresponding classification according to the ringing mode and rhythm of the alarm bell corresponding to the type of the accident in auditory aspect;
the step type alarm module is divided into three steps type alarms according to the degree of faults under the category of the accident, wherein the first step alarm is an alarm lamp flashing alarm, the second step alarm is an alarm bell playing bell alarm, and the third step alarm is an alarm lamp flashing alarm and an alarm bell playing bell alarm.
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| CN202311073635.XA CN117129796A (en) | 2023-08-23 | 2023-08-23 | Power grid fault identification system based on big data |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118035227A (en) * | 2024-04-15 | 2024-05-14 | 山东云擎信息技术有限公司 | Data intelligent processing method and system based on big data evaluation |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118035227A (en) * | 2024-04-15 | 2024-05-14 | 山东云擎信息技术有限公司 | Data intelligent processing method and system based on big data evaluation |
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Application publication date: 20231128 |