CN113592128A - Big data electric wire netting operation monitoring system - Google Patents
Big data electric wire netting operation monitoring system Download PDFInfo
- Publication number
- CN113592128A CN113592128A CN202110039981.0A CN202110039981A CN113592128A CN 113592128 A CN113592128 A CN 113592128A CN 202110039981 A CN202110039981 A CN 202110039981A CN 113592128 A CN113592128 A CN 113592128A
- Authority
- CN
- China
- Prior art keywords
- data
- power
- evaluation coefficient
- acquiring
- module
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 72
- 230000002159 abnormal effect Effects 0.000 claims abstract description 26
- 238000012423 maintenance Methods 0.000 claims abstract description 21
- 230000008439 repair process Effects 0.000 claims abstract description 4
- 238000011156 evaluation Methods 0.000 claims description 87
- 238000013500 data storage Methods 0.000 claims description 35
- 238000003062 neural network model Methods 0.000 claims description 28
- 230000007613 environmental effect Effects 0.000 claims description 21
- 238000007405 data analysis Methods 0.000 claims description 19
- 238000004458 analytical method Methods 0.000 claims description 14
- 238000004891 communication Methods 0.000 claims description 14
- 238000012360 testing method Methods 0.000 claims description 10
- 238000012549 training Methods 0.000 claims description 10
- 238000012795 verification Methods 0.000 claims description 10
- 230000005611 electricity Effects 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 9
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 230000004927 fusion Effects 0.000 claims description 3
- 230000010354 integration Effects 0.000 description 4
- 238000007726 management method Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- Game Theory and Decision Science (AREA)
- General Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Educational Administration (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Molecular Biology (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Power Engineering (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Human Computer Interaction (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Primary Health Care (AREA)
Abstract
The invention discloses a big data power grid operation monitoring system, relates to the technical field of electric power safe operation, and solves the technical problems that the abnormal operation problem of a power grid cannot be predicted in advance and the maintenance and repair efficiency is low in the prior art; the data acquisition module is arranged, so that the real-time performance of power grid monitoring is guaranteed, and the abnormal problem of power grid operation can be early warned in time; the early warning scheduling module is arranged, and after the early warning scheduling module receives the abnormal signal of the power grid and the target position, the route is planned and the target route is screened out, so that the efficiency of maintenance of workers is improved, and the power supply of the target position can be quickly recovered; the invention is provided with the data prediction module, and the future electric power data is predicted through the prediction module, so that the preparation of workers is facilitated, and the occurrence of abnormal operation of the power grid is avoided.
Description
Technical Field
The invention belongs to the field of electric power safe operation, relates to a big data technology, and particularly relates to a big data power grid operation monitoring system.
Background
In many links of the system in the shop, the distribution network is directly oriented to users, the operation management of the distribution network relates to multiple levels and multiple departments, the operation, state overhaul, construction transformation, engineering construction, distribution automation, marketing, power supply service and other multiple specialties and contents of the distribution network are related, the storage of information such as operation, marketing, overhaul and the like of the distribution network and the monitoring of indexes are managed in different departments respectively, and a global comprehensive management and control cannot be achieved.
The invention patent with the publication number of CN104124756A discloses a provincial power distribution network operation monitoring system based on whole network data; the device comprises a three-layer structure: the system comprises an information integration layer, a platform layer and an application layer; the information integration layer mainly collects distribution network basic and operation data and provides online and offline distribution network operation data to the platform layer through the information interaction bus; the platform layer is mainly used for collecting and counting various data of the distribution network and providing corresponding services for the application layer; the application layer is mainly used for carrying out operation monitoring, index monitoring and data analysis on the provincial power distribution network based on the whole network equipment and operation data under the support of the platform layer.
According to the scheme, data such as production and marketing of distribution networks in provinces, cities and counties are integrated, an information integration platform is built, the operation state of the distribution networks in the provinces and the counties is monitored in a centralized manner, various operation data and indexes of the distribution networks are monitored, and operation management of the distribution networks in the provinces and the counties is facilitated; however, the above scheme only realizes data integration and monitoring, does not fully utilize the integrated data, and does not take advantage of big data; therefore, the above solution still needs further improvement.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides a big data power grid operation monitoring system.
The purpose of the invention can be realized by the following technical scheme: a big data power grid operation monitoring system comprises a processor, a data prediction module, an alarm scheduling module, a data storage module, a data acquisition module, a data analysis module and a disaster analysis module;
the disaster analysis module is used for analyzing the influence of natural disasters on the operation of the power grid, and comprises:
when a disaster analysis module receives a disaster early warning request, acquiring the total number of electric power equipment in a natural disaster coverage area, and marking the total number of the electric power equipment as DSZ; the disaster early warning request is sent out through a meteorological platform and an intelligent terminal of a power consumer;
when the total number DSZ of the power equipment meets the condition that the DSZ is more than 0 and less than or equal to K1, judging that the influence range of the natural disaster is small, and sending a disaster yellow early warning signal to an alarm scheduling module through a processor; when the total number DSZ of the power equipment meets the condition that K1 is smaller than DSZ, judging that the natural disaster influence range is large, and sending a disaster red early warning signal to an alarm scheduling module through a processor; wherein K1 is the power device total number threshold;
sending the sending record of the disaster signal to a data storage module for storage through a processor;
the data prediction module is used for predicting the power data of a monitoring area, and comprises:
acquiring historical data of N1 days through a data storage module; wherein N1 > 366, and the historical data includes environmental parameters of the current day, population volume of the monitored area, residence volume of the monitored area, plant volume of the monitored area, and power data;
after normalization processing is carried out on historical data, dividing the historical data into a verification set and a test set according to a set proportion; the set ratio comprises 4:1, 3:2 and 1: 1;
constructing a neural network model; the neural network model comprises an error reverse feedback neural network and an RBF neural network;
training the neural network model through a verification set and a test set, judging that the training of the neural network model is finished when the target precision of the neural network model meets the requirement, and marking the trained neural network model as a prediction model;
acquiring environmental parameters, population total amount of a monitoring area, residence total amount of the monitoring area and factory total amount of the monitoring area of the future N2 days, and marking the environmental parameters, the population total amount of the monitoring area and the factory total amount of the monitoring area as prediction input data; normalizing the prediction input data and inputting the normalized prediction input data into a prediction model to obtain a prediction result of the power data; transmitting a prediction result of the power data to a data display unit; wherein N2 > 5;
and sending the prediction model and the prediction result of the power data to a data storage module for storage through a processor.
Further, the specific steps of acquiring the total number of the electric power devices include:
obtaining a remote sensing image of a monitoring area, and marking the remote sensing image as a first image after remote sensing preprocessing; the remote sensing preprocessing comprises geometric correction, atmospheric correction and image fusion;
extracting the total number of the electric power equipment in the coverage area of the natural disaster in the first image; the power equipment comprises a special transformer acquisition terminal and a concentrator;
and sending the total number of the electric power equipment to the data storage module for storage through the processor.
Furthermore, the data acquisition module is respectively in communication connection with the power acquisition unit and the environment acquisition unit, and the power acquisition unit is respectively connected with the special transformer acquisition terminal and the concentrator; the special transformer acquisition terminal comprises a large special transformer acquisition terminal and a medium and small special transformer acquisition terminal; the electric power data that the electric power collection unit user gathered the user includes:
acquiring the power consumption voltage of a large-scale special transformer acquisition terminal, and marking as UAiI ═ 1, 2, … …, n; acquiring the electricity frequency of a large-scale special transformer acquisition terminal, and marking the electricity frequency as HAi(ii) a Acquiring power utilization harmonic waves of a large-scale special transformer acquisition terminal, calculating distortion rate of the power utilization harmonic waves, and marking as TAi；
Acquiring the power consumption voltage of the small and medium-sized special transformer acquisition terminal, and marking the power consumption voltage as UBjJ is 1, 2, … …, m; acquiring the electricity frequency of a small and medium-sized special transformer acquisition terminal, and marking as HBj(ii) a Acquiring power utilization harmonic waves of small and medium-sized special transformer acquisition terminals, calculating distortion rate of the power utilization harmonic waves, and marking as TBj；
The voltage of the concentrator is obtained and marked as UCkK is 1, 2, … …, p; the frequency of the concentrator power utilization is obtained and labeled HCk(ii) a Acquiring power utilization harmonic waves of the concentrator, calculating distortion rate of the power utilization harmonic waves, and marking the distortion rate as TCk；
By the formula
Acquiring a voltage evaluation coefficient DPX; by the formula
Acquiring a frequency evaluation coefficient PPX; by the formula
Obtaining a distortion evaluation coefficient JPX; wherein α 1, α 2, α 03, α 14, α 25, α 36, α 47, α 58, and α 69 are all proportionality coefficients, and α 71, α 2, α 3, α 4, α 5, α 6, α 7, α 8, and α 9 are all real numbers greater than 0;
the power data, the voltage evaluation coefficient, the frequency evaluation coefficient and the distortion evaluation coefficient are sent to a data storage module through a processor to be stored, and meanwhile, the voltage evaluation coefficient, the frequency evaluation coefficient and the distortion evaluation coefficient are sent to a data analysis module;
the environment acquisition unit acquires environmental parameters of a monitoring area, and comprises:
acquiring an average temperature value, an average humidity value and an average wind force value of a monitoring area; the monitoring area is a coverage area of the power distribution network;
marking the average temperature value, the average humidity value and the average wind force value as PW, PS and PF respectively;
acquiring an environment evaluation coefficient HPX through a formula HPX ═ beta 1 × PW × PS × PF + beta 2; wherein β 1 and β 2 are proportionality coefficients, and both β 1 and β 2 are real numbers greater than 0;
and sending the average temperature value, the average humidity value, the average wind force value and the environment evaluation coefficient to a data storage module for storage through a processor, and sending the environment evaluation coefficient to a data analysis module.
Further, the data analysis module receives and analyzes the voltage evaluation coefficient, the frequency evaluation coefficient, the distortion evaluation coefficient and the environment evaluation coefficient, and comprises:
when the voltage evaluation coefficient, the frequency evaluation coefficient, the distortion evaluation coefficient and the environment evaluation coefficient are all in the corresponding threshold value ranges, judging that the power grid operates normally; otherwise, acquiring and judging the abnormal operation of the power grid, acquiring the abnormal position of the power grid and marking the abnormal position as a target position;
sending the abnormal signal of the power grid and the target position to an alarm scheduling module through a processor; and meanwhile, sending the sending record of the abnormal signal of the power grid and the target position to a data storage module for storage.
Furthermore, the alarm scheduling module comprises a data display unit and a maintenance scheduling unit; the data display unit is used for displaying monitoring data, and the monitoring data comprises power data, a voltage evaluation coefficient, a frequency evaluation coefficient, a distortion evaluation coefficient and an environment evaluation coefficient; the maintenance scheduling unit is used for scheduling workers to a target position for maintenance and repair, and comprises:
when the maintenance scheduling unit receives the power grid abnormal signal and the target position, a circular area is defined by taking the target position as the center and taking R1 as the radius and is marked as a screening area; wherein R1 is the set radius value;
acquiring the position of a worker in the screening area and marking the position as a primary selection position; acquiring a path for planning the primary selection position and the target position through a third-party map platform and marking the path as a primary selection path; the third-party map platform comprises a high-grade map, a Baidu map and an Tencent map;
acquiring the running distance and the congestion degree of the initially selected route, and respectively marking the running distance and the congestion degree as XJ and YD;
acquiring a path evaluation coefficient LPX through a formula LPX ═ beta 3 xJyYD; wherein β 3 is a proportionality coefficient and β 3 is a real number greater than 0;
when the path evaluation coefficient LPX satisfies 0 < LPX ≤ L1, determining that the corresponding initially selected path satisfies the requirement; otherwise, judging that the corresponding initial selection path does not meet the requirements; wherein L1 is the path evaluation coefficient threshold;
marking the shortest travel distance in the primary selected routes meeting the requirements as a target route;
sending the target path to an intelligent terminal of a worker; the intelligent terminal comprises an intelligent mobile phone, a tablet computer and a notebook computer;
after receiving the target path, the worker reaches the target position to perform maintenance and overhaul; and the running track of the worker is sent to the data display unit for displaying, and meanwhile, the dispatching record of the worker is sent to the data storage module for storage.
Further, the power data includes distortion rates of power usage voltage, power usage frequency, and power usage harmonics; the environmental parameters comprise an average temperature value, an average humidity value and an average wind force value of the monitoring area.
Further, the processor is respectively in communication connection with the data prediction module, the alarm scheduling module, the data storage module, the data acquisition module, the data analysis module and the disaster analysis module; the alarm scheduling module is respectively in communication connection with the data storage module and the data prediction module, and the data acquisition module is in communication connection with the data analysis module.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention is provided with a data acquisition module which is respectively in communication connection with a power acquisition unit and an environment acquisition unit; the power data are collected through the special transformer collection terminal and the concentrator, the environmental parameters are collected at the same time, and the judgment of the operation fault of the power grid is completed through the analysis of the power data and the environmental parameters, so that the real-time performance of the monitoring of the power grid is ensured, and the early warning can be timely carried out on the abnormal operation problem of the power grid;
2. the invention is provided with an early warning scheduling module, wherein the early warning scheduling module comprises a data display unit and a maintenance scheduling unit; after the early warning scheduling module receives the abnormal signal of the power grid and the target position, planning a path and screening out the target path, which is beneficial to improving the maintenance efficiency of workers and ensuring that the power supply of the target position can be quickly recovered;
3. the invention is provided with a data prediction module, which is used for predicting the power data of a monitoring area; acquiring historical data of N1 days through a data storage module; after normalization processing is carried out on historical data, dividing the historical data into a verification set and a test set according to a set proportion; constructing a neural network model; training the neural network model through a verification set and a test set, judging that the training of the neural network model is finished when the target precision of the neural network model meets the requirement, and marking the trained neural network model as a prediction model; acquiring environmental parameters, population total amount of a monitoring area, residence total amount of the monitoring area and factory total amount of the monitoring area of the future N2 days, and marking the environmental parameters, the population total amount of the monitoring area and the factory total amount of the monitoring area as prediction input data; normalizing the prediction input data and inputting the normalized prediction input data into a prediction model to obtain a prediction result of the power data; transmitting a prediction result of the power data to a data display unit; the data prediction module predicts future electric power data through the prediction model, and is helpful for workers to prepare in advance, so that abnormal operation of the power grid is avoided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of the principle of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a big data power grid operation monitoring system includes a processor, a data prediction module, an alarm scheduling module, a data storage module, a data acquisition module, a data analysis module, and a disaster analysis module;
the disaster analysis module is used for analyzing the influence of natural disasters on the operation of the power grid, and comprises:
when a disaster analysis module receives a disaster early warning request, acquiring the total number of electric power equipment in a natural disaster coverage area, and marking the total number of the electric power equipment as DSZ; the disaster early warning request is sent out through a meteorological platform and an intelligent terminal of a power consumer;
when the total number DSZ of the power equipment meets the condition that the DSZ is more than 0 and less than or equal to K1, judging that the influence range of the natural disaster is small, and sending a disaster yellow early warning signal to an alarm scheduling module through a processor; when the total number DSZ of the power equipment meets the condition that K1 is smaller than DSZ, judging that the natural disaster influence range is large, and sending a disaster red early warning signal to an alarm scheduling module through a processor; wherein K1 is the power device total number threshold;
sending the sending record of the disaster signal to a data storage module for storage through a processor;
the data prediction module is used for predicting the power data of a monitoring area, and comprises:
acquiring historical data of N1 days through a data storage module; wherein N1 > 366, and the historical data includes environmental parameters of the current day, population volume of the monitored area, residence volume of the monitored area, plant volume of the monitored area, and power data;
after normalization processing is carried out on historical data, dividing the historical data into a verification set and a test set according to a set proportion; the set ratio comprises 4:1, 3:2 and 1: 1;
constructing a neural network model; the neural network model comprises an error reverse feedback neural network and an RBF neural network;
training the neural network model through a verification set and a test set, judging that the training of the neural network model is finished when the target precision of the neural network model meets the requirement, and marking the trained neural network model as a prediction model;
acquiring environmental parameters, population total amount of a monitoring area, residence total amount of the monitoring area and factory total amount of the monitoring area of the future N2 days, and marking the environmental parameters, the population total amount of the monitoring area and the factory total amount of the monitoring area as prediction input data; normalizing the prediction input data and inputting the normalized prediction input data into a prediction model to obtain a prediction result of the power data; transmitting a prediction result of the power data to a data display unit; wherein N2 > 5;
and sending the prediction model and the prediction result of the power data to a data storage module for storage through a processor.
Further, the specific steps of acquiring the total number of the electric power devices include:
obtaining a remote sensing image of a monitoring area, and marking the remote sensing image as a first image after remote sensing preprocessing; the remote sensing preprocessing comprises geometric correction, atmospheric correction and image fusion;
extracting the total number of the electric power equipment in the coverage area of the natural disaster in the first image; the power equipment comprises a special transformer acquisition terminal and a concentrator;
and sending the total number of the electric power equipment to the data storage module for storage through the processor.
Furthermore, the data acquisition module is respectively in communication connection with the power acquisition unit and the environment acquisition unit, and the power acquisition unit is respectively connected with the special transformer acquisition terminal and the concentrator; the special transformer acquisition terminal comprises a large special transformer acquisition terminal and a medium and small special transformer acquisition terminal; the electric power data that the electric power collection unit user gathered the user includes:
acquiring the power consumption voltage of a large-scale special transformer acquisition terminal, and marking as UAiI ═ 1, 2, … …, n; acquiring the electricity frequency of a large-scale special transformer acquisition terminal, and marking the electricity frequency as HAi(ii) a Acquiring power utilization harmonic waves of a large-scale special transformer acquisition terminal, calculating distortion rate of the power utilization harmonic waves, and marking as TAi；
Acquiring the power consumption voltage of the small and medium-sized special transformer acquisition terminal, and marking the power consumption voltage as UBjJ is 1, 2, … …, m; acquiring the electricity frequency of a small and medium-sized special transformer acquisition terminal, and marking as HBj(ii) a Acquiring power utilization harmonic waves of small and medium-sized special transformer acquisition terminals, calculating distortion rate of the power utilization harmonic waves, and marking as TBj；
The voltage of the concentrator is obtained and marked as UCkK is 1, 2, … …, p; the frequency of the concentrator power utilization is obtained and labeled HCk(ii) a Acquiring power utilization harmonic waves of the concentrator, calculating distortion rate of the power utilization harmonic waves, and marking the distortion rate as TCk；
By the formula
Acquiring a voltage evaluation coefficient DPX; by the formula
Acquiring a frequency evaluation coefficient PPX; by the formula
Obtaining a distortion evaluation coefficient JPX; wherein α 1, α 2, α 03, α 14, α 25, α 36, α 47, α 58, and α 69 are all proportionality coefficients, and α 71, α 2, α 3, α 4, α 5, α 6, α 7, α 8, and α 9 are all real numbers greater than 0;
the power data, the voltage evaluation coefficient, the frequency evaluation coefficient and the distortion evaluation coefficient are sent to a data storage module through a processor to be stored, and meanwhile, the voltage evaluation coefficient, the frequency evaluation coefficient and the distortion evaluation coefficient are sent to a data analysis module;
the environment acquisition unit acquires environmental parameters of a monitoring area, and comprises:
acquiring an average temperature value, an average humidity value and an average wind force value of a monitoring area; the monitoring area is a coverage area of the power distribution network;
marking the average temperature value, the average humidity value and the average wind force value as PW, PS and PF respectively;
acquiring an environment evaluation coefficient HPX through a formula HPX ═ beta 1 × PW × PS × PF + beta 2; wherein β 1 and β 2 are proportionality coefficients, and both β 1 and β 2 are real numbers greater than 0;
and sending the average temperature value, the average humidity value, the average wind force value and the environment evaluation coefficient to a data storage module for storage through a processor, and sending the environment evaluation coefficient to a data analysis module.
Further, the data analysis module receives and analyzes the voltage evaluation coefficient, the frequency evaluation coefficient, the distortion evaluation coefficient and the environment evaluation coefficient, and comprises:
when the voltage evaluation coefficient, the frequency evaluation coefficient, the distortion evaluation coefficient and the environment evaluation coefficient are all in the corresponding threshold value ranges, judging that the power grid operates normally; otherwise, acquiring and judging the abnormal operation of the power grid, acquiring the abnormal position of the power grid and marking the abnormal position as a target position;
sending the abnormal signal of the power grid and the target position to an alarm scheduling module through a processor; and meanwhile, sending the sending record of the abnormal signal of the power grid and the target position to a data storage module for storage.
Furthermore, the alarm scheduling module comprises a data display unit and a maintenance scheduling unit; the data display unit is used for displaying monitoring data, and the monitoring data comprises power data, a voltage evaluation coefficient, a frequency evaluation coefficient, a distortion evaluation coefficient and an environment evaluation coefficient; the maintenance scheduling unit is used for scheduling workers to a target position for maintenance and repair, and comprises:
when the maintenance scheduling unit receives the power grid abnormal signal and the target position, a circular area is defined by taking the target position as the center and taking R1 as the radius and is marked as a screening area; wherein R1 is the set radius value;
acquiring the position of a worker in the screening area and marking the position as a primary selection position; acquiring a path for planning the primary selection position and the target position through a third-party map platform and marking the path as a primary selection path; the third-party map platform comprises a high-grade map, a Baidu map and an Tencent map;
acquiring the running distance and the congestion degree of the initially selected route, and respectively marking the running distance and the congestion degree as XJ and YD;
acquiring a path evaluation coefficient LPX through a formula LPX ═ beta 3 xJyYD; wherein β 3 is a proportionality coefficient and β 3 is a real number greater than 0;
when the path evaluation coefficient LPX satisfies 0 < LPX ≤ L1, determining that the corresponding initially selected path satisfies the requirement; otherwise, judging that the corresponding initial selection path does not meet the requirements; wherein L1 is the path evaluation coefficient threshold;
marking the shortest travel distance in the primary selected routes meeting the requirements as a target route;
sending the target path to an intelligent terminal of a worker; the intelligent terminal comprises an intelligent mobile phone, a tablet computer and a notebook computer;
after receiving the target path, the worker reaches the target position to perform maintenance and overhaul; and the running track of the worker is sent to the data display unit for displaying, and meanwhile, the dispatching record of the worker is sent to the data storage module for storage.
Further, the power data includes distortion rates of power usage voltage, power usage frequency, and power usage harmonics; the environmental parameters comprise an average temperature value, an average humidity value and an average wind force value of the monitoring area.
Further, the processor is respectively in communication connection with the data prediction module, the alarm scheduling module, the data storage module, the data acquisition module, the data analysis module and the disaster analysis module; the alarm scheduling module is respectively in communication connection with the data storage module and the data prediction module, and the data acquisition module is in communication connection with the data analysis module.
The above formulas are all calculated by removing dimensions and taking values thereof, the formula is one closest to the real situation obtained by collecting a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The working principle of the invention is as follows:
acquiring power consumption voltage UA of large-scale special transformer acquisition terminali(ii) a Acquiring power consumption frequency HA of large-scale special transformer acquisition terminali(ii) a Acquiring power utilization harmonic waves of a large-scale special transformer acquisition terminal, and calculating the distortion rate TA of the power utilization harmonic wavesi(ii) a Acquiring power utilization voltage UB of small and medium-sized special transformer acquisition terminalj(ii) a Acquiring power consumption frequency HB of small and medium-sized special transformer acquisition terminalj(ii) a Acquiring power utilization harmonic waves of small and medium-sized special transformer acquisition terminals, and calculating distortion rate TB of the power utilization harmonic wavesj(ii) a Obtaining the power consumption UC of a concentratork(ii) a Obtaining the power frequency HC of a concentratork(ii) a Acquisition setCalculating the distortion TC of the power harmonic of the repeaterk(ii) a Acquiring a voltage evaluation coefficient DPX; acquiring a frequency evaluation coefficient PPX; obtaining a distortion evaluation coefficient JPX; acquiring an average temperature value, an average humidity value and an average wind force value of a monitoring area; acquiring an environment evaluation coefficient HPX;
when a disaster analysis module receives a disaster early warning request, acquiring the total number DSZ of power equipment in a natural disaster coverage area; when the total number DSZ of the power equipment meets the condition that the DSZ is more than 0 and less than or equal to K1, judging that the influence range of the natural disaster is small, and sending a disaster yellow early warning signal to an alarm scheduling module through a processor; when the total number DSZ of the power equipment meets the condition that K1 is smaller than DSZ, judging that the natural disaster influence range is large, and sending a disaster red early warning signal to an alarm scheduling module through a processor;
acquiring historical data of N1 days through a data storage module; after normalization processing is carried out on historical data, dividing the historical data into a verification set and a test set according to a set proportion; constructing a neural network model; training the neural network model through a verification set and a test set, judging that the training of the neural network model is finished when the target precision of the neural network model meets the requirement, and marking the trained neural network model as a prediction model; acquiring environmental parameters, population total amount of a monitoring area, residence total amount of the monitoring area and factory total amount of the monitoring area of the future N2 days, and marking the environmental parameters, the population total amount of the monitoring area and the factory total amount of the monitoring area as prediction input data; normalizing the prediction input data and inputting the normalized prediction input data into a prediction model to obtain a prediction result of the power data; transmitting a prediction result of the power data to a data display unit;
when the maintenance scheduling unit receives the power grid abnormal signal and the target position, a circular area is defined by taking the target position as the center and taking R1 as the radius and is marked as a screening area; acquiring the position of a worker in the screening area and marking the position as a primary selection position; acquiring a path for planning the primary selection position and the target position through a third-party map platform and marking the path as a primary selection path; acquiring a running distance and a congestion degree of a primary selected route to acquire a route evaluation coefficient LPX; when the path evaluation coefficient LPX satisfies 0 < LPX ≤ L1, determining that the corresponding initially selected path satisfies the requirement; otherwise, judging that the corresponding initial selection path does not meet the requirements; marking the shortest travel distance in the primary selected routes meeting the requirements as a target route; sending the target path to an intelligent terminal of a worker; and the staff arrives at the target position for maintenance and overhaul after receiving the target path.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (7)
1. A big data power grid operation monitoring system is characterized by comprising a processor, a data prediction module, an alarm scheduling module, a data storage module, a data acquisition module, a data analysis module and a disaster analysis module;
the disaster analysis module is used for analyzing the influence of natural disasters on the operation of the power grid, and comprises:
when a disaster analysis module receives a disaster early warning request, acquiring the total number of electric power equipment in a natural disaster coverage area, and marking the total number of the electric power equipment as DSZ; the disaster early warning request is sent out through a meteorological platform and an intelligent terminal of a power consumer;
when the total number DSZ of the power equipment meets the condition that the DSZ is more than 0 and less than or equal to K1, judging that the influence range of the natural disaster is small, and sending a disaster yellow early warning signal to an alarm scheduling module through a processor; when the total number DSZ of the power equipment meets the condition that K1 is smaller than DSZ, judging that the natural disaster influence range is large, and sending a disaster red early warning signal to an alarm scheduling module through a processor; wherein K1 is the power device total number threshold;
sending the sending record of the disaster signal to a data storage module for storage through a processor;
the data prediction module is used for predicting the power data of a monitoring area, and comprises:
acquiring historical data of N1 days through a data storage module; wherein N1 > 366, and the historical data includes environmental parameters of the current day, population volume of the monitored area, residence volume of the monitored area, plant volume of the monitored area, and power data;
after normalization processing is carried out on historical data, dividing the historical data into a verification set and a test set according to a set proportion; the set proportion comprises 4: 1. 3:2 and 1: 1;
constructing a neural network model; the neural network model comprises an error reverse feedback neural network and an RBF neural network;
training the neural network model through a verification set and a test set, judging that the training of the neural network model is finished when the target precision of the neural network model meets the requirement, and marking the trained neural network model as a prediction model;
acquiring environmental parameters, population total amount of a monitoring area, residence total amount of the monitoring area and factory total amount of the monitoring area of the future N2 days, and marking the environmental parameters, the population total amount of the monitoring area and the factory total amount of the monitoring area as prediction input data; normalizing the prediction input data and inputting the normalized prediction input data into a prediction model to obtain a prediction result of the power data; transmitting a prediction result of the power data to a data display unit;
and sending the prediction model and the prediction result of the power data to a data storage module for storage through a processor.
2. The big data power grid operation monitoring system according to claim 1, wherein the specific steps of obtaining the total number of the electric devices comprise:
obtaining a remote sensing image of a monitoring area, and marking the remote sensing image as a first image after remote sensing preprocessing; the remote sensing preprocessing comprises geometric correction, atmospheric correction and image fusion;
extracting the total number of the electric power equipment in the coverage area of the natural disaster in the first image; the power equipment comprises a special transformer acquisition terminal and a concentrator;
and sending the total number of the electric power equipment to the data storage module for storage through the processor.
3. The big data power grid operation monitoring system according to claim 1, wherein the data acquisition module is in communication connection with a power acquisition unit and an environment acquisition unit respectively, and the power acquisition unit is in communication connection with a special transformer acquisition terminal and a concentrator respectively; the special transformer acquisition terminal comprises a large special transformer acquisition terminal and a medium and small special transformer acquisition terminal; the electric power data that the electric power collection unit user gathered the user includes:
acquiring the power consumption voltage of a large-scale special transformer acquisition terminal, and marking as UAiI ═ 1, 2, … …, n; acquiring the electricity frequency of a large-scale special transformer acquisition terminal, and marking the electricity frequency as HAi(ii) a Acquiring power utilization harmonic waves of a large-scale special transformer acquisition terminal, calculating distortion rate of the power utilization harmonic waves, and marking as TAi；
Acquiring the power consumption voltage of the small and medium-sized special transformer acquisition terminal, and marking the power consumption voltage as UBjJ is 1, 2, … …, m; acquiring the electricity frequency of a small and medium-sized special transformer acquisition terminal, and marking as HBj(ii) a Acquiring power utilization harmonic waves of small and medium-sized special transformer acquisition terminals, calculating distortion rate of the power utilization harmonic waves, and marking as TBj；
The voltage of the concentrator is obtained and marked as UCkK is 1, 2, … …, p; the frequency of the concentrator power utilization is obtained and labeled HCk(ii) a Acquiring power utilization harmonic waves of the concentrator, calculating distortion rate of the power utilization harmonic waves, and marking the distortion rate as TCk；
By the formula
Acquiring a voltage evaluation coefficient DPX; by the formula
Acquiring a frequency evaluation coefficient PPX; by the formula
Obtaining a distortion evaluation coefficient JPX; wherein α 1, α 2, α 03, α 14, α 25, α 36, α 47, α 58, and α 69 are all proportionality coefficients, and α 71, α 2, α 3, α 4, α 5, α 6, α 7, α 8, and α 9 are all real numbers greater than 0;
the power data, the voltage evaluation coefficient, the frequency evaluation coefficient and the distortion evaluation coefficient are sent to a data storage module through a processor to be stored, and meanwhile, the voltage evaluation coefficient, the frequency evaluation coefficient and the distortion evaluation coefficient are sent to a data analysis module;
the environment acquisition unit acquires environmental parameters of a monitoring area, and comprises:
acquiring an average temperature value, an average humidity value and an average wind force value of a monitoring area; the monitoring area is a coverage area of the power distribution network;
marking the average temperature value, the average humidity value and the average wind force value as PW, PS and PF respectively;
acquiring an environment evaluation coefficient HPX through a formula HPX ═ beta 1 × PW × PS × PF + beta 2; wherein β 1 and β 2 are proportionality coefficients, and both β 1 and β 2 are real numbers greater than 0;
and sending the average temperature value, the average humidity value, the average wind force value and the environment evaluation coefficient to a data storage module for storage through a processor, and sending the environment evaluation coefficient to a data analysis module.
4. The big data power grid operation monitoring system according to claim 1, wherein the data analysis module receives and analyzes a voltage evaluation coefficient, a frequency evaluation coefficient, a distortion evaluation coefficient and an environment evaluation coefficient, and comprises:
when the voltage evaluation coefficient, the frequency evaluation coefficient, the distortion evaluation coefficient and the environment evaluation coefficient are all in the corresponding threshold value ranges, judging that the power grid operates normally; otherwise, acquiring and judging the abnormal operation of the power grid, acquiring the abnormal position of the power grid and marking the abnormal position as a target position;
sending the abnormal signal of the power grid and the target position to an alarm scheduling module through a processor; and meanwhile, sending the sending record of the abnormal signal of the power grid and the target position to a data storage module for storage.
5. The big data power grid operation monitoring system according to claim 1, wherein the alarm scheduling module comprises a data display unit and a maintenance scheduling unit; the data display unit is used for displaying monitoring data, and the monitoring data comprises power data, a voltage evaluation coefficient, a frequency evaluation coefficient, a distortion evaluation coefficient and an environment evaluation coefficient; the maintenance scheduling unit is used for scheduling workers to a target position for maintenance and repair, and comprises:
when the maintenance scheduling unit receives the power grid abnormal signal and the target position, a circular area is defined by taking the target position as the center and taking R1 as the radius and is marked as a screening area; wherein R1 is the set radius value;
acquiring the position of a worker in the screening area and marking the position as a primary selection position; acquiring a path for planning the primary selection position and the target position through a third-party map platform and marking the path as a primary selection path; the third-party map platform comprises a high-grade map, a Baidu map and an Tencent map;
acquiring the running distance and the congestion degree of the initially selected route, and respectively marking the running distance and the congestion degree as XJ and YD;
acquiring a path evaluation coefficient LPX through a formula LPX ═ beta 3 xJyYD; wherein β 3 is a proportionality coefficient and β 3 is a real number greater than 0;
when the path evaluation coefficient LPX satisfies 0 < LPX ≤ L1, determining that the corresponding initially selected path satisfies the requirement; otherwise, judging that the corresponding initial selection path does not meet the requirements; wherein L1 is the path evaluation coefficient threshold;
marking the shortest travel distance in the primary selected routes meeting the requirements as a target route;
sending the target path to an intelligent terminal of a worker; the intelligent terminal comprises an intelligent mobile phone, a tablet computer and a notebook computer;
after receiving the target path, the worker reaches the target position to perform maintenance and overhaul; and the running track of the worker is sent to the data display unit for displaying, and meanwhile, the dispatching record of the worker is sent to the data storage module for storage.
6. A big data electric network operation monitoring system according to claim 1, characterized in that, the electric power data comprises distortion rates of power utilization voltage, power utilization frequency and power utilization harmonic; the environmental parameters comprise an average temperature value, an average humidity value and an average wind force value of the monitoring area.
7. The big data power grid operation monitoring system according to claim 1, wherein the processor is in communication connection with a data prediction module, an alarm scheduling module, a data storage module, a data acquisition module, a data analysis module and a disaster analysis module, respectively; the alarm scheduling module is respectively in communication connection with the data storage module and the data prediction module, and the data acquisition module is in communication connection with the data analysis module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110039981.0A CN113592128A (en) | 2021-04-28 | 2021-04-28 | Big data electric wire netting operation monitoring system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110039981.0A CN113592128A (en) | 2021-04-28 | 2021-04-28 | Big data electric wire netting operation monitoring system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113592128A true CN113592128A (en) | 2021-11-02 |
Family
ID=78238010
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110039981.0A Pending CN113592128A (en) | 2021-04-28 | 2021-04-28 | Big data electric wire netting operation monitoring system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113592128A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115082545A (en) * | 2022-06-08 | 2022-09-20 | 国网黑龙江省电力有限公司大庆供电公司 | Safety system applied to electric power field |
| CN115755738A (en) * | 2022-11-22 | 2023-03-07 | 湘煤立达矿山装备股份有限公司 | Mining intelligent power monitoring system |
| CN115765159A (en) * | 2022-10-31 | 2023-03-07 | 国网河南省电力公司新乡供电公司 | Transmission line safety early warning system based on data analysis |
| CN115833402A (en) * | 2023-02-20 | 2023-03-21 | 国网山东省电力公司日照供电公司 | Artificial intelligence network command management and issuing system based on power dispatching |
| CN116054167A (en) * | 2023-03-06 | 2023-05-02 | 国网山东省电力公司聊城供电公司 | Power grid comprehensive dispatching management system and method based on power distribution network flexible controller |
| CN116404745A (en) * | 2023-02-27 | 2023-07-07 | 合肥威标科技有限公司 | Intelligent regulation and control auxiliary decision-making system and method for power distribution network based on big data |
| CN116720983A (en) * | 2023-08-10 | 2023-09-08 | 上海飞斯信息科技有限公司 | Power supply equipment abnormality detection method and system based on big data analysis |
| CN117726959A (en) * | 2024-02-09 | 2024-03-19 | 国网安徽省电力有限公司巢湖市供电公司 | Unmanned aerial vehicle power line safety inspection system and method based on intelligent image recognition |
| WO2024160294A1 (en) * | 2023-03-03 | 2024-08-08 | 浙江浙能能源服务有限公司 | Power control method and system based on large-scale power grid power flow |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109685277A (en) * | 2018-12-28 | 2019-04-26 | 国网冀北电力有限公司经济技术研究院 | Electricity demand forecasting method and device |
| CN112034287A (en) * | 2020-09-03 | 2020-12-04 | 淮阴工学院 | Electric power safety monitoring system based on big data |
| CN112083279A (en) * | 2020-08-26 | 2020-12-15 | 深圳供电局有限公司 | Electric power automatic distributed information acquisition system and method |
| CN112711053A (en) * | 2021-01-15 | 2021-04-27 | 千居智人工智能科技昆山有限公司 | Automatic monitoring and early warning system for communication network safety operation |
-
2021
- 2021-04-28 CN CN202110039981.0A patent/CN113592128A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109685277A (en) * | 2018-12-28 | 2019-04-26 | 国网冀北电力有限公司经济技术研究院 | Electricity demand forecasting method and device |
| CN112083279A (en) * | 2020-08-26 | 2020-12-15 | 深圳供电局有限公司 | Electric power automatic distributed information acquisition system and method |
| CN112034287A (en) * | 2020-09-03 | 2020-12-04 | 淮阴工学院 | Electric power safety monitoring system based on big data |
| CN112711053A (en) * | 2021-01-15 | 2021-04-27 | 千居智人工智能科技昆山有限公司 | Automatic monitoring and early warning system for communication network safety operation |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115082545A (en) * | 2022-06-08 | 2022-09-20 | 国网黑龙江省电力有限公司大庆供电公司 | Safety system applied to electric power field |
| CN115765159B (en) * | 2022-10-31 | 2024-01-23 | 国网河南省电力公司新乡供电公司 | Transmission line safety precaution system based on data analysis |
| CN115765159A (en) * | 2022-10-31 | 2023-03-07 | 国网河南省电力公司新乡供电公司 | Transmission line safety early warning system based on data analysis |
| CN115755738A (en) * | 2022-11-22 | 2023-03-07 | 湘煤立达矿山装备股份有限公司 | Mining intelligent power monitoring system |
| CN115833402A (en) * | 2023-02-20 | 2023-03-21 | 国网山东省电力公司日照供电公司 | Artificial intelligence network command management and issuing system based on power dispatching |
| CN115833402B (en) * | 2023-02-20 | 2023-05-23 | 国网山东省电力公司日照供电公司 | Artificial intelligent network command management and issuing system based on power dispatching |
| CN116404745A (en) * | 2023-02-27 | 2023-07-07 | 合肥威标科技有限公司 | Intelligent regulation and control auxiliary decision-making system and method for power distribution network based on big data |
| CN116404745B (en) * | 2023-02-27 | 2023-10-03 | 合肥威标科技有限公司 | Intelligent regulation and control auxiliary decision-making system and method for power distribution network based on big data |
| WO2024160294A1 (en) * | 2023-03-03 | 2024-08-08 | 浙江浙能能源服务有限公司 | Power control method and system based on large-scale power grid power flow |
| CN116054167A (en) * | 2023-03-06 | 2023-05-02 | 国网山东省电力公司聊城供电公司 | Power grid comprehensive dispatching management system and method based on power distribution network flexible controller |
| CN116720983A (en) * | 2023-08-10 | 2023-09-08 | 上海飞斯信息科技有限公司 | Power supply equipment abnormality detection method and system based on big data analysis |
| CN117726959A (en) * | 2024-02-09 | 2024-03-19 | 国网安徽省电力有限公司巢湖市供电公司 | Unmanned aerial vehicle power line safety inspection system and method based on intelligent image recognition |
| CN117726959B (en) * | 2024-02-09 | 2024-05-10 | 国网安徽省电力有限公司巢湖市供电公司 | Unmanned aerial vehicle power line safety inspection system and method based on intelligent image recognition |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113592128A (en) | Big data electric wire netting operation monitoring system | |
| CN102193555B (en) | Panoramic-state monitoring system for centralized control centers | |
| CN113644745A (en) | Energy storage power station intelligence operation and maintenance analytic system based on cloud pipe limit end | |
| CN109522380B (en) | Mobile application-oriented power grid disaster comprehensive monitoring and early warning data system and method | |
| Hu et al. | A two-stage dynamic capacity planning approach for agricultural machinery maintenance service with demand uncertainty | |
| CN116937575A (en) | Energy monitoring management system for grid system | |
| CN102708411A (en) | Method for evaluating risk of regional grid on line | |
| CN103034213A (en) | Remote monitoring and controlling system | |
| CN109636672A (en) | Operation of power networks collaboration monitoring platform and monitoring method | |
| CN116451876B (en) | Power distribution network fault prediction and active overhaul system based on artificial intelligence | |
| CN103560590B (en) | Electric network intelligent scheduling framework and its implementation | |
| CN106712296A (en) | Online monitoring and managing system for electric power | |
| CN116522746A (en) | Power distribution hosting method for high-energy-consumption enterprises | |
| Ali et al. | Integration of data driven technologies in smart grids for resilient and sustainable smart cities: A comprehensive review | |
| CN115549292A (en) | Power grid operation monitoring system and method thereof | |
| CN116468243A (en) | Power grid trend early warning method based on monitoring operation data | |
| CN104281983A (en) | Method and system for dispatching resources needed by emergency repair of power distribution network | |
| CN116633018B (en) | Power industry fault power failure full-flow management and control platform | |
| CN109146206B (en) | Disaster early warning system and method based on software defined network | |
| CN117614137A (en) | Power distribution network optimization system based on multi-source data fusion | |
| CN106845780A (en) | A kind of generating set operating condition analysis system and its analysis method | |
| CN118487276B (en) | Power grid safety dynamic management and control method and system for power guarantee object | |
| CN111917187B (en) | Intelligent substation cloud monitored control system | |
| CN111815106A (en) | Power grid emergency method based on multi-source information | |
| CN111857015B (en) | Power transmission and transformation cloud intelligent controller |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20211102 |