CN113052340A - Early warning method, system and storage medium for aggregating multi-source data - Google Patents

Early warning method, system and storage medium for aggregating multi-source data Download PDF

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Publication number
CN113052340A
CN113052340A CN202110354277.4A CN202110354277A CN113052340A CN 113052340 A CN113052340 A CN 113052340A CN 202110354277 A CN202110354277 A CN 202110354277A CN 113052340 A CN113052340 A CN 113052340A
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data
early warning
target
machine patrol
health degree
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CN113052340B (en
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李雄刚
张英
陈浩
朱凌
郭锦超
林俊省
殷明
王年孝
饶成成
陈赟
陈义龙
彭炽刚
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Guangdong Power Grid Co Ltd
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Guangdong Power Grid Co Ltd
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    • 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
    • G07—CHECKING-DEVICES
    • G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
    • G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
    • G07C1/20—Checking timed patrols, e.g. of watchman
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The application discloses an early warning method, a system and a storage medium for aggregating multi-source data, wherein the method comprises the following steps: acquiring service data of a target, and associating machine patrol data of the target based on the service data; acquiring real-time monitoring data of a target, and calculating the equipment health degree of the target according to the machine patrol data and the real-time monitoring data; and setting a risk early warning value, and sending an early warning prompt to maintain the target when the equipment health degree is lower than the risk early warning value. Through carrying out operations such as fusing, resolving, analyzing and recognizing machine patrol data and real-time detection data, calculating the equipment health degree of the target, compared with a conventional data processing mode, the early warning method for machine patrol big data association and aggregation multi-source data can effectively improve the processing efficiency of the multi-source data, can timely overhaul and maintain the target according to the equipment health degree, and has good practical value.

Description

Early warning method, system and storage medium for aggregating multi-source data
Technical Field
The application relates to the technical field of big data processing, in particular to an early warning method, system and storage medium for aggregating multi-source data.
Background
With the development of smart power grids, information model fusion becomes one of key technologies for promoting compatibility, interaction and optimization of power grid heterogeneous systems, and in order to better realize information interaction, the international electrotechnical commission defines a Common Information Model (CIM) to standardize common semantics and syntax of information interaction between heterogeneous systems. However, in the related art, the processing mode for the machine patrol big data cannot meet the current business requirements, the utilization efficiency of multi-source data is not high, and the operation and maintenance efficiency of the power system is low.
Disclosure of Invention
The application provides an early warning method, system and storage medium for aggregating multi-source data, and aims to solve the problems that in the prior art, the utilization efficiency of the multi-source data is not high, and the operation and maintenance efficiency of an electric power system is low.
In order to solve the technical problem, the application provides an early warning method for machine patrol big data association and multi-source data aggregation, which comprises the following steps: acquiring service data of a target, and associating machine patrol data of the target based on the service data; acquiring real-time monitoring data of a target; calculating the equipment health degree of the target according to the machine patrol data and the real-time monitoring data; and setting a risk early warning value, and sending an early warning prompt to maintain the target when the equipment health degree is lower than the risk early warning value.
Optionally, the business data includes equipment accounts, monitoring terminal accounts, plan management data, historical defect data, operator accounts, and pollution source data.
Optionally, the machine patrol data includes a visible light picture, an infrared picture, video data, and point cloud data.
Optionally, the real-time monitoring data includes meteorological data and operational data.
Optionally, the meteorological data comprises temperature data, barometric data, wind direction data, wind speed data, rainfall data, humidity data, mountain fire data.
Optionally, the operational data includes operational current, circulating current data, partial discharge data, and monitoring photographs.
Optionally, calculating the health degree of the target device according to the machine patrol data and the real-time monitoring data, including the steps of: and carrying out image identification processing on the machine patrol data to obtain the defect type and the defect number of the target in the machine patrol data.
Optionally, after calculating the health degree of the target device according to the patrol data and the real-time monitoring data, the method comprises the following steps: and generating a visual chart according to the change curve of the health degree of the equipment.
In order to solve the technical problem, the application provides an early warning system for machine patrol big data association and aggregation multi-source data, which comprises: the early warning method for the machine patrol big data association and aggregation of the multi-source data is realized when the processor executes the computer program.
In order to solve the technical problem, the present application provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and the computer-executable instructions are used to enable a computer to execute the above early warning method for associating and aggregating large data with multi-source data.
The application provides an early warning method, a system and a storage medium for aggregating multi-source data, wherein the method comprises the following steps: acquiring service data of a target, and associating machine patrol data of the target based on the service data; acquiring real-time monitoring data of a target, and calculating the equipment health degree of the target according to the machine patrol data and the real-time monitoring data; and setting a risk early warning value, and sending an early warning prompt to maintain the target when the equipment health degree is lower than the risk early warning value. The early warning method for the machine patrol big data association aggregation multi-source data can effectively improve the processing efficiency of the multi-source data, can timely overhaul and maintain the target according to the health degree of the equipment, and has good practical value.
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In order to more clearly illustrate the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart of an embodiment of an early warning method for aggregating multi-source data by associating big data of an applicant;
FIG. 2 is a schematic structural diagram of an embodiment of an early warning system for aggregating multi-source data by associating big data of the applicant;
FIG. 3 is a schematic structural diagram of an embodiment of a computer-readable storage medium of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present application, the following provides a detailed description of the method, system and storage medium for aggregating multi-source data provided in the present application with reference to the accompanying drawings and detailed description.
The application provides an early warning method for machine patrol big data association and aggregation of multi-source data, which can effectively improve the processing efficiency of the multi-source data and improve the operation and maintenance capacity of a power transmission line.
The embodiments of the present application will be further explained with reference to the drawings.
Referring to fig. 1, fig. 1 is a flowchart illustrating an embodiment of an early warning method for machine patrol big data association and aggregation multi-source data according to the present application, where the early warning method for machine patrol big data association and aggregation multi-source data includes, but is not limited to, steps S110 to S140.
Step S110, acquiring service data of a target, and associating machine patrol data of the target based on the service data;
step S120, acquiring real-time monitoring data of a target;
step S130, calculating the equipment health degree of the target according to the machine patrol data and the real-time monitoring data;
and step S140, setting a risk early warning value, and sending an early warning prompt to maintain the target when the equipment health degree is lower than the risk early warning value.
The method comprises the steps of obtaining service data of a target, and associating machine patrol data of the target on the basis of the service data, wherein the machine patrol data can comprise visible light pictures, infrared pictures, video data and point cloud data, such as visible light image videos, infrared temperature measurement data, oblique photography data and laser point cloud data.
The machine patrols data and shoots the target equipment through helicopter or unmanned aerial vehicle and acquires, certainly also can patrol and examine through the manual work and shoot the photo, and power supply unit often sets up in the field, uses helicopter or unmanned aerial vehicle to shoot the target equipment efficient.
The visible light picture of the machine patrol data can show the state of the target, and the defect type and the defect number of the target can be seen according to the visible light picture.
The infrared picture is used for displaying temperature information of different areas of the target, and whether the target is in a normal working state or not can be judged according to the target temperature.
The video data can visually show the defect occurrence process of the target, the occurrence reason of the target defect can be found according to the video data, the state of the target can be displayed in a three-dimensional scene by means of the point cloud data, and visual display is facilitated.
It should be noted that the machine patrol data is usually video or picture data, and image recognition processing needs to be performed on the machine patrol data to obtain the target defect type and defect number.
The method comprises the steps of obtaining real-time monitoring data of a target, automatically classifying, automatically fusing, automatically resolving and automatically analyzing and identifying service data, machine patrol data and the real-time monitoring data, mining internal incidence relations among the multi-source data, calculating to obtain equipment health degree of the target, presetting a risk early warning value of the target, and sending an early warning prompt to maintain the target when the equipment health degree is lower than the risk early warning value.
The early warning method for the machine-patrolling big data association aggregation multi-source data can effectively improve the efficiency of establishing a data model for the machine-patrolling big data association aggregation multi-source data, automatically classifies, automatically fuses, automatically resolves and automatically analyzes and identifies the service data, the machine-patrolling data and the real-time monitoring data by using the data model, effectively improves the processing efficiency of the multi-source data, can timely overhaul and maintain a target according to the health degree of equipment, and has good practical value.
In one embodiment, the processor patrol data and the multi-source data are associated based on business data, and the business data can comprise an equipment account, a monitoring terminal account, plan management data, historical defect data, an operator account and pollution source data.
The equipment ledger can include information such as the model, price, overhaul period, last overhaul time, etc. of the equipment.
The monitoring terminal ledger may be information including the model, price, maintenance cycle, and the like of the monitoring terminal of the target device.
The schedule management data may be a schedule management table made for the target according to which the maintenance schedule is to be serviced.
The historical defect data can comprise the fault types and the fault numbers of the target in history, a scientific and reasonable inspection plan can be made for the target according to the historical defect data, and regular maintenance and repair of the target are facilitated.
The operator standing book is a record of historical operation of an operator on a target, and the target can be reasonably managed according to the operation of the operator.
The dirty source data is used for recording the source of the historical defect of the target, accurately grasping the reason of the defect, effectively protecting the target before the defect occurs, well reducing the probability of the defect, improving the reliability of target equipment and having very good practical value.
In one embodiment, the real-time monitoring data includes meteorological data and operational data. The power supply equipment is often arranged in the field, the field which has the greatest influence on the power supply equipment is the meteorological factor, and the health degree of the target can be better mastered by combining real-time monitoring of meteorological data of the position where the target is located and operation data of the target.
The meteorological data comprises temperature data, air pressure data, wind direction data, wind speed data, rainfall data, humidity data and mountain fire data.
The meteorological data generally have great influence to setting up the power supply unit in the field, carry out real-time supervision to meteorological data to record the storage with the monitoring result, evaluate the equipment health degree of target according to the monitoring data in real time, can real-time dynamic perception external environment change.
The temperature data also has a great influence on the operation of the equipment, for example, under the condition of low-temperature weather and rainfall, certain parts of the power supply equipment cannot work normally, the normal operation of the power supply equipment is influenced, and the health degree of the equipment is reduced; when the wind speed is too high, certain damage can be caused to power supply equipment, so that the equipment cannot normally run, and the health degree of the equipment is reduced; the health degree of equipment can be dynamically mastered by acquiring meteorological data in real time, and the equipment can be overhauled and maintained in time according to the health degree of the equipment, so that the maintenance efficiency is effectively improved, the operation of the power supply equipment is safer and more reliable, and powerful guarantee is provided for the normal operation of the power equipment.
In one embodiment, the operational data may include operational current, circulating current data, partial discharge data, and monitoring photographs.
The real-time monitoring data also comprises operation data of the equipment, the operation data comprises operation current, circulation data, partial discharge data and monitoring photos, the operation state of the real-time monitoring equipment can monitor whether the equipment is in a normal working state, when the operation current, the circulation data and the partial discharge data are abnormal, the fact that the power supply equipment is in an abnormal operation state is shown, the health degree of the equipment can be mastered in real time according to the operation state, the health degree of the equipment is monitored in real time by combining meteorological data, and an alarm is timely sent out when the health degree of the equipment is lower than a risk early warning value.
In one embodiment, the machine patrol data is subjected to image recognition processing, and the defect type and the defect number of the target in the machine patrol data are acquired. Because the machine patrol data is usually video data or picture data, the machine patrol data comprises visible light pictures, infrared pictures, video data, point cloud data and oblique photography data, the defect types and the defect quantity of corresponding targets can be obtained only after image identification is carried out on the image data or the video data, a defect database can be established according to the identified defect types and quantity, the defect types can be conveniently and intelligently learned, and the speed and the efficiency of data processing are improved.
The method comprises the steps of associating machine patrol data of a target intelligently according to the target based on service data, and obtaining defect types and defect data of target equipment according to the machine patrol data in a defect library, automatically evaluating the health degree of the target equipment according to the defect types and the defect quantity, and intelligently arranging a maintenance plan according to the position of the equipment and the number of users covering the power supply equipment, wherein the maintenance plan can comprise arrangement of personnel, materials and vehicles, and the maintenance efficiency of the power equipment is effectively improved.
In one embodiment, a visualization chart is generated according to the variation curve of the health degree of the equipment. The visual chart is generated, so that personnel can directly observe the health degree curve of the target conveniently, and a maintenance plan can be scientifically and reasonably formulated according to the health degree change curve of the target.
The health degree change rule of the target equipment can be found according to the chart, the chart can comprise information such as position information, equipment model and equipment price of the equipment, historical defect information of the equipment can be further included, the damage reason of the equipment can be found according to the historical defect information of the equipment, and the overhaul and maintenance plan of the target equipment is intelligently arranged by combining the historical damage reason and the pollution source data of the equipment.
In summary, the evaluation factors for the health degree of the equipment may include service data, machine patrol data and real-time monitoring data, the service data includes an equipment account, a monitoring terminal account, plan management data, historical defect data, an operator account and pollution source data, the machine patrol data may include visible light pictures, infrared pictures, video data, point cloud data and oblique photography data, the real-time monitoring data may include meteorological data and operation data, the meteorological data may include temperature data, air pressure data, wind direction data, wind speed data, rainfall data, humidity data and forest fire data, and the operation data includes operation current, circulation data, partial discharge data and monitoring photos. The method comprises the steps of establishing an automatic evaluation mathematical model of the health degree of target equipment by combining service data, machine patrol data and real-time monitoring data, correspondingly establishing a defect database of the target on the basis of an equipment ledger, intelligently learning historical defect data of the target equipment, intelligently evaluating the health degree of the target equipment according to real-time monitoring meteorological data and operation data, and timely reminding of overhauling and maintaining the target equipment when the health degree of the equipment is lower than a risk early warning value.
The equipment health degree can be provided with normal operating, slightly impaired, needs the maintenance, can't move, damages totally, need not to overhaul when equipment is in normal operating, and equipment is in need to maintain, can't move, damages totally under the three state in time to patrol and maintain equipment.
The application also provides an early warning system for associating and aggregating big data of machine patrol, please refer to fig. 2, and fig. 2 is a schematic structural diagram of an embodiment of the early warning system for associating and aggregating the big data of machine patrol. In this embodiment, the early warning system 200 for associating and aggregating multi-source data with machine patrol big data may include a memory 210, a processor 220, and a computer program stored in the memory 210 and executable on the processor 220.
The processor 220, when executing the computer program, implements the early warning method for aggregating multi-source data by associating large data in a machine tour as in the first aspect. The early warning method for the machine-patrolling big data association aggregation multi-source data can effectively improve the processing efficiency of the multi-source data, can timely overhaul and maintain a target according to the health degree of equipment, improves the operation and maintenance capacity of a power transmission line, and has good practical value.
In addition, an embodiment of the present application further provides a computer-readable storage medium, please refer to fig. 3, and fig. 3 is a schematic structural diagram of an embodiment of the computer-readable storage medium of the present application. The computer-readable storage medium 300 stores computer-executable instructions 310, the computer-executable instructions 310 being executable by one or more control processors, for example, the control processors are capable of performing method steps S110 to S140 of fig. 1.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit.
Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art.
Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer.
In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. In addition, for convenience of description, only a part of structures related to the present application, not all of the structures, are shown in the drawings. The step numbers used herein are also for convenience of description only and are not intended as limitations on the order in which the steps are performed. 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 application.
The terms "first", "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.

Claims (10)

1. The early warning method for associating and aggregating multi-source data with machine patrol big data is characterized by comprising the following steps of:
acquiring service data of a target, and associating the machine patrol data of the target on the basis of the service data;
acquiring real-time monitoring data of the target;
calculating the equipment health degree of the target according to the machine patrol data and the real-time monitoring data;
and setting a risk early warning value, and sending an early warning prompt to maintain the target when the equipment health degree is lower than the risk early warning value.
2. The early warning method for the machine patrol big data association and multi-source data aggregation according to claim 1, wherein the business data comprises an equipment ledger, a monitoring terminal ledger, plan management data, historical defect data, an operator ledger and pollution source data.
3. The early warning method for the machine patrol big data association and multi-source data aggregation according to claim 1, wherein the machine patrol data comprises visible light pictures, infrared pictures, video data and point cloud data.
4. The early warning method for the machine patrol big data association and multi-source data aggregation according to claim 1, wherein the real-time monitoring data comprises meteorological data and operation data.
5. The early warning method for the machine patrol big data association aggregation multi-source data according to claim 4, wherein the meteorological data comprises temperature data, air pressure data, wind direction data, wind speed data, rainfall data, humidity data and mountain fire data.
6. The early warning method for machine patrol big data association and multi-source data aggregation according to claim 4, wherein the operation data comprises operation current, circulation data, partial discharge data and monitoring photos.
7. The early warning method for the machine patrol big data association and multi-source data aggregation according to claim 1, wherein the step of calculating the equipment health degree of the target according to the machine patrol data and the real-time monitoring data comprises the following steps:
and carrying out image identification processing on the machine patrol data to obtain the defect type and the defect number of the target in the machine patrol data.
8. The early warning method for the machine patrol big data association and multi-source data aggregation according to claim 1, wherein after the calculation of the equipment health degree of the target according to the machine patrol data and the real-time monitoring data, the method comprises the following steps:
and generating a visual chart according to the change curve of the equipment health degree.
9. The utility model provides an early warning system of machine patrol big data association aggregation multisource data, includes: the memory, the processor and the computer program stored in the memory and executable on the processor, wherein the processor implements the early warning method for the machine patrol data association aggregation multi-source data according to any one of claims 1 to 8 when executing the computer program.
10. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the early warning method for machine-patrolling big data association and multi-source data aggregation according to any one of claims 1 to 8.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114154591A (en) * 2021-12-14 2022-03-08 南方电网深圳数字电网研究院有限公司 Intelligent early warning method and device for equipment state based on multi-source information

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106446426A (en) * 2016-09-29 2017-02-22 国网山东省电力公司电力科学研究院 Health index based power transformer evaluation method
CN109102171A (en) * 2018-07-24 2018-12-28 上海欣影电力科技股份有限公司 A kind of substation equipment condition intelligent evaluation system and method based on big data
CN110209552A (en) * 2019-05-29 2019-09-06 北京百度网讯科技有限公司 A kind of device management method and device
CN110298932A (en) * 2019-07-26 2019-10-01 国网浙江省电力有限公司衢州供电公司 A kind of UAV Intelligent patrol inspection integral control platform
CN111784026A (en) * 2020-05-28 2020-10-16 国网信通亿力科技有限责任公司 Cloud-side cooperative sensing-based all-dimensional physical examination system for electrical equipment of transformer substation
CN112036694A (en) * 2020-10-27 2020-12-04 重庆首讯科技股份有限公司 Expressway electromechanical equipment life cycle prediction method and system
CN112561238A (en) * 2020-11-24 2021-03-26 国家电网有限公司 Pumped storage power station auxiliary equipment state health evaluation system and method

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111460027B (en) * 2020-03-31 2023-12-05 贵州电网有限责任公司 Intelligent dynamic monitoring method and system suitable for energy Internet
CN111812427A (en) * 2020-05-29 2020-10-23 中电普瑞电力工程有限公司 Health state assessment method and system for power electronic device

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106446426A (en) * 2016-09-29 2017-02-22 国网山东省电力公司电力科学研究院 Health index based power transformer evaluation method
CN109102171A (en) * 2018-07-24 2018-12-28 上海欣影电力科技股份有限公司 A kind of substation equipment condition intelligent evaluation system and method based on big data
CN110209552A (en) * 2019-05-29 2019-09-06 北京百度网讯科技有限公司 A kind of device management method and device
CN110298932A (en) * 2019-07-26 2019-10-01 国网浙江省电力有限公司衢州供电公司 A kind of UAV Intelligent patrol inspection integral control platform
CN111784026A (en) * 2020-05-28 2020-10-16 国网信通亿力科技有限责任公司 Cloud-side cooperative sensing-based all-dimensional physical examination system for electrical equipment of transformer substation
CN112036694A (en) * 2020-10-27 2020-12-04 重庆首讯科技股份有限公司 Expressway electromechanical equipment life cycle prediction method and system
CN112561238A (en) * 2020-11-24 2021-03-26 国家电网有限公司 Pumped storage power station auxiliary equipment state health evaluation system and method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114154591A (en) * 2021-12-14 2022-03-08 南方电网深圳数字电网研究院有限公司 Intelligent early warning method and device for equipment state based on multi-source information

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