CN111786460A - Power grid information operation and maintenance active early warning method based on big data - Google Patents
Power grid information operation and maintenance active early warning method based on big data Download PDFInfo
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- H—ELECTRICITY
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- 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]
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Abstract
The embodiment of the invention discloses a big data-based active early warning method for operation and maintenance of power grid information, which comprises the following steps: grading the data acquisition equipment and the power grid equipment, and binding the graded data acquisition equipment and the power grid equipment; collecting real-time operation data of the power grid equipment of the corresponding grade by adopting data collection equipment of different grades; performing early warning evaluation according to the real-time operation data to obtain early warning results of different grades; and according to the early warning result, adopting different early warning modes to send an early warning outwards. For some less important power grid equipment, data acquisition equipment with lower cost can be adopted for real-time operation data acquisition, so that the cost is reduced. Furthermore, early warning evaluation is carried out according to real-time operation data so as to obtain early warning results of different levels, and for different early warning results, different early warning modes are adopted to send early warnings outwards, so that maintenance personnel can know the degree of urgency of the early warning, and more reasonable processing is carried out.
Description
Technical Field
The invention relates to the technical field of power distribution network monitoring of a power system, in particular to a big data-based active early warning method for operation and maintenance of power grid information.
Background
In recent years, with the development of economy, the situation of domestic and foreign power supply is tense, and a power system has a plurality of serious power failure accidents in sequence, so that huge economic loss is caused, the living order of people is influenced, and the society is greatly influenced.
Power distribution networks are an important component in power systems. The safe and stable operation of the power distribution network is an important link for the safe operation of the whole power grid and is a key link for improving the operation level of a power supply system at present. However, the existing power distribution network is a relatively fragile system, and once a large-area fault or power failure accident occurs, the consequences are serious and even catastrophic; meanwhile, the operation of the power distribution network is also influenced by the conditions of the power distribution network and meteorological conditions, so that in order to improve the safety stability and reliability of the power system, safety early warning is necessary to be carried out on risks faced by the operation of the power distribution network, and potential failure risks of the power distribution network are found out.
The existing power distribution network early warning scheme is generally as follows: the method comprises the steps of collecting various index data of the power distribution equipment in real time, and sending out early warning according to whether the index exceeds a threshold. However, the existing power distribution network early warning scheme does not grade the data acquisition equipment and the power grid equipment, so that more advanced or more complete-function or more expensive data acquisition equipment is adopted for some less important power grid equipment, and thus the cost is wasted.
Disclosure of Invention
The embodiment of the invention aims to provide a big data-based active early warning method for operation and maintenance of power grid information.
In order to achieve the above object, in a first aspect, an embodiment of the present invention provides a big data-based active early warning method for operation and maintenance of power grid information, including:
grading the data acquisition equipment and the power grid equipment, and binding the graded data acquisition equipment and the graded power grid equipment;
acquiring real-time operation data of the power grid equipment of corresponding levels by adopting the data acquisition equipment of different levels;
performing early warning evaluation according to the real-time operation data to obtain early warning results of different grades;
according to the early warning result, adopting different early warning modes to send early warning outwards; different early warning modes comprise mails, short messages or telephones.
Wherein the real-time operational data includes bus voltage, line load rate, branch current, cable tap temperature, common low voltage, and common over voltage.
As a specific implementation manner of the present application, performing early warning evaluation according to the real-time operation data to obtain early warning results of different levels specifically includes:
comparing the bus voltage, the line load rate, the branch power flow, the cable tap temperature, the common-to-low voltage and the common-to-overvoltage with respective preset thresholds to obtain comparison results;
and determining early warning results of different levels according to the comparison result.
Further, in certain preferred embodiments of the present application, after obtaining the comparison result, the method further comprises:
and generating different active early warning work orders according to the comparison result, and sending the active early warning work orders outwards by adopting different early warning modes.
As a specific embodiment of the present application, the grade of the early warning result includes a severity grade and a general grade;
when the grade of the early warning result is a serious grade, notifying a processor by adopting a telephone mode;
and when the grade of the early warning result is a common grade, notifying a processor by adopting an email or a short message to remind the processor to follow up the processing in time.
In a second aspect, an embodiment of the present invention provides another active early warning method for operation and maintenance of power grid information based on big data, including:
grading the data acquisition equipment and the power grid equipment, and binding the graded data acquisition equipment and the graded power grid equipment;
acquiring real-time operation data of the power grid equipment of corresponding levels by adopting the data acquisition equipment of different levels;
collecting real-time meteorological data of the power grid equipment by adopting environment monitoring equipment;
performing preliminary early warning evaluation according to the real-time meteorological data to obtain a preliminary early warning result;
performing secondary early warning evaluation by combining the real-time operation data and the primary early warning result to obtain final early warning results of different grades;
and sending early warning outwards by adopting different early warning modes according to the final early warning result.
Wherein the real-time operational data includes bus voltage, line load rate, branch current, cable tap temperature, common low voltage, and common over voltage; the real-time meteorological data comprise weather abnormal temperature values, strong rainfall capacity, strong snowfall capacity, lightning grades and ice disaster grades.
Further, in some preferred embodiments of the present application, after obtaining final warning results of different levels, the method further includes:
and generating an active early warning work order according to the final early warning results of different grades, and sending the active early warning work order outwards by adopting different early warning modes.
By implementing the big data-based active early warning method for operation and maintenance of power grid information, the data acquisition equipment and the power grid equipment are graded, the graded data acquisition equipment and the power grid equipment are bound, the data acquisition equipment of different grades is adopted to acquire the real-time operation data of the power grid equipment of corresponding grades, and for some less important power grid equipment, the data acquisition equipment with lower cost can be adopted to acquire the real-time operation data, so that the cost is reduced. Furthermore, early warning evaluation is carried out according to real-time operation data so as to obtain early warning results of different levels, and for different early warning results, different early warning modes are adopted to send early warnings outwards, so that maintenance personnel can know the degree of urgency of the early warning, and more reasonable processing is carried out.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a schematic flow chart of a big data-based active early warning method for operation and maintenance of power grid information according to a first embodiment of the present invention;
fig. 2 is a schematic flowchart of a big data-based active early warning method for operation and maintenance of power grid information according to a first embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. 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.
Fig. 1 is a schematic flow chart of a power grid dispatching method for a power grid enterprise according to a first embodiment of the present invention. As shown, the method may include the steps of:
s101, grading the data acquisition equipment and the power grid equipment, and binding the graded data acquisition equipment and the graded power grid equipment.
Specifically, the data acquisition devices are classified into class a, class B, class C, and the like. . . . . . Meanwhile, the power grid equipment is divided into a first level, a second level, a third level and the like. . . . . . It should be noted that, here, when dividing the class of the data acquisition device, the function or price of the data acquisition device is mainly used as a basis, and when dividing the class of the power grid device, the importance degree of the data acquisition device in the whole power distribution network is mainly used as a basis. For example, the a-level data acquisition equipment has the most complete functions and the most expensive price, and the first-level power grid equipment is the most important in the power distribution network.
And after the grading is finished, binding the data acquisition equipment and the power grid equipment. For example, the a-level data acquisition device is bound to the first-level power grid device, so that the a-level data acquisition device is dedicated to acquiring relevant data of the first-level power grid device during subsequent data acquisition.
And S102, acquiring real-time operation data of the power grid equipment of the corresponding grade by adopting the data acquisition equipment of different grades.
The real-time operation data includes, but is not limited to, bus voltage, line load rate, branch power flow, cable tap temperature, common low voltage, common overvoltage, etc.
And S103, performing early warning evaluation according to the real-time operation data to obtain early warning results of different grades.
And S104, generating different active early warning work orders, and sending the active early warning work orders outwards by adopting different early warning modes.
Specifically, the bus voltage, the line load rate, the branch power flow, the cable tap temperature, the common-to-low voltage and the common-to-overvoltage are compared with respective preset thresholds to obtain comparison results; and determining early warning results of different levels according to the comparison result.
For example, if the bus voltage of the first-stage power grid equipment is higher than a preset voltage value, an early warning result is obtained, an active early warning work order is generated, and the active early warning work order is sent outwards.
If the line load rate is greater than or equal to the preset value and the time for maintaining the state that the line load rate is greater than or equal to the preset value is greater than the preset time, obtaining an early warning result, generating an active early warning work order, and sending the active early warning work order outwards.
Similarly, the early warning result can be obtained for other parameters, such as branch load flow, cable tap temperature, common low voltage and common overvoltage, and different active early warning work orders can be generated.
After the early warning result and the active early warning work order are obtained, the early warning result can be classified into grades according to the situation, for example, the early warning result of the bus voltage and the cable tap temperature is classified into a serious grade, and the early warning result of the branch tide is classified into a general grade.
And S105, sending early warning outwards in different early warning modes according to the early warning result.
Wherein, different early warning modes include but not limited to mail, short message or telephone.
Specifically, when the grade of the early warning result is a serious grade, a processing person is notified in a telephone mode;
and when the grade of the early warning result is a common grade, notifying a processor by adopting an email or a short message to remind the processor to follow up the processing in time.
By implementing the big data-based active early warning method for operation and maintenance of power grid information, the data acquisition equipment and the power grid equipment are graded, the graded data acquisition equipment and the power grid equipment are bound, the data acquisition equipment of different grades is adopted to acquire the real-time operation data of the power grid equipment of corresponding grades, and for some less important power grid equipment, the data acquisition equipment with lower cost can be adopted to acquire the real-time operation data, so that the cost is reduced. Furthermore, early warning evaluation is carried out according to real-time operation data so as to obtain early warning results of different levels, and for different early warning results, different early warning modes are adopted to send early warnings outwards, so that maintenance personnel can know the degree of urgency of the early warning, and more reasonable processing is carried out.
Based on the same inventive concept, the embodiment of the invention provides another power grid information operation and maintenance active early warning method based on big data. As shown in fig. 2, the method mainly includes:
s201, grading the data acquisition equipment and the power grid equipment, and binding the graded data acquisition equipment and the graded power grid equipment.
And S202, acquiring real-time operation data of the power grid equipment of the corresponding grade by adopting the data acquisition equipment of different grades.
And S203, collecting real-time meteorological data of the power grid equipment by adopting environment monitoring equipment.
It should be noted that, similarly, the present invention also performs the operations of grading and binding the environmental monitoring devices. For example, the environment monitoring device is divided into a first level, a second level and a third level according to functions or prices. . . . . . And then, binding the first environment monitoring equipment with the first-level power grid equipment, wherein the first-level environment monitoring equipment is mainly used for acquiring meteorological data of the first-level power grid equipment.
The real-time meteorological data comprise weather abnormal temperature values, strong rainfall, strong snowfall, lightning grades and ice disaster grades.
And S204, performing preliminary early warning evaluation according to the real-time meteorological data to obtain a preliminary early warning result.
And S205, performing secondary early warning evaluation by combining the real-time operation data and the primary early warning result to obtain final early warning results of different grades.
It should be noted that, because the weather also has a certain influence on the operation of the power grid equipment, the weather data is taken into account when performing the early warning evaluation of the power grid equipment in this embodiment.
Specifically, the power grid equipment is preliminarily early-warned and evaluated according to the real-time meteorological data, for example, when the meteorological data corresponding to the first-stage power grid equipment is abnormal in weather temperature, the first-stage power grid equipment can be preliminarily early-warned to obtain that the first-stage power grid equipment possibly breaks down. Further, the real-time operation data (such as cable tap temperature) of the first-stage power grid equipment is combined for re-evaluation, so that a final early warning result is obtained.
And S206, generating an active early warning work order according to the final early warning results of different grades, and sending the active early warning work order outwards by adopting different early warning modes.
And S207, sending early warning outwards in different early warning modes according to the final early warning result.
It should be noted that, for the parts not described in detail in this embodiment, please refer to the foregoing method embodiments, and further description is omitted here.
By implementing the big data-based active early warning method for operation and maintenance of power grid information, the data acquisition equipment and the power grid equipment are graded, the graded data acquisition equipment and the power grid equipment are bound, the data acquisition equipment of different grades is adopted to acquire the real-time operation data of the power grid equipment of corresponding grades, and for some less important power grid equipment, the data acquisition equipment with lower cost can be adopted to acquire the real-time operation data, so that the cost is reduced. Furthermore, early warning evaluation is carried out according to real-time operation data so as to obtain early warning results of different levels, and for different early warning results, different early warning modes are adopted to send early warnings outwards, so that maintenance personnel can know the degree of urgency of the early warning, and more reasonable processing is carried out.
In addition, when early warning evaluation is performed in the embodiment, the meteorological data of the power grid equipment are taken into consideration, so that the early warning evaluation can be performed on the operation of the power grid equipment more comprehensively.
Corresponding to the foregoing method embodiment, an embodiment of the present invention provides an electronic device. As shown in fig. 3, the electronic device may include: one or more processors 101, one or more input devices 102, one or more output devices 103, and memory 104, the processors 101, input devices 102, output devices 103, and memory 104 being interconnected via a bus 105. The memory 104 is used for storing a computer program comprising program instructions, the processor 101 is configured for invoking the program instructions to perform the steps of:
grading the data acquisition equipment and the power grid equipment, and binding the graded data acquisition equipment and the graded power grid equipment;
acquiring real-time operation data of the power grid equipment of corresponding levels by adopting the data acquisition equipment of different levels;
performing early warning evaluation according to the real-time operation data to obtain early warning results of different grades;
according to the early warning result, adopting different early warning modes to send early warning outwards; different early warning modes comprise mails, short messages or telephones.
The real-time operation data includes, but is not limited to, bus voltage, line load rate, branch power flow, cable tap temperature, common low voltage, common overvoltage, etc.
Further, the processor 101 is configured to call the program instruction to perform the following steps:
comparing the bus voltage, the line load rate, the branch power flow, the cable tap temperature, the common-to-low voltage and the common-to-overvoltage with respective preset thresholds to obtain comparison results;
determining early warning results of different levels according to the comparison result;
and generating different active early warning work orders according to the comparison result, and sending the active early warning work orders outwards by adopting different early warning modes.
Further, the processor 101 is configured to call the program instruction to perform the following steps:
when the grade of the early warning result is a serious grade, notifying a processor by adopting a telephone mode;
and when the grade of the early warning result is a common grade, notifying a processor by adopting an email or a short message to remind the processor to follow up the processing in time.
Further, the processor 101 is configured to call the program instruction to perform the following steps:
grading the data acquisition equipment and the power grid equipment, and binding the graded data acquisition equipment and the graded power grid equipment;
acquiring real-time operation data of the power grid equipment of corresponding levels by adopting the data acquisition equipment of different levels;
collecting real-time meteorological data of the power grid equipment by adopting environment monitoring equipment;
performing preliminary early warning evaluation according to the real-time meteorological data to obtain a preliminary early warning result;
performing secondary early warning evaluation by combining the real-time operation data and the primary early warning result to obtain final early warning results of different grades;
and sending early warning outwards by adopting different early warning modes according to the final early warning result.
Further, the processor 101 is configured to call the program instruction to perform the following steps:
and generating an active early warning work order according to the final early warning results of different grades, and sending the active early warning work order outwards by adopting different early warning modes.
It should be understood that, in the embodiment of the present invention, the Processor 101 may be a Central Processing Unit (CPU), and the Processor may also be other general processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 102 may include a keyboard or the like, and the output device 103 may include a display (LCD or the like), a speaker, or the like.
The memory 104 may include read-only memory and random access memory, and provides instructions and data to the processor 101. A portion of the memory 104 may also include non-volatile random access memory. For example, the memory 104 may also store device type information.
In specific implementation, the processor 101, the input device 102, and the output device 103 described in the embodiment of the present invention may execute the implementation manner described in the embodiment of the big data-based active early warning method for operation and maintenance of power grid information provided in the embodiment of the present invention, and details are not described here again.
It should be noted that, for a specific workflow of the electronic device according to the embodiment of the present invention, please refer to the foregoing method embodiment, which is not described herein again.
The electronic equipment of the embodiment of the invention reduces the cost. Furthermore, early warning evaluation is carried out according to real-time operation data so as to obtain early warning results of different levels, and for different early warning results, different early warning modes are adopted to send early warnings outwards, so that maintenance personnel can know the degree of urgency of the early warning, and more reasonable processing is carried out.
In addition, when early warning evaluation is performed in the embodiment, the meteorological data of the power grid equipment are taken into consideration, so that the early warning evaluation can be performed on the operation of the power grid equipment more comprehensively.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A big data-based active early warning method for operation and maintenance of power grid information is characterized by comprising the following steps:
grading the data acquisition equipment and the power grid equipment, and binding the graded data acquisition equipment and the graded power grid equipment;
acquiring real-time operation data of the power grid equipment of corresponding levels by adopting the data acquisition equipment of different levels;
performing early warning evaluation according to the real-time operation data to obtain early warning results of different grades;
and sending early warning outwards by adopting different early warning modes according to the early warning result.
2. The big data based active pre-warning method for operation and maintenance of power grid information as claimed in claim 1, wherein the real-time operation data comprises bus voltage, line load rate, branch load flow, cable tap temperature, common low voltage and common overvoltage.
3. The big data-based active early warning method for operation and maintenance of power grid information according to claim 2, wherein performing early warning evaluation according to the real-time operation data to obtain early warning results of different levels specifically comprises:
comparing the bus voltage, the line load rate, the branch power flow, the cable tap temperature, the common-to-low voltage and the common-to-overvoltage with respective preset thresholds to obtain comparison results;
and determining early warning results of different levels according to the comparison result.
4. The big data-based active early warning method for operation and maintenance of power grid information according to claim 3, wherein after the comparison result is obtained, the method further comprises:
and generating different active early warning work orders according to the comparison result, and sending the active early warning work orders outwards by adopting different early warning modes.
5. The active early warning method for operation and maintenance of power grid information based on big data as claimed in claim 1 or 4, wherein the different early warning modes include mail, short message or telephone.
6. The big-data-based active early warning method for operation and maintenance of power grid information, according to claim 5, wherein the grades of the early warning result comprise a severity grade and a general grade;
when the grade of the early warning result is a serious grade, notifying a processor by adopting a telephone mode;
and when the grade of the early warning result is a common grade, notifying a processor by adopting an email or a short message to remind the processor to follow up the processing in time.
7. A big data-based active early warning method for operation and maintenance of power grid information is characterized by comprising the following steps:
grading the data acquisition equipment and the power grid equipment, and binding the graded data acquisition equipment and the graded power grid equipment;
acquiring real-time operation data of the power grid equipment of corresponding levels by adopting the data acquisition equipment of different levels;
collecting real-time meteorological data of the power grid equipment by adopting environment monitoring equipment;
performing preliminary early warning evaluation according to the real-time meteorological data to obtain a preliminary early warning result;
performing secondary early warning evaluation by combining the real-time operation data and the primary early warning result to obtain final early warning results of different grades;
and sending early warning outwards by adopting different early warning modes according to the final early warning result.
8. The big data based active pre-warning method for operation and maintenance of power grid information as claimed in claim 7, wherein the real-time operation data comprises bus voltage, line load rate, branch load flow, cable tap temperature, common low voltage and common overvoltage; the real-time meteorological data comprise weather abnormal temperature values, strong rainfall capacity, strong snowfall capacity, lightning grades and ice disaster grades.
9. The big data-based active early warning method for operation and maintenance of power grid information according to claim 8, wherein after the final early warning results of different levels are obtained, the method further comprises:
and generating an active early warning work order according to the final early warning results of different grades, and sending the active early warning work order outwards by adopting different early warning modes.
10. The active early warning method for operation and maintenance of power grid information based on big data as claimed in claim 7 or 9, wherein the different early warning modes include mail, short message or telephone.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112186902A (en) * | 2020-10-28 | 2021-01-05 | 施耐德电气（中国）有限公司 | Power distribution monitoring system and monitoring information display and control method and device thereof |
| CN113852876A (en) * | 2021-08-17 | 2021-12-28 | 哈工大机器人南昌智能制造研究院 | Control logic method for automatic alarm of factory power utilization overload |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102832711A (en) * | 2012-08-16 | 2012-12-19 | 北海市深蓝科技发展有限责任公司 | Multifunctional measurement and control integrated method of power system |
| CN106250993A (en) * | 2016-08-11 | 2016-12-21 | 中国船舶重工集团公司第七〇九研究所 | A kind of measurement equipment metering cycle method of adjustment based on warship maintenance section |
| CN205910570U (en) * | 2016-08-25 | 2017-01-25 | 华润水泥技术研发有限公司 | Need not people intelligent monitoring system of transformer substation that always falls on duty |
| CN106779096A (en) * | 2016-11-10 | 2017-05-31 | 国网山东省电力公司济南供电公司 | Power distribution network reports situation active forewarning system for repairment |
| CN207320949U (en) * | 2017-10-30 | 2018-05-04 | 南方电网科学研究院有限责任公司 | Substation equipment importance detection device |
| CN108173778A (en) * | 2017-12-27 | 2018-06-15 | 中国电力科学研究院有限公司 | Electric power information collection system data processing method based on business classification |
| CN109559043A (en) * | 2018-11-30 | 2019-04-02 | 天津大学 | A kind of power distribution system equipment Decision-making of Condition-based Maintenance method based on risk assessment |
| CN111047177A (en) * | 2019-12-06 | 2020-04-21 | 国网湖南省电力有限公司 | Multi-source monitoring and early warning method and system for icing of power transmission line |
-
2020
- 2020-06-30 CN CN202010616412.3A patent/CN111786460A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102832711A (en) * | 2012-08-16 | 2012-12-19 | 北海市深蓝科技发展有限责任公司 | Multifunctional measurement and control integrated method of power system |
| CN106250993A (en) * | 2016-08-11 | 2016-12-21 | 中国船舶重工集团公司第七〇九研究所 | A kind of measurement equipment metering cycle method of adjustment based on warship maintenance section |
| CN205910570U (en) * | 2016-08-25 | 2017-01-25 | 华润水泥技术研发有限公司 | Need not people intelligent monitoring system of transformer substation that always falls on duty |
| CN106779096A (en) * | 2016-11-10 | 2017-05-31 | 国网山东省电力公司济南供电公司 | Power distribution network reports situation active forewarning system for repairment |
| CN207320949U (en) * | 2017-10-30 | 2018-05-04 | 南方电网科学研究院有限责任公司 | Substation equipment importance detection device |
| CN108173778A (en) * | 2017-12-27 | 2018-06-15 | 中国电力科学研究院有限公司 | Electric power information collection system data processing method based on business classification |
| CN109559043A (en) * | 2018-11-30 | 2019-04-02 | 天津大学 | A kind of power distribution system equipment Decision-making of Condition-based Maintenance method based on risk assessment |
| CN111047177A (en) * | 2019-12-06 | 2020-04-21 | 国网湖南省电力有限公司 | Multi-source monitoring and early warning method and system for icing of power transmission line |
Non-Patent Citations (1)
| Title |
|---|
| 陈秀华 等: ""移动群智感知中面向任务需求的用户选择激励机制"", 《计算机应用》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112186902A (en) * | 2020-10-28 | 2021-01-05 | 施耐德电气（中国）有限公司 | Power distribution monitoring system and monitoring information display and control method and device thereof |
| CN113852876A (en) * | 2021-08-17 | 2021-12-28 | 哈工大机器人南昌智能制造研究院 | Control logic method for automatic alarm of factory power utilization overload |
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