CN112767193A - Situation awareness-based distribution network production differentiation operation and maintenance strategy method - Google Patents
Situation awareness-based distribution network production differentiation operation and maintenance strategy method Download PDFInfo
- Publication number
- CN112767193A CN112767193A CN202110282492.8A CN202110282492A CN112767193A CN 112767193 A CN112767193 A CN 112767193A CN 202110282492 A CN202110282492 A CN 202110282492A CN 112767193 A CN112767193 A CN 112767193A
- Authority
- CN
- China
- Prior art keywords
- equipment
- data
- production
- risk
- perception
- 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
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 63
- 238000012423 maintenance Methods 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000009826 distribution Methods 0.000 title claims abstract description 27
- 230000004069 differentiation Effects 0.000 title claims abstract description 17
- 230000008447 perception Effects 0.000 claims abstract description 53
- 238000011156 evaluation Methods 0.000 claims abstract description 43
- 238000012502 risk assessment Methods 0.000 claims abstract description 20
- 230000010485 coping Effects 0.000 claims abstract description 19
- 238000011157 data evaluation Methods 0.000 claims abstract description 10
- 230000036541 health Effects 0.000 claims abstract description 10
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 238000005516 engineering process Methods 0.000 claims description 8
- 230000004044 response Effects 0.000 claims description 5
- 230000002159 abnormal effect Effects 0.000 claims description 4
- 230000007613 environmental effect Effects 0.000 claims description 4
- 238000007621 cluster analysis Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000013441 quality evaluation Methods 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 description 4
- 238000007726 management method Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000005215 recombination Methods 0.000 description 2
- 230000006798 recombination Effects 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000004140 cleaning Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000007654 immersion Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011158 quantitative evaluation Methods 0.000 description 1
- 238000010223 real-time analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 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
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- 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
Abstract
The invention discloses a situation awareness-based distribution network production differentiation operation and maintenance strategy method, which comprises the following steps: s1, collecting production data, collecting real-time and non-real-time production data, and evaluating the quality of the data; s2, equipment risk perception, namely establishing an equipment risk evaluation perception model and carrying out equipment risk identification; s3, risk assessment of equipment, namely performing risk assessment on the equipment according to the equipment perception health degree and the influence of the equipment on the net rack; s4, establishing a strategy level and a coping strategy according to the risk assessment result; and S5, executing an operation and maintenance strategy, developing production operation and maintenance according to the risk coping strategy, and providing data and evaluation support for the steps S1-S4 through information backfilling. The method and the device can ensure the evaluation sensing integrity and result preparation of the equipment, realize the dynamic adjustment of production operation modes such as a situation sensing model, a risk coping strategy and the like, ensure the scientific and effective data evaluation, reduce the equipment failure rate and improve the power supply reliability of a power grid.
Description
Technical Field
The invention relates to the technical field of operation and maintenance methods of distribution network equipment, in particular to a situation awareness-based distribution network production differentiation operation and maintenance strategy method for real-time analysis of the operation state of power equipment.
Background
The distribution network is at the end of the whole power grid and is a window facing the society of power enterprises, the operation management of the distribution network is directly related to thousands of households, and the social responsibility and influence are huge. With the fact that power grid construction enters a new stage, the scale of power grid equipment is greatly increased, application of new equipment and new technology is accelerated, and higher requirements are provided for the construction of state control capacity of the power grid equipment. At present, the state control of the power grid in the aspects of equipment, environment and personnel still lacks an effective means, and the increasing maintenance amount cannot be met by adopting the traditional operation and maintenance mode.
The operation and maintenance of the distribution network equipment comprise: patrol, overhaul, test, acceptance check, first-aid repair and the like. In the prior art, the running state of the distribution network equipment cannot be known, and the daily patrol mode is mainly adopted. At present, a common operation and maintenance mode is to make a strategy according to state evaluation and risk evaluation, and to make full life cycle evaluation on equipment, including equipment state evaluation, equipment risk evaluation and operation and maintenance strategy making application, and some enterprises or research institutions make some improvements on equipment evaluation, but still stay in method improvement and algorithm optimization of the three applications.
The current operation and maintenance mode has the following defects:
1) the device has insufficient evaluation perception capability: neglecting the source data quality problem and the later data learning capacity, and still adopting a periodic evaluation mode;
2) the equipment evaluation result has low accuracy: the quality of source data is not checked and analyzed, and the accuracy of later evaluation is seriously influenced;
3) the device evaluation perception real-time performance is poor: current risk assessment does not utilize real-time class information and the assessment of equipment lacks timeliness.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a situation awareness-based distribution network production differentiation operation and maintenance strategy method, so that the problems of insufficient equipment evaluation and perception capability, low equipment evaluation result accuracy and poor equipment evaluation and perception real-time performance of the existing distribution network equipment operation and maintenance method are solved, the equipment evaluation and perception integrity and result readiness are ensured, the data evaluation is scientific and effective, the equipment failure rate is reduced, and the power supply reliability of a power grid is improved.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
A situation awareness-based distribution network production differentiation operation and maintenance strategy method comprises the following steps:
s1, collecting production data, collecting real-time and non-real-time production data, and evaluating the quality of the data;
s2, equipment risk perception, namely establishing an equipment risk evaluation perception model and carrying out equipment risk identification;
s3, risk assessment of equipment, namely performing risk assessment on the equipment according to the equipment perception health degree and the influence of the equipment on the net rack;
s4, establishing a strategy level and a coping strategy according to the risk assessment result;
and S5, executing an operation and maintenance strategy, developing production operation and maintenance according to the risk coping strategy, and providing data and evaluation support for the steps S1-S4 through information backfilling.
Further optimizing the technical solution, in the step S1, the quality evaluation of the data is performed to perform integrity and validity evaluation of the data.
Further optimizing the technical solution, the step S1 specifically includes the following steps:
s101, carrying out production real-time data acquisition, carrying out data acquisition on environmental quantity and equipment quantity through an equipment monitoring technology, carrying out real-time diagnosis and analysis on the data, generating a real-time task, and realizing real-time perception capability of equipment;
s102, carrying out production service data acquisition, including service data access, and then carrying out ledger analysis by combining equipment assets to generate a production plan task;
s103, according to the collected real-time data, carrying out real-time data evaluation and eliminating problem data;
and S104, according to the collected business data, carrying out production business data evaluation, carrying out integrity and validity verification on the business data, and perfecting the problem data.
In step S2, the unknown risk and the known risk of the device are identified by the perceptual evaluation item and the perceptual evaluation weight of the device.
Further optimizing the technical solution, the step S2 specifically includes the following steps:
s201, analyzing historical data, establishing an equipment perception model, forming a support, and accurately identifying known risks and unknown risks of equipment;
s202, establishing known risks of the equipment, establishing influence weights and diagnostic value weights according to the environment quantity of real-time data and the state quantity of the equipment, and forming equipment rapid identification and equipment abnormal rapid response to external risks and operation risks;
s203, establishing external risks and internal risks of the equipment according to the production historical data, carrying out risk identification on the equipment, and predicting the future risks of the equipment.
Further optimizing the technical solution, the step S3 specifically includes the following steps:
s301, evaluating the influence of equipment faults in the whole net rack, and dividing importance;
s302, collecting real-time data and historical working data of the power distribution equipment according to the equipment risk perception evaluation item, then quantizing indexes, and calculating a real-time health index of the equipment;
and S303, performing risk assessment on the equipment according to the risk assessment matrix by combining the health degree and the importance degree of the equipment.
Further optimizing the technical solution, the step S4 specifically includes the following steps:
s401, establishing an equipment operation and maintenance strategy level and a coping strategy according to the risk assessment result value;
s402, according to the coping strategy, the system automatically generates corresponding real-time and non-real-time task work orders, and meanwhile automatically dispatches the work orders to corresponding equipment owners;
and S403, comprehensively considering all production events to form a comprehensive strategy.
Further optimizing the technical solution, in the step S5, the information backfilling includes field policy execution information backfilling and policy adjustment information backfilling.
Further optimizing the technical solution, the step S5 specifically includes the following steps:
s501, the system automatically generates a planned work order and a temporary work order according to a strategy, develops production field work according to a scheme according to the type of the work order, backfills field data, and adjusts the field according to the non-conforming scheme by combining field working conditions;
s502, performing big data analysis on the historical data, and revising the equipment perception item and the perception item weight by utilizing cluster analysis, relevance analysis and an artificial neural network algorithm.
Further optimizing the technical solution, the step S5 further includes the following steps:
and (5) simulating the dynamic perception model generated by the neural network in the step (S502), intervening, and adjusting perception items or weight values.
Due to the adoption of the technical scheme, the technical progress of the invention is as follows.
The method is used for analyzing the running state of the power equipment in real time, and compared with the conventional mode of periodically evaluating and evaluating limited data of the equipment, the method increases the detection of the data influencing the running evaluation of the equipment, realizes the real-time data perception evaluation capability of the equipment, and can ensure the perception integrity and result preparation of the equipment evaluation.
The equipment risk coping level is linked with production operation and maintenance, work orders or reminders and the like are automatically generated, data are backfilled through field scheme execution and adjustment, and weight adjustment or perception model recombination is carried out on data influence through a neural network algorithm, so that dynamic adjustment of production operation modes such as a situation perception model and a risk coping strategy is realized, and scientific and effective data evaluation is guaranteed.
According to the method, the risk perception capability and the quick response capability of the equipment are improved, the failure rate of the equipment is reduced, and the power supply reliability of a power grid is improved.
The invention constructs a production situation perception operation and maintenance model by fusing the modern information communication technology, the equipment state detection technology and the traditional operation and maintenance service and the like in an intelligent direction, realizes the differentiated operation and maintenance of equipment, and leads the operation and maintenance management and the technical change which are suitable for the rapid development of a power grid.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the figures and specific examples.
A situation awareness-based distribution network production differentiation operation and maintenance strategy method is shown in a combined figure 1 and comprises the following steps:
and S1, collecting production data, collecting real-time and non-real-time production data, evaluating the quality of the data, and providing basic support for equipment perception. And the quality evaluation of the data is to evaluate the integrity and the effectiveness of the data.
Step S1 specifically includes the following steps:
s101, carrying out production real-time data acquisition, carrying out data acquisition on environmental quantity and equipment quantity through monitoring technologies such as an intelligent power distribution room, an intelligent platform area, an intelligent pipe gallery and intelligent equipment, carrying out data real-time diagnosis and analysis, generating a real-time task, and realizing real-time perception capability of the equipment.
S102, carrying out production business data acquisition, including business data access, and then carrying out ledger analysis by combining equipment assets to generate a production plan task. The service data access comprises production inspection, first-aid repair, overhaul, test, acceptance, defect and the like.
S103, according to the collected real-time data, real-time data evaluation is carried out, the special data are grouped, data fusion and analysis are divided into data discrimination and cleaning, data storage and processing, and data fusion mining and analysis, so that problem data are eliminated in advance, and error report of abnormal work orders is reduced.
And S104, according to the collected business data, carrying out production business data evaluation, carrying out integrity and validity verification on the business data, sending the data with problems to a production business system, and improving the business data in the business system to effectively support and sense the evaluation data, reduce invalid data and avoid the condition that the invalid data affects the production operation and maintenance plan.
And S2, sensing the equipment risk, establishing an equipment risk evaluation sensing model, and identifying the equipment risk. In step S2, unknown risk and known risk of the device are identified by the perception evaluation item and the perception evaluation weight of the device.
Step S2 specifically includes the following steps:
s201, analyzing the historical data, establishing an equipment perception model, forming a support, and accurately identifying the known risks and the unknown risks of the equipment.
S202, setting known risks of the equipment, setting influence weights and diagnostic value weights according to the environment quantity of real-time data and the state quantity of the equipment, and forming equipment rapid identification and equipment abnormal rapid response to external risks and operation risks.
Wherein the environmental quantity comprises temperature, humidity, gas, water immersion, border crossing and the like; the device state quantities include current, voltage, partial discharge, temperature, and the like.
S203, establishing external risks and internal risks of the equipment according to the production historical data, carrying out risk identification on the equipment, and predicting the future risks of the equipment. And support is provided for annual and monthly production.
And S3, evaluating equipment risk, and performing risk evaluation on the equipment according to the equipment perception health degree and the influence of the equipment on the net rack.
Step S3 specifically includes the following steps:
s301, evaluating the influence in the whole network frame through the influence caused by equipment faults, and dividing the importance according to multiple aspects such as equipment influence range, power failure electric quantity loss, power failure social influence and the like.
S302, collecting the real-time data and the historical working data of the power distribution equipment according to the equipment risk perception evaluation items, then quantifying indexes, and calculating the real-time health degree index of the equipment.
And S303, performing risk assessment on the equipment according to the risk assessment matrix by combining the health degree and the importance degree of the equipment.
And S4, establishing a strategy level and a coping strategy according to the risk assessment result.
Step S4 specifically includes the following steps:
s401, according to the risk assessment result value, establishing an equipment operation and maintenance strategy level and a coping strategy, and dividing the strategy into a maintenance strategy and a maintenance strategy. The influence of the equipment operation and maintenance strategy on the system reliability is considered, and the influence on the equipment fault is lack of quantitative evaluation. The state maintenance strategy focuses on controlling the operation and maintenance cost of the equipment and improves the maintenance efficiency.
S402, according to the coping strategy, the system automatically generates corresponding real-time and non-real-time task work orders, automatically sends the work orders to corresponding equipment owners, and informs the equipment owners in a reminding mode of short messages or system bulletins and the like.
And S403, comprehensively considering all production events to form a comprehensive strategy. A comprehensive strategy is a task that is performed in conjunction with other tasks, such as a comprehensive solution that considers major network production or other production events together when formulating a strategy. And a better solution is provided, and the method has great significance for reducing the workload and the repeated power failure.
And S5, executing an operation and maintenance strategy, developing production operation and maintenance according to the risk coping strategy, and providing data and evaluation support for the steps S1-S4 through information backfilling. The information backfilling comprises field strategy execution information backfilling, strategy adjustment information backfilling and the like.
Step S5 specifically includes the following steps:
s501, the system automatically generates a planned work order and a temporary work order according to a strategy, carries out production field work according to a scheme according to the type of the work order, backfills field data, and adjusts the field according to the non-conforming scheme by combining field working conditions.
S502, performing big data analysis on the historical data, and revising the equipment perception item and the perception item weight by utilizing cluster analysis, relevance analysis and an artificial neural network algorithm.
S503, because the quality problem of the historical data or the sample size is not enough to support learning, the dynamic perception model generated by the neural network in the step S502 needs to be simulated, manual intervention is adopted, and the perception item or the weight value is adjusted to achieve the optimal value.
The method is used for analyzing the running state of the power equipment in real time, and compared with the conventional mode of periodically evaluating and evaluating limited data of the equipment, the method increases the detection of the data influencing the running evaluation of the equipment, realizes the real-time data perception evaluation capability of the equipment, and can ensure the perception integrity and result preparation of the equipment evaluation.
The equipment risk coping level is linked with production operation and maintenance, work orders or reminders and the like are automatically generated, data are backfilled through field scheme execution and adjustment, and weight adjustment or perception model recombination is carried out on data influence through a neural network algorithm, so that dynamic adjustment of production operation modes such as a situation perception model and a risk coping strategy is realized, and scientific and effective data evaluation is guaranteed.
According to the method, the risk perception capability and the quick response capability of the equipment are improved, the failure rate of the equipment is reduced, and the power supply reliability of a power grid is improved.
The invention constructs a production situation perception operation and maintenance model by fusing the modern information communication technology, the equipment state detection technology and the traditional operation and maintenance service and the like in an intelligent direction, realizes the differentiated operation and maintenance of equipment, and leads the operation and maintenance management and the technical change which are suitable for the rapid development of a power grid.
Claims (10)
1. A situation awareness-based distribution network production differentiation operation and maintenance strategy method is characterized by comprising the following steps:
s1, collecting production data, collecting real-time and non-real-time production data, and evaluating the quality of the data;
s2, equipment risk perception, namely establishing an equipment risk evaluation perception model and carrying out equipment risk identification;
s3, risk assessment of equipment, namely performing risk assessment on the equipment according to the equipment perception health degree and the influence of the equipment on the net rack;
s4, establishing a strategy level and a coping strategy according to the risk assessment result;
and S5, executing an operation and maintenance strategy, developing production operation and maintenance according to the risk coping strategy, and providing data and evaluation support for the steps S1-S4 through information backfilling.
2. The situation awareness-based distribution network production differentiation operation and maintenance strategy method according to claim 1, wherein in step S1, the quality evaluation of the data is performed as integrity and effectiveness evaluation of the data.
3. The situation awareness-based distribution network production differentiation operation and maintenance strategy method according to claim 1, wherein the step S1 specifically comprises the following steps:
s101, carrying out production real-time data acquisition, carrying out data acquisition on environmental quantity and equipment quantity through an equipment monitoring technology, carrying out real-time diagnosis and analysis on the data, generating a real-time task, and realizing real-time perception capability of equipment;
s102, carrying out production service data acquisition, including service data access, and then carrying out ledger analysis by combining equipment assets to generate a production plan task;
s103, according to the collected real-time data, carrying out real-time data evaluation and eliminating problem data;
and S104, according to the collected business data, carrying out production business data evaluation, carrying out integrity and validity verification on the business data, and perfecting the problem data.
4. The situation awareness-based distribution network production differentiation operation and maintenance strategy method according to claim 1, wherein in step S2, unknown risk and known risk identification of a device is realized through a perception evaluation item and a perception evaluation weight of the device.
5. The situation awareness-based distribution network production differentiation operation and maintenance strategy method according to claim 4, wherein the step S2 specifically comprises the following steps:
s201, analyzing historical data, establishing an equipment perception model, forming a support, and accurately identifying known risks and unknown risks of equipment;
s202, establishing known risks of the equipment, establishing influence weights and diagnostic value weights according to the environment quantity of real-time data and the state quantity of the equipment, and forming equipment rapid identification and equipment abnormal rapid response to external risks and operation risks;
s203, establishing external risks and internal risks of the equipment according to the production historical data, carrying out risk identification on the equipment, and predicting the future risks of the equipment.
6. The situation awareness-based distribution network production differentiation operation and maintenance strategy method according to claim 5, wherein the step S3 specifically comprises the following steps:
s301, evaluating the influence of equipment faults in the whole net rack, and dividing importance;
s302, collecting real-time data and historical working data of the power distribution equipment according to the equipment risk perception evaluation item, then quantizing indexes, and calculating a real-time health index of the equipment;
and S303, performing risk assessment on the equipment according to the risk assessment matrix by combining the health degree and the importance degree of the equipment.
7. The situation awareness-based distribution network production differentiation operation and maintenance strategy method according to claim 1, wherein the step S4 specifically comprises the following steps:
s401, establishing an equipment operation and maintenance strategy level and a coping strategy according to the risk assessment result value;
s402, according to the coping strategy, the system automatically generates corresponding real-time and non-real-time task work orders, and meanwhile automatically dispatches the work orders to corresponding equipment owners;
and S403, comprehensively considering all production events to form a comprehensive strategy.
8. The situation awareness-based distribution network production differentiation operation and maintenance strategy method according to claim 1, wherein in the step S5, the information backfilling comprises field strategy execution information backfilling and strategy adjustment information backfilling.
9. The situation awareness-based distribution network production differentiation operation and maintenance strategy method according to claim 1, wherein the step S5 specifically comprises the following steps:
s501, the system automatically generates a planned work order and a temporary work order according to a strategy, develops production field work according to a scheme according to the type of the work order, backfills field data, and adjusts the field according to the non-conforming scheme by combining field working conditions;
s502, performing big data analysis on the historical data, and revising the equipment perception item and the perception item weight by utilizing cluster analysis, relevance analysis and an artificial neural network algorithm.
10. The situation awareness-based distribution network production differentiation operation and maintenance strategy method according to claim 9, wherein the step S5 further comprises the steps of:
and (5) simulating the dynamic perception model generated by the neural network in the step (S502), intervening, and adjusting perception items or weight values.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110282492.8A CN112767193A (en) | 2021-03-16 | 2021-03-16 | Situation awareness-based distribution network production differentiation operation and maintenance strategy method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110282492.8A CN112767193A (en) | 2021-03-16 | 2021-03-16 | Situation awareness-based distribution network production differentiation operation and maintenance strategy method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112767193A true CN112767193A (en) | 2021-05-07 |
Family
ID=75691070
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110282492.8A Pending CN112767193A (en) | 2021-03-16 | 2021-03-16 | Situation awareness-based distribution network production differentiation operation and maintenance strategy method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112767193A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114019901A (en) * | 2021-11-04 | 2022-02-08 | 北京安盟信息技术股份有限公司 | Method and device for integrally controlling information and production safety risk of numerical control machine tool |
| CN115760074A (en) * | 2022-12-07 | 2023-03-07 | 中国南方电网有限责任公司超高压输电公司广州局 | Power equipment operation and maintenance method and device, computer equipment and storage medium |
| CN116992399A (en) * | 2023-09-27 | 2023-11-03 | 北京前景无忧电子科技股份有限公司 | Power equipment operation and maintenance assessment method based on power data analysis |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103442003A (en) * | 2013-08-26 | 2013-12-11 | 北京科东电力控制系统有限责任公司 | Data acquisition backfill system for SCADA system and method thereof |
| CN103679296A (en) * | 2013-12-24 | 2014-03-26 | 云南电力调度控制中心 | Grid security risk assessment method and model based on situation awareness |
| CN109190781A (en) * | 2018-11-19 | 2019-01-11 | 国网四川省电力公司眉山供电公司 | Distribution network repairs intelligent assistance application system |
| 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 |
-
2021
- 2021-03-16 CN CN202110282492.8A patent/CN112767193A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103442003A (en) * | 2013-08-26 | 2013-12-11 | 北京科东电力控制系统有限责任公司 | Data acquisition backfill system for SCADA system and method thereof |
| CN103679296A (en) * | 2013-12-24 | 2014-03-26 | 云南电力调度控制中心 | Grid security risk assessment method and model based on situation awareness |
| CN109190781A (en) * | 2018-11-19 | 2019-01-11 | 国网四川省电力公司眉山供电公司 | Distribution network repairs intelligent assistance application system |
| 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 |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114019901A (en) * | 2021-11-04 | 2022-02-08 | 北京安盟信息技术股份有限公司 | Method and device for integrally controlling information and production safety risk of numerical control machine tool |
| CN115760074A (en) * | 2022-12-07 | 2023-03-07 | 中国南方电网有限责任公司超高压输电公司广州局 | Power equipment operation and maintenance method and device, computer equipment and storage medium |
| CN116992399A (en) * | 2023-09-27 | 2023-11-03 | 北京前景无忧电子科技股份有限公司 | Power equipment operation and maintenance assessment method based on power data analysis |
| CN116992399B (en) * | 2023-09-27 | 2024-02-27 | 北京前景无忧电子科技股份有限公司 | Power equipment operation and maintenance assessment method based on power data analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112767193A (en) | Situation awareness-based distribution network production differentiation operation and maintenance strategy method | |
| CN107194621B (en) | Water supply network management system and method | |
| CN105426980B (en) | Power distribution network health index assessment engineering application system | |
| CN109298379B (en) | Data monitoring-based intelligent electric meter field error abnormity identification method | |
| CN111695800B (en) | Method for locating abnormal electricity behavior user through cloud edge collaborative progressive analysis | |
| CN107123982B (en) | Power distribution network reliability economic benefit analysis method based on equipment transaction | |
| CN105811402A (en) | Electric power load forecasting system and forecasting method | |
| CN101673363A (en) | Method and system for evaluating energy-consuming efficiency | |
| CN108805429A (en) | " one continuous line " hardware and software platform fine-grained management method for taiwan area line loss | |
| CN116937575A (en) | Energy monitoring management system for grid system | |
| CN116683500A (en) | Active power scheduling method and system for electrochemical energy storage power station | |
| CN104574004A (en) | Auxiliary distribution network state maintenance system | |
| CN111612019A (en) | Method for identifying and analyzing fault abnormality of intelligent electric meter based on big data model | |
| CN109063863B (en) | Distribution network maintenance plan optimization method considering area equipment life cycle cost-efficiency ratio | |
| CN114879619A (en) | Digital workshop energy optimization method and system | |
| CN110866695A (en) | Auxiliary maintenance decision method based on association of equipment running state and maintenance work | |
| CN113608975A (en) | Method and system for online evaluation of running state of electric energy metering system in electric power spot market | |
| CN111552686B (en) | Power data quality assessment method and device | |
| CN117332952A (en) | Autonomous regulation and control method and system for carbon emission | |
| CN112418662A (en) | Power distribution network operation reliability analysis method using artificial neural network | |
| CN115905319B (en) | Automatic identification method and system for abnormal electricity fees of massive users | |
| CN115936663A (en) | Maintenance method and device for power system | |
| CN116384622A (en) | Carbon emission monitoring method and device based on electric power big data | |
| CN116522746A (en) | Power distribution hosting method for high-energy-consumption enterprises | |
| CN103530805B (en) | A kind of automatic settlement system based on database and system interface and method |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210507 |