CN110311369B - Power grid stable section short-term load curve prediction method and system - Google Patents
Power grid stable section short-term load curve prediction method and system Download PDFInfo
- Publication number
- CN110311369B CN110311369B CN201910473198.8A CN201910473198A CN110311369B CN 110311369 B CN110311369 B CN 110311369B CN 201910473198 A CN201910473198 A CN 201910473198A CN 110311369 B CN110311369 B CN 110311369B
- Authority
- CN
- China
- Prior art keywords
- topology
- load
- section
- power grid
- line switch
- 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.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
Abstract
The invention relates to the technical field of power grid dispatching, in particular to a method and a system for predicting a short-term load curve of a stable section of a power grid, which comprises the following steps: obtaining 220kV main transformer prediction data; traversing the sections and judging whether all the sections are traversed or not; predicting the load of the components of the section; predicting section load according to the tidal current direction; and generating a section prediction curve. The method can quickly analyze the incidence relation between the section load and the 220kV main transformer load in real time, and fit and generate a section short-term load curve according to the incidence main transformer load data, and has strong timeliness and high accuracy; the method can be written into a program module and placed in a computer system, so that the high efficiency and automation of the predictive analysis are realized.
Description
Technical Field
The invention relates to the technical field of power grid dispatching, in particular to a method and a system for predicting a short-term load curve of a stable section of a power grid.
Background
The power grid stable section is a whole formed by a plurality of lines or main transformer switches, power is supplied to a certain load area, when any switch fails, the load is transferred to the section residual switch, and therefore power grid stable section load prediction has important significance for improving the safety and stability of a power system and improving the management level of a power demand side. The main reason is that the load composition is considered to be unchanged during the prediction of the short-term load curve in the whole city, and the load curve is strongly related to external factors such as air temperature and the like. The section load curve mainly depends on the current and future section load composition, and the section load composition is often in dynamic change due to the reasons of operation mode change, equipment maintenance, distribution network ring network transfer and the like, so that the load composition cannot be accurately obtained in real time, and the load curve cannot be accurately predicted. At present, the change situation of the section load composition is generally analyzed one by adopting a manual mode, and further load prediction is carried out according to historical data of each load composition element, and no means is provided for implementing computer intelligent operation.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a method and a system for predicting a power grid stable section short-term load curve, which are used for quickly analyzing the incidence relation between a section load and a 220kV main transformer load in real time and generating a section short-term load curve by fitting according to the incidence main transformer load data, and have strong timeliness and high accuracy.
In order to solve the technical problems, the invention adopts the technical scheme that:
the method for predicting the short-term load curve of the stable section of the power grid comprises the following steps:
s1, acquiring 220kV main transformer load prediction data of a power grid in 48 hours in the future;
s2, determining an analysis object, and taking all monitoring sections existing in the current power grid as the analysis object; and judging whether all the monitoring sections have traversed: if yes, go to step S6; if not, go to step S3;
s3, judging whether all components of the monitored section are traversed or not: if yes, go to step S5; if not, go to step S4;
s4, acquiring equipment connection relation, a power flow direction, a switch state and a disconnecting link state of any 220kV line switch in the composition elements of the cross section, analyzing all 220kV main transformers connected with the 220kV line switch in the same station according to the acquired information topology, and converting the load prediction data of the 220kV line switch by combining the load prediction data of all 220kV main transformers acquired in the step S1 according to the proportion of the sum of the real-time loads of the 220kV line switch and all 220kV main transformers in the same station; (ii) a After the load prediction data of the 220kV line switch is obtained, the step S3 is skipped to judge whether all the cross section components are traversed;
s5, setting the load predicted values of all the 220kV line switches obtained in the step S4 as positive numbers or negative numbers according to the real-time load value positive and negative attributes of the 220kV line switches, and adding to obtain predicted data of the target section; after the prediction data of the target section is obtained, the step S2 is skipped to judge whether all sections are traversed;
and S6, generating a current section prediction curve according to the target section prediction data in the step S5.
According to the method for predicting the power grid stable section short-term load curve, data in an automatic E file, an SCADA Web database and an auxiliary decision system database are used as data sources, the method can be used for analyzing the association relation between the section load and the 220kV main transformer load quickly and in real time, and the section short-term load curve is generated through fitting according to the associated main transformer load data, so that the timeliness is high, and the accuracy is high.
Preferably, in step S1, the prediction data of each 220kV main transformer load is obtained from the database of the assistant decision system.
Preferably, in step S3, the components of the monitoring profile may be obtained from a profile composition table stored in a user maintenance table, in which the user substation information is stored.
Preferably, in step S4, the switch status and the disconnecting link status data are obtained from the automation E file, and the device connection relationship and the power flow direction are obtained from the SCADA Web database.
Preferably, step S4 is performed as follows:
s401, judging whether a target 220kV line switch is marked: if so, judging that the predicted load value of the target 220kV line switch is 0, and finishing topology; if not, go to step S402;
s402, according to the connection relation of the equipment, acquiring adjacent equipment connected with the current equipment, and putting the adjacent equipment into an equipment topology table for screening;
s403, performing topology one by one on all the devices acquired in the step S402 and judging whether all the devices in the device topology table are completely traversed: if yes, the topology is finished, the topology process is exited, and the step S410 is switched to; if not, continuing to judge the topology according to the judgment rules of the steps S404 to S409;
s404, judging whether the current equipment is hung: if yes, the topology is finished, the topology process is exited, and the step S403 is carried out; if not, the step S405 is carried out to continue topology judgment;
s405, judging whether the current equipment belonging substation and the target 220kV line switch belong to the same substation: if yes, go to step S406 to continue topology judgment; if not, finishing the topology judgment of the current equipment, and entering the step S403;
s406, judging whether the current equipment appears in the parent node: if yes, the topology is finished, the topology process is exited, and the step S403 is carried out; if not, the step S407 is carried out to continue topology judgment;
s407, judging whether the current equipment belongs to a main transformer: if so, stopping topology, arranging the main transformer into a co-station 220kV main transformer connected with a target 220kV line switch, and turning to the step S403; if not, the step S408 is carried out to continue topology judgment;
s408, judging whether the current equipment belongs to a switch: if yes, the step S409 is carried out to continue topology judgment; if not, stopping topology and turning to the step S402;
s409, judging whether the current equipment is closed: if yes, stopping topology and turning to step S403; if not, go to step S402;
s410, obtaining the current actual load of the target 220kV line switch as a numerator for calculating the proportional relationship, taking the sum of all real-time loads of the 220kV main transformers in the same station obtained by analyzing in the step S407 as a denominator of the proportional relationship, and calculating to obtain the proportion of the 220kV line switch and the real-time loads of the 220kV main transformers in the same station;
s411, taking out the 220kV main transformer load prediction data obtained by analyzing in the step S407 from the 220kV main transformer load prediction data of the power grid obtained in the step S1, adding and summing the 220kV main transformer load prediction data and multiplying the sum by the proportion obtained in the step S410, and calculating the load prediction value of the target 220kV line switch; and repeating the steps S401 to S411, and calculating the load predicted values of all the 220kV line switches forming the section.
Preferably, in step S5, the positive and negative attributes of the real-time load value of the 220kV line switch are obtained from the SCADA Web database.
The invention also provides a power grid stable section short-term load curve prediction system which comprises a program module written into the power grid stable section short-term load curve prediction method and a data storage system stored with an automatic E file, an SCADA Web database, an auxiliary decision system database and a user maintenance table, wherein the program module is embedded in the control module, the data storage system is connected with the control module, and the input end of the control module is connected with an input module.
According to the power grid stable section short-term load curve prediction system, each database is stored in a computer system, the method writing program module is embedded in the computer system, so that the incidence relation between the section load and the 220kV main transformer load is analyzed rapidly in real time, the section short-term load curve is generated by fitting according to the incidence main transformer load data, the timeliness is strong, the accuracy is high, and the defects that manual operation consumes time and labor and cannot meet the real-time, accurate and efficient requirements of short-term load prediction are overcome effectively.
Compared with the prior art, the invention has the beneficial effects that:
the method and the system for predicting the power grid stable section short-term load curve can quickly analyze the incidence relation between the section load and the 220kV main transformer load in real time, generate the section short-term load curve through fitting according to the incidence main transformer load data, and have the advantages of strong timeliness and high accuracy.
Drawings
FIG. 1 is a flow chart of a power grid stable section short-term load curve prediction method of the invention;
FIG. 2 is a schematic diagram of a data source of a power grid stable section short-term load curve prediction method;
fig. 3 is a flowchart of the analysis process of step S4.
Detailed Description
The present invention will be further described with reference to the following embodiments.
Example one
Fig. 1 to 3 show an embodiment of a method for predicting a grid stable section short-term load curve according to the present invention, which includes the following steps:
s1, acquiring 220kV main transformer load prediction data of a power grid in 48 hours in the future;
s2, determining an analysis object, and taking all monitoring sections existing in the current power grid as the analysis object; and judging whether all the monitoring sections have traversed: if yes, go to step S6; if not, go to step S3;
s3, judging whether all components of the monitored section are traversed or not: if yes, go to step S5; if not, go to step S4;
s4, acquiring equipment connection relation, a power flow direction, a switching state and a disconnecting link state of any 220kV line switch in the composition elements of the cross section, analyzing all 220kV main transformers connected with the 220kV line switch in the same station according to the acquired information topology, and converting load prediction data of the 220kV line switch by combining the load prediction data of all 220kV main transformers acquired in the step S1 according to the proportion of the sum of real-time loads of the 220kV line switch and all 220kV main transformers in the same station; after the load prediction data of the 220kV line switch is obtained, the step S3 is skipped to judge whether all the cross section components are traversed;
s5, setting the load predicted values of all the 220kV line switches obtained in the step S4 as positive numbers or negative numbers according to the real-time load value positive and negative attributes of the 220kV line switches, and adding to obtain predicted data of the target section; after the prediction data of the target section is obtained, the step S2 is skipped to judge whether all sections are traversed;
and S6, generating a current section prediction curve according to the target section prediction data in the step S5.
In this embodiment, the short-term load prediction is to predict the load curve of the equipment in 48 hours, and the prediction precision is one point every 15 minutes.
In step S1, the prediction data of the main transformer loads of 220kV is obtained from the database of the assistant decision system.
In step S3, the components of the monitored cross section may be obtained from a cross section composition table, which is stored in a user maintenance table, in which the user substation information is stored.
In step S4, the data of the switch state and the disconnecting link state are obtained from the automation E file, and the device connection relationship and the power flow direction are obtained from the SCADA Web database.
Step S4 is performed as follows:
s401, judging whether a target 220kV line switch is marked: if yes, judging that the predicted load value of the target 220kV line switch is 0, and finishing topology; if not, go to step S402;
s402, according to the connection relation of the equipment, acquiring adjacent equipment connected with the current equipment, and putting the adjacent equipment into an equipment topology table for screening;
s403, performing topology one by one on all the devices acquired in the step S402 and judging whether all the devices in the device topology table are completely traversed: if yes, the topology is finished, the topology process is exited, and the step S410 is switched to; if not, continuing to judge the topology according to the judgment rules of the steps S404 to S409;
s404, judging whether the current equipment is hung: if yes, the topology is finished, the topology process is exited, and the step S403 is carried out; if not, the step S405 is carried out to continue topology judgment;
s405, judging whether the current equipment belonging substation and the target 220kV line switch belong to the same substation: if yes, go to step S406 to continue topology judgment; if not, finishing the topology judgment of the current equipment, and entering the step S403;
s406, judging whether the current equipment appears in the parent node: if yes, the topology is finished, the topology process is exited, and the step S403 is carried out; if not, the step S407 is carried out to continue topology judgment;
s407, judging whether the current equipment belongs to a main transformer: if yes, stopping topology, arranging the main transformer into a co-station 220kV main transformer connected with a target 220kV line switch, and turning to the step S403; if not, the step S408 is carried out to continue topology judgment;
s408, judging whether the current equipment belongs to a switch: if yes, the step S409 is carried out to continue topology judgment; if not, stopping topology and turning to the step S402;
s409, judging whether the current equipment is closed: if yes, stopping topology and turning to step S403; if not, go to step S402;
s410, obtaining the current actual load of the target 220kV line switch as a numerator for calculating the proportional relationship, taking the sum of all real-time loads of the 220kV main transformers in the same station obtained by analyzing in the step S407 as a denominator of the proportional relationship, and calculating to obtain the proportion of the 220kV line switch and the real-time loads of the 220kV main transformers in the same station;
s411, taking out the 220kV main transformer load prediction data obtained by analyzing in the step S407 from the 220kV main transformer load prediction data of the power grid obtained in the step S1, adding and summing the 220kV main transformer load prediction data and multiplying the sum by the proportion obtained in the step S410, and calculating the load prediction value of the target 220kV line switch; and repeating the steps S401 to S411, and calculating the load predicted values of all the 220kV line switches forming the section.
In step S5, the positive and negative attributes of the real-time load value of the 220kV line switch are obtained from the SCADA Web database.
Through the steps, the incidence relation between the section load and the 220kV main transformer load can be analyzed rapidly in real time, a section short-term load curve is generated through fitting according to the incidence main transformer load data, and the method is high in timeliness and accuracy.
Example two
The embodiment is an embodiment of a power grid stable section short-term load curve prediction system, and comprises a written program module of a power grid stable section short-term load curve prediction method and a data storage system in which an automation E file, an SCADA Web database, an auxiliary decision system database and a user maintenance table are stored, wherein the program module is embedded in a control module, the data storage system is connected to the control module, and the input end of the control module is connected with an input module.
According to the embodiment, the computer system is utilized, the incidence relation between the section load and the 220kV main transformer load can be analyzed rapidly in real time, the section short-term load curve is generated by fitting according to the incidence main transformer load data, the timeliness is strong, the accuracy is high, and the defects that manual operation consumes time and labor and the requirements for short-term load prediction in real time, accuracy and high efficiency cannot be met are overcome effectively.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (7)
1. A power grid stable section short-term load curve prediction method is characterized by comprising the following steps:
s1, acquiring 220kV main transformer load prediction data of a power grid in 48 hours in the future;
s2, determining an analysis object, and taking all monitoring sections existing in the current power grid as the analysis object; and judging whether all the monitoring sections have traversed: if yes, go to step S6; if not, go to step S3;
s3, judging whether all components of the monitored section are traversed or not: if yes, go to step S5; if not, go to step S4;
s4, acquiring equipment connection relation, a power flow direction, a switch state and a disconnecting link state of any 220kV line switch in the composition elements of the cross section, analyzing all 220kV main transformers connected with the 220kV line switch in the same station according to the acquired information topology, and converting the load prediction data of the 220kV line switch by combining the load prediction data of all 220kV main transformers acquired in the step S1 according to the proportion of the sum of the real-time loads of the 220kV line switch and all 220kV main transformers in the same station; after the load prediction data of the 220kV line switch is obtained, the step S3 is skipped to judge whether all the cross section components are traversed;
s5, setting the load predicted values of all the 220kV line switches obtained in the step S4 as positive numbers or negative numbers according to the real-time load value positive and negative attributes of the 220kV line switches, and adding to obtain predicted data of the target section; after the prediction data of the target section is obtained, the step S2 is skipped to judge whether all sections are traversed;
and S6, generating a current section prediction curve according to the target section prediction data in the step S5.
2. The method for predicting the grid stability profile short-term load curve as claimed in claim 1, wherein in step S1, the prediction data of each 220kV main transformer load is obtained from an assistant decision system database.
3. The method for predicting the grid stable section short-term load curve as claimed in claim 1, wherein in step S3, the components of the monitored section are obtained from a section composition table, the section composition table is stored in a user maintenance table, and the user maintenance table stores user substation information within a year.
4. The method for predicting the short-term load curve of the stable section of the power grid as claimed in claim 1, wherein in step S4, the data of the switch state and the switch state are obtained from an automation E file, and the equipment connection relation and the power flow direction are obtained from a SCADAWeb database.
5. The method for predicting the grid stable section short-term load curve as claimed in claim 1, wherein the step S4 is performed according to the following steps:
s401, judging whether a target 220kV line switch is marked: if so, judging that the predicted load value of the target 220kV line switch is 0, and finishing topology; if not, go to step S402;
s402, according to the connection relation of the equipment, acquiring adjacent equipment connected with the current equipment, and putting the adjacent equipment into an equipment topology table for screening;
s403, performing topology one by one on all the devices acquired in the step S402 and judging whether all the devices in the device topology table are completely traversed: if yes, the topology is finished, the topology process is exited, and the step S410 is switched to; if not, continuing to judge the topology according to the judgment rules of the steps S404 to S409;
s404, judging whether the current equipment is hung: if yes, the topology is finished, the topology process is exited, and the step S403 is carried out; if not, the step S405 is carried out to continue topology judgment;
s405, judging whether the current equipment belonging substation and the target 220kV line switch belong to the same substation: if yes, go to step S406 to continue topology judgment; if not, finishing the topology judgment of the current equipment, and entering the step S403;
s406, judging whether the current equipment appears in the parent node: if yes, the topology is finished, the topology process is exited, and the step S403 is carried out; if not, the step S407 is carried out to continue topology judgment;
s407, judging whether the current equipment belongs to a main transformer: if so, stopping topology, arranging the main transformer into a co-station 220kV main transformer connected with a target 220kV line switch, and turning to the step S403; if not, the step S408 is carried out to continue topology judgment;
s408, judging whether the current equipment belongs to a switch: if yes, the step S409 is carried out to continue topology judgment; if not, stopping topology and turning to the step S402;
s409, judging whether the current equipment is closed: if yes, stopping topology and turning to step S403; if not, go to step S402;
s410, obtaining the current actual load of the target 220kV line switch as a numerator for calculating the proportional relationship, taking the sum of all real-time loads of the 220kV main transformers in the same station obtained by analyzing in the step S407 as a denominator of the proportional relationship, and calculating to obtain the proportion of the 220kV line switch and the real-time loads of the 220kV main transformers in the same station;
s411, taking out the 220kV main transformer load prediction data obtained by analyzing in the step S407 from the 220kV main transformer load prediction data of the power grid obtained in the step S1, adding and summing the 220kV main transformer load prediction data and multiplying the sum by the proportion obtained in the step S410, and calculating the load prediction value of the target 220kV line switch; and repeating the steps S401 to S411, and calculating the load predicted values of all the 220kV line switches forming the section.
6. The method for predicting the power grid stable section short-term load curve according to any one of claims 1 to 5, wherein in the step S5, the positive and negative attributes of the real-time load value of the 220kV line switch are obtained from a SCADA Web database.
7. A power grid stable section short-term load curve prediction system is characterized by comprising a program module written with the power grid stable section short-term load curve prediction method according to any one of claims 1 to 6 and a data storage system stored with an automatic E file, a SCADA Web database, an assistant decision system database and a user maintenance table, wherein the program module is embedded in a control module, the data storage system is connected with the control module, and the input end of the control module is connected with an input module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910473198.8A CN110311369B (en) | 2019-05-31 | 2019-05-31 | Power grid stable section short-term load curve prediction method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910473198.8A CN110311369B (en) | 2019-05-31 | 2019-05-31 | Power grid stable section short-term load curve prediction method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110311369A CN110311369A (en) | 2019-10-08 |
| CN110311369B true CN110311369B (en) | 2020-09-25 |
Family
ID=68075072
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910473198.8A Active CN110311369B (en) | 2019-05-31 | 2019-05-31 | Power grid stable section short-term load curve prediction method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110311369B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111931980A (en) * | 2020-07-02 | 2020-11-13 | 广东电网有限责任公司 | Line historical load recording method for marking operation mode |
| CN112396293B (en) * | 2020-10-23 | 2023-10-13 | 中国南方电网有限责任公司 | Intelligent analysis matching and form conversion method for stable section of power grid |
| CN113972664B (en) * | 2021-10-29 | 2024-02-20 | 国网上海市电力公司 | Electric power data complement method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201975794U (en) * | 2011-01-31 | 2011-09-14 | 山东电力工程咨询院有限公司 | Substation voltage reactive power synthetic control device based on short-term load forecasting |
| KR101396094B1 (en) * | 2012-11-12 | 2014-05-15 | 삼성물산 주식회사 | Intelligent energy storing system for communal house |
| CN107918639A (en) * | 2017-10-19 | 2018-04-17 | 广东电网有限责任公司云浮供电局 | Based on electric power big data main transformer peak load forecasting method and data warehouse |
| CN108551166A (en) * | 2018-04-11 | 2018-09-18 | 广东电网有限责任公司 | A kind of grid equipment and section ultra-short term, alarm and steady prosecutor method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10585445B2 (en) * | 2015-02-02 | 2020-03-10 | Opus One Solutions Energy Corporation | Systems and methods for volt/VAR control in electric power management and automation systems |
-
2019
- 2019-05-31 CN CN201910473198.8A patent/CN110311369B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201975794U (en) * | 2011-01-31 | 2011-09-14 | 山东电力工程咨询院有限公司 | Substation voltage reactive power synthetic control device based on short-term load forecasting |
| KR101396094B1 (en) * | 2012-11-12 | 2014-05-15 | 삼성물산 주식회사 | Intelligent energy storing system for communal house |
| CN107918639A (en) * | 2017-10-19 | 2018-04-17 | 广东电网有限责任公司云浮供电局 | Based on electric power big data main transformer peak load forecasting method and data warehouse |
| CN108551166A (en) * | 2018-04-11 | 2018-09-18 | 广东电网有限责任公司 | A kind of grid equipment and section ultra-short term, alarm and steady prosecutor method |
Non-Patent Citations (2)
| Title |
|---|
| Dispatch of main transformer ULTC and capacitors in a distribution system;Ruey-Hsun Liang;《IEEE Transactions on Power Delivery》;20011031;全文 * |
| 永久性负荷割接后变电站主变压器的负荷预测方法;辛洁晴;《电力系统自动化》;20140825;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110311369A (en) | 2019-10-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110311369B (en) | Power grid stable section short-term load curve prediction method and system | |
| KR101683256B1 (en) | Asset management system and method for electric power apparatus | |
| CN110363326B (en) | Power grid 110kV line short-term load curve prediction method | |
| CN110348596B (en) | Method for predicting short-term load curve of 220kV main transformer of power grid | |
| CN107302264B (en) | Control method for stable operation of secondary automation equipment of transformer substation | |
| CN109884473B (en) | Electric power overhaul system and method | |
| CN110380412B (en) | Main and auxiliary integrated online real-time loop closing analysis method based on CIM/SVG | |
| CN105930930B (en) | Load data calibration method based on load characteristic analysis and load prediction technology | |
| WO2018162198A1 (en) | Method and system for determining the expected useful life of electrical apparatus | |
| CN103077476A (en) | GIS (Gas Insulated Switching gear) equipment condition assessment method and GIS equipment condition assessment system | |
| CN116451876B (en) | Power distribution network fault prediction and active overhaul system based on artificial intelligence | |
| CN110348597B (en) | Method for predicting short-term load curve of 110kV main transformer of power grid | |
| Wang et al. | Research on reliability evaluation method of catenary of high speed railway considering weather condition | |
| CN105488572A (en) | Health state evaluation method of power distribution equipment | |
| CN108596450B (en) | Power grid risk early warning method and system | |
| CN107862459B (en) | Metering equipment state evaluation method and system based on big data | |
| CN108494095B (en) | Bus power supply range based automatic identification and display method | |
| CN112307652B (en) | Quick maintenance method for complex equipment system based on average remaining life importance | |
| CN115689532A (en) | Power system fault analysis method and device | |
| Liu et al. | An optimization method of maintenance strategy for power equipment | |
| CN112256922B (en) | Method and system for quickly identifying faults and power failure | |
| CN115224687A (en) | Power failure overhaul safety checking method and system for power transmission and transformation equipment below 220KV | |
| CN110942161B (en) | Method for improving power supply reliability based on business middle station | |
| CN103166322A (en) | Monitoring method and system of under frequency load shedding device | |
| CN111985794A (en) | Line loss management method based on marketing and distribution information integration platform |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |