CN109474067B - Power grid dispatching fault processing aid decision-making method - Google Patents
Power grid dispatching fault processing aid decision-making method Download PDFInfo
- Publication number
- CN109474067B CN109474067B CN201810926198.4A CN201810926198A CN109474067B CN 109474067 B CN109474067 B CN 109474067B CN 201810926198 A CN201810926198 A CN 201810926198A CN 109474067 B CN109474067 B CN 109474067B
- Authority
- CN
- China
- Prior art keywords
- fault
- power grid
- scheme
- evaluation coefficient
- power
- 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
- 238000000034 method Methods 0.000 title claims abstract description 24
- 239000006057 Non-nutritive feed additive Substances 0.000 title claims abstract description 7
- 238000012545 processing Methods 0.000 claims abstract description 43
- 238000004458 analytical method Methods 0.000 claims abstract description 20
- 238000003745 diagnosis Methods 0.000 claims abstract description 13
- 238000012216 screening Methods 0.000 claims abstract description 4
- 230000011664 signaling Effects 0.000 claims abstract description 3
- 238000011156 evaluation Methods 0.000 claims description 39
- 238000013528 artificial neural network Methods 0.000 claims description 12
- 238000003062 neural network model Methods 0.000 claims description 9
- 238000011084 recovery Methods 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000000518 rheometry Methods 0.000 description 1
Images
Classifications
-
- H02J13/0006—
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/085—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution lines, e.g. overhead
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
-
- 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
- Y04S10/52—Outage or fault management, e.g. fault detection or location
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
A power grid dispatching fault processing aid decision method comprises the following steps: fault diagnosis: constructing a regional power grid topological graph, and establishing a power grid fault information association table for storing an association relation between one fault occurrence and corresponding fault information; screening characteristic information corresponding to OPEN3000 when a fault occurs, searching a target element fault and a corresponding fault caused by the target element fault, calculating the goodness of fit between the characteristic information and the corresponding fault, and converting the goodness of fit into a first probability of occurrence of each fault; the characteristic information comprises a fault message, photon information, telemetering change and remote signaling deflection when a fault occurs; calling data of a fault recording device when a fault occurs, carrying out waveform analysis by using a wavelet analysis method, and calculating a second probability of occurrence of each fault by combining a fault analysis result obtained by a recording report; and weighting the first probability and the second probability of the fault occurrence, determining the fault range and the fault type according to the probability highest principle, and forming a fault processing scheme according to the fault range and the fault type.
Description
Technical Field
The invention relates to the field of power grid operation, in particular to a power grid dispatching fault processing aid decision method.
Background
At present, a power grid dispatching automation integration system (OPEN3000) only can display alarm and abnormal information, and has no functions of signal analysis, fault judgment, auxiliary decision and the like, information submitted to a dispatcher under the fault condition is often data and simple prompt in some lists, comprehensive analysis and system judgment of overall relevant alarm data are not available, and decision support for power grid dispatching is lacked. When a power grid fault occurs, the system can transmit a large amount of accident messages in time, but the fault cannot be analyzed and judged, and the fault condition cannot be directly prompted from the system. The fault diagnosis and the fault treatment completely depend on manual analysis and treatment, operation experience of scheduling personnel is relied on to a great extent, an auxiliary decision system aiming at the fault treatment is lacked, a direct and effective fault treatment decision scheme cannot be provided, and finally the efficiency and the accuracy of the power grid fault treatment in the power scheduling treatment are reduced.
Disclosure of Invention
The invention aims to solve the problems that the power grid dispatching automation integration system (OPEN3000) in the prior art only can display alarm and abnormal information and lacks decision support for power grid dispatching, and provides an auxiliary decision method for processing power grid dispatching faults.
The technical scheme adopted by the invention for solving the technical problems is as follows: a power grid dispatching fault processing aid decision method comprises the following steps:
1) fault diagnosis:
constructing a regional power grid topological graph, and establishing a power grid fault information association table for storing an association relation between one fault occurrence and corresponding fault information;
screening characteristic information corresponding to OPEN3000 when a fault occurs, searching a target element fault and a corresponding fault caused by the target element fault, calculating the goodness of fit between the characteristic information and the corresponding fault, and converting the goodness of fit into a first probability of occurrence of each fault; the characteristic information comprises a fault message, photon information, telemetering change and remote signaling deflection when a fault occurs;
calling data of a fault recording device when a fault occurs, carrying out waveform analysis by using a wavelet analysis method, and calculating a second probability of occurrence of each fault by combining a fault analysis result obtained by a recording report; the fault recording device records the change condition of the electrical quantity before and after the fault occurs;
weighting the first probability and the second probability of the fault occurrence, and determining a fault range and a fault type according to a probability highest principle;
2) forming a fault handling scheme;
extracting fault equipment, a wiring mode and a voltage grade as key characteristic quantities and quantizing according to the fault range and the fault type, and establishing a similar fault database by taking the actual fault of the power grid of the past region as a basis;
when a power grid fails, firstly, fault diagnosis is carried out, fault equipment, a wiring mode and voltage grade information are extracted, the similar fault database is automatically matched, whether pressure exists in each transfer path and whether overload exists in the equipment or not is checked, a plurality of preliminary fault processing schemes are formed through correction, the plurality of preliminary fault processing schemes are evaluated, and the optimal scheme is selected as a preparation processing scheme.
Further, the method for determining the fault range includes: the method comprises the steps of searching available power supply points of a power failure area when a fault occurs, radiating outwards through each port of a power failure network, finding a main transformer, reflecting, allowing a signal to disappear when a disconnected circuit breaker is met, analyzing the limitation of each element on the path by taking the path with the reflected signal as an effective path, analyzing the quantity of available loads and determining the effective power supply points, presetting all circuit breakers of the power failure area as a closed state, dividing all power supply ends, emitting a signal by an interface between the power failure area and a fault front interface, reflecting when the circuit breaker is met, storing the reflected path as an effective power supply recovery path, and storing the non-reflected path as a fault path.
Further, a BP neural network is selected for carrying out solution matching training; acquiring all switch positions of a regional power grid topological graph; selecting power grid faults and actual processing schemes which actually occur in recent years, and acquiring power grid parameters and switch positions related to the actual processing schemes when the power grid faults occur each time; taking the power grid parameters when each power grid fault occurs as input variables of the BP neural network, taking the switch positions related to the actual processing scheme as output variables of the BP neural network, and carrying out BP neural network training to obtain an applicable BP neural network model; selecting the preparation processing scheme in the step 2) to input the applicable BP neural network model, and outputting a final fault processing scheme by the applicable BP neural network model.
Further, the power grid parameters when each power grid fault occurs comprise a fault element type G, a power loss station wiring mode F, a maximum grid supply load Lg, a present peak value Lsm of a power loss load, a 3-day peak value Lsm-1, Lsm-2, Lsm-3 before the power loss load, a 220kV main variable capacitance-to-load ratio B of a supply area and a communication channel integrity rate W.
Further, the method for evaluating the plurality of preliminary fault handling schemes and selecting the optimal scheme as a preliminary handling scheme comprises the following specific steps: acquiring a load recovery number evaluation coefficient, an important load power loss number evaluation coefficient, an equipment operation task number evaluation coefficient, an operating equipment overrun evaluation coefficient and a section flow evaluation coefficient which are related to each preliminary fault processing scheme; weighting the load recovery number evaluation coefficient, the important load power loss number evaluation coefficient, the equipment operation task number evaluation coefficient, the operating equipment overrun evaluation coefficient and the section flow evaluation coefficient, and sequencing weighted results; and selecting a preliminary fault processing scheme with the optimal weighting result as a preparation processing scheme.
Further, the optimal weighting result is generated by combining the settings of the evaluation coefficients, and if the smaller the evaluation coefficient is, the better the preliminary fault handling scheme is, the preliminary fault handling scheme with the smallest weighting result is the preliminary fault handling scheme; if the larger the evaluation coefficient is, the better the preliminary fault handling scheme is, the preliminary fault handling scheme with the largest weighting result is the preliminary fault handling scheme.
The substantial effects of the invention are as follows: when a power grid fails, fault diagnosis is carried out, a power grid fault information association table is established, a similar library is automatically matched according to characteristic information of the failure, a target element fault and a corresponding fault caused by the target element fault are searched, a fault range and a fault type are determined, and a corresponding fault solution is provided, so that the power grid fault handling efficiency and accuracy of power dispatching processing are improved, meanwhile, the accident development can be effectively prevented, and the accident power failure range is reduced.
Drawings
Fig. 1 is a flowchart of fault diagnosis according to an embodiment of the present invention.
Fig. 2 is characteristic information corresponding to the OPEN3000 during a fault according to the embodiment of the present invention.
Fig. 3 is a schematic diagram illustrating a method for determining a fault range according to an embodiment of the present invention.
FIG. 4 is a diagram of similar library matching according to an embodiment of the present invention.
FIG. 5 is a flowchart illustrating a protocol optimization process according to an embodiment of the present invention.
FIG. 6 is a diagram of a fault-handling neural network architecture of the present invention.
Detailed Description
The technical solution of the present invention is further specifically described below by way of specific examples in conjunction with the accompanying drawings.
A power grid dispatching fault processing aid decision method comprises the following steps:
1) fault diagnosis:
constructing a regional power grid topological graph, and establishing a power grid fault information association table for storing an association relation between one fault occurrence and corresponding fault information;
as shown in fig. 1, screening feature information corresponding to OPEN3000 when a fault occurs, searching for a target element fault and a corresponding fault caused by the target element fault, calculating the goodness of fit between the feature information and the corresponding fault, and converting the goodness of fit into a first probability of occurrence of each fault; the characteristic information comprises a fault message, photon information, telemetering change and telesignalling deflection when a fault occurs, as shown in fig. 2;
calling data of a fault recording device when a fault occurs, carrying out waveform analysis by using a wavelet analysis method, and calculating a second probability of occurrence of each fault by combining a fault analysis result obtained by a recording report; the fault recording device records the change condition of the electrical quantity before and after the fault occurs;
and weighting the first probability and the second probability of the fault occurrence, and determining the fault range and the fault type according to the probability highest principle.
The method for determining the fault range comprises the steps of searching available power supply points of the whole power loss area, and searching a recovery path of the power loss load. The main transformer is found to be reflected by radiating outwards through each port of the power loss network, when the main transformer is disconnected, the signal disappears, the path with the reflected signal is an effective path, and the quota of each element on the path is analyzed, so that the number of available loads is analyzed and an effective power supply point is determined, as shown in fig. 3, the effective power supply point is JBSXV; and secondly, all the breakers in the power-losing area are preset to be closed, all the power supply ends are divided, a signal is transmitted by an interface between the power-losing area and the fault front surface, the signal is reflected when the breaker is disconnected, a reflection path is stored as an effective power supply restoration path, VO, XP, SR, BA and JK are provided, and then, according to the power supply capacity of a power supply point, a disconnection point is set, and the load is reasonably distributed.
2) Forming a fault handling scheme;
extracting fault equipment, a wiring mode and a voltage grade as key characteristic quantities and quantizing according to the fault range and the fault type, and establishing a similar fault database by taking the actual fault of the power grid of the past region as a basis;
as shown in fig. 4, when a power grid fails, firstly, fault diagnosis is performed, information of faulty equipment, a connection mode and a voltage level is extracted, the similar fault database is automatically matched, whether each transfer path has voltage, whether equipment has overload or not and whether power loss equipment remains or not are checked, a plurality of preliminary fault processing schemes are formed, the plurality of preliminary fault processing schemes are evaluated, and the optimal scheme is selected as a preliminary processing scheme.
The optimal scheme is selected as a preparation processing scheme, as shown in fig. 5, the specific method is as follows: acquiring a load recovery number evaluation coefficient, an important load power loss number evaluation coefficient, an equipment operation task number evaluation coefficient, an operating equipment overrun evaluation coefficient and a section flow evaluation coefficient which are related to each preliminary fault processing scheme; weighting the load recovery number evaluation coefficient, the important load power loss number evaluation coefficient, the equipment operation task number evaluation coefficient, the operating equipment overrun evaluation coefficient and the section flow evaluation coefficient, and sequencing weighted results; and selecting a preliminary fault processing scheme with the optimal weighting result as a preparation processing scheme. The optimal weighting result is generated by combining the setting of each evaluation coefficient, if the smaller the evaluation coefficient is, the more optimal the preliminary fault processing scheme is, the preliminary fault processing scheme with the minimum weighting result is the preliminary processing scheme; if the larger the evaluation coefficient is, the better the preliminary fault handling scheme is, the preliminary fault handling scheme with the largest weighting result is the preliminary fault handling scheme.
And selecting a BP neural network for solution matching training, acquiring all switch positions of a topological graph of the regional power grid, selecting power grid faults and actual processing schemes which actually occur in recent years, and acquiring power grid parameters when each power grid fault occurs and the switch positions related to the actual processing schemes.
As shown in fig. 6, all switch positions of the regional power grid topology are used as output variables (the on bit is 1, the off bit is 0), the method comprises the steps of taking fault element types (main transformers, buses, lines, voltage transformers and rheologies) G, a power loss station wiring mode (double buses, subsections, outer bridges, inner bridges and line transformer groups) F, a network supply highest load Lg, a power loss load current peak value Lsm, a power loss load previous 3 day peak value Lsm-1, Lsm-2, Lsm-3, a supply area 220kV main variable capacitance load ratio B and a communication channel integrity ratio W, 9 variables as input variables and initializing, selecting a power grid accident and an actual processing scheme which actually occur in nearly 3 years from an input sample, selecting the BP network input variables according to an empirical formula, setting the number of hidden layers of the BP neural network as 1 layer, setting the number of nodes of the hidden layers as 5 hidden layers, and training the BP neural network to obtain the BP neural network model suitable for the power loss station.
Selecting the preparation processing scheme in the step 2) to input the applicable BP neural network model, and outputting a final fault processing scheme by the applicable BP neural network model.
After anti-accident drilling, under the condition of single fault, the fault diagnosis is carried out by adopting the fault processing scheme provided by the invention, the fault diagnosis time is reduced from the average 10.6 minutes of manual analysis to the average 4.7 minutes of manual confirmation of tool analysis, the processing scheme setting time is reduced from the average 35.2 minutes of manual analysis to the average 18.9 minutes of automatic generation and complete manual selection, the overall time is reduced from the average 45.6 minutes to 23.6 minutes, and the time is shortened by 48.25%; under the condition of multiple and developmental faults, the fault diagnosis time is reduced from 15.1 minutes on average in manual analysis to 6.3 minutes on average in manual confirmation in tool analysis, the treatment scheme making time is reduced from 65.4 minutes in manual to 25.9 minutes in automatic generation and complete manual selection, the overall time is reduced from 80.5 minutes on average to 32.2 minutes, and the time is reduced by 60%; the invention can effectively prevent the accident development and reduce the power failure range of the accident by considering the combination of remote control operation of a dispatching end.
The above-described embodiment is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the scope of the invention as set forth in the claims.
Claims (5)
1. A power grid dispatching fault processing aid decision method is characterized by comprising the following steps:
1) fault diagnosis:
constructing a regional power grid topological graph, and establishing a power grid fault information association table for storing an association relation between one fault and corresponding fault information;
screening characteristic information corresponding to OPEN3000 when a fault occurs, searching a target element fault and a corresponding fault caused by the target element fault, calculating the goodness of fit between the characteristic information and the corresponding fault, and converting the goodness of fit into a first probability of occurrence of each fault; the characteristic information comprises a fault message, photon information, telemetering change and remote signaling deflection when a fault occurs;
calling data of a fault recording device when a fault occurs, carrying out waveform analysis by using a wavelet analysis method, and calculating a second probability of occurrence of each fault by combining a fault analysis result obtained by a recording report; the fault recording device records the change condition of the electrical quantity before and after the fault occurs;
weighting the first probability and the second probability of the fault occurrence, and determining a fault range and a fault type according to a probability highest principle;
2) forming a fault handling scheme;
extracting fault equipment, a wiring mode and a voltage grade as key characteristic quantities and quantizing according to the fault range and the fault type, and establishing a similar fault database by taking the actual fault of the power grid of the past region as a basis;
when a power grid fails, firstly, fault diagnosis is carried out, fault equipment, a wiring mode and voltage grade information are extracted, the similar fault database is automatically matched, whether each transfer path has pressure or not, whether equipment has overload or not and whether residual power-losing equipment exists or not are checked, a plurality of preliminary fault processing schemes are formed and evaluated, and the optimal scheme is selected as a preparation processing scheme;
the method comprises the following steps of evaluating a plurality of preliminary fault processing schemes, and selecting an optimal scheme as a preparation processing scheme, wherein the specific method comprises the following steps:
acquiring a load recovery number evaluation coefficient, an important load power loss number evaluation coefficient, an equipment operation task number evaluation coefficient, an operating equipment overrun evaluation coefficient and a section flow evaluation coefficient which are related to each preliminary fault processing scheme;
weighting the load recovery number evaluation coefficient, the important load power loss number evaluation coefficient, the equipment operation task number evaluation coefficient, the operating equipment overrun evaluation coefficient and the section flow evaluation coefficient, and sequencing weighted results; and selecting a preliminary fault processing scheme with the optimal weighting result as a preparation processing scheme.
2. The power grid dispatching fault handling aid decision method according to claim 1, wherein the method for determining the fault range is as follows:
the method comprises the steps of searching available power supply points of a power failure area when a fault occurs, radiating outwards through each port of a power failure network, finding a main transformer, reflecting, allowing a signal to disappear when a disconnected circuit breaker is met, analyzing the limitation of each element on the path by taking the path with the reflected signal as an effective path, analyzing the quantity of available loads and determining the effective power supply points, presetting all circuit breakers of the power failure area as a closed state, dividing all power supply ends, emitting a signal by an interface between the power failure area and a fault front interface, reflecting when the circuit breaker is met, storing the reflected path as an effective power supply recovery path, and storing the non-reflected path as a fault path.
3. The grid dispatching fault handling aid decision method according to claim 1,
selecting a BP neural network to carry out solution matching training;
acquiring all switch positions of a regional power grid topological graph;
selecting power grid faults and actual processing schemes which actually occur in recent years, and acquiring power grid parameters and switch positions related to the actual processing schemes when the power grid faults occur each time; taking the power grid parameters when each power grid fault occurs as input variables of the BP neural network, taking the switch positions related to the actual processing scheme as output variables of the BP neural network, and carrying out BP neural network training to obtain an applicable BP neural network model;
selecting the preparation processing scheme in the step 2) to input the applicable BP neural network model, and outputting a final fault processing scheme by the applicable BP neural network model.
4. The power grid dispatching fault handling assistant decision-making method according to claim 3, wherein the power grid parameters at each occurrence of a power grid fault include a fault element type G, a power loss station wiring mode F, a grid supply highest load Lg, a power loss load today peak value Lsm, a power loss load 3 day previous peak value Lsm-1, Lsm-2, Lsm-3, a supply area 220kV main variable capacitance-to-load ratio B and a communication channel integrity ratio W.
5. The power grid dispatching fault handling aid decision-making method according to claim 1, wherein the optimal weighting result is generated by combining settings of evaluation coefficients, and if the smaller the evaluation coefficient is, the better the preliminary fault handling scheme is, the preliminary fault handling scheme with the smallest weighting result is the preliminary handling scheme;
if the larger the evaluation coefficient is, the better the preliminary fault handling scheme is, the preliminary fault handling scheme with the largest weighting result is the preliminary fault handling scheme.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810926198.4A CN109474067B (en) | 2018-08-14 | 2018-08-14 | Power grid dispatching fault processing aid decision-making method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810926198.4A CN109474067B (en) | 2018-08-14 | 2018-08-14 | Power grid dispatching fault processing aid decision-making method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109474067A CN109474067A (en) | 2019-03-15 |
| CN109474067B true CN109474067B (en) | 2022-01-14 |
Family
ID=65661438
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810926198.4A Active CN109474067B (en) | 2018-08-14 | 2018-08-14 | Power grid dispatching fault processing aid decision-making method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109474067B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110854843B (en) * | 2019-10-15 | 2023-08-18 | 保定市冀能电力自动化设备有限公司 | Intelligent power distribution network metering and fault protection demarcation switch controller control calculation method |
| CN111107050B (en) * | 2019-10-22 | 2021-10-22 | 国网浙江省电力有限公司电力科学研究院 | Distributed wave recording method and device for power distribution network dynamic simulation system |
| CN111082401B (en) * | 2019-11-15 | 2022-07-08 | 国网河南省电力公司郑州供电公司 | Self-learning mechanism-based power distribution network fault recovery method |
| CN111064620A (en) * | 2019-12-20 | 2020-04-24 | 广东电网有限责任公司 | Power grid multimedia conference room equipment maintenance method and system based on operation and maintenance knowledge base |
| CN112649696A (en) * | 2020-10-26 | 2021-04-13 | 国网河北省电力有限公司邢台供电分公司 | Power grid abnormal state identification method |
| CN112632505A (en) * | 2020-12-18 | 2021-04-09 | 中国南方电网有限责任公司 | Power grid dispatcher login authentication system based on big data analysis and face recognition |
| CN113222140B (en) * | 2021-05-10 | 2022-09-20 | 重庆邮电大学 | C4.5 algorithm and BP neuron-based power distribution network fault auxiliary decision-making method |
| CN116990744B (en) * | 2023-09-25 | 2023-12-05 | 北京志翔科技股份有限公司 | Electric energy meter detection method, device, equipment and medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105224667A (en) * | 2015-10-10 | 2016-01-06 | 国家电网公司 | Based on multistation end fault diagnosis and the aid decision-making method of electrical network intelligent monitoring information |
-
2018
- 2018-08-14 CN CN201810926198.4A patent/CN109474067B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105224667A (en) * | 2015-10-10 | 2016-01-06 | 国家电网公司 | Based on multistation end fault diagnosis and the aid decision-making method of electrical network intelligent monitoring information |
Non-Patent Citations (1)
| Title |
|---|
| 《城市电网智能调度故障辅助决策系统设计开发》;靖宇宸等;《电力系统保护与控制》;20160416;第44卷(第8期);第133-136页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109474067A (en) | 2019-03-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109474067B (en) | Power grid dispatching fault processing aid decision-making method | |
| CN103823433A (en) | Method for realizing relay protection equipment on-line monitoring by use of communication process analysis | |
| CN103871004A (en) | Power distribution network failure cause analyzing method based on expert system and D-S evidence theory | |
| CN112467724A (en) | Low-voltage distribution network fault studying and judging method | |
| CN114977483B (en) | Fault diagnosis system for intelligent power grid regulation control equipment | |
| CN108710099A (en) | Capacitance type potential transformer monitoring alarm method and system | |
| CN105548744A (en) | Substation equipment fault identification method based on operation-detection large data and system thereof | |
| CN105245001A (en) | Event-driven intelligent alarm processing method and device for transformer station accidents | |
| CN117332215B (en) | High-low voltage power distribution cabinet abnormal fault information remote monitoring system | |
| CN112098715A (en) | Electric energy monitoring and early warning system based on 5G and corrected GCN diagram neural network | |
| CN108512222A (en) | A kind of intelligent substation complex automatic system | |
| CN114814467A (en) | Power distribution network fault positioning method | |
| CN112905670B (en) | Electric energy meter system for indoor power failure fault research and judgment and indoor power failure fault research and judgment method | |
| CN117614141B (en) | Multi-voltage-level coordination management method for power distribution network | |
| CN108594075B (en) | Power distribution network power failure fault positioning method based on improved ant colony algorithm | |
| CN111060780B (en) | Probability evaluation method for fault tolerance online fault location of power distribution network | |
| CN115588961B (en) | Setting value self-adaptive setting method based on power distribution network full-model protection | |
| CN112256922A (en) | Fault power failure rapid identification method and system | |
| CN110889649A (en) | Intelligent power distribution monitoring management system with high reliability | |
| CN116739550A (en) | Intelligent auxiliary decision-making method and system for rush repair | |
| CN111751655B (en) | Fault self-healing method and device for distribution line, computer equipment and storage medium | |
| CN104392102A (en) | Automatic extraction and calculation method for online monitoring indexes of relaying protection equipment | |
| CN104375482A (en) | Relay protection device online evaluation method | |
| CN106709158B (en) | Performance improvement method of power grid feeder automation system | |
| CN102571519B (en) | Intelligent scheduling device suitable for carrier communication of ring distribution network and method therefor |
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 |