CN105093063B - The online electric network failure diagnosis method judged based on the combination of multi-source data feature unit - Google Patents
The online electric network failure diagnosis method judged based on the combination of multi-source data feature unit Download PDFInfo
- Publication number
- CN105093063B CN105093063B CN201510477094.6A CN201510477094A CN105093063B CN 105093063 B CN105093063 B CN 105093063B CN 201510477094 A CN201510477094 A CN 201510477094A CN 105093063 B CN105093063 B CN 105093063B
- Authority
- CN
- China
- Prior art keywords
- data
- rule
- feature unit
- fault
- source
- 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.)
- Expired - Fee Related
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 30
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000004458 analytical method Methods 0.000 claims description 15
- 238000012546 transfer Methods 0.000 claims description 10
- 238000012544 monitoring process Methods 0.000 claims description 8
- 230000011664 signaling Effects 0.000 claims description 6
- 230000002123 temporal effect Effects 0.000 claims description 6
- 238000013506 data mapping Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 4
- 230000000737 periodic effect Effects 0.000 claims description 3
- 230000009471 action Effects 0.000 description 5
- 230000008859 change Effects 0.000 description 5
- 238000006073 displacement reaction Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000004888 barrier function Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000009131 signaling function Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
Classifications
-
- 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
- Supply And Distribution Of Alternating Current (AREA)
- Remote Monitoring And Control Of Power-Distribution Networks (AREA)
Abstract
The invention discloses a kind of online electric network failure diagnosis methods judged based on the combination of multi-source data feature unit, include the following steps：S1 obtains individual data source from multi-source data, according to the characteristics of data source, each data source is split out multiple feature units；S2 is combined by the feature unit to different data sources, forms multinomial fault reasoning rule, will be in fault reasoning rule storage to rule base；S3, the operation data of equipment, matches the fault reasoning rule in rule base, if successful match, is alerted, and provide basis for estimation one by one during online acquisition operation of power networks.This method can timely and effectively diagnose electric network fault, and timely and accurately send out alarm, meet the real time execution demand of power grid well.
Description
Technical field
The present invention relates to a kind of online electric network failure diagnosis methods more particularly to one kind to be based on multi-source data feature unit group
The online electric network failure diagnosis method judged is closed, belongs to dispatching automation of electric power systems technical field.
Background technology
With the continuous development of computer technology, network communications technology, automation system for the power network dispatching has also obtained considerable
Development, access capability is stronger, processing speed faster, run relatively reliable, application software intelligent level higher.At present, it saves
The dispatch automated system of grade and Yi Shang scheduling institution is substantially finished the construction of unified platform, makes all kinds of of different manufacturers
Using being deployed on same set of platform, the standard interface provided by platform realizes the data sharing between application.
In order to meet the needs of power grid real time execution and safe operation, the diagnostic method of electric network fault by diagnosing hair offline
Open up inline diagnosis.In addition the construction of dispatch automated system unified platform, the online diagnosing technique of support shaft of electric network fault is also from original
The application detection that comes, using alarm develop into using detection, it is unified alert, by the synthesis to multi-source warning information and analysis,
The accuracy of alarm and the comprehensive of fault message have larger promotion.
But since the diagnosis of current failure is still realized by each application, using single application data, failure is caused to examine
The basic data quality of the disconnected result heavy dependence single application.In order to ensure the accurate index using alarm function, event is improved
Barrier diagnosis and the efficiency of alarm, are necessarily required to the wrong report two when reliably quoting and reduce non-faulting when ensureing electric network fault
Compromise is carried out in a striving direction.Electric network fault detects the built-in function for belonging to the application, and criterion is also distributed in using soft
In part, very big difficulty is brought to the criterion management of user.Since each application data characteristics is different, judgment method is different, examines
Disconnected process is opaque, and diagnosis is realized with alerting by different function, is also counteracted that fault message is comprehensive and is further improved.It cannot
Timely and effectively electric network fault is diagnosed, and timely and accurately sends out alarm, having seriously affected the real time execution of power grid needs
It asks.
Invention content
In view of the deficiencies of the prior art, the technical problems to be solved by the invention are to provide a kind of special based on multi-source data
Levy the online electric network failure diagnosis method that unit combination judges.
For achieving the above object, the present invention uses following technical solutions：
A kind of online electric network failure diagnosis method judged based on the combination of multi-source data feature unit, is included the following steps：
S1 obtains individual data source from multi-source data, and according to the characteristics of data source, each data source is split out more
A feature unit；
S2 is combined by the feature unit to different data sources, multinomial fault reasoning rule is formed, by the failure
In inference rule storage to rule base；
S3, the operation data of equipment, carries out the fault reasoning rule in rule base during online acquisition operation of power networks
It matches, if successful match, is alerted, and basis for estimation is provided one by one.
Wherein more preferably, in step s 2, multinomial fault reasoning rule is formed, after being stored in rule base, is led to
It crosses and historical data is analyzed and processed, carry out the perfect of the rule base.
Wherein more preferably, by being analyzed and processed to historical data, the perfect of the rule base is carried out, including walking as follows
Suddenly：
S21 obtains the historical data stored in electric system；
S22 analyzes the warning information in historical data, splits out multiple feature units；
The multiple feature units split out are combined into a fault reasoning rule and are searched in rule base by S23, when
When the fault reasoning rule being not present in rule base, the fault reasoning rule is added in rule base；Otherwise, step is turned to
Rapid S24；
S24 repeats step S21~S23, until the historical data stored in electric system traversal is finished.
Wherein more preferably, it is the fault reasoning rule setting priority in rule base according to the operation demand of electric system,
When the operation data of primary equipment meets multinomial fault reasoning rule simultaneously, the high fault reasoning rule precedence of priority carries out
Display.
Wherein more preferably, in step s3, the data during operation of power networks are obtained online, and the failure in rule base is pushed away
Reason rule is matched one by one, if successful match, is alerted, and provide basis for estimation, included the following steps：
S31 conjugates signal as the trigger condition of fault diagnosis using remote signalling, is found by Topology Analysis Based relevant primary
Equipment brings primary equipment into monitoring range；
S32 carries out the primary equipment for being included in monitoring range periodic scan, during online acquisition operation of power networks
The operation data of the primary equipment；
S33, the operation data of the primary equipment of acquisition carries out the fault reasoning rule that is stored in rule base by
One matching, is alerted, and provide basis for estimation when the condition is satisfied.
Wherein more preferably, in step s3, following sub-step is further included：
S34, if the operation data of the primary equipment obtained meets a plurality of fault reasoning rule in rule base, choosing simultaneously
It selects the high fault reasoning rule of priority to be alerted, continues to monitor the primary equipment, if it find that the institute obtained
The higher fault reasoning rule of operation data matching priority of primary equipment is stated, alarm before is updated.
Wherein more preferably, in step s3, following sub-step is further included：
S35, if the operation data of acquired primary equipment is advised more than any fault reasoning is still unsatisfactory for after setting time
Then, stop the intermittent scanning to the primary equipment.
Wherein more preferably, the feature unit is the minimal characteristic member of data mapping.
Wherein more preferably, the multi-source data includes steady state data, dynamic data, Temporal Data, protection information and power transformation
It stands and alerts the data that direct transfer.
The online electric network failure diagnosis method provided by the present invention judged based on the combination of multi-source data feature unit, from more
Individual data source is obtained in source data, according to the characteristics of individual data source, each data source is split out into multiple feature units.It is logical
It crosses and the feature unit of different data sources is combined, the fault reasoning rule of a set of various fault conditions of covering is formed, by it
It stores in rule base, ensure that when the equipment in power grid breaks down, can accurately analyze fault message.Finally, online
The data during operation of power networks are obtained, the fault reasoning rule in rule base are matched one by one, the condition of satisfaction is accused
It is alert, and basis for estimation is provided, timely and effectively electric network fault can be diagnosed, and timely and accurately send out alarm.
Description of the drawings
Fig. 1 is the online electric network failure diagnosis method provided by the present invention judged based on the combination of multi-source data feature unit
Flow chart.
Specific embodiment
Detailed specific description is carried out to the technology contents of the present invention in the following with reference to the drawings and specific embodiments.
As shown in Figure 1, the online electric network fault provided by the present invention judged based on the combination of multi-source data feature unit is examined
Disconnected method, includes the following steps：First, individual data source is obtained from multi-source data, it, will be every according to the characteristics of individual data source
A data source splits out multiple feature units.Then, it is combined by the feature unit to different data sources, forms a set of cover
The fault reasoning rule of various fault conditions is covered, is stored in rule base.Finally, online during acquisition operation of power networks
Data match the fault reasoning rule in rule base one by one, and the condition of satisfaction is alerted, and provides basis for estimation.Under
Detailed specific description is done in face of this process.
S1 obtains individual data source from multi-source data, according to the characteristics of individual data source, each data source is split out
Multiple feature units.
According to provincial and above scheduling institution main website data access and using deployment scenario, when selecting device fails
Data below source is as signature analysis and the object split：Steady state data, dynamic data, Temporal Data, protection information and power transformation
It stands and alerts the data that direct transfer.Steady state data in acquisition power grid during equipment fault, dynamic data, Temporal Data, protection information in real time
The data that direct transfer are alerted with substation.It, will be per number from multiple data sources after the data (multi-source data) for obtaining multiple data sources
It is opened according to the data separating in source, in electric system, the data of each data source have the characteristics of exclusive, according to individual data source
Each data source is split out multiple feature units by feature.Feature unit is that the minimal characteristic of data mapping is first, online power grid
The fault data (operation data after device fails) of middle arbitrary equipment can be made of single or multiple feature units.
Wherein, steady state data is most reliable alarm source in scheduling station, has wide coverage, real-time is higher, mould
The advantages that type is complete, reflection power grid overall operation situation is always the Main Basiss of main website side fault diagnosis, the drawback is that data
When sampling different, the sampling period is longer.Steady state data includes breaker, the action message of banister and active and reactive, voltage change
Change feature.
Power grid dynamic data from PMU has many advantages, such as that the whole network samples, is high using frequency height, data precision simultaneously, right
Very strong support is capable of providing in Circuit fault diagnosis, also has the diagnosis of other type equipment failures with reference to work
With, but the considerations of for cost and volume of transmitted data, being limited in scope for PMU coverings is concentrated mainly on 500kV substations
In.
With the independent networking of oscillograph is concentrated, power grid Temporal Data is greatly improved to the speed of master station transmission, comprehensive
First and second information can diagnose the support provided quasi real time for main station failure.
Protection information is acquired by substation system of notifying and is transferred to main website, all secondary informations, does not have independent progress
The ability of fault diagnosis, but its protection exit signal and signal type can play booster action for accident analysis.
Substation alerts the function that direct transfers and plant stand monitoring of tools information is transferred to main website in the form of text with text mode, main
Side of standing does not correspond to model, is only capable of positioning primary equipment by key message therein and distinguishing signal type
Know.
With reference to power grid in the process of running, it the characteristics of each data source, selects to obtain steady state data, the dynamic of equipment in real time
Data, Temporal Data, protection information and substation alert the data that direct transfer, and carry out comprehensive analysis processing to multi-source data, can
Analysis electric network fault comprehensively, and alarm is timely and accurately sent out, meet the real time execution demand of power grid.Multi-source data is divided
From rear, according to the characteristics of individual data source, each data source can be split out multiple feature units.During grid collapses,
The data of each application can be reflected, for example the displacement information of the breaker of steady state data, the accident resultant signal of plant stand move
Make information, equipment telemetering change information etc., the mode of connection is different, the difference of operating condition and breaker actuation situation before failure
Difference, performance during failure are also different.In embodiment provided by the present invention, feature unit should meet claimed below：
1) feature unit represents data mapping feature；
2) feature unit is the minimal characteristic member of data mapping, and the fault data of arbitrary equipment can be by online power grid
Single or multiple feature unit compositions.
For example, for the transmission line of electricity of 3/2 mode of connection, part that steady state data is split out according to the data characteristics of itself
Feature unit includes：Side switch is by closing variation, middle switch is graded by conjunction variation, all switches by closing to become.
The Partial Feature unit that dynamic data feature is split out includes：Circuit three-phase current is more than zero value, circuit A phases electricity
Stream mutates, circuit B phase currents mutate, circuit C phase currents mutate, circuit three-phase current is below zero value
Deng.
Substation alerts the Partial Feature unit that data are split out that direct transfers and includes：Tripping outlet, is broken at reclosing action outlet
Road device failure protection action outlet, three bounces are for export etc..
S2 is combined by the feature unit to different data sources, is formed multinomial fault reasoning rule, is stored to
In rule base.
Feature unit, which is combined, can generate Failure Diagnostic Code, the whole the type equipment of Failure Diagnostic Code support,
The multiple grades such as certain voltage class the type equipment, certain plant stand, specific equipment, to cover whole network equipment.It is carried in the present invention
In the embodiment of confession, according to the operation demand of electric system, to the judgment rule of various kinds of equipment failure under the various modes of connection into
Row is established, and a plurality of rule can be established for same category of device failure, takes into account reliable alarm and complex fault analysis ability.
Wherein, reliable alarm type rule is under the premise of meeting accurately, using minimum feature unit, is made full use of more
The advantage of source data avoids the influence of the single source quality of data.Complicated analytic type rule is using more characteristic element, is realized
Comprehensive analysis of complex electric network fault condition.
In embodiment provided by the present invention, a specific fault reasoning rule includes following information：
1) feature unit before one or more failures；
2) feature unit after one or more failures；
3) one or more fault signature units；
4) only one failure analysis result.
Such as：For reliably alarm type rule：The feature unit that 220kV circuit permanent fault rules include is combined as：
Before failure：It runs (stable state)；
After failure：(stable state) out of service；
Feature：At least one switch closes variation (stable state), plant stand accident always acts (stable state or alarm direct transfer)；
Analysis result：Permanent fault occurs for 22kV circuits.
For complicated analytic type rule：The feature unit that 220kV circuit permanent fault rules include is combined as：
Before failure：It runs (stable state)；
After failure：(stable state) out of service；
Feature：Both sides switch conjunction variation (stable state), both sides switch point, which become, closes (stable state), circuit re-switching signalizing activity (surely
State or alarm are direct transferred or protects), plant stand accident always acts (stable state or alarm direct transfer), A phase currents exist and are mutated (WAMS)；
Analysis result：The short circuit of 22kV*** circuit A phases, reclosing failure, three jump.
For complicated analytic type rule：The feature unit that 220kV circuit permanent fault rules include combines：
Before failure：It runs (stable state)；
After failure：(stable state) out of service；
Feature：Single sided switched closes variation (stable state), plant stand accident always acts (stable state or alarm direct transfer), breaker failure is protected
Shield signalizing activity (stable state monitors or alarm is direct transferred or protected), adjacent busbar are out of service；
Analysis result：220kV*** line faults, side breaker movement cause more equipment to have a power failure.
It is combined by the feature unit to different data sources, forms multinomial fault reasoning rule, be stored to rule
Then in library, later, by being analyzed and processed to the historical data stored in electric system, the perfect of rule base is carried out, specifically
Include the following steps：
S21 obtains the historical data stored in electric system.
S22 analyzes the warning information in historical data, splits out multiple feature units.
The multiple feature units split out are combined into a fault reasoning rule and are searched in rule base by S23, when
There is no during this fault reasoning rule, it is added in rule base；Otherwise, step S24 is turned to.
S24 repeats step S21~S23, until the historical data stored in electric system traversal is finished, rule base is complete
It is kind to complete.
It is the fault reasoning in rule base according to the operation demand of electric system in embodiment provided by the present invention
Rule setting priority, when the operation data of primary equipment meets multinomial fault reasoning rule simultaneously, the high failure of priority
Inference rule is preferentially shown.
S3, the operation data of equipment, carries out one by one the application rule in rule base during online acquisition operation of power networks
Judge, alerted when the condition is satisfied, and basis for estimation is provided.
After the storage to rule base of fault reasoning rule, the operation data of equipment during online acquisition operation of power networks,
Application rule in rule base is matched one by one, (successful match) is alerted when the condition is satisfied, and provide judge according to
According to specifically comprising the following steps：
S31 conjugates signal as the trigger condition of fault diagnosis using remote signalling, finds correlation by Topology Analysis Based and once set
It is standby, bring primary equipment into monitoring range.
Remote signaling function is commonly used to measure lower column signal：The position signal of switch, is protected power transformer interior fault integrated signal
Action signal, communication equipment operation conditions signal, adjustable transformer tap position signal of protection unit etc..Provided by the present invention
Embodiment in, when these measuring signals change, occur remote signalling displacement signal.It is examined using remote signalling displacement signal as failure
After there is remote signalling displacement signal, the plant stand mode of connection, equipment and breaker are obtained by Topology Analysis Based for disconnected trigger condition
Bi-directional association relationship finds relevant primary equipment, brings primary equipment into monitoring range.
S32 carries out the primary equipment for being included in monitoring range periodic scan, primary during online acquisition operation of power networks
The operation data of equipment.
S33 carries out the fault reasoning rule that is stored in rule base the operation data of the primary equipment of acquisition one by one
Match, alerted when the condition is satisfied, and basis for estimation is provided.
S34, if the operation data of the primary equipment obtained meets a plurality of fault reasoning rule in rule base, choosing simultaneously
It selects the high fault reasoning rule of priority to be alerted, and continue to monitor the primary equipment, if it find that obtain one
The higher fault reasoning rule of operation data matching priority of secondary device, is updated alarm before.
S35, if the operation data of acquired primary equipment be more than setting time after be still unsatisfactory for any bar fault reasoning
Rule stops the intermittent scanning to the primary equipment.
In conclusion the online electric network failure diagnosis provided by the present invention judged based on the combination of multi-source data feature unit
Method ensure that the comprehensive, accurate of warning information by the data for obtaining multiple data sources.Single number is obtained from multi-source data
According to source, according to the characteristics of individual data source, each data source is split out into multiple feature units.Then, by different data
The feature unit in source is combined, and is formed the fault reasoning rule of a set of various fault conditions of covering, is stored to rule base
In.It ensure that when the operation data failure of equipment, can timely and accurately analyze fault message.Finally, it is online to obtain
Data during operation of power networks match the fault reasoning rule in rule base one by one, and the condition of satisfaction is alerted, and
Basis for estimation is provided.This method can timely and effectively diagnose electric network fault, and timely and accurately send out alarm, well
Meet the real time execution demand of power grid.
Above to the online electric network failure diagnosis side provided by the present invention judged based on the combination of multi-source data feature unit
Method is described in detail.For those of ordinary skill in the art, under the premise of without departing substantially from true spirit
It to any obvious change that it is done, will all form to infringement of patent right of the present invention, corresponding law duty will be undertaken
Appoint.
Claims (8)
- A kind of 1. online electric network failure diagnosis method judged based on the combination of multi-source data feature unit, it is characterised in that including such as Lower step：S1 obtains individual data source from multi-source data, and according to the characteristics of data source, each data source is split out multiple spies Unit is levied, wherein the feature unit is the minimal characteristic member of data mapping；S2 is combined by the feature unit to different data sources, multinomial fault reasoning rule is formed, by the fault reasoning In rule storage to rule base；S3, the operation data of equipment, carries out one by one the fault reasoning rule in rule base during online acquisition operation of power networks Matching, if successful match, is alerted, and provide basis for estimation.
- 2. the online electric network failure diagnosis method judged as described in claim 1 based on the combination of multi-source data feature unit, It is characterized in that：In step s 2, multinomial fault reasoning rule is formed, after being stored in rule base, by being carried out to historical data Analyzing and processing, carries out the perfect of the rule base.
- 3. the online electric network failure diagnosis method judged as claimed in claim 2 based on the combination of multi-source data feature unit, It is characterized in that, by analyzing and processing historical data, carrying out the perfect of the rule base, including the following steps：S21 obtains the historical data stored in electric system；S22 analyzes the warning information in historical data, splits out multiple feature units；The multiple feature units split out are combined into a fault reasoning rule and are searched in rule base, work as rule by S23 When the fault reasoning rule being not present in library, the fault reasoning rule is added in rule base；Otherwise, step is turned to S24；S24 repeats step S21~S23, until the historical data stored in electric system traversal is finished.
- 4. the online electric network failure diagnosis method judged as described in claim 1 based on the combination of multi-source data feature unit, It is characterized in that：It is the fault reasoning rule setting priority in rule base, when the fortune of primary equipment according to the operation demand of electric system When row data meet multinomial fault reasoning rule simultaneously, the high fault reasoning rule precedence of priority is shown.
- 5. the online electric network failure diagnosis method judged as described in claim 1 based on the combination of multi-source data feature unit, It is characterized in that in step s3, the online data obtained during operation of power networks carry out the fault reasoning rule in rule base It matches one by one, if successful match, is alerted, and basis for estimation is provided, included the following steps：S31 conjugates signal as the trigger condition of fault diagnosis using remote signalling, is found and relevant once set by Topology Analysis Based It is standby, bring primary equipment into monitoring range；S32 carries out the primary equipment for being included in monitoring range periodic scan, described during online acquisition operation of power networks The operation data of primary equipment；S33 carries out the fault reasoning rule that is stored in rule base the operation data of the primary equipment of acquisition one by one Match, alerted when the condition is satisfied, and basis for estimation is provided.
- 6. the online electric network failure diagnosis method judged as described in claim 1 based on the combination of multi-source data feature unit, It is characterized in that in step s3, further including following sub-step：S34 if the operation data of the primary equipment obtained meets a plurality of fault reasoning rule in rule base simultaneously, is selected excellent The high fault reasoning rule of first grade is alerted, and continues to monitor the primary equipment, if it find that obtain described one The higher fault reasoning rule of operation data matching priority of secondary device, is updated alarm before.
- 7. the online electric network failure diagnosis method judged as described in claim 1 based on the combination of multi-source data feature unit, It is characterized in that in step s3, further including following sub-step：S35, if the operation data of acquired primary equipment stops more than any fault reasoning rule is unsatisfactory for after setting time To the intermittent scanning of the primary equipment.
- 8. the online electric network failure diagnosis method judged as described in claim 1 based on the combination of multi-source data feature unit, It is characterized in that：The multi-source data includes steady state data, dynamic data, Temporal Data, protection information and substation and alerts the data that direct transfer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510477094.6A CN105093063B (en) | 2015-08-06 | 2015-08-06 | The online electric network failure diagnosis method judged based on the combination of multi-source data feature unit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510477094.6A CN105093063B (en) | 2015-08-06 | 2015-08-06 | The online electric network failure diagnosis method judged based on the combination of multi-source data feature unit |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105093063A CN105093063A (en) | 2015-11-25 |
| CN105093063B true CN105093063B (en) | 2018-06-26 |
Family
ID=54573945
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510477094.6A Expired - Fee Related CN105093063B (en) | 2015-08-06 | 2015-08-06 | The online electric network failure diagnosis method judged based on the combination of multi-source data feature unit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105093063B (en) |
Families Citing this family (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105510866B (en) * | 2015-11-27 | 2018-10-12 | 江苏省电力公司电力科学研究院 | A kind of fault monitoring method of automatic calibration of electric energy meter line |
| CN106909974B (en) * | 2015-12-22 | 2020-11-03 | 中国电信股份有限公司 | Method, data analyzer and system for analyzing multi-terminal state |
| CN106056315B (en) * | 2016-07-05 | 2019-08-16 | 国电南瑞科技股份有限公司 | A kind of analysis and evaluation system and method for the grid faults characteristics quality of data |
| CN106787169B (en) * | 2016-11-23 | 2019-12-13 | 国网福建省电力有限公司 | Method for diagnosing telemetering fault of transformer substation by using multiple data source comparison technology |
| CN106771883B (en) * | 2017-01-22 | 2022-07-26 | 中国电力科学研究院 | Multi-source information power distribution fault positioning method and system based on cloud |
| CN110095144B (en) * | 2018-01-30 | 2021-07-09 | 中电长城(长沙)信息技术有限公司 | Method and system for identifying local fault of terminal equipment |
| CN110109038B (en) * | 2019-04-19 | 2020-10-20 | 中国南方电网有限责任公司超高压输电公司南宁局 | Relay protection device measurement loop detection method and device |
| CN112964960B (en) * | 2021-02-04 | 2024-04-12 | 国网上海市电力公司 | Power grid fault diagnosis method based on multi-source data fusion of scheduling fault fingerprint library |
| CN113030652A (en) * | 2021-04-19 | 2021-06-25 | 国网上海市电力公司 | KMP algorithm-based power distribution network line fault identification and processing method |
| CN113433419B (en) * | 2021-06-04 | 2023-06-16 | 国电南瑞南京控制系统有限公司 | Intelligent alarm method and system based on polymorphic data cooperative processing |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5343155A (en) * | 1991-12-20 | 1994-08-30 | The Research And Development Institute, Inc. At Montana State University | Fault detection and location system for power transmission and distribution lines |
| CN103020219A (en) * | 2012-12-10 | 2013-04-03 | 广东电网公司电力科学研究院 | Network-based expert system tool for power grid fault diagnosis |
| CN103218756A (en) * | 2013-04-25 | 2013-07-24 | 国家电网公司 | Grid fault accident identification system |
| CN103427417A (en) * | 2013-07-31 | 2013-12-04 | 国电南瑞科技股份有限公司 | Power distribution network fault processing method based on multi-source information fusion |
| CN103728507A (en) * | 2013-11-18 | 2014-04-16 | 芜湖大学科技园发展有限公司 | Grid fault diagnosis method based on data mining |
-
2015
- 2015-08-06 CN CN201510477094.6A patent/CN105093063B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5343155A (en) * | 1991-12-20 | 1994-08-30 | The Research And Development Institute, Inc. At Montana State University | Fault detection and location system for power transmission and distribution lines |
| CN103020219A (en) * | 2012-12-10 | 2013-04-03 | 广东电网公司电力科学研究院 | Network-based expert system tool for power grid fault diagnosis |
| CN103218756A (en) * | 2013-04-25 | 2013-07-24 | 国家电网公司 | Grid fault accident identification system |
| CN103427417A (en) * | 2013-07-31 | 2013-12-04 | 国电南瑞科技股份有限公司 | Power distribution network fault processing method based on multi-source information fusion |
| CN103728507A (en) * | 2013-11-18 | 2014-04-16 | 芜湖大学科技园发展有限公司 | Grid fault diagnosis method based on data mining |
Non-Patent Citations (1)
| Title |
|---|
| "基于特征挖掘的电网故障诊断方法";李再华等;《中国电机工程学报》;20100405;第30卷(第10期);正文第18页第2栏 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105093063A (en) | 2015-11-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105093063B (en) | The online electric network failure diagnosis method judged based on the combination of multi-source data feature unit | |
| US10539618B2 (en) | High-voltage circuit breaker opening and closing time online monitoring apparatus, smart multi-dimensional big data analyzing expert system for high-voltage circuit breaker in power grid and method therefor | |
| CN106597231B (en) | GIS fault detection system and method based on Multi-source Information Fusion and deep learning network | |
| CN106093627B (en) | Digital transformer substation power quality event recording monitoring device and monitoring method | |
| CN111596170A (en) | Fault diagnosis comprehensive positioning method for intelligent distribution network | |
| CN106124935A (en) | Middle and low voltage network Fault Locating Method | |
| CN110048507B (en) | Automatic inspection method and system for power distribution automation system | |
| CN104753178A (en) | Power grid fault handling system | |
| CN111208385A (en) | Online fault layered diagnosis method for power grid | |
| CN102937675A (en) | Real-time data collecting and fault positioning system of power distribution network and working method thereof | |
| CN105974267B (en) | A kind of small current grounding fault positioning system and its method based on IEC 61850 | |
| CN103995172B (en) | Method for on-line monitoring of load current of GIS bus of substation | |
| CN111478324B (en) | Power station alarm analysis system | |
| CN202854252U (en) | Real-time data acquisition and fault locating system of power distribution network | |
| CN203691057U (en) | Arc online monitoring and protecting system | |
| CN103915898A (en) | Operation and maintenance method for intelligent substation network | |
| CN103472355A (en) | Distribution line short-circuit fault positioning system and method | |
| CN105606931A (en) | Quantum-genetic-algorithm-based fault diagnosis method for medium-voltage distribution network | |
| CN113922326B (en) | Bus/line grounding line selection wheel cutting method based on SCADA data | |
| CN113820567A (en) | Small-current grounding single-phase grounding fault processing, analyzing and positioning method and device | |
| CN203164360U (en) | Transformer device insulation online monitoring system | |
| CN112363025A (en) | Power distribution network single-phase earth fault diagnosis method and system | |
| CN112798906A (en) | System for identifying and positioning short-circuit fault of high-voltage line based on low-voltage power distribution | |
| CN109165837B (en) | Power protection and supply area grading operation risk analysis method based on main distribution integration | |
| CN116169778A (en) | Processing method and system based on power distribution network anomaly analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20180626 Termination date: 20180806 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |