CN111858852B - Full-error-point-prevention table checking method based on data similarity - Google Patents
Full-error-point-prevention table checking method based on data similarity Download PDFInfo
- Publication number
- CN111858852B CN111858852B CN202010644751.2A CN202010644751A CN111858852B CN 111858852 B CN111858852 B CN 111858852B CN 202010644751 A CN202010644751 A CN 202010644751A CN 111858852 B CN111858852 B CN 111858852B
- Authority
- CN
- China
- Prior art keywords
- equipment
- data
- primary
- similarity
- model
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/334—Query execution
- G06F16/3344—Query execution using natural language analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
- G06F16/2282—Tablespace storage structures; Management thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
- G06F16/287—Visualization; Browsing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention relates to a full-error-point-prevention table checking method based on data similarity, which comprises the following steps of: step S1, importing a CIME model file of the primary equipment, searching the association relation of the primary equipment by adopting a topological analysis algorithm, and performing similarity grouping; step S2, importing a secondary equipment data model, analyzing the name of a secondary device and the name of secondary equipment by adopting a natural language fuzzy matching algorithm, and acquiring the equipment attribute of the secondary equipment; step S3, importing the similarity-grouped CIME model files of the primary equipment and the analyzed data model of the secondary equipment into a database, and establishing an association relationship between the data of the primary equipment and the data of the secondary equipment by using an equipment relationship model to form an equipment association table; step S4, performing statistical analysis according to the obtained equipment association table to obtain auditing result data; step S5: and outputting the data of the audit result to an interface for displaying, and generating an audit report for consulting. The invention solves the problem that a large amount of time is spent on comparing the installation of equipment in manual checking, and improves the checking efficiency.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a full-defense error point table checking method based on data similarity.
Background
With the continuous and deep application of the transformer substation full anti-misoperation system, the works of switching, overhauling and the like on site become safer and faster, however, the secondary point table is the basic data of secondary anti-misoperation, and comprises a large amount of secondary equipment data such as pressing plates, air switches, handles, abnormal signals and the like on site, and the correctness of the point table directly influences the judgment result of the secondary anti-misoperation lockout logic, so that the correctness and integrity of the secondary point table data need to be checked by the secondary anti-misoperation system before the secondary anti-misoperation system is put into operation, so as to ensure the correctness of the secondary anti-misoperation execution.
In the existing auditing method, personnel with relay protection professional knowledge are used for auditing, and the auditing personnel model and check secondary anti-error point table data according to certain constraints according to the operation mode of primary equipment of a transformer substation, and the field installation conditions of secondary devices and secondary equipment (pressure plates, air switches, handles and abnormal signals) of a protection small room and a local cabinet so as to achieve the purposes of correctness and integrity of the secondary point table data. However, the manual auditing efficiency is low, the requirement on the technical quality of personnel is high, and the checking efficiency is seriously influenced.
Disclosure of Invention
In view of this, the present invention provides a method for checking a full error prevention dot table based on data similarity, which greatly shortens the time for checking the dot table and improves the error prevention efficiency of a secondary error prevention system.
In order to achieve the purpose, the invention adopts the following technical scheme:
a full error point prevention table checking method based on data similarity comprises the following steps:
step S1, importing a CIME model file of the primary equipment, searching the association relation of the primary equipment by adopting a topological analysis algorithm, and performing similarity grouping;
step S2, importing a secondary equipment data model, analyzing the name of a secondary device and the name of secondary equipment by adopting a natural language fuzzy matching algorithm, and acquiring the equipment attribute of the secondary equipment;
step S3, importing the similarity-grouped CIME model files of the primary equipment and the analyzed data model of the secondary equipment into a database, and establishing an association relationship between the data of the primary equipment and the data of the secondary equipment by using an equipment relationship model to form an equipment association table;
step S4, according to the obtained equipment association table, carrying out statistical analysis on the primary equipment, the secondary device and the secondary equipment to obtain auditing result data;
step S5: and outputting the data of the audit result to an interface for displaying, and generating an audit report for consulting.
Further, in step S1, the devices are grouped according to the device voltage level, the device type, and the device location.
Further, the device attributes of the secondary device include a device type and a function type.
Further, the step S4 is specifically:
step S41, calculating a few abnormal data in the similarity group by using the device type and the device position field as the primary device data which are grouped and have established the device association relation;
step S42, grouping the secondary device data associated with the primary equipment with the same analysis result in the step S41 by interval, secondary device type, dualization attribute, associated primary equipment position and associated primary equipment type, and calculating a few abnormal data in each group by adopting a statistical analysis algorithm;
and S43, grouping the secondary equipment data associated with the secondary devices with the same analysis result in the step S42 according to the device names, the secondary equipment function types, the set attributes and the associated primary equipment positions, and calculating a few abnormal data in each group by adopting a statistical analysis algorithm.
Compared with the prior art, the invention has the following beneficial effects:
1. before statistical analysis is carried out on the full-fault-prevention point table data, fuzzy matching is carried out on secondary equipment names with different names and electric power professional terms by adopting a natural language fuzzy matching algorithm, so that the purpose of standardized processing of the equipment names is achieved, and the problem that different transformer substations in different regions have different and non-uniform equipment naming methods is solved.
2. The invention solves the problem that the manual check needs to spend a large amount of time comparing the installation of the primary and secondary devices on site, greatly improves the check efficiency, solves the problem that the requirement on the capability of the auditor is high due to the high requirement on the degree of secondary specialty, and releases a large amount of manpower resources of the secondary specialty.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, the present invention provides a method for checking a total error point prevention table based on data similarity, comprising the following steps:
step S1, importing a CIME model file of the primary equipment, searching the incidence relation of the primary equipment by adopting a topological analysis algorithm, and carrying out similarity grouping on the equipment according to the voltage level of the equipment, the type of the equipment and the position of the equipment; for example, the interval of an incoming line I281 and the interval of an incoming line II 282 of 220kV voltage level are a group;
step S2, importing a secondary equipment data model, analyzing the name of a secondary device and the name of secondary equipment (a pressure plate, a blank switch, a handle and a signal) by adopting a natural language fuzzy matching algorithm, and acquiring equipment attributes such as the device type, the function type and the like of the secondary equipment;
step S3, importing the similarity-grouped CIME model files of the primary equipment and the analyzed data model of the secondary equipment into a database, and establishing an association relationship between the data of the primary equipment and the data of the secondary equipment by using an equipment relationship model to form an equipment association table;
step S4, performing statistical analysis on the primary equipment, the secondary device and the secondary equipment according to the obtained equipment association table to obtain audit result data;
and step S41, calculating a few abnormal data in the similar group by using the device type and the device position field as the main key according to the grouped primary device data with the device association relationship established, such as a switch, a disconnecting link, a grounding switch or a gate device which is short of a certain position at a certain interval.
Step S42, grouping the secondary device data associated with the primary equipment with the same analysis result in the step S41 by interval, secondary device type, dualization attribute, associated primary equipment position and associated primary equipment type, and calculating a few abnormal data in each group by adopting a statistical analysis algorithm; for example, the interval between the incoming line I281 and the incoming line II 282 is short of the in-situ cabinet.
Step S43, grouping the secondary equipment data associated with the secondary devices with the same analysis result in the step S42 by device name, secondary equipment function type, set attribute and associated primary equipment position, and calculating a few abnormal data in each group by adopting a statistical analysis algorithm; for example, the attribute data of the power sleeve of the I-th group control loop in the interval of the incoming line I281 is lack of an A-phase tripping outlet hard pressing plate, and the interval of the incoming line II 282 is abnormal.
Step S5: and displaying the data form of the grouped checking results on a software interface, and generating a checking report document for a user to look up.
The above description is only a preferred embodiment of the present invention, and all equivalent changes and modifications made in accordance with the claims of the present invention should be covered by the present invention.
Claims (3)
1. A full error point prevention table checking method based on data similarity is characterized by comprising the following steps:
step S1, importing a CIME model file of the primary equipment, searching the association relation of the primary equipment by adopting a topological analysis algorithm, and performing similarity grouping;
step S2, importing a secondary equipment data model, analyzing the name of a secondary device and the name of secondary equipment by adopting a natural language fuzzy matching algorithm, and acquiring the equipment attribute of the secondary equipment;
step S3, importing the similarity-grouped CIME model files of the primary equipment and the analyzed data model of the secondary equipment into a database, and establishing an association relationship between the data of the primary equipment and the data of the secondary equipment by using an equipment relationship model to form an equipment association table;
step S4, according to the obtained equipment association table, carrying out statistical analysis on the primary equipment, the secondary device and the secondary equipment to obtain auditing result data;
step S5: outputting the audit result data to an interface for display, and generating an audit report for consulting;
the step S4 specifically includes:
step S41, calculating a few abnormal data in the similarity group by using the device type and the device position field as the primary device data which are grouped and have established the device association relation;
step S42, grouping the secondary device data associated with the primary equipment with the same analysis result in the step S41 by interval, secondary device type, dualization attribute, associated primary equipment position and associated primary equipment type, and calculating a few abnormal data in each group by adopting a statistical analysis algorithm;
and S43, grouping the secondary equipment data associated with the secondary devices with the same analysis result in the step S42 according to the device names, the secondary equipment function types, the set attributes and the associated primary equipment positions, and calculating a few abnormal data in each group by adopting a statistical analysis algorithm.
2. The method for checking the full anti-error point table based on the data similarity according to claim 1, wherein: the step S1 specifically groups the devices according to the device voltage levels, the device types, and the device locations.
3. The method for checking the full anti-error point table based on the data similarity according to claim 1, wherein: the device attributes of the secondary device include a device type and a function type.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010644751.2A CN111858852B (en) | 2020-07-07 | 2020-07-07 | Full-error-point-prevention table checking method based on data similarity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010644751.2A CN111858852B (en) | 2020-07-07 | 2020-07-07 | Full-error-point-prevention table checking method based on data similarity |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111858852A CN111858852A (en) | 2020-10-30 |
| CN111858852B true CN111858852B (en) | 2022-07-05 |
Family
ID=73152370
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010644751.2A Active CN111858852B (en) | 2020-07-07 | 2020-07-07 | Full-error-point-prevention table checking method based on data similarity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111858852B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113743539B (en) * | 2021-11-03 | 2022-02-08 | 南京云问网络技术有限公司 | Form retrieval method based on deep learning |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009075249A (en) * | 2007-09-19 | 2009-04-09 | Ntt Data Corp | Audiotyped content confirmation method, audiotyped content confirming device and computer program |
| CN103761413A (en) * | 2013-12-06 | 2014-04-30 | 云南电网公司大理供电局 | Substation anti-misoperation logic intelligent generating method based on topology analysis |
| CN106710051A (en) * | 2017-01-19 | 2017-05-24 | 珠海优特电力科技股份有限公司 | Management control system and method for preventing error operations of electrical secondary equipment |
| CN107436567A (en) * | 2017-08-17 | 2017-12-05 | 长园共创电力安全技术股份有限公司 | The anti-error system and anti-misoperation method of a kind of industrial flow |
| CN109685376A (en) * | 2018-12-26 | 2019-04-26 | 国家电网公司华中分部 | A kind of power customer abnormal behaviour method for early warning based on similarity analysis theory |
-
2020
- 2020-07-07 CN CN202010644751.2A patent/CN111858852B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009075249A (en) * | 2007-09-19 | 2009-04-09 | Ntt Data Corp | Audiotyped content confirmation method, audiotyped content confirming device and computer program |
| CN103761413A (en) * | 2013-12-06 | 2014-04-30 | 云南电网公司大理供电局 | Substation anti-misoperation logic intelligent generating method based on topology analysis |
| CN106710051A (en) * | 2017-01-19 | 2017-05-24 | 珠海优特电力科技股份有限公司 | Management control system and method for preventing error operations of electrical secondary equipment |
| CN107436567A (en) * | 2017-08-17 | 2017-12-05 | 长园共创电力安全技术股份有限公司 | The anti-error system and anti-misoperation method of a kind of industrial flow |
| CN109685376A (en) * | 2018-12-26 | 2019-04-26 | 国家电网公司华中分部 | A kind of power customer abnormal behaviour method for early warning based on similarity analysis theory |
Non-Patent Citations (1)
| Title |
|---|
| 智能变电站二次设备规范化运检研究;谷栋;《中国优秀硕士学位论文全文数据库（工程科技Ⅱ辑）》;20200215;第1-67页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111858852A (en) | 2020-10-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111505433B (en) | Low-voltage transformer area indoor variable relation error correction and phase identification method | |
| CN107180314B (en) | Operation and maintenance business model modeling method based on primary and secondary system incidence relation | |
| CN106815373B (en) | Distribution network first-aid repair big data display method and system based on BI analysis | |
| CN108666992B (en) | Power grid fault comprehensive analysis platform and working method | |
| CN109919451B (en) | Digital power grid system and method based on real power grid topology analysis and safety knowledge base | |
| CN103853765A (en) | Method for comparing waveform features of power system faults | |
| CN111342454B (en) | Method and system for analyzing big data of low voltage cause at platform area outlet | |
| CN111858852B (en) | Full-error-point-prevention table checking method based on data similarity | |
| CN111398859B (en) | User low-voltage cause big data analysis method and system | |
| CN112698123A (en) | Low-voltage distribution area user topological relation identification method based on decision tree | |
| CN112256872B (en) | Intelligent billing method for overhaul work ticket of transformer substation | |
| CN109191007A (en) | A kind of method and system that grid equipment N-1 consequence grade determines automatically | |
| CN110310048B (en) | Distribution network planning overall process evaluation method and device | |
| CN113282594B (en) | Automatic extraction and analysis method and system for basic data of distribution transformer | |
| CN107748819B (en) | Natural language processing-based electrical secondary equipment modeling method and system | |
| CN112990764B (en) | Power grid equipment maintenance condition monitoring method based on reimbursement certificate | |
| CN113783298B (en) | Secondary equipment anti-error method based on regulation cloud | |
| CN112446619B (en) | Power distribution network rush-repair processing method and device | |
| CN110717725B (en) | Power grid project selection method based on big data analysis | |
| CN112949271A (en) | Maintenance safety measure analysis method and system | |
| CN114221312A (en) | Topological relation-based power distribution network protection rapid setting and checking method | |
| CN114498910A (en) | One-key sequential control system with anti-error function and control method | |
| CN111143622A (en) | Fault data set construction method based on big data platform | |
| CN110650049A (en) | Visual matching method for soft pressing plate and virtual circuit | |
| Mao et al. | Research on active emergency repair technology of distribution network based on large power data |
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 |