Case-based intelligent local discharge fault identification system and identification method

A partial discharge and fault identification technology, applied in the direction of testing dielectric strength, etc., can solve problems such as rapid search of unfavorable on-site problems, uncertainty of fault diagnosis, etc.

Inactive Publication Date: 2011-07-20
HANGZHOU KELIN ELECTRIC POWER EQUIP
View PDF5 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Not to mention pattern recognition for partial discharge faults
It can only give an alarm when the detected on-site signal amplitude is abnormal. The judgment of specific faults requires electric power experts to go to the site for test and analysis, which is not conducive to the rapid search of on-site problems.
Moreover, the failure mode recognition completely depends on the experience and level of on-site experts, which brings great uncertainty to the final fault diagnosis

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Case-based intelligent local discharge fault identification system and identification method
  • Case-based intelligent local discharge fault identification system and identification method
  • Case-based intelligent local discharge fault identification system and identification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0026] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0027] The example-based intelligent partial discharge fault identification system includes: a signal sampling module, which is used to sample and store samples continuously when the signal is abnormal; a sample retrieval module, which accepts the sample as a target template, calls the retrieval method module to obtain the retrieval method, and according to The retrieval method searches the source example module to obtain a batch of source examples similar to the target example, and then sends the source example to the example sorting module; the retrieval method module accepts the query task of the example retrieval module and returns the example identification method; the example sorting module, the example The sorting module calls the selection criteria module to obtain the selection criteria, and after obtaining the selection crit...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a case-based intelligent local discharge fault identification system and a case-based intelligent local discharge fault identification method, and relates to a fault identification system and a fault identification method. A local discharge detector can only display field acquired waveform and has no signal analysis function, the judgment of faults needs power experts to test and analyze on site, the search is slow, and the fault mode identification totally depends on experience and level of the field experts so as to bring great uncertainty to final fault diagnosis. The system comprises a signal sampling module, an example search module, a search method module, an example ordering module, a preferential standard module, a conclusion interpretation module, an example storage module and a source example module. Similar source examples are acquired form target examples, fault modes are determined through a built-in case by using a scientific geometric graph search method, and specific fault parts and serious degree are determined by using a Euclidean distance algorithm, so accuracy and reliability of fault diagnosis are improved.

Description

technical field [0001] The invention relates to a fault identification system and identification method, in particular to an example-based intelligent partial discharge fault identification system and identification method. Background technique [0002] At present, the partial discharge detector can only display the waveform collected on site, without the signal analysis function. It is even more impossible to carry out pattern recognition on partial discharge faults. It can only give an alarm when the detected on-site signal amplitude is abnormal. The judgment of specific faults requires electric power experts to go to the site for test and analysis, which is not conducive to the rapid search of on-site problems. Moreover, the failure mode recognition completely depends on the experience and level of on-site experts, which brings great uncertainty to the final fault diagnosis. Contents of the invention [0003] The technical problem to be solved and the technical task p...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/12
Inventor 谢炜谢东汪业
Owner HANGZHOU KELIN ELECTRIC POWER EQUIP
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap