Design of Diagnosis System for Insulation Degradation by Using Neurofuzzy Model | Zendy

Yigon Kim | Zendy; Yang Hee Jung | Zendy; Yong Chul Bae | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Design of Diagnosis System for Insulation Degradation by Using Neurofuzzy Model

Author(s) -

Yigon Kim,

Yang Hee Jung,

Yong Chul Bae

Publication year - 2000

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2000.p0368

Subject(s) - partial discharge , computer science , warning system , nonlinear system , wavelet , reliability engineering , production line , line (geometry) , electrical equipment , medical diagnosis , degradation (telecommunications) , artificial neural network , artificial intelligence , mechanical engineering , electrical engineering , engineering , voltage , telecommunications , medicine , physics , geometry , mathematics , pathology , quantum mechanics

Insulation aging diagnosis provides early warning of electrical equipment defects that helps avoid loss from unexpected production line shutdown. Since relations of insulation aging and partial discharge dynamics are nonlinear, it is very difficult to provide early warning in electrical equipment. This paper suggests a new method for diagnosing insulation aging that measures partial discharge on-line from DAS(Data Acquisition System) and acquires 2D patterns from analyzing it using wavelets. Using this data, design of a neurofuzzy model that diagnoses electrical equipment is investigated. Validity of the new method is confirmed by numerical simulation.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research