An adaptive GALM neural model and its application for fault diagnoses

Author

Ni, Yuanping

Author_Institution

Sch. of Inf. & Autom., Kunming Univ. of Sci. & Technol., Kunming

fYear

2008

fDate

25-27 June 2008

Firstpage

9327

Lastpage

9332

Abstract

The studies analyzed the idea of Levenberg-Marquardt (LM) algorithm, and also improved genetic algorithm (GA), finally developing an adaptive model of neural algorithm through the GA-LM. This model was applied to the fault diagnoses for power transformers. The results show that the adaptive GALM neural model is able to overcome its local minimum and increase the converging speed in comparison with BP. Meanwhile, the model can diagnose the faults of transformers efficiently and also increase the ratio of fault recognition greatly. The model is supposed to have a reference value in fault diagnoses for similar electrical equipment.

Keywords

fault diagnosis; genetic algorithms; neural nets; power engineering computing; power transformers; Levenberg-Marquardt algorithm; electrical equipment; fault recognition; genetic algorithm; power transformer fault diagnoses; Adaptive control; Algorithm design and analysis; Automation; Electronic mail; Genetic algorithms; Information analysis; Intelligent control; Jacobian matrices; Power transformers; Programmable control; fault diagnosis; genetic algorithm; levenberg-marquardt; neural model; transformer;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on

Conference_Location

Chongqing

Print_ISBN

978-1-4244-2113-8

Electronic_ISBN

978-1-4244-2114-5

Type

conf

DOI

10.1109/WCICA.2008.4594409

Filename

4594409