An Adaptive GALM Neural Model and Its Application for Fault Diagnoses

被引：0

作者：

Ni, Yuanping ^{[1
]}

机构：

[1] Kunming Univ Sci & Technol, Sch Informat & Automat, Kunming 650051, Peoples R China

来源：

2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23 | 2008年

关键词：

fault diagnosis; genetic algorithm; levenberg-marquardt; neural model; transformer;

D O I：

10.1109/WCICA.2008.4594409

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：9327 / 9332

页数：6

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