Title :
An adaptive GALM neural model and its application for fault diagnoses
Author_Institution :
Sch. of Inf. & Autom., Kunming Univ. of Sci. & Technol., Kunming
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;
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
DOI :
10.1109/WCICA.2008.4594409
