DocumentCode :
2058264
Title :
Identification of Transformer Internal Faults by Using an RBF Network Based on Dynamical Principle Component Analysis
Author :
Ozgonenel, Okan ; Kilic, Erdal ; Thomas, David ; Ozdemir, Ali Ekber
Author_Institution :
Ondokuz Mayis University, Electrical & Electronics Engineering Department, Samsun, TURKEY. e-mail: okanoz@omu.edu.tr
fYear :
2007
fDate :
12-14 April 2007
Firstpage :
719
Lastpage :
724
Abstract :
In this paper; a method is proposed to detect and identify parameter faults in nonlinear dynamical systems. The approach is based on the principal component analysis (PCA) and artificial neural networks (ANNs) based on radial basis functions (RBFs). A nonlinear system´s input and output data is manipulated without taking consideration any model in the approach. The method is applied to a three phase custom built transformer in order to detect and identify internal short circuit faults. It is obsered theughgh various application examples that the proposed method leads to satisfactory results in terms of detecting parameter faults in non-linear dynamical systems.
Keywords :
Circuit faults; Electronic mail; Fault detection; Fault diagnosis; Power system transients; Power transformer insulation; Power transformers; Principal component analysis; Radial basis function networks; Transient analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Engineering, Energy and Electrical Drives, 2007. POWERENG 2007. International Conference on
Conference_Location :
Setubal, Portugal
Print_ISBN :
978-1-4244-0895-5
Electronic_ISBN :
978-1-4244-0895-5
Type :
conf
DOI :
10.1109/POWERENG.2007.4380215
Filename :
4380215
Link To Document :
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