Date Available
12-7-2011
Year of Publication
2007
Document Type
Thesis
College
Engineering
Department
Computer Science
First Advisor
Yuan Liao
Abstract
This thesis aims at detecting and classifying the power system transmission line faults. To deal with the problem of an extremely large data set with different fault situations, a three step optimized Neural Network approach has been proposed. The approach utilizes Discrete Wavelet Transform for detection and two different types of self-organized, unsupervised Adaptive Resonance Theory Neural Networks for classification. The fault scenarios are simulated using Alternate Transients Program and the performance of this highly improved scheme is compared with the existing techniques. The simulation results prove that the proposed technique handles large data more efficiently and time of operation is considerably less when compared to the existing methods.
Recommended Citation
Kasinathan, Karthikeyan, "POWER SYSTEM FAULT DETECTION AND CLASSIFICATION BY WAVELET TRANSFORMS AND ADAPTIVE RESONANCE THEORY NEURAL NETWORKS" (2007). University of Kentucky Master's Theses. 452.
https://uknowledge.uky.edu/gradschool_theses/452
