Date of Award
Spring 2004
Document Type
Thesis - Restricted
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering
First Advisor
Arkadan, Abdul-Rahman A.
Second Advisor
Yaz, Edwin E.
Third Advisor
Brown, Ronald H.
Abstract
The performance characteristics of synchronous generator systems in stand alone or no-break power transfer (NBPT) mode are predicted using computational electromagnetics in conjunction with Artificial Neural Network based Data Mining approach. The approach has two main building blocks: The first involves the use of off-line FE-SS method to set up a data base. The second component uses Data Mining technique and is used to predict "quickly" and "accurately" the performance characteristics of highly nonlinear systems. The approach was applied stand alone synchronous generator systems typically used in aerospace applications. In this work, the system included prototype three-phase, 90 kVA, 208 V, 400Hz, synchronous generators feeding a combination of ac and dc loads in stand alone and NBPT mode.
Recommended Citation
Abou-Samra, Youri, "RBF Based Data Mining for the Characterization of Stand Alone AC Generator Systems" (2004). Master's Theses (1922-2009) Access restricted to Marquette Campus. 3868.
https://epublications.marquette.edu/theses/3868