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Filtering is an important aspect of the modern power system. By reducing the effects of harmonics, power transmission and utilization becomes more efficient. This research examines the use of neural networks for the estimation and prediction of harmonics. The utilization of neural networks for adaptive harmonic prediction, allows the cancellation of harmonics before their creation. A large part of this research focuses on the estimation of Fourier coefficients. By identifying the strengths and weaknesses of neural networks for Fourier coefficient estimation future direction for research was determined. The deficiencies of the developed networks prevent the application of this system in real-life situations. Despite the need for future research, the performance of the neural networks shows significant possibilities.
artificial intelligence, power systems, harmonics, neural networks
Date of Defense
October 15, 2009.
A Thesis submitted to the Department of Electrical & Computer Engineering in partial fulfillment of the requirements for the degree of Master of Electrical Engineering.
Includes bibliographical references.
Simon Foo, Professor Directing Thesis; Rodney Roberts, Committee Member; Anke Meyer-Baese, Committee Member.
Florida State University
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