Type of Document	Thesis
Author	Malinconico, Brian
Author's Email Address	bsm05@fsu.edu
URN	etd-11072010-105333
Title	Predictive Harmonic Cancellation using Neural Networks
Degree	Master of Science
Department	Electrical and Computer Engineering, Department of
Advisory Committee	Advisor Name Title Simon Foo Committee Chair Anke Meyer-Baese Committee Member Rodney Roberts Committee Member
Keywords	artificial intelligence power systems harmonics neural networks
Date of Defense	2010-10-15
Availability	unrestricted
Abstract 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.
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