Type of Document Thesis Author Malinconico, Brian Author's Email Address email@example.com 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
- neural networks
Date of Defense 2010-10-15 Availability unrestricted AbstractFiltering 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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