Predictive Harmonic Cancellation Using Neural Networks

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.