Real-Time Selective Harmonic Minimization for Multilevel Inverters Using Genetic Algorithm and Artificial Neural Network Angle Generation

Author

Faete Jacques Teixeira FilhoFollow

Date of Award

5-2012

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Electrical Engineering

Major Professor

Leon M. Tolbert

Committee Members

Burak Ozpineci, Bin Hu, Fred Wang

Abstract

This work approximates the selective harmonic elimination problem using Artificial Neural Networks (ANN) to generate the switching angles in an 11-level full bridge cascade inverter powered by five varying DC input sources. Each of the five full bridges of the cascade inverter was connected to a separate 195W solar panel. The angles were chosen such that the fundamental was kept constant and the low order harmonics were minimized or eliminated. A non-deterministic method is used to solve the system for the angles and to obtain the data set for the ANN training. The method also provides a set of acceptable solutions in the space where solutions do not exist by analytical methods. The trained ANN is a suitable tool that brings a small generalization effect on the angles' precision and is able to perform in real time (50/60Hz time window).

Recommended Citation

Filho, Faete Jacques Teixeira, "Real-Time Selective Harmonic Minimization for Multilevel Inverters Using Genetic Algorithm and Artificial Neural Network Angle Generation. " PhD diss., University of Tennessee, 2012.
https://trace.tennessee.edu/utk_graddiss/1309