Real time selective harmonic minimization for multilevel inverters connected to solar panels using Artificial Neural Network angle generation

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. Five 195 W solar panels were used as the DC source for each full bridge. 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 shows to be a suitable tool that brings a small generalization effect on the angles' precision.

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