Performance Comparison of HSA, ICA and PSO Algorithms for Selective Harmonic Elimination in Cascaded Multilevel Inverter with Variable DC Sources

In this paper، a new approach for selective harmonic elimination (SHE) in a cascaded multilevel inverter is proposed. The switching angles are determined with the assumption of varying input DC sources and at this condition the fundamental component is remained adjusted and undesired harmonic components are eliminated. The on-line switching angles determination is done by an Artificial Neural Network (ANN). The training data set for ANN are produced by application of heuristic algorithms to solve the SHE problem. In this paper، various algorithms such as harmony search algorithm، imperialist competitive algorithm and particle swarm optimization are applied to obtain the switching angles and by comparing the results of these methods، the better one is used for ANN training.