Selective Harmonic Elimination in a Cascaded Multilevel Inverter of Distributed Power Generators Using Water Cycle Algorithm

This research paper proposes the application of a meta-heuristic algorithm, namely the water cycle algorithm (WCA), for optimizing the performance of a multi-level inverter for a distributed energy resources-based smart grid system. The aim is to find the optimal switching angles to achieve selective harmonic elimination. To exhibit the effectiveness of the proposed algorithm and evaluate the results, a three-phase seven-level cascaded multilevel inverter (CHBMLI) is used. This paper demonstrates the efficacy of the proposed algorithm by performing a rigorous comparison with existing meta-heuristic algorithms. Independent sample t-tests for different population sizes are demonstrated, which reflect the better performance of the proposed algorithm's results. For the comparison, crucial parameters for optimization, including population size and number of iterations, are kept the same for the proposed WCA and other algorithms. Since we are solving a minimization problem, a lower fitness value is focused. In our research paper, we show how the proposed algorithm attains a lower fitness value and fast rate of convergence. For different values of the modulation index, WCA performs better than particle swarm optimization (PSO) and the firefly algorithm (FA). For a particular case of a modulation index value of 0.8, the minimum value found by WCA over 50 samples is 0.0001, whereas that of PSO and FA are 0.0223 and 0.0433, respectively, which shows that WCA has better accuracy. The results clearly present that the proposed algorithm provides a competitive percentage of elimination of selected harmonics when compared with other meta-heuristic algorithms.