Design of modified long short-term memory-based zebra optimization algorithm for limiting the issue of SHEPWM in multi-level inverter.

Abstract

This research work proposes unique MLSTM-ZOA to quickly measure SAs for an MLI. Within a certain MI range, the suggested method may calculate a greater count of SAs with various solutions. Here, the main objective lies in minimizing the THD with consideration of three parameters such as MI, number of pulses per quarter cycle, and duty cycle. Furthermore, by achieving a lower fitness value in less iteration, the suggested method has definitely outperformed other methods with respect to convergence behavior, according to the data. Lastly, analysis and performance associated with the experimental validation of SHE in multi-level inverter are also carried out. The proposed MLSTM-ZOA model in terms of THD is 56.25%, 81.58%, 53.33%, and 41.67% better than MPA, HHO, MGMPA, and SF-BOA respectively. Similarly, the proposed MLSTM-ZOA model with respect to HDP is 64.03%, 47.16%, 84.01%, and 27.62% advanced than MPA, HHO, MGMPA, and SF-BOA respectively.