Multilayer inverter with DQZ and neuro-fuzzy control for single maximum power point tracking of hybrid renewable sources

Multilayer inverters (MLIs) play an important role in their efficiency and effectiveness. This study proposes a new MLI that is optimally adapted using DQZ control and a vague neurological approach for tracking the single maximum power point of a hybrid renewable energy source. This MLI has a bidirectional fixed switch, the purpose of which is to reduce harmonics and increase the voltage level. The maximum power point tracking (MPPT) method proposed here is the only MPPT method that uses neuro-fuzzy control algorithms, making it superior to other methods. The proposed inverter consists of 12 power semiconductor switches (IGBTs) connected to three DC power sources—that is, photovoltaic, wind, and tidal energy power sources. The switching angle for pulse-width modulation can be calculated using the DQZ principle in the proposed MLI. Evaluation of the effectiveness of the proposed method uses MATLAB/Simulink simulations, the results being compared to those of the prototype mechanism. We also compare the performance of the MPPT algorithm and prototype mechanism, which is connected to a single-phase microgrid. The proposed method achieves total harmonic distortion (THD) efficiency with a satisfactory performance increase.