Artificial Neural Network-Based Multilevel Inverter for Renewable Energy Applications

In contemporary times, utilization will be multilevel inverter in high-power applications is becoming increasingly prominent. These inverters are being favored for those abilities for provide pure-quality result voltage of minimal distortion, along with the advantage of requiring semiconductor switches with lower blocking voltage, as comparing to traditional voltage sources inverters. In the project, we introduce a novel configuration known as the dual-T-type five-level CMI, designed specifically for renewable energy applications. This innovation aims to address these challenges without comparing those desirable voltage-boosting capabilities. The core concept revolves around the integration of a half bridge and an inductor to facilitate the gradual charging of a capacitor connected in series with the DC source. The control strategy for this proposed topology is implemented using an Artificial Neural Network (ANN) Algorithm. Furthermore, uniform, and coordinated operation can be achieved when extending this configuration in a cascaded manner. The operational principle of this innovative topology is thoroughly analyzed and elucidated. To validate its performance, simulation results from a prototype are presented using the MATLAB Simulink platform.