Power quality analysis of three stage fifteen level cascaded multilevel inverter by using FPGA controller for EV application

Sustainable power generation is characterized by zero carbon emissions in specific circumstances. However, this is primarily owing to their inherent generating mode, which involves the use of direct current (DC) power, as seen in solar photovoltaic (PV) systems, fuel cells, and other similar technologies. It is intended to encourage inverters to provide electricity to alternate current (AC) loads. Ensuring power quality is crucial while performing DC/AC inversion and is also vital for major electric vehicle (EV) applications, such as powering the EV motor. The required inverters can be the typical three-stage fifteen-level cascaded multilevel inverter. Enhancing the reliability of renewable energy sources can be achieved by employing EV motors and developing a 3-stage, 15-level inverter. This approach improves performance by lowering the number of switches and minimizing total harmonic distortion (THD). The utilization of pulse width modulation technology, in conjunction with an Artificial Neural Network (ANN) algorithm, results in a reduction in the number of switches and facilitates the identification of faults through diagnostic processes. The recommended controller employs an Artificial Neural Network (ANN) and Field Programmable Gate Array (FPGA) to precisely categorize errors in the proposed Multi-Level Inverter (MLI). In addition, the Total Harmonic Distortion (THD) is evaluated for various load conditions. The datasets are acquired by MATLAB simulation. The datasets are used for training and testing to identify the most optimal Artificial Neural Network (ANN) technique.