This paper presents a design of a low cost, low loss 31-level multilevel inverter (MLI) topology with a reduce the number of switches and power electronic devices. The increase in the levels of MLI leads to limiting the THD to the desired value. The 31-level output voltage is created using four PV sources with a specific ratio. The SPWM is used to control the gating signals for the switches of MLI. The PV system is integrated into the MLI using a boost converter to maximize the power capacity of the solar cells and the Incremental Conductance (IC) algorithm is employed for maximum power point tracking (MPPT) of the PV system. Simulation results of 31-level MLI indicate the THD of voltage and current waveforms are 3.73% within an acceptable range of IEEE standards.
Due to the nonlinear electrical properties of PV generators, the width and performance of these frames could be enhanced by carrying them to operate at ultimate energy mark tracking. In this study, a versatile maximum power point tracking (MPPT) model using a modified Flyback controller with artificial neural network (ANN) technique as our proposed system. The hybrid Flyback/ANN controller is based on teaching and training a neural network, where the dataset is utilized to adjust the levitation converter which is taken care of by a stand-alone photovoltaic generator (PVG) with a Flyback controller. It is assumed that the results will be obtained by the ANN-MPPT system with the Flyback controller which provides low motions and shows a great implementation around the maximum power point compared to the PVG used with traditional MPPT algorithms such as Perturbation and Observation (P & O).