Power Quality Enhancement in a Grid-Integrated Photovoltaic System Using Hybrid Techniques

Abstract

In recent years, the photovoltaic (PV) system was designed to supply solar power through photovoltaic arrays. The PV generator exhibits nonlinear voltage–current characteristics and its maximum power point tracking (MPPT), which varies with temperature and radiation. In the event of non-uniform solar insolation, several multiple maximum power points (MPPs) appear in the power–voltage characteristic of the PV module. Thus, a hybrid combination of binary particle swarm optimization (BPSO) and grey wolf optimization (GWO) is proposed herein to handle multiple MPPs. This combination is nowhere found in the literature, so the author chose this hybrid technique; and the main advantage of the proposed method is its ability to predict the global MPP (GMPP) in a very short time and to maintain accurate performance, even under different environmental conditions. Moreover, a 31-level multilevel inverter (MLI) was designed with a lower blocking voltage process to reduce the complexity of the circuit design. The entire system was executed in the MATLAB platform to examine the performance of the PV system, which was shown to extract a maximum power of 92.930 kW. The simulation design clearly showed that the proposed method with a 31-level MLI achieved better results in terms of total harmonic distortion (THD) at 1.60%, which is less when compared to the existing genetic algorithm (GA) and artificial neural networks (ANNs).