AI-based global MPPT for partial shaded grid connected PV plant via MFO approach

Abstract The photovoltaic (PV) energy production depends on the conditions surrounding the PV array such as irradiance (G), temperature and the array surface state. These factors directly affect the photonic absorption and the productivity of the PV panels. The phenomenon of partial shading condition (PSC) is one of the problems that disturb the proper operation of PV plants. In the recent literature, several algorithms have been developed to solve this problem. This paper principally aims at extensively presenting the PSC problem that had been considered by numerous articles and cited in this paper. Then, the paper presents combination of two techniques, the first one is the Global Maximum Power Point Tracking (GMPPT) for 100 kW array. The second technique is the Distributed Maximum Power point tracking configuration (DMPPT) for 1 MW PV plant under PSC. This combination aims to overcome the drawbacks related to PSC and enhance the PV system performance. A novel technique GMPPT controller is proposed using Moth-Flame Optimization algorithm (MFO) as solution for PSC shading. A comparative study is performed among different MPPT algorithms such as: Classical Incremental Conductance algorithm (IC), Fuzzy Logic approach based on IC (FL), Particles Swarms Optimization method (PSO) and MFO. Simulation results prove the capability of the proposed approach for seeking the GMPPT of the PV array system.