A Comparative Study of MPPT Techniques on THD Current for PV Systems Under Partial Shading Conditions

Partial shading conditions (PSCs) can have a considerable impact on the photovoltaic (PV) performance of solar panels, leading to the emergence of multiple maximum power points (MPPs) on the power-voltage ($\mathrm{P}-\mathrm{V}$) characteristics. These MPPs comprise various local peaks and a single global peak, rendering traditional MPPT techniques ineffective in accurately tracing the latter due to the inherent complexity and non-linearity of PV characteristic curves under PSCs. Consequently, the development of MPPT methods grounded in artificial intelligence (AI), is crucial for optimizing energy generation performance under these conditions. In this study, three MPPT approaches were investigated: Fuzzy Logic Controller (FLC), Particle Swarm Optimization (PSO), and Backstepping Controller (BC). The performance of these algorithms was assessed by quantifying Total Harmonic Distortion (THD) of currents fed into the grid, and overall energy efficiency from the PV system to the grid. This evaluation was conducted under various solar irradiation levels while maintaining a constant temperature using MATLAB/Simulink. PSO and BC demonstrate superior performance relative to FLC in power injection and power quality under both optimal and shaded conditions.