Optimized Maximum Power Point Tracking for PV System Under Partial Shading Conditions

This paper proposes an improved meta-heuristic MPPT approach to compensate for the incompetency of the most modern algorithms in achieving optimal maximum power point tracking (MPPT) for photovoltaic (PV) systems. Photovoltaic systems play an important role in achieving net zero energy and sustainable development, which normally require optimal MPPT algorithms to be effective and efficient. Due to the partial shading condition in a PV system, several local maximum power points (LMPPs) and a global maximum power point (GMPP) are created, preventing PV systems from achieving GMPP by simply using standard MPPT techniques. A hybrid MPPT approach is developed and compared with the commonly used modified Perturb and Observe (P&O) and the recent advanced Cuckoo Search (CS) approach. Furthermore, a comprehensive evaluation and comparison is conducted to validate the performance of the proposed MPPT techniques. The results indicate that the proposed enhanced MPPT algorithm can mitigate the adverse trade-offs of the existing approaches with parameter optimization for tracking speed and steady-state oscillations while resolving the GMPP location under partially shaded PV conditions.

Keywords: Photovoltaic, maximum power point tracking, global maximum power point, hybrid, optimization, partial shading condition, P&O, CS algorithm.