Comparative Performance Analysis of P&O and Incremental Conductance MPPT Algorithms for Photovoltaic Systems

Abstract

Photovoltaic (PV) energy systems play a vital role in sustainable power generation; however, their nonlinear electrical characteristics and strong dependence on environmental conditions significantly limit energy extraction efficiency. Variations in solar irradiance, temperature, and partial shading cause continuous shifts in the maximum power point (MPP), leading to power losses if not properly controlled. To address this challenge, this work presents a comparative performance evaluation of conventional Maximum Power Point Tracking (MPPT) techniques applied to a PV system under identical operating conditions. The study models a PV array integrated with a DC–DC boost converter and implements Perturb and Observe (P&O) and Incremental Conductance (IC) MPPT algorithms. A Solar Panel PV System Dataset containing 50,000 operating samples under low, medium, and high irradiance as well as partial shading conditions is utilized. Data preprocessing techniques including filtering, normalization, and segmentation are employed to ensure reliable analysis. Simulation results demonstrate that IC MPPT achieves higher tracking efficiency (≈98.6%) and faster convergence time (≈0.18 s) compared to P&O MPPT (≈97.9% efficiency and 0.32 s), while P&O exhibits lower steady-state oscillations. The findings highlight performance trade-offs and provide guidance for practical MPPT selection.