A Review and Comparative Analysis of Maximum Power Point Tracking Techniques in Solar Photovoltaic Systems

Solar photovoltaic (PV) systems have become one of the fastest-growing sources of electricity worldwide, yet a recurring challenge in their operation is ensuring that the module always works at its peak output. This peak shifts continuously with sunlight intensity and temperature, which is why Maximum Power Point Tracking (MPPT) controllers are built into virtually every grid-tied and off-grid inverter today. The choice of MPPT algorithm, however, is not trivial it affects efficiency, hardware cost, and how well the system handles sudden changes in weather. This paper examines four techniques that appear most frequently in both academic literature and commercial hardware: Perturb and Observe (P&O), Incremental Conductance (INC), Fuzzy Logic Control (FLC), and Artificial Neural Network (ANN)-based MPPT. Each method is reviewed in terms of its operating principle, tracking accuracy, speed of convergence, implementation cost, and response under partial shading. Structured comparison tables are included to make the differences concrete. The findings indicate that while P&O and INC cover the needs of most low-cost applications adequately, the intelligent methods FLC and ANN offer clear advantages in plants where variable weather and partial shading are regular occurrences.