Comparative performance assessment of fuzzy logic, incremental conductance, and hybrid fuzzy–incremental conductance MPPT techniques for photovoltaic systems

Maximum Power Point Tracking (MPPT) plays a vital role in maximizing the energy extraction efficiency of Solar Photovoltaic (SPV) systems under dynamic environmental conditions. This paper presents a comparative performance assessment of Fuzzy Logic Control (FLC), Incremental Conductance (IC), and Hybrid Fuzzy–Incremental Conductance (HFIC) MPPT techniques for SPV applications. Unlike conventional standalone FLC and IC methods, the proposed HFIC controller coordinates the normalized PV power variation ( P r ) and the IC-derived conductance indicator ( V in ) through a fuzzy inference system to generate the boost-converter duty cycle, thereby improving dynamic tracking performance and reducing steady-state oscillations. To evaluate the controller under realistic operating conditions, multiple case studies involving rapid irradiance and temperature variations are conducted. The comparative analysis includes tracking efficiency, response time, settling time, steady-state PV power ripple, and tracking stability under identical operating conditions. Simulation results demonstrate that the proposed HFIC controller achieves superior performance compared with the standalone FLC and IC methods, with a tracking efficiency of 97.33%, a response time of 15 ms, and a steady-state PV power ripple of 0.08 W. Furthermore, the HFIC controller exhibits the shortest average settling time of 9.6 ms, compared with 15.6 ms for FLC and 19.0 ms for IC, indicating faster convergence following rapid irradiance and temperature transitions. The main contribution of this work is the coordinated use of the IC-derived conductance indicator and normalized PV power variation within a unified fuzzy inference framework for adaptive duty-cycle generation, together with a comprehensive dynamic performance evaluation under sequential irradiance and temperature variations. The simulation results demonstrate the effectiveness of the proposed HFIC controller under the investigated operating conditions. All simulations were carried out using the MATLAB/Simulink environment.