A Comparative Analysis of Fuzzy Logic Control and Model Predictive Control in Photovoltaic Maximum Power Point Tracking
Zehan Li, Gunawan Dewantoro, Tuohan Xiao, Akshya Swain
Abstract
Operating PV panels at the Maximum Power Point (MPP) is crucial for increasing efficiency and reducing the payback period of the system. However, the voltage and current characteristics of PV panels are nonlinear and depend on environmental conditions like temperature and irradiance. This paper presents a comparative analysis of Fuzzy Logic Control (FLC) and Model Predictive Control (MPC) for Maximum Power Point Tracking (MPPT) applied to a photovoltaic generation system. The study focuses on FLC due to its rapid response and robustness against circuit parameter variations. MPC, known for its predictive capabilities, is also investigated for comparison. A PI control strategy is employed to maintain the desired current and voltage during battery charging. The results show that, under standard test conditions (1000 W/m2 irradiance and 25 °C temperature), the FLC-based MPPT achieved an average efficiency of 98.298%, with a response time of 12 ms. In comparison, the MPC-based MPPT achieved 96.598% efficiency and a 25 ms response time. During dynamic irradiance changes, FLC demonstrated faster adaptation with a peak tracking error of 2.398%, while MPC had a tracking error of 4.598%. These findings highlight the superior dynamic performance of FLC in real-time PV tracking and the stability of MPC for long-term optimization.