Performance Evaluation of PV MPPT Using ANFIS Subtractive Clustering, GWO, and IT2FL: A Comparative Study

Solar energy is eco-friendly nature. To maximize its utilization, this paper presents a comparative performance evaluation of four advanced maximum power point tracking (MPPT) techniques for photovoltaic (PV) systems: ANFIS subtractive clustering, Interval Type-2 Fuzzy Logic (IT2FL), and Grey Wolf Optimization (GWO)-based MPPT. The proposed ANFIS approach leverages subtractive clustering (ANFIS-SC), which offers significant advantages over conventional grid partitioning, including adaptability to complex data structures, automatic identification of optimal cluster centers, and reduced computational complexity by focusing on high-density regions. The resulting Sugeno-type ANFIS-SC MPPT system employs a single rule and one membership function per input, achieving simplicity, fast dynamic response, and superior tracking precision compared to traditional P&O methods. To further benchmark performance, an IT2FL-based MPPT is implemented, leveraging its inherent robustness to system uncertainties, while the GWO-based MPPT is introduced for its exceptional global search capability and adaptability to PV system. The three techniques are rigorously compared in terms of power extraction efficiency, voltage stability, and current dynamics under varying irradiance and temperature conditions. Simulation results demonstrate that while all intelligent methods outperform conventional P&O. This study provides critical insights for selecting MPPT strategies based on operational priorities, paving the way for optimized PV system.