AI Summary & Key Takeaways
AI InsightThe simulation findings demonstrated that the proposed hybrid intelligent controller displays a highly stable and robust performance in terms of tracking maximum power point compared to a conventional AIC MPPT method against various uncertainties stemming from disturbing inputs such as solar irradiance and panel temperature variations.
📌 3 Key Takeaways
Abstract
Photovoltaic (PV) systems have low power conversion efficiency, so maximum power point tracking (MPPT) control methods are utilized to maximize the efficiency of PV systems. The present study proposes an improved hybrid intelligent controller design for the MPPT of stand-alone PV system. The hybrid intelligent control structure is integrated into Angle of Incremental Conductance (AIC) method and Interval Type-2 Takagi-Sugeno-Kang Fuzzy Logic Controller (IT2-TSKFLC). The proposed hybrid intelligent controller offers a superior performance in terms of dealing with uncertainties of sudden changes under different environmental conditions. A simulation model is created in Matlab/Simulink using daily data from a real solar PV plant to evaluate the performance of the proposed hybrid intelligent controller. The simulation findings demonstrated that the proposed hybrid intelligent controller displays a highly stable and robust performance in terms of tracking maximum power point compared to a conventional AIC MPPT method against various uncertainties stemming from disturbing inputs such as solar irradiance and panel temperature variations.