Photovoltaic System Optimization: Showing the Benefit of Artificial Intelligence

In photovoltaic systems, the DC/DC converter operates in an environment characterized by disturbances, imprecise and uncertain variations in climatic parameters, and sensitivity to intrinsic photovoltaic module (PVM) parameters. These factors can significantly impact the operational efficiency and performance of both the DC/DC converters and the connected load. The objective of this research is to design an intelligent system comprising a fuzzy controller and a single inductance DC/DC converter to enhance the performance of PV systems. Two distinct Maximum Power Point Tracking (MPPT) methods are proposed: the first employs a traditional technique, while the second leverages fuzzy logic. The developed system incorporates a photovoltaic module that supplies power to a load through a step-down chopper. The experimental setup comprises two primary sections, namely an acquisition section and a power interface one. Signals acquired from the photovoltaic module are transmitted to the PC via the serial port using a PIC16F877A microcontroller. This microcontroller undertakes signal processing and control of the power interface for the chopper, employing a MOSFET power transistor. The outcomes obtained during the implementation underscore the necessity for the fuzzy controller in optimizing the overall system performance.