Enhancement of solar energy utilization through an artificial neural network controller featuring a dynamic learning rate and a positive output super lift luo converter

With the increasing demand for sustainable energy solutions, enhancing the efficiency of photovoltaic (PV) systems is critical. PV systems, although effective, often face efficiency variations due to changes in irradiance and temperature. This study aims to address these challenges by integrating a Positive Output Super Lift Luo (P/O SLL) converter with an Artificial Neural Network (ANN) controller utilizing a dynamic learning rate. Known for its high voltage conversion gain, the P/O SLL converter is ideal for various load requirements in PV systems. The ANN controller with a dynamic learning rate adapts to real-time environmental changes, outperforming static learning rate methods. This approach allows the ANN to manage non-linear and variable inputs from PV cells more efficiently, optimizing the Maximum Power Point Tracking (MPPT) process. The research involved simulating a PV system model using MATLAB/Simulink, integrating the P/O SLL converter and ANN controller. The study compared the performance of the ANN controller with a dynamic learning rate against a static learning rate, using 201 data samples for training. The results showed improvements in energy conversion efficiency, with the dynamic learning rate providing more stable voltage and power outputs under varying conditions, demonstrating its potential in enhancing PV system performance.