Solar PV modules offer clean, renewable energy, reducing carbon footprint and lowering electricity costs. They provide energy independence and require low maintenance. However, previously adopted techniques like simple MPPT methods often struggled with efficiency under variable irradiance and partial shading conditions. These methods lacked adaptability and precision, leading to suboptimal power extraction and increased reliance on grid electricity. Advanced algorithms and better optimization techniques address these drawbacks by enhancing efficiency and responsiveness. A novel hybrid technique is introduced for maximizing the power output of Solar Bifacial Half cut single PV panels while ensuring consistent power flow within the system. These panels incorporate bifacial technology, capturing sunlight on both their front and rear surfaces, and utilize half-cut solar cells, dividing conventional cells into two smaller ones for increased efficiency and reduced losses, especially in shaded or non-uniform irradiance conditions. The proposed hybrid technique combines the Random Forest Algorithm (RFA) with the Osprey Algorithm (OA), enhancing the prediction accuracy of RFA. In order to maximise PV output power, this combined strategy known as RFA-OA focuses on continuously tracking the Maximum Power Point (MPP). Based on voltage and current parameters, the RFA-OA algorithm specifically calculates the precise duty cycles required for the PV's DC-DC converter under various shading situations. This control method minimises fluctuations in system parameters and outside disturbances to provide the best possible load demand satisfaction. The suggested approach is put into practice in the MATLAB/Simulink environment and contrasted with current practices. It achieves a remarkable maximum output power efficiency of 99.951% for the PV panel, showcasing its efficacy in maximizing power generation while maintaining system stability and reliability.
