Bridging Human-Machine Interaction for Intelligent MPPT Control in Photovoltaic Energy Infrastructure

In recent decades, most countries rely more and more on renewable energy sources such as solar, wind, biomass and geothermal energies, etc. Solar and wind energy, in particular, are widely used because they are naturally abundant and environmentally friendly. Electricity generation based on renewable energy sources is increasing as a core component of modern energy infrastructure. To increase the output power of photovoltaic solar power generation, it is extremely important to force the system to operate at the highest possible efficiency. Maximum Power Point Tracking (MPPT) algorithms play a crucial role in optimizing the performances of the photovoltaic (PV) power generation system. This paper contributes to the modeling and development of the complete PV pumping system(Youssef,#6). Additionally, this research seeks to improve human-machine communication in energy systems by incorporating industrial oversight instruments. This communication happens through monitoring and controlling the system using STEP 7 software, which helps create a smooth link between the operator and the automated process, while comparing three Maximum Power Point Tracking (MPPT) methods: the widely used Perturb and Observe (P&O) and Incremental Conductance (INC) techniques, along with a method based on Artificial Neural Networks (ANN). The use of the ANN method demonstrates the benefits of smart control techniques. especially where their use is potential with rapidly varying irradiance. While the INC method has an appeal to its simplicity, and minimal installation costs, a comparative evaluation made reference to important metrics like voltage, current, and power production in different environments.