Adaptive MPPT Control in Autonomous Photovoltaic Systems With Human-Driven Algorithm Selection via HMI

This chapter presents an adaptive Maximum Power Point Tracking (MPPT) control framework for photovoltaic systems featuring real-time human-driven algorithm selection via an intuitive Human-Machine Interface (HMI). The platform integrates four classical algorithms—Perturb and Observe (P&O), Incremental Conductance (INC), Constant Voltage (CV), and enhanced P&O (dP-P&O)—implemented on dSPACE DS1104 hardware. Experimental evaluation under standardized staircase irradiance profiles reveals INC achieves highest efficiency (99.946%), followed by P&O (99.884%) and dP-P&O (99.780%), while CV offers computational simplicity despite lower efficiency (95.324%). This human-in-the-loop approach enables context-aware algorithm optimization without interrupting power generation, demonstrating superior adaptability compared to fixed MPPT implementations. The modular architecture supports future integration of AI-based techniques and comprehensive PV diagnostics.