Hybrid-Intelligent P&O-InC Algorithm with Deterministic Adaptive Weighting for Maximum Power Point Tracking under Dynamic Conditions - Handaya, Devi; Bayhan, Nevra; Purwanti, Bernadeta Siti Rahayu; Ridlwan, Hasvienda Mohammad (Komunitas Ilmuwan dan Profesional Muslim Indonesia, 2025)
Electricity generation remains dominated by fossil fuel–based sources, underscoring the necessity to optimize the utilization of solar energy through photovoltaic (PV) systems in support of the Sustainable Development Goals (SDGs). Variations in solar irradiance and temperature significantly influence PV performance, necessitating effective Maximum Power Point Tracking (MPPT) methods. This present study proposes HI-POnIC as an adaptive development of conventional MPPT algorithms using a deterministic, feature-based decision mechanism. The method employed dynamic weighting and adaptive step adjustment to modify the control response to changes in PV operating characteristics, without any reliance on learning processes. Performance of the system was evaluated through convergence analysis, energy and power tracking efficiency, and spatial accuracy assessment. The findings from the simulation demonstrated that HI-POnIC achieved faster convergence and enhanced stability around the maximum power point when compared with conventional methods. Its lightweight and easily implementable adaptive structure has rendered HI-POnIC suitable for PV systems operating under dynamically varying environmental conditions.