AN IOT-BASED ADAPTIVE PHOTOVOLTAIC SYSTEM EMPLOYING FUZZY MPPT AND DUAL-AXIS TRACKING TO MAINTAIN POWER STABILITY UNDER NON-UNIFORM IRRADIANCE CONDITIONS

Abstrak

This study presents an adaptive photovoltaic system integrated with Internet of Things (IoT) technology, which combines a Fuzzy Logic–based Maximum Power Point Tracking (MPPT) algorithm with a dual-axis solar tracking mechanism to maintain stable power generation under non-uniform irradiance conditions. The system is experimentally implemented with real-time monitoring and data transmission through an IoT platform, enabling continuous performance evaluation in dynamic outdoor environments. Experimental results indicate that the proposed system improves MPPT tracking efficiency from 96% to 99.98%, with optimal operation achieved within a Pulse Width Modulation (PWM) duty cycle range of 71.8%–72.2%. The most rapid transient response and stable oscillatory behavior are observed at a duty cycle of 0.03, ensuring consistent operation at the maximum power point. Compared to a system without Fuzzy Logic control, the proposed approach increases output power by 1.6%, and achieves up to a 15% improvement relative to a conventional MPPT system without Fuzzy Logic and dual-axis tracking under unfavorable weather conditions. These findings confirm that the proposed IoT-based adaptive photovoltaic system effectively enhances energy harvesting efficiency and operational reliability in real-world applications.