<![CDATA[The continuous diurnal shift in solar trajectory prevents fixed-tilt photovoltaic (PV) panels from maintaining an optimal angle of incidence, thereby limiting overall electrical energy yield. This study presents the design, implementation, and empirical evaluation of an ESP32- and Internet of Things (IoT)-based dual-axis solar tracking system engineered to maximize PV power generation. The experimental setup comprises a 10 Wp monocrystalline solar module, a light-sensing array of four Light Dependent Resistors (LDRs), dual MG996R servo actuators for azimuth and elevation adjustments, and an INA219 sensor for real-time electrical telemetry. System performance was benchmarked against a stationary PV panel across a five-day field trial during effective irradiance hours (08:00–16:00 WITA). Experimental results demonstrate that the dual-axis tracker achieved a mean daily energy yield of 55.0 Wh, exhibiting a 19.7% increase compared to the fixed baseline (46.1 Wh). Nevertheless, parasitic load analysis indicates that the control electronics and motor actuation consumed an average of 3.8 Wh/day, resulting in a net energy gain of 11.1%. These findings confirm the efficacy of dual-axis tracking in enhancing solar radiation capture, while underscoring the critical need for low-power management algorithms to optimize net energy efficiency in micro-scale PV applications.]]>
Copyrights © 2026
