Mustafa Al-Sheikh
This paper presents an IoT-enabled dual-axis solar tracking system that integrates a Kalman filter and a Proportional-Integral-Derivative (PID) controller to enhance tracking accuracy, energy efficiency, and operational stability. Addressing the ongoing challenge of maxi- mizing photovoltaic (PV) panel output, the proposed system leverages an ESP32 microcontroller and the Blynk platform to provide real-time monitoring, remote parameter adjustments, and flexible connectivity. Light Dependent Resistor (LDR) sensors measure sunlight intensity from multiple directions, while MG90S servo motors dynamically adjust the panel’s azimuth and elevation. The Kalman filter refines noisy sensor data to yield precise sun position estimates, enabling the PID controller to respond quickly and accurately to deviations in panel orientation. Through extensive testing conducted over several days, including both clear and partially cloudy conditions, the system achieved an average Root Mean Square Error (RMSE) as low as 1.2° under clear skies and maintained RMSE below 2.0° even under partial shading. Compared to a fixed-panel baseline, daily energy harvesting improved by approximately 43%. These results confirm that advanced estimation and control algorithms, when combined with IoT functionali- ties, significantly outperform simpler tracking methods and static installations. Furthermore, the low-cost, compact design and user-friendly interface facilitate practical deployment in a range of scenarios, including small-scale and off-grid installations. By ensuring continuous alignment of the PV panel with the sun, the system not only increases overall energy capture but also reduces maintenance requirements through remote oversight. This research thus offers a robust, scalable approach to improving solar energy utilization in diverse and evolving environmental conditions.