Design and evaluation of a microcontroller-based prototype of automatic dual-axis solar tracking system with integrated wind protection

¹ Prospective Technology of Electrical Engineering and Computer Science Department, National Chin-Yi University of Technology Taiwan, Taichung 411, Taiwan
² Department of Refrigeration, Air Conditioning, and Energy Engineering, National Chin-Yi University of Technology Taiwan, Taichung 411, Taiwan

^* Corresponding author: bkuncoro@ncut.edu.tw

Abstract

Solar photovoltaic (PV) energy plays a crucial role in sustainable energy transition, yet fixed PV installations often operate below optimal irradiance conditions, limiting their contribution to clean-energy generation. Improving panel orientation through dual-axis solar tracking can significantly enhance PV efficiency and support broader renewable-energy objectives. This study presents the design and evaluation of a low-cost, microcontroller-based dual-axis solar tracking (DAST) prototype developed to improve small-scale PV harvesting in distributed and educational settings. The system integrates four light-dependent resistors (LDRs) for real-time irradiance sensing, two high-torque servo motors for azimuth–elevation adjustment, and an Arduino Mega 2560 for environmental monitoring and control. A wind sensor enables automatic stow protection to reduce structural exposure under adverse conditions, enhancing system resilience for outdoor deployment. Experimental tests conducted under clear-sky conditions show that the DAST prototype increased the daily average output to 0.20 W, compared to 0.12 W for a fixed panel, representing an approximate 66% net improvement after accounting for actuator energy use. The tracker maintained an orientation accuracy of ±5° and demonstrated reliable wind-response behavior. Although validation was limited to short- term testing, the results indicate that accessible dual-axis tracking can meaningfully improve PV performance and support sustainable energy applications. Future work will focus on long-term field validation, advanced tracking algorithms, and integration with IoT-based monitoring to enhance scalability and environmental impact.