Design and implementation of a low-cost IoT-enabled dual-axis photovoltaic tracking system with experimental and simulation-based validation

Abstract Photovoltaic (PV) systems have become one of the most important solutions to sustainable energy production due to the growing demand of renewable energy and the necessity to harness energy in the most efficient manner. But traditional fixed-tilt PV systems have lower power collection because they are not able to maintain optimal orientation towards the sun over the day and under varying weather conditions. This study suggests a low-cost Internet of Things (IoT)-based dual-axis solar tracking system with in-built real-time monitoring and validation to overcome these constraints. This system employs LDR-based sensing and proportional control to actively align the PV panel and electrical and environmental parameters are constantly measured and sent to cloud platforms to be analyzed. A six months field test in Dhaka, Bangladesh has indicated a stable performance of the system and the average voltage of the system is 10.05 ± 0.12 V, the current is 0.135 ± 0.003 A and the power output is 1.43 ± 0.05 W. Comparative analysis indicates that the system is more efficient with an improvement in the energy of about 40–45. The significance of the observed improvements is statistically proven with the help of t-tests and ANOVA (p < 0.05), although simulations with MATLAB/Simulink prove the simulated behavior of the system when the conditions are controlled. The main contribution of this research is the combination of low-cost dual-axis tracking, real-time monitoring with the IoT, and experimental and simulation-based validation in one framework. The proposed system shows the feasibility of a working and scalable solution to the enhancement of solar energy harvesting in the resource-limited areas.