Data-Driven Assessment of Low-Speed Wind Energy Harvesting Potential Using MATLAB/Simulink Modeling - Igbinoba, C. K.; Okhaifoh, J. E. (European Journal of Science, Innovation and Technology, 2026)
This study presents a data-driven evaluation of a wind energy harvesting system designed for low power applications, particularly wireless sensor networks (WSNs). High resolution wind speed data were collected over a one-year period from January-December 2025 at the Federal University of Petroleum Resources, Effurun (FUPRE), Nigeria, using a calibrated Automated Weather Observing Station (AWOS). A comprehensive system model was developed in MATLAB/Simulink, incorporating aerodynamic wind turbine dynamics, electromagnetic energy conversion, rectification, DC-DC conversion, maximum power point tracking (MPPT) using the perturb and observe (P&O) algorithm, and a power management system (PMS) with battery storage. Simulation results reveal strong seasonal variations in harvested power, with peak performance observed in March, where average DC-DC output reaches approximately 38.022 mW and daily harvested energy peaks at about 0.766 Wh/day. Lower performance is recorded during periods of reduced wind activity, particularly between October and November. Despite these fluctuations, the PMS effectively regulates power flow, ensuring stable energy delivery to the load. The system demonstrates a relatively constant efficiency of approximately 16.47% across all months, indicating stable internal conversion performance. Overall, the results confirm the feasibility of utilizing low-speed wind energy for continuous power supply in autonomous systems. The study highlights the effectiveness of integrating real environmental data with intelligent power management strategies to enhance reliability and energy utilization in wind energy harvesting applications.