Development of Anemometer Based on Inertial Sensor
Álvaro B. Rocha,
Eisenhawer de M. Fernandes,
Joyce I. V. Souto,
Ricardo S. Gomez,
João M. P. Q. Delgado,
Felipe S. Lima,
Railson M. N. Alves,
André L. D. Bezerra,
Antonio G. B. Lima
Affiliations
Álvaro B. Rocha
Department of Mechanical Engineering, Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil
Eisenhawer de M. Fernandes
Laboratory of Electronic Instrumentation and Control (LIEC), Department of Electrical Engineering, Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil
Joyce I. V. Souto
Department of Mechanical Engineering, Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil
Ricardo S. Gomez
Department of Mechanical Engineering, Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil
João M. P. Q. Delgado
CONSTRUCT-LFC, Civil Engineering Department, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
Felipe S. Lima
Department of Mechanical Engineering, Federal University of Paraíba, João Pessoa 58051-900, Brazil
Railson M. N. Alves
Department of Mechanical Engineering, Federal University of Paraíba, João Pessoa 58051-900, Brazil
André L. D. Bezerra
Postgraduate Program in Process Engineering, Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil
Antonio G. B. Lima
Department of Mechanical Engineering, Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil
The current article elucidates a study centered on the development of an anemometer leveraging an inertial sensor for wind speed measurement in the northeast region of Brazil, focusing on renewable energy generation. The study encompassed a series of experiments aimed at calibrating the anemometer, analyzing the noise generated by the inertial sensor, and scrutinizing the data acquired during wind speed measurement. The calibration process unfolded in three stages: initial noise analysis, subsequent inertial data analysis, and the derivation of calibration curves. The first two stages involved experiments conducted at an average sampling rate of 10 Hz. Simultaneously, the third stage incorporated data collected over a 1 h duration while maintaining the same sampling rate. The outcomes underscore the suitability of the anemometer based on an inertial sensor for wind energy systems and diverse applications. While the wind readings from the prototype exhibit considerable fluctuations, a three-length moving average filter is applied to the prototype’s output to mitigate these fluctuations. The calibration surface was established using observational data, and the resultant surface is detailed. Data analysis assumes paramount significance in wind speed measurement, and the K-NN algorithm demonstrated superior efficacy in estimating the correspondence between measured and control data.