Rocha Álvaro B, Fernandes Eisenhawer de M, Souto Joyce I V, Gomez Ricardo S, Delgado João M P Q, Lima Felipe S, Alves Railson M N, Bezerra André L D, Lima Antonio G B
Department of Mechanical Engineering, Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil.
Laboratory of Electronic Instrumentation and Control (LIEC), Department of Electrical Engineering, Federal University of Campina Grande (UFCG), Campina Grande 58429-900, Brazil.
Micromachines (Basel). 2024 Sep 25;15(10):1186. doi: 10.3390/mi15101186.
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.
当前文章阐述了一项以开发利用惯性传感器测量巴西东北地区风速的风速计为核心的研究,重点在于可再生能源发电。该研究包括一系列实验,旨在校准风速计、分析惯性传感器产生的噪声以及仔细检查风速测量过程中获取的数据。校准过程分三个阶段展开:初始噪声分析、随后的惯性数据分析以及校准曲线的推导。前两个阶段的实验以平均10赫兹的采样率进行。同时,第三阶段纳入了在保持相同采样率的情况下持续1小时收集的数据。结果强调了基于惯性传感器的风速计适用于风能系统及各种应用。虽然原型的风速读数存在相当大的波动,但对原型的输出应用了一个三段移动平均滤波器来减轻这些波动。利用观测数据建立了校准曲面,并详细说明了所得曲面。数据分析在风速测量中至关重要,并且K近邻算法在估计测量数据与控制数据之间的对应关系方面显示出卓越的功效。