微风速流场数据采集与融合方法研究

在微风速(0～1 m/s)空间流场测量中,对传感器精度要求高,实时在线仪表数据精度不够,数据采集滞后性大;考虑采用多个传感器测量提高精度,但也存在数据融合的问题.针对微风速流场测量,提出基于K均值RBF神经网络的数据采集预处理软测量模型,首先选取中间变量(电流值),运用K均值聚类,用RBF网络训练得到单个传感器数据;提出基于相关性kalman滤波的传感器数据融合算法,剔除无效数据点,并融合得到精确风速预测值.测量实验和数据结果表明该方法处理的数据结果滞后性小,处理速度快,数据精度高.

Wind velocity flow field data acquisition and fusion method research

In the wind speed (0～ 1 m/s) space flow field measurement,the sensor accuracy requirement is high,the real time online instrument data accuracy is not enough,hysteresis of data acquisition;Consider using multiple sensors measurement improve accuracy,but also has the problem of data fusion.Velocity flow field measurement,the author of this paper,based on k means data collection and pretreatment of RBF neural network soft measurement model,firstly,intermediate variable (current value),using the k means clustering,use RBF network training to get a single sensor data;Based on correlation sensor data fusion algorithm of kalman filtering,eliminate invalid data points,and get accurate fusion wind speed prediction.Measurement experiment and data results show that this method processing data results of hysteresis is small,processing speed,high precision of data.