School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China.
Sensors (Basel). 2020 Jan 17;20(2):523. doi: 10.3390/s20020523.
The wind power industry continues to experience rapid growth worldwide. However, the fluctuations in wind speed and direction complicate the wind turbine control process and hinder the integration of wind power into the electrical grid. To maximize wind utilization, we propose to precisely measure the wind in a three-dimensional (3D) space, thus facilitating the process of wind turbine control. Natural wind is regarded as a 3D vector, whose direction and magnitude correspond to the wind's direction and speed. A semi-conical ultrasonic sensor array is proposed to simultaneously measure the wind speed and direction in a 3D space. As the ultrasonic signal transmitted between the sensors is influenced by the wind and environment noise, a Multiple Signal Classification algorithm is adopted to estimate the wind information from the received signal. The estimate's accuracy is evaluated in terms of root mean square error and mean absolute error. The robustness of the proposed method is evaluated by the type A evaluation of standard uncertainty under a varying signal-to-noise ratio. Simulation results validate the accuracy and anti-noise performance of the proposed method, whose estimated wind speed and direction errors converge to zero when the SNR is over 15 dB.
风力发电行业在全球范围内继续快速发展。然而，风速和方向的波动使风力涡轮机的控制过程变得复杂，并阻碍了风力发电并入电网。为了最大限度地利用风力，我们建议精确测量三维（3D）空间中的风力，从而便于风力涡轮机的控制过程。自然风被视为一个 3D 向量，其方向和大小对应于风的方向和速度。提出了一种半圆锥形超声传感器阵列，用于同时测量 3D 空间中的风速和风向。由于传感器之间传输的超声信号受到风和环境噪声的影响，因此采用多信号分类算法从接收信号中估计风信息。根据均方根误差和平均绝对误差来评估估计的准确性。通过在变化的信噪比下进行标准不确定度 A 类评定来评估所提出方法的稳健性。仿真结果验证了所提出方法的准确性和抗噪声性能，当 SNR 超过 15dB 时，其估计的风速和方向误差收敛于零。
