The measurement accuracy of the wind direction sensor directly affects the data reliability in fields such as meteorological monitoring and environmental research, and the installation height and environmental shielding are key factors that interfere with the measurement results.
First, the impact of the installation height on the wind direction measurement results stems from the wind speed and direction gradient characteristics of the atmospheric boundary layer. Near the ground, due to the surface friction, the wind speed increases with the increase in height, and the wind direction will also differ due to the airflow shear at different heights. According to the atmospheric boundary layer theory, above the rough surface, the wind direction at a lower altitude is easily affected by the local terrain and buildings, showing a turbulent state; while at a higher altitude, the wind direction is closer to the true wind direction of the free atmosphere. For example, in urban areas, if the wind direction sensor is installed at an altitude of 10 meters and 50 meters, the measurement results may have significant deviations due to the flow around the building and turbulence. Therefore, clarifying the relationship between the installation height and the characteristics of the atmospheric boundary layer is the basis for evaluating the interference of wind direction measurement.
Secondly, environmental shielding will directly change the airflow pattern, resulting in distortion of wind direction measurement. Buildings, trees, mountains and other obstructions will cause the airflow to be blocked on the windward side and form vortices, and produce wake areas on the leeward side. The wind direction in these disturbed areas is very different from the actual wind direction. Taking urban high-rise buildings as an example, the wind direction sensor around them may measure accelerated and distorted airflow due to the narrow tube effect of the building, which cannot reflect the actual wind direction. In addition, although the impact range is small, obstructions such as low bushes or fences will also change the airflow direction in local areas, causing measurement errors. Accurately identifying the type, size and relative position of obstructions to the sensor is the key to quantifying the degree of interference.
Furthermore, establishing a mathematical model is an effective means to evaluate the interference between installation height and environmental obstruction. The computational fluid dynamics (CFD) software is used to simulate the distribution of airflow fields under different installation heights and obstruction conditions, and the law of wind direction changes is intuitively presented. For example, CFD is used to simulate the airflow around the building and analyze the measurement errors of sensors at different positions; combined with empirical formulas, such as the wind profile power law formula, a quantitative relationship between installation height and wind direction deviation is established. In addition, based on the machine learning algorithm, input terrain data, obstruction parameters and historical wind direction data, train the prediction model, and quickly evaluate the interference degree in a specific installation scenario, providing a scientific basis for sensor layout.
Then, the reasonable selection of installation location and height is a direct strategy to reduce interference. If conditions permit, the wind direction sensor should be installed in an open area, away from tall buildings and dense vegetation, to ensure that there is no obstruction around the sensor within a range of at least 10 times the height of the obstacle. For unavoidable obstruction environments, the sensor can be placed at a relatively stable height layer by increasing the installation height. For example, in a mountain meteorological station, the sensor is installed on the top of a tower higher than the surrounding mountains; in a city, choose an open area on the roof of a high-rise building and ensure that the sensor is kept at a sufficient distance from the edge of the building. In addition, the method of installing sensors in layers at multiple heights can be used to obtain more accurate wind direction information through data fusion.
Then, the data correction algorithm can effectively compensate for the measurement error caused by installation factors. For the wind direction deviation caused by the installation height, the wind profile model can be used to correct the low-altitude measurement data and convert it into a wind direction value at a standard height (such as 10 meters). For interference caused by environmental occlusion, algorithms such as Kalman filtering and adaptive weighted fusion can be used to smooth and correct the interfered data in combination with sensor data from surrounding unobstructed areas. For example, when a sensor is detected to have abnormal wind direction data due to building occlusion, the real wind direction is estimated by the algorithm by fusing the data of multiple surrounding sensors to achieve error compensation. The application of these algorithms requires continuous optimization of parameters in combination with actual scenarios to improve the correction accuracy.
In addition, regular calibration and maintenance are necessary measures to ensure measurement accuracy. During long-term operation, wind direction sensors may produce measurement errors due to mechanical wear, aging of electronic components, and changes in the installation environment (such as new buildings and vegetation growth) will also affect the measurement results. Therefore, it is necessary to formulate a regular calibration plan and use a standard wind direction source to calibrate the sensor; at the same time, inspect the installation environment, clear the obstructions in time, and evaluate the impact of environmental changes on the measurement. In addition, a sensor performance monitoring system is established to analyze the stability and consistency of the measurement data in real time. When an abnormality is found, the calibration or fault warning process is automatically triggered to ensure the reliability of the measurement data.
Finally, the formulation of standardized installation specifications and technical guidelines will help unify interference assessment and correction methods. Industry authorities and scientific research institutions should combine theoretical research with practical experience to develop technical standards for wind direction sensor installation, clarify installation height requirements, environmental assessment methods, and data correction processes in different application scenarios. For example, differentiated installation specifications should be developed for different fields such as meteorological observation and wind power generation; a unified interference assessment index system should be established to make measurement data from different regions and units comparable. Through standardization construction, the standardization of wind direction sensor installation and data processing can be promoted, and the measurement level and data quality of the entire industry can be improved.
