Prediction of Clearance Value Based on Kalman Filtering

The laser clearance radar is a type of lidar designed for real-time monitoring of the blade tip clearance distance. In rainy or foggy conditions, the water vapor and droplets in the atmosphere cause scattering and absorption of laser signals, leading to a significant increase in invalid data obtained by the laser clearance radar. To address this issue, on the basis of data pre-processing a clearance value prediction model based on Kalman filtering is developed using the clearance data acquired by the laser clearance radar. Experiments are conducted to predict clearance values, followed by accuracy evaluations of the predicted clearance data. The results demonstrate that the proposed clearance value prediction method achieves high prediction accuracy, with a correlation coefficient of 0.99 between predicted and measured clearance values, a root mean square error of 0.07, and an average relative error of 0.48 %, which improves the validity and integrity of the laser clearance radar data. The study provides a valuable reference for the clearance warning control of wind turbines.