Study on Noise Reduction of Hydrostatic Leveling Signals for Wind Turbine Foundations Based on CEEMDAN‐SG Algorithm

The hydrostatic leveling monitoring data related to the settlement of the wind turbine foundation display substantial fluctuations along with considerable noise. In this study, based on the characteristic of the hydrostatic level measurement data of wind turbine foundation, a joint denoising method that integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm with Savitzky–Golay (SG) filtering is proposed. Several commonly used denoising algorithms were presented to verify the effectiveness of the proposed joint algorithm. The denoising performance of each algorithm was evaluated through quantitative analysis, which included calculating the signal‐to‐noise ratio, mean square error, and coefficient of determination derived from the simulated signal data. The corresponding results affirm the effectiveness and superiority of the proposed algorithms. Furthermore, the proposed algorithm was employed to mitigate the inherent noise present in field measurement data. Subsequently, a qualitative analysis was undertaken to elucidate the correlation between the denoised results and the fundamental stress state of the actual wind turbine foundation. The results demonstrate that the joint noise reduction method exhibits significant advantages compared to the individual CEEMDAN and SG algorithms. Moreover, the refined hydrostatic leveling signal precisely captures the settlement trends within the wind turbine foundation, providing a clearer understanding of its structural stability and performance.