Identification of equivalent wind and wave loads for monopile-supported offshore wind turbines in operating condition

Abstract

Wind and wave loads are critical to monopile-supported offshore wind turbines (OWTs) in determining whether OWTs are in safe conditions to generate electricity. However, these random and complex loads vary with time and location, and are difficult to measure directly. Theoretically, the loads can be identified based on structural responses. Nevertheless, it is challenging due to the complexity of external loads and the existence of harmonic loads generated by rotor rotation. This study proposes a new load identification framework using a limited amount of monitoring data to estimate the equivalent wind and wave loads of OWTs simultaneously. Both loads are modeled as Gaussian processes with exponential covariance function and incorporated into the augmented state-space model. Kalman filter is employed to identify the equivalent loads. The effectiveness and accuracy of the proposed method were validated based on a numerical OWT model and a scaled OWT test model. The results demonstrate that the framework can successfully identify equivalent wind and wave loads of OWTs in both parked and operating conditions. The probability distribution of identified load data is consistent with actual load data, with an error of less than 9.1 %. The proposed framework can find vast applications in operational management for OWTs.