Data-Driven Fault Detection in Floating Offshore Wind Turbines Using Machine Learning and Benchmark Simulations

In this paper, a data-driven Fault Detection strategy is proposed for detecting faults in generator speed sensors of floating offshore wind turbines using Machine Learning (ML) techniques. To generate reliable datasets in both healthy and faulty conditions, a seven-turbine offshore wind farm is set up using the FOWLTY MATLAB/Simulink benchmark framework. A systematic approach is applied for signal-based feature extraction, followed by dimensionality reduction through feature selection using LightGBM. Two classifiers-Support Vector Machines (SVM) and Decision Trees (DT)-are trained and evaluated on the resulting datasets. The results show that both methods achieve acceptable accuracy in fault classification, but the SVM demonstrates better performance compared to the DT. These findings confirm that the proposed methods can accurately detect generator speed sensor faults in offshore wind turbines. The results confirm that the proposed methods can accurately detect generator speed sensor faults in offshore wind turbines.