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Floating Offshore Wind Turbines (FOWTs) operate in the harsh marineenvironment with limited accessibility and maintainability. Not only failuresare more likely to occur than in land-based turbines, but also correctivemaintenance is more expensive. In the present study, a mixed model andsignal-based Fault Diagnosis (FD) architecture is developed to detect andisolate critical faults in FOWTs. More specifically, a model-based scheme isdeveloped to detect and isolate the faults associated with the turbine system.It is based on a fault detection and approximation estimator and faultisolation estimators, with time-varying adaptive thresholds to guaranteeagainst false-alarms. In addition, a signal-based scheme is established, withinthe proposed architecture, for detecting and isolating two representativemooring lines faults. For the purpose of verification, a 10MW FOWT benchmark isdeveloped and its operating conditions, which contains predefined faults, aresimulated by extending the high-fidelity simulator. Based on it, theeffectiveness of the proposed architecture is illustrated. In addition, theadvantages and limitations are discussed by comparing its fault detection tothe results delivered by other approaches. Results show that the proposedarchitecture has the best performance in detecting and isolating the criticalfaults in FOWTs under diverse operating conditions.

Fault Diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: a Mixed Model and Signal-based Approach