Fault diagnosis of the 10MW Floating Offshore Wind Turbine Benchmark: A mixed model and signal-based approach. (February 2021)

• Record Type: Journal Article
• Authors: Liu, Yichao; Ferrari, Riccardo; Wu, Ping; Jiang, Xiaoli; Li, Sunwei; Wingerden, Jan-Willem van
• Is Part Of: Renewable energy. Volume 164(2021)
• Page Start: 391
• Page End: 406
• Publication Date: 2021-02
• Subjects: Fault diagnosis -- Floating offshore wind turbine -- Model-based scheme -- Signal-based scheme -- FAST simulation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.06.130
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Floating Offshore Wind Turbines (FOWTs) operate in the harsh marine environment with limited accessibility and maintainability. Not only failures are more likely to occur than in land-based turbines, but also corrective maintenance is more expensive. In the present study, a mixed model and signal-based Fault Diagnosis (FD) architecture is developed to detect and isolate critical faults in FOWTs. More specifically, a model-based scheme is developed to detect and isolate the faults associated with the turbine system. It is based on a fault detection and approximation estimator and fault isolation estimators, with time-varying adaptive thresholds to guarantee against false-alarms. In addition, a signal-based scheme is established, within the proposed architecture, for detecting and isolating two representative mooring lines faults. For the purpose of verification, a 10 MW FOWT benchmark is developed and its operating conditions, which contains predefined faults, are simulated by extending the high-fidelity simulator. Based on it, the effectiveness of the proposed architecture is illustrated. In addition, the advantages and limitations are discussed by comparing its fault detection to the results delivered by other approaches. Results show that the proposed architecture has the best performance in detecting and isolating the critical faults in FOWTs under diverse operating conditions. Highlights: A mixed fault diagnosis architecture is developed for FOWTs. A FOWT benchmark is set up by extending the FAST code for verification. A fault generator, within the benchmark, is developed to simulate the fault scenarios. The fault diagnosis architecture has been verified via the benchmark. Results show that the mixed fault diagnosis architecture is effective for FOWTs.