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

RENEWABLE ENERGY

卷:164

Article

Liu, Yichao¹  Ferrari, Riccardo¹  Wu, Ping²  Jiang, Xiaoli¹  Li, Sunwei³  van Wingerden, Jan-Willem¹ 

[1] Delft Univ Technol, NL-2628 CD Delft, Netherlands

[2] Zhejiang Sci Tech Univ, Hangzhou 310018, Peoples R China

[3] Tsinghua Univ, Grad Sch Shenzhen, Shenzhen 518055, Peoples R China

关键词: Fault diagnosis;    Floating offshore wind turbine;    Model-based scheme;    Signal-based scheme;    FAST simulation;   

DOI  :  10.1016/j.renene.2020.06.130

来源: Elsevier

【 摘 要 】

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. (c) 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

【 授权许可】

Free