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

Liu, Yichao; Ferrari, Riccardo; Wu, Ping; Jiang, Xiaoli; Li, Sunwei; Wingerden, Jan‐Willem van

doi:10.1016/j.renene.2020.06.130

Cited by 41 publications

(26 citation statements)

References 35 publications

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“…In light of these facts, the extended Kalman filter observer is used for the rope stiffness estimation in order to identify problematic ropes using acceleration data. The data are obtained at various places along parts of modular offshore platforms and modular multi-level converters [ 349 , 360 , 361 , 362 ]. This technique allows for the detection of faults in the rope connection elements, such as cracks or ruptured strands, as a change in the stiffness of the connection element.…”

Section: Fault Monitoring Using Sensors On Offshore Facilitiesmentioning

confidence: 99%

Guidelines on Asset Management of Offshore Facilities for Monitoring, Sustainable Maintenance, and Safety Practices

Amaechi

Reda

Kgosiemang

et al. 2022

Sensors

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Recent activities in the oil and gas industry have shown an increasing need for monitoring engagements, such as in shipping, logistics, exploration, drilling, or production. Hence, there is a need to have asset management of these offshore assets (or facilities). Much of the offshore infrastructure is currently approaching or past its operational life expectancy. The study presents an overview on asset management of offshore facilities towards monitoring, safe practices, maintenance, and sustainability. This study outlines the major considerations and the steps to take when evaluating asset life extensions for an aging offshore structure (or asset). The design and construction of offshore structures require some materials that are used to make the structural units, such as offshore platform rigs, ships, and boats. Maintaining existing assets in the field and developing new platforms that are capable of extracting future oil and gas resources are the two key issues facing the offshore sector. This paper also discusses fault diagnosis using sensors in the offshore facilities. The ocean environment is constantly corrosive, and the production activities demand extremely high levels of safety and reliability. Due to the limited space and remote location of most offshore operations, producing cost-effective, efficient, and long-lasting equipment necessitates a high level of competence. This paper presents the guidelines on asset monitoring, sustainable maintenance, and safety practices for offshore structures. In this study, the management of offshore structures were also presented with some discussions on fault monitoring using sensors. It also proposes sustainable asset management approaches as guidelines that are advised, with policy implications.

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Section: Fault Monitoring Using Sensors On Offshore Facilitiesmentioning

confidence: 99%

Guidelines on Asset Management of Offshore Facilities for Monitoring, Sustainable Maintenance, and Safety Practices

Amaechi

Reda

Kgosiemang

et al. 2022

Sensors

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“…In this section, a 10MW three‐bladed variable speed reference wind turbine with the Triple‐Spar floating platform is simulated to verify the performance of the proposed RDCCA‐FDA method. The simulation is based on the high‐fidelity benchmark model developed by the Technical University of Denmark (DTU)[42] and Stuttgart Wind Energy (SWE) institute [13, 43]. The benchmark model of the operational FOWT is simulated by the physics‐based FAST v8 simulator.…”

Section: Case Studymentioning

confidence: 99%

“…The wind model is using the IEC61400-3 design regulation, where a turbulent wind field is modeled by mixing a mean wind and a fluctuating component. The turbulent wind is generated by using Turbsim [44] according to the Kaimal turbulence model including the turbulence intensity with IEC Class C. Similar to [13], to simulate the realistic wind fields, the turbulence intensity is a function of the wind speed at the hub height. In this study, two load cases (LCs) are employed.…”

Section: Case Studymentioning

confidence: 99%

“…Additionally, a band-limited Gaussian white noise is added to the measurements. The specification of the band-limited Gaussian white noise can be found in [13].…”

Section: Case Studymentioning

confidence: 99%

“…Wind turbine fault detection and classification methods can be roughly categorized into two classes: model-based and datadriven approaches. For model-based approaches, Kalman filter and estimators, parity equations, and observer-based fault detection techniques have been reported in the literature for wind turbines or their subsystems [9][10][11][12][13]. The advantage of modelbased methods is that they can offer the faults insight.…”

Section: Introductionmentioning

confidence: 99%

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Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis

Liu

Ferrari

et al. 2021

IET Renewable Power Gen

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Over the past decades, Floating Offshore Wind Turbine (FOWT) has gained increasing attention in wind engineering due to the rapidly growing energy demands. However, difficulties in turbine maintenance will increase due to the harsh operational conditions. Fault diagnosis techniques play a crucial role to enhance the reliability of FOWTs and reduce the cost of offshore wind energy. In this paper, a novel data‐driven fault diagnosis method using regularized dynamic canonical correlation analysis (RDCCA) and Fisher discriminant analysis (FDA) is proposed for FOWTs. Specifically, to overcome the collinearity problem that exists in measured process data, dynamic canonical correlation analysis with a regularization scheme, is developed to exploit the relationship between input and output signals. Then, the residual signals are generated from the established RDCCA model for fault detection. To further classify the fault type, an FDA model is trained from the residual signals of different training faulty data sets. Simulations on a FOWT baseline model based on the widely used National Renewable Energy Laboratory FAST simulator are carried out to demonstrate the feasibility and efficacy of the proposed fault detection and classification method. Results have shown many salient features of the proposed method with potential applications in FOWTs.

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Load response of a two‐rotor floating wind turbine undergoing blade‐pitch system faults

2023

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Multi‐rotor floating wind turbines are among the innovative technologies proposed in the last decade in the effort to reduce the cost of wind energy. These systems are able to offer advantages in terms of smaller blades deployed offshore, cheaper operations, fewer installations, and sharing of the floating platform. As the blade‐pitch actuation system is prone to failures, the assessment of the associated load scenarios is commonly required. Load assessment of blade‐pitch fault scenarios has only been performed for single‐rotor solutions. In this work, we address the effect of blade‐pitch system faults and emergency shutdown on the dynamics and loads of a two‐rotor floating wind turbine. The concept considered employs two NREL 5‐MW baseline wind turbines and the OO‐Star semi‐submersible platform. The blade‐pitch faults investigated are blade blockage and runaway, that is, the seizure at a given pitch angle and the uncontrolled actuation of one of the blades, respectively. Blade‐pitch faults lead to a significant increase in the structural loads of the system, especially for runaway fault conditions. Emergency shutdown significantly excites the platform pitch motion, the tower‐bottom bending moment, and tower torsional loads, while suppressing the faulty blade flapwise bending moment after a short peak. Shutdown delay between rotors increases significantly the maxima of the torsional loads acting on the tower. Comparison of blade loads with data from single‐rotor spar‐type study show great similarity, highlighting that the faulty blade loads are not affected by (1) the type of platform used and (2) the multi‐rotor deployment.

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

Cited by 41 publications

References 35 publications

Guidelines on Asset Management of Offshore Facilities for Monitoring, Sustainable Maintenance, and Safety Practices

Guidelines on Asset Management of Offshore Facilities for Monitoring, Sustainable Maintenance, and Safety Practices

Floating offshore wind turbine fault diagnosis via regularized dynamic canonical correlation and fisher discriminant analysis

Load response of a two‐rotor floating wind turbine undergoing blade‐pitch system faults

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