Global Sensitivity Analysis of High Speed Shaft Subsystem of a Wind Turbine Drive Train

• Published in: International journal of rotating machinery Vol. 2018; no. 2018; pp. 1 - 20
• Main Authors: Asadi, Saeed, Johansson, Håkan, Berbyuk, Viktor
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Alternative energy sources; Decomposition; Design optimization; Dynamic structural analysis; Economic models; Fault detection; High speed; Mathematical models; Methods; Parameter sensitivity; Powertrain; Random variables; Reliability engineering; Sensitivity analysis; Shafts (machine elements); Studies; System dynamics; Turbines; Variance analysis; Wind turbines
• ISSN: 1023-621X; 1542-3034; 1542-3034
• DOI: 10.1155/2018/9674364

The wind turbine dynamics are complex and critical area of study for the wind industry. Quantification of the effective factors to wind turbine performance is valuable for making improvements to both power performance and turbine health. In this paper, the global sensitivity analysis of validated mathematical model for high speed shaft drive train test rig has been developed in order to evaluate the contribution of systems input parameters to the specified objective functions. The drive train in this study consists of a 3-phase induction motor, flexible shafts, shafts’ coupling, bearing housing, and disk with an eccentric mass. The governing equations were derived by using the Lagrangian formalism and were solved numerically by Newmark method. The variance based global sensitivity indices are introduced to evaluate the contribution of input structural parameters correlated to the objective functions. The conclusion from the current research provides informative beneficial data in terms of design and optimization of a drive train setup and also can provide better understanding of wind turbine drive train system dynamics with respect to different structural parameters, ultimately designing more efficient drive trains. Finally, the proposed global sensitivity analysis (GSA) methodology demonstrates the detectability of faults in different components.