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Digital Twin Framework For Time To Failure Forecasting Of Wind Turbine Gearbox: A Concept

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preprint
posted on 02.05.2022, 20:40 by Mili WadhwaniMili Wadhwani, Sakshi Deshmukh, Harsh S. Dhiman
Wind turbine is a complex machine with its rotating and non-rotating equipment being sensitive to faults. Due to increased wear and tear, the maintenance aspect of a wind turbine is of critical importance. Unexpected failure of wind turbine components can lead to increased O&M costs which ultimately reduces effective power capture of a wind farm. Fault detection in wind turbines is often supplemented with SCADA data available from wind farm operators in the form of time-series format with a 10-minute sample interval. Moreover, time-series analysis and data representation has become a powerful tool to get a deeper understating of the dynamic processes in complex machinery like wind turbine. Wind turbine SCADA data is usually available in form of a multivariate time-series with variables like gearbox oil temperature, gearbox bearing temperature, nacelle temperature, rotor speed and active power produced. In this preprint, we discuss the concept of a digital twin for time to failure forecasting of the wind turbine gearbox where a predictive module continuously gets updated with real-time SCADA data and generates meaningful insights for the wind farm operator.

History

Email Address of Submitting Author

miliwadhwani.ele19@aii.ac.in

Submitting Author's Institution

Adani Institute of Infrastructure Engineering, Ahmedabad, India

Submitting Author's Country

India