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Kalman-Filter Based Data Fusion for Neutral Axis Tracking for Damage Detection in Wind-Turbine Towers

Abstract : Wind Energy is seen as one of the most promising solutions to manÕs ever increasing demands of a clean source of energy. But there is a need to reduce the high initial costs for setting up and the maintenance costs. The maintenance cost may be lowered through the use of condition monitoring (CM) and structural health monitoring (SHM). SHM allows early detection of damage and allows maintenance planning which reduces the cost. In this paper, change in Neutral Axis (NA) position is proposed as a metric for damage detection. A discrete Kalman filter (KF) is employed for the estimation of the NA in the presence of measurement noise from the strain sensors. The KF allows data fusion from the strain sensors and the yaw mechanism for the accurate estimation of the NA. Any change in the NA position may be used as an indicator for the presence and location of the damage. The study has been carried out on the simulated FE model of the wind turbine tower and indicates that NA tracking based on data fusion is sensitive to damage and robust enough to overcome the effects of measurement noise, and yawing of the nacelle.
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Submitted on : Tuesday, July 8, 2014 - 9:57:56 AM
Last modification on : Monday, November 23, 2020 - 12:52:03 PM
Long-term archiving on: : Wednesday, October 8, 2014 - 11:31:41 AM


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  • HAL Id : hal-01020345, version 1



Rohan Soman, Pawel Henryk Malinowski, Wiesław Ostachowicz. Kalman-Filter Based Data Fusion for Neutral Axis Tracking for Damage Detection in Wind-Turbine Towers. EWSHM - 7th European Workshop on Structural Health Monitoring, IFFSTTAR, Inria, Université de Nantes, Jul 2014, Nantes, France. ⟨hal-01020345⟩



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