A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Kalman filter based data fusion for neutral axis tracking in wind turbine towers
Wind energy is seen as one of the most promising solutions to man's ever increasing demands of a clean source of energy. In particular to reduce the cost of energy (COE) generated, there are efforts to increase the life-time of the wind turbines, to reduce maintenance costs and to ensure high availability. Maintenance costs may be lowered and the high availability and low repair costs ensured through the use of condition monitoring (CM) and structural health monitoring (SHM). SHM allows early detection of damage and allows maintenance planning. Furthermore, it can allow us to avoid unnecessary downtime, hence increasing the availability of the system. The present work is based on the use of neutral axis (NA) for SHM of the structure. The NA is tracked by data fusion of measured yaw angle and strain through the use of Extended Kalman Filter (EKF). The EKF allows accurate tracking even in the presence of changing ambient conditions. NA is defined as the line or plane in the section of the beam which does not experience any tensile or compressive forces when loaded. The NA is the property of the cross section of the tower and is independent of the applied loads and ambient conditions. Any change in the NA position may be used for detecting and locating the damage. The wind turbine tower has been modelled with FE software ABAQUS and validated on data from load measurements carried out on the 34m high tower of the Nordtank, NTK 500/41 wind turbine.
Kalman filter based data fusion for neutral axis tracking in wind turbine towers
Wind energy is seen as one of the most promising solutions to man's ever increasing demands of a clean source of energy. In particular to reduce the cost of energy (COE) generated, there are efforts to increase the life-time of the wind turbines, to reduce maintenance costs and to ensure high availability. Maintenance costs may be lowered and the high availability and low repair costs ensured through the use of condition monitoring (CM) and structural health monitoring (SHM). SHM allows early detection of damage and allows maintenance planning. Furthermore, it can allow us to avoid unnecessary downtime, hence increasing the availability of the system. The present work is based on the use of neutral axis (NA) for SHM of the structure. The NA is tracked by data fusion of measured yaw angle and strain through the use of Extended Kalman Filter (EKF). The EKF allows accurate tracking even in the presence of changing ambient conditions. NA is defined as the line or plane in the section of the beam which does not experience any tensile or compressive forces when loaded. The NA is the property of the cross section of the tower and is independent of the applied loads and ambient conditions. Any change in the NA position may be used for detecting and locating the damage. The wind turbine tower has been modelled with FE software ABAQUS and validated on data from load measurements carried out on the 34m high tower of the Nordtank, NTK 500/41 wind turbine.
Kalman filter based data fusion for neutral axis tracking in wind turbine towers
Soman, Rohan (author) / Malinowski, Pawel (author) / Ostachowicz, Wieslaw (author) / Schmidt Paulsen, Uwe (author) / Kundu, Tribikram
2015-01-01
Soman , R , Malinowski , P , Ostachowicz , W & Schmidt Paulsen , U 2015 , Kalman filter based data fusion for neutral axis tracking in wind turbine towers . in T Kundu (ed.) , Health Monitoring of Structural and Biological Systems 2015 . vol. 9438 , SPIE - International Society for Optical Engineering , Proceedings of SPIE - The International Society for Optical Engineering , pp. 94381B , Health Monitoring of Structural and Biological Systems 2015 , San Diego , California , United States , 09/03/2015 . https://doi.org/10.1117/12.2084145
Article (Journal)
Electronic Resource
English
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