Detection of Damage in Metallic Structures for Offshore Applications

Abstract

Wind energy is seen as one of the most promising solutions to man’s ever increasing demands of a clean source of energy. However, a major drawback of the wind energy is the high initial cost for setting up and maintenance. This makes the energy more expensive than the conventional energy sources like fossil fuels and nuclear and hence it has not been widely accepted. Thus, there is a concerted effort to reduce the cost of energy production. This can be achieved by increasing the life-time of the wind turbines; reducing maintenance costs and ensuring low downtime of the turbine. The lifetime may be increased by ensuring a more robust design while the maintenance cost and the downtime of the equipment 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 becomes more important in the off-shore industry where the cost of unscheduled maintenance are high due to the need of specialized equipment. Also, maintenance planning can allow us to avoid unnecessary downtime, hence increasing the availability of the system. In wind turbine structures, tower damage is the third most common damage in wind turbines. Thus, this work concentrates on SHM of a wind turbine tower. A decision level data fusion based on bi-axial tracking of change in Neutral Axis (NA) position is proposed. 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 ratio of the change in the NA along two perpendicular axes is taken and used for the localization. The study has been carried out on the simulated finite element (FE) model of the wind turbine tower and indicates that bi-axial NA tracking based on data fusion is indeed necessary and at the same time is sensitive to damage. The proposed methodology is then validated on real strain data from the Nordtank NTK 500/41 wind turbine. Based on the results presented, the change in NA is indeed a robust damage indicator insensitive to ambient condition changes, and the applied loads.