Lead Session Chair:
Stephan Barth, Managing Director, ForWind - Center for Wind Energy Research, Germany
Rohan Soman (1) F P Pawel Malinowski (1) Wieslaw Ostachowicz (1)
(1) Institute of Fluid Fow and Machinery, PASci, Gdansk, Poland
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Presenter's biographyBiographies are supplied directly by presenters at OFFSHORE 2015 and are published here unedited
Rohan Soman has a background in Structural Engineering. He has been working in Structural Health Monitoring with primary interest in vibration based damage detection techniques and optimization of sensor placements. He later joined the department of intelligent structures at the Institute of Fluid Flow Machinery, Polish Academy of Sciences (Poland), as a PhD candidate, where he works at present.His present research is mainly focused on the Damage Detection in Metallic Structures (tower) as well as composite structures (blades). His research interests include sensor placement optimization; Modal based Structural Health Monitoring, FBG sensors and Data fusion.
Threshold determination for neutral axis tracking based damage detection 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. The use of wind energy has received an impetus due to the advancements in the field of materials engineering. Newer, bigger wind turbines are now possible which are more robust, and lighter in weight. The main drawback of the wind energy is the high initial costs and high maintenance costs. In order to reduce the cost of generation, there is effort to increase the life-time of the wind turbines, reduce maintenance costs and ensure high availability.
In this paper, a robust metric for damage detection of towers is proposed. The Neutral Axis (NA) is the property of the cross section of the tower independent of bulk temperature effects, and ambient wind loading. The position of the NA can be assessed by measuring strains on opposite surfaces of the tower in bending. The estimation of the NA of the tower subject to unknown loading both in magnitude and direction is presented here. The discrete kalman filter is employed for the estimation of the NA subject to wind loads in the presence of measurement noise from the sensors.
Main body of abstract
Structural Health Monitoring (SHM) and Non Destructive Evaluation in structures needs to be low cost and, allow global damage detection. The selection of the SHM strategy depends on the application at hand and the component to be monitored. Wind turbine systems are a complex collection of many smaller systems, such as the blades, gear box, generator, tower etc. Many studies have been undertaken to study the failure frequency of each component or system and the resulting down time for replacement or maintenance. The failures in towers amount to 4%, but the downtime and costs of replacement for tower failure are very high. The cost of maintenance and replacement is high, as there is need to deploy specialised equipment for dis-assembling the blades, gearbox and the generator, before the replacement of the tower. Thus keeping in mind the high cost of replacement, the SHM of tower structures is necessary.
The vibration-based damage identification (VBDI) has received great attention in SHM of large structures bridges, towers etc. VBDI allows low cost technique for SHM of structures, but is insensitive to lower levels of damage and if the damage occurs away from the sensors. So there is an increasing interest shown in the use of dynamic strain measurements for SHM of structure. Unfortunately, strain sensors are local level sensors and are highly sensitive to the ambient loading on the structure and the ambient condition changes which may lead to false detections. Thus there is a need for a robust damage indicator.
The study on the simulated FE model of the wind turbine tower indicates that NA tracking is sensitive to damage and robust enough to overcome the effects of measurement noise. The use of data fusion at the information and decision level allows better use of the information at hand and allows us to locate the damage.
The study tries to optimize the use of the information provided by the sensors. A multi-level data fusion allows accurate damage localization while giving robustness to the damage indicator,