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Delegates are invited to meet and discuss with the poster presenters during the poster presentation sessions between 10:30-11:30 and 16:00-17:00 on Thursday, 19 November 2015.

Lead Session Chair:
Stephan Barth, ForWind - Center for Wind Energy Research, Germany
Dick Breteler University of Twente, The Netherlands
Co-authors:
Dick Breteler (1) F Tiedo Tinga (1) Richard Loendersloot (1) Christos Kaidis (2)
(1) University of Twente, Enschede, The Netherlands (2) Mecal, Enschede, The Netherlands

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Presenter's biography

Biographies are supplied directly by presenters at EWEA 2015 and are published here unedited

Mr. Breteler is a mechanical engineering student at the University of Twente. He is currently doing his master's thesis at Mecal Independent Experts in Enschede.


Poster

Poster Download poster (8.89 MB)

Abstract

Physics based methodology for wind turbine failure detection, diagnostics & prognostics

Introduction

The wind turbine market shows a trend towards larger turbines and also more turbines are placed offshore. For these turbines the costs of downtime in case of component failure are higher compared to smaller turbines, primarily due to higher energy loss per unit time and higher replacement costs. For this reason the need of an accurate degradation model of critical components becomes more and more important. In order to reduce these operational and maintenance (O&M) costs a generally applicable degradation model based on physics of failure has been developed. This is in line with latest research trends on prognostics and structural health management.

The model estimates the impact of misalignment of the high speed shaft between the generator and gearbox on fatigue lifetime of the helical gear of the third gearbox stage and all connected bearings. The model relates the size and kind of misalignment to fatigue loads in order to show the fatigue lifetime reduction. Subsequently, data from supervisory control and data acquisition (SCADA) and condition monitoring system (CMS) are used to extract a load spectrum and detect misalignment. Most modern wind turbines have CMS and SCADA which makes the model easy to use.


Approach

The helical gear and bearing experience high dynamic loads and therefore fatigue failures frequently occur. In order to investigate which geometry parameters are important a sensitivity analysis is applied. The fatigue lifetime is very sensitive to changing load parameters. Consequently, how the fatigue load will change during misalignment is investigated. SCADA data in combination with Minor’s rule is used to define a relation between the wind speed and load distribution.

A multi body simulation is performed in order to get knowledge about the dynamic behavior of the high speed shaft during misalignment. Operating CMS data is used to validate the simulation.


Main body of abstract

The sensitivity analysis shows that only a few geometry parameters are needed to determine a well-defined geometry of the helical gear and bearings. In case of misalignment the direction of the force and the size of the contact area will change. A simulation for both parameters has been executed showing that the influence of the changing force direction is minimal whereas the changing fatigue load is primarily based on the reduction of the contact area. The simulation also gives a relation between the angle of misalignment and fatigue load. A good load representation is obtained from SCADA which is transferred by means of Minor’s rule to the fatigue lifetime.

A multi body simulation of the gearbox including high speed shaft is performed for an aligned and misaligned case. Consequently a clear feature extractor is recognized during misalignment. An operating wind turbine with a misaligned high speed shaft is used as verification and here the feature is recognized.


Conclusion

The developed degradation model based on physics of failure gives more accurate information about the helical gear and bearing status compared to conventional methods. In this case the cause of a failure is detected instead of detecting a component which is already heavily degraded. By using this model it is shown that it is possible to detect misalignment through operational data and relate it to the remaining fatigue lifetime of the helical gear and bearings.


Learning objectives
The methodology developed in this research project provides information on how to set up a degradation model based on physics of failure. Also a new methodology is developed on how to detect misalignment by using CMS. The introduction of a multi body simulation in combination with SCADA and CMS shows how to use different approaches for one analysis.