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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
Antonio Romero Camacho TWI Ltd, United Kingdom
Co-authors:
Romero Camacho Antonio (1) F Estefania Artigao (2) Slim Soua (1) Tat-Hean Gan (1)
(1) TWI Ltd, Cambridge, United Kingdom (2) TWI Ltd, Cambridge, United Kingdom (3) Brunel University, London, United Kingdom

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Presenter's biography

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

Mr Antonio Romero Camacho has been working in the renewable energy field for the last two years. He is currently a PhD Student who is carrying out his research at the National Structural Integrity Research Centre (NSIRC) in TWI Ltd. He studied electrical and electronics engineering at the University of Castilla La Mancha in Ciudad Real. After his studies he spent 7 months at TWI Ltd as a Placement Student and was involved in some Condition monitoring projects such as VARCM, CMSWind and REMO. His research is focused on the development of vibration based condition monitoring for the assesment of rotating parts and statics structures specially in wind and tidal turbines.


Poster

Poster Download poster (15.94 MB)

Abstract

Vestas V90-3MW Wind Turbine rotating machinery Health Assessment using an advanced Condition Monitoring System

Introduction

An Advanced Condition Monitoring System has been developed as part of the CMSWind FP7 Project
partly funded by the European Commission (Grant Agreement no. 286854)
for the assessment of wind turbine rotating parts. The validation of the CMSWind System is presented in this paper. It was carried out during field trials at Bandirma Wind Energy Power Plant on a Vestas V90-3MW Wind Turbine.

Approach

The current wind turbine Condition Monitoring process can be time consuming and costly and fail to achieve the reliability and operational efficiency that the industry requires. Existing reports on the monitoring of wind turbine data are restricted to a few historical cases. In the case of Acoustic Emission (AE) this is yet scarcer. In order to overcome this issue
the development of a pattern recognition signal processing software based on similarity analysis and Euclidean Distance has been implemented to generate a baseline as the strategy for subsequent Condition Monitoring. The work described in this manuscript will show the application of the CMSWind System for enabling the prompt detection of changes in the machinery status.

Main body of abstract

'This integrated Condition Monitoring System
which utilizes three new and novel techniques specifically designed for wind turbines and their components
will improve wind turbine machinery reliability. This estimation is made from the fact that unnecessary maintenance and out of service wind turbines are reduced or even eliminated
improving reliability and operation. Motor Current Signature Analysis (MCSA)
Operational Modal Analysis (OMA) and Acoustic Emission (AE) techniques have been used for the development of a baseline that indicates the behaviour under normal operation of three different components: the generator
the drive train (including the gearbox) and high speed shaft respectively. Different signal processing methods have been used to extract the wind turbine features depending on the monitoring technique. The features extracted during this initial period have been used to create a baseline (or signature) that defines the behaviour of the generator
gearbox and high speed shaft of the wind turbine under normal operation. A range of maximum and minimum values for these features have been calculated based on trends (average ± standard deviation). Following on
the drive train monitoring process is performed by comparing each new set of data acquired to the original baseline created during the initial stage. This gives the CMSWind System the capability of identifying deviations from a normal operation situation based on the Euclidean Distance dissimilarity.


Conclusion

The analysis of the data gathered during a period of three months is presented in the current manuscript. The baseline has been developed with the data collected during the first month. The data acquired during the following months is then compared to the mentioned baseline for a complete health assessment of the wind turbine drive train.


Learning objectives