ENBIS-16 in Sheffield

11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016

Monitoring a Wind Turbine by Combining Sensor Data

14 September 2016, 09:00 – 09:20

Abstract

Submitted by
Alessandro Di Bucchianico
Authors
Stella Kapodistria (Eindhoven University of Technology), Alessandro Di Bucchianico (Eindhoven University of Technology), Thomas Kenbeek (Eindhoven University of Technology)
Abstract
Undetected damage to parts of a wind turbine such as blade cracks due to lightning or broken gear wheels may have disastrous consequences possibly leading to loss of the entire wind turbine. It is therefore important to continuously monitor the condition of wind turbines, in particular when they are placed at remote locations (e.g., off-shore wind farms). Technological advances make it economically feasible to equip wind turbines with sensors for various physical variables (including vibration).
We describe our experiences when applying Statistical Process Control to monitor the condition of wind turbines in the Netherlands that are equipped with various sensors. Our approach is based on jointly monitoring variables using regression analysis to correct for external influences. This was an eye opener for the wind turbine engineers who use to think in threshold values for individual sensor variables. Analysis of historical data showed that malfunctioning of one the generators of a specific wind turbine could have detected several months before the actual breakdown of the complete gearbox. This research was performed within the DAISY4OFFSHORE (Dynamic Asset Information System for Offshore Wind Farm Optimisation) project funded by the Dutch government through its “Wind at Sea” Top Consortium Knowledge and Innovation. The work of Kapodistria is also supported by the Dutch Science Foundation Gravitation Project “Networks” (www.thenetworkcenter.nl).

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