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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/24762

Title: Optimisation of data acquisition in wind turbines with data-driven conversion functions for sensor measurements
Authors: Colone, L.
Reder, M.
Tautz-Weinert, Jannis
Melero, J.J.
Natarajan, Anand
Watson, Simon J.
Keywords: Wind turbine
Condition monitoring
Data optimisation
Data mining
Operation and maintenance (O&M)
Issue Date: 2017
Publisher: Elsevier (© The Authors)
Citation: COLONE, L. ... et al, 2017. Optimisation of data acquisition in wind turbines with data-driven conversion functions for sensor measurements. Energy Procedia, 137, pp.571–578
Abstract: Operation and Maintenance (O&M) is an important cost driver of modern wind turbines. Condition monitoring (CM) allows the implementation of predictive O&M strategies helping to reduce costs. In this work a novel approach for wind turbine condition monitoring is proposed focusing on synergistic effects of coexisting sensing technologies. The main objective is to understand the predictability of signals using information from other measurements recorded at different locations of the turbine. The approach is based on a multi-step procedure to pre-process data, train a set of conversion functions and evaluate their performance. A subsequent sensitivity analysis measuring the impact of the input variables on the predicted response reveals hidden relationships between signals. The concept feasibility is tested in a case study using Supervisory Control And Data Acquisition (SCADA) data from an offshore turbine.
Description: This paper was published as Open Access by Elsevier. It was presented at the 14th Deep Sea Offshore Wind R&D Conference, EERA DeepWind'2017, 18-20 January 2017, Trondheim, Norway. Shared first authorship - authors L.Colone, M.Reder and J.Tautz-Weinert contributed equally to the publication and are presented in alphabetical order.
Sponsor: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642108.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/24762
Publisher Link: http://dx.doi.org/10.1016/j.egypro.2017.10.386
ISSN: 1876-6102
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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