Deepwind2017_ColoneRederTautzWeinert_revised_paper.pdf (289.69 kB)
Optimisation of data acquisition in wind turbines with data-driven conversion functions for sensor measurements
journal contribution
posted on 2017-04-21, 10:40 authored by L. Colone, M. Reder, Jannis Weinert, J.J. Melero, Anand Natarajan, Simon WatsonOperation 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.
Funding
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.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Published in
Energy ProcediaCitation
COLONE, L. ... et al, 2017. Optimisation of data acquisition in wind turbines with data-driven conversion functions for sensor measurements. Energy Procedia, 137, pp.571–578Publisher
Elsevier (© The Authors)Version
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Acceptance date
2017-03-27Publication date
2017Notes
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.ISSN
1876-6102Publisher version
Language
- en