Cost effective maintenance of wind turbines using components reliability
Wind turbine system reliability is a critical factor. Poor reliability directly affects both the project’s revenue stream through increased operation and maintenance (O&M) costs and reduced availability to generate power due to turbine downtime. This task will develop component level reliability models by using SCADA data together with maintenance logbooks. These models will allow optimizing O&M costs for the whole wind farm.
The previously defined objectives to realize this task are, firstly, to check and improve the existent models in a bigger scenario to assure their functionality, secondly, to define appropriate models for older wind turbines. Most of the published studies are focused on new wind turbines but there are old wind farms that need this kind of studies in order to be optimized, and finally to include the environmental parameters in the models in a proper way. Considering variable affections between the different weather and reliability parameters, in order to define the costs model more accurately including lack of production, main parts of the turbine and the weather conditions. Historical corrective maintenance costs will be used to obtain better results.
The expected outcome should include the following three objectives:
Improved reliability model of the WT components verified with a high amount of data.
Reliability model for older wind turbine components.
O&M cost model based on the reliability model, the weather conditions and historical respective maintenance data.
Enel Green Power (EGP)
Tentative schedule: Spring – Summer 2016
Technical University of Denmark (DTU)
Tentative schedule: Spring – Summer 2017
Reder M, Yürüşen NY, Melero JJ (2018) Data-driven learning framework for associating weather conditions and wind turbine failures, Reliability Engineering and System Safety 169 pp. 554–569, ISSN: 09518320, DOI: 10.1016/j.ress.2017.10.004, Full text: https://www.sciencedirect.com/science/article/pii/S0951832017300832?via%3Dihub
Reder M and Melero JJ (2017) Modelling Wind Turbine Failures based on Weather Conditions, Journal of Physics: Conf. Series 926 (012012), DOI: 10.1088/1742-6596/926/1/012012. Full text: http://iopscience.iop.org/article/10.1088/1742-6596/926/1/012012/meta
Reder M, Melero JJ (2017) Time series data mining for analysing the effects of wind speed on wind turbine reliability, Safety and Reliability – Theory and Applications, CRC Press Taylor & Francis , DOI: 10.1201/9781315210469-93, Full text
Yürüşen NY, Reder M (2017) Failure Event Definitions & their Effects on Survival and Risk Analysis of Wind Turbines, Safety and Reliability – Theory and Applications, CRC Press Taylor & Francis, DOI: 10.1201/9781315210469-93, Full text
Reder, M, Gonzalez, E and Melero, JJ (2016) Wind Turbine Failures – Tackling current Problems in Failure Data Analysis, Journal of Physics: Conference Series, 753(072027), DOI: 10.1088/1742-6596/753/7/072027. Full text: http://iopscience.iop.org/article/10.1088/1742-6596/753/7/072027
Gonzalez, E, Reder, M and Melero, JJ (2016) SCADA alarms processing for wind turbine component failure detection, Journal of Physics: Conference Series, 753(072019), DOI: 10.1088/1742-6596/753/7/072019. Full text: http://iopscience.iop.org/article/10.1088/1742-6596/753/7/072019
Reder M and Melero JJ (2017) Wind Turbine Reliability Modelling based on Weather Conditions, 13th EAWE PhD Seminar on Wind Energy in Europe.
Reder M and Melero JJ (2017) Modelling Wind Turbine Failures based on Weather Conditions, In WindEurope Summit 2017, Amsterdam. DOI: http://proceedings.windeurope.org/confex2017/posters/PO191.pdf
Reder, M and Melero, JJ (2016) Assessing Wind Speed Effects on Wind Turbine Reliability. In WindEurope Summit 2016, Hamburg. DOI: 10.13140/RG.2.2.11134.59200. Full text
Reder, M, Gonzalez, E and Melero, JJ (2016) Wind Turbine Failures – Tackling current Problems in Failure Data Analysis, The Science of Making Torque from Wind (TORQUE 2016), Munich. Full text
Gonzalez, E, Reder, M and Melero, JJ (2016) SCADA alarms processing for wind turbine component failure detection, The Science of Making Torque from Wind (TORQUE 2016), Munich. Full text
Colone, M. Reder, J. Tautz-Weinert, J.J. Melero, A. Natarajana, S.J. Watson (2017). Optimisation of Data Acquisition in Wind Turbines with Data-Driven Conversion Functions for Sensor Measurements. IEERA DEEPWIND’2017, 14TH DEEP SEA OFFSHORE WIND R&D CONFERENCE, Trondheim, Norway. Download Poster
Julio J. Melero
Host Institution: Circe