Wind Farm O&M cost reduction through predictive maintenance
The principle of planning maintenance is to minimize life cycle cost (LCC), i.e. the cost associated to inspection, on site testing, replenishment, future maintenance, future failures and repairs. The cost of energy can be potentially driven down by improving the use of condition based maintenance. In this project, data acquired from monitoring systems will be used for validating a broad range of models and to reduce the uncertainties in the decision system used to plan maintenance. The initial part will be dedicated to understand the key benefits of using preventive actions, which components can be addressed and how to implement the system on a wind farm scale. The work will offset from initial analysis under the ongoing project ODIN-WIND decommissioning of offshore wind farms in cooperation with industrial partners.
Tentative schedule: September – November 2016
Technical University of Munich (TUM)
Tentative schedule: June – September 2017
L. 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
Host Institution: Technical University of Denmark (DTU)