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An Optimization Framework for Opportunistic Maintenance of Offshore Wind Power System
KTH, School of Electrical Engineering (EES).
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2009 (English)In: 2009 IEEE BUCHAREST POWERTECH, VOLS 1-5 / [ed] Toma L; Otomega B, NEW YORK: IEEE , 2009, 2970-2976 p.Conference paper (Refereed)
Abstract [en]

A sound maintenance planning is of crucial importance for wind power farms, and especially for offshore locations. There is a large potential in cost savings by maintenance optimization to make the projects more cost-efficient. This paper presents an opportunistic maintenance optimization model for offshore wind power system. The model takes advantage of wind forecasts and corrective maintenance activities in order to perform preventive maintenance tasks at low costs. The approach is illustrated with an example to demonstrate the value of the optimization. In this example 43% of the cost to perform preventive maintenance could be saved using the proposed method.

Place, publisher, year, edition, pages
NEW YORK: IEEE , 2009. 2970-2976 p.
Keyword [en]
Maintenance optimization, offshore wind power, opportunistic maintenance
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-25041DOI: 10.1109/PTC.2009.5281868ISI: 000276834601170ScopusID: 2-s2.0-74949136228ISBN: 978-1-4244-2234-0OAI: diva2:355258
Bucharest PowerTech Conference. Bucharest, ROMANIA, JUN 28-JUL 02, 2009
QC 20101006Available from: 2010-10-06 Created: 2010-10-06 Last updated: 2010-10-06Bibliographically approved
In thesis
1. On Optimal Maintenance Management for Wind Power Systems
Open this publication in new window or tab >>On Optimal Maintenance Management for Wind Power Systems
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Sound maintenance strategies and planning are of crucial importance for wind power systems, and especially for offshore locations. In the last decades, an increased awareness of the impact of human living on the environment has emerged in the world. The importance of developing renewable energy is today highly recognized and energy policies have been adopted towards this development. Wind energy has been the strongest growing renewable source of energy this last decade. Wind power is now developing offshore where sites are available and benefits from strong and steady wind. However, the initial investments are larger than onshore, and operation and maintenance costs may be substantially higher due to transportation costs for maintenance and accessibility constrained by the weather.

Operational costs can be significantly reduced by optimizing decisions for maintenance strategies and maintenance planning. This is especially important for offshore wind power systems to reduce the high economic risks related to the uncertainties on the accessibility and reliability of wind turbines.

This thesis proposes decision models for cost efficient maintenance planning and maintenance strategies for wind power systems. One model is proposed on the maintenance planning of service maintenance activities. Two models investigate the benefits of condition based maintenance strategies for the drive train and for the blades of wind turbines, respectively. Moreover, a model is proposed to optimize the inspection interval for the blade. Maintenance strategies for small components are also presented with simple models for component redundancy and age replacement.

The models are tested in case studies and sensitivity analyses are performed for parameters of interests. The results show that maintenance costs can be significantly reduced through optimizing the maintenance strategies and the maintenance planning.

Place, publisher, year, edition, pages
Stockholm: KTH, 2009. 76 p.
Trita-EE, ISSN 1653-5146 ; 2009:051
Wind Power, Maintenance, Reliability, Optimization
urn:nbn:se:kth:diva-11793 (URN)978-91-7415-482-5 (ISBN)
2009-12-04, Sal D3, KTH, Lindstedtvägen 5, Stockholm, 13:00 (English)
Available from: 2009-12-21 Created: 2009-12-18 Last updated: 2010-11-03Bibliographically approved

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Besnard, FrancoisPatriksson, MichaelBertling, Lina
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