Economic evaluation of maintenance strategies for wind turbines: a stochastic analysis. Issue 7 (1st September 2015)

• Record Type: Journal Article
• Authors: Kerres, Bertrand; Fischer, Katharina; Madlener, Reinhard
• Is Part Of: IET renewable power generation. Volume 9:Issue 7(2015)
• Page Start: 766
• Page End: 774
• Publication Date: 2015-09-01
• Subjects: wind turbines -- power system economics -- maintenance engineering -- condition monitoring -- costing -- life cycle costing -- power system reliability; stochastic model -- lifecycle cost assessment -- cost effective maintenance strategy -- field data‐based reliability model -- serial connection -- production losses -- Vestas V44 wind turbine -- reliability centred maintenance analysis -- critical wind turbine components -- power 600 kW -- economic evaluation -- turbine capacity -- electricity price -- online condition monitoring systems -- condition‐based maintenance -- gearbox; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2014.0260
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : The authors develop a stochastic model for assessing the life‐cycle cost and availability of wind turbines resulting from different maintenance scenarios, with the objective to identify the most cost‐effective maintenance strategy. Using field‐data‐based reliability models, the wind turbine – in terms of reliability – is modelled as a serial connection of the most critical components. Both direct cost for spare parts, labour and access to the turbine, as well as indirect cost from production losses are explicitly taken into account. The model is applied to the case of a Vestas V44–600 kW wind turbine. Results of a reliability‐centred maintenance analysis of this wind turbine are used to select the most critical wind turbine components and to identify possible maintenance scenarios. This study reveals that corrective maintenance is the most cost‐effective maintenance strategy for the gearbox and the generator of the V44 turbine, while the cost benefit of condition‐based maintenance using online condition‐monitoring systems increases with higher electricity price, turbine capacity and remoteness of sites.