CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

On the economic benefits of using condition monitoring systems for maintenance management of wind power systems

François Besnard (Institutionen för energi och miljö, Elteknik) ; Julia Nilsson ; Lina Bertling (Institutionen för energi och miljö, Elteknik)
2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2010 p. 160-165. (2010)
[Konferensbidrag, refereegranskat]

The large growth in the wind power industry in the past years mainly focuses on a growing market and the development of large turbines and offshore farms. The high technical availability of wind turbines comes with a high need for frequent maintenance. Current maintenance planning is generally not optimized, and it is possible to make maintenance more efficient. Condition Monitoring Systems (CMS) are commonly used in other industries and can reduce the consequential damage at failure and provides advantages for the planning of the maintenance. It is of interest to determine if the wind industry would benefit of the use of CMS. This paper shows results from Life-Cycle-Cost (LCC) evaluated with probabilistic methods and sensitivity analysis to identify the benefit of using CMS. The results highlight that there is a high economic benefit of using CMS, as well as benefits on the risk. The benefit is highly influenced by the reliability of the gearbox.

Nyckelord: Condition monitoring, Life cycle cost, Maintenance, Wind energy, Probability



Den här publikationen ingår i följande styrkeområden:

Läs mer om Chalmers styrkeområden  

Denna post skapades 2010-09-21. Senast ändrad 2012-11-30.
CPL Pubid: 126682

 

Läs direkt!


Länk till annan sajt (kan kräva inloggning)


Institutioner (Chalmers)

Institutionen för energi och miljö, Elteknik

Ämnesområden

Energi
Elektroteknik och elektronik

Chalmers infrastruktur

Relaterade publikationer

Denna publikation ingår i:


On maintenance optimization for offshore wind farms