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Enabling Multi-Agent-Systems for wind turbine maintenance optimization through a common database

Presentation held at ESReDA Conference 2012, 42nd ESReDA Seminar on Risk and Reliability for Wind Energy and other Renewable Sources, 15 - 16 May 2012, Glasgow, Scotland
 
: Rafik, K.; Faulstich, S.; Pfaffel, S.; Kühn, P.

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Volltext urn:nbn:de:0011-n-3960228 (413 KByte PDF)
MD5 Fingerprint: 9c3f51ce9283887a450f0bb5ba0900f9
Erstellt am: 21.6.2016


2012, 12 S.
Seminar on Risk and Reliability for Wind Energy and Other Renewable Sources <42, 2012, Glasgow>
Englisch
Vortrag, Elektronische Publikation
Fraunhofer IWES ()
maintenance; optimization; Artificial Intelligence; Multi-Agent-Systems; Failure Database

Abstract
Maintenance management for wind turbines (WT) aims on the one hand at reducing the overall maintenance cost and on the other hand at improving the availability.Although modern onshore WT attain high technical availability of up to 98 %, the evaluation of maintenance work in previous projects shows, that high WT availability requires additional maintenance work and costs. There is a considerable scope for optimizing reliability and maintenance procedures. A possibility therefore is to systematically make use of available knowledge and past experience. Thus, necessary steps have to be introduced for operation and maintenance of wind turbines to bring several readings together and to use them for improvements. At this point, information coming from databases, statistical methods as well as sound statements is essential. The consideration of several conditions e.g. weather conditions, power prognostics, stock keeping etc. are essential for optimal decisions. However, due to this enormous amount of information sophisticated tools are needed. This paper is going to show the possible application of high-performance computing methodologies, which may help wind farm operators (WFO) examining optimal maintenance strategies. The so called Multi-Agent-System (MAS) which is a new discipline in the world of Artificial Intelligence (AI) and the Data Mining (DM), which is a high-performance computing methodology used to observe and deduce hidden knowledge and logical dependencies of a great amount of data using several appropriate algorithms, should be investigated and a methodology for the use of AI in WT maintenance is proposed.

: http://publica.fraunhofer.de/dokumente/N-396022.html