Application of neural networks for failure detection on wind turbines

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Mesquita Brandao, RF (Author) Other; Beleza Carvalho, JA (Author) Other; Fernando Maciel Barbosa (Author) FEUP

Conference proceedings International

Title: 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011

2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011

Trondheim, 19 June 2011 through 23 June 2011

Indexing

Scopus - 0 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Technological sciences > Engineering > Electronic engineering

Other information

Authenticus ID: P-008-0GV

DOI: 10.1109/ptc.2011.6019229

Abstract (EN): Wind energy is the renewable energy source with a higher growth rate in the last decades. The huge proliferation of wind farms across the world has arisen as an alternative to the traditional power generation and also as a result of economic issues which necessitate monitoring systems in order to optimize availability and profits. Tools to detect the onset of mechanical and electrical faults in wind turbines at a sufficiently early stage are very important for maintenance actions to be well planned, because these actions can reduce the outage time and can prevent bigger faults that may lead to machine stoppage. The set of measurements obtained from the wind turbines are enormous and as such the use of neural networks may be beneficial in understanding if there is any important information that may help the prevention of big failures. © 2011 IEEE.

Language: English

Type (Professor's evaluation): Scientific