http://www.cnr.it/ontology/cnr/individuo/prodotto/ID231553
Growing Neural Gas based Maximum Power Point Tracking for High Performance VOC-FOC based Wind Generator System with Induction Machine (Contributo in atti di convegno)
- Type
- Label
- Growing Neural Gas based Maximum Power Point Tracking for High Performance VOC-FOC based Wind Generator System with Induction Machine (Contributo in atti di convegno) (literal)
- Anno
- 2009-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/ECCE.2009.5316549 (literal)
- Alternative label
M. Pucci, G. Vitale (2009)
Growing Neural Gas based Maximum Power Point Tracking for High Performance VOC-FOC based Wind Generator System with Induction Machine
in 2009 IEEE Energy Conversion Congress and Exposition, ECCE 2009; San Jose, CA; United States; 20 September 2009 through 24 September 2009; Category numberCFP09ECD-PRT; Code 78727, San Jose, California, USA, 20-24 sept , 2009
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- M. Pucci, G. Vitale (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
- http://www.scopus.com/record/display.url?eid=2-s2.0-72449126688&origin=resultslist&sort=plf-f&src=s&nlo=&nlr=&nls=&sid=1HsExd0NrK76WAPS5dk0QN1%3a240&sot=aut&sdt=a&sl=38&s=AU-ID%28%22Vitale%2c+Gianpaolo%22+12790600000%29&relpos=33&relpos=13&citeCnt=1&searchTerm=AU-ID%28%5C%26quot%3BVitale%2C+Gianpaolo%5C%26quot%3B+12790600000%29 (literal)
- Note
- Scopus (literal)
- ISI Web of Science (WOS) (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- ISSIA-CNR (Insitute of Intelligent Systems for the Automation), Palermo, Italy (literal)
- Titolo
- Growing Neural Gas based Maximum Power Point Tracking for High Performance VOC-FOC based Wind Generator System with Induction Machine (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
- Abstract
- This paper presents a MPPT technique for high
performance wind generator with induction machine based on
the Growing Neural Gas (GNG) network. Here a GNG network
has been trained off-line to learn the turbine characteristic
surface torque versus wind speed and machine speed, and
implemented on-line so to perform the inversion of this function
obtaining the wind free speed on the basis of the estimated
torque and measured machine speed. The machine reference
speed is then computed on the basis of the optimal tip speed
ratio. For the experimental application, a back-to-back
configuration with two voltage source converters has been
considered, one on the machine side and the other on the grid
side. (literal)
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