http://www.cnr.it/ontology/cnr/individuo/prodotto/ID191611
Growing Neural Gas-Based MPPT of Variable Pitch (Articolo in rivista)
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- Growing Neural Gas-Based MPPT of Variable Pitch (Articolo in rivista) (literal)
- Anno
- 2012-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1109/TIA.2012.2190964 (literal)
- Alternative label
Cirrincione Maurizio, Pucci MArcello, Vitale Gianpaolo. (2012)
Growing Neural Gas-Based MPPT of Variable Pitch
in IEEE transactions on industry applications; IEEE-Institute Of Electrical And Electronics Engineers Inc., Piscataway (Stati Uniti d'America)
(literal)
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- Cirrincione Maurizio, Pucci MArcello, Vitale Gianpaolo. (literal)
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- University of Technology of Belfort-Montbéliard,
90010 Belfort, France (literal)
- Titolo
- Growing Neural Gas-Based MPPT of Variable Pitch (literal)
- Abstract
- This paper proposes a maximum power point tracking
(MPPT) technique for variable pitch wind generators with
induction machines, which can suitably be adopted in both the
maximum power range and the constant power range of the wind
speed. For this purpose, anMPPT technique based on the growing
neural gas (GNG) wind turbine surface identification and the corresponding
function inversion has been adopted to cover also the
situation of constant rated power region. This has been obtained
by including the blade pitch angle in the space of the data learnt by
the GNG and feeding back the estimated wind speed to compute
the correct value of the pitch angle, which permits the machine to
work at rated power and torque. A further enhancement of the
pitch angle selection by a simple perturb & observe method has
been added to cope with slight wind estimation errors occurring at
machine rated speed. The proposed methodology has been verified
both in numerical simulation and experimentally on a properly
devised test setup. The correct behavior of the system has been
proved also on a real wind speed profile on a daily scale. (literal)
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