Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloDevelopment of a High-Performance, FPGA-Based Virtual Anemometer for Model-Based MPPT of Wind Generators
Anno di pubblicazione2020
Autore/iGiuseppe La Tona, Massimiliano Luna, Maria Carmela Di Piazza, Marcello Pucci, Angelo Accetta
Affiliazioni autoriNational Research Council (CNR), Institute of Marine Engineering (INM)
Autori CNR e affiliazioni
  • inglese
AbstractModel-based maximum power point tracking (MPPT) of wind generators (WGs) eliminates dead times and increases energy yield with respect to iterative MPPT techniques. However, it requires the measurement of wind speed. Under this premise, this paper describes the implementation of a high-performance virtual anemometer on a field programmable gate array (FPGA) platform. Said anemometer is based on a growing neural gas artificial neural network that learns and inverts the mechanical characteristics of the wind turbine, estimating wind speed. The use of this device in place of a conventional anemometer to perform model-based MPPT of WGs leads to higher reliability, reduced volume/weight, and lower cost. The device was conceived as a coprocessor with a slave serial peripheral interface (SPI) to communicate with the main microprocessor/digital signal processor (DSP), on which the control system of the WG was implemented. The best compromise between resource occupation and speed was achieved through suitable hardware optimizations. The resulting design is able to exchange data up to a 100 kHz rate; thus, it is suitable for high-performance control of WGs. The device was implemented on a low-cost FPGA, and its validation was performed using input profiles that were experimentally acquired during the operation of two different WGs.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da83
Pagine a-
Pagine totali16
RivistaElectronics (Basel)
Attiva dal 2010
Editore: MDPI - Basel
Lingua: inglese
ISSN: 2079-9292
Titolo chiave: Electronics (Basel)
Numero volume della rivista9
Fascicolo della rivista1
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)
  • Scopus (Codice:2-s2.0-85077548370)
Parole chiavevirtual sensors, anemometer, maximum power point tracking, wind generator, field-programmable gate array, growing neural gas, artificial neural network
Link (URL, URI)
Titolo parallelo-
Scadenza embargo-
Data di accettazione10/12/2019
Note/Altre informazioniOPEN ACCESS: Liberamente e gratuitamente accessibile
Strutture CNR
  • INM — Istituto di iNgegneria del Mare
Moduli/Attività/Sottoprogetti CNR
  • DIT.AD017.072.001 : Energy conversion and management
Progetti Europei-