Induction Machines based Wind Generators with Neural Maximum Power Point Tracking and Minimum Losses Techniques
Academic Article
Publication Date:
2016
abstract:
This paper presents an Induction Machine (IM) based wind generation unit with integrated Maximum Power Point Tracking (MPPT), Electric Losses Minimization Technique (ELMT) and Discontinuous PWM (D-PWM) features. The target is the development of a highly efficient wind generation unit with high dynamic performance, able not only to rapidly track the maximum generable power according to any wind speed variation, but also to minimize, at the same time, the converter's and the machine's losses. The proposed wind generator is based on a back-to-back power converter topology with two VSIs and contains a previously developed neural based MPPT as well as an original version of an ELMT. The proposed wind generation unit has been tested experimentally on a suitably developed test setup. Results clearly show that the integration of ELMT+D-PWM into the MPPT based wind generator control permits the active power injected into the power grid to be increased up to 37.5 % at high wind speeds and up to 240 % at low wind speeds.
Iris type:
01.01 Articolo in rivista
Keywords:
Discontinuous Pulsewidth Modulation (D-PWM); Electrical Losses Minimization Technique (ELMT); Induction machine (IM); Neural networks (NN); Wind generator (WG); maximum power point tracking (MPPT)
List of contributors:
DI PIAZZA, MARIA CARMELA; Pucci, Marcello
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