Adaptive hybrid intelligent MPPT controller to approximate effectual wind speed and optimal rotor speed of variable speed wind turbine
Author
dc.contributor.author
Sitharthan, R.
Author
dc.contributor.author
Karthikeyan, Madurakavi
Author
dc.contributor.author
Shanmuga Sundar, D.
Author
dc.contributor.author
Rajasekaran, S.
Admission date
dc.date.accessioned
2020-01-07T12:24:25Z
Available date
dc.date.available
2020-01-07T12:24:25Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
ISA Transactions 96 (2020) 479–489
es_ES
Identifier
dc.identifier.other
10.1016/j.isatra.2019.05.029
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/173071
Abstract
dc.description.abstract
Operating wind power generation system at optimal power point is essential which is achieved
by employing a Maximum Power Point Tracking (MPPT) control strategy. This literature focuses on
developing a novel particle swarm optimization algorithm enhanced radial basis function neural
network supported TSR based MPPT control strategy for Doubly Fed Induction Generator (DFIG) based
wind power generation system. The proposed hybrid MPPT control strategy estimates the effective
wind speed and estimates the optimal rotor speed of the wind power generation system to track the
maximum power. The proposed controller extremely reduces the speed dissimilarity range of wind
power generation system, which leads to rationalizing the pulse width inflection of DFIG rotor side
converter. This in turn, increases the system’s reliability and delivers an effective power tracking with
reduced converter losses. Furthermore, by utilizing the proposed MPPT controller, the converter size
can be reduced to 40%. Therefore, the overall cost of the system can be gradually decreased. To validate
the performance of the proposed MPPT controller, an extensive simulation study has been carried out
under medium and high wind speed conditions in MATLAB/Simulink. The obtained results have been
justified using experimental analysis.