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Abstract
<p class="first" id="d2415119e91">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.
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