%0 Journal Article
%T Neural network每based improved active and reactive power control of wind
%A Debirupa Hore
%A Runumi Sarma
%J Wind Engineering
%@ 2048-402X
%D 2018
%R 10.1177/0309524X18780402
%X Artificial neural network每based power controllers are trained using back propagation algorithm for controlling the active and reactive power of a wind-driven double fed induction generator under varying wind speed conditions and fault conditions. Vector control scheme is used for control of the double fed induction generator. Here stator flux每oriented vector control scheme is implemented for the rotor side converter and grid voltage vector scheme is used for control of grid side converter using tuned proportional每integral active and reactive power controllers, which is later replaced by artificial neural network每based controllers. The artificial neural network controllers are trained using the data obtained from simulation of conventional proportional每integral controllers under varying operating conditions. The intelligent controller makes the generated stator active power to track the reference active power more precisely at specified power factor in both sub-synchronous and super-synchronous modes of operations. Simulation results reveal that the neural network每based controller significantly improves the performance of variable speed wind power generating double fed induction generator under various conditions
%K Double fed induction generator
%K neural network
%K vector control
%K wind power generation
%U https://journals.sagepub.com/doi/full/10.1177/0309524X18780402