Neural network–based improved active and reactive power control of wind-driven double fed induction generator under varying operating conditions

Neural network–based improved active and reactive power control of wind-driven double fed... Artificial neural network–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–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–integral active and reactive power controllers, which is later replaced by artificial neural network–based controllers. The artificial neural network controllers are trained using the data obtained from simulation of conventional proportional–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–based controller significantly improves the performance of variable speed wind power generating double fed induction generator under various conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wind Engineering SAGE

Neural network–based improved active and reactive power control of wind-driven double fed induction generator under varying operating conditions

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Publisher
SAGE Publications
Copyright
© The Author(s) 2018
ISSN
0309-524X
eISSN
2048-402X
D.O.I.
10.1177/0309524X18780402
Publisher site
See Article on Publisher Site

Abstract

Artificial neural network–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–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–integral active and reactive power controllers, which is later replaced by artificial neural network–based controllers. The artificial neural network controllers are trained using the data obtained from simulation of conventional proportional–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–based controller significantly improves the performance of variable speed wind power generating double fed induction generator under various conditions.

Journal

Wind EngineeringSAGE

Published: Jun 1, 2018

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