Adaptive neuro‐fuzzy controller for static VAR compensator to damp out wind energy conversion system oscillation

Abstract

Wind shear and tower shadow produce a periodic pulse reduction in mechanical torque captured from wind energy resulting in wind energy conversion system (WECS) active power oscillations. In this study, an adaptive neuro‐fuzzy controller for static VAR compensator, used in power networks integrated with WECS, is presented to address the torque oscillation problem. The proposed controller consists of a radial basis function neural network representing a third‐order auto‐regressive and moving average system model and performing the prediction, and a main controller with adaptive neuro‐fuzzy inference system providing the damping signal. A modified two‐area four‐machine power network with WECS integration is applied to validate the proposed implementation, compared with conventional lead/lag compensation. Time‐domain simulations prove that the proposed controller can provide a damping signal to improve the active power oscillation and system dynamic stability, influenced by torque oscillations under WECSs synchronised operating condition.