Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10887
Title: Adaptive neuro-fuzzy controller for static VAR compensator to damp out wind energy conversion system oscillation
Authors: Huang, H
Chung, CY
Issue Date: 2013
Publisher: Institution of Engineering and Technology
Source: IET generation, transmission & distribution, 2013, v. 7, no. 2, p. 200-207 How to cite?
Journal: IET generation, transmission & distribution 
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.
URI: http://hdl.handle.net/10397/10887
ISSN: 1751-8687
EISSN: 1751-8695
DOI: 10.1049/iet-gtd.2012.0609
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