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.
Original language | English |
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Pages (from-to) | 200-207 |
Number of pages | 8 |
Journal | IET Generation, Transmission and Distribution |
Volume | 7 |
Issue number | 2 |
DOIs | |
Publication status | Published - Feb 2013 |
ASJC Scopus subject areas
- Control and Systems Engineering
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering