Intelligent control of SVC using wavelet neural network to enhance transient stability

Authors:
Mohsen Farahani
Affiliations:
Department of Electrical Engineering, Bu-Ali Sina University, Hamedan, Iran
Venue:
Engineering Applications of Artificial Intelligence
Year:
2013

Citing 5
Cited 0

Indirect adaptive control of nonlinear dynamic systems using self recurrent wavelet neural networks via adaptive learning rates

Information Sciences: an International Journal
Brief paper: Adaptive RTRL based neurocontroller for damping subsynchronous oscillations using TCSC

Engineering Applications of Artificial Intelligence
Wavelet neural networks for function learning

IEEE Transactions on Signal Processing
A neural network regulator for turbogenerators

IEEE Transactions on Neural Networks
Wavelet networks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In order to enhance transient stability in a power system, a new intelligent controller is proposed to control a Static VAR compensator (SVC) located at center of the transmission line. This controller is an online trained wavelet neural network controller (OTWNNC) with adaptive learning rates derived by the Lyapunov stability. During the online control process, the identification of system is not necessary, because of learning ability of the proposed controller. One of the proposed controller features is robustness to different operating conditions and disturbances. The test power system is a two-area two-machine system power. The simulation results show that the oscillations are satisfactorily damped out by the OTWNNC.