Optimal Thyristor Control Series Capacitor Neuro-Controller for Damping Oscillations
M. Magaji and M. W. Mustafa
DOI : 10.3844/jcssp.2009.980.987
Journal of Computer Science
Volume 5, Issue 12
This study applies a neural-network-based optimal TCSC controller for damping oscillations. Optimal neural network controller is related to model-reference adaptive control, the network controller is developed based on the recursive "pseudo-linear regression". Problem statement: The optimal NN controller is designed to damp out the low frequency local and inter-area oscillations of the large power system. Approach: Two multilayer-perceptron neural networks are used in the design-the identifier/model network to identify the dynamics of the power system and the controller network to provide optimal damping. By applying this controller to the TCSC devices the damping of inter-area modes of oscillations in a multi-machine power system will be handled properly. Results: The effectiveness of the proposed optimal controller is demonstrated on two power system problems. The first case involves TCSC supplementary damping control, which is used to provide a comprehensive evaluation of the learning control performance. The second case aims at addressing a complex system to provide a very good solution to oscillation damping control problem in the Southern Malaysian Peninsular Power Grid. Conclusion: Finally, several fault and load disturbance simulation results are presented to stress the effectiveness of the proposed TCSC controller in a multi-machine power system and show that the proposed intelligent controls improve the dynamic performance of the TCSC devices and the associated power network.
© 2009 M. Magaji and M. W. Mustafa. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.