Research Article Open Access

Optimal Thyristor Control Series Capacitor Neuro-Controller for Damping Oscillations

M. Magaji1 and M. W. Mustafa1
  • 1 ,
Journal of Computer Science
Volume 5 No. 12, 2009, 980-987


Published On: 31 December 2009

How to Cite: Magaji, M. & Mustafa, M. W. (2009). Optimal Thyristor Control Series Capacitor Neuro-Controller for Damping Oscillations. Journal of Computer Science, 5(12), 980-987.


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.

  • 7 Citations



  • TCSC
  • neural network
  • power system oscillations
  • linear models
  • NARMA and MLP