TY  - JOUR
T1  - ARNF Coordinated Controller Design for SVC and TCSC in Power System
AU - Sangeetha, J. AU - Renuga, P. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 1
SP  - 38
EP  - 45
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.38.45
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.38.45
KW  - Thyristor controlled series capacitor
KW  -static var compensator
KW  -adaptive recurrent neuro fuzzy control
KW  -ACO-NPU
KW  -India
AB  - This study discusses and compares various co-ordinated controller design techniques for oscillation damping in nonlinear, complex power systems during transient disturbances. Multi-machine power systems are equipped with Thyristor Controlled Series Capacitor (TCSC) and Static Var Compensators (SVC) to enhance the stability of the systems. The damping controller co-ordinates measurement signals and control signals to control the TCSC and SVC devices. The Adaptive Recurrent based Neuro Fuzzy (ARNF) controller is employed to provide co-ordinated control signals to TCSC, SVC at each step depends upon the deviation in generator rotor speeds to enhance the stability of the Power System. To train NeuroFuzzy controller parameters, this study proposes the pheromone information updating in Ant Colony Optimization algorithms (ACO).The ACO algorithms based on Novel Pheromone Updating (ACO-NPU) scheme is employed to minimize the cost function and make the adaptive networks performance similar to a targeted training data. ARNF systems enable an extraction of rule-based knowledge from data and the introduction of a priori knowledge in the process of data analysis and system identification. The performance of proposed control strategy is evaluated in a three machine test system development in MATLAB for different scenarios. The nonlinear simulation results were compared with several modified PSO and continuous ACO algorithms. The results show that ACO with Pheromone Updating mechanism (ACO-NPU) handles continuous problems very well within a reasonable solution time without being trapped in local minimum.
ER  -