In this work neural network controlled UPFC and TCSC device was used for the reduction of losses in power transmission network. The main problem addressed in this work is the optimal placements and control of UPFC and TCSC for the minimization of power transmission networks. To address these problems, Artificial Neural networks and genetic algorithm be used for the control and placements of the FACTS devices respectively for optimal active power loss reduction. The novel contribution of this work is to produce a model and train ANN for UPFC control using critical disruptive voltage and thyrist or firing and variation. Genetic algorithm was used for the optimal placement of the FACTS devise in the MATLAB/SIMULINK model of the Nigeria 330KV transmission system. Findings showed that the proposed neural network controlled UPFC achieved better active and reactive power loss reduction that the TCSC. It outperformed the TCSC by 6.08% in the reduction of active loss and by 15.34% in the reduction of reactive power loss in the power system.

Keywords : TRAINING, NEURAL NETWORK, UPFC, TCSC.