Using Neural Networks Based Devices to Reduce Power Loss in the Nigerian Transmission Line Network
Keywords:
UPFC, TCSC, Eigenvalue, Participation Factor, Damping RatioAbstract
Three necessary analyses were carried out on the Nigerian 330 kV power transmission network that consists of 14 generators, 59 buses, 39 load points and 111 transmission lines. These analyses viz; eigenvalue, participation factor and damping ratio, were carried out to obtain buses that contributed to the stability of the network. The network was modelled with MATLAB Simulink. The load bus Eket TS was stable during the analyses and selected as the best location for the simulation of Unified power flow controller (UPFC). The load, on the bus, was varied using a 50% increment (198 MW, 297 MW, 396 MW, 495 MW, and 594 MW). The performance of the proposed system was compared with Thyristor Controlled Series Capacitor (TCSC). The active power loss in the system was reduced by 7.965% with TCSC and 14.04% with UPFC. For the reactive power loss, the TCSC reduced the reactive loss by 8.255%; UPFC reduced the reactive loss by 24.67%. The UPFC outperformed the TCSC by 6.08% in reducing active power losses and by 15.345% in reducing reactive power losses. The neural network-controlled UPFC achieved a better loss reduction than the TCSC.