The Role of Artificial Neural Network (ANN) Based Unified Power Flow Controller (UPFC) in Reduction of Transmission Line Losses: A Case Study of Nigeria 330 kV 58-Bus Network

The Nigeria 330 kV Power transmission network is beset with high losses due to weak transmission lines and greater radial network, resistive as well as losses due to corona. The network consists of 87 transmission lines, 58-buses, 22 generation stations and 36 load buses. To mitigate the losses, power flow was carried out using PSAT to determine the steady state voltage, active power, reactive power, active power loss and reactive power losses which forms input to ANN based UPFC to reduce the active and reactive power losses and also improve voltage profile of the buses. The load flow was based on the singularity of the Jacobian Matrix. The data used was real-time data of the Transmission Company of Nigeria (TCN) Osogbo. The result showed that, the active and reactive power losses without FACTS device was 5.237 MW and 7.03 MW and with UPFC FACTS it was 2.6788 MW and 4.658 MW with ANN based UPFC FACTS 1.2952MW and 1.9150 MW. Also, the active power loss reduction with UPFC FACTS was 48.8% and reactive power loss reduction with UPFC FACTS was 33.8% compared with ANN-based UPFC controller, the active power loss reduction was 75.3% and the reactive power loss reduction was 73%. It is therefore evident that ANN-based UPFC controllers reduced active and reactive power losses greatly and should be integrated into 330 kV network.