TRANSMISSION NETWORK LOSS MINIMIZATION USING ARTIFICIAL NEURAL NETWORK AND FACTS DEVICES

Abstract: Due to the increase in voltage instability problem and power losses in Nigeria grid are a serious operational challenger facing electricity supply utilities. The Nigeria 330Kv power grid was used as a case study for the evaluation of the proposed power loss reduction system a simulink model of the Nigeria 330Kv transmission system with the proposed neural network controlled TCSC integrated was created in the MATLAB/SIMULINK programming environment. Genetic algorithm was used for optimal placement of the FACTS device in the MATLAB/SIMULINK model of the Nigeria 330Kv transmission system .The proposed approach has been implemented on IEEE 67 bus system, 39 load points, 111 transmission lines and 14 generators. The simulation and evaluation were carried out with TCSC installed with each variation of the load at the bus; load flow is run to determine total system losses. Findings showed that the proposed neural network controlled TCSC to Achieved an average active power loss reduction of 13.11378 (p.u) and average reactive power loss reduction of 78.16378 (p.u). This shows that TCSC reduced both active and reactive power loss in the system. Keywords: TCSC, FACTS Devices, Neural Network, GA, Modeling and training, Transmission Grid, Classification of FACTS Devices. Title: TRANSMISSION NETWORK LOSS MINIMIZATION USING ARTIFICIAL NEURAL NETWORK AND FACTS DEVICES Author: Chukwuagu .I.M, Aneke. E. C International Journal of Novel Research in Electrical and Mechanical Engineering ISSN 2394-9678 Vol. 10, Issue 1, September 2022 - August 2023 Page No: 9-27 Novelty Journals Website: www.noveltyjournals.com Published Date: 04-November-2022 DOI: doi.org Paper Download Link (Source) noveltyjournals.com International Journal of Novel Research in Electrical and Mechanical Engineering, ISSN 2394-9678, Novelty Journals, Website: www.noveltyjournals.com